
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39251805
71924
10.1038/s41598-024-71924-z
Article
The relationship between brain structure volumes, depressive symptoms and body composition in obese/overweight and normal-/underweight women
Łapińska Lidia lidia.lapinska@umb.edu.pl

1
Szum-Jakubowska Aleksandra 2
Krentowska Anna 1
Pawlak Mikołaj 34
Hładuński Marcin 5
Waszkiewicz Napoleon 6
Karczewska-Kupczewska Monika 1
Kamiński Karol 2
Kowalska Irina 1
1 https://ror.org/00y4ya841 grid.48324.39 0000 0001 2248 2838 Department of Internal Medicine and Metabolic Diseases, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Bialystok, Poland
2 https://ror.org/00y4ya841 grid.48324.39 0000 0001 2248 2838 Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Bialystok, Poland
3 https://ror.org/02zbb2597 grid.22254.33 0000 0001 2205 0971 Department of Neurology, Poznan University of Medical Sciences, Poznan, Poland
4 https://ror.org/018906e22 grid.5645.2 0000 0004 0459 992X Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, The Netherlands
5 grid.48324.39 0000000122482838 Independent Laboratory of Molecular Imaging, Medical University of Bialystok, Bialystok, Poland
6 https://ror.org/00y4ya841 grid.48324.39 0000 0001 2248 2838 Department of Psychiatry, Medical University of Bialystok, Bialystok, Poland
9 9 2024
9 9 2024
2024
14 2102113 10 2023
2 9 2024
© The Author(s) 2024
2024
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Depressive symptoms are highly prevalent and heterogeneous in women. Different brain structures might be associated with depressive symptoms and body composition in women with obesity/overweight and normal-/underweight, although the data is limited. The analysis included 265 women from Bialystok PLUS population study, untreated with antidepressive or antipsychotic medications. The subjects underwent brain magnetic resonance imaging and body composition analysis. Beck Depression Inventory (BDI) score was inversely associated with nucleus accumbens volume (β = −0.217, p = 0.008) in women with BMI ≥ 25 kg/m2, but with insula volume (β = −0.147, p = 0.027) in women with BMI < 25 kg/m2 after adjustment for age and estimated intracranial volume (eTIV). In women with BMI ≥ 25 kg/m2, nucleus accumbens volume was inversely associated with the percentage of visceral fat and BDI score (β = −0.236, p = 0.012, β = −0.192, p = 0.017) after adjustment for age and eTIV. In women with BMI < 25 kg/m2, insula volume was positively associated with total fat-free mass and negatively with the BDI score (β = 0.142, p = 0.030, β = −0.137, p = 0.037) after adjustment for age and eTIV. Depressive symptoms might be associated with nucleus accumbens volume in overweight/obese women, while in normal-/ underweight women—with alterations in insula volume.

Subject terms

Depression
Human behaviour
issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Depressive disorders are highly prevalent. One-year occurrence of clinical depression (major depressive disorder) is approximately 6% and the risk of depression during the whole life is 15–18%, while subclinical depression is even more frequent compared to major depressive disorder1,2. Additionally, coronavirus pandemic had an impact on mental health3. Depression is almost twice as common in women than men and can present as complex manifestations including affected mood, cognitive disturbance, and somato-vegetative dysfunction1,2. Typical, melancholic depressive symptoms are manifested by loss of appetite and/or weight, while individuals with atypical depressive symptoms can present opposite symptoms, such as increased appetite and/or weight gain1,4.

A meta-analysis which included 183 studies examined the association between the indices of body weight and depression. The authors showed that both underweight and obesity significantly increased the risk of depression5. A possible U-shaped relationship between body mass index (BMI) and depression can indicate heterogeneity within this disease. The association between obesity and depression is bidirectional. A meta-analysis of longitudinal studies showed that obesity at baseline increased the risk of onset of depression at follow-up, while depression increased the odds for developing obesity later in life6. Metabolic and neurobiological mechanisms by which obesity is connected with depression are complex and little known. According to the current literature, psychiatric consequences of obesity can derive from diet rich in sugar and saturated fat, lack of physical activity, and body composition changes7.

According to the literature, depression is also associated with body composition8,9. Previous studies showed that abdominal fat accumulation has a particularly negative impact on depressive symptoms in women9–11. Lee et al. observed that depressive mood is related with increased visceral fat mass, but not with subcutaneous adipose tissue11. Everson-Rose et al. examined middle-aged women and showed that visceral fat volume is higher in women with depression, particularly in overweight and obese women12. Guedes et al. demonstrated a significant inverse correlation between total fat-free mass and severity of depressive symptoms in individuals with metabolic syndrome13. Adipose and muscle tissues secrete adipokines, myokines and cytokines which can pass through blood–brain barrier and impact brain structures14.

Previous studies showed that depressive symptoms can be connected with changes in dimensions and function of frontal lobe, hippocampus, stratum, insula and amygdala1,15,16. These brain structures are responsible for emotion, memory, motivation, attention, executive function, regulation of systemic metabolism, and behaviour related with food intake7,15–17. Different clinical or biological characteristics of depression could be related to specific brain structure alterations18.

According to the literature, obesity might be related to the changes in brain structure volumes and thickness19. Data from the United Kingdom Biobank study (18.7% participants with BMI ≥ 30 kg/m2) showed that higher BMI was related to lower grey matter volume. Obesity was associated with lower volume of putamen, pallidum, and nucleus accumbens regions20. Raji et al. observed that elevated abdominal fat (both subcutaneous and visceral) predicted lower brain volume21. Another study from United Kingdom Biobank analysed the relationship between fat distribution and brain volume. Central obesity, defined by waist-hip ratio, dual-energy X-ray absorptiometry (DXA) scans, and abdominal magnetic resonance imaging (MRI), negatively correlated with grey matter volume22. Kilgour et al. analysed 53 articles and observed that there can be a positive association between volumes of selected grey matter regions (right temporal lobe and bilateral ventromedial prefrontal cortex) and muscle size23.

There is limited data concerning brain structure volumes in subjects with normal-/underweight and depressive symptoms. Additionally, few studies analysed the differences in brain structure volumes between women with low body weight or high body weight and depressive disorders.

The present study aimed to examine the association of specific brain structures volumes with depressive symptoms and body composition in women with obesity/overweight and normal-/underweight women. We hypothesized that different brain structures can be associated with depressive symptoms severity and body composition in women with BMI ≥ 25 kg/m2 and with BMI < 25 kg/m2.

