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American Journal of Preventive Cardiology logoLink to American Journal of Preventive Cardiology
. 2026 Jan 3;25:101405. doi: 10.1016/j.ajpc.2025.101405

Population estimates, trends, characteristics and prognostic outcomes of cardiovascular-kidney-liver-metabolic health: A population-based study

Yiming Chen a,1, Haryo Raden Himan b,1, Yalkin Yazicioglu a,1, Rachel Goh a, Yip Han Chin c, Bryan Chong c, Srinithy Nagarajan a, Martin Tze Wah Kueh d,e, Jiong-Wei Wang n,o,p, Mark M Muthiah f,g, Xin Zhou h, Mark Y Chan a,b, Anurag Mehta i, Mamas A Mamas j,2, Muhammad Shahzeb Khan k,l,m,2, Nicholas WS Chew a,b,2,⁎
PMCID: PMC12848996  PMID: 41613353

Abstract

Background

Cardiovascular-kidney-liver-metabolic (CKLM) diseases constitute the presence of MASLD, T2D, CKD, obesity and/or CVD, that often co-exist and synergistically increase cardiovascular risk. However, the prevalence, extent and outcomes of the CKLM burden remain poorly understood.

Methods

This population-based study utilised National Health and Nutrition Examination Survey (NHANES) 2007–2018 database, examining individuals’ number (0–4) and permutations of CKLM diseases, namely: type 2 diabetes (T2D), obesity, metabolic dysfunction-associated steatotic liver disease (MASLD), chronic kidney disease (CKD). The primary outcome was all-cause mortality. Cox regression models were constructed to evaluate the relationship between CKLM phenotypes and all-cause mortality, adjusting for age, sex, race, socioeconomic status, and physical activity.

Results

The weighted cohort represented 97.8 million US adults (mean age 47.7 ± 16.7 years). 54.16% of the cohort had ≥1 CKLM diseases. From 2007 to 2018, the proportion of individuals with ≥2 CKLM diseases increased from 32.4% to 55.6% of the population, with the largest increase in proportion of individuals with 4 diseases. The most common CKLM phenotype was MASLD-obesity (23.0%), followed by MASLD-obesity-T2D (5.1%). The highest mortality rates were observed in individuals with 4 CKLM diseases (21.4%), followed by 3 diseases (12.2%). Cox regression revealed that 4 diseases predicted the highest mortality risk (aHR 2.24, 95%CI: 1.66–3.02, p < 0.001), followed by 3 diseases (aHR 1.52, 95%CI: 1.25–1.85, p < 0.001). The MASLD-T2D-CKD phenotype (aHR 3.13, 95%CI: 1.80–5.42, p < 0.001) and T2D-CKD phenotype (aHR 3.26, 95%CI: 2.33–4.55, p < 0.001) predicted the highest mortality risk.

Conclusions

The CKLM multimorbidity burden is rising in the US population. Higher CKLM burden (≥3 CKLM diseases) and CKD-centric phenotypes (MASLD-T2D-CKD or T2D-CKD) independently predict the highest mortality risk.

Keywords: Cardiovascular disease, Metabolic dysfunction-associated steatotic liver disease (MASLD), Cardiovascular-kidney-metabolic, Chronic kidney disease, Cardiovascular multimorbidity

List of abbreviations

CKD

Chronic kidney disease

CKM

Cardiovascular-kidney-metabolic

CKLM

Cardiovascular-kidney-liver-metabolic

CVD

Cardiovascular disease

MASLD

Metabolic dysfunction-associated steatotic liver disease

T2D

Type 2 diabetes

1. Introduction

Cardiovascular disease (CVD) has remained the leading cause of mortality worldwide, with an estimated 19.2 million deaths in 2022 representing 32 % of global mortality[1]. The burden of CVD is set to almost double from 2025 to 2050, a reflection of the anticipated rising trends of metabolic risk factors over the same timeframe[2,3]. The rising epidemic of metabolic dysfunction-associated steatotic liver disease (MASLD), type 2 diabetes mellitus (T2D), obesity, chronic kidney disease (CKD) and CVD are often intertwined [[3], [4], [5]]. Recent studies have described the high prevalence of cardiovascular-kidney-metabolic (CKM) syndrome, affecting nearly 90 % of the United States (US) population between 2011 and 20206, and has been associated with increased lifetime risk of CVD events, kidney function decline, and all-cause mortality[6].

Despite the growing recognition of systemic metabolic health under the CKM framework, the inclusion of MASLD within the wider cardiovascular-kidney-liver-metabolic (CKLM) construct has gained traction. The CKLM framework constitutes the presence of MASLD, T2D, CKD, obesity and CVD[3] that tend to co-exist and interact, and are often underpinned by shared mechanistic pathways and socioeconomic influences[[7], [8], [9], [10], [11]]. The mechanistic pathways of MASLD are underscored by the multisystemic, deleterious effects of excess or dysfunctional visceral adiposity, that drives insulin resistance, systemic inflammation, oxidative stress, atherogenic dyslipidemia, and vascular dysfunction[12]. Dose-response relationships exist between MASLD and the CKLM constituents, as increasing MASLD severity has been associated with higher incidence of T2D, CKD, and CVD [5,6,[13], [14], [15], [16], [17], [18]]. In addition, the mounting evidence on cardiometabolic multimorbidity has shown increasing number of CKLM diseases associated with multiplicative risk of mortality[19]. There is increasing clinical justification for the expanded CKLM syndromic model[11] as this has the potential to enhance early detection and prevention of individuals at risk of cardiometabolic diseases, and improving clinical risk stratification beyond the existing American Heart Association CKM guidance[12,20,21].

Therefore the present study seeks to address the knowledge gaps by examining the population estimates, cardiovascular outcomes, and long-term mortality of the CKLM syndrome. We provide insights into the impact of the extent, patterns and intersections of the CKLM disease burden on cardiovascular outcomes and long-term survival in the community-dwelling adult population. The proposed CKLM framework is an important next step in enhancing contemporary risk stratification models for the early detection and prevention of CVD.

2. Methods

2.1. Setting and design

This study utilised data from the National Health and Nutrition Examination Survey (NHANES) 2007–2018, a national database for health and nutrition in the US. The NHANES is a prospective cohort study of adults aged ≥18 years, and is a nationally representative study conducted by the National Center for Health Statistics of the Centers for Disease Control and Prevention in the US. The study design has been described previously (www.cdc.gov/nchs/nhanes/about_nhanes.htm). The longitudinal data on death and death date was identified through linkage to the National Death Index. Follow-up duration was derived from the date of examination until the date of death or the end of the study (31 December 2019). The mortality causes were classified based on the International Classification of Diseases.

