Evidence-based interventions for identifying candidate quality indicators to assess quality of care in diabetic foot clinics: a scoping review

Flora Mbela Lusendi1,2* , An‑Sofie Vanherwegen1 , Kris Doggen1 , Frank Nobels3 and Giovanni Arnoldo Matricali2,4 *Correspondence: Flora Mbela Lusendi 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。

1 Health Services Research, Sciensano, Rue Juliette Wytsmanstraat 14, Brussels 1050, Belgium

2 Department of Development and Regeneration, KU Leuven, Leuven, Belgium

3 Multidisciplinary Diabetic Foot Clinic, Onze-Lieve-Vrouwziekenhuis, Aalst, Belgium

4 Multidisciplinary Diabetic Foot Clinic, University Hospital Leuven, Leuven, Belgium

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the  original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecom‑ mons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Abstract

Background Foot ulcers in people with diabetes are a serious complication requiring a complex management and have a high societal impact. Quality monitoring systems to optimize diabetic foot care exist, but a formal and more evidence-based approach to develop quality indicators (QIs) is lacking. We aimed to identify a set of candi‑ date indicators for diabetic foot care by adopting an evidence-based methodology.

Methods A systematic search was conducted across four academic databases: PubMed, Embase CINAHL and Cochrane Library. Studies that reported evidence-based interventions related to organization or delivery of dia‑ betic foot care were searched. Data from the eligible studies were summarized and used to formulate process and structure indicators. The evidence for each candidate QI was described in a methodical and transparent manner. The review process was reported according to the “Preferred Reported Items for Systematic reviews and Meta-Analy‑ sis” (PRISMA) statements and its extension for scoping reviews.

Results In total, 981 full-text articles were screened, and 322 clinical studies were used to formulate 42 candidate QIs.

Conclusions An evidence-based approach could be used to select candidate indicators for diabetic foot ulcer care, relating to the following domains: wound healing interventions, peripheral artery disease, offloading, secondary pre‑ vention, and interventions related to organization of care. In a further step, the feasibility of the identified set of indica‑ tors will be assessed by a multidisciplinary panel of diabetic foot care stakeholders.

Keywords Diabetic foot ulcer, Quality of healthcare, Quality indicators, Evidence-based medicine, Health service research

Sex and APOE ε4 allele differences in longitudinal white matter microstructure in multiple cohorts of aging and Alzheimer’s disease

Amalia Peterson1,2 | Aditi Sathe1 | Dimitrios Zaras1 | Yisu Yang1 | Alaina Durant1 | Kacie D. Deters3 | Niranjana Shashikumar1 | Kimberly R. Pechman1 | Michael E. Kim4 | Chenyu Gao5 | Nazirah Mohd Khairi5 | Zhiyuan Li5 | Tianyuan Yao4 | Yuankai Huo4,5 | Logan Dumitrescu1,2,6,7 | Katherine A. Gifford1,2 | Jo Ellen Wilson1,8,9 | Francis E. Cambronero1 | Shannon L. Risacher10,11 | Lori L. Beason-Held12 | Yang An12 | Konstantinos Arfanakis13,14,15 | Guray Erus16 | Christos Davatzikos16 | Duygu Tosun17 | Arthur W. Toga18 | Paul M. Thompson19 | Elizabeth C. Mormino20 | Mohamad Habes21 | Di Wang21 | Panpan Zhang1,22 | Kurt Schilling23,24 | Alzheimer’s Disease Neuroimaging Initiative (ADNI) | The BIOCARD | Study Team | The Alzheimer’s Disease Sequencing Project (ADSP) | Marilyn Albert25 | Walter Kukull26 | Sarah A. Biber26 | Bennett A. Landman2,4,5,7,23,24,27 | Sterling C. Johnson28,29 | Julie Schneider14 | Lisa L. Barnes14 | David A. Bennett14 | Angela L. Jefferson1,2,4 | Susan M. Resnick12 | Andrew J. Saykin10,11 | Timothy J. Hohman1,2,6,7 | Derek B. Archer1,2,6,7

Correspondence

Derek B. Archer, Vanderbilt Memory and Alzheimer’s Center, 3319 West End Ave., Nashville, TN 37203, USA. Email: 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。

