Combined versus Individual Effects of Dietary Energy Reduction and Physical Activity on Body Composition in Older Adults with Metabolic Syndrome Isa Galvão-Rodrigues, Laura Gallardo-Nuell, Basma El Emrani-Azariah, Rafael Ramos, Josep Garre-Olmo, Josep Puig, José María Moreno-Navarrete, Jordi Mayneris-Perxachs, José Manuel Fernández-Real Journal of Obesity and Metabolic Syndrome, 2026 Background: Serum ferritin is well known to be associated with chronic inflammation and insulin resistance. This study investigates urinary ferritin as a potential non-invasive biomarker for metabolic alterations and insulin resistance. Methods: This study analyzed ferritin levels in urine samples from two independent cross-sectional cohorts: IRONMET-continuous glucose monitoring (CGM) (a middle-aged cohort, n=78) and Aging Imageomics (an aged cohort, n=47). Associations between urinary ferritin and clinical and biochemical parameters were assessed using correlation analyses. Untargeted plasma metabolomics was performed in the IRONMET-CGM cohort, and differentially abundant metabolites associated with urinary ferritin and ultrasensitive C-reactive protein were identified using linear models for microarray regression analysis. Results: In the IRONMET-CGM cohort, urinary ferritin correlated positively with serum glucose, serum ferritin, and transferrin saturation and negatively with transferrin and total iron-binding capacity. In the Aging Imageomics cohort, urinary ferritin showed positive associations with serum glucose, fasting plasma insulin, low-density lipoprotein cholesterol, serum ferritin, right ankle diastolic blood pressure, and the percentage of mean arterial pressure of the right brachial pulse wave and a negative association with high-density lipoprotein cholesterol. Plasma metabolomics profiling revealed that urinary ferritin was positively associated with succinic acid, adipic acid and cis-4-decenoic acid concentrations, suggesting disruptions in fatty acid metabolism. In contrast, metabolites linked to elevated ultrasensitive C-reactive protein, including 3-hydroxyanthranilic acid and formyl-5-hydroxykynurenamine, were associated with the kynurenine pathway and systemic inflammation. Conclusion: Urinary ferritin is a promising, non-invasive marker of iron metabolism disturbances and metabolic dysregulation. Its associations with glucose metabolism, lipid profiles, and distinct metabolomics signatures suggest potential utility for early detection of metabolic alterations.
Ultra-Processed Food Consumption and Childhood Allergic Diseases: Increased Risk of Asthma Onset in the SENDO Project O. Galindo, M. J. Goikoetxea, L. Moreno‐Galarraga, L. Argiz, J. M. Moreno‐Villares, Victor de la O, N. Martín‐Calvo Allergy European Journal of Allergy and Clinical Immunology, 2026 Background While the role of genetic predisposition in asthma and other allergic conditions is well established, the contribution of nutritional patterns is heterogeneous and has been demonstrated in cross‐sectional studies but not in prospective cohorts. Methods We analyzed data from 1546 participants enrolled in the SENDO cohort between January 2015 and June 2024. Children aged 4–5 years were prospectively recruited. UPF consumption was assessed at baseline and updated at the 3‐year follow‐up. Information on asthma and allergic diseases was collected at baseline and updated annually during the follow‐up. A final sample of 691 participants was classified into tertiles (T1, T2, T3) according to UPF consumption. Prevalent cases were excluded from the analysis to ensure that incident cases were included during the follow‐up. In the main analyses, we calculated the adjusted hazard ratios (HRs) and 95% CI with survival analyses. Results After a mean follow‐up of 3.4 years, the adjusted risk for asthma in each tertile (T1, T2, T3) of UPF consumption was 2.6%, 9.9%, and 7.6% respectively (p for trend: 0.03). In the fully adjusted model of the survival analysis, children with greater UPF consumption (T2 + T3) showed a significantly higher risk of asthma (HR 3.76; 95% CI 1.15–11.51, p = 0.02) but not of AA, nor other allergic outcomes compared with their peers in the lowest tertile (T1) of UPF consumption. Conclusion Higher UPF consumption may be associated with an increased risk of developing asthma in school‐age children.
