Correlation between overall lifestyle score and advanced stages among patients with cardiovascular–kidney–metabolic syndrome: NHANES 2011–2020

Abstract

Background

Cardiovascular–kidney–metabolic (CKM) syndrome, a new multistage disorder recently introduced by the American Heart Association, underscores the intricate connection between cardiovascular, renal, and metabolic diseases. Since maintaining a healthy lifestyle is a reliable way to promote regression of CKM stages, we aimed to examine the associations between overall lifestyle scores and advanced CKM stages in affected patients.

Methods

This cross-sectional study used nationally representative data from five National Health and Nutrition Examination Survey cycles (2011–2020) among adults aged 20 years or older. The overall lifestyle score was constructed based on four factors: never smoking, no heavy drinking, sufficient leisure-time physical activity, and a healthy diet. Participants were divided into five CKM stages (0–4) based on the clinical severity, with stages 3 and 4 considered advanced. Multivariable weighted logistic regression was used to explore the relationship between overall lifestyle score and advanced CKM stages.

Results

A total of 18,664 patients with CKM syndrome were included in the final analysis, of whom 3073 were classified as having advanced CKM syndrome, and 15,591 were not. After adjusting for potential confounders, compared with participants with an unfavorable overall lifestyle score (0–1 healthy lifestyle factors), patients with CKM syndrome with intermediate (2 healthy lifestyle factors) and favorable (3–4 healthy lifestyle factors) overall lifestyle scores had a 31% and 44% lower risk of being in advanced stages, respectively. Further subgroup and sensitivity analyses yielded consistent results.

Conclusions

Patients with CKM syndrome who had a favorable and intermediate overall lifestyle score were significantly less likely to be in advanced stages of the disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40001-025-03339-z.

Introduction

Non-communicable diseases (NCDs), primarily including cardiovascular disease (CVD), chronic kidney disease (CKD), chronic respiratory diseases, cancers, and diabetes, are the leading cause of morbidity and mortality globally. The Global Burden of Disease Study reported that the proportion of deaths attributable to NCDs elevated from 56.8% in 1990 to 74.4% in 2019. Over the same period, the proportion of disability-adjusted life years due to NCDs rose from 37.8% to 66.0% [1]. The cardiovascular, kidney, and metabolic systems are deeply interconnected, with dysfunction in one often exacerbating disorders in the others [2]. For instance, diabetes and CKD are strongly linked to increased cardiovascular mortality [3, 4]. Given these complex interactions, a holistic approach to CVD prevention that also incorporates kidney and metabolic health is essential.

In response to the substantial burden of poor cardiovascular–kidney–metabolic (CKM) health, the American Heart Association (AHA) issued a presidential advisory in 2023 defining CKM syndrome. This syndrome reflects the overlapping pathophysiology and clinical outcomes of these disorders [5]. Regarding risk factors and established disease, this advisory proposed a new model that classifies CKM syndrome into five stages, ranging from stage 0 (no risk factors present) to stage 4 (clinical CVD). Recent research indicates that CKM syndrome affects a large portion of U.S. adults, with 90% classified as (stage 1 or higher), and 15% diagnosed with advanced stages (stage 3 or 4) [6]. Moreover, higher CKM stages have been associated with an increased risk of all-cause mortality [7]. The poor CKM syndrome imposes a significant economic and social burden, underscoring the urgent need for effective strategies to slow its progression.

Modifiable lifestyle factors, including smoking, alcohol drinking, physical activity, and diet quality, play a crucial role in the development and progression of CVD, CKD, and diabetes [8–11]. Studies have indicated that cigarette smoking, alcohol misuse, physical inactivity, and poor diet contribute up to 60% of premature deaths [10, 12, 13]. However, most existing studies have focused on individual lifestyle components or single diseases, lacking a holistic perspective that reflects the interconnected nature of CKM syndrome. Lifestyle factors often cluster together, and their combined effects can be more detrimental to health than the sum of their individual effects [14]. In its presidential advisory, the AHA underscored a healthy lifestyle approach to prevent the progression of CKM syndrome [5]. However, evidence regarding the combined impact of a healthy lifestyle score on CKM syndrome remains limited. While seminal work has begun to use composite metrics like the AHA’s Life’s Essential 8 in CKM syndrome populations [15], the specific role of a focused lifestyle score encompassing core modifiable behaviors—smoking, alcohol, physical activity, and diet—in relation to advanced CKM stages warrants further investigation in contemporary, nationally representative samples. Clarifying this relationship may have important individual, clinical, and public health implications.

Consequently, in this study, we investigated the association between an overall lifestyle score—incorporating smoking, alcohol consumption, physical activity, and diet—with advanced stages of CKM syndrome. We aimed to offer evidence supporting the adoption of multidimensional lifestyle interventions as a unified strategy to decrease the combined burden of cardiovascular, kidney, and metabolic diseases. While prior research has demonstrated the benefits of healthy lifestyle behaviors for cardiometabolic and kidney outcomes, few studies have examined the role of an overall lifestyle score in the context of advanced CKM syndrome, a high-risk state characterized by concurrent cardiovascular, kidney, and metabolic dysfunction. This study will examine the relationship between an overall lifestyle score and advanced stages of CKM syndrome in a nationally representative cohort of adults in the U.S.

