Risk factor target achievement in relation to incident cardiovascular disease among individuals with cardiovascular-kidney-metabolic syndrome stage 1–3: results from a nationwide cohort study

Abstract

Background and objective

The association between risk factor target achievement and the risk of incident cardiovascular disease (CVD) among individuals with cardiovascular-kidney-metabolic (CKM) syndrome stages 1–3 remains unclear. We investigated this association within the China Health and Retirement Longitudinal Study (CHARLS) cohort.

Methods

This study included 6,501 individuals with CKM stages 1–3. The exposure of interest was the number of risk factor targets achieved at baseline, including systolic blood pressure (SBP) < 130 mm Hg, fasting blood glucose < 6.1 mmol/L, low-density lipoprotein cholesterol (LDL-C) < 2.6 mmol/L, and moderate-to-vigorous physical activity. Cox proportional hazards regression models were used to analyse the association between the number of targets achieved and the risk of CVD.

Results

During a median follow-up of 9.0 years, 1,221 incident CVD events and 539 stroke events were documented. Compared with participants achieving no risk factor targets at baseline, those achieving all four targets had a significantly lower risk of both CVD (adjusted hazard ratio [aHR]: 0.40, 95% CI: 0.26–0.62) and stroke (aHR: 0.39, 95% CI: 0.20–0.75). Stratified analyses indicated that the inverse association between the number of achieved targets and CVD risk was stronger among participants younger than 60 years than among those aged 60 years or older (P for interaction < 0.05).

Conclusion

A greater number of risk factor targets achieved at baseline was associated with a lower subsequent risk of CVD in individuals with CKM stages 1–3.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12872-026-06162-4.

Introduction

The cardiovascular-kidney-metabolic (CKM) syndrome, recently defined by the American Heart Association (AHA), is a systemic disorder characterized by complex pathophysiological interactions among metabolic risk factors, chronic kidney disease (CKD), and the cardiovascular system [1]. The condition is highly prevalent worldwide. Epidemiological data suggest that approximately 90% of U.S. adults are affected by CKM syndrome (stage 1 or higher) [2, 3]. Similarly, the burden of CKM syndrome in China is substantial and increasing, driven by accelerated population aging and the rising prevalence of type 2 diabetes, cardiovascular disease (CVD), and CKD [4].

Among the adverse clinical outcomes associated with CKM syndrome, CVD constitutes a primary driver of reduced quality of life, premature mortality, and economic strain on healthcare systems [5, 6]. Therefore, achieving guideline-recommended risk factor targets in individuals with CKM syndrome may be clinically relevant for lowering CVD risk and prolonging CVD-free survival.

Substantial evidence demonstrates that traditional cardiovascular risk factors, including elevated blood pressure [7, 8], hyperglycemia [9, 10], dyslipidemia [11, 12], and physical inactivity [6, 13], are associated with an increased risk of CVD. While achieving multiple risk factor targets has been associated with lower CVD risk in populations with single conditions, such as diabetes or hypertension [14, 15], these findings are largely derived from disease-specific models. Given that CKM syndrome involves multiorgan interactions and complex pathophysiology [1], evidence extrapolated from single-disease contexts may not fully capture the risk profile of this population. To date, data quantifying the association between joint achievement of risk factor targets and CVD risk specifically within individuals with CKM syndrome are limited.

To address this knowledge gap, we used data from the China Health and Retirement Longitudinal Study (CHARLS) cohort to investigate the association between the number of baseline risk factor targets achieved (SBP < 130 mm Hg, FBG < 6.1 mmol/L, LDL-C < 2.6 mmol/L, and engagement in moderate-to-vigorous physical activity) and the subsequent risk of incident CVD among individuals with CKM syndrome stage 1–3 and no prior history of CVD.

Methods

Study population

The CHARLS is a nationally representative longitudinal cohort. Details regarding its cohort profile, sampling strategy, standardized survey questionnaires, and biomarker collection have been reported previously [16]. The questionnaire-based information used in the present analysis was obtained from the standardized CHARLS survey questionnaires and was not developed specifically for this study.

Briefly, between June 2011 and March 2012, the baseline survey enrolled 17,708 community-dwelling residents from 150 counties/districts across 28 provinces in mainland China, using a multistage, stratified probability sampling strategy covering both urban and rural areas. Follow-up surveys were conducted every 2–3 years following standardized protocols, with four follow-up waves completed to date. The study protocol was approved by the Biomedical Ethics Committee of Peking University (IRB approval number: IRB00001052-11015). All participants provided written informed consent.

