Annals of Nutrition and Metabolism最新文献

Background: Abdominoperineal resection (APR) for low rectal cancer results in permanent stoma, psychosocial distress, and systemic inflammation, impairing quality of life and survival. This study evaluated whether app-based cognitive behavioral therapy (CBT) plus biomarker-guided nutrition could improve recovery and survival.

Methods: In this prospective, single-center, non-randomized controlled cohort, 186 patients with stage I-III low rectal adenocarcinoma undergoing curative abdominoperineal resection (APR) all received standard Enhanced Recovery After Surgery (ERAS) care. Patients were managed with ERAS alone or ERAS plus an integrative program combining digital cognitive behavioral therapy (CBT) and inflammation-guided precision nutrition, tailored according to prognostic nutritional index (PNI), C-reactive protein (CRP), and interleukin-6 (IL-6). Interventions began two weeks preoperatively and continued for six months. Primary outcomes were longitudinal changes in depression, sleep quality, sexual function, and health-related quality of life. Secondary outcomes included inflammatory markers, skeletal muscle index (SMI), and two-year disease-free and overall survival.

Results: By month 3, the intervention group showed greater improvements in all primary outcomes, sustained through 24 months (all p < 0.001). CRP and IL-6 were reduced by day 7 (both p < 0.001), with faster recovery and shorter hospitalization. SMI was better preserved at 24 months (p = 0.046). Two-year DFS (hazard ratio [HR] = 0.48, 95% confidence interval [CI] 0.26-0.89, p = 0.021) and OS (HR = 0.44, 95% CI 0.20-0.95, p = 0.036) were significantly improved. Findings remained robust in multivariable and sensitivity analyses.

Conclusions: Among patients undergoing APR managed within an ERAS pathway, the addition of digital CBT and inflammation-guided nutrition was associated with improved multidimensional recovery and favorable survival signals. Given the non-randomized design, these results should be regarded as hypothesis-generating and support further evaluation of ERAS-based integrative strategies in randomized multicenter trials.

Introduction: Sarcopenic obesity (SO) is defined as co-existence of obesity and sarcopenia. The co-occurrence of sarcopenia and obesity might act synergistically and SO might have a greater effect on adverse health outcomes than sarcopenia or obesity alone. The term osteosarcopenic obesity was introduced recently to describe the impairment of bone, muscle and fat tissues. This study aimed to evaluate cardiometabolic and bone parameters in relation with the presence of sarcopenia and their association with components of sarcopenia (muscle strength, muscle mass and physical performance) in children and adolescents with obesity.

Methods: A total of 95 children and adolescents with obesity (diagnosed with the use of International Obesity Task Force criteria) with a mean age of 12.7( ± 3) years participated in this study. Body composition was evaluated by bioelectrical impedance (BIA) and dual- energy x-ray absorptiometry (DXA). Grip strength and physical performance (six-minute walk test, timed up and go test) were evaluated. Blood pressure was measured and blood samples were taken. Sarcopenia was defined as co-occurrence of weak handgrip strength (≤10 th centile) according to Dodds et al. and low DXA-ASMM% (appendicular muscle mass/weight x 100%) (≤9 nd centile) according to McCarthy et al. Results: The overall prevalence of sarcopenia was 17.89%. In comparison with patients without sarcopenia, those with sarcopenia had significantly higher high-density lipoprotein cholesterol (HDL-C) and significantly lower total body less head bone mineral content (BMC-TBLH) and total body less head bone mineral density (BMD-TBLH). Partial correlation analyses controlling for age and sex were performed. BIA-ASMM% and DXA-ASMM% were negatively correlated with insulin 0', homeostasis model assessment of insulin resistance (HOMA-IR) and diastolic blood pressure. DXA-ASMM% correlated negatively with total cholesterol. Handgrip strength was negatively correlated with total cholesterol, low-density lipoprotein (LDL) and HDL cholesterol. Maximal handgrip was positively correlated with bone parameters (BMC-TBLH, BMC-lumbar spine(LS), BMD-TBLH and BMD-LS). The highest diagnostic value for detecting sarcopenia in children and adolescents with obesity was achieved by HDL-C (cut-off value >44.8mg/dl), area under ROC curve (AUC)=0.73).

Conclusion: Muscle mass and muscle strength were significantly associated with cardiometabolic parameters in children and adolescents with obesity. The best independent predictive risk factor for diagnosing sarcopenia in obese children and adolescents is HDL-C (cut-off value >44.8mg/dl). The results of our study regarding bone parameters suggest the presence of osteosarcopenic obesity.

Background: Arterial stiffness (AS) is a well-established independent predictor of cardiovascular events and all-cause mortality. However, few studies have examined the comparative predictive performance of traditional obesity indicators (BMI, WC) versus novel indices (WWI, ABSI, BRI, CI) for AS.

