Evidence map›Paper›PMID 41381318›Full record

ArticlePrimary care diabetes2026

The U.S. diabetes belt and factors explaining the excess risk: Multifactorial modeling and machine learning analysis.

Longjian Liu, Nathalie S May, Yuwei Hou, Jingyi Shi, Edward J Gracely, Arthur L Frank, Howard J Eisen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Primary care diabetes, 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

7 authors.

Longjian LiuDepartment of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, PA 19104, United States. Electronic address: Longjian.Liu@Drexel.edu.
Nathalie S MayDepartment of Medicine, College of Medicine, Drexel University, Philadelphia, PA 19104, United States.
Yuwei HouCenter for Center for Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, United States.
Jingyi ShiDepartment of Mathematics and Statistics, Mississippi State University, Starkville, MS, USA.
Edward J GracelyDepartment of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, PA 19104, United States; Department of Family, Community & Preventive Medicine, College of Medicine, Drexel University, Philadelphia, PA 19104, United States.
Arthur L FrankDepartment of Environmental and Occupational Health, Dornsife School of Public Health at Drexel University, Philadelphia, PA 19104, United States.
Howard J EisenClinical Research for the Advanced Cardiac and Pulmonary Vascular Disease Program, Thomas Jefferson University Hospital, Philadelphia, PA 19107, United States.

Funding

The predicative values of vascular and metabolic disorders for risk of incident mild cognitive impairment and dementiaR21AG082210 · NIA · DREXEL UNIVERSITY · PI LIU, LONGJIAN · 2023 to 2024
$403k
NIA NIH HHS 1R21AG082210–01NIA NIH HHS R21 AG082210
6 · The paper itself

Abstract

aimResearch on the epidemiology of diabetes mellitus (DM) has identified a geographically distinct region in the United States (U.S.) known as the diabetes belt (DM Belt), which represents a significant public health concern. This study aimed to examine the factors contributing to the increased risk of DM in the DM Belt compared to the non-DM Belt.

methodsData were analyzed from 398,243 adults aged ≥ 18 years who participated in the 2019 Behavior Risk Factor Surveillance System. DM status was based on participants' self-reported physician-diagnosed DM. The DM Belt was defined at the state level according to the U.S. Center for Disease Control and Prevention's classification. Logistic regression (LR) was used to estimate odds ratios for DM and assess the excess DM risk in the DM Belt versus the non-DM Belt. Random Forest (RF) and stepwise LR were employed to identify and rank key contributors to the excess DM risk.

resultsResidents of the DM Belt had a significantly higher prevalence of DM than those in the non-DM Belt (age-sex-adjusted rate: 12.5 % versus 10.5 %, p < 0.001). Low socioeconomic status (SES), physical inactivity, and hypertension were identified as the top three factors explaining the excess DM risk in the DM Belt.

conclusionsThese findings underscore the importance of an integrated approach to improving SES, promoting healthy behaviors, managing chronic conditions for reducing DM risk. Addressing these factors can help mitigate health disparities in DM risk across the U.S.

Indexed as

Diabetes MellitusMachine LearningAdolescentAdultAgedBehavioral Risk Factor Surveillance SystemCross-Sectional StudiesFemaleHealth BehaviorHumansMaleMiddle AgedPrevalenceRisk AssessmentRisk FactorsSedentary BehaviorDiabetes mellitusGeographic variationRisk factors

Identifiers

PMID41381318
PMCPMC13410521

What OpenQuestion holds

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LicenceCC BY
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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.