Search Results (13,650)

Background: Long noncoding RNAs (lncRNAs) have emerged as critical regulators of hepatic metabolism and disease progression. The hepatocyte nuclear factor 1 alpha antisense 1 (HNF1A-AS1) lncRNA modulates liver-specific transcription factors; however, its physiological role in diet-dependent lipid homeostasis remains poorly defined. Methods: In this study, we investigated the mouse ortholog, Hnf1a opposite strand 1 (Hnf1aos1), using AAV-mediated knockdown in C57BL/6J mice fed either a chow diet (10% kcal from fat) or a high-fat diet (HFD; 60% kcal from fat) for 12 weeks. Metabolic phenotyping included hepatic lipid quantification, histological analysis, serum biochemistry, and quantitative gene expression profiling. Results: Loss of Hnf1aos1 produced distinct, diet-dependent alterations in hepatic lipid handling. Under chow conditions, knockdown mice exhibited selective hepatic cholesterol accumulation (6.10 ± 2.9 mg/g tissue vs. 3.51 ± 1.1 mg/g in controls), accompanied by dysregulation of cholesterol clearance pathways. In contrast, under HFD conditions, knockdown precipitated severe macrovesicular degeneration, with hepatic triglyceride levels approximately doubled relative to HFD-fed controls (51.72 ± 19.8 mg/g vs. 26.34 ± 11.9 mg/g) and a numerically elevated triglyceride-to-cholesterol ratio (TG:TC ≈ 6.1:1; p = 0.0621, trend). Chow/Kd mice gained significantly less weight than chow-fed controls, whereas HFD/Kd mice exhibited weight gain comparable to HFD controls despite severe hepatic steatosis. This paradoxical phenotype suggests impaired metabolic feedback at the post-transcriptional level, in which compensatory upregulation of Hnf1a mRNA is insufficient to suppress lipid-associated genes such as Cd36, despite profound lipid overload; however, HNF1A protein levels were not directly measured in this study. Conclusion: Collectively, these findings identify Hnf1aos1 as a regulator of hepatic lipid homeostasis whose loss produces a phenotype consistent with inappropriate lipid accumulation during nutrient excess, without defining the underlying molecular mechanism. Our results support a role for Hnf1aos1 in shaping hepatic metabolic plasticity and provide insight into lncRNA-associated MASLD phenotypes. Full article

Background/Objective: Intermittent fasting (IF) shows promise for metabolic and mental health benefits, but evidence in older adults remains limited. This study systematically evaluated the safety and effectiveness of IF in adults aged ≥60 years, comparing different protocols using network meta-analysis. Methods: Systematic review and network meta-analysis following Cochrane and PRISMA guidelines were conducted, producing a literature search until June 2025 across PubMed, Scopus, and ScienceDirect databases, with inclusion criteria comprising randomized controlled trials, clinical trials, and observational studies evaluating IF in adults ≥60 years. Network meta-analysis compared time-restricted eating (TRE), IF 5:2 method, Islamic Sunnah fasting (ISF), Healthy Living Diet and usual diet. The NMA was conducted exclusively using randomized controlled trials (RCTs; n = 7); pre–post trials and observational studies were included solely in the narrative systematic review component and did not contribute to any pooled NMA estimates. Observational data contributed exclusively to the narrative synthesis. Results: Thirty-one studies were included; seven RCTs were eligible for network meta-analysis. ISF and TRE 16:8 were most effective for weight (ISF: −2.36 kg; TRE 16:8: −1.92 kg) and BMI reduction (−0.81 and −1.01 kg/m²) without lean mass loss. Findings on cardiometabolic parameters, mental health, and cognitive function are based on the narrative synthesis of individual studies. Long-term structured IF was associated with improvements in standardized cognitive performance assessed via validated instruments. However, very restrictive eating windows (≤10 h) and prolonged fasting (>12.38 h) were associated with adverse outcomes, including lower cognitive scores and 58% increased cardiovascular mortality. Conclusions: TRE 16:8 and ISF showed the strongest comparative evidence for weight reduction in the RCT-based NMA, with acceptable short-term safety profiles in the included trials. In the narrative review, these protocols were associated with clinically meaningful improvements in body weight, metabolic markers, and blood pressure while generally preserving lean muscle mass in older adults. The cardiovascular mortality risk associated with very restrictive eating windows may emphasize the importance of moderate fasting approaches in this vulnerable population. Further long-term research is needed to confirm optimal protocols and identify at-risk subgroups. Full article