Funding information

BAL, Grant/Award Number: R01-EB017230; DBA, Grant/Award Number: K01-AG073584; TJH, Grant/Award Number: U24-AG074855; Vanderbilt Clinical Translational Science Award, Grant/Award Numbers: UL1-TR000445, UL1-TR002243; Vanderbilt’s High-Performance Computer Cluster for Biomedical Research, Grant/Award Numbers: R01-AG080821, S10-OD023680; Alzheimer’s Association, Grant/Award Number: IIRG-08-88733(ALJ); NIH, Grant/Award Numbers: K01-EB032898 (KGS), R01-EB017230 (BAL), K01-AG073584 (DBA), U24-AG074855 (TJH), R01-AG059716 (TJH), UL1-TR000445 (Vanderbilt Clinical Translational Science Award), UL1-TR002243 (Vanderbilt Clinical Translational Science Award), S10-OD02380 (Vanderbilt’s High-Performance Computer Cluster for Biomedical Research), R01-AG080821 (MH), R01-AG034962 (ALJ), R01-AG056534 (ALJ), R01-AG062826 (KAG), U19-AG03655 (MA); Intramural NIH, Grant/Award Number: 75N95D22P00141

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2024 The Author(s). Alzheimer’s & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer’s Association.

Abstract

INTRODUCTION: The effects of sex and apolipoprotein E (APOE)—Alzheimer’s disease (AD) risk factors—on white matter microstructure are not well characterized.

METHODS: Diffusion magnetic resonance imaging data from nine well-established longitudinal cohorts of aging were free water (FW)–corrected and harmonized. This dataset included 4741 participants (age = 73.06 ± 9.75) with 9671 imaging sessions over time. FW and FW-corrected fractional anisotropy (FAFWcorr) were used to assess differences in white matter microstructure by sex and APOE ε4 carrier status.

RESULTS: Sex differences in FAFWcorr in projection tracts and APOE ε4 differences in FW limbic and occipital transcallosal tracts were most pronounced.

DISCUSSION: There are prominent differences in white matter microstructure by sex and APOE ε4 carrier status. This work adds to our understanding of disparities in AD. Additional work to understand the etiology of these differences is warranted.

KEYWORDS

aging, Alzheimer’s disease, sex differences, white matter disease

Highlights

∙ Sex and apolipoprotein E (APOE) ε4 carrier status relate to white matter microstruc tural integrity.

∙ Females generally have lower free water–corrected fractional anisotropy compared to males.

∙ APOE ε4 carriers tended to have higher free water than non-carriers.

Overview of Alzheimer’s Disease Neuroimaging Initiative and future clinical trials

Michael W. Weiner1,2,3,4,5,6  | Shaveta Kanoria1,6 | Melanie J. Miller1,6 | Paul S. Aisen7 | Laurel A. Beckett8 | Catherine Conti1,6 | Adam Diaz1,6 | Derek Flenniken6 | Robert C. Green9 | Danielle J. Harvey8 | Clifford R. Jack Jr.10 | William Jagust11 | Edward B. Lee12 | John C. Morris13,14,15 | Kwangsik Nho16,17 | Rachel Nosheny1,4 | Ozioma C. Okonkwo18 | Richard J. Perrin13,14,15 | Ronald C. Petersen19 | Monica Rivera-Mindt20,21 | Andrew J. Saykin16,22 | Leslie M. Shaw23 | Arthur W. Toga24 | Duygu Tosun1,2 | Dallas P. Veitch1,6 for the Alzheimer’s Disease Neuroimaging Initiative

1 Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, California, USA

2 Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA

3 Department of Medicine, University of California San Francisco, San Francisco, California, USA

4 Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, California, USA

5 Department of Neurology, University of California San Francisco, San Francisco, California, USA

6 Northern California Institute for Research and Education (NCIRE), San Francisco, California, USA

7 Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, USA

8 Division of Biostatistics, Department of Public Health Sciences, University of California, Medical Sciences 1C, Davis, California, USA

9 Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Broad Institute Ariadne Labs and Harvard Medical School, Boston, Massachusetts, USA

10 Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA

11 Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California, USA

12 Translational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of

Pennsylvania, Philadelphia, Pennsylvania, USA

13 Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, Saint Louis, Missouri, USA

14 Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, USA

15 Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA

16 Department of Radiology and Imaging Sciences and the Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA

17 Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, USA

18 Wisconsin Alzheimer’s Disease Research Center and Department of Medicine, University of Wisconsin School of Medicine and Public Health, Clinical Science Center, Madison, Wisconsin, USA

