Purpose This paper aims to examine associations of the hemoglobin, albumin, lymphocyte and platelet (HALP) score with dyslipidemia, hypercholesterolemia, and hypertriglyceridemia in a nationally representative sample of US adults. Design/methodology/approach Data from NHANES 2005–2018 were analyzed. HALP was calculated from hemoglobin, albumin, lymphocyte count and platelet count. Survey-weighted logistic regression incorporated fasting subsample weights, strata and primary sampling units. Natural cubic splines assessed continuous dose-response patterns. Fully adjusted models excluded the four HALP components, and multiple imputation was used as a sensitivity analysis. Findings Among 11,350 complete-case participants, fully adjusted odds ratios comparing HALP quartile 4 with quartile 1 were 1.46 (95% confidence interval [CI] 1.23–1.74) for dyslipidemia, 1.31 (95% CI 1.05–1.65) for hypercholesterolemia, and 1.67 (95% CI 1.30–2.15) for hypertriglyceridemia. Dyslipidemia and hypertriglyceridemia were also associated with quartile 3, whereas the hypercholesterolemia association was weaker and significant only in quartile 4. Natural cubic splines showed nonlinear dose-response associations for all three outcomes. The cross-sectional findings do not establish causality or clinical predictive utility. Research limitations/implications Even with attempts to control for interfering variables as much as possible, the influence of other potential covariates remains. Attempts to control interfering variables are made, yet the influence of other potential covariates continues. The exclusion of numerous participants due to incomplete data or unmet inclusion criteria may have led to selection bias, impacting the generalizability of the findings. This exclusion could affect the generalizability of the results to broader populations. Practical implications The investigation may provide an understanding of causal association between HALP score and dyslipidemia. Using the NHANES database, the research selected a typical group of individuals through detailed multi-stage sampling methods. The four parts of HALP score are also quantifiable and provide considerable clinical benefits. HALP score consists of four parts that are easy to measure and offer substantial benefits in a clinical setting. Social implications Robust statistical methods were used, such as adjusting for confounding variables, to minimize potential bias. Future investigations with larger and more inclusive datasets are recommended to authenticate these conclusions and enhance their applicability to diverse populations. Originality/value This study provides nationally representative, survey-weighted evidence comparing HALP associations across three lipid outcomes and characterizing their continuous dose-response patterns. Longitudinal and externally validated studies are required before clinical application.
Purpose The purpose of this review is to examine how provitamin A accumulation in maize, post-harvest retention, food processing, carotenoid bioavailability and host-specific bioconversion collectively influence vitamin A delivery, and to propose the delivered retinol activity equivalent (dRAE) framework for scenariobased assessment. Vitamin A deficiency (VAD) remains a major global public health problem, particularly in populations reliant on staple-based diets. Provitamin A (PVA)-biofortified maize has emerged as a sustainable food-based intervention to improve vitamin A intake. Design/methodology/approach This study integrates current knowledge on the molecular basis of PVA accumulation, carotenoid stability, post-harvest retention and host factors affecting vitamin A utilization. Literature was collected from Web of Science, Scopus and PubMed using keywords related to PVA maize breeding, processing, storage and bioavailability. Priority was given to recent peer-reviewed high-quality studies reporting evidence on PVA concentration, carotenoid retention, bioavailability and bioconversion. Findings Molecular breeding has increased kernel ß-carotene through key carotenoid pathway genes, including lcyE and crtRB1. However, kernel carotenoid concentration alone does not ensure effective vitamin A delivery. Post-harvest degradation during storage, processing and cooking, together with dietary and physiological factors, can substantially influence carotenoid availability and conversion to retinol. Host genetic variability, particularly polymorphisms in BCMO1, further contributes to differences in ß-carotene conversion efficiency. Practical implications The dRAE framework provides a conceptual, scenario-based approach for considering crop composition, post-harvest retention, bioavailability and bioconversion together when evaluating the potential vitamin A contribution of PVA-biofortified maize. Originality/value Effective vitamin A delivery from PVA-biofortified maize depends on kernel carotenoid concentration, post-harvest retention, bioavailability and bioconversion. The authors propose a delivery-oriented framework, Delivered Retinol Activity Equivalent (dRAE), to integrate these factors under specified conditions.
