Single-Point Insulin Sensitivity Estimator and Incident Cardiovascular Disease Across Stages 0–3 of Cardiovascular-Kidney-Metabolic Syndrome: A Nationwide Prospective Cohort Study

Single-Point Insulin Sensitivity Estimator and Incident Cardiovascular Disease Across Stages 0–3 of Cardiovascular-Kidney-Metabolic Syndrome: A Nationwide Prospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Single-Point Insulin Sensitivity Estimator and Incident Cardiovascular Disease Across Stages 0–3 of Cardiovascular-Kidney-Metabolic Syndrome: A Nationwide Prospective Cohort Study Xiao Zhang, He Jin, Hui Yuan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9341764/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The cardiovascular–kidney–metabolic (CKM) syndrome, recently proposed by the American Heart Association, provides a unified framework linking metabolic, renal, and cardiovascular risk. The single-point insulin sensitivity estimator (SPISE) represents a potential indicator to assess insulin sensitivity and metabolic disorder. However, the longitudinal association between SPISE and incident cardiovascular disease across CKM stages remains unclear. Methods We analyzed prospective data from the China Health and Retirement Longitudinal Study (CHARLS). The primary outcome was incident CVD. The SPISE was calculated by 600×HDL-C ^0.185 / (TG^0.2×BMI^1.338). Associations between SPISE and incident CVD were evaluated using Cox proportional hazards models, Kaplan–Meier curves, and restricted cubic spline (RCS) analysis. Stratified analyses were further conducted to examine potential effect modification by socio - demographic characteristics. Results Among 6,176 participants, 1,509 developed incident CVD during follow-up. After multivariable adjustment, each 1-unit increment in SPISE was associated with an 8% lower risk of incident CVD (HR 0.92, 95% CI 0.90–0.95). Compared with the lowest SPISE quartile, the highest quartile was associated with a 36% lower risk of incident CVD (HR 0.64, 95% CI 0.55–0.75; P for trend < 0.001).Restricted cubic spline analysis demonstrated a linear inverse association between SPISE and CVD risk, with no evidence of non-linearity (P for non-linearity = 0.460). The inverse association was stronger in men, participants aged < 60 years, and those with diabetes (all P for interaction < 0.05). Conclusions Higher SPISE was associated with a lower risk of incident CVD across CKM stages 0–3, suggesting that SPISE may serve as a practical marker for cardiovascular risk stratification and metabolic risk assessment in this population. Single-point insulin sensitivity estimator Cardiovascular kidney metabolic syndrome Cardiovascular diseases CHARLS Figures Figure 1 Figure 2 Figure 3 Figure 4 Research insights What is currently known about this topic? Previous studies have shown that SPISE can serve as a simple marker for assessing insulin sensitivity. What is the key research question? What is the association between SPISE and the incidence of CVD in patients with CKM syndrome at stages 0–3? What is new? This research represents the first comprehensive large-scale evidence evaluating the linear inverse association between the SPISE index and incident CVD among individuals at CKM syndrome stages 0–3, while further highlighting how this relationship varies across different age, gender, and glucose conditions. Introduction Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide[ 1 ]. To better characterize the interrelated progression of metabolic dysfunction, chronic kidney disease, and cardiovascular abnormalities, the American Heart Association recently introduced the cardiovascular–kidney–metabolic (CKM) syndrome framework. Importantly, individuals at CKM stages 0–3 may already exhibit an increased burden of cardiometabolic and cardiovascular risk, underscoring the need for improved tools for early risk assessment and preventive intervention[ 2 , 3 ]. Insulin resistance (IR) is a central pathophysiological driver of CKM progression[ 3 – 5 ]. In addition to its adverse effects on glucose metabolism, IR is closely associated with a cluster of interrelated metabolic and vascular abnormalities, including increased visceral adiposity, atherogenic dyslipidemia characterized by elevated triglyceride levels and reduced high-density lipoprotein cholesterol, as well as early vascular dysfunction manifested by endothelial impairment and reduced vascular homeostasis, all of which act synergistically to promote the development and progression of cardiovascular risk[ 6 – 9 ]. Given the central role of insulin resistance in CKM syndrome, practical surrogate markers reflecting IR-related metabolic abnormalities may be useful for risk stratification and prognostic evaluation across the CKM continuum. The Single-Point Insulin Sensitivity Estimator (SPISE), integrating triglycerides, HDL-C, and BMI, has emerged as a robust and accessible surrogate for evaluating insulin sensitivity and associated metabolic disturbances[ 10 – 12 ]. By encompassing both lipid profiles and adiposity, SPISE effectively captures the multi-faceted nature of IR-driven dysfunction. While previous evidence suggests that lower SPISE levels correlate with increased cardiovascular risk in general and diabetic populations[ 13 , 14 ], its predictive utility within the specific context of CKM staging remains poorly defined. Despite the central role of insulin resistance in CKM syndrome, whether SPISE is associated with incident CVD across CKM stages 0–3 remains unclear. Using a nationwide prospective cohort, we investigated this association and further characterized its dose–response pattern to assess the potential relevance of SPISE for cardiovascular risk assessment within the CKM framework. Methods Study Population and Data Source This study used data from the Harmonized China Health and Retirement Longitudinal Study (Harmonized CHARLS), a nationally representative prospective cohort of Chinese adults aged ≥ 45 years. The Harmonized dataset provides standardized variables for longitudinal analyses. The baseline survey was conducted in 2011–2012, with follow-up waves in 2013, 2015, and 2018.The CHARLS protocol was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015), and all participants provided written informed consent. Study Design and Participant Selection This study used data from the Harmonized CHARLS. Participants enrolled in the 2011–2012 baseline survey were followed through the 2018 wave. The following exclusion criteria were applied: (1) age < 45 years or missing age data (n = 8,655, primarily refreshment