Methods

Study population

The National Health and Nutrition Examination Survey (NHANES) is a series of cross-sectional surveys conducted by the Centers for Disease Control and Prevention. It provides nationally representative information focused on population health and nutritional status in the U.S. using a stratified, multistage probability cluster design sampling methodology. Data collection involved in-home interviews on basic health and demographics, followed by in-depth examinations at mobile examination centers. Details about NHANES are available on the NHANES website [16], including the protocol and data collection procedures. All NHANES procedures were approved by the National Center for Health Statistics' Ethics Committee, and all participants provided written informed consent. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. The completed STROBE checklist is provided as Supplementary File 1.

The continuous NHANES began in 1999 and has collected data biennially since then. For CKM syndrome stages 0 and 1, lower body mass index (BMI) and waist circumference cutoffs are recommended for Asian populations, given their predisposition to developing metabolic abnormalities at lower levels of adiposity [17]. Considering these race-specific anthropometric criteria for stages 0 and 1 and the availability of a dedicated Non-Hispanic Asian category only from 2011 to 2012 onward, we used data on adults in the U.S aged 20 years and older in NHANES 2011–2020 for the present study. Participants were further excluded based on the following criteria: (1) a pregnancy at baseline (n = 336); (2) missing information on demographics information (marital status, household income, and education attainment; n = 3768); (3) missing data on lifestyle factors (smoking status, drinking status, physical activity, and diet quality; n = 3994); and (4) missing information on CKM syndrome indicators (n = 2639). Finally, 21,112 adults were included in this study. The flowchart for participant enrollment is illustrated in Fig. 1.

Fig. 1.

Screening flow of participants. CKM: cardiovascular–kidney–metabolic; NHANES: National Health and Nutrition Examination Survey

Assessment of lifestyle factors

Multiple lifestyle factors are inter-related. In line with prior studies conducted in the NHANES [18, 19] and other large-scale population-based studies [9, 20], we considered four lifestyle factors, including smoking status, alcohol status, physical activity, and diet quality, to generate an overall lifestyle score. These lifestyle factors were consistent with recommendations from the World Health Organization (WHO) [21]. We selected these four factors for our primary score, because they represent well-established, core modifiable risk factors with standardized measurements, allowing for clear interpretation and direct comparison with a large body of existing literature. Information on participant lifestyles was obtained through self-reported questionnaires. Never smoking was considered a healthy behavior, defined in the questionnaire as smoking fewer than 100 cigarettes over a lifetime [19]. A healthy level of alcohol consumption was defined as daily consumption of no more than one drink per day for women and two drinks per day for men. Based on the dietary guidelines in the U.S., one drink is defined as 14 g of ethanol [22]. According to the 2018 Physical Activity Guidelines for Americans, 2nd Edition [23], sufficient physical activity is defined as at least 150 min per week of moderate leisure-time physical activity or 75 min per week of vigorous leisure-time physical activity (LTPA), or an equivalent combination. Dietary intake information was collected using the first 24-h dietary recall, conducted by trained interviewers. The NHANES processed the dietary data using the U.S. Department of Agriculture Food and Nutrient Database for Dietary Studies. Diet quality was assessed using the Healthy Eating Index 2015 (HEI-2015), which reflects adherence to the 2015–2020 Dietary Guidelines for Americans. The HEI-2015 total score ranges from 0 (nonadherence) to 100 (perfect adherence) [24]. A healthy diet was defined as an HEI-2015 score in the top two quintiles of the distribution [25].

Definition of overall lifestyle score

Similarly, a healthy lifestyle factor and overall lifestyle score were defined following a previous study [19]. Never smoking, abstaining from heavy drinking, engaging in sufficient LTPA, and maintaining a healthy diet were considered healthy lifestyle factors. Participants were assigned a score of one point if they met the criterion for a healthy level, and zero points otherwise. Overall lifestyle scores were constructed as the total of all four factors, ranging from 0 (worst) to 4 (best), as detailed in Table S1. To avoid extreme groups with limited cases, the overall lifestyle score was subsequently categorized into three groups: unfavorable overall lifestyle score (score ≤ 1), intermediate overall lifestyle score (score = 2), and favorable overall lifestyle score (score ≥ 3). The overall lifestyle score was calculated as an unweighted sum of four lifestyle factors, based on their simplicity and clinical interpretability. Although this simple additive approach has been widely used [26–28], it assumes that the correlation between different lifestyle factors and the outcome is identical, which may be inaccurate. Consequently, we established a weighted overall lifestyle score, where the weight of each lifestyle factor was examined by its relationship with the outcome. The details are presented in the sensitivity analyses.

Definitions of the components of CKM syndrome

BMI was calculated as weight in kilograms divided by height in meters squared. Hypertension was characterized by a blood pressure of 130/80 mmHg or higher, a self-reported history of high blood pressure, or the use of ongoing antihypertensive medication. The criteria of 130/80 mmHg refer to the 2025 American College of Cardiology/AHA Hypertension Guidelines [29]. Prediabetes and diabetes were diagnosed based on the American Diabetes Association 2025 criteria [30]. Prediabetes was identified by having a fasting blood glucose level from 100 to < 126 mg/dL, or a glycated hemoglobin from 5.7% to < 6.5%. In addition, diabetes was defined as a fasting blood glucose level of ≥ 126 mg/dL, a glycated hemoglobin level of ≥ 6.5%, a self-reported history of diabetes, or current hypoglycemic treatment. Metabolic syndrome was defined according to the National Cholesterol Education Program's Adult Treatment Panel III report [31]. Participants were diagnosed with metabolic syndrome if they met at least three of the following five criteria: (1) the fasting blood glucose ≥ 100 mg/dL or drug treatment for diabetes mellitus; (2) high-density lipoprotein cholesterol < 50 mg/dL in females, < 40 mg/dL in males or drug treatment for decreased high-density lipoprotein cholesterol; (3) plasma triglyceride > 150 mg/dL or drug treatment for increased triglyceride; (4) waist circumference > 88 cm in women or > 102 cm in men; and (5) blood pressure ≥ 130/80 mmHg or drug treatment for high blood pressure. Based on the Kidney Disease Improving Global Outcomes clinical practice guideline recommended by the AHA [5], the CKD risk stage was determined using the estimated glomerular filtration rate and urinary albumin–creatinine ratio categories, including low risk, moderate risk, high risk, and very high risk (Table S2).

