U-shaped associations between cardiometabolic index (CMI) and all-cause and cardiovascular mortality among elderly Americans with diabetes or prediabetes: NHANES 1999–2018

U-shaped associations between cardiometabolic index (CMI) and all-cause and cardiovascular mortality among elderly Americans with diabetes or prediabetes: NHANES 1999–2018 | 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 U-shaped associations between cardiometabolic index (CMI) and all-cause and cardiovascular mortality among elderly Americans with diabetes or prediabetes: NHANES 1999–2018 Lu Long, Lei He, Hao Sun, Zhiwen Shu, Weixue Wang, Qing Li, Juxiang Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6525763/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: As a relatively novel and integrated metric, the Cardiometabolic Index (CMI) has not been thoroughly explored for its utility in assessing mortality risk among patients with diabetes or prediabetes. This study focuses on analyzing the relationship between CMI and all-cause along with cardiovascular disease (CVD) mortality in older U.S. adults diagnosed with diabetes or prediabetes. Methods: The research encompassed 3,062 older adults diagnosed with diabetes or prediabetes, drawn from the National Health and Nutrition Examination Survey (NHANES) conducted between 1999 - 2018. The mortality results were ascertained by cross-referencing with the records of the National Death Index (NDI) up until December 31, 2019. Moreover, the DeLong test was utilized to validate the predictive capability of CMI relative to other cardiometabolic indices. Results: Across a follow-up period averaging 85.44 months, 1,277 all-cause deaths were documented among patients with diabetes or prediabetes, including 445 occurrences of cardiovascular mortality. The analysis of threshold effects based on restricted cubic splines demonstrated a U-shaped, nonlinear association between CMI and all-cause as well as CVD mortality in individuals with diabetes or prediabetes. Notably, Interestingly, when the baseline CMI was below the cutoff values (1.12 for all-cause mortality and 1.04 for cardiovascular mortality), it showed a negative association solely with all-cause mortality (HR: 0.78, 95% CI: 0.63–0.95). However, exceeding these thresholds was significantly linked to a higher risk of both all-cause mortality (HR: 1.13, 95% CI: 1.03–1.24) and CVD mortality (HR: 1.19, 95% CI: 1.03–1.37). Conclusion: In older adults with diabetes or prediabetes, a U-shaped association was identified between initial CMI and both all-cause and CVD mortality, with respective thresholds of 1.12 and 1.04. Cardiometabolic index diabetes prediabetes All-cause mortality Cardiovascular mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Background Diabetes ranks among the top global causes of illness and death. Its prevalence is increasing in tandem with population growth and aging ( 1 ). The global prevalence of diabetes is predicted to rise to 12.2% (783.2 million individuals) by 2045, and healthcare spending is anticipated to grow to $ 1.054 trillion, imposing a substantial burden on both individuals and healthcare systems worldwide ( 2 ). A significant reason that diabetes mellitus poses a threat to human health is attributed to its macrovascular and microvascular complications ( 3 ). Cardiovascular disease (CVD) is a common complication of type 2 diabetes mellitus (T2DM) and remains one of the leading causes of death among diabetic patients globally ( 4 ) ( 5 ). With the rapid growth of the elderly population, the prevalence of CVD and diabetes is also increasing rapidly. In the United States, the prevalence of CVD among individuals aged 60 years and older is 77.8% in men and 76.3% in women, while among those aged 80 years and older, the prevalence rises to 85.9% in men and 85.1% in women ( 6 ). According to the 2024 National Diabetes Statistics Report, the current incidence of diabetes among individuals aged 65 and older is 29.2% ( 7 ). Therefore, identifying modifiable risk factors in the elderly population plays a crucial role in reducing the burden on healthcare systems and socioeconomic resources. Research indicates that adults diagnosed with diabetes mellitus are 2 to 4 times more likely to experience cardiovascular events than their non-diabetic counterparts ( 3 ) ( 4 ). This elevated risk may be attributed to the pathological effects of hyperglycemia and insulin resistance on endothelial dysfunction and the progression of atherosclerosis ( 8 – 10 ). Dyslipidemia in diabetic patients is also a contributing factor to the pathogenesis of atherosclerosis. Previous studies have proposed using certain lipid-related indicators to monitor the occurrence of cardiovascular disease in diabetic patients, such as the triglyceride-glucose (TyG) index or lipid accumulation product (LAP) ( 11 ) ( 12 ). In recent years, the cardiometabolic index (CMI) has developed as an innovative biomarker for measuring abdominal adiposity and lipid metabolism( 13 ). Results from a retrospective Japanese cohort study demonstrated that the CMI not only identifies the risk of cardiometabolic diseases but also exhibits a nonlinear association with T2DM ( 14 ). Other studies have shown that CMI is associated with coronary artery disease, erectile dysfunction, and renal cell carcinoma, among others ( 15 – 17 ). Nevertheless, the clinical utility of CMI in predicting mortality among older adults with diabetes or prediabetes remains under-researched. Our study systematically investigates the relationships between CMI along with both all-cause and cardiovascular disease (CVD) mortality in older adults with diabetes or prediabetes, offering evidence-based insights for prevention and clinical management in this high-risk group. 2 Methods 2.1 Population and Study Design The National Health and Nutrition Examination Survey (NHANES) serves as a comprehensive national study evaluating the physical condition and dietary patterns across various age groups within the U.S. population( 18 ). The National Center for Health Statistics (NCHS) Ethics Review Board authorized the study protocol, and written informed consent was obtained from all participants. Relevant data are publicly accessible on the NHANES website ( https://www.cdc.gov/nchs/nhanes/index.html ). From the NHANES 1999–2018 database, we screened individuals (n = 5592) who were 65 years or older at baseline and had a confirmed diagnosis of type 2 diabetes or prediabetes. Diabetes and prediabetes were defined according to ADA guidelines( 19 ), with detailed diagnostic criteria shown in Table S1 . We further excluded participants lacking CMI data or those who were pregnant (n = 2,525), as well as cases with unavailable mortality data (n = 5). The final analytical cohort comprised 3,062 participants (Fig. S1 ). 2.2 Assessment of CMI CMI, as a composite index, is calculated utilizing the formula: CMI = ( \(\:\frac{TG(mmol/L)}{HDL-C(mmol/L)}\) )* Waist-to-Height Ratio (WHtR) WHtR = WC (cm)/ height (cm) Our study population was stratified into quartile-based groups according to their CMI values, with the lowest quartile (Q1: 1.106). 2.3 Definitions of outcome variables We accessed the NHANES-linked mortality files, with data current through December 31, 2019, to determine all-cause and CVD mortality within the follow-up cohort. Mortality certificate records from the National Death Index (NDI) were linked to survey participant data via a probabilistic matching algorithm. Mortality outcomes were classified based on diagnostic codes from the International Classification of Diseases, Tenth Revision (ICD-10) system to identify specific death causes. Deaths attributable to cardiovascular disease included the following ICD-10 code ranges: I00-I09, I11, I13, and I20-I51( 20 ). Total deaths from all specified causes were referred to as all-cause mortality 2.4 Assessment of covariates In this study, covariate selection involved a comprehensive consideration of multiple factors, including demographic characteristics, laboratory measures, and health-related information. Demographic characteristics obtained through NHANES household interviews included sex, age, race, educational attainment, income-to-poverty ratio (PIR), and body mass index (BMI). Based on blood samples provided by participants at the Mobile Examination Center (MEC), we measured baseline levels of fasting plasma glucose (FBG), HbA1c, estimated glomerular filtration rate (eGFR), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), urinary albumin-to-creatinine ratio (uACR). Health-related covariates (smoking, drinking, physical activity, hypertension, hyperlipidemia, CVD) were adjusted in the analysis (see Table S2 for details). 