Biomedical and environmental sciences : BES最新文献

Objective: To assess relationships between cold spells and genitourinary hospitalization risk.

Methods: Hospitalization records for genitourinary system diseases (GUDs) from 16 districts in Beijing (2013-2018) were analyzed. Cold spells were defined based on varying intensity thresholds. A two-stage analytical method was employed: first, generalized linear models assessed district-specific associations between cold spells and hospitalizations; second, random-effects meta-analysis aggregated the district-level results. Subgroup analyses were performed by admission type (emergency vs. outpatient), age, and sex.

Results: A total of 271,579 GUD-related hospitalizations were recorded. Cold spells (p1day2，daily mean temperature below the 1 ^st percentiles of the daily mean temperature distribution from January 1, 2013, to December 31, 2018, lasting for two or more consecutive days) were linked to a significant rise in hospitalization risks: 1.43 (95% CI: 1.32-1.56) for all GUDs, 1.35 (95% CI: 1.23-1.49) for urinary system diseases, and 1.46 (95% CI: 1.28-1.67) for renal failure, when compared to non-cold spell days. Emergency admissions showed higher risk increases than outpatient admissions.

Conclusion: Extreme cold spells significantly elevate hospitalization risks for GUDs. This highlights the urgent need for targeted public health interventions to mitigate cold-related health impacts, especially for vulnerable populations.

Ambient air pollution is increasingly being recognized as a risk factor for heart failure; however, its effects on cardiac biomarkers remain unclear. This scoping review assessed the existing evidence on the association between air pollution and cardiac biomarkers in heart failure, described the key concepts, synthesized data, and identified research gaps. Following the PRISMA-ScR guidelines, PubMed, Embase, Web of Science, and CNKI databases were searched for studies on air pollution, heart failure, and biomarkers. A total of 765 records were screened, and 81 full texts were assessed for eligibility, resulting in 15 studies. The results showed that the exposure to particulate matter was associated with elevated N-terminal pro-B-type natriuretic peptide and troponin levels. Several studies have linked particulate matter exposure to a higher cardiovascular risk and heart failure biomarkers. Inflammatory and oxidative stress markers were consistently elevated across studies, supporting the biological relevance of these associations. However, few studies have focused specifically on populations with heart failure or clinically relevant biomarkers, and the evidence for gaseous pollutants remains inconclusive. These findings highlight the need to integrate environmental risk assessment into heart failure care and inform policy efforts to reduce the pollution-related cardiovascular burden. Further research should address these gaps through improved exposure assessments and the integration of mechanistic evidence.

Objective: To assess the short-term lag effects of climate and air pollution on hospital admissions for cardiovascular and respiratory diseases, and to develop deep learning-based models for daily hospital admission prediction.

Methods: A multi-city study was conducted in Tokyo's 23 wards, Osaka City, and Nagoya City. Random forest models were employed to assess the synergistic short-term lag effects (lag0, lag3, and lag7) of climate and air pollutants on hospitalization for five cardiovascular diseases (CVDs) and two respiratory diseases (RDs). Furthermore, we developed hybrid deep learning models that integrated an autoencoder (AE) with a Long Short-Term Memory network (AE+LSTM) to predict daily hospital admissions.

Results: On the day of exposure (lag0), air pollutants, particularly nitrogen oxides (NO x), exhibited the strongest influence on hospital admissions for CVD and RD, with pronounced effects observed for hypertension (I10-I15), ischemic heart disease (I20), arterial and capillary diseases (I70-I79), and lower respiratory infections (J20-J22 and J40-J47). At longer lags (lag3 and lag7), temperature and precipitation were more influential predictors. The AE+LSTM model outperformed the standard LSTM, improving the prediction accuracy by 32.4% for RD in Osaka and 20.94% for CVD in Nagoya.

Conclusion: Our findings reveal the dynamic, time-varying health risks associated with environmental exposure and demonstrate the utility of deep learnings in predicting short-term hospital admissions. This framework can inform early warning systems, enhance healthcare resource allocation, and support climate-adaptive public health strategies.

Objective: To investigate the association between long-term exposure to ambient fine particulate matter (PM _2.5) and its constituents and the risk of incident type 2 diabetes mellitus (T2DM), and to examine the modification roles of overweight status.

Methods: This prospective study included 27,507 adults living in rural China. The annual mean residential exposure to PM _2.5 and its constituents was estimated using a satellite-based statistical model. Cox models were used to estimate the risk of T2DM associated with PM _2.5 and its constituents. Stratified analysis quantified the role of overweight status in the association between PM _2.5 constituents and T2DM.

Results: Over a median follow-up of 9.4 years, 3,001 new T2DM cases were identified. The hazard ratio ( HR) for a 10 μg/m ³ increase in ambient PM _2.5 was 1.30 (95% confidence interval [ CI]: 1.17, 1.45). Among the constituents, the strongest association was observed with black carbon. Being overweight significantly modified the association between certain constituents and the risk of T2DM. Participants who were overweight and exposed to the highest quartile of PM _2.5 constituents had the highest risk of T2DM ( HR: 2.46, 95% CI: 2.04, 2.97).

Conclusions: Our findings indicate that PM _2.5 was associated with an increased risk of T2DM, with black carbon potentially being the primary contributor. Being overweight appeared to enhance the association between PM _2.5 and T2DM. This suggests that controlling both PM _2.5 exposure and overweight status may reduce the burden of T2DM.