# The cholesterol, high-density lipoprotein, and glucose index and its derivatives related to obesity and inflammation as novel metabolic markers for predicting new-onset cardiovascular-liver-metabolic multimorbidity: A prospective cohort study

Researcher: Ge Zhang
Chinese name: 张格
ORCID identity anchor: https://orcid.org/0000-0002-3116-3246
Canonical researcher: https://drgezhang.com/#person

Journal: Journal of Diabetes Investigation
Year: 2026
Type: Article
DOI: 10.1111/jdi.70462
Canonical page: https://drgezhang.com/papers/doi-10-1111-jdi-70462.html
Markdown record: https://drgezhang.com/papers/doi-10-1111-jdi-70462.md

## Full Authors

1. Zhonghua Sun
2. Aikeliyaer Ainiwaer
3. Yameng Xu
4. Ge Zhang
5. Xiangyu Sun
6. Xinliang Peng
7. Yuanhui Dai
8. Kaisaierjiang Kadier

## Official Abstract

### Background

Cardiovascular‐liver‐metabolic multimorbidity (CLMM), defined as the co‐occurrence of at least two of cardiovascular disease, type 2 diabetes, and metabolic dysfunction‐associated steatotic liver disease, is an increasing health burden. We evaluated the cholesterol, high‐density lipoprotein, and glucose (CHG) index and its obesity‐ and inflammation‐derived variants for predicting incident CLMM.

### Methods

This prospective cohort included 6,534 adults aged ≥45 years from the China Health and Retirement Longitudinal Study. CHG, eight obesity‐derived indices, and CHG‐CRP were assessed, with the triglyceride‐glucose index and TG/HDL‐C ratio as comparators. Associations with incident CLMM were evaluated using logistic regression, restricted cubic splines, and threshold analysis. Predictive performance was compared using receiver operating characteristic analysis, the integrated discrimination improvement (IDI), and the net reclassification improvement (NRI), with additional joint‐effect, interaction, subgroup, and sensitivity analyses.

### Results

At the 2015 follow‐up, 406 participants (6.2%) met the criteria for CLMM. Higher CHG was linearly associated with incident CLMM (highest vs lowest quartile: OR 1.64, 95% confidence interval [CI] 1.22–2.22). Obesity‐derived variants showed stronger associations, particularly CHG‐BMI (OR 3.13, 95% CI 2.21–4.43), which also had the highest discrimination (AUC 0.643, 95% CI 0.616–0.669) and continuous NRI (34.08%). Participants with both high CHG and high BMI had the greatest CLMM risk (OR 2.59, 95% CI 1.94–3.51). Results were robust in sensitivity analyses.

### Conclusions

The CHG index and its obesity‐derived variants, particularly CHG‐BMI, are accessible and inexpensive candidate metabolic markers for predicting new‐onset CLMM. CHG‐BMI showed the highest apparent discrimination among the evaluated indices, although its AUC was modest. External population validation is needed.

Text reproduced verbatim from PMC ([source](https://pmc.ncbi.nlm.nih.gov/articles/PMC13651581/)); [DOI](https://doi.org/10.1111/jdi.70462); [CC-BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/).

<a id="paper-discovery"></a>
## Research & review context

### Research fields

- Cardiometabolic epidemiology
- Metabolic biomarker research

### Review topics

- Multimorbidity risk-marker evaluation
- Obesity-derived biochemical indices

### Scientific role

Prospective observational association study with apparent discrimination analysis, requiring independent validation.

### Evidence position

Population-level candidate-marker evidence with a surrogate liver-disease component; causal and clinical-utility claims remain unestablished.

### Related concepts

- CHG-BMI
- CHARLS
- LAP-based suspected MASLD

### Possible citation contexts

- Reviews comparing metabolic indices for incident, surrogate-defined cardiometabolic multimorbidity.
- Methodological discussions separating observational odds ratios and apparent AUCs from validated clinical prediction.

Possible literature-review contexts, not evidence of existing citations. Read the findings and Evidence Scope before citing the original article.

## Evidence Snapshot

**CHG-derived metabolic indices and incident cardiovascular–liver–metabolic multimorbidity in CHARLS**

In a prospective CHARLS analysis, baseline CHG and obesity-derived variants were associated with subsequently meeting a surrogate-defined multimorbidity endpoint. CHG-BMI showed the largest observed AUC among the evaluated indices, but discrimination was modest and assessed in the analysis sample without validation.

| Evidence | Value |
| --- | --- |
| Unique total | n=6,534 |
| CHARLS analytic cohort | n=6,534 |

## Research Question

Are baseline CHG and its obesity- or inflammation-derived indices associated with new-onset cardiovascular–liver–metabolic multimorbidity, and how well do they discriminate follow-up cases within CHARLS?

