DNA damage in inhabitants exposed to heavy metals near Hudiara drain, Lahore, Pakistan.

The current study was conducted on the inhabitants living in the area adjacent to the Hudiara drain using bore water and vegetables adjacent to the Hudiara drain. Toxic heavy metals badly affect human health because of industrial environmental contamination. Particularly hundreds of millions of individuals globally have faced the consequences of consuming water and food tainted with pollutants. Concentrations of heavy metals in human blood were elevated in Hudiara drainings in Lahore city, Pakistan, due to highly polluted industrial effluents. The study determined the health effects of high levels of heavy metals (Cd, Cu, Zn, Fe, Pb, Ni, Hg, Cr) on residents of the Hudiara draining area, including serum MDA, 8-Isoprostane, 8-hydroxyguanosine, and creatinine levels. An absorption spectrophotometer was used to determine heavy metals in wate water, drinking water, soil, plants and human beings blood sampleas and ELISA kits were used to assess the level of 8-hydroxyguanosine, MDA, 8-Isoprostane in plasma serum creatinine level. Waste water samples, irrigation water samples, drinking water samples, Soil samples, Plants samples and blood specimens of adult of different weights and ages were collected from the polluted area of the Hudiara drain (Laloo and Mohanwal), and control samples were obtained from the unpolluted site Sheiikhpura, 60 km away from the site. Toxic heavy metals in blood damage the cell membrane and DNA structures, increasing the 8-hydroxyguanosine, MDA, creatinine, and 8-Isoprostane. Toxic metals contaminated bore water and vegetables, resulting in increased levels of creatinine, MDA, Isoprostane, and 8-hydroxy-2-guanosine in the blood of inhabitants from the adjacent area Hudiara drain compared to the control group. In addition,. This study also investigated heavy metal concentrations in meat and milk samples from buffaloes, cows, and goats. In meat, cow samples showed the highest Cd, Cu, Fe and Mn concentrations. In milk also, cows exhibited elevated Cu and Fe levels compared to goats. The results highlight species-specific variations in heavy metal accumulation, emphasizing the need for targeted monitoring to address potential health risks. The significant difference between the two groups i.e., the control group and the affected group, in all traits of the respondents (weight, age, heavy metal values MDA, 8-Isoprostane, 8-hydroxyguaniosine, and serum creatinine level). Pearson's correlation coefficient was calculated. The study has shown that the level of serum MDA, 8-Isoprostane, 8-hydroxyguaniosine, or creatinine has not significantly correlated with age, so it is independent of age. This study has proved that in Pakistan, the selected area of Lahore in the villages of Laloo and Mohanwal, excess of heavy metals in the human body damages the DNA and increases the level of 8-Isoprostane, MDA, creatinine, and 8-hydroxyguaniosine. As a result, National and international cooperation must take major steps to control exposure to heavy metals.

Occurrence, distribution, and ecological risk assessment of heavy metals in Chao Phraya River, Thailand.

Understanding heavy metals in rivers is crucial, as their presence and distribution impact water quality, ecosystem health, and human well-being. This study examined the presence and levels of nine heavy metals (Cd, Cr, Cu, Fe, Hg, Mn, Ni, Pb, and Zn) in 16 surface water samples along the Chao Phraya River, identifying Fe, Mn, Zn, and Cr as predominant metals. Although average concentrations in both rainy and dry seasons generally adhered to WHO guidelines, Mn exceeded these limits yet remained within Thailand's acceptable standards. Seasonal variations were observed in the Chao Phraya River, and Spearman's correlation coefficient analysis established significant associations between season and concentrations of heavy metals. The water quality index (WQI) demonstrated varied water quality statuses at each sampling point along the Chao Phraya River, indicating poor conditions during the rainy season, further deteriorating to very poor conditions in the dry season. The hazard potential index (HPI) was employed to assess heavy metal contamination, revealing that during the dry season in the estuary area, the HPI value exceeded the critical threshold index, indicating the presence of heavy metal pollution in the water and unsuitable for consumption. Using the species sensitivity distribution model, an ecological risk assessment ranked the heavy metals' HC5 values as Pb > Zn > Cr > Cu > Hg > Cd > Ni, identifying nickel as the most detrimental and lead as the least toxic. Despite Cr and Zn showing a moderate risk, and Cu and Ni posing a high risk to aquatic organisms, the main contributors to ecological risk were identified as Cu, Ni, and Zn, suggesting a significant potential ecological risk in the Chao Phraya River's surface water. The results of this study provide fundamental insights that can direct future actions in preventing and managing heavy metal pollution in the river ecosystem.

