Combined effects of age and BMI are related to altered cortical thickness in adolescence and adulthood

Westwater, Margaret L.; Vilar-López, Raquel; Ziauddeen, Hisham; Verdejo-García, Antonio; Fletcher, Paul C.

doi:10.1016/j.dcn.2019.100728

Cited by 19 publications

(17 citation statements)

References 84 publications

(100 reference statements)

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“…Lou et al (2014) showed greater GMVs within the bilateral putamen compared with the control group [44]. However, two studies showed that a higher BMI was associated with a lower GMV within the bilateral putamen [49] and left putamen [54]. Taki et al (2008) showed a negative correlation between the BMI and the GMV and the gray matter ratio (percentage of GMV in the intracranial volume) within all lobes, the anterior cerebellum and the midbrain and a positive correlation within regions of the frontal and temporal lobes, posterior cerebellum, thalami and caudate heads in men (no significant association was described in women) [57], and another study found that there was no significant association between the BMI and brain volumes (measured by total brain volume and GMV) [61].…”

Section: Cross-sectional Designmentioning

confidence: 83%

“…As shown in Table 1, the publications were released between July 2006 and February 2021, and the sample size in each study varied from 32 to 2344 participants. A total of 11 studies had samples exclusively comprised of healthy participants [7,22,[41][42][43][44][45][46][47][48], and two studies included a healthy subgroup [49,50]. The main characteristics of the participants are shown under "Sample Size, Type of Groups" in the table.…”

Section: Study Characteristicsmentioning

confidence: 99%

“…However, three studies used the BMI for Asian populations instead of the regular BMI, considering underweight, normal weight, overweight, mild obesity and moderate-severe obesity BMIs to be below 18.5, 18.5-22.9, 23-24.9 and 25-27.4 and above 27.5 kg/m 2 , respectively [53,54,60]. The number of studies and type of obesity evaluation were as follows: 2 studies evaluated the waistto-hip ratio [57,61], 4 studies used the waist circumference (cm) [44,46,60,61], 3 studies used the plasma leptin level (ng/mL) [48,63,65] (in 2 of them, it was the exclusive obesity measurement [48,65]), 2 studies used the body fat percentage (%) [47,49,57], 1 study used the fat mass index (kg/m 2 ) [47] as the level of adiposity, one study used the visceral adipose tissue (I) [43] and 1 study evaluated the visceral fat mass (g) and volume (cm 3 ) as the exclusive obesity measurement [58]. One study used the BMI, visceral fat ratio (VFR) (obtained by dividing the area of the intraabdominal fat by the total area of the abdominal cavity) and hepatorenal gradient (value obtained by subtracting the echogenicity of the hepatic region of interest (ROI) and the renal ROI) [51].…”

Section: Study Characteristicsmentioning

confidence: 99%

“…However, it is interesting to assess the possible relation between obesity, GMV and cognition in case subtle cognitive impairment was related to the GMV loss in relation to obesity. Thus, 11 articles included behavioral or neuropsychological assessment in their methods [6][7][8][9]49,52,[55][56][57][58][59]. As was said above, 34 studies were included in this systematic review, and regarding the chosen neuroimaging analysis, there were 21 VBM studies and 13 CT studies, whose results are about to be exposed.…”

Section: Study Characteristicsmentioning

confidence: 99%

“…In relation with the type of obesity measurement and the differences found after the gender-stratified analysis (in case it was applied), three studies showed lower GMVs in the obese group in comparison with the lean controls [7,45,64], and two studies concluded the existence of a negative correlation between the CT and BMI [42,43,49], especially within the orbitofrontal cortex and other regions in the prefrontal and frontal cortex, in addition to the insula, temporal, parietal and occipital cortex. One of those studies also demonstrated that higher visceral adipose tissue was related with reduced CT [43].…”

Section: Cross-sectional Designmentioning

confidence: 99%

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Obesity and Gray Matter Volume Assessed by Neuroimaging: A Systematic Review

2021

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Obesity has become a major public and individual health problem due to its high worldwide prevalence and its relation with comorbid conditions. According to previous studies, obesity is related to an increased risk of cognitive impairment and dementia. This systematic review aims to further examine the present state of the art about the association between obesity and gray matter volume (GMV) as assessed by magnetic resonance imaging (MRI). A search was conducted in Pubmed, SCOPUS and Cochrane of those studies released before 1 February 2021 including MRIs to assess the GMVs in obese participants. From this search, 1420 results were obtained, and 34 publications were finally included. Obesity was mainly measured by the body mass index, although other common types of evaluations were used (e.g., waist circumference, waist-to-hip ratio and plasma leptin levels). The selected neuroimaging analysis methods were voxel-based morphometry (VBM) and cortical thickness (CT), finding 21 and 13 publications, respectively. There were 30 cross-sectional and 2 prospective longitudinal studies, and 2 articles had both cross-sectional and longitudinal designs. Most studies showed a negative association between obesity and GMV. This would have important public health implications, as obesity prevention could avoid a potential risk of GMV reductions, cognitive impairment and dementia.

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Section: Cross-sectional Designmentioning

confidence: 83%

Section: Study Characteristicsmentioning

confidence: 99%

Section: Study Characteristicsmentioning

confidence: 99%

Section: Study Characteristicsmentioning

confidence: 99%

Section: Cross-sectional Designmentioning

confidence: 99%

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Obesity and Gray Matter Volume Assessed by Neuroimaging: A Systematic Review

2021

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A machine learning‐derived neuroanatomical pattern predicts delayed reward discounting in the Human Connectome Project Young Adult sample

MacKillop

2023

J of Neuroscience Research

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Delayed reward discounting (DRD) is defined as the extent to which person favors smaller rewards that are immediately available over larger rewards available in the future. Higher levels of DRD have been identified in individuals with a wide range of clinical disorders. Although there have been studies adopting larger samples and using only gray matter volume to characterize the neuroanatomical correlates of DRD, it is still unclear whether previously identified relationships are generalizable (out‐of‐sample) and how cortical thickness and cortical surface area contribute to DRD. In this study, using the Human Connectome Project Young Adult dataset (N = 1038), a machine learning cross‐validated elastic net regression approach was used to characterize the neuroanatomical pattern of structural magnetic resonance imaging variables associated with DRD. The results revealed a multi‐region neuroanatomical pattern predicted DRD and this was robust in a held‐out test set (morphometry‐only R2 = 3.34%, morphometry + demographics R2 = 6.96%). The neuroanatomical pattern included regions implicated in the default mode network, executive control network, and salience network. The relationship of these regions with DRD was further supported by univariate linear mixed effects modeling results, in which many of the regions identified as part of this pattern showed significant univariate associations with DRD. Taken together, these findings provide evidence that a machine learning‐derived neuroanatomical pattern encompassing various theoretically relevant brain networks produces robustly predicts DRD in a large sample of healthy young adults.

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