## Author Evidence Summary

This observational study related baseline metabolic indices to a later binary multimorbidity outcome in Chinese middle-aged and older adults. Multimorbidity required at least two component conditions and included an LAP-based suspected MASLD phenotype rather than imaging-confirmed liver disease. CHG-BMI had a stronger quartile association and a higher apparent AUC than the other evaluated indices. The findings support candidate-marker research; they do not establish causation, independent validation, an actionable clinical threshold, or benefit from modifying CHG-BMI.

## Key Findings

<a id="kf1"></a>
### KF1: A prospective CHARLS analysis identified incident, surrogate-defined cardiovascular–liver–metabolic multimorbidity at follow-up.

Context: Chinese community-dwelling adults without baseline CLMM; component conditions were evaluated at baseline and the follow-up wave, rather than using exact onset dates.

| Evidence | Value |
| --- | --- |
| Analytic participants | 6,534 adults aged 45 years or older |
| Exposure and outcome waves | 2011 baseline and 2015 follow-up |
| Incident CLMM | 406 participants; 6.2% |
| Outcome definition | At least two of CVD, type 2 diabetes, and LAP-based suspected MASLD |

Source locator: Methods: Study population and Outcome assessment (jdi70462-sec-0007; jdi70462-sec-0009); Results 3.1 (jdi70462-sec-0013); Figure 1; Table 1

<a id="kf2"></a>
### KF2: Higher baseline CHG was associated with higher odds of meeting the CLMM criteria at follow-up.

Context: Highest versus lowest CHG quartile in the fully adjusted logistic model; these are odds ratios for a binary follow-up outcome, not hazard ratios or treatment effects.

| Evidence | Value |
| --- | --- |
| CHG Q4 versus Q1 adjusted OR | 1.64; 95% CI 1.22–2.22 |
| Adjusted covariates | Age, sex, education, residence, marital status, smoking, drinking, hypertension, and CKD |

Source locator: Results 3.2 (jdi70462-sec-0014); Table 2 (jdi70462-tbl-0002), CHG Q4, Model 2

<a id="kf3"></a>
### KF3: CHG combined with BMI showed a stronger quartile association with incident CLMM than CHG alone within this analysis.

Context: Comparing the highest and lowest CHG-BMI quartiles within the same cohort and fully adjusted model; relative effect-size ranking does not establish a causal role or clinical utility.

| Evidence | Value |
| --- | --- |
| CHG-BMI Q4 versus Q1 adjusted OR | 3.13; 95% CI 2.21–4.43 |
| Comparison | CHG alone Q4 versus Q1: OR 1.64; 95% CI 1.22–2.22 |

Source locator: Results 3.2 (jdi70462-sec-0014); Table 2 (jdi70462-tbl-0002), CHG-BMI Q4 and CHG Q4, Model 2

<a id="kf4"></a>
### KF4: CHG-BMI had the largest observed AUC among the evaluated indices, with modest apparent discrimination.

Context: Each index was evaluated in the same analytic sample without internal or external validation; the reported AUCs do not establish transportable clinical prediction.

| Evidence | Value |
| --- | --- |
| CHG-BMI AUC | 0.643; 95% CI 0.616–0.669 |
| CHG AUC | 0.575; 95% CI 0.546–0.603 |
| TyG AUC | 0.558; 95% CI 0.529–0.587 |
| TG/HDL AUC | 0.534; 95% CI 0.506–0.562 |

Source locator: Methods: Statistical analysis (jdi70462-sec-0011); Results 3.4 (jdi70462-sec-0016); Figure 3; Table 3 (jdi70462-tbl-0003)

<a id="kf5"></a>
### KF5: Jointly high CHG and BMI were associated with higher follow-up CLMM odds, without statistically significant interaction.

Context: High/low groups used sample-specific median cut-offs and a low-CHG/low-BMI reference; these cut-offs are not established clinical thresholds.

| Evidence | Value |
| --- | --- |
| High CHG plus high BMI adjusted OR | 2.59; 95% CI 1.94–3.51 |
| Multiplicative interaction | OR 0.90; 95% CI 0.58–1.40; P=0.643 |

Source locator: Results 3.5 (jdi70462-sec-0017), Joint effects and interaction of CHG and BMI; Table S4A–B

