Risk Of Type 2 Diabetes Research Articles

Sex differences in type 2 diabetes genetic susceptibility

Introduction. In the last few years gender and sex differences in type 2 diabetes (T2D) predisposition are paid much attention. This gap can be caused by the hormonal and genetic background and require further traits.Aim. The aim of this study was to identify genetic markers of T2D predisposition depending on sex using 17 polymorphic markers of genes involved in various links of T2D pathogenesis.Material and Methods. 536 patients with T2D and 1,476 healthy individuals were examined. Amplification of 17 polymorphic gene loci was performed by polymerase chain reaction. Associations of DNA polymorphisms with T2D were evaluated by logistic regression using the SNPStats program. The degree of association was assessed in the odds ratio (OR) scores. Correction for multiplicity of comparisons and multivariate analysis was used.Results. The genotypes of increased T2D risk for men are CC of CCL20 rs6749704 marker (OR = 3.85, P = 0.0002) in recessive model, D/I of CCR5 rs333 marker (OR = 4.42, P = 0.0208); by GRIA1 marker rs2195450 - CT and TT in the dominant model (OR = 2.42, P = 0.0002), TT - in the recessive model (OR = 2.89, P = 0.0070). The genotypes of increased risk of T2D in women according to the marker TCF7L2 rs7903146 - CT and TT in the dominant model (OR = 1.69, P = 0.0003), TT - in recessive (OR = 1.61, P = 0.0124), according to the marker ADIPOQ rs17366743 - TC (OR = 2.55, P = 0.0168).Conclusion. The established genotypes of increased T2D risk depending on gender make it possible to personalize approaches to primary T2D prevention.

Factors assessed in the first year of a longitudinal study predict subsequent study visit compliance: the TEDDY study

BackgroundCompliance with a study protocol is central to meeting its research goals. In longitudinal research studies, data loss due to missed visits limit statistical power and introduce bias. The Environmental Determinants of Diabetes in the Young (TEDDY) study is a longitudinal multinational (US, Finland, Germany, and Sweden) investigation of children at risk for type 1 diabetes (T1D) that seeks to identify the environmental triggers of islet autoimmunity and T1D. The purpose of the current study was to identify sociodemographic variables and maternal characteristics assessed in the first year of TEDDY that were associated with study visit compliance in the subsequent 3 years.MethodsSociodemographic variables, maternal life-style behaviors, post-partum depression, maternal reactions to the child’s T1D risk, and study-related variables were collected at child-age 6 months and 15 months. Multiple linear regression was used to examine the association of these variables to study visit compliance in the subsequent 3 years.ResultsStudy visit compliance was highest in Sweden (p > 0.001), in children who were their mother’s first child (p > 0.001), and whose mothers were older (p > 0.001) and more satisfied with the TEDDY study (p > 0.001). Father participation was also associated with better study visit compliance (p > 0.001). In contrast, children whose mothers smoked (p > 0.001), suffered from post-partum depression (p = 0.034), and were more anxious about their child’s T1D risk (p = 0.002), completed fewer visits. Father’s study satisfaction was also associated with study visit compliance (p = 0.029); however, it was not significant in models that included maternal study satisfaction.ConclusionsSociodemographic variables, maternal characteristics—including study satisfaction—and fathers’ participation in the first year of a longitudinal study were associated with subsequent study visit compliance in a sample of children genetically at-risk for T1D followed for 4 years. This information can inform future strategies designed to improve study visit compliance in longitudinal pediatric studies.Trial registration: NCT00279318, 06/09/2004.

Olive oil consumption, plasma metabolites, and risk of type 2 diabetes and cardiovascular disease

