Evidence map›Paper›PMID 36204553›Full record

ArticleFrontiers in aging neuroscience2022

Metabolic syndrome predictors of brain gray matter volume in an age-stratified community sample of 776 Mexican- American adults: Results from the genetics of brain structure image archive.

Eithan Kotkowski, Larry R Price, Ralph A DeFronzo, Crystal G Franklin, Maximino Salazar, Amy S Garrett, Mary Woolsey, John Blangero, Ravindranath Duggirala, David C Glahn and 1 more

Abstract read
In one paragraph

Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Review
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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

11 authors.

Eithan KotkowskiResearch Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States.
Larry R PriceDepartments of Mathematics and Education, Texas State University, San Marcos, TX, United States.
Ralph A DeFronzoDiabetes Research Unit and Diabetes Division, Texas Diabetes Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States.
Crystal G FranklinResearch Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States.
Maximino SalazarResearch Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States.
Amy S GarrettResearch Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States.
Mary WoolseyResearch Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States.
John BlangeroGenomics Computing Center, South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, United States.
Ravindranath DuggiralaGenomics Computing Center, South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, United States.
David C GlahnDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT, United States.
Peter T FoxResearch Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States.

Funding

South Texas Alzheimer's Disease Research CenterP30AG066546 · NIA · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI Sudha Seshadri · 2021 to 2026
$24.0M
PRECURSORS OF STROKE INCIDENCE AND PROGNOSISR01NS017950 · NINDS · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI Hugo Javier Aparicio, Jose Rafael Romero · 1985 to 2026
$22.9M
Meta-anaylsis in human brain mappingR01MH074457 · NIMH · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI FOX, PETER THORNTON · 2006 to 2024
$10.5M
Cognitively Healthy Nonagenarians in the Cross Cohort Collaboration (CCC)RF1AG059421 · NIA · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI SESHADRI, SUDHA · 2018 to 2018
$6.1M
SOLAR-Eclipse Computational Tools for Imaging GeneticsR01EB015611 · NIBIB · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI KOCHUNOV, PETER V. · 2012 to 2024
$5.0M
NRSA Training Core (TL1)TL1TR002647 · NCATS · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI CHUN, YONG-HEE PATRICIA, FREI, CHRISTOPHER R. · 2018 to 2022
$1.8M
South Texas Medical Scientist Training Program (STX-MSTP)T32GM113896 · NIGMS · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI CAVAZOS, JOSE E · 2018 to 2022
$1.1M
Hybrid GPU/CPU Computing Resource to Support Connectomic and GenomicsS10OD023696 · OD · UNIVERSITY OF MARYLAND BALTIMORE · PI KOCHUNOV, PETER V. · 2018 to 2018
$593k
NCATS NIH HHS TL1 TR002647NIA NIH HHS P30 AG066546NIA NIH HHS RF1 AG059421NIBIB NIH HHS R01 EB015611NIGMS NIH HHS T32 GM113896NIH HHS S10 OD023696NIMH NIH HHS R01 MH074457NINDS NIH HHS R01 NS017950
6 · The paper itself

Abstract

Introduction: This project aimed to investigate the association between biometric components of metabolic syndrome (MetS) with gray matter volume (GMV) obtained with magnetic resonance imaging (MRI) from a large cohort of community-based adults ( Methods: Lipid profiles, biometrics, and regional brain GMV were obtained from the Genetics of Brain Structure (GOBS) image archive. Participants underwent T1-weighted MR imaging. MetS components (waist circumference, fasting plasma glucose, triglycerides, HDL cholesterol, and blood pressure) were defined using the National Cholesterol Education Program Adult Treatment Panel III. Subjects were grouped by age: early adult (18-25 years), young adult (26-45 years), and middle-aged adult (46-65 years). Linear regression modeling was used to investigate associations between MetS components and GMV in five brain regions comprising the NS-MetS: cerebellum, brainstem, orbitofrontal cortex, right insular/limbic cluster and caudate. Results: In both men and women of each age group, waist circumference was the single component most strongly correlated with decreased GMV across all NS-MetS regions. The brain region most strongly correlated to all MetS components was the posterior cerebellum. Conclusion: The posterior cerebellum emerged as the region most significantly associated with MetS individual components, as the only region to show decreased GMV in young adults, and the region with the greatest variance between men and women. We propose that future studies investigating neurological effects of MetS and its comorbidities-namely diabetes and obesity-should consider the NS-MetS and the differential effects of age and sex.

Indexed as

central obesitycerebellar cognitive affective syndrome (CCAS)insulin resistancemagnetic resonance imaging (MRI)metabolic syndromeposterior cerebellumvoxel-based morphometry (VBM)waist circumference

Identifiers

PMID36204553
PMCPMC9531122

What Socratic holds

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LicenceCC BY
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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 Socratic graph.