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Malnutrition is classified into marasmus and kwashiorkor in children. However, the clinical significance of these aspects is unclear in adult patients with heart failure (HF). We divided 2308 adult patients with HF into four groups according to marasmus type (body mass index < 18.5 kg/m2) and kwashiorkor type (serum albumin < 3.4 g/dL) malnutrition: Group C (no malnutrition, n = 1511, 65.5%), Group M (marasmus type malnutrition, n = 133, 5.8%), Group K (kwashiorkor type malnutrition, n = 554, 24.0%) and Group MK (marasmic-kwashiorkor type malnutrition, n = 110, 4.8%). Group M showed the lowest blood pressure. Groups K and MK showed higher levels of B-type natriuretic peptide. Right atrial pressure was lowest in Groups M and MK. Kaplan-Meir analysis demonstrated that Group MK had the lowest event-free rate of all-cause death and cardiac death. In the multivariable Cox proportional hazard analysis, Groups M, K, and MK were associated with all-cause death (hazard ratio 1.790, 1.657 and 2.313, respectively) and cardiac death (hazard ratio 2.053, 1.855 and 3.001, respectively) compared to Group C as a reference. Marasmus type and kwashiorkor type malnutrition are associated with distinct profiles and high mortality, and marasmic-kwashiorkor type malnutrition has the poorest prognosis. [ABSTRACT FROM AUTHOR]

Cardiometabolic risk factors increase the chance of developing cardiovascular disease (CVD) and type 2 diabetes. Most CVD risk factors are influenced by total and regional obesity. A higher risk of developing CVD may be linked to vitamin D deficiency, which is more prevalent in the older population. With the goal of evaluating the association between vitamin D and cardiometabolic risk factors and total and regional obesity in older adults, this research included 25 (OH) vitamin D3 concentrations and biochemical markers associated with cardiometabolic diseases, as well as total and regional adiposity, which was measured by DXA. A total of 1991 older participants in the PoCOsteo study were included. Overall, 38.5% of participants had vitamin D deficiency. After adjusting for confounders, the results of multiple linear and logistic regression suggested an inverse association between vitamin D and body mass index (P = 0.04), waist circumference (P = 0.001), total fat (P = 0.02), android fat (P = 0.001), visceral fat (P < 0.001), subcutaneous fat (P = 0.01), trunk fat (P = 0.006), arm fat (P = 0.03), high systolic blood pressure (P = 0.004), high total cholesterol (P < 0.001), high LDL-cholesterol (P < 0.001), high serum triglycerides (P = 0.001), and high fasting glucose (P < 0.001). Additionally, higher vitamin D concentrations decreased the risk of dyslipidemia by 2%. Our results showed a significant association between serum vitamin D and a number of cardiometabolic risk factors, including total and regional obesity. [ABSTRACT FROM AUTHOR]

The 24-h movement guidelines for children and adolescents comprise recommendations for adequate sleep, moderate to vigorous physical activity (MVPA) and sedentary behaviour (SB). However, whether adolescents who meet these 24-h movement guidelines may be less likely to have high blood pressure (HBP) has not been established. The present study assessed the association between meeting 24-h movement guidelines and HBP in a school-based sample of 996 adolescents between 10–17 years (13.2 ± 2.4 years, 55.4% of girls). Blood pressure was measured using a digital oscillometric device, while sleep, MVPA and SB were measured using the Baecke questionnaire. The association between the 24-h movement guidelines and HBP was performed using binary logistic regression adjusted for sex, age, socioeconomic status, and body mass index. It was observed that less than 1% of the sample meet the three 24-h movement guidelines. The prevalence of HBP was lower in adolescents who meet all three movement 24-h guidelines (11.1%) compared to those who did not meet any guidelines (27.2%). Individual 24-h movement guidelines analysis showed that adolescents with adequate sleep were 35% less likely to have HBP (OR = 0.65; 95% CI 0.46–0.91). Meeting sleep guidelines combined with meeting MVPA (OR = 0.69; 95% CI 0.50–0.95) or SB (OR = 0.67; 95% CI 0.48–0.94) was inversely associated with HBP. Adolescents who meet two or three 24-h movement guidelines were respectively 47% (OR = 0.53; 95% CI 0.29–0.98) and 34% (OR = 0.66; 95% CI 0.48–0.91) less likely to have HBP. In adolescents, meeting sleep and 24-h movement guidelines were inversely associated with HBP. [ABSTRACT FROM AUTHOR]

