Objective To study the associations between heterogeneity of gestational diabetes mellitus (GDM) subtype/prepregnancy body mass index (pre‐BMI) and large‐for‐gestational‐age (LGA) infants of Chinese women. Methods We performed a retrospective case‐control study of 299 women with GDM and 204 women with normal glucose tolerance (NGT), using oral glucose tolerance test‐based indices performed at 24‐25 weeks of gestation. Women with GDM were classified into the following three physiologic subtypes: GDM with a predominant insulin‐secretion defect (GDM‐dysfunction), GDM with a predominant insulin‐sensitivity defect (GDM‐resistance), or GDM with both defects (GDM‐mixed). We then used a binary logistic regression model to evaluate the potential associations of GDM subtypes and pre‐BMI with newborn macrosomia or LGA. Results Women with GDM‐resistance had a higher pre‐BMI (P < 0.001), whereas women in the GDM‐dysfunction and GDM‐mixed groups had pre‐BMIs comparable to the NGT group. In the logistic regression model, women in the GDM‐mixed group exhibited an increased risk of bearing newborns with macrosomia and LGA, and women in the GDM‐dysfunction group tended to have newborns with LGA after adjusting for pre‐BMI and other potential confounders. Women who were overweight or obese prepregnancy manifested an increased risk of having newborns with macrosomia and LGA relative to normal‐weight women, regardless of whether values were unadjusted or adjusted for all potential confounders. There was no significant interaction between GDM subtype and pre‐BMI for any of the studied outcomes. Conclusions Heterogeneity of GDM (GDM‐dysfunction and GDM‐mixed) and prepregnancy overweight/obesity were independently associated with LGA in Chinese women. There was no significant interaction between GDM subtypes and pre‐BMI for LGA.

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.

Background 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. Methods 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. Results 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). Conclusions 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.

The coronavirus disease 2019 (COVID-19) pandemic has brought severe challenges to global public health. Many studies have shown that obesity plays a vital role in the occurrence and development of COVID-19. Obesity exacerbates COVID-19, leading to increased intensive care unit hospitalization rate, high demand for invasive mechanical ventilation, and high mortality. The mechanisms of interaction between obesity and COVID-19 involve inflammation, immune response, changes in pulmonary dynamics, disruptions of receptor ligands, and dysfunction of endothelial cells. Therefore, for obese patients with COVID-19, the degree of obesity and related comorbidities should be evaluated. Treatment methods such as administration of anticoagulants and anti-inflammatory drugs like glucocorticoids and airway management should be actively initiated. We should also pay attention to long-term prognosis and vaccine immunity and actively address the physical and psychological problems caused by longterm staying-at-home during the pandemic. The present study summarized the research to investigate the role of obesity in the incidence and progression of COVID-19 and the psychosocial impact and treatment options for obese patients with COVID-19, to guide the understanding and management of the disease.