Materials and methods

Study population

The study was a part of the Bialystok PLUS population study. Ethical approval for the study was obtained from the Ethics Committee of the Medical University of Bialystok, Poland (approval number: R-I-002/108/2016). All procedures performed in the study were in accordance with the Declaration of Helsinki and all participants gave written informed consent. The study recruitment was described previously24. Overall, 1134 individuals were recruited and examined as a population cohort of Białystok PLUS study between August 2017 and February 2022. The flow of study participants into this analytic sample is described in Fig. 1. Men were excluded from this study due to distinct clinical manifestation of depressive symptoms compared to women. People above 70 years of age were not included in this analysis due to higher prevalence of cognitive disorders in this age group than in younger subjects. Women aged 20–70 years were included in this study. Finally, taking into account the exclusion criteria, we included 265 women. The exclusion criteria were: 1/history of stroke, 2/transient ischemic attack, 3/multiple sclerosis, 4/epilepsy, 5/dementia, 6/Parkinson’s disease, 7/schizophrenia, 8/bipolar disorder, 9/use of antidepressive or antipsychotic medications, 10/decompensated hypothyroidism or hyperthyroidism (thyroid-stimulating hormone, TSH, < 0.1 or > 5 µU/ml), 11/type 1 diabetes, 12/oral glucocorticosteroid use, 13/acute infection (high-sensitivity C-reactive protein, hsCRP, ≥ 10 mg/l), 14/alcohol abuse (≥ 8 points in Alcohol Use Disorder Identification Test, AUDIT), 15/illicit or recreational drug use in last year, 16/incomplete Beck Depression Inventory (BDI), 17/contraindications or lack of consent for MRI.Figure 1 Selection of the study groups from the population cohort study.

Depressive symptoms assessment

To assess depressive symptoms in the present study, we used BDI test, which is a commonly available self-report rating inventory for diagnostic screening. The study participants completed the Polish version of the BDI, which measured the severity of depressive symptoms within the preceding month. In our previous studies, the cut-off point for the presence of depressive symptoms was ≥ 10 points in BDI (≥ 21 points in BDI for clinical depressive symptoms and 10–20 BDI score for subclinical depressive symptoms)25,26. According to the published data, subclinical depressive symptoms are associated with structural brain changes similar to those in clinical depression27.

Anthropometric and sociodemographic parameters

All study participants underwent general physical examination. Anthropometric measurements including height and weight were taken. BMI was calculated as body weight in kilograms divided by height in meters squared. Obesity was recognized at BMI ≥ 30 kg/m2, overweight 25–29.99 kg/m2, normal weight 18.5–24.99 kg/m2, underweight < 18.5 kg/m2.

We used self-report questionnaires to collect data on illicit or recreational drugs. Evaluation of alcohol consumption and alcohol-related problems was done by the self-report version of a 10-item screening test developed by the World Health Organization—AUDIT. All individuals received AUDIT questionnaires and were asked to complete the survey. Body composition was analysed using DXA (Lunar iDXA, GE Healthcare, Chicago, Illinois, United States) at the Clinical Research Centre, Medical University of Bialystok. Body composition analyses were performed and controlled by qualified staff members of Bialystok PLUS study. The equipment was calibrated before each examination. Study participants were positioned on the examination table in a supine position, with their feet secured with an adjustable strap and their arms by their side. Using this method, body composition including body fat and fat-free tissue was estimated. For each area of the body (trunk, arms, and legs), DXA assessed fat-free mass and fat mass with the precision (coefficient of variation) of 2.0 and 8.0%, respectively. In our study, we used measurements of total fat mass (kg), total fat-free mass (kg), percentage of android fat (%, android fat mass divided by total fat mass and multiplied by 100), percentage of gynoid fat, (%, gynoid fat mass divided by total fat mass and multiplied by 100), percentage of visceral fat, (%, visceral fat mass divided by total fat mass and multiplied by 100).

Brain imaging and data processing

MRI scans were acquired with a 3.0 T Siemens Biograph nMR scanner. Brain analysis was conducted with the Portable Batch System server. Morphometric analysis of brain structure was conducted with Freesurfer version 7.2.0 (available at https://surfer.nmr.mgh.harvard.edu/) using a recon-all stream28,29. Freesurfer includes modules to segment cortical and subcortical brain structures. In this study, we analysed the volumes of depression-related brain structures: superior frontal gyrus, middle frontal gyrus (caudal + rostral middle frontal), anterior cingulate gyrus (caudal + rostral anterior cingulate), insula, hippocampus, amygdala, and nucleus accumbens. The means of each brain structure’s volumes were analysed (right + left/2)30–35. Additionally, we calculated estimated total intracranial volume (eTIV). The reconstruction and segmentation were visually inspected.

Statistical analysis

Statistical analyses were performed using Statistica 13.0 (Statsoft, OK, USA) and STATA 16 (StataCorp, TX, USA). The variables were tested for normal distribution using the Shapiro–Wilk test. Due to the non-normal distribution of data, all values were expressed as median and interquartile range. The comparisons between two groups were performed using Mann–Whitney U test for continuous variables and Chi-squared test for nominal variables. Multivariate linear regression was used to assess the relationship between brain structure volumes and BDI score after controlling for age and eTIV separately in women with obesity/overweight and in women with normal-/underweight. Multivariate linear regression was used to evaluate the association between brain structure volumes and body composition (total fat mass, total fat-free mass, percentage of android fat, percentage of gynoid fat, percentage of visceral fat). The level of significance was set at p < 0.05.

Results

The study sample consisted of 265 women with median age 47 years and BMI 24.79 kg/m2. The analysed population was divided according to BMI into a group of women with obesity/overweight (n = 131) and with normal-/underweight (n = 134). Median age and BMI of women with BMI ≥ 25 kg/m2 were 53 years and 28.91 kg/m2, whereas median age and BMI of women with BMI < 25 kg/m2 were 41 years and 22.00 kg/m2. The group of women with obesity/overweight had higher age and BMI compared to women with normal-/underweight (p < 0.001 and p < 0.001). Additionally, the group of women with BMI ≥ 25 kg/m2 had higher total fat mass, total fat-free mass, percentage of android and visceral fat and lower percentage of gynoid fat compared to women with BMI < 25 kg/m2 (all p < 0.001). The presence of depressive symptoms (BDI ≥ 10 points) was comparable in the group with obesity/overweight and normal-/underweight women (28.24% vs. 27.61%, p = 0.909) (Table 1). Only nucleus accumbens volume was significantly decreased in women with obesity/ overweight compared to normal-/underweight group (p = 0.041) (Table 1).Table 1 The characteristics of total population, women with overweight/obesity, and women with normal-/ underweight.

