case control study on type 1 diabetes

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• Published: 16 October 2020

Risk factors for type 1 diabetes, including environmental, behavioural and gut microbial factors: a case–control study

• Deborah Traversi 1 , 8 ,
• Ivana Rabbone 2 , 7 ,
• Giacomo Scaioli 1 , 8 ,
• Camilla Vallini 2 ,
• Giulia Carletto 1 , 8 ,
• Irene Racca 1 ,
• Ugo Ala 5 ,
• Marilena Durazzo 4 ,
• Alessandro Collo 4 , 6 ,
• Arianna Ferro 4 ,
• Deborah Carrera 3 ,
• Silvia Savastio 3 ,
• Francesco Cadario 3 ,
• Roberta Siliquini 1 , 8 &
• Franco Cerutti 1 , 2  

Scientific Reports volume  10 , Article number:  17566 ( 2020 ) Cite this article

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• Microbiology
• Molecular biology
• Risk factors

Type 1 diabetes (T1D) is a common autoimmune disease that is characterized by insufficient insulin production. The onset of T1D is the result of gene-environment interactions. Sociodemographic and behavioural factors may contribute to T1D, and the gut microbiota is proposed to be a driving factor of T1D. An integrated preventive strategy for T1D is not available at present. This case–control study attempted to estimate the exposure linked to T1D to identify significant risk factors for healthy children. Forty children with T1D and 56 healthy controls were included in this study. Anthropometric, socio-economic, nutritional, behavioural, and clinical data were collected. Faecal bacteria were investigated by molecular methods. The findings showed, in multivariable model, that the risk factors for T1D include higher Firmicutes levels (OR 7.30; IC 2.26–23.54) and higher carbohydrate intake (OR 1.03; IC 1.01–1.05), whereas having a greater amount of Bifidobacterium in the gut (OR 0.13; IC 0.05 – 0.34) was a protective factor for T1D. These findings may facilitate the development of preventive strategies for T1D, such as performing genetic screening, characterizing the gut microbiota, and managing nutritional and social factors.

Introduction

Type 1 diabetes (T1D) is a multifactor disease caused by β-cell destruction (which is mostly immune-mediated) and absolute insulin deficiency. At present, the management of T1D has been improved, but the disease remains incurable. T1D onset is most common in childhood. T1D represents approximately 5–10% of all diabetes diagnoses 1 . Between 70 and 90% of T1D patients at diagnosis exhibit evidence of an immune-mediated process with β-cell autoantibodies. T1D onset is preceded by a preclinical period that lasts approximately 3 years, in which autoantibodies appear in the circulatory system 2 . Immune destruction of the β-cells can be detected by the evaluation of some haematic markers 3 . The disease has strong HLA associations, which explain nearly half of the genetic disease predisposition, while the remainder is due to other genetic polymorphisms 3 , 4 .

Analysis of genetic disease susceptibility suggests that there is a greater risk of T1D development when the father is affected by the disease than when the mother is affected 5 . On the other hand, there is evidence that a critical role is played by non-genetic factors, including both environmental and host-related factors, which are considered to play decisive roles in the disease process, leading to the manifestation of clinical T1D 6 .

The worldwide incidence of T1D in the age group of 0–15 years varies considerably by region (from 0.5 to 60 per 100,000 children), and the yearly increase ranges from 0.6% to 9.3%. In Europe, the percentage of cases in the age group of 0–15 years will rise by 70% 7 . In the Piedmont region, up to 2013, there were approximately 8,000 cases in this age group with an incidence of 27 new diagnoses per 100,000 8 . Migrant populations tend to show an incidence of diabetes similar to that of most host populations; therefore, a higher T1D incidence in migrant children was observed in Europe 6 , 9 , 10 . Such a pronounced increase in incidence cannot be attributable to genetic factors alone. Other major risk factors may include the environment, Western lifestyle and nutrition 10 . Other diseases with immune involvement, such as allergies, exhibit a similar trend, suggesting an inductor role for exogenous factors regarding the increased predisposition to autoimmunity 11 . Preventive measures to reduce the incidence of T1D have not been defined to date. Various factors seem to be involved in modulating the incidence of T1D, including birth delivery mode, feeding, birth weight, infections (especially viral), dietary behaviour, and pharmaceutical use (especially antibiotics). Such factors may contribute to T1D development during the early disease stage 12 ; however, compared with genetic factors, environmental factors are less well characterized 13 . β- Cell vulnerability to stress factors has been discussed as the basis of the overload hypothesis 14 . Associations among the microbiome, metabolome, and T1D were shown, highlighting a host-microbiota role in the onset of the disease 12 , 15 . The origin of the disease process was suspected to be gut microbiota dysbiosis (imbalances in the composition and function of intestinal microbes) associated with altered gut permeability and a major vulnerability of the immune system 6 . Accordingly, evidence obtained from both animal models and human studies suggests that the gut microbiota and the immune system interact closely, emphasizing the role of the intestinal microbiota in the maturation and development of immune functions 16 . Recently, mycobiome-bacteriome interactions, as well as intestinal virome and islet autoimmunity, were hypothesized to be drivers of dysbiosis 17 . Several studies have specifically investigated microbiota composition in children with T1D 18 , 19 , 20 , but the results have not been consistent. Interestingly, most studies are in agreement regarding the reduced microbial diversity observed in subjects with T1D compared with controls; moreover, the microbiota structure in T1D subjects was found to be different from that of control subjects 21 , 22 . To date, a typical T1D-associated microbiota has not been identified 23 , 24 , 25 , 26 . The research also determined that T1D clinical management could be improved by in-depth analysis of the partial remission phase 27 ; however, preventive measures are limited and generally focus only on genetic susceptibility 28 and general population screening for islet autoimmunity 29 . The development of an integrated prediction strategy could be useful for increasing early diagnosis while avoiding onset complications by identifying children at risk of T1D to place under observation and, in the future, to treat with preventive methods 10 .

The aim of this study is to identify environmental, behavioural, and microbial risk factors of T1D onset to develop an integrated T1D preventive management strategy that is suitable for paediatricians in the Piedmont region.

Subject description and origin factor analysis

To analyse the origin factor, the study population was subdivided by the children's origins (Italian and migrant, 69 and 27 children, respectively). An analysis of the socio-demographic and behavioural factors examined in the study showed many differences between Italian and migrant children, while other variables appear to be quite homogeneous (Table 1 ). In the studied cohort, migrant status did not produce a significant increase in T1D onset.

Approximately 79% of the children in the cohort had siblings; approximately 40% of the included children lived with a pet in the house, and more than 65% of the children took antibiotics during the first two years of life. The residency zone was notably different between Italians and migrants: the percentage of migrant children living in urban sites was higher but not significant following the adjusted model. Regular sports activities seem to be practised more by Italian children than by migrant children (73.5% vs 51.8%, p = 0.054). A total of 77.9% of Italian children and 55.6% of migrant children were subjected to regular health check-ups (p = 0.017). A significant difference was confirmed for the ages of the migrant mother and father (Table 1 ), meanly 6 years and 4 years younger respectively at recruitment, respect the Italians (p = 0.017 and p = 0.0425). The analysis of eating habits and nutritional intake revealed that the majority of the children were breastfed. Moreover, the weaning age was 6 months, as recommended. Migrant children showed higher total carbohydrate intake (+ 12%, p = 0.044) and simple carbohydrate intake (+ 24%, p = 0.0045). Moreover, among migrants, the children tended to access food by themselves and to consume meals alone. The percentage of migrant children who ate meals while watching TV was higher but not significant. Finally, the one-course meal was more frequent in migrant families (ratio 1:3, p = 0.006).

The analysis of microbiota and bioindicator species displayed no significant differences between Italian and migrant children: the qRT-PCR measurements showed a trend of greater value for the total bacteria (both for the experimental design with and without probe), Bacteroides and M. smithii (both using 16S rDNA and nifH) in migrant children. The DGGE profile and dendrogram analysis did not show a different clustering pattern based on the origin, and the migrant group showed a trend towards greater α-diversity of the faecal microbiota profiles (Shannon index + 5%). Additionally, the α-diversity analyses in next generation sequencing (NGS) showed a difference in taxonomic units (OTUs), i.e., there were more OTUs in migrants than in Italians, but the difference was not significant, though it was close to the limit of significance (p = 0.057). Furthermore, the phylogenetic diversity index (Faith PD) suggested that the origin of the subjects could influence the structure of the microbial community. Although the overall number of OTUs did not change significantly, the phylogenetic distance of the individual OTUs was greater in the migrant group than in the Italian group, as the OTUs occupied a broader ecological niche in the migrant group.

T1D risk factors

Previous results indicated that being a migrant child in the Piedmont region is not a significant risk factor for T1D onset 30 . Table 2 shows single logistic regressions performed to estimate the impact of the different variables on the outcome. Notably, the analysis of socio-demographic, behavioural, and nutritional determinants revealed that having parents with at least a high school certificate seems to be a protective factor for T1D onset, even if not significant after adjusted comparisons.

High total caloric intake, as well as high protein intake and consumption of total carbohydrates, are associated with only a slightly increased risk of T1D onset.

The DGGE gel and the results of the cluster analysis are shown in Fig.  1 . The Pearson similarity clustering showed macro beta-diversity differences between the T1D patients and healthy children, with the main division being in two different clusters.

DGGE banding patterns and the results of the analysis in which the Pearson coefficient (numbers reported near the nodes) was used for measuring similarity in banding patterns. The cluster identifies T1D patients (red lines) and healthy children (green lines).

Firmicutes and Bacteroidetes followed by Proteobacteria and Actinobacteria (Table 3 ) predominantly composed the gut microbiota of all children. In the children with diabetes, an increase in the levels of three members of Bacteroidetes ( Alistipes senegalensis , Bacteroides timonensis , and Barnesiella intestinihominis ) and three members of Firmicutes ( Christensenella timonensis ,

Ruminococcus bromii , and Urmitella timonensis ) was observed by sequencing.

Furthermore, other notable results were obtained by NGS analyses. The taxonomic analysis revealed that the gut microbiota of the study participants was composed of nine relevant phyla: Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria, Verrucomicrobia, Euryarchaeota, Tenericutes, Cyanobacteria, and an unclassified phylum.

Moreover, beta-diversity analyses were carried out to highlight the differences among the samples based on the structures of their microbial communities. The weighted UniFrac metric showed that the samples were not subdivided into clusters. The intragroup and intergroup distances were comparable, and there was no separation between the clusters. These findings were confirmed by the Permanova test. Finally, analyses of the differential abundance were performed to compare the increase or decrease in the abundance of one or more bacteria in the case and control groups. DeSeq2 showed 48 significantly abundant OTUs (p < 0.001). The most abundant OTU was Rikenellaceae followed by Prevotellaceae ( Prevotella copri ), Barnesiellaceae , Lachnospiraceae, and Ruminococcaceae ( Ruminococcus bromii ), which were significantly more abundant in children with diabetes.

The difference in the results observed between methods is an interesting discussion point. The methods are characterized by different sensitivities; they represent different molecular perspectives regarding the faecal microbiota. When a method with a higher sensibility is used (NGS), a flattening effect is possible. On the other hand, the major abundance of such genera as Ruminococcu s was confirmed by different microbiota study methods, which is in keeping with the qRT-PCR results. A group of 23 samples showed different clusterization compared to the others (Fig.  2 , left). This small group was not different from the main group regarding any characteristics. The only significant difference was observed for the M. smithii presence and the A. muciniphila levels, both of which were higher in the separated group (Fig.  2 , right). A. muciniphila was proposed as a probiotic 31 , while M. smithii has been characterized as the most abundant methanogen in the gut 32 .

Left-Unweighted UniFrac graph of the NGS results. There are two identifiable groups: the blue circle (main group) and the red circle (separated group). No experimental hypothesis was confirmed for the cluster definition. On the Right: box plot of the qRT-PCR results for some microbiological targets ( Akkermansia muciniphila and Methanobrevibacter smithii ), the difference between the groups is significant (t-test p < 0.05).

The qRT-PCR gut microbiota analysis indicated significant differences among T1D patients and healthy children (Table 2 ). The logistic regression analysis showed that the increase in the Margalef index was associated with a decrease in the likelihood of disease onset (OR 0.20; 95% CI 0.09–0.46, p = 0.000). Increased Firmicutes levels and decreased Bacteroidetes levels were significant risk factors for T1D (OR 7.49; 95% CI 3.25–17.28, p = 0.0001; OR 0.28; 95% CI 0.15–0.51 p = 0.0001, respectively). Moreover, Bifidobacterium spp. was a protective factor for T1D onset (OR 0.20; 95% CI 0.10–0.38, p = 0.0001).

The multivariable analysis produced a R 2  = 0.6259 (p < 0.001). After adjusting for confounding factors, the likelihood of having diabetes is significantly higher in those with higher amount of Firmicutes, lower amount of Bifidobacterium spp and a higher amount of total carbohydrate intake (Table 4 ).

T1D is an important disease that affects health with onset primarily occurring in childhood. At present, there is no cure for this disease, and only disease management is possible. The disease burden of T1D is immense, especially considering the number of years of life lost due to disability but also the years of life lost due to premature death. The life expectancy for T1D patients is approximately 16 years shorter than that of the comparable healthy population 33 . Even if relevant risk factors are known, to date, such scientific determinants do not include a screening programme for preventive purposes. Of course, preventive action must be considered as a systematic process that focuses on the main risk factors to identify children at higher risk of T1D and to suggest efficacious preventive treatments. In the study, the main T1D onset risk factors seem to be identifiable in the composition of the microbiota and, in particular, the microbiota α-diversity, Firmicutes and Bacteroidetes levels and their ratio, as well as the Bifidobacterium level. Similar evidence was obtained by other studies, which observed both higher Bacteroidetes in T1D patients 34 , 35 and less abundant anti-inflammatory genera in children with multiple islet autoantibodies 36 . Reduced microbial diversity appears to become significant between seroconversion and overt T1D 15 . A significant difference in the Bifidobacterium level was observed in different studies, including both a small cohort of autoimmune children 37 , 38 and a larger population associated with such protective factors as breastfeeding 21 . At the genus level, a significant difference in, for example, Blautia (increased in patients), was observed 39 ; however, in other studies, different single species ( Bacteroides ovatus ) seem to be more abundant in patients than in the controls 18 . However, prior studies suggest the presence of duodenal mucosa abnormalities in the inflammatory profile for T1D patients 22 , 40 and on the T1D-related changes in the gut microbiota, even if proving the causality of these factors has remained challenging 21 .

The characterization of the microbiota is rapidly evolving. Traditional methods that are not as sensitive as PCR-DGGE are still suitable, while NGS methods are expanding. Sophisticated whole-genome sequencing methods integrated with metabolomics and proteomics have been proposed. However, the large amount of data, being affected by multiple confounding factors, has not had a clear impact on T1D prevention strategies. The development of a simple method to describe microbiota modulation using validated biomarkers, which could serve as a rapid screening test, may be warranted.

Another risk factor is the occurrence of stress due to a traumatic or emotional experience. This stress seems to be able to affect the autoimmunity process. Therefore, particular attention could be paid to such risk factors for T1D risk in children.

A high education level of one or both parents could be also protective, suggesting that socioeconomic factors affect the T1D risk. Other factors, identified as significant risk modulators among behavioural and nutritional factors, had minor effects.

The study has some potential limitations, including susceptibility to bias in recollection about exposure and reverse causality. The exposure recollection could be biased, but this issue can be less influential at the onset, as in this study. Moreover, recruitment at the onset guarantees a temporal coherence of the exposure with respect to the disease onset.

T1D is one of the most frequently diagnosed diseases in children; however, it is not a high-incidence disease. The prospective inclusion of a large number of healthy children, which is needed for the observation of enough cases, requires a very long time of observation. Moreover, a restricted age range was necessary in children for the rapid changes in behaviour and microbiota. This requirement resulted in an additional included subject restriction. On the other hand, the study of multifactorial diseases with poorly understood pathogenic pathways is imperative, even if it is at risk for obtaining less conclusive evidence. Of course, such a study alone could not elucidate the causation process, but the evidence obtained could be important for the selection of higher-risk subpopulations, planning of future research, and improving prevention.

Identification of a higher-risk subpopulation is strictly relevant for the subsequent validation of an efficient preventive screening to be produced with a prospective method. Of course, the pathogenesis of type 1 diabetes has not been fully elucidated to date; however, in this study, various factors (associated with both the disease and the microbiota composition) were included, such as the origin of the children, the age of the mother, the age of breastfeeding and the age of weaning. Other possible confounding factors not included in our analysis are viral infections, particularly enteroviruses, and preterm birth; however, there was no clear consensus regarding these novel factors at the beginning of the study.

Concerning the microbiota, the knowledge is still incomplete, and various factors can interact to produce a T1D risk modulation that is not explainable at present. Moreover, the results obtained using different techniques were also dissimilar (for example, clusterization due to β-diversity analysis). This finding is likely due to the different sensitivities of the applied methods 41 . Furthermore, even if the time between the symptom comparison and the diagnosis is very short, there is a danger of biased estimates due to reverse causality.

In conclusion, this study confirmed that T1D onset risk is modulated by compositional changes in the gut microbiota and that such evidence must be employed to devise preventive measure. The results showed that the gut microbial indicators found in children with T1D differ from those found in healthy children. These findings also pave the way for new research attempting to develop strategies to control T1D development by modifying the gut microbiota. However, a better knowledge of gut microbial composition associated with the development of T1D must be obtained to choose the best treatment 10 , 42 , 43 , 44 , 45 .

In brief, direct or indirect manipulations of the intestinal microbiome may provide effective measures for preventing or delaying the disease process leading to the manifestation of clinical T1D. At present, a preventive strategy could be developed that includes the main genetic and microbiome risk factors. Then, this strategy could be applied to healthy children to reduce the burden of T1D.

Study design and participants

The case–control study began in January 2016 46 and ended in September 2018 (case–control phase of clinicaltrial.gov Protocol ID: G12114000080001). The work was conducted following the STROBE Statement for a case–control study. The activity is bicentric and includes the two main paediatric hospitals in the Piedmont region (located in Torino and Novara), which cover the clinical management for cases of T1D in the region. The ethics committees of the two hospitals approved the research activities during 2015 (“Comitato etico interaziendale A.O.U. Ordine Mauriziano di Torino ASLTO1” with record number 0117120 and “Comitato etico Interaziendale A.O.U. “Maggiore della Carità” ASL BI, NO, VCO” record number 631/CE).

The recruitment included 40 paediatric patients with T1D (cases) and 56 healthy children (controls), who were comparable in terms of age, gender, and ethnicity to avoid bias. The included subjects represent the most convenient sample possible. The inclusion criteria were age (5–10 years), normal weight, and residence in Piedmont. Exclusion criteria were celiac disease, chronic disease diagnosis, eating disorders, active infections, use of antibiotics and/or probiotics and/or any other medical treatment that influences intestinal microbiota during the 3 months before recruitment and children with parents of mixed origins (Italian and migrant) for the exclusion of important confounding factors due to genetic and cultural mixed backgrounds 19 .

The T1D children were integrated into the study at disease onset, with hyperglycaemia, with or without ketoacidosis, polyuria symptoms, a high value of glycated haemoglobin (HbA1c > 42 mmol/mol) and T1D-specific autoantibody positivity. Healthy children were contacted by paediatricians in the territory of the acute care system. The guardians of the enlisting children read, understood, and then signed informed consent forms following the declaration of Helsinki. A module is prepared for parents, children, and mature children 47 . All the following methods were carried out following relevant guidelines and regulations when available. A questionnaire was given to the parents containing items and questions to retrieve data on the family contest with particular regards to emotive stressors, such as mourning or separation, anthropometrics, and socio-demographic, nutritional, and behavioural information.

Anthropometric and nutritional data included weight, height, body mass index (BMI), food frequency based on 24-h recall and a food frequency questionnaire (FFQ), neonatal feeding, and age of weaning. The anthropometric parameters (weight and height) were measured according to standard recommendations. The BMI values were interpreted according to the WHO criterion. The 24-h recall technique reconstructed the meals and food intake on a recent "typical" day, estimating the bromatological inputs according to a food composition database for epidemiological studies in Italy (BDA). The FFQ, developed for the study, focused on the consumption of certain food categories (those containing sugars, fibre, omega-3, calcium, vitamin D, condiments, and cereals) and eating habits (e.g., alone or with adults, in front of the TV).

Twenty-eight percent of the involved population is migrants (both parents not Italian). Such data are consistent with the percentage of newborns from non-Italian mothers, which is approximately 30% in northern Italy 48 . The migrant group included children coming mainly from northern Africa and Eastern Europe. The migration involved the parents and sometimes the children; on average, the included children as migrants were residents in Italy for less than 5 years. At the end of recruitment, no significant differences were observed between the case and control groups for age, sex composition, and origins (criteria for pairing) or for height, weight, and BMI (T-test, p > 0.05) (Table 5 ).

Sample collection and DNA extraction

A kit for stool collection was delivered to each study participant following a validated procedure 49 , 50 and using a Fecotainer device (Tag Hemi VOF, Netherlands). Faecal samples were homogenized within 24 h in the laboratory, and five 2 g aliquots were stored at − 80 °C until DNA isolation was performed. Total DNA extractions from the stool samples were performed using the QiaAmp PowerFecal DNA Kit (QIAGEN, Hilden, Germany). The nucleic acids were quantified using a NanoQuant Plate (TECAN Trading AG, Switzerland), which allows quantification using a spectrophotometer read at 260 nm. The spectrophotometer used was the TECAN Infinite 200 PRO, and the software was i-Control (version 1.11.10). The extracted DNA concentrations ranged from 1.1–155.5 ng/μl (mean 41.35 ± 38.70 ng/μL). Samples were stored at –20 °C until molecular analysis was performed.

The PCR products for denaturing gradient gel electrophoresis (DGGE) were obtained by amplifying the bacterial 16S rRNA genes following a marker gene analysis approach 51 . The primer pairs were 357F-GC and 518R (Table 6 ) 52 . All PCRs were performed with the T100 Bio-Rad Thermocycler in a 25-μl reaction volume containing 1X Master Mix (166–5009, Bio-Rad, Berkeley, CA, USA), 0.02 bovine serum albumin (BSA), 0.4 μM of each primer, and 2 μl of DNA diluted 1:10 in sterile DNase-treated water. DGGE was carried out using a DCode System (Bio-Rad) with a 30–50% denaturing gradient of formamide and urea 53 . Electrophoresis ran at 200 V for 5 h at 60 °C in 1X TAE buffer. Gels were stained for 30 min with SYBR Green I nucleic acid gel stain (10.000X in DMSO, S9430, Sigma-Aldrich, USA) and were visualized using the D-Code XR apparatus from Bio-Rad. Then, DGGE bands were excised, incubated overnight at − 20 °C, washed, and crushed in 20 μl of molecular-grade water. The supernatant (2 μl) was used as a template and reamplified, as previously described, without BSA and using modified linker-PCR bacterial primers (357F-GC; 518R-AT-M13) (Table 6 ) 19 , 52 , 54 , 55 , 56 , 57 , 58 , 59 , 60 . The obtained PCR products were sequenced with Sanger sequencing (Genechron-Ylichron S.r.l.). The sequence similarities were obtained by the National Centre for Biotechnology Information (NCBI) database using nucleotide Basic Local Alignment Search Tool (BLASTn) analysis.

High-throughput DNA sequencing and analysis were conducted by BMR Genomics s s.r.l. The V3-V4 region of 16S rDNA was amplified using the MiSeq 300PEPro341F and Pro805R primer pair 6 . The sample reads were above 12*10 6 . The reaction mixture (25 μl) contained 3–10 ng/μl genomic DNA, Taq Platinum HiFi (Invitrogen, Carlsbad, CA), and 10 μM of each primer. The PCR conditions for amplification of DNA were as follows: 94 °C for 1 min (1X), 94 °C for 30 s, 55 °C for 30 s, 68 °C for 45 s (25X), and 68 °C for 7 min (1X). PCR products were purified through Agencourt XP 0.8X Magnetic Beads and amplified shortly with the Index Nextera XT. The amplicons were normalized with SequalPrep (Thermo Fisher) and multiplexed. The pool was purified with Agencourt XP 1X Magnetic Beads, loaded onto MiSeq, and sequenced with the V3 chemistry-300PE strategy.

