Search Results (6,872)

Background: Critical illness is characterized by profound and rapidly evolving metabolic derangements driven by systemic inflammation, hypercatabolism, fluid shifts, and endocrine dysregulation. These dynamic changes markedly limit the accuracy of predictive equations, increasing the risk of both underfeeding and overfeeding. Indirect Calorimetry Energy represents the gold standard for measuring energy expenditure, while bioelectrical impedance vector analysis (BIVA) provides complementary insights into hydration status, cellular integrity, and body cell mass. In palliative care, AI-supported integration of indirect calorimetry and BIVA enables goal-concordant artificial nutrition by aligning energy delivery with real-time metabolic status while minimizing symptom burden. Artificial intelligence (AI) has emerged as a promising tool to integrate these heterogeneous data streams and support adaptive nutritional strategies. Methods: We conducted a structured narrative review of the literature published between 2000 and 2025 using PubMed, Scopus, Embase, and Web of Science. Artificial intelligence was not used to perform the literature search or study selection. Instead, AI was analyzed as a clinical and technological component within the included studies and explored as a future enabling strategy. Eligible publications involved adult critically ill patients and addressed indirect calorimetry, BIVA-derived parameters, or AI-based metabolic modeling applied to nutritional support. Given the heterogeneity of study designs and outcomes, findings were synthesized qualitatively. Results: Predictive equations showed substantial inaccuracy in unstable metabolic states, with errors frequently exceeding ±20–40%. Indirect calorimetry enabled individualized assessment of energy expenditure but remained limited by intermittent availability. Serial BIVA assessments consistently identified clinically relevant alterations in hydration status, body cell mass, and phase angle, the latter being strongly associated with adverse outcomes. Studies incorporating AI demonstrated improved integration of calorimetry, BIVA, and clinical variables, allowing identification of metabolic phenotypes, anticipation of metabolic shifts, and generation of adaptive nutritional recommendations. Conclusions: This narrative review highlights the complementary roles of Indirect Calorimetry and BIVA in characterizing metabolic needs in critical illness. Artificial intelligence does not replace these tools but enhances their clinical utility by integrating multidimensional data into dynamic, patient-specific nutritional strategies. The combined AI–IC–BIVA approach represents a promising framework for metabolic precision nutrition in the ICU, warranting prospective validation. Full article

Background: Reliable biomarkers that capture tumor–host interactions and predict treatment resistance in extensive-stage small cell lung cancer (SCLC) remain limited. We evaluated the prognostic and predictive value of the pretreatment lactate dehydrogenase-to-albumin ratio (LAR), an integrative biomarker reflecting metabolic activity, systemic inflammation, and host nutritional status. Methods: This multicenter, retrospective cohort study included patients with extensive-stage SCLC treated at five tertiary centers between 2016 and 2024. Pretreatment LAR was calculated using baseline serum lactate dehydrogenase and albumin levels and dichotomized using a Youden index-derived cut-off at the 12-month overall survival (OS) horizon. Time-dependent receiver operating characteristic (ROC) analyses using inverse probability weighting were performed to assess discriminative performance. Survival outcomes were evaluated using Kaplan–Meier estimates and Cox proportional hazards models. Associations with platinum resistance and lack of objective treatment benefit (defined as progressive disease as best response) were examined using logistic regression models. Results: A total of 223 patients were included. Elevated LAR was associated with inferior OS (median, 15.8 vs. 25.2 months; log-rank p < 0.001) and progression-free survival (7.9 vs. 11.5 months; p < 0.001). In multivariable analysis, LAR remained independently associated with OS (HR, 1.43; 95% CI, 1.04–1.95; p = 0.028). LAR demonstrated modest but consistently superior discriminative performance compared with other inflammatory indices for both 12-month OS (area under the curve [AUC], 0.692) and 6-month progression-free survival (PFS) (AUC, 0.646), with statistically significant differences in DeLong comparisons. Higher LAR was independently associated with increased odds of platinum resistance (adjusted odds ratio [aOR], 2.31; 95% CI, 1.41–3.81; p = 0.001) and lack of objective treatment benefit (adjusted OR, 2.04; 95% CI, 1.33–3.14; p = 0.001). Conclusions: Pretreatment LAR is a clinically accessible and biologically integrative biomarker associated with survival and treatment resistance in extensive-stage SCLC. By capturing tumor–host interactions, LAR may support risk stratification and identify patients at increased risk of early treatment failure. Prospective validation is warranted to define its role in biomarker-driven clinical decision-making. Full article

