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Associations of composite metabolic indices with adverse outcomes in stage 0–3 cardiovascular-kidney-metabolic syndrome: a scoping review and evidence map

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  • Published: 09 November 2026
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Associations of composite metabolic indices with adverse outcomes in stage 0–3 cardiovascular-kidney-metabolic syndrome: a scoping review and evidence map
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  • Hongwei Wang1,
  • Caizheng Yang2,
  • Ming Zhao3,
  • Xinyu Wang4,
  • Fangying Tian5,
  • Shanshan Ge6,
  • Ruihua Jin2 &
  • …
  • Hua He1 
  • 28 Accesses

  • Explore all metrics

We’re sharing this article early to provide faster access to peer-reviewed, accepted research. It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record. All legal disclaimers apply.

Abstract

Background

Stage 0–3 of cardiovascular-kidney-metabolic (CKM) syndrome represents a critical window for primary prevention, yet the prognostic value of numerous composite metabolic indices derived from routine clinical and laboratory parameters in this population remains unclear. We mapped the available evidence and assessed the direction, consistency, and certainty of associations.

Methods

Following PRISMA-ScR guidelines, we searched PubMed, Web of Science, Scopus, and the Cochrane Library from inception to 15 July 2026. We included prospective cohort studies that reported multivariable-adjusted hazard ratios for any composite metabolic index against all-cause mortality (ACM), cardiovascular mortality (CVM), incident cardiovascular disease (iCVD), coronary heart disease (CHD), stroke, cardiometabolic multimorbidity (CMM), heart failure (HF), or chronic kidney disease (CKD) progression in adults with stage 0–3 CKM. Two reviewers independently screened records, extracted data, assessed risk of bias using Quality In Prognosis Studies (QUIPS), and performed an exploratory GRADE appraisal. No meta-analysis was conducted due to substantial heterogeneity.

Results

We included 34 study reports, predominantly derived from the China Health and Retirement Longitudinal Study (CHARLS). TyG-family indices accounted for over 45% of evaluated exposures. The evidence landscape was heavily concentrated on iCVD, whereas no eligible prospective study evaluated HF or CKD progression as a primary endpoint. In within-study comparisons, TyG-WC and TyG-WHtR most frequently demonstrated stronger adjusted associations with adverse outcomes than the basal TyG index; conversely, eGDR consistently showed inverse associations. Most studies were classified as having a moderate risk of bias, primarily driven by unvalidated outcome ascertainment and residual confounding. Exploratory GRADE appraisal rated the vast majority of index-outcome pairs as low or very low certainty.

Conclusion

In stage 0–3 CKM, TyG-WC and TyG-WHtR showed the most consistent associations with adverse outcomes in within-study comparisons, whereas eGDR was predominantly inversely associated. However, overall evidence certainty remains low, and critical gaps persist for HF and CKD progression. Future research must shift from index proliferation to external validation against PREVENT and prospective adjudication of hard endpoints.

Research insights

What is currently known about this topic?

Numerous composite metabolic indices have been proposed as surrogates for insulin resistance and adiposity, but their comparative prognostic value in CKM stages 0–3 remains uncharted.

What is the key research question?

Which composite metabolic indices demonstrate the most robust and consistent associations with adverse outcomes in CKM stages 0–3, and what are the critical evidence gaps?

What is new?

This is the first scoping review and evidence map covering the full spectrum of composite metabolic indices in CKM stages 0–3. In within-study comparisons, TyG-WC and TyG-WHtR most frequently demonstrated stronger positive associations with adverse outcomes; however, this evidence does not establish predictive superiority or a synergistic biological mechanism. Crucially, no eligible prospective study evaluated HF or CKD progression as a primary endpoint, highlighting a mismatch between the current evidence base and the 2026 AHA/ACC/ADA/ASN guideline priorities. Furthermore, exploratory GRADE appraisal indicates that the vast majority of these associations remain of low or very low certainty.

How might this study influence clinical practice?

TyG-WC and TyG-WHtR represent promising, low-cost tools for early risk stratification in CKM. However, their clinical adoption requires rigorous external validation across diverse ancestries and direct benchmarking against the PREVENT equations. To bridge the current evidence gaps, future research must prioritize the prospective adjudication of hard heart failure and renal endpoints, shifting the paradigm from mere index proliferation toward demonstrating tangible incremental value.

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Abbreviations

ACM:

All-cause mortality

CVM:

Cardiovascular mortality

iCVD:

incident Cardiovascular disease

CHD:

Coronary Heart Disease

CMM:

Cardiometabolic multimorbidity

ASCVD:

Atherosclerotic Cardiovascular Disease

MACE:

Major Adverse Cardiovascular Events

HD:

Heart Disease

UKB:

UK Biobank

CHARLS:

China Health and Retirement Longitudinal Study

NHANES:

National Health and Nutrition Examination Survey

NHID:

National Health Insurance Database

PUTH:

Peking University Third Hospital

TyG:

Triglyceride-glucose index

TyG-ABSI:

Triglyceride-glucose-body shape index

TyG-BMI:

Triglyceride-glucose-body mass index

TyG-WC:

Triglyceride-glucose-waist circumference

TyG-WHtR:

Triglyceride-glucose-waist-to-height ratio

TyG-BRI:

Triglyceride-glucose-body roundness index

TyG-WWI:

Triglyceride-glucose-weight-adjusted waist index

TyG-CVAI:

Triglyceride-glucose-Chinese visceral adiposity index

TyG-KDM-BA:

