{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T12:36:48Z","timestamp":1770813408434,"version":"3.50.1"},"reference-count":48,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T00:00:00Z","timestamp":1770768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell. Infect. Microbiol."],"abstract":"\n Background<\/jats:title>\n \n Helicobacter pylori (\n H.pylori<\/jats:italic>\n ) has been implicated in peripheral atherosclerosis (PA); however, its predictive value for PA risk in large population-based cohorts remains insufficiently characterized.\n <\/jats:p>\n <\/jats:sec>\n \n Objectives<\/jats:title>\n \n This study aimed to evaluate assess the predictive contribution of\n H.pylori<\/jats:italic>\n infection to PA risk, in combination with traditional clinical factors, using interpretable machine learning (ML) models.\n <\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n A retrospective cohort of 5,862 individuals undergoing routine health check-ups was analyzed. Demographic data, laboratory indices, and lower-extremity vascular ultrasound findings were collected to determine PA status and\n H.pylori<\/jats:italic>\n infection. Key risk factors were identified through univariate and multivariate logistic regressions. Subgroup and restricted cubic spline (RCS) analyses were applied to evaluate effect modification and nonlinear associations. Fourteen ML algorithms were developed and evaluated using the area-under-curve (AUC), sensitivity, accuracy, specificity, positive-predictive-value (PPV), negative-predictive-value (NPV), F1-score, Youden\u2019s index, and calibration. Models were subsequently retrained using regression-identified variables to assess stability. SHapley Additive exPlanations (SHAP) analysis was employed to interpret feature importance from across models. Longitudinal and mediation analyses were conducted to explore temporal relationships and the potential mediating role of the triglyceride-glucose (TyG) index.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n H.pylori<\/jats:italic>\n infection was independently associated with PA (odds ratio (OR)=5.27, 95%CI=4.27-6.54,\n P<\/jats:italic>\n &lt;0.001), alongside male (OR = 5.88, 95%CI=4.22-8.29,\n P<\/jats:italic>\n &lt;0.001), smoking (OR = 2.11, 95%CI=1.67-2.67,\n P<\/jats:italic>\n &lt;0.001), and elevated low-density lipoprotein levels (OR = 2.10, 95%CI=1.47-3.04,\n P<\/jats:italic>\n &lt;0.001). Subgroup and RCS analyses demonstrated consistent and nonlinear associations with PA outcomes. ML models using all variables achieved excellent predictive performance, including AUC = 0.993, accuracy=0.981, sensitivity=0.954, specificity=0.990, PPV = 0.966, NPV = 0.986, F1-score=0.960 and Youden\u2019s index=0.944. After restricting predictors to regression-identified variables, the CatBoost model maintained acceptable discrimination, with an AUC = 0.754, accuracy=0.785, sensitivity=0.342, specificity=0.925, PPV = 0.621, NPV = 0.823, F1-score =0.441 and Youden\u2019s index=0.279. SHAP analysis consistently ranked\n H.pylori<\/jats:italic>\n infection as the top predictor. Longitudinal analysis revealed a higher proportion of persistent\n H.pylori<\/jats:italic>\n infection in emerging PA cases. Mediation analysis indicated a negligible indirect effect of the TyG index (1.11%,\n P<\/jats:italic>\n = 0.124).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n \n H.pylori<\/jats:italic>\n infection is independently associated with PA and represents a critical contributor to PA risk stratification. The integration of epidemiological analysis with interpretable ML provides a robust framework for identifying high-risk individuals and supports the potential value of incorporating infectious markers into vascular risk assessment.\n <\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fcimb.2026.1693262","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T07:37:07Z","timestamp":1770795427000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Interpretable machine learning analysis for relationships between Helicobacter pylori infection and peripheral atherosclerosis: a retrospective cohort study"],"prefix":"10.3389","volume":"16","author":[{"given":"Chenlu","family":"Li","sequence":"first","affiliation":[{"name":"Department of Gastroenterology, Affiliated Yueqing Hospital, Wenzhou Medical University","place":["Wenzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingshi","family":"Lin","sequence":"additional","affiliation":[{"name":"Interventional therapy Department, Affiliated Yueqing Hospital, Wenzhou Medical University","place":["Wenzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liaoliao","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Affiliated Yueqing Hospital, Wenzhou Medical University","place":["Wenzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,11]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1410","DOI":"10.3390\/s25051410","article-title":"A review of machine learning and deep learning methods for person detection, tracking and identification, and face recognition with applications","volume":"25","author":"Amirgaliyev","year":"2025","journal-title":"Sensors (Basel)"},{"key":"B2","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1186\/s13098-021-00765-x","article-title":"Association of Helicobacter pylori infection with the risk of metabolic syndrome and insulin resistance: an updated systematic review and meta-analysis","volume":"13","author":"Azami","year":"2021","journal-title":"Diabetol. 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