samples from later waves); (2) prevalent cardiovascular disease (CVD) at baseline or missing baseline CVD status (n = 2,650); (3) follow-up duration < 2 years or missing outcome data (n = 4,080); (4) missing data required to calculate SPISE, estimate eGFR, or define CKM stage (n = 3,869); (5) extreme SPISE values (± 3 standard deviations; n = 34); and (6) missing key covariates, including sex, smoking status, education level, and histories of hypertension or diabetes (n = 122).After these exclusions, 6,176 participants were included in the final analysis (Fig. 1 ). Definition of CKM syndrome stage 0 to 3 CKM syndrome was classified into four stages (0–3) according to the American Heart Association Presidential Advisory: stage 0, no CKM risk factors; stage 1, excess or dysfunctional adiposity or impaired glucose tolerance; stage 2, presence of metabolic risk factors or chronic kidney disease (CKD); and stage 3, subclinical cardiovascular disease. In this study, very high-risk CKD (KDIGO stages G4–G5) and a high 10-year predicted CVD risk based on the Framingham risk score were considered risk equivalents for subclinical CVD and classified as stage 3. Estimated glomerular filtration rate (eGFR) was calculated using the 2009 CKD-EPI creatinine equation and categorized according to KDIGO guidelines. Calculation of SPISE SPISE was calculated as: SPISE = 600 × HDL-C^0.185 / (TG^0.2 × BMI^1.338), where lipid parameters were expressed in mg/dL and BMI in kg/m². Study Outcome and Follow-up The primary outcome was incident cardiovascular disease (CVD), defined as a composite of newly diagnosed heart disease and stroke. Events were ascertained through self-reported physician diagnoses at each follow-up wave. For survival analyses, time-to-event was assigned based on the wave of first report (2,4, or 7 years for the 2013, 2015, and 2018 waves, respectively). Participants without CVD events were right-censored at 7 years. Data collection Baseline covariates included demographic characteristics (age, sex, education, and marital status), lifestyle factors (smoking and drinking status), physical measurements (body mass index [BMI], waist circumference, systolic blood pressure [SBP], and diastolic blood pressure [DBP]), and medical history (lung and liver diseases).Laboratory measurements included fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), renal function markers (blood urea nitrogen [BUN], serum creatinine, and uric acid), lipid profiles (total cholesterol [TC], triglycerides [TG], high-density lipoprotein cholesterol [HDL-C], and low-density lipoprotein cholesterol [LDL-C]), and C-reactive protein (CRP).Derived variables, including estimated glomerular filtration rate (eGFR), SPISE, and CKM stage, were calculated as described above. Hypertension and diabetes were defined based on biomarker thresholds, self-reported diagnoses, or the use of relevant medications (antihypertensive agents, insulin, or glucose-lowering medications).Detailed measurement protocols are available on the CHARLS website.( http://charls.pku.edu.cn/ ). Statistical Analysis Baseline characteristics were summarized across SPISE quartiles as means ± standard deviations (SDs) for continuous variables and counts (percentages) for categorical variables. Differences between groups were assessed using analysis of variance (ANOVA) for continuous variables and the chi-square test for categorical variables. Cumulative incidence of CVD was estimated using Kaplan–Meier methods and compared with the log-rank test. Associations between SPISE and incident CVD were evaluated using Cox proportional hazards models, with results reported as hazard ratios (HRs) and 95% confidence intervals (CIs). SPISE was analyzed both as a continuous variable (per 1-unit increase) and as quartiles, with the lowest quartile as the reference. Linear trends across quartiles were assessed by assigning the median value to each quartile and modeling this variable as continuous. Five models with increasing levels of adjustment were constructed: Model 1, unadjusted; Model 2, adjusted for age and gender; Model 3, further adjusted for marital status, education level, smoking status and drinking status; Model 4, additionally adjusted for LDL-c, eGFR, CRP, UA; and Model 5, further adjusted for hypertension, diabetes, lung disease and liver disease. The proportional hazards assumption was evaluated using Schoenfeld residuals. Restricted cubic spline (RCS) models with four knots were applied to examine the dose–response relationship between SPISE and CVD risk, with adjustment for covariates included in Model 5. Analyses were conducted in the overall population and within CKM strata. Subgroup analyses were performed according to age (< 60 and ≥ 60 years), sex, smoking status, drinking status, hypertension, diabetes, and CKM stages. Interactions were assessed using likelihood ratio tests comparing models with and without cross-product terms. All analyses were performed using R4.5.2 software. A two-sided P value < 0.05 was considered statistically significant. Results Baseline Characteristics The study cohort included 6,176 participants (mean age 58.37 ± 8.83 years; 53.9% female). Baseline characteristics according to SPISE quartiles are shown in Table 1. Compared with participants in the lowest quartile (Q1), those in the highest quartile (Q4) were older, more likely to be male, and had higher proportions of current smoking and drinking (all P < 0.001).Across increasing SPISE quartiles, cardiometabolic profiles differed substantially. Higher SPISE levels were associated with lower values of adiposity measures (waist circumference and BMI), blood pressure, glycemic markers, and atherogenic lipids, along with higher HDL-C levels (all P < 0.001). The prevalence of hypertension and diabetes decreased across quartiles, from 38.0% and 9.4% in Q1 to 12.4% and 2.1% in Q4, respectively. CKM stage distribution differed significantly across SPISE quartiles (P < 0.001). The proportion of participants in stages 0–1 increased from 2.2% in Q1 to 44.2% in Q4, whereas the proportion in stages 2–3 decreased from 97.8% to 55.9%. Cumulative Incidence of CVD and Kaplan-Meier Analysis During a mean follow-up of 6.60 years, 1,509 participants (24.4%) developed incident CVD. The proportion of events across SPISE quartiles was 30.2% (Q1), 24.5% (Q2), 23.8% (Q3), and 19.3% (Q4). Kaplan–Meier analysis showed significant differences in cumulative incidence across quartiles (log-rank P < 0.001; Fig. 2 ). Participants in Q1 had the highest cumulative incidence, whereas those in Q4 had the