[Association between exposure to PM_(2.5) and chronic kidney disease in a population with metabolic syndrome].

OBJECTIVE: To investigate the association between long-term fine particulate matter(PM_(2.5)) exposure and the risk of chronic kidney disease(CKD) in people with abnormal metabolism syndrome(MS) components. METHODS: Based on health checkup data from a hospital in Beijing, a retrospective cohort study was used to collect annual checkup data from 2013-2019. A questionnaire was used to obtain information on demographic characteristics and lifestyle habits. We measured blood pressure, height, weight, waist circumference, concentrations of triglycerides(TG), fasting glucose, and high-density lipoprotein cholesterol(HDL-C). Longitude and latitude were also extracted from the addresses of the study subjects for pollutant exposure data estimation. Logistic regression models were used to explore the estimated effect of long-term PM_(2.5) exposure on the risk of CKD prevalence in people with abnormal MS components. Two-pollutant and multi-pollutant models were developed to test the stability of these result. Subgroup analysis was conducted based on age, the presence of MS, individual MS component abnormalities, and dual-component MS abnormalities. RESULTS: The study included 1540 study subjects with abnormal MS components at baseline, 206 with CKD during the study period. The association between long-term PM_(2.5) exposure and increased risk of CKD in people with abnormal MS fractions was statistically significant, with a 2.26-fold increase in risk of CKD for every 10 µg/m~3 increase in PM_(2.5) exposure(OR=3.26, 95% CI 2.72-3.90). The result in the dual-pollutant models and multi-pollutant models suggested that the association between long-term PM_(2.5) exposure and increased risk of CKD in people with abnormal MS fractions remained stable after controlling for contemporaneous confounding by other air pollutants. The result of subgroup analysis revealed that individuals aged 45 or older, without MS, with TG<1.7 mmol/L, HDL-C≥1.04 mmol/L, without hypertension, and with central obesity and high blood sugar had a stronger association between PM_(2.5) exposure and CKD-related health effects. CONCLUSION: Long-term exposure to PM_(2.5) may increase the risk of CKD in people with abnormal MS components. More attention should be paid to middle-aged and elderly people aged ≥45 years, people with central obesity and hyperglycemia.

Greater variability in HDL-C was associated with an increased risk of cognitive decline in the middle- and elderly Chinese: A cohort study.

BACKGROUND: Previous studies into relationship between high-density lipoprotein cholesterol (HDL-C) and cognitive decline were constrained to a single measurement, leaving the association between HDL-C variability and risk of cognitive decline unclear. METHODS: We identified 5930 participants from the China Health and Retirement Longitudinal Study (CHARLS) who were devoid for stroke, dementia, and memory-related diseases at baseline and underwent a minimum of 2 sequential health examinations during 2011-2015. Variability in HDL-C was defined as (1) variability independent of the mean (VIM), (2) average real variability (ARV), and (3) standard deviation (SD) of HDL-C change from baseline and follow-up visits. Cognitive function was evaluated in 2018 by Mini-mental state examination (MMSE) in the Chinese version. Logistic regression was employed to explore the association between HDL-C variability and cognitive decline. Odd ratios (OR) and 95 % confidence intervals (CI) were reported. RESULTS: The study included participants from CHARLS, mean age of 57.84±8.44 years and 44 % male. After adjustment for covariates, the highest quartile of VIM was associated with an increased risk of cognitive decline [OR:1.049, 95 %CI: 1.014-1.086] compared to the lowest quartile. For each SD increment of VIM, the OR was 1.015 (95 %CI:1.003-1.027). Strong dose-response relationships were identified (P for trend: 0.005). Consistent results were obtained for other measures of HDL-C variability (ARV and SD). Similar patterns were identified in different dimensions of cognition. CONCLUSIONS: Elevated HDL-C variability was associated with increased cognitive decline risk. Strategies to reducing HDL-C variability may lower the risks of cognitive decline among the general population.

Association of obesity with cardiovascular disease in the absence of traditional risk factors.