Starting from the extracted DNA, the following microbial targets were quantified by qRT-PCR using a CFX Touch Real-Time PCR Detection System (Bio-Rad-Hercules, CA) and CFX Manager (3.1 Software): total Bacteria, Bacteroidetes, Bacteroides spp., Firmicutes, Bifidobacterium spp., Akkermansia muciniphila, and Methanobrevibacter smithii . Total bacteria and M. smithii were detected following two reaction designs. For M. smithii , the analysis was performed using as target both the 16S rDNA and then a specific functional gene ( nifH ). For total bacteria, quantification was carried out using a protocol with or without a probe. For the determination of total bacteria (method without probe), Bacteroidetes, Bacteroides spp., Firmicutes, Bifidobacterium spp. and Akkermansia muciniphila , 2 µl of 1:10 extracted DNA was added to a reaction mixture consisting of 10 µl Sso Advance SYBR Green Supermix (172–5261, Bio-Rad), 0.5 µl each of the forward and reverse primers (10 µM final concentration) and 7 µl of ultrapure water in a 20 µl final reaction volume. The reaction conditions were set as follows: 95 °C for 3 min (1X), 95 °C for 10 s, and 59 °C for 15 s (57 °C for Bacteroidetes spp. and 60 °C for Firmicutes), 72 °C for 10 s (39X), 65 °C for 31 s, 65 °C for 5 s + 0.5 °C/cycle, ramp 0.5 °C/s (60X). Moreover, for the determinations of M. smithii and total bacteria (method with probe), the reaction was as follows. Two microlitres of 1:10 extracted DNA was added to a reaction mixture consisting of 10 µl IQ Multiplex PowerMix (Bio-Rad-Hercules, CA), 0.2 µl of the molecular probe (10 µM), 0.5 µl each of the forward and reverse primers (10 µM final concentration) and 6.8 µl of ultrapure water in a 20 µl final reaction volume. The reaction conditions were 95 °C for 3 min (1X), 95 °C for 10 s, 59 °C for 15 s, 72 °C for 15 s (39X), and 72 °C for 5 min. Standard curves were produced with serial six-fold dilutions of genomic DNA from the microorganism target, provided by ATCC (Manassas, Virginia, USA) or DSMZ (Braunschweig, Germany). All PCR tests were carried out in triplicate. Table 6 provides detailed information regarding oligonucleotide sequences and genomic standards 19 , 54 , 55 , 56 , 57 , 58 , 59 , 60 . The PCR efficiencies were always between 90 and 110%. To confirm the amplification of each target, gel electrophoresis was performed on 2% agarose gels.

Data elaboration and statistical analyses

The statistical analysis was performed using STATA version 11.0. Moreover, the data on the included T1D patients and healthy controls were elaborated to highlight the likelihood of having diabetes. A descriptive analysis of the variables was conducted. The data were reported as absolute numbers and percentages for categorical variables and as means and standard deviations for continuous variables. Moreover, the subjects were divided by individual origins into two groups: Italian and migrant, considering the origin of the children and their families, to show differences in the distribution of disease determinants and to assess whether being a migrant could be associated with T1D onset. Differences between Italian and migrant children were assessed using the χ 2 test with Fisher’s correction for categorical variables and Student’s t-test for continuous variables. Univariable logistic regression was then performed to estimate the impact of sociodemographic, nutritional, and microbiota-related variables on the outcome. These associations were expressed as odds ratios (OR) at a 95% confidence interval (CI). Moreover, the adjusted p-value for multiple comparisons was calculated using the Benjamini and Hochberg false discovery rate method. We conducted multivariable analyses including various variables (age, gender, Firmicutes, Bifidobacterium spp ., and total carbohydrate intake) and the risk of type 1 diabetes using logistic regression models. The Spearman rank-order correlation coefficient was also determined to assess the relationships between variables. A p-value p < 0.05 was considered significant for all analyses.

The DGGE gel analysis was performed with Bionumerics 7.2. The hierarchical classification was performed with a UPGMA system (1% tolerance and optimization level) and Pearson correlation. Simpson's diversity index, Shannon’s index, and Margalef index were calculated for each DGGE profile to evaluate alpha diversity.

NGS bioinformatics analysis was performed with the software pipeline Qiime2. The reads were cleaned up by the primers using the software Cutadapt (version 2018.8.0) and processed with the software DADA2. The sequences were trimmed at the 3′ end (forward: 270 bp; reverse 260 bp), filtered by quality, and merged with default values. Subsequently, the sequences were elaborated to obtain unique sequences. In this phase, the chimaeras (denoised-paired) are also eliminated. The sequences were clustered against unique sequences at 99% similarity. The taxonomies of both GreenGenes (version 13–8) and Silva (version 132) were assigned to the OTU sequences. Alpha-diversity analyses were performed on all samples using the observed OTUs, Shannon, Pielou's evenness, and Faith PD indices, and for each index, the Kruskal–Wallis test was used to verify the significance of the comparisons between samples. Beta-diversity analyses were performed on all samples using the Bray–Curtis, Jaccard, and UniFrac metrics (weighted and unweighted). Multivariable statistical analyses were performed using the PERMANOVA, Adonis, and ANOSIM tests; instead, the analysis of the differential abundance was based on the packages of R (MetagenomeSeq, DeSeq2, and ANCOM).

Data availability

The database includes human data that are available upon reasonable request.

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Acknowledgements

The authors are grateful to the Italian Ministry of Health (RF-2011-02350617), the University of the Study of Torino and the Città della salute e e della scienza di Torino and the Hospital “Maggiore della Carità" di Novara for co-funding this project. Moreover, the authors wish to thank dr. Barbara Di Stefano (Sanitary Direction AOU Novara) and Mrs Rim Maatoug, Mrs Shpresa Xheka, and Mrs Daniela Elena Zelinschi (cultural intermediaries) at Novara Hospital for the translation of the questionnaire for migrant people. Finally, the authors make a special acknowledgement to the participant children and their families.

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F.C. and R.S. coordinate the work. F.C., I.R., R.S., D.T.: design the work. F.C., I.R., S.S., and F.C.: patient inclusion and questionnaire administration. C.V., D.C.: clinical data collection, Torino and Novara, respectively. I.R.: patient sample collection and transport, questionnaire elaboration. D.T., G.C.: sample processing and extraction, molecular analysis. G.S., U.A., D.T. : statistical analysis and bioinformatics. M.D., A.C., A.F.: nutritional data elaboration. G.C., G.S.: drafted the work. F.C., I.R., R.S., M.D.: revised the work. D.T.: substantively revised the work.

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Traversi, D., Rabbone, I., Scaioli, G. et al. Risk factors for type 1 diabetes, including environmental, behavioural and gut microbial factors: a case–control study. Sci Rep 10 , 17566 (2020). https://doi.org/10.1038/s41598-020-74678-6

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DOI : https://doi.org/10.1038/s41598-020-74678-6

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• Research and Discoveries

Study provides preliminary evidence in favor of a new type 1 diabetes treatment

November 1, 2023

Written By Grace Niewijk

• Call Us At 1-888-824-0200

Type 1 diabetes is an autoimmune disease that causes the body's immune system to attack and destroy insulin-producing beta cells in the pancreas. Traditional management of type 1 diabetes has primarily involved replacing the missing insulin with injections which, though effective, can be expensive and burdensome. A new study led by researchers at the University of Chicago Medicine and Indiana University suggests that an existing drug could be repurposed to treat type 1 diabetes, potentially reducing dependence on insulin as the sole treatment.

The research centers on a medication known as α-difluoromethylornithine (DFMO), which inhibits an enzyme that plays a key role in cellular metabolism. The latest translational results are a culmination of years of research: In 2010, while corresponding author Raghu Mirmira, MD, PhD , was at Indiana University, he and his lab performed fundamental biochemistry experiments on beta cells in culture. They found that suppressing the metabolic pathway altered by DFMO helped protect the beta cells from environmental factors, hinting at the possibility of preserving and even restoring these vital cells in patients diagnosed with type 1 diabetes.

The researchers confirmed their observations preclinically in zebrafish and then in mice before senior author Linda DiMeglio, MD, MPH, Edwin Letzter Professor of Pediatrics at Indiana University School of Medicine and a pediatric endocrinologist at Riley Children's Health, launched a clinical trial to evaluate the safety and tolerability of the drug in type 1 diabetes patients. The results of the trial, which was funded by the Juvenile Diabetes Research Foundation (JDRF) and used DMFO provided by Panbela Therapeutics, indicated that the drug is safe for type 1 diabetes patients and can help keep insulin levels stable by protecting beta cells.

“As a physician-scientist, this is the kind of thing we’ve always strived for – to discover something at a very basic, fundamental level in cells and find a way to bring it into the clinic,” said Mirmira, who is now Professor of Medicine and an endocrinologist at UChicago Medicine. “It definitely underscores the importance of supporting basic science research.”

"It's been truly thrilling to witness the promising results in the pilot trial after this long journey, and we're excited to continue our meaningful collaboration," said DiMeglio.

Importantly, DFMO has already been FDA-approved as a high dose injection since 1990 for treating African Sleeping Sickness and received breakthrough therapy designation for neuroblastoma maintenance therapy after remission in 2020. Pre-existing regulatory approval could potentially facilitate its use in type 1 diabetes, saving effort and expense and getting the treatment to patients sooner.

“For a drug that’s already approved for other indications, the approval timeline can be a matter of years instead of decades once you have solid clinical evidence for safety and efficacy,” said Mirmira. “Using a new formulation of DFMO as a pill allows patients to take it by mouth instead of needing to undergo regular injections, and it has a very favorable side effect profile. It’s exciting to say we have a drug that works differently from every other treatment we have for this disease.”

To follow up on the recently published results, first and co-corresponding author Emily K. Sims, MD, Associate Professor of Pediatrics at IU School of Medicine and a pediatric endocrinologist at Riley Children's Health, launched a multi-center clinical trial, also funded by JDRF – with UChicago among the trial sites – to gather even stronger data regarding the efficacy of DFMO as a type 1 diabetes treatment.

"With our promising early findings, we hold hope that DFMO, possibly as part of a combination therapy, could offer potential benefits to preserve insulin secretion in individuals with recent-onset type 1 diabetes and ultimately also be tested in those who are at risk of developing the condition," said Sims.

“A new era is dawning where we’re thinking of novel ways to modify the disease using different types of drugs and targets that we didn’t classically think of in type 1 diabetes treatment,” said Mirmira.

The study, “Inhibition of Polyamine Biosynthesis Preserves β-Cell Function in Type 1 Diabetes,” was published in Cell Medicine Reports in November 2023. Co-authors include Emily K. Sims, Abhishek Kulkarni, Audrey Hull, Stephanie E. Woerner, Susanne Cabrera, Lucy D. Mastrandrea, Batoul Hammoud, Soumyadeep Sarkar, Ernesto S. Nakayasu, Teresa L. Mastracci, Susan M. Perkins, Fangqian Ouyang, Bobbie-Jo Webb-Robertson, Jacob R. Enriquez, Sarah A. Tersey, Carmella Evans-Molina, S. Alice Long, Lori Blanchfield, Eugene W. Gerner, Raghavendra Mirmira, and Linda A. DiMeglio.

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This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:

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Study provides preliminary evidence in favor of a new type 1 diabetes treatment

by University of Chicago Medical Center

More information: Emily K. Sims et al, Inhibition of polyamine biosynthesis preserves β cell function in type 1 diabetes, Cell Reports Medicine (2023). DOI: 10.1016/j.xcrm.2023.101261 Journal information: Cell Reports Medicine

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• Open access
• Published: 27 May 2022

Breastfeeding, nutrition and type 1 diabetes: a case-control study in Izmir, Turkey

• İpek Çiçekli   ORCID: orcid.org/0000-0003-4717-6145 1 &
• Raika Durusoy   ORCID: orcid.org/0000-0003-1041-8462 2  

International Breastfeeding Journal volume  17 , Article number:  42 ( 2022 ) Cite this article

4914 Accesses

3 Citations

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The relationship between infant breastfeeding and type 1 diabetes mellitus (DM) is unclear but it has been suggested that there may be a link between many environmental factors, including dietary antigens affecting diabetes epidemiology.

The main objective of this study is to investigate nutritional risk factors, especially breastfeeding early in life that may be associated with the development of type 1 DM and to determine the relationship these factors have with the disease.

This research is a case-control study and was carried out in Ege University Children’s Hospital in İzmir, Turkey between 13 January 2020 and 5 March 2020. A total of 246 children aged between 4 and 14 years were included in the study. The case group consisted of patients diagnosed with type 1 DM followed-up by Ege University Children’s Hospital’s Endocrinology Unit and the control group included non-diabetic children attending the same hospital’s General Pediatric Outpatient Clinic. A structured questionnaire was created by the researchers after reviewing the literature related to nutritional and other risk factors for type 1 DM. The questionnaire was administered by interviewing the parents and it was related to the child, mother and family of the child. In this study, breastfeeding duration was defined as the total duration of breastfeeding and exclusive breastfeeding meant that the child received only breast milk from the mother.

The mean age at diagnosis was 6.30 ± 4.03 years for cases and 7.48 ± 2.56 years for controls. We found that each monthly increase in exclusive breastfeeding duration provided a 0.83-fold (95% CI 0.72, 0.96) decrease in the risk of type 1 DM. Introduction of cereals in the diet at the sixth month or earlier was associated with a 2.58-fold (95% CI 1.29, 5.16) increased risk.

Conclusions

Determining the contribution of exclusive breastfeeding to the disease is important in establishing preventive policies. A longer duration of exclusive breastfeeding may be an important role in preventing the disease. This free intervention that truly works will be cost-effective. Future studies are needed to clarify the role of both exclusive and non-exclusive breastfeeding on the development of type 1 DM.

Diabetes mellitus (DM) is a chronic metabolic disease characterized by hyperglycemia due to impairments in either insulin secretion and / or insulin effect [ 1 ]. As of today, 537 million people worldwide have diabetes [ 2 ]. This number is estimated to reach 643 million in 2030 and 783 million in 2045, which can be considered alarming levels [ 2 ].

Type 1 DM is characterized by insulin deficiency and hyperglycemia, usually starting in childhood, when the beta-cells of the pancreas are destroyed by autoimmune or non-autoimmune processes [ 2 ]. In individuals with genetic predisposition (human leukocyte antigen or HLA groups at risk), autoimmunity is triggered by the effect of environmental factors (viruses, toxins, emotional stress, others) and progressive beta-cell damage begins. Clinical symptoms of diabetes occur when beta-cell reserves are reduced by 80–90% [ 3 ].

It has been suggested that there are many environmental factors, including dietary antigens [ 4 , 5 , 6 ], as well as genetic risk factors [ 7 , 8 , 9 , 10 , 11 ] that affect the epidemiology of type 1 DM [ 12 ]. Although not all genotypes with risk have yet been identified, only about 10–15% of individuals at genetic risk develop type 1 DM [ 5 ]. In studies conducted on migrants, it has been shown that the incidence of type 1 DM increases in those who migrate from a region where the incidence of type 1 DM is low to a region with high incidence, and the effect of environmental conditions has been emphasized [ 13 ]. These data were found to be consistent with the results of studies finding that environmental triggers increase and accelerate the development of clinical type 1 DM despite lower genetic predisposition [ 13 ].

Some nutritional factors contribute to the development of the disease. Studies in 40 countries worldwide have shown that dietary patterns may impact the development of type 1 DM [ 14 ]. Vitamin D, another nutritional factor, may have a protective effect on glycemic control in patients with type 1 DM [ 15 ] and according to a birth cohort study, the provision of vitamin D supplementation for infants early in life could help to reduce the risk of the disease [ 16 ]. The introduction of cow’s milk-based infant formulas in the first three postnatal months was found to be associated with an increase in pancreatic beta-cell auto-antibodies [ 17 ]. However, another study had shown that cow’s milk did not play an important role in the development of type 1 DM [ 18 ].

Although many studies have been performed to investigate the role of nutrition in pregnancy and early in life on type 1 DM, the results have been inconsistent. Breastfeeding [ 19 ], probiotic supplementation [ 20 ], vitamin C, and zinc supplementation [ 21 ] have been shown as possible protective factors against type 1 DM whereas early exposure to eggs, gluten [ 22 , 23 ] and vegetables [ 24 ] might increase the risk.

Studies with school-age children have shown that diabetic children are significantly more prone to stress and depression compared to non-diabetic children [ 25 ]. Beyond the psychological and somatic effects of the disease on the individuals, diabetic individuals also encounter socio-economic consequences affecting their families and entire societies [ 26 ]. Frequent co-morbidities further increase negative socioeconomic consequences, especially in low- and middle-income countries [ 26 ].

According to the Social Security Institution’s data in Turkey, the costs of diabetes and its complications amount to approximately 23% of the total health expenditure [ 27 ]. In addition, indirect costs such as the loss of productivity of diabetics, the persons caring for the patient and their family are not included in these cost estimates. Therefore the cost does not reflect the psychosocial effects of the losses of quality-adjusted life years. Knowledge of modifiable environmental risk factors in type 1 DM can assist authorities in planning and implementing preventive policies to reduce the burden of the disease. It is as yet uncertain how and which nutritional or other environmental factors are important in the development of type 1 DM. Moreover, epigenetic mechanisms are not clearly defined.

The main objective of this study is to investigate potential nutritional risk factors, especially breastfeeding early in life, that may be associated with the development of type 1 DM and to determine the relationship of these factors with the disease, independent of other established risk factors.

Participants

A case-control study was carried out at Ege University Children’s Hospital, İzmir City, Turkey, over a period of two months from January to March 2020.

A minimum sample size of 105 cases and 105 controls with a total of 210 participants was calculated with G-Power using the t-test group, with an effect size of 0.5, an error margin of 0.05, and a power of 95%. About 20% more sample size was added to account for possible non-response and a total of 246 children (120 cases and 126 controls) were included in the study.

The study data were collected at Ege University Faculty of Medicine Children’s Hospital in Bornova, Izmir between 13 January 2020 and 5 March 2020. Children and their parents who attended the general pediatrics and endocrinology / metabolic diseases outpatient clinics of the hospital and who met the study criteria were examined. The case group consisted of 120 children in the age group of 4–14 years who were diagnosed with type 1 DM based on World Health Organization and International Diabetes Federation guidelines [ 28 ] and who were being followed-up at Ege University Children’s Hospital Endocrinology / metabolic diseases outpatient clinic.

The diabetes outpatient clinic is held once a week (on Thursdays) and on the first Monday of every month. The mean number of diabetic patients attending the research was 15 patients per day. The control group comprised 126 non-diabetic children selected from the general pediatric outpatient clinic of the same hospital. A questionnaire was applied face-to-face to the parents of the children. All questions in the study were asked to the parents and separately written informed consent was obtained from children and their parents. In addition, the files of the case group were examined and the date of diagnosis, height, body weight and HbA1c levels at the time of diagnosis were collected as data.

Children who were followed up in the Endocrine and Metabolic Diseases Outpatient Clinic, diagnosed with type 1 DM and aged between 4 and 14 years were included in the case group. Children who attended the General Pediatrics Outpatient Clinic, were not diagnosed with type 1 DM, and aged 4–14 years were included in the control group. Those who did not want to share their information and could not remember answers to the study questions were excluded. The response rates were 96 and 91% among cases and controls, respectively, for all eligible cases and controls attending the hospital.

Questionnaires

A structured questionnaire was created by the researchers after reviewing the literature related to nutritional and other risk factors for type 1 DM [ 21 , 29 , 30 , 31 , 32 , 33 , 34 ]. The questionnaire was administered by interviewing the parents and its content was related to the child, mother and family of the child. For children: anthropometric data, breastfeeding duration, infant formula consumption, the introduction of some foods into the diet, infections, supplementations (vitamin D and probiotic) early in life and physical activity were questioned; for mothers, anthropometric data and history during pregnancy; for family, socio-demographic characteristics such as education, whether the child lived with parents, and family history were asked. In addition, the case group was examined about the age at diagnosis of the disease, the HbA1c level and the percentiles at diagnosis.

In this study, breastfeeding duration was defined as the total duration of breastfeeding and exclusive breastfeeding meant that the child received only breast milk (no other liquids or solids given, not even water with the exception of oral rehydration solution, or drops / syrups of vitamins, minerals or medicines) from the mother [ 35 ].

The percentiles were calculated based on the percentile values table of Neyzi et al. [ 36 ]. Parents’ body mass index (BMI) was classified according to the World Health Organization’s obesity scale [ 37 ]. Finally, high-intensity physical activity was defined as “physical activities that increase the maximum heart rate by 70 − 85%” [ 38 ]. Examples of physical activities were given (running, basketball, football, tennis, swimming, skipping rope) by the researcher.

Statistical analysis

The data were analyzed by using SPSS software. The quality of the data had been checked prior to analysis. Descriptive variables of cases and controls were compared with Student t-tests (continuous variables), Mann Whitney U tests (non-parametric) and chi-square tests (categorical variables). In order to reveal the relationship between significant parameters and the development of type 1 DM independently from other factors, age and sex-adjusted logistic regression analysis were performed. Since the difference in mean ages of the two groups was found to be significant (both age of enrolment in the study and age at diagnosis type 1 DM), other variables were evaluated adjusting for age and gender.

General pediatric outpatient clinic admissions are due to newly developing acute conditions and 85–90% are first visits to the hospital. Ten to 15 % are invited for follow-up one month later, so the follow-up is also at the same age. If they also have a chronic condition, they are referred to pediatric specialization clinics and start follow-up in those clinics.

Among the cases, six were diagnosed with type 1 DM at zero years, three of whom were excluded from the multivariate analysis since they were diagnosed in their first month of life, so the diagnosis would be before the environmental exposures could happen. The remaining three children were diagnosed at 10, 11 and 11 months, thus they were kept in the analysis since they could be exposed to potential nutritional risk factors in question.

Sex and age-adjusted multivariable logistic analysis, adjusted odds ratios (aORs) and 95% confidence intervals (95% CIs) were used to identify possible risk factors of the disease. In all analyses, p  <  0.05 was considered statistically significant. The dependent variable was having type 1 DM. Maternal factors, family history, family characteristics, nutritional characteristics early in life were the independent variables.

Population Attributable Risk (PAR) and Population Attributable Risk Percent (PAR %) were calculated to estimate the proportion of cases for whom the disease is attributable to exclusive breastfeeding and to estimate the excess rate of type 1 DM in the study population of both exposed and non-exposed children that is attributable to being non-breastfed exclusively up to the first six months. This measure was calculated as [ 39 ]:

Characteristics of children

A total of 246 children were included in the study, with 120 cases and 126 controls. The mean age of the case and control groups was 10.43 ± 3.31 and 7.48 ± 2.56 years, respectively ( p  <  0.05). The cases’ mean age at diagnosis was 6.30 ± 4.03 years and was found to be significantly lower than the control group. The mean duration of their disease was 4.2 ± 3 .85 years. The mean height percentile was higher in controls (means 45.66 ± 31.16 and 58.00 ± 31.88, p  = 0.003) and the mean BMI percentile was higher in the case group (means 55.20 ± 29.86 and 40.32 ± 35.02, p  < 0.001). A significant difference was found in the family history of type 1 DM. There was a type 1 DM history in 10.7% of the case group and 0.8% in the control group ( p  = 0.001). No significant difference was found in the child’s living status with parents and parents’ education level. However, a significant difference was found in physical activity levels ( p  = 0.014). There was no difference between the duration of vitamin D use. In both groups, no infant was supplemented with probiotics in the first year postpartum. The controls’ rate of living in urban areas was found to be significantly higher (Table  1 ).

Maternal characteristics

The mean birth interval was higher in the case group. A significant difference was found in the birth intervals with univariate analysis ( p  = 0.036) but not in multivariate analysis. Those with a birth interval of more than six years constituted 20.7% of the cases and 8.0% of controls (Table  2 ). In the case group, no mother was supplemented with probiotics during pregnancy and 98.4% of the control group were not supplemented with probiotics during pregnancy.

Nutritional profiles of children

The mean duration of exclusive breastfeeding was higher in the control group ( p  = 0.009). In the case group, the rate of exclusive breastfeeding for less than one month was 47.8, and 30.6% in the controls (Table  3 ). This difference was statistically significant ( p  = 0.037). No statistically significant differences were found between colostrum consumption, total breastfeeding duration, infant formula consumption and formula preferences.

No statistically significant difference was observed between which month the cow’s milk, eggs, fruits, vegetables, and berry fruits were introduced. However the introduction of cereals was statistically significant and the cases’ introduction to them was earlier ( p  = 0.008). For the case group 5.5% were introduced to cereals before the sixth month as compared to 3.2% of controls, while 44.2% of controls were introduced to cereals after the eighth month, compared to 24.7% of cases (Table  4 ).