Upgraded Estimation of Dietary Intake Using Phenotypic and Biochemical Markers by Supervised Equations: Applicability for Categorizing DQI Edwin Fernández-Cruz, Víctor de la O, Cristina M. Fernández, M. Ángel Rubio-Herrera, Pilar Matía-Martín, Alfonso L. Calle-Pascual, Ana Barabash, J. Alfredo Martínez Journal of the American Nutrition Association, 2026 OBJECTIVE: Dietary and nutrient intake directly impact health, whereby adherence to certain dietary patterns is linked to positive outcomes. Traditional methods like the Food Frequency Questionnaire (FFQ) and 24-hour recall are subjective, highlighting the need for advanced techniques that incorporate phenotypic and metabolic data. This pilot exploratory study aimed to assess the feasibility of using machine-learning techniques that integrate routinely collected phenotypic and biochemical data to predict adherence to well-characterized dietary quality indices. METHOD: A total of 138 participants were recruited in the Dietary Deal cross-sectional study to collect data on dietary intake (FFQ, 24-hour recall), biochemical markers, physical activity estimation, quality-of-life questionnaires, and anthropometric determinations. The Mediterranean Diet Adherence Screener (MEDAS 17p), the Alternative Healthy Eating Index (AHEI), the Dietary Approaches to Stop Hypertension (DASH), and a pro-vegetarian model were tested as quality indices. Biochemical and dietary data were integrated using adjusted logistic regressions through STATA (v. 18.0) statistical program to identify biochemical markers associated with food consumption to predict dietary quality. Subsequently, an algorithm based on machine-learning techniques was developed, and the predictive capacity of the obtained models was determined using receiver operating characteristic (ROC) curves and related metrics (area under the curve). RESULTS: = 22.07% to 35.76%, depending on the index. The model's accuracy ranged from 72.46% to 78.26%, with ROC values between 0.79 and 0.87, indicating moderate to good predictive validity of the training data on itself. CONCLUSIONS: This pilot exploratory analysis demonstrates the feasibility of integrating dietary and biochemical data to suitably predict adherence to validated dietary quality indices, Although not intended as a deployable prediction tool, the study provides preliminary evidence supporting the potential of routinely collected clinical data to inform personalized precision dietary advice through objective computational algorithms for precision nutrition implementation.
Branched-Chain Amino Acid Intake and Risk of Incident Type 2 Diabetes: Results from the SUN Cohort Víctor de la O, Telmo Bretos-Azcona, Francisco Javier Basterra-Gortari, Carmen de la Fuente-Arrillaga, Miguel Ruiz-Canela, Miguel Ángel Martínez-González, Maira Bes-Rastrollo Biomedicines, 2025 Background/Objectives: While many studies have explored the association between circulating branched-chain amino acids (BCAAs) and type 2 diabetes mellitus (T2DM), evidence on the prospective relationship between dietary BCAA intake and T2DM risk remains limited. We aimed to explore this relationship—both total and by dietary source—in a Mediterranean cohort. Methods: We used data from the SUN Project, a prospective and dynamic cohort of Spanish university graduates initiated in 1999. Dietary intake was assessed with a validated 136-item food frequency questionnaire at baseline and at 10 years. BCAA intake (valine, leucine, isoleucine) was estimated using the USDA amino acid database and adjusted for energy intake by the residual method. Participants were followed biennially through questionnaires to identify incident T2DM cases, confirmed by a supplementary questionnaire and medical report, following the ADA diagnostic criteria. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs), adjusting for potential confounders across four multivariable models. BCAA intake was modeled both categorically (tertiles) and continuously (per 0.5% energy or 5 g/day increase). Analyses were stratified by age and recruitment period. Results: After exclusions, 20,154 participants were included (mean follow-up: 14.67 ± 5.8 years), with 220 incident T2DM cases identified. For each 0.5% energy increment intake from BCAA, there was no association with T2DM (adjusted HR: 1.01; 95% CI: 0.69–1.20). Among men, the adjusted HR was 0.91, 95% CI: 0.69–1.20. Among women, it was 1.40, 95% CI: 0.94–2.09. In the overall cohort, higher BCAA intake showed a non-significant inverse association with the T2DM risk when comparing extreme tertiles (HR = 0.81; 95% CI: 0.48–1.37), which strengthened when repeated dietary measures were considered (HR = 0.70; 95% CI: 0.46–1.06, p-trend = 0.06). Analyses by BCAA sources (animal vs. plant) and stratified by sex, weight status, and age did not reveal consistent patterns, though exploratory findings suggested potential effect modification by sex and adiposity. Sensitivity analyses confirmed the lack of robust associations, with some subgroup-specific signals being limited by low event numbers and wide CIs. Conclusions: Given the power limitations and the modest, non-significant associations observed, these findings should be considered preliminary evidence that may help guide future research on the role of dietary BCAAs in glucose metabolism and diabetes risk.
