The Chasm in Percutaneous Coronary Intervention and In-Hospital Mortality Rates Among Acute Myocardial Infarction Patients in Rural and Urban Hospitals in China: A Mediation Analysis

Aldehyde Dehydrogenase 2 rs671 G/A and a/A Genotypes are Associated with the Risk of Acute Myocardial Infarction

Background

Aldehyde dehydrogenase 2 (ALDH2) is a key catalytic enzyme involved in the aldehyde metabolism that plays an important role in the occurrence and development of acute myocardial infarction (AMI). However, the relationship of ALDH2 polymorphism and susceptibility to AMI may differ among different regions and populations, and it has not yet been reported in Hakka population. The purpose of the present study was to investigate it in this population.

Methods

Four hundred and nineteen AMI patients and 636 individuals without AMI were included in the present study. The ALDH2 rs671 polymorphism was genotyped using polymerase chain reaction (PCR)-microarray. Differences in ALDH2 rs671 genotypes and alleles between patients and controls were compared, and the relationship between ALDH2 rs671 genotypes and AMI risk was analyzed.

Results

Patients with AMI had a lower frequency of ALDH2 rs671 G/G genotype (43.2% vs 52.7%, p=0.003), and a higher G/A genotype (45.6% vs 38.5%, p=0.025) than controls. And AMI patients had a lower frequency of ALDH2 rs671 G allele (66.0% vs 71.9%), and a higher A allele (34.0% vs 28.1%) (p=0.004) than controls. Logistic regression analysis showed that overweight (body mass index (BMI)≥24.0 kg/m2 vs BMI 18.5-23.9 kg/m2: odds ratio (OR) 2.046, 95% confidence interval (CI): 1.520-2.754, p<0.001), history of hypertension (yes vs no: OR 3.464, 95% CI: 2.515-4.770, p<0.001), ALDH2 rs671 G/A genotype (G/A vs G/G: OR 1.476, 95% CI: 1.102-1.976, p=0.009), and A/A genotype (A/A vs G/G: OR 1.656, 95% CI: 1.027-2.668, p=0.038) maybe the independent risk factors for AMI.

Conclusion

Overweight (BMI≥24.0 kg/m2), a history of hypertension, and ALDH2 rs671 G/A or A/A genotypes increased the risk of developing AMI in Hakka population.

Ambient fine particulate matter chemical composition associated with in-hospital case fatality, hospital expenses, and length of hospital stay among patients with heart failure in China

*Joint senior authorship.

BACKGROUND

Previous studies have observed the adverse effects of ambient fine particulate matter pollution (PM2.5) on heart failure (HF). However, evidence regarding the impacts of specific PM2.5 components remains scarce.

METHODS

We included 58 129 patients hospitalised for HF between 2013 and 2017 in 11 cities of Shanxi, China from inpatient discharge database. We evaluated exposure to PM2.5 and its components ((sulphate (SO42-), nitrate (NO3-), ammonium (NH4+), organic matter (OM) and black carbon (BC)), along with meteorological factors using bilinear interpolation at each patients' residential address. We used multivariable logistic and linear regression models to assess the associations of these components with in-hospital case fatality, hospital expenses, and length of hospital stay.

RESULTS

Increase equivalents to the interquartile range (IQR) in OM (odds ratio (OR) = 1.13; 95% confidence interval (CI) = 1.02, 1.26) and BC (OR = 1.14; 95% CI = 1.02, 1.26) were linked to in-hospital case fatality. Per IQR increments in PM2.5, SO42-, NO3-, OM, and BC were associated with cost increases of 420.62 (95% CI = 285.75, 555.49), 221.83 (95% CI = 96.95, 346.71), 214.93 (95% CI = 68.66, 361.21), 300.06 (95% CI = 176.96, 423.16), and 303.09 (95% CI = 180.76, 425.42) CNY. Increases of 1 IQR in PM2.5, SO42-, OM, and BC were associated with increases in length of hospital stay of 0.10 (95% CI = 0.02, 0.19), 0.09 (95% CI = 0.02, 0.17), 0.10 (95% CI = 0.03, 0.17), and 0.16 (95% CI = 0.08, 0.23) days.

CONCLUSIONS

Our findings suggest that ambient SO42-, OM, and BC might be significant risk factors for HF, emphasising the importance of formulating customised guidelines for the chemical constituents of PM and controlling the emissions of the most dangerous components.

Predicting preterm birth using auto-ML frameworks: a large observational study using electronic inpatient discharge data

Background

To develop and compare different AutoML frameworks and machine learning models to predict premature birth.

Methods

The study used a large electronic medical record database to include 715,962 participants who had the principal diagnosis code of childbirth. Three Automatic Machine Learning (AutoML) were used to construct machine learning models including tree-based models, ensembled models, and deep neural networks on the training sample (N = 536,971). The area under the curve (AUC) and training times were used to assess the performance of the prediction models, and feature importance was computed via permutation-shuffling.