2.5 Statistical Analysis The NHANES survey utilizes a multi-stage probability sampling approach to construct the study framework; in this research, Appropriate sampling weights should be incorporated into statistical analyses to strengthen the credibility and robustness of the outcomes. Continuous data is offered as mean ± standard deviation (SD), and categorical variables are displayed as frequencies and percentages. One-way ANOVA was used to compare continuous variables at baseline, and Pearson's chi-squared test was used to analyze categorical variables. To address missing values in the study data, we applied multiple imputations by chained equations (MICE) to estimate incomplete covariates. Different imputation strategies were employed based on variable types: predictive mean matching was used for continuous variables, logistic regression for binary variables, and multinomial logistic regression for unordered categorical variables. A comprehensive assessment confirmed that the proportion of missing data for all covariates was below 10%. The Kaplan-Meier curves were employed to the survival probability over time for patients with diabetes or prediabetes at different CMI levels. A weighted multiple Cox proportional hazards model was utilized to examine the links between CMI and both all-cause and cardiovascular mortality, while a piecewise Cox model was further applied to analyze these relationships on both sides of the threshold in individuals with diabetes or prediabetes. A nonlinear analysis was performed to examine the dose-response relationship between CMI and mortality, employing a weighted restricted cubic spline (RCS) regression model with three nodes. The best threshold was established by pinpointing the maximum likelihood ratio within the nonlinear relationship. Further research investigated several other indicators related to cardiometabolic, which were calculated according to the following formulas: ( 1 ) AIP= \(\:{{log}}_{10}\frac{TG(mmol/L)}{HDL-L(mmol/L)}\) ( 2 ) NHHR= \(\:\frac{TC(mmol/L)-HDL-L(mmol/L)}{HDL-L(mmol/L)}\) ( 3 ) SHR= \(\:\frac{FPG(mmol/L)}{1.59*HbA1c\left(\%\right)-2.59}\) ( 4 ) TyG index = ln \(\:\frac{fasting\:triglycerides(mg/dL)*fasting\:glucose(mg/dL)}{2}\) ( 5 ) HOMA-IR index = \(\:\frac{fasting\:glucose\left(mmol/L\right)*fasting\:insulin(uU/mL)}{22.5}\) ( 6 ) HOMA-β index = \(\:\frac{20\:\times\:\:fasting\:insulin\:(\mu\:IU/ml)}{fasting\:glucose\:(mmol/ml)\:-\:3.5}\) Multivariable linear regression analyses adjusted according to Model 2 were utilized to evaluate the relationship between the CMI as well as various cardiometabolic parameters. Based on the Akaike Information Criterion (AIC), the best covariates were selected using the bidirectional stepwise method. In comparing the prognostic value of CMI with other cardiometabolic indicators, we introduced each indicator separately into the baseline model adjusted for the best covariates. The DeLong test was employed to assess variations in the C-index among different models, comparing their predictive performance. Stratified analysis was performed taking into account sex, race, BMI (< 25.00 or ≥ 25.00), hypertension, CVD, and diabetes classification. To guarantee the validity of our findings, we conducted multiple sensitivity analyses in order to verify what we discovered. First, we removed samples with missing covariate data to mitigate potential bias introduced by data missingness. Second, participants with pre-existing cancer diagnoses were excluded from the analysis, considering the substantial impact of malignancy on mortality outcomes and its strong relationship with all-cause death in the diabetic population. Finally, to mitigate potential reverse causation bias, mortality events documented during the initial 24-month follow-up period were excluded from the analysis. Data analyses were conducted in R software (version 4.3.2), and statistical significance was defined as a p-value < 0.05. 3 Results 3.1 Characteristics of the Participants This study enrolled 3062 participants, representing a weighted population of 8,539,653 participants. The average weighted age was 72.86, with women making up 50.85%. Enrolled patients had a mean CMI of 0.91 (0.02). Table 1 depicts a detailed distribution of baseline characteristics across CMI quartile stratifications. Compared to the lowest CMI quartile, the highest CMI quartile group exhibited significant differences in demographic characteristics, including younger age, a higher proportion of males, a greater prevalence of obesity, a larger proportion of Mexican White individuals, lower educational attainment, more prevalent smoking history, less physical activity, and higher rates of hypertension and CVD. Furthermore, regarding biochemical markers, the highest quartile group demonstrated statistically significant differences in LDL, HDL, FDG, HbA1c, ALT, uric acid, and eGFR. 3.2 Associations of CMI with Mortality During an average follow-up of 85.44 months, 1,277 all-cause deaths were documented among patients with diabetes or prediabetes, including 445 occurrences of cardiovascular mortality. Kaplan-Meier survival curves revealed significant differences in all-cause mortality (p = 0.012) as well as CVD mortality (p = 0.045) across the four groups (Fig.1). To evaluate the independent relationship between CMI and mortality outcomes, we employed a weighted multivariable Cox regression analysis with proportional hazards assumption (Table 2). After full adjustment for potential confounders in Model 2, the analysis revealed the following associations: for all-cause mortality, the adjusted HRs across ascending CMI quartiles were 1.00 (reference, Q1), 0.80 (95% CI: 0.65-0.98, Q2), 0.79 (95% CI: 0.68-1.00, Q3), and 0.93 (95% CI: 0.76-1.14, Q4); for cardiovascular mortality, the corresponding HRs were 1.00 (Q1), 0.80 (95% CI: 0.56-1.15, Q2), 0.71 (95% CI: 0.50-1.01, Q3), and 0.99 (95% CI: 0.71-1.39, Q4). 3.3The detection of nonlinear relationships The multivariable-adjusted smooth curve analysis demonstrated a notable U-shaped nonlinear association between CMI and both all-cause mortality (Figure 2A) and CVD mortality (Figure 2B). Threshold effect analysis, conducted using piecewise Cox proportional hazards regression models, revealed critical thresholds of 1.12 and 1.04 for CMI concerning all-cause mortality and CVD mortality, respectively, supported by statistically significant log-likelihood ratio tests (P < 0.05). (Table 3). Interestingly, our findings revealed a significant inverse relationship between the CMI along with all-cause mortality within a defined threshold range (HR: 0.78, 95% CI: 0.63-0.95). However, the inverse association between CMI and CVD mortality was not statistically significant (p = 0.113). Above the critical threshold, CMI was significantly positively linked to all-cause mortality (HR: 1.13, 95% CI: 1.03-1.24) and cardiovascular disease mortality (HR: 1.19, 95% CI: 1.03-1.37). Furthermore, the link between CMI and mortality was evaluated independently in patients with diabetes along with prediabetes (Fig.3). The findings demonstrated a marked U-shaped dose-response connection between CMI and both all-cause mortality ( P = 0.011 ) along with CVD mortality ( P = 0.049 ) among the diagnosed diabetic population. Nevertheless, no clear non-linear associations were identified between CMI and either all-cause mortality ( P = 0.100 ) or CVD mortality ( P = 0.235 ) in the prediabetes group. 3.4 Association and Predictive Ability of CMI with Other Cardiometabolic Indicators Our investigation revealed statistically significant associations between the CMI and multiple metabolic parameters (all p<0.001). Specifically, CMI demonstrated strong positive correlations with the AIP (r = 0.84), NHHR (r = 0.69), and TyG (r = 0.76). Moderate but significant positive correlations were observed between CMI and both SHR (r = 0.14) and HOMA-IR (r = 0.19). However, no statistically significant association was detected between CMI and HOMA-β (r = 0.02, p = 0.528), as illustrated in Fig. 4. Table 4 presents the predictive performance of various models with related cardiometabolic indicators. The C-indices of the AIP, NHHR, SHR, TyG, HOMA-IR, and HOMA-β models did not show significant differences from the CMI model, indicating that the predictive performance of CMI is consistent with that of other cardiometabolic indicators. 3.5 Sensitivity analysis First, we finalized a sample of 2490 participants for analysis after excluding individuals with missing baseline covariate data. Statistical analysis demonstrated a steady link between CMI and both all-cause mortality as well as CVD mortality (Table S2). Second, excluding subjects with pre-existing malignant tumors at baseline yielded data analysis results consistent with the main conclusions (Table S3). Third, the association patterns between CMI and both types of mortality remained similar after removing deaths occurring in the initial two years of follow-up, confirming the robustness of our findings (Table S4). 3.6 Subgroup analyses Compared with the population with a lower CMI (all-cause mortality < 1.12, cardiovascular disease mortality < 1.04), the population with a higher CMI (all-cause mortality risk ≥ 1.12, cardiovascular disease mortality risk ≥ 1.04) still showed a significant correlation in mortality across different subgroups (Fig.S2). Moreover, baseline CMI showed no significant interaction with the stratification variables (P > 0.05). 