Multivariate analysis

According to non-parametric correlation analyses, exclusive breastfeeding duration and total breastfeeding duration were not found to be associated with age when type 1 DM was diagnosed. The birth interval was found to be significant in the age and sex-adjusted regression analysis. In addition, regardless of age and gender, it was observ ed. that the risk of type 1 DM decreased 0.85 ( p  = 0.007; 95% CI0.76, 0.96) times with each monthly increase in the duration of exclusive breastfeeding (Fig.  1 ). Having a birth interval of more than six years increased the risk of the disease by 2.79 ( p  = 0.018; 95% CI 1.19, 6.54) times.

Sex and age adjusted (the cases’ age at diagnosis) logistic regression analysis of risk factors independent of other risk factors

According to results of the multivariate logistic regression, longer exclusive breastfeeding duration, living in a rural area and not consuming infant formula were identified as protective factors. Although there was no significant difference found in type 1 DM risk with introduction to cereals at 12 months and after, it was found that the introduction to cereals at the sixth month and earliere increased the risk of type 1 DM by 2.58 ( p  = 0.008; 95% CI 1.29, 5.16) times compared to between months7–11, independent of other risk factors. Similarly, infant formula consumption after the sixth month was associated with an increased risk of type 1 DM compared to no infant formula consumption (Table  5 ).

Sensitivity analyses

The potential impacts on our results of age at which the cases developed DM, (whether including or excluding data for the three children who were diagnosed at the first month after birth) and with data missing for the father’s education level variable, was assessed using multiple imputations, as described in the Supplementary Data . Among the cases, six of them were diagnosed with type 1 DM in the first year of life, three of whom were excluded from the multivariate analysis since they were diagnosed in their first month of life, so the diagnosis would have occurred before the environmental exposures could happen. The remaining one child was diagnosed at ten months and two children at 11 months, thus they were kept in the analysis since they could have been exposed to the potential nutritional risk factors in question.

The main analyses were repeated after adjusting for age and the father’s education level. Multiple imputations changed some of the conclusions based on the research sample as attached (Supplementary Table  1 ). The missing data on the father’s education level in the study was assessed using multiple imputations and this did not change the conclusions. So the model excluding the father’s education level and cases that developed type 1 DM before the first month was used.

PAR was calculated as 0.111 in the study and PAR% was calculated as 38.3% .

Elimination of preventable environmental risk factors associated with type 1 DM is an important step in the prevention of the disease. However, it has not been precisely explained which factors play a key role and when and in which situations the factors should be eliminated [ 22 ]. In this research we have explored possible preventable environmental triggers and determinants, especially breastfeeding early in life.

We found that each monthly increase in the duration of exclusive breastfeeding but not total breastfeeding provides a reduction in type 1 DM risk. However, introducing the cereals before the sixth month was found to be an important risk factor. The birth interval which was significant in univariate analyzes, lost its significance in multivariate analysis.

• Breastfeeding

The effect of breast milk, the first food of the newborn, on type 1 DM is a controversial issue. There are many studies in the literature that show no effect [ 40 ], a protective effect [ 19 ] and an effect [ 21 ]. It has been suggested that the protective effect of breastmilk is through reducing neonatal intestinal permeability [ 41 ]. The World Health Organization recommends feeding exclusively breast milk in the first six months of life and breastmilk up to the age of two, because feeding children with exclusively breast milk for the first six months after birth prevents diarrhea, respiratory diseases and provides all the nutrients and fluids the infant needs for optimal growth and development [ 42 ]. For participants in our study, it was observed that the rates of those who did not receive breast milk at all or those who were exclusively breastfed for less than a month were quite high. It has been observed that the accomplishment rate of the World Health Organization target for six months exclusive breastfeeding is low.

According to the Turkey Demographic and Health Survey data 2018, approximately two in five children were exclusively breastfed up to six months old and the proportion of children who are exclusively breastfed decreases with age; from 59% among 0–1 month-old infants to 14% among 4–5 months old infants [ 43 ]. On the other hand, the National Immunization Survey results indicated that only one in four children was breastfed exclusively through six months in the U.S. [ 44 ]. In our study, only the median month of breastfeeding was close to the Turkey Demographic and Health Survey data. The exclusive breast milk receiving rates through six months were found to be lower than the worldwide, National Immunization Survey and Turkey Demographic and Health Survey data in both the case and control groups. This was not surprising because our sample was quite low compared to the aforementioned samples, and the data mentioned reflected a population of children younger than two years old in 2018. So the mean age of the children in our study was higher. This result may be different in studies to be conducted with a larger population and adjusted for age. There are large differences in breastfeeding rates between regions, between and within countries. But unfortunately, these rates are insufficient both in the world and in Turkey. We can estimate that 38.3% of type 1 DM cases would be avoided by an increase in the proportion of infants exclusively breastfed to six months. Keep in mind that almost two in five infants who are not breastfed exclusively for the first six months will have type 1 DM so any intervention that can promote breastfeeding may have a big impact in preventing the disease.

In the study of Çarkçı and Altuğ (2020), conducted in the same city as this study (İzmir) the rate of children with type 1 DM who received exclusive breast milk up to the first six months was found to be more than four times compared to our study [ 45 ]. While asking the duration of exclusive breastfeeding, the definition of exclusive breastfeeding was explained as “the total time in which the baby takes only breast milk, and no other liquid (including water) or solids other than oral rehydration solution or vitamins, minerals or drugs/syrups are given” in this study. While making a statement, after the parents answered, “Have you ever given water during this period?” was asked again to be sure. In this process, there were parents who changed their answers after the second question. Therefore, different results may have been obtained in studies where this distinction was not made clear.

There are many studies on the relationship between breastfeeding and type 1 DM. Holmberg et al. (2007) found that the duration of total breastfeeding for less than four months is a risk factor for the development of beta-cell autoimmunity in children under five years old. The same study reported that the duration of exclusive breastfeeding for less than four months increased the risk of developing beta-cell autoantibodies two times [ 17 ]. In another study, it was shown that the risk of type 1 DM in childhood can be reduced by 15%, even by breastfeeding exclusively in the early weeks of life. However, the observed relationship between exclusive breastfeeding and type 1 DM could not be explained independently of certain risk factors for DM such as gestational DM, birth weight, gestational age, maternal age, birth order and mode of delivery [ 19 ]. However in a series of prospective and birth cohort studies investigating the relationship between breastfeeding and the development of islet autoimmunity, no effect of breastfeeding has been reported [ 46 , 47 ]. Similarly, a series of prospective studies investigating the relationship between breastfeeding and the development of type 1 DM reported that breastfeeding had no effect [ 48 , 49 ].

We found that longer exclusive breastfeeding duration was a significant protective factor against type 1 DM but the same effect was not observed with total breastfeeding duration. In some studies, exclusive breastfeeding and total breastfeeding duration were not compared and the duration of total or any breastfeeding was researched. Therefore the differences in studies’ results can be attributed to their methods.

In addition to distinguishing between exclusive breastfeeding and total breastfeeding, there are also differences between studies in defining exclusive breastfeeding. For instance, in two Large Scandinavian Birth Cohorts, breastfed infants were found to be at doubled risk of type 1 DM compared to infants who did not receive breast milk at all but no evidence indicated that longer duration of breastfeeding was associated with a reduced risk of the disease [ 50 ].

Similarly, in two large cohort studies, breastfeeding duration was not associated with type 1 DM [ 51 , 52 ]. Infants classified as exclusively breastfed were allowed water-based drinks in the aforementioned study and duration of exclusive breastfeeding was not taken into account.

As can be seen, many studies only look at total breastfeeding duration without making any distinction between exclusive breastfeeding duration and total breastfeeding duration. In addition, striking differences in the breastfeeding practices of governments and health authorities may be a confounding factor in the results of the studies. Positive social norms that support and encourage breastfeeding, including in public spaces, encourage mothers to breastfeed [ 53 ]. As observed in our study, the duration of exclusive breastfeeding of children after birth is quite low and it has been observed that parents do not attach sufficient importance to the period of exclusive breastfeeding.

Support from trained counselors and peers, including mothers and other family members, is as important as postpartum health care in maintaining breastfeeding in communities. The support of men, spouses and partners should not be ignored in this process [ 53 ]. In studies on breastfeeding, the mother has always been at the center and studies on the role of fathers / partners are insufficient. Tohotoa et al. highlighted the importance of the role of fathers in encouraging and supporting a successful breastfeeding process [ 54 ]. Moreover, paternal practical, physical and emotional support could make a difference [ 54 ].

When challenges experienced by mothers are shared with their partners, babies might have a better chance of receiving exclusive breast milk for the recommended six months and could keep going on breastfeeding for up to two years. In this way, the early introduction of complementary foods, especially cereals, which we found a significant risk factor for type 1 DM in our study, could be prevented.

Cow’s milk and infant formula consumption

Early exposure to cow’s milk proteins has been studied in terms of beta-cell autoimmunity and the risks of clinical disease development [ 55 ]. Early introduction of cow’s milk proteins into the diet may trigger inflammation of the intestinal mucosa and increase intestinal permeability [ 56 ]. The introduction of infant formula reflects the total duration of the exclusive breastfeeding [ 31 ]. Therefore, it should be considered together with the duration of exclusive breastfeeding. These may have led to contradictory results [ 31 ].

Some studies have shown that early exposure to cow’s milk proteins increases the risk of beta-cell autoimmunity [ 57 ] and type 1 DM [ 58 ] while others found no relationship between type 1 DM and cow’s milk proteins [ 31 , 59 ]. We also did not find an association with the timing of cow’s milk introduction. It has been observed that consumption of infant formula at six months and later increased the risk of type 1 DM in this research. However, while the risk of type 1 DM was expected to increase with the consumption of infant formula at six months and earlier compared to those who did not consume it, a statistically significant change was not observed.

This result may be explained in three ways: First, there may be a bias in choosing the control group from the same tertiary care and university hospitals with type 1 DM patients. Considering the socioeconomic status of children attending a university hospital, infant formula may have been introduced earlier than the general community and might not represent the healthy control group clearly. Second, there may have been a response bias. Third, since the mean age of children with type 1 DM is significantly higher, parents may have recall bias. Although it is easy to identify potential sources of bias, it is not possible to predict the true impact of these biases on results.

Introduction to cereals

Gluten, a protein found in barley, wheat and rye has been hypothesized to be one of the nutritional risk factors related to the development of type 1 DM [ 60 ]. A study in non-obese diabetic rats concluded that the intra-epithelial infiltration of T cells, the incidence of autoimmune type 1 DM and enteropathies decreased with a gluten-free diet compared to the controls [ 61 ]. Introduction of gluten before four months of age was associated with an increased risk of type 1 DM in another study [ 62 ]. These results were explained by the hypothesis that the gluten-free diet may prevent gliadin peptides from crossing the intestinal barrier by reducing intestinal permeability, thus preventing the development of pancreatic autoimmunity [ 63 ]. Our study supports these arguments since introducing cereals before the sixth month was found to be an important risk factor.

However in our study, the cereals were not questioned for their gluten content, they were questioned overall, but wheat production and consumption ranks in the first place among cereals in Turkey [ 64 ]. So wheat-containing cereals (including gluten) are expected to be added into the diet of infants in the transition to complementary foods at first. Nevertheless, it could not be confirmed specifically that gluten exposure was earlier in cases, but it was found that cereal introduction prior to the sixth month was associated with an increased risk of type 1 DM.

Limitations

We have many limitations in the study. As in our study, case-control studies always have the potential for bias. It is not easy to collect accurate and unbiased data on past exposures. Therefore case-control studies are prone to some sources of bias like recall bias or the control group’s selection from the hospital. Many of the established risk factors were questioned, in order to overcome confounding. However, the gestation week was not questioned at birth, so it could not be evaluated whether the birth weight was normal for gestational age. It was questioned whether they drank water during exclusive breastfeeding, but we did not collect data on when they started to consume water. Therefore this variable maybe could provide a better comparison for exclusive breastfeeding duration in future studies. In addition, the vaccination status of the children was not asked and abortion was not researched while questioning the birth interval and birth order so they may be confounding factors. Since infections in the first three years were questioned by anamnesis, their bacterial / viral status could not be determined and their relationship with enteroviruses could not be investigated.

Longer exclusive breastfeeding duration may prevent the early introduction of certain nutrients in the diet. Determining the contribution of exclusive breastfeeding and its interactions with protective factors to the disease is important in establishing preventive policies. Breastfeeding is cost-effective and may be a free intervention for the prevention of type 1 DM. Support from partners is a key factor in maintaining breastfeeding in communities. Considering the limitations of the study, systematic reviews with meta-analysis are needed in determining the role of both exclusive and non-exclusive breastfeeding on the development of type 1 DM.

Availability of data and materials

The dataset could be obtained from the corresponding author upon reasonable request.

Abbreviations

Human leukocyte antigen

Population attributable risk

Population attributable risk percent

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Acknowledgments

The authors are grateful for help and support provided by Prof. Dr. Damla Gökşen Şimşek, Assoc. Prof. Dr. Aslı Aslan, Spec. Dr. Eren Er and all health professionals from Ege University who enabled contact with potential cases and controls during the data collection.

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İpek Çiçekli

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R.D., and İ.Ç. contributed to the design and implementation of the research and to the analysis of the results. İ.Ç. contributed to the writing of the manuscript. R.D. encouraged İ.Ç. to investigate and she supervised this work. All authors read and approved the final manuscript for submission.

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Çiçekli, İ., Durusoy, R. Breastfeeding, nutrition and type 1 diabetes: a case-control study in Izmir, Turkey. Int Breastfeed J 17 , 42 (2022). https://doi.org/10.1186/s13006-022-00470-z

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Gut microbiota in children with type 1 diabetes differs from that in healthy children: a case-control study

• Mora Murri 1 ,
• Isabel Leiva 2 ,
• Juan Miguel Gomez-Zumaquero 3 ,
• Francisco J Tinahones 4 , 7 ,
• Fernando Cardona 1 , 4 ,
• Federico Soriguer 5 , 6 &
• María Isabel Queipo-Ortuño 1 , 4  

BMC Medicine volume  11 , Article number:  46 ( 2013 ) Cite this article

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A recent study using a rat model found significant differences at the time of diabetes onset in the bacterial communities responsible for type 1 diabetes modulation. We hypothesized that type 1 diabetes in humans could also be linked to a specific gut microbiota. Our aim was to quantify and evaluate the difference in the composition of gut microbiota between children with type 1 diabetes and healthy children and to determine the possible relationship of the gut microbiota of children with type 1 diabetes with the glycemic level.

A case-control study was carried out with 16 children with type 1 diabetes and 16 healthy children. The fecal bacteria composition was investigated by polymerase chain reaction-denaturing gradient gel electrophoresis and real-time quantitative polymerase chain reaction.

The mean similarity index was 47.39% for the healthy children and 37.56% for the children with diabetes, whereas the intergroup similarity index was 26.69%. In the children with diabetes, the bacterial number of Actinobacteria and Firmicutes, and the Firmicutes to Bacteroidetes ratio were all significantly decreased, with the quantity of Bacteroidetes significantly increased with respect to healthy children. At the genus level, we found a significant increase in the number of Clostridium, Bacteroides and Veillonella and a significant decrease in the number of Lactobacillus, Bifidobacterium, Blautia coccoides/Eubacterium rectale group and Prevotella in the children with diabetes. We also found that the number of Bifidobacterium and Lactobacillus , and the Firmicutes to Bacteroidetes ratio correlated negatively and significantly with the plasma glucose level while the quantity of Clostridium correlated positively and significantly with the plasma glucose level in the diabetes group.

Conclusions

This is the first study showing that type 1 diabetes is associated with compositional changes in gut microbiota. The significant differences in the number of Bifidobacterium, Lactobacillus and Clostridium and in the Firmicutes to Bacteroidetes ratio observed between the two groups could be related to the glycemic level in the group with diabetes. Moreover, the quantity of bacteria essential to maintain gut integrity was significantly lower in the children with diabetes than the healthy children. These findings could be useful for developing strategies to control the development of type 1 diabetes by modifying the gut microbiota.

Peer Review reports

Type 1 diabetes is a worldwide problem, mainly in children, and it is associated with a significant burden, mostly related to the development of vascular complications [ 1 ]. Type 1 diabetes is the result of a complex interaction between different degrees of genetic susceptibility and environmental factors [ 2 – 4 ]. The intestinal microbiota is one of these environmental factors currently under study, partly as a result of observations in both non-obese diabetic (NOD) mice and BioBreeding diabetes-prone rats, where the use of antibiotics was shown to prevent the onset of diabetes [ 5 , 6 ]. Moreover, a recent study using NOD mice suggested that the development of type 1 diabetes can be prevented through modulation of the intestinal microbiota [ 7 ]. Newly, Vaarala et al . suggested that the interaction between the intestinal environment, the barrier function and the immune system are crucial in the onset of type 1 diabetes [ 4 ]. Using a rat model, Roesch et al . found significant differences at the time of diabetes onset in the bacterial communities responsible for type 1 diabetes modulation [ 8 ]. Moreover, other studies have shown that beneficial bacteria, such as probiotic bacteria, have a protective effect in rodent models by delaying or preventing the onset of type 1 diabetes [ 9 , 10 ]. With respect to mechanisms of action, Wen et al . found that the gut microbiome of NOD mice lacking an adaptor for multiple innate immune receptors responsible for recognizing microbial stimuli correlates with the disease onset, revealing a relationship between gut microbiota and the immune system [ 11 ]. Recent studies have demonstrated that commensal bacteria are crucial for maturation and function of the mucosal immune system. The balance between two major effector T cell populations in the intestine, IL-17+ T helper 17 cells and Foxp3+ regulatory T cells, requires signals from commensal bacteria and is dependent on the composition of the intestinal microbiota [ 12 – 14 ]. In addition, increased gut permeability has been observed in patients with type 1 diabetes as well as in NOD mouse and BioBreeding rat models [ 15 – 18 ]. It has been suggested that this increased gut permeability (commonly called leaky gut) may affect the absorption of antigens that can attack and damage pancreatic beta cells [ 19 ]. Because gut microbes can affect intestinal permeability, the gut ecology may play a role in the development of type 1 diabetes [ 20 ].

Only a few studies have evaluated the ecology of intestinal microbiota in autoimmune children who were not yet diabetic [ 21 , 22 ]. These studies used a very low number of participants (four patients and four controls) and neither of them have controlled for such an important factor as the mode of delivery (natural birth or Cesarean) or the type and time of infant feeding (formula-fed or breast-fed), both of which determine the gut microbial composition during infancy [ 23 , 24 ].

The aim of the present study, therefore, was to characterize the composition of fecal microbiota in children with type 1 diabetes as compared with children without diabetes (controlling for such factors as mode of delivery and breastfeeding time) using PCR-denaturing gradient gel electrophoresis (DGGE) and real-time quantitative PCR (qPCR) analysis. This was to determine whether there were significant differences in the gut microbiota composition between these groups and, if so, to quantify the differences and determine the possible relation of the gut microbiota of children with type 1 diabetes with their glycemic level.

Study participants and design

The case-control study included 16 Caucasian children with type 1 diabetes, aged 7.16 ±0.72 years, and 16 healthy Caucasian children, aged 7.48 ±0.87 years. Type 1 diabetes was diagnosed following the criteria of the American Diabetes Association [ 25 , 26 ] and the appearance of at least two persistent, confirmed anti-islet autoantibodies (insulin autoantibodies, glutamic acid decarboxylase autoantibodies or tyrosine phosphatase autoantibodies). The patients with diabetes were treated and monitored according to a standard medical protocol. Patients were excluded if they had any other acute or chronic inflammatory diseases or infectious diseases at study entry. The study participants received no antibiotic treatment, probiotics, prebiotics or any other medical treatment influencing intestinal microbiota during the 3 months before the start of the study. The selected healthy children were all type 1 diabetes autoantibody negative and they were matched to the children with diabetes for age, gender, race, mode of delivery and duration of breastfeeding. The parents of the patients and controls completed a structured interview to obtain the following data: health status, lifestyle aspects (such as living environment and physical activity) and dietary habit. The dietary intake patterns in patients and controls were determined from a food frequency questionnaire that allowed us to assess the consumption of groups of foods. The written guardian or parental consents of the children were obtained. The sampling and experimental processes were performed with the approval of the local Ethics Committee of Ciudad de Jaen hospital. Stool samples were collected by parents at home and delivered to the storage area for frozen storage at -80ºC within one hour [ 27 ].

Anthropometric measurements

Body weight and height were measured according to standardized procedures [ 28 ].

Laboratory measurements

Fasting venous blood samples were collected. The serum was separated in aliquots and immediately frozen at -80ºC. Serum biochemical parameters were measured in duplicate. Serum glucose, cholesterol and triglycerides were measured using a standard enzymatic method (Randox Laboratories Ltd., Antrim, UK). The quantitative detection of autoantibodies to islet cell antigens was done using the Elisa RSR GADAb Kit, Elisa RSR IA-2Ab Kit and RIA RSR IAA Kit (RSR Limited, Cardiff, UK).

DNA extraction from fecal samples

Fecal samples were immediately kept after collection at -80°C and stored until analyzed. DNA extraction from 200 mg of stools was done using the QIAamp DNA Stool Mini Kit (Qiagen, Hilden, Germany) following the manufacturer's instructions. The DNA concentration was determined by absorbance at 260 nm (A260), and the purity was estimated by determining the A260 to A280 ratio with a Nanodrop spectrophotometer (Nanodrop Technologies, Wilmington, DE, USA).

Analysis of fecal microbiota by PCR-DGGE

Fecal samples from each participant were examined by determining PCR-DGGE profiles as recently published by us [ 29 ]. The V2 to V3 regions of the 16S rRNA genes (positions 339 to 539 in the Escherichia coli gene) of bacteria in the fecal samples were amplified by primers HDA1-GC (5′- CGC CCG CCG CGC GCG GCG GGC GGG GCG GGG GCA CGG GGG G CC TAC GGG AGG CAG CAG T-3′; (the GC clamp is in boldface)) and HDA2 (5′-GTA TTA CCG CGG CTG CTG GCA C-3′) generating a 200 bp product. Aliquots (2 μL) of DNA were amplified by real-time PCR (20 μL final volume) in a 7500 Fast Real-Time PCR Systems instrument using Fast SYBR Green Master Mix and 200 nM of each of the universal primers HDA1-GC or HDA2 with the following amplification program: initial denaturation at 95°C for 20 s; amplification using 45 cycles including denaturation at 95°C for 3 s; annealing at 55°C for 30 s; and extension at 72°C for 1 min. Negative controls without a DNA template were included in each analysis.

After real-time PCR, 15 μL of products were mixed with 6 μL of loading dye before loading. Electrophoresis was performed with a DCode Universal Mutation Detection System instrument (Bio-Rad Laboratories, S.A, Madrid, Spain). Six percent polyacrylamide gels were prepared and electrophoresed with 1× TAE buffer prepared from 50× TAE buffer (2 M Tris base, 1 M glacial acetic acid, 50 mM ethylenediaminetetraacetic acid (EDTA)). The denaturing gradient was formed by using two 6% acrylamide (acrylamide to bisacrylamide ratio 37.5:1) stock solutions (Bio-Rad). The gels contained a 20% to 80% gradient of urea and formamide that increases in the direction of electrophoresis. Electrophoretic runs were in a TAE buffer (40 mmol/L Tris, 20 mmol/L acetic acid, and 1 mmol/L EDTA, pH 7.4) at 130 V and 60°C for 4.5 h. Electrophoresis was stopped when a xylene cyanol dye marker reached the bottom of a gel. Gels were stained with ethidium bromide (0.5 mg/L) for 5 min, rinsed with deionized water, viewed by UV transillumination and photographed with Gelcapture image acquisition software (DNR Bio-Imaging Systems Ltd, Mahale HaHamisha, Jerusalen, Israel). All the samples were analyzed on the same DGGE run to avoid the possible influence of variations in electrophoretic conditions between different runs. No band was observed in the negative controls. Similarities between banding patterns in the DGGE profile were calculated based on the presence and absence of bands and expressed as a similarity coefficient. Gels were analyzed using BioNumerics software (Applied Maths, Sint-Martens-Latem, Belgium). Normalized banding patterns were used for cluster analysis. The Dice similarity coefficient was used to calculate pairwise comparisons of the DGGE fingerprint profiles obtained. A similarity coefficient value of 100% indicates that DGGE profiles are identical while completely different profiles result in a similarity coefficient value of 0%. The unweighted pair group method with arithmetic mean algorithm was used for construction of dendrograms.

