Fasting Blood Glucose to High-Density Lipoprotein Cholesterol Ratio and MASLD Risk: Non-Linear Association and BMI Mediation in Non-Diabetic Adults

Why It Matters

GHR offers clinicians a readily available biomarker to flag high‑risk individuals before liver damage progresses, potentially reducing the need for costly imaging or biopsies. Its strong predictive power and partial mediation by BMI highlight a modifiable pathway for early intervention.

Key Takeaways

• GHR predicts MASLD better than FBG or HDL‑C alone
• Each 1‑unit GHR rise raises MASLD odds by 23%
• BMI mediates ~60% of GHR‑MASLD link
• Risk escalates sharply until GHR ≈4.62, then plateaus
• GHR cutoff ~3.97 offers 80% sensitivity for screening

Pulse Analysis

Metabolic dysfunction‑associated steatotic liver disease (MASLD) has eclipsed viral hepatitis as the most common chronic liver condition worldwide, driving a surge in demand for non‑invasive risk‑stratification tools. Traditional markers such as fasting glucose or HDL‑cholesterol capture only a slice of the underlying metabolic disturbance, prompting researchers to explore composite indices. The fasting blood glucose‑to‑HDL‑C ratio (GHR) integrates glucose and lipid pathways, offering a more holistic snapshot of glucolipotoxic stress that fuels hepatic fat accumulation.

In the recent Frontiers in Nutrition study, investigators leveraged the NAGALA cohort to evaluate GHR’s predictive capacity. Logistic models revealed a 23% increase in MASLD odds per unit GHR, while generalized additive models identified a non‑linear curve with a critical inflection at 4.62—beyond which additional risk plateaus. Receiver‑operating‑characteristic analysis showed GHR’s AUC of 0.815, significantly surpassing fasting glucose (0.727) and HDL‑C (0.787) alone. Mediation analysis further demonstrated that body‑mass index explains nearly 60% of the GHR‑MASLD link, underscoring obesity’s central role in the metabolic cascade.

For clinicians, the practical implication is clear: a routine lab panel can generate GHR, enabling early identification of patients who merit liver imaging or lifestyle counseling. The optimal GHR threshold of roughly 3.97 delivers 80% sensitivity, making it a viable first‑line screen, especially in settings where ultrasound resources are limited. Nonetheless, the cross‑sectional design and reliance on ultrasound diagnosis temper causal claims, and performance wanes in severely obese or hypertriglyceridemic subgroups. Prospective validation in diverse populations and integration with advanced imaging modalities will be essential before GHR can be embedded into standard MASLD screening algorithms.