Increasing plasma glucose before the development of type 1 diabetes-the TRIGR study.

OBJECTIVE: The ß-cell stress hypothesis suggests that increased insulin demand contributes to the development of type 1 diabetes. In the TRIGR trial we set out to assess the profile of plasma glucose and HbA1c before the diagnosis of clinical diabetes compared to nondiabetic children. RESEARCH DESIGN AND METHODS: A cohort of children (N = 2159) with an affected first-degree relative and increased HLA risk were recruited 2002-2007 and followed until 2017. To study the relationship between plasma glucose/HbA1c and the development of autoantibodies or clinical disease Kaplan-Meir curves were developed. Mixed models were constructed for plasma glucose and HbA1c separately. RESULTS: A family history of type 2 diabetes was related to an increase in plasma glucose (p < 0.001). An increase in glucose from the previous sample predicted clinical diabetes (p < 0.001) but not autoantibodies. An increase of HbA1c of 20% or 30% from the previous sample predicted the development of any autoantibody (p < 0.003 resp <0.001) and the development of diabetes (p < 0.002 resp <0.001. Participants without autoantibodies had lower HbA1c (mean 5.18%, STD 0.24; mean 33.08 mmol/mol, STD 2.85) than those who progressed to clinical disease (5.31%, 0.42; 34.46 mmol/mol, 4.68; p < 0.001) but higher than those who developed any autoantibody (5.10%, 0.30; 32.21 mmol/mol, 3.49; p < 0.001), or multiple autoantibodies (5.11%, 0.35; 32.26 mmol/mol, 3.92; p < 0.003). CONCLUSIONS: A pronounced increase in plasma glucose and HbA1c precedes development of clinical diabetes, while the association between plasma glucose or HbA1c and development of autoantibodies is complex. Increased insulin demand may contribute to development of type 1 diabetes.

Childhood body mass index in relation to subsequent risk of type 1 diabetes-A Danish cohort study.

The incidence of type 1 diabetes (T1D) is increasing, and obesity may be a contributing factor by increasing the risk and accelerating the onset. We investigated the relation between childhood body mass index z-scores (BMIz) and the later risk of T1D, including association with age at onset of T1D. The study included 238 cases and 10 147 controls selected from the Copenhagen School Health Record Register (CSHRR). Cases of T1D were identified in the Danish Registry of Childhood and Adolescent Diabetes and 2 regional studies and linked to CSHRR. Using conditional logistic regression models, the association of childhood prediagnostic BMIz at 7 and 13 years of age and changes between these ages with subsequent risk (odds ratio, OR) of T1D was estimated. A greater BMIz at 7 and 13 years of age was associated with increased risk of T1D with OR of 1.23 (confidence interval, CI 1.09-1.37; P = .0001) and 1.20 (CI 1.04-1.40; P = .016), respectively. The risk was increased by upward changes in z-scores from birth to 7 years (OR=1.21, P = .003) and from 7 to 13 years of age (OR=1.95, P = .023), but in the latter age interval also by a decline in BMIz (OR = 1.91, P = .034). There were no associations between BMIz at 7 and 13 years of age and the age of onset (P = .34 and P = .42, respectively). Increased BMIz is associated with a moderate increase in risk of T1D, but with no relation to age at onset within the analyzed age range. Increased BMIz over time is unlikely to explain the rising incidence of T1D.

The relationship between BMI and insulin resistance and progression from single to multiple autoantibody positivity and type 1 diabetes among TrialNet Pathway to Prevention participants.

AIMS/HYPOTHESIS: The incidence of type 1 diabetes is increasing at a rate of 3-5% per year. Genetics cannot fully account for this trend, suggesting an influence of environmental factors. The accelerator hypothesis proposes an effect of metabolic factors on type 1 diabetes risk. To test this in the TrialNet Pathway to Prevention (PTP) cohort, we analysed the influence of BMI, weight status and insulin resistance on progression from single to multiple islet autoantibodies (Aab) and progression from normoglycaemia to diabetes. METHODS: HOMA1-IR was used to estimate insulin resistance in Aab-positive PTP participants. Cox proportional hazards models were used to evaluate the effects of BMI, BMI percentile (BMI%), weight status and HOMA1-IR on the progression of autoimmunity or the development of diabetes. RESULTS: Data from 1,310 single and 1,897 multiple Aab-positive PTP participants were included. We found no significant relationships between BMI, BMI%, weight status or HOMA1-IR and the progression from one to multiple Aabs. Similarly, among all Aab-positive participants, no significant relationships were found between BMI, weight status or HOMA1-IR and progression to diabetes. Diabetes risk was modestly increased with increasing BMI% among the entire cohort, in obese participants 13-20 years of age and with increasing HOMA1-IR in adult Aab-positive participants. CONCLUSIONS/INTERPRETATION: Analysis of the accelerator hypothesis in the TrialNet PTP cohort does not suggest a broad influence of metabolic variables on diabetes risk. Efforts to identify other potentially modifiable environmental factors should continue.