	Total population (n = 265)	Women with BMI ≥ 25 kg/m2 (n = 131)	Women with BMI < 25 kg/m2 (n = 134)	
Age, years	47.00 (38.00–60.00)	53.00 (44.00–63.00)*	41.00 (34.00–53.00)	
BMI, kg/m2	24.79 (21.98–28.78)	28.91 (26.76–31.90)*	22.00 (20.19–23.21)	
BDI, points	6 (3–11)	6 (3–11)	6 (3–10)	
BDI ≥ 10 points, n; %	74; 27.92	37; 28.24	37; 27.61	
BDI ≥ 21 points	12; 4.53	6; 4.58	6; 4.48	
10–21 points n; %	62; 23.40	31; 23.66	31; 23.13	
Total fat mass, kg	24.06 (18.74–31.34)	31.38 (28.13–36.87)*	18.76 (15.30–21.42)	
Total fat-free mass, kg	41.41 (37.99–44.59)	43.61 (41.44–47.11)*	38.77 (36.34–41.42)	
Percentage of android fat, %	7.86 (6.33–8.91)	8.65 (7.91–9.53)*	6.48 (5.06–7.86)	
Percentage of gynoid fat, %	17.45 (15.94–19.30)	16.38 (15.43–17.69)*	18.66 (16.87–20.33)	
Percentage of visceral fat, %	2.36 (1.38–3.66)	3.41 (2.24–4.46)*	1.55 (9.38 -2.41)	
Superior frontal gyrus, cm3	18.37 (17.37–20.07)	18.43 (17.37–19.95)	18.31 (17.33–20.11)	
Middle frontal gyrus, cm3	18.66 (17.34–20.05)	18.56 (17.38–19.88)	18.70 (17.33–20.23)	
Anterior cingulate gyrus, cm3	3.74 (3.36–4.13)	3.78 (3.41–4.15)	3.69 (3.35–4.12)	
Insula, cm3	6.35 (5.97–6.75)	6.41 (5.97–6.79)	6.31 (5.95–6.73)	
Hippocampus, cm3	3.55 (3.35–3.73)	3.54 (3.34–3.74)	3.55 (3.37–3.71)	
Amygdala, cm3	1.75 (1.64–1.83)	1.74 (1.64–1.82)	1.75 (1.64–1.83)	
Nucleus accumbens, cm3	0.52 (0.47–0.58)	0.51 (0.47–0.57)*	0.53 (0.48–0.59)	
eTIV, cm3	1359.13 (1293.75–1424.14)	1373.60 (1307.57–1441.91)	1350.63 (1289.23–1409.35)	
Data is presented as median (interquartile range) or n; %.

*p < 0.05 for the differences between women with BMI ≥ 25 kg/m2 and women with BMI < 25 kg/m2. p values were derived from Mann–Whitney U test or Chi-squared test.

Abbreviations: BDI, Beck Depression Inventory; BMI, body mass index; eTIV, estimated total intracranial volume.

Analysis of the groups of women with BMI ≥ 25 kg/m2

In the group of women with BMI ≥ 25 kg/m2, nucleus accumbens volume was inversely associated with BDI score as continuous variable after adjustment for age and eTIV (Table 2). In this group, 37 women had BDI score ≥ 10 points. We used multivariate regression analysis with nucleus accumbens volume as a dependent variable and the presence of depressive symptoms (BDI score ≥ 10) as an independent variable, adjusted for age and eTIV. We observed an inverse relationship between the volume of nucleus accumbens and the presence of depressive symptoms (≥ 10 points in BDI) (β = −0.142, p = 0.031) (Supplementary Table S1). In the group of women with BMI ≥ 25 kg/m2, 6 women had BDI score ≥ 21 and 31 subjects 10–20 points. To assess distinct relations between nucleus accumbens volume and clinical or subclinical depressive symptoms, in the next step we performed multivariate regression analysis with the presence of depressive symptoms divided into clinical depressive symptoms (BDI score ≥ 21) and subclinical depressive symptoms (BDI score 10–20) as an independent variable, adjusted for age and eTIV. We observed an inverse relationship between the volume of nucleus accumbens and the presence of ≥ 21 points in BDI (β = −0.213, p = 0.008) but not a significant association between the volume of nucleus accumbens and the presence of BDI score 10–20 (β = −0.140, p = 0.083) (Supplementary Table S2).Table 2 Multivariate regression analysis results with each brain structure volume as a dependent variable and BDI score as an independent variable in the group of women with BMI ≥ 25 kg/m2 (n = 131).

Brain structure volume	Multivariate regression analysis results	
Superior frontal gyrus, cm3	B < −0.001, β = −0.002, p = 0.979	
Middle frontal gyrus, cm3	B = −0.015, β = −0.045, p = 0.497	
Anterior cingulate gyrus, cm3	B = 0.003, β = 0.028, p = 0.690	
Insula, cm3	B = −0.007, β = −0.076, p = 0.294	
Hippocampus, cm3	B < 0.001, β = 0.020, p = 0.792	
Amygdala, cm3	B < −0.001, β = −0.006, p = 0.938	
Nucleus accumbens, cm3	B = −0.003, β = −0.217, p = 0.008*	
*p < 0.05.

Linear regression analyses were additionally adjusted to age (years) and eTIV (cm3).

Abbreviations: B, unstandardized regression coefficient; β, standardized regression coefficient; BDI, Beck Depression Inventory; BMI, body mass index; eTIV, estimated total intracranial volume.

In addition, we used multivariate linear regression with nucleus accumbens volume as a dependent variable and each body composition parameter and BDI score as independent variables, adjusting each model for age and eTIV. In this group, nucleus accumbens volume was inversely associated with the percentage of visceral fat and BDI score (Table 3, Supplementary Tables S3–S8).Table 3 The associations of nucleus accumbens volume (dependent variable) with body composition and BDI score (independent variables) in women with BMI ≥ 25 kg/m2 (n = 131).

Model 1	
  Total fat mass, kg	B = −0.001, β = −0.128, p = 0.115	
  BDI score, points	B = −0.003, β = −0.218, p = 0.007*	
  Age, years	B = −0.002, β = −0.329, p < 0.001*	
  eTIV, cm3	B < 0.001, β = 0.242, p = 0.003*	
  Adjusted R2 = 0.217	
Model 2	
  Total fat-free mass, kg	B = −0.001, β = −0.074, p = 0.375	
  BDI score, points	B = −0.002, β = −0.211, p = 0.011*	
  Age, years	B = −0.002, β = −0.367, p < 0.001*	
  eTIV, cm3	B < 0.001, β = 0.217, p = 0.010*	
  Adjusted R2 = 0.183	
Model 3	
  Gynoid fat percentage, %	B = 0.002, β = 0.049, p = 0.575	
  BDI score, points	B = −0.003, β = −0.210, p = 0.011*	
  Age, years	B = −0.002, β = −0.338, p < 0.001*	
  eTIV, cm3	B =  < 0.001, β = 0.227, p = 0.006*	
  Adjusted R2 = 0.205	
Model 4	
  Android fat percentage, %	B = −0.006, β = −0.101, p = 0.228	
  BDI score, points	B = −0.003, β = −0.214, p = 0.009*	
  Age, years	B = −0.002, β = −0.323, p < 0.001*	
  eTIV, cm3	B < 0.001, β = 0.238, p = 0.004*	
  Adjusted R2 = 0.210	
Model 5	
  Visceral fat percentage, %	B = −0.013, β = −0.231, p = 0.014*	
  BDI score, points	B = −0.002, β = −0.192, p = 0.017*	
  Age, years	B = −0.001, β = −0.233, p = 0.012*	
  eTIV, cm3	B < 0.001, β = 0.225, p = 0.005*	
  Adjusted R2 = 0.239	
*p < 0.05.

