{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T16:45:44Z","timestamp":1793810744553,"version":"4.6.1"},"reference-count":35,"publisher":"American Medical Association (AMA)","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"<jats:sec><jats:title>Importance<\/jats:title><jats:p>Excess salt intake is a major modifiable risk factor for hypertension and cardiovascular disease. Personalized interventions using genetic information and digital tools, such as smartphone applications, may enhance sodium reduction, but evidence remains limited.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objective<\/jats:title><jats:p>To evaluate the effectiveness of a sodium reduction program incorporating a genetic profile and an artificial intelligence (AI)\u2013based mobile app among adults with elevated blood pressure and a sodium-sensitive genotype.<\/jats:p><\/jats:sec><jats:sec><jats:title>Design, Setting, and Participants<\/jats:title><jats:p>A 3-arm randomized clinical trial, conducted at a Japanese electronics company from September 17 to December 16, 2024, included employees aged 20 to 65 years with elevated blood pressure and the sodium-sensitive <jats:italic>AGT M235T<\/jats:italic> genotype.<\/jats:p><\/jats:sec><jats:sec><jats:title>Interventions<\/jats:title><jats:p>Participants were randomized on a 1:1:0.2 basis to (1) a treatment group receiving a genetic profile and an AI-based app incorporating sodium-specific information; (2) a control group receiving no intervention; or (3) an app-only group using the app without a genetic profile or information.<\/jats:p><\/jats:sec><jats:sec><jats:title>Main Outcomes and Measures<\/jats:title><jats:p>The primary outcome was daily salt intake, estimated from spot urine samples using the INTERSALT (International Study of Electrolyte Excretion and Blood Pressure) formula. Secondary outcomes included self-reported body mass index, behavior change intentions, and systolic and diastolic blood pressure. Analysis was performed on an intention-to-treat basis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Of 312 randomized participants, 289 completed follow-up (mean [SD] age, 51.3 [8.3] years; 252 of 279 men [90.3%]). The mean (SD) baseline salt intake was 11.3 (2.0) g\/d. At 3 months, no significant difference in salt intake was observed between the treatment and control groups (mean difference, \u22120.2 g\/d; 95% CI, \u22120.7 to 0.3 g\/d) or between the treatment and app-only groups (mean difference, \u22120.04 g\/d; 95% CI, \u22120.9 to 0.8 g\/d). No significant differences were observed between groups in any secondary outcomes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions and Relevance<\/jats:title><jats:p>In this randomized clinical trial conducted among individuals with elevated blood pressure and a sodium-sensitive genotype, a sodium reduction program combining a genetic profile and an AI-based app with sodium-specific information did not significantly reduce salt intake. These findings highlight the challenges of achieving dietary behavior change through genetic personalized plus digital interventions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Trial Registration<\/jats:title><jats:p><jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/umin.ac.jp\/ctr\">http:\/\/umin.ac.jp\/ctr<\/jats:ext-link> Identifier: <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/center6.umin.ac.jp\/cgi-open-bin\/ctr\/ctr_view.cgi?recptno=R000060118\">UMIN000052685<\/jats:ext-link><\/jats:p><\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2025.37540","type":"journal-article","created":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T15:00:30Z","timestamp":1760626830000},"page":"e2537540","source":"Crossref","is-referenced-by-count":5,"title":["Sodium Reduction Program Incorporating Genetic Profile and an AI-Based App"],"prefix":"10.1001","volume":"8","author":[{"given":"Koryu","family":"Sato","sequence":"first","affiliation":[{"name":"Faculty of Policy Management, Keio University, Kanagawa, Japan"},{"name":"Department of Social Epidemiology, Graduate School of Medicine and School of Public Health, Kyoto University, Kyoto, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kosuke","family":"Inoue","sequence":"additional","affiliation":[{"name":"Department of Social Epidemiology, Graduate School of Medicine and School of Public Health, Kyoto University, Kyoto, Japan"},{"name":"Hakubi Center for Advanced Research, Kyoto University, Kyoto, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taihei","family":"Yamaguchi","sequence":"additional","affiliation":[{"name":"Innovation Laboratory, Next Business Development Division, Toshiba Corporation, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maiko","family":"Yagi","sequence":"additional","affiliation":[{"name":"Innovation Laboratory, Next Business Development Division, Toshiba Corporation, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masashi","family":"Ebisawa","sequence":"additional","affiliation":[{"name":"Innovation Laboratory, Next Business Development Division, Toshiba Corporation, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shinichiro","family":"Mizuno","sequence":"additional","affiliation":[{"name":"Department of Research & Development, Wellmira Inc, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomoko","family":"Yanagisawa","sequence":"additional","affiliation":[{"name":"Department of Research & Development, Wellmira Inc, Tokyo, 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