19 Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA

20 Department of Psychology, Latin American and Latino Studies Institute, African and African American Studies, Fordham University, Bronx, New York, USA

21 Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA

22 Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA

23 Department of Pathology and Laboratory Medicine and the PENN Alzheimer’s Disease Research Center, Center for Neurodegenerative Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA

24 Laboratory of Neuro Imaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of the University of Southern California, San Diego, California, USA

Correspondence

Michael W. Weiner, Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, 4150 Clement St, San Francisco, CA 94121, USA. Email: 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data. Some ADNI investigators participated in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp content/uploads/how_to_apply/ ADNI_Acknowledgement_List.pd

Funding information

NIH, Grant/Award Number: U19 -AG 024904; National Institute on Aging, Grant/Award Number: U19AG024904

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

© 2024 The Author(s). Alzheimer’s & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer’s Association.

Abstract

The overall goal of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) is to opti mize and validate biomarkers for clinical trials while sharing all data and biofluid samples with the global scientific community. ADNI has been instrumental in stan dardizing and validating amyloid beta (Aβ) and tau positron emission tomography (PET) imaging. ADNI data were used for the US Food and Drug Administration (FDA) approval of the Fujirebio and Roche Elecsys cerebrospinal fluid diagnostic tests. Additionally, ADNI provided data for the trials of the FDA-approved treatments aducanumab, lecanemab, and donanemab. More than 6000 scientific papers have been published using ADNI data, reflecting ADNI’s promotion of open science and data sharing. Despite its enormous success, ADNI has some limitations, particularly in generalizing its data and findings to the entire US/Canadian population. This introduction provides a historical overview of ADNI and highlights its significant accomplishments and future vision to pioneer “the clinical trial of the future” focusing on demographic inclusivity.

KEYWORDS

Alzheimer’s disease, Alzheimer’s disease biomarkers, Alzheimer’s disease clinical trials, Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s disease progression, amyloid, Lab oratory of Neuro Imaging, magnetic resonance imaging, neurodegeneration, positron emission tomography, post-traumatic stress disorder, tau, underrepresented populations

Highlights

∙ The Alzheimer’s Disease Neuroimaging Initiative (ADNI) introduced a novel model for public-private partnerships and data sharing.

∙ It successfully validated amyloid and Tau PET imaging, as well as CSF and plasma biomarkers, for diagnosing Alzheimer’s disease.

∙ ADNI generated and disseminated vital data for designing AD clinical trials.

APOE ε4–associated heterogeneity of neuroimaging biomarkers across the Alzheimer’s disease continuum

Jason Mares1,2 Gautam Kumar1,3,4 Anurag Sharma1,3 Sheina Emrani5 Laura Beth McIntire6 Jia Guo7,8 Vilas Menon1,2 Tal Nuriel1,3 for the Alzheimer’s Disease Neuroimaging Initiative

1 Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, New York, USA

2 Department of Neurology, Columbia University, New York, New York, USA

3 Department of Pathology and Cell Biology, Columbia University, New York, New York, USA

4 Department of Neurobiology, University of Maryland, Baltimore, Maryland, USA

5 Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA

6 Lipidomics and Biomarker Discovery Lab, Brain Health Imaging Institute, Department of Radiology, Weill Cornell Medicine, New York, New York, USA

7 Department of Psychiatry, Columbia University, New York, New York, USA

8 Zuckerman Institute, Columbia University, New York, New York, USA

Correspondence

Tal Nuriel, Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, 630 W. 168th St., P&S 12-420E, New York, NY 10032, USA. Email: 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 The Alzheimer’s Disease Neuroimaging Initiative: Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data, but did not participate in analysis or writing of this report.

Funding information

NIA, Grant/Award Numbers: K01 AG061264, R01 AG070202, R01 AG078800, R01 AG066831, U19 AG024904 This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

© 2025 The Author(s). Alzheimer’s & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer’s Association

Abstract

INTRODUCTION: While the role of apolipoprotein E (APOE) ε4 in Alzheimer’s dis ease (AD) susceptibility has been studied extensively, much less is known about the differences in disease presentation in APOE ε4 carriers versus non-carriers.

METHODS: To help elucidate these differences, we performed a broad analysis com paring the regional levels of six different neuroimaging biomarkers in the brains of APOE ε4 carriers versus non-carriers who participated in the Alzheimer’s Disease Neuroimaging Initiative (ADNI).

