Key result
Novel lipid-metabolic model identifies prediabetes with ~88% sensitivity in a case-control cohort.
Why the study?
Does a biochemical model integrating age, ADPN, NEFA, and TG/HDL-C accurately identify prediabetes in Chinese adults?
Case-Control (n=296)
No
Does a biochemical model integrating age, ADPN, NEFA, and TG/HDL-C accurately identify prediabetes in Chinese adults?
Effect estimate: AUC 0.736 (95% CI 0.679-0.793)
A biochemical model incorporating age, adiponectin, NEFA, and TG/HDL-C provides moderate discrimination for identifying prediabetes, offering a potential screening tool when OGTT is not feasible.
Prediabetes is highly prevalent in China and often remains undetected in primary-care settings where fasting plasma glucose (FPG) alone may miss individuals with impaired glucose tolerance (IGT). Early identification of metabolic deterioration requires practical, biochemically informed identification tools. This study aimed to develop and evaluate a multifactorial model for identifying prediabetes among Chinese adults. We conducted a retrospective case-control study including 296 adults undergoing routine health examinations at Pingshan Hospital between August 2023 and December 2024. Based on WHO 2019 criteria and 2010 ADA standards, 123 individuals with prediabetes and 173 normoglycemic controls were enrolled. Clinical and biochemical variables—including adiponectin (ADPN), non-esterified fatty acids (NEFA), lipid parameters, and the triglyceride-to-HDL-cholesterol (TG/HDL-C) ratio—were measured. Multivariable logistic regression analysis was performed using the enter method, with variables selected based on clinical relevance and prior evidence. Model performance was evaluated using receiver operating characteristic (ROC) analysis. Age, ADPN, NEFA, and TG/HDL-C were incorporated into the final model. ADPN was inversely associated with prediabetes risk, whereas age showed a positive association, TG/HDL-C and NEFA demonstrated a borderline positive association. The model demonstrated moderate discriminative ability, with an area under the curve (AUC) of 0.736 (95% CI 0.679–0.793), sensitivity of 88%, and specificity of 46%. The Hosmer-Lemeshow test indicated acceptable model calibration (p = 0.076). We developed a biochemical identification model integrating ADPN, NEFA, TG/HDL-C, and age that demonstrated moderate discriminatory ability for identifying prediabetes. The model offers a practical pre-screening approach for community and outpatient settings where oral glucose tolerance test (OGTT) is not routinely feasible. Prospective multicenter validation is warranted. Not applicable.
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Zhang et al. (2026) conducted a case-control in Prediabetes (n=296). Biochemical model integrating adiponectin, non-esterified fatty acids, TG/HDL-C, and age vs. Normoglycemic controls was evaluated on Discriminative ability for identifying prediabetes (AUC 0.736, 95% CI 0.679-0.793). A biochemical model integrating adiponectin, NEFA, TG/HDL-C, and age identified prediabetes with an AUC of 0.736 (95% CI 0.679-0.793), 88% sensitivity, and 46% specificity.
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