Gut Microbiota Shifts After a Weight Loss Program in Adults with Obesity: The WLM3P Study Vanessa Pereira, Amanda Cuevas-Sierra, Victor de la O, Rita Salvado, Inês Barreiros-Mota, Inês Castela, Alexandra Camelo, Inês Brandão, Christophe Espírito Santo, Ana Faria, Conceição Calhau, Marta P. Silvestre, André Moreira-Rosário Nutrients, 2025 Background: The gut microbiota is increasingly recognized as a key modulator in obesity management, influencing host energy balance, lipid metabolism, and inflammatory pathways. With obesity prevalence continuing to rise globally, dietary interventions that promote beneficial microbial shifts are essential for enhancing weight loss outcomes and long-term health. Objective: This study investigated the effects of the multicomponent Weight Loss Maintenance 3 Phases Program (WLM3P), which integrates caloric restriction, a high-protein low-carbohydrate diet, time-restricted eating (10h TRE), dietary supplementation (prebiotics and phytochemicals), and digital app-based support on gut microbiota composition compared to a standard low-carbohydrate diet (LCD) in adults with obesity. The analysis focused exclusively on the 6-month weight loss period corresponding to Phases 1 and 2 of the WLM3P intervention. Methods: In this sub-analysis of a randomized controlled trial (ClinicalTrials.gov Identifier: NCT04192357), 58 adults with obesity (BMI 30.0–39.9 kg/m2) were randomized to the WLM3P (n = 29) or LCD (n = 29) groups. Stool samples were collected at baseline and 6 months for 16S rRNA sequencing. Alpha and beta diversity were assessed, and genus-level differential abundance was determined using EdgeR and LEfSe. Associations between microbial taxa and clinical outcomes were evaluated using regression models. Results: After 6-month, the WLM3P group showed a significant increase in alpha diversity (p = 0.03) and a significant change in beta diversity (p < 0.01), while no significant changes were observed in the LCD group. Differential abundance analysis revealed specific microbial signatures in WLM3P participants, including increased levels of Faecalibacterium. Notably, higher Faecalibacterium abundance was associated with greater reductions in fat mass (kg, %) and visceral adiposity (cm2) in the WLM3P group compared to LCD (p < 0.01). Conclusions: These findings suggest a potential microbiota-mediated mechanism in weight loss, where Faecalibacterium may enhance fat reduction effectiveness in the context of the WLM3P intervention.
Urinary Hippuric Acid as a Sex-Dependent Biomarker for Fruit and Nut Intake Raised from the EAT-Lancet Index and Nuclear Magnetic Resonance Analysis Edwin Fernández-Cruz, Víctor de la O, Cristina M. Fernández-Diaz, Pilar Matía-Martín, M. Ángel Rubio-Herrera, Nuria Amigó, Alfonso L. Calle-Pascual, J. Alfredo Martínez Metabolites, 2025 Background/Objectives: Assessing nutrient intake is essential for understanding body homeostasis and diet–health interactions. Traditional methods, such as dietary questionnaires and quality indices, are limited by subjectivity and variability in food composition tables. Metabolomic markers, like urinary hippuric acid, provide an objective means to estimate food and nutrient intake, helping to link dietary patterns with metabolic outputs and health outcomes. This study uniquely evaluates urinary hippuric acid as a putative biomarker of nut intake, expanding the previously known role as a fruit intake marker, and investigates sex-related differences in the excretion. Methods: Using Nuclear Magnetic Resonance (NMR) spectroscopy, 34 urinary metabolites from 138 participants (69.7% women) in the Dietary Deal project were analyzed. Metabolite concentrations were categorized by median adherence to the EAT-Lancet score (≤p50 or >p50). A validated Food Frequency Questionnaire (FFQ) assessed dietary and energy intake. Correlation analyses linked metabolites to the 14 EAT-Lancet food groups, and a linear regression adjusted model examined associations between urinary hippuric acid and fruit/nut consumption, with sensitivity analysis for sex. Results: The EAT-Lancet index, stratified by median adherence, effectively distinguished between high and low dietary intake of fruits (p = 0.012) and nuts (p < 0.001). Urinary hippuric acid concentrations were found to be influenced by sex (p = 0.020), with females showing a 44.7% higher mean concentration. Overall, urinary hippuric acid levels were positively associated with FFQ-estimated nut consumption (p = 0.049), providing the first evidence of potential suitability as a nut intake biomarker. Conclusions: Hippuric acid emerges as a promising dietary biomarker for assessing nut intake in healthy populations. This study provides novel insights that extend the application of hippuric acid to dietary nut assessment and emphasizes the importance of a sex-specific interpretation for precision nutrition purposes using NMR technology.