Definition of CKM syndrome stages

In the current study, the definition of CKM syndrome stage was determined according to the AHA Presidential Advisory Statement on CKM syndrome [5], with modifications for NHANES data based on the criteria from Aggarwal et al. [6]. Stage 0 was characterized by a normal BMI and waist circumference, normoglycemia, normotension, a normal lipid status, and no evidence of CKD or subclinical or clinical CVD. Stage 1 was defined as having excess weight (BMI ≥ 25 kg/m² or ≥ 23 kg/m² if Asian ethnicity), or abdominal obesity (waist circumference ≥ 88/102 cm in women/men or 80/90 cm in women/men if Asian ethnicity), or prediabetes. In stages 0 and 1 of CKM syndrome, it is indicated that Asian populations use a lower anthropometric cut point, because they are prone to metabolic abnormalities even with less adiposity [32]. These criteria are aligned with current BMI categories. The U.S. Centers for Disease Control and Prevention [33] and the WHO [34] define a normal BMI range as 18.5–24.9, overweight as a BMI of 25.0–29.9, obesity as a BMI > 30.0, and severe obesity as a BMI ≥ 35. The WHO expert consultation recommended lower BMI ranges for overweight and obesity in Asian populations. Specifically, they recommended 23.0–27.4 for overweight and 27.5 and higher for obesity [35]. Stage two included participants with metabolic risk factors (hypertriglyceridemia [≥ 135 mg/dL], hypertension, metabolic syndrome, and diabetes), or moderate-to-high-risk CKD. Stage three was identified based on the presence of a high predicted 10 year CVD risk (as determined by the Framingham risk score) or very-high-risk CKD. Stage four comprised individuals with established clinical CVD (coronary heart disease, angina, heart attack, heart failure, or stroke). The detailed descriptions of CKM syndrome stage definitions are illustrated in Table S3. According to the prevalence of CKM syndrome among adults in the U.S. [6], CKM syndrome involved stage one or higher, with advanced stages being stage 3 or 4, as these indicated participants with or at high risk for CVD.

Assessment of covariates

Possible confounding factors were assessed using questionnaires and included: age (continuous variable), sex (female or male), ethnicity (non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, Hispanic and other race or ethnicity), education level (less than high school, high school or equivalent, and college or above), ratio of family income to poverty (< 1, 1–3, or > 3), and marital status (coupled, and single or separated).

Statistical analysis

Considering the complex, multistage, and probability sampling design of NHANES, we applied sampling weights and sample design variables in our formal analyses. In accordance with the National Center for Health Statistics weighting guidelines [36], pooling data from four continuous NHANES survey cycles (2011–2020) requires the construction of appropriate weights to ensure that estimates are representative of the U.S. civilian non-institutionalized population at the midpoint of the combined period. Due to the suspension of NHANES 2019–2020 field operations in March 2020 as a result of the COVID-19 pandemic, the 2017–March 2020 pre-pandemic data represent a 3.25 year period [37]. Combining the 2011–2012, 2013–2014, 2015–2016, and 2017–March 2020 data yields a combined data set spanning 9.25 years. Accordingly, the survey weights were adjusted as follows: the weights for the 2011–2012, 2013–2014, and 2015–2016 cycles were multiplied by 2/9.25, while the weights for the 2017–March 2020 cycle were multiplied by 3.25/9.25.

Mean values with standard errors (SE) were employed to present continuous variables, while categorical variables are depicted as absolute numbers with their corresponding percentages. Considering the potential interactions between these four healthy lifestyle factors, we tested for two-way interactions between all pairs of lifestyle factors (smoking status and alcohol status, smoking status and LTPA, smoking status and diet quality, alcohol status and LTPA, alcohol status and diet quality, LTPA and diet quality) in a multivariable logistic regression model with the result (advanced CKM syndrome). The p > 0.05 was used for all interaction terms (Table S4), indicating non-significant interactions with the risk of advanced CKM syndrome in our study population. Logistic regression models were used to examine the relationship between the overall lifestyle score and advanced CKM syndrome. The results were reported as odds ratios (ORs) with confidence intervals (CIs). We fitted two statistical models: an unadjusted model (adjusted for no covariates) and an adjusted model (adjusted for age, sex, ethnicity, educational level, family income, and marital status). The variance inflation factors procedure was used to test collinearity for all covariates, and all covariables’ variance inflation factors were less than 5 (Table S5).