Abbreviations: see Table 2.

Analysis of the groups of women with BMI < 25 kg/m2

In the group of women with BMI < 25 kg/m2, insula volume was inversely associated with BDI score as continuous variable after adjustment for age and eTIV (Table 4). In this group, 37 women had BDI score ≥ 10 points. We used multivariate regression analysis with insula volume as a dependent variable and age, eTIV, and the presence of depressive symptoms (BDI score ≥ 10) as independent variables. We observed an inverse relationship between the volume of insula and the presence of ≥ 10 points in BDI (β = −0.198, p = 0.016) (Supplementary Table S9). In this group of women with BMI < 25 kg/m2, 6 women had BDI score ≥ 21 and 31 subjects 10–20 points. Similar to the analysis described above, we then performed multivariate regression analysis with insula volume as a dependent variable and the presence of depressive symptoms divided into clinical depressive symptoms (BDI score ≥ 21) and subclinical depressive symptoms (BDI score 10–20) as an independent variable, adjusted for age and eTIV. We observed an inverse relationship between the volume of insula and the presence of ≥ 21 points in BDI (β = −0.155, p = 0.019), but not a significant association with the presence of BDI score 10–20 (β = −0.099, p = 0.131) (Supplementary Table S10).Table 4 Multivariate regression analysis results with each brain structure volume as a dependent variable and BDI score as an independent variable in the group of women with BMI < 25 kg/m2 (n = 134).

Brain structure volume	Multivariate regression analysis results	
Superior frontal gyrus, cm3	B = −0.014, β = −0.046, p = 0.542	
Middle frontal gyrus, cm3	B = −0.005, β = −0.017, p = 0.795	
Anterior cingulate gyrus, cm3	B = 0.008, β = 0.091, p = 0.264	
Insula, cm3	B = −0.013, β = −0.147, p = 0.027*	
Hippocampus, cm3	B = −0.001, β = −0.025, p = 0.745	
Amygdala, cm3	B < −0.001, β = −0.006, p = 0.935	
Nucleus accumbens, cm3	B < −0.001, β = −0.020, p = 0.800	
*p < 0.05.

Linear regression analyses were additionally adjusted to age (years) and eTIV (cm3).

Abbreviations: see Table 2.

In the next step, we used multivariate linear regression with insula volume as a dependent variable and each body composition parameter and BDI score as independent variable, adjusting each model for age and eTIV. In normal-/underweight women, insula volume was positively connected only with total fat-free mass and negatively with BDI score (Table 5, Supplementary Tables S11–S16).Table 5 The associations of insula volume (dependent variable) with body composition and BDI score (independent variables) in women with BMI < 25 kg/m2 (n = 134).

Model 1	
  Total fat mass, kg	B = 0.015, β = 0.113 p = 0.089	
  BDI score, points	B = −0.012, β = −0.132, p = 0.046*	
  Age, years	B = −0.008, β = −0.176, p = 0.009*	
  eTIV, cm3	B = 0.004, β = 0.636, p < 0.001*	
  Adjusted R2 = 0.457	
Model 2	
  Total fat-free mass, kg	B = 0.021, β = 0.137, p = 0.037*	
  BDI score, points	B = −0.012, β = −0.133, p = 0.042*	
  Age, years	B = −0.007, β = −0.145, p = 0.027*	
  eTIV, cm3	B = 0.003, β = 0.610, p < 0.001*	
  Adjusted R2 = 0.463	
Model 3	
  Gynoid fat percentage, %	B = −0.033, β = −0.133, p = 0.053	
  BDI score, points	B = −0.014, β = −0.157, p = 0.017*	
  Age, years	B = −0.009, β = −0.191, p = 0.006*	
  eTIV, cm3	B = 0.004, β = 0.649, p < 0.001*	
  Adjusted R2 = 0.461	
Model 4	
  Android fat percentage, %	B = 0.016, β = 0.045, p = 0.505	
  BDI score, points	B = −0.014, β = −0.149, p = 0.025*	
  Age, years	B = −0.007, β = −0.160, p = 0.019*	
  eTIV, cm3	B = 0.004, β = 0.639, p < 0.001*	
  Adjusted R2 = 0.447		
Model 5	
  Visceral fat percentage, %	B = −0.021, β = −0.036, p = 0.634	
  BDI score, points	B = −0.013, β = −0.142 p = 0.035*	
  Age, years	B = −0.006, β = −0.136, p = 0.074	
  eTIV, cm3	B = 0.004, β = 0.629, p < 0.001*	
  Adjusted R2 = 0.445	
*p < 0.05.

Abbreviations: see Table 2.

Discussion

The main finding of our study was that the associations between brain structure volumes, body composition and depressive symptoms are distinct in women with BMI ≥ 25 kg/m2 and BMI < 25 kg/m2. Depressive symptoms in women with obesity/overweight can be associated with nucleus accumbens volume, while in women with normal-/underweight they show an association with insula volume. In women with obesity/overweight, nucleus accumbens volume was inversely associated with the content of visceral fat and the depressive symptoms’ severity, while in women with normal-/underweight, insula volume was positively associated with total fat-free mass and negatively with the depressive symptoms’ severity.

The present study was performed in a population of women with a wide range of BMI from mild underweight to severe obesity. In agreement with previous observations from general population, higher prevalence of obesity or overweight was shown in elderly compared to young women36. In our study, obesity/overweight was associated with decreased nucleus accumbens volume. García-García et al. analysed the dataset of United Kingdom Biobank (participant ages range from 40 to 80 years), finding a negative association between BMI and nucleus accumbens volume37. This brain structure is a region in the ventral striatum which receives dopaminergic inputs from the ventral tegmental area in the midbrain and belongs to mesolimbic dopamine system, which is a part of brain reward circuit38. A number of studies concern the relationship between nucleus accumbens volume and obesity or depression. However, the data regarding women with these both diseases is limited. Our analysis demonstrated that BDI score was inversely connected with nucleus accumbens volume only in women with obesity/overweight. Blunted sensitivity of brain reward circuit in people with depression can lead to compensating mechanisms, such as quick rewarding by eating food rich in fat and sugar7,38. In consequence, atypical depressive symptoms, such as increased appetite and weight gain, can be observed in women with obesity/overweight.

