ReviewDiabetes, obesity & metabolism2026
Toward Personalized Medicine in Type 1 Diabetes: Understanding How Patient Heterogeneity Influences Therapeutic Efficacy.
Review in Diabetes, obesity & metabolism, 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
2 authors.
Funding
Abstract
Pharmacologic interventions for type 1 diabetes (T1D) have advanced significantly in recent years with the advent of the first FDA approved therapy teplizumab for delaying symptomatic disease onset in 2022. Despite this progress, major hurdles remain in moving toward personalized medicine approaches for T1D. Here, we highlight the examples of heterogeneity in therapeutic responses to recent beta cell and immune interventions and what these studies can teach us about how to tailor therapy for maximizing benefit to patients at risk of or living with T1D. We examine the differences between proposed endotypes, such as childhood-onset versus adult-onset disease, and how these distinctions may inform the use of different therapies. We also explore the importance of disease staging in determining therapeutic windows, as early interventions appear most effective before extensive beta cell loss. Emerging biomarkers including autoantibody profiles, metabolic indices, and circulating nucleic acids offer additional tools for stratifying patients and predicting responses. Ultimately, recognizing and leveraging patient heterogeneity provides an opportunity to align therapies with underlying physiology, moving beyond 'one-size-fits all' approaches. The promise of personalized T1D therapy will be realized by surmounting the barriers to implementation, including trial design, paediatric underrepresentation, and cost.
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.