Journal of Health, Population, and Nutrition最新文献

Background: Tobacco exposure is a major public health concern and has been implicated in accelerated female reproductive aging. However, most evidence relies on self-reported smoking history, which may introduce bias. Cotinine, a reliable biomarker of nicotine exposure, provides an objective measure to clarify the association between tobacco exposure and reproductive lifespan (RLS).

Methods: We analyzed 11,944 women from two nationally representative cohorts: NHANES (n = 6,081, U.S., 1999-2018) and KNHANES (n = 5,863, Korea, 2014-2020). Serum cotinine (NHANES) and urinary cotinine (KNHANES) were quantified using standardized laboratory assays. Multivariable linear regression and restricted cubic spline (RCS) models were employed to assess the relationship between cotinine levels and age at menopause, menarche, and RLS, adjusting for demographic, socioeconomic, and metabolic covariates. Subgroup analyses were conducted to explore effect modification.

Results: Higher cotinine levels were significantly associated with earlier menopause (NHANES β = -0.23; KNHANES β = -0.10) and shorter RLS (NHANES β = -0.22; KNHANES β = -0.08). RCS models confirmed linear dose-response associations in both cohorts, with threshold effects observed at higher exposure levels (NHANES ln-cotinine > - 3.47: β = -0.303, 95% CI: -0.386 to - 0.220, P < 0.001). Subgroup analyses indicated stronger associations among younger women, non-diabetic individuals, and lower-income groups, with pronounced differences across racial and educational strata.

Conclusions: Cotinine, as an objective biomarker of tobacco exposure, was robustly associated with shortened reproductive lifespan across two national cohorts. The associations were linear, with stronger reproductive toxicity at higher exposure levels, particularly among U.S. women. These findings highlight the reproductive risks of smoking and underscore the importance of biomarker-based assessments in reproductive aging research.

Background: Although extensive evidence has identified epidemiological risk factors for comorbidities related to obstructive sleep apnea (OSA), few studies have explored the linear relationship between the atherogenic index of plasma (AIP) and OSA, particularly regarding the mediating role of weight-adjusted-waist index (WWI). This study aimed to elucidate the linear relationship between AIP and OSA symptoms and to quantify the mediating effect of WWI.

Methods: This cross-sectional study utilized data from the National Health and Nutrition Examination Survey (NHANES) 2015-2020, comprising 6,033 participants. To investigate the association between AIP and OSA symptoms, we used multivariable logistic regression, restricted cubic spline models, subgroup analyses, interaction tests, and sensitivity analysis. Additionally, mediation analysis was performed to explore the mediating role of WWI in the AIP-OSA symptoms relationship.

Results: A positive association between AIP and OSA symptoms was observed. In the fully adjusted model, each 1-unit increase in AIP was associated with a 121% higher risk of OSA. Subgroup analysis revealed that the age interacted with the association, with AIP being associated with increased risk of OSA only in a subgroup of subjects younger than 60 years. Mediation analysis indicated that WWI explained 19.75% of this relationship. Sensitivity analyses using subsamples and a stricter OSA definition confirmed the robustness of these findings.

Conclusions: The AIP is positively associated with OSA symptoms, and WWI plays a mediating role in this relationship. These findings suggest that monitoring AIP levels and managing WWI may be effective strategies for preventing and reducing the risk of OSA.

Malnutrition, including both undernutrition and overnutrition, remains a major public health concern in Bangladesh, particularly among women of reproductive age. This study aims to identify key determinants of women's malnutrition in Bangladesh and compare the predictive performance of ordinal logistic regression and machine learning methods for predicting women's malnutrition using data from the 2022 Bangladesh Demographic and Health Survey. This study utilized data from 8,728 ever-married women aged 15-49 years extracted from the BDHS 2022. Six ML algorithms, including Random Forest, Extreme Gradient Boosting (XGBoost), Support Vector Machine, Naïve Bayes, AdaBoost, and Multilayer Perceptron (MLP), were compared with ordinal logistic regression by evaluating their performances using accuracy, precision, recall, [Formula: see text] score, Cohen's kappa, and area under the curve (AUC). Data preprocessing included SMOTE to address class imbalance, and models were assessed using stratified k-fold cross-validation. Findings of Ordinal Logistic Regression (OLR) suggest that age, division, residence, wealth index, current breastfeeding status, husband's education, currently working, and age at first marriage are the significant predictors of women's malnutrition. However, its predictive performance was modest, with an accuracy of 49% and macro-averaged [Formula: see text] score was 0.47. In contrast, ML models outperformed OLR across all evaluation metrics. Random Forest and XGBoost achieved the highest test accuracy (64%), with Random Forest attaining a macro-averaged [Formula: see text] score of 0.64 and achieved 66.2% accuracy (10-fold CV). Traditional models, such as OLR, are more explainable, but machine learning models demonstrate higher accuracy in classifying malnutrition. The findings can help policymakers and health professionals prioritize resources and plan targeted nutrition programs, considering the risk factors identified in this study, to lessen the burden of both undernutrition and overnutrition among women in Bangladesh.

