0c98d737a675a963ebbf26f95c75bc0c nutrients-17-02651.pdf 927630744a50c1acaed714b32aa2aa9fe60842ff nutrients-17-02651.pdf f06c6e265b0e06bb06bbedb5e9990e1619dc47f7dce636fd847d0b4b3fcd49ac nutrients-17-02651.pdf Title: Predicting Metabolic and Cardiovascular Healthy from Nutritional Patterns and Psychological State Among Overweight and Obese Young Adults: A Neural Network Approach Subject: Background and objectives: Overweight and obesity are global public health problems, as they increase the risk of chronic diseases, reduce quality of life, and generate a significant economic and healthcare burden. This study evaluates the capacity of nutritional patterns and psychological status to predict the presence of cardiometabolic risk among overweight and obese young adults, from a neural network approach. Method: The study included N = 188 overweight or obese students, who provided measures on their dietary intake, physical and psychological state, and sociodemographic profile. Neural networks were used to predict their metabolic status, classified into two categories based on anthropometric, biochemical, and cardiometabolic risk factors: metabolically unhealthy obesity (MUO) versus metabolically healthy obesity (MHO). Results: The predictive models demonstrated differences in specificity and sensitivity capacity depending on the criteria employed for the classification of MUO/MHO and gender. Among the female subsample, MUO was predicted by poor diet (low consumption of mineral and vitamins, and high consumption of fats and sodium) and high levels of depression and stress, while among the male subsample high body mass index (BMI), depression, and anxiety were the key factors. Protective factors associated to MHO were lower BMI, lower psychopathology distress and more balanced diets. Predictive models based on the HOMA-IR criterion yielded very high specificity and low sensibility (high capacity to identify MHO but low accuracy to identify MUO). The models based on the IDF criterion achieved excellent discriminative capacity for men (specificity and sensitivity around 92.5%), while the model for women obtained excellent sensitivity and low specificity. Conclusions: The results provide empirical support for personalized prevention and treatment programs, accounting for individual differences with the aim of promoting healthy habits among young adults, especially during university education. Keywords: overweight; obesity; nutrient patterns; cardiometabolic; body mass index; HOMA-IR Author: Geovanny Genaro Reivan Ortiz, Laura Maraver-Capdevila and Roser Granero Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25; modified using OpenPDF 1.4.2 CreationDate: Fri Aug 15 12:33:58 2025 CEST ModDate: Fri Aug 15 16:04:41 2025 CEST Custom Metadata: yes Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 19 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 2098946 bytes Optimized: no PDF version: 1.5 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- MIKRSI+VnURWPalladioL Type 1 Custom yes yes yes 125 0 MVNEQN+URWPalladioL-Bold Type 1 Custom yes yes yes 126 0 XHYGOW+URWPalladioL-Roma Type 1 Custom yes yes yes 127 0 SSODKM+URWPalladioL-Ital Type 1 Custom yes yes yes 128 0 ZIPEAN+EURM10 Type 1 Builtin yes yes yes 272 0 VBAQIR+CMSY10 Type 1 Builtin yes yes yes 273 0 ICBUDW+URWPalladioL-BoldItal Type 1 Custom yes yes yes 302 0 GKCBAA+ArialNarrow,Italic TrueType WinAnsi yes yes no 328 0 GKBNLM+PalatinoLinotype TrueType WinAnsi yes yes no 329 0 GKCBAC+Arial TrueType WinAnsi yes yes no 330 0 GKBPCK+PalatinoLinotype,BoldItalic TrueType WinAnsi yes yes no 331 0 GKCBAE+Arial,Bold TrueType WinAnsi yes yes no 332 0 GKBNLO+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 344 0 GKBNLM+PalatinoLinotype TrueType WinAnsi yes yes no 345 0 GKBPCK+PalatinoLinotype,BoldItalic TrueType WinAnsi yes yes no 346 0 GKBNLM+PalatinoLinotype TrueType WinAnsi yes yes no 398 0 GKBPCK+PalatinoLinotype,BoldItalic TrueType WinAnsi yes yes no 399 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2025-08-27 03:07:44 CEST RepresentationInformation: nutrients-17-02651.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2025-08-26 06:22:42 CEST Size: 2098946 Format: PDF Version: 1.5 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 611 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: ViewerPreferences: HideToolbar: false HideMenubar: false HideWindowUI: false FitWindow: true CenterWindow: false DisplayDocTitle: false NonFullScreenPageMode: UseNone Direction: L2R ViewArea: CropBox ViewClip: CropBox PrintArea: CropBox PageClip: CropBox PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Item: Title: Materials and Methods Children: Item: Title: Participants Item: Title: Measures Item: Title: Procedure Item: Title: Neural Networks Item: Title: Statistical Analysis Item: Title: Results Children: Item: Title: Characteristics of the Sample Item: Title: Neural Network Model for Women Predicting the Presence of MUO (HOMA-IR Criterion) Item: Title: Neural Network Model for Men Predicting the Presence of MUO (HOMA-IR Criterion) Item: Title: Neural Network Model for Women Predicting the Presence of MUO (IDF Criterion) Item: Title: Neural Network Model for Men Predicting the Presence of MUO (IDF Criterion) Item: Title: Assessmen of the Validity of the Neural Predictive Models for Discriminating MUO/MHO Item: Title: Discussion Children: Item: Title: Limitations and Proposals for Future Studies Item: Title: Strengths Item: Title: Conclusions Item: Title: References Info: Title: Predicting Metabolic and Cardiovascular Healthy from Nutritional Patterns and Psychological State Among Overweight and Obese Young Adults: A Neural Network Approach Author: Geovanny Genaro Reivan Ortiz, Laura Maraver-Capdevila and Roser Granero Subject: Background and objectives: Overweight and obesity are global public health problems, as they increase the risk of chronic diseases, reduce quality of life, and generate a significant economic and healthcare burden. This study evaluates the capacity of nutritional patterns and psychological status to predict the presence of cardiometabolic risk among overweight and obese young adults, from a neural network approach. Method: The study included N = 188 overweight or obese students, who provided measures on their dietary intake, physical and psychological state, and sociodemographic profile. Neural networks were used to predict their metabolic status, classified into two categories based on anthropometric, biochemical, and cardiometabolic risk factors: metabolically unhealthy obesity (MUO) versus metabolically healthy obesity (MHO). Results: The predictive models demonstrated differences in specificity and sensitivity capacity depending on the criteria employed for the classification of MUO/MHO and gender. Among the female subsample, MUO was predicted by poor diet (low consumption of mineral and vitamins, and high consumption of fats and sodium) and high levels of depression and stress, while among the male subsample high body mass index (BMI), depression, and anxiety were the key factors. Protective factors associated to MHO were lower BMI, lower psychopathology distress and more balanced diets. Predictive models based on the HOMA-IR criterion yielded very high specificity and low sensibility (high capacity to identify MHO but low accuracy to identify MUO). The models based on the IDF criterion achieved excellent discriminative capacity for men (specificity and sensitivity around 92.5%), while the model for women obtained excellent sensitivity and low specificity. Conclusions: The results provide empirical support for personalized prevention and treatment programs, accounting for individual differences with the aim of promoting healthy habits among young adults, especially during university education. 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