BackgroundOlive oil consumption has been inversely associated with the risk of type 2 diabetes (T2D) and cardiovascular disease (CVD). However, the impact of olive oil consumption on plasma metabolites remains poorly understood. This study aims to identify plasma metabolites related to total and specific types of olive oil consumption, and to assess the prospective associations of the identified multi-metabolite profiles with the risk of T2D and CVD.MethodsThe discovery population included 1837 participants at high cardiovascular risk from the PREvención con DIeta MEDiterránea (PREDIMED) trial with available metabolomics data at baseline. Olive oil consumption was determined through food-frequency questionnaires (FFQ) and adjusted for total energy. A total of 1522 participants also had available metabolomics data at year 1 and were used as the internal validation sample. Plasma metabolomics analyses were performed using LC–MS. Cross-sectional associations between 385 known candidate metabolites and olive oil consumption were assessed using elastic net regression analysis. A 10-cross-validation (CV) procedure was used, and Pearson correlation coefficients were assessed between metabolite-weighted models and FFQ-derived olive oil consumption in each pair of training–validation data sets within the discovery sample. We further estimated the prospective associations of the identified plasma multi-metabolite profile with incident T2D and CVD using multivariable Cox regression models.ResultsWe identified a metabolomic signature for the consumption of total olive oil (with 74 metabolites), VOO (with 78 metabolites), and COO (with 17 metabolites), including several lipids, acylcarnitines, and amino acids. 10-CV Pearson correlation coefficients between total olive oil consumption derived from FFQs and the multi-metabolite profile were 0.40 (95% CI 0.37, 0.44) and 0.27 (95% CI 0.22, 0.31) for the discovery and validation sample, respectively. We identified several overlapping and distinct metabolites according to the type of olive oil consumed. The baseline metabolite profiles of total and extra virgin olive oil were inversely associated with CVD incidence (HR per 1SD: 0.79; 95% CI 0.67, 0.92 for total olive oil and 0.70; 0.59, 0.83 for extra virgin olive oil) after adjustment for confounders. However, no significant associations were observed between these metabolite profiles and T2D incidence.ConclusionsThis study reveals a panel of plasma metabolites linked to the consumption of total and specific types of olive oil. The metabolite profiles of total olive oil consumption and extra virgin olive oil were associated with a decreased risk of incident CVD in a high cardiovascular-risk Mediterranean population, though no associations were observed with T2D incidence.Trial registration: The PREDIMED trial was registered at ISRCTN (http://www.isrctn.com/, ISRCTN35739639).

Genotypes predisposing for celiac disease and autoimmune diabetes and risk of infections in early childhood.

Infections in early childhood have been associated with risk of celiac disease (CD) and type 1 diabetes (T1D). We investigated whether this is driven by susceptibility genes for autoimmune disease by comparing infection frequency by genetic susceptibility variants for CD or T1D. We genotyped 373 controls and 384 children who developed CD or T1D in the population-based Norwegian Mother, Father and Child Cohort study (MoBa) study for human leukocyte antigen(HLA)-DQ, FUT2, SH2B3, and PTPN22, and calculated a weighted non-HLA genetic risk score (GRS) for CD and T1D based on over 40 SNPs. Parents reported infections in questionnaires when children were 6 and 18 months old. We used negative binomial regression to estimate incidence rate ratio (IRR) for infections by genotype. HLA genotypes for CD and T1D or non-HLA GRS for T1D were not associated with infections. The non-HLA GRS for CD was associated with a nonsignificantly lower frequency of infections (aIRR: 0.95, 95%CI: 0.87-1.03 per weighted allele score), and significantly so when restricting to healthy controls (aIRR: 0.89, 0.81-0.99). Participants homozygous for rs601338(A;A) at FUT2, often referred to as nonsecretors, had a nonsignificantly lower risk of infections (aIRR: 0.91, 95%CI: 0.83-1.01).SH2B3 and PTPN22 genotypes were not associated with infections. The association between infections and risk of CD (OR: 1.15 per five infections) was strengthened after adjustment for HLA genotype and non-HLA GRS (OR: 1.24). HLA variants and non-HLA GRS conferring susceptibility for CD were not associated with increased risk of infections in early childhood and is unlikely to drive the observed association between infections and risk of CD or T1D in many studies.

Extremely Early Appearance of Islet Autoantibodies in Genetically Susceptible Children

