Evidence map›Paper›PMID 34952840›Full record

Observational studyBMJ open diabetes research & care2021

Maximum body mass index before onset of type 2 diabetes is independently associated with advanced diabetic complications.

Harutoshi Ozawa, Kenji Fukui, Sho Komukai, Megu Y Baden, Shingo Fujita, Yukari Fujita, Takekazu Kimura, Ayumi Tokunaga, Hiromi Iwahashi, Junji Kozawa and 1 more

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in BMJ open diabetes research & care, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 27% 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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Differential Efficacy of Weight Loss Interventions in Patients with Versus Without Diabetes.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2024
    Review
  3. Article
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

11 authors at 2 institutions in 1 country.

Harutoshi OzawaDepartment of Metabolic Medicine, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.ORCID 0000-0001-7590-6745
Kenji FukuiDepartment of Metabolic Medicine, Osaka University Graduate School of Medicine, Suita, Osaka, Japan kfukui@endmet.med.osaka-u.ac.jp.ORCID 0000-0003-2468-7559
Sho KomukaiDepartment of Integrated Medicine Division of Biomedical Statistics, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
Megu Y BadenDepartment of Metabolic Medicine, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
Shingo FujitaDepartment of Metabolic Medicine, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
Yukari FujitaDepartment of Metabolic Medicine, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.ORCID 0000-0002-8847-5654
Takekazu KimuraDepartment of Metabolic Medicine, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
Ayumi TokunagaDepartment of Metabolic Medicine, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
Hiromi IwahashiDiabetes Center, Toyonaka Municipal Hospital, Toyonaka, Osaka, Japan.
Junji KozawaDepartment of Metabolic Medicine, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.ORCID 0000-0001-9665-8947
Iichiro ShimomuraDepartment of Metabolic Medicine, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
The University of Osaka · JPToyonaka Municipal Hospital · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe maximum body mass index (BMI) before onset of type 2 diabetes (MBBO) might be used to estimate a patient's insulin secretion capacity. There have been few factors that can predict future diabetic complications at the time of diagnosis of diabetes mellitus. This study aimed to clarify the clinical usefulness of MBBO for predicting the development of advanced diabetic microvascular complications. RESEARCH DESIGN AND

methodsThis was a cross-sectional observational study. Of 1304 consecutively admitted patients with type 2 diabetes, we enrolled 435 patients for whom we could confirm their MBBO. Univariate and multivariate logistic regression analyses were performed to examine whether MBBO or BMI on admission was associated with advanced diabetic retinopathy or nephropathy. To evaluate the predictive performance of these indexes, we performed cross-validation in various models with MBBO or BMI and evaluated the areas under the curve (AUCs) yielded by these analyses.

resultsUnivariate analyses suggested that MBBO was associated with advanced retinopathy and nephropathy, while BMI on admission was associated only with advanced nephropathy. In multivariate analyses, MBBO was significantly associated with advanced complications, while BMI on admission was not. For advanced diabetic retinopathy, the AUCs were 0.70-0.72, and for advanced nephropathy, the AUCs were 0.81-0.83. When comparing the AUCs among models, the models with MBBO sustained high predictive performance for diabetic complications.

conclusionsMBBO was independently associated with advanced diabetic complications, while BMI on admission was not. Diabetic microvascular complications in patients with high MBBO could progress more rapidly. At the time of the diagnosis of diabetes mellitus, MBBO would enable us to predict the progress of diabetic complications.

Indexed as

Diabetes Mellitus, Type 2Diabetic NephropathiesDiabetic RetinopathyBody Mass IndexCross-Sectional StudiesHumansclinical studydiabetes complicationsdiabetes mellitusobesitytype 2

Identifiers

PMID34952840
PMCPMC8710853
OpenAlexW4200250604

What Socratic holds

Textmetadata
LicenceCC BY-NC
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.