Purpose This study aims to culturally adapt the Gestational Weight Gain Psychosocial Risk Assessment Tool (GWG tool) and evaluate its psychometric properties among pregnant women in Türkiye. Design/methodology/approach This methodological study included 312 pregnant women aged ≥ 19 years. Factorial validity and internal consistency were evaluated using confirmatory factor analysis (CFA) and Cronbach’s alpha, respectively. Associations between the T-GWG factors and the Body Understanding Measure for Pregnancy (BUMPs) and Eating Attitudes Test-26 (EAT-26) scores were also examined. Findings CFA supported the three-factor structure of the T-GWG, comprising self-efficacy (SE), attitudes towards weight gain (ATW) and body image (BI), with acceptable model fit. Cronbach’s alpha coefficients were 0.97 for SE, 0.95 for ATW and 0.92 for BI. SE was positively correlated with BUMPs total and factor scores and the EAT-26 social pressure factor, whereas ATW was negatively correlated with BUMPs and EAT-26 total and factor scores. No significant correlations were observed for BI. These findings provide preliminary evidence supporting the factorial validity and internal consistency of the T-GWG. Originality/value To the best of the authors’ knowledge, this is the first study to culturally adapt and psychometrically evaluate the GWG tool among pregnant women in Türkiye. The T-GWG provides a brief measure of psychosocial dimensions related to gestational weight management for further research in this population.
Purpose Ultra-processed foods (UPFs) have become dominant in the global food system. They are considered one of the etiological factors in the global syndemic of obesity, malnutrition and climate change. UPFs are characterized by poor nutritional composition and pose a threat to local food diversity and cultural traditions. Furthermore, their environmental impacts have recently come under scrutiny. This study aims to identify existing evidence on the environmental impacts associated with UPFs. Design/methodology/approach The review was conducted in three databases (PubMed/MEDLINE, Scopus and EMBASE) in March 2025. The PECOS acronym was used to establish the eligibility criteria. The search and selection of studies, as well as data extraction, were performed by two researchers, with discrepancies resolved by a third. The methodological quality of the included studies was also assessed. Findings UPFs contribute to multiple environmental impacts and are associated with increased greenhouse gas emissions, water use, land use, higher acidification and eutrophication potential, water scarcity, plastic consumption, larger carbon, water and ecological footprints, depletion of fossil resources and increased energy demand. Originality/value UPFs contribute significantly to current environmental degradation, with profound implications for human, animal and ecosystem health.
Purpose The purpose is the study was to evaluate the accuracy and consistency of artificial intelligence (AI) models in estimating environmental parameters of different diet types. Design/methodology/approach A model performance evaluation study was conducted using four generative AI systems (ChatGPT-5.2, Gemini-3, Grok-4.1 and DeepSeek-V3.2). Four standardized dietary scenarios were developed (current diet, model diet, lacto-ovo vegetarian diet and vegan diet), each providing 2000 kcal/day. Carbon footprint (CF), water footprint (WF), energy footprint (EF) and land use were calculated using life cycle assessment–based databases and used as reference values. Each AI model was queried three times using the same standardized prompt and mean outputs were analyzed. Findings Significant differences between AI-generated estimates and reference values were observed across most dietary scenarios and sustainability parameters (p < 0.05). ChatGPT-5.2 demonstrated the best overall performance, particularly for WF [mean absolute error (MAE): 124.19; absolute error: 6.11%] and EF (MAE: 5.46; absolute error: 13.82%). DeepSeek-V3.2 showed the lowest prediction error for CF (MAE: 0.25; absolute error: 12.18%) and land use (MAE: 1.12; absolute error: 23.99%). In contrast, Grok-4.1 exhibited the highest deviations, with CF errors reaching 48.38% and substantial overestimations of land use (absolute error: 199.78%). Large deviations were particularly evident for WF and land use across several models. Originality/value To the best of the authors’ knowledge, this study provides one of the first comparative evaluations of generative AI models in estimating environmental sustainability indicators of dietary patterns. By benchmarking AI-generated outputs against life cycle assessment–based reference values across standardized diet scenarios, the study offers new evidence regarding the reliability and limitations of AI in sustainable nutrition research.
