INTRODUCTION: Endometriosis (EMS) is a common gynaecological disorder linked to metabolic disturbances. However, evidence on the associations between lipid accumulation product (LAP) and visceral adiposity index (VAI) with the risk of EMS remains limited. This study aimed to explore the potential associations between LAP, VAI and EMS. METHOD: Data were obtained from the 1999-2006 National Health and Nutrition Examination Survey (NHANES), including a total of 2046 samples. Weighted multivariable logistic regression models and smoothed curve fitting were used to assess the associations between LAP, VAI and EMS. Additionally, subgroup analyses and interaction tests were conducted to evaluate intergroup differences in the associations between LAP, VAI and EMS. RESULTS: In the fully adjusted model, higher Log2 LAP (odds ratio [OR] 1.256, 95% confidence interval [CI] 1.102-1.431, P=0.0014) and Log2 VAI (OR 1.287, 95% CI 1.105-1.498, P=0.0022) were significantly associated with increased EMS risk. Participants in the highest quartile of Log2 LAP (OR 1.983, P=0.0029) and Log2 VAI (OR 1.690, P=0.0486) had a higher risk of EMS. Subgroup analysis showed stronger associations among women with diabetes (Log2 LAP OR 3.681, P=0.009; Log2 VAI OR 4.849, P=0.041). CONCLUSION Elevated LAP and VAI were independently associated with an increased risk of EMS. LAP and VAI may serve as potential indicators for assessing EMS-related risk, suggesting that visceral obesity and lipid metabolic disturbances might play roles in the pathophysiological process of EMS. These findings underscore the potential of LAP and VAI as non-invasive markers for EMS risk, warranting further validation in clinical settings.
Humans ; Female ; Cross-Sectional Studies ; Endometriosis/metabolism* ; Adult ; Nutrition Surveys ; Intra-Abdominal Fat ; Lipid Accumulation Product ; Middle Aged ; Obesity, Abdominal/complications* ; Adiposity ; Risk Factors ; Logistic Models

BACKGROUND: The benefits of ideal cardiovascular-health metrics (ICVHMs) in patients with renal insufficiency remain unclear. This study aimed to investigate the associations between ICVHM and prognosis in a renal insufficiency population. METHODS: The trial enrolled 29,682 participants from the US National Health and Nutrition Examination Survey (NHANES), 2007-2018, with mortality follow-up through December 31, 2019. Participants were divided into three groups based on estimated glomerular filtration rates. Cardiovascular health was assessed using new "Life's Essential 8" metrics. Cox regression analyses based on NHANES data were used to determine the associations between ICVHMs and cardiovascular mortality in patients with renal insufficiency. RESULTS: During a mean follow-up of 6.58 years, ideal cardiovascular health (hazard ratio [HR] = 0.42; 95% confidence interval [CI]; 0.25-0.70) and ideal health behavior (HR = 0.53; 95% CI; 0.39-0.73) reduced cardiovascular mortality in participants with renal insufficiency. For each one ICVHM increment, a 25% reduction in cardiovascular mortality was recorded (95% CI; 0.69-0.82). When compared with participants with normal renal function, for those with mild renal insufficiency, the HR for cardiovascular mortality gradually decreased from 1.47 (95% CI; 0.85-2.52) in those who had ≤1 ICVHMs to 0.30 (95% CI; 0.12-0.77) in participants who had >6 ICVHMs. CONCLUSIONS From an ICVHM perspective, enhanced cardiovascular benefits were observed in individuals with renal insufficiency, coupled with a reduced risk of all-cause mortality. Furthermore, when compared with individuals with normal renal function, increased ICVHMs can mitigate adverse risks associated with renal impairment.
Humans ; Male ; Female ; Nutrition Surveys ; Middle Aged ; Renal Insufficiency/physiopathology* ; Aged ; Prognosis ; Adult ; Cardiovascular Diseases/mortality* ; Glomerular Filtration Rate/physiology* ; Proportional Hazards Models

