(1) Background: We investigated the effects of a 12-week exercise intervention with or without vitamin D supplementation on insulin resistance and the plasma lipidome of participants with type 2 diabetes. We further explored whether the effects of the intervention on glycemic parameters could be associated with the baseline lipidome. (2) Methods: Sixty-one participants were randomly allocated to control (Con), exercise (EX), vitamin D (VD), and EX + VD groups. Multiple glycemic and anthropometric parameters were evaluated before and after intervention. The homeostasis model assessment of insulin resistance (HOMA-IR) was the primary outcome. The plasma lipidome was analyzed before, after, and at an additional 12-week follow-up. Machine learning was applied to establish prediction models for responsiveness of glycemic control. (3) Results: Our interventions failed to improve the HOMA-IR index while fasting glucose was reduced in the EX + VD group (change%, −11.9%; effect size, 0.65; p < 0.05). Both EX and VD interventions altered the plasma lipidome, with EX + VD intervention considerably affecting levels of lyso-phosphatidylcholines and triglycerols containing long-chain unsaturated fatty acids. Such effects could last until 12 weeks after intervention. Notably, there was high inter-individual variability in glycemic parameters including HOMA-IR in response to the interventions, which could be predicted with great accuracy using an optimal panel of baseline lipid predictors alone or in combination with clinical indices, as assessed by an area under the receiver operating characteristic curve value of over 0.9. (4) Conclusions: Although substantial alterations were observed in the plasma lipidome related to glycemic control, our intervention failed to improve HOMA-IR scores, which may have been predominately due to the large inter-individual variability in responses. Basal plasma lipid levels could potentially predict an individual’s response to intervention, highlighting the necessity of personalized nutrition.

Background: This study aimed to investigate the roles of insulin related gene IGF2BP2, HMG20A, and HNF1B variants in the susceptibility of type 2 diabetes mellitus (T2DM), and to identify their association with age, gender, BMI, and smoking and alcohol drinking behavior among the Han Chinese population. Methods: 508 patients with T2DM and 503 healthy controls were enrolled. Rs11927381 and rs7640539 in IGF2BP2, rs7178572 in HMG20A, rs4430796 and rs11651052 in HNF1B were genotyped by using the Agena MassARRAY. Odds ratio (OR) and 95% confidence intervals (CI) were calculated by logistic regression. Results: We found that HMG20A rs7178572 (OR = 1.25, p = 0.015) and HNF1B rs11651052 (OR = 1.26, p = 0.019) increased the risk of T2DM. Rs7178572, rs4430796, and rs11651052 might be related to the higher T2DM susceptibility not only by itself but also by interacting with age, gender smoking and alcohol drinking. Rs11927381 also conferred the higher T2DM susceptibility at age £ 59 years. Besides, rs7178572-AA (p = 0.032) genotype and rs11651052 GG (p = 0.018) genotype were related to higher glycated hemoglobin and insulin level, respectively. Conclusion: Specifically, we firstly found that rs11927381, rs7640539 and rs11651052 were associated with risk of T2DM among the Han Chinese population. We also provide evidence that age, gender, BMI, smoking and drinking status have an interactive effect with these variants on T2DM susceptibility.

Background: Evidence from genetic epidemiology indicates that type 2 diabetes (T2D) has a strong genetic basis. Activated STAT4 has an inflammatory effect, and STAT4 is an important mediator of inflammation in diabetes. Our study aimed to study the association between STAT4 single nucleotide polymorphisms (SNPs) and T2D susceptibility in Chinese Han population. Methods: We conducted a 'case–control' study among 500 T2D patients and 501 healthy individuals. 5 candidate STAT4 SNPs were successfully genotyped. The association between SNPs and T2D susceptibility under different genetic models was evaluated by logistic regression analysis. ‘SNP-SNP’ interaction was analyzed and completed by multi-factor dimensionality reduction (MDR). Finally, we evaluated the differences of clinical characteristics under different genotypes by one-factor analysis of variance. Results: The overall results showed that STAT4 rs3821236 was associated with increasing T2D risk under allele (OR 1.23, p = 0.020), homozygous (OR 1.51, p = 0.025), dominant (OR 1.36, p = 0.029), and additive models (OR 1.23, p = 0.020). The results of stratified analysis showed that rs3821236, rs11893432, and rs11889341 were risk factors for T2D among participants ≤ 60 years old. Only rs11893432 was associated with increased T2D risk among female participants. There was also a potential association between rs3821236 and T2D with nephropathy risk. STAT4 rs11893432, rs7574865 and rs897200 were significantly associated with lysophosphatidic acid, cystatin C and thyroxine t4, respectively. Conclusion: The genetic polymorphisms of STAT4 is potentially associated with T2D susceptibility of Chinese population. In particular, rs3821236 is significantly associated with T2D risk both in the overall and several subgroup analyses. Our study may provide new ideas for T2D individualized diagnosis/protection.

Diabetes mellitus (DM) is a complex metabolic disease that is caused by a complex interplay between genetic and environmental factors. This research aimed to investigate the association of genetic polymorphisms in PDX1 and MC4R with T2DM risk. The genotypes of 10 selected SNPs in PDX1 and MC4R were identified using the Agena MassARRAY platform. We utilized odds ratio (OR) and 95% confidence intervals (CIs) to assess the correlation between genetic polymorphisms and T2DM risk. We found that PDX1-rs9581943 decreased susceptibility to T2DM among in a Chinese Han population (OR = 0.76, p = 0.045). We also found that selected genetic polymorphisms in PDX1 and MC4R could modify the risk of T2DM, which might also be influenced by age, sex, BMI, smoking status, and drinking status (p < 0.05). We concluded that PDX1 and MC4R genetic variants were significantly associated with T2DM risk in a Chinese Han population. These single polymorphic markers may be considered to be new targets in the assessment and prevention of T2DM among Chinese Han people.