Hasil untuk "Nutritional diseases. Deficiency diseases"

J. Harig, K. Soergel, R. Komorowski et al.

Yuhua Yin, Meng Ye, Meiqi Shi et al.

Abstract Background The distribution of dietary macronutrients is critical for blood glucose management and adverse birth outcomes among pregnant women with gestational diabetes mellitus (GDM); however, the relationship between plant-based food intake and the risk of large-for- gestational-age (LGA) birth is unclear. Here, based on a large-scale birth cohort, we aim to investigate the prospective association between plant-based diet intake and the risk of LGA birth in pregnant women with GDM. Methods We included 1768 GDM participants and assessed dietary plant-based diet patterns by constructing plant-based diet index (PDI), healthy plant-based diet index (hPDI) and unhealthy plant-based diet index (uPDI) using data collected from food frequency questionnaires (FFQ). LGA were defined as gender- and gestational age-adjusted birth weight of newborns greater than 90th percentile. Results We found that individuals in the highest quartile of uPDI had a significantly higher risk of LGA compared to those in the lowest quartile (OR :1.62, 95% CI: 1.02 to 2.62, p = 0.05), after adjusting for potential confounding factors. Moreover, the dose-response analysis indicated a significant nonlinear relationship, with the risk of LGA increasing as uPDI rose from the first to the second quartile (OR: 1.74, 95% CI: 1.06 to 2.88, p = 0.03) and then plateauing upon reaching the third quartile (OR: 2.00, 95% CI: 1.27 to 3.21, p < 0.01). The sensitivity analyses showed that this association was generally robust across different adjustment models, but was attenuated by further adjustment for glycated hemoglobin A1c (HbA1c), which indicated potential mediation roles of HbA1c between unhealthy plant-based diets and LGA risk. Conclusions This study suggests that unhealthy plant food intake is a potential risk factor for LGA among pregnant women with GDM. The quality of plant-based foods should be considered when promoting plant-based dietary patterns.

Siyu Li, Chenwei Song, Qi Zhou et al.

This paper proposes a graph-augmented reasoning framework for tobacco pest and disease management that integrates structured domain knowledge into large language models. Building on GraphRAG, we construct a domain-specific knowledge graph and retrieve query-relevant subgraphs to provide relational evidence during answer generation. The framework adopts ChatGLM as the Transformer backbone with LoRA-based parameter-efficient fine-tuning, and employs a graph neural network to learn node representations that capture symptom-disease-treatment dependencies. By explicitly modeling diseases, symptoms, pesticides, and control measures as linked entities, the system supports evidence-aware retrieval beyond surface-level text similarity. Retrieved graph evidence is incorporated into the LLM input to guide generation toward domain-consistent recommendations and to mitigate hallucinated or inappropriate treatments. Experimental results show consistent improvements over text-only baselines, with the largest gains observed on multi-hop and comparative reasoning questions that require chaining multiple relations.

Catalina Yepez, Kimberly McKeirnan, Megan Undeberg et al.

Abstract Background Access to a primary care physician (PCP) is essential for managing chronic diseases. Limited PCP access in underserved populations can lead to undiagnosed or poorly managed hypertension. This cross-sectional study assessed the association between having a PCP and uncontrolled blood pressure (BP) among predominantly Hispanic or Latino attendees of a community health fair, adjusting for identified confounders. Methods Data were collected from participants aged 18 years or older at Fiesta de Salud, a community health fair held in Yakima County, Washington State, in August 2023. Blood pressure, PCP status, insurance coverage, demographics, and self-reported hypertension diagnoses were recorded. Uncontrolled BP was defined as systolic ≥ 140 mmHg or diastolic ≥ 90 mmHg. Bivariate analyses utilized Pearson’s chi-squared tests. Variable selection for adjusted robust Poisson regression was informed by a directed acyclic graph (DAG), subsequently adjusting for demographics (gender and age), socioeconomic (insurance status), and clinical (prior hypertension diagnosis) confounders. Only complete cases were included for analysis. Results Of the 218 participants analyzed, 84% identified as Hispanic, 63% were female. Uncontrolled BP prevalence was 33%. Participants without a PCP had significantly higher prevalence of uncontrolled BP (59% vs. 42%, p = 0.002). Having a PCP consistently lowered the prevalence of uncontrolled BP across all models, remaining significant in the fully adjusted model (PR = 0.54; 95% CI: 0.35–0.85, p = 0.007). Adjustment variables identified by the DAG were included to reduce confounding. Conclusions Having a PCP was independently associated with better BP control after adjusting for demographic, socioeconomic, and clinical confounders. Expanding access to consistent PCP relationships, particularly in underserved communities, may improve hypertension management and reduce disparities. Community health fairs can play an important role by identifying individuals with uncontrolled blood pressure and facilitating linkage to ongoing primary care.