Background: Albania has undergone a rapid demographic transition characterized by pronounced population aging. Comprehensive geriatric assessment—functional performance, validated nutritional screening tools, and systematic evaluation of morbidities—is essential for accurately characterizing frailty and identifying the risk of malnutrition in its early stages. The objective of the present study was to improve the assessment of the health status of Albanian older adults, both community-dwelling and residing in long-term care facilities, by addressing both functional and nutritional components. Methods: This observational study included Albanian older adults aged ≥ 65 years, both institutionalized and community-dwelling. Frailty and nutritional status were assessed using validated questionnaires (Grauer Geriatric Functional Evaluation and Mini Nutritional Assessment—MNA), alongside body composition analysis performed by bioelectrical impedance analysis (BIA). Results: Data for 123 older adults were analyzed (56.9% female; mean age 71.3 ± 7.4 years; 54.5% institutionalized vs. 45.5% community-dwelling). A high prevalence of frailty and multimorbidity was observed, particularly among institutionalized older adults. With regard to nutritional status, marked age-related differences were identified among females, with a pronounced deterioration in those aged over 75 years. Body-composition-derived parameters identified a substantially higher proportion of individuals at risk of malnutrition compared with other conventional anthropometric measures. Low body cell mass index (BCMI) and institutionalization were the factors with the strongest independent associations with frailty (AOR 5.02, 95% CI 1.69–14.87, p = 0.004, and AOR 5.71, 95% CI 1.76–18.54, p = 0.004, respectively), while low BCMI was the only variable associated with an increased risk of malnutrition (AOR 4.88, 95% CI 1.78–13.40, p = 0.002). Conclusions: These exploratory findings suggest that incorporating body composition parameters into geriatric assessment may provide complementary information alongside traditional screening tools to support the development of targeted preventive and therapeutic strategies. Full article

Background: Blood flow restriction training (BFRT) is a low-load resistance training modality capable of inducing muscle hypertrophy and strength adaptations that are comparable to traditional high-load resistance training. Beyond athletic performance settings, BFRT has growing relevance for musculoskeletal health, rehabilitation and populations unable to tolerate high mechanical loads. However, substantial inter-individual variability in adaptive responses has been reported. Genetic and molecular factors may partly contribute to this variability and inform more individualised exercise strategies. Other intrinsic and extrinsic factors, including age, sex, training status, nutrition, and protocol-related differences, may also influence adaptive responses. Objective: This scoping review aimed to map available evidence on molecular modulators of adaptation to BFRT and to identify gaps in the literature regarding genetic influences on BFRT responses. Methods: A structured search of PubMed, Web of Science and Google Scholar was conducted till 1 February 2026. Experimental and quasi-experimental studies examining BFRT in relation to genetic polymorphisms, gene expression, and molecular signalling pathways associated with strength and hypertrophy outcomes were included. Primary outcomes were genetic and molecular factors relevant to BFRT adaptation, including genetic polymorphisms, gene expression, and molecular signalling markers. Secondary outcomes included muscle strength, hypertrophy, vascular responses, and related functional outcomes where reported. Study selection and data extraction were conducted according to PRISMA-ScR guidelines. The methodological quality of randomised controlled trials was assessed using the PEDro scale. This scoping review was registered retrospectively in the Open Science Framework on 17 March 2026, after completion of the literature search. Results: From an initial 47 records, only three studies (n = 3) met the inclusion criteria. The included studies reported molecular responses associated with BFRT, including downregulation of proteolytic genes, suppression of myostatin expression, and upregulation of angiogenic markers. Notably, no studies directly examined genetic polymorphism or genotype–BFRT interactions, highlighting a clear need for these studies in this field. Conclusions: This scoping review therefore identifies a critical evidence gap, with genotype-informed BFRT prescription remaining unsupported by the current literature. Limited evidence supports the possible role of BFRT in molecular responses associated with muscle adaptation. Future research should prioritise well-designed studies integrating both genetic and molecular analyses to better understand inter-individual variability in BFRT adaptations. Full article