4 Discussion As a large-scale, nationally representative prospective cohort study, this research is the first to investigate the association between CMI and mortality risk (all-cause and CVD-specific) among older adults with diabetes or prediabetes. The analysis demonstrated a clear U-shaped pattern between CMI as well as mortality. Additionally, threshold analysis identified specific inflection points: 1.12 for all-cause mortality along with 1.04 for CVD mortality. Furthermore, the DeLong test analysis confirmed that the predictive performance was on par with other cardiometabolic indices. These results underscore the potential of CMI as a robust metric for predicting mortality among older adults with diabetes or prediabetes. As a novel composite metric, CMI is designed to assess cardiovascular and metabolic health status. Its predictive potential has been increasingly recognized in diverse disease contexts. In a large-scale study involving 40,275 participants, Liu et al. observed an L-shaped nonlinear correlation between CMI and all-cause mortality, with a critical threshold of 0.98. Notably, when CMI levels fell below 0.98, they were inversely linked with all-cause mortality ( 25 ). Further supporting this, Xu et al. found that CMI levels were associated with increased all-cause mortality risk in the general population aged 65 years and older( 26 ). Our study emphasizes that, in elderly individuals with diabetes or prediabetes, CMI significantly modifies the relationship between cardiovascular and all-cause mortality. Additionally, previous research has demonstrated a significant positive correlation between CMI and the progression of arteriosclerosis in patients with diabetes( 17 ). Similarly, a retrospective cohort study involving 2,067 participants identified CMI as an independent predictor of coronary artery disease and cardiovascular events in patients with hypertension and obstructive sleep apnea (OSA) ( 27 ). These findings provide indirect support for our study’s conclusions. CMI can effectively reflect the level of fat accumulation in the body ( 28 ). The potential mechanisms linking diabetes and cardiovascular disease involve dyslipidemia, insulin resistance (IR), hyperglycemia, and endothelial dysfunction. Specifically, endothelial cells (ECs) exert vasodilatory effects on vascular smooth muscle cells (VSMCs) primarily through the production of nitric oxide (NO), prostacyclin, and hyperpolarizing factors( 29 ) ( 30 ). Furthermore, NO generated by ECs inhibits VSMC proliferation and migration, excessive oxidative stress, inflammation, and leukocyte adhesion( 31 ). However, insulin resistance and hyperglycemia reduce endothelial nitric oxide synthase (eNOS) activity, leading to decreased NO production, impaired vasodilation, and excessive oxidative stress. These conditions also increase endothelial permeability and dysfunction, promoting LDL oxidation and foam cell formation, ultimately contributing to atherosclerotic plaque development( 8 , 32 , 33 ). Meanwhile, during insulin resistance, lipolysis in adipocytes is increased, leading to the release of large amounts of free fatty acids (FFA) into the bloodstream. This process enhances the synthesis of LDL ( 34 , 35 ), reduces the expression of hepatic LDL receptors (LDLR) ( 34 ), increases the secretion of small dense LDL particles( 36 ), and promotes the degradation of high-density lipoprotein cholesterol (HDL-C), thereby impairing reverse cholesterol transport (RCT) ( 37 ). Pathological studies demonstrate that the sustained accumulation of abnormal lipid metabolites in the vascular intima promotes the development of atherosclerotic lesions. Critically, disruption of the structural integrity of atherosclerotic plaques can initiate platelet aggregation, leading to the formation of occlusive thrombi and, ultimately, fatal cardiovascular events such as acute coronary syndrome ( 38 – 40 ). The data from this study demonstrate that the CMI exhibits significant positive correlations with indicators like AIP, NHHR, SHR, TyG, and HOMA-IR. However, the correlation with HOMA-β was not significant. Prior research has established the clinical utility of these indicators when forecasting all-cause and CVD mortality in individuals with diabetes or prediabetes( 41 – 44 ). Notably, we found that CMI possesses comparable predictive performance to other cardiometabolic indicators when assessing all-cause and CVD mortality in patients with diabetes and pre-diabetic conditions. This finding offers robust evidence, grounded in evidence-based medicine, supporting CMI as a valuable predictor of mortality. This study's empirical analysis further revealed a significant U-shaped relationship between CMI and both all-cause and cardiovascular mortality. Notably, after comprehensive adjustment for confounders, a one-unit increase in CMI among individuals with a baseline CMI below 1.12 was associated with a 22% decreased risk of all-cause mortality (p < 0.05). Research indicates that abnormally low TG levels may pose health risks and play a role in the pathogenesis of certain disorders ( 45 ). For example, a large-scale study involving 127,124 heart failure patients (2000–2020) revealed that TG levels below 1.2 mmol/L were associated with an increased risk of hypertension-related rehospitalization or death ( 46 ). Similarly, a large cohort study revealed that low serum TG levels were correlated with higher mortality rates among ischemic stroke patients ( 47 ). No significant relationship was detected between CMI and cardiovascular mortality at lower baseline CMI levels (< 1.04). A nationwide study encompassing 18,781 participants revealed that triglyceride levels were independently associated solely with all-cause mortality ( 48 ). Additionally, findings from a cohort study involving 40,275 individuals from the general population showed that, after fully adjusting for confounding variables, baseline CMI below the threshold was negatively linked with all-cause mortality yet not significantly associated with CVD mortality, which is consistent with our study( 25 ). Given the nonlinear relationship between the CMI and the risk of adverse outcomes, maintaining the CMI within an optimal range holds considerable clinical significance. The study conducted separate analyses for diabetic and prediabetic patients. A notable U-shaped trend was found between CMI and both all-cause along with CVD mortality in diabetic patients (P 0.05). This discrepancy might be attributed to the narrower range of CMI values observed in the prediabetic group, a hypothesis requiring confirmation through further empirical research with larger sample sizes. This nationwide, prospective study design enables robust statistical analysis and reliable estimation of the associations between CMI and all-cause along with cardiovascular mortality. Additionally, our study further compared the predictive performance of CMI with other cardiac metabolic indices and performed sensitivity analyses, which enhanced the robustness of the results. Nevertheless, there are several limitations to consider. First, as a single-center observational study, establishing a causal link between CMI and mortality is limited. Second, the study design focused solely on the prognostic value of baseline CMI levels, without considering the influence of dynamic CMI changes on mortality risk. Third, a portion of diabetes diagnoses were derived from self-reported data, which may be prone to recall bias and classification errors, possibly impacting the validity of the study's findings. 5 Conclusion The research findings indicate that the CMI exhibits significant predictive capability for all-cause along with CVD mortality in elderly patients with diabetes or prediabetes. Moreover, a distinct U-shaped dose-response relationship exists between CMI and these two mortality outcomes. Integrating CMI into standard monitoring practices in clinical settings could offer a valuable reference for more precise, personalized risk stratification in individuals with diabetes and prediabetes. Abbreviations CMI cardiometabolic index CVD cardiovascular disease DM diabetes mellitus HR hazard ratio CI confidence interval BMI body mass index PIR Poverty income ratio TC total cholesterol TG triglycerides AST Aspartate transferase ALT alanine transferase FPG fasting plasma glucose; eGFR estimated glomerular filtration rate uACR urine albumin-creatinine ratio HbA1c glycated hemoglobin A1c LDL-C low-density lipoprotein cholesterol HDL-C high-density lipoprotein cholesterol AIP lipid accumulation product NHHR non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio SHR stress hyperglycemia ratio TyG triglyceride-glucose HOMA-IR homeostasis model assessment of insulin resistance HOMA-β homeostasis model assessment of β-cell function ICD-10 International Statistical Classification of Disease,10th Revision NHANES National Health and Nutrition Examination Survey RCS restricted cubic spline US United States Declarations Ethics approval and consent to participate The study protocol was approved by the NHANES Institutional Review Board and carried out according to the Declaration of Helsinki, with all NHANES participants signing informed consent. Consent for publication The authors have no relevant conflicts of interest to disclose. Availability of data and materials The datasets generated and analyzed in this study were available from the NHANES database, https://www.cdc.gov/nchs/nhanes/ Funding The National Natural Science Foundation of China (No.82360072 to Q-L). Second Affiliated of Nanchang University Funded Clinical Research Projects (No.2022efyA03 to J.X-L). Author contribution L-L: Methodology, Software, Formal Analysis, Data Curation, Writing - Original Draft. L-H: Methodology, Investigation, Data curation., L-H: Methodology, Investigation, Data curation. H-S: Methodology, Investigation, Data curation. Z.W-S: Methodology, Investigation. W.X-W: Methodology, Investigation. Q-L: Methodology, Investigation, J.X-L: Conceptualization, Methodology, Writing - Review & Editing, Supervision, Project administration. Acknowledgment None References Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, et al. 