Abstract
Introduction
Cardiovascular-Kidney-Metabolic (CKM) syndrome is common among cancer survivors, largely driven by overlapping risk factors and therapy-induced toxicities; however, its contribution to long-term mortality has not been clarified.
Methods
From the 1999–2018 National Health and Nutrition Examination Survey (NHANES) cycles, 2,274 cancer survivors with CKM syndrome stages 1–4 were included. Associations between CKM syndrome staging and long-term mortality were analyzed using weighted Cox regression, and further explored through subgroup and sensitivity analyses.
Results
During a median follow-up of 10.2 years, 854 deaths occurred (277 cancer-related, 224 cardiovascular). Higher CKM stages were associated with increased all-cause and cardiovascular mortality, but not cancer-specific mortality. Adjusted HRs (95% CIs) for all-cause mortality were 1.215 (95% CI: 0.757–1.950) for stage 2, 1.772 (95% CI: 1.089–2.885) for stage 3, and 2.560 (95% CI: 1.597–4.103) for stage 4; for cardiovascular mortality, HRs were 1.406 (95% CI: 0.582–3.393), 2.910 (95% CI: 1.108–7.644), and 5.960 (95% CI: 2.158–16.458).
Conclusion
Progression of CKM syndrome was linked to increased all-cause and cardiovascular mortality among cancer survivors, underscoring the need for early recognition and intervention.
Trial registration
Not applicable.
Supplementary Information
The online version contains supplementary material available at 10.1186/s40959-026-00450-w.
Keywords: Cardiovascular–kidney–metabolic syndrome, Cancer survivors, Mortality, National health and nutrition examination survey
Introduction
The American Heart Association (AHA) recently defined cardiovascular-kidney-metabolic (CKM) syndrome as a systemic disorder encompassing cardiovascular, renal, and metabolic dysfunctions [1]. Far from being rare, CKM syndrome is now nearly ubiquitous: recent U.S. data indicate that almost 90% of adults meet criteria for CKM stage 1 or higher, with approximately 15% progressing to advanced stages (stage 3–4) that confer substantially elevated morbidity risks [2]. Meanwhile, the prevalence of multimorbid CKM conditions (≥ 2 concurrent disorders) has risen markedly, from 5.3% in 1999 to 8.0% in 2020, and the coexistence of cardiovascular disease (CVD), chronic kidney disease (CKD), and diabetes currently affects 1.5% of U.S. adults [3].
While the escalating burden of CKM syndrome demands urgent attention, advances in oncology have simultaneously expanded the population of cancer survivors, which is projected to exceed 22.1 million worldwide by 2030 [4]. This growing population is increasingly burdened by chronic comorbidities, with CKM syndrome emerging as a pivotal concern [5]. Epidemiological evidence shows that nearly half of cancer survivors present with metabolic syndrome (45.44%), and almost one in five have CVD (19.23%)—two hallmark components of CKM syndrome and established predictors of therapy-related cardiotoxicity [6, 7].
This convergence raises critical questions about shared pathophysiology. Beyond treatment-related toxicity, CKM syndrome reflects endogenous mechanisms—including systemic inflammation, oxidative stress, and neurohormonal activation—that accelerate cardiac and renal dysfunction and drive a self-perpetuating cycle of metabolic deterioration [5, 8], These processes may also directly foster oncogenesis. Yet, despite plausible biological links, population-level evidence remains limited. Although preliminary findings suggest CKM-related mechanisms influence survivorship, the distribution of CKM stages and their prognostic relevance for all-cause, cardiovascular, and cancer-specific mortality in this population remain poorly defined. Most crucially, whether advanced CKM stages independently contribute to cancer-specific death—potentially mediated by the above mechanisms—has not been explored.
Therefore, this study aimed to delineate CKM stage distribution among U.S. cancer survivors and to determine its prognostic significance for mortality, with the ultimate goal of informing risk stratification and personalized survivorship care.
Method
Study population
This study utilized data from the 1999–2018 cycles of the National Health and Nutrition Examination Survey (NHANES). Cancer survivor status was defined as a self-reported history of cancer in the NHANES Medical Conditions questionnaire (MCQ220), which is administered to adults aged ≥ 20 years; therefore, pediatric cancer survivors were not captured. Given the low proportion of CKM stage 0 among participants with CKM staging available (n = 87/2,361) and the limited number of outcome events (cancer deaths: n = 5; cardiovascular deaths: n = 2; all-cause deaths: n = 11), using CKM stage 0 as the comparator could result in unstable regression estimates. Accordingly, our main analyses used CKM stage 1 as the reference group and were restricted to participants with CKM stages 1–4. Based on predefined inclusion and exclusion criteria (Fig. 1), a total of 2,274 eligible participants were included in the final analysis.
Fig. 1.
Flowchart of participants selection
Definitions of CKM syndrome stages
The staging of CKM syndrome followed the AHA Presidential Advisory Statement, adapted to variables available in NHANES as previously described (Supplementary Table S1) [2, 9]. Stage 0 included participants without any CKM-related health risk factors. Stage 1 included individuals with excessive or dysfunctional adiposity. Stage 2 comprised those with metabolic risk factors or moderate-to-high-risk chronic kidney disease (CKD). Stage 3 included participants with very-high-risk CKD or subclinical cardiovascular disease (CVD). Stage 4 referred to cases with a history of clinically diagnosed CVD based on self-report [9]. The definition of metabolic syndrome followed the guidelines of the National Cholesterol Education Program III [10]. CKD staging followed the Kidney Disease Improving Global Outcome criteria [11]. Ten-year CVD risk was estimated using the Framingham risk score, consistent with AHA recommendations [12].
Participants with CKM stage ≥ 1 were considered to have CKM syndrome. Those in stages 3–4 were classified as having advanced CKM syndrome, reflecting established or elevated CVD risk [2].
