https://orcid.org/0000-0001-8608-2219
Figures
Abstract
Background
The cardiometabolic index (CMI) has been utilized in recent years to detect patients with type 2 diabetes mellitus (T2DM). Given the consistent annual rise in the incidence of T2DM among young adults, the relationship between CMI and mortality risk among those with early-onset T2DM remains to be determined.
Methods
We enrolled 2,188 participants aged 20–65 with diabetes from the National Health and Nutrition Examination Survey (NHANES) 1999–2018. Weighted Cox proportional hazard regression models were employed to evaluate hazard ratios (HRs) with 95% confidence intervals (CIs). Restricted cubic spline (RCS) curves were utilized to assess linear associations. Stratified and interaction analyses were performed.
Results
We find that higher CMI is strongly associated with an increased risk of all-cause mortality (p = 0.005) and cardiovascular mortality (p = 0.020) in the strictest model. RCS analysis revealed a linear relationship between CMI and mortality. Subgroup and interaction analysis showed no statistical significance.
Citation: Li A, Zhou Z, Chen X, Liu Y, Ma Q (2026) Impact of cardiometabolic index on long-term mortality in young adults with type 2 diabetes mellitus. PLoS One 21(5): e0348952. https://doi.org/10.1371/journal.pone.0348952
Editor: Shukri AlSaif, Saud Al-Babtain Cardiac Centre, SAUDI ARABIA
Received: June 10, 2025; Accepted: April 23, 2026; Published: May 21, 2026
Copyright: © 2026 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All data utilized in the study are available from the NHANES database (https://www.cdc.gov/nchs/nhanes/index.html).
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
In recent years, the prevalence of diabetes mellitus (DM) has continued rising globally, presenting a formidable public health concern. According to estimates from the International Diabetes Federation (IDF), DM affected 536.6 million people, representing 10.5% of the population worldwide in 2021. Projections suggest that by 2045 this number will reach 783.2 million, accounting for 12.2% of the global population [1]. The Global Burden of Disease Study 2021 estimated that diabetes was responsible for as many as 1.7 million deaths globally in 2021 (1.57–1.79 million) [2]. Cardiovascular complications, including peripheral artery disease (PAD), coronary artery disease (CAD), heart failure (HF), and stroke, remain the leading causes of morbidity and mortality among individuals with DM [3]. These conditions are twice as prevalent in individuals with type 2 diabetes mellitus (T2DM) compared to their counterparts without the disease [4]. With the decreasing onset age of T2DM, its prevalence among young adults is rising steadily each year [5]. Early-onset T2DM exposes patients to prolonged metabolic derangements, which accelerate and hasten the progression of atherosclerosis, heart failure, and other cardiovascular conditions. Several studies have demonstrated that patients with young-onset type 2 diabetes (YOD) face a significantly greater risk of macrovascular complications compared to those with late-onset type 2 diabetes (LOD) or type 1 diabetes, as well as elevated mortality rates from all-causes and cardiovascular disease (CVD) [6–8]. The rising incidence of YOD presents a significant clinical challenge. Thus, identifying reliable biomarkers in young individuals with diabetes is crucial for reducing mortality rates and enhancing patient prognosis.
Patients with DM are often predisposed to obesity and dyslipidemia, which can further accelerate the progression of CVD. The Cardiometabolic Index (CMI) has recently emerged as an innovative marker for comprehensively assessing cardiometabolic risk by integrating two key indicators—waist-to-height ratio (WHtR) and triglycerides-to-high-density lipoprotein cholesterol (TG/HDL-C) ratio—which reflect central adiposity and lipid metabolism, both of which are closely linked to cardiovascular health [9,10]. CMI has been evaluated as a predictive marker across various conditions. For instance, it has been shown to be effective in identifying the existence and intensity of metabolic syndrome in adults with obesity [11]. Additionally, emerging research highlights CMI’s association with metabolic-associated fatty liver disease (MAFLD) [12] and hypertension [13]. Furthermore, elevated CMI has been positively correlated with all-cause mortality in the elderly, suggesting that it may function as a valuable prognostic indicator for poor outcomes in this population. Studies have demonstrated a significant relationship between elevated CMI and the development of diabetes [14], as well as its association with arteriosclerosis in patients with T2DM, further highlighting its value as a cardiovascular risk marker [15]. However, while CMI has been studied with diabetes, research on its potential as an indicator of all-cause and cardiovascular mortality, particularly among younger diabetic populations, remains limited. Given the growing prevalence of young-onset diabetes and the heightened cardiovascular risk in this group, it is crucial to explore whether CMI can function as a reliable predictor of mortality.