Background: Plant-based diets are associated with reduced cardiometabolic risk and lower systemic inflammation. Nurses, as the largest group of healthcare professionals, play a central role in health education; however, limited research has examined healthcare providers' own adoption of plant-based diets and related knowledge, particularly in Asian healthcare settings. This study addresses this gap by examining dietary patterns, plant-based diet knowledge, and associated occupational and health factors among healthcare providers in Taiwan.

Methods: A cross-sectional study was conducted between February and July 2024 at a Buddhist teaching hospital in southern Taiwan. Using convenience sampling and self-administered, data was collected from 344 healthcare providers, exceeding the minimum sample size required for multivariable regression and proportionally representing major professional groups within the hospital. Knowledge of plant-based diets was the primary outcome, while white blood cell and monocyte count, commonly used clinical indicators of systemic inflammatory status, were examined as secondary outcomes. Multiple linear regression analyses were performed.

Results: Participants included nurses (n = 175, 50.9%) and other healthcare professionals. Most followed an omnivorous diet (n = 281, 81.7%), while 18.3% (n = 63) adhered to a plant-based diet. Nurses demonstrated lower knowledge scores than other professionals (mean 5.7 vs. 6.4, p < .001). Only 38.4% answered more than half of the knowledge correctly, and 71.3% relied on non-professional information sources. Adherence to a plant-based diet was associated with lower white blood cell and monocyte counts, suggesting a more favorable inflammatory profile within clinically normal reference ranges. In regression analyses, higher knowledge scores were independently associated with plant-based diet adherence (β = 0.277), fixed daytime work schedules (β = -0.154), physician-confirmed diagnoses (β = 0.128), and access to non-professional information sources (β = -0.110) (all p < .05).

Conclusion: Despite frequent abnormal health check-up findings, adoption of plant-based diets and related knowledge were limited among nurses. This well-powered hospital-based study provides novel evidence from a Buddhist healthcare context and highlights the need for targeted nutrition education and supportive workplace strategies to strengthen nurses' health literacy and dietary counseling capacity.

Objective: Dietary habits may influence functional dyspepsia (FD), but their role remains inconclusive and unclear. These inconsistencies highlight the need for further research to establish evidence-based dietary guidelines. This systematic review and meta-analysis aimed to assess the link between dietary habits and FD, and their potential as modifiable factors in FD management.

Design: In this systematic review and meta-analysis, a comprehensive search was conducted up to December 2025.

Setting: Studies were identified in PubMed, ISI Web of Science, and Scopus.

Participants: We included observational studies involving adults with FD that investigated the association between various dietary habits and FD.

Results: After screening 2,640 articles, we identified 11 studies comprising 21,220 participants. The meta-analysis revealed that spicy food consumption significantly increased the risk of FD by 32% (n = 4, OR = 1.32, 95% CI: 1.04-1.67), as well as epigastric pain (n = 4, OR = 1.46, 95% CI: 1.22-1.75) and epigastric burning (n = 4, OR = 1.47, 95% CI: 1.21-1.78). Additionally, higher meal frequency was associated with a reduced risk of FD (n = 2, OR = 0.52, 95% CI: 0.32-0.86). There was no significant association between spicy food consumption and early satiety or postprandial fullness.

Conclusion: This study indicated that spicy food consumption might be associated with an increased risk of FD, epigastric pain, and burning. However, higher meal frequency might have protective effect on FD. Further high-quality studies are warranted to confirm these results.

Objective: This systematic review aims to synthesize the current evidence and develop evidence-based recommendations regarding time-restricted eating (TRE) for weight management in adults with overweight and obesity, addressing a gap in specific clinical guidelines.

Methods: We conducted a systematic search of nine databases and six websites for relevant literature up to September 2024. Included studies comprised randomized controlled trials (RCTs), clinical guidelines, expert consensus statements, and systematic reviews focusing on TRE in the target population. Two reviewers independently performed study selection, data extraction, and methodological quality assessment using standardized tools (e.g., AMSTAR 2, AGREE II, JBI checklists). Evidence was synthesized thematically, and recommendations were graded using the JBI framework.

Results: The search identified 5535 records. After screening, 25 articles were included: five guidelines, three expert consensuses, eight systematic reviews, and nine RCTs. The synthesis yielded 39 key evidence points across six domains: applicable populations, intervention protocols, dietary considerations, psychological and sleep effects, efficacy, and safety. The synthesized evidence suggests that TRE can induce significant weight loss and improve cardiometabolic parameters (e.g., blood glucose and lipid profiles) in the short to medium term. While heterogeneity exists across individual studies, this review identifies key factors (e.g., eating window protocols, adherence) that may influence outcomes and provides a framework for clinical decision-making.

Conclusions: TRE represents a promising dietary intervention for adults with overweight and obesity. This review provides a structured evidence summary and practical recommendations to guide its clinical application. Future research should focus on the long-term efficacy, sustainability, and impact of TRE on hard clinical endpoints.

Level of evidence: Level I, systematic review.

