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Abstract Background Due to the high prevalence of multimorbidity and realistic health service demands for fall prevention, there is growing interest in the association between multimorbidity and falls. Our study aimed to identify multimorbidity patterns among Chinese older adults and explore the association between multimorbidity patterns and falls. Methods Data from 4,579 Chinese community-dwelling older adults was included in this analysis. Information regarding falls and 10 chronic conditions was collected. An exploratory factor analysis was performed to determine multimorbidity patterns. Regression models were fitted to explore the associations of individual chronic disease or multimorbidity patterns with falls. Results Among 4,579 participants, 368 (8.0%) were defined as fallers, including 92 (2.0%) frequent fallers, and multimorbidity affected 2,503 (54.7%) participants. Older adults with multimorbidity were more likely to be fallers [odds ratio (OR) = 1.3, P = 0.02] and frequent fallers (OR = 1.7, P = 0.04). Three multimorbidity patterns were identified (i.e., cardiovascular-metabolic diseases, psycho-cognitive diseases and organic diseases), and the associations between psycho-cognitive diseases/organic diseases and prevalent falls or frequent falls were found to be significant. Conclusions The psycho-cognitive disease pattern and organic disease pattern are significantly associated with falls. Therefore, more attention should be paid to patients with psycho-cognitive diseases and timely, targeted diagnostic and treatment services should be provided in fall prevention.

Abstract BackgroundPersonality has an impact on the health-related quality of life (HRQoL) of older adults. However, the relationship and mechanisms of the 2 variables are controversial, and few studies have been conducted on older adults. ObjectiveThe aim of this study was to explore the relationship between personality and HRQoL and the mediating and moderating roles of sleep quality and place of residence in this relationship. MethodsA total of 4123 adults 60 years and older were from the Psychology and Behavior Investigation of Chinese Residents survey. Participants were asked to complete the Big Five Inventory, the Brief version of the Pittsburgh Sleep Quality Index, and EQ-5D-5L. A backpropagation neural network was used to explore the order of factors contributing to HRQoL. Path analysis was performed to evaluate the mediation hypothesis. ResultsAs of August 31, 2022, we enrolled 4123 older adults 60 years and older. Neuroticism and extraversion were strong influencing factors of HRQoL (normalized importance >50%). The results of the mediation analysis suggested that neuroticism and extraversion may enhance and diminish, respectively, HRQoL (index: β=−.262, PPPPPPP ConclusionsThis study sheds light on the potential mechanisms of personality and HRQoL among older Chinese adults and can help health care providers and relevant departments take reasonable measures to promote healthy aging.

BackgroundThe increasing prevalence of nonalcoholic fatty liver disease (NAFLD) in China presents a significant public health concern. Traditional ultrasound, commonly used for fatty liver screening, often lacks the ability to accurately quantify steatosis, leading to insufficient follow-up for patients with moderate-to-severe steatosis. Transient elastography (TE) provides a more quantitative diagnosis of steatosis and fibrosis, closely aligning with biopsy results. Moreover, machine learning (ML) technology holds promise for developing more precise diagnostic models for NAFLD using a variety of laboratory indicators. ObjectiveThis study aims to develop a novel ML-based diagnostic model leveraging TE results for staging hepatic steatosis. The objective was to streamline the model’s input features, creating a cost-effective and user-friendly tool to distinguish patients with NAFLD requiring follow-up. This innovative approach merges TE and ML to enhance diagnostic accuracy and efficiency in NAFLD assessment. MethodsThe study involved a comprehensive analysis of health examination records from Suzhou Municipal Hospital, spanning from March to May 2023. Patient data and questionnaire responses were meticulously inputted into Microsoft Excel 2019, followed by thorough data cleaning and model development using Python 3.7, with libraries scikit-learn and numpy to ensure data accuracy. A cohort comprising 978 residents with complete medical records and TE results was included for analysis. Various classification models, including logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost), were constructed and evaluated based on the area under the receiver operating characteristic curve (AUROC). ResultsAmong the 916 patients included in the study, 273 were diagnosed with moderate-to-severe NAFLD. The concordance rate between traditional ultrasound and TE for detecting moderate-to-severe NAFLD was 84.6% (231/273). The AUROC values for the RF, LightGBM, XGBoost, SVM, KNN, and LR models were 0.91, 0.86, 0.83, 0.88, 0.77, and 0.81, respectively. These models achieved accuracy rates of 84%, 81%, 78%, 81%, 76%, and 77%, respectively. Notably, the RF model exhibited the best performance. A simplified RF model was developed with an AUROC of 0.88, featuring 62% sensitivity and 90% specificity. This simplified model used 6 key features: waist circumference, BMI, fasting plasma glucose, uric acid, total bilirubin, and high-sensitivity C-reactive protein. This approach offers a cost-effective and user-friendly tool while streamlining feature acquisition for training purposes. ConclusionsThe study introduces a groundbreaking, cost-effective ML algorithm that leverages health examination data for identifying moderate-to-severe NAFLD. This model has the potential to significantly impact public health by enabling targeted investigations and interventions for NAFLD. By integrating TE and ML technologies, the study showcases innovative approaches to advancing NAFLD diagnostics.

The widespread use of pesticides performs a vital role in safeguarding crop yields and quality, providing the opportunity for multiple pesticides to co-exist, which poses a significant potential risk to human health. To assess the toxic effects caused by exposures to individual pesticides (chlorpyrifos, carbofuran and acetamiprid), binary combinations and ternary combinations, individual and combined exposure models were developed using HepG2 cells and the types of combined effects of pesticide mixtures were assessed using concentration addition (CA), independent action (IA) and combination index (CI) models, respectively, and the expression of biomarkers related to oxidative stress, apoptosis and cell necrosis was further examined. Our results showed that both individual pesticides and mixtures exerted toxic effects on HepG2 cells. The CI model indicated that the toxic effects of pesticide mixtures exhibited synergistic effects. The results of the lactate dehydrogenase (LDH) release and apoptosis assay revealed that the pesticide mixture increased the release of LDH and apoptosis levels. Moreover, our results also showed that individual pesticides and mixtures disrupted redox homeostasis and that pesticide mixtures produced more intense oxidative stress effects. In conclusion, we have illustrated the enhanced combined toxicity of pesticide mixtures by in-vitro experiments, which provides a theoretical basis and scientific basis for further toxicological studies.

Obesity, especially central obesity, is a strong risk factor for developing type 2 diabetes (T2D). However, the mechanism underlying the progression from central obesity to T2D remains unknown. Therefore, we analyzed the gut microbial profiles of central obese individuals with or without T2D from a Chinese population. Here we reported both the microbial compositional and gene functional alterations during the progression from central obesity to T2D. Several opportunistic pathogens were enriched in obese T2D patients. We also characterized thousands of genes involved in sugar and amino acid metabolism whose abundance were significantly depleted in obese T2D group. Moreover, the abundance of those genes was negatively associated with plasma glycemia level and percentage of individuals with impaired plasma glucose status. Therefore, our study indicates that the abundance of those depleted genes can be used as a potential biomarker to identify central obese individuals with high risks of developing T2D.