In this study, we utilized National Health and Nutrition Examination Survey (NHANES) data from younger diabetic patients to investigate the correlation between CMI and both all-cause and cardiovascular mortality. Our analysis aims to determine whether CMI serves as a reliable predictor of mortality in this population, offering a potential tool for risk stratification and personalized management in young diabetic patients.
Materials and methods
Source of data and study participants
The data for this prospective cohort study were collected from NHANES between 1999–2018 administered by the National Center for Health Statistics (NCHS) and the Centers for Disease Control and Prevention (CDC). The participants underwent comprehensive interviews, physical examinations, and laboratory assessments in specialized mobile centers, administered by skilled and experienced professionals and technical staff. This study encompassed participants (n = 101,316) from the NHANES 1999–2018 cohort, subsequently screening those aged 20–65 years who had available CMI levels. Subsequently, DM participants with missing data of follow-up information and any covariates in this study were excluded, resulting in a final sample size of 2188 individuals (Fig 1). Each participant provided written informed consent for participation in the program, thus additional written consent was not required for further analysis.
Abbreviation: NHANES: National Health and Nutrition Examination Survey; CMI: cardiometabolic index.
Determination of all-cause and cardiovascular mortality
In this study, we assessed outcomes encompassing both all-cause mortality and CVD mortality. Data on all-cause mortality were obtained by probabilistically linking NHANES records to the National Death Index (NDI) documentation. Supplementary mortality surveillance data were sourced from the US Social Security Administration, the Centers for Medicare and Medicaid Services, and official death certificates. The cause of death was classified using standardized codes from the International Classification of Diseases, Tenth Revision (ICD-10). Mortality attributable to heart disease including coronary heart disease (CHD), angina, congestive heart failure (CHF), cardiac arrest, and stroke was identified by ICD-10 codes I00-I09, I11, I13, and I20–I51. The follow-up period was defined as the interval extending from the date of the examination to either the date of death or the end of the follow-up period, which concluded on December 31, 2019.
Definition of diabetes
Diabetes was diagnosed based on one or more of the following criteria: i. fasting plasma glucose (FPG) levels ≥7 mmol/L (126 mg/dL); ii. glycated hemoglobin A1c (HbA1c) ≥6.5%; iii. a 2-hour blood glucose level ≥200 mg/dL from an oral glucose tolerance test (OGTT). Additionally, a self-reported diagnosis or the use of insulin or oral hypoglycemic agents was also considered indicative of diabetes [16].
Assessment of the Cardiometabolic Index
To compute the CMI, the following formula was employed: , where TG refers to triglycerides, HDL-C denotes high-density lipoprotein cholesterol, and WHtR represents the waist circumference to height ratio. In our study, CMI was treated as an exposure variable, and all participants were stratified into high or low CMI groups based on their CMI values for subsequent analyses
Covariates
In the present study, we considered essential covariates including the following: age, sex (female/male), race (Mexican American/Non-Hispanic Black/Non-Hispanic White/Other Hispanic/ Others), education levels (under high school/ high school or equivalent/above high school), smoking status (never/former/current), drinking status (no/low-to-moderate/heavy), body mass index (BMI), waist circumference, WHtR, total cholesterol (TC), TG, estimated glomerular filtration rate (eGFR), low-density lipoprotein cholesterol (LDL), HDL, and disease status (hypertension/CVD). BMI was calculated by dividing weight (measured in kilograms) by the square of height (expressed in meters). Participants were categorized into categories of normal weight (BMI < 25 kg/m²), overweight (BMI ranging from 25 to 30 kg/m²), or obese (BMI ≥ 30 kg/m²). Hypertension was defined by the following clinical criteria: three consecutive measurements of systolic blood pressure surpassing 140 mmHg or diastolic blood pressure exceeding 90 mmHg, a clinical diagnosis of hypertension, or employment of antihypertensive pharmaceuticals.
