With the development of data mining, machine learning offers opportunities to improve discrimination by analyzing complex interactions among massive variables. To test the ability of machine learning ...algorithms for predicting risk of type 2 diabetes mellitus (T2DM) in a rural Chinese population, we focus on a total of 36,652 eligible participants from the Henan Rural Cohort Study. Risk assessment models for T2DM were developed using six machine learning algorithms, including logistic regression (LR), classification and regression tree (CART), artificial neural networks (ANN), support vector machine (SVM), random forest (RF) and gradient boosting machine (GBM). The model performance was measured in an area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value and area under precision recall curve. The importance of variables was identified based on each classifier and the shapley additive explanations approach. Using all available variables, all models for predicting risk of T2DM demonstrated strong predictive performance, with AUCs ranging between 0.811 and 0.872 using laboratory data and from 0.767 to 0.817 without laboratory data. Among them, the GBM model performed best (AUC: 0.872 with laboratory data and 0.817 without laboratory data). Performance of models plateaued when introduced 30 variables to each model except CART model. Among the top-10 variables across all methods were sweet flavor, urine glucose, age, heart rate, creatinine, waist circumference, uric acid, pulse pressure, insulin, and hypertension. New important risk factors (urinary indicators, sweet flavor) were not found in previous risk prediction methods, but determined by machine learning in our study. Through the results, machine learning methods showed competence in predicting risk of T2DM, leading to greater insights on disease risk factors with no priori assumption of causality.
Previous meta-analyses on associations between air pollution (AP) and type 2 diabetes mellitus (T2DM) were mainly focused on studies conducted in high-income countries. Evidence should be updated by ...including more recent studies, especially those conducted in low- and middle-income countries. We therefore conducted a systematic review and meta-analysis of epidemiological studies to conclude an updated pooled effect estimates between long-term AP exposure and the prevalence and incidence of T2DM. We searched PubMed, Embase, and Web of Science to identify studies regarding associations of AP with T2DM prevalence and incidence prior to January 2019. A random-effects model was employed to analyze the overall effects. A total of 30 articles were finally included in this meta-analysis. The pooled results showed that higher levels of AP exposure were significantly associated with higher prevalence of T2DM (per 10 μg/m3 increase in concentrations of particles with aerodynamic diameter < 2.5 μm (PM2.5): odds ratio (OR) = 1.09, 95% confidence interval (95%CI): 1.05, 1.13; particles with aerodynamic diameter < 10 μm (PM10): OR = 1.12, 95%CI: 1.06, 1.19; nitrogen dioxide (NO2): OR = 1.05, 95%CI:1.03, 1.08). Besides, higher level of PM2.5 exposure was associated with higher T2DM incidence (per 10 μg/m3 increase in concentration of PM2.5: hazard ratio (HR) = 1.10, 95%CI:1.04, 1.16), while the associations between PM10, NO2 and T2DM incidence were not statistically significant. The associations between AP exposure and T2DM prevalence showed no significant difference between high-income countries and low- and middle-incomes countries. However, different associations were identified between PM2.5 exposure and T2DM prevalence in different geographic areas. No significant differences were found in associations of AP and T2DM prevalence/incidence between females and males, except for the effect of NO2 on T2DM incidence. Overall, AP exposure was positively associated with T2DM. There still remains a need for evidence from low- and middle-income countries on the relationships between AP and T2DM.
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•Long-term exposure to air pollution was associated with an increased risk of T2DM.•Different associations between females and males were not identified.•Epidemiologic studies on exposure-response relationship should be encouraged.
Long-term exposure to air pollution was positively associated with T2DM.
Background
Stroke is still the main cause of death and disability worldwide, numerous studies of recurrence risk have been reported, while systematic estimates of stroke recurrence risk in the last ...10 years are variable. This review aims to estimate the cumulative stroke recurrence risk in the last 10 years for secondary prevention management in future.
Methods
A systematic search from January 2009 to March 2019 was conducted through PubMed, EMBASE, Web of Science, Wan-fang, and CNKI. Search terms were in English and Chinese.
