One Blood Test May Flag Stroke Risk Early in Metabolic Syndrome, Chinese Study Finds

A simple index calculated from three routine blood test results—total cholesterol, high-density lipoprotein cholesterol, and fasting blood glucose—may help identify middle-aged and older adults at elevated risk of stroke before clinical cardiovascular disease develops. In a prospective analysis of the nationally representative China Health and Retirement Longitudinal Study (CHARLS), researchers found that both a single baseline measurement and a cumulative multi-year measure of this cholesterol, high-density lipoprotein, and glucose (CHG) index were independently and linearly associated with the risk of newly diagnosed stroke among people in the early stages of cardiovascular-kidney-metabolic (CKM) syndrome.

The study, published in Cardiovascular Diabetology – Endocrinology Reports, included 4,661 Chinese adults aged 45 and older who were classified in CKM stages 0 to 3, meaning they had metabolic risk factors or subclinical cardiovascular abnormalities but not yet established clinical cardiovascular disease. During follow-up, 476 participants, or 10.2 percent, reported an incident stroke. The CKM framework, proposed by the American Heart Association, describes a continuum running from metabolic dysfunction through kidney disease to overt cardiovascular events, and stroke imposes a particularly heavy burden within that continuum.

The CHG index is computed by taking the logarithm of total cholesterol in milligrams per deciliter multiplied by fasting blood glucose, divided by twice the HDL level. Unlike the triglyceride-glucose index, it incorporates HDL, which matters because the relationship between HDL and cardiovascular risk is not strictly linear; in hypertensive patients, very high HDL levels above 80 milligrams per deciliter have been associated paradoxically with increased risk. The index was designed as a low-cost surrogate marker of insulin resistance, a fundamental driver of both CKM progression and stroke, in settings where the gold-standard hyperinsulinaemic-euglycaemic clamp is impractical for large-scale screening.

To capture long-term metabolic exposure, the researchers calculated a cumulative CHG index as the average of the 2012 and 2015 values multiplied by the three-year interval between them. Stroke outcomes were drawn from self-reported physician diagnoses at the 2018 and 2020 follow-up waves, and anyone diagnosed with stroke before 2015 was excluded to avoid reverse causality. Participants with CKM stage 4, which already includes clinical cardiovascular events such as stroke, were also excluded to focus on the early prevention window.

After full adjustment for demographics, lifestyle factors, anthropometric measurements, laboratory parameters, medication use, and comorbidities, each one-unit increase in the baseline CHG index was associated with a 42 percent higher odds of stroke (odds ratio 1.42, 95 percent confidence interval 1.12–1.81), while each one-unit increase in the cumulative index was associated with a 14 percent higher odds (odds ratio 1.14, 95 percent confidence interval 1.03–1.26). Participants in the highest tertile of the baseline index had 35 percent higher odds than those in the lowest tertile, and the equivalent comparison for the cumulative index showed a 34 percent elevation, with statistically significant trend tests for both measures.

Restricted cubic spline analysis indicated that both associations were linear rather than threshold-shaped, with p-values for non-linearity of 0.331 for the baseline index and 0.817 for the cumulative index and significant overall associations for both. Higher CHG tertiles also tracked with a less favorable metabolic profile, including higher body mass index, C-reactive protein, fasting glucose, total cholesterol, and triglycerides, lower HDL, and greater prevalence of hypertension, diabetes, and advanced CKM stage.

The study’s central comparison, however, yielded a more sobering result. When the two measures were assessed with receiver operating characteristic curves, the area under the curve was 0.672 for the baseline index and 0.671 for the cumulative index, a difference that was not statistically significant. In other words, repeated measurement over three years added no meaningful predictive information beyond a single blood draw. Both values around 0.67 indicate only moderate discriminatory capacity, well below what would be needed for a standalone diagnostic or risk-stratification tool.

Subgroup analyses suggested the association might be modified by gender and drinking status, with significant interactions for both measures. The authors reported that risk estimates appeared higher in women and in never-drinkers, possibly reflecting the loss of estrogen’s vascular protection after menopause and the ability of the index to flag people with concealed metabolic risk who lack traditional behavioral risk factors. The researchers themselves cautioned that these subgroup findings are exploratory, potentially underpowered, and susceptible to type I error, requiring independent validation.

Several sensitivity analyses supported the robustness of the main findings. Multiple imputation of missing data produced nearly identical estimates, and modeling the indices as continuous variables or tertiles, fitting Cox proportional hazards models, and applying competing-risk regression that treated all-cause death as a competing event all led to the same conclusions. The authors also noted that borderline significant p-values near 0.05 should be interpreted cautiously because no formal multiplicity adjustment was applied in this exploratory study.

The study has important limitations. Stroke was ascertained by self-report without objective medical records or imaging validation, raising the possibility of recall and misclassification bias, although non-differential misclassification would tend to bias results toward the null. The cumulative index relied on only two time points and assumed linear change, residual confounding from factors such as diet quality, physical activity, and medication adherence cannot be excluded, ischemic and hemorrhagic stroke could not be distinguished, and the findings from a Chinese population aged 45 and older may not generalize to other groups. The authors conclude that the CHG index, derived from routine and inexpensive blood tests, may serve as a preliminary screening alert in primary care and population screening, but it should be used alongside established risk tools such as the Framingham Risk Score or the China-PAR model rather than in isolation.

Subject of Research: Association of baseline and cumulative cholesterol-HDL-glucose index with incident stroke risk in adults with cardiovascular-kidney-metabolic syndrome stages 0–3 in the CHARLS cohort.

Article Title: Baseline versus cumulative cholesterol, high-density lipoprotein, and glucose index and stroke risk in individuals with cardiovascular-kidney-metabolic syndrome stages 0–3: evidence from the CHARLS study

Article References: Chen, C., Yang, X., Li, C., Xi, Q., & Ding, Y. (2026). Baseline versus cumulative cholesterol, high-density lipoprotein, and glucose index and stroke risk in individuals with cardiovascular-kidney-metabolic syndrome stages 0–3: evidence from the CHARLS study. Cardiovascular Diabetology – Endocrinology Reports, 12(1), Article 49. https://doi.org/10.1186/s40842-026-00315-2

Image Credits: AI Generated

DOI: 10.1186/s40842-026-00315-2

Keywords: stroke, CHG index, cardiovascular-kidney-metabolic syndrome, CHARLS, insulin resistance, lipid metabolism, blood glucose, risk prediction, cohort study, China, epidemiology, screening