lowest. The curves for Q2 and Q3 were largely overlapping throughout follow-up. Independent association between SPISE and incident CVD The association between SPISE and incident CVD was evaluated using Cox proportional hazards models (Table 2). In the unadjusted model, each 1-unit increase in SPISE was associated with a lower risk of CVD (HR 0.92, 95% CI 0.89–0.94). This association remained after adjustment for potential confounders. In the fully adjusted model (Model 5), each 1-unit increase in SPISE was associated with an HR of 0.92 (95% CI 0.90–0.95). When SPISE was analyzed in quartiles, higher quartiles were associated with lower risks of incident CVD (P for trend < 0.001). Compared with the lowest quartile (Q1), the adjusted HRs were 0.81 (95% CI 0.70–0.93) for Q2, 0.82 (95% CI 0.71–0.94) for Q3, and 0.64 (95% CI 0.55–0.75) for Q4. Subgroup and Interaction Analyses Subgroup analyses were performed according to age, sex, smoking status, drinking status, hypertension, diabetes, and CKM stage (Fig. 3 ). Significant interactions were observed for age, sex, and diabetes (all P for interaction < 0.05). The association between SPISE and CVD was stronger among participants aged < 60 years (HR 0.91, 95% CI 0.87–0.94), male (HR 0.88, 95% CI 0.85–0.92), and those with diabetes (HR 0.86, 95% CI 0.79–0.92). No significant interactions were observed for smoking status, drinking status, hypertension, or CKM stage (all P for interaction > 0.05). Dose-Response Relationship between the SPISE Index and Incident CVD Restricted cubic spline analyses based on the fully adjusted model were used to examine the association between SPISE and CVD risk (Fig. 4 ). In the overall cohort (CKM stages 0–3), SPISE showed a linear inverse association with CVD risk (P for overall < 0.001; P for non-linearity = 0.460).Similar patterns were observed in stratified analyses. The association remained linear in participants with CKM stage 2 (P for overall = 0.017; P for non-linearity = 0.353) and stage 3 (P for overall = 0.038; P for non-linearity = 0.201). Discussion In this nationwide prospective cohort, a higher SPISE index—a robust surrogate for insulin sensitivity—was consistently associated with a reduced risk of incident CVD across CKM stages 0 through 3. This inverse relationship remained linear and resilient even among individuals with advanced metabolic derangement (stages 2–3). While the hyperinsulinemic–euglycemic clamp remains the diagnostic benchmark, its complexity precludes its use in large-scale epidemiology[ 15 ]. By integrating the SPISE index into the AHA’s CKM framework[ 16 , 17 ], our study demonstrates that this lipid-BMI-based metric[ 18 ] provides a scalable tool for risk stratification across the full continuum of cardiometabolic dysfunction. Subgroup analyses revealed significant heterogeneity, with the protective association of preserved insulin sensitivity being more pronounced in younger individuals, men, and those with diabetes. In younger populations, insulin resistance and dyslipidemia often serve as the primary drivers of early-onset atherosclerosis[ 19 , 20 ]; conversely, in older adults, cumulative vascular injury and arterial stiffness may overshadow these metabolic factors[ 21 ]. The sex-specific divergence[ 22 ] likely reflects the vasculoprotective effects of estrogen on endothelial function and lipid metabolism, which may partially buffer women against metabolic insults prior to menopause[ 23 – 25 ]. Furthermore, the heightened risk-stratification benefit in diabetic participants suggests that maintaining insulin sensitivity may alleviate vascular damage even in the presence of chronic hyperglycemia[ 14 ]. The observed link between a low SPISE index and CVD risk is anchored in well-defined biological pathways. Insulin resistance impairs PI3K/Akt signaling, leading to diminished nitric oxide bioavailability and subsequent endothelial dysfunction—a critical precursor to atherogenesis[ 17 , 26 , 27 ]. Simultaneously, the lipid components of SPISE (elevated triglycerides and low HDL-C) reflect a milieu of atherogenic dyslipidemia characterized by small, dense LDL particles[ 11 ]. These particles are highly susceptible to oxidation and vascular infiltration, thereby accelerating plaque formation and luminal narrowing. The inclusion of BMI in the SPISE index further captures the systemic impact of adiposity[ 28 , 29 ]. Excess visceral fat promotes a chronic low-grade inflammatory state and a prothrombotic environment through the dysregulated secretion of adipokines[ 30 – 32 ]. These interconnected metabolic and inflammatory cascades collectively drive the progression from early-stage metabolic disturbance to overt cardiovascular events within the CKM framework. A major strength of this study is its nationwide prospective design and the use of restricted cubic splines to provide a granular dose–response evaluation. However, several constraints merit consideration. First, the observational nature of the data precludes definitive causal inference. Second, SPISE was only assessed at baseline, leaving potential longitudinal shifts in insulin sensitivity uncaptured. Third, although we utilized the CHARLS cohort, CVD outcomes were based on self-reported diagnoses, which may introduce misclassification bias compared to clinically adjudicated events. Finally, the focus on middle-aged and older Chinese adults may limit generalizability to other ethnic groups. Despite adjusting for multiple confounders, residual confounding from unmeasured factors—such as dietary patterns or genetic predisposition—cannot be entirely ruled out. Conclusion This cohort study demonstrated an association between a lower SPISE index and an increased incidence of CVD within the population at CKM syndrome stages 0–3. Notably, this dose-response relationship indicates that integrating the easily accessible SPISE index into clinical assessments could serve as a convenient and effective strategy to identify high-risk individuals among those in the early-to-intermediate stages of CKM syndrome. Declarations Ethics approval and consent to participate CHARLS was approved by the Institutional Review Board of Peking University, all participants provided written informed consent. Competing interests The authors declare no competing interests. Funding This work was supported by the 2022 Beijing Major Epidemic Prevention and Control Key Specialty Project. Author Contribution ZX conceptualized and designed the study, analyzed the data, and drafted the manuscript.YH and JH reviewed and edited the manuscript. All authors read and approved the final manuscript. Acknowledgement The authors thank the CHARLS research team for providing the dataset and express their appreciation to all study participants. Data Availability The data supporting the findings of this study are available on the CHARLS website (http://charls.pku.edu.cn/). References Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, Barengo NC, Beaton AZ, Benjamin EJ, Benziger CP, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update From the GBD 2019 Study. J Am Coll Cardiol. 