This study aimed to progress the understanding of idiopathic hypersomnia (IH) by assessing the moderating influence of individual characteristics, such as age, sex, and body mass index (BMI) on sleep architecture. In this retrospective study, 76 IH participants (38.1 ± 11.3 years; 40 women) underwent a clinical interview, an in-laboratory polysomnography with a maximal 9-h time in bed and a multiple sleep latency test (MSLT). They were compared to 106 healthy controls (38.1 ± 14.1 years; 60 women). Multiple regressions were used to assess moderating influence of age, sex, and BMI on sleep variables. We used correlations to assess whether sleep variables were associated with Epworth Sleepiness Scale scores and mean sleep onset latency on the MSLT in IH participants. Compared to controls, IH participants had shorter sleep latency (p = 0.002), longer total sleep time (p < 0.001), more time spent in N2 sleep (p = 0.008), and showed trends for a higher sleep efficiency (p = 0.023) and more time spent in rapid eye movement (REM) sleep (p = 0.022). No significant moderating influence of age, sex, or BMI was found. More severe self-reported sleepiness in IH patients was correlated with shorter REM sleep latency and less N1 sleep in terms of proportion and duration (ps < 0.01). This study shows that, when compared to healthy controls, patients with IH had no anomalies in their sleep architecture that can explain their excessive daytime sleepiness. Moreover, there is no moderating influence of age, sex, and BMI, suggesting that the absence of major group differences is relatively robust. [ABSTRACT FROM AUTHOR]

To examine the influence of Body Mass Index (BMI) on laparoscopic gastrectomy (LG) short-term and long-term outcomes for gastric cancer. A retrospective analysis was conducted on gastric cancer patients undergoing LG at the Third Hospital of Nanchang City from January 2013 to January 2022. Based on WHO BMI standards, patients were categorized into normal weight, overweight, and obese groups. Factors such as operative time, intraoperative blood loss, postoperative complications, and overall survival were assessed. Across different BMI groups, it was found that an increase in BMI was associated with longer operative times (average times: 206.22 min for normal weight, 231.32 min for overweight, and 246.78 min for obese), with no significant differences noted in intraoperative blood loss, postoperative complications, or long-term survival among the groups. The impact of BMI on long-term survival following LG for gastric cancer was found to be insignificant, with no notable differences in survival outcome between different BMI groups. Although higher BMI is associated with increased operative time in LG for gastric cancer, it does not significantly affect intraoperative blood loss, postoperative complications, recovery, or long-term survival. LG is a feasible treatment choice for obese patients with gastric cancer. [ABSTRACT FROM AUTHOR]

Sarcopenia has been associated with higher toxicity induced by anti-cancer treatments and shorter survival in patients with squamous cell lung carcinoma (SqCLC). Over the past few decades, immune checkpoint inhibitors (ICIs) significantly improves the prognosis. However, few clinical studies explored the effectiveness of immunotherapy in the elderly population. Here, we performed a retrospective analysis to determine the prognostic role of sarcopenia in older patients with SqCLC receiving ICIs. We retrospectively assessed SqCLC patients who were treated with PD-1 inhibitors and all patients were at least 70 years old. Pre-treatment sarcopenic status was determined by analyzing L3 skeletal muscle index (SMI) with chest CT. Progression-free survival (PFS), disease-specific survival (DSS) and overall survival (OS) were estimated using the Kaplan–Meier method, and the differences in survival were compared using the log-rank test. Among 130 male SqCLC patients, 93 had sarcopenia. Patients with sarcopenia were older and had a lower body mass index (BMI). Over an average follow-up of 20.8 months, 92 patients died. For all 130 patients, the mean OS was 13.3 months. Patients with sarcopenia had a significantly shorter OS and PFS than those without sarcopenia (OS, 12.4 ± 5.2 months vs. 15.5 ± 10.5 months, P = 0.028; PFS, 6.4 ± 2.9 months vs. 7.7 ± 4.2 months; P = 0.035). Multivariable analysis showed that sarcopenia was an independent prognostic factor for shorter OS and PFS. CT-determined sarcopenia is an independent prognostic factor for older patients with SqCLC receiving ICIs. [ABSTRACT FROM AUTHOR]