Assessment of mortality
Mortality status was ascertained by linking NHANES participants with the National Death Index (NDI), enabling follow-up through December 31, 2019. Causes of death were classified using the International Classification of Diseases, 10th Revision ༈ICD-10༉ and included both all-cause and cause-specific mortality, such as cardiovascular deaths (ICD-10: I00–I09, I11, I13, I20–I51, I60–I69) and cancer deaths (C00–C97). Follow-up duration was calculated from the date of the NHANES interview to the date of death or end of follow-up, whichever came first. Time windows are illustrated in Figure S1.
Covariates
Covariates were selected according to prior studies and included demographic, behavioral, comorbidity, and examination variables [13, 14]. Demographics covered age, sex, race/ethnicity, marital status, education level, and poverty income ratio (PIR). Behavioral factors comprised smoking status and alcohol consumption. Comorbidities included hypertension and diabetes, identified by self-report, medication use, or clinical criteria. Examination measures included body mass index (BMI). Detailed descriptions of questionnaires, protocols, and laboratory assessments are available in NHANES documentation.
Statistical analysis
All analyses were conducted in accordance with the complex, multistage sampling design of NHANES and the corresponding analytic guidelines. Figure S2 illustrates missing data patterns, with most covariates showing < 10% missingness. Missing values were imputed using random forest methods in the mice package of R (excluding exposure and outcome variables). Baseline characteristics across CKM stages were compared using weighted chi-square tests and t-tests. Continuous variables were summarized as weighted means with standard errors (SE), and categorical variables as weighted frequencies and percentages.
Weighted Cox proportional hazards models were used to assess the associations between CKM stages and all-cause and cause-specific mortality. Proportional hazards assumptions were tested using Schoenfeld residuals. Results are presented as hazard ratios (HRs) with 95% confidence intervals (CIs). Four models were constructed with stepwise adjustment for potential confounders: Model 1 was unadjusted; Model 2 adjusted for age, sex, and race; Model 3 additionally included marital status, educational attainment, and PIR; and Model 4 further incorporated alcohol consumption and smoking status. A trend test across exposure categories was conducted using integer scores (0, 1, 2, and 3). Kaplan-Meier (K-M) curves with log-rank tests compared survival across CKM stages.
Subgroup analyses were conducted to examine potential effect modification. Sensitivity analyses included using the non-imputed dataset, excluding participants with < 2 years of follow-up, and further adjustment for BMI, hypertension, and diabetes. Landmark analyses were prespecified at 24, 36, and 60 months to evaluate the association between CKM stage and long-term mortality. Finally, we applied the Fine-Gray subdistribution hazards model to account for competing risks and to examine the associations between CKM stages and long-term mortality among U.S. cancer survivors. All analyses were performed in R (version 4.4.1), with two-sided p < 0.05 considered significant.
Results
Baseline characteristics
Of the 2,274 cancer survivors analyzed, 57.03% were female (n = 1,198), with a mean age of 63.53 years (SE = 0.39). Table 1 summarizes demographic characteristics by CKM stage. Compared with those in the non-advanced stages, individuals in the advanced stages tended to be of older age, predominantly male, widowed/divorced/separated, with lower educational attainment and reduced income levels. Individuals in this group also showed higher proportions of former smokers, non-drinkers, and documented histories of hypertension and diabetes mellitus (all P < 0.05). Supplementary Table S2 presents participant characteristics according to CKM staging (stages 1–4).
Table 1.
Baseline characteristics of US cancer survivors according to the CKM syndrome (Non-advanced stages and advanced stages)
| Characteristics | Patients with CKM syndrome | |||
|---|---|---|---|---|
| Total (N = 2274) |
Non-advanced stages (N = 1289) |
Advanced stages (N = 985) |
P
value |
|
| Age (years) | 63.53(0.39) | 58.75(0.49) | 72.76(0.42) | < 0.001 |
| Age group (%) | < 0.001 | |||
| < 60 | 576(35.24) | 488(47.64) | 88(11.25) | |
| ≥ 60 | 1698(64.76) | 801(52.36) | 897(88.75) | |
| Sex (%) | < 0.001 | |||
| Male | 1076(42.97) | 507(37.82) | 569(52.92) | |
| Female | 1198(57.03) | 782(62.18) | 416(47.08) | |
| Race and Ethnicity (%) | 0.120 | |||
| Non-Hispanic White | 1554(85.33) | 832(85.59) | 722(84.84) | |
| Non-Hispanic Black | 311(5.65) | 173(5.04) | 138(6.84) | |
| Mexican American | 180(2.58) | 124(2.96) | 56(1.84) | |
