Biomedical and environmental sciences : BES最新文献

Objective: To investigate the associations between eight serum per- and polyfluoroalkyl substances (PFASs) and regional fat depots, we analyzed the data from the National Health and Nutrition Examination Survey (NHANES) 2011-2018 cycles.

Methods: Multiple linear regression models were developed to explore the associations between serum PFAS concentrations and six fat compositions along with a fat distribution score created by summing the concentrations of the six fat compositions. The associations between structurally grouped PFASs and fat distribution were assessed, and a prediction model was developed to estimate the ability of PFAS exposure to predict obesity risk.

Results: Among females aged 39-59 years, trunk fat mass was positively associated with perfluorooctane sulfonate (PFOS). Higher concentrations of PFOS, perfluorohexane sulfonate (PFHxS), perfluorodecanoate (PFDeA), perfluorononanoate (PFNA), and n-perfluorooctanoate (n-PFOA) were linked to greater visceral adipose tissue in this group. In men, exposure to total perfluoroalkane sulfonates (PFSAs) and long-chain PFSAs was associated with reductions in abdominal fat, while higher abdominal fat in women aged 39-59 years was associated with short-chain PFSAs. The prediction model demonstrated high accuracy, with an area under the curve (AUC) of 0.9925 for predicting obesity risk.

Conclusion: PFAS exposure is associated with regional fat distribution, with varying effects based on age, sex, and PFAS structure. The findings highlight the potential role of PFAS exposure in influencing fat depots and obesity risk, with significant implications for public health. The prediction model provides a highly accurate tool for assessing obesity risk related to PFAS exposure.

Objective: The relationship between non-high-density lipoprotein (NHDL) cholesterol to high-density lipoprotein cholesterol (HDL-C) ratio (NHHR) and stoke remains unknown. This study aimed to evaluate the association between the adult NHHR and stroke occurrence in the United States of America (USA).

Methods: To clarify the relationship between the NHHR and stroke risk, this study used a multivariable logistic regression model and a restricted cubic spline (RCS) model to investigate the association between the NHHR and stroke, and data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018. Subgroup and sensitivity analyses were conducted to test the robustness of the results.

Results: This study included 29,928 adult participants, of which 1,165 participants had a history of stroke. Logistic regression analysis of variables demonstrated a positive association between NHHR and stroke ( OR 1.24, 95% CI: 1.03-1.50, P = 0.026). Compared with the lowest reference group of NHHR, participants in the second, third, and fourth quartile had a significantly increased risk of stroke after full adjustments ( OR: 1.35, 95% CI: 1.08-1.69) ( OR: 1.83, 95% CI: 1.42-2.36) ( OR: 2.04, 95% CI: 1.50-2.79). In the total population, a nonlinear dose-response relationship was observed between the NHHR and stroke risk ( P non-linearity = 0.002). This association remained significant in several subgroup analyses. Further investigation of the NHHR may enhance our understanding of stroke prevention and treatment.

Conclusion: Our findings suggest a positive correlation between the NHHR and an increased prevalence of stroke, potentially serving as a novel predictive factor for stroke. Timely intervention and management of the NHHR may effectively mitigate stroke occurrence. Prospective studies are required to validate this association and further explore the underlying biological mechanisms.

Objective: Observational studies have found associations between inflammatory bowel disease (IBD) and the risk of dementia, including Alzheimer's dementia (AD) and vascular dementia (VD); however, these findings are inconsistent. It remains unclear whether these associations are causal.

Methods: We conducted a meta-analysis by systematically searching for observational studies on the association between IBD and dementia. Mendelian randomization (MR) analysis based on summary genome-wide association studies (GWASs) was performed. Genetic correlation and Bayesian co-localization analyses were used to provide robust genetic evidence.

Results: Ten observational studies involving 80,565,688 participants were included in this meta-analysis. IBD was significantly associated with dementia (risk ratio [ RR] =1.36, 95% CI = 1.04-1.78; I ² = 84.8%) and VD ( RR = 2.60, 95% CI = 1.18-5.70; only one study), but not with AD ( RR = 2.00, 95% CI = 0.96-4.13; I ² = 99.8%). MR analyses did not supported significant causal associations of IBD with dementia (dementia: odds ratio [ OR] = 1.01, 95% CI = 0.98-1.03; AD: OR = 0.98, 95% CI = 0.95-1.01; VD: OR = 1.02, 95% CI = 0.97-1.07). In addition, genetic correlation and co-localization analyses did not reveal any genetic associations between IBD and dementia.