## Study Design & Model Development

### Study profile

- Study Design: Prospective observational analysis of the 2011 and 2015 CHARLS waves; baseline-index exposures and binary outcome ascertainment at follow-up; logistic regression, restricted cubic splines, and apparent ROC evaluation.
- Evidence Type: Longitudinal observational association and unvalidated, in-sample discrimination
- Unique total: 6,534
- Population: Community-dwelling adults aged at least 45 years in mainland China, free of baseline CLMM; persons with zero or one component condition could enter the analysis.
- Primary Endpoint: Newly meeting at least two criteria for CVD, type 2 diabetes, and LAP-based suspected MASLD at the 2015 follow-up.
- Secondary Endpoint: Quartile associations, nonlinear relationships, apparent discrimination, joint CHG/BMI effects, and exploratory reclassification, subgroup, and sensitivity analyses.
- External validation: No
- Data modalities: Baseline fasting blood biochemistry, Anthropometric obesity indices, Interview and clinical covariates, Follow-up questionnaire, laboratory, and surrogate liver-phenotype ascertainment

### Cohort hierarchy

| Cohort | Role | n | Relationship |
| --- | --- | ---: | --- |
| CHARLS analytic cohort | Single analysis cohort; not partitioned into independent development or validation populations | 6534 | Independent cohort |

### Model development

- Analytical Framework: Multivariable logistic regression and restricted cubic splines; index-specific ROC analysis
- Performance Scope: Apparent discrimination in the same analysis sample; no internal or external validation
- Endpoint Timing: Exact onset dates were unavailable; ORs are not hazard ratios
- Clinical Thresholds: Sample-derived median and Youden cut-offs are not established clinical decision thresholds

## What This Study Adds

- Compares CHG with obesity- and inflammation-derived variants for a prospectively ascertained, surrogate-defined multimorbidity endpoint.
- Separates the strength of adjusted associations from the modest, apparent discrimination of the evaluated indices.
- Reports joint CHG/BMI associations while distinguishing them from evidence of statistical interaction.

## Evidence Scope

### Supports

- Associations between baseline indices and meeting study-defined CLMM criteria at follow-up in this CHARLS analysis.
- CHG-BMI ranking highest among evaluated indices for apparent ROC discrimination in this sample.
- Hypothesis generation and independent validation of accessible metabolic-marker combinations.

### Does Not Establish

- A causal effect of CHG, obesity, or inflammation on multimorbidity.
- Imaging- or biopsy-confirmed MASLD in every participant.
- Hazard ratios, exact onset timing, or a time-to-event prediction model.
- Independent internal or external validation, clinical net benefit, treatment efficacy, or a validated clinical cut-off.

### Limitations

- Residual confounding and observational design prevent causal inference.
- Exact component-disease onset dates were unavailable; logistic ORs cannot be interpreted as hazards or incidence rates.
- The MASLD component used an LAP-based suspected phenotype and may be misclassified; metabolic components overlap conceptually with the exposure indices.
- The ROC analyses used the same analysis sample without internal or external validation, so apparent AUCs may be optimistic.
- Single baseline exposure measurements, substantial complete-data selection, medication changes, correlated comparisons, and exploratory subgroup analyses limit interpretation.
- Generalizability beyond Chinese adults aged at least 45 years requires independent evaluation.

<a id="citation-use-cases"></a>
## When This Study Is Useful to Cite

### CU1: What prospective population evidence links CHG-derived indices to incident CLMM?

Observational CHARLS associations with a surrogate-defined follow-up outcome, without causal inference.

Evidence: [KF1](#kf1), [KF2](#kf2), [KF3](#kf3)

### CU2: How well did CHG-BMI discriminate follow-up CLMM in the analysis sample?

Modest apparent discrimination without internal or external validation.

Evidence: [KF4](#kf4)

<a id="citation-boundaries"></a>
## What This Study Should Not Be Cited to Claim

- A causal effect of CHG, obesity, or inflammation on multimorbidity.
- Imaging- or biopsy-confirmed MASLD in every participant.
- Hazard ratios, exact onset timing, or a time-to-event prediction model.
- Independent internal or external validation, clinical net benefit, treatment efficacy, or a validated clinical cut-off.

<a id="evidence-matrix"></a>
## Evidence Matrix

<a id="evidence-jdi70462-1"></a>
### A prospective CHARLS analysis identified incident, surrogate-defined cardiovascular–liver–metabolic multimorbidity at follow-up.