| Other Race | 229(6.44) | 160(6.41) | 69(6.48) | |
| Marital status (%) | < 0.001 | |||
| Married/Living with partner | 1414(67.04) | 851(71.40) | 563(58.62) | |
| Never married | 118(4.50) | 83(5.51) | 35(2.55) | |
| Widowed/Divorced/Separated | 742(28.45) | 355(23.09) | 387(38.83) | |
| Education level (%) | < 0.001 | |||
| Less than high school graduate | 538(15.41) | 244(11.24) | 294(23.47) | |
| High school graduate or GED | 555(24.67) | 315(23.90) | 240(26.16) | |
| Some college or above | 1181(59.93) | 730(64.86) | 451(50.37) | |
| PIR, mean (SE) | 3.18(0.05) | 3.42(0.06) | 2.71(0.07) | < 0.001 |
| PIR, n (%) | < 0.001 | |||
| ≤ 1.3 | 560(16.47) | 296(13.80) | 264(21.63) | |
| 1.3∼3.5 | 937(38.02) | 471(33.15) | 466(47.42) | |
| > 3.5 | 777(45.52) | 522(53.05) | 255(30.96) | |
| Smoking (%) | 0.001 | |||
| Never | 1011(44.71) | 619(47.43) | 392(39.46) | |
| Ex-smoker | 929(39.31) | 470(35.74) | 459(46.23) | |
| Current-smoker | 334(15.97) | 200(16.83) | 134(14.31) | |
| Drinking (%) | 0.020 | |||
| No | 340(11.84) | 186(10.44) | 154(14.54) | |
| Yes | 1934(88.16) | 1103(89.56) | 831(85.46) | |
| BMI (kg/m2) | 29.10(0.15) | 29.25(0.21) | 28.80(0.26) | 0.220 |
| BMI category (%) | 0.980 | |||
| < 25 | 615(27.32) | 332(27.18) | 283(27.58) | |
| ≥ 25, < 30 | 821(35.43) | 465(35.55) | 356(35.20) | |
| ≥ 30 | 838(37.25) | 492(37.27) | 346(37.22) | |
| Hypertension, n (%) | < 0.001 | |||
| No | 747(37.92) | 540(46.09) | 207(22.13) | |
| Yes | 1527(62.08) | 749(53.91) | 778(77.87) | |
| Diabetes mellitus, n (%) | < 0.001 | |||
| No | 1568(74.41) | 1005(82.43) | 563(58.89) | |
| Yes | 706(25.59) | 284(17.57) | 422(41.11) | |
Data are shown as the weighted mean ± standard errors or proportion as appropriate
Figure 2 shows trends in CKM prevalence among U.S. cancer survivors aged ≥ 20 years from 1999 to 2000 to 2017–2018. Stage 1 prevalence ranged from 8.14% to 16.27%, stage 2 from 50.45% to 60.56%, stage 3 from 8.33% to 15.29%, and stage 4 from 18.58% to 27.51%. Stage 2 consistently had the highest prevalence, whereas stages 1 and 3 remained lower and relatively stable. Stage 2 thus accounted for the largest disease burden, while the increasing prevalence of stage 4 deserves attention.
Fig. 2.
Trends in prevalence of CKM syndrome stages among US cancer survivors aged 20 years or older, NHANES 1999–2018. Abbreviations: NHANES, National Health and Nutrition Examination Survey; CKM, cardiovascular-kidney-metabolic. Data were weighted to be nationally representative. Error bars indicate 95% CIs
CKM stages and all-cause mortality
The 25th, 50th (median), and 75th percentiles of follow-up duration were 43, 83, and 134 months, corresponding to 3.6, 6.9, and 11.2 years. 854 deaths occurred (294 in non-advanced and 560 in advanced CKM stages) (Table 2). K-M curves showed significant survival differences by CKM stage (Figure S3). In the fully adjusted model, HRs for all-cause mortality versus stage 1 were 1.215 (95% CI: 0.757–1.950) for stage 2, 1.772 (95% CI: 1.089–2.885) for stage 3, and 2.560 (95% CI: 1.597–4.103) for stage 4 (P for trend < 0.001) (Table 2; Fig. 3A). Advanced stages showed significantly higher mortality risk than non-advanced stages (HR = 1.922, 95% CI: 1.563–2.364; P < 0.001) (Table 2; Fig. 3A).
Table 2.
Association of CKM syndrome stage with all-cause mortality among US cancer survivors
| Variables | Event/no. | Model 1 | Model 2 | Model 3 | Model 4 | ||||
|---|---|---|---|---|---|---|---|---|---|
| HR (95%CI) | P value | HR (95%CI) | P value | HR (95%CI) | P value | HR (95%CI) | P value | ||
| All-cause mortality | 854/2274 | ||||||||
| CKM Syndrome stage | |||||||||
| Stage 1 | 31/205 | 1[Reference] | 1[Reference] | 1[Reference] | 1[Reference] | ||||
| Stage 2 | 263/1084 | 1.828(1.078, 3.099) | 0.025 | 1.247(0.776,2.004) | 0.361 | 1.204(0.749,1.933) | 0.443 | 1.215(0.757,1.950) | 0.421 |
| Stage 3 | 206/352 | 8.751(5.222,14.665) | < 0.001 | 1.971(1.200,3.236) | 0.007 | 1.839(1.126,3.004) | 0.015 | 1.772(1.089,2.885) | 0.021 |
| Stage 4 | 354/633 | 8.215(4.969,13.582) | < 0.001 | 2.908(1.788,4.729) | < 0.001 | 2.635(1.633,4.254) | < 0.001 | 2.560(1.597,4.103) | < 0.001 |
| P for trend | 0.061 | < 0.001 | < 0.001 | < 0.001 | |||||
| Non-advanced stages | 294/1289 | 1[Reference] | 1[Reference] | 1[Reference] | 1[Reference] | ||||
| Advanced stages | 560/985 | 4.993(4.149,6.010) | < 0.001 | 2.120(1.718,2.617) | < 0.001 | 1.998(1.622,2.462) | < 0.001 | 1.922(1.563,2.364) | < 0.001 |
Abbreviations: CKM Cardiovascular–kidney–metabolic syndrome, HR Hazard ratio, CI Confidence interval, PIR Poverty Income Ratio
Model 1 was unadjusted
Model 2 was adjusted for age, sex, and race/ethnicity
Model 3 was adjusted for age, sex, race/ethnicity, marital status, education level, and PIR
Model 4 was adjusted for age, sex, race/ethnicity, marital status, education level, PIR, alcohol intake, and smoking status
Fig. 3.
