Cited by 427Atrial Fibrillation Management and OutcomesDiabetes, Cardiovascular Risks, and LipoproteinsCardiovascular Health and Risk Factors

Metabolic Syndrome and Risk of Development of Atrial Fibrillation: The Niigata Preventive Medicine Study

Open access
Watanabe, Hiroshi, Tanabe, Naohito, Watanabe, Toru, Darbar, Dawood, Roden, Dan M., Sasaki, Shigeru, Aizawa, Yoshifusa·2008·Circulation

Abstract

Background— The metabolic syndrome consists of a cluster of atherosclerotic risk factors, many of which also have been implicated in the genesis of atrial fibrillation (AF). However, the precise role of the metabolic syndrome in the development of AF is unknown. Methods and Results— This prospective, community-based, observational cohort study was based on an annual health check-up program in Japan. We studied 28 449 participants without baseline AF. We used 2 different criteria for the metabolic syndrome—the guidelines of the National Cholesterol Education Program Third Adult Treatment Panel (NCEP-ATP III) and those of the American Heart Association/National Heart, Lung, and Blood Institute (AHA/NHLBI)—to study the risk of development of new-onset AF. The metabolic syndrome was present in 3716 subjects (13%) and 4544 subjects (16%) using the NCEP-ATP III and AHA/NHLBI definitions, respectively. During a mean follow-up of 4.5 years, AF developed in 265 subjects (105 women). Among the metabolic syndrome components, obesity (age- and sex-adjusted hazard ratio [HR], 1.64), elevated blood pressure (HR, 1.69), low high-density lipoprotein cholesterol (HR, 1.52), and impaired insulin tolerance (HR, 1.44 [NCEP-ATP III] and 1.35 [AHA/NHLBI]) showed an increased risk for AF. The association between the metabolic syndrome and AF remained significant in subjects without treated hypertension or diabetes by the NCEP-ATP III definition (HR, 1.78) but not by the AHA/NHLBI definition (HR, 1.28). Conclusions— The metabolic syndrome was associated with increased risk of AF. The metabolic derangements of the syndrome may be important in the pathogenesis of AF.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1161/circulationaha.107.744466

Cites

47 of 47 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Diabetes is probably not the most potent independent risk factor for stroke in atrial fibrillation compared with the other items in the CHA2DS2-VASc score.

    “Diabetes is probably not the most potent independent risk factor for stroke in AF compared with the other items in the CHA 2 DS 2 -VASc score, but it is included in this risk-stratification tool, giving a point alongside most other items.600,618,[622][623][624]”
  • Patients with diabetes may have a lower risk of inappropriate therapy or implantable cardioverter-defibrillator shock.

    “In contrast, patients with diabetes may have a lower risk of inappropriate therapy or ICD shock, since they may be more sedentary with consequently less frequent exercise-induced sinus tachycardia and also lower incidence of lead fracture.600,658,659”

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. Kannel WB, Wolf PA, Benjamin EJ, Levy D. Prevalence, incidence, prognosis, and predisposing conditions for atrial fibrillation: population-based estimates. Am J Cardiol. 1998;82:2N-9N.
  2. Fujishima F. Cardiac diseases as risk factors for cerebrovascular diseases [in Japanese]. Junkankisenmoni. 1998;6:19 -26.

Cited by

Sorted by citations

Authors
Watanabe, H., Tanabe, N., Watanabe, T., Darbar, D., Roden, D. M., Sasaki, S., & Aizawa, Y.
Affiliations
Vanderbilt University (US)
Venue
Circulation
Published
2008-03-11
Volume
117
Issue
10
Pages
1255-1260
ISSN
  • 0009-7322 (ISSN-L)
  • 1524-4539
OpenAlex
W2010305313
Cited by
427
References
47
Citations by year
2026: 15 · 2025: 14 · 2024: 8 · 2023: 21 · 2022: 21 · 2021: 30 · 2020: 27 · 2019: 21 · 2018: 24 · 2017: 21 · 2016: 20 · 2015: 24 · 2014: 41 · 2013: 28 · 2012: 28
FWCI
20.12
Type
journal article
Language
en
Open access
Locations
  • Circulation (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • Vanderbilt University
  • School of Medicine, Vanderbilt University
  • National Heart, Lung, and Blood Institute (K23HL075266, U01HL065962)
Topics
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Atrial Fibrillation Management and Outcomes
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Risk Factors
Keywords
metabolic syndrome, atrial fibrillation, new-onset atrial fibrillation, obesity, hypertension, low HDL cholesterol, impaired fasting glucose, NCEP ATP III criteria, AHA/NHLBI criteria, atherosclerosis risk factors, atrial fibrillation pathogenesis
MeSH terms
Aged, Atrial Fibrillation, Female, Humans, Male, Middle Aged, Risk Factors, Cohort Studies, Incidence, Multivariate Analysis, Proportional Hazards Models, Observation, Metabolic Syndrome

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review