Objective. We studied the characteristics of children who developed islet autoantibodies by the age of 0.50 years and hypothesized that the appearance of extremely early islet autoimmunity differs between four birth cohorts within 1994–2019 according to the change in the incidence of Type 1 diabetes (T1D) in Finland. Methods. Data from Finnish children participating in the Type 1 Diabetes Prediction and Prevention (DIPP) study, or the Environmental Determinants of Diabetes in the Young (TEDDY) study were analyzed. These studies follow children with increased HLA-conferred risk for T1D with regular measurements of islet autoantibodies. Maternally transferred antibodies were excluded by comparing islet autoantibodies in cord serum, child’s first follow-up serum and the maternal serum. Results. Among 20,979 Finnish children at increased risk to T1D, 53 (0.25%) developed at least one islet autoantibody at the age of ≤0.50 years. During a mean follow-up of 8.1 years, 15.1% progressed to T1D (median age at diagnosis 2.0 years), 43.4% developed confirmed islet autoimmunity but no T1D, and 41.5% had only transient islet autoantibodies. IAA was the most common first-appearing autoantibody. Among progressors, age at diagnosis was 1.0–2.4 years in children with IAA-initiated autoimmunity and 4.5–16.1 years in ZnT8A-initiated autoimmunity. When comparing children developing autoantibodies either at the age of ≤0.50 years or 0.51–0.75 years, confirmed positivity during follow-up was more common in the older group (81.7% vs. 58.5%; p = 0.002 ). In four birth cohorts within 1994–2019 appearance of islet autoantibodies at the age of ≤0.50 years decreased towards the most recent birth cohorts ( p = 0.016 ). Conclusion. Islet autoimmunity by the age of 0.50 years was rare in genetically susceptible children and was typically initiated with IAA. Confirmed positivity was less common in children with autoantibodies at age ≤0.50 than at slightly older age. The secular decrease of islet autoimmunity before age 0.50 years was observed. This trial is registered with NCT03269084 and NCT00279318.

Sleep and insulin sensitivity in adolescents at risk of type 2 diabetes: the Sleep Manipulation in Adolescents at Risk of Type 2 Diabetes randomized crossover study.

To investigate the effect of increasing sleep duration for 1 week, compared to a week of habitual and decreased sleep, on insulin sensitivity (IS) in adolescents at risk for type 2 diabetes (T2D). Adolescents, 13-18 years old, at risk for T2D, with obesity and other risk factors, were recruited for a randomized (1:1), open-label, sex-stratified crossover study, that manipulated time-in-bed to modify sleep duration (measured by actigraphy). Following a week of habitual (HB) sleep, time-in-bed was increased (IN) and decreased (DE) by 1 hour 30 min/night for 1 week, counterbalanced across participants (HBINDE or HBDEIN), and separated by a week of washout sleep. The main outcome measure was IS, obtained via 2-hour oral-glucose-tolerance-test conducted after each sleep week. Of the 43 participants recruited, 36 (84%) completed all sleep interventions (52.8% female, age = 15.1 years, body mass index = 99.9th percentile, order: HBINDE = 18 and HBDEIN = 18). On average, during the HB week, participants slept 7 hours 31 min/night; sleep duration was 1 hour 02 min/night higher during the IN week and 1 hour 19 min/night lower during the DE week. We found a significant effect of sleep week on IS with a large effect size. Following the IN sleep week, IS was 20% higher compared to after the HB and DE sleep weeks, but there was no significant difference in IS following HB versus DE sleep weeks. Whenever possible, clinicians should empower youth at risk of T2D to improve their sleep duration, since even a modest increase in sleep duration of 1 h/night for 1 week can have a positive impact on IS in this population. Sleep Extension and IS in Adolescents, https://clinicaltrials.gov/study/NCT03754036, November 23rd, 2018. ClinicalTrials.gov (ID:NCT03754036).

Sex Differences in Branched-chain Amino Acid and Tryptophan Metabolism and Pathogenesis of Youth-onset Type 2 Diabetes.

Insulin resistance is associated with elevations in plasma branched-chain amino acids (BCAAs). BCAAs compete with aromatic amino acids including tryptophan for uptake into β cells. To explore relationships between BCAAs and tryptophan metabolism, adiposity, and glucose tolerance, we compared urine metabolites in overweight/obese youth with type 2 diabetes (T2D) with those in nondiabetic overweight/obese and lean youth. Metabolites were measured in 24-hour and first-morning urine samples of 56 nondiabetic adolescents with overweight/obesity, 42 adolescents with T2D, and 43 lean controls, aged 12 to 21 years. Group differences were assessed by Kruskal Wallis or ANOVA. Groups were comparable for age, pubertal status, and ethnicity. Youth with T2D were predominantly female and had highest percent body fat. BCAAs, branched-chain ketoacids (BCKAs), tryptophan, and kynurenine were higher in urine of subjects with T2D. There were no differences between lean controls and nondiabetic youth with overweight/obesity. T2D was associated with diversion of tryptophan from the serotonin to the kynurenine pathway, with higher urinary kynurenine/serotonin ratio and lower serotonin/tryptophan and 5-HIAA/kynurenine ratios. Urinary BCAAs, BCKAs, tryptophan, and ratios reflecting diversion to the kynurenine pathway correlated positively with metrics of body fat and hemoglobin A1c. Increases in these metabolites in the obese T2D group were more pronounced and statistically significant only in adolescent girls. Increases in urinary BCAAs and BCKAs in adolescent females with T2D are accompanied by diversion of tryptophan metabolism from the serotonin to the kynurenine pathway. These adaptations associate with higher risks of T2D in obese adolescent females than adolescent males.