BACKGROUND: The association between obesity and cardiovascular disease (CVD) in people without traditional CVD risk factors is unclear. This study aimed to investigate the association of obesity with CVD and its subtypes in people without traditional CVD risk factors. METHODS: Based on the Kailuan cohort study, the included participants were divided into different groups according to levels of body mass index (BMI) and waist height ratio (WHtR), respectively. Multivariate Cox proportional hazard models were used to evaluate the associations. RESULTS: This study included 31,955 participants [men 63.99%; mean age (48.14 ± 3.33) years]. During a median follow-up period of 12.97 (interquartile range: 12.68-13.17) years, 1298 cases of CVD were observed. Compared with the normal BMI group, the hazard ratios (HRs) for CVD, stroke, and myocardial infarction (MI) in the BMI obese group were 1.31 (95% confidence interval [CI] 1.11-1.55), 1.21 (95%CI 1.01-1.46), 1.62 (95%CI 1.13-2.33), respectively. Compared with the WHtR non-obese group, the HRs for CVD, stroke, and MI in the obese group were 1.25(95%CI 1.11-1.41), 1.18 (95%CI 1.03-1.34), 1.57 (95%CI 1.18-2.09), respectively. There was an interaction between age and WHtR (P for interaction was 0.043). The association between WHtR and CVD was stronger in people under 60 years old, with a HR of 1.44 (95%CI 1.24-1.67). CONCLUSION: We found that obesity increased the risk of CVD in people without traditional CVD risk factors. The association of WHtR with CVD was stronger in people under 60 years old.

Association Between Long-Term Exposure to Fine Particulate Matter Constituents and Progression of Cerebral Blood Flow Velocity in Beijing: Modifying Effect of Greenness.

Few studies have explored the effects of fine particulate matter (PM2.5) and its constituents on the progression of cerebral blood flow velocity (BFV) and the potential modifying role of greenness. In this study, we investigated the association of PM2.5 and its constituents, including sulfate (SO4 2-), nitrate (NO3 -), ammonium (NH4 +), organic matter (OM), and black carbon (BC), with the progression of BFV in the middle cerebral artery. Participants from the Beijing Health Management Cohort who underwent at least two transcranial Doppler sonography examinations during 2015-2020 were recruited. BFV change and BFV change rate were used to define the progression of cerebral BFV. Linear mixed effects models were employed to analyze the data, and the weighted quantile sum regression assessed the contribution of PM2.5 constituents. Additionally, greenness was examined as a modifier. Among the examined constituents, OM exhibited the strongest association with BFV progression. An interquartile range increase in PM2.5 and OM exposure concentrations was associated with a decrease of -16.519 cm/s (95% CI: -17.837, -15.201) and -15.403 cm/s (95% CI: -16.681, -14.126) in BFV change, and -10.369 cm/s/year (95% CI: -11.387, -9.352) and -9.615 cm/s/year (95% CI: -10.599, -8.632) in BFV change rate, respectively. Furthermore, stronger associations between PM2.5 and BFV progression were observed in individuals working in areas with lower greenness, those aged under 45 years, and females. In conclusion, reducing PM2.5 levels in the air, particularly the OM constituent, and enhancing greenness could potentially contribute to the protection of cerebrovascular health.

Long-term exposure to PM2.5 and PM10 and chronic kidney disease: the Beijing Health Management Cohort, from 2013 to 2018.

Long-term exposure to ambient particulate pollutants (PM2.5 and PM10) may increase the risk of chronic kidney disease (CKD), but the results of previous research were limited and inconsistent. The purpose of this study was to assess the relationships of PM2.5 and PM10 with CKD. This study was a cohort study based on the physical examination data of 2082 Beijing residents from 2013 to 2018 in the Beijing Health Management Cohort (BHMC). A land-use regression model was used to estimate the individual exposure concentration of air pollution based on the address provided by each participant. CKD events were identified based on self-report or medical evaluation (estimated glomerular filtration rate, eGFR less than 60 ml/min/1.73 m2). Finally, the associations of PM2.5 and PM10 with CKD were calculated using univariate and multivariate logistic regression models. During the research period, we collected potentially confounding information. After adjusting for confounders, each 10 µg/m3 increase in PM2.5 and PM10 exposure was associated with an 84% (OR: 1.84; 95% CI: 1.45, 2.33) and 37% (OR: 1.37; 95% CI: 1.15, 1.63) increased risk of CKD. Adjusting for the four common gaseous air pollutants (CO, NO2, SO2, O3), the effect of PM2.5 and PM10 on CKD was significantly enhanced, but the effect of PM10 was no longer significant in the multi-pollutant model. The results of the stratified analysis showed that PM2.5 and PM10 were more significant in males, middle-aged and elderly people over 45 years old, smokers, drinkers, BMI ≥ 24 kg/m2, and abnormal metabolic components. In conclusion, long-term exposure to ambient PM2.5 and PM10 was associated with an increased risk of CKD.