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Nature. 2001;414(6865):813-20. Ginsberg HN. Insulin resistance and cardiovascular disease. The Journal of clinical investigation. 2000;106(4):453-8. Boden G. Role of fatty acids in the pathogenesis of insulin resistance and NIDDM. Diabetes. 1997;46(1):3-10. Dixon JL, Ginsberg HN. Regulation of hepatic secretion of apolipoprotein B-containing lipoproteins: information obtained from cultured liver cells. Journal of lipid research. 1993;34(2):167-79. Zakai NA, Minnier J, Safford MM, Koh I, Irvin MR, Fazio S, et al. Race-Dependent Association of High-Density Lipoprotein Cholesterol Levels With Incident Coronary Artery Disease. Journal of the American College of Cardiology. 2022;80(22):2104-15. Farb A, Burke AP, Tang AL, Liang TY, Mannan P, Smialek J, et al. Coronary plaque erosion without rupture into a lipid core. A frequent cause of coronary thrombosis in sudden coronary death. Circulation. 1996;93(7):1354-63. Falk E, Nakano M, Bentzon JF, Finn AV, Virmani R. Update on acute coronary syndromes: the pathologists' view. European heart journal. 2013;34(10):719-28. Tabas I, Bornfeldt KE. Macrophage Phenotype and Function in Different Stages of Atherosclerosis. Circulation research. 2016;118(4):653-67. Yu B, Li M, Yu Z, Zheng T, Feng X, Gao A, et al. The non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) as a predictor of all-cause and cardiovascular mortality in US adults with diabetes or prediabetes: NHANES 1999-2018. BMC medicine. 2024;22(1):317. Ding L, Zhang H, Dai C, Zhang A, Yu F, Mi L, et al. The prognostic value of the stress hyperglycemia ratio for all-cause and cardiovascular mortality in patients with diabetes or prediabetes: insights from NHANES 2005-2018. Cardiovascular diabetology. 2024;23(1):84. Shi Y, Wen M. Sex-specific differences in the effect of the atherogenic index of plasma on prediabetes and diabetes in the NHANES 2011-2018 population. Cardiovascular diabetology. 2023;22(1):19. Zhang Q, Xiao S, Jiao X, Shen Y. The triglyceride-glucose index is a predictor for cardiovascular and all-cause mortality in CVD patients with diabetes or pre-diabetes: evidence from NHANES 2001-2018. Cardiovascular diabetology. 2023;22(1):279. Abbasi F, Reaven GM. Comparison of two methods using plasma triglyceride concentration as a surrogate estimate of insulin action in nondiabetic subjects: triglycerides × glucose versus triglyceride/high-density lipoprotein cholesterol. Metabolism: clinical and experimental. 2011;60(12):1673-6. Ren QW, Teng TK, Ouwerkerk W, Tse YK, Tsang CTW, Wu MZ, et al. Triglyceride levels and its association with all-cause mortality and cardiovascular outcomes among patients with heart failure. Nature communications. 2025;16(1):1408. Ryu WS, Lee SH, Kim CK, Kim BJ, Yoon BW. Effects of low serum triglyceride on stroke mortality: a prospective follow-up study. Atherosclerosis. 2010;212(1):299-304. Huang YQ, Liu XC, Lo K, Feng YQ, Zhang B. A dose-independent association of triglyceride levels with all-cause mortality among adults population. Lipids in health and disease. 2020;19(1):225. Tables Tables 1 to 4 are available in the Supplementary Files section Additional Declarations No competing interests reported. Supplementary Files SupplementalMaterials.docx Table.docx 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-6525763","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469217627,"identity":"c65584e5-b1f9-484b-ad03-d34f24b354db","order_by":0,"name":"Lu Long","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Long","suffix":""},{"id":469217628,"identity":"f72a9ec8-80b1-4340-af19-a4bc49c074df","order_by":1,"name":"Lei He","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"He","suffix":""},{"id":469217629,"identity":"dac9cbb4-ff5f-4c2e-b863-c73a8f31552a","order_by":2,"name":"Hao Sun","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Sun","suffix":""},{"id":469217634,"identity":"99be1635-bd6f-4f92-90bb-d9d993714173","order_by":3,"name":"Zhiwen Shu","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Zhiwen","middleName":"","lastName":"Shu","suffix":""},{"id":469217635,"identity":"173311c4-9764-4672-bac7-430458ecad42","order_by":4,"name":"Weixue Wang","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Weixue","middleName":"","lastName":"Wang","suffix":""},{"id":469217636,"identity":"cdf4d10d-c277-48e6-b9ed-6e47cae34320","order_by":5,"name":"Qing Li","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Li","suffix":""},{"id":469217638,"identity":"43ba82fa-6ee4-4ceb-aea3-02044b42f351","order_by":6,"name":"Juxiang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBACPoYDjA8SKmx4+NkbiNTCxnCA2eDBmTQZyZ4DRGthYJN82HbYxuCGA7FaGM+YSSSwnedhuMHA+OFjDlG2HEu2SOC5zcM4u4FZcuY2orQcPngjQeI2D7PMATZmXuK0HGyQSDA4x8MGdB6xWg4fkkhIOMDDQ4KWY8kGCQeSeSR4DjYT5xd+iTOGD3/+s7O3P9588MNHYrQwSByAsRgbiFEPsoZYhaNgFIyCUTByAQB8kzWMNS2d0AAAAABJRU5ErkJggg==","orcid":"","institution":"Nanchang University","correspondingAuthor":true,"prefix":"","firstName":"Juxiang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-04-25 06:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6525763/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6525763/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84528885,"identity":"63434a1f-f215-4dac-8eb1-6788b783628f","added_by":"auto","created_at":"2025-06-13 05:47:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":610502,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves for (A) all-cause mortality and (B) cardiovascular mortality stratified by CMI quartiles.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6525763/v1/1ca48c6a28f00f1a6685b8c3.png"},{"id":84530516,"identity":"c889c841-af29-4762-98ed-3951bd6932ec","added_by":"auto","created_at":"2025-06-13 06:03:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":431971,"visible":true,"origin":"","legend":"\u003cp\u003eThe nonlinear relationships between CMI and all-cause mortality (A) as well as CVD mortality (B) in patients with diabetes or prediabetes. For hazard ratio (HR) calculations, CMI values of 1.12 (A) and 1.04 (B) were used as reference points. Adjusted for age, sex, race, education level, PIR, ALT, AST, uric acid, eGFR, UACR, smoking status, alcohol consumption, physical activity, hypertension, and CVD. The relationship between CMI and mortality risk is depicted by a solid line, and the red shaded area represents the 95% confidence interval (CI). CMI: cardiometabolic index; CVD: cardiovascular disease\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6525763/v1/7d11beb5432258df7b09da86.png"},{"id":84528892,"identity":"0a5e833d-2ad0-4120-ae37-e85bf32a83aa","added_by":"auto","created_at":"2025-06-13 05:47:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":772041,"visible":true,"origin":"","legend":"\u003cp\u003eThe nonlinear associations between CMI and all-cause mortality (A) as well as CVD mortality (B) in diabetic individuals. For hazard ratio calculations, diabetic individuals were referenced at CMI values of 1.25 (A) and 1.06 (B). Similarly, the nonlinear relationships between CMI and all-cause mortality (C) and CVD mortality (D) in prediabetic individuals, with prediabetic individuals referenced at CMI values of 1.04 (A) and 1.06 (B). Adjusted for age, sex, race, education level, PIR, ALT, AST, uric acid, eGFR, UACR, smoking status, alcohol consumption, physical activity, hypertension, and CVD. The relationship between CMI and mortality risk is depicted by a solid line, and the red shaded area represents the 95% confidence interval (CI). CMI: cardiometabolic index; CVD: cardiovascular disease\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6525763/v1/577cc140f7f93acb7e21c117.png"},{"id":84528898,"identity":"fd27c8be-ca0d-4e48-a6a0-c0c0067241a6","added_by":"auto","created_at":"2025-06-13 05:47:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5249224,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between CMI and Other Cardiometabolic Indicators.