Background/Objectives: A blunted growth hormone (GH) response in stimulation tests (GHSTs) in obese patients is well documented, with less evidence for insulin-like growth factor-1 (IGF-1) concentrations. The aim of this study was to assess the relationships between nutritional status, GH peak in GHST, and IGF-1 concentrations, and to develop machine learning prediction models of GH deficiency (GHD) in children with short stature. Methods: A case–control study included 1592 children with short stature, whose height, weight, body mass index (BMI), GH peak in two GHSTs, IGF-1 concentration and bone age (BA) were assessed. The cut-off of GH peak in two GHSTs between GHD and idiopathic short stature (ISS) was 10.0 µg/L; additionally, a lower cut-off of 7.0 µg/L was used in repeated analysis. Univariate statistical analyses and classification models were used to identify variables related to the normal and subnormal results of GHST. Results: Depending on the cut-off of GH peak (10.0 vs. 7.0 µg/L), GHD was diagnosed in 604 vs. 279 patients (37.9% vs. 17.5%). Children with GHD had significantly lower (p < 0.001) BMI SDS and IGF-1 SDS than ones with ISS for both cut-offs of GH peak. Overnutrition was associated with the lowest GH peak but the highest IGF-1 SDS; the opposite results were observed in undernutrition. A decision tree predicted GHD in 156 patients, in 149 based on BMI SDS > 0.91. A Naïve Bayes classifier predicted GHD in 118 cases, with BMI SDS and IGF-1 SDS being the only significant variables. The best multilayer perceptron (MLP) neural network predicted GHD in 310 patients, while a logistic regression model did so in 269 patients.. Conclusions: Interpretation of GHST should include the patient’s nutritional status in order to avoid overdiagnosis of GHD in overweight and obese children. Full article

Despite the productivity and economic limitations imposed by environmental and climatic conditions, livestock systems play a fundamental role in preserving habitats and high-conservation-value species, while delivering a broad spectrum of ecosystem services to rural populations. Breeders need timely information to produce safe, inexpensive, environmentally, and welfare-friendly food products. Information on feeding and nutrition is of particular importance since it represents a significant percentage of animal breeding costs. Automating the collection, analysis, and use of production-related information on livestock feeding systems represents one of the central challenges facing the sector. Precision feeding systems (PFSs) have deeply changed farm management by providing new information on the health status of animals, their welfare, and nutritional requirements. PFSs encompass modern electronic and ICT-related (information and communication technologies) technologies that facilitate the electronic measurement of critical components, ensuring optimum efficiency of both resource use and animal productivity. This review analyzes the current state and potential applications of precision feeding systems for sustainable livestock production. The implementation and feasibility of PFSs have been investigated across the major animal production species and contexts. Based on the available literature, real-time monitoring and control systems can improve the production efficiency of livestock farms. However, further research is needed, as several components of PFSs are still at different stages of development and commercial readiness. Full article