Associations between ambient air pollution, meteorology, and daily hospital admissions for ischemic stroke: a time-stratified case-crossover study in Beijing.

Air pollution and ischemic stroke (IS) are both vital factors affecting the health of Beijing citizens. This study aims at exploring the associations between air pollution, meteorology, and the hospital admission of IS (IS HA). Information on 476,659 IS inpatients in secondary and higher hospitals in Beijing from 2013 to 2018 were collected. A time-stratified case-crossover design with the generalized additive model and the distributed lag nonlinear model were used. In the single-pollutant models, an inter-quartile range increase in O3, SO2, CO, and NO2 resulted in a significant highest increase in IS HA by 2.23% (95% CI: 1.56%, 2.90%), 1.53% (95% CI: 1.12%, 1.95%), 1.05% (95% CI: 0.70%, 1.40%), and 0.51% (95% CI: 0.24%, 0.79%) on the day of pollution, so did PM2.5 and PM10 by 1.13% (95% CI: 0.68%, 1.59%) and 1.19% (95% CI: 0.74%, 1.64%) at a lag of 0-5 days. There was a nonlinear relationship between meteorology and IS HA. In the multivariate model, the cumulative relative risks with a maximum lag time of 21 days of PM2.5 and NO2 were 1.11 (95% CI: 1.04, 1.19) and 0.88 (95% CI: 0.82, 0.94), while the effects of SO2, O3, and meteorology were insignificant. The findings suggested that particulate pollutants could increase the risk of IS, and the elderly were more sensitive to it, while the results of gaseous pollutants are still discordant. The control of air pollution and the protection of susceptible populations should receive higher attention from policymakers.

Acute effect of air pollutants' peak-hour concentrations on ischemic stroke hospital admissions among hypertension patients in Beijing, China, from 2014 to 2018.

Air pollutants' effect on ischemic stroke (IS) has been widely reported. But the effect of high-level concentrations during people's outdoor periods among hypertension patients was unknown. Peak-hour concentrations were defined considering air pollutants' high concentrations as well as people's outdoor periods. We conducted a time-series study and used the generalized additive model to analyze peak-hour concentrations' acute effect. A total of 315,499 IS patients comorbid with hypertension were admitted to secondary and above hospitals in Beijing from 2014 to 2018. A 10 µg/m3 (CO: 1 mg/m3) increase of the peak-hour concentrations was positively associated with IS hospital admissions among hypertension patients. The maximum effect sizes were as follows: for PM2.5, 0.17% (95% confidence interval [CI]: 0.10-0.24%) at Lag0 and 0.22% (95% CI: 0.12-0.33%) at Lag0-5; for PM10, 0.09% (95% CI: 0.05-0.13%) at Lag5 and 0.17% (95% CI: 0.09-0.26%) at Lag0-5; for SO2, 0.87% (95% CI: 0.46-1.29%) at Lag5; for NO2, 0.83% (95% CI: 0.62-1.04%) at Lag0 and 0.86% (95% CI: 0.59-1.13%) at Lag0-1; for CO 1.23% (95% CI: 0.66-1.80%) at Lag0 and 1.33% (95% CI: 0.33-2.35%) at Lag0-5; for O3 0.23% (95% CI: 0.12-0.35%) at Lag0 and 0.20% (95% CI: 0.05-0.34%) at Lag0-1. The effect sizes of PM2.5, NO2, and O3 remained significant after adjusting daily mean. Larger effect sizes were observed for PM2.5 and PM10 in cool season and for O3 in warm season. As significant exposure indicators of air pollution, peak-hour concentrations exposure increased the risk of IS hospital admissions among hypertension patients and it is worthy of consideration in relative environmental standard. It is suggested for hypertension patients to avoid outdoor activity during peak hours. More relevant searches are required to further illustrate air pollutant's effect on chronic disease population.