Sequencing of selected bands from DGGE gels

Bands were excised from DGGE gels with a sterile razor, placed in 40 μL sterile water and incubated at 4°C for diffusion of DNA into the water. DNA were used in a second PCR with HDA1/2 primers without a GC-clamp (initial denaturation at 95°C for 20 s, followed by 45 cycles including denaturation at 95°C for 3 s, annealing at 55°C for 15 s and extension at 72°C for 10 s). Subsequently, the PCR products were directly cloned into pCR 4-TOPO (Invitrogen, Carlsbad, CA, USA) according to the manufacturer's instructions. Plasmid DNA was isolated from the cells using the Qiagen Mini Spin Prep Kit (Qiagen), and subjected to PCR (HDA1/2-GC) as earlier described. PCR products were diluted until 20 ng/μL, purified with ExoSAP-IT (USB Corporation, Cleveland, OH, USA) and sequenced in an ABI 3130 (Applied Biosystems, Inc., Foster City, CA, USA) using the BigDie-Kit-Standard. Nucleotide sequence data obtained were analyzed using MicroSeqID v2.1.1 software (Applied Biosystems).

Microbial quantification by real-time qPCR

Specific primers targeting different bacterial genera were used to characterize the fecal microbiota by quantitative real-time qPCR (Table 1 ) [ 30 – 36 ]. Briefly, quantitative PCR experiments were performed with a LightCycler 2.0 PCR sequence detection system using the FastStart DNA Master SYBR Green kit (Roche Diagnostics, Indianapolis, IN, USA). All PCR tests were carried out in duplicate, with a final volume of 20 μL containing 1 μL of each fecal DNA preparation and 200 nM of each primer (Table 1 ). The thermal cycling conditions used were as follows: an initial DNA denaturation step at 95°C for 10 min; 45 cycles of denaturation at 95°C for 10 s; primer annealing at optimal temperature for 20 s; extension at 72°C for 15 s. Finally, melt curve analysis was performed by slowly cooling the PCRs from 95°C to 60°C (0.05°C per cycle) with simultaneous measurement of the SYBR Green I signal intensity. Melting-point-determination analysis allowed the confirmation of the specificity of the amplification products. Each participant's extracted DNA was subjected to a human β-globin PCR to ensure that amplifiable DNA was successfully extracted from the sample and to monitor for PCR inhibitors with the reaction conditions described previously [ 37 ]. The bacterial concentration from each sample was calculated by comparing the threshold cycle values obtained from the standard curves with the LightCycler 4.0 software. Standard curves were constructed for each experiment using serial 10-fold dilutions of bacterial genomic DNA (of known concentration) from pure cultures, corresponding to 10 1 to 10 10 copies per gram of feces.

The different strains used were obtained from the Spanish Collection of Type Cultures (CECT) ( Bacteroides vulgatus NCTC 11154, Fusobacterium varium NCTC 10560, Enterococcus faecalis CECT 184, Enterobacter cloacae CECT 194, Clostridium perfringens CECT 376) and the American Type Culture Collection (ATCC) ( Bifidobacterium bifidum ATCC 15696, Lactobacillus casei ATCC 334D-5, Prevotella intermedia ATCC 25611D-5, Ruminococcus productus ATCC 27340D-5 and Veillonella dispar ATCC 17745). Standard curves were normalized to the copy number of the 16S rRNA gene for each species. For species for which the copy number of 16S rRNA operon was not published, the copy number was calculated by averaging the operon numbers of the closest bacterial taxa from the ribosomal RNA database rrnDB [ 38 ]. Negative controls containing all the elements of the reaction mixture except template DNA were performed in every analysis and no product was ever detected. The data presented are the mean values of duplicate real-time qPCR analyses. The amplification efficiency of the qPCR for all primer pairs was determined using the linear regression slope of a dilution series based on the following equation E = 10 (-1/slope) . We found that for 13 primer pairs the efficiency ranged from 98% (E = 1.96) to 100% (E = 2) with slopes values in the range of -3.4 to -3.32.

Statistical analysis

Results are expressed as mean values and standard deviations. The statistical analysis was performed with SPSS 15.0 software (SPSS Inc., Chicago, IL, USA). The sample size was calculated to obtain a difference in the mean bacterial number between the healthy children and those with type 1 diabetes of at least 2 × 10 5 copies per gram of feces. With a power of 80%, an alpha error of 0.05 and an estimated standard deviation between group of 1.13 × 10 5 copies per gram of feces (data obtained from Wu et al . [ 39 ]), six children were needed in each group. However, we increased the number of participants to 16 children and 16 controls. The bacterial copy number values were converted into logarithmic values before the statistical analysis. Given the low number of participants analyzed, the Mann-Whitney U test was used to check changes in bacterial number and biochemical variables between the two groups. The Spearman correlation coefficient was calculated to estimate the linear correlations between variables. A multivariate regression analysis was performed to identify individual bacteria as independent predictors for plasma glucose level. Statistical significance was set at a P value of <0.05. All data are presented in the text as the mean ± SD.

The healthy children and those with diabetes all had similar physical activity and dietary habits. The analysis of the food frequency questionnaires showed no significant differences in the consumption patterns of rice, wheat, vegetables, fish or meat between the two study groups, although the children with diabetes had a fast carbohydrate restriction (foods made with white flour and refined sugar).

Anthropometric and biochemical measurements

The anthropometric and biochemical variables of the healthy children and those with diabetes are shown in Table 2 . Apart from the levels of glucose and HbA1c, which were significantly higher in the children with diabetes, no other significant differences were seen between the groups in the anthropometric and biochemical variables. In addition, because the healthy children and the children with diabetes were matched for breastfeeding time and mode of delivery, no significant differences were noted in these variables.

PCR-DGGE and bacterial band identification

Variations were found in the presence or absence (qualitative) and intensity (quantitative) of the bands between the healthy children and the children with diabetes in the host-specific fingerprints generated. DGGE band profiles showed differences in band richness between the two groups. Analysis of the diversity of the microbiota showed that the mean of the DGGE bands was 13.85 ±3.87 for the healthy children and 11.63 ±3.64 for the children with diabetes, though the difference was not significant. Some bands were seen in fingerprints from all the children (in different lanes but at the same position), indicating that specific species of the predominant microbiota were common to all the children.

The Dice similarity coefficient was used to calculate the similarity index between DGGE band profiles related to sampling of healthy children and those with diabetes. The mean similarity index was 47.39% for the healthy children and 37.56% for the children with diabetes. The mean similarity index between the groups was 26.69%, lower than the intra-group similarity (Table 3 ). The DGGE gel and the results of the cluster analysis are shown in Figure 1 . The cluster analysis showed that the intra-group similarity for the diabetic and the healthy groups was significantly higher than the inter-group similarity. These results demonstrate that the dominant microbiota in the healthy group was different from that of the diabetic group.

Cluster analysis . Dendrograms of electrophoretic band patterns obtained in the denaturing gel gradient electrophoresis experiment with universal primers in the fecal samples collected from healthy children (H) and those with type 1 diabetes (D). (A) Cluster analysis; (B) DGGE profiles related to fecal samples; (C) line graph.

All the bands from the profiles of all the healthy children and the children with diabetes were cloned and sequenced to identify the dominant microbiota. The sequence similarity matches for bands were analyzed by MicroSeqID v2.1.1 software. Bacterial identification showed that the majority of bacteria represented in the fingerprints obtained corresponded to five phyla (Table 4 ). Most of the sequences belonged to Firmicutes and Bacteroidetes, with the rest distributed among Actinobacteria, Fusobacteria and Proteobacteria. Nevertheless, we observed important differences between the healthy children and the children with diabetes in the distribution ratio of the different genera within Bacteroidetes, Firmicutes and Actinobacteria phyla. In the children with diabetes, we found an increase in the Clostridium, Bacteroides, Veillonella, Eggerthella and Bacillus frequencies and a disappearance of Prevotella and Bifidobacterium as compared with the healthy children (Table 4 ).

Comparative analysis of gut microbiota communities in healthy children and children with diabetes

Changes in the bacterial population abundance were assessed in the fecal samples of both groups. The results obtained in the real-time qPCR experiments with the different primers are shown in Tables 5 and 6 . Relevant differences were found in the bacteria number of three phyla between the diabetic and the healthy children. The number of Actinobacteria, Firmicutes and Bacteroidetes were significantly different between groups whilst the quantity of Proteobacteria and Fusobacteria were similar between the groups. In the children with diabetes, the bacterial number of Actinobacteria and Firmicutes was significantly decreased while that of Bacteroidetes was significantly increased with respect to the healthy children. Moreover, the Firmicutes to Bacteroidetes ratio was significantly lower in the children with diabetes than the healthy children.

Within Firmicutes, the quantity of Veillonella was significantly higher and the number of bacteria from the Blautia coccoides - Eubacterium rectale group was significantly lower in the children with diabetes compared with the healthy children. The Lactobacillus number was significantly lower and Clostridium levels significantly higher in the children with diabetes. However, no significant differences were found in Enterococcus levels between the two groups. Within Bacteroidetes, the quantity of Bacteroides was significantly higher whereas the number of Prevotella was significantly lower in the children with diabetes compared with the healthy children. Finally, within Actinobacteria, the number of Bifidobacterium was significantly lower in the children with diabetes.

Relationship between gut microbiota composition in children with type 1 diabetes and glycemic level

In the children with diabetes, we found a significant univariate correlation between the amount of specific bacterial groups and the plasma glucose levels ( Bifidobacterium r = -0.797, P = 0.008; Clostridium r = 0.676, P <0.05; Lactobacillus r = -0.698, P <0.05; and Firmicutes to Bacteroidetes ratio r = -0.473, P <0.05) and HbA1c levels ( Bifidobacterium r = -0.573, P <0.05; Clostridium r = 0.452, P <0.05; Firmicutes r = -0.559, P <0.05; Firmicutes to Bacteroidetes ratio r = -0.765, P = 0.012). A multivariate regression analysis that included all the bacterial groups analyzed showed that only the reduction in the number of Bifidobacterium and Lactobacillus was associated with the plasma glucose level ( P <0.05, β = -0.476, R 2 = 0.587; and P = 0.012, β = -0.687, R 2 = 0.539, respectively) whereas the higher HbA1c level was associated with the decrease in the Firmicutes to Bacteroidetes ratio ( P <0.001, β = -1.047, R 2 = 0.781) and the increase in the number of Clostridium ( P = 0.016, β = 0.867, R 2 = 0.499).

In the present study we found significant differences in the fecal microbial composition between healthy children and children with type 1 diabetes. We are unaware of any other similar studies in children with type 1 diabetes using simultaneously DGGE molecular profiling, unweighted pair group method with arithmetic mean algorithm dendrogram construction, sequencing and real-time qPCR analysis. To determine the characteristics of the gut microbiota based on the condition of just type 1 diabetes, we excluded the influence of physiological factors such as age, gender, dietary habits and race. In addition, we also controlled for the mode of delivery at birth and the duration of breastfeeding. This was because the first year of life has a crucial impact on gut microbiota composition and epidemiological studies in humans at genetic risk for type 1 diabetes have suggested that a short duration of breastfeeding and early feeding in infancy with complex dietary proteins such as cow's milk proteins can modulate the development of beta cell autoimmunity, clinical type 1 diabetes, or both [ 40 – 42 ]. No significant differences were found between the two groups of children (type 1 diabetes and controls).

The DGGE analysis of the fecal microbiota revealed a significantly lower intra-group similarity index in children with diabetes than in healthy children. In other words, the DGGE profiles in healthy children were more similar to each other, whereas in children with diabetes they were less similar. A similar result was found by Giongo et al . [ 21 ]. These data suggest that diabetic status may influence specific bacterial groups of the gut microbiota community.

Sequence analysis of the DGGE bands cloned enable the association of specific bacterial genotypes with health or diabetes situations. Consistent with previous human and animal studies [ 11 , 21 , 39 , 37 , 43 ], the gut microbiota of healthy children and children with diabetes was predominately composed of Firmicutes and Bacteroidetes and the main difference lies in the proportion of genus-division bacteria within this two phyla and the Actinobacteria phylum between both the group with diabetes and the healthy group. These results suggest that the dominant microbiota genera are different in children with type 1 diabetes compared with healthy children. Recently, three robust clusters, referred to as "enterotypes", which are not nation or continent specific have been identified. Assignment of an individual microbiome into a given enterotype is based upon the relative enrichment of that microbiome in one of three genera: Bacteroides (enterotype 1), Prevotella (enterotype 2) or Ruminococcus (enterotype 3) [ 44 ]. In this study, within Bacteroidetes, the Bacteroides genus was prevalent in the diabetic group, whereas the Prevotella genus was associated with the healthy group. Thus, the type 1 diabetic gut microbiomes could be classified into enterotype 1 and the healthy microbiomes could be classified into enterotype 2.

As DGGE is considered a semiquantitative tool for monitoring the dynamics of the predominant bacterial species of fecal microbiota, an additional analysis with real-time qPCR was performed to obtain a quantitative estimation of the changes found in the gut microbiota between children with diabetes and healthy children. We noted significant quantitative differences between the major microbial phyla present in the feces of healthy children and those with diabetes. In contrast to the situation in healthy children, we found a significant increase in the quantity of Bacteroidetes and a significant decrease in the number of Firmicutes and Actinobacteria in children with type 1 diabetes. Our data showed a significantly lower Firmicutes to Bacteroidetes ratio in children with type 1 diabetes compared with healthy children. Moreover, we saw a negative correlation between this ratio and both the glucose and the HbA1C levels in children with diabetes, which could help to explain the significantly higher glycemic level in this group. In agreement with this, Giongo et al . observed that the Firmicutes to Bacteroidetes ratio in study participants with type 1 diabetes was changing during the first 6 months after birth before the development of the autoimmune disease. These authors showed a successive decline in Firmicutes and an increase in Bacteroidetes number in the gut microbiome over time until the children became diabetic [ 21 ]. Moreover, this imbalance observed at the phylum level between Bacteroidetes and Firmicutes has been previously described in several human disorders. A decline in the Firmicutes to Bacteroidetes ratio compared with controls has been described in human type 2 diabetes [ 45 ], whereas in Crohn´s disease, both Bacteroidetes and Firmicutes seem to decline [ 46 ]. The opposite happens in obesity, where the imbalance is due to the increase in the Firmicutes to Bacteroidetes ratio [ 47 ], indicating that obesity and diabetes are associated with different groups of intestinal microbiota.

However, the major difference between the two groups was found in the number of bacteria at genus-division level. The most remarkable result was the significant increase in the number of Clostridium, Bacteroides and Veillonella in the children with diabetes, whereas the number of Lactobacillus, Bifidobacterium , the Blautia coccoides/Eubacterium rectale group and Prevotella genus were all significantly decreased in children with diabetes. Our findings concerning the microbiota of children with diabetes are in line with observations in other animal studies. Roesch et al . found higher levels of Lactobacillus and Bifidobacterium in BioBreeding diabetes-resistant rats whereas Bacteroides and Clostridium were more abundant in BioBreeding diabetes-prone rats [ 27 ]. In contrast with this, however, Brown et al . found that Lactobacillus and Bifidobacterium were more abundant in participants with type 1 diabetes than in healthy participants [ 22 ].

The significant decrease in the number of Lactobacillus and Bifidobacterium observed in children with type 1 diabetes in our study was associated with their higher levels of plasma glucose, as indicated by the negative correlation found. Also, the regression analysis showed that the decrease in the number of Lactobacillus and Bifidobacterium could be associated with the plasma glucose level in the children with diabetes. In previous studies, the levels of Bifidobacterium have also been related to improved glucose metabolism, insulin resistance and low-grade inflammation [ 48 , 49 ]. Moreover, Valladares et al . determined that the administration of Lactobacillus johnsonii isolated from BioBreeding diabetes-resistant rats delays or inhibits the onset of type 1 diabetes in BioBreeding diabetes-prone rats [ 10 ].

Both Lactobacillus and Bifidobacterium have members with probiotic characteristics and these have been associated with positive effects for the host in the large intestine [ 50 ]. In addition, both bacterial groups have the capacity to produce the beneficial organic acid lactate, which is converted into butyrate by butyrate-producing bacteria in the gut [ 22 ]. Barcenilla et al . [ 51 ] showed that most of the butyrate-producing isolates from human fecal samples are related to the Blautia coccoides-Eubacterium rectale group. Previous studies have shown that butyrate induces mucin synthesis (a glycoprotein produced by the host that could maintain the integrity of the gut epithelium) [ 52 ], decreases bacterial transport across the epithelium [ 53 ], and improves gut integrity by increasing tight junction assembly [ 54 ]. In addition, the genera Prevotella are responsible for the degradation of this mucin [ 55 ]; thus, the significant decline in the numbers of the Blautia coccoides-Eubacterium rectale group and Prevotella that we found in children with type 1 diabetes compared with healthy children could indicate a reduction in mucin synthesis by the host and a lack of this mucin on the epithelial layer of the gut, which would lead to a significant alteration in intestinal permeability. Other studies have described an association between type 1 diabetes and compromised barrier permeability in humans and both the NOD mouse and BioBreeding rat models [ 16 – 18 , 20 ].

The significant increase in the number of Clostridium, Bacteroides and Veillonella in the children with diabetes with respect to the healthy children was accompanied by a significant positive correlation between both the plasma levels of glucose and HbA1c and the quantity of Clostridium . These bacteria are able to ferment glucose and lactate to propionate, acetate and succinate. However, these short fatty acids do not induce mucin synthesis [ 52 ]. This situation would, though, reduce the tight junction assembly, generating an increase in the gut permeability in children with type 1 diabetes [ 22 ].

Finally, we propose a possible mechanism to explain the relationship we have found between the gut microbiota present in children with type 1 diabetes and the glycemic levels observed. The short-chain fatty acids (such as butyrate and propionate) formed by this gut microbiota have a role in the regulation of the levels of gut hormones such as glucose-dependent insulinotropic polypeptide, glucagon-like peptide 1 and ghrelin. These hormones have important effects on carbohydrate metabolism [ 56 ], thus allowing gut microbiota to affect glycemic levels. In addition, Huml et al . have previously demonstrated an altered secretion pattern of gut hormones in children with type 1 diabetes that may impact on the metabolic control of diabetes in these patients [ 57 ]. Further studies will be necessary to demonstrate this proposed mechanism.

A limitation of the 16S rRNA gene-based method is that the function of the identified bacteria is unknown. Future studies using a microbial metagenomic sequencing analysis will be carried out to obtain information about the functional diversity of the bacterial community analyzed here.

This is the first study showing that type 1 diabetes is associated with compositional changes in gut microbiota. Our results show that gut microbiota found in children with type 1 diabetes differed significantly from that found in healthy children. The gut microbiota in the children with diabetes was less similar than the gut microbiota in the healthy children. The significant differences between the diabetic and the healthy children in the number of Bifidobacterium, Lactobacillus and Clostridium and the Firmicutes to Bacteroidetes ratio could be implicated in the glycemic level of the children with diabetes. In addition, the numbers of lactic acid-producing bacteria, butyrate-producing bacteria and mucin-degrading bacteria, essential to maintain gut integrity, were significantly lower in the children with diabetes than the healthy children. These bacterial differences could be responsible for the altered gut permeability previously described in patients with type 1 diabetes. These findings could be useful for developing strategies to control the development of type 1 diabetes by modifying the gut microbiota.

Abbreviations

denaturing gradient gel electrophoresis

ethylenediaminetetraacetic acid

non-obese diabetic mice

polymerase chain reaction

quantitative polymerase chain reaction

standard deviation

buffer with Tris base, glacial acetic acid and ethylenediaminetetraacetic acid.

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Acknowledgements

The authors wish to thank all the participants for their collaboration and the Plataforma de Secuenciacion y Genotipado and the Unidad Central de Biología Molecular (Servicio de PCR a tiempo real) of FIMABIS for their help in laboratory assays. We also gratefully acknowledge the help of Ian Johnstone for his expertise in preparing this manuscript. The research group belongs to the Centros de Investigación en Red (CIBEROBN, CB06/03/0018 and CIBERDEM, CB07/08/0019) of the Instituto de Salud Carlos III. This work was partially funded by a grant from CIBER, CB06/03/0018 of the Instituto de Salud Carlos III to MM, FC, FJT and MIQO; the Instituto de Salud Carlos III, Madrid, Spain (CP07/0095) to FJT; and the Servicio Andaluz de Salud, Andalucía, Spain (PI0696/2010) to FJT. The funding agencies had no role in the design and performance of the study, the interpretation of the data or the writing of the manuscript.

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Authors' contributions

IL, FC, FS, FJT and MIQO conceived the study and developed the experimental design. IL, FS, FJT and MIQO were responsible for acquisition and selection of all samples utilized in this study. MM, IL, FC, JMGZ and MIQO performed all laboratory assays. MM, IL, JMGZ, FC and MIQO compiled the database and performed statistical analysis and data interpretation. MM, IL, JMGZ, FC, FS, FJT and MIQO wrote the paper. FS, FJT and MIQO provided critical revision. All the authors have read and approved the final manuscript.

Mora Murri, Isabel Leiva contributed equally to this work.

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Murri, M., Leiva, I., Gomez-Zumaquero, J.M. et al. Gut microbiota in children with type 1 diabetes differs from that in healthy children: a case-control study. BMC Med 11 , 46 (2013). https://doi.org/10.1186/1741-7015-11-46

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Systematic review article, association between enterovirus infection and type 1 diabetes risk: a meta-analysis of 38 case-control studies.

• 1 Jinhua Maternity and Child Health Care Hospital, Jinhua, China
• 2 Jinhua Women and Children’s Hospital, Jinhua, China
• 3 First Department of Neurology, Affiliated Jinhua Hospital, Jinhua Municipal Central Hospital, Zhejiang University School of Medicine, Jinhua, China

Objective: The association between enterovirus infection and type 1 diabetes (T1D) is controversial, and this meta-analysis aimed to explore the correlation.

Methods: PubMed, Embase, Web of Science, and Cochrane Database were searched from inception to April 2020. Studies were included if they could provide sufficient information to calculate odds ratios and 95% confidence intervals. All analyses were performed using STATA 15.1.

Results: Thirty-eight studies, encompassing 5921 subjects (2841 T1D patients and 3080 controls), were included. The pooled analysis showed that enterovirus infection was associated with T1D ( P < 0.001). Enterovirus infection was correlated with T1D in the European ( P < 0.001), African ( P = 0.002), Asian ( P = 0.001), Australian ( P = 0.011), and Latin American ( P = 0.002) populations, but no conclusion could be reached for North America. The association between enterovirus infection and T1D was detected in blood and tissue samples (both P < 0.001); no association was found in stool samples.

Conclusion: Our findings suggest that enterovirus infection is associated with T1D.

Introduction

Type 1 diabetes (T1D) is a multifactorial disease resulting from the autoimmune destruction or dysfunction of pancreatic β cells ( 1 ). T1D has become a global burden, and at least 13 million individuals suffer from the disease worldwide ( 2 , 3 ). Exogenous insulin injection cannot produce an optimal control of glucose homeostasis, leading to microvascular complications in the heart, brain, eye, kidney, and peripheral nervous system ( 4 ).

Although several environmental factors have been reported to be associated with T1D, enterovirus infection is under intensive focus ( 4 – 6 ). It is a ubiquitous, small, non-enveloped positive-strand RNA virus. Enterovirus genus belongs to the Picornaviridae family and consists of 15 species, seven of which contain human pathogens. These human infecting enteroviruses are classified into four species (Enterovirus A-D and Rhinovirus A-C) and contain more than 250 serologically distinct viruses. Enterovirus A-D consists of over 100 different types, including polioviruses, coxsackievirus types A and B (CVA and CVB), numbered enteroviruses, and echoviruses ( 7 , 8 ). Enteroviruses potentially interact with several receptors ( 9 ), among which the coxsackie and adenovirus receptor (CAR) is the most studied with respect to T1D. Enteroviruses can infect pancreatic β cells in pancreatic islets via the CAR, which is expressed on β and α cells, and the viruses replicate in both these cell types ( 10 , 11 ). Both acute and persistent enterovirus infections have been shown to affect the functions of the host cell, inducing β cell death, decreasing insulin mRNA expression and insulin secretion, and disrupting the Golgi apparatus ( 11 – 16 ).