Vaspin: A Novel Biomarker Linking Gluteofemoral Body Fat and Type 2 Diabetes Risk.

To determine whether adiposity depots modulate vaspin levels and whether vaspin predicts type 2 diabetes (T2D) risk, through epidemiological and genetic analyses. We assessed the relationship of plasma vaspin concentration with incident and prevalent T2D and adiposity-related variables in 1) the Prospective Urban and Rural Epidemiology (PURE) biomarker substudy (N = 10,052) and 2) the Outcome Reduction with Initial Glargine Intervention (ORIGIN) trial (N = 7,840), using regression models. We then assessed whether vaspin is causally associated with T2D and whether genetic variants associated with MRI-measured adiposity depots modulate vaspin levels, using two-sample Mendelian randomization (MR). A 1-SD increase in circulating vaspin levels was associated with a 16% increase in incident T2D in the PURE cohort (hazard ratio 1.16; 95% CI 1.09-1.23; P = 4.26 × 10-7) and prevalent T2D in the ORIGIN cohort (odds ratio [OR] 1.16; 95% CI 1.07-1.25; P = 2.17 × 10-4). A 1-unit increase in BMI and triglyceride levels was associated with a 0.08-SD (95% CI 0.06-0.10; P = 2.04 × 10-15) and 0.06-SD (95% CI 0.04-0.08; P = 4.08 × 10-13) increase, respectively, in vaspin in the PURE group. Consistent associations were observed in the ORIGIN cohort. MR results reinforced the association between vaspin and BMI-adjusted T2D risk (OR 1.01 per 1-SD increase in vaspin level; 95% CI 1.00-1.02; P = 2.86 × 10-2) and showed that vaspin was increased by 0.10 SD per 1-SD decrease in genetically determined gluteofemoral adiposity (95% CI 0.02-0.18; P = 2.01 × 10-2). No relationships were found between subcutaneous or visceral adiposity and vaspin. These findings support that higher vaspin levels are related to increased T2D risk and reduced gluteofemoral adiposity, positioning vaspin as a promising clinical predictor for T2D.

Plasma Metabolites Related to the Consumption of Different Types of Dairy Products and Their Association with New-Onset Type 2 Diabetes: Analyses in the Fenland and EPIC-Norfolk Studies, United Kingdom.

To identify metabolites associated with habitual dairy consumption and investigate their associations with type 2 diabetes (T2D) risk. Metabolomics assays were conducted in the Fenland (n = 10,281) and EPIC-Norfolk (n = 1,440) studies. Using 82 metabolites assessed in both studies, we developed metabolite scores to classify self-reported consumption of milk, yogurt, cheese, butter, and total dairy (Fenland Study-discovery set; n = 6035). Internal and external validity of the scores was evaluated (Fenland-validation set, n = 4246; EPIC-Norfolk, n = 1440). The study assessed associations between each metabolite score and T2D incidence in EPIC-Norfolk (n = 641 cases; 16,350 person-years). The scores classified low and high consumers for all dairy types with internal validity, and milk, butter, and total dairy with external validity. The scores were further associated with lower incident T2D: hazard ratios (95% confidence interval) per standard deviation: milk 0.71 (0.65, 0.77); butter 0.62 (0.57, 0.68); total dairy 0.66 (0.60, 0.72). These associations persisted after adjustment for known dairy-fat biomarkers. Metabolite scores identified habitual consumers of milk, butter, and total dairy products, and were associated with lower T2D risk. These findings hold promise for identifying objective indicators of the physiological response to dairy consumption.

