{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T12:20:02Z","timestamp":1783167602401,"version":"3.54.6"},"reference-count":26,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:00:00Z","timestamp":1758844800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Mild cognitive impairment (MCI) is a significant and increasingly recognized problem in individuals with type 2 diabetes mellitus (T2DM). This study aims to develop a machine-learning model to predict MCI in patients with T2DMThe dataset was obtained from a prospective cohort conducted at the Sheikh Khalifa Ibn Zaid Hospital, Casablanca, Morocco, and was randomly split into three parts. Machine learning models were trained using the training dataset, and their performance was assessed on the test dataset. The unseen data was reserved for final validation. Subsequently, recursive feature elimination was applied with the top two algorithms to identify and retain the most impactful features for predicting MCI. Then, we retrained the models using the selected variables. Finally, the variables most contributing to the prediction of MCI were represented in a Shapley Additive Explanations SHAPE value plot to better understand their contribution and their ranking in MCI prediction.The dataset included 100 patients. Extra Trees classifier was the best-performing model for mild cognitive impairment (Accuracy: 0\u00b79310\/AUC: 0\u00b79667\/ Recall: 0\u00b79333\/ Precision: 0\u00b79333\/F1: 0\u00b79333). The most contributing factors to MCI in patients with type 2 diabetes were, respectively, diabetic retinopathy, age, serum LDL cholesterol level, microalbuminuria, HbA1c, and, serum creatinine level. Our findings suggest avenues for early intervention that could prevent the progression of cognitive impairment among patients with type 2 diabetes.<\/jats:p>","DOI":"10.1371\/journal.pone.0332442","type":"journal-article","created":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T17:30:21Z","timestamp":1758907821000},"page":"e0332442","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["Diabetic retinopathy as the primary predictor of mild cognitive impairment in type 2 diabetes: Insights from machine learning models"],"prefix":"10.1371","volume":"20","author":[{"given":"Fatima Zahra","family":"Rhmari Tlem\u00e7ani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fai\u00e7al","family":"Aitlahbib","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soukaina","family":"Laidi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6872-7060","authenticated-orcid":true,"given":"Najib","family":"Alidrissi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0318-5816","authenticated-orcid":true,"given":"Jehanne","family":"Aasfara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saloua","family":"Elamari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asma","family":"Chadli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8426-3929","authenticated-orcid":true,"given":"Imane","family":"Motaib","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,9,26]]},"reference":[{"issue":"1","key":"pone.0332442.ref001","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.cger.2014.08.021","article-title":"Diabetes and cognition","volume":"31","author":"ER Mayeda","year":"2015","journal-title":"Clin Geriatr Med"},{"issue":"23","key":"pone.0332442.ref002","doi-asserted-by":"crossref","first-page":"2551","DOI":"10.1001\/jama.2014.13806","article-title":"The diagnosis and management of mild cognitive impairment: a clinical review","volume":"312","author":"KM Langa","year":"2014","journal-title":"JAMA"},{"issue":"6","key":"pone.0332442.ref003","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1007\/s00592-020-01648-9","article-title":"The prevalence of mild cognitive impairment in type 2 diabetes mellitus patients: a systematic review and meta-analysis","volume":"58","author":"Y You","year":"2021","journal-title":"Acta Diabetol"},{"issue":"7","key":"pone.0332442.ref004","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1515\/revneuro-2017-0016","article-title":"Mild cognitive impairment in type 2 diabetes mellitus and related risk factors: a review","volume":"28","author":"X-Y Yuan","year":"2017","journal-title":"Rev Neurosci"},{"issue":"4","key":"pone.0332442.ref005","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1210\/er.2007-0034","article-title":"Cognitive dysfunction and diabetes mellitus","volume":"29","author":"CT Kodl","year":"2008","journal-title":"Endocr Rev"},{"issue":"2","key":"pone.0332442.ref006","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1177\/1932296817706375","article-title":"Machine Learning Methods to Predict Diabetes Complications","volume":"12","author":"A Dagliati","year":"2018","journal-title":"J Diabetes Sci Technol"},{"issue":"6","key":"pone.0332442.ref007","doi-asserted-by":"crossref","first-page":"3317","DOI":"10.3390\/ijerph18063317","article-title":"Prediction of Type 2 Diabetes Based on Machine Learning Algorithm","volume":"18","author":"HM Deberneh","year":"2021","journal-title":"Int J Environ Res Public Health"},{"issue":"8","key":"pone.0332442.ref008","article-title":"Factors Associated With Mild Cognitive Impairment in Patients With Type 2 Diabetes: A Cohort Study","volume":"14","author":"FZ Rhmari Tlem\u00e7ani","year":"2022","journal-title":"Cureus"},{"issue":"4","key":"pone.0332442.ref009","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1001\/archneur.64.4.570","article-title":"Relation of diabetes to mild cognitive impairment","volume":"64","author":"JA Luchsinger","year":"2007","journal-title":"Arch Neurol"},{"issue":"1","key":"pone.0332442.ref010","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.jalz.2013.01.001","article-title":"Association of diabetes with amnestic and nonamnestic mild cognitive impairment","volume":"10","author":"RO Roberts","year":"2014","journal-title":"Alzheimers Dement"},{"issue":"4","key":"pone.0332442.ref011","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1176\/appi.ajp.2014.14070878","article-title":"Modifiable