2020;76(25):2982–3021. Ndumele CE, Rangaswami J, Chow SL, Neeland IJ, Tuttle KR, Khan SS, Coresh J, Mathew RO, Baker-Smith CM, Carnethon MR, et al. 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Insulin resistance assessed by estimated glucose disposal rate and risk of incident cardiovascular diseases among individuals without diabetes: findings from a nationwide, population based, prospective cohort study. Cardiovasc Diabetol. 2024;23(1):194. Tian X, Chen S, Wang P, Xu Q, Zhang Y, Luo Y, Wu S, Wang A. Insulin resistance mediates obesity-related risk of cardiovascular disease: a prospective cohort study. Cardiovasc Diabetol. 2022;21(1):289. Kawai T, Autieri MV, Scalia R. Adipose tissue inflammation and metabolic dysfunction in obesity. Am J Physiol Cell Physiol. 2021;320(3):C375–91. Tables Tables are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Onlinefloatimage1.png floatimage3.jpeg Table1.Baseline characteristics of individuals classified by quartiles of the SPISE index floatimage4.jpeg Table 2 Association between the SPISE index and CVD incidence in a population with CKM syndrome stages 0–3 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9341764","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622939975,"identity":"ca8a0dc0-4eb7-482d-8b88-e92ac3f7d572","order_by":0,"name":"Xiao Zhang","email":"","orcid":"","institution":"Beijing Anzhen Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Zhang","suffix":""},{"id":622939976,"identity":"cff0ef4f-fdb5-4984-8b3c-2538923c0a28","order_by":1,"name":"He Jin","email":"","orcid":"","institution":"Beijing Anzhen Hospital","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Jin","suffix":""},{"id":622939977,"identity":"c6321d7e-bfe4-42bb-b526-c4aa72317d70","order_by":2,"name":"Hui Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYJACZgYGCQY2+TNmEh8MbOyI18LPcMZMckZBWjKxWhgYJBuOJRvzfDjE2EBIucGN5GePC9ssGAwONh98bGNwgJmB/fDRDfi1pJkbz2yTYDA4zNhwOMfgDh8DT1raDfxaEsykeUFajoG1PGNmkOAxI6Al/RtYi/0ZoBYLkF2EteRAbbkB1MJAjBbJM2/KpHnOQbQc7DFIS2Yj5Be+4+nbpHnK6hgM7h9sOPDjj40dP/vhY3i1KByA0PUNMBE2fMpBQL6BkIpRMApGwSgYBQCSxUsVSFFZRwAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing Anzhen Hospital","correspondingAuthor":true,"prefix":"","firstName":"Hui","middleName":"","lastName":"Yuan","suffix":""}],"badges":[],"createdAt":"2026-04-07 08:23:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9341764/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9341764/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107254588,"identity":"0ff5a563-6c11-4dda-94d5-69653d71a968","added_by":"auto","created_at":"2026-04-19 12:04:04","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":20254,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study population\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9341764/v1/103e19514aea73e6b941c75c.jpg"},{"id":107254589,"identity":"c1bd3932-6f53-46d3-9490-1e648450c17f","added_by":"auto","created_at":"2026-04-19 12:04:04","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52148,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan - Meier curves for the cumulative incidence of CVD according to SPISE quartiles.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9341764/v1/1b012b28726e4ed06aa8381e.jpg"},{"id":107483688,"identity":"e75db056-a40c-49f4-9c4f-897ad7e6df32","added_by":"auto","created_at":"2026-04-22 02:28:49","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":130199,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses of the association between the SPISE index and incident CVD in a population with CKM syndrome stages 0–3\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9341764/v1/d71ce3fca9ef1d0c0fb1585e.jpg"},{"id":107254593,"identity":"79d803f1-685c-4ec0-bc25-df121afda0a5","added_by":"auto","created_at":"2026-04-19 12:04:04","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":67222,"visible":true,"origin":"","legend":"\u003cp\u003eThe RCS analysis between the SPISE index and CVD incidence in a population with CKM syndrome stages 0–3. The model was adjusted for age, gender, marital status, education level, smoking status, drinking status, LDL-c, eGFR, CRP, UA, hypertension, diabetes, lung disease, liver disease.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9341764/v1/f88158511f1d93a5152e46c6.jpg"},{"id":107705984,"identity":"6c9643eb-a974-474f-a502-5b792cda48ec","added_by":"auto","created_at":"2026-04-24 09:17:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":460287,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9341764/v1/47c5c485-9165-4d09-a7a1-6195cfe137b5.pdf"},{"id":107483195,"identity":"08d286c5-2724-499d-9220-709b87def493","added_by":"auto","created_at":"2026-04-22 02:26:45","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":31032,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9341764/v1/72b0960b6a7cc20e49a75019.png"},{"id":107484537,"identity":"afed66a1-2919-4602-b048-e0b24a82ab63","added_by":"auto","created_at":"2026-04-22 02:32:21","extension":"jpeg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":932602,"visible":true,"origin":"","legend":"\u003cp\u003eTable1.Baseline characteristics of individuals classified by quartiles of the SPISE index\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9341764/v1/0acff62b84049852d06f50e4.jpeg"},{"id":107254592,"identity":"44451d03-db0c-4c68-beaa-0a5595bb384d","added_by":"auto","created_at":"2026-04-19 12:04:04","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":582771,"visible":true,"origin":"","legend":"\u003cp\u003eTable 2 Association between the SPISE index and CVD incidence in a population with CKM syndrome stages 0–3\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9341764/v1/2e345d65dbfb1d26b4d8d6f9.jpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Single-Point Insulin Sensitivity Estimator and Incident Cardiovascular Disease Across Stages 0–3 of Cardiovascular-Kidney-Metabolic Syndrome: A Nationwide Prospective Cohort Study","fulltext":[{"header":"Research insights ","content":"\u003cp\u003e\u003cstrong\u003eWhat is currently known about this topic?