Landscape and risk assessment of microplastic contamination in farmed oysters and seawater along the coastline of China.

Microplastic (MP) pollution poses a significant threat to marine ecosystem and seafood safety. However, comprehensive and comparable assessments of MP profiles and their ecological and health in Chinese farming oysters are lacking. This study utilized laser infrared imaging spectrometer (LDIR) to quantify MPs in oysters and its farming seawater at 18 sites along Chinese coastlines. Results revealed a total of 3492 MPs in farmed oysters and seawater, representing 34 MP types, with 20-100 µm MP fragments being the dominant. Polyurethane (PU) emerged as the predominant MP type in oysters, while polysulfones were more commonly detected in seawater. Notably, oysters from the Bohai Sea exhibited a higher abundance of MPs (13.62 ± 2.02 items/g) and estimated daily microplastic intake (EDI, 2.14 ± 0.26 items/g/kg·bw/day), indicating a greater potential health risk in the area. Meanwhile, seawater from the Yellow Sea displayed a higher level (193.0 ± 110.7 items/L), indicating a greater ecological risk in this region. Given the pervasiveness and abundance of PU and its high correlation with other MP types, we proposed PU as a promising indicator for monitoring and assessing the risk MP pollution in mariculture in China. These findings provide valuable insights into the extent and characteristics of MP pollution in farmed oysters and seawater in China.

A Review of Recent Low-dose Research and Recommendations for Moving Forward.

ABSTRACT: The linear no-threshold (LNT) model has been the regulatory "law of the land" for decades. Despite the long-standing use of LNT, there is significant ongoing scientific disagreement on the applicability of LNT to low-dose radiation risk. A review of the low-dose risk literature of the last 10 y does not provide a clear answer, but rather the body of literature seems to be split between LNT, non-linear risk functions (e.g., supra- or sub-linear), and hormetic models. Furthermore, recent studies have started to explore whether radiation can play a role in the development of several non-cancer effects, such as heart disease, Parkinson's disease, and diabetes, the mechanisms of which are still being explored. Based on this review, there is insufficient evidence to replace LNT as the regulatory model despite the fact that it contributes to public radiophobia, unpreparedness in radiation emergency response, and extreme cleanup costs both following radiological or nuclear incidents and for routine decommissioning of nuclear power plants. Rather, additional research is needed to further understand the implications of low doses of radiation. The authors present an approach to meaningfully contribute to the science of low-dose research that incorporates machine learning and Edisonian approaches to data analysis.

Replace the Linear No-threshold Model with a Risk-informed Targeted Approach to Radiation Protection.

ABSTRACT: The linear no-threshold (LNT) model may be useful as a simple basis for developing radiation protection regulations and standards, but it bears little resemblance to scientific reality and is probably overly conservative at low doses and low dose rates. This paper is an appeal for a broader view of radiation protection that involves more than just optimization of radiation dose. It is suggested that the LNT model should be replaced with a risk-informed, targeted approach to limitation of overall risks, which include radiation and other types of risks and accidents/incidents. The focus should be on protection of the individual. Limitation of overall risk does not necessarily always equate to minimization of individual or collective doses, but in some cases it might. Instead, risk assessment (hazards analysis) should be performed for each facility/and or specific job or operation (straightforward for specialized work such as radiography), and this should guide how limited resources are used to protect workers and the public. A graded approach could be used to prioritize the most significant risks and identify exposure scenarios that are unlikely or non-existent. The dose limits would then represent an acceptable level of risk, below which no further reduction in dose would be needed. Less resources should be spent on ALARA and tracking small individual and collective doses. Present dose limits are thought to be conservative and should suffice in general. Two exceptions are possibly the need for a lower (lifetime) dose limit for lens of the eye for astronauts and raising the public limit to 5 mSv y -1 from 1 mSv y -1 . This would harmonize the public limit with the current limit for the embryo fetus of the declared pregnant worker. Eight case studies are presented that emphasize how diverse and complex radiation risks can be, and in some cases, chemical and industrial risks outweigh radiation risks. More focus is needed on prevention of accidents and incidents involving a variety of types of risks. A targeted approach is needed, and commitments should be complied with until they are changed or exemptions are granted. No criticism of regulators or nuclear industry personnel is intended here. Protection of workers and the public is everyone's goal. The question is how best to accomplish that.

A Revised System of Radiological Protection Is Needed.