The high burden of anaemia during pregnancy underscores the urgent need to gain a comprehensive understanding of the factors contributing to its widespread occurrence. Our study assessed the prevalence and the trends of moderate-to-severe anaemia (MSA) in late pregnancy (28 to 36 weeks) and then investigated the key determinants driving this prevalence among women in Lagos, Nigeria. We conducted a secondary data analysis involving 1216 women enrolled in the Predict-PPH study between January and March 2023. We employed a multivariate binary logistic regression model with a backward stepwise selection approach to identify significant predictors of MSA. The study revealed a 14.5% prevalence of MSA during pregnancy. Independent predictors of MSA included having given birth to two or more children (adjusted odds ratio = 1.46, 95% confidence interval: 1.03–2.07), having a maternal body mass index (BMI) of 28 kg/m2 or higher (adjusted odds ratio = 1.84, 95% confidence interval: 1.29–2.61), having less than tertiary education (adjusted odds ratio = 1.51, 95% confidence interval: 1.08–2.11), and being unemployed (adjusted odds ratio = 1.97, 95% confidence interval: 1.19–3.26). It is crucial for pregnant women, particularly those with higher parities and elevated BMI, to be monitored regularly for anaemia and its consequences during their antenatal care. Additionally, addressing the link between low education, unemployment, and anaemia necessitates comprehensive strategies that empower women in terms of education and economic status to enhance the overall well-being of individuals and communities, ultimately reducing the prevalence of anaemia and associated health issues in pregnancy. [ABSTRACT FROM AUTHOR]

Plyometric training (PT) is an effective training method for improving physical fitness among trained individuals; however, its impact on health-related physical fitness in untrained participants remains ambiguous. Therefore, this meta-analysis aimed to evaluate the effects of PT on health-related physical fitness among untrained participants. Six electronic databases (PubMed, CINAHL Plus, MEDLINE Complete, Web of Science Core Collection, SCOPUS, and SPORTDiscus) were systematically searched until March 2024. We included controlled trials that examined the effects of PT on health-related physical fitness indices in untrained participants. Twenty-one studies were eligible, including a total of 1263 participants. Our analyses revealed small to moderate effects of PT on body mass index, muscular strength, cardiorespiratory fitness, and flexibility (ES = 0.27–0.61; all p > 0.05). However, no significant effects were detected for body fat percentage and lean mass (ES = 0.21–0.41; all p > 0.05). In conclusion, the findings suggest that PT may be potentially effective in improving health-related physical fitness indices (i.e., body mass index, muscular strength, cardiorespiratory fitness, and flexibility) in untrained participants. However, the results should be interpreted cautiously due to data limitations in some fitness variables. [ABSTRACT FROM AUTHOR]