Yijia Wang, Songting Gao, Qiang Xu et al.

Abstract Background Type 2 diabetes mellitus (T2DM) and sarcopenia have emerged as significant public health challenges that disproportionately affect middle-aged adults. However, the relationship between T2DM and muscle parameters in this critical age remains inadequately elucidated. Methods We conducted a comprehensive analysis of cross-sectional data derived from 2,446 middle-aged participants (aged 40–59 years; 1,230 men and 1,216 women) enrolled in the National Health and Nutrition Examination Survey (NHANES) 2011–2014. T2DM was defined by physician-confirmed diagnosis and/or glycohemoglobin (HbA1c) ≥ 6.5%. We systematically evaluated appendicular lean mass index (ALMI) and grip strength in relation to T2DM status, employing multiple linear regression models with hierarchical adjustment for demographic, anthropometric, and clinical covariates. Results Following comprehensive covariate adjustment, T2DM exhibited a significant inverse association with combined grip strength (β=-3.14; 95% CI: -4.55, -1.73) but showed no significant association with ALMI (β=-0.05; 95% CI: -0.13, 0.02). Sex-stratified analyses revealed significant reductions in both muscle mass and strength exclusively among men with T2DM, whereas no significant associations were observed in women. The adverse impact of T2DM on grip strength was particularly pronounced among individuals with BMI < 25 kg/m² (β=-6.72; 95% CI: -10.38, -3.06). Furthermore, glycohemoglobin demonstrated positive associations with ALMI in non-diabetic individuals, while serum glucose levels exhibited inverse associations with ALMI across the total study population. Conclusions Our findings demonstrated negative associations between T2DM and muscle mass and strength in middle-aged men, but not in women. These observations underscore the critical importance of early muscle health surveillance and targeted therapeutic interventions in T2DM patients, particularly among men and those with BMI < 25 kg/m².

Moretta D Fauzi, Sahar B Toulabi, Emma Bovaird et al.

Maria F Gomez, Stephanie R Hogue, Jason L Salemi et al.

Ziyang Gao, Annie Cheung, Yihao Ou

Accurate prediction of protein-ligand binding affinity plays a pivotal role in accelerating the discovery of novel drugs and vaccines, particularly for gastrointestinal (GI) diseases such as gastric ulcers, Crohn's disease, and ulcerative colitis. Traditional computational models often rely on structural information alone and thus fail to capture the genetic determinants that influence disease mechanisms and therapeutic responses. To address this gap, we propose GastroDL-Fusion, a dual-modal deep learning framework that integrates protein-ligand complex data with disease-associated gene sequence information for drug and vaccine development. In our approach, protein-ligand complexes are represented as molecular graphs and modeled using a Graph Isomorphism Network (GIN), while gene sequences are encoded into biologically meaningful embeddings via a pre-trained Transformer (ProtBERT/ESM). These complementary modalities are fused through a multi-layer perceptron to enable robust cross-modal interaction learning. We evaluate the model on benchmark datasets of GI disease-related targets, demonstrating that GastroDL-Fusion significantly improves predictive performance over conventional methods. Specifically, the model achieves a mean absolute error (MAE) of 1.12 and a root mean square error (RMSE) of 1.75, outperforming CNN, BiLSTM, GIN, and Transformer-only baselines. These results confirm that incorporating both structural and genetic features yields more accurate predictions of binding affinities, providing a reliable computational tool for accelerating the design of targeted therapies and vaccines in the context of gastrointestinal diseases.

