New Cholesterol-Glucose Index Matches Classic Marker in Flagging Diabetic Kidney Disease

Diabetic kidney disease remains one of the most feared complications of type 2 diabetes, quietly eroding the filtering capacity of the kidneys in a large share of patients long before symptoms appear. Now, a cross-sectional study from China suggests that a relatively simple composite blood marker, the cholesterol–high-density lipoprotein–glucose (CHG) index, is strongly associated with the presence of this complication and performs essentially on par with the better-known triglyceride–glucose (TyG) index. The findings, published in BMC Endocrine Disorders by Ting Wang, Xiaotong Zhao, and Ruifeng Shi of the First Affiliated Hospital of Anhui Medical University, add a potentially useful tool to the ongoing search for inexpensive, widely available indicators of kidney risk in diabetes.

The research team analyzed data from 1,379 adults with type 2 diabetes recruited in a hospital-based setting. Diabetic kidney disease was defined using standard clinical criteria: an estimated glomerular filtration rate below 60 mL/min/1.73 m², the presence of albuminuria with a urinary albumin-to-creatinine ratio of at least 30 mg/g, or both. Of the participants, 554 individuals, or 40.2 percent, met the definition of diabetic kidney disease. That high prevalence underscores why clinicians are keen to find markers that can be computed from routine laboratory panels rather than relying solely on more specialized or invasive assessments.

Both indices under investigation are built from everyday blood tests. The TyG index, which has gained considerable traction in metabolic research over the past decade, combines fasting triglycerides and fasting glucose into a logarithmic surrogate for insulin resistance. The CHG index is a newer proposal that blends total cholesterol, high-density lipoprotein cholesterol, and fasting glucose, aiming to capture a joint lipid-glycemic burden in a single number. The logic is that kidney damage in diabetes is driven not only by high blood sugar but also by lipid abnormalities and the metabolic stress they jointly impose on the delicate filtration barriers of the nephron.

To test whether CHG carries real signal, the researchers used multivariable logistic regression with progressive layers of covariate adjustment. In the primary model, which accounted for age, sex, body mass index, smoking, alcohol drinking, and diabetes duration, each one-standard-deviation increase in the CHG index was associated with 46 percent higher odds of diabetic kidney disease (odds ratio 1.46, 95 percent confidence interval 1.29 to 1.65). The TyG index performed almost identically, with an odds ratio of 1.44 per standard deviation (95 percent confidence interval 1.27 to 1.63). Both associations were statistically significant at P less than 0.001.

Crucially, the associations survived tougher scrutiny. When the team added systolic and diastolic blood pressure and the use of lipid-lowering medication to the model, and then further adjusted for glycated hemoglobin, the links between both indices and kidney disease remained robust. This matters because blood pressure and long-term glucose control are themselves major drivers of diabetic kidney damage; a marker that stays significant after accounting for them is capturing something beyond the usual suspects. Restricted cubic spline analysis, a flexible technique for mapping the shape of a dose-response relationship, showed significant overall associations for both indices without meaningful departures from linearity, with P values for nonlinearity of 0.348 for CHG and 0.904 for TyG. In other words, risk climbed steadily with each index rather than spiking only above some threshold.

The authors then went beyond simple association and asked how well each index actually discriminates between patients with and without diabetic kidney disease. Receiver operating characteristic analysis produced nearly identical areas under the curve: 0.684 for CHG and 0.683 for TyG, with a DeLong test P value of 0.715 confirming no statistically meaningful difference. An AUC around 0.68 indicates moderate discrimination, better than a coin flip but not accurate enough to serve as a stand-alone diagnostic test. The real question for any new index is whether it adds value on top of information clinicians already collect.

On that front, the exploratory results were cautiously encouraging. Adding CHG to a base clinical model yielded small but measurable improvements in reclassification, with an integrated discrimination improvement of 0.026 (95 percent confidence interval 0.012 to 0.046) and a continuous net reclassification improvement of 0.277 (95 percent confidence interval 0.160 to 0.392). Decision curve analysis, a method that evaluates the net benefit of a model across a range of decision thresholds, showed higher net benefit for the CHG-augmented model across threshold probabilities of 0.30 to 0.60. The authors were careful to flag that these were nominal P values not adjusted for multiple testing, so the reclassification findings should be treated as hypothesis-generating rather than definitive.

One of the more intriguing signals emerged from the prespecified subgroup analyses. A nominally significant interaction was observed for diabetes duration (P equal to 0.006), with the CHG–kidney disease association appearing stronger in patients who had lived with diabetes for ten years or longer. This pattern is biologically plausible: the microvascular damage that culminates in diabetic nephropathy typically accumulates over years of metabolic exposure, so a composite lipid-glycemic marker might exert a more visible statistical footprint in a longer-duration population. The analysis also indicated that the overall association was driven predominantly by albuminuria rather than by reduced glomerular filtration rate, pointing toward early injury of the glomerular filtration barrier as the relevant pathological process.

The study’s strengths lie in its reasonably large sample, its systematic comparison of two competing indices, and its use of modern analytical methods ranging from spline modeling to decision curve analysis. The authors also conducted exploratory sensitivity analyses and handled missing data with both multiple imputation and complete-case approaches to probe robustness. Still, the limitations are substantial and the authors state them plainly. The cross-sectional design means the data capture a single moment in time; elevated CHG and TyG are associated with prevalent kidney disease, but the study cannot establish that the indices predict who will develop it. Reverse causation remains possible, since declining kidney function itself alters lipid metabolism and glucose handling, potentially inflating the indices in people who already have kidney damage.

For now, the practical takeaway is that a marker computable from three routine blood values performs comparably to the TyG index in identifying patients with type 2 diabetes who have diabetic kidney disease, and may modestly sharpen risk assessment when layered onto standard clinical variables. The CHG index does not outshine its established rival, but matching it with a different lipid backbone gives researchers and clinicians a second, independent lens on the same metabolic territory. Whether that lens proves genuinely predictive will require prospective cohort studies that follow patients forward in time, ideally across multiple centers and ethnic groups. Until such validation arrives, the study stands as a careful, methodologically transparent demonstration of association, and a reminder that the intersection of blood lipids and blood sugar continues to hold clues to one of diabetes most consequential complications.

Subject of Research: Association of lipid-glycemic composite indices with diabetic kidney disease in type 2 diabetes

Article Title: Association of the CHG index with diabetic kidney disease and its discriminative performance in patients with type 2 diabetes: a cross-sectional comparison with the TyG index

Article References: Wang, T., Zhao, X., & Shi, R. (2026). Association of the CHG index with diabetic kidney disease and its discriminative performance in patients with type 2 diabetes: a cross-sectional comparison with the TyG index. BMC Endocrine Disorders. https://doi.org/10.1186/s12902-026-02620-4

Image Credits: AI Generated

DOI: 10.1186/s12902-026-02620-4

Keywords: diabetic kidney disease, type 2 diabetes, CHG index, TyG index, insulin resistance, albuminuria, cross-sectional study, logistic regression, ROC analysis, lipid metabolism, chronic kidney disease, biomarkers