Background/Objectives: Osteonecrosis of the femoral head (ONFH) is difficult to diagnose, particularly in the early stages, because radiological findings may be subtle. Delayed or inaccurate staging may increase the risk of femoral head collapse and functional loss. Although magnetic resonance imaging is highly sensitive for early-stage lesion detection, interpretation may vary depending on observer experience. Therefore, reliable and explainable automated decision support approaches are needed. Methods: In this study, a deep learning-based approach was proposed to classify ONFH into early and late stages according to the Ficat–Arlet staging system. Stage I–II cases were defined as early-stage, whereas Stage III–IV cases were defined as late-stage. Axial and coronal MR images were evaluated separately to investigate plane-dependent classification performance. The images were converted into a three-channel format, resized to a common spatial resolution, normalized, and augmented during training. Feature extraction was performed using transfer learning with modern convolutional neural network architectures. ConvNeXt Tiny was used as the main classification backbone. Weighted loss was applied to reduce the effect of class imbalance, and the decision threshold was optimized on validation data to reduce missed clinically critical late-stage cases. Results: A dataset collected from the Orthopedics and Traumatology Department of Firat University Hospital was used in the experimental evaluation. The dataset was divided into training and test sets using an 80:20 split, and 10-fold cross-validation was additionally performed to assess model stability. In the hold-out test, the axial plane model achieved 94.51% accuracy, 96.80% sensitivity, 93.49% specificity, 0.9162 F1-score, and 0.981 AUC. In the coronal plane model, 92.84% accuracy, 96.13% sensitivity, 90.96% specificity, 0.9072 F1-score, and 0.988 AUC were obtained. The 10-fold cross-validation results provided a more conservative estimate of generalization performance. Conclusions: The findings indicate that deep learning-based plane-wise analysis of MR images can distinguish early- and late-stage ONFH with high performance. Grad-CAM-based visual explanations showed that the model focused mainly on clinically relevant subchondral and weight-bearing regions of the femoral head. The proposed approach may serve as an explainable decision support tool for reducing observer-dependent variability in clinical staging. Future studies should validate the method using external, multicenter datasets and paired patient-level axial–coronal images. Full article

Background/Objectives: Pancreatic adenocarcinoma remains one of the most lethal malignancies, largely due to aggressive biological behavior and limited available insight into biomarker-based prognostic stratification. The aim of our research was to investigate the role of macrophage migration inhibitory factors (MIFs), interleukin-8 (IL-8/CXCL8), and stem cell factors (SCFs) in pancreatic adenocarcinoma. Methods: In this single-center study, sixty hospitalized patients diagnosed with pancreatic adenocarcinoma were prospectively enrolled, and a cross-sectional analysis of baseline cytokine levels was performed. Serum MIF, IL-8/CXCL8, and SCF were assessed in a single analytical run using Luminex xMAP technology. Results: Elevated MIF and IL-8/CXCL8 levels characterized an inflammatory phenotype, associated with leukocytosis, neutrophilia, increased fibrinogen levels, and unequal prevalence of new-onset diabetes. Higher MIF levels were further associated with larger tumor dimension, while IL-8/CXCL8 was associated with increased bilirubin level and recent weight loss (p < 0.05). In contrast, increased SCF predicted a dissemination phenotype as defined by metastasis occurrence (65.4% vs. 28.6%, p = 0.012). SCF demonstrated significant discriminatory ability for metastasis (AUC 0.712, p = 0.013) and remained significantly associated in multivariable analysis. Conclusions: MIF and IL-8/CXCL8 primarily reflect inflammation-driven processes, whereas SCF identifies a dissemination-dominant phenotype, suggesting distinct biological pathways underlying disease progression in pancreatic cancer. Full article

