{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:31:09Z","timestamp":1753893069532,"version":"3.41.2"},"reference-count":47,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:00:00Z","timestamp":1750291200000},"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. Public Health"],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>This study innovatively investigates the cumulative associations between behavioral determinants of health (BDoH), metabolic determinants of health (MDoH), and systemic inflammation biomarkers in U.S. adults, using a novel cross-sectional framework to quantify their synergistic effects.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Utilizing cross-sectional data from 18,500 participants in the National Health and Nutrition Examination Survey (NHANES 2005\u20132018\u202fcycle), we developed a composite exposure model integrating BDoH (smoking status, physical activity, dietary quality) and MDoH (obesity metrics, hypertension, diabetes) through standardized questionnaires and clinical measurements. Systemic immune-inflammation index (SII) and systemic inflammatory response index (SIRI) were calculated from peripheral blood cell counts. Multivariable-adjusted logistic regression models examined dose\u2013response relationships, with trend analysis explicitly testing cumulative BDoH-MDoH interactions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The cohort (mean age 44.3\u202f\u00b1\u202f0.3\u202fyears; 52.4% male) demonstrated significant positive associations between adverse health determinants and inflammatory indices. Current tobacco use (OR\u202f=\u202f1.32, 95%CI\u202f=\u202f1.18\u20131.47), suboptimal diet (HEI\u202f&amp;lt;\u202f52: OR\u202f=\u202f1.24, 95%CI\u202f=\u202f1.11\u20131.38), obesity (BMI\u202f\u2265\u202f30\u202fkg\/m<jats:sup>2<\/jats:sup>: OR\u202f=\u202f1.41, 95%CI\u202f=\u202f1.27\u20131.56), and central adipometry (OR\u202f=\u202f1.39, 95%CI\u202f=\u202f1.25\u20131.54) showed strongest correlations with elevated SII\/SIRI. Metabolic disorders exhibited distinct patterns: hypertension and diabetes associated specifically with SIRI elevation (OR\u202f=\u202f1.19, 95%CI\u202f=\u202f1.06\u20131.33 and OR\u202f=\u202f1.17, 95%CI\u202f=\u202f1.03\u20131.32, respectively), while physical inactivity (&amp;lt;600 MET-min\/week) uniquely correlated with SII increase (OR\u202f=\u202f1.26, 95%CI\u202f=\u202f1.13\u20131.40). Notably, our cumulative model revealed synergistic effects: exposure to \u22653 adverse behavioral determinants amplified inflammation risks (SII: OR\u202f=\u202f1.57, 95%CI\u202f=\u202f1.42\u20131.73; SIRI: OR\u202f=\u202f1.49, 95%CI\u202f=\u202f1.35\u20131.64), with significant dose-dependent trends (P-trend&amp;lt;0.001). Co-occurring metabolic abnormalities demonstrated additive inflammatory effects (P-trend&amp;lt;0.001), exceeding individual risk factor impacts.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This cross-sectional study provides the first evidence that integrated BDoH-MDoH cumulative exposure models uncover distinct and synergistic inflammatory pathways. Both individual and combined behavioral-metabolic risk factors significantly associate with systemic inflammation biomarker elevation, highlighting the necessity of dual-target intervention strategies.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fpubh.2025.1602629","type":"journal-article","created":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:30:16Z","timestamp":1750311016000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Cumulative behavioral and metabolic determinants of health are associated with higher inflammation-related indices: insights from a cross-sectional study (NHANES 2005\u20132018)"],"prefix":"10.3389","volume":"13","author":[{"given":"Anzhi","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youping","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyan","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xunge","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenshen","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,6,19]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.7326\/M23-0507","article-title":"Social, 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