In addition, the primary analysis was conducted in several subgroups, and sensitivity analyses were performed to test the strength and the reliability of our results. Analyses were performed with stratification according to age (< 60 years and ≥ 60 years), sex, ethnicity (white and non-white participants), educational level, family income, and marital status. The sensitivity analyses were conducted as follows. First, we excluded individuals with a self-reported history of cancer, as lifestyle behaviors may be influenced by cancer diagnosis. Second, to address potential selection bias from excluding participants with missing covariate data, iterative imputation for missing covariates was performed using a machine learning algorithm via the R package ‘missRanger’. MissRanger is a method for iterative imputation that utilizes random forests [38]. It imputes missing values for a variable by making predictions with a random forest that uses all other variables as covariates. The method iterates over all variables repeatedly until the average out-of-bag prediction error no longer improves, serving as the stopping criterion. In addition, the missRanger package incorporates predictive mean matching to prevent the introduction of values not observed in the original data, thereby preserving the distributional characteristics of the data set. The imputation was conducted with parameters set to pmm.k = 3 and num.trees = 500. Third, considering the potential relationship of medication with CKM syndrome, we excluded participants who were treated with sodium–glucose cotransporter-2 inhibitors and glucagon-like peptide 1 receptor agonists. Fourth, considering that CKM syndrome is an ordinal variable, we constructed an ordinal logistic regression model to evaluate the relationship between the overall lifestyle score and the stages of CKM syndrome. Fifth, in addition to the four primary lifestyle factors, we incorporated two emerging lifestyle factors (sleep duration and sedentary behavior) to construct the overall lifestyle score [39]. Sixth, we employed the individual healthy lifestyle factor instead of the overall lifestyle score in the models to assess whether the findings were similar to those of the primary analysis. Finally, to account for potential differences in the association between each lifestyle factor and the risk of advanced CKM syndrome, we constructed a weighted overall lifestyle score. Weights were derived from the regression coefficients (beta values) of a multivariate logistic regression model with advanced CKM syndrome as the dependent variable and the four lifestyle factors as independent variables. Participants were equally divided into three groups based on their weighted overall lifestyle score [19].

All analyses were performed using R software (version 4.4.2), and p < 0.05 was considered statistically significant.

Results

Characteristics of the CKM syndrome patients

Among the 21,112 adults in the U.S. from NHANES 2011 to 2020, the proportions were 11.59%, 21.77%, 52.08%, 3.90%, and 10.66%, respectively, for stages 0 to 4. A total of 88.41% of U.S. adults met criteria for CKM syndrome (stage 1 or higher), and 14.56% met criteria for advanced stages. We then removed 2448 participants in CKM stage 0, and finally, this study included 18,664 patients with CKM syndrome (Fig. 1). Table 1 indicates the baseline characteristics of patients with CKM syndrome with or without advanced stages. According to the weighted analyses, the weighted mean (weighted SE) age of the 18,664 patients with CKM syndrome was 49.4 (0.3) years, and 9298 (weighted, 49.9%) were females. Compared with patients with non-advanced CKM syndrome, those with advanced CKM syndrome were more likely to be older (weighted mean [weighted SE], 66.0 [0.3] versus 46.3 [0.3]), males (1803 [weighted, 56.9%] versus 7,563 [weighted, 48.9%]), non-Hispanic white (1862 [weighted, 82.0%] versus 5410 [weighted, 64.3%]), have less highly educated people (1524 [weighted, 55.7%] versus 9250 [weighted, 65.2%]), and less high-income households (930 [weighted, 42.2%] versus 6125 [weighted, 52.5%]). For healthy lifestyle factors, patients with advanced CKM syndrome tended to be former or current smokers (1835 [weighted, 59.7%] versus 6434 [weighted, 41.8%]), non-heavy drinkers (2,711 [weighted, 87.7%] versus 12,180 [weighted, 76.8%]), have more insufficient physical activity people (2793 [weighted, 89.7%] versus 11,657 [weighted, 71.6%]), and healthier diet (1,295 [weighted, 43.5%] versus 6,172 [weighted, 38.4%]). In addition, the baseline characteristics of the unweighted samples exhibited the same findings (Table S6).

Table 1.

Baseline characteristics of patients with cardiovascular–kidney–metabolic syndrome

Characteristic	CKM syndrome patients			p value
	Total (n = 18664)	Non-advanced stages (n = 15591)	Advanced stages (n = 3073)
Age (years), mean (SE)	49.4 (0.3)	46.3 (0.3)	66.0 (0.3)	< 0.0001
Sex, n (%)				< 0.0001
Female	9298 (49.9)	8028 (51.1)	1270 (43.1)
Male	9366 (50.1)	7563 (48.9)	1803 (56.9)
Race or ethnicity, n (%)				< 0.0001
Hispanic	4312 (14.2)	3888 (15.6)	424 (6.0)
Non-Hispanic White	7272 (67.0)	5410 (64.3)	1862 (82.0)
Non-Hispanic Black	4377 (10.8)	3796 (11.3)	581 (7.6)
Non-Hispanic Asian	1975 (4.5)	1855 (5.0)	120 (1.5)
Other	728 (3.5)	642 (3.7)	86 (2.7)
Education level, n (%)				< 0.0001
Less than high school	3630 (12.5)	2904 (11.9)	726 (15.9)
High school or equivalent	4260 (23.7)	3437 (22.9)	823 (28.4)
College or above	10774 (63.8)	9250 (65.2)	1524 (55.7)
Family PIR, n (%)				< 0.0001
< 1.0	3771 (13.4)	3123 (13.3)	648 (14.0)
1.0–3.0	7838 (35.7)	6343 (34.2)	1495 (43.8)
> 3.0	7055 (50.9)	6125 (52.5)	930 (42.2)
Marital, n (%)				0.2
Coupled	11145 (64.5)	9403 (64.7)	1742 (63.1)
Single or separated	7519 (35.5)	6188 (35.3)	1331 (36.9)
Smoking status, n (%)				< 0.0001
Never	10395 (55.4)	9157 (58.2)	1238 (40.3)
Former or now	8269 (44.6)	6434 (41.8)	1835 (59.7)
Alcohol status, n (%)				< 0.0001
No heavy drinking	14891 (78.5)	12180 (76.8)	2711 (87.7)
Heavy drinking	3773 (21.5)	3411 (23.2)	362 (12.3)
LTPA, n (%)				< 0.0001
Sufficient	4214 (25.7)	3934 (28.4)	280 (10.3)
Insufficient	14450 (74.3)	11657 (71.6)	2793 (89.7)
Healthy diet, n (%)				< 0.001
Yes	7467 (39.2)	6172 (38.4)	1295 (43.5)
No	11197 (60.8)	9419 (61.6)	1778 (56.5)
Healthy lifestyle score, n (%)				< 0.0001
0-point	1301 (7.1)	1141 (7.3)	160 (5.6)
1-point	4664 (24.9)	3616 (23.3)	1048 (33.2)
2-point	6898 (36.9)	5705 (36.7)	1193 (38.0)
3-point	4697 (24.6)	4099 (25.4)	598 (20.3)
4-point	1104 (6.6)	1030 (7.2)	74 (2.9)
Overall lifestyle score, n (%)				< 0.0001
Unfavorable	5965 (31.9)	4757 (30.7)	1208 (38.7)
Intermediate	6898 (36.9)	5705 (36.7)	1193 (38.0)
Favorable	5801 (31.2)	5129 (32.6)	672 (23.2)