The inclusion criteria were adults aged 20–80 years, with complete data on T2D, obesity, MASLD, CKD and CVD status, as well as mortality. Participants with missing data were excluded from the analysis (Supplementary Figure 1). Participants were allocated into 5 groups based on the number of CKLM diseases (namely, the presence of T2D, obesity, MASLD and CKD), from 0 CKLM diseases to 4 diseases.

MASLD was defined as: evidence of hepatic steatosis and the presence of at least one of five cardiometabolic criteria, (1) body mass index (BMI) ≥23 kg/m2 (Asian) or ≥25 kg/m2 (Caucasian), or waist-circumference ≥94 cm for males or ≥80 cm for females; (2) T2D (fasting plasma glucose ≥5.6mmol/L, haemoglobin A1C (HbA1C) ≥5.7 %, or on glucose-lowering treatment); (3) hypertension (blood pressure ≥130/80 mmHg from 3 readings or on anti-hypertensive treatment); (4) hypertriglyceridaemia (plasma triglyceride ≥1.7mmol/L or lipid-lowering treatment) and (5) hyperlipidaemia (high-density lipoprotein (HDL) ≤1.0mmol/L for males or <1.3mmol/L for females, or lipid-lowering treatment)[22]. As liver biopsy data was unavailable, hepatic steatosis was determined using the Fatty Liver Index (FLI), calculated with the formula

FLI=(e0.953*loge(triglycerides[mg/dL])+0.139*BMI[kg/m2]+0.718*loge(gamma−glutamyl−transferase[U/L])+0.053*waistcircumference[cm]−15.745)(1+e0.953*loge(triglycerides[mg/dL])+0.139*BMI[kg/m2]+0.718*loge(gamma−glutamyl−transferase[U/L])+0.053*waistcircumference[cm]−15.745)*100.

[23]. A threshold of FLI ≥60 has been externally validated in population studies with ultrasound-proven hepatic steatosis[18,24,25], demonstrating a sensitivity of 61 %, specificity of 86 % and positive likelihood ratio of 4.323. Advanced hepatic fibrosis was assessed using the Fibrosis-4 Index (FIB-4), which includes alanine aminotransferase (ALT), aspartate aminotransferase (AST), age and platelet count. The formula used was FIB-4 = age [years] × AST [IU/L] / (platelet [× 109/L] × ALT [IU/L]1/2). A FIB-4 threshold of ≥2.67 was defined as advanced fibrosis, with a 80 % positive predictive value and 83 % negative predictive value[26,27].

CKD was categorized based on the Kidney Disease: Improving Global Outcomes (KDIGO) definition[24], which encompasses kidney damage for >3 months, defined by structural or functional abnormalities of the kidney with or without decreased glomerular filtration rate (GFR), manifesting by either: (1) pathologic abnormalities; (2) markers of kidney damage, including abnormalities in the composition of blood or urine, or abnormalities on imaging (3) GFR <60 mL/min/1.73 m2 for ≥3 months with or without kidney damage or (4) presence of albuminuria defined as urine albumin-creatinine ratio (ACR) >30 mg/g. The estimated GFR (eGFR) was calculated according to the CKD-EPI 2021 formula[28]. An eGFR of <60 mL/min/1.73 m2, urine ACR >30mg/g or individuals on dialysis were used to define CKD. T2D was defined based on the World Health Organization guidelines as (1) fasting plasma blood glucose ≥7.0 mmol/L or (2) HbA1C of ≥6.5 % or (3) self-reported T2DM or (4) individuals on anti-diabetic medications. Obesity[29] was defined as BMI >30 kg/m2.

Other study definitions can be found in Supplementary Table 1.

2.2. Study outcomes

The primary outcome of the study was all-cause mortality. The secondary outcomes were cardiovascular-related mortality, heart failure, myocardial infarction, coronary heart disease and stroke. These outcomes were adjudicated based on the cardiovascular endpoints established in previous primary and secondary prevention studies on CKLM syndrome, derived from prior NHANES studies[[30], [31], [32]] as well as other study cohorts[4,7,[33], [34], [35], [36], [37], [38], [39]].

2.3. Statistical analysis

All statistical analyses were conducted using R version 4.5.1 within RStudio, employing the survey and srvyr packages to process the complex multistage probability sampling design of NHANES and produce robust estimates of the US population. Survey weights, primary sampling units, and strata were incorporated into all analyses to ensure accurate variance estimation, population inference, and nationally representative estimates. The detailed methodology of the weight methods has been described previously[40]. Continuous variables were presented as mean (standard deviation), and categorical variables as number (%). Continuous variables were compared by analysis of variance (ANOVA), while categorical variables were compared using Pearson chi-squared test.

Kaplan-Meier survival curves were constructed to examine time-to-death across the categories of CKLM disease burden, with survey weights applied to estimate population-level mortality patterns. Sex-specific subgroup analysis was performed for the survival analysis. Weighted log-rank tests were used to assess statistical significance between survival curves. Weighted prevalence heatmaps were generated to visualize the pairwise co-occurrence patterns among the CKLM diseases. Colour gradients represented the prevalence percentage from 0 to 100 %. UpSet plots were constructed using survey-weighted data to display the prevalence of all possible combinations of the four CKLM diseases. These visualizations display intersection sizes and marginal distributions, utilizing dual-layout plots that incorporate both standard intersection bars and weighted prevalence estimates for each unique combination. Additional analysis was performed by sex, race/ethnicity (Non-Hispanic white, Non-Hispanic Black, Non-Hispanic Asian, Hispanic, and other), and socioeconomic status. Income levels were categorized using the poverty-to-income ratio (PIR), with thresholds of PIR <1 as low, 1–4 as middle, and >4 as high.

Cox proportional hazards regression models were used to investigate the all-cause mortality risk across CKLM disease patterns, including a count-based model with five levels (0, 1, 2, 3, 4 CKLM diseases), and a specific combination model with categorical variables representing all observed combinations of the 4 CKLM diseases, both using no CKLM disease as the reference. Based on established predictors that demonstrated significant associations with mortality in individuals with CKLM diseases as described in prior studies[[41], [42], [43], [44]], we performed a systematic covariate selection approach to identify the covariates that would maximize the model discriminative ability, measured by the concordance (C) statistic. The selected Cox regression models were adjusted for age, sex, race or ethnic group, socioeconomic status, and physical activity. The final model selections also underwent tests of proportionality using three complementary approaches – the Grambsch-Therneau test with Schoenfeld residuals, the Variance Inflation Factor analysis, and the log-log plots (Supplementary Table 2, Supplementary Figures 2–5). In addition, the odds ratios and their 95 % confidence intervals (CI) were calculated for the secondary outcomes across the CKLM disease categories. A p-value of ≤0.05 was considered statistically significant. The detailed methodology can be found in Supplementary Materials 1–2.