\u003c/p\u003e\n\u003cp\u003eCMI: cardiometabolic index; AIP: atherogenic index of plasma; NHHR: non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio; SHR: stress hyperglycemia ratio; TyG index: triglyceride-glucose index; HOMA-IR: homeostasis model assessment of insulin resistance; HOMA-β: homeostasis model assessment of β-cell function\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6525763/v1/10b64d454839dc41d7fb60ed.png"},{"id":88608097,"identity":"dcc790af-af93-4874-b282-df985de91f17","added_by":"auto","created_at":"2025-08-08 09:09:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7355732,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6525763/v1/bf0b2aef-8a66-4839-b14e-74b275489f06.pdf"},{"id":84528891,"identity":"d0641e4a-2691-4aee-85b5-28dce0fb62ad","added_by":"auto","created_at":"2025-06-13 05:47:18","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":791863,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6525763/v1/e3d418c06474ac5911fd47e3.docx"},{"id":84530031,"identity":"5ba173da-0f1e-4538-a1ac-eddf45a5ba2d","added_by":"auto","created_at":"2025-06-13 05:55:18","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":38182,"visible":true,"origin":"","legend":"","description":"","filename":"Table.docx","url":"https://assets-eu.researchsquare.com/files/rs-6525763/v1/9cdbaaec347aed44ea551cc0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"U-shaped associations between cardiometabolic index (CMI) and all-cause and cardiovascular mortality among elderly Americans with diabetes or prediabetes: NHANES 1999–2018","fulltext":[{"header":"1 Background","content":"\u003cp\u003eDiabetes ranks among the top global causes of illness and death. Its prevalence is increasing in tandem with population growth and aging (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The global prevalence of diabetes is predicted to rise to 12.2% (783.2\u0026nbsp;million individuals) by 2045, and healthcare spending is anticipated to grow to \u003cspan\u003e$\u003c/span\u003e1.054 trillion, imposing a substantial burden on both individuals and healthcare systems worldwide (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). A significant reason that diabetes mellitus poses a threat to human health is attributed to its macrovascular and microvascular complications (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Cardiovascular disease (CVD) is a common complication of type 2 diabetes mellitus (T2DM) and remains one of the leading causes of death among diabetic patients globally (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). With the rapid growth of the elderly population, the prevalence of CVD and diabetes is also increasing rapidly. In the United States, the prevalence of CVD among individuals aged 60 years and older is 77.8% in men and 76.3% in women, while among those aged 80 years and older, the prevalence rises to 85.9% in men and 85.1% in women (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). According to the 2024 National Diabetes Statistics Report, the current incidence of diabetes among individuals aged 65 and older is 29.2% (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Therefore, identifying modifiable risk factors in the elderly population plays a crucial role in reducing the burden on healthcare systems and socioeconomic resources.\u003c/p\u003e \u003cp\u003eResearch indicates that adults diagnosed with diabetes mellitus are 2 to 4 times more likely to experience cardiovascular events than their non-diabetic counterparts (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). This elevated risk may be attributed to the pathological effects of hyperglycemia and insulin resistance on endothelial dysfunction and the progression of atherosclerosis (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Dyslipidemia in diabetic patients is also a contributing factor to the pathogenesis of atherosclerosis. Previous studies have proposed using certain lipid-related indicators to monitor the occurrence of cardiovascular disease in diabetic patients, such as the triglyceride-glucose (TyG) index or lipid accumulation product (LAP) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In recent years, the cardiometabolic index (CMI) has developed as an innovative biomarker for measuring abdominal adiposity and lipid metabolism(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Results from a retrospective Japanese cohort study demonstrated that the CMI not only identifies the risk of cardiometabolic diseases but also exhibits a nonlinear association with T2DM (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Other studies have shown that CMI is associated with coronary artery disease, erectile dysfunction, and renal cell carcinoma, among others (\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Nevertheless, the clinical utility of CMI in predicting mortality among older adults with diabetes or prediabetes remains under-researched.\u003c/p\u003e \u003cp\u003eOur study systematically investigates the relationships between CMI along with both all-cause and cardiovascular disease (CVD) mortality in older adults with diabetes or prediabetes, offering evidence-based insights for prevention and clinical management in this high-risk group.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Population and Study Design\u003c/h2\u003e \u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) serves as a comprehensive national study evaluating the physical condition and dietary patterns across various age groups within the U.S. population(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The National Center for Health Statistics (NCHS) Ethics Review Board authorized the study protocol, and written informed consent was obtained from all participants. Relevant data are publicly accessible on the NHANES website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/index.html\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). From the NHANES 1999\u0026ndash;2018 database, we screened individuals (n\u0026thinsp;=\u0026thinsp;5592) who were 65 years or older at baseline and had a confirmed diagnosis of type 2 diabetes or prediabetes. Diabetes and prediabetes were defined according to ADA guidelines(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), with detailed diagnostic criteria shown in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. We further excluded participants lacking CMI data or those who were pregnant (n\u0026thinsp;=\u0026thinsp;2,525), as well as cases with unavailable mortality data (n\u0026thinsp;=\u0026thinsp;5). The final analytical cohort comprised 3,062 participants (Fig.\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Assessment of CMI\u003c/h2\u003e \u003cp\u003eCMI, as a composite index, is calculated utilizing the formula:\u003c/p\u003e \u003cp\u003eCMI = (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{TG(mmol/L)}{HDL-C(mmol/L)}\\)\u003c/span\u003e\u003c/span\u003e)* Waist-to-Height Ratio (WHtR)\u003c/p\u003e \u003cp\u003eWHtR\u0026thinsp;=\u0026thinsp;WC (cm)/ height (cm)\u003c/p\u003e \u003cp\u003eOur study population was stratified into quartile-based groups according to their CMI values, with the lowest quartile (Q1: \u0026lt;0.414) serving as the reference group, followed by Q2 (0.414\u0026ndash;0.684), Q3 (0.684\u0026ndash;1.106), and Q4 (\u0026gt;\u0026thinsp;1.106).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Definitions of outcome variables\u003c/h2\u003e \u003cp\u003eWe accessed the NHANES-linked mortality files, with data current through December 31, 2019, to determine all-cause and CVD mortality within the follow-up cohort. Mortality certificate records from the National Death Index (NDI) were linked to survey participant data via a probabilistic matching algorithm. Mortality outcomes were classified based on diagnostic codes from the International Classification of Diseases, Tenth Revision (ICD-10) system to identify specific death causes. Deaths attributable to cardiovascular disease included the following ICD-10 code ranges: I00-I09, I11, I13, and I20-I51(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Total deaths from all specified causes were referred to as all-cause mortality\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Assessment of covariates\u003c/h2\u003e \u003cp\u003eIn this study, covariate selection involved a comprehensive consideration of multiple factors, including demographic characteristics, laboratory measures, and health-related information. Demographic characteristics obtained through NHANES household interviews included sex, age, race, educational attainment, income-to-poverty ratio (PIR), and body mass index (BMI). Based on blood samples provided by participants at the Mobile Examination Center (MEC), we measured baseline levels of fasting plasma glucose (FBG), HbA1c, estimated glomerular filtration rate (eGFR), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), urinary albumin-to-creatinine ratio (uACR). Health-related covariates (smoking, drinking, physical activity, hypertension, hyperlipidemia, CVD) were adjusted in the analysis (see Table S2 for details).