The scale of the analysis is one of its distinguishing features. Drawing on outpatient and inpatient records from Zhongshan Hospital between January 1, 2014, and November 5, 2024, the investigators assembled a cohort of 13,966 people with prediabetes. Laboratory test results and imaging examination data were collected from the hospital’s electronic systems, and the study endpoint was defined as the first occurrence of chronic kidney disease during follow-up. Because the cohort was so large and the observation window so long, the researchers had enough events to model risk not as a single static number but as something that evolves with time, which is precisely where their methodological choice becomes important.

That choice was the time-dependent Cox regression model, a statistical framework that extends the classical survival analysis approach of David Cox to accommodate predictor variables whose values change over the course of observation. In a conventional Cox model, a patient’s covariates are typically fixed at baseline, which can be a serious limitation in longitudinal medicine: blood pressure, medication regimens, lipid profiles, and cardiac function all drift over years, and a model that ignores that drift can misjudge risk. A time-dependent formulation allows the hazard of developing kidney disease to be recalculated as new clinical information accumulates, effectively letting the model update its forecast the way a weather model updates as fresh satellite data arrive.

Building such a model on more than a decade of real-world hospital records also posed a data-engineering challenge, and the team addressed it with a strikingly contemporary tool. During the research process, the authors report, DeepSeek-R1 was utilized for feature extraction from clinical data, an early example of a large language model being embedded in the pipeline of a clinical prediction study. Rather than hand-coding every relevant variable from free-text records and structured entries, the researchers used the model to help identify and organize the clinical features that would ultimately feed the survival analysis. The final predictor set that emerged is a portrait of the prediabetic patient as a whole-body system rather than a glucose value alone.

The variables incorporated into the model span several organ systems. Uric acid disorders, hypertension, and anemia each contributed predictive signal, as did low high-density lipoprotein cholesterol, the so-called good cholesterol whose protective vascular role is well established. Age entered the model per ten-year increment, and gender was included as well. Cardiac and vascular measures appeared in the form of left ventricular ejection fraction, a standard echocardiographic index of pumping efficiency, and carotid intima-media thickness, an ultrasound measurement of arterial wall thickening that serves as a surrogate marker of early atherosclerosis. The medication history proved informative in its own right: the number of distinct classes of glucose-lowering medications a patient was taking, whether the patient was on a renal-protective glucose-lowering agent, use of insulin, and use of novel oral anticoagulants all entered the final equation.

Each of these predictors tells a coherent biological story. Elevated uric acid is associated with endothelial dysfunction and renal injury, while hypertension imposes mechanical stress on the delicate filtering structures of the kidney. Anemia can both reflect and exacerbate renal impairment, since failing kidneys produce less erythropoietin. The finding that the intensity and type of glucose-lowering therapy carried predictive information suggests that treatment burden functions as a proxy for disease severity and trajectory, and the specific value of renal-protective agents echoes the growing clinical recognition that some modern drugs, such as SGLT-2 inhibitors, shield the kidneys directly rather than merely lowering blood sugar. Meanwhile, reduced ejection fraction and thicker carotid arteries tie kidney risk to the broader cardiovascular continuum, reinforcing the idea that the heart, the vasculature, and the kidneys fail together more often than in isolation.

To test whether the model actually worked, the researchers turned to five-fold cross-validation, a technique in which the dataset is split into five parts and the model is repeatedly trained on four of them and evaluated on the fifth, rotating through all partitions. This internal validation strategy guards against the most common failure mode of clinical prediction models: overfitting, in which an algorithm memorizes the quirks of its training data and then collapses when confronted with new patients. The performance metric was the time-dependent area under the curve, or AUC, which measures how well the model separates, at each time point, the patients who go on to develop kidney disease from those who do not.

The results showed a model that is strong in the near term and gracefully degrading over longer horizons. The time-dependent AUC values were 0.818 at one year, 0.815 at three years, 0.801 at five years, 0.782 at seven years, 0.744 at nine years, and 0.685 at nearly ten years, with confidence intervals reported for each estimate. In practical terms, an AUC above 0.80 is generally considered good discrimination, meaning the model correctly ranked the risk of two randomly chosen patients more than four times out of five during the first five years of follow-up. The authors characterized this as moderate but stable discriminative ability with satisfactory calibration over short- and intermediate-term follow-up, and they were explicit that the tool should serve as an early risk-screening reference rather than a definitive clinical decision-making instrument.

That framing matters, because the clinical stakes are enormous. Chronic kidney disease is a silent progression: kidney function can decline for years before symptoms appear, and by the time routine markers such as estimated glomerular filtration rate or urinary albumin-to-creatinine ratio cross diagnostic thresholds, much of the renal reserve may already be lost. Prediabetes, defined by impaired fasting glucose or impaired glucose tolerance, affects hundreds of millions of people worldwide according to estimates from bodies such as the International Diabetes Federation, and it represents the single largest identifiable reservoir of future diabetes and diabetic kidney disease. A screening tool that can flag, at the prediabetic stage, which patients are quietly heading toward renal failure would allow clinicians to intensify monitoring, optimize blood pressure and lipid management, and prioritize renal-protective therapies long before irreversible damage occurs.

The study, which was registered as a clinical trial under number ChiCTR2400089463 and approved by the Institutional Review Board of Zhongshan Hospital, Fudan University, also hints at where predictive medicine is heading. The combination of a classical survival framework with large language model-assisted feature extraction from electronic health records suggests a hybrid future in which decades-old biostatistics and cutting-edge artificial intelligence reinforce one another, each covering the other’s weaknesses. The authors are careful about the limits of their work: the model was internally validated in a single Chinese hospital cohort, and external validation in independent populations will be needed before broad deployment. Yet the core message stands on its own. For the vast population living in the gray zone between normal glucose and diabetes, kidney risk is not a distant abstraction but a measurable, modelable quantity, and with the right data, clinicians may soon be able to see it coming years in advance.

Subject of Research: Development and validation of a time-dependent Cox regression model predicting incident chronic kidney disease in prediabetic patients

Article Title: Development and validation of a risk predictive model for incident chronic kidney disease in prediabetic patients based on time-dependent Cox regression

Article References: Liu, P., Lan, C.-D., Jin, Y., Hu, B.-S., & Yang, H. (2026). Development and validation of a risk predictive model for incident chronic kidney disease in prediabetic patients based on time-dependent Cox regression. BMC Endocrine Disorders. https://doi.org/10.1186/s12902-026-02587-2

Image Credits: AI Generated

DOI: 10.1186/s12902-026-02587-2

Keywords: prediabetes, chronic kidney disease, risk prediction model, time-dependent Cox regression, machine learning, electronic health records, uric acid, hypertension, anemia, carotid intima-media thickness, predictive medicine, cross-validation