ABSTRACT: The system of radiological protection has been based on linear no-threshold theory and related dose-response models for health detriment (in part related to cancer induction) by ionizing radiation exposure for almost 70 y. The indicated system unintentionally promotes radiation phobia, which has harmed many in relationship to the Fukushima nuclear accident evacuations and led to some abortions following the Chernobyl nuclear accident. Linear no-threshold model users (mainly epidemiologists) imply that they can reliably assess the cancer excess relative risk (likely none) associated with tens or hundreds of nanogray (nGy) radiation doses to an organ (e.g., bone marrow); for 1,000 nGy, the excess relative risk is 1,000 times larger than that for 1 nGy. They are currently permitted this unscientific view (ignoring evolution-related natural defenses) because of the misinforming procedures used in data analyses of which many radiation experts are not aware. One such procedure is the intentional and unscientific vanishing of the excess relative risk uncertainty as radiation dose decreases toward assigned dose zero (for natural background radiation exposure). The main focus of this forum article is on correcting the serious error of discarding risk uncertainty and the impact of the correction. The result is that the last defense of the current system of radiological protection relying on linear no-threshold theory (i.e., epidemiologic studies implied findings of harm from very low doses) goes away. A revised system is therefore needed.

Health risk assessment and prevalence of work-related musculoskeletal disorders among the aged brick molders, in India.

The Brickfield Industry is the major and oldest informal industry in India, where millions of brickfield workers make their livelihood. Aged brickfield workers are also involved in different activities in the brickfield, especially in brick mold activities owing to poor socioeconomic conditions. A cross-sectional study was designed to determine the prevalence of work-related musculoskeletal disorders among the aged brick molders and compare them with aged control subjects. A Nordic Questionnaire was applied to assess the discomfort felt among both groups of workers and the Rapid Upper Limb Assessment (RULA) method was used to evaluate posture during their job. The RULA posture analysis showed that the posture adopted by aged brick molders required changes immediately. The result of the ART tool also stated that the brick molding activities' exposure level was high and required further investigation urgently. The study concluded that due to working in a forward bending posture for a prolonged period, aged brickmolders suffered from severe low back and knee pain along with upper-limb disorders due to repetitive activities.

The coefficient of conformism of a correlative prediction (CCCP): Building up reliable nano-QSPRs/QSARs for endpoints of nanoparticles in different experimental conditions encoded via quasi-SMILES.

Simulation of the physicochemical and biochemical behavior of nanomaterials has its own specifics. However, the main goal of modeling for both traditional substances and nanomaterials is the same. This is an ecologic risk assessment. The universal indicator of toxicity is the n-octanol/water partition coefficient. Mutagenicity indicates the possibility of future undesirable environmental effects, possibly greater than toxicity. Models have been proposed for the octanol/water distribution coefficient of gold nanoparticles and the mutagenicity of silver nanoparticles. Unlike the previous studies, here the models are built using an updated scheme, which includes two improvements. Firstly, the computing involves a new criterion for prediction potential, the so-called coefficient of conformism of a correlative prediction (CCCP); secondly, the Las Vegas algorithm is used to select the potentially most promising models from a group of models obtained by the Monte Carlo algorithm. Apparently, CCCP is a measure of the predictive potential (not only correlation). This can give an advantage in developing a model in comparison to using the classic determination coefficient. Likely, CCCP can be more informative than the classical determination coefficient. The Las Vegas algorithm is able to improve the model obtained by the Monte Carlo method.

Pretreatment mortality risk prediction model in patients with polymyositis/dermatomyositis-associated interstitial lung disease.

OBJECTIVES: Risk prediction for patients with polymyositis/dermatomyositis-associated interstitial lung disease (PM/DM-ILD) is challenging due to heterogeneity in the disease course. We aimed to develop a mortality risk prediction model for PM/DM-ILD. METHODS: This prognostic study analysed patients with PM/DM-ILD admitted to Nanjing Drum Hospital from 2016 to 2021. The primary outcome was mortality within 1 year. We used a least absolute shrinkage and selection operator (LASSO) logistic regression model to identify predictive laboratory indicators. These indicators were used to create a laboratory risk score, and we developed a mortality risk prediction model by incorporating clinical factors. The evaluation of model performance encompassed discrimination, calibration, clinical utility and practical application for risk prediction and prognosis. RESULTS: Overall, 418 patients with PM/DM-ILD were enrolled and randomly divided into development (n=282) and validation (n=136) cohorts. LASSO logistic regression identified four optimal features in the development cohort, forming a laboratory risk score: C reactive protein, lactate dehydrogenase, CD3+CD4+ T cell counts and PO2/FiO2. The final prediction model integrated age, arthralgia, anti-melanoma differentiation-associated gene 5 antibody status, high-resolution CT pattern and the laboratory risk score. The prediction model exhibited robust discrimination (area under the receiver operating characteristic: 0.869, 95% CI 0.811 to 0.910), excellent calibration and valuable clinical utility. Patients were categorised into three risk groups with distinct mortality rates. The internal validation, sensitivity analyses and comparative assessments against previous models further confirmed the robustness of the prediction model. CONCLUSIONS: We developed and validated an evidence-based mortality risk prediction model with simple, readily accessible clinical variables in patients with PM/DM-ILD, which may inform clinical decision-making.