- Context: Chinese community-dwelling adults without baseline CLMM; component conditions were evaluated at baseline and the follow-up wave, rather than using exact onset dates.
- Finding: Analytic participants: 6,534 adults aged 45 years or older; Exposure and outcome waves: 2011 baseline and 2015 follow-up; Incident CLMM: 406 participants; 6.2%; Outcome definition: At least two of CVD, type 2 diabetes, and LAP-based suspected MASLD
- Evidence Level: Prospective observational cohort analysis
- Scope: Chinese community-dwelling adults without baseline CLMM; component conditions were evaluated at baseline and the follow-up wave, rather than using exact onset dates.
- Source Locator: Methods: Study population and Outcome assessment (jdi70462-sec-0007; jdi70462-sec-0009); Results 3.1 (jdi70462-sec-0013); Figure 1; Table 1
- Evidence refs: [KF1](#kf1)

<a id="evidence-jdi70462-2"></a>
### Higher baseline CHG was associated with higher odds of meeting the CLMM criteria at follow-up.

- Context: Highest versus lowest CHG quartile in the fully adjusted logistic model; these are odds ratios for a binary follow-up outcome, not hazard ratios or treatment effects.
- Finding: CHG Q4 versus Q1 adjusted OR: 1.64; 95% CI 1.22–2.22; Adjusted covariates: Age, sex, education, residence, marital status, smoking, drinking, hypertension, and CKD
- Evidence Level: Prospective observational cohort analysis
- Scope: Highest versus lowest CHG quartile in the fully adjusted logistic model; these are odds ratios for a binary follow-up outcome, not hazard ratios or treatment effects.
- Source Locator: Results 3.2 (jdi70462-sec-0014); Table 2 (jdi70462-tbl-0002), CHG Q4, Model 2
- Evidence refs: [KF2](#kf2)

<a id="evidence-jdi70462-3"></a>
### CHG combined with BMI showed a stronger quartile association with incident CLMM than CHG alone within this analysis.

- Context: Comparing the highest and lowest CHG-BMI quartiles within the same cohort and fully adjusted model; relative effect-size ranking does not establish a causal role or clinical utility.
- Finding: CHG-BMI Q4 versus Q1 adjusted OR: 3.13; 95% CI 2.21–4.43; Comparison: CHG alone Q4 versus Q1: OR 1.64; 95% CI 1.22–2.22
- Evidence Level: Prospective observational cohort analysis
- Scope: Comparing the highest and lowest CHG-BMI quartiles within the same cohort and fully adjusted model; relative effect-size ranking does not establish a causal role or clinical utility.
- Source Locator: Results 3.2 (jdi70462-sec-0014); Table 2 (jdi70462-tbl-0002), CHG-BMI Q4 and CHG Q4, Model 2
- Evidence refs: [KF3](#kf3)

<a id="evidence-jdi70462-4"></a>
### CHG-BMI had the largest observed AUC among the evaluated indices, with modest apparent discrimination.

- Context: Each index was evaluated in the same analytic sample without internal or external validation; the reported AUCs do not establish transportable clinical prediction.
- Finding: CHG-BMI AUC: 0.643; 95% CI 0.616–0.669; CHG AUC: 0.575; 95% CI 0.546–0.603; TyG AUC: 0.558; 95% CI 0.529–0.587; TG/HDL AUC: 0.534; 95% CI 0.506–0.562
- Evidence Level: Prospective observational cohort analysis
- Scope: Each index was evaluated in the same analytic sample without internal or external validation; the reported AUCs do not establish transportable clinical prediction.
- Source Locator: Methods: Statistical analysis (jdi70462-sec-0011); Results 3.4 (jdi70462-sec-0016); Figure 3; Table 3 (jdi70462-tbl-0003)
- Evidence refs: [KF4](#kf4)

<a id="evidence-jdi70462-5"></a>
### Jointly high CHG and BMI were associated with higher follow-up CLMM odds, without statistically significant interaction.

- Context: High/low groups used sample-specific median cut-offs and a low-CHG/low-BMI reference; these cut-offs are not established clinical thresholds.
- Finding: High CHG plus high BMI adjusted OR: 2.59; 95% CI 1.94–3.51; Multiplicative interaction: OR 0.90; 95% CI 0.58–1.40; P=0.643
- Evidence Level: Prospective observational cohort analysis
- Scope: High/low groups used sample-specific median cut-offs and a low-CHG/low-BMI reference; these cut-offs are not established clinical thresholds.
- Source Locator: Results 3.5 (jdi70462-sec-0017), Joint effects and interaction of CHG and BMI; Table S4A–B
- Evidence refs: [KF5](#kf5)

## Q&A

### Who was included in the analysis?

The final analytic cohort comprised 6,534 CHARLS participants aged at least 45 years without baseline CLMM. Baseline measurements were from 2011 and outcome ascertainment from 2015.

