Mediating Effect of Body Mass Index and Dyslipidemia on the Relation of Uric Acid and Type 2 Diabetes: Results From China Health and Retirement Longitudinal Study

Sex-specific associations between serum uric acid levels and risk of hypertension for different diagnostic reference values of high blood pressure

The association between uric acid (UA) and hyperuricemia with 5-year hypertension incidence using different blood pressure (BP) diagnostic references in men and women without cardiometabolic diseases is unknown. We used the checkup data from Kagoshima Kouseiren Hospital. All participants with hypertension or on BP medication, diabetes, dyslipidemia, obesity, estimated glomerular filtration rate<60 ml/min/1.73m2, metabolic syndrome, history of gout, and UA-lowering medication were excluded. UA was categorized into sex-specific quartiles and hyperuricemia was defined as UA > 7 mg/dl in men and UA > 6 mg/dl in women. We performed multivariate logistic regression to assess the effects of UA on hypertension development. The 5-year hypertension incidence was defined as subsets of BP ≥ 140/90 mmHg in cohort 1 and BP ≥ 130/80 mmHg in cohort 2. The study enrolled 21,443 participants (39.8%, men) in cohort 1 and 15,245 participants (36.5%, men) in cohort 2. The incidence of hypertension in cohorts 1 and 2 over 5 years was 16.3% and 29.7% in men and 10.9% and 21.4% in women, respectively. When comparing the fourth to the first UA quartile, there was an association with hypertension in men in cohort 1, with odds ratio (OR): 1.36 (95% confidence interval [CI], 1.13-1.63, p < 0.01) and cohort 2, OR: 1.31 (95%CI, 1.09-1.57, p < 0.01), respectively, but not in women. Additionally, an association between hyperuricemia and hypertension was observed in men only in cohort 1, with OR: 1.23 (95%CI, 1.07-1.42, p = 0.02), and in women in cohort 2, OR: 1.57 (95%CI, 1.14-2.16, p < 0.01). The effect of UA on the development of hypertension is influenced by sex and incidence differs with the BP reference used. Uric acid effect on the development of hypertension is affected by sex and incidence differs with the BP reference used.

Synergistic effect of schizandrin A and DNase I knockdown on high glucose induced beta cell apoptosis by decreasing intracellular calcium concentration

OBJECTIVE

To explore the synergistic effect of deoxyribonuclease I (DNase I) knockdown combined with Schizandrin A (Sch A) in protecting islet beta-cells (β-cells) from apoptosis under high-glucose (HG) conditions.

METHODS

The concentration of Sch A was detected by Cell Counting Kit-8 (CCK-8). High glucose-cultured rat insulinoma beta cell line (RIN-M5F) cells were treated with Sch A and transfected with DNase I small interfering RNA (siRNA). Cell apoptosis rate and apoptosis-related protein level were examined by flow cytometry and Western blot method respectively. In addition, Na-K-adenosine triphosphatease (Na-K-ATPase) and Ca-Mg-ATPase activity, cell membrane potential, and intracellular Ca concentration was also examined respectively.

RESULTS

Our study revealed that HG stimulation can cause a significant increase in DNase I level and cell apoptosis rate. However, Sch A combined with DNase I knockdown can significantly decrease the cell apoptosis rate and apoptosis-related protein levels such as BAX ( 0.05) and Caspase-3 ( 0.01). In addition, we also found that the combination of Sch A and DNase I knockdown can dramatically increase cell membrane potential level, Na-K-ATPase, and Ca-Mg-ATPase activity. Meanwhile, intracellular Ca concentration was also found to be significantly decreased by the synergistic effect of Sch A and DNase I knockdown.

CONCLUSION

Overall, our study reveals a synergistic effect of Sch A and DNase I knockdown in protecting β-cells from HG-induced apoptosis.

Fatty liver mediates the association of hyperuricemia with prediabetes and diabetes: a weighting-based mediation analysis

Background

Fatty liver, obesity, and dyslipidemia are associated with prediabetes or diabetes risk, and hyperuricemia co-exists. The present study evaluated the role of multiple mediators, namely, fatty liver, body mass index (BMI), and dyslipidemia, in the association between hyperuricemia and diabetes status.

Methods

Baseline data from the ongoing Fuqing cohort (5,336 participants) were analyzed to investigate the association of hyperuricemia with diabetes status using a multinomial logistic regression model. Furthermore, causal mediation analysis with the weighting-based approach was performed to estimate hyperuricemia's total natural direct effect (tnde), total natural indirect effect (tnie), and total effect (te) on prediabetes and diabetes risk, mediating jointly via fatty liver, BMI, and dyslipidemia.

Results

In multinomial analysis without considering mediators' effects, hyperuricemia was associated with a higher risk of prediabetes only (odds ratio: 1.25; 95% CI: 1.09-1.43; p < 0.001). When fatty liver, BMI, and dyslipidemia were considered as multiple mediators in the association, hyperuricemia was linked to both prediabetes [tnde: 1.11, 95% CI: 1.04-1.11; tnie: 1.07, 95% CI: 1.05-1.09; and overall proportion mediated (pm): 42%, 95% CI: 27%-73%] and diabetes risk (tnde: 0.96, 95% CI: 0.82-1.14; tnie: 1.25, 95% CI: 1.18-1.33; and pm: 100%, 95% CI: 57%-361%). Hyperuricemia showed significant tnde, te, and tnie, mediated by fatty liver jointly with dyslipidemia (pm = 17%) or BMI (pm = 35%), on prediabetes risk.

Conclusion

Hyperuricemia could increase prediabetes or diabetes risk, partially mediated by fatty liver, BMI, and dyslipidemia. Fatty liver is the crucial mediator in the association between hyperuricemia and prediabetes.

Spatiotemporal Analysis and Risk Assessment Model Research of Diabetes among People over 45 Years Old in China

Diabetes, which is a chronic disease with a high prevalence in people over 45 years old in China, is a public health issue of global concern. In order to explore the spatiotemporal patterns of diabetes among people over 45 years old in China, to find out diabetes risk factors, and to assess its risk, we used spatial autocorrelation, spatiotemporal cluster analysis, binary logistic regression, and a random forest model in this study. The results of the spatial autocorrelation analysis and the spatiotemporal clustering analysis showed that diabetes patients are mainly clustered near the Beijing−Tianjin−Hebei region, and that the prevalence of diabetes clusters is waning. Age, hypertension, dyslipidemia, and smoking history were all diabetes risk factors (p < 0.05), but the spatial heterogeneity of these factors was weak. Compared with the binary logistic regression model, the random forest model showed better accuracy in assessing diabetes risk. According to the assessment risk map generated by the random forest model, the northeast region and the Beijing−Tianjin−Hebei region are high-risk areas for diabetes.