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe NHANES survey utilizes a multi-stage probability sampling approach to construct the study framework; in this research, Appropriate sampling weights should be incorporated into statistical analyses to strengthen the credibility and robustness of the outcomes. Continuous data is offered as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and categorical variables are displayed as frequencies and percentages. One-way ANOVA was used to compare continuous variables at baseline, and Pearson's chi-squared test was used to analyze categorical variables.\u003c/p\u003e \u003cp\u003eTo address missing values in the study data, we applied multiple imputations by chained equations (MICE) to estimate incomplete covariates. Different imputation strategies were employed based on variable types: predictive mean matching was used for continuous variables, logistic regression for binary variables, and multinomial logistic regression for unordered categorical variables. A comprehensive assessment confirmed that the proportion of missing data for all covariates was below 10%.\u003c/p\u003e \u003cp\u003eThe Kaplan-Meier curves were employed to the survival probability over time for patients with diabetes or prediabetes at different CMI levels. A weighted multiple Cox proportional hazards model was utilized to examine the links between CMI and both all-cause and cardiovascular mortality, while a piecewise Cox model was further applied to analyze these relationships on both sides of the threshold in individuals with diabetes or prediabetes. A nonlinear analysis was performed to examine the dose-response relationship between CMI and mortality, employing a weighted restricted cubic spline (RCS) regression model with three nodes. The best threshold was established by pinpointing the maximum likelihood ratio within the nonlinear relationship. Further research investigated several other indicators related to cardiometabolic, which were calculated according to the following formulas:\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) \u003cem\u003eAIP=\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{log}}_{10}\\frac{TG(mmol/L)}{HDL-L(mmol/L)}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) \u003cem\u003eNHHR=\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{TC(mmol/L)-HDL-L(mmol/L)}{HDL-L(mmol/L)}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) \u003cem\u003eSHR=\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{FPG(mmol/L)}{1.59*HbA1c\\left(\\%\\right)-2.59}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) \u003cem\u003eTyG index\u0026thinsp;=\u0026thinsp;ln\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{fasting\\:triglycerides(mg/dL)*fasting\\:glucose(mg/dL)}{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) \u003cem\u003eHOMA-IR index =\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{fasting\\:glucose\\left(mmol/L\\right)*fasting\\:insulin(uU/mL)}{22.5}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) \u003cem\u003eHOMA-β index =\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{20\\:\\times\\:\\:fasting\\:insulin\\:(\\mu\\:IU/ml)}{fasting\\:glucose\\:(mmol/ml)\\:-\\:3.5}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eMultivariable linear regression analyses adjusted according to Model 2 were utilized to evaluate the relationship between the CMI as well as various cardiometabolic parameters. Based on the Akaike Information Criterion (AIC), the best covariates were selected using the bidirectional stepwise method. In comparing the prognostic value of CMI with other cardiometabolic indicators, we introduced each indicator separately into the baseline model adjusted for the best covariates. The DeLong test was employed to assess variations in the C-index among different models, comparing their predictive performance. Stratified analysis was performed taking into account sex, race, BMI (\u0026lt;\u0026thinsp;25.00 or \u0026ge;\u0026thinsp;25.00), hypertension, CVD, and diabetes classification. To guarantee the validity of our findings, we conducted multiple sensitivity analyses in order to verify what we discovered. First, we removed samples with missing covariate data to mitigate potential bias introduced by data missingness. Second, participants with pre-existing cancer diagnoses were excluded from the analysis, considering the substantial impact of malignancy on mortality outcomes and its strong relationship with all-cause death in the diabetic population. Finally, to mitigate potential reverse causation bias, mortality events documented during the initial 24-month follow-up period were excluded from the analysis. Data analyses were conducted in R software (version 4.3.2), and statistical significance was defined as a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003e\u003cstrong\u003e3.1\u0026nbsp;\u003c/strong\u003eCharacteristics of the Participants\u003c/p\u003e\n\u003cp\u003eThis study enrolled 3062 participants, representing a weighted population of 8,539,653 participants. The average weighted age was 72.86, with women making up 50.85%. Enrolled patients had a mean CMI of 0.91 (0.02). Table 1 depicts a detailed distribution of baseline characteristics across CMI quartile stratifications. Compared to the lowest CMI quartile, the highest CMI quartile group exhibited significant differences in demographic characteristics, including younger age, a higher proportion of males, a greater prevalence of obesity, a larger proportion of Mexican White individuals, lower educational attainment, more prevalent smoking history, less physical activity, and higher rates of hypertension and CVD. Furthermore, regarding biochemical markers, the highest quartile group demonstrated statistically significant differences in LDL, HDL, FDG, HbA1c, ALT, uric acid, and eGFR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Associations of CMI with Mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring an average follow-up of 85.44 months, 1,277 all-cause deaths were documented among patients with diabetes or prediabetes, including 445 occurrences of cardiovascular mortality. Kaplan-Meier survival curves revealed significant differences in all-cause mortality (p = 0.012) as well as CVD mortality (p = 0.045) across the four groups (Fig.1). To evaluate the independent relationship between CMI and mortality outcomes, we employed a weighted multivariable Cox regression analysis with proportional hazards assumption (Table 2). After full adjustment for potential confounders in Model 2, the analysis revealed the following associations: for all-cause mortality, the adjusted HRs across ascending CMI quartiles were 1.00 (reference, Q1), 0.80 (95% CI: 0.65-0.98, Q2), 0.79 (95% CI: 0.68-1.00, Q3), and 0.93 (95% CI: 0.76-1.14, Q4); for cardiovascular mortality, the corresponding HRs were 1.00 (Q1), 0.80 (95% CI: 0.56-1.15, Q2), 0.71 (95% CI: 0.50-1.01, Q3), and 0.99 (95% CI: 0.71-1.39, Q4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3The detection of nonlinear relationships\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe multivariable-adjusted smooth curve analysis demonstrated a notable U-shaped nonlinear association between CMI and both all-cause mortality (Figure 2A) and CVD mortality (Figure 2B). Threshold effect analysis, conducted using piecewise Cox proportional hazards regression models, revealed critical thresholds of 1.12 and 1.04 for CMI concerning all-cause mortality and CVD mortality, respectively, supported by statistically significant log-likelihood ratio tests (P \u0026lt; 0.05).