Evidence map›Paper›PMID 36495057›Full record

ArticleJournal of diabetes investigation2023

Association of cardiovascular health metrics with annual incidence of prediabetes or diabetes: Analysis of a nationwide real-world database.

Akira Okada, Hidehiro Kaneko, Satoshi Matsuoka, Hidetaka Itoh, Yuta Suzuki, Katsuhito Fujiu, Nobuaki Michihata, Taisuke Jo, Norifumi Takeda, Hiroyuki Morita and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of diabetes investigation, 2023. 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
–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

3 citing papers in PubMed.

  1. Article
  2. Article
  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

15 authors.

Akira OkadaDepartment of Prevention of Diabetes and Lifestyle-Related Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Hidehiro KanekoThe Department of Cardiovascular Medicine, The University of Tokyo, Tokyo, Japan.ORCID https://orcid.org/0000-0003-2553-6170
Satoshi MatsuokaThe Department of Cardiovascular Medicine, The University of Tokyo, Tokyo, Japan.
Hidetaka ItohThe Department of Cardiovascular Medicine, The University of Tokyo, Tokyo, Japan.
Yuta SuzukiThe Department of Cardiovascular Medicine, The University of Tokyo, Tokyo, Japan.
Katsuhito FujiuThe Department of Cardiovascular Medicine, The University of Tokyo, Tokyo, Japan.
Nobuaki MichihataThe Department of Health Services Research, The University of Tokyo, Tokyo, Japan.
Taisuke JoThe Department of Health Services Research, The University of Tokyo, Tokyo, Japan.
Norifumi TakedaThe Department of Cardiovascular Medicine, The University of Tokyo, Tokyo, Japan.ORCID https://orcid.org/0000-0003-4818-3347
Hiroyuki MoritaThe Department of Cardiovascular Medicine, The University of Tokyo, Tokyo, Japan.
Satoko YamaguchiDepartment of Prevention of Diabetes and Lifestyle-Related Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Koichi NodeDepartment of Cardiovascular Medicine, Saga University, Saga, Japan.
Toshimasa YamauchiDepartment of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Hideo YasunagaThe Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, Tokyo, Japan.
Issei KomuroThe Department of Cardiovascular Medicine, The University of Tokyo, Tokyo, Japan.

Funding

Ministry of Education, Culture, Sports, Science and Technology, Japan 20H03907Ministry of Education, Culture, Sports, Science and Technology, Japan 21H03159Ministry of Education, Culture, Sports, Science and Technology, Japan 21K08123Ministry of Health, Labour and Welfare 21AA2007
6 · The paper itself

Abstract

AIMS/

introductionLittle is known about the relationship between cardiovascular health (CVH) metrics and the risk of developing prediabetes or diabetes. We examined the association of CVH metrics with the annual risk of developing prediabetes or diabetes. MATERIALS AND

methodsWe carried out this study including 403,857 participants aged 18-71 years with available data on fasting plasma glucose (FPG) data for five consecutive years and with normal FPG (<100 mg/dL) at the initial health checkup. We identified the following ideal CVH metrics: non-smoking, body mass index of <25 kg/m

resultsThe median age was 44 years, and 65.6% were men. An increasing number of non-ideal CVH metrics was associated with an elevated risk of prediabetes or diabetes. A non-ideal body mass index was most strongly associated with the risk of prediabetes or diabetes. The risk of developing prediabetes or diabetes rose as the number of non-ideal CVH metrics increased over 1 year.

conclusionsCVH metrics could stratify the risk of the annual development of prediabetes or diabetes. The risk of developing prediabetes or diabetes might be reduced by improving CVH metrics.

Indexed as

Cardiovascular DiseasesDiabetes MellitusPrediabetic StateAdultBlood PressureFemaleHumansIncidenceMaleQuality Indicators, Health CareRisk FactorsCardiovascular health metricsDiabetesPrediabetes

Identifiers

PMID36495057
PMCPMC9951564

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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.