Mendelian randomization analyses suggest a causal role for circulating GIP and IL-1RA levels in homeostatic model assessment-derived measures of β-cell function and insulin sensitivity in Africans without type 2 diabetes

BackgroundIn vitro and in vivo studies have shown that certain cytokines and hormones may play a role in the development and progression of type 2 diabetes (T2D). However, studies on their role in T2D in humans are scarce. We evaluated associations between 11 circulating cytokines and hormones with T2D among a population of sub-Saharan Africans and tested for causal relationships using Mendelian randomization (MR) analyses.MethodsWe used logistic regression analysis adjusted for age, sex, body mass index, and recruitment country to regress levels of 11 cytokines and hormones (adipsin, leptin, visfatin, PAI-1, GIP, GLP-1, ghrelin, resistin, IL-6, IL-10, IL-1RA) on T2D among Ghanaians, Nigerians, and Kenyans from the Africa America Diabetes Mellitus study including 2276 individuals with T2D and 2790 non-T2D individuals. Similar linear regression models were fitted with homeostatic modelling assessments of insulin sensitivity (HOMA-S) and β-cell function (HOMA-B) as dependent variables among non-T2D individuals (n = 2790). We used 35 genetic variants previously associated with at least one of these 11 cytokines and hormones among non-T2D individuals as instrumental variables in univariable and multivariable MR analyses. Statistical significance was set at 0.0045 (0.05/11 cytokines and hormones).ResultsCirculating GIP and IL-1RA levels were associated with T2D. Nine of the 11 cytokines and hormones (exceptions GLP-1 and IL-6) were associated with HOMA-S, HOMA-B, or both among non-T2D individuals. Two-stage least squares MR analysis provided evidence for a causal effect of GIP and IL-RA on HOMA-S and HOMA-B in multivariable analyses (GIP ~ HOMA-S β = − 0.67, P-value = 1.88 × 10−6 and HOMA-B β = 0.59, P-value = 1.88 × 10−5; IL-1RA ~ HOMA-S β = − 0.51, P-value = 8.49 × 10−5 and HOMA-B β = 0.48, P-value = 5.71 × 10−4). IL-RA was partly mediated via BMI (30-34%), but GIP was not. Inverse variance weighted MR analysis provided evidence for a causal effect of adipsin on T2D (multivariable OR = 1.83, P-value = 9.79 × 10−6), though these associations were not consistent in all sensitivity analyses.ConclusionsThe findings of this comprehensive MR analysis indicate that circulating GIP and IL-1RA levels are causal for reduced insulin sensitivity and increased β-cell function. GIP’s effect being independent of BMI suggests that circulating levels of GIP could be a promising early biomarker for T2D risk. Our MR analyses do not provide conclusive evidence for a causal role of other circulating cytokines in T2D among sub-Saharan Africans.

Serum neuregulin 1 in relation to ventricular function and subclinical atherosclerosis in type 2 diabetes patients

Background and aimsNeuregulin 1 (NRG-1) is one of the members of the epidermal growth factors proteins. The present study provides novel insights into the relationship between serum levels of NRG-1 and insulin resistance, subclinical atherosclerosis and cardiac dysfunction that occur in type 2 diabetes (T2D). MethodsThe study included 50 patients with T2D and 40 healthy age- and gender-matched controls. Serum NRG-1 was measured using ELISA. Glycemic parameters, lipid profile and insulin resistance were assessed. Trans-thoracic echocardiography and carotid intima media thickness (CIMT) were studied for all study subjects. ResultsT2D patients had significantly lower serum NRG-1 levels than controls. Serum NRG-1 was negatively correlated with age, fasting blood glucose, HbA1c, insulin resistance, blood urea, serum creatinine and LDL-C, and positively correlated with HDL-C, eGFR and CIMT. Regarding echocardiographic variables, serum NRG-1 was found to correlate positively with left ventricular global longitudinal strain and negatively with E/Ea ratio. NRG-1 was found to predict subclinical atherosclerosis in type 2 diabetes patients at a cut-off value<108.5pg/ml with 78% sensitivity and 80% specificity. ConclusionsA robust relationship was found between serum NRG-1 levels and hyperglycemia, insulin resistance, subclinical atherosclerosis, and cardiac dysfunction in patients with type 2 diabetes. These results shed light on a possible role of NRG-1 as a potential noninvasive biomarker for detection of cardiometabolic risk in T2D.