The present study utilized data from all survey waves from baseline through 2020. Participants were excluded if they met any of the following criteria: (1) age < 45 years; (2) prevalent CVD or using cardiovascular medications at baseline; (3) missing data required for CKM staging or classified as CKM stage 0; (4) missing data on FBG, SBP, LDL-C, or physical activity; (5) lost to follow-up. A total of 6,501 participants were included in the final analysis. A detailed flowchart of the study population selection is presented in Fig. 1.

Fig. 1.

Flowchart of participant selection. CVD, cardiovascular disease; CKM, cardiovascular-kidney-metabolic; SBP, systolic blood pressure; FBG, fasting blood glucose; LDL-C, low-density lipoprotein cholesterol

Assessment of the risk factors

Blood pressure was measured three times by trained interviewers using an automated electronic monitor (Omron HEM-7200), with 45-second intervals between readings. The mean of the three measurements was used for analysis. Venous blood samples were collected after an overnight fast of at least 8 h. LDL-C was quantified using a direct method, and FBG was measured using the hexokinase method [17]. Physical activity was assessed via a standardized structured questionnaire, in which participants reported weekly physical activity levels, including vigorous, moderate, light, and no activity. Risk factor target achievement was defined using baseline measurements only. Target achievement was determined according to guideline-recommended thresholds: SBP < 130 mm Hg [6], LDL-C < 2.6 mmol/L [18, 19], FBG < 6.1 mmol/L [20], and engagement in moderate-to-vigorous physical activity [21]. The number of achieved targets was calculated by summing these four baseline-defined indicators, ranging from 0 to 4.

Assessment of covariates

Demographic characteristics (age, sex, education, marital status, household registration), health-related behaviors (smoking, alcohol consumption), medical history (including diabetes and hypertension), and medication use (antihypertensive, glucose-lowering, and lipid-lowering agents) were collected by trained interviewers using standardized questionnaires. Anthropometric measurements (height, weight, and waist circumference) were obtained by trained personnel following standard protocols. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Laboratory assessments included high-sensitivity C-reactive protein (hs-CRP), creatinine, blood urea nitrogen (BUN), hemoglobin A1c (HbA1c), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG). The estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation [22]. Hypertension was defined as SBP ≥ 140 mm Hg, diastolic blood pressure (DBP) ≥ 90 mm Hg, self-reported physician diagnosis, or use of antihypertensive medication. Diabetes was defined as FBG ≥ 7.0 mmol/L, or HbA1c ≥ 6.5%, self-reported physician diagnosis, or use of glucose-lowering medication.

CKM syndrome stage 0–3

Based on the AHA guideline framework, CKM syndrome stage 0–3 were defined as follows [1]: Stage 0, no CKM-related health risk factors; Stage 1, overweight/obesity, abdominal obesity, or prediabetes, without other metabolic risk factors or CKD; Stage 2, the presence of metabolic disorders (e.g., hypertension, metabolic syndrome, hypertriglyceridemia, or diabetes) or moderate-to-high risk CKD, or both; and Stage 3, subclinical CVD in individuals with metabolic dysfunction or CKD. Detailed staging criteria are provided in Table S1. Due to data limitations in CHARLS, two AHA staging criteria could not be fully operationalised: CKD risk stratification for Stage 2 was based on eGFR alone, as UACR data were not systematically collected; and Stage 3 identification relied on risk-equivalent proxies rather than direct imaging or biomarker evidence of subclinical CVD (echocardiography and NT-proBNP were unavailable). Accordingly, participants were classified as CKM Stage 3 if they met either of the following operational criteria: (1) an estimated 10-year CVD risk ≥ 20% calculated using the AHA’s PREVENT equations [23]; or (2) very high-risk CKD (KDIGO categories G4 or G5, defined as eGFR < 30 mL/min/1.73 m²) [24]. Of the 329 participants classified as CKM Stage 3, 321 (97.6%) met the PREVENT criterion only, 8 (2.4%) met the eGFR criterion only, and no participant met both criteria simultaneously. Both CKM staging and risk factor target achievement were defined at baseline; therefore, our analysis estimates the association between baseline-defined target achievement and subsequent CVD risk, not the effect of dynamic risk factor management.

Ascertainment of outcomes

The primary outcome was incident CVD, defined as a new self-reported, physician-diagnosed history of heart disease (including angina, myocardial infarction, congestive heart failure, or other heart conditions) and stroke. The secondary outcome was incident stroke. During each follow-up survey, participants were asked: (1) “Have you ever been told by a doctor that you have heart disease…?” and (2) “Have you ever been told by a doctor that you have had a stroke?” Follow-up time was calculated from the baseline survey date to the date of the survey wave at which CVD was first self-reported, the date of death, or the date of the last available follow-up interview, whichever occurred first.

