ArticleFrontiers in endocrinology2025
Stratifying metabolic-related risk factors using latent class analysis to explore the risk of renal composite endpoints in patients with type 2 diabetes mellitus and associated chronic kidney disease.
Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Metabolic syndrome is a key independent risk factor for the progression of chronic kidney disease (CKD) in patients with Type 2 diabetes (T2DM). Traditional studies often focus on isolated metabolic markers, but our research aims to comprehensively assess the metabolic landscape of these patients. Existing approaches have been limited in integrating multiple metabolic parameters and stratifying patients based on the severity of metabolic dysregulation, hindering the understanding of disease progression. Methods: This single-center, retrospective cohort study was conducted at Guangdong Provincial People's Hospital, enrolling 860 participants from January 2010 to December 2023. A total of 65.0% were male, and 35.0% were female. Using Latent Class Analysis (LCA), we stratified CKD patients with T2DM into two distinct classes based on a comprehensive set of baseline clinical metabolic indicators, including glycated hemoglobin (HbA1c), lipid profiles, serum uric acid, blood pressure and body mass index (BMI). Cox proportional hazards models were used to assess renal outcome risks across these identified metabolic phenotypes. Results: LCA revealed that Class 2 exhibited significantly higher values for clinical parameters including systolic and diastolic blood pressure, BMI, total cholesterol, triglycerides, LDL-C, HbA1c, and protein-to-creatinine ratio. Longitudinal analysis showed increased hazard ratios for renal outcomes at 3, 5, and 10 years for Class 2 (HR: 1.718, 1.662, and 1.826, respectively; all P < 0.05). Conclusions: This study highlights the utility of comprehensive metabolic profiling through LCA for stratifying CKD patients with T2DM, identifying two distinct subgroups with differential renal prognoses, and offering insights for precision nephrology interventions and personalized risk management.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.