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrevious studies have shown that SPISE can serve as a simple marker for assessing insulin sensitivity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is the key research question?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhat is the association between SPISE and the incidence of CVD in patients with CKM syndrome at stages 0–3?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is new?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research represents the first comprehensive large-scale evidence evaluating the linear inverse association between the SPISE index and incident CVD among individuals at CKM syndrome stages 0–3, while further highlighting how this relationship varies across different age, gender, and glucose conditions.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eCardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. To better characterize the interrelated progression of metabolic dysfunction, chronic kidney disease, and cardiovascular abnormalities, the American Heart Association recently introduced the cardiovascular\u0026ndash;kidney\u0026ndash;metabolic (CKM) syndrome framework. Importantly, individuals at CKM stages 0\u0026ndash;3 may already exhibit an increased burden of cardiometabolic and cardiovascular risk, underscoring the need for improved tools for early risk assessment and preventive intervention[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInsulin resistance (IR) is a central pathophysiological driver of CKM progression[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In addition to its adverse effects on glucose metabolism, IR is closely associated with a cluster of interrelated metabolic and vascular abnormalities, including increased visceral adiposity, atherogenic dyslipidemia characterized by elevated triglyceride levels and reduced high-density lipoprotein cholesterol, as well as early vascular dysfunction manifested by endothelial impairment and reduced vascular homeostasis, all of which act synergistically to promote the development and progression of cardiovascular risk[\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Given the central role of insulin resistance in CKM syndrome, practical surrogate markers reflecting IR-related metabolic abnormalities may be useful for risk stratification and prognostic evaluation across the CKM continuum.\u003c/p\u003e \u003cp\u003eThe Single-Point Insulin Sensitivity Estimator (SPISE), integrating triglycerides, HDL-C, and BMI, has emerged as a robust and accessible surrogate for evaluating insulin sensitivity and associated metabolic disturbances[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. By encompassing both lipid profiles and adiposity, SPISE effectively captures the multi-faceted nature of IR-driven dysfunction. While previous evidence suggests that lower SPISE levels correlate with increased cardiovascular risk in general and diabetic populations[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], its predictive utility within the specific context of CKM staging remains poorly defined.\u003c/p\u003e \u003cp\u003eDespite the central role of insulin resistance in CKM syndrome, whether SPISE is associated with incident CVD across CKM stages 0\u0026ndash;3 remains unclear. Using a nationwide prospective cohort, we investigated this association and further characterized its dose\u0026ndash;response pattern to assess the potential relevance of SPISE for cardiovascular risk assessment within the CKM framework.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population and Data Source\u003c/h2\u003e \u003cp\u003eThis study used data from the Harmonized China Health and Retirement Longitudinal Study (Harmonized CHARLS), a nationally representative prospective cohort of Chinese adults aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years. The Harmonized dataset provides standardized variables for longitudinal analyses. The baseline survey was conducted in 2011\u0026ndash;2012, with follow-up waves in 2013, 2015, and 2018.The CHARLS protocol was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015), and all participants provided written informed consent.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Design and Participant Selection\u003c/h3\u003e\n\u003cp\u003eThis study used data from the Harmonized CHARLS. Participants enrolled in the 2011\u0026ndash;2012 baseline survey were followed through the 2018 wave. The following exclusion criteria were applied: (1) age\u0026thinsp;\u0026lt;\u0026thinsp;45 years or missing age data (n\u0026thinsp;=\u0026thinsp;8,655, primarily refreshment samples from later waves); (2) prevalent cardiovascular disease (CVD) at baseline or missing baseline CVD status (n\u0026thinsp;=\u0026thinsp;2,650); (3) follow-up duration\u0026thinsp;\u0026lt;\u0026thinsp;2 years or missing outcome data (n\u0026thinsp;=\u0026thinsp;4,080); (4) missing data required to calculate SPISE, estimate eGFR, or define CKM stage (n\u0026thinsp;=\u0026thinsp;3,869); (5) extreme SPISE values (\u0026plusmn;\u0026thinsp;3 standard deviations; n\u0026thinsp;=\u0026thinsp;34); and (6) missing key covariates, including sex, smoking status, education level, and histories of hypertension or diabetes (n\u0026thinsp;=\u0026thinsp;122).After these exclusions, 6,176 participants were included in the final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eDefinition of CKM syndrome stage 0 to 3\u003c/h3\u003e\n\u003cp\u003eCKM syndrome was classified into four stages (0\u0026ndash;3) according to the American Heart Association Presidential Advisory: stage 0, no CKM risk factors; stage 1, excess or dysfunctional adiposity or impaired glucose tolerance; stage 2, presence of metabolic risk factors or chronic kidney disease (CKD); and stage 3, subclinical cardiovascular disease. In this study, very high-risk CKD (KDIGO stages G4\u0026ndash;G5) and a high 10-year predicted CVD risk based on the Framingham risk score were considered risk equivalents for subclinical CVD and classified as stage 3. Estimated glomerular filtration rate (eGFR) was calculated using the 2009 CKD-EPI creatinine equation and categorized according to KDIGO guidelines.\u003c/p\u003e\n\u003ch3\u003eCalculation of SPISE\u003c/h3\u003e\n\u003cp\u003eSPISE was calculated as: SPISE\u0026thinsp;=\u0026thinsp;600 \u0026times; HDL-C^0.185 / (TG^0.2 \u0026times; BMI^1.338), where lipid parameters were expressed in mg/dL and BMI in kg/m\u0026sup2;.