Triglyceride-glucose-Klemera-Doubal biological age acceleration

TyG-FI:

Triglyceride-glucose-frailty index

TyG-RFM:

Triglyceride-glucose-relative fat mass

RCII:

Residual cholesterol inflammatory index

CTI:

C-reactive protein-triglyceride-glucose index

CTI-BMI:

C-reactive protein-triglyceride-glucose index-body mass index

CTI-WC:

C-reactive protein-triglyceride-glucose index-waist circumference

CTI-WHtR:

C-reactive protein-triglyceride-glucose index-waist-to-height ratio

CTI-WWI:

C-reactive protein-triglyceride-glucose index-weight-adjusted waist index

CTI-BRI:

C-reactive protein-triglyceride-glucose index-body roundness index

CTI-CVAI:

C-reactive protein-triglyceride-glucose index-Chinese visceral adiposity index

CTI-CI:

C-reactive protein-triglyceride-glucose index-conicity index

CTI-FI:

C-reactive protein-triglyceride-glucose index-frailty index

cuCTI-FI:

cumulative C-reactive protein-triglyceride-glucose index-frailty index

AIP:

Atherogenic index of plasma

AIP-BMI:

Atherogenic index of plasma-body mass index

AIP-WC:

Atherogenic index of plasma-waist circumference

AIP-WHtR:

Atherogenic index of plasma-waist-to-height ratio

AIP-BRI:

Atherogenic index of plasma-body roundness index

AIP-ABSI:

Atherogenic index of plasma-a body shape index

AIP-VAI:

Atherogenic index of plasma-visceral adiposity index

AIP-WWI:

Atherogenic index of plasma-weight-adjusted waist index

eGDR:

estimated glucose disposal rate

VAI:

Visceral adiposity index

LAP:

Lipid accumulation product

CVAI:

Chinese visceral adiposity index

Acknowledgements

The authors thank the participants and investigators of the National Health and Nutrition Examination Survey (NHANES), the China Health and Retirement Longitudinal Study (CHARLS), the UK Biobank, the Korean National Health Insurance Database (NHID), and the GOLD-Health cohort for making their data publicly available. We are grateful to the research librarians at Shanxi Medical University for their assistance with the literature search.

Funding

This study was supported in part by grants from the 2024 Shanxi Province High-Quality Development Research Project (Program No. SXGZL202423 for Caizheng Yang); the 2023 Shanxi Association for Science and Technology Science and Technology Innovation Think Tank Construction Research Project (Program No. KXKT202315 for Caizheng Yang); the 2024 Shanxi Province Higher Education Innovation Project (Program No. 2024L090 for Hongwei Wang); and the 2025 Major Decision-making Consultation Project of the People’s Government of Shanxi Province (Program No. ZB3006 for Caizheng Yang).

Author information

Authors and Affiliations

  1. Department of Critical Care Medicine, Shanxi Provincial People’s Hospital, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan, Shanxi, China

    Hongwei Wang & Hua He

  2. College of Nursing, Shanxi Technology and Business University, Taiyuan, Shanxi, China

    Caizheng Yang & Ruihua Jin

  3. Department of Infectious Diseases, Ningxia Medical University General Hospital, Yinchuan, Ningxia, China

    Ming Zhao

  4. College of Nursing, Shanxi Medical University, Taiyuan, Shanxi, China

    Xinyu Wang

  5. Department of Hospital-Acquired Infection Management, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China

    Fangying Tian

  6. Health Management Center, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China

    Shanshan Ge

Authors
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Corresponding authors

Correspondence to Caizheng Yang or Hua He.

Ethics declarations

Ethical approval and consent to participate

Since this study is a comprehensive review of previously published, anonymized aggregated data and an integration of evidence maps, no ethical approval from the institutional review board is required for this study, nor is informed consent from individuals necessary.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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Supplementary Information

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12933_2026_3440_MOESM1_ESM.docx (download DOCX )

Supplementary Material 1: Table S1. PRISMA-ScR Checklist. Table S2. Full electronic search strategies. Table S3a. Diagnostic Criteria for CKM Components. Table S3b. Definition of CKM Syndrome Staging. Table S4. The Quality In Prognosis Studies (QUIPS) Tool: Domains, Core Items, and Rating Criteria. Table S5. GRADE evidence profile for all reported composite metabolic index-outcome pairings. Table S6. QUIPS Risk of Bias Assessment for Each Included Study. Table S7. Full data extraction of included studies. Table S8. Full adjustment sets, outcome definitions, exposure timing, and outcome ascertainment for included studies. Table S9. Formulae for composite metabolic indices included in the review.

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Cite this article

Wang, H., Yang, C., Zhao, M. et al. Associations of composite metabolic indices with adverse outcomes in stage 0–3 cardiovascular-kidney-metabolic syndrome: a scoping review and evidence map. Cardiovasc Diabetol (2026). https://doi.org/10.1186/s12933-026-03440-5

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  • Received: 12 September 2026

  • Accepted: 03 November 2026

  • Published: 09 November 2026

  • DOI: https://doi.org/10.1186/s12933-026-03440-5

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Keywords

  • Cardiovascular-kidney-metabolic syndrome
  • Composite metabolic indices
  • Triglyceride-glucose index
  • Scoping review
  • Evidence mapping

Associated Content

Part of a collection:

Cardiovascular-Kidney-Metabolic (CKM) Syndrome: Risk Stratification, Prognostic Implications and Therapeutic Innovation

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