... younger age at onset of type 1 diabetes is typically associated with more acute symptoms at presentation, including an increased risk of diabetic ketoacidosis and admission to hospital.9 Second, ...the changing disease patterns mean that young people with diabetes will have a longer duration of exposure to an altered metabolic milieu, which substantially increases the risk of chronic microvascular and macrovascular complications.
Growth and risk for obesity Researchers at the Diabetes in Pregnancy Center at Northwestern University in Chicago have reported excessive growth in a multiethnic population of offspring of women with ...diabetes during pregnancy, including both gestational diabetes mellitus (GDM) and insulin-treated preexistent diabetes (2). ... extensive maternal diabetes information based on glucose testing rather than on assessment of family history of diabetes is available for the offspring of women who had diabetes before or during pregnancy (diabetic mothers), for those mothers who developed diabetes only after pregnancy (pre-diabetic mothers), as well as for those who remained nondiabetic.
This study ascertained cases of type 1 and type 2 diabetes among youths from 2002 through 2012 at five U.S. study centers. After adjustment for age, sex, and race or ethnic group, there were relative ...annual increases in the incidences of type 1 and type 2 diabetes.
Diagnoses of type 1 and type 2 diabetes in youths present a substantial clinical and public health burden owing to the challenges of disease management and the risks of acute and chronic complications.
1
The SEARCH for Diabetes in Youth study (hereafter, the SEARCH study) previously showed increases in the prevalences of both diseases in the 2001–2009 period.
2
However, data on the trends in incidence are needed to understand the current and potential burden of diabetes more fully.
Previous reports have shown that the incidence of type 1 diabetes has increased worldwide over the past three decades.
3
–
8
Data from Australia . . .
Advances in molecular methods and the ability to share large population-based datasets are uncovering heterogeneity within diabetes types, and some commonalities between types. Within type 1 ...diabetes, endotypes have been discovered based on demographic (e.g. age at diagnosis, race/ethnicity), genetic, immunological, histopathological, metabolic and/or clinical course characteristics, with implications for disease prediction, prevention, diagnosis and treatment. In type 2 diabetes, the relative contributions of insulin resistance and beta cell dysfunction are heterogeneous and relate to demographics, genetics and clinical characteristics, with substantial interaction from environmental exposures. Investigators have proposed approaches that vary from simple to complex in combining these data to identify type 2 diabetes clusters relevant to prognosis and treatment. Advances in pharmacogenetics and pharmacodynamics are also improving treatment. Monogenic diabetes is a prime example of how understanding heterogeneity within diabetes types can lead to precision medicine, since phenotype and treatment are affected by which gene is mutated. Heterogeneity also blurs the classic distinctions between diabetes types, and has led to the definition of additional categories, such as latent autoimmune diabetes in adults, type 1.5 diabetes and ketosis-prone diabetes. Furthermore, monogenic diabetes shares many features with type 1 and type 2 diabetes, which make diagnosis difficult. These challenges to the current classification framework in adult and paediatric diabetes require new approaches. The ‘palette model’ and the ‘threshold hypothesis’ can be combined to help explain the heterogeneity within and between diabetes types. Leveraging such approaches for therapeutic benefit will be an important next step for precision medicine in diabetes.
Graphical abstract
Insights from prospective, longitudinal studies of individuals at risk for developing type 1 diabetes have demonstrated that the disease is a continuum that progresses sequentially at variable but ...predictable rates through distinct identifiable stages prior to the onset of symptoms. Stage 1 is defined as the presence of β-cell autoimmunity as evidenced by the presence of two or more islet autoantibodies with normoglycemia and is presymptomatic, stage 2 as the presence of β-cell autoimmunity with dysglycemia and is presymptomatic, and stage 3 as onset of symptomatic disease. Adoption of this staging classification provides a standardized taxonomy for type 1 diabetes and will aid the development of therapies and the design of clinical trials to prevent symptomatic disease, promote precision medicine, and provide a framework for an optimized benefit/risk ratio that will impact regulatory approval, reimbursement, and adoption of interventions in the early stages of type 1 diabetes to prevent symptomatic disease.
Abstract
Context
Previous studies have shown that exposure to maternal gestational diabetes mellitus (GDM) is associated with increased offspring body mass index (BMI) and risk for overweight or ...obesity.
Objective
This study aimed to explore differences in BMI trajectories among youth exposed or not exposed to maternal GDM and understand whether these associations differ across life stages.