Identifying factors linked to autism traits in the general population may improve our understanding of the mechanisms underlying divergent neurodevelopment. In this study we assess whether factors increasing the likelihood of childhood autism are related to early autistic trait emergence, or if other exposures are more important. We used data from 536 toddlers from London (UK), collected at birth (gestational age at birth, sex, maternal body mass index, age, parental education, parental language, parental history of neurodevelopmental conditions) and at 18 months (parents cohabiting, measures of socio-economic deprivation, measures of maternal parenting style, and a measure of maternal depression). Autism traits were assessed using the Quantitative Checklist for Autism in Toddlers (Q-CHAT) at 18 months. A multivariable model explained 20% of Q-CHAT variance, with four individually significant variables (two measures of parenting style and two measures of socio-economic deprivation). In order to address variable collinearity we used principal component analysis, finding that a component which was positively correlated with Q-CHAT was also correlated to measures of parenting style and socio-economic deprivation. Our results show that parenting style and socio-economic deprivation correlate with the emergence of autism traits at age 18 months as measured with the Q-CHAT in a community sample. [ABSTRACT FROM AUTHOR]

Both underweight and obesity have been associated with poor prognosis in COVID-19. In an older populations of patients hospitalized for SARS-CoV-2 infection, we aimed to evaluate the association between body mass index (BMI) and short and long-term prognosis. Among 434 consecutive patients aged ≥ 70 years and hospitalized for suspected COVID-19 at a university hospital, 219 patients (median age of 83 years, 53% male) testing positive for COVID-19 and for whom BMI was recorded at admission, agreed to participate. Among them, 39 had a BMI < 20 kg/m2, 73 had a BMI between 20 and 24.9 kg/m2 and 107 had a BMI ≥ 25 kg/m2. After adjustment for confounders, BMI < 20 kg/m2 was associated with a higher risk of one-year mortality (hazard ratio (HR) [95% confidence interval]: 1.75 [1.00–3.05], p = 0.048), while BMI ≥ 25 kg/m2 was not (HR: 1.04 [0.64–1.69], p = 0.9). However, BMI was linearly correlated with both in-hospital acute respiratory failure (p = 0.02) and cardiovascular events (p = 0.07). In this cohort of older patients hospitalized for COVID-19, low BMI, rather than high BMI, appears as an independent risk factor for death after COVID-19. The pathophysiological patterns underlying this excess mortality remain to be elucidated. [ABSTRACT FROM AUTHOR]

This cross-sectional study aimed to develop and validate population-based machine learning models for examining the association between breastfeeding and metabolic syndrome in women. The artificial neural network, the decision tree, logistic regression, the Naïve Bayes, the random forest and the support vector machine were developed and validated to predict metabolic syndrome in women. Data came from 30,204 women, who aged 20 years or more and participated in the Korean National Health and Nutrition Examination Surveys 2010–2019. The dependent variable was metabolic syndrome. The 86 independent variables included demographic/socioeconomic determinants, cardiovascular disease, breastfeeding duration and other medical/obstetric information. The random forest had the best performance in terms of the area under the receiver-operating-characteristic curve, e.g., 90.7%. According to random forest variable importance, the top predictors of metabolic syndrome included body mass index (0.1032), medication for hypertension (0.0552), hypertension (0.0499), cardiovascular disease (0.0453), age (0.0437) and breastfeeding duration (0.0191). Breastfeeding duration is a major predictor of metabolic syndrome for women together with body mass index, diagnosis and medication for hypertension, cardiovascular disease and age. [ABSTRACT FROM AUTHOR]

This cross-sectional study aimed to develop and validate population-based machine learning models for examining the association between breastfeeding and metabolic syndrome in women. The artificial neural network, the decision tree, logistic regression, the Naïve Bayes, the random forest and the support vector machine were developed and validated to predict metabolic syndrome in women. Data came from 30,204 women, who aged 20 years or more and participated in the Korean National Health and Nutrition Examination Surveys 2010–2019. The dependent variable was metabolic syndrome. The 86 independent variables included demographic/socioeconomic determinants, cardiovascular disease, breastfeeding duration and other medical/obstetric information. The random forest had the best performance in terms of the area under the receiver-operating-characteristic curve, e.g., 90.7%. According to random forest variable importance, the top predictors of metabolic syndrome included body mass index (0.1032), medication for hypertension (0.0552), hypertension (0.0499), cardiovascular disease (0.0453), age (0.0437) and breastfeeding duration (0.0191). Breastfeeding duration is a major predictor of metabolic syndrome for women together with body mass index, diagnosis and medication for hypertension, cardiovascular disease and age. [ABSTRACT FROM AUTHOR]

