The Uric Acid-to-HDL Ratio (UHR) and its Association with Prediabetic Phenotypes: A Cross-sectional Population Study
Renjie Yin, Juan Chen, Zihao Shi, Qunyan Zhou, Chunhua WuIntroduction:
The uric acid to high-density lipoprotein cholesterol ratio (UHR) is an emerging biomarker reflective of metabolic and inflammatory dysregulation associated with nutrient metabolism disorders. While elevated UHR has been identified as a significant risk factor for prediabetes, its association with distinct prediabetes phenotypes—categorized by impaired glucose tolerance (IGT), impaired fasting glucose (IFG), and elevated glycated hemoglobin A1c (HbA1c)—remains unexplored.
Methods:
We conducted a cross-sectional analysis using data from the 2011–2016 National Health and Nutrition Examination Survey (NHANES). Prediabetes phenotypes were defined based on standardized criteria for IGT, IFG, and HbA1c-defined prediabetes. Multivariable linear regression models were employed to assess the independent association between UHR levels and specific prediabetes phenotypes, with normoglycemia as the reference. A trend test was used to evaluate the relationship between UHR and the number of coexisting glucometabolic defects.
Results:
UHR levels differed significantly across prediabetes phenotypes compared to the normoglycemic group (p < 0.001). In fully adjusted models, higher UHR levels were independently associated with the combined phenotype of IGT + IFG (β = 0.515, 95% CI: 0.144 to 0.885) and the triad phenotype of IFG + IGT + HbA1c (β = 0.660, 95% CI: 0.313 to 1.007). No significant associations were found for other isolated phenotypes. Furthermore, a significant positive trend was observed, with UHR increasing progressively as the number of concurrent glucometabolic defects increased (P for trend < 0.001).
Discussion:
Our findings indicate that UHR elevation is not a uniform marker across all prediabetes phenotypes. Instead, it specifically associates with more complex metabolic dysregulation characterized by concurrent glucometabolic defects — particularly the combination of IGT and IFG, or all three abnormalities together. This pattern suggests that UHR may help clinicians identify individuals at risk for a higher-burden prediabetes state, beyond simple dysglycemia. However, given the crosssectional design, longitudinal studies are needed to determine whether UHR predicts differential progression across these phenotypes.
Conclusion:
UHR was associated with prediabetes phenotypes involving multiple glucometabolic abnormalities. Because this is a cross-sectional study, the findings are hypothesis-generating: UHR may be linked to a more adverse metabolic profile. Prospective studies are needed to confirm this.