SynthesisFrontiers in endocrinology2026
Lipid biomarkers for the prediction of type 2 diabetes risk, an umbrella review and updated meta-analyses of prospective observational studies.
Synthesis in Frontiers in endocrinology, 2026. 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Although multiple prospective studies have examined associations between lipid metabolism disorders and the risk of type 2 diabetes (T2D), a systematic review and data updates are still lacking. Nowadays, some noval lipid metabolism biomarkers have received increasing attention, but who is the most predictive biomarkers of T2D? this study aims to conduct the in-depth exploration. Methods: We conducted an umbrella review of meta-analyses of prospective cohort studies by searching PubMed, Web of Science, Cochrane Library, and Embase from inception to 14 February 2025. Methodological quality was assessed using the AMSTAR 2, and evidence strength was evaluated using predefined credibility criteria. The study protocol was registered in PROSPERO (CRD420250649341). Results: A total of 13 meta-analyses, 133 original articles (including 39 newly included articles) and 1, 226, 322 participants were included. The results of meta-analysis showed significant positive correlation between several lipid metabolism parameters and the risk of T2D. The strongest associations were observed for the hypertriglyceridemic waist (HTW) phenotype [Relative risk (RR) = 3.54, 95% CI: 1.92, 6.53]. Significant positive correlations were also confirmed for the lipid accumulation product (LAP) (RR = 2.94, 95% CI: 2.31, 3.73), the triglyceride glucose (TyG) index (RR = 2.51, 95% CI: 2.13, 2.95), the atherogenic index of plasma (AIP) (RR = 1.97, 95% CI: 1.54, 2.52), the visceral adiposity index (VAI) (RR = 1.77, 95% CI: 1.61, 1.94), the triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio (RR = 1.51, 95% CI: 1.36, 1.69), and non-high-density lipoprotein cholesterol (non-HDL-C) (RR = 1.27, 95% CI: 1.07, 1.52). In contrast, lipoprotein(a) [Lp(a)] showed an inverse association (RR = 0.73, 95% CI: 0.56, 0.96). Dose-response meta-analysis suggested that there was a significant linear relationship between the TG/HDL-C ratio and T2D risk ( Conclusion: This systematic review supportsed the predictive value of multiple lipid biomarkers for the risk of T2D, especially some composite indicators such as the HTW, LAP, TyG index, AIP, VAI and TG/HDL-C ratio, but larger-scale and longer-term prospective studies are warranted to validate these associations.
Indexed as
Identifiers
What Socratic 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 Socratic graph.