NHANES is approved by the National Center for Health Statistics Research Ethics Review Board, and all participants provided informed consent.

3. Results

3.1. Population estimates and trends of cardiovascular-kidney-liver-metabolic burden

Out of a nationally representative dataset of 59,842 adults, 2428 were excluded for missing survey weight data, 40,164 for missing data on CKLM diseases, 2322 for missing follow-up time data, and 776 were further excluded for missing CVD data. Following which, the remaining 14,152 adults were included in the final study population. This represented a weighted sample of 97,787,555 US adults, aged 20–80 years old with data on T2D, obesity, MASLD, CKD and CVD status, from 2007 to 2018, which was analysed for the present study (Supplementary Figure 1). The mean study follow-up duration was 6.7 ± 3.5 years. When stratified according to number of CKLM diseases, the majority of the study population had 0 CKLM disease (N = 44.8 million, 45.84 %), followed by 2 (N = 25.7 million, 26.2 %), 1 (N = 17.0 million, 17.3 %), 3 (N = 8.0 million, 8.19 %), and 4 diseases (N = 2.3 million, 2.35 %) (Table 1). From 2007 to 2018, the number of individuals with ≥2 CKLM diseases increased by 38.9 % from 29.9 million to 41.5 million (32.4 % to 55.6 % of the population), with the largest increase observed in those with 4 CKLM diseases (from 0.91 % to 3.49 %). The proportion of individuals with only 1 disease decreased over the decade (from 17.24 % to 15.53 %) (Supplementary Figure 6).

Table 1.

Baseline demographics of NHANES 2007–2018 cohort stratified by number of CKLM diseases.

Number of CKLM diseases
Overall (N = 97,787,555) 0 (N = 44,829,839) 1 (N = 17,004,383) 2 (N = 25,651,827) 3 (N = 8004,633) 4 (N = 2296,874) P-value
Follow-up time, years 6.70 (3.46) 6.96 (3.46) 6.65 (3.40) 6.55 (3.51) 6.25 (3.32) 5.27 (2.92) <0.001
Age, y 48 (16.73) 44 (16.36) 51 (16.94) 48 (15.87) 57 (14.26) 61 (13.76) <0.001
Sex, female 50,224,551 (51.4) 24,187,053 (54.0) 7988,462 (47.0) 12,446,574 (48.5) 4428,823 (55.3) 1173,639 (51.1) <0.001
Body mass index*
 Obesity 35,983,132 (36.8) 0 3314,896 (19.5) 22,831,599 (89.0) 7539,764 (94.2) 2296,874 (100.0) <0.001
 Non-obesity 61,804,423 (63.2) 44,829,839 (100.0) 13,689,487 (80.5) 2820,228 (11.0) 464,869 (5.8) 0 <0.001
Waist circumference, cm 99.40 (16.50) 87.66 (9.24) 97.00 (9.17) 113.62 (12.67) 118.32 (13.14) 121.72 (12.90) <0.001
Systolic blood pressure, mmHg 121.10 (16.81) 116.57 (14.93) 123.75 (17.28) 123.63 (15.97) 129.01 (19.07) 134.32 (21.04) <0.001
Diastolic blood pressure, mmHg 70.15 (11.94) 68.33 (10.71) 70.61 (12.21) 72.59 (12.10) 71.40 (14.06) 70.82 (16.56) <0.001
Current smoking* 42,190,011 (43.1) 21,780,643 (48.6) 7607,578 (44.7) 10,007,765 (39.0) 2565,371 (32.0) 545,808 (23.8) <0.001
Family history of coronary heart disease* 12,754,200 (13.0) 4830,021 (10.8) 2111,699 (12.4) 3640,220 (14.2) 1760,001 (22.0) 418,117 (18.2) <0.001
Ethnic group*
 Non-Hispanic White 65,508,667 (67.0) 30,635,151 (68.3) 11,221,568 (66.0) 17,085,671 (66.6) 5201,285 (65.0) 1364,992 (59.4) 0.014
 Non-Hispanic Black 10,126,182 (10.4) 3932,370 (8.8) 1748,965 (10.3) 2947,787 (11.5) 1137,159 (14.2) 359,901 (15.7) <0.001
 Non-Hispanic Asian 3592,375 (3.7) 2272,312 (5.1) 739,963 (4.4) 415,755 (1.6) 131,117 (1.6) 33,227 (1.4) <0.001
 Hispanic 14,461,067 (14.8) 5914,168 (13.2) 2651,647 (15.6) 4229,069 (16.5) 1265,160 (15.8) 401,023 (17.5) <0.001
 Other 4099,264 (4.1) 2075,838 (4.6) 642,240 (3.7) 973,545 (3.8) 269,911 (3.4) 137,730 (6.0) 0.290
Poverty-to-income ratio*
 Low income 14,214,704 (14.5) 6153,478 (13.7) 2500,922 (14.7) 3939,917 (15.4) 1257,462 (15.7) 362,845 (15.8) 0.199
 Middle income 48,299,905 (49.4) 20,595,402 (45.9) 8708,634 (51.2) 13,238,264 (51.6) 4463,175 (55.8) 1292,270 (56.3) <0.001
 High income 35,272,947 (36.1) 18,080,959 (40.4) 5794,827 (34.1) 8473,646 (33.0) 2283,996 (28.5) 641,758 (27.9) <0.001
Comorbidity
Hypertension* 35,435,056 (36.2) 9030,006 (20.1) 7224,885 (42.5) 11,427,267 (44.5) 5716,178 (71.4) 1972,422 (85.9) <0.001
Diabetes mellitus 12,111,950 (12.4) 0 2031,545 (11.9) 2245,963 (8.8) 5537,569 (69.2) 2296,874 (100.0) <0.001
Dyslipidemia* 47,113,349 (48.2) 16,965,248 (37.8) 9675,196 (56.9) 13,515,719 (52.7) 5326,157 (66.5) 1647,777 (71.7) <0.001
MASLD 42,245,296 (43.2) 0 7611,784 (44.8) 24,412,674 (95.2) 7923,964 (99.0) 2296,874 (100.0) <0.001
CKD status
 CKD 11,169,052 (11.4) 0 4046,158 (23.8) 1813,418 (7.1) 3012,602 (37.6) 2296,874 (100.0) <0.001
 On dialysis* 365,800 (0.4) 0 146,140 (0.9) 73,843 (0.3) 72,611 (0.9) 73,206 (3.2) <0.001
Chronic obstructive pulmonary disease* 3683,155 (3.8) 1247,136 (2.8) 675,673 (4.0) 988,892 (3.9) 505,593 (6.3) 217,019 (9.4) <0.001
Biochemical data
Serum creatinine, mg/dL 0.87 (0.33) 0.83 (0.17) 0.91 (0.39) 0.88 (0.37) 0.91 (0.51) 1.16 (0.75) <0.001
eGFR, mL/min per 1.73m[2] 97.04 (20.64) 101.03 (17.31) 93.29 (22.66) 97.23 (19.76) 88.66 (24.07) 74.22 (30.47) <0.001
Proteinuria* 9157,658 (9.4) 0 3137,310 (18.5) 1447,501 (5.6) 2515,018 (31.4) 1778,666 (77.4) <0.001
Uric acid, mg/dL 5.47 (1.40) 5.00 (1.21) 5.61 (1.36) 5.95 (1.35) 6.03 (1.59) 6.46 (1.73) <0.001
Albumin, g/dL 4.23 (0.34) 4.31 (0.32) 4.25 (0.34) 4.16 (0.33) 4.08 (0.34) 4.01 (0.36) <0.001
AST, U/L 25.02 (17.32) 23.78 (13.38) 26.17 (21.18) 25.99 (18.73) 26.48 (22.74) 25.03 (13.36) <0.001
ALT, U/L 25.10 (17.93) 21.54 (12.83) 26.35 (22.63) 29.50 (20.31) 27.77 (18.90) 26.79 (18.37) <0.001
γGGT, U/L 27.66 (33.89) 19.72 (16.80) 33.41 (45.34) 33.56 (39.28) 36.35 (41.18) 43.68 (51.63) <0.001
Fasting plasma glucose, mmol/L 5.94 (1.74) 5.38 (0.57) 5.93 (1.67) 5.99 (1.42) 7.91 (3.08) 9.44 (3.87) <0.001
HbA1C, 5.6 (0.94) 5.3 (0.35) 5.6 (0.84) 5.7 (0.81) 6.8 (1.52) 7.8 (1.87) <0.001
Total cholesterol, mmol/L 4.97 (1.06) 4.91 (1.01) 5.14 (1.19) 5.04 (1.01) 4.83 (1.16) 4.74 (1.27) <0.001
LDL cholesterol, mmol/L 2.94 (0.91) 2.89 (0.87) 3.03 (0.99) 3.05 (0.86) 2.80 (0.98) 2.61 (1.03) <0.001
HDL cholesterol, mmol/L 1.40 (0.42) 1.55 (0.43) 1.39 (0.46) 1.25 (0.32) 1.21 (0.34) 1.18 (0.30) <0.001
Triglycerides, mmol/L 1.40 (1.14) 1.03 (0.55) 1.62 (1.44) 1.66 (1.14) 1.87 (1.72) 2.22 (2.08) <0.001
Medications
Antihypertensive drugs 88,186 (0.1) 23,131 (0.1) 7345 (0) 33,242 (0.1) 24,468 (0.3) 0 0.077
Glucose-lowering therapies 3595,522 (3.7) 0 671,635 (0.7) 581,541 (0.6) 1245,024 (1.3) 1097,321 (1.1) 0.546
Lipid lowering drugs 50,259 (0.1) 2425 (0) 4039 (0) 20,195 (0.1) 12,751 (0.2) 10,850 (0.5) <0.001
Aspirin 34,198,778 (35.0) 11,939,981 (26.6) 6500,590 (38.2) 8099,870 (31.6) 4266,878 (53.3) 1608,259 (70.0) <0.001
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Categorical variables are presented as n ( %) and continuous variables are presented as mean (standard deviation). p < 0.05 is taken as statistical significance (in bold).