Aetiology and predictors of major bleeding events in patients with heart failure with reduced ejection fraction undergoing percutaneous coronary intervention.

OBJECTIVES: We sought to determine the relationship between the degree of left ventricular ejection fraction (LVEF) impairment and the frequency and type of bleeding events after percutaneous coronary intervention (PCI). DESIGN: This was an observational retrospective cohort analysis. Patients who underwent PCI from 2009 to 2017 were identified from our institutional National Cardiovascular Disease Registry (NCDR) CathPCI database. Patients were stratified by pre-PCI LVEF: preserved (≥50%), mildly reduced (41%-49%) and reduced (≤40%) LVEF. PRIMARY OUTCOME MEASURES: The outcome was major bleeding, defined by NCDR criteria. Events were classified based on bleeding aetiology and analysed by multivariable logistic regression. RESULTS: Among 13 537 PCIs, there were 817 bleeding events (6%). The rate of bleeding due to any cause, blood transfusion, gastrointestinal bleeding and coronary artery perforation or tamponade each increased in a stepwise fashion comparing preserved, mildly reduced and reduced LVEF reduction (p<0.05 for all comparisons). However, there were no differences in bleeding due to asymptomatic drops in haemoglobin, access site haematoma or retroperitoneal bleeding. After multivariable adjustment, mildly reduced and reduced LVEF remained independent predictors of bleeding events (OR 1.36, 95% CI 1.06 to 1.74, p<0.05 and OR 1.73, 95% CI 1.45 to 2.06, p<0.0001). CONCLUSIONS: The degree of LV dysfunction is an independent predictor of post-PCI major bleeding events. Patients with mildly reduced or reduced LVEF are at greatest risk of post-PCI bleeding, driven by an increased need for blood transfusion, major GI bleeding events and coronary artery perforation or tamponade. Pre-PCI LV dysfunction does not predict asymptomatic declines in haemoglobin, access site haematoma or retroperitoneal bleeding.

Study on agricultural drought disaster risk assessment in Heilongjiang reclamation area based on SSAPSO optimization projection pursuit model.

Heilongjiang reclamation area serves as a crucial hub for commodity grain production and strategic reserves in China, playing a vital role in maintaining national food security. Investigating the assessment of agricultural drought risk in this region can yield valuable insights into spatial and temporal variations in drought risk. Such insights can aid in formulating effective strategies for disaster prevention and mitigation, thereby minimizing food losses caused by drought disasters. This study employs a comprehensive indicator system comprising 17 indicators categorized into hazard, exposure, vulnerability, and resistance capacity. The projection pursuit model is applied to evaluate regional drought risk, while the PSO algorithm, optimized by the SSA algorithm, addresses the limitations of low local search ability and search accuracy during the large-scale search process of the PSO optimization algorithm. This study examines and compares the optimization and convergence capabilities of three algorithms: real number encoding-based genetic algorithm (RAGA), particle swarm optimization algorithm (PSO), and sparrow algorithm-based improved particle swarm optimization algorithm (SSAPSO). The analysis demonstrates that SSAPSO exhibits superior optimization performance and convergence properties, establishing it as a highly effective algorithm for optimization tasks. The findings reveal the following trends: over time, agricultural drought risk in Heilongjiang reclamation area has generally declined, with fluctuations observed in hazard and vulnerability, an increase in exposure, and a continuous enhancement of resistance capacity. Spatially, the western region exhibits significantly higher agricultural drought risk compared to the eastern region, primarily due to elevated hazard and vulnerability, coupled with lower resistance capacity. As the agricultural economy grows and agricultural expertise accumulates, the risk of agricultural drought decreases. However, variations in economic growth among different regions lead to diverse spatial distributions of risk.

A comprehensive stroke risk assessment by combining atrial computational fluid dynamics simulations and functional patient data.