\u003c/p\u003e\n\u003ch3\u003eStudy Outcome and Follow-up\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was incident cardiovascular disease (CVD), defined as a composite of newly diagnosed heart disease and stroke. Events were ascertained through self-reported physician diagnoses at each follow-up wave. For survival analyses, time-to-event was assigned based on the wave of first report (2,4, or 7 years for the 2013, 2015, and 2018 waves, respectively). Participants without CVD events were right-censored at 7 years.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eBaseline covariates included demographic characteristics (age, sex, education, and marital status), lifestyle factors (smoking and drinking status), physical measurements (body mass index [BMI], waist circumference, systolic blood pressure [SBP], and diastolic blood pressure [DBP]), and medical history (lung and liver diseases).Laboratory measurements included fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), renal function markers (blood urea nitrogen [BUN], serum creatinine, and uric acid), lipid profiles (total cholesterol [TC], triglycerides [TG], high-density lipoprotein cholesterol [HDL-C], and low-density lipoprotein cholesterol [LDL-C]), and C-reactive protein (CRP).Derived variables, including estimated glomerular filtration rate (eGFR), SPISE, and CKM stage, were calculated as described above. Hypertension and diabetes were defined based on biomarker thresholds, self-reported diagnoses, or the use of relevant medications (antihypertensive agents, insulin, or glucose-lowering medications).Detailed measurement protocols are available on the CHARLS website.(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://charls.pku.edu.cn/\u003c/span\u003e\u003cspan address=\"http://charls.pku.edu.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eBaseline characteristics were summarized across SPISE quartiles as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SDs) for continuous variables and counts (percentages) for categorical variables. Differences between groups were assessed using analysis of variance (ANOVA) for continuous variables and the chi-square test for categorical variables.\u003c/p\u003e \u003cp\u003eCumulative incidence of CVD was estimated using Kaplan\u0026ndash;Meier methods and compared with the log-rank test. Associations between SPISE and incident CVD were evaluated using Cox proportional hazards models, with results reported as hazard ratios (HRs) and 95% confidence intervals (CIs). SPISE was analyzed both as a continuous variable (per 1-unit increase) and as quartiles, with the lowest quartile as the reference. Linear trends across quartiles were assessed by assigning the median value to each quartile and modeling this variable as continuous.\u003c/p\u003e \u003cp\u003eFive models with increasing levels of adjustment were constructed: Model 1, unadjusted; Model 2, adjusted for age and gender; Model 3, further adjusted for marital status, education level, smoking status and drinking status; Model 4, additionally adjusted for LDL-c, eGFR, CRP, UA; and Model 5, further adjusted for hypertension, diabetes, lung disease and liver disease. The proportional hazards assumption was evaluated using Schoenfeld residuals.\u003c/p\u003e \u003cp\u003eRestricted cubic spline (RCS) models with four knots were applied to examine the dose\u0026ndash;response relationship between SPISE and CVD risk, with adjustment for covariates included in Model 5. Analyses were conducted in the overall population and within CKM strata. Subgroup analyses were performed according to age (\u0026lt;\u0026thinsp;60 and \u0026ge;\u0026thinsp;60 years), sex, smoking status, drinking status, hypertension, diabetes, and CKM stages. Interactions were assessed using likelihood ratio tests comparing models with and without cross-product terms.\u003c/p\u003e \u003cp\u003eAll analyses were performed using R4.5.2 software. A two-sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e \u003cp\u003eThe study cohort included 6,176 participants (mean age 58.37\u0026thinsp;\u0026plusmn;\u0026thinsp;8.83 years; 53.9% female). Baseline characteristics according to SPISE quartiles are shown in Table\u0026nbsp;1. Compared with participants in the lowest quartile (Q1), those in the highest quartile (Q4) were older, more likely to be male, and had higher proportions of current smoking and drinking (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).Across increasing SPISE quartiles, cardiometabolic profiles differed substantially. Higher SPISE levels were associated with lower values of adiposity measures (waist circumference and BMI), blood pressure, glycemic markers, and atherogenic lipids, along with higher HDL-C levels (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The prevalence of hypertension and diabetes decreased across quartiles, from 38.0% and 9.4% in Q1 to 12.4% and 2.1% in Q4, respectively. CKM stage distribution differed significantly across SPISE quartiles (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The proportion of participants in stages 0\u0026ndash;1 increased from 2.2% in Q1 to 44.2% in Q4, whereas the proportion in stages 2\u0026ndash;3 decreased from 97.8% to 55.9%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCumulative Incidence of CVD and Kaplan-Meier Analysis\u003c/h2\u003e \u003cp\u003eDuring a mean follow-up of 6.60 years, 1,509 participants (24.4%) developed incident CVD. The proportion of events across SPISE quartiles was 30.2% (Q1), 24.5% (Q2), 23.8% (Q3), and 19.3% (Q4). Kaplan\u0026ndash;Meier analysis showed significant differences in cumulative incidence across quartiles (log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Participants in Q1 had the highest cumulative incidence, whereas those in Q4 had the lowest. The curves for Q2 and Q3 were largely overlapping throughout follow-up.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIndependent association between SPISE and incident CVD\u003c/h2\u003e \u003cp\u003eThe association between SPISE and incident CVD was evaluated using Cox proportional hazards models (Table\u0026nbsp;2). In the unadjusted model, each 1-unit increase in SPISE was associated with a lower risk of CVD (HR 0.92, 95% CI 0.89\u0026ndash;0.94). This association remained after adjustment for potential confounders. In the fully adjusted model (Model 5), each 1-unit increase in SPISE was associated with an HR of 0.92 (95% CI 0.90\u0026ndash;0.95).