⁎

Missing data COPD (N = 47,997,459), current smoking (N = 54,100,708), dyslipidemia (N = 184,644), family history of coronary heart disease (N = 2191,730) hypertension (N = 2455,255), poverty-to-income ratio (N = 6883,595), proteinuria (N = 15,762,150),

ALT, alanine aminotransferase; AST, aspartate aminotransferase; CKD, chronic kidney disease; CKLM, cardiovascular-kidney-liver-metabolic; eGFR, estimated glomerular filtration rate; HbA1C, hemoglobin A1C; LDL cholesterol, low-density lipoprotein cholesterol; HDL cholesterol, high-density lipoprotein cholesterol; MASLD, metabolic dysfunction-associated steatotic liver disease; γGGT, gamma-glutamyl transpeptidase.

3.2. Clinical characteristics and intersectionality of cardiovascular-kidney-liver-metabolic phenotypes

The mean age of the study population was 47.7 ± 16.7 years, and 51.4 % were female. Individuals with increasing number of CKLM diseases tended to be older (p < 0.001), with a greater proportion of females (p < 0.001). Larger proportions of individuals were non-Hispanic Black (p < 0.001), and from low and middle-income groups (p < 0.001), with an increasing number of CKLM diseases (Fig. 1). There were higher rates of hypertension (p < 0.001), dyslipidemia (p < 0.001), chronic obstructive pulmonary disease (p < 0.001) with increasing number of CKLM diseases (Table 1).

Fig. 1.

Fig 1

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Distribution of the burden of cardiovascular-kidney-liver-metabolic syndrome, stratified by sex, race or ethnic group, and socio-economic status.

Among CKLM diseases, the greatest intersectionality among diseases was observed between obesity and MASLD, with 89.5 % of the obesity population with concomitant MASLD, and 76.2 % of the MASLD population with concomitant obesity. In the population with T2D, substantial proportions of individuals have concomitant MASLD (73.7 %), followed by obesity (62.7 %). In the population with CKD, large proportions of individuals have concomitant MASLD (53.9 %), followed by obesity (44.9 %) (Fig. 2). The most common CKLM phenotype was concomitant MASLD-obesity (N = 22.4 million; 23.0 %), followed by MASLD only (N = 7.6 million; 7.8 %), and concomitant MASLD-obesity-T2D (N = 4.9 million; 5.1 %) (Fig. 3).

Fig. 2.

Fig 2

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Intersectionality of cardiovascular-kidney-liver-metabolic diseases.

Y-axis represents the population subgroup with a CKLM-related disease, X-axis represents the presence of the coexistent CKLM disease within the population subgroup.