OBJECTIVE: To explore the relationship between the geriatric nutritional risk index (GNRI) and cognitive function. METHODS: A cross-sectional study method was conducted. People aged ≥ 60 years from the National Health and Nutrition Examination Survey (NHANES) databases from 1999 to 2002 and 2011 to 2014 were included as study subjects. The participants were divided into three groups based on their GNRI scores: a medium-high risk group (82 ≤ GNRI < 92), a low risk group (92 ≤ GNRI < 98), and a no-risk group (GNRI ≥ 98). Demographic characteristics (gender, age, race, education), chronic diseases [chronic bronchitis, emphysema, thyroid problems, coronary heart disease, angina pectoris, stroke, hypertension, diabetes mellitus, and depression score on the patient health questionnaire (PHQ-9)], lifestyle habits (history of smoking, hours of sleep), etc., were collected. Cognitive function was assessed using the consortium to establish a registry for Alzheimer's disease word learning subtest (CERAD-WL), animal fluency test (AFT), and digit symbol substitution test (DSST) for the 2011-2014 data, while only the DSST was used for the 1999-2002 data. Differences in the above information among the GNRI cohorts were compared. Factors affecting cognitive function in the population were analyzed using multifactorial Logistic regression. RESULTS: 2 653 participants from 2011 to 2014 and 2 380 participants from 1999 to 2002 were enrolled, with a total of 5 033 participants in the study. There were statistically significant differences in age, stroke, diabetes mellitus, DSST score, AFT score, CERAD score test 1 recall (Cst1), and CERAD score test 2 recall (Cst2) among the GNRI groups. Multifactorial Logistic regression analysis of data from 2011 to 2014 showed that in model 3 (DSST score, age, gender, race, marriage, education, hours of sleep, history of smoking, emphysema, thyroid problems, chronic bronchitis, coronary heart disease, angina pectoris, hypertension, diabetes mellitus, depression score on the PHQ-9, and stroke) adjusted for all covariates, GNRI was a protective factor for DSST [odds ratio (OR) = 1.03, 95% confidence interval (95%CI) was 1.00 to 1.05, P = 0.03]; Logistic regression analyse for 1999 to 2002 and 2011 to 2014 showed a significant association even after adjustment for covariates (OR = 1.02, 95%CI was 1.00 to 1.03, P = 0.02). Subgroup Logistic regression analyses of the total population from 2011 to 2014 showed a significant association between GNRI and DSST scores (OR = 1.02, 95%CI was 1.01 to 1.03, P < 0.001), with significant associations in the age subgroups of 60 to 64 years old, across gender, non-Hispanic Whites and Blacks, by education, and by marital status associations were significant (all P < 0.05). Subgroup Logistic regression analyse of the total populations from 1999 to 2002 and 2011 to 2014 showed a significant association between the GNRI and DSST score (OR = 1.01, 95%CI was 1.01 to 1.02, P < 0.001), but did not show a significant year difference (interaction P = 0.503), and the newly found in the smoking population the association was also more significant (P < 0.01). CONCLUSION The GNRI correlates with the presence of cognitive functions related to processing speed, sustained attention, and executive function, and may be able to serve as an indicator for the assessment or prediction of related cognitive functions.
Humans ; Cross-Sectional Studies ; Aged ; Middle Aged ; Nutrition Surveys ; Cognition ; Female ; Male ; Nutritional Status ; Risk Factors ; Geriatric Assessment

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.
Humans ; Fluorocarbons/blood* ; Female ; Adult ; Middle Aged ; Male ; Environmental Pollutants/blood* ; Abdominal Fat ; Nutrition Surveys ; Alkanesulfonic Acids/blood* ; Obesity ; Environmental 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.
Humans ; Stroke/blood* ; United States/epidemiology* ; Male ; Female ; Middle Aged ; Cross-Sectional Studies ; Nutrition Surveys ; Adult ; Aged ; Cholesterol, HDL/blood* ; Cholesterol/blood* ; Risk Factors

OBJECTIVE: Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health. Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment, including high dimensionality, correlated exposure, and subtle individual effects. METHODS: We proposed a novel statistical approach, the generalized functional linear model (GFLM), to analyze the health effects of exposure mixtures. GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation. The robustness and efficiency was evaluated under various scenarios through extensive simulation studies. RESULTS: We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey (NHANES). In the first application, we examined the effects of 37 nutrients on BMI (2011-2016 cycles). The GFLM identified a significant mixture effect, with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI, respectively. For the second application, we investigated the association between four pre- and perfluoroalkyl substances (PFAS) and gout risk (2007-2018 cycles). Unlike traditional methods, the GFLM indicated no significant association, demonstrating its robustness to multicollinearity. CONCLUSION GFLM framework is a powerful tool for mixture exposure analysis, offering improved handling of correlated exposures and interpretable results. It demonstrates robust performance across various scenarios and real-world applications, advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
Humans ; Environmental Exposure/analysis* ; Linear Models ; Nutrition Surveys ; Environmental Pollutants ; Body Mass Index

