Evidence mapPaperPMID 27044508Full record

ArticleLipids in health and disease2016

Lipidomic risk score independently and cost-effectively predicts risk of future type 2 diabetes: results from diverse cohorts.

Manju Mamtani, Hemant Kulkarni, Gerard Wong, Jacquelyn M Weir, Christopher K Barlow, Thomas D Dyer, Laura Almasy, Michael C Mahaney, Anthony G Comuzzie, David C Glahn and 8 more

Open access · goldAbstract read
In one paragraph

Article in Lipids in health and disease, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 1 pooled it
3.1field-weighted citation impact, top 8% 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

28 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.

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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

18 authors at 4 institutions in 2 countries.

Manju MamtaniSouth Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA. manju.mamtani@utrgv.edu.
Hemant KulkarniSouth Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA.
Gerard WongBaker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia.
Jacquelyn M WeirBaker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia.
Christopher K BarlowBaker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia.
Thomas D DyerSouth Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA.
Laura AlmasySouth Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA.
Michael C MahaneySouth Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA.
Anthony G ComuzzieDepartment of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA.
David C GlahnDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
Dianna J MaglianoBaker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia.
Paul ZimmetBaker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia.
Jonathan ShawBaker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia.
Sarah Williams-BlangeroSouth Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA.
Ravindranath DuggiralaSouth Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA.
John BlangeroSouth Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA.
Peter J MeikleBaker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia.
Joanne E CurranSouth Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA.
The University of Texas Rio Grande Valley · USBaker Heart and Diabetes Institute · AUHartford Hospital · USTexas Biomedical Research Institute · US

Funding

PEDIGREE ANALYSIS OF LIPOPROTEIN PHENOTYPESP01HL045522 · SOUTHWEST FOUNDATION FOR BIOMEDICAL RES · 1991 to 2005
$12.5M
Quantitative Trait Locus Mapping in Human PedigreesR37MH059490 · SOUTHWEST FOUNDATION FOR BIOMEDICAL RES · 2001 to 2005
$3.2M
EXTRAMULAR RESEARCH FACILITIES CONSTRUCTIONC06RR017515 · SOUTHWEST FOUNDATION FOR BIOMEDICAL RES · 2003 to 2003
$3.1M
MOLECULAR AND BIOCHEMICAL GENETICS LABORATORY RENOVATIONC06RR013556 · SOUTHWEST FOUNDATION FOR BIOMEDICAL RES · 1998 to 1998
NCRR NIH HHS C06 RR013556NCRR NIH HHS C06 RR017515NCRR NIH HHS S10 RR029392NHLBI NIH HHS P01 HL045522NHLBI NIH HHS R01 HL045522NIDDK NIH HHS 1R01DK088972-01NIDDK NIH HHS R01 DK079169NIDDK NIH HHS R01 DK082610NIDDK NIH HHS R01 DK088972NIMH NIH HHS R01 MH078111NIMH NIH HHS R01 MH078143NIMH NIH HHS R01 MH083824NIMH NIH HHS R37 MH059490
6 · The paper itself

Abstract

backgroundDetection of type 2 diabetes (T2D) is routinely based on the presence of dysglycemia. Although disturbed lipid metabolism is a hallmark of T2D, the potential of plasma lipidomics as a biomarker of future T2D is unknown. Our objective was to develop and validate a plasma lipidomic risk score (LRS) as a biomarker of future type 2 diabetes and to evaluate its cost-effectiveness for T2D screening.

methodsPlasma LRS, based on significantly associated lipid species from an array of 319 lipid species, was developed in a cohort of initially T2D-free individuals from the San Antonio Family Heart Study (SAFHS). The LRS derived from SAFHS as well as its recalibrated version were validated in an independent cohort from Australia--the AusDiab cohort. The participants were T2D-free at baseline and followed for 9197 person-years in the SAFHS cohort (n = 771) and 5930 person-years in the AusDiab cohort (n = 644). Statistically and clinically improved T2D prediction was evaluated with established statistical parameters in both cohorts. Modeling studies were conducted to determine whether the use of LRS would be cost-effective for T2D screening. The main outcome measures included accuracy and incremental value of the LRS over routinely used clinical predictors of T2D risk; validation of these results in an independent cohort and cost-effectiveness of including LRS in screening/intervention programs for T2D.

resultsThe LRS was based on plasma concentration of dihydroceramide 18:0, lysoalkylphosphatidylcholine 22:1 and triacyglycerol 16:0/18:0/18:1. The score predicted future T2D independently of prediabetes with an accuracy of 76%. Even in the subset of initially euglycemic individuals, the LRS improved T2D prediction. In the AusDiab cohort, the LRS continued to predict T2D significantly and independently. When combined with risk-stratification methods currently used in clinical practice, the LRS significantly improved the model fit (p < 0.001), information content (p < 0.001), discrimination (p < 0.001) and reclassification (p < 0.001) in both cohorts. Modeling studies demonstrated that LRS-based risk-stratification combined with metformin supplementation for high-risk individuals was the most cost-effective strategy for T2D prevention.

conclusionsConsidering the novelty, incremental value and cost-effectiveness of LRS it should be used for risk-stratification of future T2D.

Indexed as

BiomarkersCohort StudiesCost-Benefit AnalysisDiabetes Mellitus, Type 2HumansInsulin ResistanceLipidsReproducibility of ResultsRisk FactorsBiomarkersLipidsDiabetesDiagnostic toolsEndocrine disordersGeneticsLipidomics

Identifiers

PMID27044508
PMCPMC4820916
OpenAlexW2337086658

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.