Urban fires are highly destructive with high casualty rates, often causing significant casualties and property losses. The obstruction of the Community Emergency Corridor System is a critical factor exacerbating fire casualties, directly related to residents’ life safety and public security governance effectiveness. Currently, community emergency corridors face severe systemic bottlenecks in the coordinated development of triadic space (physical, social, and information spaces), and the lag of information space has become a fatal shortcoming restricting emergency response efficiency, highlighting the urgent need for a comprehensive evaluation framework. However, existing studies mostly focus on a single spatial dimension, lacking a systematic framework for the coordinated patency of triadic space. Based on this, this study adopts the triadic space perspective, takes 77 typical communities in China as research objects, and uses the Entropy Weighted TOPSIS method to construct an evaluation index system for the accessibility of the Community Emergency Corridor System and systematically measure its level. The results show that the patency of triadic space is unbalanced overall; social space outperforms physical and information spaces (with the latter being the lowest), reflecting deficiencies in emergency information release and acquisition. Regionally, accessibility in Northeast China is significantly higher than in other regions (Northeast > West > Central > East), and eastern China has the lowest scores in physical and information spaces due to high urbanization, dense buildings, and land scarcity. Corresponding countermeasures are proposed to address regional disparities. The triadic space evaluation framework and methodological path provide a replicable analytical tool for urban fire-oriented community emergency management and references for fire resilience governance in other countries or high-density communities. Full article

Background: Weight loss and skeletal muscle wasting are frequent in cancer and may influence treatment tolerance and outcomes. Computed tomography (CT) based body composition analysis at the third lumbar vertebra (L3) is an accurate method to quantify skeletal muscle in routine oncology care. Methods: We performed a multicenter retrospective cohort study including 202 adults with locally advanced (stage IB–III) gastric cancer treated in four Portuguese hospitals (January 2020–December 2022). Skeletal muscle area (SMA) was assessed on baseline CT at the L3 vertebral level, using Data Analysis Facilitation Suite (DAFS) software v3.11.2, and skeletal muscle index (SMI) was subsequently calculated. Patients with low muscle quantity were classified as sarcopenic (below sex-specific SMI mean). We evaluated associations with relapse-free survival (RFS), overall survival (OS), FLOT chemotherapy dose-limiting toxicities (DLTs), and postoperative complications after gastrectomy. Results: Mean age was 69 years, 65% had ECOG PS 0, 53% received FLOT chemotherapy protocol. Mean SMI was 49.6 cm²/m² in males and 40.9 cm²/m² in females and correlated positively, though moderately, with BMI (p < 0.01; r = 0.424). Sarcopenia was not significantly associated with RFS (p = 0.186) or OS (p = 0.168) at 30-month follow-up. Although numerical differences were observed (64% vs. 56% of patients did not relapse and 74% vs. 63% were alive, for non-sarcopenic vs. sarcopenic patients). Sarcopenia was associated with a higher risk of DLTs (p = 0.021; OR 2.56, 95% CI 1.15–5.73) and postoperative complications (p = 0.024; OR 2.16, 95% CI 1.11–4.21). Conclusions: Sarcopenia significantly increases the risk of chemotherapy toxicity and postoperative complications in locally advanced gastric cancer. However, its effect on OS and RFS was not statistically significant at 30-month follow-up. Standardization of CT-based sarcopenia cut-offs remains a major barrier to clinical implementation. Full article

