# Cardiometabolic risk phenotypes and chronic kidney disease incidence in older adults: a nationwide longitudinal cohort study

**Authors:** Zhihe Zeng, Zhaoyang Xiao

PMC · DOI: 10.1186/s12889-025-23868-w · BMC Public Health · 2025-07-29

## TL;DR

This study shows that different cardiometabolic risk profiles in older adults are linked to varying levels of chronic kidney disease risk, with metabolic syndrome and cardiovascular disease being the strongest predictors.

## Contribution

The study introduces a novel phenotypic classification of cardiometabolic risk factors and demonstrates its utility in predicting CKD risk in older adults.

## Key findings

- Three cardiometabolic classes were identified: healthy, metabolic syndrome, and cardiovascular disease, with CKD incidence increasing from 5.9% to 12.7%.
- Metabolic syndrome and cardiovascular disease phenotypes showed significantly higher CKD risk compared to the healthy phenotype after adjusting for confounders.
- Sensitivity analyses confirmed the robustness of the classification model with over 90% consistency in class assignments.

## Abstract

There is mixed evidence for an association between cardiometabolic risk factors and chronic kidney disease risk (CKD). This study aimed to determine whether different latent classes of cardiometabolic conditions were associated with chronic kidney disease risk.

Data from 7,195 participants in the China Health and Retirement Longitudinal Study (CHARLS) were analyzed. Latent class analysis was performed using data on obesity, high-density lipoprotein cholesterol, triglyceride, hypertension, diabetes, arthritis or rheumatism, and systemic inflammatory conditions and heart disease. Confounder-adjusted multiple logistic regressions were conducted to estimate CKD incidence by cardiometabolic latent classes. Sensitivity analyses were performed across cross-sectional and longitudinal samples, as well as derivation and validation cohorts.

Three cardiometabolic classes were identified: relatively healthy cardiometabolic (RHC) phenotype, metabolic syndrome (MetS) phenotype, and cardiovascular disease (CVD) phenotype, which accounted for 66.2%, 19.9%, and 13.8%, respectively. The incidence of CKD was 12.7% in the CVD group, 9.4% in the MetS group, and 5.9% in the RHC group. After adjusting for confounding factors, it was found that the metabolic syndrome type had a 54% increased risk of newly diagnosed CKD compared to the healthy heart type (OR = 1.54, 95% CI: 1.22–1.93), while the cardiovascular type increased by 104% (OR = 2.04, 95% CI: 1.61–2.57). Sensitivity analyses showed high consistency (> 90%) in class assignments, confirming model robustness.

Different cardiometabolic phenotypes are associated with an increased risk of new-onset CKD. Gender and age are important factors influencing the strength of this association. Phenotypic classification may improve CKD risk stratification and guide early prevention efforts.

The online version contains supplementary material available at 10.1186/s12889-025-23868-w.

## Linked entities

- **Diseases:** chronic kidney disease (MONDO:0005300), metabolic syndrome (MONDO:0000816), cardiovascular disease (MONDO:0004995), diabetes (MONDO:0005015)

## Full-text entities

- **Genes:** CST3 (cystatin C) [NCBI Gene 1471] {aka ADLDWA, ARMD11, HEL-S-2}, CRP (C-reactive protein) [NCBI Gene 1401] {aka PTX1}, COG2 (component of oligomeric golgi complex 2) [NCBI Gene 22796] {aka CDG2Q, LDLC}, CKM (creatine kinase, M-type) [NCBI Gene 1158] {aka CKMM, CPK-M, M-CK}, REN (renin) [NCBI Gene 5972] {aka ADTKD4, HNFJ2, RTD}
- **Diseases:** sleep deprivation (MESH:D012892), CHARLS (OMIM:603663), overweight (MESH:D050177), declining kidney function (MESH:D007680), CVD (MESH:D002318), lipid abnormalities (MESH:D011017), high (MESH:D008228), rheumatism (MESH:D012216), digestive diseases (MESH:D004066), Metabolic syndrome (MESH:D024821), abnormal lipid metabolism (MESH:D052439), insulin resistance (MESH:D007333), stroke (MESH:D020521), AKI (MESH:D058186), adiposity (MESH:D018205), heart disease (MESH:D006331), gestational diabetes (MESH:D016640), kidney failure (MESH:D051437), Impaired cardiac output (MESH:D002303), inflammatory (MESH:D007249), cancer (MESH:D009369), metabolic abnormalities (MESH:D008659), ID (MESH:C537985), excessive (MESH:D006970), chronic lung disease (MESH:D029424), arthritis (MESH:D001168), lung disease (MESH:D008171), hyperuricemia (MESH:D033461), abdominal obesity (MESH:D056128), obesity (MESH:D009765), dyslipidemia (MESH:D050171), Hypertension (MESH:D006973), atherosclerosis (MESH:D050197), CKM syndrome (MESH:D007674), diabetes (MESH:D003920), HF (MESH:D006333), CKD (MESH:D051436)
- **Chemicals:** TG (MESH:D014280), lipid (MESH:D008055), urea nitrogen (MESH:C530477), testosterone (MESH:D013739), aldosterone (MESH:D000450), Uric acid (MESH:D014527), TC (MESH:D013667), glucose (MESH:D005947), cholesterol (MESH:D002784), BIC (-), creatinine (MESH:D003404)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

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## References

1 references — full list in the complete paper: https://tomesphere.com/paper/PMC12309190/full.md

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Source: https://tomesphere.com/paper/PMC12309190