Statistical analysis

Continuous variables are presented as mean ± standard deviation for normally distributed data or median (interquartile range) for skewed data. Categorical variables are summarized as frequencies (percentages). Baseline characteristics were compared between groups using one-way analysis of variance, the Kruskal-Wallis test, the chi-square test, or Fisher’s exact test, as appropriate.

Multivariable Cox proportional hazards regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between the number of risk factor targets achieved at baseline and the risk of incident CVD, with participants achieving no targets serving as the reference group. Three sequential models were constructed: Model 1 adjusted for age and sex; Model 2 further adjusted for smoking status, alcohol consumption, marital status, residence, eGFR, BUN, and hs-CRP, and Model 3 additionally adjusted for the use of antihypertensive, antidiabetic, and lipid-lowering medications. Model 2 is therefore presented as the primary result, and Model 3 is retained as a supplementary reference to quantify the potential contribution of medication use. To assess the robustness and explore potential heterogeneity, stratified analyses were conducted by age (< 60 years vs. ≥60 years) and sex. Multiplicative interaction was tested by including a product term of the main exposure and the stratification variable in the models. Additionally, restricted cubic splines with three knots placed at the 10th, 50th, and 90th percentiles were fitted to characterize potential dose-response relationships of SBP, FBG, and LDL-C with the risk of CVD.

Several sensitivity analyses were conducted to evaluate the stability of the findings. First, we excluded participants who developed CVD within the first two years of follow-up to minimize potential reverse causality. Second, we performed a complete-case analysis by excluding individuals with any missing covariates. Third, the target blood pressure was redefined as SBP < 130 mm Hg and DBP < 80 mm Hg. Fourth, to account for the competing risk of death, we conducted analyses using the Fine-Gray subdistribution hazards model within the Cox proportional hazards framework. Fifth, to evaluate whether the mathematical coupling between the PREVENT equations and the exposure variables influenced the primary findings, we repeated the primary analysis after excluding all CKM Stage 3 participants. Sixth, to assess sensitivity to the time-to-event assignment, we conducted a midpoint analysis in which event time was assigned to the midpoint of the interval between the last event-free wave and the first wave at which CVD was reported.

All statistical analyses were performed using R software (version 4.1.2). A two-tailed P value < 0.05 was considered statistically significant.

Baseline characteristics

Baseline characteristics of the study participants stratified by the number of risk factor targets achieved are presented in Table 1. The study included 6,501 individuals with CKM stages 1–3. The proportions of participants achieving 0, 1, 2, 3, and 4 targets were 8.7%, 30.5%, 37.2%, 20.0%, and 3.6%, respectively. Participants achieving more targets were generally younger, more likely to be female and non-current smokers, and had higher levels of physical activity. They also exhibited lower waist circumference and BMI, and reported lower use of antihypertensive, antidiabetic, or lipid-lowering medications. Additionally, these individuals had lower levels of hs-CRP, BUN, FBG, HbA1c, SBP, DBP, LDL-C, and TG (Table 1). Baseline characteristics of the study participants stratified by CKM stage are shown in Table S2.

Table 2.

HRs (95%CIs) of cardiovascular disease according to the risk factor targets achieved

	Target-achievement group	Case/Total	IR, per 1,000 person-years	Model 1 HR (95%CI)	Model 2 HR (95%CI)	Model 3 HR (95%CI)
CVD	SBP ≥ 130	658/3,127	26.6	Ref.	Ref.	Ref.
	SBP < 130	563/3,374	20.2	0.79 (0.71,0.89)	0.79 (0.71,0.89)	0.87 (0.77,0.98)
	FBG ≥ 6.1	444/2,247	24.6	Ref.	Ref.	Ref.
	FBG < 6.1	777/4,254	22.5	0.91 (0.81,1.03)	0.91 (0.81,1.02)	0.94 (0.83,1.06)
	LDL-C ≥ 2.6	867/4,362	24.6	Ref.	Ref.	Ref.
	LDL-C < 2.6	354/2,139	20.5	0.87 (0.77,0.98)	0.86 (0.76,0.98)	0.87 (0.76,0.98)
	MVPA: NO	902/4,609	24.4	Ref.	Ref.	Ref.
	MVPA: Yes	319/1,892	20.5	0.86 (0.76,0.98)	0.87 (0.77,0.99)	0.89 (0.78 ,1.01)
	0 targets achieved	145/565	33.3	Ref.	Ref.	Ref.
	1 target achieved	401/1,980	25.2	0.77 (0.64,0.93)	0.78 (0.65,0.95)	0.82 (0.68,1.00)
	2 targets achieved	438/2,422	22.4	0.70 (0.58,0.85)	0.71 (0.57,0.86)	0.78 (0.64,0.95)
	3 targets achieved	212/1,301	19.8	0.64 (0.52,0.80)	0.64 (0.52,0.80)	0.72 (0.58,0.90)
	4 targets achieved	25/233	12.6	0.40 (0.26,0.62)	0.40 (0.26,0.62)	0.47 (0.30,0.72)
	Per 1 additional target achieved			0.86 (0.81,0.91)	0.86 (0.81,0.91)	0.89 (0.84,0.95)

