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. Author manuscript; available in PMC: 2021 Nov 29.
Published in final edited form as: Psychoneuroendocrinology. 2020 May 23;119:104722. doi: 10.1016/j.psyneuen.2020.104722

Developmental stage-dependent relationships between ghrelin levels and hippocampal white matter connections in low-weight anorexia nervosa and atypical anorexia nervosa

Lauren Breithaupt 1,2, Natalia Chunga-Iturry 2,3, Amanda E Lyall 2,3, Suheyla Cetin-Karayumak 2,3, Kendra R Becker 1,2, Jennifer J Thomas 1,2, Meghan Slattery 4, Nikos Makris 2,3,5,6, Franziska Plessow 2,4, Ofer Pasternak 2,3,6, Laura M Holsen 2,7, Marek Kubicki 2,3,5,6, Madhusmita Misra 2,4,8,*, Elizabeth A Lawson 2,4,*, Kamryn T Eddy 1,2,*
PMCID: PMC8629489  NIHMSID: NIHMS1601554  PMID: 32512249

Abstract

Introduction.

Disruptions in homeostatic and hedonic food motivation are proposed to underlie anorexia nervosa (AN) and atypical AN, restrictive eating disorders which commonly onset in puberty. Ghrelin, a neuroprotective hormone that drives hedonic eating is increased in AN and is expressed in the hippocampus. White matter (WM) undergoes significant change during puberty in regions involved in food motivation, particularly WM tracts connected with the hippocampus. The association between ghrelin and WM region of interest (ROI) with hippocampal connections in restrictive eating disorders, particularly in adolescence during key neurodevelopmental growth, is unknown.

Methods.

We evaluated fasting plasma ghrelin and WM microstructure (measured by free-water corrected fractional anisotropy (FA-t)) in WM ROIs with hippocampal connections - the fornix and the hippocampal portion of the cingulum - in 56 adolescent females (age range: 11.9 – 22.1 y; mean: 19.0 y) with low-weight eating disorders including AN and atypical AN (N = 36) and healthy controls (N = 20).

Results.

FA-t in the fornix or hippocampal portion of the fornix did not differ between groups. Ghrelin was higher in AN/atypical AN vs. HC and was positively correlated with puberty stage in the AN/atypical AN group, but not the HC group. The correlation between ghrelin and FA-t in the fornix was significantly different in females with AN/atypical AN compared to controls. In AN/atypical AN, pubertal stage moderated the relation between fasting plasma ghrelin and FA-t in the fornix: higher fasting ghrelin was associated with lower FA-t in the fornix in late-post-puberty, but was not associated with FA-t in the early to mid stages of puberty.

Conclusions.

In post-pubertal females with low-weight AN/atypical AN, higher levels of ghrelin are associated with lower FA-t in the fornix. This relationship is not evident in the early to mid stages of puberty in AN/atypical AN or in HC, and may reflect a lack of possible neuroprotective effects of ghrelin in late-post puberty only. Understanding the effects of ghrelin on WM microstructure longitudinally and following recovery from AN/Atypical AN and how this differs across pubertal stages will be an important next step. These findings could ultimately inform treatment staging and aid in diagnosis and detection of AN/atypical AN.

Keywords: Anorexia nervosa, puberty, total ghrelin, white matter, diffusion tensor imaging

1. Introduction

Neurobiological abnormalities in homeostatic and hedonic food motivation may underlie restrictive eating behaviors in anorexia nervosa (AN) and atypical AN (Holsen et al., 2012; Kaye et al., 2013). While both conditions are characterized by restrictive eating and weight loss, in AN, severe restriction leads to dangerously low body weight, whereas in atypical AN, weight is not necessarily low (APA, 2013). Both disorders typically onset in adolescence around the time of puberty (Volpe et al., 2016). Developmental changes to the homeostatic and hedonic food motivation system during puberty may help explain the peak incidence of disease onset during this time (Luciana et al. 2012).