RESULTS:We observed significant APOE ε4–associated heterogeneity in regional amy loid beta deposition, tau accumulation, glucose uptake, brain volume, cerebral blood flow, and white matter hyperintensities within each AD diagnostic group. We also observed important APOE ε4–associated differences in cognitively unimpaired indi viduals who converted to mild cognitive impairment/AD versus those who did not

DISCUSSION: This observed heterogeneity in neuroimaging biomarkers between APOE ε4 carriers versus non-carriers may have important implications regarding the prevention, diagnosis, and treatment of AD in different subpopulations.

KEYWORDS

Alzheimer’s disease, Alzheimer’s Disease Neuroimaging Initiative, apolipoprotein E, biomarkers, heterogeneity, neuroimaging

Highlights

∙ An extensive study was performed on the apolipoprotein E (APOE) ε4–associated heterogeneity in neuroimaging biomarkers from the Alzheimer’s Disease Neu roimaging Initiative.

∙ Robust APOE ε4–associated increases in amyloid beta (Aβ) deposition throughout the brain, in every diagnostic group, were observed.

∙ APOE ε4–associated increases in tau pathology, decreases in glucose uptake, and increases in brain atrophy, which expand in regional scope and magnitude with disease progression, were observed.

∙ Significant sex- and age-related differences in APOE ε4–associated neuroimaging biomarker heterogeneity, with overall increases in pathological presentation in female APOE ε4 carriers, were observed.

∙ Regional differences in Aβ deposition, tau accumulation, glucose uptake, ventricle size, and white matter hyperintensities were observed in cognitively normal partic ipants who converted to mild cognitive impairment/Alzheimer’s disease, which may hold potential predictive value.

Nutritional Interventions for Pressure Ulcer Prevention in Hip Fracture Patients: A Systematic Review and Meta-Analysis of Controlled Trials

Jose M. Moran 1,* , Laura Trigo-Navarro 2 , Esther Diestre-Morcillo 3 , Elena Pastor-Ramon 4 and Luis M. Puerto-Parejo 5

1 Nursing and Occupational Therapy College, University of Extremadura, 10001 Caceres, Spain

2 Área de Salud de Badajoz, Supervisora del Bloque Quirúrgico, Hospital Materno Infantil de Badajoz, Calle Violeta 3, 06010 Badajoz, Spain; 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。

3 Área de Salud de Badajoz, Banco de Sangre, Hospital Universitario de Badajoz, Av. de Elvas, s/n, 06080 Badajoz, Spain

4 Biblioteca Virtual de ciencias de la Salud de las Illes Balears (Bibliosalut), Ctra. De Valldemossa, 79, mòdul L+1, 07120 Palma, Spain; 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。

5 Gerencia del Área de Salud de Badajoz, Supervisor del Área de Investigación, Proyectos y Gestión, Av. de Huelva, 8, 06005 Badajoz, Spain; 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。

* Correspondence: 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。

Academic Editor: Yi-Chia Huang

Received: 22 January 2025

Revised: 6 February 2025

Accepted: 8 February 2025

Published: 11 February 2025

Citation: Moran, J.M.; Trigo-Navarro, L.; Diestre-Morcillo, E.; Pastor-Ramon, E.; Puerto-Parejo, L.M. Nutritional Interventions for Pressure Ulcer Prevention in Hip Fracture Patients: A Systematic Review and Meta-Analysis of Controlled Trials. Nutrients 2025, 17, 644. https://doi.org/10.3390/ nu17040644

Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/)

Abstract: Background/Objective: Pressure ulcers represent a significant complication in patients with reduced mobility, such as those recovering from hip fractures. In the present study, we aimed to comprehensively assess the impact of oral nutritional interventions on  the development of pressure ulcers in hip fracture patients via a systematic review and meta analysis of controlled studies evaluating the effectiveness of oral nutritional supplements compared with standard care. Methods: In accordance with PRISMA standards, this systematic review and meta-analysis of controlled studies evaluated the effectiveness of any type of oral nutritional supplements compared with standard care in hip fracture patients. The risk of bias was evaluated using the Cochrane ROB2 tool for randomized controlled trials and the ROBINS-1 tool for nonrandomized trials. Results: Fourteen studies (10 randomized controlled trials and 4 controlled trials) published since 1990 (n = 1648) were included. Oral nutritional supplementation was associated with a statistically significant decrease in the odds ratio of developing pressure ulcers in hip fracture patients (OR 0.54, 95% CI: 0.40–0.73, p < 0.001). Conclusions: The incidence and evolution of pressure ulcers can be improved by oral dietary supplementation in patients who have undergone hip fracture surgery. Accordingly, we propose that oral nutritional supplementation should be considered an essential component of comprehensive post-hip-fracture care.