Methods: This study utilized baseline data from the Shaanxi cohort of the Regional Ethnic Cohort Study in Northwest China (RECS). Anthropometric measurements were collected to calculate obesity indicators, and estimated pulse wave velocity (ePWV) served as a surrogate for AS. Logistic regression and restricted cubic spline models assessed the associations and dose-response relationships between obesity indicators and ePWV. Receiver operating characteristic (ROC) curves were used to compare the predictive performance of these indicators.

Results: After adjusting for confounders, all obesity indices exhibited significant positive nonlinear associations with ePWV (P for nonlinearity < 0.001). Participants with overweight (OR = 1.63, 95% CI: 1.51-1.77) or obesity (OR = 2.13, 95% CI: 1.90-2.40) had a higher risk of elevated ePWV compared to those with normal BMI. Central adiposity (based on WC) was associated with a 70% increased risk (OR = 1.70, 95% CI: 1.58-1.83). Similarly, individuals in the highest quartile (Q4) of WWI, ABSI, BRI, and CI showed 1.40-, 1.14-, 2.24-, and 1.52-fold higher risks of elevated ePWV, respectively, compared to the lowest quartile (Q1). Novel obesity indices demonstrated superior predictive performance over traditional measures, with WWI exhibiting the strongest discriminative ability.

Conclusions: Obesity significantly increases AS risk in Chinese adults, and novel obesity indicators (particularly WWI) could outperform BMI and WC in predicting AS. Managing abdominal obesity and reducing visceral adiposity may mitigate AS risk, thereby lowering the likelihood of cardiovascular disease.

Introduction Milk fat globule epidermal growth factor 8 (MFGE8) represents a recently characterized secreted integrin ligand involved in modulating systemic metabolic processes through impacts on nutrient absorption, gastrointestinal motility, and lipid homeostasis. Current literature supports its relevance in obesity pathogenesis. However, the exploration of MFGE8's function specifically within pediatric obesity contexts remains insufficient. This investigation aimed to delineate relationships between circulating MFGE8 concentrations and diverse obesity-associated parameters in children. Methods Our study included 104 participants (32 females, 72 males), consisting of 64 children diagnosed with obesity and 40 normal-weight controls. We measured serum MFGE8 levels employing enzyme-linked immunosorbent assay (ELISA) methodology. Multiple clinical and biochemical measures were evaluated, encompassing body mass index (BMI), waist and hip circumferences, blood pressure, hepatic enzyme patterns, cholesterol parameters, and fasting insulin. Results Obese participants exhibited substantially increased serum MFGE8 concentrations relative to non-obese controls (P < 0.05). MFGE8 levels correlated positively with numerous anthropometric measures: systolic blood pressure (SBP), heart rate (BPM), bone age (BA), height, weight, BMI, waist circumference, hip circumference, waist-to-hip ratio (WHR), and waist-to-height ratio (WHTR). Additionally, MFGE8 demonstrated significant positive relationships with multiple metabolic and inflammatory indicators: alanine aminotransferase (ALT), triglyceride (TG), fasting insulin (FINS), homeostatic model assessment of insulin resistance (HOMA-IR), basal metabolic rate (BMR), body fat percentage (BFR), uric acid (UA), glutamyl transpeptidase (GGT), tumor necrosis factor-α (TNF-α), adiponectin (ADP), and leptin (LEP). Inverse correlations were identified with albumin (ALB) and direct bilirubin (DBIL). Multivariable linear regression modeling identified BMI, HOMA-IR, and male gender as independent positive predictors of MFGE8 concentrations. Conclusion Our results demonstrate that elevated MFGE8 levels are linked to pediatric obesity, implying its potential contribution to the development of this condition and associated metabolic complications.

Introduction and Aim In intoxication-type inherited metabolic disorders (IMDs), strict protein restriction is vital; noncompliance may lead to coma or death. Meat and eggs are lifelong prohibited. This study developed personalized low-protein alternatives and chewable amino acid supplements to improve dietary adherence, palatability, and overall quality of life. Methods Low-protein meat, egg, and amino acid-based candy prototypes were produced using a 3D-food-printer. Proximate composition, color, texture, and pH measurements were performed. Sensory evaluation was conducted with fifteen patients. Results Fifteen patients with IMDs participated in sensory testing of 3D-printed low-protein foods. Visual and overall acceptability scores were high, with 93.3% expressing willingness to include products in their diets. Isoleucine and valine SCs, and LP meat analogs received the highest ratings for texture and appearance. Arginine SC scored lower in taste and texture, aligning with instrumental softness data. Open-ended feedback emphasized emotional and dietary impact. Texture profile analysis and proximate composition confirmed feasibility of protein restriction with preserved palatability. Conclusion 3D food printing provides visually and texturally acceptable low-protein options for patients with inherited metabolic disorders, enhancing dietary adherence and social participation. This pioneering study is the first to develop low-protein meat and egg analogs, offering significant contributions to the field.

