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OBJECTIVE: 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% CIbca = 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% CIbca = -1.26 to -0.26) and healthy-weight controls (t = -2.92, p = .019, d = -0.72, 95% CIbca = -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.

Life expectancy and obesity rates have drastically increased in recent years. An unhealthy weight is related to long-lasting biological deregulations that might compromise the normal course of development and the so-called “successful aging”. The aim of the current study was to test whether an obesity status could mimic the functional organization of an otherwise healthy aged brain. To this end, we included adults with (N = 32, mean age 34.5 ± 6.49) and without obesity (N = 34, mean age 32.7 ± 6.79) as well as adolescents with obesity (N = 30, mean age 15.3 ± 2.64) and normal-weight (N = 31, mean age 15.6 ± 2.60). A sample of stroke-free non-obese and non-demented seniors was also entered (N = 32, mean age 66.1 ± 7.43). Participants underwent a resting-state MRI acquisition and graph-based measurements of segregation, integration and robustness (i.e., mean degree and strength) were calculated. Obesity in adults was accompanied by a broad pattern of losses in network robustness when compared to healthy-weight adults and seniors, as well as increases in network segregation relative to elders. Differences in adolescents followed the same direction yet did not survive multiple comparison adjustment. No interaction emerged when exploring the transition from childhood to adulthood accounting for body-weight status. While more research is needed, we offer preliminary evidence of an obesity status negatively rendering network resilience, which could compromise the normal course of aging.

Objective: Overweight (body mass index or BMI = 25 kg/m(2)) and stress interact with each other in complex ways. Overweight promotes chronic low-inflammation states, while stress is known to mediate caloric intake. Both conditions are linked to several avoidable health problems and to cognitive decline, brain atrophy, and dementia. Since it was proposed as a framework for the onset of mental illness, the allostatic load model has received increasing attention. Although changes in health and cognition related to overweight and stress are well-documented separately, the association between allostatic load and brain integrity has not been addressed in depth, especially among overweight subjects. Method: Thirty-four healthy overweight-to-obese and 29 lean adults underwent blood testing, neuropsychological examination, and magnetic resonance imaging to assess the relationship between cortical thickness and allostatic load, represented as an index of 15 biomarkers (this is, systolic and diastolic arterial tension, glycated hemoglobin, glucose, creatinine, total cholesterol, HDL and LDL cholesterol, triglycerides, c-reactive protein, interleukin-6, insulin, cortisol, fibrinogen, and leptin). Results: Allostatic load indexes showed widespread positive and negative significant correlations (p < 0.01) with cortical thickness values depending on body-weight status. Conclusion: The increase of allostatic load is linked to changes in the gray matter composition of regions monitoring behavior, sensory-reward processing, and general cognitive function.