\u003c/p\u003e \u003cp\u003eWhen SPISE was analyzed in quartiles, higher quartiles were associated with lower risks of incident CVD (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Compared with the lowest quartile (Q1), the adjusted HRs were 0.81 (95% CI 0.70\u0026ndash;0.93) for Q2, 0.82 (95% CI 0.71\u0026ndash;0.94) for Q3, and 0.64 (95% CI 0.55\u0026ndash;0.75) for Q4.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup and Interaction Analyses\u003c/h2\u003e \u003cp\u003eSubgroup analyses were performed according to age, sex, smoking status, drinking status, hypertension, diabetes, and CKM stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Significant interactions were observed for age, sex, and diabetes (all P for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The association between SPISE and CVD was stronger among participants aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years (HR 0.91, 95% CI 0.87\u0026ndash;0.94), male (HR 0.88, 95% CI 0.85\u0026ndash;0.92), and those with diabetes (HR 0.86, 95% CI 0.79\u0026ndash;0.92). No significant interactions were observed for smoking status, drinking status, hypertension, or CKM stage (all P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDose-Response Relationship between the SPISE Index and Incident CVD\u003c/h2\u003e \u003cp\u003eRestricted cubic spline analyses based on the fully adjusted model were used to examine the association between SPISE and CVD risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the overall cohort (CKM stages 0\u0026ndash;3), SPISE showed a linear inverse association with CVD risk (P for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001; P for non-linearity\u0026thinsp;=\u0026thinsp;0.460).Similar patterns were observed in stratified analyses. The association remained linear in participants with CKM stage 2 (P for overall\u0026thinsp;=\u0026thinsp;0.017; P for non-linearity\u0026thinsp;=\u0026thinsp;0.353) and stage 3 (P for overall\u0026thinsp;=\u0026thinsp;0.038; P for non-linearity\u0026thinsp;=\u0026thinsp;0.201).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this nationwide prospective cohort, a higher SPISE index\u0026mdash;a robust surrogate for insulin sensitivity\u0026mdash;was consistently associated with a reduced risk of incident CVD across CKM stages 0 through 3. This inverse relationship remained linear and resilient even among individuals with advanced metabolic derangement (stages 2\u0026ndash;3). While the hyperinsulinemic\u0026ndash;euglycemic clamp remains the diagnostic benchmark, its complexity precludes its use in large-scale epidemiology[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. By integrating the SPISE index into the AHA\u0026rsquo;s CKM framework[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], our study demonstrates that this lipid-BMI-based metric[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] provides a scalable tool for risk stratification across the full continuum of cardiometabolic dysfunction.\u003c/p\u003e \u003cp\u003eSubgroup analyses revealed significant heterogeneity, with the protective association of preserved insulin sensitivity being more pronounced in younger individuals, men, and those with diabetes. In younger populations, insulin resistance and dyslipidemia often serve as the primary drivers of early-onset atherosclerosis[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]; conversely, in older adults, cumulative vascular injury and arterial stiffness may overshadow these metabolic factors[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The sex-specific divergence[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] likely reflects the vasculoprotective effects of estrogen on endothelial function and lipid metabolism, which may partially buffer women against metabolic insults prior to menopause[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Furthermore, the heightened risk-stratification benefit in diabetic participants suggests that maintaining insulin sensitivity may alleviate vascular damage even in the presence of chronic hyperglycemia[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe observed link between a low SPISE index and CVD risk is anchored in well-defined biological pathways. Insulin resistance impairs PI3K/Akt signaling, leading to diminished nitric oxide bioavailability and subsequent endothelial dysfunction\u0026mdash;a critical precursor to atherogenesis[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Simultaneously, the lipid components of SPISE (elevated triglycerides and low HDL-C) reflect a milieu of atherogenic dyslipidemia characterized by small, dense LDL particles[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These particles are highly susceptible to oxidation and vascular infiltration, thereby accelerating plaque formation and luminal narrowing.\u003c/p\u003e \u003cp\u003eThe inclusion of BMI in the SPISE index further captures the systemic impact of adiposity[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Excess visceral fat promotes a chronic low-grade inflammatory state and a prothrombotic environment through the dysregulated secretion of adipokines[\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These interconnected metabolic and inflammatory cascades collectively drive the progression from early-stage metabolic disturbance to overt cardiovascular events within the CKM framework.\u003c/p\u003e \u003cp\u003eA major strength of this study is its nationwide prospective design and the use of restricted cubic splines to provide a granular dose\u0026ndash;response evaluation. However, several constraints merit consideration. First, the observational nature of the data precludes definitive causal inference. Second, SPISE was only assessed at baseline, leaving potential longitudinal shifts in insulin sensitivity uncaptured. Third, although we utilized the CHARLS cohort, CVD outcomes were based on self-reported diagnoses, which may introduce misclassification bias compared to clinically adjudicated events. Finally, the focus on middle-aged and older Chinese adults may limit generalizability to other ethnic groups. Despite adjusting for multiple confounders, residual confounding from unmeasured factors\u0026mdash;such as dietary patterns or genetic predisposition\u0026mdash;cannot be entirely ruled out.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis cohort study demonstrated an association between a lower SPISE index and an increased incidence of CVD within the population at CKM syndrome stages 0\u0026ndash;3. Notably, this dose-response relationship indicates that integrating the easily accessible SPISE index into clinical assessments could serve as a convenient and effective strategy to identify high-risk individuals among those in the early-to-intermediate stages of CKM syndrome.