In:
Psychosomatic Medicine, Ovid Technologies (Wolters Kluwer Health), Vol. 83, No. 7 ( 2021-9), p. 700-706
Abstract:
Life expectancy and obesity rates have drastically increased in recent years. An unhealthy weight is related to long-lasting medical disorders that might compromise the normal course of aging. The aim of the current study of brain connectivity patterns was to examine whether adults with obesity would show signs of premature aging, such as lower segregation, in large-scale networks. Methods Participants with obesity ( n = 30, mean age = 32.8 ± 5.68 years) were compared with healthy-weight controls ( n = 33, mean age = 30.9 ± 6.24 years) and senior participants who were stroke-free and without dementia ( n = 30, mean age = 67.1 ± 6.65 years) using resting-state magnetic resonance imaging and graph theory metrics (i.e., small-world index, clustering coefficient, characteristic path length, and degree). Results Contrary to our hypothesis, participants with obesity exhibited a higher clustering coefficient compared with senior participants ( t = 5.06, p 〈 .001, d = 1.23, 95% CI bca = 0.64 to 1.88). Participants with obesity also showed lower global degree relative to seniors ( t = −2.98, p = .014, d = −0.77, 95% CI bca = −1.26 to −0.26) and healthy-weight controls ( t = −2.92, p = .019, d = −0.72, 95% CI bca = −1.19 to −0.25). Regional degree alterations in this group were present in several functional networks. Conclusions Participants with obesity displayed greater network clustering than did seniors and also had lower degree compared with seniors and individuals with normal weight, which is not consistent with the notion that obesity is associated with premature aging of the brain. Although the cross-sectional nature of the study precludes causal inference, the overly clustered network patterns in obese participants could be relevant to age-related changes in brain function because regular networks might be less resilient and metabolically inefficient.
Type of Medium:
Online Resource
ISSN:
1534-7796
,
0033-3174
DOI:
10.1097/PSY.0000000000000952
Language:
English
Publisher:
Ovid Technologies (Wolters Kluwer Health)
Publication Date:
2021
