Prevalence of dyslipidemia and gene polymorphisms of ABCB1 and SLCO1B1 in Han, Uygur, Kazak, Hui, Tatar, Kirgiz, and Sibe populations with coronary heart disease in Xinjiang, China

Association between the metabolic score for insulin resistance and prostate cancer: a cross-sectional study in Xinjiang

Background

Insulin resistance is associated with the development and progression of various cancers. However, the epidemiological evidence for the association between insulin resistance and prostate cancer is still limited.

Objectives

To investigate the associations between insulin resistance and prostate cancer prevalence.

Methods

A total of 451 patients who were pathologically diagnosed with prostate cancer in the First Affiliated Hospital of Xinjiang Medical University were selected as the case population; 1,863 participants who conducted physical examinations during the same period were selected as the control population. The metabolic score for insulin resistance (METS-IR) was calculated as a substitute indicator for evaluating insulin resistance. The Chi-square test and Mann-Whitney U test were performed to compare the basic information of the case population and control population. Univariate and multivariate logistic regression analyses to define factors that may influence prostate cancer prevalence. The generalized additive model (GAM) was applied to fit the relationship between METS-IR and prostate cancer. Interaction tests based on generalized additive model (GAM) and contour plots were also carried out to analyze the interaction effect of each factor with METS-IR on prostate cancer.

Results

METS-IR as both a continuous and categorical variable suggested that METS-IR was negatively associated with prostate cancer prevalence. Smoothed curves fitted by generalized additive model (GAM) displayed a nonlinear correlation between METS-IR and prostate cancer prevalence (P < 0.001), and presented that METS-IR was negatively associated with the odds ratio (OR) of prostate cancer. The interaction based on the generalized additive model (GAM) revealed that METS-IR interacted with low-density lipoprotein cholesterol (LDL-c) to influence the prostate cancer prevalence (P = 0.004). Contour plots showed that the highest prevalence probability of prostate cancer was achieved when METS-IR was minimal and low-density lipoprotein cholesterol (LDL-c) or total cholesterol (TC) was maximal.

Conclusions

METS-IR is nonlinearly and negatively associated with the prevalence of prostate cancer. The interaction between METS-IR and low-density lipoprotein cholesterol (LDL-c) has an impact on the prevalence of prostate cancer. The study suggests that the causal relationship between insulin resistance and prostate cancer still needs more research to confirm.

Effects of long-term exposure to PM2.5 and chemical constituents on blood lipids in an essential hypertensive population: A multi-city study in China

Previous studies on the effects of fine particulate matter (PM_2.5) and chemical constituents on lipid disorder among hypertension populations, particularly in China, are very limited. We aimed to examine the effects of long-term exposure to PM_2.5 and chemical constituents on dyslipidemias in China. Finally, we included 34,841 participants with essential hypertension from 19 regions in China during 2010-2011. Data were modeled using the generalized additive mixed model. We found that PM_2.5 and chemical constituents exposure were positively associated with the increased risk of dyslipidemias and increased levels of total cholesterol (TC) and triglyceride (TG). The odds ratio for hypercholesterolemia was 1.356 [95% confidence interval (CI): 1.246, 1.477] for PM_2.5, and the strongest association with PM_2.5 constituents was found for nitrate. Each 10 μg/m³ increase in PM_2.5 showed a significant increase of TC by 2.60% (95% CI: 2.03, 3.17) and TG by 2.91% (95% CI: 1.60, 4.24), respectively. Meanwhile, an interquartile range increase in nitrate, ammonium and organic matter had stronger associations with TC and TG parameters than black carbon, sulfate, and mineral dust. Our findings may contribute to a better understanding of the chronic effects of PM_2.5 and chemical constituents on lipid disorder in an essential hypertensive population.