A meta-analysis identified the correlation between enterovirus infection and T1D in 2011 ( 17 ). Although several original studies have been reported from 2012 to 2020 ( 10 , 18 – 34 ), no updated meta-analysis has been performed to explore and refine the correlation. The conclusions of the original studies are conflicting, and thus, a meta-analysis including the latest studies is still needed to evaluate the association between enterovirus infection and T1D.

Materials and Methods

Search strategy.

PubMed, Embase, Web of Science, and Cochrane Database were searched for relevant studies. We used the following terms for searching: “enterovirus” AND (“type 1 diabetes” OR “type 1 diabetic patients” OR “type 1 diabetes mellitus” OR “insulin-dependent diabetes” OR “insulin-dependent diabetic patients” OR “T1D” OR “T1DM”). The searches were restricted to English-language articles published up to April 2020. We also reviewed the references of included articles to identify any potential additional study.

Inclusion and Exclusion Criteria

The studies were eligible if they met the following criteria: (1) study design: case-control; (2) outcomes: investigated the association between enterovirus infection and T1D and reported the number of subjects with and without enterovirus infection for each group; (3) subjects: patients with insulin-dependent diabetes (i.e., T1D); and (4) controls: non-diabetic individuals. When there were multiple publications from the same study population, only the publication with the largest sample size was included. Studies were excluded if they were (1) reviews, letters, or case reports, (2) cell or animal studies, or (3) duplicate publications from the same population.

Data Extraction

Data were extracted independently by two authors. Disagreements were resolved by a third author. The following information was extracted: first author, publication year, country, mean age, the gender ratio of the cases, number of patients in the case and control group, number of enterovirus infections in each group, detection method, sample source, and enterovirus type. We also contacted the corresponding author to obtain details of the missing relevant data.

Quality Assessment

The Newcastle-Ottawa quality assessment scale (NOS) ( 35 ), a 9-star system, was used for quality assessment. Two authors assessed the studies independently. Any differences were resolved by consulting a third author. The assessment scale included the selection method of the exposed group (with enterovirus infection) and the non-exposed group (without enterovirus infection), the matching of the two groups, and the outcome assessment. A study awarded more than 5 stars was considered a high-quality study.

Statistical Analysis

Odds ratio (OR) and 95% confidence interval (CI) were used to estimate the strength of the association between enterovirus infection and T1D. The fixed-effect model was used for non-heterogeneous data, and the random effect model was used for heterogeneous data. The Q and I 2 statistics were used to test for heterogeneity. If statistically significant heterogeneity was present (Q statistic P < 0.05 or I 2 ≥ 50%), the random-effect model was applied; otherwise, the fixed-effect model was used ( 36 ). In order to explore the potential sources of heterogeneity, we conducted subgroup analyses by continents (Asia, Europe, North America, or Africa), detection methods (PCR, ELISA, or immunostaining), sample sources (blood, tissue, or stool), and study quality (NOS score ≥ 6 or < 6). The sensitivity analysis was conducted by the sequential removal of each study. Begg’s correlation and Egger’s regression were used to assess the potential publication bias ( 37 , 38 ). All analyses were conducted using STATA 15.1 (Stata, College Station, TX, USA).

Characteristics of the Studies Included in the Meta-Analysis

The study process is shown in Figure 1 . Among 1501 potentially relevant studies, 38 met the inclusion criteria ( 10 , 18 – 34 , 39 – 58 ). The dataset included 5921 subjects (2841 T1D patients and 2841 controls). The included studies were published from 1990 to 2019, with sample sizes ranging from 7 to 766. Of these studies, 25 were from Europe, four from Africa, two from Asia, two from Australia, one from North America, and one from Latin America. Most studies were in Caucasians. No study was excluded due to poor quality. Detailed information of all the included studies is listed in Table 1 . The results of the quality evaluation are shown in Supplement Table 1 .

Figure 1 Schematic of the process of selecting studies for the meta-analysis.

Table 1 Characteristics of the 38 studies included in the present meta-analysis.

Pooled Analysis

A total of 38 studies reported the association between enterovirus infection and T1D. Enterovirus infection was associated with T1D (OR = 7.8, 95% CI = 4.9–12.4, P < 0.001) ( Figure 2 ), and substantial heterogeneity was observed among the studies ( P < 0.001, I 2 = 80.7%).

Figure 2 Forest plot of ORs of enterovirus and type 1 diabetes.

Subgroup Analysis

Studies were categorized by continent, detection method, sample source, and study quality in the subgroup analysis. Enterovirus infection was correlated with T1D in the European (OR = 7.5, 95% CI = 4.4–12.6, P < 0.001), African (OR = 16.5, 95% CI = 2.8–95.1, P = 0.002), Asian (OR = 245.0, 95% CI = 4.1–15000.0, P = 0.001), Australian (OR = 5.8, 95% CI = 1.5–22.9, P = 0.011), and Latin American (OR = 11.9, 95% CI = 2.4–58.8, P = 0.002) populations. The study from North America reported no association between enterovirus and T1D, but since only one study was included, no conclusion could be reached. The association between enterovirus infection and T1D was shown in blood samples (OR = 8.8, 95% CI = 4.9–15.9, P < 0.001) and tissue samples (OR = 9.9, 95% CI = 5.5–17.8, P < 0.001), but none was detected in stool samples. Furthermore, no significant difference was observed between different detection methods and study quality ( Table 2 ).

Table 2 Subgroup analysis results.

Sensitivity Analysis

In order to evaluate the influence of each study on the pooled OR, the sensitivity analysis was performed by the sequential removal of every study. The results showed no significant variation in OR, which reflected the stability and robustness of our results ( Figure 3 ).

Figure 3 Sensitivity analysis of the association between enterovirus and type 1 diabetes. The odds ratios and 95% confidence intervals (CIs) for the association between enterovirus and type 1 diabetes were recalculated by sequentially excluding each study indicated on the left.

Publication Bias

Funnel plots showed a slight asymmetry. Publication bias was indicated by P-values from Egger’s regression ( P < 0.001); however, no significant publication bias was indicated by P-values from Begg’s test ( P = 0.151).

The incidence of T1D is rising in many countries. Environmental factors, especially enterovirus infection, might be involved in the initiation and acceleration of the pathogenesis of T1D ( 60 ). Although a previous meta-analysis was conducted to identify whether enterovirus infection was associated with T1D ( 17 ), the present meta-analysis consisted of the largest number of original studies and subjects available to evaluate the association. Furthermore, we conducted a subgroup analysis of the detection method and sample source, which was not performed in the previous meta-analysis.

In the present study, 38 case-control studies, consisting of 5921 subjects (2841 T1D subjects and 3080 controls) were included. The pooled analysis showed that enterovirus infection is associated with T1D, with almost 8-fold the odds of enterovirus infection in T1D compared with the controls, consistent with the previous meta-analysis ( 17 ). As the new studies included Asian and African populations, a finding of significant association in these populations suggests that the correlation with relatively high T1D rates found in European populations is also observed in other populations. Karaoglan et al. ( 25 ) investigated the serologic epidemiological and molecular evidence on enteroviruses and respiratory viruses in patients with newly-diagnosed T1D during the cold season and showed that enteroviruses and respiratory viruses, in addition to seasonal infections, could play a role in the etiopathogenesis and clinical onset of T1D. Honkanen et al. ( 27 ) evaluated whether the presence of enterovirus was associated with the appearance of islet autoimmunity in T1D and found that enterovirus infection diagnosed by detecting viral RNA was associated with the development of islet autoimmunity with an interval of several months. In the subgroup analysis, enterovirus infection was correlated with T1D in Europe, Africa, Asia, Australia, and Latin American, but no conclusion could be reached for North America. Moreover, the association between enterovirus infection and T1D was shown in blood and tissue samples, but no association was detected in stool samples, possibly because only two studies presented data from stool specimens and because stool sampling and handling are subject to more technical variability than blood, for example, especially if stool sampling is performed at home. Thus, the subgroup variability needs to be investigated in future studies. Sensitivity analysis showed that this meta-analysis results were robust, without a single study influencing the results by itself, indicating statistical stability and reliability. Still, a significant publication bias was observed in Egger’s test, suggesting a possible under-reporting of negative results or no reports from smaller centers with less experience.

Enterovirus infection is associated with the destruction of β cells ( 1 ). Two recent studies have shown that CVB1 is associated with an increased risk of β-cell autoimmunity, while CVB3 and CVB6 are associated with a reduced T1D risk ( 31 , 61 ). CVB1 has been reported to infect human pancreatic islets in vitro ; it is one of the most cytolytic enterovirus serotypes in this model ( 62 ). In addition, an in vivo study performed in CBS/j mice demonstrated that the CVB3 virus did not affect glucose tolerance, while CVB4 did ( 63 ). Only a few original studies in our meta-analysis have provided the enterovirus type for cases and controls, and hence, we could not establish the correlation between enterovirus type and T1D. Still, those studies ( 31 , 61 – 63 ) suggest that different strains of enteroviruses could have different impacts on the development of T1D through variations in the genome of the viruses. One of the limitations in all these studies is the difficulty of obtaining not just the evidence of serotype but the complete enterovirus genomes from human patients at the time of T1D diagnosis. In addition, obtaining the complete genomes from stool samples is technically difficult. Future studies will have to examine more closely the strains associated with T1D as well as the genomes and mechanisms involved since the development of T1D might vary with serotypes.

Some limitations should be noted. First, the sample size is still small in this meta-analysis, especially in the subgroup analysis. Second, although some of the original studies detected the enterovirus types, most of them did not provide the number of T1D patients per enterovirus type. Therefore, we could not examine the correlation between enterovirus type and T1D. Third, although subgroup and sensitivity analyses were conducted, a source of heterogeneity was still not found, which could be attributed to the insufficient information obtained from the original studies. Fourth, the further evaluation of potential gene-gene or gene-environment interactions was limited by the insufficient original data. Despite the limitations, our meta-analysis significantly increased the statistical power based on substantial data from different studies.

Our findings suggest that enterovirus infection is associated with T1D. This study might provide a scientific basis for identifying the infectious agents associated with T1D and for the possible prevention of T1D through vaccines and other means. Studies with a larger sample size, especially from the US and China, are needed to reach a definitive conclusion.

Data Availability Statement

The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.

Author Contributions

KW and FY, study design and manuscript writing. YC, data collection and data analysis. JX, data interpretation. YZ, preparation of the manuscript. YW and TL, literature analysis. All authors contributed to the article and approved the submitted version.

This work was supported by the Jinhua Science and Technology Bureau (Grant no.2020-3-036).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2021.706964/full#supplementary-material

Abbreviations

T1D, type 1 diabetes; CAR, coxsackie and adenovirus receptor; NOS, Newcastle-Ottawa quality assessment scale; OR, odds ratio; CI, confidence interval; CVA, coxsackievirus type A; CVB, coxsackievirus type B.

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Keywords: enterovirus infection, type 1 diabetes, case-control studies, odds ratio, meta-analysis

Citation: Wang K, Ye F, Chen Y, Xu J, Zhao Y, Wang Y and Lan T (2021) Association Between Enterovirus Infection and Type 1 Diabetes Risk: A Meta-Analysis of 38 Case-Control Studies. Front. Endocrinol. 12:706964. doi: 10.3389/fendo.2021.706964

Received: 08 May 2021; Accepted: 09 August 2021; Published: 07 September 2021.

Reviewed by:

Copyright © 2021 Wang, Ye, Chen, Xu, Zhao, Wang and Lan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Kan Wang, [email protected] ; Fei Ye, [email protected]

† These authors have contributed equally to this work

This article is part of the Research Topic

The Contribution of Viruses and Innate Immune System in the Pathogenesis of Type 1 Diabetes

What Is Type 1 Diabetes?

People of all ages can develop type 1 diabetes.

If you have type 1 diabetes, your pancreas doesn’t make insulin or makes very little insulin. Insulin helps blood sugar enter the cells in your body for use as energy. Without insulin, blood sugar can’t get into cells and builds up in the bloodstream. High blood sugar is damaging to the body and causes many of the symptoms and complications of diabetes.

Type 1 diabetes was once called insulin-dependent or juvenile diabetes, but it can develop at any age.

Type 1 diabetes is less common than  type 2 —about 5-10% of people with diabetes have type 1. Currently, no one knows how to prevent type 1 diabetes, but it can be treated successfully by:

• Following your doctor’s recommendations for living a healthy lifestyle.
• Managing your blood sugar.
• Getting regular health checkups.
• Getting diabetes self-management education and support .

For Parents

If your child has type 1 diabetes—especially a young child—you’ll handle diabetes care on a day-to-day basis. Daily care will include serving healthy foods, giving insulin injections, and watching for and  treating hypoglycemia  (low blood sugar). You’ll also need to stay in close contact with your child’s health care team. They will help you understand the treatment plan and how to help your child stay healthy.

Much of the information that follows applies to children as well as adults. You can also  visit JDRF’s T1D Resources  for more information on managing your child’s type 1 diabetes.

What Causes Type 1 Diabetes?

Type 1 diabetes is thought to be caused by an autoimmune reaction (the body attacks itself by mistake). This reaction destroys the cells in the pancreas that make insulin, called beta cells. This process can go on for months or years before any symptoms appear.

Some people have certain genes (traits passed on from parent to child) that make them more likely to develop type 1 diabetes. However, many of them won’t go on to have type 1 diabetes even if they have the genes. A trigger in the environment, such as a virus, may also play a part in developing type 1 diabetes. Diet and lifestyle habits don’t cause type 1 diabetes.

Symptoms and Risk Factors

It can take months or years before  symptoms  of type 1 diabetes are noticed. Type 1 diabetes symptoms can develop in just a few weeks or months. Once symptoms appear, they can be severe.

Some type 1 diabetes symptoms are similar to symptoms of other health conditions. Don’t guess! If you think you could have type 1 diabetes, see your doctor to get your blood sugar tested. Untreated diabetes can lead to very serious—even fatal—health problems.

Risk factors  for type 1 diabetes are not as clear as for prediabetes and type 2 diabetes. However, studies show that family history plays a part.

Testing for Type 1 Diabetes

A  simple blood test  will let you know if you have diabetes. If you were tested at a health fair or pharmacy, follow up at a clinic or doctor’s office. That way you’ll be sure the results are accurate.

If your doctor thinks you have type 1 diabetes, your blood may also be tested for autoantibodies. These substances indicate your body is attacking itself and are often found with type 1 diabetes but not with type 2. You may have your urine tested for ketones too. Ketones are produced when your body burns fat for energy. Having ketones in your urine indicates you have type 1 diabetes instead of type 2.

Managing Diabetes

Unlike many health conditions, diabetes is  managed  mostly by you, with support from your health care team:

• Primary care doctor
• Foot doctor
• Registered dietitian nutritionist
• Diabetes educator

Also ask your family, teachers, and other important people in your life for help and support. Managing diabetes can be challenging, but everything you do to improve your health is worth it!

If you have type 1 diabetes, you’ll need to take insulin shots (or wear an insulin pump) every day. Insulin is needed to manage your blood sugar levels and give your body energy. You can’t take insulin as a pill. That’s because the acid in your stomach would destroy it before it could get into your bloodstream. Your doctor will work with you to figure out the most effective type and dosage of insulin for you.

You’ll also need to  do regular blood sugar checks . Ask your doctor how often you should check it and what your target blood sugar levels should be. Keeping your blood sugar levels as close to target as possible will help you prevent or delay diabetes-related  complications .

Stress is a part of life, but it can make managing diabetes harder. Both managing your blood sugar levels and dealing with daily diabetes care can be tougher to do. Regular physical activity, getting enough sleep, and exercises to relax can help. Talk to your doctor and diabetes educator about these and other ways you can manage stress.

Healthy lifestyle habits are really important too:

• Making  healthy food choices
• Being  physically active
• Controlling your  blood pressure
• Controlling your  cholesterol

Make regular appointments with your health care team. They’ll help you stay on track with your treatment plan and offer new ideas and strategies if needed.

Hypoglycemia and Diabetic Ketoacidosis

These 2 conditions are common complications of diabetes, and you’ll need to know how to handle them. Meet with your doctor for step-by-step instructions. You may want to bring a family member with you to the appointment so they learn the steps too.

Hypoglycemia  (low blood sugar) can happen quickly and needs to be  treated  quickly. It’s most often caused by:

• Too much insulin.
• Waiting too long for a meal or snack.
• Not eating enough.
• Getting extra physical activity.

Talk to your doctor if you have low blood sugar several times a week. Your treatment plan may need to be changed.

Diabetic ketoacidosis  (DKA) is a serious complication of diabetes that can be life-threatening. DKA develops when you don’t have enough insulin to let blood sugar into your cells. Very high blood sugar and low insulin levels lead to DKA. The two most common causes are illness and missing insulin shots. Talk with your doctor and make sure you understand how you can prevent and treat DKA.

Get Diabetes Education

Meeting with a diabetes educator is a great way to get support and guidance, including how to:

• Develop and stick to a healthy eating and activity plan
• Test your blood sugar and keep a record of the results
• Recognize the signs of high or low blood sugar and what to do about it
• Give yourself insulin by syringe, pen, or pump
• Monitor your feet, skin, and eyes to catch problems early
• Buy diabetes supplies and store them properly
• Manage stress and deal with daily diabetes care

Ask your doctor about  diabetes self-management education and support services and to recommend a diabetes educator. You can also search this nationwide directory  for a list of programs in your community.

Get Support

Tap into online diabetes communities for encouragement, insights, and support. Check out the American Diabetes Association’s Community page and JDRF’s TypeOneNation . Both are great ways to connect with others who share your experience.

• Type 1 Diabetes Resources and Support from JDRF
• Living With Diabetes
• Just Diagnosed With Type 1 Diabetes
• Learn About Diabetic Ketoacidosis
• 4 Ways To Take Insulin
• Making the Leap From Type 1 Teen to Adult

To receive updates about diabetes topics, enter your email address:

• Diabetes Home
• State, Local, and National Partner Diabetes Programs
• National Diabetes Prevention Program
• Native Diabetes Wellness Program
• Chronic Kidney Disease
• Vision Health Initiative
• Heart Disease and Stroke
• Overweight & Obesity

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• Short report
• Open access
• Published: 26 June 2017

Metabolic profiling of type 1 diabetes mellitus in children and adolescents: a case–control study

• Liene Bervoets 1 ,
• Guy Massa 1 , 2 ,
• Wanda Guedens 3 ,
• Evelyne Louis 1 ,
• Jean-Paul Noben 4 &
• Peter Adriaensens 3 , 5  

Diabetology & Metabolic Syndrome volume  9 , Article number:  48 ( 2017 ) Cite this article

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Type 1 diabetes mellitus (T1DM) is one of the most common pediatric diseases and its incidence is rising in many countries. Recently, it has been shown that metabolites other than glucose play an important role in insulin deficiency and the development of diabetes. The aim of our study was to look for discriminating variation in the concentrations of small-molecule metabolites in the plasma of T1DM children as compared to non-diabetic matched controls using proton nuclear magnetic resonance ( 1 H-NMR)-based metabolomics.

A cross-sectional study was set-up to examine the metabolic profile in fasting plasma samples from seven children with poorly controlled T1DM and seven non-diabetic controls aged 8–18 years, and matched for gender, age and BMI-SDS. The obtained plasma 1 H-NMR spectra were rationally divided into 110 integration regions, representing the metabolic phenotype. These integration regions reflect the relative metabolite concentrations and were used as statistical variables to construct (train) a classification model in discriminating between T1DM patients and controls.

The total amount of variation explained by the model between the groups is 81.0% [R 2 Y(cum)] and within the groups is 75.8% [R 2 X(cum)]. The predictive ability of the model [Q 2 (cum)] obtained by cross-validation is 50.7%, indicating that the discrimination between the groups on the basis of the metabolic phenotype is valid. Besides the expected higher concentration of glucose, the relative concentrations of lipids (triglycerides, phospholipids and cholinated phospholipids) are clearly lower in the plasma of T1DM patients as compared to controls. Also the concentrations of the amino acids serine, tryptophan and cysteine are slightly decreased.

Conclusions

The present study demonstrates that metabolic profiling of plasma by 1 H-NMR spectroscopy allows to discriminate between T1DM patients and controls. The metabolites that significantly differ between both groups might point to disturbances in biochemical pathways including (1) choline deficiency, (2) increased gluconeogenesis, and (3) glomerular hyperfiltration. Although the sample size of this study is still somewhat limited and a validation should be performed, the proof of principle looks promising and justifies a deeper investigation of the diagnostic possibilities of 1 H-NMR metabolomics in follow-up studies.

Trial registration NCT03014908. Registered 06/01/2017. Retrospectively registered

Type 1 diabetes mellitus (T1DM) is one of the most common pediatric diseases and its incidence is rising in many countries [ 1 ]. T1DM is a chronic metabolic disorder that results from a lack of pancreatic β-cell insulin production by autoimmune mechanisms [ 2 ]. Insulin is a key hormone to maintain metabolic homeostasis, regulating carbohydrate, lipid and protein metabolism, and insulin deficiency in T1DM subsequently induces a variety of metabolic derangements [ 3 , 4 ]. To identify novel pathways or early biomarkers indicative of metabolic alterations that are involved in the development of diabetes, metabolomics is an increasingly used tool [ 5 ]. Metabolomics research on pediatric study populations is still in its infancy. Up to now, only a few researchers investigated the plasma metabolic fingerprint of T1DM in children, using mass spectrometry as analytical tool [ 6 , 7 , 8 ]. One study showed that children who later progressed to T1DM had reduced serum levels of succinic acid and phosphatidylcholine at birth, pointing towards a dysregulated metabolism preceding β-cell autoimmunity and overt T1DM [ 6 ]. In addition, methionine deficits in early childhood may lead to an increased risk to develop T1DM later in life [ 7 ]. When comparing the plasma metabolic profile of T1DM and healthy children, differences were observed in lipid metabolism (non-esterified fatty acids, lysophospholipids and other fatty acid-derivatives), and some markers of differential activity of the gut microbiota (bile acids, p-cresol sulfate) [ 8 ]. However, the use of nuclear magnetic resonance (NMR)-based metabolomics to obtain a deeper knowledge of the plasma metabolic profile of T1DM has not been fully explored in the pediatric population. Proton ( 1 H)-NMR spectroscopy has proven to be a robust and reproducible technique with very limited sample preparation (no extractions) [ 9 ], that can detect and quantify a wide variety of metabolites simultaneously, providing information regarding the biochemical pathways involved [ 10 ]. The objective of the current study was to investigate metabolic variations in the plasma of T1DM children and adolescents as compared to plasma of non-diabetic matched controls using 1 H-NMR spectroscopy combined with multivariate statistics.

Subjects and characteristics

Children with poorly controlled T1DM (n = 7) and non-diabetic controls (n = 7) were recruited at the Department of Pediatrics of the Jessa Hospital Hasselt (Belgium) between June 2012 and November 2013. Inclusion criteria were: (1) aged between 8 and 18; (2) normal-weight according to the International Obesity Task Force (IOTF) BMI criteria [ 11 ]; and (3) fasted for at least 8 h. All subjects were matched for gender (four males and three females in both groups), age (12.0 ± 3.0 and 13.5 ± 2.7 years, respectively), and BMI-SDS (0.03 ± 0.62 and 0.05 ± 0.55, respectively). Subject characteristics are presented in Table  1 . T1DM patients were diagnosed according to international consensus guidelines [ 12 ]. T1DM patients had diabetes for 6.8 ± 3.8 years, and were treated with exogenous insulin (mean insulin dose per kg: 0.83 ± 0.22/kg). T1DM patients show high fasting plasma glucose levels (mean: 187 ± 82 mg/dl) and hemoglobin A1c (HbA1c) concentrations above 6.5% (mean: 9.7 ± 2.8%) (Additional file 1 : Table S1). None of the subjects was using lipid-lowering drugs or other medication. The study was conducted in accordance with the ethical rules of the Helsinki Declaration and Good Clinical Practice. The study protocol was approved by the medical-ethical committees of the Jessa Hospital and Hasselt University (12.27/ped12.02). Informed and written consent was obtained from all participants and their parents or legal guardian.

Biochemical measurements

Fasting venous blood of T1DM patients was collected in 2-ml fluoride-oxalate tubes for biochemical analysis at the Clinical Laboratory of Jessa Hospital. Plasma glucose was measured by the glucose oxidase method using a Synchron LX20 analyzer (Beckman Coulter, Brea, CA, USA) and HbA1c was measured using ion exchange chromatography (Menarini HA-8160 HbA1c auto-analyzer, Menarini Diagnostics, Belgium).