Results
A total of 37 studies involving 1,075,014 stroke patients were included. The pooled stroke recurrence rate was 7.7% at 3 months, 9.5% at 6 months, 10.4% at 1 year, 16.1% at 2 years, 16.7% at 3 years, 14.8% at 5 years, 12.9% at 10 years, and 39.7% at 12 years after the initial stroke. In addition, the pooled recurrence rate of 32 studies including stroke patients over 50 years only at seven time points except for subgroup of 10 years was 7.7%, 9.5%, 11.2%, 16.1%, 19.3%, 18.1%, and 39.7%, respectively. Meta-regression showed that the time points explained 23.02% of the variance among studies, while regions, age, and stroke types showed no significant contribution to heterogeneity.
Conclusions
The risk of stroke recurrence varies greatly from 3 months to over 10 years and increases significantly over time in both young and old subgroup. The heterogeneity may be explained by follow-up time, regions, age, methodology differences, and stroke types, which was needed further exploration in future.
Objective: The aim of this study was to explore the latest prevalence of hyperuricemia and influencing factors in Chinese rural population.
Methods: A survey was conducted from July 2015 to September ...2017. A total of 38,855 (15,371 men and 23,484 women) subjects were recruited from the Henan Rural Cohort Study. Hyperuricemia was defined as a serum urate level of >7.0 mg/dL for men and >6.0 mg/dL for women. A meta-analysis of 19 studies that focused on hyperuricemia prevalence was performed to validate the result of the cross-sectional survey.
Results: The crude and age-standardized prevalence of hyperuricemia was 10.24% and 12.60%, respectively. The prevalence of hyperuricemia decreased in men with increasing age, but the opposite trend was observed in women. The results of meta-analysis demonstrated that hyperuricemia prevalence in Chinese rural areas was 11.7%, consistent with the result of current survey. Multivariate logistic regression revealed that overweight or obesity, hypercholesterolemia, hypertriglyceridemia, hypoalphalipoproteinemia and high serum creatinine level could increase the risk of hyperuricemia, while high physical activity and fasting plasma glucose were associated with a lower risk of hyperuricemia in all participants.
Conclusion: The latest prevalence of hyperuricemia is high in rural China and is associated with multiple factors, indicating that prevention and control strategies for hyperuricemia are needed urgently.
Understanding the temporal trend of the disease burden of stroke and its attributable risk factors in China, especially at provincial levels, is important for effective prevention strategies and ...improvement. The aim of this analysis from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) is to investigate the disease burden of stroke and its risk factors at national and provincial levels in China from 1990 to 2019.
Following the methodology in the GBD 2019, the incidence, prevalence, mortality, and disability-adjusted life-years (DALYs) of stroke cases in the Chinese population were estimated by sex, age, year, stroke subtypes (ischaemic stroke, intracerebral haemorrhage, and subarachnoid haemorrhage), and across 33 provincial administrative units in China from 1990 to 2019. Attributable mortality and DALYs of underlying risk factors were calculated by a comparative risk assessment.
In 2019, there were 3·94 million (95% uncertainty interval 3·43–4·58) new stroke cases in China. The incidence rate of stroke increased by 86·0% (73·2–99·0) from 1990, reaching 276·7 (241·3–322·0) per 100 000 population in 2019. The age-standardised incidence rate declined by 9·3% (3·3–15·5) from 1990 to 2019. Among 28·76 million (25·60–32·21) prevalent cases of stroke in 2019, 24·18 million (20·80–27·87) were ischaemic stroke, 4·36 million (3·69–5·05) were intracerebral haemorrhage, and 1·58 million (1·32–1·91) were subarachnoid haemorrhage. The prevalence rate increased by 106·0% (93·7–118·8) and age-standardised prevalence rate increased by 13·2% (7·7–19·1) from 1990 to 2019. In 2019, there were 2·19 million (1·89–2·51) deaths and 45·9 million (39·8–52·3) DALYs due to stroke. The mortality rate increased by 32·3% (8·6–59·0) from 1990 to 2019. Over the same period, the age-standardised mortality rate decreased by 39·8% (28·6–50·7) and the DALY rate decreased by 41·6% (30·7–50·9). High systolic blood pressure, ambient particulate matter pollution exposure, smoking, and diet high in sodium were four major risk factors for stroke burden in 2019. Moreover, we found marked differences of stroke burden and attributable risk factors across provinces in China from 1990 to 2019.