Raquel Norel, Michele Merler, Pavitra Modi

Patients with rare neurological diseases report cognitive symptoms -"brain fog"- invisible to traditional tests. We propose continuous neurocognitive monitoring via smartphone speech analysis integrated with Relational Graph Transformer (RELGT) architectures. Proof-of-concept in phenylketonuria (PKU) shows speech-derived "Proficiency in Verbal Discourse" correlates with blood phenylalanine (p = -0.50, p < 0.005) but not standard cognitive tests (all |r| < 0.35). RELGT could overcome information bottlenecks in heterogeneous medical data (speech, labs, assessments), enabling predictive alerts weeks before decompensation. Key challenges: multi-disease validation, clinical workflow integration, equitable multilingual deployment. Success would transform episodic neurology into continuous personalized monitoring for millions globally.

Falih Gozi Febrinanto, Adonia Simango, Chengpei Xu et al.

Graph neural networks (GNNs) have been developed to model the relationship between regions of interest (ROIs) in brains and have shown significant improvement in detecting brain diseases. However, most of these frameworks do not consider the intrinsic relationship of causality factor between brain ROIs, which is arguably more essential to observe cause and effect interaction between signals rather than typical correlation values. We propose a novel framework called CGB (Causal Graphs for Brains) for brain disease classification/detection, which models refined brain networks based on the causal discovery method, transfer entropy, and geometric curvature strategy. CGB unveils causal relationships between ROIs that bring vital information to enhance brain disease classification performance. Furthermore, CGB also performs a graph rewiring through a geometric curvature strategy to refine the generated causal graph to become more expressive and reduce potential information bottlenecks when GNNs model it. Our extensive experiments show that CGB outperforms state-of-the-art methods in classification tasks on brain disease datasets, as measured by average F1 scores.

Mingyan Ju, Tingting Deng, Xuemin Jia et al.

Abstract Background The incidence of diabetic gastrointestinal diseases is increasing year by year. This study aimed to investigate the causal relationship between antidiabetic medications and gastrointestinal disorders, with the goal of reducing the incidence of diabetes-related gastrointestinal diseases and exploring the potential repurposing of antidiabetic drugs. Methods We employed a two-sample Mendelian randomization (TSMR) design to investigate the causal association between antidiabetic medications and gastrointestinal disorders, including gastroesophageal reflux disease (GERD), gastric ulcer (GU), chronic gastritis, acute gastritis, Helicobacter pylori infection, gastric cancer (GC), functional dyspepsia (FD), irritable bowel syndrome (IBS), ulcerative colitis (UC), Crohn’s disease (CD), diverticulosis, and colorectal cancer (CRC). To identify potential inhibitors of antidiabetic drug targets, we collected single-nucleotide polymorphisms (SNPs) associated with metformin, GLP-1 receptor agonists, SGLT2 inhibitors, DPP-4 inhibitors, insulin, and its analogs, thiazolidinediones, sulfonylureas, and alpha-glucosidase inhibitors from published genome-wide association study statistics. We then conducted a drug-target Mendelian randomization (MR) analysis using inverse variance weighting (IVW) as the primary analytical method to assess the impact of these inhibitors on gastrointestinal disorders. Additionally, diabetes was selected as a positive control. Results Sulfonylureas were found to significantly reduce the risk of CD (IVW: OR [95% CI] = 0.986 [0.978, 0.995], p = 1.99 × 10− 3), GERD (IVW: OR [95% CI] = 0.649 [0.452, 0.932], p = 1.90 × 10− 2), and chronic gastritis (IVW: OR [95% CI] = 0.991 [0.982, 0.999], p = 4.50 × 10− 2). However, they were associated with an increased risk of GU development (IVW: OR [95%CI] = 2 0.761 [1.259, 6.057], p = 1 0.12 × 10− 2). Conclusions The results indicated that sulfonylureas had a positive effect on the prevention of CD, GERD, and chronic gastritis but a negative effect on the development of gastric ulcers. However, our research found no causal evidence for the impact of metformin, GLP-1 agonists, SGLT2 inhibitors, DPP 4 inhibitors, insulin and its analogs, thiazolidinediones, or alpha-glucosidase inhibitors on gastrointestinal diseases. Graphical abstract