Stroke, a major global health concern often rooted in cardiac dynamics, demands precise risk evaluation for targeted intervention. Current risk models, like the CHA 2 DS 2 -VASc score, often lack the granularity required for personalized predictions. In this study, we present a nuanced and thorough stroke risk assessment by integrating functional insights from cardiac magnetic resonance (CMR) with patient-specific computational fluid dynamics (CFD) simulations. Our cohort, evenly split between control and stroke groups, comprises eight patients. Utilizing CINE CMR, we compute kinematic features, revealing smaller left atrial volumes for stroke patients. The incorporation of patient-specific atrial displacement into our hemodynamic simulations unveils the influence of atrial compliance on the flow fields, emphasizing the importance of LA motion in CFD simulations and challenging the conventional rigid wall assumption in hemodynamics models. Standardizing hemodynamic features with functional metrics enhances the differentiation between stroke and control cases. While standalone assessments provide limited clarity, the synergistic fusion of CMR-derived functional data and patient-informed CFD simulations offers a personalized and mechanistic understanding, distinctly segregating stroke from control cases. Specifically, our investigation reveals a crucial clinical insight: normalizing hemodynamic features based on ejection fraction fails to differentiate between stroke and control patients. Differently, when normalized with stroke volume, a clear and clinically significant distinction emerges and this holds true for both the left atrium and its appendage, providing valuable implications for precise stroke risk assessment in clinical settings. This work introduces a novel framework for seamlessly integrating hemodynamic and functional metrics, laying the groundwork for improved predictive models, and highlighting the significance of motion-informed, personalized risk assessments.

Effects on physical activity, physical fitness and well-being in a 36-months randomized controlled study, comparing a multimodal hospital-based intervention programme for primary cardiovascular prevention with usual care.

BACKGROUND: Cardiovascular disease is a major cause of mortality and morbidity worldwide, and primary prevention efforts are poorly developed in people at high cardiovascular risk. On this background, we performed the Hjerteløftet Study and demonstrated that participation over 36 months in a multimodal primary prevention programme, significantly reduced validated cardiovascular risk scores. In the current substudy we aimed to further explore several elements and effects following the intervention programme. METHODS: A random sample from the original Hjerteløftet Study was included for further examinations (n = 255, 40% women), and these patients were already randomized to an intervention group (IG) (n = 127), or a control group (CG) (n = 128). We compared changes from baseline to 36-months follow-up in physical activity, cardiorespiratory fitness, psychological well-being (WHO-5), cardiovascular medication use, smoking habits, and cardiometabolic risk factors (blood pressure, lipids, blood glucose, HbA1c, Apolipoprotein A-I, Apolipoprotein B and high-sensitive C-reactive protein). RESULTS: Self-reported physical activity increased significantly with absolute difference in mean delta Physical Activity Index score in the IG compared to the CG: 0.90, 95% CI: 0.10 to 1.70, p = 0.028 (ANCOVA). There were no corresponding differences in cardiorespiratory fitness. The participation resulted in psychological well-being improvement in both groups with a larger increase in the IG compared to the CG. The mean difference in delta WHO-5 score was 5.06, 95% CI: 0.68 to 9.45, p = 0.024, and 3.28, 95% CI: -0.69 to 5.25, p = 0.104 when controlled for baseline values (ANCOVA). The use of antihypertensive medication increased significantly more in the CG (p = 0.044). Only minor, nonsignificant changes were observed for traditional risk factors and cardiometabolic variables. CONCLUSIONS: Participation in the Hjerteløftet Study intervention programme resulted in an improved physical activity level, but without changing cardiorespiratory fitness. Participation in the programme also tended to improve psychological well-being, possibly related to increased physical activity, less smoking and less use of cardiovascular medication. Concerning the metabolic status, no major differences were observed, but minor changes may have been concealed by a larger increase in cardiovascular medication use in the control group. TRIAL REGISTRATION: ClinicalTrials.gov (NCT01741428), 04/12/2012.

Analysis of differential metabolites in serum metabolomics of patients with aortic dissection.

BACKGROUND: Pathogenesis and diagnostic biomarkers of aortic dissection (AD) can be categorized through the analysis of differential metabolites in serum. Analysis of differential metabolites in serum provides new methods for exploring the early diagnosis and treatment of aortic dissection. OBJECTIVES: This study examined affected metabolic pathways to assess the diagnostic value of metabolomics biomarkers in clients with AD. METHOD: The serum from 30 patients with AD and 30 healthy people was collected. The most diagnostic metabolite markers were determined using metabolomic analysis and related metabolic pathways were explored. RESULTS: In total, 71 differential metabolites were identified. The altered metabolic pathways included reduced phospholipid catabolism and four different metabolites considered of most diagnostic value including N2-gamma-glutamylglutamine, PC(phocholines) (20:4(5Z,8Z,11Z,14Z)/15:0), propionyl carnitine, and taurine. These four predictive metabolic biomarkers accurately classified AD patient and healthy control (HC) samples with an area under the curve (AUC) of 0.9875. Based on the value of the four different metabolites, a formula was created to calculate the risk of aortic dissection. Risk score = (N2-gamma-glutamylglutamine × -0.684) + (PC (20:4(5Z,8Z,11Z,14Z)/15:0) × 0.427) + (propionyl carnitine × 0.523) + (taurine × -1.242). An additional metabolic pathways model related to aortic dissection was explored. CONCLUSION: Metabolomics can assist in investigating the metabolic disorders associated with AD and facilitate a more in-depth search for potential metabolic biomarkers.

