Evidence mapPaperPMID 36883732Full record

ArticleJournal of the American Geriatrics Society2023

Data-driven classification of health status of older adults with diabetes: The diabetes and aging study.

Elbert S Huang, Jennifer Y Liu, Kasia J Lipska, Richard W Grant, Neda Laiteerapong, Howard H Moffet, L Philip Schumm, Andrew J Karter

Open access · hybridAbstract read
In one paragraph

Article in Journal of the American Geriatrics Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.1field-weighted citation impact, top 12% of its field
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

4 citing papers in PubMed, 10 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 1 country.

Elbert S HuangSection of General Internal Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, USA.ORCID 0000-0002-4628-2061
Jennifer Y LiuDivision of Research, Kaiser Permanente Northern California, Oakland, California, USA.
Kasia J LipskaSection of Endocrinology, Yale School of Medicine, New Haven, Connecticut, USA.
Richard W GrantDivision of Research, Kaiser Permanente Northern California, Oakland, California, USA.
Neda LaiteerapongSection of General Internal Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, USA.
Howard H MoffetDivision of Research, Kaiser Permanente Northern California, Oakland, California, USA.ORCID 0000-0002-8728-774X
L Philip SchummBiostatistics Laboratory, University of Chicago, Chicago, Illinois, USA.
Andrew J KarterDivision of Research, Kaiser Permanente Northern California, Oakland, California, USA.
Kaiser Permanente · USUniversity of Illinois Chicago · USYale University · US

Funding

Yale Clinical and Translational Science AwardUL1TR001863 · YALE UNIVERSITY · 2025 to 2025
$9.9M
Translational Research Core - Health Engagement & Action Translational (HEAT)P30DK092924 · KAISER FOUNDATION RESEARCH INSTITUTE · 2025 to 2025
$700k
Research Design, Data, and Analytics CoreP30DK092949 · UNIVERSITY OF CHICAGO · 2025 to 2025
$660k
NCATS NIH HHS UL1 TR001863NIA NIH HHS K24 AG069080NIA NIH HHS R01 AG060756NIA NIH HHS R01 AG063391NIDDK NIH HHS P30 DK092924NIDDK NIH HHS P30 DK092949
6 · The paper itself

Abstract

backgroundWe set out to identify empirically-derived health status classes of older adults with diabetes based on clusters of comorbid conditions which are associated with future complications.

methodsWe conducted a cohort study among 105,786 older (≥65 years of age) adults with type 2 diabetes enrolled in an integrated healthcare delivery system. We used latent class analysis of 19 baseline comorbidities to derive health status classes and then compared incident complication rates (events per 100 person-years) by health status class during 5 years of follow-up. Complications included infections, hyperglycemic events, hypoglycemic events, microvascular events, cardiovascular events, and all-cause mortality.

resultsThree health status classes were identified: Class 1 (58% of the cohort) had the lowest prevalence of most baseline comorbidities, Class 2 (22%) had the highest prevalence of obesity, arthritis, and depression, and Class 3 (20%) had the highest prevalence of cardiovascular conditions. The risk for incident complications was highest for Class 3, intermediate for Class 2 and lowest for Class 1. For example, the age, sex and race-adjusted rates for cardiovascular events (per 100 person-years) for Class 3, Class 2 and Class 1 were 6.5, 2.3, and 1.6, respectively; 2.1, 1.2, 0.7 for hypoglycemia; and 8.0, 3.8, and 2.3 for mortality.

conclusionsThree health status classes of older adults with diabetes were identified based on prevalent comorbidities and were associated with marked differences in risk of complications. These health status classes can inform population health management and guide the individualization of diabetes care.

Indexed as

Cardiovascular DiseasesDiabetes Mellitus, Type 2AgedAged, 80 and overAgingCohort StudiesHealth StatusHumansagingcomorbiditycomplicationsdiabeteslatent class analysis

Identifiers

PMID36883732
PMCPMC10363208
OpenAlexW4323532335

What Socratic holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.