Values were weighted mean (SE) for continuous variables or unweighted numbers (weighted percentage) for categorical variables. Because all numbers were rounded, percentages may not total 100%

CKM: Cardiovascular–Kidney–Metabolic; LTPA: Leisure-time physical activity; PIR: Poverty income ratio

Association between overall lifestyle score and advanced CKM syndrome

Among patients with CKM syndrome, those with a healthy lifestyle score of zero tended to be in advanced stages compared to those with a score of 4 (160 [weighted, 12.1%] versus 74 [weighted, 6.8%]), and those having unfavorable overall lifestyle score were more likely to be in advanced stages than those with favorable overall lifestyle score (1208 [weighted, 18.6%] versus 672 [weighted, 11.5%]; Tables S7 and S8).

The results of the weighted logistic regression analyses are presented in Table 2. Following adjusting age, sex, ethnicity, education level, family income, and marital status, compare to patients with CKM syndrome in the unfavorable overall lifestyle score group, the adjusted OR for advanced CKM syndrome were 0.69 (95% CI 0.61–0.77; p < 0.0001) for those in the intermediate group and 0.56 (95% CI 0.48–0.65; p < 0.0001) for those in the favorable group, respectively. Besides, for each one-point increment in the overall lifestyle score, the adjusted OR was 0.79 (95% CI 0.75–0.84; p < 0.0001).

Table 2.

Association between overall lifestyle score and advanced CKM syndrome

	Unadjusted model		Adjusted model
	OR (95% CI)	p value	OR (95% CI)	p value
Overall lifestyle score
Unfavorable	1 (reference)		1 (reference)
Intermediate	0.82 (0.73,0.92)	< 0.001	0.69 (0.61,0.77)	< 0.0001
Favorable	0.56 (0.49,0.64)	< 0.0001	0.56 (0.48,0.65)	< 0.0001
One-point increase in the overall lifestyle score	0.82 (0.79,0.87)	< 0.0001	0.79 (0.75,0.84)	< 0.0001

Unadjusted model: no adjusted. Adjusted model: adjusted for sociodemographic variables (age, sex, ethnicity, educational level, family income, and marital status)

CI: confidence intervals; CKM: cardiovascular–kidney–metabolic; LTPA: Leisure-time physical activity; OR: odds ratio

Subgroup and sensitivity analyses

Consistent findings were observed in analyses with stratification by age, sex, ethnicity, educational level, family income, and marital status (Table 3). In sensitivity analyses, the results were consistent with the primary findings. Similar associations were observed when participants with a history of cancer were excluded (Table S9), when missing covariates of values were estimated with use of iterative imputation with machine learning algorithm (Table S10), when patients treated with sodium–glucose cotransporter-2 inhibitors and glucagon-like peptide 1 receptor agonists were excluded (Table S11), when the link between overall lifestyle score and CKM syndrome stages were assessed by ordinal logistic regression models (Table S12), and when we incorporated two emerging lifestyle factors to construct overall lifestyle score (Table S13). For the four lifestyle factors, the statistically significant correlation with advanced CKM syndrome stages was observed in never smoking, adequate LTPA, and healthy diet (the 95% CI not covering one, and p < 0.05), but not in no heavy drinking (the 95% CI covering one, and p > 0.05; Table S13). The multivariable-adjusted OR for patients with CKM syndrome having a healthy lifestyle score of 4 was 0.51 (95% CI 0.35–0.74; p < 0.001) for advanced stages, as compared with patients having a healthy lifestyle score of zero (Table S14). In addition, the associations remained robust when the weighted overall lifestyle score was applied (Table S15).

Table 3.