\u0026nbsp;(Table 3). Interestingly, our findings revealed\u0026nbsp;a significant inverse relationship between the CMI along with all-cause mortality within a defined threshold range (HR: 0.78, 95% CI: 0.63-0.95). However, the inverse association between CMI and CVD mortality was not statistically significant (p = 0.113). Above the critical threshold, CMI was significantly positively linked to all-cause mortality (HR: 1.13, 95% CI: 1.03-1.24) and cardiovascular disease mortality (HR: 1.19, 95% CI: 1.03-1.37). Furthermore, the link between CMI and mortality was evaluated independently in patients with diabetes along with prediabetes (Fig.3). The findings demonstrated a marked U-shaped dose-response connection between CMI and both all-cause mortality (\u003cem\u003eP = 0.011\u003c/em\u003e) along with CVD mortality (\u003cem\u003eP = 0.049\u003c/em\u003e) among the diagnosed diabetic population. Nevertheless, no clear non-linear associations were identified between CMI and either all-cause mortality (\u003cem\u003eP = 0.100\u003c/em\u003e) or CVD mortality (\u003cem\u003eP = 0.235\u003c/em\u003e) in the prediabetes group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Association and Predictive Ability of CMI with Other Cardiometabolic Indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur investigation revealed statistically significant associations between the CMI and multiple metabolic parameters (all p\u0026lt;0.001). Specifically, CMI demonstrated strong positive correlations with the AIP (r = 0.84), NHHR (r = 0.69), and TyG (r = 0.76). Moderate but significant positive correlations were observed between CMI and both SHR (r = 0.14) and HOMA-IR (r = 0.19). However, no statistically significant association was detected between CMI and HOMA-β (r = 0.02, p = 0.528), as illustrated in Fig. 4. Table 4 presents the predictive performance of various models with related cardiometabolic indicators. The C-indices of the AIP, NHHR, SHR, TyG, HOMA-IR, and HOMA-β models did not show significant differences from the CMI model, indicating that the predictive performance of CMI is consistent with that of other cardiometabolic indicators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Sensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, we finalized a sample of 2490 participants for analysis after excluding individuals with missing baseline covariate data. Statistical analysis demonstrated a steady link between CMI and both all-cause mortality as well as CVD mortality (Table S2). Second, excluding subjects with pre-existing malignant tumors at baseline yielded data analysis results consistent with the main conclusions (Table S3). Third, the association patterns between CMI and both types of mortality remained similar after removing deaths occurring in the initial two years of follow-up, confirming the robustness of our findings (Table S4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Subgroup analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompared with the population with a lower CMI (all-cause mortality \u0026lt; 1.12, cardiovascular disease mortality \u0026lt; 1.04), the population with a higher CMI (all-cause mortality risk ≥ 1.12, cardiovascular disease mortality risk ≥ 1.04) still showed a significant correlation in mortality across different subgroups (Fig.S2). Moreover, baseline CMI showed no significant interaction with the stratification variables (P \u0026gt; 0.05).\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eAs a large-scale, nationally representative prospective cohort study, this research is the first to investigate the association between CMI and mortality risk (all-cause and CVD-specific) among older adults with diabetes or prediabetes. The analysis demonstrated a clear U-shaped pattern between CMI as well as mortality. Additionally, threshold analysis identified specific inflection points: 1.12 for all-cause mortality along with 1.04 for CVD mortality. Furthermore, the DeLong test analysis confirmed that the predictive performance was on par with other cardiometabolic indices.\u003c/p\u003e \u003cp\u003eThese results underscore the potential of CMI as a robust metric for predicting mortality among older adults with diabetes or prediabetes. As a novel composite metric, CMI is designed to assess cardiovascular and metabolic health status. Its predictive potential has been increasingly recognized in diverse disease contexts. In a large-scale study involving 40,275 participants, Liu et al. observed an L-shaped nonlinear correlation between CMI and all-cause mortality, with a critical threshold of 0.98. Notably, when CMI levels fell below 0.98, they were inversely linked with all-cause mortality (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Further supporting this, Xu et al. found that CMI levels were associated with increased all-cause mortality risk in the general population aged 65 years and older(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Our study emphasizes that, in elderly individuals with diabetes or prediabetes, CMI significantly modifies the relationship between cardiovascular and all-cause mortality. Additionally, previous research has demonstrated a significant positive correlation between CMI and the progression of arteriosclerosis in patients with diabetes(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Similarly, a retrospective cohort study involving 2,067 participants identified CMI as an independent predictor of coronary artery disease and cardiovascular events in patients with hypertension and obstructive sleep apnea (OSA) (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). These findings provide indirect support for our study\u0026rsquo;s conclusions.\u003c/p\u003e \u003cp\u003eCMI can effectively reflect the level of fat accumulation in the body (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The potential mechanisms linking diabetes and cardiovascular disease involve dyslipidemia, insulin resistance (IR), hyperglycemia, and endothelial dysfunction. Specifically, endothelial cells (ECs) exert vasodilatory effects on vascular smooth muscle cells (VSMCs) primarily through the production of nitric oxide (NO), prostacyclin, and hyperpolarizing factors(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Furthermore, NO generated by ECs inhibits VSMC proliferation and migration, excessive oxidative stress, inflammation, and leukocyte adhesion(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). However, insulin resistance and hyperglycemia reduce endothelial nitric oxide synthase (eNOS) activity, leading to decreased NO production, impaired vasodilation, and excessive oxidative stress. These conditions also increase endothelial permeability and dysfunction, promoting LDL oxidation and foam cell formation, ultimately contributing to atherosclerotic plaque development(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Meanwhile, during insulin resistance, lipolysis in adipocytes is increased, leading to the release of large amounts of free fatty acids (FFA) into the bloodstream. This process enhances the synthesis of LDL (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), reduces the expression of hepatic LDL receptors (LDLR) (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), increases the secretion of small dense LDL particles(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), and promotes the degradation of high-density lipoprotein cholesterol (HDL-C), thereby impairing reverse cholesterol transport (RCT) (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Pathological studies demonstrate that the sustained accumulation of abnormal lipid metabolites in the vascular intima promotes the development of atherosclerotic lesions. Critically, disruption of the structural integrity of atherosclerotic plaques can initiate platelet aggregation, leading to the formation of occlusive thrombi and, ultimately, fatal cardiovascular events such as acute coronary syndrome (\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe data from this study demonstrate that the CMI exhibits significant positive correlations with indicators like AIP, NHHR, SHR, TyG, and HOMA-IR. However, the correlation with HOMA-β was not significant. Prior research has established the clinical utility of these indicators when forecasting all-cause and CVD mortality in individuals with diabetes or prediabetes(\u003cspan additionalcitationids=\"CR42 CR43\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Notably, we found that CMI possesses comparable predictive performance to other cardiometabolic indicators when assessing all-cause and CVD mortality in patients with diabetes and pre-diabetic conditions. This finding offers robust evidence, grounded in evidence-based medicine, supporting CMI as a valuable predictor of mortality. This study's empirical analysis further revealed a significant U-shaped relationship between CMI and both all-cause and cardiovascular mortality. Notably, after comprehensive adjustment for confounders, a one-unit increase in CMI among individuals with a baseline CMI below 1.12 was associated with a 22% decreased risk of all-cause mortality (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Research indicates that abnormally low TG levels may pose health risks and play a role in the pathogenesis of certain disorders (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). For example, a large-scale study involving 127,124 heart failure patients (2000\u0026ndash;2020) revealed that TG levels below 1.2 mmol/L were associated with an increased risk of hypertension-related rehospitalization or death (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Similarly, a large cohort study revealed that low serum TG levels were correlated with higher mortality rates among ischemic stroke patients (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNo significant relationship was detected between CMI and cardiovascular mortality at lower baseline CMI levels (\u0026lt;\u0026thinsp;1.04). A nationwide study encompassing 18,781 participants revealed that triglyceride levels were independently associated solely with all-cause mortality (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Additionally, findings from a cohort study involving 40,275 individuals from the general population showed that, after fully adjusting for confounding variables, baseline CMI below the threshold was negatively linked with all-cause mortality yet not significantly associated with CVD mortality, which is consistent with our study(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Given the nonlinear relationship between the CMI and the risk of adverse outcomes, maintaining the CMI within an optimal range holds considerable clinical significance. The study conducted separate analyses for diabetic and prediabetic patients. A notable U-shaped trend was found between CMI and both all-cause along with CVD mortality in diabetic patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, in prediabetic individuals, the U-shaped trend for mortality failed to achieve statistical significance (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This discrepancy might be attributed to the narrower range of CMI values observed in the prediabetic group, a hypothesis requiring confirmation through further empirical research with larger sample sizes.