Fig. 3.

Fig 3

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Population estimates of cardiovascular-kidney-liver-metabolic phenotypes in the NHANES 2007–2018 cohort.

3.3. Study outcomes

The study population with at least one CKLM disease had higher all-cause mortality rate (8.3 %, p < 0.001) compared to those without CKLM diseases (3.7 %, p < 0.001). The highest all-cause mortality rates were found in the group with 4 CKLM diseases (21.4 %), followed by those with 3 (12.2 %, p < 0.001). Lower all-cause mortality rates were observed in the groups with 1 (9.3 %) or 2 CKLM diseases (5.4 %, p < 0.001) (Table 2). Kaplan-Meier curves demonstrated individuals with 4 CKLM diseases portended the least favourable survival across 10-years, followed by those with 3 diseases (p < 0.0001) (Fig. 4). These trends were similar in the sex-specific subgroup analysis for males and females (Supplementary Figure 7). Compared to the population without CKLM disease, Cox regression demonstrated that the group with 1 CKLM disease (adjusted Hazard Ratio [aHR] 1.40, 95 % CI: 1.15–1.70, p < 0.001) was independently associated with increased all-cause mortality following adjustment. There was a larger magnitude of all-cause mortality risk in groups with 3 (aHR 1.52, 95 % CI: 1.25–1.85, p < 0.001) or 4 CKLM diseases (aHR 2.24, 95 % CI: 1.66–3.02, p < 0.001). There were trends towards increased all-cause mortality risk in the group with 2 CKLM diseases (aHR 1.21; 95 % CI: 0.96–1.52, p = 0.106), although this did not reach statistical significance after adjusting for confounders (Table 3, Supplementary Figure 8).

Table 2.

Study outcomes stratified by number of CKLM diseases.

Number of CKLM diseases
Overall (N = 97,787,555) 0 (N = 44,829,839) 1 (N = 17,004,383) 2 (N = 25,651,827) 3 (N = 8004,633) 4 (N = 2296,874) P-value
Long-term all-cause mortality 6091,347 1671,966 (3.7) 1573,906 (9.3) 1378,980 (5.4) 974,906 (12.2) 491,589 (21.4) <0.001
Cardiovascular mortality 1723,942 383,603 (0.9) 408,216 (2.4) 459,196 (1.8) 248,142 (3.1) 224,785 (9.8) <0.001
Heart failure 2242,911 307,061 (0.7) 502,188 (3.0) 580,218 (2.3) 517,327 (6.5) 336,117 (14.7) <0.001
Myocardial infarction 3362,347 839,389 (1.9) 703,181 (4.2) 752,158 (2.9) 700,164 (8.8) 367,455 (16.1) <0.001
Coronary heart disease 3193,893 679,673 (1.5) 676,816 (4.0) 907,756 (3.5) 642,913 (8.1) 286,735 (12.6) <0.001
Stroke 2670,525 720,142 (1.6) 634,403 (3.7) 653,400 (2.6) 350,939 (4.4) 311,641 (13.7) <0.001
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CKLM, cardiovascular-kidney-liver-metabolic.

Categorical variables are presented as n ( %).

Fig. 4.

Fig 4

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Kaplan-Meier curves of all-cause mortality of the overall population with varying burden of cardiovascular-kidney-liver-metabolic syndrome.

Table 3.

Cox regression analysis for long-term all-cause mortality, stratified by number and types of CKLM diseases.

Unadjusted Hazard Ratio (95 % CI) P-value Adjusted Hazard Ratio (95 % CI)
Number of participants Number of deaths Age, sex, race or ethnic group P-value Age, sex, race or ethnic group, socioeconomic status and physical activity P-value
4 diseases 2296,874 491,589 8.21 (6.10–11.04) <0.001 2.51 (1.86–3.39) <0.001 2.24 (1.66–3.02) <0.001
3 diseases 8004,633 974,906 3.75 (3.04–4.61) <0.001 1.78 (1.44–2.20) <0.001 1.52 (1.25–1.85) <0.001
2 diseases 25,651,827 1378,980 1.55 (1.24–1.94) <0.001 1.22 (0.98–1.53) 0.080 1.21 (0.96–1.52) 0.106
1 disease 17,004,383 1573,906 2.63 (2.22–3.12) <0.001 1.49 (1.24–1.80) <0.001 1.40 (1.15–1.70) <0.001
MASLD-T2D-Obesity 4992,031 433,926 2.48 (1.88–3.29) <0.001 1.45 (1.12–1.89) 0.005 1.23 (0.94–1.62) 0.132
MASLD-CKD-Obesity 2467,064 373,710 5.20 (3.59–7.54) <0.001 2.07 (1.38–3.09) <0.001 1.79 (1.26–2.55) 0.001
MASLD-T2D-CKD 464,869 147,595 14.85 (9.20–23.97) <0.001 3.74 (2.26–6.20) <0.001 3.13 (1.80–5.42) <0.001
T2D-CKD-Obesity 80,669 19,675 11.11 (3.54–34.85) <0.001 2.02 (0.60–6.77) 0.255 2.00 (0.56–7.15) 0.289
MASLD-T2D 1173,658 128,292 3.17 (1.95–5.16) <0.001 1.27 (0.79–2.06) 0.320 1.32 (0.79–2.22) 0.286
MASLD-Obesity 22,445,376 746,494 0.94 (0.71–1.26) 0.690 0.91 (0.68–1.21) 0.508 0.93 (0.69–1.25) 0.614
MASLD-CKD 793,640 168,887 7.50 (4.74–11.86) <0.001 1.96 (1.28–2.99) 0.002 1.57 (1.01–2.45) 0.047
Obesity-T2D 219,374 2796 0.32 (0.07–1.44) 0.138 0.21 (0.05–0.86) 0.030 0.22 (0.06–0.87) 0.030
Obesity-CKD 166,848 23,882 4.92 (1.63–14.87) 0.005 2.18 (0.75–6.37) 0.154 2.82 (0.99–7.98) 0.051
T2D-CKD 852,930 308,629 14.85 (9.20–23.97) <0.001 3.74 (2.26–6.20) <0.001 3.26 (2.33–4.55) <0.001
MASLD only 7611,784 459,269 1.51 (1.14–2.00) 0.004 1.13 (0.85–1.50) 0.390 1.06 (0.79–1.43) 0.680
Obesity only 3314,896 129,197 1.12 (0.65–1.93) 0.687 1.38 (0.78–2.44) 0.273 1.47 (0.81–2.66) 0.209
T2D only 2031,545 226,663 3.63 (2.61–5.03) <0.001 1.49 (1.09–2.03) 0.012 1.48 (1.07–2.06) 0.018
CKD only 4046,158 758,777 6.69 (5.38–8.33) <0.001 2.08 (1.69–2.56) <0.001 1.88 (1.50–2.36) <0.001
0 diseases 1671,966 [Reference] — [Reference] — [Reference] —
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CKLM, cardiovascular-kidney-liver-metabolic.

