The Role of Metabolic Dysfunction–Associated Fatty Liver Disease in Developing Chronic Kidney Disease: Longitudinal Cohort Study
Bibliographic record
Abstract
Background The association between metabolic dysfunction–associated fatty liver disease (MAFLD) and chronic kidney disease (CKD) is unclear. Objective This longitudinal cohort study aimed to test whether MAFLD plays an important role in the development of CKD. Methods This cohort study included 41,246 participants who had undergone 3 or more health examinations from 2008 to 2015 at the People’s Hospital of Guangxi Zhuang Autonomous Region, China. Participants were categorized into 2 groups according to whether they presented with or without MAFLD. The occurrence of new-onset CKD was stated as either an estimated glomerular filtration rate of <60 mL/min per 1.73 m2 or a higher level of albuminuria during their follow-up appointment. The association between MAFLD and CKD was evaluated using a Cox regression method. Results Of the 41,246 participants, 11,860 (28.8%) were diagnosed with MAFLD. Over the course of the 14-year follow-up (median 10.0 years), 5347 (13%) participants experienced a new incident of CKD (135.73 per 10,000 person-years). MAFLD was discovered as an important risk factor for new incidents of CKD (hazard ratio 1.18, 95% CI 1.11-1.26) by using the multivariable Cox proportional hazard regression model. When stratified by gender, the adjusted hazard ratio for the incidence of CKD in men and women with MAFLD were 1.16 (95% CI 1.07-1.26) and 1.32 (95% CI 1.18-1.48), respectively. According to the subgroup analysis results, after adjusting for confounding factors, the MAFLD-related CKD risk was greater in men aged <60 years (Pinteraction=.001) and in those with combined dyslipidemia (Pinteraction=.02), but this relationship was not found in women (all Pinteraction>.05). Conclusions MAFLD plays an important role in the development of new incidents of CKD in the long run. Trial Registration Chinese Clinical Trial Registry ChiCTR2200058543; https://www.chictr.org.cn/showproj.html?proj=153109
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".