The researchers divided admissions from Changsha Central Hospital chronologically rather than randomly. The earliest 70 percent, from January 2020 through November 2024, formed a derivation cohort of 593 patients, including 67 with hemorrhagic transformation. The remaining 254 admissions, recorded from late November 2024 through May 2026, formed the internal temporal-validation cohort and included 27 hemorrhagic transformation events. Overall, 94 of 847 admissions, or 11.1 percent, had imaging-confirmed in-hospital hemorrhagic transformation. The design was intended to test whether laboratory markers added useful information beyond readily available clinical variables under changing real-world conditions.

The baseline model captured age, baseline National Institutes of Health Stroke Scale score, atrial fibrillation, large-vessel occlusion, rapid-glucose measurement and onset-to-treatment time. The candidate additions were the natural logarithm of the neutrophil-to-lymphocyte ratio, or ln(NLR), the atherogenic index of plasma, or AIP, and residual cholesterol. In temporal validation, the clinical model had an AUC of 0.801 with a 95 percent confidence interval of 0.707 to 0.881. Adding ln(NLR) raised the AUC to 0.818, a difference of 0.017 with a DeLong test p value of 0.220. Adding AIP and residual cholesterol produced an AUC of 0.803, while the full nine-variable model reached 0.818, a gain of 0.018 with p = 0.346.

Discrimination was therefore only numerically better after the biomarkers were introduced. Calibration and overall prediction error were virtually unchanged: both the clinical and full models had Brier scores of 0.081 in temporal validation, and their calibration slopes were 1.029 and 1.060, respectively. Decision-curve analysis likewise showed closely overlapping net-benefit trajectories, without a broad or consistently separated advantage for the full model. In other words, the laboratory additions did not demonstrate that they would improve clinical decisions more reliably than the simpler model across the range of decision thresholds examined.

The adjusted analysis identified baseline stroke severity as the clearest independent correlate of the broader hemorrhagic transformation outcome. Each one-point increase in the NIHSS score was associated with 8.4 percent higher odds of hemorrhagic transformation, with an odds ratio of 1.084, a 95 percent confidence interval of 1.044 to 1.126 and p below 0.001. Atrial fibrillation was associated with approximately 2.7-fold higher odds, with an odds ratio of 2.66, a confidence interval of 1.34 to 5.27 and p = 0.005. Large-vessel occlusion had a positive but statistically imprecise association. By contrast, ln(NLR), AIP and residual cholesterol were not independently associated with the primary outcome after the clinical variables were included.

That contrast extended to the unadjusted patient characteristics. Admissions with hemorrhagic transformation had a median baseline NIHSS score of 15, compared with 5 among those without transformation, and large-vessel occlusion was present in 75.9 percent versus 41.4 percent. Atrial fibrillation was also more frequent, at 46.2 percent versus 15.7 percent. Patients with transformation also had higher NLR and other inflammatory indices, lower total and low-density lipoprotein cholesterol, and a lower AIP. These differences did not translate into independent biomarker effects or robust incremental prediction once the clinical model was included, illustrating why an association in a group comparison is not equivalent to useful patient-level discrimination.

A separate exploratory analysis produced a narrower signal for strict symptomatic intracranial hemorrhage, or sICH, defined using the ECASS II framework and restricted to events within seven days. There were 37 sICH events overall, including only 12 in the temporal-validation cohort. In the derivation data, ln(NLR) was associated with sICH after adjustment for NIHSS, atrial fibrillation and large-vessel occlusion, with an odds ratio of 1.89, a 95 percent confidence interval of 1.04 to 3.42 and p = 0.037. Adding NLR to the exploratory clinical model increased its validation AUC from 0.816 to 0.888, but the increment of 0.071 did not reach statistical significance, with p = 0.097. The small number of validation events makes this finding hypothesis-generating rather than evidence of clinical readiness.

The robustness checks did not establish a reproducible advantage from adding biomarkers. Excluding patients who received bridging thrombectomy produced validation AUCs of 0.770 for the clinical model and 0.761 for the full model. Restricting the analysis to patients with known large-vessel occlusion status produced AUCs of 0.800 and 0.815, respectively, while limiting hemorrhagic transformation to events documented within seven days yielded 0.802 and 0.809. Excluding three events whose archived documentation could not be re-verified and adopting a conservative approach to uncertain laboratory timing likewise failed to demonstrate consistent biomarker gains. A separate gradient-boosting analysis within the same cohort also ranked NIHSS, atrial fibrillation and large-vessel occlusion above the three biomarkers, but it was not an independent validation.