Model 1: adjusted for age and sex; Model 2: adjusted for age, sex, smoking status, alcohol consumption, marital status, residential area, eGFR, BUN, and hs-CRP; Model 3: adjusted for all the variables in model 2 and the use of antihypertensive, antidiabetic, and lipid-lowering medications

Abbreviations: CVD Cardiovascular disease, HR Hazard ratio, CI Confidence interval, IR Incidence rate, SBP Systolic blood pressure, FBG Fasting blood glucose, LDL-C Low-density lipoprotein cholesterol, MVPA Moderate-to-vigorous physical activity

Table 1.

Baseline characteristics of the study group according to the risk factor targets achieved

Variables	Total	0 targets achieved	1 target achieved	2 targets achieved	3 targets achieved	4 targets achieved	P Value
	(N = 6,501)	(N = 565)	(N = 1,980)	(N = 2,422)	(N = 1,301)	(N = 233)
Age, years	59.28 ± 9.36	63.12 ± 9.87	61.03 ± 9.59	58.69 ± 9.13	56.82 ± 8.27	54.97 ± 8.00	< 0.01
Men, n (%)	2,985 (45.9)	244 (43.2)	915 (46.2)	1,114 (46.0)	617 (47.4)	95 (40.8)	0.03
Current drinker, n (%)	2,167 (33.3)	170 (30.1)	671 (33.9)	796 (32.9)	453 (34.8)	77 (33.0)	0.1
Current smoker, n (%)	1,952 (30.0)	155 (27.4)	601 (30.4)	733 (30.3)	396 (30.4)	67 (28.8)	0.03
Physical activity level, n (%)							< 0.01
Almost no physical activity	4,036 (62.1)	483 (85.5)	1,550 (78.3)	1,526 (63.0)	477 (36.7)	0 (0.0)
Light physical activity	573 (8.8)	82 (14.5)	216 (10.9)	219 (9.0)	56 (4.3)	0 (0.0)
Moderate-to-vigorous physical activity	1,892 (29.1)	0 (0.0)	214 (10.8)	677 (28.0)	768 (59.0)	233 (100.0)
Education, n (%)							0.09
Less than lower secondary	5,948 (91.5)	521 (92.2)	1,825 (92.2)	2,221 (91.7)	1,171 (90.0)	210 (90.1)
Upper secondary& vocational training	500 (7.7)	41 (7.3)	141 (7.1)	178 (7.3)	119 (9.2)	21 (9.0)
Tertiary	53 (0.8)	3 (0.5)	14 (0.7)	23 (1.0)	11 (0.8)	2 (0.9)
Currently married, n (%)	5,754 (88.5)	471 (83.4)	1,693 (85.5)	2,173 (89.7)	1,201 (92.3)	216 (92.7)	< 0.01
Rural residence, n (%)	4367 (67.2)	352 (62.3)	1300 (65.7)	1654 (68.3)	908 (69.8)	153 (65.7)	< 0.01
hs-CRP, mg/L	1.05 (0.56–2.19)	1.48 (0.69–3.01)	1.16 (0.60–2.44)	0.99 (0.54–2.07)	0.90 (0.51–1.90)	0.71 (0.44–1.40)	< 0.01
eGFR, mL/min/1.73 m²	92.14 ± 14.91	86.99 ± 16.56	90.23 ± 15.20	92.84 ± 14.31	94.73 ± 14.17	98.98 ± 12.05	< 0.01
BUN, mg/dL	15.77 ± 4.61	15.87 ± 4.76	16.10 ± 4.68	15.71 ± 4.48	15.46 ± 4.77	14.98 ± 3.90	< 0.01
WC, cm	84.99 ± 12.24	88.48 ± 11.92	86.17 ± 12.34	84.31 ± 12.07	83.64 ± 12.02	81.22 ± 12.14	< 0.01
BMI, kg/m2	23.98 ± 10.69	24.68 ± 4.08	24.23 ± 9.98	23.54 ± 3.71	24.29 ± 19.60	22.96 ± 3.07	< 0.01