Background: Obesity is a heterogeneous chronic disease in which eating behavior phenotypes may influence treatment response. Yet, anti-obesity medication (AOM) selection is still largely guided by anthropometric and metabolic parameters, with limited use of behavioral phenotyping in routine practice. We evaluated whether multidimensional eating behavior changes, measured by the Brazilian Eating Behavior Phenotype Scale (Escala de Fenótipos do Comportamento Alimentar, EFCA), differ across commonly used AOMs in a real-world cohort. Methods: We conducted a retrospective, observational, real-world study in obesity outpatient care settings in São Paulo, Brazil. Adults with obesity (18–65 years) treated with a single principal AOM for 6 months and paired baseline/6-month follow-up EFCA and anthropometric data were included. Analyses focused on early responders (≥5% total body weight loss at 3 months). Five AOM groups available in Brazil were analyzed: semaglutide (oral or subcutaneous), naltrexone/bupropion, sibutramine, topiramate, and tirzepatide. Outcomes included percent weight loss, EFCA total score, and five EFCA subscales (hedonic, emotional, compulsive, hyperphagic, disorganized). Within-medication behavioral changes were assessed using paired tests and standardized effect sizes (Cohen’s dz, 95% CI), summarized in heatmap form. Results: The analytical cohort comprised 66 early responders with paired EFCA assessments at baseline and 6 months. EFCA profiling revealed distinct behavioral response fingerprints across AOMs. Effect size mapping showed predominantly large behavioral effects (many dz ≥ 0.8) in hedonic, emotional, hyperphagic, and compulsive domains. Strongest signals included emotional eating reductions with naltrexone/bupropion (dz 2.04), tirzepatide (dz 1.77), semaglutide (dz 1.52), and topiramate (dz 1.54); hedonic reductions with tirzepatide (dz 2.06), semaglutide (dz 1.55), and naltrexone/bupropion (dz 1.52); hyperphagic reductions with tirzepatide (dz 1.50) and semaglutide (dz 1.34); and compulsive reductions with topiramate (dz 1.41) and consistent effects across tirzepatide, semaglutide, and sibutramine (≈dz 0.95–0.96). Disorganized eating showed heterogeneous/attenuated responsiveness, from near-null with tirzepatide (dz 0.03) to large but imprecise effects in smaller groups (e.g., topiramate dz 1.24, wide CI). Conclusions: In this responder-enriched real-world cohort, AOMs showed distinct and reproducible EFCA behavioral signatures, supporting a clinically actionable phenotype-informed framework to prioritize, sequence, and monitor obesity pharmacotherapy beyond nonspecific weight reduction, while highlighting disorganization as a potential target for adjunctive behavioral strategies. Full article

Background and Objectives: We aimed to evaluate early postoperative radiographic changes in sagittal foot alignment following laparoscopic sleeve gastrectomy and to investigate the association between early weight loss and sagittal foot alignment parameters. Materials and Methods: This study included 72 consecutive patients who underwent primary laparoscopic sleeve gastrectomy. Standardized lateral foot radiographs were obtained preoperatively and at the fourth postoperative month. Meary’s angle, calcaneal pitch, and talar declination angle were measured on all radiographs. Demographic and clinical variables, including age, sex, height, body weight, and body mass index (BMI), were recorded. Results: Meary’s angle demonstrated a significant postoperative decrease from 15° (IQR, 8°) to 11° (IQR, 12°) (p < 0.001), indicating improvement in medial longitudinal arch alignment. In contrast, no significant postoperative changes were observed in the calcaneal pitch (p = 0.227) or talar declination angles (p = 0.751). The proportion of patients within the normal range for all measured sagittal alignment parameters increased postoperatively, without showing statistical significance. Statistical analysis revealed that all postoperative sagittal alignment parameters showed significant correlation with preoperative values. Notably, postoperative Meary’s angle demonstrated a very strong positive correlation with preoperative Meary’s angle (r = 0.80, p < 0.001), whereas no significant correlation was identified between postoperative Meary’s angle and either postoperative weight or weight/BMI loss (p > 0.05). Although BMI loss showed a significant correlation with postoperative calcaneal pitch and talar declination angles, these correlations were weak to moderate (r = −0.403, and r = −0.362, respectively). Conclusions: Early postoperative body weight/BMI loss following sleeve gastrectomy is associated with modest, parameter-specific improvements in sagittal foot alignment, primarily reflected by changes in Meary’s angle, suggesting that the medial longitudinal arch may be more responsive to early postoperative unloading than other sagittal alignment parameters. The strong association between preoperative and postoperative measurements underscores the central role of baseline alignment in determining early postoperative outcomes. Full article