Luc L Hagenaars, Tera L Fazzino, Joreintje Dingena Mackenbach

Abstract Objective: Food taxation can improve diets by making unhealthy foods more expensive and by making healthy foods cheaper. In the Netherlands, a political window of opportunity arose in December 2021 to reduce the value-added tax (VAT) on fruits and vegetables to zero percent. The policy is now facing institutional friction along several fronts, however, delaying and potentially averting its implementation. We analysed this institutional friction to inform future food tax policies. Design: We qualitatively analysed open-access fiscal and health experts’ position papers about benefits and downsides of the zero-rate that were discussed with members of parliament in June 2023. Setting: The Netherlands. Participants: Not applicable. Results: Health and fiscal experts expressed noticeably different viewpoints towards the utility of the zero-rate. One important argument fiscal experts based their negative advice upon pertained to the legal restrictions for distinguishing between healthier and unhealthier forms of fruits and vegetables (i.e. the principle of neutrality). A zero-rate VAT on unhealthier forms of fruits and vegetables, e.g. processed cucumber, mixed with salt and sugar, would be undesirable, but differentiating between raw and processed cucumber would offend the neutrality principle. Conclusions: The Dutch attempt to give fruits and vegetables a tax break highlights the need for crystal-clear food classifications when designing food tax policies. Public health nutritionists should combine classifications based on caloric density, palatability, degree of processing and nutrient content to provide a database for evidence-informed tax differentiation according to food item healthfulness.

Miguel Angel Hernández Real, María Magdalena Sánchez Jesús , Patricia Muñoz-Ledo Rábago et al.

Introducción: La asfixia perinatal (AP) es una complicación obstétrica de alto riesgo para el desarrollo de alteraciones orgánicas y funcionales que pueden conllevar a estados de malnutrición infantil, con todos los efectos adversos que estos suponen sobre la salud y desarrollo humano. El objetivo del presente estudio fue describir los patrones de crecimiento físico presentados durante los primeros dos años de vida en una muestra de niños con antecedentes de AP. Metodología: Estudio descriptivo, retrospectivo y longitudinal, en 52 casos de niños con antecedentes de AP que asistieron a un programa de seguimiento y cuidado integral durante los primeros 24 meses de edad. Las tendencias de crecimiento se determinaron con base en lo establecido por la OMS (2006), clasificando a los niños de acuerdo con los cambios observados en el puntaje z de los indicadores antropométricos. Todos los niños elegidos para el estudio fueron tratados con hipotermia terapéutica.    Resultados: 38.5% de los casos presentaron trayectorias de desnutrición sin recuperación, siendo la desnutrición crónica la alteración predominante. El retraso en el crecimiento intrauterino (RP = 1.61, IC95%: 1.028 – 2.529), la razón peso-longitud para la edad gestacional menor al percentil 10 (RP= 1.37, IC95%: 1.021 – 1.849), el trabajo (RP = 1.59, IC95%: 1.119 – 2.265) y un menor nivel educativo de la madre (RP = 1.40, IC 95%: 1.041 – 1.899), se asociaron significativamente con la desnutrición infantil.        Conclusiones: Las trayectorias de desnutrición representaron un problema frecuente en el grupo de estudio por lo que se considera importante continuar esta línea de investigación a fin de determinar la prevalencia de alteraciones del crecimiento en niños con esta y otras condiciones patológicas.   

Jairo Diaz-Rodriguez, Juan Pablo Gomez, Jeremy P. Orange et al.

Identifying areas in a landscape where individuals have a higher likelihood of disease infection is key to managing diseases. Unlike conventional methods relying on ecological assumptions, we perform a novel epidemiological tomography for the estimation of landscape propensity to disease infection, using GPS animal tracks in a manner analogous to tomographic techniques in positron emission tomography (PET). Treating tracking data as random Radon transforms, we analyze Cervid movements in a game preserve, paired with antibody levels for epizootic hemorrhagic disease virus (EHDV) -- a vector-borne disease transmitted by biting midges. After discretizing the field and building the regression matrix of the time spent by each deer (row) at each point of the lattice (column), we model the binary response (infected or not) as a binomial linear inverse problem where spatial coherence is enforced with a total variation regularization. The smoothness of the reconstructed propensity map is selected by the quantile universal threshold. To address limitations of small sample sizes and evaluate significance of our estimates, we quantify uncertainty using a bootstrap-based data augmentation procedure. Our method outperforms alternative ones when using simulated and real data. This tomographic framework is novel, with no established statistical methods tailored for such data.