Sample collection, preparation and 1 H-NMR analysis

Fasting venous blood was collected in 6-ml lithium heparin tubes and stored at 4 °C within 10 min. Within 30 min, samples were centrifuged at 1600 g for 15 min and plasma aliquots of 500 µl were transferred into cryovials and stored at −80 °C [ 13 ]. Detailed protocols regarding sample preparation and 1 H-NMR analysis have been previously described elsewhere [ 14 ]. Plasma 1 H-NMR spectra were rationally divided into 110 integration regions defined on the basis of spiking experiments with known metabolites [ 15 ]. These integration regions reflect the relative metabolite concentrations—i.e. the metabolic phenotype—and were used as statistical variables to construct (train) a classification model in discriminating between T1DM patients and controls.

Statistical analysis

Multivariate statistics was performed using SIMCA-P + (Version 13.0, Umetrics, Sweden). After mean-centering and Pareto scaling of the variables, unsupervised principal component analysis (PCA) was performed in order to look for clustering and possible confounders within the dataset, and to identify possible outliers by a Hotelling’s T2 range test and a distance to model plot. In a next step, orthogonal partial least squares discriminant analysis (OPLS-DA) was used to build (train) a model (statistical classifier) to discriminate between T1DM patients and controls [ 16 ]. The validity of the established model was evaluated on one hand by the total amount of variation between and within the groups explained by the model [denoted as R 2 Y(cum) and R 2 X(cum), respectively] and on the other hand by the predictive ability of the model as determined by a sevenfold cross-validation [denoted as Q 2 (cum)]. To be classified as a variable that strongly contributes to the group discrimination, three selection criteria have to be fulfilled: (1) significantly different in univariate statistics (a student t test corrected for multiple testing by the Benjamini–Hochberg method), (2) an OPLS-DA absolute value of p(corr), i.e. the loading scaled as a correlation coefficient, exceeding 0.6 and (3) an OPLS-DA variable importance for the projection (VIP) value exceeding 0.5 [ 16 ].

Multivariate OPLS-DA statistics was used to train a classification model (classifier) in discriminating between T1DM patients and controls based on data input from their metabolic profile or phenotype. A PCA analysis was conducted first to look for clustering and possible confounders. Figure  1 shows that the subjects were clustered in a way that allowed T1DM patients to be clearly differentiated from controls and no outliers were detected. Moreover, staining the PCA score plots for gender, age and BMI-SDS clearly shows that none of these factors have a confounding effect on the discrimination between T1DM patients and controls, as was expected for matched groups (data not shown).

PCA score plot obtained for T1DM patients ( filled triangle ) and healthy controls ( circle ). Each participant is represented by its metabolic profile and visualized as a single symbol of which the location is determined by the contributions of the 110 variables in the 1 H-NMR spectrum. The PCA score plot shows the first principal component (PC1: 69.4%), explaining the largest variance within the dataset, versus the second principal component (PC2: 12.6%) that explains the second largest variance

In a next step, OPLS-DA was used to build a model (statistical classifier) to differentiate between T1DM patients and controls (Fig.  2 a). The total amount of variation between the groups that can be explained by the model is 81.0% [R 2 Y(cum)] while this within the groups is 75.8% [R 2 X(cum)]. The predictive ability of the model, obtained by cross-validation, is quite high with a Q 2 (cum) of 50.7%, indicating that the discrimination between the groups on the basis of the metabolic phenotype is valid. The OPLS-DA S-line plot shown in Fig.  2 b visualizes the covariance [left y-axis; p(ctr)] and correlation coefficient [right y-axis; abs(p(corr)] between the variables and the classification score in the model (see caption of Fig.  2 b for more information). Strongly discriminating variables combine a clear covariance [p(ctr)] with a high absolute value for the correlation coefficient [abs(p(corr)] (Table  2 ).

OPLS-DA score plot ( a ) and S-line plot ( b ) obtained for T1DM patients ( filled triangle ) and healthy controls ( circle ). Each participant is represented by its metabolic profile and visualized as a single symbol of which the location is determined by the contributions of the 110 variables in the 1 H-NMR spectrum. The OPLS-DA score plot shows the first predictive component (t[1]P: 51.8%), explaining the variation between the groups, versus the first orthogonal component (t[1]O: 24.0%) that explains the variation within the groups. The OPLS-DA S-line plot visualizes differences between T1DM patients (negative) and controls (positive). The left y-axis represents p(ctr)[1], the covariance between a variable and the classification score. It indicates if an increase or decrease of a variable is correlated to the classification score. The magnitude of the covariance is however difficult to interpret since covariance is scale dependent. This means that a high value for the covariance does not necessary imply a strong correlation, as the covariance is also influenced by the intensity of the signal with respect to the noise level. Therefore this measure will likely indicate variables with large signal intensities. The right y-axis shows p(corr)[1], the correlation coefficient between a variable and the classification score (i.e. the normalized covariance). It gives a linear indication of the strength of the correlation. As the correlation is independent of the intensity of the variable, it will be a better measure for the reliability of the variable in the classification process. In   b , the red color stands for the highest absolute value of the correlation coefficient. Strongly discriminating variables have a large intensity and large reliability

The plot shows that the concentrations of lipids (triglycerides, phospholipids and cholinated phospholipids) are clearly decreased in the plasma of T1DM patients as compared to controls, whereas serine, tryptophan and cysteine concentrations seem to be decreased slightly. The glucose levels on the other hand are clearly increased in the plasma of T1DM patients. These changes were also observed as significant by a univariate t test with post hoc Benjamini–Hochberg correction (Table  3 ). The plot further shows that ketone levels (i.e. acetoacetate and β-hydroxybutyrate) are slightly elevated in T1DM patients. In order to look if T1DM patients and controls can be differentiated by a model constructed without the 15 variables related to the strong glucose signals in the 1 H-NMR spectra, the variables representing glucose were removed from the metabolic profile prior to the OPLS-DA model building (construction of the model with only 95 variables). The results are also presented in Table  2 and confirm that the relative concentrations of lipids (triglycerides, phospholipids and cholinated phospholipids) and some amino acids (serine, tryptophan and cysteine) are reduced in the plasma of T1DM patients.

Type 1 diabetes mellitus is a serious health concern worldwide that is usually diagnosed in children and young adults [ 1 ]. T1DM is a metabolic disorder, and in recent decades it has been shown that metabolites other than glucose play an important role in insulin deficiency and the development of diabetes [ 4 , 5 ]. Metabolomics, the study of small-molecule metabolites, has developed into an important tool in diabetes research [ 5 ]. In this study, we investigated metabolic variations in T1DM children and adolescents using NMR-spectroscopy-based metabolomics to gain inside into biochemical pathways that are altered in early stages of T1DM. Besides the expected higher concentration of glucose, we found lower relative concentrations for lipids (triglycerides, phospholipids and cholinated phospholipids) and the amino acids serine, tryptophan and cysteine in plasma of T1DM children and adolescents as compared to non-diabetic controls.

Our findings of relatively lower levels of lipids in the plasma of T1DM as compared to controls are in agreement with other metabolomics studies [ 3 , 4 , 6 ]. In a prospective Finnish study, it was found that children who developed T1DM have reduced serum levels of phosphatidylcholine at birth, next to lower levels of multiple triglycerides and phospholipids throughout the follow-up [ 6 ]. In addition, it has been demonstrated that children developing T1DM before 4 years of age have low cord-blood levels of phospholipids, mainly phosphatidylcholines [ 17 ]. It is suggested that T1DM progressors are choline-deficient at birth, and that choline deficiency is the key mechanism leading to lower serum triglyceride levels and their increased accumulation in the liver [ 6 ]. Choline metabolism also depends on the gut microbial composition [ 18 ], making the latter an attractive target for early prevention and treatment of T1DM [ 19 ]. In addition, low levels of phosphatidylcholine may play a role in oxidative damage affecting the pancreatic β-cell insulin production, because phosphatidylcholines are thought to have anti-inflammatory properties [ 20 ]. According to an NMR-based metabolomics study in adults, lower levels of triglycerides in T1DM patients may also be attributable to treatment with insulin [ 3 ], which is known to have an anti-lipolytic action [ 21 ]. We further observed lower plasma levels of serine, tryptophan and cysteine in T1DM as compared to controls. In current literature, only a limited number of papers can be found regarding the relationship between these amino acids and T1DM. A study in diabetic db−/db− mice suggested that a strongly decreased concentration of the gluconeogenic amino acids serine, alanine and glycine, resulted in increased gluconeogenesis [ 22 ]. In addition, a study in rats suggested that tryptophan suppresses the elevation of blood glucose and lessens the burden associated with insulin secretion from β-cells [ 23 ]. Finally, reduced plasma levels of cysteine in T1DM patients can be explained by glomerular hyperfiltration, resulting in an increased renal clearance of cysteine [ 24 ].

Although the sample size of this study is still somewhat limited, the experiments were carried out according to a strictly controlled protocol. This pilot study demonstrates the proof of principle that metabolic phenotyping of T1DM in children by proton-NMR spectroscopy allows to differentiate between T1DM patients and controls and therefore justifies the start-up of larger studies.

Because NMR metabolomics can be used to search for subtle changes in the plasma of children prone to develop T1DM, it might become an important tool for the early diagnosis and prognosis of T1DM in children. Hence, restoring or improving the plasma metabolic profile, e.g. by re-establishing lipid and amino acid availability or by modulating gut microbial composition, might prevent β-cell destruction and delay T1DM progression in children and adolescents.

The present study demonstrates the proof of principle that metabolic phenotyping of plasma by 1 H-NMR spectroscopy allows to discriminate between T1DM patients and controls. T1DM children and adolescents show lower relative plasma concentrations of lipids (triglycerides, phospholipids and cholinated phospholipids), serine, tryptophan and cysteine as compared to non-diabetic controls. NMR-spectroscopy-based metabolomics appears to be a promising tool for the identification of disturbed biochemical pathways related to the development of T1DM. Nevertheless, further identification and validation is needed in order to evaluate the use of NMR metabolomics in the prediction, diagnosis and monitoring of T1DM in children. Therefore, deeper follow-up studies in larger pediatric cohorts are of utmost importance to further explore the potential of metabolomics in the field of pediatric diabetes.

Abbreviations

proton nuclear magnetic resonance

body mass index

body mass index standard deviation score

standard error of cross-validation

hemoglobin A1c

International Obesity Task Force

orthogonal partial least squares discriminant analysis

principal component analysis

correlation scaled loading

type 1 diabetes mellitus

variable influence on projection

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Authors’ contributions

LB, GM and PA conceived and designed the study. LB and GM collected the data. LB carried out the experiments. LB analyzed the data statistically. LB, PA, WG, EL and JPN interpreted the data. LB did literature research. LB generated figures and tables. LB wrote the manuscript with help of GM and PA. GM, WG, EL, JPN and PA revised the paper. LB had full access to all of the data in the study and takes responsibility for the integrity of the data and accuracy of the data analysis. All authors read and approved the final manuscript.

Acknowledgements

We thank all children and adolescents for their participation in this study. This study is part of the ‘Limburg Clinical Research Program (LCRP) UHasselt-ZOL-Jessa’, supported by the foundation Limburg Sterk Merk, province of Limburg, Flemish government, Hasselt University, Ziekenhuis Oost-Limburg and Jessa Hospital. We also thank the Research Foundation Flanders for their support via the MULTIMAR project and G. Reekmans for his assistance in the 1 H-NMR analysis of plasma samples.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

The study was conducted in accordance with the ethical rules of the Helsinki Declaration and Good Clinical Practice. The study protocol was approved by the medical-ethical committees of the Jessa Hospital and Hasselt University (12.27/ped12.02).

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Liene Bervoets, Guy Massa & Evelyne Louis

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Wanda Guedens & Peter Adriaensens

Biomedical Research Institute, Hasselt University, Agoralaan 1 Building C, 3590, Diepenbeek, Belgium

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Bervoets, L., Massa, G., Guedens, W. et al. Metabolic profiling of type 1 diabetes mellitus in children and adolescents: a case–control study. Diabetol Metab Syndr 9 , 48 (2017). https://doi.org/10.1186/s13098-017-0246-9

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Clinical features of patients with type 2 diabetes with and without Covid-19: A case control study (CoViDiab I)

Ernesto maddaloni.

a Umberto I “Policlinico” General Hospital, Sapienza University of Rome, Italy

Luca D'Onofrio

Francesco alessandri, carmen mignogna, gaetano leto.

b Santa Maria Goretti Hospital, Polo Pontino Sapienza University, Latina, Italy

Lucia Coraggio

Sara sterpetti, giuseppe pascarella.

c Campus Bio-Medico University of Rome, Italy

Ivano Mezzaroma

Miriam lichtner, paolo pozzilli, felice eugenio agrò, monica rocco.

d Sant’Andrea Hospital, Sapienza University of Rome, Italy

Francesco Pugliese

Claudio maria mastroianni, raffaella buzzetti, associated data.

To evaluate whether subjects with diabetes hospitalized for Coronavirus disease-19 (Covid-19) represent a subgroup of patients with high-risk clinical features compared to patients with diabetes without Covid-19.

In this case-control study 79 patients with type 2 diabetes out of 354 adults hospitalized for Covid-19 and 158 controls with type 2 diabetes but without Covid-19, matched for age and gender, were enrolled. Medical history and concomitant therapies were retrieved from medical charts and compared between cases and controls, controlling for confounders.

Fully-adjusted multivariate logistic regression model showed that previous CVD history did not differ between patients with and without Covid-19 (odds ratio 1.40, 95% confidence interval [CI]: 0.59–3.32, p = 0.45). A higher prevalence of chronic obstructive pulmonary disease (COPD) (OR 3.72, 95%CI: 1.42–9.72, p = 0.007) and of chronic kidney disease (CKD) (OR 3.08, 95%CI: 1.18–8.06, p = 0.022) and a lower prevalence of ever smokers (OR 0.30, 95%CI: 0.13–0.67, p = 0.003), of users of lipid lowering agents (OR 0.26, 95%CI: 0.12–0.54, p < 0.001), and of anti-hypertensive drugs (OR 0.39, 95%CI: 0.16–0.93, p = 0.033) were found among cases.

Conclusions

CVD prevalence does not differ between people with diabetes with and without Covid-19 requiring hospitalization. An increased prevalence of COPD and of CKD in Covid-19 patients with type 2 diabetes is suggested. These findings aid to clarify the relationship between underlying conditions and SARS-CoV-2 infection in the high-risk group of patients with diabetes.

1. Introduction

At the end of 2019 the beta-coronavirus SARS-CoV-2 has spread in Wuhan, China, causing coronavirus disease 2019 (Covid-19). The infection outbreak has rapidly reached the dimension of a pandemic and thousands of people are dying around the word [1] . Elderly with underlying conditions, especially cardiometabolic comorbidities, seems more vulnerable to Covid-19. Age > 65 years, hypertension, diabetes and history of cardiovascular events are the most prevalent conditions among patients hospitalized for Covid-19 [2] , [3] , [4] , [5] , and they are even more frequently described among those patients requiring intensive care or dying for this disease, irrespective of the geographical variation in both Covid-19 and comorbidities prevalence [6] , [7] , [8] . The proinflammatory and hypercoagulable states characterizing these conditions have been hypothesized to contribute to the deadly interaction between cardiometabolic disorders and SARS-CoV-2 infection [9] , [10] , [11] . However, it is not known if the coexistence of comorbidities potentially acting on similar pathways to increase patients’ vulnerability to Covid-19, such as diabetes and cardiovascular disease, results in a stepwise increased risk. Indeed, while it has been shown that both diabetes and cardiovascular disease may exacerbate Covid-19 in the general population [12] , [13] , [14] , whether and at what extent the presence of overt cardiovascular disease or of other underlying conditions provide an incremental risk for Covid-19 in patients with type 2 diabetes is unknown.

We hypothesized that patients with type 2 diabetes hospitalized for Covid-19 represent a group of patients with high risk clinical features, such as history of cardiovascular events or presence of other relevant comorbidities.

Therefore, in order to identify, quantify and further explore the risk profile of patients with type 2 diabetes hospitalized for Covid-19, we compared their clinical features to those of patients with type 2 diabetes without signs or symptoms of SARS-CoV-2 infection.

2. Materials and methods

2.1. study design and population.

The Covid-19 & Diabetes (CoViDiab) Study is a collaborative research project involving 4 different Covid academic centers in Rome (Umberto I “Policlinico” General hospital, Campus Bio-Medico hospital, Sant’Andrea hospital) and Latina (Santa Maria Goretti hospital), aimed to evaluate Covid-19 features and progression in people with diabetes. The present case-control study was designed to primarily test whether the prevalence of cardiovascular disease, and secondarily of other underlying conditions, differ between patients with type 2 diabetes hospitalized for Covid-19 (cases), compared to patients with type 2 diabetes without signs or symptoms of SARS-CoV-2 infection (controls). Cases were identified among adult patients (>18 years old) hospitalized for Covid-19 in one of the four study centers and enrolled in the CoViDiab Study no later than May 15th, 2020. Controls matched for age and sex with cases were identified among patients with type 2 diabetes referring to the Diabetes Unit of Policlinico Umberto I General hospital and enrolled at a case-control ratio of 1:2. Briefly, medical charts of patients aged > 18 years and with a diagnosis of type 2 diabetes attending the clinic up to May 15th, 2020 were screened backwards and data were retrieved from patients fulfilling the inclusion/exclusion criteria, according to the matching strategy fully described in the Supplementary material . Inclusion criteria for this study were: being aged > 18 years old and having a diagnosis of type 2 diabetes, defined as at least one random blood glucose value > 200 mg/dl, or fasting blood glucose > 126 mg/dl, or HbA1c > 6.5% (48 mmol/mol), or self-reported history of diabetes. A diagnosis of type 1 diabetes or of monogenic diabetes were considered exclusion criteria. All controls were screened for signs and symptoms of SARS-CoV-2 infection and patients hospitalized for any respiratory infection or with any of the following signs or symptoms experienced in the 30 days before enrolment were excluded: fever, cough, chill, chest tightness, worsening dispnoea, conjunctivitis, nausea, vomiting, diarrhea.

2.2. Data collection strategy

The following data were retrieved from medical records of both cases and controls: age, gender, smoking habits, height, weight, body mass index (calculated as weight in kilograms divided by the square of height in meters), serum creatinine, concomitant lipid lowering and anti-hypertensive therapies, previous history of major adverse cardiovascular events (MACE: any among myocardial infarction, stroke, percutaneous coronary revascularization, coronary artery by-pass graft), of heart failure, of chronic obstructive pulmonary disease (COPD). Estimated glomerular filtration rate (eGFR) was calculated according to the Chronic Kidney Disease (CKD) Epidemiology Collaboration formula [15] and the percentage of participants in CKD stage IIIb (eGFR < 45 ml/min/1.73 m 2 ) was calculated. Data retrieved for cases are those collected at the time of hospitalization, while data retrieved for controls are those collected during the last follow-up visit attended at the outpatient diabetes clinic.

Data about anti-diabetes therapeutic regimens were retrieved from the web-based reimbursement system of Lazio region (WebCare Lazio) and therefore reflect the therapeutic regimens as reported in the system: euglycemic agents (EuGlA: metformin, dipeptidyl peptidase 4 inhibitors [DPP4i], glucagon-like peptide 1 receptor agonists [GLP1RA], sodium-glucose co-transporter 2 inhibitors [SGLT2i] and/or pioglitazone); oral hypoglycemic agents (OHA: sulphonylureas or glinides); basal insulin (alone or in combination with EuGlA or OHA); multiple daily insulin injections (MDI: ≥ 3 insulin injections per day). Data about the use of each specific anti-diabetes drug class were available for all controls and for a subsample of cases (n = 61).

2.3. Statistical analysis

Continuous variables are presented as median [25th-75th percentile] and categorical variables as number (%). Kruskal-Wallis test was used to evaluate differences in continuous variables between groups. Chi-squared test or Fisher exact test were used to test differences of the distribution of categorical variables among groups.

Logistic regression was used for multivariate analyses with hospitalization for symptomatic Covid-19 as dichotomous outcome and positive history of MACE as main exposure. Based on the literature showing a significant association between hypertension and Covid-19 [5] , [6] , we pre-specified use of anti-hypertensive drugs as confounder to be forced in the model irrespective of its level of association with the outcome. Further variables found to differ between subjects with and without Covid-19 at a nominal p-value < 0.1 were also tested in the multivariate logistic regression model with the main exposure and the outcome. Results of the logistic regression models are expressed as odds ratio (OR) with 95% confidence intervals (CI). Since eGFR values were missing for 26 cases and for 3 controls, predictive mean matching imputation was used to impute CKD categories before being tested in the model.

The sample size of this study provided > 80% power to detect a hypothesized 2-times higher prevalence of MACE among Covid-19 cases compared to controls at a two-sided alpha level of 0.05. Two-sided tests at the 0.05 level of significance were used for all statistical comparisons. Stata/IC 12.1 software used for data analysis and Prism 8.4 Software for graphical representations.

2.4. Ethics

The study complies with the principle of Helsinki Declaration and was approved by the Ethical Committee of Umberto I “Policlinico” General hospital. Because of the study’s retrospective design, verbal informed consent was obtained from participants who were not able to reach the study center due to the lockdown restrictions, while informed consent was waived in cases of impossibility of contact with patients and in case of exitus. The privacy and anonymity of the data collected was guaranteed in accordance with current regulations.

Medical charts of 354 adults enrolled in the CoViDiab study and with known diabetes status were screened. After exclusion of 2 patients with type 1 diabetes, 79 patients with type 2 diabetes hospitalized for Covid-19 were enrolled. According to the prespecified enrolment plan and matching strategy ( Supplementary material ), 158 controls matched for age and sex with the 79 Covid-19 cases were enrolled.

When this analysis was carried out, 9 out of the 79 cases (11.4%) died 20 [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] days after hospitalization, 38 (48.1%) were discharged after 23 [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] days and 32 (40.5%) were still hospitalized. Admission to an intensive care unit was required in 21 cases (26.6%).

Features of cases and controls are summarized in table 1 . Cases and controls had comparable age (76 [66–83] vs 74 [65–82] years, p = 0.88), gender distribution (63.9% vs 63.3% males, p = 0.92) and body mass index. Cases were less likely ever smokers than controls (22.8% vs 45.6%, p = 0.001.)

Clinical features of patients with diabetes with and without Covid-19.

Abbreviations: eGFR, estimated Glomerular Filtration Rate, ACEi, angiotensin-converting enzyme inhibitors; ARB, angiotensin receptor blockers; EuGlA, Euglycemic agents (metformin, DPP4i, GLP1RA, SGLT2i and/or pioglitazone); OHA, oral hypoglycemic agents (sulphonylureas or glinides); MDI, multiple daily insulin injections. *CKD data available for 155 controls and for 53 controls; **Data available for 74 cases and for all controls.

Previous history of MACE was present in 13 (16.5%) cases and in 43 (27.2%) controls (p = 0.066), COPD in 17 (21.5%) cases and 14 (8.9%) controls (p = 0.006) and heart failure in 10 (12.8%) cases and 21 (13.3%) controls (p = 0.92). Among participants with available eGFR data, CKD stage IIIb was present in 37.7% cases and in 11.6% controls (p < 0.001).

Anti-hypertensive agents, lipid lowering drugs, EuGlA and basal insulin alone or in combination were more frequently used in controls than in cases, while OHA and MDI were more frequently observed among Covid-19 cases (p ≤ 0.01 for all, table 1 ). When considering the single anti-diabetes drug-classes, a less frequent use of all EuGlA was confirmed in the subsample of 61 Covid-19 cases with available data compared to controls (supplementary table S1).

The multivariate regression model showed no differences in previous history of MACE between cases and controls (OR 1.40, 95%CI: 0.59–3.32, p = 0.45), while COPD (OR 3.72, 95%CI: 1.42–9.72, p = 0.007), CKD stage IIIb (OR 3.08, 95%CI: 1.18–8.06, p = 0.022), lower prevalence of smoking habits (OR 0.30, 95%CI: 0.13–0.67, p = 0.003) and less frequent use of lipid lowering agents (OR 0.26, 95%CI: 0.12–0.54, p < 0.001), of anti-hypertensive drugs (OR 0.39, 95%CI: 0.16–0.93, p = 0.033), and of basal insulin (OR 0.18, 95%CI: 0.06–0.59, p = 0.005), all remained significantly associated with Covid-19 ( Fig. 1 ).