FBG, mmol/L	6.22 ± 2.04	7.89 ± 2.98	6.64 ± 2.48	5.92 ± 1.51	5.60 ± 1.02	5.35 ± 0.58	< 0.01
HbA1c, %	5.29 ± 0.83	5.74 ± 1.23	5.41 ± 1.01	5.20 ± 0.64	5.13 ± 0.53	5.02 ± 0.47	< 0.01
SBP, mmHg	131.75 ± 21.41	148.96 ± 16.01	141.65 ± 20.38	127.81 ± 20.48	119.50 ± 15.73	115.26 ± 8.46	< 0.01
DBP, mmHg	76.39 ± 12.01	82.55 ± 10.17	80.58 ± 11.67	74.90 ± 11.88	71.42 ± 10.58	69.10 ± 8.30	< 0.01
LDL-C, mmol/L	3.02 ± 0.92	3.58 ± 0.76	3.29 ± 0.91	3.00 ± 0.88	2.59 ± 0.80	2.13 ± 0.37	< 0.01
HDL-C, mmol/L	1.31 ± 0.39	1.30 ± 0.35	1.30 ± 0.40	1.31 ± 0.39	1.32 ± 0.41	1.33 ± 0.42	< 0.01
TG, mmol/L	2.84 (2.01–4.14)	3.30 (2.27–4.74)	3.03 (2.13–4.35)	2.79 (1.99–4.01)	2.59 (1.81–3.82)	2.43 (1.65–3.62)	< 0.01
Anti-hypertensive drugs, n (%)	1,114 (17.1)	180 (31.9)	434 (21.9)	353 (14.6)	130 (10.0)	17 (7.3)	< 0.01
Antidiabetic medications, n (%)	216 (3.3)	51 (9.0)	94 (4.8)	51 (2.1)	19 (1.5)	1 (0.4)	< 0.01
Lipid-lowering drugs, n (%)	245 (3.8)	37 (6.6)	90 (4.55)	77 (3.18)	38 (2.9)	3 (1.3)	< 0.01
CVD, n (%)	1,221 (18.8)	145 (25.7)	401 (20.3)	438 (18.1)	212 (16.3)	25 (10.7)	< 0.01
CKM syndrome stages, n (%)							< 0.01
Stage 1	1,659 (25.5)	19 (3.4)	243 (12.3)	722 (29.8)	555 (42.6)	120 (51.5)
Stage 2	4,513 (69.4)	464 (82.1)	1,591 (80.3)	1,617 (66.8)	728 (56.0)	113 (48.5)
Stage 3	329 (5.1)	82 (14.5)	146 (7.4)	83 (3.4)	18 (1.4)	0 (0.0)

Abbreviations: hs-CRP high-sensitivity C-reactive protein, eGFR estimated glomerular filtration rate, BUN Blood urea nitrogen, WC Waist circumference, BMI Body mass index, FBG Fasting blood glucose, HbA1c glycated hemoglobin A1c, SBP Systolic blood pressure, DBP Diastolic blood pressure, LDL-C Low-density lipoprotein cholesterol, HDL-C High-density lipoprotein cholesterol, CVD Cardiovascular disease, CKM Cardiovascular-kidney-metabolic

Baseline risk factor target achievement and CVD among individuals with CKM stages 1–3

During a median follow-up of 9.0 years, 1,221 incident CVD events and 539 incident strokes were documented among the individuals with CKM stages 1–3. Compared with participants achieving no targets, those achieving all four targets had a significantly lower risk of CVD (aHR: 0.40; 95% CI: 0.26–0.62) and stroke (aHR: 0.39; 95% CI: 0.20–0.75) (Tables 2 and 3). Each additional target achieved was associated with a 14% lower risk of CVD (HR: 0.86; 95% CI: 0.81–0.91), and a 25% lower risk of stroke (HR: 0.75; 95% CI: 0.69–0.82). Among the four individual targets, achieving the SBP target was associated with the lowest adjusted HRs for both CVD (HR: 0.79; 95% CI: 0.71–0.89) and stroke (HR: 0.56; 95% CI: 0.47–0.68) (Tables 2 and 3).