Food motivation and appetitive function are complex processes in which various peripherally-derived endocrine and centrally-derived neuropeptidergic hormones signal to the hypothalamus and insula, which contains the primary taste cortex (Bailer and Kaye, 2003; Caron and Richard, 2017; Fudge et al., 2005). The primary components of food reward, including anticipation and consumption, are controlled by a network of hedonic and homeostatic regions, including the ventral tegmental area, and its dopaminergic projections to the nucleus accumbens (Wise, 2004a, 2004b). Neuroanatomically interconnected with these regions is the hippocampus. Traditionally implicated in memory processes, the hippocampus has emerged as a key structure in regulatory systems and a neural substrate involved in control of food intake (Bubb et al., 2017; Davidson et al., 2019; Kanoski and Grill, 2017; Stevenson and Francis, 2017). For example, our prior work suggests a key role of the hippocampus in food motivation in AN (Holsen et al., 2012), as indicated by hypoactivation of the hippocampus in adults with AN compared to healthy controls on viewing high calorie foods (Holsen et al., 2012). Adolescents with AN and atypical AN have significant gray matter volume reductions compared to healthy controls in several hippocampus subfields, even after adjusting for global brain volume loss, suggesting that the hippocampus may be particularly vulnerable in restrictive eating disorders (Myrvang et al., 2018). The fornix is the principal white matter fiber tract that connects the hippocampus to other regions including projections into several subcortical areas known to play a role in controlling food intake, such as the nucleus accumbens and hypothalamus. Earlier studies have reported alterations in white matter microstructure in the fornix in acute stages of AN (Frank et al., 2013; Kazlouski et al., 2011; von Schwanenflug et al., 2018), which may normalize with weight gain (von Schwanenflug et al., 2018).

Levels of ghrelin, a hormone that drives homeostatic and hedonic eating, are high in AN (Holsen et al., 2014; Schalla and Stengel, 2018). Though primarily secreted by the stomach, ghrelin can cross the blood-brain barrier (Diano et al., 2006; McEwen, 2007) and is highly expressed [along with its receptor the growth hormone secretagogue receptor 1a (GHSR-1a)] in thehypothalamus and the hippocampus (Guan et al., 1997; Zigman et al., 2006), and in white matter in glial cells (Baquedano et al., 2013; Fuente-Martín et al., 2016; García-Cáceres et al., 2014) Ghrelin levels rise sharply just before meals and fall to a nadir within an hour of food intake (Cummings et al., 2004). Ghrelin binds to GHS-R1a-expressing neurons in the arcuate nucleus of the hypothalamus, which regulates homeostatic appetite (Guan et al., 1997; Schellekens et al., 2012; Willesen et al., 1999). Ghrelin also acts as an endogenous modulator of hedonic eating by stimulating dopamine release from VTA neurons that project to the NAc. Ghrelin levels typically spike before puberty and then decline in later pubertal stages (Whatmore et al., 2003). Compared to healthy controls, women and girls with AN have high ghrelin levels, reflecting an adaptive response to starvation (Méquinion et al., 2013; Ogiso et al., 2011), and levels normalize with weight gain (Hübel et al., 2019). However, increases in ghrelin in AN are not associated with increased eating or weight gain (Hübel et al., 2019), suggesting an ineffective compensatory mechanism to chronic starvation. Of note, ghrelin can be measured in its active form (acyl ghrelin), “inactive” form (des-acyl ghrelin), or the combination of the two (total ghrelin). All three measures of ghrelin have been reported to be high in AN (see Schalla and Stengel, 2018 for a review). The effects of active ghrelin on food intake and BMI are well described. However, less is known about des-acyl ghrelin which may have an opposing role to acyl ghrelin on food intake (See Schalla and Stengel 2018 for a review). Finally, ghrelin protects against degenerative diseases (Bayliss and Andrews, 2013; Chung et al., 2007; Diano et al., 2006; Santos et al., 2017; Spencer et al., 2013), by inhibiting neuronal apoptosis (Chung et al., 2007) and protecting neurons from inflammation, oxidative stress, excitotoxicity, starvation, and hypoxia (Erşahin et al., 2010; Fujitsuka et al., 2016; Gahete et al., 2011; Lopez et al., 2012; Spencer et al., 2013). At present, the association between ghrelin and white matter integrity in AN is unknown.

Diffusion tensor imaging (DTI), a type of magnetic resonance imaging (MRI), allows for the investigation of in vivo microstructural changes of white matter tracts by quantifying the diffusion of water molecules in the brain, and assesses microstructural properties such as axonal coherence, density or degree of myelination (O’Donnell and Pasternak, 2015). The most commonly studied DTI metric is fractional anisotropy (FA), which reflects white matter properties such as fiber density, coherence of axons, axonal diameter, and myelination. Further, myelination occurs through adolescence and continues into the third decade of life (Benes, 1989; Huttenlocher, 1990). Reductions in FA are often interpreted as a decline in white matter health, which may impact cognitive functions such as task performance and mental processing speed.