Background/Objectives: Metastatic pancreatic cancer is a highly lethal disease, and clinically useful biomarkers for outcome stratification are limited. Uric acid reflects systemic metabolic stress and inflammatory signaling, suggesting potential relevance as a tumor–host biomarker. However, the clinical significance of uric acid-based composite biomarkers in pancreatic cancer remains unclear. Methods: In this multicenter retrospective cohort study, 110 patients with metastatic pancreatic adenocarcinoma treated between 2015 and 2024 were analyzed. Sex-adjusted uric acid-based biomarkers were calculated using uric acid z-scores normalized by sex and integrated with markers of nutritional and immune status, including the uric acid z-score-to-albumin ratio (UAzAR) and uric acid z-score-to-lymphocyte ratio (UAzLR). Associations with overall survival (OS), progression-free survival (PFS), and chemotherapy response were evaluated using Kaplan–Meier analysis, Cox proportional hazards models, receiver operating characteristic (ROC) analyses, and multivariate logistic regression. Results: The median OS and PFS for the entire cohort were 12.6 months (95% CI 11.3–13.9) and 7.5 months (95% CI 6.6–8.4), respectively. Patients with high UAzAR had significantly shorter OS than those with low UAzAR (7.3 vs. 16.4 months; log-rank p < 0.001), and similar findings were observed for UAzLR (7.4 vs. 16.4 months; p < 0.001). In multivariate Cox models, elevated UAzAR independently predicted inferior OS (HR] 3.10, 95% CI 1.58–6.09; p = 0.001) and PFS (HR 2.35, 95% CI 1.22–4.52; p = 0.010), while elevated UAzLR was similarly associated with reduced OS (HR 3.28, 95% CI 1.68–6.39; p < 0.001) and PFS (HR 2.47, 95% CI 1.30–4.70; p = 0.006). High UAzAR and UAzLR were also independently associated with chemotherapy failure (adjusted odds ratio [OR] 5.52, 95% CI 2.16–14.06 and OR 6.42, 95% CI 2.49–16.55; both p < 0.001). In ROC analyses, UAzAR and UAzLR demonstrated moderate discrimination for 12-month OS (AUC 0.659 and 0.658) and stronger discrimination for 6-month PFS (AUC 0.705 and 0.692). Conclusions: Sex-adjusted uric acid-derived composite biomarkers independently predict survival and chemotherapy response in metastatic pancreatic cancer and may identify a high-risk metabolic phenotype relevant for clinical risk stratification. Full article

Diet quality, nutrition knowledge, and psychosomatic literacy—defined as the understanding of the interactions between diet, gut microbiota, and mental well-being—may shape weight-related behaviours in youth. This study used a cross-sectional design to integrate these domains with digital information pathways in Central–Eastern Europe. This study assessed diet quality, nutrition, and psychosomatic knowledge, supplement use, and health-information sources among Polish adolescents and young adults, with emphasis on age-related differences and the role of social media. A cross-sectional, anonymous online survey (October 2025–January 2026) was conducted in Poland (final analytical sample: n = 478; adolescents 15–19 years vs. young adults 20–30 years). Of 591 individuals who accessed the survey, 478 were included in the final analytical sample. Diet quality was estimated from FFQ data using KomPAN-derived indices (pHDI-10, nHDI-14, DQI). Nutrition knowledge (0–25 points), psychosomatic/gut–brain indicators, supplementation, and information sources were analysed using χ²/Fisher tests and Mann–Whitney U tests with effect sizes. The primary outcomes measured were dietary supplement use and excess body weight (BMI ≥ 25 kg/m²). Multivariable logistic regression examined predictors of supplement use and BMI ≥ 25 kg/m². Overall diet quality was low to moderate, with limited intake of whole grains, legumes, and fish, and common nutrition misconceptions. Social media was the most frequently indicated source of diet/supplement information and was independently associated with more frequent supplement use (OR = 2.29; 95% CI: 1.43–3.64). Adolescents reported lower whole-grain intake and more misconceptions than young adults. Predictors of BMI ≥ 25 kg/m² included male sex (OR = 2.46; 95% CI: 1.46–4.15), lower education, and lower nutrition knowledge, while age showed a non-linear positive association with excess body weight. Polish adolescents and young adults show gaps between declared pro-health attitudes and actual diet quality/competencies. Social media reliance appears particularly linked to product-oriented behaviours (supplementation). Prevention should strengthen nutrition and food safety education, digital health literacy, and professional guidance on supplementation, especially in adolescents. Our findings suggest that social media is a primary driver for dietary supplementation among Polish youth, more so than objective nutrition knowledge. While diet quality is linked to weight status, the relationship is complex. These results may inform future public health interventions targeting digital health literacy to promote balanced nutrition and safe supplementation practices. Full article