Statistical analysis
All statistical analyses took into account the complex survey design of NHANES, incorporating sample weights, clustering, and stratification. In this study, participant characteristics at baseline were presented as means with standard deviations (SD) for continuous variables, and as counts with weighted percentages for categorical variables. The enrolled participants were stratified into two categories according to the optimal cutoff of the CMI and intergroup analyses were performed. Where applicable, weighted chi-square tests and Kruskal-Wallis tests were utilized to assess the distribution of fundamental characteristics between the different CMI groups. The survey-weighted univariable and multivariable Cox proportional hazards regression models were employed to evaluate the hazard ratios (HRs) with 95% confidence intervals (CIs) for all-cause and CVD risk concerning CMI. Three models were employed, model 1 adjusted none; model 2 adjusted by age, sex, and ethnicity; model 3 additionally adjusted by education level, smoking status, drinking status, weight, TC, eGFR, hypertension and CVD based on model 2. Kaplan-Meier curves and inter-group comparisons by log-rank tests demonstrated survival outcomes. Restricted cubic spline (RCS) analysis was employed to examine the potential nonlinear relationships. Stratified and interaction analyses concerned covariates contained age, sex, ethnicity, education level, smoking status, drinking status, weight, TC, eGFR, hypertension and CVD. In addition, Spearman correlation coefficients were calculated to assess the correlations among CMI and its components and metabolic markers (fasting glucose, HbA1c, fasting insulin). Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were employed to predict the performance of CMI, its components (TG/HDL, WHtR), and metabolic markers (fasting glucose, HbA1c, fasting insulin) for all-cause events and CVD events. Differences in AUC between CMI and comparator markers were tested using DeLong’s test (for correlated ROC curves). All statistical analyses were employed by R (version 4.3.1), with a two-tailed p-value of less than 0.05 regarded as statistically significant.
Results
Baseline Characteristics
This study included 2,188 participants from a young diabetic population, representing an estimated 17,955,290 young individuals with diabetes across the United States. Using an optimal CMI cutoff of 1.84, determined by maximally selected rank statistics for the strongest survival association, participants were divided into two groups: a higher CMI group (CMI > 1.84, n = 366) and a lower CMI group (CMI ≤ 1.84, n = 1822) (Fig 2). Compared to the lower CMI group, participants in the higher CMI group were predominantly male, had a greater proportion of Mexican Americans, and exhibited lower LDL and HDL levels, alongside higher BMI, WC, WHtR, TC, TG, HbA1c, and fasting glucose levels. Additionally, this group had a higher prevalence of hypertension and CVD. Further characteristics of the participants are detailed in Table 1.
The cut-off point of CMI was calculated using the maximally selected rank statistics based on the ‘maxstat’ package. Abbreviation: CMI: cardiometabolic index.