Phenotype-Driven Variability in Longitudinal Body Composition Changes After a Very Low-Calorie Ketogenic Intervention: A Machine Learning Cluster Approach Victor de la O, Begoña de Cuevillas, Miksa Henkrich, Barbara Vizmanos, Maitane Nuñez-Garcia, Ignacio Sajoux, Daniel de Luis, J. Alfredo Martínez Journal of Personalized Medicine, 2025 Background: Obesity is a major global public health issue with no fully satisfactory solutions. Most nutritional interventions rely on caloric restriction, with varying degrees of success. Very low-calorie ketogenic diets (VLCKD) have demonstrated rapid and sustained weight loss by inducing ketone bodies through lipolysis, reducing appetite, and preserving lean mass while maintaining metabolic health. Methods: A prospective clinical study analyzed sociodemographic, anthropometric, and adherence data from 7775 patients undergoing a multidisciplinary nutritional single-arm intervention based on a commercial weight-loss program. This method, using protein preparations with a specific balanced nutritional profile, aimed to identify key predictors of weight-loss success and classify population phenotypes with shared baseline characteristics and weight-loss patterns to optimize treatment personalization. Results: Statistical and machine learning analyses revealed that male gender (−9.2 kg vs. −5.9 kg) and higher initial body weight (−8.9 kg vs. −4.0 kg) strongly predict greater weight loss on a VLCKD, while age has a lesser impact. Two distinct population clusters emerged, differing in age, sex, follow-up duration, and medical visits, demonstrating unique weight-loss success patterns. These clusters help define individualized strategies for optimizing outcomes. Conclusions: These findings translationally support associations with the efficacy of a multidisciplinary VLCK weight-loss program and highlight predictors of success. Recognizing variables such as sex, age, and initial weight enhances the potential for a precision-based approach in obesity management, enabling more tailored and effective treatments for diverse patient profiles and prescribe weight loss personalized recommendations.
Computational algorithm based on health and lifestyle traits to categorize lifemetabotypes in the NUTRiMDEA cohort Andrea Higuera-Gómez, Víctor de la O, Rodrigo San-Cristobal, Rosa Ribot-Rodríguez, Isabel Espinosa-Salinas, Alberto Dávalos, María P. Portillo, J. Alfredo Martínez Scientific Reports, 2024 Classifying individuals based on metabotypes and lifestyle phenotypes using exploratory factor analyses, cluster definition, and machine-learning algorithms is promising for precision chronic disease prevention and management. This study analyzed data from the NUTRiMDEA online cohort (baseline: n = 17332 and 62 questions) to develop a clustering tool based on 32 accessible questions using machine-learning strategies. Participants ranged from 18 to over 70 years old, with 64.1% female and 35.5% male. Five clusters were identified, combining metabolic, lifestyle, and personal data: Cluster 1 ("Westernized Millennial", n = 967) included healthy young individuals with fair lifestyle habits; Cluster 2 ("Healthy", n = 10616) consisted of healthy adults; Cluster 3 ("Mediterranean Young Adult", n = 2013) represented healthy young adults with a healthy lifestyle and showed the highest adherence to the Mediterranean diet; Cluster 4 ("Pre-morbid", n = 600) was characterized by healthy adults with declined mood; Cluster 5 ("Pro-morbid", n = 312) comprised older individuals (47% >55 years) with poorer lifestyle habits, worse health, and a lower health-related quality of life. A computational algorithm was elicited, which allowed quick cluster assignment based on responses ("lifemetabotypes"). This machine-learning approach facilitates personalized interventions and precision lifestyle recommendations, supporting online methods for targeted health maintenance and chronic disease prevention.