Table 3.

HRs (95%CIs) of stroke according to the risk factor targets achieved

	Target-achievement group	Case/Total	IR, per 1,000 person-years	Model 1HR (95%CI)	Model 2HR (95%CI)	Model 3 HR (95%CI)
Stroke	SBP ≥ 130	340/3,127	13.1	Ref.	Ref.		Ref.
	SBP < 130	199/3,374	6.8	0.56 (0.47,0.67)	0.56 (0.47,0.68)		0.64 (0.53,0.77)
	FBG ≥ 6.1	213/2,247	11.2	Ref.	Ref.		Ref.
	FBG < 6.1	326/4,254	9.0	0.82 (0.69,0.98)	0.82 (0.69,0.98)		0.88 (0.73,1.05)
	LDL-C ≥ 2.6	375/4,362	10.1	Ref.	Ref.		Ref.
	LDL-C < 2.6	164/2,139	9.1	0.91 (0.76,1.09)	0.92 (0.76,1.10)		0.92 (0.76,1.10)
	MVPA: NO	414/4,609	10.6	Ref.	Ref.		Ref.
	MVPA: Yes	125/1,892	7.6	0.74 (0.61,0.91)	0.74 (0.61,0.91)		0.77 (0.63,0.94)
	0 targets achieved	69/565	14.8	Ref.	Ref.		Ref.
	1 target achieved	209/1,980	12.6	0.86 (0.65,1.13)	0.88 (0.67,1.15)		0.99 (0.75,1.30)
	2 targets achieved	188/2,422	9.1	0.65 (0.49,0.86)	0.66 (0.50,0.88)		0.79 (0.59,1.05)
	3 targets achieved	63/1,301	5.6	0.41 (0.29,0.58)	0.42 (0.29,0.59)		0.51 (0.36,0.72)
	4 targets achieved	10/233	4.9	0.37 (0.19,0.72)	0.39 (0.20,0.75)		0.49 (0.25,0.96)
	Per 1 additional target achieved			0.75 (0.68,0.82)	0.75 (0.69,0.82)		0.80 (0.73,0.88)

Model 1: adjusted for age and sex; Model 2: adjusted for age, sex, smoking status, alcohol consumption, marital status, residential area, eGFR, BUN, and hs-CRP; Model 3: adjusted for all the variables in model 2 and the use of antihypertensive, antidiabetic, and lipid-lowering medications

Abbreviations: CVD Cardiovascular disease, HR Hazard ratio, CI Confidence interval, IR Incidence rate, SBP Systolic blood pressure, FBG Fasting blood glucose, LDL-C Low-density lipoprotein cholesterol, MVPA Moderate-to-vigorous physical activity

Stratified analyses by age (< 60 years vs. ≥ 60 years) and sex revealed a significant interaction between age and the number of achieved targets in relation to CVD risk (P for interaction = 0.04). Specifically, among individuals with all four risk factor targets achieved, those younger than 60 years had a lower risk of CVD than those aged 60 years or older (Table S3).

Dose-response and sensitivity analyses

Restricted cubic spline analyses were conducted to examine the dose-response relationships of SBP, FBG, and LDL-C with the risk of incident CVD after multivariable adjustment. The results demonstrated a non-linear association between SBP (P for nonlinear = 0.010) and CVD risk (Fig. 2A) and a non-linear association between FBG (P for nonlinear = 0.037) and CVD risk (Fig. 2B). In contrast, no statistically significant dose-response relationship was observed for LDL-C (P for overall: SBP, P < 0.001; FBG, P = 0.009; LDL-C, P = 0.263) (Fig. 2C).

Fig. 2.

Dose‑response relationships of (A) SBP, (B) FBG, and C LDL-C levels with between SBP, FBG, and LDL‑C levels and cardiovascular disease in individuals with CKM stages 1-3. Hazard ratios were adjusted for age, sex, smoking status, alcohol consumption, marital status, residential area, eGFR, BUN, and hs-CRP. Abbreviations: SBP, systolic blood pressure; FBG, fasting blood glucose;LDL-C, low-density lipoprotein cholesterol; CKM, cardiovascular-kidney-metabolic

In sensitivity analyses, the observed inverse association between the number of targets achieved and CVD risk among individuals with CKM remained consistent under the following conditions: after excluding participants who developed CVD events within the first two years of follow-up (Table S4); after performing a complete-case analysis excluding individuals with missing covariates (Table S5); after redefining the blood pressure control target as SBP < 130 mm Hg and DBP < 80 mm Hg (Table S6); after accounting for the competing risk of death using Fine-Gray subdistribution hazard models (Table S7); after excluding all CKM Stage 3 participants to address the potential mathematical coupling between the PREVENT equations and the exposure variables (Table S8); and after assigning event time to the midpoint of the censoring interval as an alternative approach to interval censoring (Table S9).