Associations of the CMI with all‑cause and cardiovascular mortality
Over a mean follow-up duration of 8.62 years, 294 of the 2188 young diabetic patients (13.4%) succumbed. Of these, 84 (3.84%) were attributed to cardiovascular causes. Using a weighted multivariable Cox regression model, we examined the correlation between CMI and all-cause and cardiovascular mortality. In the unadjusted analysis, individuals with higher CMI had a significantly elevated risk of all-cause mortality (HR = 1.91, p < 0.001) and cardiovascular mortality (HR = 2.28, p = 0.002). The association remained robust after multivariate adjustments in both Model 2 (HR = 1.96, p < 0.001) and Model 3 (HR = 1.67, p = 0.005) for all-cause mortality and (HR = 2.34, p = 0.002) (HR = 2.03, p = 0.020) for cardiovascular mortality (Table 2). In addition, Kaplan‒Meier survival analysis revealed notable disparities in all-cause mortality rates between the groups with higher and lower CMI levels (p = 0.0048), with the higher-CMI cohort exhibiting a notably reduced survival rate (Fig 3A). RCS analysis revealed a linear relationship between CMI and all-cause (p for non-linearity = 0.219) and cardiovascular mortality (p for non-linearity = 0.186) (Fig 3B and 3C). Furthermore, the Spearman correlation coefficients between CMI and TG/HDL revealed a high correlation (0.99) (S1 Fig.). RCS analysis revealed a non-linear relationship between TG/HDL, WHtR, glucose and all-cause and cardiovascular mortality (all p for non-linearity < 0.05), and a linear relationship between HbA1c, fast insulin and all-cause mortality (all p for non-linearity > 0.05) (S2 Fig.). Moreover, after adjustment, the association between glucose, HbA1c, fast insulin and all-cause mortality was statistically significant (HR = 1.004, p < 0.001; HR = 1.142, p < 0.001; HR = 1.003, p = 0.033), and the association between glucose and CVD mortality was statistically significant (HR = 1.003, p = 0.003). The association between WHtR, TG/HDL and all-cause mortality and CVD mortality was not statistically significant (S1 Table). In addition, the AUC of CMI and the other biomarkers in all-cause mortality and CVD mortality were demonstrated in S3 Fig. and S2 Table.
Kaplan–Meier analysis of all-cause mortality (A) based on CMI groups among young individuals with diabetes; Restricted cubic spline analysis between CMI and the risk of all-cause mortality (B) and CVD mortality (C) in young participants with diabetes. Abbreviation: CMI: cardiometabolic index; CVD: cardiovascular disease.
Subgroup analysis
Subgroup analyses were conducted to assess the associations between CMI and all-cause and cardiovascular mortality (Fig 4). The results found that being male, obese, never smoking, a non-drinker or low-to-moderate alcohol drinker, and having hypertension were more strongly associated with an increased risk of all-cause mortality. Furthermore, being male, never smoking, being a low-to-moderate alcohol drinker, and having hypertension were more strongly associated with an elevated risk of cardiovascular mortality. Importantly, no significant interactions were detected between CMI and any of the stratification variables.
Stratified analyses of the associations between CMI and all-cause mortality and CVD mortality among young individuals with diabetes. Abbreviation: CMI: cardiometabolic index; CVD: cardiovascular disease.
Discussion
In the present study, we explored the association of CMI with CVD and mortality in a cohort of young individuals with diabetes, utilizing NHANES data spanning from 1999 to 2018. Our findings indicate that a higher CMI is strongly associated with an increased risk of all-cause and cardiovascular mortality, even after adjusting for multiple confounding factors. The linear relationship between CMI and mortality, as revealed by RCS analysis, suggests that even modest increases in CMI are associated with heightened mortality risk. Subgroup analysis uncovered that the relationship between elevated CMI and mortality risk was stronger among male participants, individuals with obesity, non-smokers, and those with hypertension. The Cox regression results and AUC results confirm that CMI outperforms its anthropometric component (WHtR) and insulin, and is non-inferior to glucose/HbA1c, supporting its utility as a composite cardiometabolic marker. The statistically significant relationships suggest that CMI may be a valuable relevant marker for overall mortality even specifically for cardiovascular outcomes in this population.