Translational Algorithms for Technological Dietary Quality Assessment Integrating Nutrimetabolic Data with Machine Learning Methods Víctor de la O, Edwin Fernández-Cruz, Pilar Matía Matin, Angélica Larrad-Sainz, José Luis Espadas Gil, Ana Barabash, Cristina M. Fernández-Díaz, Alfonso L. Calle-Pascual, Miguel A. Rubio-Herrera, J. Alfredo Martínez Nutrients, 2024 Recent advances in machine learning technologies and omics methodologies are revolutionizing dietary assessment by integrating phenotypical, clinical, and metabolic biomarkers, which are crucial for personalized precision nutrition. This investigation aims to evaluate the feasibility and efficacy of artificial intelligence tools, particularly machine learning (ML) methods, in analyzing these biomarkers to characterize food and nutrient intake and to predict dietary patterns. Methods: We analyzed data from 138 subjects from the European Dietary Deal project through comprehensive examinations, lifestyle questionnaires, and fasting blood samples. Clustering was based on 72 h dietary recall, considering sex, age, and BMI. Exploratory factor analysis (EFA) assigned nomenclature to clusters based on food consumption patterns and nutritional indices from food frequency questionnaires. Elastic net regression identified biomarkers linked to these patterns, helping construct algorithms. Results: Clustering and EFA identified two dietary patterns linked to biochemical markers, distinguishing pro-Mediterranean (pro-MP) and pro-Western (pro-WP) patterns. Analysis revealed differences between pro-MP and pro-WP clusters, such as vegetables, pulses, cereals, drinks, meats, dairy, fish, and sweets. Markers related to lipid metabolism, liver function, blood coagulation, and metabolic factors were pivotal in discriminating clusters. Three computational algorithms were created to predict the probabilities of being classified into the pro-WP pattern. The first is the main algorithm, followed by a supervised algorithm, which is a simplified version of the main model that focuses on clinically feasible biochemical parameters and practical scientific criteria, demonstrating good predictive capabilities (ROC curve = 0.91, precision–recall curve = 0.80). Lastly, a reduced biochemical-based algorithm is presented, derived from the supervised algorithm. Conclusions: This study highlights the potential of biochemical markers in predicting nutritional patterns and the development of algorithms for classifying dietary clusters, advancing dietary intake assessment technologies.
Coronavirus disease 2019 is associated with long-term depressive symptoms in Spanish older adults with overweight/obesity and metabolic syndrome Sangeetha Shyam, Carlos Gómez-Martínez, Indira Paz-Graniel, José J. Gaforio, Miguel Ángel Martínez-González, Dolores Corella, Montserrat Fitó, J. Alfredo Martínez, Ángel M. Alonso-Gómez, Julia Wärnberg, Jesús Vioque, Dora Romaguera, José López-Miranda, Ramon Estruch, Francisco J. Tinahones, José Manuel Santos-Lozano, J. Luís Serra-Majem, Aurora Bueno-Cavanillas, Josep A. Tur, Vicente Martín Sánchez, Xavier Pintó, María Ortiz Ramos, Josep Vidal, Maria Mar Alcarria, Lidia Daimiel, Emilio Ros, Fernando Fernandez-Aranda, Stephanie K. Nishi, Oscar García Regata, Estefania Toledo, Jose V. Sorli, Olga Castañer, Antonio Garcia-Rios, Rafael Valls-Enguix, Napoleon Perez-Farinos, M. Angeles Zulet, Elena Rayó-Gago, Rosa Casas, Mario Rivera-Izquierdo, Lucas Tojal-Sierra, Miguel Damas-Fuentes, Pilar Buil-Cosiales, Rebeca Fernández-Carrion, Albert Goday, Patricia J. Peña-Orihuela, Laura Compañ-Gabucio, Javier Diez-Espino, Susanna Tello, Ana González-Pinto, Víctor de la O, Miguel Delgado-Rodríguez, Nancy Babio, Jordi Salas-Salvadó Psychological Medicine, 2024