Objective: This study evaluated the relationships between the development and clinical course of pressure injuries (PIs) and neurological status, nutritional risk, and laboratory parameters among patients admitted to a tertiary intensive care unit. Materials and Methods: The single-center, retrospective, observational study included 220 patients hospitalized in the intensive care unit for at least 5 days. On the day of admission, Glasgow Coma Scale (GCS), Acute Physiology and Chronic Health Evaluation II (APACHE II), Braden, and Nutritional Risk Screening 2002 (NRS-2002) scores were assessed. Demographic characteristics, comorbidities, need for sedation and vasopressors, and laboratory parameters during the first 24 h (albumin, C-reactive protein, lactate, D-dimer) were analyzed. Factors independently associated with new PI development and clinical improvement were identified using binary logistic regression. Results: New PIs developed in 25% of patients. Patients with PI progression were older and had lower GCS and Braden scores, higher NRS-2002 scores, lower albumin levels, and higher D-dimer levels (p < 0.05). In multivariable analysis, low GCS (OR = 0.824), presence of comorbidity (OR = 2.327), and a high NRS-2002 risk level were independent predictors of new PI development. The model’s discriminative ability was acceptable (AUC = 0.756). Among patients with existing PIs, NRS-2002 score (OR = 0.450) and age (OR = 1.058) were independently associated with clinical improvement in an exploratory multivariable model. Conclusions: NRS-2002 was the only variable independently associated with both new PI development and the clinical improvement of existing lesions, underscoring the central role of nutritional risk assessment in ICU-based PI prevention and prognosis. Full article

Background: We investigated the prognostic value of nutritional indices in patients with atrial fibrillation (AF) undergoing percutaneous left atrial appendage occlusion (LAAO). Methods: This two-center retrospective study enrolled 151 patients (median age 75, IQR: 69–80) undergoing LAAO. The Controlling Nutritional Status (CONUT) score, Geriatric Nutritional Risk Index (GNRI), and Prognostic Nutritional Index (PNI) were calculated preoperatively. Endpoints included all-cause mortality (primary), postoperative bleeding, and stroke. Associations with mortality were analyzed using multivariable Cox regression models. Results: Over a median follow-up of 8 months (IQR: 5–13), 28 patients (18.5%) died. In multivariable analyses (adjusted for age, sex, diabetes, and chronic kidney disease), each 1-point increase in the CONUT score was associated with a higher risk of all-cause mortality (HR 1.196, 95% CI 1.029–1.390; p = 0.020), whereas higher GNRI values were associated with a lower mortality risk (HR 0.956, 95% CI 0.915–0.998; p = 0.042). In contrast, PNI was not associated with mortality (p = 0.993). Nutritional indices did not significantly predict secondary outcomes like bleeding or stroke. Conclusions: These findings suggest that malnutrition is strongly and independently associated with mortality in high-risk AF patients receiving LAAO. The CONUT score demonstrates the most robust association in this population, highlighting the importance of metabolic reserves. Full article

Background/Objectives: Nutritional vulnerability (NV) describes the interaction of diet quality, access to food, health status and socioeconomic factors and may differ between neighborhoods. Nevertheless, there is still a limited amount of evidence regarding local NV variations in contrasting resource landscapes. The purpose of this study was to operationalize NV in Windham, Connecticut and conduct an analysis of its spatial distribution and the differences between neighborhoods for NV and specifically diet quality. Methods: NV was measured with four indicators, including two diet quality measures (liking-based DQI and short food frequency-based sHEI), food security, obesity, and SNAP participation. Areas of vulnerable concentration were determined through spatial mapping. Indicators related to each other were measured by Spearman correlation. To compare the contrasting neighborhoods (resource-dense vs. resource-limited), contextual differences were studied and differences in NV indicators, sociodemographic and movement factors were compared with the help of chi-square tests. Diet quality measures were jointly examined for concordance (both measures low or high) and discordance. Results: Area-level comparisons showed significant differences in mobility-related and sociodemographic characteristics, including vehicle access and education level (p < 0.05). High diet quality (measure concordance) was reported by individuals living in high-resourced regions; low diet quality (measure concordance) by individuals in low-resourced regions. Conclusions: The NV Map illustrated focal patterns of vulnerability determined by the interplay of sociodemographic disadvantage and mobility-related limitations and not by distance to food resources. These results give practical spatial data to promote specific nutrition and resource intervention. Full article