High cortisol production in Cushing's syndrome leads to fat centralisation. The influence of modest cortisol variations on body shape, however, is less clear. We examined potentially causal associations between morning plasma cortisol and body shape and obesity with inverse-variance weighted random-effects models in a two-sample Mendelian randomisation analysis. We used publicly available summary statistics from the CORtisol NETwork (CORNET) consortium, UK Biobank, and the Genetic Investigation of Anthropometric Traits (GIANT) consortium. Only in women, morning plasma cortisol (proxied by ten genetic polymorphisms) was associated positively with waist size reflected in waist-to-hip index (WHI, 0.035 standard deviation (SD) units change per one SD cortisol increase; 95% confidence interval (0.002–0.067); p = 0.036) and "a body shape index" (ABSI; 0.039 (0.006–0.071); p = 0.021). There was no evidence for associations with hip index (HI) or body mass index (BMI). Among individual polymorphisms, rs7450600 stood out (chromosome 6; Long Intergenic Non-Protein-Coding RNA 473 gene, LINC00473). Morning plasma cortisol proxied by rs7450600 was associated positively with WHI and inversely with HI and BMI in women and men. Our findings support a causal association of higher morning plasma cortisol with larger waist size in women and highlight LINC00473 as a genetic link between morning plasma cortisol and body shape. [ABSTRACT FROM AUTHOR]

Predicting the transition of kidney function in chronic kidney disease is difficult as specific symptoms are lacking and often overlooked, and progress occurs due to complicating factors. In this study, we applied time-series cluster analysis and a light gradient boosting machine to predict the trajectories of kidney function in non-dialysis dependent chronic kidney disease patients with baseline estimated glomerular filtration rate (GFR) ≥ 45 mL/min/1.73 m2. Based on 5-year changes in estimated GFR, participants were stratified into groups with similar trajectories by cluster analysis. Next, we applied the light gradient boosting machine algorithm and Shapley addictive explanation to develop a prediction model for clusters and identify important parameters for prediction. Data from 780 participants were available for analysis. Participants were classified into five classes (Class 1: n = 78, mean [± standard deviation] estimated GFR 100 ± 19.3 mL/min/1.73 m2; Class 2: n = 176, 76.0 ± 9.3 mL/min/1.73 m2; Class 3: n = 191, 59.8 ± 5.9 mL/min/1.73 m2; Class 4: n = 261, 52.7 ± 4.6 mL/min/1.73 m2; and Class 5: n = 74, 53.5 ± 12.0 mL/min/1.73 m2). Declines in estimated GFR were 8.9% in Class 1, 12.2% in Class 2, 4.9% in Class 3, 12.0% in Class 4, and 45.1% in Class 5 during the 5-year period. The accuracy of prediction was 0.675, and the top three most important Shapley addictive explanation values were 1.61 for baseline estimated GFR, 0.12 for hemoglobin, and 0.11 for body mass index. The estimated GFR transition of patients with preserved chronic kidney disease mostly depended on baseline estimated GFR, and the borderline for estimated GFR trajectory was nearly 50 mL/min/1.73 m2. [ABSTRACT FROM AUTHOR]

Disparities in socio-economic status (SES) predict many immune system-related diseases, and previous research documents relationships between SES and the immune cell transcriptome. Drawing on a bioinformatically-informed network approach, we situate these findings in a broader molecular framework by examining the upstream regulators of SES-associated transcriptional alterations. Data come from the National Longitudinal Study of Adolescent to Adult Health (Add Health), a nationally representative sample of 4543 adults in the United States. Results reveal a network—of differentially expressed genes, transcription factors, and protein neighbors of transcription factors—that shows widespread SES-related dysregulation of the immune system. Mediational models suggest that body mass index (BMI) plays a key role in accounting for many of these associations. Overall, the results reveal the central role of upstream regulators in socioeconomic differences in the molecular basis of immunity, which propagate to increase risk of chronic health conditions in later-life. [ABSTRACT FROM AUTHOR]