Variables-stratified analyses for the association between overall lifestyle score and advanced CKM syndrome

Characteristic	Overall lifestyle score			p interaction
	Unfavorable	Intermediate	Favorable
Age				0.203
< 60	1 (reference)	0.44 (0.35,0.54)	0.47 (0.36,0.62)
≥ 60	1 (reference)	0.99 (0.85,1.15)	0.79 (0.67,0.94)
Sex				0.324
Female	1 (reference)	0.63 (0.51,0.78)	0.55 (0.46,0.67)
Male	1 (reference)	0.74 (0.60,0.91)	0.60 (0.47,0.77)
Race or ethnicity				0.775
White	1 (reference)	0.66 (0.57,0.78)	0.55 (0.46,0.66)
Non-white	1 (reference)	0.69 (0.57,0.82)	0.48 (0.38,0.59)
Educational level				0.965
Less than high school	1 (reference)	0.60 (0.47,0.78)	0.55 (0.38,0.81)
High school or equivalent	1 (reference)	0.73 (0.53,1.01)	0.61 (0.42,0.88)
College or above	1 (reference)	0.68 (0.56,0.83)	0.56 (0.46,0.67)
Marital status	0.374
Married	1 (reference)	0.70 (0.59,0.83)	0.54 (0.44,0.65)
Single or separated	1 (reference)	0.67 (0.54,0.82)	0.62 (0.47,0.80)
PIR				0.872
< 1	1 (reference)	0.73 (0.57,0.94)	0.57 (0.38,0.85)
1–3	1 (reference)	0.67 (0.54,0.84)	0.53 (0.43,0.66)
> 3	1 (reference)	0.70 (0.53,0.91)	0.60 (0.46,0.78)

Values are weighted odds ratios (95% confidence interval). Model: each stratification was adjusted for age, sex, ethnicity, education level, marital status, and family income except the stratification factor itself

CKM: Cardiovascular–kidney–metabolic; PIR: poverty-to-income ratio

Discussion

This study is the first to examine the correlations of overall lifestyle score, including smoking status, alcohol status, physical activity, and diet quality, with advanced CKM syndrome stages based on a large U.S. cohort. Compared to their counterparts with an unfavorable overall lifestyle score, patients with CKM syndrome intermediate and favorable overall lifestyle scores had 31% and 44% lower prevalence of advanced stages, respectively. Our findings suggest that integrated modifiable lifestyle behaviors could provide crucial insights for improving risk management methods for CKM syndrome in future studies and clinical experiments.

The cardiovascular, kidney, and metabolic functions are closely inter-related, and issues in one system can cause problems in the others [2]. Particularly, CVD is often accompanied by comorbidity risk factors, including CKD and metabolic diseases, such as obesity and diabetes [40]. In October 2023, the AHA indicated the concept of CKM syndrome, which explained the interplay among obesity, diabetes, CKD, and CVD [5]. Based on the risk factors and established disease, CKM syndrome is staged, beginning with stage zero, where there are no CKM risk factors; stage 1, which involves more or dysfunctional body fat; stage 2, which features metabolic risk factors or moderate to high-risk CKD; stage 3, which features subclinical CVD or very high-risk CKD; stage 4, which is proposed by clinical CVD [5]. A recent study indicated that CKM syndrome is highly prevalent among adults in the U.S., with about 90% meeting the requirement for CKM stage 1 or higher, and 15% of U.S. adults are identified as having advanced CKM stages (stages 3 and 4). The high incidence rate reflected poor CKM syndrome health in the U.S. population, making the measures to recognize and decrease the risk-improving factors of CKM syndrome extremely urgent.

Since CKM syndrome is a multistage condition, it is essential to identify that the CKM staging path can move in both directions, allowing individuals to advance or retreat through the CKM stages. In its presidential advisory, the AHA underscored the importance of a healthy lifestyle as a reliable way to promote CKM stage regression [5]. Significant lifestyle changes have been linked with reductions in adipose tissue and developments in glucose tolerance (stage 1) [41, 42]; remission of diabetes [43], hypertension [44], and hyperlipidemia [45], as well as improvements in kidney function (stage 2) [46]; reduction in risk of changed cardiac structure and function (stage 3) [47]. These findings underscore the critical role of adopting a healthy lifestyle in preventing patients from advanced stages of CKM syndrome.