Ivo V. Stoepker

We consider outbreak detection settings of endemic diseases where the population under study consists of various subpopulations available for stratified surveillance. These subpopulations can for example be based on age cohorts, but may also correspond to other subgroups of the population under study such as international travellers. Rather than sampling uniformly across the population, one may elevate the effectiveness of the detection methodology by optimally choosing a sampling subpopulation. We show (under some assumptions) the relative sampling efficiency between two subpopulations is inversely proportional to the ratio of their respective baseline disease risks. This implies one can increase sampling efficiency by sampling from the subpopulation with higher baseline disease risk. Our results require careful treatment of the power curves of exact binomial tests as a function of their sample size, which are non-monotonic due to the underlying discreteness. A case study of COVID-19 cases in the Netherlands illustrates our theoretical findings.

Jie Li, Cillian Hourican, Pashupati P. Mishra et al.

Cardiovascular diseases (CVD) and depression exhibit significant comorbidity, which is highly predictive of poor clinical outcomes. Yet, the underlying biological pathways remain challenging to decipher, presumably due to the non-linear associations across multiple mechanisms. In this study, we introduced a multipartite projection method based on mutual information correlations to construct multilayer disease networks as a novel approach to explore such intricate relationships. We applied this method to a cross-sectional dataset from a wave of the Young Finns Study, which includes data on CVD and depression, along with related risk factors and two omics of biomarkers: metabolites and lipids. Rather than directly correlating CVD-related phenotypes and depressive symptoms, we extended the notion of bipartite networks to create a multipartite network, linking these phenotypes and symptoms to intermediate biological variables. Projecting from these intermediate variables results in a weighted multilayer network, where each link between CVD and depression variables is marked by its layer (i.e., metabolome or lipidome). Applying this projection method, we identified potential mediating biomarkers that connect CVD with depression. These biomarkers may therefore play significant roles in the biological pathways underlying CVD-depression comorbidity. Additionally, the network projection highlighted sex and BMI as key risk factors, or confounders, in this comorbidity. Our method is scalable to incorporate any number of omics layers and various disease phenotypes, offering a comprehensive, system-level perspective on the biological pathways contributing to comorbidity.

Mario Padilla Rodriguez, Mohamed Nafea

Cardiovascular diseases are a leading cause of mortality worldwide, highlighting the need for accurate diagnostic methods. This study benchmarks centralized and federated machine learning algorithms for heart disease classification using the UCI dataset which includes 920 patient records from four hospitals in the USA, Hungary and Switzerland. Our benchmark is supported by Shapley-value interpretability analysis to quantify features' importance for classification. In the centralized setup, various binary classification algorithms are trained on pooled data, with a support vector machine (SVM) achieving the highest testing accuracy of 83.3\%, surpassing the established benchmark of 78.7\% with logistic regression. Additionally, federated learning algorithms with four clients (hospitals) are explored, leveraging the dataset's natural partition to enhance privacy without sacrificing accuracy. Federated SVM, an uncommon approach in the literature, achieves a top testing accuracy of 73.8\%. Our interpretability analysis aligns with existing medical knowledge of heart disease indicators. Overall, this study establishes a benchmark for efficient and interpretable pre-screening tools for heart disease while maintaining patients' privacy. This work is available at https://github.com/padillma1/Heart-Disease-Classification-on-UCI-dataset-and-Shapley-Interpretability-Analysis.

Rikathi Pal, Anik Basu Bhaumik, Arpan Murmu et al.

Diseases in plants cause significant danger to productive and secure agriculture. Plant diseases can be detected early and accurately, reducing crop losses and pesticide use. Traditional methods of plant disease identification, on the other hand, are generally time-consuming and require professional expertise. It would be beneficial to the farmers if they could detect the disease quickly by taking images of the leaf directly. This will be a time-saving process and they can take remedial actions immediately. To achieve this a novel feature extraction approach for detecting tomato plant illnesses from leaf photos using low-cost computing systems such as mobile phones is proposed in this study. The proposed approach integrates various types of Deep Learning techniques to extract robust and discriminative features from leaf images. After the proposed feature extraction comparisons have been made on five cutting-edge deep learning models: AlexNet, ResNet50, VGG16, VGG19, and MobileNet. The dataset contains 10,000 leaf photos from ten classes of tomato illnesses and one class of healthy leaves. Experimental findings demonstrate that AlexNet has an accuracy score of 87%, with the benefit of being quick and lightweight, making it appropriate for use on embedded systems and other low-processing devices like smartphones.

J. Bourre

S. Schiffman