Similar trends were observed with cardiovascular-related mortality across the CKLM phenotypes. The highest cardiovascular mortality rate was observed in the group with 4 CKLM diseases (9.8 %), followed by those with 3 (3.1 %) (p < 0.001) (Table 2). Kaplan-Meier curves demonstrated individuals with 4 CKLM diseases portended the least favourable cardiovascular-related mortality, with early divergence of the survival curves at 2 years of follow-up (p < 0.0001) (Fig. 5). These cardiovascular-related mortality trends remained unchanged in the sex-specific subgroup analysis (Supplementary Figure 9).

Fig. 5.

Fig 5

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Kaplan-Meier curves of cardiovascular mortality of the overall population with varying burden of cardiovascular-kidney-liver-metabolic syndrome.

Individuals with 4 CKLM diseases had the highest rates of heart failure (14.7 %), myocardial infarction (16.1 %), coronary heart disease (12.6 %) and stroke (13.7 %), followed by those with 3 and 2 CKLM diseases (p < 0.001 for all) (Table 2). Compared to the population without CKLM disease, the population with ≥1 CKLM disease had significantly higher risk of heart failure, myocardial infarction, coronary heart disease and stroke. There were large increments in the magnitude of risks in heart failure, myocardial infarction and coronary heart disease in the groups with 3 and 4 CKLM diseases, compared to those with 1 and 2 CKLM diseases (Table 4).

Table 4.

Odds ratio of secondary outcomes across the number of CKLM diseases.

Number of participants Number of cases Odds ratio P-value
Heart failure
 4 diseases 491,589 336,117 25.04 (16.36–38.31) <0.001
 3 diseases 8004,633 517,327 10.11 (6.65–15.36) <0.001
 2 diseases 25,651,827 580,218 3.36 (2.31–4.88) <0.001
 1 disease 17,004,383 502,188 4.42 (3.08–6.36) <0.001
 0 diseases 44,829,839 307,061 [Reference] —
Myocardial infarction
 4 diseases 491,589 286,735 9.33 (5.95–14.64) <0.001
 3 diseases 8004,633 642,913 5.73 (4.21–7.79) <0.001
 2 diseases 25,651,827 907,756 2.39 (1.84–3.09) <0.001
 1 disease 17,004,383 676,816 2.70 (1.86–3.92) <0.001
 0 diseases 44,829,839 679,673 [Reference] —
Coronary heart disease
 4 diseases 491,589 367,455 10.06 (6.73–15.03) <0.001
 3 diseases 8004,633 700,164 5.07 (3.26–7.89) <0.001
 2 diseases 25,651,827 752,158 1.59 (1.19–2.12) 0.003
 1 disease 17,004,383 703,181 2.27 (1.56–3.29) <0.001
 0 diseases 44,829,839 839,389 [Reference] —
Stroke
 4 diseases 491,589 311,641 9.69 (6.01–15.60) <0.001
 3 diseases 8004,633 350,939 2.83 (1.89–4.25) <0.001
 2 diseases 25,651,827 653,400 1.60 (1.15–2.24) 0.007
 1 disease 17,004,383 634,403 2.38 (1.65–3.44) <0.001
 0 diseases 44,829,839 720,142 [Reference] —
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CKLM, cardiovascular-kidney-liver-metabolic.

3.4. Subgroup analysis on specific cardiovascular-kidney-liver-metabolic intersections

In the subgroup analysis of the population with 3 CKLM diseases, individuals with concomitant MASLD-CKD-obesity (aHR 1.79, 95 % CI: 1.26–2.55, p = 0.001) and MASLD-T2D-CKD phenotypes (aHR 3.13, 95 % CI: 1.80–5.42, p < 0.001) were independently associated with higher risk of all-cause mortality. Although there were trends toward increased all-cause mortality in the MASLD-T2D-obesity (aHR 1.23, 95 % CI: 0.94–1.62, p = 0.132) and T2D-CKD-obesity phenotypes (aHR 2.00, 95 % CI: 0.56–7.15, p = 0.289), these did not reach statistical significance (Table 3).

In the subgroup with 2 CKLM diseases, only individuals with MASLD-CKD (aHR 1.57, 95 % CI: 1.01–2.45, p = 0.047) and T2D-CKD phenotypes (aHR 3.26, 95 % CI: 2.33–4.55, p < 0.001) were independent predictors of all-cause mortality. While there were trends towards increased all-cause mortality in the MASLD-T2D (aHR 1.32, 95 % CI: 0.79–2.22, p = 0.286) and obesity-CKD (aHR 2.82, 95 % CI: 0.99–7.98, p = 0.051) groups, these did not reach statistical significance after adjustments. On the other hand, the obesity-T2D phenotype demonstrated a protective effect against all-cause mortality (aHR 0.22, 95 % CI: 0.06–0.87, p = 0.030) (Table 3).

In the subgroup with only 1 CKLM disease, individuals with T2D (aHR 1.48, 95 % CI: 1.07–2.06, p = 0.018) or CKD in silos (aHR 1.88, 95 % CI: 1.50–2.36, p < 0.001) were independently associated with increased all-cause mortality, but this was not observed for obesity (aHR 1.47, 95 % CI: 0.81–2.66, p = 0.209) or MASLD-only phenotypes (aHR 1.06, 95 % CI: 0.79–1.43, p = 0.680) (Table 3).

