Evidence map›Paper›PMID 32195313›Full record

ArticleSSM - population health2020

Beyond recent BMI: BMI exposure metrics and their relationship to health.

Carmen D Ng, Michael R Elliott, Fernando Riosmena, Solveig A Cunningham

Abstract read
In one paragraph

Article in SSM - population health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Correlates of chronic pain onset and recovery in the CoLaus cohort.European journal of pain (London, England) · 2025
    Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Economic Disadvantage During Childhood, Obesity, and Diabetes Across Three Birth Cohorts of Older Mexicans.The journals of gerontology. Series B, Psychological sciences and social sciences · 2024
    Article
  7. Aging and Adiposity-Focus on Biological Females at Midlife and Beyond.International journal of molecular sciences · 2024
    Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. 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

4 authors.

Carmen D NgEmory University, United States.
Michael R ElliottUniversity of Michigan, United States.
Fernando RiosmenaUniversity of Colorado, United States.
Solveig A CunninghamEmory University, United States.

Funding

Dynamics and Health Consequences of Obesity between Infancy and Young Adulthood in the United StatesR01DK115937 · NIDDK · EMORY UNIVERSITY · PI ARGESEANU CUNNINGHAM, SOLVEIG · 2018 to 2021
$1.6M
NIDDK NIH HHS R01 DK115937
6 · The paper itself

Abstract

Body mass index (BMI) is generally used to classify adiposity. Despite the fact that the consequences of adiposity for chronic health accumulate and manifest over time, most population health research exploring the implications of high BMI measures only its recent intensity. Some studies have used retrospective measures involving maximum weight, and even fewer have used BMI at multiple time points to estimate cumulative exposure to adiposity. The goal of this study was to compare BMI exposure metrics that captured different dimensions of body mass - intensity, history, and duration - in models of health indicators linked with adiposity. We used self-reported BMI of young adults (ages 18 - 33 years, n = 8,608) across 11 waves of data from the National Longitudinal Survey of Youth 1997 to evaluate eight BMI exposure metrics: most recent, maximum, mean, and median BMI, proportion of time with overweight/obesity, and excess BMI-years with overweight/obesity. We used these metrics in models of self-reported general health, chronic condition, and diabetes, and ascertained how most recent BMI performed when compared with other metrics that better capture the dynamics of BMI. The Akaike information criteria and Vuong tests were used for model comparison, and the strengths of associations were also compared. Most recent BMI was the best metric for explaining general health. Median BMI was best for explaining diabetes, with most recent BMI under-estimating the association by 13% relative to median BMI. For chronic condition, there was no clear best metric. We concluded that most recent BMI is useful for explaining health outcomes, though other metrics should also be given consideration, particularly for conditions that develop over time. Metrics that accounted for both intensity and history performed quite well, but the duration measures might be less useful.

Indexed as

Body mass indexChronic healthLongitudinal studiesObesity

Identifiers

PMID32195313
PMCPMC7078435

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.