At present, white matter studies in AN are small and inconclusive. Cross-sectional studies in adults with AN have reported reductions in FA in several brain tracts (Frieling et al., 2012; Hu et al., 2017; Kazlouski et al., 2011; Nagahara et al., 2014; Via et al., 2014), while studies in adolescents have variably reported lower, higher, or no differences compared to controls (Frank et al., 2013; Travis et al., 2015; Vogel et al., 2016); Gaudio et al., 2017). Divergent results may be due to partial volume effects (PVE) due to cerebrospinal fluid (CSF) (Kaufmann et al. 2017; Seitz 2017), which can be corrected by calculating free-water-corrected FA maps (tissue FA; FA-t) (Pasternak et al., 2009). Further, a recent meta-analysis of white matter in eating disorders advocates for free-water correction, particularly in AN where partial volume effects are greater due to malnutrition (Meneguzzo et al. 2019; Gaudio et al. 2019).

Differential findings in adolescents versus adults may also reflect the widespread neurobiological changes that emerge in healthy individuals with onset and progression of puberty(Tamnes et al., 2018). Healthy adolescents have a linear increase in FA white matter health from childhood to adolescence (Lebel et al., 2012, 2008), from increased axonal caliber and/or by increased myelination (Paus, 2018). The relationship between AN and white matter has not been assessed in the context of pubertal status (King et al., 2018), and studies have not examined the effect of chronological age alone (Frank et al., 2013; Frieling et al., 2012; Kazlouski et al., 2011; Nagahara et al., 2014; Shott et al., 2016; Travis et al., 2015; Via et al., 2014). Possible influences of pubertal status on white matter maturation may contribute to the well-characterized vulnerabilities during adolescence and early adult years to reward disruption in AN. Further, due to the observed ghrelin dysregulation in AN and neuroprotective role of ghrelin in healthy controls (HC), understanding the association between ghrelin and white matter integrity in AN, particularly in adolescence during key neurodevelopmental growth, is an important next step.

We completed a cross-sectional study investigating white matter tracts carrying connections to the hippocampus (fornix, hippocampal portion of the cingulum) as well as a control tract (the gyrus portion of the cingulum) in a cohort of adolescents and young adults with low-weight eating disorders including AN and atypical AN (AN/atypical AN group) compared to HC. Based on recent meta-analyses regarding white matter in AN (Barona et al., 2019) suggesting widespread alterations in AN and the above literature on ghrelin and white matter development, we hypothesized that individuals with AN/atypical AN would show less white matter microstructure measured by FA-t in the hippocampal cingulum portion and fornix and increased fasting plasma ghrelin levels compared to age-matched HC. Next, we hypothesized that within AN/atypical AN, higher levels of circulating ghrelin would be associated with less white matter integrity in hippocampal connections. Finally, we predicted that pubertal status would moderate the relationship between ghrelin and FA-t. We did not make specific hypotheses about the direction of the relationship due to the exploratory nature of this final aim.

2. Methods

2.1. Subjects

Data from 56 consecutively recruited adolescent and adult females (age - range: 11.9 – 22.1 years; mean, standard deviation: 19.0, 2.6) with AN (n=22) or atypical-AN (n = 14) (AN/atypical AN group, N = 36) or healthy controls of normal-weight (HC group; N = 20) participating in a larger ongoing study of the neurobiology of low-weight eating disorders (R01MH103402) were included. Inclusion in the AN/atypical AN group was predicated on having a current low-weight eating disorder defined as < or = 90% of expected median body weight determined by 50th percentile BMI for age, bone age, or height/bone age for gender percentile and a DSM-5 diagnosis of AN or atypical AN conferred through assessment by KSADS (Kaufman et al., 2013) and confirmed via symptom count on the Eating Disorder Examination version 17.0 (EDE; (Fairburn, C. G., Cooper, Z., & O’Connor, M., 2014). While individuals with both AN and atypical AN met the low-weight definition for study inclusion, those with AN met the additional diagnostic criterion of being <10th percentile for BMI for age and gender (if under 18 years old) and <85% expected median body weight (if 18 years or older) (APA, 2013). Healthy controls had no lifetime psychiatric disorder as determined by the K-SADS (Kaufman et al., 1997) and had a BMI between 25th and 85th percentiles for age, regular menses if more than 2 years post-menarchal, and no pubertal delay. Exclusion criteria for both groups included use of systemic hormones, pregnancy or breastfeeding within eight weeks, history of psychosis, active substance abuse, hematocrit less than 30%, potassium level less than 3 mmol/L, history of gastrointestinal tract surgery or other conditions that could lead to low weight (e.g. neoplasia, diabetes mellitus), and any contraindication to MRI scanning. Controls were excluded if they ran >25 miles or exercised >10 hours in any one week in the three months preceding study enrollment, due to prior research demonstrating that these activities increase ghrelin levels (Ackerman et al., 2012). Subjects were provided with a detailed description of the study, following which they provided written consent for study participation if over 18 years old. Parental written consent and child assent was collected for subjects under 18 years old. The study was approved by the Partners Human Research Committee. A subset of the participants’ clinical characteristics (Aulinas et al., 2019a, 2019b; Izquierdo et al., 2019) were previously reported. DTI data and their relation to ghrelin, the focus of this manuscript, have not been previously published.