Background: In recent years, artificial intelligence (AI) methods, including deep convolutional neural networks (CNNs), have gained increasing importance in supporting the automated analysis of echocardiograms. The aim of this study was to evaluate the impact of selected image artifacts—motion blur, acoustic shadowing, and speckle noise—on the performance of automatic classification of standard transthoracic echocardiographic (TTE) views using deep learning models. Methods: The analysis included 217 TTE video clips (2170 frames) covering apical views: two-chamber (A2C), three-chamber (A3C), four-chamber (A4C), and five-chamber (A5C). Two convolutional neural network architectures—ResNet-18 and ResNet-34—were applied, initialized with weights pretrained on the ImageNet dataset (transfer learning). In a limited comparative scope, EfficientNet-B0, a ViT model used as a frozen feature extractor combined with Logistic Regression, and a classical HOG + SVM model, were also included as reference methods. Classification performance was evaluated under conditions of controlled image degradation caused by motion blur, acoustic shadowing, and speckle noise. Results: All analyzed artifacts reduced classification performance, although the magnitude of this effect depended on artifact type. Speckle noise proved to be the most destructive, causing performance collapse across all evaluated methods at high severity. Motion blur and acoustic shadowing produced more differentiated degradation profiles. The ResNet models achieved the highest performance under reference conditions; however, after degradation, the ranking of models was no longer stable. In the comparative analysis, HOG + SVM showed the smallest relative performance loss under motion blur and the highest balanced accuracy under severe acoustic shadowing, whereas severe speckle remained critical for all models. Conclusions: Image quality degradation significantly impairs TTE view classification performance, and evaluation based solely on reference-quality images does not fully reflect model robustness to artifacts. These findings indicate the need to complement standard model evaluation with a structured robustness analysis under degraded imaging conditions and highlight the importance of training and validation settings that better reflect real clinical practice. Full article

Orbital maneuver detection is a core component of space situational awareness. The multi-scale characteristics of satellite orbital behavior and sample imbalance issues lead to challenges in existing methods, including insufficient feature adaptation and limited detection accuracy. This paper proposes an Adaptive Spiking Gating Multi-Scale Liquid State Machine (ASG-MSLSM) for orbital maneuver detection based on variations in satellite orbital parameters. The method integrates multi-scale reservoir pools with different scale-dependent decay factors and Leaky Integrate-and-Fire (LIF) neurons to enhance multi-scale temporal feature extraction capability. A spiking gating network is designed to adaptively learn fusion weights for multi-scale features, replacing traditional fixed equal-weight fusion strategies. During training, weighted binary cross-entropy loss is employed to address class imbalance. Experimental results based on real satellite data demonstrate that the proposed method significantly outperforms baseline models in maneuver detection metrics, achieving higher recall, improving feature separability, and reducing both missed detections and false alarms. These results indicate that the proposed method provides a robust solution for orbital maneuver detection. Full article