According to the published data, depressive disorders in people with obesity are associated with inflammation in the brain: the activation of microglial cells, an increase in the production of proinflammatory cytokines in the brain, release of reactive oxygen species and, ultimately, neurotoxic effects in the mesolimbic system7,39,40. Peripheral low-grade inflammation is also observed in people with chronic psychological stress and depression38,41. Visceral fat accumulation is also associated with persistent low-grade inflammation40. In our previous study, we observed increased mass of visceral fat in women with depressive symptoms25. In this study, percentage of visceral fat and the presence of depressive symptoms were inversely connected with nucleus accumbens volume in women with obesity/overweight. The results of a large population-based cohort study from the United Kingdom Biobank showed a negative association between central obesity and volume of nucleus accumbens in people with obesity22. Impaired blood–brain barrier integrity in mood disorders and obesity can promote increased permeation of peripheral immune cells from visceral fat to brain and elicit sustained neuroinflammatory actions that ultimately reduce volume of structures associated with depressive-like behaviour, such as nucleus accumbens7,38. In a previous study, we observed that in women with depressive symptoms, excess visceral fat deposits can be connected with insulin resistance25. Insulin modulates dopamine release, and insulin receptors are expressed in structures of mesolimbic system. According to the published data, peripheral insulin resistance can expand to central nervous system and lead to brain insulin resistance42,43. The combination of depressive symptoms and excess visceral fat might possibly influence the volume of nucleus accumbens in women with obesity/overweight.

Only in the group of women with normal-/underweight, we observed an inverse association between the volume of insula and BDI score. Insula has a key role in cognitive and emotional functions, motivational processes and interoception (neural mapping of body states)17. The insula-frontal functional connectivity plays a role in cognitive processes during depression17. Our findings add new information to data from literature and they suggest that insula volume might be connected with total fat-free mass and the depressive symptoms severity only in women with normal-/underweight. We suspect that one possible explanation of our results is a tendency to being physically inactive in persons with depression1. Killgory et al. found that total number of minutes of weekly physical exercise was positively associated with grey matter volume within a region of the posterior left insula44. Peter et al. showed that aerobic capacity positively correlated with grey matter density in the right anterior insula45. One explanation of this phenomenon could be the secretion capacity of skeletal muscles14. Myokines are secreted from muscle cells in response to muscle contractions14. According to the published studies, myokines penetrate the blood–brain barrier to enhance brain-derived neurotrophic factor (BDNF) production14. Mood disorders are connected with decreased BDNF level in central nervous system1. Serum BDNF positively correlated with cortical thickness and volume in multiple brain regions in minor depression46. Additionally, we observed a positive relationship between plasma BDNF and insulin sensitivity in previous studies47. Another possible explanation of our results in the group of women with normal-/underweight is the observation regarding frequent occurrence of eating disorders in people with depression48. Curzio et al. analysed brain structure alterations in adolescents with anorexia nervosa and observed reduced grey matter volumes in left insula and both frontal lobes in this group compared to healthy controls49. The observed brain structure alterations in the group of women with normal-/underweight may be related with typical melancholic symptoms of depression in this group. The presence of melancholic depression in women can cause BMI decrease in long-term observation50. Based on the results of the previous studies, melancholic depression is associated with a reduction of insula volume and caudal anterior cingulate cortex thickness17,51.

Lamers et al. observed different roles of hypothalamic–pituitary–adrenal axis function, inflammation, and metabolic syndrome in melancholic versus atypical depression. Individuals with melancholic depression had higher level of awakening saliva cortisol and diurnal cortisol slope compared with persons with atypical depression and with controls. People with atypical depression had significantly higher levels of inflammatory markers in blood (C-reactive protein, interleukin-6, tumor necrosis factor-α), BMI and waist circumference than persons with melancholic depression and controls4. In women with obesity/overweight and normal-/ underweight, different depressive symptoms can be dominant. Atypical depressive symptoms can mainly be observed in women with obesity/overweight, while melancholic depressive symptoms in women with normal-/underweight4,7,50. The results of a previous study showed that visceral fat volume was connected with depression, but this relationship was weaker in normal-/underweight women compared to overweight and obese women12. This might explain why we did not observe a significant association between nucleus accumbens volume and depressive symptoms severity in women with BMI < 25 kg/m2. Women with normal-/underweight and depressive symptoms can present hypercortisolemia, which is associated with loss of muscle mass. Depression and obesity cooccurrence is often defined by atypical features, which is mostly associated with normal or decreased cortisol levels7. However, the discussed reasons could not fully elucidate the pathomechanism of this different relationship in the study groups. Further studies will be needed to understand the associated mechanism.

There are several limitations to the present study. The main limitation is a small sample size, which did not permit us to separate four BMI groups (underweight, normal weight, overweight, obese women). Additionally, we used BDI questionnaires to define the group of women with depressive symptoms and did not perform a comprehensive psychiatric evaluation. Moreover, in our analyses we only used measurements from structural, and not functional, magnetic resonance imaging. The main strength of our study is the analysis of women without antidepressive and/or antipsychotic drugs.

In conclusion, depressive symptoms might be associated with nucleus accumbens volume in overweight/obese women, while in normal-/ underweight women—with alterations in insula volume. These relationships can be connected with body composition. Further structural and functional research is needed to investigate which pathway disturbance can be responsible for depressive symptoms in women with underweight, normal weight, overweight and obesity. In view of the current data, few studies have investigated an association between low body weight and depressive symptoms. The understanding of depressive disorders in different groups of women can help choose proper medication or develop targeted therapies.

Supplementary Information

Supplementary Tables.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-71924-z.

Acknowledgements

Bialystok PLUS study was supported by the Municipal Office in Bialystok, Grant Number W/UB/DSP/1640/UMBIAŁYSTOK/2017, and from funds from the Medical University of Bialystok for the Białystok PLUS study, including Grant Number SUB/1/00/19/001/1201.

Author contributions

L.Ł., I.K. conception, A.S.J., M.H. data collection, A.S.J., M.H., M.P. brain magnetic resonance imaging analysis, L.Ł., A.S.J., A.K., M.P., K.K., I.K. analysis and interpretation of data, L.Ł. writing original draft preparation, I.K., K.K., N.W., M.K.K. supervision. All authors reviewed the manuscript.