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e CHARLS was approved by the Institutional Review Board of Peking University, all participants provided written informed consent.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the 2022 Beijing Major Epidemic Prevention and Control Key Specialty Project.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZX conceptualized and designed the study, analyzed the data, and drafted the manuscript.YH and JH reviewed and edited the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank the CHARLS research team for providing the dataset and express their appreciation to all study participants.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study are available on the CHARLS website (http://charls.pku.edu.cn/).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRoth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, Barengo NC, Beaton AZ, Benjamin EJ, Benziger CP, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990\u0026ndash;2019: Update From the GBD 2019 Study. 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Metab Clin Exp. 2021;119:154766.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaulmichl K, Hatunic M, H\u0026oslash;jlund K, Jotic A, Krebs M, Mitrakou A, Porcellati F, Tura A, Bergsten P, Forslund A, et al. Modification and Validation of the Triglyceride-to-HDL Cholesterol Ratio as a Surrogate of Insulin Sensitivity in White Juveniles and Adults without Diabetes Mellitus: The Single Point Insulin Sensitivity Estimator (SPISE). Clin Chem. 2016;62(9):1211\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNigro J, Osman N, Dart AM, Little PJ. Insulin resistance and atherosclerosis. Endocr Rev. 2006;27(3):242\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrooijmans J, Singh S, Naqshband M, Bruikman CS, Pinto-Sietsma SJ. Premature atherosclerosis: An analysis over 39 years in the Netherlands. Implications for young individuals in high-risk families. 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Atherosclerosis. 2019;281:150\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhitlock G, Lewington S, Sherliker P, Clarke R, Emberson J, Halsey J, Qizilbash N, Collins R, Peto R. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet (London England). 2009;373(9669):1083\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoliaki C, Liatis S, Kokkinos A. Obesity and cardiovascular disease: revisiting an old relationship. Metab Clin Exp. 2019;92:98\u0026ndash;107.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z, Zhao L, Lu Y, Xiao Y, Zhou X. Insulin resistance assessed by estimated glucose disposal rate and risk of incident cardiovascular diseases among individuals without diabetes: findings from a nationwide, population based, prospective cohort study. Cardiovasc Diabetol. 2024;23(1):194.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian X, Chen S, Wang P, Xu Q, Zhang Y, Luo Y, Wu S, Wang A. Insulin resistance mediates obesity-related risk of cardiovascular disease: a prospective cohort study. Cardiovasc Diabetol. 2022;21(1):289.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKawai T, Autieri MV, Scalia R. Adipose tissue inflammation and metabolic dysfunction in obesity. Am J Physiol Cell Physiol. 2021;320(3):C375\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Single-point insulin sensitivity estimator, Cardiovascular kidney metabolic syndrome, Cardiovascular diseases, CHARLS","lastPublishedDoi":"10.21203/rs.3.rs-9341764/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9341764/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe cardiovascular\u0026ndash;kidney\u0026ndash;metabolic (CKM) syndrome, recently proposed by the American Heart Association, provides a unified framework linking metabolic, renal, and cardiovascular risk. The single-point insulin sensitivity estimator (SPISE) represents a potential indicator to assess insulin sensitivity and metabolic disorder. However, the longitudinal association between SPISE and incident cardiovascular disease across CKM stages remains unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed prospective data from the China Health and Retirement Longitudinal Study (CHARLS). The primary outcome was incident CVD. The SPISE was calculated by 600\u0026times;HDL-C ^0.185 / (TG^0.2\u0026times;BMI^1.338). Associations between SPISE and incident CVD were evaluated using Cox proportional hazards models, Kaplan\u0026ndash;Meier curves, and restricted cubic spline (RCS) analysis. Stratified analyses were further conducted to examine potential effect modification by socio - demographic characteristics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 6,176 participants, 1,509 developed incident CVD during follow-up. After multivariable adjustment, each 1-unit increment in SPISE was associated with an 8% lower risk of incident CVD (HR 0.92, 95% CI 0.90\u0026ndash;0.95). Compared with the lowest SPISE quartile, the highest quartile was associated with a 36% lower risk of incident CVD (HR 0.64, 95% CI 0.55\u0026ndash;0.75; P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001).Restricted cubic spline analysis demonstrated a linear inverse association between SPISE and CVD risk, with no evidence of non-linearity (P for non-linearity\u0026thinsp;=\u0026thinsp;0.460). The inverse association was stronger in men, participants aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years, and those with diabetes (all P for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eHigher SPISE was associated with a lower risk of incident CVD across CKM stages 0\u0026ndash;3, suggesting that SPISE may serve as a practical marker for cardiovascular risk stratification and metabolic risk assessment in this population.\u003c/p\u003e","manuscriptTitle":"Single-Point Insulin Sensitivity Estimator and Incident Cardiovascular Disease Across Stages 0–3 of Cardiovascular-Kidney-Metabolic Syndrome: A Nationwide Prospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-19 12:04:00","doi":"10.21203/rs.3.rs-9341764/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2dd13024-7bff-4339-b68f-5f3f2f29e6d5","owner":[],"postedDate":"April 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-19T12:55:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-19 12:04:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9341764","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9341764","identity":"rs-9341764","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}