Evidence map›Paper›PMID 42889813›Full record

ReviewFrontiers in public health2026

ICD-11 and diabetes: opportunities for precision public health through enhanced classification.

Jingya Zhou, Peng Xia, Yuanmeng Li, Naishi Li

Abstract readReview
In one paragraph

Review in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jingya ZhouDepartment of Medical Records, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Peng XiaState Key Laboratory of Complex Severe and Rare Diseases, Department of Nephrology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Yuanmeng LiKey Laboratory of Endocrinology of National Health Commission of People's Republic of China, Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Naishi LiDepartment of Medical Records, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Diabetes mellitus poses a major global health challenge, yet its intrinsic heterogeneity has been poorly captured by traditional classification systems. The international classification of diseases, tenth revision (ICD-10), built on a phenotype-based framework, lacks the granularity needed to incorporate contemporary molecular insights into diabetes subtypes, hampering both clinical precision and public health surveillance. Main body: This review examines how the recently implemented international classification of diseases, eleventh revision (ICD-11) addresses these limitations through its ontology-based architecture. We analyze three key areas. First, ICD-11 reclassifies prediabetes as a distinct reportable entity with formal subtyping of impaired fasting glucose and impaired glucose tolerance, which may support more precise epidemiological tracking and evidence-based prevention. Second, its Uniform Resource Identifier (URI)-based taxonomy provides granular classification across the diabetes spectrum, from type 1 and type 2 diabetes to monogenic forms such as maturity-onset diabetes of the young (MODY), with etiological stratification that can inform both clinical decision-making and population-level surveillance. Third, its postcoordination mechanism allows multidimensional documentation of complications, drug-induced diabetes, and unstable disease states, generating structured, machine-readable data with potential for real-world evidence generation and health system performance assessment. However, early implementation experiences across multiple countries reveal substantial challenges-including heterogeneous adoption timelines, coding inconsistencies, and workforce training gaps-that may constrain the realization of these benefits in practice. We also discuss the implications of these advances for health systems, including value-based reimbursement models and the production of actionable evidence for policy makers. Conclusion: ICD-11 repositions diabetes classification toward a framework that offers substantially enhanced capacity for etiological and granular representation of the disease. By generating standardized, granular population-level data, it offers a potential infrastructure for precision public health. Yet realizing this potential will depend on health system capacity, workforce training, and sustained investment in implementation-particularly in resource-limited settings, where the risk of widening disparities is most acute. Future refinements should integrate genomic descriptors and dynamic update mechanisms to keep pace with rapid advances in diabetes research, with continued attention to health equity and global applicability.

Indexed as

Diabetes MellitusInternational Classification of DiseasesPrecision MedicinePublic HealthHumansdiabetes mellitusdisease classificationdisease surveillancehealth policyICD-11postcoordinationprecision public healthprediabetes

Identifiers

PMID42889813
PMCPMC13670241

What OpenQuestion holds

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Registered trials

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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.