Background: Malnutrition and systemic inflammation are increasingly recognized as important determinants of prognosis in patients with heart failure. Several immunonutritional indices, including the Prognostic Nutritional Index (PNI), the Controlling Nutritional Status (CONUT) score, and the C-reactive protein–albumin–lymphocyte (CALLy) index, have been proposed as markers of nutritional and inflammatory status. However, their prognostic value in elderly patients with heart failure with preserved ejection fraction (HFpEF) remains incompletely defined. This study aimed to evaluate the prognostic significance of these immunonutritional indices in elderly patients with HFpEF over a long-term follow-up period. Methods: This retrospective observational study included 200 elderly patients hospitalized with HFpEF (mean age 86.6 ± 6.5 years). Clinical, laboratory, and echocardiographic parameters were collected at admission. Nutritional status was assessed using PNI, CONUT score, and CALLy index. Patients were followed for mortality during long-term follow-up. Survival analyses were performed using Cox regression models, receiver operating characteristic (ROC) curves, and Kaplan–Meier analysis. Median follow-up was 3.8 years (IQR 2.1–5.9). Results: During follow-up, 123 patients (61.5%) died, while 77 patients (38.5%) were alive at the end of observation. In univariate analysis, PNI, CONUT score, left ventricular ejection fraction (LVEF), and the tricuspid annular plane systolic excursion to systolic pulmonary artery pressure (TAPSE/sPAP) ratio were significantly associated with mortality. In multivariate analysis, the CONUT score, LVEF, and the TAPSE/sPAP ratio remained independent predictors of mortality. ROC analysis showed strong prognostic performance for the TAPSE/sPAP ratio (AUC 0.932), CONUT score (AUC 0.925), and LVEF (AUC 0.897). Optimal cut-off values for mortality prediction were CONUT ≥ 6, LVEF ≥ 65%, and TAPSE/sPAP ≤ 0.55 mm/mmHg. Kaplan–Meier analysis confirmed significantly reduced survival among patients with higher CONUT scores, higher LVEF, and an impaired TAPSE/sPAP ratio. Conclusions: In elderly patients with HFpEF, nutritional status and cardiopulmonary functional parameters are important determinants of long-term prognosis. The CONUT score emerged as the most informative immunonutritional index, while echocardiographic parameters reflecting ventricular function and right ventricular–pulmonary arterial coupling provided additional prognostic information. Integrating nutritional assessment with echocardiographic evaluation may improve risk stratification in elderly patients with HFpEF. Full article

Background/Objectives: Aging is associated with physiological, biochemical, and psychosocial changes that can significantly affect nutritional status and overall health. In Sub-Saharan Africa (SSA), older adults face unique age-related challenges that may compromise healthy aging, yet evidence remains fragmented. This systematic review synthesized the existing literature on the nutritional status, age-related challenges, and strategies to promote healthy aging of older adults in SSA. Methods: A systematic literature search was conducted on PubMed, Scopus, ScienceDirect, and Cochrane Library to identify relevant studies published up to 10 December 2025. Results: Fifty-five studies met the inclusion criteria, with most of the studies coming from South Africa, Ghana, and Nigeria. Amongst community-dwelling populations, approximately 30–65% of the older adults were either malnourished or at risk of malnutrition, while hospital-based studies reported markedly higher burdens, with malnutrition prevalence exceeding 70% in some settings. Undernutrition, micronutrient deficiencies, and the coexistence of overweight and obesity were frequently observed, reflecting the region’s ongoing nutrition transition. Frailty emerged as the predominant age-related challenge, with prevalence ranging around 10–60%. Other common challenges included sarcopenia, reduced muscle strength, functional disability, cognitive impairment, and dysphagia, all of which were closely related to poor nutritional status, food insecurity, multimorbidity, and reduced quality of life. Few studies reported on healthy aging strategies, with the limited evidence suggesting that nutrition education, physical activity, and psychosocial interventions may enhance nutritional and functional outcomes. Conclusions: The need for context-specific, nutrition-sensitive interventions, and stronger health and social support systems is warranted to promote healthy aging in SSA older adults. Full article