A machine learning screening model for identifying the risk of high-frequency hearing impairment in a general population.

BACKGROUND: Hearing impairment (HI) has become a major public health issue in China. Currently, due to the limitations of primary health care, the gold standard for HI diagnosis (pure-tone hearing test) is not suitable for large-scale use in community settings. Therefore, the purpose of this study was to develop a cost-effective HI screening model for the general population using machine learning (ML) methods and data gathered from community-based scenarios, aiming to help improve the hearing-related health outcomes of community residents. METHODS: This study recruited 3371 community residents from 7 health centres in Zhejiang, China. Sixty-eight indicators derived from questionnaire surveys and routine haematological tests were delivered and used for modelling. Seven commonly used ML models (the naive Bayes (NB), K-nearest neighbours (KNN), support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGBoost), boosting, and least absolute shrinkage and selection operator (LASSO regression)) were adopted and compared to develop the final high-frequency hearing impairment (HFHI) screening model for community residents. The model was constructed with a nomogram to obtain the risk score of the probability of individuals suffering from HFHI. According to the risk score, the population was divided into three risk stratifications (low, medium and high) and the risk factor characteristics of each dimension under different risk stratifications were identified. RESULTS: Among all the algorithms used, the LASSO-based model achieved the best performance on the validation set by attaining an area under the curve (AUC) of 0.868 (95% confidence interval (CI): 0.847-0.889) and reaching precision, specificity and F-score values all greater than 80%. Five demographic indicators, 7 disease-related features, 5 behavioural factors, 2 environmental exposures, 2 hearing cognitive factors, and 13 blood test indicators were identified in the final screening model. A total of 91.42% (1235/1129) of the subjects in the high-risk group were confirmed to have HI by audiometry, which was 3.99 times greater than that in the low-risk group (22.91%, 301/1314). The high-risk population was mainly characterized as older, low-income and low-educated males, especially those with multiple chronic conditions, noise exposure, poor lifestyle, abnormal blood indices (e.g., red cell distribution width (RDW) and platelet distribution width (PDW)) and liver function indicators (e.g., triglyceride (TG), indirect bilirubin (IBIL), aspartate aminotransferase (AST) and low-density lipoprotein (LDL)). An HFHI nomogram was further generated to improve the operability of the screening model for community applications. CONCLUSIONS: The HFHI risk screening model developed based on ML algorithms can more accurately identify residents with HFHI by categorizing them into the high-risk groups, which can further help to identify modifiable and immutable risk factors for residents at high risk of HI and promote their personalized HI prevention or intervention.

Predictive model and risk analysis for coronary heart disease in people living with HIV using machine learning.

OBJECTIVE: This study aimed to construct a coronary heart disease (CHD) risk-prediction model in people living with human immunodeficiency virus (PLHIV) with the help of machine learning (ML) per electronic medical records (EMRs). METHODS: Sixty-one medical characteristics (including demography information, laboratory measurements, and complicating disease) readily available from EMRs were retained for clinical analysis. These characteristics further aided the development of prediction models by using seven ML algorithms [light gradient-boosting machine (LightGBM), support vector machine (SVM), eXtreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), decision tree, multilayer perceptron (MLP), and logistic regression]. The performance of this model was assessed using the area under the receiver operating characteristic curve (AUC). Shapley additive explanation (SHAP) was further applied to interpret the findings of the best-performing model. RESULTS: The LightGBM model exhibited the highest AUC (0.849; 95% CI, 0.814-0.883). Additionally, the SHAP plot per the LightGBM depicted that age, heart failure, hypertension, glucose, serum creatinine, indirect bilirubin, serum uric acid, and amylase can help identify PLHIV who were at a high or low risk of developing CHD. CONCLUSION: This study developed a CHD risk prediction model for PLHIV utilizing ML techniques and EMR data. The LightGBM model exhibited improved comprehensive performance and thus had higher reliability in assessing the risk predictors of CHD. Hence, it can potentially facilitate the development of clinical management techniques for PLHIV care in the era of EMRs.

Long-term survival in stroke patients: insights into triglyceride-glucose body mass index from ICU data.

