Vascular health in children and adolescents: effects of obesity and diabetes

The foundations for cardiovascular disease in adults are laid in childhood and accelerated by the presence of comorbid conditions, such as obesity, diabetes, hypertension, and dyslipidemia. Early detection of vascular dysfunction is an important clinical objective to identify those at risk for subsequent cardiovascular morbidity and events, and to initiate behavioral and medical interventions to reduce risk. Typically, cardiovascular screening is recommended for young adults, especially in people with a family history of cardiovascular conditions. Children and adolescents were once considered to be at low risk, but with the growing health concerns related to sedentary lifestyle, poor diet and obesity, cardiovascular screening may be needed earlier so that interventions to improve cardiovascular health can be initiated. This review describes comorbid conditions that increase cardiovascular risk in youth, namely obesity and diabetes, and describes noninvasive methods to objectively detect vascular disease and quantify vascular function and structure through measurements of endothelial function, arterial compliance, and intima-media thickness. Additionally, current strategies directed toward prevention of vascular disease in these populations, including exercise, dietary interventions and pharmacological therapy are described

A Bidirectional Mendelian Randomization Study to evaluate the causal role of reduced blood vitamin D levels with type 2 diabetes risk in South Asians and Europeans.

Context Multiple observational studies have reported an inverse relationship between 25-hydroxyvitamin D concentrations (25(OH)D) and type 2 diabetes (T2D). However, the results of short- and long-term interventional trials concerning the relationship between 25(OH)D and T2D risk have been inconsistent. Objectives and methods To evaluate the causal role of reduced blood 25(OH)D in T2D, here we have performed a bidirectional Mendelian randomization study using 59,890 individuals (5,862 T2D cases and 54,028 controls) from European and Asian Indian ancestries. We used six known SNPs, including three T2D SNPs and three vitamin D pathway SNPs, as a genetic instrument to evaluate the causality and direction of the association between T2D and circulating 25(OH)D concentration. Results Results of the combined meta-analysis of eight participating studies showed that a composite score of three T2D SNPs would significantly increase T2D risk by an odds ratio (OR) of 1.24, p = 1.82 × 10–32; Z score 11.86, which, however, had no significant association with 25(OH)D status (Beta -0.02nmol/L ± SE 0.01nmol/L; p = 0.83; Z score -0.21). Likewise, the genetically instrumented composite score of 25(OH)D lowering alleles significantly decreased 25(OH)D concentrations (-2.1nmol/L ± SE 0.1nmol/L, p = 7.92 × 10–78; Z score -18.68) but was not associated with increased risk for T2D (OR 1.00, p = 0.12; Z score 1.54). However, using 25(OH)D synthesis SNP (DHCR7; rs12785878) as an individual genetic instrument, a per allele reduction of 25(OH)D concentration (-4.2nmol/L ± SE 0.3nmol/L) was predicted to increase T2D risk by 5%, p = 0.004; Z score 2.84. This effect, however, was not seen in other 25(OH)D SNPs (GC rs2282679, CYP2R1 rs12794714) when used as an individual instrument. Conclusion Our new data on this bidirectional Mendelian randomization study suggests that genetically instrumented T2D risk does not cause changes in 25(OH)D levels. However, genetically regulated 25(OH)D deficiency due to vitamin D synthesis gene (DHCR7) may influence the risk of T2D

Sex differences in HDL ApoC-III in American Indian youth

Abstract Background Since American Indians are predisposed to type 2 diabetes (DM2) and associated cardiovascular risk, Cherokee boys and girls (n = 917) were studied to determine whether BMI Z (body mass index Z score) is associated with the apoC-III (apolipoprotein C-III) content of HDL (high density lipoprotein), a previously reported predictor of DM2. Methods An ad hoc cross-sectional analysis was conducted on a previously studied cohort. Participants were grouped by gender-specific age groups (5 to 9, 10 to 14 and 15 to 19 years). ApoA-I (apolipoprotein A-I) and HDL apoC-III were assayed by electroimmunoassay. ApoC-III was measured in whole plasma, and in HDL to determine the molar proportion to apoA-I. General linear models were used to assess association. Results The HDL apoC-III to apoA-I molar ratio increased by BMI Z quartile in girls aged 10–14 years (p  Conclusions ApoC-III showed an obesity-related increase relative to apoA-I during adolescence beginning in girls aged 10 to 14 years and in boys aged 15 to 19 years. The earlier changes in girls may alter HDL’s protective properties on the β-cell and contribute to their increased risk for DM2.</p

Assessing the prediction of type 2 diabetes risk using polygenic and clinical risk scores in South Asian study populations

Background: Genome-wide polygenic risk scores (PRS) have shown high specificity and sensitivity in predicting type 2 diabetes (T2D) risk in Europeans. However, the PRS-driven information and its clinical significance in non-Europeans are underrepresented. We examined the predictive efficacy and transferability of PRS models using variant information derived from genome-wide studies of Asian Indians (AIs) (PRS AI ) and Europeans (PRS EU ) using 13,974 AI individuals. Methods: Weighted PRS models were constructed and analyzed on 4602 individuals from the Asian Indian Diabetes Heart Study/Sikh Diabetes Study (AIDHS/SDS) as discovery/training and test/validation datasets. The results were further replicated in 9372 South Asian individuals from UK Biobank (UKBB). We also assessed the performance of each PRS model by combining data of the clinical risk score (CRS). Results: Both genetic models (PRS AI and PRS EU ) successfully predicted the T2D risk. However, the PRS AI revealed 13.2% odds ratio (OR) 1.80 [95% confidence interval (CI) 1.63–1.97; p  = 1.6 × 10 −152 ] and 12.2% OR 1.38 (95% CI 1.30–1.46; p  = 7.1 × 10 −237 ) superior performance in AIDHS/SDS and UKBB validation sets, respectively. Comparing individuals of extreme PRS (ninth decile) with the average PRS (fifth decile), PRS AI showed about two-fold OR 20.73 (95% CI 10.27–41.83; p  = 2.7 × 10 −17 ) and 1.4-fold OR 3.19 (95% CI 2.51–4.06; p  = 4.8 × 10 −21 ) higher predictability to identify subgroups with higher genetic risk than the PRS EU . Combining PRS and CRS improved the area under the curve from 0.74 to 0.79 in PRS AI and 0.72 to 0.75 in PRS EU . Conclusion: Our data suggest the need for extending genetic and clinical studies in varied ethnic groups to exploit the full clinical potential of PRS as a risk prediction tool in diverse study populations