Hazard ratios (HR) and 95% confidence intervals (CI) for all-cause mortality A cardiovascular mortality B and cancer mortality C stratified by CKM syndrome stages among US cancer survivors aged 20 years or older, NHANES 1999–2018. Model 1 was unadjusted; Model 2 was adjusted for age, sex, and race/ethnicity; Model 3 was adjusted for age, sex, race/ethnicity, marital status, education level, and PIR; Model 4 was adjusted for age, sex, race/ethnicity, marital status, education level, PIR, alcohol intake, and smoking status
CKM stages and cardiovascular mortality
During follow-up, 224 cardiovascular deaths were recorded, including 47 in non-advanced and 177 in advanced CKM stages (Table 3). K-M curves showed significant survival differences by CKM stage (Figure S4). In the fully adjusted model, using stage 1 as reference, HRs for cardiovascular mortality were 1.406 (95% CI: 0.582–3.393) for stage 2, 2.910 (95% CI: 1.108–7.644) for stage 3, and 5.960 (95% CI: 2.158–16.458) for stage 4 (P for trend < 0.001) (Table 3; Fig. 3B). Compared with non-advanced stages, advanced CKM stages were linked to a substantially greater cardiovascular mortality risk (HR = 3.532, 95% CI: 2.171–5.746; P < 0.001), even after adjusting for demographic, socioeconomic, and lifestyle factors (Table 3; Fig. 3B).
Table 3.
Association of CKM syndrome stage with cardiovascular mortality among US cancer survivors
| Variables | Event/no. | Model 1 | P | Model 2 | P | Model 3 | P | Model 4 | P |
|---|---|---|---|---|---|---|---|---|---|
| HR (95%CI) | value | HR (95%CI) | value | HR (95%CI) | value | HR (95%CI) | value | ||
| Cardiovascular mortality | 224/2274 | ||||||||
| CKM Syndrome stage | |||||||||
| Stage 1 | 6/205 | 1[Reference] | 1[Reference] | 1[Reference] | 1[Reference] | ||||
| Stage 2 | 41/1084 | 2.572(1.050, 6.301) | 0.039 | 1.510(0.644, 3.544) | 0.343 | 1.380(0.570, 3.337) | 0.475 | 1.406(0.582, 3.393) | 0.449 |
| Stage 3 | 54/352 | 24.439(10.164,58.766) | < 0.001 | 3.379(1.324, 8.627) | 0.011 | 2.960(1.129, 7.763) | 0.027 | 2.910(1.108, 7.644) | 0.030 |
| Stage 4 | 123/633 | 27.438(11.078,67.954) | < 0.001 | 6.993(2.646,18.484) | < 0.001 | 5.883(2.124,16.296) | < 0.001 | 5.960(2.158,16.458) | < 0.001 |
| P for trend | 0.036 | < 0.001 | < 0.001 | < 0.001 | |||||
| Non-advanced stages | 47/1289 | 1[Reference] | 1[Reference] | 1[Reference] | 1[Reference] | ||||
| Advanced stages | 177/985 | 11.581(7.677,17.470) | < 0.001 | 3.892(2.365,6.406) | < 0.001 | 3.563(2.173,5.843) | < 0.001 | 3.532(2.171,5.746) | < 0.001 |
Abbreviations: CKM Cardiovascular–kidney–metabolic syndrome, HR Hazard ratio, CI Confidence interval, PIR Poverty Income Ratio
Model 1 was unadjusted
Model 2 was adjusted for age, sex, and race/ethnicity
Model 3 was adjusted for age, sex, race/ethnicity, marital status, education level, and PIR
Model 4 was adjusted for age, sex, race/ethnicity, marital status, education level, PIR, alcohol intake, and smoking status
CKM stages and cancer mortality
K-M curves showed significant differences in cancer survival across CKM stages (Figure S5). However, in the fully adjusted Cox model, no significant associations were found (Table 4; Fig. 3C). Compared with stage 1, HRs for cancer mortality were 0.677 (95% CI: 0.338–1.358) for stage 2, 0.803 (95% CI: 0.374–1.724) for stage 3, and 1.095 (95% CI: 0.536–2.241) for stage 4, with no significant trend (P = 0.182). Similarly, grouping stages into non-advanced versus advanced showed no significant association (HR = 1.409, 95% CI: 0.951–2.086; P = 0.087) (Table 4; Fig. 3C).
Table 4.
Association of CKM syndrome stage with cancer mortality among US cancer survivors
| Variables | Event/no. | Model 1 | P | Model 2 | P | Model 3 | P | Model 4 | P |
|---|---|---|---|---|---|---|---|---|---|
| HR (95%CI) | value | HR (95%CI) | value | HR (95%CI) | value | HR (95%CI) | value | ||
| Cancer mortality | 277/2274 | ||||||||
| CKM Syndrome stage | |||||||||
| Stage 1 | 17/205 | 1[Reference] | 1[Reference] | 1[Reference] | 1[Reference] | ||||
| Stage 2 | 99/1084 | 1.010(0.494,2.066) | 0.978 | 0.751(0.374,1.509) | 0.421 | 0.677(0.336,1.366) | 0.276 | 0.677(0.338,1.358) | 0.272 |
| Stage 3 | 53/352 | 3.150(1.526,6.502) | 0.002 | 0.984(0.446,2.169) | 0.968 | 0.806(0.369,1.762) | 0.589 | 0.803(0.374,1.724) | 0.574 |
| Stage 4 | 108/633 | 3.298(1.654,6.576) | < 0.001 | 1.412(0.667,2.989) | 0.367 | 1.118(0.536,2.332) | 0.766 | 1.095(0.536,2.241) | 0.803 |
| P for trend | 0.347 | 0.098 | 0.167 | 0.182 | |||||
| Non-advanced stages | 116/1289 | 1[Reference] | 1[Reference] | 1[Reference] | 1[Reference] | ||||
| Advanced stages | 161/985 | 3.223(2.371,4.382) | < 0.001 | 1.632(1.080,2.466) | 0.020 | 1.432(0.959,2.137) | 0.079 | 1.409(0.951,2.086) | 0.087 |
Abbreviations: CKM Cardiovascular–kidney–metabolic syndrome, HR Hazard ratio, CI Confidence interval, PIR Poverty Income Ratio
Model 1 was unadjusted
Model 2 was adjusted for age, sex, and race/ethnicity
Model 3 was adjusted for age, sex, race/ethnicity, marital status, education level, and PIR
Model 4 was adjusted for age, sex, race/ethnicity, marital status, education level, PIR, alcohol intake, and smoking status
Subgroup analyses
Subgroup analyses showed consistent associations across age and race/ethnicity (Table 5). The link between CKM stages and all-cause mortality was stronger in females (HR = 3.322, 95% CI: 2.498–4.418) than in males (HR = 1.987, 95% CI: 1.574–2.509), with significant sex interaction (P for interaction = 0.009). No significant interactions were found for cardiovascular mortality.