Data availability

The datasets analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References

1. Malhi GS Mann JJ Depression Lancet 2018 392 2299 2312 10.1016/S0140-6736(18)31948-2 30396512
Malhi, G. S. & Mann, J. J. Depression. Lancet 392, 2299–2312 (2018).30396512 10.1016/S0140-6736(18)31948-2
2. Noyes BK Munoz DP Khalid-Khan S Brietzke E Booij L Is subthreshold depression in adolescence clinically relevant? J. Affect. Disord. 2022 309 123 130 10.1016/j.jad.2022.04.067 35429521
Noyes, B. K., Munoz, D. P., Khalid-Khan, S., Brietzke, E. & Booij, L. Is subthreshold depression in adolescence clinically relevant?. J. Affect. Disord. 309, 123–130 (2022).35429521 10.1016/j.jad.2022.04.067
3. Moniuszko-Malinowska A COVID-19 pandemic influence on self-reported health status and well-being in a society Sci. Rep. 2022 12 8767 10.1038/s41598-022-12586-7 35610250
Moniuszko-Malinowska, A. et al. COVID-19 pandemic influence on self-reported health status and well-being in a society. Sci. Rep. 12, 8767 (2022).35610250 10.1038/s41598-022-12586-7
4. Lamers F Evidence for a differential role of HPA-axis function, inflammation and metabolic syndrome in melancholic versus atypical depression Mol. Psychiatry 2013 18 692 699 10.1038/mp.2012.144 23089630
Lamers, F. et al. Evidence for a differential role of HPA-axis function, inflammation and metabolic syndrome in melancholic versus atypical depression. Mol. Psychiatry 18, 692–699 (2013).23089630 10.1038/mp.2012.144
5. Jung SJ Association between body size, weight change and depression: Systematic review and meta-analysis Br. J. Psychiatry 2017 211 14 21 10.1192/bjp.bp.116.186726 28428339
Jung, S. J. et al. Association between body size, weight change and depression: Systematic review and meta-analysis. Br. J. Psychiatry 211, 14–21 (2017).28428339 10.1192/bjp.bp.116.186726
6. Luppino FS Overweight, obesity, and depression: A systematic review and meta-analysis of longitudinal studies Arch. Gen. Psychiatry 2010 67 220 229 10.1001/archgenpsychiatry.2010.2 20194822
Luppino, F. S. et al. Overweight, obesity, and depression: A systematic review and meta-analysis of longitudinal studies. Arch. Gen. Psychiatry 67, 220–229 (2010).20194822 10.1001/archgenpsychiatry.2010.2
7. Fulton S Décarie-Spain L Fioramonti X Guiard B Nakajima S The menace of obesity to depression and anxiety prevalence Trends Endocrinol. Metab. 2022 33 18 35 10.1016/j.tem.2021.10.005 34750064
Fulton, S., Décarie-Spain, L., Fioramonti, X., Guiard, B. & Nakajima, S. The menace of obesity to depression and anxiety prevalence. Trends Endocrinol. Metab. 33, 18–35 (2022).34750064 10.1016/j.tem.2021.10.005
8. Cosan AS Fat compartments in patients with depression: A meta-analysis Brain Behav. 2021 11 e01912 10.1002/brb3.1912 33150726
Cosan, A. S. et al. Fat compartments in patients with depression: A meta-analysis. Brain Behav. 11, e01912 (2021).33150726 10.1002/brb3.1912
9. Chlabicz M Subjective well-being in non-obese individuals depends strongly on body composition Sci. Rep. 2021 11 21797 10.1038/s41598-021-01205-6 34750456
Chlabicz, M. et al. Subjective well-being in non-obese individuals depends strongly on body composition. Sci. Rep. 11, 21797 (2021).34750456 10.1038/s41598-021-01205-6
10. Cho SJ The relationship between visceral adiposity and depressive symptoms in the general Korean population J. Affect. Disord. 2019 244 54 59 10.1016/j.jad.2018.09.046 30316052
Cho, S. J. et al. The relationship between visceral adiposity and depressive symptoms in the general Korean population. J. Affect. Disord. 244, 54–59 (2019).30316052 10.1016/j.jad.2018.09.046
11. Lee ES Kim YH Beck SH Lee S Oh SW Depressive mood and abdominal fat distribution in overweight premenopausal women Obes. Res. 2005 13 320 325 10.1038/oby.2005.43 15800290
Lee, E. S., Kim, Y. H., Beck, S. H., Lee, S. & Oh, S. W. Depressive mood and abdominal fat distribution in overweight premenopausal women. Obes. Res. 13, 320–325 (2005).15800290 10.1038/oby.2005.43
12. Everson-Rose SA Depressive symptoms and increased visceral fat in middle-aged women Psychosom. Med. 2009 71 410 416 10.1097/PSY.0b013e3181a20c9c 19398501
Everson-Rose, S. A. et al. Depressive symptoms and increased visceral fat in middle-aged women. Psychosom. Med. 71, 410–416 (2009).19398501 10.1097/PSY.0b013e3181a20c9c
13. Guedes EP Body composition and depressive/anxiety symptoms in overweight and obese individuals with metabolic syndrome Diabetol. Metab. Syndr. 2013 5 82 10.1186/1758-5996-5-82 24364839
Guedes, E. P. et al. Body composition and depressive/anxiety symptoms in overweight and obese individuals with metabolic syndrome. Diabetol. Metab. Syndr. 5, 82 (2013).24364839 10.1186/1758-5996-5-82
14. Pedersen BK Physical activity and muscle-brain crosstalk Nat. Rev. Endocrinol. 2019 15 383 392 10.1038/s41574-019-0174-x 30837717
Pedersen, B. K. Physical activity and muscle-brain crosstalk. Nat. Rev. Endocrinol. 15, 383–392 (2019).30837717 10.1038/s41574-019-0174-x
15. Zhang FF Peng W Sweeney JA Jia ZY Gong QY Brain structure alterations in depression: Psychoradiological evidence CNS Neurosci. Ther. 2018 24 994 1003 10.1111/cns.12835 29508560
Zhang, F. F., Peng, W., Sweeney, J. A., Jia, Z. Y. & Gong, Q. Y. Brain structure alterations in depression: Psychoradiological evidence. CNS Neurosci. Ther. 24, 994–1003 (2018).29508560 10.1111/cns.12835
16. Pandya M Altinay M Malone DA Jr Anand A Where in the brain is depression? Curr. Psychiatry Rep. 2012 14 634 642 10.1007/s11920-012-0322-7 23055003
Pandya, M., Altinay, M., Malone, D. A. Jr. & Anand, A. Where in the brain is depression?. Curr. Psychiatry Rep. 14, 634–642 (2012).23055003 10.1007/s11920-012-0322-7
17. Namkung H Kim SH Sawa A The insula: An underestimated brain area in clinical neuroscience, psychiatry, and neurology Trends Neurosci. 2017 40 200 207 10.1016/j.tins.2017.02.002 28314446
Namkung, H., Kim, S. H. & Sawa, A. The insula: An underestimated brain area in clinical neuroscience, psychiatry, and neurology. Trends Neurosci. 40, 200–207 (2017).28314446 10.1016/j.tins.2017.02.002
18. Toenders YJ The association between clinical and biological characteristics of depression and structural brain alterations J. Affect. Disord. 2022 312 268 274 10.1016/j.jad.2022.06.056 35760189
Toenders, Y. J. et al. The association between clinical and biological characteristics of depression and structural brain alterations. J. Affect. Disord. 312, 268–274 (2022).35760189 10.1016/j.jad.2022.06.056