Low birth weight (LBW) is associated with neonatal morbidity, mortality and long-term health complications. Global studies report an association between air pollution, such as tropospheric ozone, and LBW. This study aims to analyze the association between ozone exposure during pregnancy and LBW in 130 municipalities in rural and semi-urban Spain. We conducted a retrospective population-based cohort study using data from the Instituto Nacional de Estadística (INE) and air quality data from the Spanish Government for the 2001–2017 period. We performed descriptive analysis, logistic regression and linear regression analyses adjusted for various covariates. In addition, we fitted generalized additive models (GAMs) to estimate non-linear relationships. An association between decreased neonatal weight and high ozone exposure was found, especially in the first and second trimester. An increase in ozone concentration could lower neonatal weight but not enough evidence demonstrates an association with LBW. More research is needed to understand the impact of ozone exposure on neonates during pregnancy. Full article

The ability to continuously learn over time by incorporating new information while holding onto previously acquired expertise is known as incremental learning (IL). Although this concept is fundamental to human learning, existing machine learning techniques have a significant propensity to forget prior experience by overwriting previously learned patterns from classes. The continuous learning of new information in K-nearest neighbor (KNN) with lazy learning strategies compounds to loss of old knowledge upon learning new information and stability-plasticity dilemma. The change in new data points and data distributions in unforeseen ways impacts KNN’s ability to adapt to changes in class label distribution, leading to concept drift. This experiment models a hybrid 3WDKNN-based incremental learning algorithm (ILA) designed for application in a heterogeneous and dynamically changing environment. This model addresses the limitations of KNN by overcoming computational costs and inefficiencies associated with loss of information in classes, while facilitating incremental learning to attain high predictive accuracy in crop yield datasets. The algorithm employs weighted voting to identify optimal assigned classes for the nearest neighbor and uses memory reconstruction strategy for class incremental learning until the memory is full without forgetting. Using weighted voting for the best assigned classes for the nearest neighbor, the algorithm uses a local mean vector to determine the best distances for the shortest-term incremental learning to achieve the highest performance accuracy in a concept drift environment. The hybrid 3WDKNN_ILA was developed and evaluated alongside advanced algorithms within the same dataset context. The model improves performance in incremental learning contexts by utilizing current concepts and minimizing errors on both current and recent data to avoid parameterization. The model achieves optimal efficient incremental learning by mitigating intentional loss and minimizing errors associated with valuable class information derived from aggregated mean values through class rectification and transfer. The hybrid model achieves the best efficient performance accuracy in all the tested weighted averages of 200W, 500W, and 1000W with tested set K values of 5, 9, and 13K. This hybrid model demonstrates performance accuracy of 97% at a value of 13K, whereas 3WD_KNN achieves 96% at 9K, HoKNN attains 89% at 13K, and 1IKNN reaches 88% at 9K accuracy, respectively. The integrated novelty in the hybrid 3WDKNN_ILA proves superior in terms of computational efficiency, accuracy, and high-level incremental performance and learning in comparison with other tested models of algorithms. Full article

This paper examines whether a residual structural state extracted from cross-asset downside-risk dependence contains incremental information for forecasting next-day market downside risk beyond a strong heterogeneous autoregressive (HAR) benchmark. The empirical analysis uses Binance intraday data from September 2019 to December 2025 and a fixed sample of 24 liquid cryptocurrencies obtained through explicit data-quality screening and sample diagnostics. The forecasting target is the log of an equal-weight cross-sectional downside-risk index constructed from strictly valid asset-level realized downside semivariance measures. The empirical design is deliberately conservative: the market sample is fixed ex ante, the target is evaluated against Bitcoin (BTC) and Ethereum (ETH) dominance diagnostics, and asset-level HAR-type models are estimated recursively to generate ex-ante one-step-ahead residuals, from which rolling residual-dependence matrices and structural signatures are constructed. The selected residual state contains four components: average residual correlation, Frobenius-type deformation, influence concentration, and influential-set turnover. The evidence supports three qualified conclusions. First, the full residual state attains the lowest average QLIKE loss relative to the HAR benchmark, although the corresponding Diebold–Mariano test under the primary QLIKE loss does not reject equal predictive accuracy at conventional levels. Complementary Clark–West evidence on the nested log-scale comparison supports incremental predictive content for the level-state and full-state augmentations. Second, the strongest forecasting evidence comes from the full state rather than from deformation-only specifications. Third, event-window diagnostics show that structural reorganization is most pronounced around stress-entry and extreme-risk episodes, supporting an onset-sensitive rather than a long-lead early-warning interpretation. Overall, the evidence supports a cautious and statistically qualified predictive conclusion: residual market structure may contain incremental information for short-horizon downside-risk forecasting in cryptocurrency markets, especially around stress onset, but the result should not be interpreted as a decisive primary-loss improvement or as evidence that deformation alone dominates a strong benchmark. Full article