Among many modifiable lifestyle factors, cigarette smoking, alcohol misuse, physical inactivity, and poor diet are prevalent, causing a significant burden of disease globally [48, 49]. These four risk factors accounted for 60% of premature deaths in the U.S. population [10]. In addition, these factors tend to be clustered, and the integrations of these lifestyle factors are more detrimental to health compared to their individual cumulative effects [14]. In this study, four healthy lifestyle factors—never smoking, no heavy drinking, sufficient LTPA, and a healthy diet—were used to construct the overall lifestyle score. This finding is consistent with results from large-scale population-based studies. In a longitudinal cohort of 480,940 middle-aged adults from the UK Biobank, adherence to a healthier lifestyle was linked with approximately 6.3 years longer life for men and 7.6 years for women [8]. Similar results have been reported in China. In the China PEACE Million Persons Project, 0.9 million adults, Zhang et al. found that participants adhering to all four healthy lifestyle behaviors had significantly lower risks of all-cause mortality (hazard ratio 0.64; 95% CI 0.52–0.79) and cardiovascular mortality (hazard ratio 0.53; 95% CI 0.37–0.76) following a median follow-up of 2.4 years [9]. Meanwhile, a cross-sectional study of NHANES III demonstrated that compared to nonadherence, adherence to all four low-risk behaviors was associated with a 63% reduced risk of all-cause mortality [18]. In addition, adherence to healthy lifestyle behavior could decrease the risk of many NCDs. For instance, healthy lifestyles were indicated to lead to a 40–76% reduction in CVD risk compared to unhealthy lifestyles [50–52]. In a meta-analysis and systematic review of 31 randomized controlled trials enrolling 23,684 patients, compared with usual care, lifestyle interventions reduced the incidence of diabetes by 41% and increased the probability of reverting to normoglycemia by 44% in adults with prediabetes [53]. Another meta-analysis and systematic review, including 104 cohort studies with 2,755,719 participants, suggested that increased vegetable and potassium intake, being physically active, moderate alcohol consumption, and never smoking status were consistently associated with reduced risk of CKD [54]. This study adds new evidence to this field that a favorable and intermediate overall lifestyle score, comprised of the four well-characterized modifiable factors (never smoking, no heavy drinking, sufficient LTPA, and a healthy diet), was associated with a 31% and 44% lower risk of being in advanced stages among patients with CKM syndrome. Our work highlights the potential of addressing modifiable lifestyle factors as a unified strategy to mitigate the burden of interconnected conditions, including CVD, CKD, and diabetes, consistent with the integrated method advocated by the CKM syndrome framework. It extends beyond the well-established link between lifestyle and chronic disease by demonstrating a graded inverse association between overall lifestyle score and advanced CKM syndrome. This indicates that overall lifestyle modifications may specifically mitigate the progression to multiorgan complications, which are crucial concerns in managing CKM syndrome. Our findings are aligned with a growing body of evidence underscoring the importance of holistic health metrics in CKM syndrome. For instance, a recent study by Tan et al. [15] demonstrated that the Life's Essential 8 health behaviors was associated with prognosis in patients with advanced CKM syndrome. This study complements this work by demonstrating that overall lifestyle score, focused on four key lifestyle behaviors, shows a strong, graded association with the odds of having advanced CKM stages in a cross-sectional setting.

The exact mechanisms linking the four lifestyle factors to advanced stages of CKM syndrome are not yet fully elucidated. However, several potential mechanisms may explain this association. Cigarette-derived compounds, including nicotine and volatile organic compounds, can induce oxidative stress on the endothelium and endothelial dysfunction, thereby initiating cardiovascular and renal injury [55, 56]. Alcohol consumption may contribute to advanced CKM syndrome in part by increasing levels of phenylacetylglutamine, a gut microbiota-derived metabolite of phenylalanine. This metabolite has been indicated to improve platelet responsiveness and thrombosis potential through adrenergic receptor signaling, thereby potentially promoting cardiovascular events and accelerating renal and cardiac injury. Higher phenylacetylglutamine levels are linked with elevated risks of heart failure, atherosclerosis, and CKD, possibly through promoting inflammatory pathways and endothelial dysfunction [57]. Conversely, vigorous LTPA lowers blood pressure and enhances endothelial function through multiple mechanisms, including increased nitric oxide bioavailability, decreased oxidative stress, and improved vascular compliance. These adaptations increase perfusion, reduce afterload on the heart, and lower glomerular pressure in the kidneys, thereby slowing the progression of cardiovascular and renal damage [58, 59]. Finally, a diet with a high HEI-2015 score, characterized by abundant minerals and vitamins from fruits, vegetables, whole grains, dairy or soy alternatives, protein foods, and unsaturated fatty acids, can decrease systemic inflammation and oxidative stress, crucial drivers in the progression of chronic cardiorenal metabolic diseases [60, 61].