Keywords: hip fracture; pressure ulcers; oral nutritional supplement; pressure sores; meta-analysis; wound healing; nutritional intervention

Tlalpan 2020 Case Study: Enhancing Uric Acid Level Prediction with Machine Learning Regression and Cross-Feature Selection

Guadalupe Gutiérrez-Esparza 1,2,* ,†, Mireya Martínez-García 3,† , Manlio F. Márquez-Murillo 2 , Malinalli Brianza-Padilla 3 , Enrique Hernández-Lemus 4,5,* and Luis M. Amezcua-Guerra 3,*

1 “Researcher for Mexico” Program under SECIHTI, Secretariat of Sciences, Humanities, Technology, and Innovation, Mexico City 08400, Mexico

2 Division of Diagnostic and Treatment Services, National Institute of Cardiology Ignacio Chávez, Mexico City 04510, Mexico; 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。

3 Department of Immunology, National Institute of Cardiology Ignacio Chávez, Mexico City 04510, Mexico; 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 (M.M.-G.); 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 (M.B.-P.)

4 Computational Genomics Division, National Institute of Genomic Medicine, Mexico City 14610, Mexico

5 Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico 

*Correspondence: 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 (G.G.-E.); 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 (E.H.-L.); 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 (L.M.A.-G.)

† These authors contributed equally to this work.

Academic Editor: Motoyuki Iemitsu Received: 11 February 2025 Revised: 3 March 2025 Accepted: 6 March 2025 Published: 17 March 2025

Citation: Gutiérrez-Esparza, G.; Martínez-García, M.; Márquez Murillo, M.F.; Brianza-Padilla, M.; Hernández-Lemus, E.; Amezcua Guerra, L.M. Tlalpan 2020 Case Study: Enhancing Uric Acid Level Prediction with Machine Learning Regression and Cross-Feature Selection. Nutrients 2025, 17, 1052. https://doi.org/ 10.3390/nu17061052

Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/)

Abstract: Background/Objectives: Uric acid is a key metabolic byproduct of purine degradation and plays a dual role in human health. At physiological levels, it acts as an antioxidant, protecting against oxidative stress. However, excessive uric acid can lead to hyperuricemia, contributing to conditions like gout, kidney stones, and cardiovascular diseases. Emerging evidence also links elevated uric acid levels with metabolic disorders, including hypertension and insulin resistance. Understanding its regulation is crucial for preventing associated health complications. Methods: This study, part of the Tlalpan 2020 project, aimed to predict uric acid levels using advanced machine learning algorithms. The dataset included clinical, anthropometric, lifestyle, and nutritional characteristics from a cohort in Mexico City. We applied Boosted Decision Trees (Boosted DTR), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Shapley Additive Explanations (SHAP) to identify the most relevant variables associated with hyperuricemia. Feature engineering techniques improved model performance, evaluated using Mean Squared Error (MSE), Root-Mean-Square Error (RMSE), and the coefficient of determination (R²). Results: Our study showed that XGBoost had the highest accuracy for anthropometric and clinical predictors, while CatBoost was the most effective at identifying nutritional risk factors. Distinct predictive profiles were observed between men and women. In men, uric acid levels were primarily influenced by renal function markers, lipid profiles, and hereditary predisposition to hyperuricemia, particularly paternal gout and diabetes. Diets rich in processed meats, high-fructose foods, and sugary drinks showed stronger associations with elevated uric acid levels. In women, metabolic and cardiovascular markers, family history of metabolic disorders, and lifestyle factors such as passive smoking and sleep quality were the main contributors. Additionally, while carbohydrate intake was more strongly associated with uric acid levels in women, fructose and sugary beverages had a greater impact in men. To enhance model robustness, a cross-feature selection approach was applied, integrating top features from multiple models, which further improved predictive accuracy, particularly in gender-specific analyses. Conclusions: These findings provide insights into the metabolic, nutritional characteristics, and lifestyle determinants of uric acid levels, supporting targeted public health strategies for hyperuricemia prevention.

Keywords: uric acid; regression-based machine learning; feature selection; feature engineering; Mexico City; Tlalpan 2020 cohort