Background: Mothers with inadequate intake of micronutrients are a serious and collective global health issue, especially in poverty stricken areas. However, the available studies in Ethiopia have been usually focused in early childhood nutrition using old statistical methods. The aim of this study is to apply multiple machine learning algorithms to construct a high fidelity predictive model and identify key predictors of Inadequate Minimum Dietary Diversity for Women among Ethiopian mothers with a child under 24 months.

Methods: A weighted sample of 3,914 women from the Ethiopian Demographic Health Survey 2016 was utilized to conduct a secondary analysis of data. The outcome variable was dichotomous: Inadequate Minimum Dietary Diversity for Women or Adequate Minimum Dietary Diversity for Women. The data was divided into 20% and 80% in the testing and training respectively. We used R software version 4.5 to apply and test ML algorithms. To deal with the harsh imbalance of classes, the Adaptive Synthetic method was utilized, and robust feature selection was performed by the Boruta algorithm. An entire set of seven machine learning algorithms classifiers was trained and tested (Accuracy, Recall, F1 score, specificity, precision and AUC).

Findings: Random forest algorithm (accuracy = 95.03%, sensitivity = 92.73%, precision = 97.28% F1-score = 94.94% and AUC = 98.34) was the best predictive model since it had better performance metrics on the test set. Rural residence, unprotected source of drink water, poor wealth index, no media exposure, unimproved toilet facility, no education, age, religion, and traditional method of contraceptive were the top factors to predict minimum dietary diversity of women.

Conclusion: Machine learning models, specifically the Random forest classifier, are well-suited to predict a mother with Minimum Dietary Diversity, which provides a useful decision-supporting tool to the health officials of the populace. The results of the study suggest evidence based guidance, including the necessity of geographically concentrated interventions and the combined programs that can integrate the effects of nutrition education, family planning, and economic empowerment to help reduce the overwhelming socioeconomic and demographic risk factors to advance poor maternal dietary diversity in Ethiopia.

Background: Ketogenic diet (KD), characterized by low carbohydrate and high fat intake, has become an increasingly popular strategy for weight management and metabolic improvement in recent years. However, its potential influence on lower urinary tract symptoms (LUTS), particularly overactive bladder (OAB) and nocturia, remains unclear. This study aimed to investigate the associations between the ketogenic diet ratio (KDR) and OAB or nocturia, and to explore the mediation roles of the frailty index (FI) and platelet-to-HDL-C ratio (PHR).

Methods: We analyzed data from 22,249 adults in the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018. KDR was calculated as (0.9 × fat + 0.46 × protein) / (0.1 × fat + 0.58 × protein + carbohydrates). Weighted multivariable logistic regression models were used to assess the associations between KDR and OAB or nocturia. Restricted cubic spline and threshold effect analyses explored nonlinear relationships, while mediation analyses examined the roles of FI and PHR. Subgroup and interaction analyses evaluated the modifying effect of physical activity.

Results: KDR showed distinct associations with LUTS phenotypes. A nonlinear, U-shaped relationship was observed between nocturia and KDR, with an inflection point at approximately 0.342. Below this point, higher KDR was associated with a lower nocturia risk, while above it, the risk increased. In contrast, KDR displayed a linear inverse association with OAB (OR = 0.48, 95% CI = 0.30-0.79, P = 0.004). The KDR-nocturia relationship was significantly modified by physical activity (P for interaction < 0.05): the inverse association was more pronounced in individuals with low physical activity (< 500 MET-min/week), whereas a threshold effect persisted among highly active participants. Mediation analyses further revealed that FI and PHR partially mediated the association between KDR and OAB, with indirect effect proportions of 21.6% and 5.8%, respectively.

Conclusions: KDR was inversely associated with OAB and showed a threshold-dependent, U-shaped relationship with nocturia, with patterns potentially influenced by physical activity. These findings provide a novel metabolic perspective on LUTS management and suggest that variations in ketogenic dietary balance and activity level may be relevant to bladder health. However, given the cross-sectional design, these associations should be interpreted cautiously, and causal relationships cannot be inferred.

Background: Malnutrition among children under five remains a pressing public health issue in Bangladesh. Identifying its determinants is critical for designing effective interventions. This study aims to evaluate the suitability of statistical models that account for the ordinal nature of malnutrition categories, comparing Multinomial Logistic Regression (MLR) and the Proportional Odds Regression Model (POM) using data from the sixth round of UNICEF's Multiple Indicator Cluster Survey (MICS).

Methods: Child nutritional status was assessed using weight-for-age Z-scores (WAZ), categorized into severely undernourished, moderately undernourished, and nourished. MLR and POM were applied to model the relationship between malnutrition and various socio-demographic and health-related factors. Model performance was compared using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC).

Results: POM demonstrated superior model fit (AIC: 8788.996, BIC: 9099.4353) compared to MLR (AIC: 8844.849, BIC: 9451.617). Significant predictors of malnutrition were identified through POM which included geographical division, child's sex, mother's BMI, mother's education, prenatal care, birth size, and household wealth index.

Conclusions: The Proportional Odds Regression Model outperformed Multinomial Logistic Regression by effectively capturing the ordinal structure of malnutrition categories. These findings underscore key determinants of child malnutrition and offer valuable guidance for targeted nutritional policies and development programs in Bangladesh.