A precise forecast of the need for blood transfusions (BT) in patients undergoing total hip arthroplasty (THA) is a crucial step toward the implementation of precision medicine. To achieve this goal, we utilized supervised machine learning (SML) techniques to establish a predictive model for BT requirements in THA patients. Additionally, we employed unsupervised machine learning (UML) approaches to identify clinical heterogeneity among these patients. In this study, we recruited 224 patients undergoing THA. To identify factors predictive of BT during the perioperative period of THA, we employed LASSO regression and the random forest (RF) algorithm as part of supervised machine learning (SML). Using logistic regression, we developed a predictive model for BT in THA patients. Furthermore, we utilized unsupervised machine learning (UML) techniques to cluster THA patients who required BT based on similar clinical features. The resulting clusters were subsequently visualized and validated. We constructed a predictive model for THA patients who required BT based on six predictive factors: Age, Body Mass Index (BMI), Hemoglobin (HGB), Platelet (PLT), Bleeding Volume, and Urine Volume. Before surgery, 1 h after surgery, 1 day after surgery, and 1 week after surgery, significant differences were observed in HGB and PLT levels between patients who received BT and those who did not. The predictive model achieved an AUC of 0.899. Employing UML, we identified two distinct clusters with significantly heterogeneous clinical characteristics. Age, BMI, PLT, HGB, bleeding volume, and urine volume were found to be independent predictors of BT requirement in THA patients. The predictive model incorporating these six predictors demonstrated excellent predictive performance. Furthermore, employing UML enabled us to classify a heterogeneous cohort of THA patients who received BT in a meaningful and interpretable manner. [ABSTRACT FROM AUTHOR]

Rural children are more at risk for childhood obesity but may have difficulty participating in pediatric weight management clinical trials if in-person visits are required. Remote assessment of height and weight observed via videoconferencing may provide a solution by improving the accuracy of self-reported data. This study aims to validate a low-cost, scalable video-assisted protocol for remote height and weight measurements in children and caregivers. Families were provided with low-cost digital scales and tape measures and a standardized protocol for remote measurements. Thirty-three caregiver and child (6–11 years old) dyads completed remote (at home) height and weight measurements while being observed by research staff via videoconferencing, as well as in-person measurements with research staff. We compared the overall and absolute mean differences in child and caregiver weight, height, body mass index (BMI), and child BMI adjusted Z-score (BMIaz) between remote and in-person measurements using paired samples t-tests and one sample t-tests, respectively. Bland–Altman plots were used to estimate the limits of agreement (LOA) and assess systematic bias. Simple regression models were used to examine associations between measurement discrepancies and sociodemographic factors and number of days between measurements. Overall mean differences in child and caregiver weight, height, BMI, and child BMIaz were not significantly different between remote and in-person measurements. LOAs were − 2.1 and 1.7 kg for child weight, − 5.2 and 4.0 cm for child height, − 1.5 and 1.7 kg/m2 for child BMI, − 0.4 and 0.5 SD for child BMIaz, − 3.0 and 2.8 kg for caregiver weight, − 2.9 and 3.9 cm for caregiver height, and − 2.1 and 1.6 kg/m2 for caregiver BMI. Absolute mean differences were significantly different between the two approaches for all measurements. Child and caregiver age were each significantly associated with differences between remote and in-person caregiver height measurements; there were no significant associations with other measurement discrepancies. Remotely observed weight and height measurements using non-research grade equipment may be a feasible and valid approach for pediatric clinical trials in rural communities. However, researchers should carefully evaluate their measurement precision requirements and intervention effect size to determine whether remote height and weight measurements suit their studies. Trial registration: ClinicalTrials.gov NCT04142034 (29/10/2019). [ABSTRACT FROM AUTHOR]