ArticleCardiovascular diabetology2026
Proteomics-derived organ-specific aging clusters predict macrovascular and microvascular complications in diabetes.
Article in Cardiovascular diabetology, 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
7 authors.
Funding
Abstract
backgroundAging is intrinsically linked to diabetes pathogenesis, yet evidence gaps persist regarding organ-specific aging and risks of long-term diabetes complications. This study aims to estimate the association between plasma proteomics-based organ-specific aging and risks of long-term diabetes complications.
methodsThis cohort study quantified biological age gaps (residuals from linear regression of predicted versus chronological age) across 11 organ systems in 1979 baseline diabetes patients based on plasma proteomic profiling (Olink Explore 3072) of 2916 proteins and three validated different aging models. Extreme aging was defined as > 1.5 standard deviation from organ-specific mean age gaps. Multivariable Cox models assessed incident complication risks in 1707 complication-free patients.
resultsDuring a median follow-up time of 12.71 years, 356 incident all-cause deaths (20.86%), 348 incident cardiovascular disease (CVD) (20.39%), 227 incident diabetic retinopathy (DR) (13.30%), 88 incident peripheral artery disease (PAD) (5.16%), and 280 incident chronic kidney disease (CKD) (16.40%) occurred. Extreme cardiac aging showed strongest CVD risk association (adjusted HR: 2.92, 95% CI: 2.13, 3.99; P < 0.001), with significant brain (adjusted HR: 1.43, 95% CI: 1.06, 1.92; P = 0.019), arterial (adjusted HR: 1.59, 95% CI: 1.06, 2.39; P = 0.024), and pancreatic (adjusted HR: 1.35, 95% CI: 1.04, 1.77; P = 0.027) aging associations. Each complication demonstrated distinct organ-aging signatures. The association remains robust and consistent across three distinct aging models. Patients with baseline extreme aging in more than 3 organs faced significantly higher risks of incident diabetes complications compared to those with no or only one to three extremely aged organs. Additive interaction analysis revealed synergistic effects of organ aging on complications risk in diabetes. For example, concurrent heart and muscle aging substantially elevated CVD risk with a relative excess risk due to interaction (RERI) of 3.49 (95% CI: 0.74, 8.16) and an attributable proportion (AP) of 0.58 (95% CI: 0.14, 0.74).
conclusionsProteomics-based organ aging assessment identifies significant complication-specific vulnerability patterns and substantially elevated risks with multiorgan involvement, supporting comprehensive aging evaluation for diabetes risk stratification. These findings require further validation in larger and more diverse populations and warrant more precise characterization of organ-specificity to enhance their robustness and generalizability.
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.