Table 5.
Stratified analysis of the association between CKM syndrome stage and all-cause mortality and cardiovascular mortality among US cancer survivors
| character | CKM Syndrome stage | |||
|---|---|---|---|---|
| Non-advanced stages | Advanced stages | P value | p for interaction | |
| All-cause mortality | ||||
| Age group | 0.067 | |||
| < 60 | 1[Reference] | 3.645(1.735,7.658) | < 0.001 | |
| ≥ 60 | 1[Reference] | 2.463(2.048,2.961) | < 0.001 | |
| Sex | 0.009 | |||
| Male | 1[Reference] | 1.987(1.574,2.509) | < 0.001 | |
| Female | 1[Reference] | 3.322(2.498,4.418) | < 0.001 | |
| Race and Ethnicity | 0.104 | |||
| Non-Hispanic White | 1[Reference] | 2.643(2.134,3.274) | < 0.001 | |
| Non-Hispanic Black | 1[Reference] | 2.392(1.454,3.934) | < 0.001 | |
| Mexican American | 1[Reference] | 1.703(0.756,3.839) | 0.199 | |
| Other Race | 1[Reference] | 1.158(0.573, 2.337) | 0.683 | |
| Cardiovascular mortality | ||||
| Age group | 0.209 | |||
| < 60 | 1[Reference] | 8.558(2.368,30.926) | 0.001 | |
| ≥ 60 | 1[Reference] | 5.480(3.609,8.322) | < 0.001 | |
| Sex | 0.139 | |||
| Male | 1[Reference] | 4.327(2.405, 7.786) | < 0.001 | |
| Female | 1[Reference] | 8.442(4.833,14.747) | < 0.001 | |
| Race and Ethnicity | 0.350 | |||
| Non-Hispanic White | 1[Reference] | 5.754(3.659, 9.047) | < 0.001 | |
| Non-Hispanic Black | 1[Reference] | 13.556(3.087,59.537) | < 0.001 | |
| Mexican American | 1[Reference] | 2.439(0.288,20.635) | 0.413 | |
| Other Race | 1[Reference] | 0.304(0.088,1.049) | 0.059 |
Each stratification was adjusted for age, sex, race/ethnicity, marital status, education level, PIR, alcohol intake, and smoking status, if not already stratified
Abbreviations: CKM Cardiovascular–kidney–metabolic syndrome, HR Hazard ratio, CI Confidence interval, PIR Poverty Income Ratio
Sensitivity analysis
Sensitivity analyses yielded similar results when using the non-imputed dataset (Supplementary Tables S3–S5), excluding participants with < 2 years of follow-up (Tables S6–S8), or additionally adjusting for BMI, hypertension, and diabetes (Tables S9–S11), confirming the robustness of the associations between CKM stages and increased all-cause and cardiovascular mortality (P for trend < 0.05). Landmark analyses at 24, 36, and 60 months (with time zero reset at each landmark and including only participants event-free up to that time point) showed directionally consistent post-landmark associations, further supporting the robustness of the findings for long-term outcomes (Supplementary Tables S12-S14). To address the small sample size and sparse events in CKM stage 0, we additionally combined CKM stages 0–1 as the reference group; the associations between higher CKM stages and mortality remained consistent with the primary analyses (Supplementary Tables S15-S17). The associations also remained robust in the Fine and Gray competing models (Supplementary Table S18).
Discussion
This nationally representative study of U.S. cancer survivors evaluated the relationship between CKM staging and mortality. Compared with stage 1, stage 3 was associated with a 77.2% higher all-cause mortality risk, and stage 4 with a 156.0% increase. For cardiovascular mortality, risks were 191.0% and 496.0% higher at stages 3 and 4, respectively. These findings indicate a progressive rise in mortality risk with advancing CKM stage, most evident in advanced stages (3–4). By contrast, no significant association was observed between CKM staging and cancer-specific mortality, suggesting limited prognostic value for this outcome.
Epidemiological evidence and clinical cohort studies consistently indicate that the cardiovascular risk is substantially elevated among cancer survivors [15–17]. Our findings reveal that CKM advanced stages were independently associated with increased risk of cardiovascular mortality among cancer survivors. Prior benchmarks substantiate this: cardiotoxic breast cancer therapies yield 15%−20% incident heart failure, whereas hematopoietic stem cell transplantation (HSCT) recipients exhibit 5–10-fold greater cardiovascular event incidence versus population controls [18, 19]. This risk amplification stems from synergistic damage between oncologic therapies and metabolic dysregulation.