The disease burden of stroke is still severe in China, although the age-standardised incidence and mortality rates have decreased since 1990. The stroke burden in China might be reduced through blood pressure management, lifestyle interventions, and air pollution control. Moreover, because substantial heterogeneity of stroke burden existed in different provinces, improved health care is needed in provinces with heavy stroke burden.
National Key Research and Development Program of China and Taikang Yicai Public Health and Epidemic Control Fund.
•Long-term exposure to PM2.5 was associated with poor sleep quality (OR = 1.15).•Long-term exposure to NO2 was associated with poor sleep quality (OR = 1.14).•Exposure to air pollution showed adverse ...effects on sleep quality in rural China.•Health effects of air pollution in rural areas should be given more attention.
Poor sleep quality is associated with poor quality of life and may even lead to mental illnesses. Several studies have indicated the association between exposure to air pollution and sleep quality. However, the evidence is very limited in China, especially in rural areas.
Participants in this study were obtained from the Henan Rural Cohort established during 2015–2017. Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI) in the baseline survey. Poor sleep quality was defined by the global score of PSQI > 5. Participants’ exposures to PM2.5, PM10 (particulate matter with aerodynamic diameters ≤2.5 μm and 10 μm, respectively) and NO2 (nitrogen dioxide) during the three years before the baseline survey were estimated using a satellite-based prediction. The associations between long-term exposure to air pollutants and sleep quality were examined using both the linear regression and logistic regression models.
The IQRs (interquartile range) of mean levels of participants’ exposures to PM2.5, PM10 and NO2 were 3.3 µg/m3, 8.8 µg/m3, and 4.8 µg/m3, respectively. After adjusted for potential confounders, the global score of PSQI (and 95%CI, 95% confidence intervals) increased by 0.16 (0.04, 0.27), 0.09 (−0.01, 0.19) and 0.14 (0.03, 0.24), associated with per IQR increase in PM2.5, PM10 and NO2, respectively. The odds ratios (and 95%CI) of poor sleep quality associated with per IQR increase in PM2.5, PM10 and NO2 were 1.15 (1.03, 1.29), 1.11 (1.02, 1.21) and 1.14 (1.03, 1.25), respectively.
Long-term exposures to PM2.5, PM10 and NO2 were associated with poor sleep quality in rural China. Improvement of air quality may help to improve sleep quality among rural population of China.
Abstract
The purpose of this study was to examine the changes in severity of anxiety and depression symptoms, stress and sleeping quality after three months of mass quarantine for COVID-19 among ...undergraduate fresh students compared to their pre-COVID-19 measures. We used participants from the Chinese Undergraduate Cohort (CUC), a national prospective longitudinal study to examine the changes in anxiety and depression symptoms severity, stress and sleep quality after being under mass quarantine for three months. Wilcoxon matched pair signed-rank test was used to compare the lifestyle indicators. Severity of anxiety, depression symptoms, stress and sleep quality were compared with Wilcoxon signed-rank test. We used generalized estimating equation (GEE) to further quantify the change in mental health indicators and sleep quality after the COVID-19 mass quarantine compared to baseline. This study found that there was no deterioration in mental health status among Chinese new undergraduate students in 2020 after COVID-19 mass quarantine compared with the baseline measures in 2019. There was an improvement in sleep quality and anxiety symptoms. After adjusting for age, sex, exercise habit, time spent on mobile gadgets, and time spent outdoors, year 2020 was significantly associated with severity of depression symptoms in males (OR:1.52. 95%CI:1.05–2.20,
p
-value = 0.027). Year 2020 was significantly associated with the improvement of sleeping quality in total (OR:0.45, 95%CI:0.38–0.52,
p
< 0.001) and in all the subgroups. This longitudinal study found no deterioration in mental health status among Chinese new undergraduate students after three months of mass quarantine for COVID-19.