Factors associated to Covid-19 in diabetes. This forest plot is a graphical representation of the final multivariate logistic regression model with main effect (previous history of major adverse cardiovascular events, MACE) and the outcome (evidence of symptomatic Covid-19 infection requiring hospitalization), adjusted for factors associated with the outcome at the bivariate analysis at a p-value < 0.1. Oral hypoglycemic agents (sulphonylureas or glinides) not represented because perfectly predict the outcome.

4. Discussion

This study shows that previous history of cardiovascular events is similarly prevalent among patients with type 2 diabetes with and without Covid-19. However, we found that, after adjustment for confounders, patients with type 2 diabetes and hospitalized for Covid-19 were more likely affected by COPD and less likely on lipid lowering treatment and smokers compared to controls. Furthermore, EuGlA and basal inulin (alone or in combination) were more frequently used in controls than in cases.

Recently, two large meta-analyses showed an increased risk of hospitalization after infection with SARS-CoV-2 in patients with cardiovascular disease [13] , [14] . The presence of underlying cardiovascular conditions also increases the risk of in-hospital death for Covid-19 [16] , further suggesting an intimate relationship between cardiovascular disease and SARS-CoV-2 infection. Differently from previous studies, here we investigated the impact of cardiovascular disease in a population affected by type 2 diabetes, often considered a “coronary heart disease equivalent” [17] . In this population we failed to find differences in the prevalence of cardiovascular disease between patients with and without Covid-19. These results suggest that mechanisms through which type 2 diabetes and cardiovascular disease increase vulnerability to Covid-19 may overlap and are not additive. Although we did not investigate for the impact of cardiovascular disease on Covid-19 progression among inpatients, our data are in line with other studies which did not find independent associations between prior history of cardiovascular events and outcomes among patients with diabetes hospitalized for Covid-19 [18] , [19] , [20] . This topic needs, however further investigations, being still debated as contrasting data are reported depending on the chosen outcome [20] .

On the contrary, our finding of a possible additional risk conferred by COPD, may suggest this condition is associated to SARS-CoV-2 infection through different pathways than type 2 diabetes. The prevalence of COPD in our control population is in line with previous reports showing about 10% of patients with diabetes are also affected by COPD [21] . However, we found a 2-times higher prevalence of COPD among type 2 diabetes patients with Covid-19 requiring hospitalization. Of note, large case series from Asia, Europe and North America showed a relatively low prevalence of COPD in patients hospitalized for Covid-19, ranging from 1.5% in China to 5.4% in New York City [5] , [6] , [22] . We are instead describing a 20% prevalence of COPD in a selected population with concomitant type 2 diabetes, suggesting that type 2 diabetes may facilitate the interaction between COPD and SARS-CoV-2 infection. In this regard, Interleukin-6 (IL6) is a key cytokine of systemic inflammation in patients with COPD, particularly increased in those with concomitant insulin resistance and metabolic syndrome [23] , [24] . Of note, IL6 has emerged as a possible target for anti-Covid-19 therapies [25] , [26] and has been described increased in hospitalized patients with Covid-19 and type 2 diabetes [27] . A possible role for IL6 as a mediator of the interaction between type 2 diabetes and COPD in Covid-19 patients may therefore be investigated in future studies.

In our study, the prevalence of patients currently smoking among those with Covid-19 was lower compared to patients without Covid-19. While surprising, this result is consistent with evidence gathered so far from large case series of patients with Covid-19 from several countries [5] , [16] , [22] , [28] . It has been hypothesized that nicotine may mediate the protective effect of smoking by modulating angiotensin converting enzyme 2 (ACE2) or by inhibiting the cytokine storm responsible of the most severe Covid-19 cases [29] . Nevertheless, contrasting results on the impact of smoking in Covid-19 progression and severity have also been described [30] , [31] .

While a lower use of lipid lowering agents and of anti-hypertensive drugs, and a higher use of MDI and of OHA were found among Covid-19 patients, these results should be interpreted with caution and no cause-effect relationship may be inferred from our study. We acknowledge that these associations may indeed be biased by the fact that controls have been sampled from a population of patients with type 2 diabetes referring to a high-specialty diabetes clinic, with a high compliance to the current standards of care for the management of type 2 diabetes. Nevertheless, in our Covid-19 cohort the percentages of patients using lipid-lowering agents and on MDI are also largely lower and largely higher than the respective percentages registered in the general Italian population with type 2 diabetes [32] . While statin use has been recently associated to lower mortality in Covid-19 [16] , this finding may also suggest that the cohort of patients with type 2 diabetes hospitalized for Covid-19 represent a group with sub-optimally managed type 2 diabetes. The higher use of MDI and of OHA, as well as the higher prevalence of CKD, also goes in the same direction, suggesting that patients with type 2 diabetes hospitalized for Covid-19 are more frequently those with advanced diabetes requiring third and fourth line therapies or those not benefitting from the newer anti-diabetes drugs [16] . Unfortunately, HbA1c and diabetes duration data were not available for the majority of Covid-19 cases.

No differences in BMI between cases and controls were found, and BMI was not retained in our final multivariate regression model at the pre-specified nominal p-value < 0.1. Differently from previous reports showing that obesity may worsen Covid-19 outcomes among hospitalized patients [5] , [20] , our study was aimed to describe the risk profile of patients with type 2 diabetes and Covid-19 and not to describe risk factors for poor Covid-19 prognosis among hospitalized patients. Furthermore, recent evidence suggests that high visceral adiposity, more than high BMI per se , may be associated with Covid-19 progression [33] , [34] . Since measures of visceral adiposity were not available in our population, ad-hoc case-control studies in people with type 2 diabetes with and without Covid-19 should be conducted to clarify this topic.

Finally, coherently with the aim of this study to evaluate factors associated with symptomatic Covid-19 requiring hospitalization, we did not enroll patients with SARS-CoV-2 infection not requiring hospitalization. Therefore, based on our data we cannot establish whether the factors we identified are associated to the infection or to the progression of cases towards hospitalization. In this regard, we also cannot exclude asymptomatic infection (not causing Covid-19) among controls.

In conclusion, to the best of our knowledge, this is the first case-control study to compare clinical features of patients with type 2 diabetes with and without Covid-19, showing no difference in positive history of established cardiovascular disease. Conversely, an increased prevalence of COPD and of CKD in Covid-19 patients with type 2 diabetes is suggested. These findings clarify the relationship between different underlying conditions and SARS-CoV-2 infection in the high-risk group of patients with diabetes.

Declaration of Competing Interest

E.M. reports research support from scientific societies with unrestricted grants from Lilly and from AstraZeneca and personal fees from Merck Serono, AstraZeneca, Abbott, PikDare.; C.M. has received speaker fees from AstraZeneca; G.L. has received honoraria from NovoNordisk, Lilly, AstraZeneca, Sanofi; P.P. has received research support from Eli Lilly and Company and serves on the speaker bureau for Sanofi‐Aventis; R.B. has received honoraria or consulting fees from Sanofi, Eli Lilly, Abbott, and AstraZeneca.

Acknowledgments

Funding . This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Appendix B Supplementary data to this article can be found online at https://doi.org/10.1016/j.diabres.2020.108454 .

Appendix A. Investigators of the CoViDiab study group (by study center, in alphabetical order).

Umberto I “Policlinico” General Hospital:

Camilla Ajassa, Rugova Alban, Francesco Alessandri, Federica Alessi, Raissa Aronica, Valeria Belvisi, Raffaella Buzzetti, Matteo Candy, Alessandra Caputi, Anna Carrara, Elena Casali, Eugenio Nelson Cavallari, Giancarlo Ceccarelli, Luigi Celani, Maria Rosa Ciardi, Lucia Coraggio, Ambrogio Curtolo, Claudia D’Agostino, Gabriella D’Ettorre, Luca D’Onofrio, Francesca De Giorgi, Gabriella De Girolamo, Valeria Filippi, Lucio Gnessi, Cecilia Luordi, Ernesto Maddaloni, Claudio Maria Mastroianni, Ivano Mezzaroma, Carmen Mignogna, Chiara Moretti, Francesco Pugliese, Gregorio Recchia, Marco Ridolfi, Francesco Eugenio Romani, Gianluca Russo, Franco Ruberto, Giulia Savelloni, Guido Siccardi, Antonio Siena, Sara Sterpetti, Serena Valeri, Mauro Vera, Lorenzo Volpicelli, Mikiko Watanabe.

Santa Maria Goretti Hospital:

Massimo Aiuti, Giuseppe Campagna, Cosmo Del Borgo, Laura Fondaco, Blerta Kertusha, Frida Leonetti, Gaetano Leto, Miriam Lichtner, Raffaella Marocco, Renato Masala, Paola Zuccalà.

Campus Bio-Medico University:

Felice Eugenio Agrò, Giulia Nonnis, Giuseppe Pascarella, Paolo Pozzilli, Alessandra Rigoli, Alessandro Strumia

Sant’Andrea Hospital:

Daniela Alampi, Monica Rocco.

Appendix B. Supplementary material

The following are the Supplementary data to this article:

Prospective Nested Case-Control Study of Dietary and Microbiological-Associated Levels and Dynamics of 5-Aminovaleric Acid Betaine (5-AVAB) in Patients with Type 2 Diabetes

• PMID: 37944985

Objective: This study aimed to evaluate the associations between dietary and microbiological factors, and the levels and dynamics of 5-amino valeric acid betaine (5-AVAB) in patients with type 2 diabetes (T2D) through a prospective nested case-control study. An added meta-analysis aimed to provide a comprehensive evaluation of the relationship between 5-AVAB levels and T2D risk.

Methods: A total of 1200 T2D patients and 1200 age- and sex-matched controls were recruited for this study. Dietary information was collected through 24-hour dietary recall questionnaires, while fecal samples were analyzed for gut microbiota composition using 16S rRNA gene sequencing. 5-AVAB levels were measured in plasma samples using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Multivariate logistic regression and general linear models were applied to evaluate the associations between 5-AVAB levels, dietary factors, and gut microbiota composition.

Results: The T2D patients exhibited significantly lower plasma 5-AVAB concentrations compared to the control group (P < .001). Lower 5-AVAB levels were associated with higher odds of T2D (adjusted OR = 2.89, 95% CI: 1.76-4.74). Higher intake of dietary factors, including fiber and polyunsaturated fatty acids (PUFAs), were positively associated with 5-AVAB levels. Furthermore, specific bacterial taxa were significantly associated with 5-AVAB levels. A meta-analysis of five studies corroborated the inverse association between 5-AVAB and T2D risk (pooled OR = 2.68, 95% CI: 1.61-4.46).

Conclusion: Our findings suggest that lower 5-AVAB levels are associated with an increased risk of T2D. Dietary factors and gut microbiota composition appear to significantly influence 5-AVAB levels. The potential use of 5-AVAB as a therapeutic target in T2D management is an exciting area of research that requires further investigation. If successful, it could lead to new treatment options for T2D patients, ultimately improving their long-term health outcomes and quality of life.

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NHS to offer 150,000 people with type 1 diabetes an artificial pancreas

Move to make more than half of those living with condition in England and Wales eligible for device hailed as gamechanger

More than 150,000 adults and children with type 1 diabetes in England and Wales are to be offered an artificial pancreas on the NHS, which experts are hailing as a “gamechanger” that will “save lives and heartbreak”.

The groundbreaking device, also called a hybrid closed-loop system, uses a hi-tech algorithm to determine the amount of insulin that should be administered and reads blood sugar levels to keep them steady. A world-first trial on the NHS showed it was more effective at managing diabetes than current devices and required far less input from patients.

Final draft guidance from the National Institute for Health and Care Excellence (Nice) recommends that people in England and Wales should benefit from the wearable device if their diabetes is not adequately controlled by their current pump or glucose monitor. The decision to give the go-ahead for widespread use of the artificial pancreas was announced on Tuesday at Nice’s annual conference in Manchester by Dr Sam Roberts, its chief executive.

There are about 290,000 people living with type 1 diabetes in England and Wales . More than half of them will now become eligible because their diabetes is not controlled with their current device.

The artificial pancreas has been found to be better at keeping blood sugar levels within a healthy range, cutting the risk of people suffering complications from diabetes. It works via a continuous glucose monitor sensor attached to the body which transmits data to a body-worn insulin pump.

This pump then calculates how much insulin is needed and delivers the precise amount to the body. Hybrid closed-loop systems mean people do not need to rely on finger-prick blood tests or injecting insulin to control their blood sugar levels.

Living with type 1 diabetes can be relentless, health experts say, and requires intense management 24 hours a day. Hundreds of individual treatment decisions must be made around the clock as extreme blood glucose highs and lows can be fatal. By automating what is currently a manual process, the artificial pancreas could lift the relentless burden and risk of burnout.

Nice said it had agreed with NHS England that all children and young people, women who are pregnant or planning a pregnancy, and people who already have an insulin pump will be first to be offered a hybrid closed-loop system as part of a five-year rollout plan.

The technology will then be rolled out to those adults with an average HbA1c reading of 7.5% or more and those who suffer abnormally low blood sugar levels. Nice guidelines recommend people should aim for an HbA1c level of 6.5% or lower.

Prof Jonathan Benger, the chief medical officer at Nice, said: “With around 10% of the entire NHS budget being spent on diabetes, it is important for Nice to focus on what matters most by ensuring the best value for money technologies are available to healthcare professionals and patients.

“Using hybrid closed-loop systems will be a gamechanger for people with type 1 diabetes. By ensuring their blood glucose levels are within the recommended range, people are less likely to have complications such as disabling hypoglycaemia, strokes and heart attacks, which lead to costly NHS care.

“This technology will improve the health and wellbeing of patients, and save the NHS money in the long term.”

Nice said that, due to the need for extra staff alongside specialist training for patients and staff, it had accepted a request from NHS England for a rollout over five years.

Karen Addington, the chief executive of JDRF UK, a type 1 diabetes charity, hailed the announcement, saying it would transform the lives of children and adults. “Hybrid closed-loop defines a new era for medicine,” she said.

“It’s a beautiful algorithm, which will save lives and heartbreak, as well as in the long term saving NHS the cost of cardiovascular and retinal surgery, kidney dialysis and transplantation.”

She added: “Today’s announcement makes Great Britain the first country in the world to make hybrid closed-loop widely available, as England and Wales follow the lead of Scotland, who approved the use of HCL earlier in 2022.”

Colette Marshall, the chief executive of Diabetes UK, said the artificial pancreas had the potential “to transform the lives of many people with type 1 diabetes”, improving both health and quality of life.

Yasmin Hopkins, who took part in trials of the artificial pancreas, said: “From day one it was amazing. Before the closed-loop system, I would experience a lot of highs, which I’d then overcorrect, go low and eat a lot of sugar. All of that has been eradicated.

“This technology gives me the freedom to get on with my life and live without fear of what might happen in a few hours, days or years.”

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• Diabetes Care for Children & Young People

Vol:05 | No:01

Children and young people’s diabetes care: Case study

• 12 Jul 2016

This case study demonstrates the physical and psychological difficulties faced by many young people with type 1 diabetes. Over the year following her diagnosis, Max had a deterioration in glycaemic control despite reporting that little had changed in her management. Detailed assessment revealed a number of psychosocial factors that were preventing her from achieving good control. However, working with her multidisciplinary team, she was able to address these issues and improve her blood glucose levels. This article outlines these issues and the action plan that Max and her diabetes team drew up to overcome them.

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This case study represents the challenges and issues, both physical and psychological, faced by a young person with type 1 diabetes and the support given by her diabetes multidisciplinary team (MDT). Implications for practice are addressed using current evidence-based research. The names of the child and family have been anonymised to protect their identity.

Case study Max (a pseudonym) is a 17-year-old girl who was diagnosed with type 1 diabetes 4 years ago at the age of 13 years. She and her mother were shocked and upset by the diagnosis, and both felt its management would be too great a task to take on by themselves.

Max is an only child and lives with her mother, a single parent. She attends the local state comprehensive school and is popular with her peer group. Her mother was very involved in her care and diabetes management from the onset. Despite this, her diabetes control deteriorated over time ( Table 1 ). In October 2012, her HbA 1c was 56 mmol/mol (7.3%); however, over the next year, this increased to 84 mmol/mol (9.8%) in July 2013. She found it difficult to count the carbohydrate portions in her food and her injections were hurting much more than when she was first diagnosed. She also expressed a fear of hypoglycaemia and of “looking stupid” in front of her friends.

Max and her MDT discussed treatment options to improve her glycaemic control. She refused insulin pump therapy but agreed to a blood glucose monitor and bolus advisor to assist with her regimen of multiple daily insulin injections (MDI). She is now using the bolus advisor confidently and has had regular one-to-one sessions with a psychologist. She is having fewer hypoglycaemic episodes and her HbA 1c has improved; in January 2016 it was 69 mmol/mol (8.5%) and in April 2016 it was 58 mmol/mol (7.5%).

Discussion Diagnosis Max and her mother were extremely shocked and upset by the diagnosis of type 1 diabetes and the potential severity of the condition and intense management required. Both felt it would be too great a task to take on by themselves.

Kübler-Ross and Kessler (2005) suggested that a diagnosis of diabetes is a life-changing event comparable to the experience of loss, and that children and families will often go through the five stages of grief defined by Kübler-Ross (1970) and outlined in Box 1 . They use this as a coping strategy to enable them to eventually acknowledge the condition. However, many families never reach the fifth stage of acceptance and many will fluctuate between the stages.

Although Max and her mum did accept the diagnosis eventually, at times both of them reverted to the earlier stages of grief. The diabetes MDT supported the family from diagnosis and will continue to support them throughout their time within the paediatric diabetes service, through the transition period with both paediatric and young people’s teams, until discharged to adult diabetes care.

The diabetes MDT was established after the Best Practice Tariff was introduced in 2012. It consists of doctors, nurses, dietitians, a psychologist and a personal assistant. It is well recognised that the MDT needs to work together in close cooperation to achieve good practice, and this can be strengthened by using written protocols, guidelines and targets (Brink, 2010). Logic would suggest that centres with MDTs and the same approaches and treatment regimens would have similar outcomes, yet the Hvidøre Childhood Diabetes Study Group has shown this is not the case (de Beaufort et al, 2013). In terms of glycaemic control, there were notable differences in patient outcomes across 21 diabetes clinics, all of which were committed to MDT-based practice. Although factors such as age, type of insulin regimen and socioeconomic status were shown to have some influence over specific outcomes, they did not explain the apparent differences between these clinics.

Family/social history Max is an only child and lives with her mother, a single parent. East et al (2006) suggested that rapid social change over the past 20 years has seen a marked increase in the number of mother-headed single-parent families. Max attends the local state comprehensive school, where she is generally doing well. She is popular with her peer group. La Greca et al (1995) suggested that peer relationships are important in diabetes management, as children and young people (CYP) may receive considerable emotional support from their friends. However, on occasions, Max’s peer relationships have had a counterproductive effect on her, and she feels she is different from her friends as the only one who has diabetes. This at times affects her self-esteem and impacts her diabetes control.

Max’s mother was very involved in her care and diabetes management from the onset. Anderson and Brackett (2005) suggested that parents typically take on most of the responsibility for management of diabetes when children are young or newly diagnosed.

Deterioration in diabetes control Max’s diabetes control had deteriorated since her diagnosis ( Table 1 ). In October 2012, her HbA 1c was 56 mmol/mol (7.3%), which indicated a good level of diabetes control and a reduced risk of diabetes complications, as suggested by the DCCT (Diabetes Control and Complications Trial; DCCT Research Group, 1994). At her subsequent diabetes clinic appointments up to July 2013, she reported that “nothing had really changed,” except she “didn’t have time to think about her diabetes,” although she felt guilty because she knew she could make herself ill and her mum would get upset. She stated that it was hard counting the carbohydrate portions in her food and her injections were hurting much more than when she was first diagnosed. Her height and weight remained static.

Diabetes care is greatly influenced by psychosocial factors when they obstruct people’s ability to manage their diabetes and achieve good metabolic control. A team-based approach to addressing an individual’s ability to cope is critical (Kent et al, 2010). It is important for healthcare professionals to be aware of how CYP think at the different stages of their development, as their understanding of illness and chronic health conditions is often greater than that of their peers. Jean Piaget (1896–1980) investigated cognitive processes in children, calling them “schemas”. By the time children reach around 12 years of age, they can describe illness in terms of non-functioning or malfunctioning of an internal organ or process. Later in development they can appreciate that a person’s thoughts or feelings can affect the way the body functions, which demonstrates an awareness of psychological factors (Taylor et al, 1999).

Spear (2013) proposed that we can begin to understand how young people with type 1 diabetes think, feel and behave if we consider the cognitive and biological changes that occur during adolescence. Glasper and Richardson (2005) suggested there is now a growing awareness that CYP are able to make their own decisions if given information in an age-appropriate manner. Gillick competence identifies children aged under 16 years as having the capacity to consent to their own treatment if they understand the consequences (NSPCC, 2016).

Butler et al (2007) suggest that adolescence is a time of upheaval when young people have to deal with the influence of peers, school life and developing their own identity, as well as all the physiological changes that occur. Young people with type 1 diabetes have the added responsibility of developing autonomy regarding the self-management of their condition. Hanas (2006) suggests that parents should continue to take part in their child’s diabetes care into adolescence and not hand the responsibility to the young person too early. Snoek and Skinner (2002) suggest that intensive self-management of diabetes is complex and time-consuming, and creates a significant psychosocial burden on children and their families.

There are significant challenges for CYP to engage in effective diabetes self-management. Several of these were identified with Max and her mother:

• Deterioration in diabetes control.
• Difficulty with carbohydrate counting.
• Insulin omission.
• Fear of hypoglycaemia.
• Painful injections.

Action plan An action plan was discussed between Max and the MDT. As she was on an MDI regimen (a long-acting insulin at bedtime and rapid-acting insulin with meals), a bolus advisor/blood glucose monitor was demonstrated and discussed with her and her mum. Max felt she would be able to use this to help eliminate the calculations which, although she was capable of doing them, she often lacked time to do so. With further discussion, Max said she was “scared of getting it wrong and having a hypo”. Insulin pump therapy was discussed but she did not want to “have a device attached to my body because it would remind me all the time that I have diabetes”. Insulin pump therapy is recommended as a treatment option for adults and children over 12 years of age with type 1 diabetes whose HbA 1c levels remain above 69 mmol/mol (8.5%) on MDI therapy despite a high level of care (NICE, 2015a).

The National Service Framework standard 3 (Department of Health, 2001) recommends empowering people with diabetes and encourages them and their carers to gain the knowledge and skills to be partners in decision-making, and giving them more personal control over the day-to-day management of their diabetes, ensuring the best possible quality of life. However, if a diabetes management plan is discussed in partnership with a (Gillick-competent) young person but they elect not to comply with the plan despite full awareness of the implications of their actions, then the diabetes team should support them whilst trying to encourage them to maintain the treatment plan. This can be very difficult and frustrating at times, as a healthcare professional is an advocate for the patient, and promotion of the best interests of the patient is paramount.

Psychology involvement Max was reviewed by the psychologist to assess her psychological health and wellbeing. The psychologist used the Wellbeing in Diabetes questionnaire (available from the Yorkshire and Humber Paediatric Diabetes Network) to assess her and identify an optimal plan of care.

The psychology sessions were focussed on her issues around the following:

• Worry about deterioration in control.
• The consequences of insulin omission.

Max had a series of one-to-one appointments and some joint sessions with the paediatric diabetes specialist nurse and/or dietitian, so this linked into other team members’ specialities.

Carbohydrate counting and use of a bolus advisor The dietitian assessed Max and her mother’s ability to carbohydrate count using a calculator, food diagrams and portion sizes, and both of them were able to demonstrate competency in this task. Garg et al (2008) have shown that the use of automated bolus advisors is safe and effective in reducing postprandial glucose excursions and improving overall glycaemic control. However, this can only be true if the bolus advisor is being used correctly and is confirmed as such by comparing blood glucose and HbA 1c results before and after initiation of the bolus advisor, and observing the patient using the device to ensure it is being used safely and correctly.

Barnard and Parkin (2012) propose that, as long as safety and lifestyle are taken into consideration, advanced technology will benefit CYP, as inaccurate bolus calculation can lead to persistent poor diabetes control. These tools can help with removing the burden of such complex maths and have the potential to significantly improve glycaemic control.