The pathophysiology of this risk escalation is rooted in multidimensional CKM system injuries induced by anticancer treatments [20]. Immune checkpoint inhibitors (ICI) exacerbate atherosclerotic progression through induction of systemic inflammation and endothelial dysfunction [21, 22]. Importantly, a pooled analysis of 761 patients across seven studies demonstrates that cancer patients developing acute kidney injury (AKI) during ICI therapy face a 42% increased mortality risk (HR = 1.42, 95% CI:1.05–1.92) compared to non-AKI counterparts. Notably, unresolved AKI (HR = 2.93, 95% CI:1.41–6.08) confers triple the death risk, suggesting a mechanistic synergy between ICI-induced vascular toxicity and renal pathological processes in driving adverse outcomes [23]. Chemotherapeutic agents—exemplified by anthracyclines’ free radical-mediated cardiotoxicity and platinum derivatives’ renal tubular toxicity—directly induce chronic kidney dysfunction in 30–40% of patients [24]. Targeted therapies potentiate metabolic syndrome via insulin signaling disruption, particularly evident in androgen deprivation therapy (ADT) recipients who develop diabetes at 20–25% 5-year incidence rates [24].
Notably, oncologic interventions not only target organs directly but also amplify CKM risks through metabolic derangement cascades. Endocrine therapy-induced visceral adiposity (waist circumference increasing by 4–6 cm annually) and dyslipidemia (LDL-C elevations ≥ 30 mg/dL) [2], radiotherapy-mediated hypothalamic-pituitary axis disruption, and subsequent insulin resistance-hypertension-lipid triad synergistically propel metabolic syndrome development—ultimately afflicting 50% of breast cancer survivors and 30–50% of HSCT recipients during long-term follow-up [2]. When these acquired metabolic disorders intersect with the high baseline prevalence of CKM syndrome (25–40%) in the general population [24], cancer survivors experience accelerated CKM pathobiological progression, culminating in exponential increases in cardiovascular mortality.
CKM syndrome during the advanced stage is associated with advanced disease progression and a substantial increase in patients’ absolute risk of cardiovascular disease CVD [1]. Stage 3 is characterized by overlapping subclinical CVD with CKM risk factors or equivalent-risk metabolic abnormalities, while stage 4 requires management of established CVD alongside CKM risk factors. These stages mark the transition from subclinical status to overt CVD with concurrent kidney impairment, reflecting escalating disease complexity that disproportionately threatens patient survival. This progression partly explains our findings of a near tripling of all-cause mortality in cancer survivors with advanced CKM syndrome.
Specifically, a significant sex-based disparity was observed in the association between CKM stages and all-cause mortality (interaction P = 0.009). Adjusted hazard ratios indicated a significantly higher risk of all-cause mortality in females compared with males, with women showing approximately a two-fold increased risk. A representative U.S. cohort study spanning > 30 years observed significantly elevated mortality risk in women compared to men, partially supporting our findings [25]. This disparity may relate to more pronounced vascular dysfunction in females under metabolic stress [26] and their higher burden of nonfatal cardiovascular events [27, 28]. Mechanistically, XX-chromosome murine models demonstrate exacerbated hepatic steatosis, enhanced insulin resistance, and elevated circulating cholesterol levels relative to XY counterparts [29], indicating substantial X-chromosome dosage effects on obesity-related pathology.
Epidemiologic insights from China reveal critical context: CVD, type 2 diabetes, and chronic kidney disease (CKD) currently affect 23.4%, 11.2%, and 10.8% of the population, respectively [30, 31]. Notably, these conditions collectively dominate national mortality patterns, with CVD alone accounting for over 45% of all deaths [32]. The interconnected pathophysiology of CKM syndrome predisposes individuals to multimorbidity-driven mortality amplification. A national cohort study demonstrated that isolated diabetes and CKD confer 7.7% and 11.5% 10-year mortality risks, respectively. Mechanistically, their co-occurrence triggers synergistic mortality elevation, doubling the risk to 31.1% over the same period [30].
The observed dissociation between CKM stages and cancer-specific mortality risk may arise from multiple intertwining mechanisms. Advanced CKM patients exhibit elevated rates of cardiovascular/metabolic-related deaths (competing risks), potentially truncating follow-up duration and obscuring longitudinal observation of tumor-specific outcomes [33]. Furthermore, CKM-associated metabolic dysregulation (e.g., insulin resistance, chronic inflammation) exerts tumor-type-dependent biological effects [19, 34], paradoxically promoting malignancies such as colorectal carcinoma while potentially suppressing others like renal cell carcinoma, thereby neutralizing aggregate associations. Clinically, CKM comorbidities necessitate therapeutic modifications (dose-limiting restrictions on nephrotoxic agents or contraindication of targeted therapies) that confound survival analyses, compounded by attribution bias wherein multiorgan failure in terminal phases may be misclassified as cancer progression. These intersecting complexities underscore the necessity for multidimensional analyses incorporating tumor-specific stratification, competing risk models, and rigorous cause-of-death audits to disentangle the pathophysiological interplay between chronic metabolic disorders and cancer prognosis. To further address these issues, future studies should rigorously apply competing risk models to better characterize the impact of CKM syndrome on cancer-specific mortality. Additionally, incorporating cause-of-death audits may help clarify attribution bias. Such approaches could improve the interpretation of null results and provide a more nuanced understanding of the relationship between CKM syndrome and cancer prognosis, potentially informing improved clinical management strategies for cancer patients with concomitant metabolic dysfunction.
