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Background: Diurnal temperature range (DTR) is an important index of climate change and variability. It is also a risk factor affecting human health. However, limited evidence is available to illustrate the effect of DTR modification on cause-specific cardiovascular disease among the elderly. Methods: A semi-parametric generalized additive model (GAM) was used to analyze the exposure-effect relationship between DTR and daily emergency room (ER) admissions for cause-specific cardiovascular diseases among the elderly from 2009 to 2011 in Beijing. We examined the effects of DTR for stratified groups by gender and age, and examined the effects of DTR in the warm season and cold season for cause-specific cardiovascular diseases. Results: Significant associations were found between DTR and ER admissions for all cardiovascular and cerebrovascular disease among elderly males, while DTR was significantly associated with ER admissions for all cardiovascular disease, ischemic heart disease and cerebrovascular disease among elderly females. People aged 75 years and older were more vulnerable to DTR. DTR caused greater adverse effects on both genders in the warm season, whereas the effect estimates on females were higher in cold season than in warm season. Conclusions: A short-term increase of DTR was significantly associated with ER admissions for cause-specific cardiovascular disease among the elderly in Beijing. Gender, age and season may modify the acute health effect of DTR. Some prevention programs that target the high risk subgroups in the elderly for impending large temperature changes may reduce the impact of DTR on people’s health.

Air pollution exposure may play an adverse role in diabetes. However, little data are available directly evaluating the effects of air pollution exposure in blood lipids of which dysfunction has been linked to diabetes or its complications. We aimed to evaluate the association between air pollution and lipids level among type 2 diabetic patients in Northwest China. We performed a population-based study of 3912 type 2 diabetes patients in an ongoing cohort study in China. Both spline and multiple linear regressions analysis were used to examine the association between short-term exposure to PM10, SO2, NO2 and total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). By spline analyses, we observed that the relationship between SO2 and HDL-C and LDL-C was shown to be non-linear (p_non-lin-association = 0.0162 and 0.000). An inverted U-shaped non-linear relationship between NO2 and LDL-C was found (p_non-lin-association < 0.0001). A J-shaped non-linear relationship between PM10 and TC, HDL-C (p_non-lin-association = 0.0173, 0.0367) was also revealed. In linear regression analyses, a 10 μg/m3 increment in SO2 was associated with 1.31% (95% CI: 0.40–2.12%), 3.52% (95% CI: 1.07–6.03%) and 7.53% (95% CI: 5.98–9.09%) increase in TC, TG and LDL-C, respectively. A 10 μg/m3 increment in PM10 was associated with 0.45% (95% CI: 0.08–0.82%), 0.29% (95% CI: 0.10–0.49%) and 0.83% (95% CI: 0.21–1.45%) increase in TC, HDL-C and LDL-C, respectively. For NO2, an increment of 10 μg/m3 was statistically associated with −3.55% (95% CI: −6.40–0.61%) and 39.01% (95% CI: 31.43–47.03%) increase in HDL-C and LDL-C. The adverse effects of air pollutants on lipid levels were greater in female and elder people. Further, we found SO2 and NO2 played a more evident role in lipid levels in warm season, while PM10 appeared stronger in cold season. The findings suggest that exposure to air pollution has adverse effects on lipid levels among type 2 diabetes patients, and vulnerable people may pay more attention on severe air pollution days.

Panel studies show a consistent association between increase in the cardiovascular hospitalizations with air pollutants in economically developed regions, but little evidence in less developed inland areas. In this study, a time-series analysis was used to examine the specific effects of major air pollutants [particulate matter less than 10 microns in diameter (PM10), sulfur dioxide (SO2), and nitrogen dioxides (NO2)] on daily hospital admissions for cardio-cerebrovascular diseases in Lanzhou, a heavily polluted city in China. We examined the effects of air pollutants for stratified groups by age and gender, and conducted the modifying effect of seasons on air pollutants to test the possible interaction. The significant associations were found between PM10, SO2 and NO2 and cardiac disease admissions, SO2 and NO2 were found to be associated with the cerebrovascular disease admissions. The elderly was associated more strongly with gaseous pollutants than younger. The modifying effect of seasons on air pollutants also existed. The significant effect of gaseous pollutants (SO2 and NO2) was found on daily hospital admissions even after adjustment for other pollutants except for SO2 on cardiac diseases. In a word, this study provides the evidence for the detrimental short-term health effects of urban gaseous pollutants on cardio-cerebrovascular diseases in Lanzhou.

Background: To study the association between anthropometric measurements and the risk of diabetes and impaired fasting glucose (IFG) and compare body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR) to determine the best indicator and its optimal cutoffs for predicting type 2 diabetes and IFG.Methods: A Chinese prospective (2011-2019) cohort named the Jingchang cohort that included 48 001 participants was studied. Using Cox proportional hazard models, hazard ratios (HRs) for incident type 2 diabetes or IFG per 1 SD change in BMI, WC, and WHtR were calculated. Area under the curve (AUC) was compared to identify the best anthropometric variable and its optimal cutoff for predicting diabetes. Results: The association of BMI, WC, and WHtR with type 2 diabetes or IFG risk was positive in the univariate and multivariable-adjusted Cox proportional hazard models. Of all three indexes, the AUC of BMI was largest and that of WC was smallest. The derived cutoff values for BMI, WC, and WHtR were 24.6 kg/m 2 , 89.5 cm, and 0.52 in men and 23.4 kg/m 2 , 76.5 cm, and 0.47 in women for predicting diabetes, respectively. The derived cutoff values for BMI, WC, and WHtR were 23.4 kg/m 2 , 87.5 cm, and 0.50 in men and 22.5 kg/m 2 , 76.5 cm, and 0.47 in women for predicting IFG, respectively [Correction added on 8 April 2020, after first online publication: '0' has been deleted from 'WC,0' in the first sentence.].Conclusions: Our derived cutoff points were lower than the values specified in the most current Asian diabetes guidelines. We recommend a cutoff point for BMI in Asians of 23 kg/m 2 and for WC a cutoff point of 89 cm in men and

The aim of the study was to explore the association between serum uric acid (SUA) and metabolic syndrome (MetS) in premenopausal and postmenopausal women in the Jinchang Cohort. We studied 3808 female Jinchuan Nonferrous Metals Corporation workers aged 40-60 years. Cohort data from epidemiological surveys and medical exams were used. MetS was defined using the 2009 Joint Interim Society criteria. The relationship between SUA and MetS was evaluated using multiple logistic regression after adjusting for potential confounders. MetS and hyperuricemia were more prevalent in postmenopausal women than premenopausal ones (35.3% versus 15.2% and 9.2% versus 4.2%, respectively). Premenopausal and postmenopausal women with hyperuricemia had 2.81 (95% CI: 1.72-4.61) and 2.10 (95% CI: 1.44-3.08), respectively, times the odds of having MetS than their counterparts without hyperuricemia. Even within normal SUA quartiles, only premenopausal women in the highest and second-highest quartile had 3.57 (95% CI: 2.24-5.68) and 2.78 (95% CI: 1.71-4.50), respectively, times the odds of having MetS than those in the lowest quartile. Even in the normal range, the odds ratios for MetS increased gradually according to SUA levels in all women (P<0.001). In conclusion, there was a significant correlation between SUA levels and MetS, and the association was stronger in premenopausal women than postmenopausal ones.

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