Abstract A045: Adhering to a culturally responsive framework: Integrating kinship into a multilevel community-based intervention for American Indian adults with comorbid type 2 diabetes and cancer

Abstract The prolific disparities of type 2 diabetes (T2D) and cancer in tribal communities is well documented. American Indian (AI) adults living with T2D are at greater risk of not only developing cancer, but also dying from it, with cooccurrence becoming increasingly prevalent. On rural tribal reservations, common environmental diabetogenic factors (e.g., obesity, lack of physical activity, poor diet, and stress) are considered as sources of elevated risk for both T2D and cancer. Critically important in improving both diabetes and cancer-related health behaviors and outcomes, particularly for AI people, is psychosocial support from family and community ties (e.g., kinship). Despite supporting evidence, few interventions include kinship as a key factor to promote best outcomes for T2D management among AI adults with a dual diagnosis of cancer. To address this growing issue, there is an urgent need for culturally and contextually adapted interventions aiming to address the cooccurrence of T2D and cancer that are, ideally, led by tribal communities. In this session, healthcare professional Early-Stage Investigators (ESIs), some of whom also identify as AI, will present community-based participatory research (CBPR) principles used to develop a multilevel Tribally tailored intervention, with kinship as a moderating factor, for AI adults living with both T2D and cancer. The ESIs represent a unique pool of providers invested in the community within which they work by co-creating an intervention which utilizes pre-existing strong community and familial norms. The ESIs further look to break away from the common trend in research to overgeneralize interventions and findings to all AI Tribal communities by tailoring the intervention to each specific Tribal Community engaged in the research intervention. To foster innovative research methods by emphasizing important historic and socio-cultural influences relevant to understanding the holistic context of health disparities in tribal communities, the guiding framework is the NIMHD Minority Health and Health Disparities Research Framework adapted for AI and Alaska Native Nations. In this research, individuals are viewed within the context of the broader social system where they function daily, while families and community are key stakeholders and leaders in the successful management of T2D and cancer. The approach focuses on diabetes-related barriers by providing family and social units with skills for successful T2D and cancer management, healthy communication, and shared commitment. In addition to these domains of influence, involving community systems is critical for a sustainable intervention that effectively addresses health disparities for T2D and cancer. Citation Format: Sheila L. Hammer, Timian M. Godfrey. Adhering to a culturally responsive framework: Integrating kinship into a multilevel community-based intervention for American Indian adults with comorbid type 2 diabetes and cancer [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr A045.

Sick individuals, sick populations revisited: a test of the Rose hypothesis for type 2 diabetes disparities

IntroductionThe Rose hypothesis predicts that since genetic variation is greater within than between populations, genetic risk factors will be associated with individuals’ risk of disease but not population disparities, and...

Affected brother as the highest risk factor of type 1 diabetes development in children and adolescents: One center data before implementing type 1 diabetes national screening.

There is an increased risk for childhood type 1 diabetes (T1D) when T1D and type 2 diabetes (T2D) are reported in relatives. Our objective was to evaluate current family risk factors for T1D development before implementing a national screening program for T1D. A population of 879 Caucasian children and adolescents with T1D and 286 healthy controls were enrolled in the study. All participants completed the same questionnaire, which collected information about family history of diabetes over 3 generations. In statistical analyses, frequency tables and χ2 tests evaluated possible multicollinearity among risk factors that were significantly associated with the outcomes. Family history of diabetes was more frequent in controls (n = 75, 26.2%) than in patients with T1D (n = 146, 16.6%, odds ratio (OR) = 1.785, 95% confidence interval (95% CI): 1.299-2.452, degrees of freedom (df) = 12.976, p = 0.004), especially with a family history of T2D (n = 62, 21.7% compared to n = 79, 9.0%, respectively, OR = 2.803, 95% CI: 1.948-4.034, df = 32.669, p < 0.001). Also, there was a tendency for the nuclear family of T1D patients to be more frequently affected by T1D (n = 74, 8.4%) than the controls (n = 15, 5.2%, OR = 1.605, 95% CI: 0.937-2.751, df = 3.081, p = 0.079). The risk of T1D was associated with the closest family members being affected and accelerated over generations. Indeed, it was highest in siblings, especially brothers (OR = 12.985, 95% CI: 0.782-215.743, Fisher's test: p < 0.001). A positive family history of T2D burden among second-degree relatives was 2.728 times more frequent in the control group than in the T1D group (OR = 2.728; 95% Cl: 1.880-3.962, p < 0.001). Furthermore, a positive family history of T1D among first-degree relatives was less frequent in the controls than in the T1D group (OR = 0.124; 95% Cl: 0.030-0.516, p = 0.004). A family history of T1D, but not T2D, is a significant risk factor for T1D development. Indeed, the priority in screening for T1D should be given to first-degree relatives of T1D patients, starting from siblings.

