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This study examined whether depression risk and protective factors differ across age groups (65\u201374, 75\u201384, 85+) among community-dwelling older adults in Turkey.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data were derived from the Turkey Elderly Profile Survey 2023 (TEPA-2023), a nationally representative multi-stage cluster sample. Among 29,785 individuals surveyed, the final analytical sample comprised 8,370 adults aged 65 years and older. Depression was defined using the Geriatric Depression Scale-30 (GDS-30) with a cut-off score of \u2265\u200911. In the theory-driven phase, binary logistic regression models including 24 independent variables were estimated for the overall sample and for separate age groups, with model validation based on stratified 10-fold cross-validation. In the data-driven phase, multiple machine learning algorithms were compared using accuracy, recall, F1-score, and area under the ROC curve (AUC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The prevalence of depression increased significantly with age, from 39.9% in the 65\u201374 group to 53.4% in the 75\u201384 group and 67.7% in the 85\u2009+\u2009group (\u03c7\u00b2 = 242.40,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). In the overall logistic regression model, the strongest risk factor was unhappiness (OR\u2009=\u20092.516), whereas the strongest protective factor was functional independence as measured by Lawton-Brody IADL (OR\u2009=\u20090.386). The overall model showed good discrimination (AUC\u2009=\u20090.812; McFadden R\u00b2 = 0.245). Age-stratified analyses showed that the number of significant predictors decreased with advancing age (13\u21929\u21925), and obesity showed a protective effect only in the 85\u2009+\u2009group (OR\u2009=\u20090.506,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.016). In the data-driven phase, ten machine learning algorithms were compared using stratified 10-fold cross-validation. Gradient Boosting achieved the highest overall AUC (0.813), while SVM-RBF yielded the best discrimination in the 75\u201384 (0.806) and 85+ (0.828) groups. Permutation importance analysis converged with logistic regression findings, consistently ranking unhappiness, self-rated health, Lawton-Brody IADL, and physical activity frequency as top predictors. Across age strata, predictor prominence shifted from education and income in the Under-75 group toward BMI and disability indicators in the 85\u2009+\u2009group.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The risk profile of geriatric depression changes substantially with advancing age. While socioeconomic factors appear more prominent in younger-old adults, health status and functional independence become more dominant in later old age. The combined use of theory-driven regression and data-driven machine learning provides a more comprehensive framework for identifying age-related depression patterns in older adults.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12913-026-14760-3","type":"journal-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T04:06:34Z","timestamp":1779422794000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Age-related determinants of geriatric depression: an analysis of theory-driven and data-driven approaches"],"prefix":"10.1186","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3562-1961","authenticated-orcid":false,"given":"Fatih","family":"Orhan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,22]]},"reference":[{"key":"14760_CR1","doi-asserted-by":"crossref","unstructured":"Collaborators, GBD 2019 Mental Disorders. 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