The global of young individuals with T2DM was increasing annually. High energy intake, obesity, a sedentary lifestyle, and decreased moderate to high-intensity training led to the rising tendency [17]. Reduced physical activity and a sedentary lifestyle are correlated with an elevated risk of long-term health outcomes, including all-cause mortality and the incidence of cardiovascular disease in individuals with T2DM [18]. This decline in physical engagement may lead to increased insulin resistance and the promotion of low-grade systemic inflammation, both of which could significantly contribute to these adverse health outcomes [19–21]. Current evidence suggests a heightened risk of cardiovascular complications, adverse pregnancy outcomes, and premature mortality among young individuals with T2DM [22–24]. The precise mechanisms by which diabetes contributes to mortality remain shrouded in uncertainty, however, insulin resistance may play a pivotal role in diabetic individuals’ fatality. Insulin resistance is a pathophysiological condition defined as the diminished ability of insulin to optimally facilitate the transport of glucose into bodily cells, which results in reduced insulin bioavailability and impaired glucose uptake, ultimately leading to hyperinsulinemia [25]. Consequently, hyperinsulinemia further triggers inflammatory responses, evidenced by elevated levels of pro-inflammatory cytokines such as nuclear factor kappa B (NF-κB), tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and interleukin-8 (IL-8) [26–28]. Moreover, insulin enhances the production of endothelial nitric oxide by activating the phosphatidylinositol 3-kinase pathway, thereby inducing vasodilation. In states of insulin resistance, this pathway is compromised, leading to diminished nitric oxide levels and subsequent stimulation of the mitogen-activated protein kinase pathway, which facilitates vasoconstriction [29]. The production of these factors subsequently promotes smooth muscle cell proliferation and collagen deposition, ultimately resulting in vascular arterial stiffness, which promotes the development of cardiovascular disease [25,30]. It is consequential to employ a predictive metric to ascertain the all-cause even cardiovascular risk for these young diabetics, thereby mitigating the likelihood of early-onset complications.
The CMI consists of the waist-to-height ratio and lipid profiles as triglycerides-to-HDL cholesterol ratio, which was initially innovated to identify diabetes mellitus. The waist-to-height ratio serves as a more effective screening instrument compared to waist circumference and BMI for assessing cardiometabolic risk factors in adults [9]. The ratio of triglycerides to HDL cholesterol functions effectively as a screening tool for detecting impaired glucose tolerance [31], the higher ratio correlated with elevated insulin resistance and increased risk of coronary heart disease (CHD), cardiovascular disease (CVD) in obese patients with T2DM [32]. CMI comprehensively presents central adiposity and lipid metabolism, tightly associated with cardiovascular health [14]. A national cohort study demonstrated elevated levels of CMI with a remarkably elevated risk of new-onset T2DM (HR = 1.78) in middle-aged and elderly, as those with lower levels of CMI changed to higher levels during follow-up time with the risk of T2DM increased by 75% [33]. Moreover, CMI is not only correlated to elevated risk of T2DM but also tightly linked with other metabolic diseases. The positive correlation found in CMI with systemic inflammatory status among the obesity group contrasts with the normal weight group in older men (P < 0.0001) [34]. Consistent investigation was reported by Xu et al. that CMI was positively associated with long-term health outcomes as all-cause mortality (HR = 1.11) in the elderly, additionally, inflammation indicators such as leukocytes and neutrophils mediated the association [35]. Zou et al. investigated the CMI-related risk of non-alcoholic fatty liver disease (NAFLD) among young individuals (OR = 2.63) considerably higher contrasts with young and middle-aged (OR = 1.38), middle-aged and elderly (OR = 1.18), and elderly (OR = 1.14) [36]. Population-based cohort studies showed that the lower CMI group was younger than the higher group. After strict adjustment, the significant positive correlation remained to verify the comprehensiveness and applicability of CMI [36,37]. Our findings suggest that CMI may be particularly useful for identifying at-risk individuals who were obese, non-smokers, or had hypertension inspired by subgroup analysis, further emphasizing the importance of personalized risk assessment in clinical practice and evaluating long-term health for individuals. Massive studies emphasized that increased CMI levels are related to deteriorated prognosis, the potential mechanisms need to be discovered by further advanced research.