Results

The H2O AutoML framework had the highest median AUC of 0.846, followed by AutoGluon (median AUC: 0.840) and Auto-sklearn (median AUC: 0.820), and the median training time was the lowest for H2O AutoML (0.14 min), followed by AutoGluon (0.16 min) and Auto-sklearn (4.33 min). Among different types of machine learning models, the Gradient Boosting Machines (GBM) or Extreme Gradient Boosting (XGBoost), stacked ensemble, and random forrest models had better predictive performance, with median AUC scores being 0.846, 0.846, and 0.842, respectively. Important features related to preterm birth included premature rupture of membrane (PROM), incompetent cervix, occupation, and preeclampsia.

Conclusions

Our study highlights the potential of machine learning models in predicting the risk of preterm birth using readily available electronic medical record data, which have significant implications for improving prenatal care and outcomes.

Ambient particulate matter and in-hospital case fatality of acute myocardial infarction: A multi-province cross-sectional study in China

The acute myocardial infarction (AMI) outcomes have been extensively linked with ambient particulate matter (PM). However, whether a smaller particle has greater impact and the consequent attributable burden associated with PM of different sizes remain unclear. We conducted a multi-province cross-sectional study among AMI patients using the inpatient discharge datasets from four Chinese provinces (Shanxi, Sichuan, Guangxi, and Guangdong) from 2014 to 2019. Ambient PM exposure for each patient was assessed using the ChinaHighAirPollutants dataset. We employed the mixed-effects logistic regression models to evaluate the association of PM of different sizes (PM1, PM2.5, PM10) on in-hospital case fatality. The potential reducible fractions in in-hospital case fatality were estimated through counterfactual analyses. Of 177,749 participants, 125,501 (70.6 %) were male and the in-hospital case fatality rate was 4.9%. For short-term (7-day average) exposure, the odds ratios (ORs) for PM1, PM2.5, and PM10 (per 10 µg/m3) were 1.052 (95 % confidence interval [CI], 1.032-1.071), 1.026 (95 % CI, 1.014-1.037), and 1.016 (95% CI, 1.008-1.024), respectively. The estimated ORs for long-term exposure (annual average) were 1.303 (95 % CI, 1.252-1.356) for PM1, 1.209 (95 % CI, 1.178-1.241) for PM2.5, 1.157 (95 % CI, 1.134-1.181) for PM10. Short-term exposure to PM1 showed the highest potential reducible fraction (8.5 %, 95 % CI, 5.0-11.7 %), followed by PM2.5 and PM10, while the greatest potential reducible fraction of long-term exposure was observed in PM10 (30.9 %, 95 % CI, 27.2-34.4%), followed by PM2.5 and PM1. In summary, PM with smaller size had a more pronounced impact on in-hospital AMI case fatality, with PM1 exhibiting greater effects than PM2.5 and PM10. Substantial health benefits for AMI patients could be achieved by mitigating ambient PM exposure.

Short-term air pollution exposure associated with death from kidney diseases: a nationwide time-stratified case-crossover study in China from 2015 to 2019

BACKGROUND

Long-term exposure to air pollution has been associated with the onset and progression of kidney diseases, but the association between short-term exposure to air pollution and mortality of kidney diseases has not yet been reported.

METHODS

A nationally representative sample of 101,919 deaths from kidney diseases was collected from the Chinese Center for Disease Control and Prevention from 2015 to 2019. A time-stratified case-crossover study was applied to determine the associations. Satellite-based estimates of air pollution were assigned to each case and control day using a bilinear interpolation approach and geo-coded residential addresses. Conditional logistic regression models were constructed to estimate the associations adjusting for nonlinear splines of temperature and relative humidity.

RESULTS

Each 10 µg/m3 increment in lag 0-1 mean concentrations of air pollutants was associated with a percent increase in death from kidney disease: 1.33% (95% confidence interval [CI]: 0.57% to 2.1%) for PM1, 0.49% (95% CI: 0.10% to 0.88%) for PM2.5, 0.32% (95% CI: 0.08% to 0.57%) for PM10, 1.26% (95% CI: 0.29% to 2.24%) for NO2, and 2.9% (95% CI: 1.68% to 4.15%) for SO2.  CONCLUSIONS: Our study suggests that short-term exposure to ambient PM1, PM2.5, PM10, NO2, and SO2 might be important environmental risk factors for death due to kidney diseases in China.

Ambient particulate matter pollution of different sizes associated with recurrent stroke hospitalization in China: A cohort study of 1.07 million stroke patients

BACKGROUND

To estimate the associations between ambient particulate matter (PM) pollution of different sizes (PM₁, PM_2.5, and PM₁₀) and risk of rehospitalization among stroke patients, as well as the attributable burden in China.

METHODS

We built a cohort of 1,066,752 participants with an index stroke hospitalization in Sichuan, China from 2017 to 2019. Seven-day and annual average exposures to PM pollution prior to the date of the index hospitalization were linked with residential address using a bilinear interpolation approach. Cox proportional hazard models were constructed to assess the association between ambient PM and the risk of rehospitalization. The burden of stroke rehospitalization was estimated using a counterfactual approach.