This study is the first to show that CKM staging, particularly advanced stages have prognostic value for all-cause and cardiovascular mortality in cancer survivors. Incorporating CKM assessment into follow-up may help identify high-risk individuals and guide personalized care. Despite these strengths, several limitations should be noted. First, CKM staging relied on fasting subsample measurements. The substantial missingness in fasting laboratory variables and the use of fasting-specific survey weights may introduce selection bias and limit generalizability; thus, the findings should be interpreted with caution. Second, CKM stage was assessed only at NHANES baseline, and longitudinal changes were not captured, which may attenuate risk estimation over extended follow-up. Third, as an observational analysis, residual confounding from unmeasured factors (e.g., physical activity, diet) cannot be excluded; moreover, the lack of detailed information on cancer diagnosis timing and treatment may further contribute to residual confounding. Accordingly, although the landmark analyses were directionally consistent with the primary results, the observed associations should not be interpreted as causal effects or post-diagnosis trajectories. Fourth, misclassification bias remains possible despite multivariable adjustment. Fifth, the relatively small number of cardiovascular deaths—especially in early CKM stages—reduced the precision of stage-specific estimates. Finally, due to limited sample size and sparse events within strata defined by cancer type and CKM stage, cancer-specific subgroup analyses were not adequately powered. Future large-scale, registry-based studies with more diverse populations and repeated CKM assessments are warranted to validate these findings and to clarify potential heterogeneity across cancer sites and treatments.
Conclusion
In conclusion, advancing CKM stages were independently linked to excess risks of all-cause and cardiovascular mortality among cancer survivors. Early recognition and integrated management of CKM syndrome should be prioritized to improve long-term outcomes in this growing population.
Supplementary Information
Acknowledgements
We appreciate all the efforts made by the staff of National Health and Nutrition Examination Survey (NHANES) in collecting and presenting data.
Declaration of generative AI and AI-assisted technologies in the writing process
No generative AI or AI-assisted technology was used in the preparation, analysis, or writing of this manuscript.
Abbreviations
- AHA
American Heart Association
- CI
Confidence intervals
- CKD
Chronic kidney disease
- CKM
Cardiovascular–kidney–metabolic
- CVD
Cardiovascular disease
- NHANES
National Health and Nutrition Examination Survey
- HR
Hazard ratios
- PIR
Poverty Income Ratio
- SE
Standard errors
Authors’ contributions
All authors read and approved the final manuscript.Jinghai Song: Conceptualization, Writing-Review, Editing and Supervision; Yangyang Zheng, and Ting Li: Methodology, Statistical analysis, Writing of Draft, Supervision, and Validation.All authors contributed to the interpretation of the results and critical revision of the manuscript for important intellectual content and approved the final version of the manuscript. Jinghai Song is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Funding
This research received no external funding.
Data availability
The survey data are publicly available on the internet for data users and researchers throughout the world (www.cdc.gov/nchs/nhanes/).
Declarations
Ethics approval and consent to participate
This study used anonymous data from the National Health and Nutrition Examination Survey and complied with the ethical guidelines and regulations of the Declaration of Helsinki. The study was approved by the National Center for Health Statistics Ethics Review Board, and all participants provided written informed consent before the study.
Consent for publication
Not Applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yangyang Zheng and Ting Li contributed equally to this work and share first authorship.
References
- 1.Ndumele CE, Neeland IJ, Tuttle KR, Chow SL, Mathew RO, Khan SS, et al. A synopsis of the evidence for the science and clinical management of Cardiovascular-Kidney-Metabolic (CKM) syndrome: a scientific statement from the American Heart Association. Circulation. 2023;148:1636–64. [DOI] [PubMed] [Google Scholar]
- 2.Aggarwal R, Ostrominski JW, Vaduganathan M. Prevalence of Cardiovascular-Kidney-Metabolic syndrome stages in US adults, 2011–2020. JAMA. 2024;331:1858–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Ostrominski JW, Arnold SV, Butler J, Fonarow GC, Hirsch JS, Palli SR, et al. Prevalence and overlap of cardiac, renal, and metabolic conditions in US adults, 1999–2020. JAMA Cardiol. 2023;8:1050–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Miller KD, Nogueira L, Mariotto AB, Rowland JH, Yabroff KR, Alfano CM, et al. Cancer treatment and survivorship statistics, 2019. CA Cancer J Clin. 2019;69:363–85. [DOI] [PubMed] [Google Scholar]
- 5.Quagliariello V, Berretta M, Bisceglia I, Giacobbe I, Iovine M, Barbato M, et al. In the era of cardiovascular-kidney-metabolic syndrome in cardio-oncology: from pathogenesis to prevention and therapy. Cancers (Basel). 2025. 10.3390/cancers17071169. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Liu AB, Zhang Y, Tian P, Meng TT, Chen JL, Zhang D, et al. Metabolic syndrome and cardiovascular disease among adult cancer patients: results from NHANES 2007–2018. BMC Public Health. 2024;24:2259. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Mauro C, Capone V, Cocchia R, Cademartiri F, Riccardi F, Arcopinto M, et al. Cardiovascular side effects of anthracyclines and HER2 inhibitors among patients with breast cancer: a multidisciplinary stepwise approach for prevention, early detection, and treatment. J Clin Med. 2023;12:2121. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Pugliese NR, Masi S, Taddei S. The renin-angiotensin-aldosterone system: a crossroad from arterial hypertension to heart failure. Heart Fail Rev. 2020;25:31–42. [DOI] [PubMed] [Google Scholar]
