Associations of sleep quality and exercise frequency and the risk of coronary heart disease in Chinese urban elderly: a secondary analysis of cross-sectional data.

BACKGROUND: Sleep quality and exercise frequency are closely associated with coronary heart disease (CHD). Few studies focused on the joint effect of initiating sleep, sleep disorders, and exercise frequency on the risk of CHD in the elderly. We used a secondary data analysis based on Boshan Elderly cross-sectional study. We explored the sleep quality, exercise frequency, and their joint effects on the risk of CHD. METHODS: We collected 678 participants whose age ≥ 60 years old from Boshan District Hospital. We used the Pittsburgh Sleep Quality Index to evaluate the sleep quality and obtained physical examination information from the hospital. RESULTS: Compared with the non-CHD group, patients with CHD spent more time in initiating sleep (time ≥ 60 min, 34.59% vs. 22.93%, P = 0.025) and less time exercising (exercise frequency < 1 times/week, 23.90% vs. 17.15%, P = 0.024). In multiple logistic regression analysis, sleep latency ≥ 60 min was associated with CHD risk (adjusted OR = 1.83; 95% CI: 1.11, 2.99; P-trend = 0.008). The adjusted OR (95% CI) of CHD was 2.24 (1.16, 4.34) for sleep duration < 5 h versus 5-9 h. Compared with exercise frequency < 1 times/week, the adjusted OR for exercise frequency ≥ 1 times/week was 0.46 (95% CI: 0.26, 0.83; P = 0.010). In addition, the joint effects of long sleep latency (≥ 60 min) and sleep disorders were associated with CHD (adjusted OR = 3.36; 95% CI: 1.41, 8.02). The joint effect of exercise frequency ≥ 1 times/week and sleep onset latency within normal limits (< 30 min) was also associated with CHD, and the adjusted OR (95% CI) was 0.42 (0.21, 0.87). CONCLUSIONS: Long sleep latency, high frequency of initiating sleep difficulty, sleep disorders, and short sleep duration were positively associated with CHD. In addition, the joint effects of long sleep latency and sleep disorders were positively correlated with CHD incidence. However, the joint effects of exercise frequency ≥ 1 times/week and normal sleep onset latency were negatively associated CHD.

The Associations of Maternal Blood Hemoglobin and Serum Triglyceride Levels and the Risk of Preterm Delivery.

The abnormal hemoglobin (HGB) and serum lipid concentrations during pregnancy will increase the risk of preterm delivery. Our study aimed to explore the correlation between prenatal HGB and serum lipid levels and preterm delivery. We enrolled 215 mother-infant pairs in a pilot cohort study. The logistic regression model and Restricted Cubic Spline model (RCS) were used to investigate the levels of prenatal blood HGB and serum lipid such as triglyceride (TG), total cholesterol, high-density lipoprotein, low density lipoprotein and preterm delivery. The results showed that moderate levels of prenatal blood HGB (OR=0.28; 95%CI: 0.10, 0.75, p-trend=0.018) and high level of serum TG (OR=0.29; 95%CI: 0.10, 0.84, p-trend=0.022) level were negatively associated with the risk of preterm delivery. The joint effect results showed that compared with lower level of prenatal blood HGB (≤123.13 g/l) and TG (≤3.7 mmol/l), we found that high levels prenatal blood HGB and serum TG (OR=0.32, 95%CI: 0.12, 0.89) had a negative association with the risk of preterm delivery. Moreover, prenatal blood HGB and serum TG levels had negative linear dose-effect relationships with the risk of preterm delivery in overall and girl group (p<0.05). Moderate levels of prenatal blood HGB and high level of serum TG were negatively associated with the risk of preterm delivery. The joint effect of high levels prenatal HGB and prenatal serum TG in the normal range were negatively correlated with preterm delivery. Moreover, the underlying mechanisms should be clarified in future studies.

The association of serum magnesium and chronic kidney disease: a two-sample mendelian randomization study of European descent.

BACKGROUND: Previous observational studies focused on the association of serum magnesium (SMg) and chronic kidney disease (CKD), but the conclusion was inconsistent. To investigate the causal relationship of SMg and CKD, we performed a two-sample mendelian randomization (TSMR) analysis using publicly datasets. METHOD: In mendelian randomization (MR) analysis, we used single nucleotide polymorphisms (SNPs) which had genetic statistical significance with SMg but not associated with kidney function and confounding factors as instrumental variable (IV). To select SNPs, we used publicly database of Genome Wide Association Study (GWAS) and Chronic Kidney Disease Genetics (CKDGen) Confirms. We used inverse-variance weighted (IVW), weighted median, MR-Egger regression, weighted mode, and simple mode approaches in TSMR analysis. RESULTS: We selected 4 SNPs (rs4072037, rs7965584, rs11144134 and rs448378) as IV. In IVW approach, the result of MR analysis for CKD was OR = 0.55, 95% CI: 0.06, 4.75, P = 0.58; for estimated glomerular filtration rate from creatinine (eGFR)crea was ß = -0.06, 95% CI: -1.08, 0.07, P = 0.39; for estimated glomerular filtration rate from cystatin C (eGFR)cys was ß = -0.03, 95% CI: -0.43, 0.36, P = 0.86, respectively per SD increase in SMg. When subgroup by diabetes mellitus (DM), the results for DM-eGFRcrea was ß = -0.33, 95% CI: -0.85, 0.19, P = 0.21; and for non-DM-eGFRcrea was ß = -0.03, 95% CI: -0.16, 0.11, P = 0.71. The results of other four MR approaches were consistent with IVW approach (all P > 0.05). CONCLUSION: Our TSMR analysis showed that SMg had no causal effect on kidney function and progress CKD in European descent. As for the results about overall population, the verified study is needed in future study.

Associations of multiple serum biomarkers and the risk of cardiovascular disease in China.

BACKGROUND: Previous studies focus on one or several serum biomarkers and the risk of cardiovascular disease (CVD). This study aims to investigate the association of multiple serum biomarkers and the risk of CVD and evaluate the dose-relationship between a single serum metabolite and CVD. METHODS: Our case-control study included 161 CVD and 160 non-CVD patients who had a physical examination in the same hospital. We used stratified analysis and cubic restricted analysis to investigate the dose-response relationship of individual serum biomarkers and the CVD incident. Moreover, to investigate serum biomarkers and CVD, we used elastic net regression and logistic regression to build a multi-biomarker model. RESULTS: In a single serum biomarker model, we found serum FT4, T4. GLU, CREA, TG and LDL-c were positively associated with CVD. In the male group, serum T4, GLU and LDL-c were positively associated with CVD; and serum TG was positively associated with CVD in the female group. When patients ≤63 years old, serum T4, GLU, CREA and TG were positively associated with CVD, and serum TG and LDL-c were positively associated with CVD when patients > 63 years old. Moreover, serum GLU had nonlinearity relationship with CVD and serum TG and LDL-c had linearity association with CVD. Furthermore, we used elastic regression selecting 5 serum biomarkers (GLU, FT4, TG, HDL-c, LDL-c) which were independently associated with CVD incident and built multi-biomarker model. And the multi-biomarker model had much better sensitivity than single biomarker model. CONCLUSION: The multi-biomarker model had much higher sensitivity than a single biomarker model for the prediction of CVD. Serum FT4, TG and LDL-c were positively associated with the risk of CVD in single and multiple serum biomarkers models, and serum TG and LDL-c had linearity relationship with CVD.