predictors of dementia in mild cognitive impairment: a systematic review and meta-analysis","volume":"172","author":"C Cooper","year":"2015","journal-title":"Am J Psychiatry"},{"key":"pone.0332442.ref012","doi-asserted-by":"crossref","first-page":"1307837","DOI":"10.3389\/fendo.2024.1307837","article-title":"A new nomogram model for the individualized prediction of mild cognitive impairment in elderly patients with type 2 diabetes mellitus","volume":"15","author":"Y Jiang","year":"2024","journal-title":"Front Endocrinol (Lausanne)"},{"issue":"8","key":"pone.0332442.ref013","doi-asserted-by":"crossref","first-page":"1721","DOI":"10.1007\/s40520-023-02456-1","article-title":"Predicting mild cognitive impairment progression to Alzheimer\u2019s disease based on machine learning analysis of cortical morphological features","volume":"35","author":"W Wang","year":"2023","journal-title":"Aging Clin Exp Res"},{"key":"pone.0332442.ref014","doi-asserted-by":"crossref","first-page":"1533317520927163","DOI":"10.1177\/1533317520927163","article-title":"Machine-Learning Algorithms Based on Screening Tests for Mild Cognitive Impairment","volume":"35","author":"J-H Park","year":"2020","journal-title":"Am J Alzheimers Dis Other Demen"},{"key":"pone.0332442.ref015","doi-asserted-by":"crossref","first-page":"866230","DOI":"10.3389\/fnagi.2022.866230","article-title":"Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network","volume":"14","author":"Y Zhou","year":"2022","journal-title":"Front Aging Neurosci"},{"key":"pone.0332442.ref016","doi-asserted-by":"crossref","first-page":"136908","DOI":"10.1016\/j.neulet.2022.136908","article-title":"Detection of mild cognitive impairment in type 2 diabetes mellitus based on machine learning using privileged information","volume":"791","author":"S Xia","year":"2022","journal-title":"Neurosci Lett"},{"key":"pone.0332442.ref017","doi-asserted-by":"crossref","first-page":"1213711","DOI":"10.3389\/fendo.2023.1213711","article-title":"Risk factors and drug discovery for cognitive impairment in type 2 diabetes mellitus using artificial intelligence interpretation and graph neural networks","volume":"14","author":"X Zhang","year":"2023","journal-title":"Front Endocrinol (Lausanne)"},{"issue":"3","key":"pone.0332442.ref018","doi-asserted-by":"crossref","DOI":"10.1002\/brb3.3456","article-title":"Predictive model for identifying mild cognitive impairment in patients with type 2 diabetes mellitus: A CHAID decision tree analysis","volume":"14","author":"R Maimaitituerxun","year":"2024","journal-title":"Brain Behav"},{"key":"pone.0332442.ref019","doi-asserted-by":"crossref","first-page":"692911","DOI":"10.3389\/fnagi.2021.692911","article-title":"Association Between Diabetic Retinopathy and Cognitive Impairment: A Systematic Review and Meta-Analysis","volume":"13","author":"D Cheng","year":"2021","journal-title":"Front Aging Neurosci"},{"issue":"1","key":"pone.0332442.ref020","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.tem.2021.10.008","article-title":"Common pathways in dementia and diabetic retinopathy: understanding the mechanisms of diabetes-related cognitive decline","volume":"33","author":"K Little","year":"2022","journal-title":"Trends Endocrinol Metab"},{"issue":"11","key":"pone.0332442.ref021","article-title":"A deep learning model for detection of Alzheimer\u2019s disease based on retinal photographs: a retrospective, multicentre case-control study","volume":"4","author":"CY Cheung","year":"2022","journal-title":"Lancet Digit Health"},{"issue":"3","key":"pone.0332442.ref022","doi-asserted-by":"crossref","DOI":"10.1136\/bmjopen-2021-053008","article-title":"Risk and incidence of cognitive impairment in patients with chronic kidney disease and diabetes: the results from a longitudinal study in a community cohort of patients and an age and gender-matched control cohort in North Wales, UK","volume":"12","author":"P Hobson","year":"2022","journal-title":"BMJ Open"},{"issue":"4","key":"pone.0332442.ref023","doi-asserted-by":"crossref","first-page":"104","DOI":"10.21037\/atm.2019.12.113","article-title":"The factors contributing to cognitive dysfunction in type 2 diabetic patients","volume":"8","author":"S-S Xia","year":"2020","journal-title":"Ann Transl Med"},{"issue":"2","key":"pone.0332442.ref024","doi-asserted-by":"crossref","first-page":"221","DOI":"10.2337\/dc08-1153","article-title":"Relationship between baseline glycemic control and cognitive function in individuals with type 2 diabetes and other cardiovascular risk factors: the action to control cardiovascular risk in diabetes-memory in diabetes (ACCORD-MIND) trial","volume":"32","author":"T Cukierman-Yaffe","year":"2009","journal-title":"Diabetes Care"},{"issue":"10","key":"pone.0332442.ref025","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1038\/s41574-018-0048-7","article-title":"Cognitive decline and dementia in diabetes mellitus: mechanisms and clinical implications","volume":"14","author":"GJ Biessels","year":"2018","journal-title":"Nat Rev Endocrinol"},{"key":"pone.0332442.ref026","doi-asserted-by":"crossref","first-page":"1149084","DOI":"10.3389\/fendo.2023.1149084","article-title":"Elevated serum creatinine levels and risk of cognitive impairment in older adults with diabetes: a NHANES study from 2011-2014","volume":"14","author":"Y Xiao","year":"2023","journal-title":"Front Endocrinol (Lausanne)"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0332442","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T17:30:26Z","timestamp":1758907826000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0332442"}},"subtitle":[],"editor":[{"given":"Amin","family":"Mansoori","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,9,26]]},"references-count":26,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9,26]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0332442","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,26]]}}}