Methods
Data from 403 mother/child dyads (76 exposed; 327 not exposed) participating in the longitudinal Exploring Perinatal Outcomes among Children (EPOCH) study in Colorado were used. Participants who had 2 or more longitudinal height measurements from 27 months to a maximum of 19 years were included in the analysis. Life stages were defined using puberty related timepoints: early childhood (27 months to pre-adolescent dip PAD, average age 5.5 years), middle childhood (from PAD to age at peak height velocity APHV, average age 12.2 years), and adolescence (from APHV to 19 years). Separate general linear mixed models, stratified by life stage, were used to assess associations between GDM exposure and offspring BMI.
Results
There was not a significant association between exposure to GDM and BMI trajectories during early childhood (P = .27). In middle childhood, participants exposed to GDM had higher BMI trajectories compared to those not exposed (males: P = .005, females: P = .002) and adolescent (P = .02) periods.
Conclusion
Our study indicates that children who are exposed to GDM may experience higher BMI trajectories during middle childhood and adolescence, but not during early childhood. These data suggest that efforts to prevent childhood obesity among those exposed in utero to maternal GDM should start before pubertal onset.
Aims/hypothesis
This study aimed to: (1) identify metabolite patterns during late childhood that differ with respect to exposure to maternal gestational diabetes mellitus (GDM); (2) examine the ...persistence of GDM/metabolite associations 5 years later, during adolescence; and (3) investigate the associations of metabolite patterns with adiposity and metabolic biomarkers from childhood through adolescence.
Methods
This study included 592 mother–child pairs with information on GDM exposure (
n
= 92 exposed), untargeted metabolomics data at age 6–14 years (T1) and at 12–19 years (T2), and information on adiposity and metabolic risk biomarkers at T1 and T2. We first consolidated 767 metabolites at T1 into factors (metabolite patterns) via principal component analysis (PCA) and used multivariable regression to identify factors that differed by GDM exposure, at α = 0.05. We then examined associations of GDM with individual metabolites within factors of interest at T1 and T2, and investigated associations of GDM-related factors at T1 with adiposity and metabolic risk throughout T1 and T2 using mixed-effects linear regression models.
Results
Of the six factors retained from PCA, GDM exposure was associated with greater odds of being in quartile (Q)4 (vs Q1–3) of ‘Factor 4’ at T1 after accounting for age, sex, race/ethnicity, maternal smoking habits during pregnancy, Tanner stage, physical activity and total energy intake, at α = 0.05 (OR 1.78 95% CI 1.04, 3.04;
p
= 0.04). This metabolite pattern comprised phosphatidylcholines, diacylglycerols and phosphatidylethanolamines. GDM was consistently associated with elevations in a subset of individual compounds within this pattern at T1 and T2. While this metabolite pattern was not related to the health outcomes in boys, it corresponded with greater adiposity and a worse metabolic profile among girls throughout the follow-up period. Each 1-unit increment in Factor 4 corresponded with 0.17 (0.08, 0.25) units higher BMI z score, 8.83 (5.07, 12.59) pmol/l higher fasting insulin, 0.28 (0.13, 0.43) units higher HOMA-IR, and 4.73 (2.15, 7.31) nmol/l higher leptin.
Conclusions/interpretation
Exposure to maternal GDM was nominally associated with a metabolite pattern characterised by elevated serum phospholipids in late childhood and adolescence at α = 0.05. This metabolite pattern was associated with greater adiposity and metabolic risk among female offspring throughout the late childhood-to-adolescence transition. Future studies are warranted to confirm our findings.
Gut microbiota phenotypes of obesity Stanislawski, Maggie A; Dabelea, Dana; Lange, Leslie A ...
NPJ biofilms and microbiomes,
07/2019, Letnik:
5, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Obesity is a disease with a complex etiology and variable prevalence across different populations. While several studies have reported gut microbiota composition differences associated with obesity ...in humans, there has been a lack of consistency in the nature of the reported changes; it has been difficult to determine whether methodological differences between studies, underlying differences in the populations studied, or other factors are responsible for this discordance. Here we use 16 S rRNA data from previously published studies to explore how the gut microbiota-obesity relationship varies across heterogeneous Western populations, focusing mainly on the relationship between (1) alpha diversity and (2)
relative abundance with BMI. We provide evidence that the relationship between lower alpha diversity and higher BMI may be most consistent in non-Hispanic white (NHW) populations and/or those with high socioeconomic status, while the relationship between higher
relative abundance and BMI may be stronger among black and Hispanic populations. We further examine how diet may impact these relationships. This work suggests that gut microbiota phenotypes of obesity may differ with race/ethnicity or its correlates, such as dietary components or socioeconomic status. However, microbiome cohorts are often too small to study complex interaction effects and non-white individuals are greatly underrepresented, creating substantial challenges to understanding population-level patterns in the microbiome-obesity relationship. Further study of how population heterogeneity influences the relationship between the gut microbiota and obesity is warranted.
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