4. Discussion

Prior studies have demonstrated that individual or clusters of diseases along the newly-conceptualised CKM axis drive cardiometabolic risk[13,14,45], however the inclusion of MASLD within the extended CKLM framework remains poorly understood[11,46]. Our study extends the current knowledge by providing insights into the long-term prognostic outcomes according to the extent, intersections and distinct phenotypes of the CKLM syndrome. The study highlights four pertinent findings: 1) The proportion of individuals with ≥2 CKLM diseases increased by 38.9 % over the past decade, affecting more than half of the US population in 2018 (41.5 million), with the largest percentage rise was observed in individuals living with the full spectrum of the CKLM syndrome (i.e. all 4 diseases). Coexistent MASLD-obesity (22.4 million; 23.0 %) and MASLD-obesity-T2D (4.9 million; 5.1 %) were the most common CKLM disease phenotypes in the US; 2) CKLM disease burden was disproportionately carried by non-Hispanic Black and Hispanic groups, individuals of low to middle income levels, highlighting the need for integrated and targeted care models for social determinants of health; 3) Individuals with 3 or more CKLM diseases were associated with 2–3 fold increase in long-term all-cause mortality risk compared to those with 1–2 CKLM diseases. Similar stepwise increase in cardiovascular outcomes – including heart failure, myocardial infarction, coronary heart disease and stroke – were observed with progressively higher CKLM multimorbidity burden; 4) CKLM phenotypes exert heterogeneous influences on mortality risk, with CKD and T2D emerging as the strongest prognostic drivers. Those with T2D-CKD or MASLD-CKD dyads portended the highest mortality risk in the population with 2 CKLM diseases, while the MASLD-T2D-CKD triad presaged the highest mortality risk in the population with 3 CKLM diseases (Central Illustration).

Unlabelled image

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Central Illustration. Population estimates and prognostic outcomes of cardiovascular-kidney-liver-metabolic phenotypes and diseases in the US population. CKD, chronic kidney disease; CKLM, cardiovascular-kidney-liver-metabolic; MASLD, metabolic dysfunction-associated steatotic liver disease; T2D, Type 2 diabetes.

More than half of the US population lived with at least 2 CKLM diseases, with predominant MASLD-obesity and MASLD-obesity-T2D phenotypes. This is underscored by the rapid epidemiological shift towards greater CKLM disease clustering, mirroring the antecedent, parallel CKM trends reported in prior cohorts[45,47] and global trends[3,[48], [49], [50]]. The high intersectionality between MASLD and obesity in our study alludes to their role as early precursors[6,13,51,52] in the later development of CKLM syndrome. Intersectionality across the CKLM spectrum is underpinned by shared pathophysiological mechanisms, including excess and dysfunctional adiposity, chronic low-grade inflammation driven by pro-inflammatory adipokines, oxidative stress, and insulin resistance[11,[53], [54], [55]]. Each individual entity can accelerate the development of CKLM diseases in multilayered and multidirectional relationships[6]. For example, obesity begets adipose dysfunction, thus accelerating MASLD progression[56], insulin resistance, and in turn accentuating renal inflammation and oxidative stress that contributes to CKD[[57], [58], [59]]. Moreover, the CKLM syndrome is contributed by shared deleterious behavioural, epidemiological and genetic factors, such as the increased consumption of cheap and accessible carbohydrate-dense diet and ultra-processed foods in food swamps[60,61], and low physical activity levels. This is exacerbated by socioeconomic-cultural-ethnic and genetic vectors[62] that include the lack of access to CKLM care in socially deprived neighbourhoods[63], poor dietary decisions[64], as well as higher genetic predisposition for the CKLM syndrome in non-Hispanic Black and Hispanic individuals[45,65]. This is reflected in our findings that CKLM disease clustering is disproportionately borne by individuals of low to middle income status, and in these specific ethnic groups. The rising prevalence and complexity of CKLM multimorbidity forewarn the worsening cardiometabolic milieu in the US, requiring an integrated and multidisciplinary care approach in the early CKLM detection and screening, and timely cardiovascular prevention strategy across the diverse populations[[66], [67], [68], [69]], tailored to specific socioeconomic-cultural-ethnic backgrounds[70,71].

In comparison to individuals without CKLM disease, those with 3 or more CKLM diseases had significantly higher all-cause mortality risk, while those with 2 CKLM diseases did not observe any significant increase in mortality risk. The observed pattern suggests CKLM syndrome may exert mortality risk via a threshold effect, where excess mortality is borne by those above a certain level of CKLM multimorbidity[47], rather than a linear relationship. Once a critical point of CKLM multimorbidity is reached, synergistic disease interactions between CKLM diseases such as chronic inflammation and aberrant inter-organ crosstalk may synergistically accelerate atherosclerosis, endothelial dysfunction and systemic metabolic disturbances, increasing the risk of cardiovascular morbidity[6,36,52,72,73] . These systemic disturbances are exacerbated by functional decline and frailty, especially in older individuals with increased CKLM diseases, which can worsen homeostatic reserve and cardio-renal-hepatic adaptability[74,75]. It is well-understood that traditional cardiometabolic risk factors such as hypertension or hyperlipidaemia can exert multiplicative CVD risk and reduce life expectancy[19,76], our study adds to the body of evidence that a similar multiplicative risk is likely to be present in CKLM syndrome[6]. Thus, consideration of the integrated CKLM syndrome is vital to optimise cardiovascular risk-stratification[47], particularly with greater awareness and screening of MASLD and CKD[77,78] as cardiovascular risk-enhancers[[79], [80], [81], [82]].

Within CKLM spectrum, specific phenotypes exert disparate effects on mortality, with certain phenotypic clusters having more deleterious effects. In the CKLM axis, CKD emerged as a strong prognostic driver of mortality within the CKLM syndrome[52,83]. When CKD was present in dyads or triads, mortality risk was higher than in CKLM clusters without CKD[57,[84], [85], [86], [87]]. On the other hand, the T2D-obesity phenotype demonstrated an apparent protective association against mortality[43,88,89]. While the “obesity paradox” has been postulated to be related to preserved cardiorespiratory fitness, greater metabolic reserves, and/or less cardiac cachexia, no exact physiological cause has been elucidated[9,43,89,90]. It is more likely that methodological explanations such as reverse causation (e.g. deleterious causes of low body weight or antecedent weight loss elevating mortality risk[91]), residual confounding (e.g. confounders related to low body weight that elevate mortality risk), or heterogeneity in body compositions (e.g. higher muscle mass associated with favourable outcomes[92]), rather than physiological considerations, may account for the apparent protective association of some of these obesity-based phenotypes. Importantly, in our study, this apparent protective effect was no longer evident in the most vulnerable groups that are more advanced in the CKLM spectrum (i.e. three or more CKLM diseases), highlighting the prognostic importance of high CKLM multimorbidity burden[11,73,93,94].