Interplay between diet, circulating indolepropionate concentrations and cardiometabolic health in US populations

ObjectivesTo identify indolepropionate (IPA)-predicting gut microbiota species, investigate potential diet–microbiota interactions, and examine the prospective associations of circulating IPA concentrations with type 2 diabetes (T2D) and coronary heart disease (CHD)...

Newly Diagnosed Type 2 Diabetes in Children and Adolescents in Bangladesh- A Cross Sectional Study

Background: Type 2 diabetes (T2D) has become a serious health problem in children and adolescents as the prevalence of obesity is increasing. Objective: To observe the baseline clinical and laboratory findings and treatment modalities of children and adolescents with newly diagnosed T2D. Materials &amp; Methods: This cross-sectional study was done from Jan- Dec.2017 in Changing Diabetes in Children (CDiC), Paediatric diabetes OPD clinic in BIRDEM among newly diagnosed T2D children and adolescents. Results: Among 68 patients majority were female (64.7%). Mean age was 13.1 ± 2.5 years. Ten patients were below 10 years. Most of the patients were from urban &amp; semi urban area and high to middle socioeconomic status. Family history was present in 91.2% and 22.1 % mother had H/O GDM. Forty two patients (61.8%) had typical and 22 (32.3%) had atypical symptoms. Thirty seven children had acanthosis nigricans (54.4%). Majority were found overweight (53/68), and (12/68) were obese. Mean HbA1c was 10.2 ± 2.8. Life style modification was advised to all. Insulin was started along with metformin in patients with high HbA1c and 33.8% patients were given metformin only. While comparing, BMI was found high in basal and metformin group. Lower fasting blood glucose and HbA1c were observed in patients with metformin only. Conclusion: T2D is emerging in children and adolescents. The screening of children at high risk of T2D is essential for preventive and timely remedial measures. BANGLADESH J CHILD HEALTH 2022; VOL 46 (1) : 9-13

Serum neuregulin 1 in relation to ventricular function and subclinical atherosclerosis in type 2 diabetes patients

Background and aimsNeuregulin 1 (NRG-1) is one of the members of the epidermal growth factors proteins. The present study provides novel insights into the relationship between serum levels of NRG-1 and insulin resistance, subclinical atherosclerosis and cardiac dysfunction that occur in type 2 diabetes (T2D). MethodsThe study included 50 patients with T2D and 40 healthy age- and gender-matched controls. Serum NRG-1 was measured using ELISA. Glycemic parameters, lipid profile and insulin resistance were assessed. Trans-thoracic echocardiography and carotid intima media thickness (CIMT) were studied for all study subjects. ResultsT2D patients had significantly lower serum NRG-1 levels than controls. Serum NRG-1 was negatively correlated with age, fasting blood glucose, HbA1c, insulin resistance, blood urea, serum creatinine and LDL-C, and positively correlated with HDL-C, eGFR and CIMT. Regarding echocardiographic variables, serum NRG-1 was found to correlate positively with left ventricular global longitudinal strain and negatively with E/Ea ratio. NRG-1 was found to predict subclinical atherosclerosis in type 2 diabetes patients at a cut-off value<108.5pg/ml with 78% sensitivity and 80% specificity. ConclusionsA robust relationship was found between serum NRG-1 levels and hyperglycemia, insulin resistance, subclinical atherosclerosis, and cardiac dysfunction in patients with type 2 diabetes. These results shed light on a possible role of NRG-1 as a potential noninvasive biomarker for detection of cardiometabolic risk in T2D.

Role of Gut Microbiota in Statin-Associated New-Onset Diabetes-A Cross-Sectional and Prospective Analysis of the FINRISK 2002 Cohort.