This study has several limitations. First, the cross-sectional design of this study means that the observed association between a favorable overall lifestyle score and lower odds of advanced CKM stages cannot be interpreted causally, and it certainly cannot demonstrate that a healthy lifestyle prevents or delays the progression to advanced stages. The fundamental issue is the inability to establish temporality. While it is plausible that a healthy lifestyle protects against CKM progression, the reverse direction of association (reverse causality) is equally plausible. For instance, pre-existing advanced CKM stages (e.g., heart failure symptoms or renal dysfunction) can directly compromise a patient’s capacity for physical activity, leading to a lower lifestyle score. Furthermore, the higher prevalence of a self-reported healthy diet among patients with advanced CKM syndrome strongly suggests that dietary improvements may have been adopted after diagnosis as a clinical management strategy, rather than representing long-term habits. Similarly, the symptom burden (e.g., fatigue and pain) or psychological distress associated with advanced disease may promote unhealthy eating patterns or smoking relapse. This bidirectional ambiguity underscores the critical need for longitudinal studies to disentangle the temporal sequence of these relationships. Additional well-designed cohort studies are needed to verify the causality. Second, the cross-sectional nature of the NHANES information imposes fundamental limitations on interpreting the dynamics of CKM syndrome progression and regression. Although we observed a significant association between a favorable overall lifestyle score and lower odds of advanced CKM stages, our analysis cannot establish a causal relationship or temporal sequence. Crucially, this design precludes any assessment of how alterations in lifestyle factors over time might influence transitions between CKM stages, especially the potential for regression from stages 3 or 4 to stages 1 or 2 after lifestyle improvement. Our results highlight a robust association that cannot elucidate these critical longitudinal dynamics. Future prospective cohort research with repeated measures of both lifestyle behaviors and CKM syndrome stage is essential to verify the directionality of the link observed here and specifically investigate the impact of lifestyle modification on the potential for CKM stage regression. Third, data on lifestyle behaviors, including diet, physical activity, smoking, and alcohol consumption, were self-reported. Self-reported exposures may introduce measurement error, particularly for diet and physical activity. As all lifestyle factors were assessed via questionnaires, they are susceptible to recall bias and social desirability bias, where participants may over-report healthy behaviors. Although NHANES uses standardized protocols and validated instruments to minimize these errors, some degree of misclassification is inevitable. However, we reason that this misclassification is likely to be non-differential with respect to CKM stage, as the clinical and laboratory assessment for CKM syndrome was independent of the self-reported lifestyle interviews. Non-differential misclassification of the exposure would typically attenuate the observed effect estimates toward the null hypothesis, suggesting that the true associations might be stronger than those we observed. Furthermore, the assessment of diet quality was based on a single 24-h dietary recall. While this method is standard in large surveys such as NHANES and the HEI-2015 is designed for such data to assess population-level diet quality, it only captures short-term intake and may not fully represent an individual's habitual long-term dietary patterns. We sought to mitigate this by categorizing participants based on the population distribution (top two quintiles of HEI-2015), but some misclassification remains likely. As with other self-reported measures, this misclassification is expected to be non-differential, which would attenuate the observed associations toward null. Fourth, our primary overall lifestyle score assigned equal weight to each of the four components. This simple additive approach, while clinically intuitive and widely used, assumes a linear and equal contribution of each factor and may mask potential non-linear or synergistic effects between them. Although our sensitivity analysis using a weighted score yielded nearly identical results (Table S15), supporting the robustness of our primary findings, it does not fully capture the potential complexity of lifestyle interactions. Future studies with even larger sample sizes could employ more advanced modeling techniques to investigate potential effect modifiers and interactions among these lifestyle factors. Fifth, the complexity of the CKM staging system, which integrates multiple biomarkers and clinical criteria, introduces a potential for outcome misclassification. Although we employed standardized NHANES protocols and strictly followed established definitions to minimize this risk, some degree of misclassification is inherent in such a multifaceted staging approach. For instance, the reliance on a single measurement for certain biomarkers (e.g., blood pressure and laboratory values) or the unavailability of more advanced diagnostic data (e.g., echocardiography for subclinical CVD) in NHANES could lead to imperfect staging. However, we reason that any such misclassification is likely to be non-differential with respect to participants' lifestyle scores, as the measurement of CKM components was independent of the self-reported lifestyle assessment. Non-differential misclassification typically biases effect estimates toward the null, suggesting that our observed associations might be conservative. Another consideration is the retrospective application of updated clinical criteria. The CKM staging and definitions for conditions such as hypertension and diabetes were based on the most current guidelines (2023–2025), which were released after the NHANES data (2011–2020) were collected. This means our staging may not reflect the clinical diagnoses participants actually received during that period. However, we applied these contemporary standards uniformly across all survey cycles to ensure consistency and to frame our findings within the most recent clinical context. The updated, often stricter criteria primarily reclassify individuals into earlier, at-risk stages (1 and 2). The misclassification affecting our primary outcome—advanced stages (3 and 4), which are based on high-risk predictions or hard clinical events—is likely to be minimal and non-differential with respect to lifestyle, potentially leading to a more conservative estimate of the association. Sixth, we must consider the potential for survivor bias and reporting bias. Survivor bias may arise, because individuals with the most severe and debilitating forms of advanced CKM syndrome (e.g., those who are institutionalized or too ill to participate) are under-represented in the NHANES sample. This could lead to an underestimation of the prevalence of advanced stages and potentially attenuate the observed associations, as the sickest individuals (who likely have poor lifestyles) are missing. In addition, despite the use of validated instruments, self-reported lifestyle data are susceptible to social desirability bias, where participants may over-report healthy behaviors. As discussed earlier, we believe such misclassification is likely non-differential, which would also bias the results toward the null. Therefore, the net effect of these potential biases would, in theory, lead to an underestimation of the true strength of the association between a favorable lifestyle and lower odds of advanced CKM stages. The robust association we observed despite these potential biases adds credibility to our findings. Finally, because this study was limited to the U.S. population, the findings may not be generalized to other countries or other ethnic groups.

Conclusion

In this nationally representative cross-sectional study, a favorable overall lifestyle was significantly associated with lower odds of advanced stages among U.S. adults with CKM syndrome. Notwithstanding the limitations of the study design for causal inference, our findings highlight a robust association and could provide crucial hypotheses-generating insights for designing future longitudinal studies and clinical trials to investigate whether lifestyle interventions can prevent or delay the progression of CKM syndrome.

Abbreviations

AHA

American Heart Association

BMI

Body mass index

CI

Confidence interval

CKD

Chronic kidney disease

CKM

Cardiovascular–kidney–metabolic

CVD

Cardiovascular disease

HEI-2015

Healthy Eating Index 2015

LTPA

Leisure-time physical activity

NCDs

Non-communicable diseases

NHANES

National Health and Nutrition Examination Survey

OR

Odds ratios

SE

Standard errors

STROBE

Strengthening the reporting of observational studies in epidemiology

Author contributions

PL, XQS and DYT designed the study and conducted the data analysis. PL, XQS and YYS drafted the manuscript. HXS and DYT proposed critical revisions to the manuscript. All authors read and approved the final manuscript.

Funding

This study was supported by the Self-funded Scientific Research Project of the 961 st Hospital of the Joint Logistics Support Force of The Chinese People’s Liberation Army (No. ZZKY2025-006).

Data availability

The National Health and Nutrition Examination Survey dataset is publicly available at the National Center for Health Statistics of the Center for Disease Control and Prevention (https://www.cdc.gov/nchs/nhanes/index.html).

Declarations

Ethics approval and consent to participate

Clinical trial number: Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.