Evidence: KF1

### What counted as cardiovascular–liver–metabolic multimorbidity?

Participants had to meet at least two criteria for CVD, type 2 diabetes, and LAP-based suspected MASLD at follow-up. The liver component was a surrogate phenotype, not universal imaging or histological confirmation.

Evidence: KF1

### How strong was the association with CHG?

In the fully adjusted logistic model, the highest versus lowest CHG quartile had an OR of 1.64 with a 95% CI of 1.22–2.22. This is an observational odds ratio for the follow-up outcome.

Evidence: KF2

### Did CHG-BMI perform better than CHG alone?

CHG-BMI had a stronger highest-versus-lowest-quartile association and the largest observed AUC among the compared indices. Its AUC of 0.643 was still modest and was evaluated in the analysis sample.

Evidence: KF3, KF4

### Was this an externally validated clinical prediction model?

No. The article explicitly describes apparent, in-sample ROC evaluation without internal or external validation. Independent population validation and assessment beyond BMI and conventional risk factors are still needed.

Evidence: KF4

### Did high CHG and high BMI interact statistically?

The jointly high group had greater follow-up CLMM odds than the low–low group, but the multiplicative interaction was not statistically significant. Joint association therefore does not establish synergy.

Evidence: KF5

### Can these findings guide a treatment intended to prevent CLMM?

The study did not test an intervention. Observational associations and apparent discrimination do not establish that changing an index prevents disease or improves clinical decisions.

Evidence: KF2, KF3, KF4

## Concepts & Entities

### Conditions

Cardiovascular–liver–metabolic multimorbidity, Cardiovascular disease, Type 2 diabetes, LAP-based suspected MASLD

### Markers

Cholesterol–HDL–glucose index, CHG-BMI, Triglyceride–glucose index

### Methods

CHARLS, Prospective observational cohort, Logistic regression, Apparent ROC discrimination

## Related Research

- [AI hybrid survival assessment for advanced heart failure patients with renal dysfunction](https://drgezhang.com/papers/aihflevel.html) — A separate heart-failure prognosis study provides a contrast in endpoint and modeling purpose; it does not validate CHG-BMI or the CLMM endpoint. (DOI: 10.1038/s41467-024-50415-9)
- [Plasma Olink Proteomics Reveals Novel Biomarkers for Prediction and Diagnosis in Dilated Cardiomyopathy with Heart Failure](https://drgezhang.com/papers/olink-dcm.html) — A separate cardiac biomarker study uses a different disease population and measurement platform; it is related biomarker research rather than replication of this analysis. (DOI: 10.1021/acs.jproteome.4c00522)

## Publication & Provenance

- DOI: https://doi.org/10.1111/jdi.70462
- Publisher: https://onlinelibrary.wiley.com/doi/10.1111/jdi.70462
- Doi Url: https://doi.org/10.1111/jdi.70462
- PubMed: https://pubmed.ncbi.nlm.nih.gov/42852842/
- PMCID: PMC13651581
- Version Of Record: Journal of Diabetes Investigation; Early View, first published 9 October 2026
- License: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
- License Url: https://creativecommons.org/licenses/by-nc/4.0/
- Data Source: China Health and Retirement Longitudinal Study, 2011 baseline and 2015 follow-up
- Supporting Material: Tables S1–S8 are linked in the version-of-record supplementary material; key estimates on this page are located in the full-text Results and principal tables.
- Author Identity: Ge Zhang is listed with the Department of Cardiology, The First Affiliated Hospital of Zhengzhou University. The DOI is present in public ORCID 0000-0002-3116-3246; the self-maintained ORCID linkage is distinct from publisher-authenticated identity.
- Evidence Basis: Version-of-record full text via Europe PMC XML; Methods, Results, Tables 1–3 and article-level permissions; PubMed and public ORCID metadata.

## Evidence-page notice

This is an original evidence synthesis linked to the published article. Cite the original article for study findings. The official abstract, when displayed separately, is reproduced verbatim under its article-level license; the synthesis preserves observational, endpoint-ascertainment, and validation limits.

## Links

- DOI: https://doi.org/10.1111/jdi.70462
- HTML page: https://drgezhang.com/papers/doi-10-1111-jdi-70462.html
- Google Scholar query: https://scholar.google.com/scholar?q=%22The%20cholesterol%2C%20high-density%20lipoprotein%2C%20and%20glucose%20index%20and%20its%20derivatives%20related%20to%20obesity%20and%20inflammation%20as%20novel%20metabolic%20markers%20for%20predicting%20new-onset%20cardiovascular-liver-metabolic%20multimorbidity%3A%20A%20prospective%20cohort%20study%22
- ORCID: https://orcid.org/0000-0002-3116-3246