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However, its role in the progression trajectory of cardiometabolic multimorbidity (CMM) remains unclear. We aimed to investigate the involvement of the CTI in the CMM progression trajectory.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This prospective study included 266,049 individuals from the UK Biobank, who were free of cardiometabolic diseases (CMD) at baseline. CMM was defined as the presence of two or more CMDs, including type 2 diabetes (T2D), coronary heart disease (CHD), and stroke. The CTI was calculated using the formula: 0.412 \u00d7 ln (CPR)\u2009+\u2009ln (TG\u00d7FPG\/2). Cox proportional hazards, Kaplan\u2013Meier curves, restricted cubic spline (RCS) and multi-state models were employed to examine associations of CTI with the incidence and progression of CMM. Receiver operating characteristic (ROC) curve, C-index analysis, net reclassification index (NRI) together with integrated discrimination improvement index (IDI) were carried out to examinate the predictive performance of CTI. The robustness of results was further evaluated via stratified and sensitivity analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>CTI was positively and significantly associated with CMM development. Compared with the low-CTI group, the high-CTI group exhibited an increased risks of T2D (HR: 3.60, 95% CI 3.39\u20133.83), stroke (HR: 1.11, 95% CI 1.03\u20131.19), CHD (HR: 1.52, 95% CI 1.46\u20131.58), first cardiometabolic disease (FCMD, HR: 1.86, 95% CI 1.81\u20131.92), CMM (HR: 2.50, 95% CI 2.23\u20132.80), and death (HR: 1.25, 95% CI 1.20\u20131.29). Among CMM and its component diseases, CTI showed the greater predictive capacity for T2D and CMM risk. Additionally, CTI exhibited incremental predictive value over TyG and CRP for incident CHD, FCMD and CMM with the highest C-index and NRI values. Stratified analyses indicated the consistent association of CTI with all outcomes except for stroke across age, gender and BMI. Specifically, stronger associations were observed in younger, female and lower BMI individuals. In state transition analysis, the high-CTI group showed elevated risks for transitions from baseline to FCMD (HR: 1.86, 95% CI 1.80\u20131.91), baseline to death (HR: 1.18, 95% CI 1.12\u20131.23), and FCMD to CMM (HR: 1.39, 95% CI 1.24\u20131.56). In disease-specific transitions, a higher CTI was linked to increased risks of transitions from baseline to T2D (HR: 3.68, 95% CI 3.45\u20133.93), baseline to CHD (HR: 1.49, 95% CI 1.43\u20131.56), baseline to death (HR: 1.18, 95% CI 1.12\u20131.23), stroke to CMM (HR: 1.43, 95% CI 1.09\u20131.86), and CHD to CMM (HR: 1.52, 95% CI 1.29\u20131.79). Similar findings were observed when the CTI was treated as a continuous variable.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our data revealed that CTI was positively correlated with the incidence and progression trajectory of CMM. CTI could serve as a simple and scalable tool for risk stratification in CMM, highlighting its potential utility in screening population with cardiometabolic-inflammatory burden.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12933-026-03174-4","type":"journal-article","created":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T07:13:46Z","timestamp":1775373226000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Associations between the C-reactive protein-triglyceride glucose index and the incidence and progression trajectory of cardiometabolic multimorbidity: a multi-state model study"],"prefix":"10.1186","volume":"25","author":[{"given":"Lei","family":"Yuan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongxiang","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Zong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juanfang","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huilong","family":"Qu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan","family":"Nilsson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,5]]},"reference":[{"issue":"1","key":"3174_CR1","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1186\/s12933-022-01632-3","volume":"21","author":"H Xie","year":"2022","unstructured":"Xie H, Li J, Zhu X, Li J, Yin J, Ma T, Luo Y, He L, Bai Y, Zhang G, et al. 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