Several design features strengthen the central conclusion. Preprocessing and multiple-imputation models were fitted in the derivation cohort and then applied to the later cohort, limiting information leakage. The analysis assessed discrimination, calibration, Brier score, decision curves and multiple sensitivity analyses rather than relying on a single performance metric. A 500-resample bootstrap check estimated a mean optimism of 0.031 for the full model’s apparent AUC, producing an optimism-corrected derivation AUC of approximately 0.786. A post-hoc adequacy calculation also indicated that more development observations would have been desirable. The authors therefore characterized the work as an exploratory incremental-value analysis rather than the development of a deployable clinical risk score.

Important limitations temper the conclusions. This was a single-center retrospective study, so institutional practices and patient selection may limit generalizability, and internal temporal validation does not establish transportability to other hospitals. Hemorrhagic transformation was identified from official radiology reports rather than independent, blinded review of archived images, and clinically triggered repeat imaging may have affected ascertainment. Although the analyzed blood and lipid measurements came from initial emergency specimens collected before thrombolysis, exact draw-to-bolus intervals were not retained for every patient, and not all results were necessarily available before treatment. Predictor missingness and biomarker distributions also changed over time. The results therefore support evaluating future markers against strong clinical baselines, but they do not justify withholding intravenous thrombolysis from otherwise eligible patients.

Subject of Research: Incremental prediction of hemorrhagic transformation after intravenous thrombolysis for acute ischemic stroke

Article Title: Clinical severity predominates over routine inflammatory and metabolic biomarkers in predicting hemorrhagic transformation after intravenous thrombolysis: internal temporal validation of incremental biomarker value

Article References: Liang, H., Zeng, B., Wang, J., Yi, L., Huang, L., Lin, Z., Zhou, C., & Yue, Z. (2026). Clinical severity predominates over routine inflammatory and metabolic biomarkers in predicting hemorrhagic transformation after intravenous thrombolysis: internal temporal validation of incremental biomarker value. Frontiers in Neurology, 17, Article 1940110. https://doi.org/10.3389/fneur.2026.1940110

Image Credits: AI Generated

DOI: 10.3389/fneur.2026.1940110

Keywords: acute ischemic stroke, intravenous thrombolysis, hemorrhagic transformation, NIHSS, neutrophil-to-lymphocyte ratio, atherogenic index of plasma, residual cholesterol, prediction model, temporal validation, Clinical, severity, predominates

Chronic kidney disease is a slow, often silent condition in which the kidneys progressively lose their ability to filter waste from the blood. Because symptoms frequently do not appear until substantial function has been lost, researchers have long searched for early, readily measurable signals that identify people at risk before the damage becomes irreversible. Dyslipidemia—an abnormal level of lipids in the blood—is extremely common in patients who already have CKD, typically manifesting as elevated triglycerides, reduced high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels that are normal or only slightly raised. What has remained less clear is whether this distinctive dyslipidemic pattern precedes the onset of kidney disease and can therefore serve as a warning sign rather than simply a consequence.

To address that question, the research team, led by Dandan Li of the First Affiliated Hospital of Henan University of Science and Technology and colleagues at Huaihe Hospital of Henan University and collaborating institutions in China, analyzed data from adults aged 40 years and older whose baseline estimated glomerular filtration rate (eGFR)—the standard measure of kidney filtering capacity—exceeded 60 mL/min/1.73 m². The participants were followed for six years. The team examined eight lipid parameters: triglycerides (TG), total cholesterol (TC), HDL-C, LDL-C, the triglyceride-to-HDL-C ratio (TG/HDL-C), the total-cholesterol-to-HDL-C ratio (TC/HDL-C), the non-HDL-cholesterol-to-HDL-C ratio (NHHR), and residual cholesterol (RC), a measure of cholesterol carried in triglyceride-rich lipoprotein remnants that is increasingly studied for its role in cardiovascular disease.

The main outcome was carefully defined: incident CKD was recorded when a participant’s eGFR fell by at least 30 percent from its baseline value and finished the follow-up period below 60 mL/min/1.73 m². This dual criterion helps ensure that the classification captures a genuine, clinically meaningful decline in kidney function rather than ordinary measurement variability. Using multivariate logistic regression, the researchers adjusted for a range of potential confounders, including demographic factors and clinical covariates, and employed restricted cubic spline analyses to explore the shape of the relationships and subgroup analyses to test whether the associations differed across patient characteristics.

The results were striking in their consistency. Over the six years of follow-up, 453 of the 3,856 participants—11.7 percent of the cohort—developed incident CKD. After full statistical adjustment, participants in the highest tertile of log-transformed triglycerides had 59 percent higher odds of developing CKD compared with those in the lowest tertile, with an odds ratio of 1.59 and a 95 percent confidence interval of 1.24 to 2.05. Total cholesterol told a similar story, with an odds ratio of 1.49 (95 percent CI: 1.15 to 1.92) for the highest versus lowest tertile.

