ArticleDigital health
Dynamics of the atherogenic index of plasma and diabetes conversion: Insights from machine learning and longitudinal pattern analysis in middle-aged and older Chinese adults with prediabetes.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Oxidative dyslipidemia in early pregnancy: integrating atherogenic index of plasma (AIP) and uric acid (UA) to refine preeclampsia risk stratification.Frontiers in pharmacology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Atherogenic dyslipidemia significantly influences diabetes development, yet the longitudinal prognostic value of the atherogenic index of plasma (AIP) in prediabetic populations remains understudied. This study investigated whether dynamic AIP measures predict diabetes conversion in middle-aged and older Chinese adults with prediabetes while exploring interactions with other metabolic risk factors through interpretable machine learning. Methods: We analyzed data from 1965 prediabetic adults (≥ 45 years) from the China Health and Retirement Longitudinal Study (2012-2015), assessing baseline AIP, cumulative AIP exposure (AIPcum), and change patterns via K-means clustering. Associations with incident diabetes were evaluated using logistic regression with progressive confounder adjustment. Five machine learning models were developed using predictors identified through the Boruta algorithm and recursive feature elimination, with the best model interpreted via SHAP (SHapley Additive exPlanations) analysis. Results: During follow-up, 15.0% of participants progressed to diabetes. Elevated AIPcum showed the strongest association with diabetes risk (OR for highest vs. lowest quartile = 2.37; 95% CI: 1.60-3.57; Conclusion: Cumulative AIP exposure and unfavorable AIP change patterns independently predict prediabetes-to-diabetes progression. Incorporating these measures into risk assessment may enhance early identification of high-risk individuals and inform targeted interventions.
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.