Background/Objectives: The emergence of SARS-CoV-2 variants and breakthrough infections underscores the need for next-generation vaccines capable of protecting from natural infection and/or preventing virus transmission. Intranasal vaccination offers a promising approach by eliciting local immune responses in the nasal mucosa, the primary site of infection and reservoir for transmissible virus. We evaluated two live-attenuated, respiratory syncytial virus-vectored vaccines in which the RSV F and G surface glycoproteins were replaced with a chimeric SARS-CoV-2 Spike protein from the ancestral USA/WA-1/2020 strain (MV-014-212) or the Delta variant (MV-014-212-delta). Methods: K18-hACE2 mice and LVG Syrian hamsters were vaccinated with a single intranasal dose of MV-014-212 or MV-014-212-delta. Systemic and mucosal immunity were assessed following vaccination, and protection was evaluated following Delta SARS-CoV-2 challenge. In vaccinated hamsters, morbidity, viral shedding, and lung inflammation and injury were also assessed following natural exposure to infected cagemates. Results: A single intranasal dose of either vaccine elicited systemic and mucosal immunity in K18-hACE2 mice, including serum neutralizing antibodies, Spike-specific memory B cells and plasmablasts, and Spike-specific CD8⁺ lung-resident memory T cells. Although MV-014-212-delta vaccination provided the best protection against the Delta variant virus challenge, both vaccines decreased viral loads in nasal discharge, lung, and brain, and reduced weight loss and mortality. In naturally acquired infection studies, vaccinated hamsters exposed to infected cagemates exhibited minimal weight loss, limited viral replication within the nasal mucosa, and attenuated lung pathology. Conclusions: Intranasal RSV-vectored vaccines can elicit broad protective respiratory immunity, suggesting that this platform could be leveraged for other respiratory pathogens. Full article

Background/Objectives: This study examines cross-sectional associations between dietary patterns, weight loss, and handgrip strength (HGS) among adults with a history of bariatric surgery. Methods: We analyzed data of 1888 adults (62.3% women; mean age 38.8 years) who attended the Qatar Biobank study. Dietary patterns were identified using factor analysis of data from a food frequency questionnaire. HGS was measured using dynamometry, and relative HGS (RHGS) was calculated as HGS/BMI. Results: The mean weight loss after bariatric surgery was 27.6 kg (23.4%), and the mean HGS was 30.1 (SD 11.2) kg. The mean duration after bariatric surgery was 3.6 years. Greater weight loss was associated with lower HGS (Q4 vs. Q1: −1.29 (95%CI −2.26 to −0.33)) but higher RHGS (Q4 vs. Q1: 0.10 (0.06 to 0.13)). Higher adherence to a “prudent diet” with high intake of fruits and vegetables was associated with stronger HGS (Q4 vs Q1: 1.07 (0.18 to 1.96)). In contrast, a “traditional diet” (high intake of mixed dishes, e.g., biryani, croissants, zaatar fatayer, lasagna, white rice, and Arabic bread) was inversely associated with HGS (Q4 vs. Q1: −1.27 (−2.19 to −0.35)). Conclusions: In conclusion, greater weight loss was associated with improved relative muscle strength, while adherence to a traditional diet was linked to weaker HGS. These findings highlight the importance of diet quality in maintaining muscle function after bariatric surgery. Full article