Conclusion: Our study did not provide genetic evidence for a causal association between IBD and dementia risk. The increased risk of dementia observed in observational studies may be attributed to unobserved confounding factors or detection bias.

Objective: The relationship between fish consumption and stroke is inconsistent, and it is uncertain whether this association varies across predicted stroke risks.

Methods: A cohort study comprising 95,800 participants from the Prediction for Atherosclerotic Cardiovascular Disease Risk in China project was conducted. A standardized questionnaire was used to collect data on fish consumption. Participants were stratified into low- and moderate-to-high-risk categories based on their 10-year stroke risk prediction scores. Hazard ratios ( HRs) and 95% confidence intervals ( CIs) were estimated using Cox proportional hazard models and additive interaction by relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (SI).

Results: During 703,869 person-years of follow-up, 2,773 incident stroke events were identified. Higher fish consumption was associated with a lower risk of stroke, particularly among moderate-to-high-risk individuals ( HR = 0.53, 95% CI: 0.47-0.60) than among low-risk individuals ( HR = 0.64, 95% CI: 0.49-0.85). A significant additive interaction between fish consumption and predicted stroke risk was observed (RERI = 4.08, 95% CI: 2.80-5.36; SI = 1.64, 95% CI: 1.42-1.89; AP = 0.36, 95% CI: 0.28-0.43).

Conclusion: Higher fish consumption was associated with a lower risk of stroke, and this beneficial association was more pronounced in individuals with moderate-to-high stroke risk.

Objective: This study examines the sequential mediating roles of body pain and self-reported health in the association between sleep duration and self-reported life satisfaction among elderly Chinese adults.

Methods: Data from the fifth wave of the China Health and Retirement Longitudinal Survey (CHARLS) were used to analyse the relationships between sleep duration and body pain, self-reported health, and life satisfaction through logistic regression and Restricted Cubic Spline (RCS) analyses. The sequential mediation effects of body pain and self-reported health status were examined via chain mediation analysis.

Results: Logistic regression analysis showed that sleeping fewer than 6 hours or 6-7 hours was linked to higher risks of body pain, poor health, and dissatisfaction with life compared to sleeping 7-8 hours (all P < 0.05). Additionally, those sleeping more than 9 hours also had increased risks of poor health and dissatisfaction with life compared to those sleeping 7-8 hours (all P < 0.05). Chain mediation analysis showed that body pain and self-reported health status sequentially mediated 46.15% of the association between sleep duration and life satisfaction.

Conclusion: Body pain and self-reported health may shape the relationship between sleep duration and life satisfaction in elderly Chinese adults.

Objective: To establish and validate a novel diabetic retinopathy (DR) risk-prediction model using a whole-exome sequencing (WES)-based machine learning (ML) method.

Methods: WES was performed to identify potential single nucleotide polymorphism (SNP) or mutation sites in a DR pedigree comprising 10 members. A prediction model was established and validated in a cohort of 420 type 2 diabetic patients based on both genetic and demographic features. The contribution of each feature was assessed using Shapley Additive explanation analysis. The efficacies of the models with and without SNP were compared.

Results: WES revealed that seven SNPs/mutations ( rs116911833 in TRIM7, 1997T>C in LRBA, 1643T>C in PRMT10, rs117858678 in C9orf152, rs201922794 in CLDN25, rs146694895 in SH3GLB2, and rs201407189 in FANCC) were associated with DR. Notably, the model including rs146694895 and rs201407189 achieved better performance in predicting DR (accuracy: 80.2%; sensitivity: 83.3%; specificity: 76.7%; area under the receiver operating characteristic curve [AUC]: 80.0%) than the model without these SNPs (accuracy: 79.4%; sensitivity: 80.3%; specificity: 78.3%; AUC: 79.3%).

Conclusion: Novel SNP sites associated with DR were identified in the DR pedigree. Inclusion of rs146694895 and rs201407189 significantly enhanced the performance of the ML-based DR prediction model.