BACKGROUND: Hypertension is a serious chronic disease that can significantly lead to various cardiovascular diseases, affecting vital organs such as the heart, brain, and kidneys. Our goal is to predict the risk of new onset hypertension using machine learning algorithms and identify the characteristics of patients with new onset hypertension. METHODS: We analyzed data from the 2011 China Health and Nutrition Survey cohort of individuals who were not hypertensive at baseline and had follow-up results available for prediction by 2015. We tested and evaluated the performance of four traditional machine learning algorithms commonly used in epidemiological studies: Logistic Regression, Support Vector Machine, XGBoost, LightGBM, and two deep learning algorithms: TabNet and AMFormer model. We modeled using 16 and 29 features, respectively. SHAP values were applied to select key features associated with new onset hypertension. RESULTS: A total of 4,982 participants were included in the analysis, of whom 1,017 developed hypertension during the 4-year follow-up. Among the 16-feature models, Logistic Regression had the highest AUC of 0.784(0.775∼0.806). In the 29-feature prediction models, AMFormer performed the best with an AUC of 0.802(0.795∼0.820), and also scored the highest in MCC (0.417, 95%CI: 0.400∼0.434) and F1 (0.503, 95%CI: 0.484∼0.505) metrics, demonstrating superior overall performance compared to the other models. Additionally, key features selected based on the AMFormer, such as age, province, waist circumference, urban or rural location, education level, employment status, weight, WHR, and BMI, played significant roles. CONCLUSION We used the AMFormer model for the first time in predicting new onset hypertension and achieved the best results among the six algorithms tested. Key features associated with new onset hypertension can be determined through this algorithm. The practice of machine learning algorithms can further enhance the predictive efficacy of diseases and identify risk factors for diseases.
Humans ; China/epidemiology* ; Hypertension/diagnosis* ; Machine Learning ; Male ; Female ; Middle Aged ; Adult ; Nutrition Surveys ; Algorithms ; Aged ; Risk Factors

BACKGROUND: Ethylene oxide (EO) is a widely utilized industrial compound known to pose health hazards. Although its carcinogenic characteristics have been thoroughly investigated, recent findings indicate possible links to respiratory disease. The correlation between EO exposure and the likelihood of developing obstructive sleep apnea (OSA) in individuals remains unclear. The study aimed to explore the association between EO exposure and OSA within the broader US population. METHODS: From 2015 to 2020, 4355 participants were analyzed cross-sectionally in the National Health and Nutrition Examination Survey (NHANES). As the primary indicator of EO exposure, hemoglobin adducts of EO (HbEO) were used in this study. The relationship between EO exposure and OSA prevalence was assessed using weighted multivariable regression analysis and smoothing curve fitting. Using subgroup analysis and interaction tests, we investigated whether this association remained consistent across populations. RESULTS: According to the study, higher HbEO level was positively correlated with a higher prevalence of OSA. Compared to the first HbEO quartile (Q1), participants within the highest quartile (Q4) presented a higher OSA prevalence in the fully model (OR = 1.32, 95% CI: 1.08-1.62, P = 0.01, P for trend = 0.001). This correlation was particularly evident among females and individuals who are insufficiently physically active. CONCLUSIONS This research found a positive relationship between the extent of exposure to EO and OSA prevalence among a representative sample of Americans.
Humans ; Sleep Apnea, Obstructive/chemically induced* ; Female ; Male ; Middle Aged ; Adult ; Cross-Sectional Studies ; Prevalence ; Ethylene Oxide/toxicity* ; United States/epidemiology* ; Nutrition Surveys ; Aged ; Environmental Exposure/adverse effects* ; Young Adult