Insulin omission and fear of hypoglycaemia Max also expressed her fear of hypoglycaemia and of “looking stupid” in front of her friends. She admitted to missing some of her injections, especially at school. Wild et al (2007) suggest that a debilitating fear of hypoglycaemia can result in poor adherence to insulin regimens and subsequent poor metabolic control. Crow et al (1998) describe the deliberate omission or reduced administration of insulin, which results in hyperglycaemia and subsequent rapid reduction in body weight. Type 1 diabetes predisposes a person to a high BMI. Adolescent girls and adult women with type 1 diabetes generally have higher BMI values than their peers without the condition (Domargård et al, 1999). Affenito et al (1998) observed that insulin misuse was the most common method of weight control used by young women with type 1 diabetes. However, Max’s weight remained stable and there was no clinical indication that she was missing insulin to lose weight; rather, it was her fear of hypoglycaemia that drove her to omitting insulin at school. With the use of the bolus calculator, she was reassured about her calculations for insulin-to-carbohydrate ratios, but it was reinforced with her that the device would only work efficiently if she used it correctly with each meal.

Painful injections Max also highlighted that her injections were now more painful than when she was first diagnosed, and this was causing her distress each time she had to inject. Injection technique was discussed with her and demonstrated using an injection model, and her injection technique was observed and appeared satisfactory. She was using 5-mm insulin needles and so was switched to 4-mm needles, as recommended by Forum for Injection Technique (2015) guidelines.

Appropriate technique when giving injections is key to optimal blood glucose control; however, evidence suggests that injection technique is often imperfect. Studies by Strauss et al (2002) and Frid et al (2010) revealed disturbing practices in relation to injection technique, with little improvement over the years. Current diabetes guidelines do not include detailed advice on injection technique, and only the guidance on type 2 diabetes in adults (NICE, 2015b) makes any reference to providing education about injectable devices for people with diabetes. However, the older Quality Standard for diabetes in adults (NICE, 2011) recommends a structured programme of education, including injection site selection and care (Diggle, 2014).

Conclusion The issues and concerns this young girl had were identified and addressed by the diabetes MDT. She was assessed by several members of the team, and a credible, evidence-based action plan was put into place to assist her and her mother to manage her diabetes at this difficult time. Max is now using the bolus advisor confidently and having fewer hypoglycaemic episodes, and her HbA 1c has improved. She prefers using the 4-mm injection pen needles, although she remains hesitant when giving injections; she will still not consider insulin pump therapy. Her one-to-one sessions with the psychologist have now ceased, but she is aware she can access a psychologist at clinic on request, or if the MDT assesses that her psychological health has deteriorated.

When a child in a family develops a chronic condition such as type 1 diabetes, effective communication is vitally important to address issues with the family at the earliest stage so that problems can be discussed and, hopefully, resolved before they escalate out of control. Upon reflection, the team could have become more intensely involved at an earlier stage to prevent Max’s diabetes management issues and stop her HbA 1c from reaching such a high level. Furthermore, the new NICE (2015a) guideline has set the target HbA 1c at ≤48 mmol/mol (6.5%), so there is still some work to be done. However, the outcome of this case appears to be favourable at present.

Affenito SG, Rodriguez NR, Backstrand JR et al (1998) Insulin misuse by women with type 1 diabetes mellitus complicated by eating disorders does not favorably change body weight, body composition, or body fat distribution. J Am Diet Assoc 98 : 686–8 Anderson BJ, Brackett J (2005) Diabetes in children. In: Snoek FJ, Skinner TC (eds). Psychology in Diabetes Care (2nd edition). John Wiley & Sons, Chichester Barnard K, Parkin C (2012) Can automated bolus advisors help alleviate the burden of complex maths and lead to optimised diabetes health outcomes? Diabetes Care for Children & Young People 1 : 6–9 Brink SJ (2010) Pediatric and adolescent multidisciplinary diabetes team care. Pediatr Diabetes 11 : 289–91 Butler JM, Skinner M, Gelfand D et al (2007) Maternal parenting style and adjustment in adolescents with type I diabetes. J Pediatr Psychol 32 : 1227–37 Crow SJ, Keel PK, Kendall D (1998) Eating disorders and insulin-dependent diabetes mellitus. Psychosomatics 39 : 233–43 de Beaufort CE, Lange K, Swift PG et al (2013) Metabolic outcomes in young children with type 1 diabetes differ between treatment centers: the Hvidoere Study in Young Children 2009. Pediatr Diabetes 14 : 422–8 Department of Health (2001) National Service Framework: Diabetes . DH, London. Available at: http://bit.ly/18OpAzL (accessed 24.02.16) Diabetes Control and Complications Trial Research Group (1994) Effect of intensive diabetes treatment on the development and progression of long-term complications in adolescents with insulin-dependent diabetes mellitus: Diabetes Control and Complications Trial. J Pediatr 125 : 177–88 Diggle J (2014) Are you FIT for purpose? The importance of getting injection technique right . Journal of Diabetes Nursing 18 : 50–7 Domargård A, Särnblad S, Kroon M et al (1999) Increased prevalence of overweight in adolescent girls with type 1 diabetes mellitus. Acta Paediatr 88 : 1223–8 East L, Jackson D, O’Brien L (2006) Father absence and adolescent development: a review of the literature. J Child Health Care 10 : 283–95 Forum for Injection Technique (2015) The UK Injection Technique Recommendations (3rd edition). Available at: http://bit.ly/1QeZU2E (accessed 24.02.16) Frid A, Hirsch L, Gaspar R et al (2010) The Third Injection Technique Workshop in Athens (TITAN). Diabetes Metab 36 (Suppl 2): 19–29 Garg SK, Bookout TR, McFann KK et al (2008) Improved glycemic control in intensively treated adult subjects with type 1 diabetes using insulin guidance software. Diabetes Technol Ther 10 : 369–75 Glasper EA, Richardson J (2005) A Textbook of Children’s and Young People’s Nursing . Churchill Livingston, London Hanas R (2006) Type 1 Diabetes in Children, Adolescents and Young Adults (3rd edition). Class Publishing, London: 329, 349–50 Kent D, Haas L, Randal D et al (2010) Healthy coping: issues and implications in diabetes education and care. Popul Health Manag 13 : 227–33 Kübler-Ross E (1970) On Death and Dying: What the Dying Have to Teach Doctors, Nurses, Clergy and Their Own Families . Tavistock Publications, London Kübler-Ross E, Kessler D (2005) On Grief and Grieving: Finding the Meaning of Grief Through the Five Stages of Loss . Simon & Schuster UK, London La Greca AM, Auslander WF, Greco P et al (1995) I get by with a little help from my family and friends: adolescents’ support for diabetes care. J Pediatr Psychol 20 : 449–76 NICE (2011) Diabetes in adults (QS6). NICE, London. Available at: www.nice.org.uk/guidance/qs6 (accessed 24.02.16) NICE (2015a) Diabetes (type 1 and type 2) in children and young people: diagnosis and management (NG18). NICE, London. Available at: www.nice.org.uk/guidance/ng18 (accessed 24.02.16) NICE (2015b) Type 2 diabetes in adults: management (NG28). NICE, London. Available at: www.nice.org.uk/guidance/ng28 (accessed 24.02.16) NSPCC (2016) A Child’s Legal Rights: Gillick Competency and Fraser Guidelines . NSPCC, London. Available at: http://bit.ly/1Tj6DcF (accessed 24.02.16) Snoek FJ, Skinner TC (2002) Psychological counselling in problematic diabetes: does it help? Diabet Med 19 : 265–73 Spear LP (2013) Adolescent neurodevelopment. J Adolesc Health 52 (Suppl 2): 7–13 Strauss K, De Gols H, Hannat I et al (2002) A pan-European epidemiologic study of insulin injection technique in patients with diabetes. Practical Diabetes International 19 : 71–76 Taylor J, Müller D, Wattley L, Harris P (1999) The development of children’s understanding. In: Nursing Children: Psychology, Research and Practice . Stanley Thornes, Cheltenham Wild D, von Maltzahn R, Brohan E et al (2007) A critical review of the literature on fear of hypoglycemia in diabetes: implications for diabetes management and patient education. Patient Educ Couns 68 : 10–5

Do youth workers have a role in improving diabetes transition services?

Cgm for children and young people with type 1 diabetes: nice criteria and effects of decision fatigue and alarm fatigue  , improving paediatric diabetes in england: areas of focus, delays in accessing continuous glucose monitoring in people with type 1 diabetes, celebrating may ng: the woman behind the obe, fiona campbell awarded an obe for services to paediatric diabetes, diabetes transition: a time to act.

Can the involvement of youth workers improve diabetes care for young people transitioning to adult diabetes services?

The impact of decision fatigue and alarm fatigue in children and young people using continuous glucose monitoring

NHSEI National Clinical Lead for Diabetes in Children and Young People, Fulya Mehta, outlines the areas of focus for improving paediatric diabetes care.

16 Nov 2022

NICE guidance urges local trusts to improve processes and advocate for CGM use in children and young people.

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Case Presentation

Case study: a patient with uncontrolled type 2 diabetes and complex comorbidities whose diabetes care is managed by an advanced practice nurse.

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Geralyn Spollett; Case Study: A Patient With Uncontrolled Type 2 Diabetes and Complex Comorbidities Whose Diabetes Care Is Managed by an Advanced Practice Nurse. Diabetes Spectr 1 January 2003; 16 (1): 32–36. https://doi.org/10.2337/diaspect.16.1.32

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The specialized role of nursing in the care and education of people with diabetes has been in existence for more than 30 years. Diabetes education carried out by nurses has moved beyond the hospital bedside into a variety of health care settings. Among the disciplines involved in diabetes education, nursing has played a pivotal role in the diabetes team management concept. This was well illustrated in the Diabetes Control and Complications Trial (DCCT) by the effectiveness of nurse managers in coordinating and delivering diabetes self-management education. These nurse managers not only performed administrative tasks crucial to the outcomes of the DCCT, but also participated directly in patient care. 1  

The emergence and subsequent growth of advanced practice in nursing during the past 20 years has expanded the direct care component, incorporating aspects of both nursing and medical care while maintaining the teaching and counseling roles. Both the clinical nurse specialist (CNS) and nurse practitioner (NP) models, when applied to chronic disease management, create enhanced patient-provider relationships in which self-care education and counseling is provided within the context of disease state management. Clement 2 commented in a review of diabetes self-management education issues that unless ongoing management is part of an education program, knowledge may increase but most clinical outcomes only minimally improve. Advanced practice nurses by the very nature of their scope of practice effectively combine both education and management into their delivery of care.

Operating beyond the role of educator, advanced practice nurses holistically assess patients’ needs with the understanding of patients’ primary role in the improvement and maintenance of their own health and wellness. In conducting assessments, advanced practice nurses carefully explore patients’ medical history and perform focused physical exams. At the completion of assessments, advanced practice nurses, in conjunction with patients, identify management goals and determine appropriate plans of care. A review of patients’ self-care management skills and application/adaptation to lifestyle is incorporated in initial histories, physical exams, and plans of care.

Many advanced practice nurses (NPs, CNSs, nurse midwives, and nurse anesthetists) may prescribe and adjust medication through prescriptive authority granted to them by their state nursing regulatory body. Currently, all 50 states have some form of prescriptive authority for advanced practice nurses. 3 The ability to prescribe and adjust medication is a valuable asset in caring for individuals with diabetes. It is a crucial component in the care of people with type 1 diabetes, and it becomes increasingly important in the care of patients with type 2 diabetes who have a constellation of comorbidities, all of which must be managed for successful disease outcomes.

Many studies have documented the effectiveness of advanced practice nurses in managing common primary care issues. 4 NP care has been associated with a high level of satisfaction among health services consumers. In diabetes, the role of advanced practice nurses has significantly contributed to improved outcomes in the management of type 2 diabetes, 5 in specialized diabetes foot care programs, 6 in the management of diabetes in pregnancy, 7 and in the care of pediatric type 1 diabetic patients and their parents. 8 , 9 Furthermore, NPs have also been effective providers of diabetes care among disadvantaged urban African-American patients. 10 Primary management of these patients by NPs led to improved metabolic control regardless of whether weight loss was achieved.

The following case study illustrates the clinical role of advanced practice nurses in the management of a patient with type 2 diabetes.

A.B. is a retired 69-year-old man with a 5-year history of type 2 diabetes. Although he was diagnosed in 1997, he had symptoms indicating hyperglycemia for 2 years before diagnosis. He had fasting blood glucose records indicating values of 118–127 mg/dl, which were described to him as indicative of “borderline diabetes.” He also remembered past episodes of nocturia associated with large pasta meals and Italian pastries. At the time of initial diagnosis, he was advised to lose weight (“at least 10 lb.”), but no further action was taken.

Referred by his family physician to the diabetes specialty clinic, A.B. presents with recent weight gain, suboptimal diabetes control, and foot pain. He has been trying to lose weight and increase his exercise for the past 6 months without success. He had been started on glyburide (Diabeta), 2.5 mg every morning, but had stopped taking it because of dizziness, often accompanied by sweating and a feeling of mild agitation, in the late afternoon.

A.B. also takes atorvastatin (Lipitor), 10 mg daily, for hypercholesterolemia (elevated LDL cholesterol, low HDL cholesterol, and elevated triglycerides). He has tolerated this medication and adheres to the daily schedule. During the past 6 months, he has also taken chromium picolinate, gymnema sylvestre, and a “pancreas elixir” in an attempt to improve his diabetes control. He stopped these supplements when he did not see any positive results.

He does not test his blood glucose levels at home and expresses doubt that this procedure would help him improve his diabetes control. “What would knowing the numbers do for me?,” he asks. “The doctor already knows the sugars are high.”

A.B. states that he has “never been sick a day in my life.” He recently sold his business and has become very active in a variety of volunteer organizations. He lives with his wife of 48 years and has two married children. Although both his mother and father had type 2 diabetes, A.B. has limited knowledge regarding diabetes self-care management and states that he does not understand why he has diabetes since he never eats sugar. In the past, his wife has encouraged him to treat his diabetes with herbal remedies and weight-loss supplements, and she frequently scans the Internet for the latest diabetes remedies.

During the past year, A.B. has gained 22 lb. Since retiring, he has been more physically active, playing golf once a week and gardening, but he has been unable to lose more than 2–3 lb. He has never seen a dietitian and has not been instructed in self-monitoring of blood glucose (SMBG).

A.B.’s diet history reveals excessive carbohydrate intake in the form of bread and pasta. His normal dinners consist of 2 cups of cooked pasta with homemade sauce and three to four slices of Italian bread. During the day, he often has “a slice or two” of bread with butter or olive oil. He also eats eight to ten pieces of fresh fruit per day at meals and as snacks. He prefers chicken and fish, but it is usually served with a tomato or cream sauce accompanied by pasta. His wife has offered to make him plain grilled meats, but he finds them “tasteless.” He drinks 8 oz. of red wine with dinner each evening. He stopped smoking more than 10 years ago, he reports, “when the cost of cigarettes topped a buck-fifty.”

The medical documents that A.B. brings to this appointment indicate that his hemoglobin A 1c (A1C) has never been <8%. His blood pressure has been measured at 150/70, 148/92, and 166/88 mmHg on separate occasions during the past year at the local senior center screening clinic. Although he was told that his blood pressure was “up a little,” he was not aware of the need to keep his blood pressure ≤130/80 mmHg for both cardiovascular and renal health. 11  

A.B. has never had a foot exam as part of his primary care exams, nor has he been instructed in preventive foot care. However, his medical records also indicate that he has had no surgeries or hospitalizations, his immunizations are up to date, and, in general, he has been remarkably healthy for many years.

Physical Exam

A physical examination reveals the following:

Weight: 178 lb; height: 5′2″; body mass index (BMI): 32.6 kg/m 2

Fasting capillary glucose: 166 mg/dl

Blood pressure: lying, right arm 154/96 mmHg; sitting, right arm 140/90 mmHg

Pulse: 88 bpm; respirations 20 per minute

Eyes: corrective lenses, pupils equal and reactive to light and accommodation, Fundi-clear, no arteriolovenous nicking, no retinopathy

Thyroid: nonpalpable

Lungs: clear to auscultation

Heart: Rate and rhythm regular, no murmurs or gallops

Vascular assessment: no carotid bruits; femoral, popliteal, and dorsalis pedis pulses 2+ bilaterally

Neurological assessment: diminished vibratory sense to the forefoot, absent ankle reflexes, monofilament (5.07 Semmes-Weinstein) felt only above the ankle

Lab Results

Results of laboratory tests (drawn 5 days before the office visit) are as follows:

Glucose (fasting): 178 mg/dl (normal range: 65–109 mg/dl)

Creatinine: 1.0 mg/dl (normal range: 0.5–1.4 mg/dl)

Blood urea nitrogen: 18 mg/dl (normal range: 7–30 mg/dl)

Sodium: 141 mg/dl (normal range: 135–146 mg/dl)

Potassium: 4.3 mg/dl (normal range: 3.5–5.3 mg/dl)

Lipid panel

    • Total cholesterol: 162 mg/dl (normal: <200 mg/dl)

    • HDL cholesterol: 43 mg/dl (normal: ≥40 mg/dl)

    • LDL cholesterol (calculated): 84 mg/dl (normal: <100 mg/dl)

    • Triglycerides: 177 mg/dl (normal: <150 mg/dl)

    • Cholesterol-to-HDL ratio: 3.8 (normal: <5.0)

AST: 14 IU/l (normal: 0–40 IU/l)

ALT: 19 IU/l (normal: 5–40 IU/l)

Alkaline phosphotase: 56 IU/l (normal: 35–125 IU/l)

A1C: 8.1% (normal: 4–6%)

Urine microalbumin: 45 mg (normal: <30 mg)

Based on A.B.’s medical history, records, physical exam, and lab results, he is assessed as follows:

Uncontrolled type 2 diabetes (A1C >7%)

Obesity (BMI 32.4 kg/m 2 )

Hyperlipidemia (controlled with atorvastatin)

Peripheral neuropathy (distal and symmetrical by exam)

Hypertension (by previous chart data and exam)

Elevated urine microalbumin level

Self-care management/lifestyle deficits

    • Limited exercise

    • High carbohydrate intake

    • No SMBG program

Poor understanding of diabetes

A.B. presented with uncontrolled type 2 diabetes and a complex set of comorbidities, all of which needed treatment. The first task of the NP who provided his care was to select the most pressing health care issues and prioritize his medical care to address them. Although A.B. stated that his need to lose weight was his chief reason for seeking diabetes specialty care, his elevated glucose levels and his hypertension also needed to be addressed at the initial visit.

The patient and his wife agreed that a referral to a dietitian was their first priority. A.B. acknowledged that he had little dietary information to help him achieve weight loss and that his current weight was unhealthy and “embarrassing.” He recognized that his glucose control was affected by large portions of bread and pasta and agreed to start improving dietary control by reducing his portion size by one-third during the week before his dietary consultation. Weight loss would also be an important first step in reducing his blood pressure.

The NP contacted the registered dietitian (RD) by telephone and referred the patient for a medical nutrition therapy assessment with a focus on weight loss and improved diabetes control. A.B.’s appointment was scheduled for the following week. The RD requested that during the intervening week, the patient keep a food journal recording his food intake at meals and snacks. She asked that the patient also try to estimate portion sizes.

Although his physical activity had increased since his retirement, it was fairly sporadic and weather-dependent. After further discussion, he realized that a week or more would often pass without any significant form of exercise and that most of his exercise was seasonal. Whatever weight he had lost during the summer was regained in the winter, when he was again quite sedentary.

A.B.’s wife suggested that the two of them could walk each morning after breakfast. She also felt that a treadmill at home would be the best solution for getting sufficient exercise in inclement weather. After a short discussion about the positive effect exercise can have on glucose control, the patient and his wife agreed to walk 15–20 minutes each day between 9:00 and 10:00 a.m.

A first-line medication for this patient had to be targeted to improving glucose control without contributing to weight gain. Thiazolidinediones (i.e., rosiglitizone [Avandia] or pioglitizone [Actos]) effectively address insulin resistance but have been associated with weight gain. 12 A sulfonylurea or meglitinide (i.e., repaglinide [Prandin]) can reduce postprandial elevations caused by increased carbohydrate intake, but they are also associated with some weight gain. 12 When glyburide was previously prescribed, the patient exhibited signs and symptoms of hypoglycemia (unconfirmed by SMBG). α-Glucosidase inhibitors (i.e., acarbose [Precose]) can help with postprandial hyperglycemia rise by blunting the effect of the entry of carbohydrate-related glucose into the system. However, acarbose requires slow titration, has multiple gastrointestinal (GI) side effects, and reduces A1C by only 0.5–0.9%. 13 Acarbose may be considered as a second-line therapy for A.B. but would not fully address his elevated A1C results. Metformin (Glucophage), which reduces hepatic glucose production and improves insulin resistance, is not associated with hypoglycemia and can lower A1C results by 1%. Although GI side effects can occur, they are usually self-limiting and can be further reduced by slow titration to dose efficacy. 14  

After reviewing these options and discussing the need for improved glycemic control, the NP prescribed metformin, 500 mg twice a day. Possible GI side effects and the need to avoid alcohol were of concern to A.B., but he agreed that medication was necessary and that metformin was his best option. The NP advised him to take the medication with food to reduce GI side effects.

The NP also discussed with the patient a titration schedule that increased the dosage to 1,000 mg twice a day over a 4-week period. She wrote out this plan, including a date and time for telephone contact and medication evaluation, and gave it to the patient.

During the visit, A.B. and his wife learned to use a glucose meter that features a simple two-step procedure. The patient agreed to use the meter twice a day, at breakfast and dinner, while the metformin dose was being titrated. He understood the need for glucose readings to guide the choice of medication and to evaluate the effects of his dietary changes, but he felt that it would not be “a forever thing.”

The NP reviewed glycemic goals with the patient and his wife and assisted them in deciding on initial short-term goals for weight loss, exercise, and medication. Glucose monitoring would serve as a guide and assist the patient in modifying his lifestyle.

A.B. drew the line at starting an antihypertensive medication—the angiotensin-converting enzyme (ACE) inhibitor enalapril (Vasotec), 5 mg daily. He stated that one new medication at a time was enough and that “too many medications would make a sick man out of me.” His perception of the state of his health as being represented by the number of medications prescribed for him gave the advanced practice nurse an important insight into the patient’s health belief system. The patient’s wife also believed that a “natural solution” was better than medication for treating blood pressure.

Although the use of an ACE inhibitor was indicated both by the level of hypertension and by the presence of microalbuminuria, the decision to wait until the next office visit to further evaluate the need for antihypertensive medication afforded the patient and his wife time to consider the importance of adding this pharmacotherapy. They were quite willing to read any materials that addressed the prevention of diabetes complications. However, both the patient and his wife voiced a strong desire to focus their energies on changes in food and physical activity. The NP expressed support for their decision. Because A.B. was obese, weight loss would be beneficial for many of his health issues.

Because he has a sedentary lifestyle, is >35 years old, has hypertension and peripheral neuropathy, and is being treated for hypercholestrolemia, the NP performed an electrocardiogram in the office and referred the patient for an exercise tolerance test. 11 In doing this, the NP acknowledged and respected the mutually set goals, but also provided appropriate pre-exercise screening for the patient’s protection and safety.

In her role as diabetes educator, the NP taught A.B. and his wife the importance of foot care, demonstrating to the patient his inability to feel the light touch of the monofilament. She explained that the loss of protective sensation from peripheral neuropathy means that he will need to be more vigilant in checking his feet for any skin lesions caused by poorly fitting footwear worn during exercise.

At the conclusion of the visit, the NP assured A.B. that she would share the plan of care they had developed with his primary care physician, collaborating with him and discussing the findings of any diagnostic tests and procedures. She would also work in partnership with the RD to reinforce medical nutrition therapies and improve his glucose control. In this way, the NP would facilitate the continuity of care and keep vital pathways of communication open.

Advanced practice nurses are ideally suited to play an integral role in the education and medical management of people with diabetes. 15 The combination of clinical skills and expertise in teaching and counseling enhances the delivery of care in a manner that is both cost-reducing and effective. Inherent in the role of advanced practice nurses is the understanding of shared responsibility for health care outcomes. This partnering of nurse with patient not only improves care but strengthens the patient’s role as self-manager.

Geralyn Spollett, MSN, C-ANP, CDE, is associate director and an adult nurse practitioner at the Yale Diabetes Center, Department of Endocrinology and Metabolism, at Yale University in New Haven, Conn. She is an associate editor of Diabetes Spectrum.

Note of disclosure: Ms. Spollett has received honoraria for speaking engagements from Novo Nordisk Pharmaceuticals, Inc., and Aventis and has been a paid consultant for Aventis. Both companies produce products and devices for the treatment of diabetes.

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