\u003c/p\u003e \u003cp\u003eThis nationwide, prospective study design enables robust statistical analysis and reliable estimation of the associations between CMI and all-cause along with cardiovascular mortality. Additionally, our study further compared the predictive performance of CMI with other cardiac metabolic indices and performed sensitivity analyses, which enhanced the robustness of the results. Nevertheless, there are several limitations to consider. First, as a single-center observational study, establishing a causal link between CMI and mortality is limited. Second, the study design focused solely on the prognostic value of baseline CMI levels, without considering the influence of dynamic CMI changes on mortality risk. Third, a portion of diabetes diagnoses were derived from self-reported data, which may be prone to recall bias and classification errors, possibly impacting the validity of the study's findings.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThe research findings indicate that the CMI exhibits significant predictive capability for all-cause along with CVD mortality in elderly patients with diabetes or prediabetes. Moreover, a distinct U-shaped dose-response relationship exists between CMI and these two mortality outcomes. Integrating CMI into standard monitoring practices in clinical settings could offer a valuable reference for more precise, personalized risk stratification in individuals with diabetes and prediabetes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCMI cardiometabolic index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCVD\u0026nbsp;cardiovascular disease\u003c/p\u003e\n\u003cp\u003eDM diabetes mellitus\u003c/p\u003e\n\u003cp\u003eHR hazard ratio\u003c/p\u003e\n\u003cp\u003eCI confidence interval\u003c/p\u003e\n\u003cp\u003eBMI body mass index\u003c/p\u003e\n\u003cp\u003ePIR Poverty income ratio\u003c/p\u003e\n\u003cp\u003eTC total cholesterol\u003c/p\u003e\n\u003cp\u003eTG triglycerides\u003c/p\u003e\n\u003cp\u003eAST Aspartate transferase\u003c/p\u003e\n\u003cp\u003eALT\u0026nbsp;alanine transferase\u003c/p\u003e\n\u003cp\u003eFPG fasting plasma glucose;\u003c/p\u003e\n\u003cp\u003eeGFR estimated glomerular filtration rate\u003c/p\u003e\n\u003cp\u003euACR urine albumin-creatinine ratio\u003c/p\u003e\n\u003cp\u003eHbA1c glycated hemoglobin A1c\u003c/p\u003e\n\u003cp\u003eLDL-C low-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eHDL-C high-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eAIP lipid accumulation product\u003c/p\u003e\n\u003cp\u003eNHHR non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio\u003c/p\u003e\n\u003cp\u003eSHR stress hyperglycemia ratio\u003c/p\u003e\n\u003cp\u003eTyG triglyceride-glucose\u003c/p\u003e\n\u003cp\u003eHOMA-IR homeostasis model assessment of insulin resistance\u003c/p\u003e\n\u003cp\u003eHOMA-β\u0026nbsp;homeostasis model assessment of\u0026nbsp;β-cell function\u003c/p\u003e\n\u003cp\u003eICD-10 International Statistical Classification of Disease,10th Revision\u003c/p\u003e\n\u003cp\u003eNHANES National Health and Nutrition Examination Survey\u003c/p\u003e\n\u003cp\u003eRCS restricted cubic spline\u003c/p\u003e\n\u003cp\u003eUS United States\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the NHANES Institutional Review Board and carried out according to the Declaration of Helsinki, with all NHANES participants signing informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed in this study were available from the NHANES database, https://www.cdc.gov/nchs/nhanes/\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Natural Science Foundation of China (No.82360072 to Q-L).\u003c/p\u003e\n\u003cp\u003eSecond Affiliated of Nanchang University Funded Clinical Research Projects (No.2022efyA03 to J.X-L).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL-L: Methodology, Software, Formal Analysis, Data Curation, Writing - Original Draft. L-H: Methodology, Investigation, Data curation., L-H: Methodology, Investigation, Data curation. H-S: Methodology, Investigation, Data curation. Z.W-S: Methodology, Investigation. W.X-W: Methodology, Investigation. Q-L: Methodology, Investigation, J.X-L: Conceptualization, Methodology, Writing - Review \u0026amp; Editing, Supervision, Project administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eVirani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, et al. Heart Disease and Stroke Statistics-2021 Update: A Report From the American Heart Association. Circulation. 2021;143(8):e254-e743.\u003c/li\u003e\n \u003cli\u003eSun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. 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Lipids in health and disease. 2020;19(1):225.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 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":"Cardiometabolic index, diabetes, prediabetes, All-cause mortality, Cardiovascular mortality","lastPublishedDoi":"10.21203/rs.3.rs-6525763/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6525763/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e As a relatively novel and integrated metric, the Cardiometabolic Index (CMI) has not been thoroughly explored for its utility in assessing mortality risk among patients with diabetes or prediabetes. This study focuses on analyzing the relationship between CMI and all-cause along with cardiovascular disease (CVD) mortality in older U.S. adults diagnosed with diabetes or prediabetes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe research encompassed 3,062 older adults diagnosed with diabetes or prediabetes, drawn from the National Health and Nutrition Examination Survey (NHANES) conducted between 1999 - 2018. The mortality results were ascertained by cross-referencing with the records of the National Death Index (NDI) up until December 31, 2019. Moreover, the DeLong test was utilized to validate the predictive capability of CMI relative to other cardiometabolic indices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eAcross a follow-up period averaging 85.44 months, 1,277 all-cause deaths were documented among patients with diabetes or prediabetes, including 445 occurrences of cardiovascular mortality. The analysis of threshold effects based on restricted cubic splines demonstrated a U-shaped, nonlinear association between CMI and all-cause as well as CVD mortality in individuals with diabetes or prediabetes. Notably, Interestingly, when the baseline CMI was below the cutoff values (1.12 for all-cause mortality and 1.04 for cardiovascular mortality), it showed a negative association solely with all-cause mortality (HR: 0.78, 95% CI: 0.63–0.95). However, exceeding these thresholds was significantly linked to a higher risk of both all-cause mortality (HR: 1.13, 95% CI: 1.03–1.24) and CVD mortality (HR: 1.19, 95% CI: 1.03–1.37).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e In older adults with diabetes or prediabetes, a U-shaped association was identified between initial CMI and both all-cause and CVD mortality, with respective thresholds of 1.12 and 1.04.\u003c/p\u003e","manuscriptTitle":"U-shaped associations between cardiometabolic index (CMI) and all-cause and cardiovascular mortality among elderly Americans with diabetes or prediabetes: NHANES 1999–2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-13 05:47:13","doi":"10.21203/rs.3.rs-6525763/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":"1799c49e-f7c8-4c94-94af-b5096746d9d4","owner":[],"postedDate":"June 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-08T09:08:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-13 05:47:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6525763","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6525763","identity":"rs-6525763","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}