Background/Objectives: This study analyzes the relationship between obesity phenotypes and sleep apnea risk in the Korean population. Methods: This study utilized data from the Korean National Health and Nutrition Examination Survey (KNHANES) collected between 2019 and 2021 (n = 10,970 adults; age ≥ 40 years). Obesity phenotypes were classified into four groups based on body mass index (BMI) and the presence of metabolic syndrome: metabolically healthy normal weight (MHNW), metabolically abnormal normal weight (MANW), metabolically healthy obese (MHO), and metabolically abnormal obese (MAO). Sleep apnea risk was assessed using the STOP-Bang questionnaire, and multivariate logistic regression analyses were performed to evaluate the association between obesity phenotypes and sleep apnea. Results: Among the 10,970 participants, the phenotypes were as follows: MHNW, 51.1%; MANW, 10.3%; MHO, 15.8%; and MAO, 21.8%. Baseline characteristics differed significantly across phenotypes, with the metabolically unhealthy groups (MANW and MAO) being older and exhibiting more cardiometabolic risk factors than the metabolically healthy groups. The prevalence of STOP-Bang questionnaire components differed significantly across phenotypes (all p < 0.001), and the mean STOP-Bang score increased from MHNW to MAO. In multivariate logistic regression analyses, the odds (adjusted odds ratio [95% CI]) of high sleep apnea risk were significantly elevated in all non-MHNW phenotypes: MAO (10.27 [7.71–13.68]), MHO (6.17 [4.35–8.75]), and MANW (1.91 [1.22–2.98]). Notably, MAO conferred a significantly higher risk than MHO (OR 1.69 [1.34–2.13]), highlighting the synergy of obesity and metabolic dysfunction. Obesity phenotypes, defined by BMI and metabolic health status, were differentially associated with sleep apnea risk in Korean adults. The highest risk was observed in individuals with both obesity and metabolic syndrome, while metabolically abnormal normal-weight adults also showed a significantly increased risk. Conclusions: Metabolic dysfunction may contribute to sleep apnea risk beyond body size alone and may be considered in risk stratification strategies. Full article

Background/Objectives: Early detection of postnatal growth failure (PGF) is essential for optimizing nutritional management in preterm infants, as PGF is associated with adverse neurodevelopmental outcomes. Early prediction remains difficult because postnatal growth is influenced by multiple clinical factors including gestation age, birth weight, nutritional status, and comorbidities. Machine-learning approaches have been proposed to predict complex neonatal outcomes. This study compared the predictive performance of neonatologists with that of a machine-learning model for predicting PGF. Methods: PGF was defined as a decrease in weight z-score greater than 1.28 at discharge compared with birth. A machine-learning model based on extreme gradient boosting (XGBoost) was trained using a dataset of 7954 very low birth weight (VLBW) infants. Nine neonatologists independently assessed 100 clinical cases through a questionnaire-based evaluation, including 50 patients with PGF. Predictive performance was evaluated using seven metrics: area under the receiver operating characteristic curve (AUROC), accuracy, error rate, positive predictive value (PPV), sensitivity, specificity, and F1 score. Results: The neonatologists had a median of 5 years (range: 4–10 years) of clinical experience. The median prediction score among the neonatologists was 52/100 (range, 44–60), whereas the XGBoost model achieved 79/100. The XGBoost model achieved an AUROC of 0.79, accuracy of 0.79, error rate of 0.21, sensitivity of 0.82, and an F1 score of 0.80, demonstrating superior overall performance compared to the neonatologists. In addition, the XGBoost model had a lower error rate than the neonatologists (0.21 vs. 0.49), whereas specificity (0.76 vs. 0.86) and PPV (0.77 vs. 0.53) did not differ significantly. Conclusions: The machine-learning model demonstrated superior or comparable predictive performance to that of neonatologists in detecting PGF. Machine-learning-based prediction models may support early risk stratification and targeted nutritional management in VLBW infants. Full article