Insulin Resistance Scores Fail to Predict Knee Osteoarthritis in Nine-Year Chinese Cohort

Knee osteoarthritis is one of the most common causes of chronic pain and disability worldwide, and for decades clinicians have blamed the usual suspects: age, excess body weight, joint injury, and the slow mechanical wear of a lifetime of walking. But in recent years a quieter suspect has entered the conversation. Insulin resistance, the metabolic state in which the body’s tissues respond sluggishly to the hormone insulin, has been implicated in a growing list of chronic diseases, from heart disease to certain cancers. Some researchers have wondered whether the same metabolic dysfunction that damages blood vessels and organs might also inflame and degrade the cartilage inside our knees. A new nine-year study from China set out to answer that question with unusual rigor, and the results offer a sobering lesson about how easy it is for a promising biological hypothesis to dissolve under statistical scrutiny.

The research, published in BMC Endocrine Disorders, drew on the China Health and Retirement Longitudinal Study, a nationally representative cohort that has tracked thousands of middle-aged and older Chinese adults since 2011. The team, led by Kan Chen and Yi Wang of the First Affiliated Hospital of Jinzhou Medical University, identified 5,871 participants who were free of knee osteoarthritis at the start of the study. These individuals were followed across four subsequent survey waves in 2013, 2015, 2018, and 2020, giving the investigators up to nine years of observation. During that window, 2,143 participants, or 36.5 percent of the cohort, developed symptomatic knee osteoarthritis as reported on standardized questionnaires. That strikingly high incidence rate alone underscores why the condition represents such an enormous public health burden in rapidly aging populations.

What made this study distinctive was its systematic approach to measuring insulin resistance. Because directly measuring insulin sensitivity with a glucose clamp is impractical in large population studies, researchers rely on surrogate indices calculated from routine blood tests and anthropometric measurements. The team evaluated six of them simultaneously. The estimated glucose disposal rate, or eGDR, models how quickly the body can clear glucose from the bloodstream. The triglyceride-glucose index, or TyG, combines fasting triglycerides and glucose into a single number that tracks insulin resistance. Variants of TyG that incorporate body mass index or waist circumference capture both metabolic and adiposity dimensions. The Chinese visceral adiposity index, or CVAI, was specifically developed to estimate harmful visceral fat in Chinese populations. The metabolic score for insulin resistance, or METS-IR, and the atherogenic index of plasma, or AIP, round out the panel. Each index offers a slightly different window into the same underlying metabolic state, and testing all six together guards against the cherry-picking that has plagued smaller studies in this field.

The statistical machinery was equally thorough. The investigators used multivariable Cox regression, the standard tool for survival analysis, to estimate hazard ratios for knee osteoarthritis across quartiles of each index while adjusting for a battery of potential confounders. They then applied restricted cubic splines, a flexible modeling technique that allows the relationship between an index and disease risk to take on any shape, including U-shaped or threshold effects, rather than assuming a straight line. Sensitivity analyses probed whether early incident cases, overlapping information about body size in the adiposity-based indices, or selection bias might be distorting the results. And crucially, the team corrected for multiple testing using the false discovery rate procedure, a step that many observational studies skip and that often separates robust signals from statistical noise.

Before that correction, the raw results looked tantalizing. The trend tests for eGDR and CVAI showed nominal P values of 0.046 and 0.044 respectively, just under the conventional 0.05 threshold. The atherogenic index of plasma told a slightly different story: participants in the second and third quartiles of AIP had nominally elevated hazard ratios of 1.14 and 1.15 compared with the lowest quartile, with confidence intervals that excluded the null value of one. Interestingly, the highest AIP quartile did not show a significant elevation, and the overall trend test for AIP was far from significant at 0.798, a pattern that would be difficult to reconcile with a simple dose-response relationship between lipid-derived insulin resistance and joint degeneration.

Then came the false discovery rate correction, and the picture changed. When the six fully adjusted trend tests were corrected for the fact that six independent hypotheses were being tested, none of them remained statistically significant. The quartile comparisons for AIP, which were not included in the multiplicity correction, are reported by the authors as nominal, exploratory findings. The restricted cubic spline analyses found no evidence of statistically significant non-linearity for any of the six indices. In plain terms, once the analysis accounted for the reality that testing six markers makes it likely that at least one will cross the 0.05 threshold by chance alone, the apparent signals vanished into the statistical background.

The authors are refreshingly candid about what this means. They describe the observed patterns as small in magnitude and explicitly state that they should be regarded as hypothesis-generating rather than as evidence that any of these indices can stratify knee osteoarthritis risk in clinical practice. This kind of restraint is worth pausing on, because it runs counter to a well-documented tendency in metabolic and musculoskeletal research, where surrogate indices are frequently promoted as screening tools on the basis of uncorrected associations. A nominal P value of 0.046, in a field where dozens of similar analyses have been published, is exactly the kind of finding that the false discovery rate framework was designed to temper.

The study does have limitations that the authors and independent readers will want to weigh. The outcome was self-reported symptomatic knee osteoarthritis rather than radiographically confirmed disease, which means the findings speak most directly to clinically apparent disease but may miss asymptomatic structural damage. Residual confounding remains possible in any observational cohort, and the sensitivity analyses, while consistent, showed attenuation of estimates after full adjustment, suggesting that some of the raw association may have been driven by factors such as body weight that lie on the causal pathway or correlate closely with it. The adiposity-based indices in particular share substantial mathematical overlap with body mass index, making it difficult to disentangle a specific insulin resistance effect from the well-established mechanical load of excess weight on knee joints.

Yet the null result is itself informative. If insulin resistance were a strong independent driver of knee osteoarthritis, one might expect at least one of six complementary indices, each capturing a different facet of metabolic dysfunction, to survive correction in a cohort of nearly six thousand people followed for nine years. The absence of such a signal suggests that whatever contribution metabolic dysfunction makes to knee osteoarthritis is likely modest, indirect, or mediated through pathways such as adiposity and systemic inflammation that are already captured by conventional risk factors. It also cautions against the growing enthusiasm for repurposing metabolic indices as universal predictors of diseases well beyond their original scope.

For the millions of people living with creaking, aching knees, the practical takeaway is unchanged: maintain a healthy weight, stay physically active, protect the joints from injury, and manage metabolic health for its own well-established benefits. For researchers, the study offers a methodological template worth emulating, one that pre-specifies multiple indices, applies rigorous multiplicity correction, and reports null findings with the same transparency as positive ones. In an era when flashy associations often travel faster than careful verification, a nine-year cohort study that concludes its own hypothesis is not yet supported is a quiet but valuable contribution to the science of aging joints.

Subject of Research: Prospective associations between surrogate insulin resistance indices and incident symptomatic knee osteoarthritis in a Chinese cohort

Article Title: Associations between insulin resistance surrogate indices and risk of incident symptomatic knee osteoarthritis: insights from a 9-year Chinese cohort

Article References: Chen, K., Wang, X., Zheng, H., Zhang, X., Wu, J., Huang, J., & Wang, Y. (2026). Associations between insulin resistance surrogate indices and risk of incident symptomatic knee osteoarthritis: insights from a 9-year Chinese cohort. BMC Endocrine Disorders. https://doi.org/10.1186/s12902-026-02526-1

Image Credits: AI Generated

DOI: 10.1186/s12902-026-02526-1

Keywords: insulin resistance, knee osteoarthritis, TyG index, eGDR, CVAI, CHARLS, prospective cohort study, false discovery rate, Cox regression, metabolic syndrome, joint health, epidemiology