Evidence map›Paper›PMID 26494809›Full record

SynthesisDiabetes care2015

Definitions of Metabolic Health and Risk of Future Type 2 Diabetes in BMI Categories: A Systematic Review and Network Meta-analysis.

Luca A Lotta, Ali Abbasi, Stephen J Sharp, Anna-Stina Sahlqvist, Dawn Waterworth, Julia M Brosnan, Robert A Scott, Claudia Langenberg, Nicholas J Wareham

Abstract readSystematic ReviewNetwork Meta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Diabetes care, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 4 pooled it
–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

42 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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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

9 authors.

Luca A LottaMRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Addenbrooke's Hospital, Cambridge, U.K.
Ali AbbasiMRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Addenbrooke's Hospital, Cambridge, U.K. Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Stephen J SharpMRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Addenbrooke's Hospital, Cambridge, U.K.
Anna-Stina SahlqvistGenetics, GlaxoSmithKline Research and Development, Stevenage, U.K.
Dawn WaterworthGenetics, GlaxoSmithKline, King of Prussia, PA.
Julia M BrosnanCardiovascular and Metabolic Diseases Research Unit, Pfizer Worldwide Research and Development, Cambridge, MA.
Robert A ScottMRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Addenbrooke's Hospital, Cambridge, U.K.
Claudia LangenbergMRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Addenbrooke's Hospital, Cambridge, U.K.
Nicholas J WarehamMRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Addenbrooke's Hospital, Cambridge, U.K. nick.wareham@mrc-epid.cam.ac.uk.

Funding

Medical Research Council MC_U106179471Medical Research Council MC_UU_12015/1
6 · The paper itself

Abstract

objectiveVarious definitions of metabolic health have been proposed to explain differences in the risk of type 2 diabetes within BMI categories. The goal of this study was to assess their predictive relevance. RESEARCH DESIGN AND

methodsWe performed systematic searches of MEDLINE records for prospective cohort studies of type 2 diabetes risk in categories of BMI and metabolic health. In a two-stage meta-analysis, relative risks (RRs) specific to each BMI category were derived by network meta-analysis and the resulting RRs of each study were pooled using random-effects models. Hierarchical summary receiver operating characteristic curves were used to assess predictive performance.

resultsIn a meta-analysis of 140,845 participants and 5,963 incident cases of type 2 diabetes from 14 cohort studies, classification as metabolically unhealthy was associated with higher RR of diabetes in all BMI categories (lean RR compared with healthy individuals 4.0 [95% CI 3.0-5.1], overweight 3.4 [2.8-4.3], and obese 2.5 [2.1-3.0]). Metabolically healthy obese individuals had a high absolute risk of type 2 diabetes (10-year cumulative incidence 3.1% [95% CI 2.6-3.5]). Current binary definitions of metabolic health had high specificity (pooled estimate 0.88 [95% CI 0.84-0.91]) but low sensitivity (0.40 [0.31-0.49]) in lean individuals and satisfactory sensitivity (0.81 [0.76-0.86]) but low specificity (0.42 [0.35-0.49]) in obese individuals. However, positive (<3.3 in all BMI categories) and negative (>0.4) likelihood ratios were consistent with insignificant to small improvements in prediction.

conclusionsAlthough individuals classified as metabolically unhealthy have a higher RR of type 2 diabetes compared with individuals classified as healthy in all BMI categories, current binary definitions of metabolic health have limited relevance to the prediction of future type 2 diabetes.

Indexed as

Body Mass IndexBody WeightDiabetes Mellitus, Type 2HumansObesityOverweightPrognosisRisk

Identifiers

PMID26494809
PMCPMC4826609

What OpenQuestion holds

Textmetadata
LicenceTDM
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.