High birth weights but not excessive weight gain prior to manifestation are related to earlier onset of diabetes in childhood: 'accelerator hypothesis' revisited.

AIMS/HYPOTHESIS: Aim of this study was to test Wilkin's 'accelerator hypothesis': whether excessive weight gain accelerates the onset of type 1 diabetes. SUBJECTS AND METHODS: Anthropometric birth data of 1117 children who developed diabetes between 1988 and April 2013 were compared with those of a sex, age, and gestational age matched, contemporary regional control group (n = 54 344). Cases were divided into three manifestation groups (G1:0-4.9 yr, G2:5-9.9 yr, and G3: 10-20 yr). Furthermore, growth data of 540 children with diabetes were compared with controls (n = 134 249) in pre-, peri-, and post-onset intervals (interval: 1-6). Also, correlation of age at onset and body mass index (BMI) standard deviation score (SDS) at this point of time were examined. RESULTS: Cases had significantly higher SDSs for birth weight when compared with controls (boys: p = 0.007, girls: p = 0.002). Children with early manifestation had the highest mean of birth weight SDS (G1>G2>G3), (p = 0.22, adjusted r(2) = 0.001). BMI SDS trend curves of cases are slightly higher compared with those of the healthy controls. This was only significant in years after diagnosis (interval 6, p < 0.000). Cases did not show excessive weight gain in any of the examined intervals before the onset of diagnosis (interval 1-3). One year after diagnosis, we found an inverse correlation between age at diagnosis and BMI SDS at diabetes manifestation. The youngest children at diagnosis (G1) had the lowest BMI SDS at manifestation and vice versa (G1

Why is the Incidence of Type 1 Diabetes Increasing?

Type 1 diabetes is a condition that can lead to serious long-term complications and can have significant psychological and quality of life implications. Its incidence is increasing in all parts of the world, but the reasons for this are incompletely understood. Genetic factors alone cannot explain such a rapid increase in incidence; therefore, environmental factors must be implicated. Lifestyle factors have been classically associated with type 2 diabetes. However, there are data implicating obesity and insulin resistance to type 1 diabetes as well (accelerator hypothesis). Cholesterol has also been shown to be correlated with the incidence of type 1 diabetes; this may be mediated by immunomodulatory effects of cholesterol. There is considerable interest in early life factors, including maternal diet, mode of delivery, infant feeding, childhood diet, microbial exposure (hygiene hypothesis), and use of anti-microbials in early childhood. Distance from the sea has recently been shown to be negatively correlated with the incidence of type 1 diabetes. This may contribute to the increasing incidence of type 1 diabetes since people are increasingly living closer to the sea. Postulated mediating mechanisms include hours of sunshine (and possibly vitamin D levels), mean temperature, dietary habits, and pollution. Ozone, polychlorinated biphenyls, phthalates, trichloroethylene, dioxin, heavy metals, bisphenol, nitrates/nitrites, and mercury are amongst the chemicals which may increase the risk of type 1 diabetes. Another area of research concerns the role of the skin and gut microbiome. The microbiome is affected by many of the factors mentioned above, including the mode of delivery, infant feeding, exposure to microbes, antibiotic use, and dietary habits. Research on the reasons why the incidence of type 1 diabetes is increasing not only sheds light on its pathogenesis but also offers insights into ways we can prevent type 1 diabetes.

Increased Incidence of Type 1 Diabetes in Children and No Change in the Age of Diagnosis and BMI-SDS at the Onset - is the Accelerator Hypothesis not Working?

Objective: One of the hypothesized reasons for the observed increase in type 1 diabetes incidence in children is weight gain, causing accelerated disease development in predisposed individuals. This so-called accelerator hypothesis is, however, controversial. The aim was to analyze whether, in the ethnically homogeneous population of Lesser Poland, an increase in the number of cases of diabetes among children was associated with younger age and higher body mass index-standard deviation score (BMI-SDS) at the time of diagnosis. Methods: Retrospective data analysis from medical records of all patients <14 years (n=559; 50.6% male), with newly diagnosed type 1 diabetes, in Lesser Poland between 1st January 2006 and 31st December 2017 (11 years). Results: The incidence ratio ranged significantly (p<0.001) from the lowest in 2006 (11.2/100,000/year) to the highest in 2012 (21.9/100,000/year). The mean age of diagnosis was 8.2±3.5 years. There was no trend in decreasing diagnosis age (p=0.43). The mean BMI-SDS was -0.4±1.2. Almost all children (91.6%) presented with BMI-SDS within the normal range at the time of diagnosis, with only 2.7% of cases being obese and 5.7% underweight at the moment of diagnosis. There was no clear trend at all in BMI-SDS over the study period. Conclusion: These results do not corroborate an increase of type 1 incidence in paediatric population being associated with younger age of diagnosis and higher BMI-SDS. This implies that the accelerator hypothesis does not hold true in the study population.