Limited evidences were reported about the risk of pre-pregnancy hair dye use or irregular menstruation with abnormal birth weight during pregnancy, and their joint effects were also unknown. The aim of our study was to explore whether the pre-pregnancy exposure of hair dye and irregular menstruation were associated with the risk of abnormal birth weight. We conducted a nested case-control study from a prospective cohort of 6203 pregnant women. Low birth weight study included 315 mother-infant pairs (105 LBW cases and 210 matched controls), and macrosomia study included 381 mother-infant pairs (127 macrosomia cases and 254 matched controls). Meanwhile, lifestyle information including hair dying custom and menstrual history were collected by face-to-face questionnaires and birth outcomes were extracted from the medical records. The logistic regressions models were used to analyze the join effect of irregular menstruation and hair dye use. Pre-pregnancy hair dye use was associated with increased risk of LBW (adjusted OR = 1.71, 95% CI: 1.01–2.92, P = 0.048). Irregular menstruation had high risk of LBW (adjusted OR = 2.79, 95% CI: 1.53–5.09, P = 0.001) and macrosomia (adjusted OR = 1.93, 95% CI: 1.09–3.44, P = 0.023). Additionally, in the LBW study, women who used hair dye with pre-pregnancy BMI < 18.5 kg/m² had higher OR than those with only one risk factor (3.07 vs 2.53, P _trend = 0.015), and women with both hair dye use and irregular menstruation also had higher risk than those with only one factor (4.53 vs 2.07, P _trend = 0.05). Moreover, in macrosomia study, women with irregular menstruation and pre-pregnancy BMI ≥ 24 kg/m² had higher risk than those with one factor (13.31 vs 2.09, P _trend = 0.001). Our study showed that either pre-pregnancy hair dye use or irregular menstruation was associated with abnormal birth weight, especially, their joint effects could furthermore increase the risk of low birth weight infants when these two factors existed simultaneously.

Background: Manganese (Mn) participates in lipid metabolism. However, the associations between Mn exposure and dyslipidaemia is unclear. Methods: This was a cross-sectional study. Data were collected from the 2017 the Mn-exposed workers healthy cohort (MEWHC). Finally, 803 occupationally Mn-exposed workers included in the study. The workers were divided into two groups. The grouping of this study was based on Mn-Time Weighted Averages (Mn-TWA). The high-exposure group included participants with Mn-TWA greater than 0.15 mg/m³. The low-exposure group included participants with Mn-TWA less than or equal to 0.15 mg/m³. Mn-TWA levels and dyslipidaemia were assessed. Results: After adjustment for seniority, sex, cigarette consumption, alcohol consumption, high-fat diet frequency, medicine intake in the past two weeks, egg intake frequency, drinking tea, WHR, and hypertension, Mn-TWA levels was negatively correlated with high triglycerides (TG) risk in workers overall (OR = 0.51; 95% CI: 0.36, 0.73; p < 0.01). The results of males and females were consistent (OR = 0.53; 95% CI: 0.34, 0.81; p < 0.01) and (OR = 0.47; 95% CI: 0.24, 0.94; p < 0.01), respectively. By performing interactions analyses of workers overall, we observed no significant interactions among confounders. Mn-TWA levels and pack-years on high TG risk (relative excess risk for the interactions (RERI = 2.29, 95% CI: − 2.07, 6.66), (RERI) = 2.98, 95% CI: − 2.30, 8.26). Similarly, smoking status, drinking status, high-fat diet frequency, and Waist-to-Hip Ratio (WHR) showed non-significant interactions with Mn-TWA levels on high TG risk. Conclusions: This research indicates that high Mn exposure was negatively related to high TG risk in workers.

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.

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.