Given the high correlation between CMI and the triglyceride-to-HDL cholesterol (TG/HDL-C) ratio observed in our study, and their comparable AUC values for mortality prediction, a legitimate question arises regarding the practical advantage of the more complex CMI formula. We acknowledge that TG/HDL-C is a simpler and well-established surrogate for insulin resistance. Nevertheless, CMI offers several distinct advantages from a clinical and pathophysiological perspective. First, CMI explicitly incorporates waist-to-height ratio (WHtR), a direct measure of central adiposity that is independent of lipid metabolism. Central obesity is not merely a proxy for dyslipidemia; it contributes to mortality through adipokine dysregulation, chronic low-grade inflammation, and mechanical effects on cardiovascular function—mechanisms that are incompletely captured by TG/HDL-C alone. For instance, visceral fat accumulation drives adipokine dysregulation, which directly impairs metabolic homeostasis and promotes insulin resistance [38,39]. Moreover, central obesity is a state of chronic low-grade inflammation. Visceral adipose tissue serves as an active compartment for secreting pro-inflammatory molecules [40]. Furthermore, central obesity exerts direct mechanical effects on the cardiovascular system. Excess visceral fat increases total peripheral resistance and cardiac workload, promotes adverse cardiac remodeling, and accelerates arterial stiffening independent of metabolic factors [41–43]. In young individuals with type 2 diabetes, visceral adiposity often precedes overt dyslipidemia, and an isolated elevation in WHtR with normal TG/HDL-C may still confer excess risk [44–46]. Second, our subgroup analyses revealed that CMI exhibited stronger associations with mortality in certain populations (e.g., non-smokers, hypertensive individuals) compared to TG/HDL-C (S4 Fig.), suggesting that the inclusion of WHtR may capture obesity-related risk that is not fully reflected by lipid ratios in specific clinical contexts [47]. Third, the two-component structure of CMI (WHtR and TG/HDL-C) allows clinicians to disentangle the relative contributions of adiposity versus lipid metabolism to an individua’s risk profile, thereby guiding more targeted interventions—e.g., lifestyle modification for elevated WHtR versus lipid-lowering therapy for elevated TG/HDL-C [12,48,49]. In contrast, TG/HDL-C as a single composite metric provides no such decomposition. Therefore, CMI offers enhanced granularity for personalized risk assessment and mechanistic understanding, particularly in young diabetic patients where central obesity is a dominant and modifiable driver of cardiometabolic risk.
The strengths of this study are the employment of representative and weighted NHANES data for generalizability, and we provide evidence of CMI positivity related to all-cause and cardiovascular mortality in young individuals with diabetes. Our findings also emphasize the clinical implications of CMI. Given the rising prevalence of young-onset diabetes, early identification of individuals at higher risk of mortality is critical for implementing timely interventions. CMI, which is a simple, non-invasive metric derived from commonly measured clinical parameters, could serve as a valuable tool for risk stratification in young diabetic patients. Subsequent research endeavors should focus on confirming these results within broader and more varied demographic groups, as well as investigating the possible mechanisms underlying the differential associations between CMI and mortality outcomes. Additionally, the limitations of this study are the following: firstly, unmeasured confounding factors exist despite adjusting for some covariates; secondly, causality could not be confirmed in this study due to the observational nature of cohort studies.
Conclusion
In this prospective population-based study, we observed significant findings that the higher CMI level correlated with mortality risk among young individuals with diabetes mellitus and served as a comprehensive and effective prognostic indicator for long-term health.
Supporting information
S1 Fig. The Spearman correlation coefficients among CMI, TG/HDL, WHtR, fast glucose, HbA1c, and fast insulin.
https://doi.org/10.1371/journal.pone.0348952.s001
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S2 Fig. The Restricted cubic spline analysis between TG/HDL, WHtR, fast glucose, HbA1c, fast insulin and the risk of all-cause mortality and CVD mortality in participants with diabetes.
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S3 Fig. Receiver operating characteristic (ROC) curves of CMI and other metabolic indicators for predicting mortality risk.
https://doi.org/10.1371/journal.pone.0348952.s003
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S4 Fig. Stratified analyses of the associations between TG/HDL and all-cause mortality and CVD mortality among young individuals with diabetes.
https://doi.org/10.1371/journal.pone.0348952.s004
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S1 Table. Association between TG/HDL, WHtR, glucose, HbA1c, fast insulin and all-cause mortality and CVD mortality in T2DM populations.
https://doi.org/10.1371/journal.pone.0348952.s005
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S2 Table. AUC of TG/HDL, WHtR, glucose, HbA1c, fast insulin and all-cause mortality and CVD mortality in T2DM populations.
https://doi.org/10.1371/journal.pone.0348952.s006
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Acknowledgments
We thank all NHANES participants and crew for their precious endeavors and dedication. We are grateful for technical support from the Center for Computational Biology and Bioinformatics, Furong Laboratory and Bioinformatics Center, Xiangya Hospital, Central South University.
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