19. Opel N Brain structural abnormalities in obesity: relation to age, genetic risk, and common psychiatric disorders: Evidence through univariate and multivariate mega-analysis including 6420 participants from the ENIGMA MDD working group Mol. Psychiatry. 2021 26 4839 4852 10.1038/s41380-020-0774-9 32467648
Opel, N. et al. Brain structural abnormalities in obesity: relation to age, genetic risk, and common psychiatric disorders: Evidence through univariate and multivariate mega-analysis including 6420 participants from the ENIGMA MDD working group. Mol. Psychiatry. 26, 4839–4852 (2021).32467648 10.1038/s41380-020-0774-9
20. Hamer M Batty GD Association of body mass index and waist-to-hip ratio with brain structure: UK Biobank study Neurology 2019 92 e594 e600 10.1212/WNL.0000000000006879 30626649
Hamer, M. & Batty, G. D. Association of body mass index and waist-to-hip ratio with brain structure: UK Biobank study. Neurology 92, e594–e600 (2019).30626649 10.1212/WNL.0000000000006879
21. Raji, C.A., et al. Visceral and subcutaneous abdominal fat predict brain volume loss at midlife in 10,001 individuals. Aging Dis.10.14336/AD.2023.0820 (2023).
22. Pflanz CP Central obesity is selectively associated with cerebral gray matter atrophy in 15,634 subjects in the UK Biobank Int. J. Obes. (Lond). 2022 46 1059 1067 10.1038/s41366-021-00992-2 35145215
Pflanz, C. P. et al. Central obesity is selectively associated with cerebral gray matter atrophy in 15,634 subjects in the UK Biobank. Int. J. Obes. (Lond). 46, 1059–1067 (2022).35145215 10.1038/s41366-021-00992-2
23. Kilgour AH Todd OM Starr JM A systematic review of the evidence that brain structure is related to muscle structure and their relationship to brain and muscle function in humans over the lifecourse BMC Geriatr. 2014 14 85 10.1186/1471-2318-14-85 25011478
Kilgour, A. H., Todd, O. M. & Starr, J. M. A systematic review of the evidence that brain structure is related to muscle structure and their relationship to brain and muscle function in humans over the lifecourse. BMC Geriatr. 14, 85 (2014).25011478 10.1186/1471-2318-14-85
24. Chlabicz M ECG indices poorly predict left ventricular hypertrophy and are applicable only in individuals with low cardiovascular risk J. Clin. Med. 2020 9 1364 10.3390/jcm9051364 32384681
Chlabicz, M. et al. ECG indices poorly predict left ventricular hypertrophy and are applicable only in individuals with low cardiovascular risk. J. Clin. Med. 9, 1364 (2020).32384681 10.3390/jcm9051364
25. Łapińska L The relationship between subclinical depressive symptoms and metabolic parameters in women: A subanalysis of the Bialystok PLUS study Pol. Arch. Intern. Med. 2022 132 16261 35579576
Łapińska, L. et al. The relationship between subclinical depressive symptoms and metabolic parameters in women: A subanalysis of the Bialystok PLUS study. Pol. Arch. Intern. Med. 132, 16261 (2022).35579576
26. Łapińska L The association between plasma N-terminal pro-brain natriuretic peptide concentration and metabolic disturbances in women with depressive symptoms Psychoneuroendocrinology 2023 158 106409 10.1016/j.psyneuen.2023.106409 37801752
Łapińska, L. et al. The association between plasma N-terminal pro-brain natriuretic peptide concentration and metabolic disturbances in women with depressive symptoms. Psychoneuroendocrinology 158, 106409 (2023).37801752 10.1016/j.psyneuen.2023.106409
27. Besteher B Gaser C Nenadić I Brain structure and subclinical symptoms: A dimensional perspective of psychopathology in the depression and anxiety spectrum Neuropsychobiology 2020 79 270 283 10.1159/000501024 31340207
Besteher, B., Gaser, C. & Nenadić, I. Brain structure and subclinical symptoms: A dimensional perspective of psychopathology in the depression and anxiety spectrum. Neuropsychobiology 79, 270–283 (2020).31340207 10.1159/000501024
28. Fischl B FreeSurfer Neuroimage 2012 62 774 781 10.1016/j.neuroimage.2012.01.021 22248573
Fischl, B. FreeSurfer. Neuroimage 62, 774–781 (2012).22248573 10.1016/j.neuroimage.2012.01.021
29. Fischl B Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain Neuron 2002 33 341 355 10.1016/S0896-6273(02)00569-X 11832223
Fischl, B. et al. Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002).11832223 10.1016/S0896-6273(02)00569-X
30. Salvadore G Prefrontal cortical abnormalities in currently depressed versus currently remitted patients with major depressive disorder Neuroimage 2011 54 2643 2651 10.1016/j.neuroimage.2010.11.011 21073959
Salvadore, G. et al. Prefrontal cortical abnormalities in currently depressed versus currently remitted patients with major depressive disorder. Neuroimage 54, 2643–2651 (2011).21073959 10.1016/j.neuroimage.2010.11.011
31. Tang Y Reduced ventral anterior cingulate and amygdala volumes in medication-naïve females with major depressive disorder: A voxel-based morphometric magnetic resonance imaging study Psychiatry Res. 2007 156 83 86 10.1016/j.pscychresns.2007.03.005 17825533
Tang, Y. et al. Reduced ventral anterior cingulate and amygdala volumes in medication-naïve females with major depressive disorder: A voxel-based morphometric magnetic resonance imaging study. Psychiatry Res. 156, 83–86 (2007).17825533 10.1016/j.pscychresns.2007.03.005
32. Cole J Costafreda SG McGuffin P Fu CH Hippocampal atrophy in first episode depression: A meta-analysis of magnetic resonance imaging studies J. Affect. Disord. 2011 134 483 487 10.1016/j.jad.2011.05.057 21745692
Cole, J., Costafreda, S. G., McGuffin, P. & Fu, C. H. Hippocampal atrophy in first episode depression: A meta-analysis of magnetic resonance imaging studies. J. Affect. Disord. 134, 483–487 (2011).21745692 10.1016/j.jad.2011.05.057
33. Treadway MT Early adverse events, HPA activity and rostral anterior cingulate volume in MDD PLoS One 2009 4 e4887 10.1371/journal.pone.0004887 19325704
Treadway, M. T. et al. Early adverse events, HPA activity and rostral anterior cingulate volume in MDD. PLoS One 4, e4887 (2009).19325704 10.1371/journal.pone.0004887
34. Peng W Chen Z Yin L Jia Z Gong Q Essential brain structural alterations in major depressive disorder: A voxel-wise meta-analysis on first episode, medication-naive patients J. Affect. Disord. 2016 199 114 123 10.1016/j.jad.2016.04.001 27100056
Peng, W., Chen, Z., Yin, L., Jia, Z. & Gong, Q. Essential brain structural alterations in major depressive disorder: A voxel-wise meta-analysis on first episode, medication-naive patients. J. Affect. Disord. 199, 114–123 (2016).27100056 10.1016/j.jad.2016.04.001