BACKGROUND: The Triglyceride Glucose-Body Mass Index (TyG-BMI) has been established as a robust indicator of insulin resistance (IR), reflecting metabolic health across various populations. In general, lower TyG-BMI values are often associated with better metabolic health outcomes and a reduced risk of adverse health events in non-critically ill populations. Previous studies have highlighted a significant negative association between TyG-BMI and all-cause mortality (ACM) among critically ill atrial fibrillation patients. Given the high prevalence and severe outcomes associated with stroke, understanding how TyG-BMI at the time of ICU admission correlates with ACM in critically ill stroke patients becomes imperative. This study aims to assess the correlation between TyG-BMI and ACM in this specific patient cohort, exploring how traditional associations between TyG-BMI and metabolic health may differ in the context of acute, life-threatening illness. METHODS: Patient data were retrieved by accessing the Medical Information Mart for Intensive Care IV (MIMIC-IV 2.2) database, categorizing patients into three groups on the basis of TyG-BMI tertiles. The study evaluated both primary and secondary outcomes: the primary outcomes included the 90-day, 180-day, and 1-year ACM, while secondary outcomes encompassed ICU, in-hospital, and 30-day ACM. Our study employed the Kaplan-Meier (K-M) curve method for outcome comparison across the groups while utilizing multivariate Cox proportional-hazards regression models and restricted cubic splines (RCS) to explore TyG-BMI association with these outcomes. Additionally, interaction and subgroup analyses were performed, focusing on different mortality time points. RESULTS: Among a cohort of 1707 individuals diagnosed with stroke, the average age was 68 years (interquartile range [IQR]: 58-78 years), with 946 (55.42%) of the participants being male. The analysis of K-M curves suggested that patients having a lower TyG-BMI level faced a heightened risk of long-term ACM, whereas the short-term ACM exhibited no statistically significant differences across the three TyG-BMI groups. Furthermore, Cox proportional-hazards regression analysis validated a statistically significant increased risk of long-term ACM among patients belonging to the lowest TyG-BMI tertile. Additionally, RCS analysis results demonstrated L-shaped correlations between the TyG-BMI index and both short- and long-term ACM. These findings underscore the TyG-BMI predictive value for long-term mortality in stroke patients, highlighting a nuanced relationship that varies over different time frames. The results revealed no interactions between TyG-BMI and the stratified variables, with the exception of age. CONCLUSION: In our study, lower TyG-BMI levels in critically ill stroke patients are significantly related to a higher risk of long-term ACM within the context of the United States. This finding suggests the potential of TyG-BMI as a marker for stratifying long-term risk in this patient population. However, it's crucial to note that this association was not observed for short-term ACM, indicating that the utility of TyG-BMI may be more pronounced in long-term outcome prediction. Additionally, our conclusion that TyG-BMI could serve as a reliable indicator for managing and stratifying stroke patients over the long term is preliminary. To confirm our findings and assess the universal applicability of TyG-BMI as a prognostic tool, it is crucial to conduct rigorously designed research across various populations.

Associations between cardiometabolic indices and the risk of diabetic kidney disease in patients with type 2 diabetes.

BACKGROUND: This study was designed to assess the associations between emerging cardiometabolic indices-the atherogenic index of plasma (AIP), the stress hyperglycemia ratio (SHR), the triglyceride-glucose (TyG) index, and the homeostasis model assessment of insulin resistance (HOMA-IR)-and the incidence of diabetic kidney disease (DKD) in type 2 diabetes (T2D) patients. METHODS: We consecutively enrolled 4351 T2D patients. The AIP, SHR, TyG index, and HOMA-IR were calculated from baseline parameters. DKD was defined as a urine albumin/creatinine ratio > 30 mg/g or an eGFR < 60 mL/min per 1.73 m. All participants were categorized into tertiles based on the cardiometabolic indices. Multivariate logistic regression models, restricted cubic splines, and receiver operating characteristic (ROC) curves were used for analysis. RESULTS: A total of 1371 (31.5%) patients were diagnosed with DKD. A restricted cubic spline showed a J-shaped association of the AIP and TyG index with DKD, a log-shaped association between HOMA-IR and DKD, and a U-shaped association between the SHR and DKD incidence. Multivariate logistic regression revealed that individuals in the highest tertile of the four cardiometabolic indices had a significantly greater risk of DKD than did those in the lowest tertile (AIP: OR = 1.08, 95% CI = 1.02-1.14, P = 0.005; SHR: OR = 1.42, 95% CI = 1.12-1.81, P = 0.004; TyG index: OR = 1.86, 95% CI = 1.42-2.45, P < 0.001; HOMA-IR: OR = 2.24, 95% CI = 1.52-3.30, P < 0.001). The receiver operating characteristic curves showed that the HOMA-IR score was better than other indices at predicting the risk of DKD, with an optimal cutoff of 3.532. CONCLUSIONS: Elevated AIP, SHR, TyG index and HOMA-IR are associated with a greater risk of DKD in patients with T2D. Among these indices, the HOMA-IR score demonstrated the strongest association with and predictive value for DKD incidence.