RESULTS

245,457 (23.0 %) participants experienced rehospitalization during a mean of 1.15 years (SD: 0.90 years) of follow-up. Seven-day average concentrations of PM were associated with increased risk of rehospitalization: the hazard ratios (HRs) per 10 μg/m³ were 1.034 (95 % confidence interval [CI]: 1.029-1.038) for PM₁, 1.033 (1.031-1.036) for PM_2.5, and 1.030 (1.028-1.031) for PM₁₀; the hazard ratios were larger for annual average concentrations: 1.082 (1.074-1.090) for PM₁, 1.109 (1.104-1.114) for PM_2.5, and 1.103 (1.099-1.106) for PM₁₀. The associations were stronger in participants who were female, of minority ethnicity (non-Han Chinese), who suffered from an ischemic stroke, and those admitted under normal conditions. Population attributable fractions for stroke rehospitalization ranged from 4.66 % (95 % CI: 1.69 % to 7.63 %) for the 7-day average of PM₁ to 17.05 % (14.27 % to 19.83 %) for the annual average of PM₁₀; the reducible average cost of rehospitalization per participant attributable to PM ranged from 492.09 (178.19 to 806) RMB for the 7-day average of PM₁ to 1801.65 (1507.89 to 2095.41) RMB for the annual average of PM₁₀.

CONCLUSIONS

Ambient PM pollution may increase the risk of rehospitalization in stroke patients and is responsible for a significant burden of stroke rehospitalization.

Long-term exposure to ambient fine particulate matter chemical composition and in-hospital case fatality among patients with stroke in China

Background

There is little evidence on the association between PM_2.5 chemical components and fatality among hospitalized stroke patients.

Methods

This study used an inpatient discharge database from 2013 to 2019 in four provinces (Sichuan, Shanxi, Guangxi, and Guangdong) in China. Annual average exposure to PM_2.5 and its five chemical components [black carbon (BC), organic matter (OM), sulphate ( S O 4 2 - ), nitrate ( N O 3 - ), and ammonium ( N H 4 + )] were estimated using bilinear interpolation at patient's residential address. Mixed-effects logistic regression models were conducted to estimate the odds ratios (ORs). Counterfactual analyses were used to estimate the population attributable burden (PAF).

Findings

Among 3,069,093 hospitalized patients with stroke, each interquartile (IQR) increment in PM_2.5 and its chemical components was significantly associated with stroke fatality: the ORs were 1.137 [95% confidence interval (CI): 1.118-1.157; IQR: 15.14 μg/m³] for PM_2.5, 1.108 (95% CI: 1.091-1.126; IQR: 0.71 μg/m³) for BC, 1.086 (95% CI: 1.069-1.104; IQR: 3.47 μg/m³) for OM, and 1.065 (95% CI: 1.048-1.083; IQR: 2.81 μg/m³) for S O 4 2 - . We did not find significant associations for N O 3 - (OR: 0.991, 95% CI: 0.975-1.008; IQR: 3.30 μg/m³). The associations were larger among patients with ischemic stroke than those with hemorrhagic stroke. The PAFs were 10.6% (95% CI: 9.1-12.2%) for BC, 9.9% (95% CI: 8.2-11.7%) for OM, and 6.6% (4.9-8.3%) for S O 4 2 - .

Interpretation

Ambient BC, OM, and S O 4 2 - might be important risk factors for stroke fatality. The findings advocate the need to develop tailored guidelines for PM chemical components and curb the emissions of the most hazardous chemical components.

Funding

Bill & Melinda Gates Foundation (INV-016826).

Association between sedentary time and metabolic syndrome: A cross-sectional study among Chinese Garze Tibetans

Background

Chinese Tibetans have long hours of sitting without much physical activity given their religious behavior, raising potential harmful health hazards. However, the relationship between sedentary time and metabolic syndrome (MetS) has not been investigated in Chinese Tibetans.

Methods

From Jan 2021 to Jun 2022, residents in Garze Tibetan Autonomous Prefecture in Southwest China's Sichuan province were recruited using a multi-stage, stratified, random-cluster sampling strategy. MetS were ascertained using definition proposed by the International Diabetes Federation. Associations between sedentary time and the prevalence of MetS in the total sample and by age and sex were estimated using logistic regression models.

Results

Among 971 Chinese Tibetan participants (mean age 41.1 years and 73.8% female), 319 (32.9%) were diagnosed as having MetS. We found positive associations of sedentary time over 11 h per day with the prevalence of MetS in crude (OR: 1.23; 95% CI: 1.12-1.36, p < 0.001), age and sex adjusted (OR: 1.18; 95% CI: 1.08-1.29, p < 0.001), and fully adjusted (OR: 1.17; 95% CI: 1.08-1.29, p < 0.001) models, compared to those who had <8 h of sedentary time per day. Sensitivity analyses suggest consistent positive association between sedentary time and each metric of MetS.

Conclusions

Sedentary time longer than 11 h per day is significantly associated with increased risk of MetS, suggesting that polices to advocate health education may alleviate the health burden of MetS among Tibetans in China.