Evidence map›Paper›PMID 29062686›Full record

ArticleNeuroImage. Clinical2018

Differential associations of metabolic risk factors on cortical thickness in metabolic syndrome.

Nicolette F Schwarz, Leslie K Nordstrom, Linda H G Pagen, Daniela J Palombo, David H Salat, William P Milberg, Regina E McGlinchey, Elizabeth C Leritz

Open access · goldAbstract read
In one paragraph

Article in NeuroImage. Clinical, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
3.0field-weighted citation impact, top 9% 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

19 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Observational
  8. Large-Scale Population-Based Studies of Blood Metabolome and Brain Health.Current topics in behavioral neurosciences · 2024
    Review
  9. Article
  10. Review
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. 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

8 authors at 2 institutions in 1 country.

Nicolette F SchwarzNeuroimaging Research for Veterans Center (NeRVe), Geriatric Research Education and Clinical Center (GRECC), Veterans Administration Boston Healthcare System, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Leslie K NordstromNeuroimaging Research for Veterans Center (NeRVe), Geriatric Research Education and Clinical Center (GRECC), Veterans Administration Boston Healthcare System, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Linda H G PagenNeuroimaging Research for Veterans Center (NeRVe), Geriatric Research Education and Clinical Center (GRECC), Veterans Administration Boston Healthcare System, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Daniela J PalomboNeuroimaging Research for Veterans Center (NeRVe), Geriatric Research Education and Clinical Center (GRECC), Veterans Administration Boston Healthcare System, Boston, MA, USA; Boston University School of Medicine, Boston, MA, USA.
David H SalatNeuroimaging Research for Veterans Center (NeRVe), Geriatric Research Education and Clinical Center (GRECC), Veterans Administration Boston Healthcare System, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; The Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, USA.
William P MilbergNeuroimaging Research for Veterans Center (NeRVe), Geriatric Research Education and Clinical Center (GRECC), Veterans Administration Boston Healthcare System, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Regina E McGlincheyNeuroimaging Research for Veterans Center (NeRVe), Geriatric Research Education and Clinical Center (GRECC), Veterans Administration Boston Healthcare System, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Elizabeth C LeritzNeuroimaging Research for Veterans Center (NeRVe), Geriatric Research Education and Clinical Center (GRECC), Veterans Administration Boston Healthcare System, Boston, MA, USA; Harvard Medical School, Boston, MA, USA. Electronic address: Elizabeth_leritz@hms.harvard.edu.
Geriatric Research Education and Clinical Center · USHarvard University · US

Funding

Neuroimaging and Neuropsychological Biomarkers of Vascular Risk FactorsR01NS086882 · NINDS · HARVARD MEDICAL SCHOOL · PI LERITZ, ELIZABETH C · 2014 to 2018
$1.7M
NINDS NIH HHS R01 NS086882
6 · The paper itself

Abstract

objectiveMetabolic syndrome (MetS) refers to a cluster of risk factors for cardiovascular disease, including obesity, hypertension, dyslipidemia, and hyperglycemia. While sizable prior literature has examined associations between individual risk factors and quantitative measures of cortical thickness (CT), only very limited research has investigated such measures in MetS. Furthermore, the relative contributions of these risk factors to MetS-related effects on brain morphology have not yet been studied. The primary goal of this investigation was to examine how MetS may affect CT. A secondary goal was to explore the relative contributions of individual risk factors to regional alterations in CT, with the potential to identify risk factor combinations that may underlie structural changes.

methodsEighteen participants with MetS (mean age = 59.78 years) were age-matched with 18 healthy control participants (mean age = 60.50 years). CT measures were generated from T1-weighted images and groups were contrasted using whole-brain general linear modeling. A follow-up multivariate partial least squares correlation (PLS) analysis, including the full study sample with complete risk factor measurements (N = 53), was employed to examine which risk factors account for variance in group structural differences.

resultsParticipants with MetS demonstrated significantly reduced CT in left hemisphere inferior parietal, rostral middle frontal, and lateral occipital clusters and in a right hemisphere precentral cluster. The PLS analysis revealed that waist circumference, high-density lipoprotein cholesterol (HDL-C), triglycerides, and glucose were significant contributors to reduced CT in these clusters. In contrast, diastolic blood pressure showed a significantly positive association with CT while systolic blood pressure did not emerge as a significant contributor. Age was not associated with CT.

conclusionThese results indicate that MetS can be associated with regionally specific reductions in CT. Importantly, a novel link between a risk factor profile comprising indices of obesity, hyperglycemia, dyslipidemia and diastolic BP and localized alterations in CT emerged. While the pathophysiological mechanisms underlying these associations remain incompletely understood, these findings may be relevant for future investigations of MetS and might have implications for treatment approaches that focus on specific risk factor profiles with the aim to reduce negative consequences on the structural integrity of the brain.

Indexed as

Brain MappingAdultAgedAged, 80 and overCase-Control StudiesCerebral CortexCohort StudiesFemaleHumansImaging, Three-DimensionalMagnetic Resonance ImagingMaleMetabolic SyndromeMiddle AgedRisk FactorsWaist CircumferenceBP, blood pressureCT, cortical thicknessDiastolic blood pressureDyslipidemiaFDR, false discovery rateGLM, general linear modelGray matterHDL-C, high-density lipoprotein cholesterolHyperglycemiaMagnetic resonance imagingMetS, Metabolic SyndromeMRI, magnetic resonance imagingObesityPLS, partial least squares correlationVA, Veterans Administration Boston Healthcare System

Identifiers

PMID29062686
PMCPMC5641920
OpenAlexW2760732820

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.