35. Chu Z Atrophy of bilateral nucleus accumbens in melancholic depression Neuroreport 2023 34 493 500 10.1097/WNR.0000000000001915 37270840
Chu, Z. et al. Atrophy of bilateral nucleus accumbens in melancholic depression. Neuroreport 34, 493–500 (2023).37270840 10.1097/WNR.0000000000001915
36. Stepaniak, U., et al. Prevalence of general and abdominal obesity and overweight among adults in Poland. Results of the WOBASZ II study (2013–2014) and comparison with the WOBASZ study (2003–2005). Pol. Arch. Med. Wewn. 126, 662–671 (2016).
37. García-García I Morys F Dagher A Nucleus accumbens volume is related to obesity measures in an age-dependent fashion J. Neuroendocrinol. 2020 32 e12812 10.1111/jne.12812 31758711
García-García, I., Morys, F. & Dagher, A. Nucleus accumbens volume is related to obesity measures in an age-dependent fashion. J. Neuroendocrinol. 32, e12812 (2020).31758711 10.1111/jne.12812
38. Baik JH Stress and the dopaminergic reward system Exp. Mol. Med. 2020 52 1879 1890 10.1038/s12276-020-00532-4 33257725
Baik, J. H. Stress and the dopaminergic reward system. Exp. Mol. Med. 52, 1879–1890 (2020).33257725 10.1038/s12276-020-00532-4
39. Han KM Ham BJ How inflammation affects the brain in depression: A review of functional and structural MRI studies J. Clin. Neurol. 2021 17 503 515 10.3988/jcn.2021.17.4.503 34595858
Han, K. M. & Ham, B. J. How inflammation affects the brain in depression: A review of functional and structural MRI studies. J. Clin. Neurol. 17, 503–515 (2021).34595858 10.3988/jcn.2021.17.4.503
40. Treadway MT Cooper JA Miller AH Can't or won't? Immunometabolic constraints on dopaminergic drive Trends Cogn. Sci. 2019 23 435 448 10.1016/j.tics.2019.03.003 30948204
Treadway, M. T., Cooper, J. A. & Miller, A. H. Can’t or won’t? Immunometabolic constraints on dopaminergic drive. Trends Cogn. Sci. 23, 435–448 (2019).30948204 10.1016/j.tics.2019.03.003
41. Siddiqui A Association of oxidative stress and inflammatory markers with chronic stress in patients with newly diagnosed type 2 diabetes Diabetes Metab. Res. Rev. 2019 35 e3147 10.1002/dmrr.3147 30801898
Siddiqui, A. et al. Association of oxidative stress and inflammatory markers with chronic stress in patients with newly diagnosed type 2 diabetes. Diabetes Metab. Res. Rev. 35, e3147 (2019).30801898 10.1002/dmrr.3147
42. Liu S Borgland SL Insulin actions in the mesolimbic dopamine system Exp. Neurol. 2019 320 113006 10.1016/j.expneurol.2019.113006 31279911
Liu, S. & Borgland, S. L. Insulin actions in the mesolimbic dopamine system. Exp. Neurol. 320, 113006 (2019).31279911 10.1016/j.expneurol.2019.113006
43. Ferrario CR Reagan LP Insulin-mediated synaptic plasticity in the CNS: Anatomical, functional and temporal contexts Neuropharmacology 2018 136 182 191 10.1016/j.neuropharm.2017.12.001 29217283
Ferrario, C. R. & Reagan, L. P. Insulin-mediated synaptic plasticity in the CNS: Anatomical, functional and temporal contexts. Neuropharmacology 136, 182–191 (2018).29217283 10.1016/j.neuropharm.2017.12.001
44. Killgore WD Olson EA Weber M Physical exercise habits correlate with gray matter volume of the hippocampus in healthy adult humans Sci. Rep. 2013 3 3457 10.1038/srep03457 24336512
Killgore, W. D., Olson, E. A. & Weber, M. Physical exercise habits correlate with gray matter volume of the hippocampus in healthy adult humans. Sci. Rep. 3, 3457 (2013).24336512 10.1038/srep03457
45. Peters J Voxel-based morphometry reveals an association between aerobic capacity and grey matter density in the right anterior insula Neuroscience 2009 163 1102 1108 10.1016/j.neuroscience.2009.07.030 19628025
Peters, J. et al. Voxel-based morphometry reveals an association between aerobic capacity and grey matter density in the right anterior insula. Neuroscience 163, 1102–1108 (2009).19628025 10.1016/j.neuroscience.2009.07.030
46. Polyakova M Serum BDNF levels correlate with regional cortical thickness in minor depression: A pilot study Sci. Rep. 2020 10 14524 10.1038/s41598-020-71317-y 32883977
Polyakova, M. et al. Serum BDNF levels correlate with regional cortical thickness in minor depression: A pilot study. Sci. Rep. 10, 14524 (2020).32883977 10.1038/s41598-020-71317-y
47. Karczewska-Kupczewska M Circulating brain-derived neurotrophic factor concentration is downregulated by intralipid/heparin infusion or high-fat meal in young healthy male subjects Diabetes Care 2012 35 358 362 10.2337/dc11-1295 22210566
Karczewska-Kupczewska, M. et al. Circulating brain-derived neurotrophic factor concentration is downregulated by intralipid/heparin infusion or high-fat meal in young healthy male subjects. Diabetes Care 35, 358–362 (2012).22210566 10.2337/dc11-1295
48. Godart N Mood disorders in eating disorder patients: Prevalence and chronology of ONSET J. Affect. Disord. 2015 185 115 122 10.1016/j.jad.2015.06.039 26162282
Godart, N. et al. Mood disorders in eating disorder patients: Prevalence and chronology of ONSET. J. Affect. Disord. 185, 115–122 (2015).26162282 10.1016/j.jad.2015.06.039
49. Curzio O Lower gray matter volumes of frontal lobes and insula in adolescents with anorexia nervosa restricting type: Findings from a Brain Morphometry Study Eur. Psychiatry 2020 63 e27 10.1192/j.eurpsy.2020.19 32172703
Curzio, O. et al. Lower gray matter volumes of frontal lobes and insula in adolescents with anorexia nervosa restricting type: Findings from a Brain Morphometry Study. Eur. Psychiatry 63, e27 (2020).32172703 10.1192/j.eurpsy.2020.19
50. Ottino C Short-term and long-term effects of major depressive disorder subtypes on obesity markers and impact of sex on these associations J. Affect. Disord. 2022 297 570 578 10.1016/j.jad.2021.10.057 34718038
Ottino, C. et al. Short-term and long-term effects of major depressive disorder subtypes on obesity markers and impact of sex on these associations. J. Affect. Disord. 297, 570–578 (2022).34718038 10.1016/j.jad.2021.10.057
51. Soriano-Mas C Cross-sectional and longitudinal assessment of structural brain alterations in melancholic depression Biol. Psychiatry 2011 69 318 325 10.1016/j.biopsych.2010.07.029 20875637
Soriano-Mas, C. et al. Cross-sectional and longitudinal assessment of structural brain alterations in melancholic depression. Biol. Psychiatry 69, 318–325 (2011).20875637 10.1016/j.biopsych.2010.07.029