Discussion

In this prospective cohort study of 6,501 individuals with CKM stages 1–3, achieving a greater number of SBP, FBG, LDL-C, and physical activity targets was associated with a lower subsequent risk of incident CVD and stroke. Compared with participants who met zero targets, those achieving all four targets had a 60% lower risk of CVD and a 61% lower risk of stroke. In addition, the inverse association between combined target achievement and CVD risk was more pronounced in participants younger than 60 years compared with those aged 60 years or older.

Previous observational studies have demonstrated that achieving multiple risk factor targets was associated with a lower risk of CVD in populations with isolated conditions, such as prediabetes, hypertension, diabetes, or CKD [25–29]. When these conditions coexist, the associated risk may not be merely additive. Rather, the interplay among metabolic, cardiovascular, and renal dysfunction likely contributes to a synergistic exacerbation of risk, accelerating atherosclerotic progression—a central pathophysiological feature of CKM syndrome [1]. The present study corroborates and extends these findings to individuals with CKM stages 1–3. Our findings are consistent with results from a post hoc analysis of the Systolic Blood Pressure Intervention Trial (SPRINT) in CKM populations, which showed that intensive blood pressure control (< 120 mm Hg) was associated with fewer cardiovascular events compared with standard control (< 140 mm Hg), with benefits appearing more pronounced in those with CKM stage 2 [8]. While AHA guidelines suggest that comprehensive management of lifestyle and metabolic indices in CKM stages 1–3 may facilitate regression to earlier stages, only 3.6% of participants in this study achieved combined control of all four risk factors. These data suggest that, despite guideline recommendations, achieving all recommended targets in real-world CKM populations remains a substantial challenge.

The restricted cubic spline analyses provided further context for the individual components of the composite score. SBP and FBG demonstrated significant dose–response associations with incident CVD, whereas the corresponding association for LDL-C did not reach statistical significance in this cohort. This finding should not be interpreted as evidence against the clinical relevance of LDL-C control in CKM populations. First, LDL-C was assessed only once at baseline, and a single measurement may inadequately capture cumulative lipid exposure or treatment-related changes in LDL-C levels over the follow-up period [30]. Second, individuals with CKM stage 1–3 characteristically present with multiple coexisting metabolic, renal, inflammatory, and lifestyle-related risk factors; in such a multivariable context, the apparent independent association of any single lipid marker may be attenuated by competing pathways and collinearity [6]. Third, the relatively low prevalence of lipid-lowering medication use in this cohort (3.8%) and the reliance on a single baseline LDL-C measurement limited our ability to evaluate treatment-related lipid changes during follow-up. Nevertheless, as the composite score was constructed a priori to reflect guideline-recommended target achievement, and given that randomised evidence has consistently demonstrated LDL-C lowering reduces major vascular events including in patients with CKD [11, 18], LDL-C target achievement was retained as a component of the score.

Stratified analyses revealed potentially meaningful heterogeneity in the association between target achievement and CVD risk. The inverse association appeared stronger among participants younger than 60 years than among older participants, a pattern consistent with the broader cardiovascular prevention literature emphasising the disproportionate long-term benefit of early risk factor modification. Younger individuals generally carry a lower cumulative metabolic burden, and their vascular endothelial function may not yet have sustained irreversible structural damage, such that timely correction of metabolic disturbances could substantially reduce lifetime exposure to atherogenic stimuli — a concept supported by evidence that earlier reductions in LDL-C and blood pressure confer benefits beyond what contemporaneous risk factor levels alone would predict [30, 31]. The UK Prospective Diabetes Study further demonstrated that early intensive glycaemic intervention yields a sustained “legacy effect” that persists well beyond the active treatment period [32]. By contrast, older individuals with CKM more frequently present with established vascular structural changes, such as arterial calcification and increased arterial stiffness, which may limit the extent to which metabolic control can reverse accumulated cardiovascular risk [33]. These observations collectively suggest that the window of greatest opportunity for comprehensive target achievement may lie earlier in the CKM trajectory, before cumulative vascular injury has progressed beyond a threshold amenable to meaningful reversal. With respect to sex, the directional consistency of the inverse association in both women and men, alongside the absence of a statistically significant interaction, suggests that the association between baseline target achievement and CVD risk did not differ substantially by sex in this CKM population. This finding is broadly aligned with evidence from multifactorial intervention studies, including a post-hoc analysis of the NID-2 randomised trial [34], in which intensive multifactorial therapy targeting blood pressure, glycaemia, and lipid levels reduced MACE risk in both women and men, without a statistically significant treatment-by-sex interaction for MACE. Nonetheless, statistical power to detect modest sex-specific differences was limited by the relatively small number of participants, particularly men, who achieved all four targets, and this question warrants further investigation in larger, sex-stratified studies.