Prediction of various insulin resistance indices for the risk of hypertension among military young adults: the CHIEF cohort study, 2014-2020.

BACKGROUND: Non-insulin-based insulin resistance (NI-IR) indices have been reported to have an association with prevalent hypertension, however, no cohort studies to date have compared their prediction of hypertension among young adults. METHODS: A total of 2,448 military men and women, aged 18-39 years, without baseline hypertension in Taiwan were followed for incident hypertension events from 2014 until the end of 2020. All subjects underwent annual health examinations including measurements of blood pressure (BP) in mmHg. Systolic BP (SBP) 130-139/diastolic BP (DBP) < 80, SBP < 130/DBP 80-89, and SBP 130-139/DBP 80-89 were respectively defined as stage I isolated systolic hypertension (ISH), isolated diastolic hypertension (IDH) and combined hypertension (CH). The cut-off levels of stage II hypertension for SBP and DBP were 140-159 and 90-99, respectively. Four NI-IR indices included the ratio of serum triglycerides (TG) to high-density lipoprotein cholesterol (HDL-C), TyG index defined as ln[TG* fasting glucose (FG)/2], Metabolic Score for IR (METS-IR) defined as ln[(2* FG) + TG)* body mass index (BMI)/(ln(HDL-C))], and ZJU index defined as BMI + FG + TG + 3* alanine transaminase/aspartate transaminase (+ 2 if female). Multivariable Cox regression analysis was performed with adjustments for baseline age, sex, body mass index, BP, substance use, family history for early onset cardiovascular diseases or hypertension, low-density lipoprotein cholesterol, kidney function, serum uric acid and physical activity to determine the associations. RESULTS: During a median follow-up of 6.0 years, there were 920 hypertension events (37.6%). Greater TyG, TG/HDL-C and METS-IR indices were associated with a higher risk of stage I IDH (hazard ratios (HRs) and 95% confidence intervals: 1.376 (1.123-1.687), 1.082 (1.039-1.127) and 3.455 (1.921-6.214), respectively), whereas only greater ZJU index was associated with a higher risk of stage II IDH [HRs: 1.011 (1.001-1.021)]. In addition, greater ZJU index was associated with a higher risk of stage II ISH [HR: 1.013 (1.003-1.023)], and greater TyG index was associated with a higher risk of stage II CH [HR: 2.821 (1.244-6.395)]. CONCLUSION: Insulin resistance assessed by various NI-IR indices was associated with a higher risk of hypertension in young adults, while the assessment ability for specific hypertension category may differ by NI-IR indices.

Effects of the stress hyperglycemia ratio on long-term mortality in patients with triple-vessel disease and acute coronary syndrome.

AIMS: Risk assessment for triple-vessel disease (TVD) remain challenging. Stress hyperglycemia represents the regulation of glucose metabolism in response to stress, and stress hyperglycemia ratio (SHR) is recently found to reflect true acute hyperglycemic status. This study aimed to evaluate the prognostic value of SHR and its role in risk stratification in TVD patients with acute coronary syndrome (ACS). METHODS: A total of 3812 TVD patients with ACS with available baseline SHR measurement were enrolled from two independent centers. The endpoint was cardiovascular mortality. Cox regression was used to evaluate the association between SHR and cardiovascular mortality. The SYNTAX (Synergy Between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery) II (SSII) was used as the reference model in the model improvement analysis. RESULTS: During a median follow-up of 5.1 years, 219 (5.8%) TVD patients with ACS suffered cardiovascular mortality. TVD patients with ACS with high SHR had an increased risk of cardiovascular mortality after robust adjustment for confounding (high vs. median SHR: adjusted hazard ratio 1.809, 95% confidence interval 1.160-2.822, P = 0.009), which was fitted as a J-shaped pattern. The prognostic value of the SHR was found exclusively among patients with diabetes instead of those without diabetes. Moreover, addition of SHR improved the reclassification abilities of the SSII model for predicting cardiovascular mortality in TVD patients with ACS. CONCLUSIONS: The high level of SHR is associated with the long-term risk of cardiovascular mortality in TVD patients with ACS, and is confirmed to have incremental prediction value beyond standard SSII. Assessment of SHR may help to improve the risk stratification strategy in TVD patients who are under acute stress.