Dyslipidemia is treated effectively with statins, but treatment has the potential to induce new-onset type-2 diabetes. Gut microbiota may contribute to this outcome variability. We assessed the associations of gut microbiota diversity and composition with statins. Bacterial associations with statin-associated new-onset type-2 diabetes (T2D) risk were also prospectively evaluated. We examined shallow-shotgun-sequenced fecal samples from 5755 individuals in the FINRISK-2002 population cohort with a 17+-year-long register-based follow-up. Alpha-diversity was quantified using Shannon index and beta-diversity with Aitchison distance. Species-specific differential abundances were analyzed using general multivariate regression. Prospective associations were assessed with Cox regression. Applicable results were validated using gradient boosting. Statin use associated with differing taxonomic composition (R2, 0.02%; q=0.02) and 13 differentially abundant species in fully adjusted models (MaAsLin; q<0.05). The strongest positive association was with Clostridium sartagoforme (β=0.37; SE=0.13; q=0.02) and the strongest negative association with Bacteroides cellulosilyticus (β=-0.31; SE=0.11; q=0.02). Twenty-five microbial features had significant associations with incident T2D in statin users, of which only Bacteroides vulgatus (HR, 1.286 [1.136-1.457]; q=0.03) was consistent regardless of model adjustment. Finally, higher statin-associated T2D risk was seen with [Ruminococcus] torques (ΔHRstatins, +0.11; q=0.03), Blautia obeum (ΔHRstatins, +0.06; q=0.01), Blautia sp. KLE 1732 (ΔHRstatins, +0.05; q=0.01), and beta-diversity principal component 1 (ΔHRstatin, +0.07; q=0.03) but only when adjusting for demographic covariates. Statin users have compositionally differing microbiotas from nonusers. The human gut microbiota is associated with incident T2D risk in statin users and possibly has additive effects on statin-associated new-onset T2D risk.

Association between birth weight/joint exposure to ambient air pollutants and type 2 diabetes: a cohort study in the UK Biobank

ABSTRACT Early life events and environmental factors are associated with type 2 diabetes (T2D) development. We assessed the combined effect of birth weight andambient air pollutants, and effect of their interaction on T2D risk. Totally, 6,474 T2D incidents were recorded over an 8.7-year follow-up period. The adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs) were 1.31 (1.26, 1.36) for each kilogram decrease in birth weight, and 1.08 (1.05, 1.11) for each standard deviation increase in air pollution score (APS). Birth weight<3000 g amplified the T2D risk associated with high APS. A combination of the lowest birth weight (<2500 g) and the highest quintile of APS led to over two-fold increase in T2D risk (aHR: 2.17; 95% CI: 1.79–2.64). There was a significant additive interaction between them. In conclusion, ambient air pollutants increase the risk for T2D, particularly in populations with low birth weight.

Predicting the Risk of Developing Type 1 Diabetes Using a One-Week Continuous Glucose Monitoring Home Test With Classification Enhanced by Machine Learning: An Exploratory Study.

Detection of two or more autoantibodies (Ab) in the blood might describe those individuals at increased risk of developing type 1 diabetes (T1D) during the following years. The aim of this exploratory study is to propose a high versus low T1D risk classifier using machine learning technology based on continuous glucose monitoring (CGM) home data. Forty-two healthy relatives of people with T1D with mean ± SD age of 23.8 ± 10.5 years, HbA1c (glycated hemoglobin) of 5.3% ± 0.3%, and BMI (body mass index) of 23.2 ± 5.2 kg/m2 with zero (low risk; N = 21), and ≥2 (high risk; N = 21) Ab, were enrolled in an NIH (National Institutes of Health)-funded TrialNet ancillary study. Participants wore a CGM for a week and consumed three standardized liquid mixed meals (SLMM) instead of three breakfasts. Glycemic features were extracted from two-hour post-SLMM CGM traces, compared across groups, and used in four supervised machine learning Ab risk status classifiers. Recursive Feature Elimination (RFE) algorithm was used for feature selection; classifiers were evaluated through 10-fold cross-validation, using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model. The percent time of glucose >180 mg/dL (T180), glucose range, and glucose CV (coefficient of variation) were the only significant differences between the glycemic features in the two groups with P values of .040, .035, and .028 respectively. The linear SVM (Support Vector Machine) model with RFE features achieved the best performance of classifying low-risk versus high-risk individuals with AUC-ROC = 0.88. A machine learning technology, combining a potentially self-administered one-week CGM home test, has the potential to reliably assess the T1D risk.