- 9.Ndumele CE, Rangaswami J, Chow SL, Neeland IJ, Tuttle KR, Khan SS, et al. Cardiovascular-Kidney-Metabolic health: A presidential advisory from the American heart association. Circulation. 2023;148:1606–35. [DOI] [PubMed] [Google Scholar]
- 10.Third Report of the National Cholesterol Education Program (NCEP). Expert panel on Detection, Evaluation, and treatment of high blood cholesterol in adults (Adult treatment panel III) final report. Circulation. 2002;106:3143–421. [PubMed] [Google Scholar]
- 11.Song L, Buggs V, Samara VA, Bahri S. Calculation of the estimated glomerular filtration rate using the 2021 CKD-EPI creatinine equation and whole blood creatinine values measured with radiometer ABL 827 FLEX. Clin Chem Lab Med. 2022;60:867–76. [DOI] [PubMed] [Google Scholar]
- 12.D’Agostino RB, Sr., Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General cardiovascular risk profile for use in primary care: the Framingham heart study. Circulation. 2008;117:743–53. [DOI] [PubMed] [Google Scholar]
- 13.Zhu R, Wang R, He J, Wang L, Chen H, Wang Y, et al. Associations of cardiovascular-kidney-metabolic syndrome stages with premature mortality and the role of social determinants of health. J Nutr Health Aging. 2025;29:100504. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Lin L, Hu Y, Lei F, Huang X, Zhang X, Sun T, et al. Cardiovascular health and cancer mortality: evidence from US NHANES and UK Biobank cohort studies. BMC Med. 2024;22:368. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Florido R, Daya NR, Ndumele CE, Koton S, Russell SD, Prizment A, et al. Cardiovascular disease risk among cancer survivors: the atherosclerosis risk in communities (ARIC) study. J Am Coll Cardiol. 2022;80:22–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Narayan HK, Narezkina A, Ehrhardt MJ. Second malignancies and cardiovascular disease in childhood cancer survivors: double trouble. JACC CardioOncol. 2023;5:804–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Howard E, Steingart RM, Armstrong GT, Lyon AR, Armenian SH, Teresa Voso M, et al. Cardiovascular events in cancer survivors. Semin Oncol. 2019;46:426–32. [DOI] [PubMed] [Google Scholar]
- 18.Ferdinand KC. An overview of cardiovascular-kidney-metabolic syndrome. Am J Manag Care. 2024;30:S181–8. [DOI] [PubMed] [Google Scholar]
- 19.Maurea N, Coppola C, Piscopo G, Galletta F, Riccio G, Esposito E, et al. Pathophysiology of cardiotoxicity from target therapy and angiogenesis inhibitors. J Cardiovasc Med (Hagerstown). 2016;17(Suppl 1):e19–26. [DOI] [PubMed] [Google Scholar]
- 20.Roth S, M’Pembele R, Matute P, Kotfis K, Larmann J, Lurati Buse G. Cardiovascular-kidney-metabolic syndrome: association with adverse events after major noncardiac surgery. Anesth Analg. 2024;139:679–81. [DOI] [PubMed] [Google Scholar]
- 21.Zarifa A, Lopez-Mattei J, Palaskas N, Iliescu C, Durand JB, Kim PY. Immune checkpoint inhibitors (ICIs)-related cardiotoxicity. Adv Exp Med Biol. 2020;1244:277–85. [DOI] [PubMed] [Google Scholar]
- 22.Kim ST, Chu Y, Misoi M, Suarez-Almazor ME, Tayar JH, Lu H, et al. Distinct molecular and immune hallmarks of inflammatory arthritis induced by immune checkpoint inhibitors for cancer therapy. Nat Commun. 2022;13:1970. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Kanbay M, Copur S, Siriopol D, Yildiz AB, Berkkan M, Popa R, et al. The association between acute kidney injury and outcomes in cancer patients receiving immune checkpoint inhibitor therapy: a systematic review and meta-analysis. Clin Kidney J. 2023;16:817–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Forte V, Pandey A, Abdelmessih R, Forte G, Whaley-Connell A, Sowers JR, et al. Obesity, diabetes, the cardiorenal syndrome, and risk for cancer. Cardiorenal Med. 2012;2:143–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Ji H, Sabanayagam C, Matsushita K, Cheng CY, Rim TH, Sheng B, et al. Sex Differences in Cardiovascular-Kidney-Metabolic Syndrome: 30-Year US Trends and Mortality Risks-Brief Report. Arterioscler Thromb Vasc Biol. 2025;45:157–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Ji H, Kim A, Ebinger JE, Niiranen TJ, Claggett BL, Merz CNB, et al. Cardiometabolic risk-related blood pressure trajectories differ by sex. Hypertension. 2020;75:e6-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Oh ES, You Z, Nowak KL, Jovanovich AJ. Sex differences in cardiovascular and all-cause mortality in adults with and without CKD: NHANES 1999–2018. Kidney360. 2023;4:1467–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Shajahan S, Amin J, Phillips JK, Hildreth CM. Relationship between sex and cardiovascular mortality in chronic kidney disease: a systematic review and meta-analysis. PLoS One. 2021;16:e0254554. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Arnold AP, Cassis LA, Eghbali M, Reue K, Sandberg K. Sex hormones and sex chromosomes cause sex differences in the development of cardiovascular diseases. Arterioscler Thromb Vasc Biol. 2017;37:746–56. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Wang Z, Ma L, Liu M, Fan J, Hu S. Summary of the 2022 report on cardiovascular health and diseases in China. Chin Med J (Engl). 2023;136:2899–908. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Clinical guidelines for prevention. And treatment of type 2 diabetes mellitus in the elderly in China (2022 edition). Zhonghua Nei Ke Za Zhi. 2022;61:12–50. [DOI] [PubMed] [Google Scholar]
- 32.Global burden. Of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019. Lancet. 2020;396:1223–49. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Li N, Li Y, Cui L, Shu R, Song H, Wang J, et al. Association between different stages of cardiovascular-kidney-metabolic syndrome and the risk of all-cause mortality. Atherosclerosis. 2024;397:118585. [DOI] [PubMed] [Google Scholar]
- 34.Rawat PS, Jaiswal A, Khurana A, Bhatti JS, Navik U. Doxorubicin-induced cardiotoxicity: an update on the molecular mechanism and novel therapeutic strategies for effective management. Biomed Pharmacother. 2021;139:111708. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The survey data are publicly available on the internet for data users and researchers throughout the world (www.cdc.gov/nchs/nhanes/).