•Short-term increase in PM2.5, PM10, NO2, SO2 and O3 concentrations was associated with exacerbation of mental disorders.•The effects of NO2 were robust when controlling PM2.5 or PM10.•NO2 had more ...serious health threat than other pollutants.•O3 showed positive associations in female, in warm season.
To determine the associations between outdoor air pollution and hospital outpatient visits for mental disorders in China.
We obtained data of 111,842 hospital outpatient visits for mental disorders from the largest hospitals of 13 cities, China, between January 01, 2013 and December 31, 2015. We collected air pollutant data including particulate matter ≤2.5 µm in diameter (PM2.5), particulate matter ≤10 µm in diameter (PM10), nitrogen dioxide (NO2), ozone (O3) and sulphur dioxide (SO2) from China National Environmental Monitoring Centre during the same period. We conducted a time-stratified case-crossover design with conditional logistic regression models to determine the associations.
A 10 µg/m3 increase in PM2.5, PM10, NO2 and SO2 was associated with a significant increase in hospital outpatient visits for mental disorders on the current day. When stratified by age, sex and season, the effects of PM2.5 and NO2 were robust among different subgroups at lag05 days. PM10 showed positive associations in males, in cold season, and in depression patients. SO2 showed positive associations in males, in cold season, and in anxiety patients. O3 showed positive associations in females, in warm season, and in depression patients. Nearly one sixth hospital outpatient visits for mental disorders can be attributable to NO2.
Short-term increase in PM2.5, PM10, NO2, SO2 and O3 concentrations was significantly associated with exacerbation of mental disorders in China as indicated by increases in hospital outpatient visits. NO2 had more serious health threat than other pollutants in terms of mental disorders. Our findings strongly suggest a need for more strict emission control regulations to protect mental health from air pollution.
•Positive associations of air pollutants with MetS were found in rural Chinese adults.•Physical activity was negatively associated with MetS in rural Chinese adults.•Air pollutants attenuated the ...negative association of physical activity with MetS.
Long-term exposure to ambient air pollution and physical activity are linked to metabolic syndrome (MetS). However, the joint effect of physical activity and ambient air pollution on MetS remains largely unknown in rural Chinese adult population.
In this study, 39 089 individuals were included from the Henan Rural Cohort study that recruited 39 259 individuals at the baseline. Participants' exposure to air pollutants (including particulate matter with an aerodynamic diameter ≤ 1.0 µm (PM1), ≤2.5 µm (PM2.5), or ≤ 10 µm (PM10) and nitrogen dioxide (NO2)) were evaluated by using a spatiotemporal model based on satellites data. Individuals were defined as MetS according to the recommendation of the Joint Interim Societies. Physical activity-metabolic equivalent (MET) was calculated based on the formula of MET coefficient of activity × duration (hour per time) × frequency (times per week). Generalized linear models were used to analyze the individual air pollutant or physical activity and their interaction on MetS. Interaction effects of individual air pollutant and physical activity on MetS were assessed by using Interaction plots which exhibited the estimated effect of physical activity on MetS as a function of individual air pollutant.
The prevalence of MetS was 30.8%. The adjusted odd ratio of MetS with a per 5 µg/m3 increase in PM1, PM2.5, PM10, NO2 or a 10 MET (hour/day) of physical activity increment was 1.251(1.199, 1.306), 1.424(1.360, 1.491), 1.228(1.203, 1.254), 1.408(1.363, 1.455) or 0.814(0.796, 0.833). The protective effect of physical activity on MetS was decreased with accompanying air pollutant concentrations increased.
The results indicated that long-term exposure to ambient air pollutants related to increased risk of MetS and physical activity attenuated the effects of ambient air pollutants on increased risk for MetS.
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