Evidence map›Paper›PMID 29796112›Full record

ArticleDiabetology & metabolic syndrome2018

Assessing the added predictive ability of a metabolic syndrome severity score in predicting incident cardiovascular disease and type 2 diabetes: the Atherosclerosis Risk in Communities Study and Jackson Heart Study.

Yi Guo, Solomon K Musani, Mario Sims, Thomas A Pearson, Mark D DeBoer, Matthew J Gurka

Open access · goldAbstract read
In one paragraph

Article in Diabetology & metabolic syndrome, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
2.4field-weighted citation impact, top 10% 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

26 citing papers in PubMed, 33 citations in OpenAlex.

  1. Impact of Setmelanotide on Metabolic Syndrome Risk in Patients With Bardet-Biedl Syndrome.The Journal of clinical endocrinology and metabolism · 2025
    Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Observational
  15. Article
  16. Metabolic Syndrome Is Associated With Poor Cognition: A Population-Based Study of 70-Year-Old Adults Without Dementia.The journals of gerontology. Series A, Biological sciences and medical sciences · 2021
    Article
  17. Article
  18. Article
  19. Contribution of Components of Metabolic Syndrome to Cognitive Performance in Middle-Aged Adults.Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists · 2021
    Article
  20. 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

6 authors at 3 institutions in 1 country.

Yi Guo1Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32608 USA.
Solomon K Musani2Department of Medicine, Jackson Heart Study, University of Mississippi Medical Center, Jackson, MS 39213 USA.
Mario Sims2Department of Medicine, Jackson Heart Study, University of Mississippi Medical Center, Jackson, MS 39213 USA.
Thomas A Pearson3Department of Epidemiology, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32608 USA.
Mark D DeBoer4Division of Pediatric Endocrinology, Department of Pediatrics, University of Virginia, Charlottesville, VA 22908 USA.
Matthew J Gurka1Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32608 USA.ORCID 0000-0002-3371-4582
University of Florida Health · USJackson Memorial Hospital · USUniversity of Virginia · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe severity of the metabolic syndrome (MetS) predicts future coronary heart disease (CHD) and diabetes independent of the individual MetS components. Our aim was to evaluate whether MetS severity conferred additional discrimination to existing scoring systems for cardiovascular disease (CVD) and diabetes risk.

methodsWe assessed Cox proportional hazard models of CHD- and diabetes risk among 13,141 participants of the Atherosclerosis Risk in Communities Study and the Jackson Heart Study, using the Framingham Risk Calculator, the American Heart Association's Atherosclerotic CVD calculator, the American Diabetes Association diabetes risk score and an additional diabetes risk score derived from ARIC data. We then added a MetS-severity Z-score to these models and assessed for added risk discrimination by assessing Akaike information criterion, c-statistic, integrated discrimination improvement (IDI) and continuous net reclassification improvement (NRI).

resultsThe MetS severity score appears to add to the predictive ability of individual CHD and diabetes risk scores. Using the IDI, MetS improved risk prediction for diabetes but not CHD risk. In all 4 scoring systems, MetS severity had a significant non-event NRI, improving the ability to exclude individuals without events. Assessing interactions between risk scores and MetS severity revealed that MetS severity was more highly associated with disease risk among those in the lowest quintiles of risk score, suggesting that MetS was particularly able to identify risk among individuals judged to be of low risk by existing algorithms.

conclusionsMets severity improved prediction of diabetes more so than CHD. Incorporation of multiple risk predictors into electronic health records may help in better identifying those at high disease risk, who can then be placed earlier on preventative therapy.

Indexed as

Cardiovascular diseaseMetabolic syndromeRisk predictionType 2 diabetes mellitus

Identifiers

PMID29796112
PMCPMC5956946
OpenAlexW2804176300

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.