BACKGROUND: Heavy metals are significant risk factors for kidney function. Numerous studies have shown that exposure to heavy metals negatively correlates with kidney function through oxidative stress pathways, and serum α-klotho is linked to oxidative stress. However, the role of α-klotho in the relationship between blood lead, mercury, and kidney function remains unclear. METHOD: This study evaluated the mediating role of alpha-klotho in the relationship between lead, mercury and renal function, using data from the 2007-2016 National Health and Nutrition Examination Survey (NHANES) in U.S. adults aged 40-79. The sample included 11,032 participants, with blood lead, mercury, α-klotho, and other relevant covariates measured. Inductively coupled plasma mass spectrometry was used to assess blood lead and mercury levels, and enzyme-linked immunosorbent assay (ELISA) was employed to measure serum α-klotho. Kidney function was evaluated using estimated glomerular filtration rate (eGFR) based on creatinine levels. Multivariable linear regression was conducted to analyze the relationships between blood lead, mercury, α-klotho, and eGFR. A mediation analysis model was used to assess whether α-klotho influenced these associations. RESULTS: We observed a significant association between blood lead and eGFR. Mediation analysis revealed that α-klotho accounted for 12.76% of the relationship between serum lead and eGFR in the NHANES population. Subgroup analysis showed that α-klotho mediated 12.43%, 6.87%, 21.50% and 5.44% of the relationship between blood lead and eGFR in women, middle-aged adults (40-59 years old), without cardiovascular disease and hypertension, respectively. However, α-klotho did not mediate the relationship between blood mercury and eGFR in terms of gender or age. This newly identified pathway may provide valuable insights for the prevention and treatment mechanisms related to kidney function impairment. CONCLUSION We found that blood lead was associated with renal function. According to the results of subgroup analysis, for blood lead, serum α-klotho mediated the association in females, middle aged 60-79 years. The relationship between blood mercury and renal function was not clinically significant, and serum α-Klotho mediated the relationship between blood mercury and renal function without significant clinical significance.
Humans ; Middle Aged ; Lead/blood* ; Female ; Klotho Proteins ; Male ; Aged ; Adult ; Mercury/blood* ; Glomerular Filtration Rate ; Nutrition Surveys ; United States ; Kidney/physiology* ; Glucuronidase/blood* ; Environmental Pollutants/blood*

BACKGROUND: Chronic kidney disease (CKD) poses a major global health challenge, often foreshadowing poor patient outcomes. The C-reactive protein to albumin ratio (CAR) serves as a pivotal biomarker, demonstrating a strong correlation with adverse outcomes in cardiovascular disease (CVD). This study sought to examine the correlation between CAR and the risk of all-cause and cardiovascular mortality in patients with CKD stages 3-5. METHODS: This study utilized data of CKD patients from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2010, with follow-up to December 31, 2019. The optimal CAR cutoff value was identified utilizing the method of maximally selected rank statistics. Multivariable Cox proportional hazards regression model, restricted cubic splines (RCS) model, and subgroup analysis were employed to assess the association between CAR and mortality among CKD patients. RESULTS: During a median (with interquartile range) follow-up period of 115 (112,117) months among 2,841 CKD individuals, 1,893 deaths were observed, including 692 deaths due to CVD events. Based on the RCS analysis, a non-linear correlation was observed between CAR and mortality. Using 0.3 as the optimal CAR cutoff value, the cohort was divided into high and low groups. In the fully adjusted model, CKD patients with high CAR values exhibited an elevated risk of all-cause mortality (hazard ratio [HR] 1.53, 95% confidence interval [CI] 1.28-1.83, P < 0.001) and cardiovascular mortality (HR 1.48, 95% CI 1.08-2.02, P = 0.014). Compared to the population aged >65 years (HR 1.32, 95% CI 0.99-1.76, P = 0.064), the risk of cardiovascular mortality was significantly higher in those aged ≤65 years (HR 2.19, 95% CI 1.18-4.09, P = 0.014) with elevated CAR levels. CONCLUSIONS A notable correlation exists between the elevation of CAR and increased all-cause and cardiovascular mortality, suggesting its potential as an independent indicator for evaluating the prognosis of patients with CKD stages 3-5.
Humans ; Renal Insufficiency, Chronic/epidemiology* ; Cardiovascular Diseases/blood* ; Male ; Female ; Middle Aged ; C-Reactive Protein/metabolism* ; Aged ; Biomarkers/blood* ; Nutrition Surveys ; Adult ; United States/epidemiology* ; Serum Albumin/analysis*