This study is not without its limitations. First, longitudinal cardiovascular outcomes were not available in the NHANES cohort. Future studies will benefit from time-to-event analysis for cardiovascular endpoints, across the diverse CKLM disease phenotypes explored in the present study. Second, while FLI is a simple and non-invasive screening tool of hepatic steatosis useful for large population studies, its reliance on BMI and waist circumference can potentially lead to the misclassification of MASLD. Thus, the use of FLI-defined hepatic steatosis may inflate the prevalence of coexistent MASLD-obesity. Nevertheless, our findings are in keeping with prior meta-analyses that have described the high prevalence of MASLD in the overweight (69.9 %) and obesity population[17,20] (75.2 %). On the other hand, the FLI may underestimate the risk of MASLD in lean individuals, potentially misclassifying individuals with lean MASLD[95]. In addition, as several studies have demonstrated that high FLI estimates are independently associated with higher risk of cardiovascular events[96,97], the use of FLI-defined MASLD may inflate the hazard ratio estimates of all-cause mortality. It is important to note, however, that the presence of MASLD was not found to be independently associated with all-cause mortality following adjustment for traditional confounders in the present study. While the gold-standard liver biopsy or noninvasive imaging will be important to confirm the study findings, the adoption of externally validated serum-based composite scores remains the more pragmatic alternative for enabling large-scale screening of MASLD in large population-based studies[4,26,98,99]. Third, the cross-sectional data did not allow for the analysis of the natural history of the various CKLM phenotypes. Future longitudinal large-scale studies will be important to ascertain the bidirectionality and chronology of the CKLM phenotypes. Fourth, lifestyle and socioeconomic covariates such as smoking and income were self-reported and hence subjected to recall bias. Moreover, the availability of data on alcohol use was not complete for all study participants, presenting limitations to the determination of MASLD based on the current criterion[37]. Last, the CKLM framework serves as a construct to examine the intersections across systemic metabolic disorders, focusing on obesity, T2D, MASLD and CKD. As such, behavioral risk factors such as smoking were not included. Furthermore, as the study intended to examine the CKLM multimorbidity burden, metabolic risk factors such as hypertension and dyslipidemia were not included in the current CKLM framework to avoid multicollinearity, given that the MASLD definition accounted for these metabolic parameters. Nevertheless, hypertension and dyslipidemia remain one of the fundamental metabolic disorders that link CKD and MASLD to cardiovascular morbidity and mortality. Moving forward, future studies are warranted to integrate the proposed CKLM concept into contemporary cardiovascular risk stratification models, and examine if this improves the discriminant value in CVD risk prediction.

5. Conclusion

The nationally representative cohort of US adults reveals the growing CKLM burden, with more than half of the US population living with at least 2 CKLM diseases. There has been a shift away from the existence of CKLM in silos, towards the coexistence of CKLM multimorbidity, with MASLD-obesity and MASLD-obesity-T2D identified as the commonest CKLM phenotypes in the US. CKLM phenotypes exert heterogeneous influences on prognosis, with the highest mortality and cardiovascular risk borne by vulnerable populations with 3 or 4 CKLM diseases, as well as those with CKD-centric phenotypes including the T2D-CKD or MASLD-CKD dyads, and the MASLD-T2D-CKD triad. Scalable and effective risk stratification systems and interdisciplinary preventive strategies are needed to be incorporated within the wider cardiovascular-kidney-liver-metabolic disease framework.

Ethics approval and consent to participate

NHANES is approved by the National Center for Health Statistics Research Ethics Review Board, and all participants provided informed consent.

Consent for publication

Not applicable.

Availability of data and materials

The datasets analysed in the current study are available from the corresponding author on reasonable request.

N.W.S.C has received research grant support from National University Health System Seed Fund (NUHSRO/2022/RO5+6/Seed-Mar/03), National Medical Research Council Research Training Fellowship (MH 095:003/008–303), National University of Singapore Yong Loo Lin School of Medicine's Academic Fellowship Scheme, and the National University Health System Clinician Scientist Program (NCSP2.0/2024/NUHS/NCWS), CSDU Clinician-Scientist Grant, and the National Medical Research Council Transition Award (TA24jul-0008).

M.S.K has received consulting fees from Novartis, Bayer, Boehringer Ingelheim and research support from Merck.

Funding

This study was supported by the CSDU Clinician-Scientist Grant and the National Medical Research Council Transition Award (TA24jul-0008).

CRediT authorship contribution statement

Yiming Chen: Writing – review & editing, Writing – original draft, Visualization, Conceptualization. Haryo Raden Himan: Writing – original draft, Methodology, Formal analysis, Data curation. Yalkin Yazicioglu: Writing – review & editing, Writing – original draft. Rachel Goh: Writing – review & editing, Data curation, Conceptualization. Yip Han Chin: Writing – review & editing, Supervision, Methodology, Investigation. Bryan Chong: Writing – review & editing, Writing – original draft, Supervision, Methodology. Srinithy Nagarajan: Writing – original draft. Martin Tze Wah Kueh: Writing – review & editing, Methodology, Investigation, Formal analysis, Data curation. Jiong-Wei Wang: Writing – review & editing, Methodology, Investigation, Formal analysis. Mark M Muthiah: Writing – review & editing, Supervision, Project administration, Investigation. Xin Zhou: Writing – review & editing, Supervision, Methodology. Mark Y Chan: Writing – review & editing, Supervision, Project administration, Investigation, Conceptualization. Anurag Mehta: Writing – review & editing, Investigation, Conceptualization. Mamas A Mamas: Writing – review & editing, Supervision, Methodology, Investigation, Formal analysis, Data curation. Muhammad Shahzeb Khan: Writing – review & editing, Supervision, Conceptualization. Nicholas WS Chew: Writing – review & editing, Supervision, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization.

Declaration of competing interest

All other authors declare no conflicts of interest associated with this publication.

Acknowledgements

All persons included in the manuscript have contributed to the manuscript in terms of its inception, data collection, data analysis, manuscript write-up, and subsequent critical revisions.

Footnotes

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.ajpc.2025.101405.

Appendix. Supplementary materials

mmc1.docx (3.3MB, docx)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

mmc1.docx (3.3MB, docx)

Data Availability Statement

The datasets analysed in the current study are available from the corresponding author on reasonable request.

N.W.S.C has received research grant support from National University Health System Seed Fund (NUHSRO/2022/RO5+6/Seed-Mar/03), National Medical Research Council Research Training Fellowship (MH 095:003/008–303), National University of Singapore Yong Loo Lin School of Medicine's Academic Fellowship Scheme, and the National University Health System Clinician Scientist Program (NCSP2.0/2024/NUHS/NCWS), CSDU Clinician-Scientist Grant, and the National Medical Research Council Transition Award (TA24jul-0008).

M.S.K has received consulting fees from Novartis, Bayer, Boehringer Ingelheim and research support from Merck.

Articles from American Journal of Preventive Cardiology are provided here courtesy of Elsevier

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