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by deficits in social interaction, communication, and the presence of restricted, repetitive behaviors. The rising global prevalence of ASD suggests a multifactorial etiology involving genetic, environmental, and neurodevelopmental factors. This review explores the establishment of the early life microbiome, highlighting rapid microbial colonization from maternal and environmental sources. Emerging evidence indicates that delivery mode and infant feeding practices may influence ASD susceptibility. Although the concept of a sterile intrauterine environment remains debated, its investigation is valuable.

Summary: This review provides a comprehensive analysis of the interplay between diet, gastrointestinal symptoms, and ASD, evaluates the gut-brain axis as a mechanistic framework, and assesses the therapeutic potential of microbial interventions. The bidirectional "microbiota-gut-brain axis" has emerged as a critical pathway linking gut microbiota and brain function, offering potential therapeutic targets for ASD. Dietary patterns in children with ASD are often characterized by selectivity and restriction, which may disrupt gut microbiota composition and exacerbate gastrointestinal symptoms, thereby increasing ASD risk. Nutritional interventions and early behavioral therapies are thus essential.

Key messages: The gluten-free, casein-free diet remains controversial, with inconsistent evidence regarding its efficacy. Probiotic supplementation shows strain-specific effects, necessitating rigorous evaluation before clinical application. Given the heterogeneity of ASD, pharmacological treatments have shown limited universal efficacy. While promising findings have emerged from research on probiotics, prebiotics, and fecal microbiota transplantation, further well-designed clinical studies are needed to elucidate the complex etiology of ASD and validate therapeutic strategies.

Introduction: Despite advances in enhanced recovery after surgery (ERAS), many colorectal cancer (CRC) patients continue to experience sleep disturbance, nutritional decline, pain, and psychological distress. Evidence on integrated psychobehavioral and immunonutritional strategies remains limited.

Methods: In this single-center randomized tcontrolled trial, 260 patients with stage I-III CRC undergoing elective curative resection were randomized (1:1) to receive standard ERAS care or an integrative program. The intervention comprised five structured sessions of resilience-oriented psychological support (two preoperatively, three postoperatively) and prognostic nutritional index (PNI)-guided early enteral nutrition (standard formula for PNI ≥45; ω-3-enriched immunonutrition with prebiotic and lactobacillus-derived postbiotic components for PNI <45, without the assumption of viable probiotic delivery). The primary endpoint was global Pittsburgh Sleep Quality Index (PSQI) score at postoperative day (POD) 15. Secondary endpoints included nutritional biomarkers, pain, anxiety and depression, and health-related quality of life (HRQoL). Exploratory endpoints were C-reactive protein (CRP) and interleukin-6 (IL-6).

Results: Of 298 screened, 260 were randomized and 240 completed per protocol. Adherence exceeded 90% with no serious adverse events. At POD15, the intervention group had lower PSQI scores (adjusted mean difference -2.4; 95% confidence interval: -3.2 to -1.6; p < 0.001), with improvements in latency, disturbance, and daytime dysfunction. Nutritional recovery was enhanced (albumin +2.8 g/L, prealbumin +20.4 mg/L, transferrin +0.3 g/L, PⅢNP +15.2 ng/mL; all p < 0.05). Pain and opioid use were reduced (VAS POD3 -1.5; opioids -30 mg, both p < 0.001). Anxiety and depression scores improved (SAS -5.8, SDS -6.1; p < 0.001), alongside clinically meaningful HRQoL gains in global health (+8.7), physical (+7.2), and emotional functioning (+6.5). Subgroup analyses confirmed consistent benefits, while CRP (-4.8 mg/L) and IL-6 (-3.1 pg/mL) were lower in the intervention group.

Conclusion: Integrative perioperative care combining psychological support with PNI-guided early enteral nutrition is safe, feasible, and effective in improving sleep, nutritional recovery, pain control, psychological well-being, and quality of life after CRC surgery.

Introduction: Malnutrition leads to negative health outcomes such as delayed recovery, increased hospital stays, and higher costs. The study aimed to develop a predictive tool to diagnose malnutrition using patient data, including anthropometric, phenotypic, and laboratory information.

Methods: A cohort of 252 adult patients was assessed at a tertiary hospital. Logistic regression and decision tree analysis were applied to evaluate the role of the data in predicting the risk of malnutrition. The performances of the models were tested with Akaike information criterion, null deviance, residual deviance, accuracy, and kappa metrics, and the statistical significance of the variables was evaluated with Wald-Z test. Attribute importance ranking was obtained by Bootstrap Optimized Random Univariate Tree Analysis algorithm.

Results: A total of 252 patients, 125 female (49.6%) and 127 male (50.4%), were included in the study. The mean age was 72.57 ± 13.6 years. Malnutrition was diagnosed in 174 patients (69%). According to the equations, the most important characteristics were determined as hand grip strength, weight loss, BMI, and gender. The model achieved high accuracy (89.8%) and a kappa value of 0.79, demonstrating its potential for clinical application.

Conclusion: Machine learning model offers a faster, more efficient alternative to traditional diagnostic tools. The study concludes that machine learning-based models provide superior prediction performance and could significantly enhance the efficiency and accuracy of malnutrition diagnosis in healthcare.