Cluster-based metabolic subphenotypes and projected cross-organ risk in Chinese adults with prediabetes

Abstract

BACKGROUND

Prediabetes is metabolically heterogeneous, but most risk assessment remains glycemia centered. It is unclear whether prediabetes subphenotypes already show coordinated or discordant cardio-kidney-liver risk-score signatures before diabetes. We hypothesized that cluster-defined subphenotypes would differ in projected cardiovascular, kidney, and fibrosis-4 (FIB-4)-defined liver fibrosis-risk profiles.

AIM

To determine whether cluster-defined prediabetes subphenotypes differ in projected cross-organ risk-score profiles.

METHODS

We performed a cross-sectional baseline analysis of a prospective multicenter high-risk diabetes program in China. K-means clustering was applied to 621 adults with prediabetes using age, body mass index, glycated hemoglobin, homeostasis model assessment of insulin resistance, and homeostasis model assessment of β-cell function. Framingham-estimated 10-year cardiovascular disease risk, chronic kidney disease (CKD) prognosis consortium-estimated 5-year CKD risk, and FIB-4-defined fibrosis-risk signal were compared across subphenotypes using Poisson regression with HC3 robust standard errors. Sensitivity analyses evaluated whether the observed patterns were robust to threshold choice, missing-data handling, albumin-to-creatinine ratio availability, reference group, and age loading.

RESULTS

Four subphenotypes were identified: (1) Mild obesity-related dysmetabolism (n = 177); (2) Mild age-related dysmetabolism (MARD) (n = 190); (3) Severe insulin resistance (n = 95); and (4) Severe insulin deficiency (SID) (n = 159). MARD and SID together accounted for most threshold-positive participants across the cardiovascular, kidney, and FIB-4 domains: (1) 75.6%; (2) 86.4%; and (3) 82.9%. Compared with mild obesity-related dysmetabolism, MARD showed the broadest age-loaded projected-risk pattern, with adjusted prevalence ratios of 1.94 (95%CI: 1.28-2.94) for Framingham risk, 8.75 (4.12-18.56) for CKD prognosis consortium risk, and 3.00 (1.76-5.13) for FIB-4. SID showed a nonclassical pattern, remaining enriched for projected CKD risk and FIB-4-defined fibrosis-risk signal despite lower adiposity, with adjusted prevalence ratios of 6.41 and 3.51, respectively (all P < 0.05)

CONCLUSION

Prediabetes subphenotypes show distinct projected cardio-kidney-liver risk patterns: MARD is broadly age-loaded, whereas SID marks a nonclassical kidney/FIB-4 signal requiring organ-outcome validation.

Keywords: Prediabetic state; Metabolic subphenotypes; Cluster analysis; Cardiovascular-kidney-metabolic syndrome; Cardiovascular diseases; Chronic renal insufficiency; Liver fibrosis; Insulin deficiency

Core Tip: Prediabetes is not a uniform metabolic state. In this Chinese multicenter cohort, cluster-defined subphenotypes showed distinct projected cardio–kidney–liver risk-score patterns. Mild age-related dysmetabolism represented a broad, age-loaded projected-risk subtype across Framingham, chronic kidney disease prognosis consortium, and fibrosis-4 domains. Severe insulin deficiency showed the most unexpected signal: Despite lower adiposity and lack of a classical insulin-resistant phenotype, it remained enriched for projected chronic kidney disease risk and fibrosis-4-defined liver fibrosis-risk signal. These findings connect prediabetes subphenotyping with an integrated organ-risk framework and support prospective validation of subtype-informed risk assessment before overt diabetes.