The composite indices performed at least as well as the conventional parameters. The TC/HDL-C ratio was associated with an odds ratio of 1.51 (95 percent CI: 1.17 to 1.95), while the TG/HDL-C ratio produced the largest effect estimate in the analysis, at 1.63 (95 percent CI: 1.26 to 2.10). The non-HDL-cholesterol-to-HDL-C ratio, a composite measure that captures the balance between atherogenic and protective cholesterol fractions, yielded an odds ratio of 1.51 (95 percent CI: 1.17 to 1.95), and residual cholesterol was associated with an odds ratio of 1.50 (95 percent CI: 1.18 to 1.92). In other words, every one of the pro-atherogenic lipid measures examined—whether conventional or composite—pointed in the same direction: higher levels corresponded to higher odds of future kidney disease.

There was one notable exception, and it was protective. Higher levels of HDL-C, often described as the “good” cholesterol because of its role in reverse cholesterol transport, were associated with lower odds of incident CKD, with an odds ratio of 0.75 (95 percent CI: 0.58 to 0.96). This inverse relationship fits mechanistically with HDL’s established anti-inflammatory and antioxidant functions, which some researchers believe may extend to protecting the delicate filtration structures of the kidney from lipid-mediated injury.

The subgroup analyses added an intriguing nuance. Most of the associations held steady across prespecified subgroups of participants, but the TG/HDL-C ratio showed a higher effect estimate in people without diabetes. This observation is scientifically interesting because diabetic kidney disease has its own well-characterized risk architecture; the suggestion that the triglyceride-HDL balance may be an especially informative predictor among non-diabetic adults raises the possibility that composite lipid indices could help identify kidney risk in a population where clinical vigilance is often lower. The authors also employed restricted cubic spline analysis, a flexible statistical technique that models non-linear dose-response relationships, to characterize how risk varied across the full range of lipid values rather than only at arbitrary cutoffs.

From a mechanistic standpoint, several pathways could link lipid disturbances to kidney damage. Elevated triglycerides and residual cholesterol reflect an abundance of triglyceride-rich lipoproteins and their remnants, particles that can deposit in tissues, provoke inflammation, and injure the glomeruli—the microscopic filtration units of the kidney. Lipid accumulation within kidney cells can trigger oxidative stress and fibrosis, processes that progressively scar renal tissue and erode filtering capacity. At the same time, the possibility of reverse causation cannot be fully dismissed in an observational, retrospective design: subtle early kidney dysfunction may itself alter lipid metabolism, and the study’s reliance on routinely collected clinical data means that unmeasured confounding remains a possibility. The authors note that the study received no external funding and was approved by the Clinical Research Ethics Committee of Huaihe Hospital, Henan University, which waived individual informed consent owing to the retrospective nature of the analysis.

The practical implications, however, are immediately relevant to everyday medicine. Lipid panels are among the most commonly ordered laboratory tests in the world, and the parameters highlighted in this study—triglycerides, HDL-C, and simple ratios derived from them—require no additional blood draws, no specialized equipment, and no added cost. If the findings are confirmed in prospective and ethnically diverse cohorts, clinicians could conceivably incorporate composite lipid indices into routine kidney risk stratification, flagging patients whose blood fat profiles warrant closer monitoring of eGFR and earlier intervention with lifestyle modification or lipid-lowering therapy. With chronic kidney disease affecting hundreds of millions of people globally and often progressing unnoticed until dialysis or transplantation becomes necessary, the idea that a number already printed on nearly every routine lab report might help predict who will lose kidney function is a compelling—and potentially consequential—step forward for preventive nephrology.

Subject of Research: The association between conventional and composite blood lipid parameters and the risk of developing incident chronic kidney disease in adults

Article Title: Lipid parameters and the risk of incident CKD: an analysis of conventional and composite lipid indices

Article References: Li, D., Song, Z., Wang, X., Xu, H., Li, X., Yan, S., Yu, T., Liu, Y., Liu, Z., & Jiang, H. (2026). Lipid parameters and the risk of incident CKD: an analysis of conventional and composite lipid indices. BMC Endocrine Disorders. https://doi.org/10.1186/s12902-026-02529-y

Image Credits: AI Generated

DOI: 10.1186/s12902-026-02529-y

Keywords: chronic kidney disease, triglycerides, HDL cholesterol, LDL cholesterol, lipid ratios, residual cholesterol, dyslipidemia, eGFR, cohort study, nephrology, cardiovascular risk, kidney disease prevention