This study has several strengths. To our knowledge, it is the first investigation using a nationally representative cohort of middle-aged and older Chinese adults to examine the association between risk factor target achievement and CVD risk specifically in individuals with CKM stages 1–3. The prospective longitudinal design, large sample size, and multi-regional coverage enhance the robustness of the findings. However, several limitations should be considered. First, risk factor target achievement was determined from a single baseline assessment, and the analysis therefore reflects baseline target-achievement status rather than sustained risk factor management over time. Second, the exposure variable and CKM staging criteria share common baseline data sources and were not fully independent. In particular, for CKM stage 2, hypertension, diabetes, and metabolic syndrome were part of the staging criteria and were closely related to the SBP and FBG target components used in the exposure definition, limiting causal interpretation. Third, CKM staging in this study was subject to several data-driven constraints. Stage 2 CKD risk stratification relied on eGFR alone, as UACR data were not systematically collected in CHARLS. Stage 3 was operationally defined using a 10-year CVD risk threshold (PREVENT equations) and very-high-risk CKD, given the absence of echocardiographic and NT-proBNP data; this proxy definition may misclassify some participants. Furthermore, as the PREVENT equations incorporate SBP, FBG, and LDL-C — variables shared with the exposure — participants achieving more risk factor targets were less likely to be classified as Stage 3, potentially attenuating the exposure–outcome association; a sensitivity analysis excluding Stage 3 participants demonstrated consistent findings. Fourth, because CVD events were ascertained at discrete survey waves, the exact date of event onset is unknown; a midpoint sensitivity analysis yielded consistent results. Fifth, residual confounding from unmeasured variables cannot be excluded. Sixth, outcome ascertainment relied on self-reported physician diagnoses, which may introduce recall bias and misclassification, although prior validation studies within CHARLS have suggested acceptable accuracy. Seventh, as the study population consisted solely of Chinese individuals, caution is warranted when generalising findings to other ethnic groups. Finally, given the observational design, causal relationships cannot be established.

Conclusion

In summary, among individuals with CKM stages 1–3, achieving a greater number of baseline-defined targets for SBP, FBG, LDL-C, and physical activity was associated with a lower subsequent risk of incident CVD and stroke. These findings support the clinical relevance of integrated metabolic and lifestyle risk factor assessment in CKM populations, while further studies with repeated measurements are needed to evaluate the impact of sustained target achievement over time.

Abbreviations

CKM

Cardiovascular-kidney-metabolic

AHA

American Heart Association

CKD

Chronic kidney disease

CVD

Cardiovascular disease

CHARLS

China Health and Retirement Longitudinal Study

SBP

Systolic blood pressure

DBP

Diastolic blood pressure

FBG

Fasting blood glucose

LDL-C

Low-density lipoprotein cholesterol

HDL-C

High-density lipoprotein cholesterol

BMI

Body mass index

WC

Waist circumference

eGFR

Estimated glomerular filtration rate

BUN

Blood urea nitrogen

hs-CRP

High-sensitivity C-reactive protein

HbA1c

Glycated hemoglobin A1c

HR

Hazard ratio

CI

Confidence interval

IR

Incidence rate

MVPA

Moderate-to-vigorous physical activity

Authors’ contributions

Bin-Bin He: Writing – original draft, Methodology, Data curation, Validation, Data curation, Formal analysis. Guo-Yan Wu: Writing – review & editing, Writing – original draft, Conceptualization **.**.

Funding

There is no research funding.

Data availability

The dataset supporting the conclusions of this article is available in the CHARLS repository, https://charls.pku.edu.cn/.

Declarations

Ethics approval and consent to participate

Approval for this project was granted by the Biomedical Ethics Committee of Peking University (Beijing, China) (IRB00001052-11015), with our study adhering strictly to the principles outlined in the Declaration of Helsinki, and obtaining informed consent from all participants.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.