2.2. Procedures

Following informed consent/assent, the following were performed at a screening visit prior to the scan: a complete medical history; physical examination, including, height, weight, and Tanner staging for puberty. Subjects arrived at the Athinoula A. Martinos Center for Biomedical Imaging having fasted overnight for at least 8 hours. Fasting plasma was drawn around 08:45 h by trained nursing staff. Our samples were immediately placed on ice following venipuncture, spun in a refrigerated centrifuge, and stored at −80 degrees Celsius until measurement. Plasma total ghrelin levels were determined by an enzyme-linked immunosorbent assay (ELISA) (EMD Millipore: Billerica, MA), with an intra-assay CV of 1.32% and inter-assay CV of 6.62%. The lower limit of detection was 50.0 pg/mL. All participants consumed a standardized mixed meal prior to diffusion weighted imaging (DW) scanning to reduce state nutritional effects (McNamara et al., 2018). Participants were instructed to consume an approximately 400kcal mixed meal standardized for macronutrient content (approximately 20% calories from protein, 20% from fat, and 60% from carbohydrates) over the course of 15 minutes before collecting the diffusion MRI (dMRI) scans.

2.3. dMRI scanning parameters and analysis

dMRI scans were acquired using a Siemens 3T Skyra scanner at the MGH Athinoula A. Martinos Center for Biomedical Imaging. Sixty-five diffusion-weighted images with a spatial resolution of 2×2×2 mm3 were obtained for each scan, including 64 volumes with diffusion gradients (b=700 s/mm2) and 1 volume without diffusion weighting (b=0). All diffusion MR images were visually inspected for motion effects and signal loss, corrected for eddy current-induced distortions and masked manually to remove non-brain areas (3D Slicer, www.slicer.org). FA maps were generated, registered to a study-specific template created using Advanced Normalization Tools (ANTs) (Avants et al., 2011, 2010), and then the template was registered to the standard template of the Illinois Institute of Technology (IIT) Human Brain Atlas as the reference (Varentsova et al., 2014).

To minimize the PVE in our analyses, FA-t maps were generated employing the Free Water Imaging pipeline. This method models the diffusion-weighted images into two compartments on a voxel-level: a free water compartment that represents water molecules that are free to diffuse, and a tissue compartment, modeled as a tensor, which represents water diffusion that is restricted or hindered (Pasternak et al., 2009). FA-t maps, calculated from the free-water corrected diffusion tensor, were registered to the study-specific template using the linear and non-linear FA transformations obtained during the template creation mentioned above.

Region of Interest (ROI) analyses were performed using the connectivity-based parcellation/definitions of the major bundles of the IIT Human Brain Atlas (Catani and de Schotten, 2012). The average FA-t was calculated for 5 brain regions: the fornix, right and left cingulum cingulate gyrus portion, right and left cingulum hippocampal portion.

2.4. Pubertal Stage

Tanner staging was performed by a research nurse practitioner or physician to determine pubertal status. Female Tanner staging categorizes individuals along an ordinal puberty scale from 1 to 5 on the basis of breast and pubic hair development (Tanner, 1962). Tanner stages were averaged across both categories to create one Tanner stage. Study participants were grouped by Tanner staging as follows: pre/early pubertal (Tanner stages 1,2) (n= 2), midpubertal (Tanner stages 3,4) (n = 16), and late-postpubertal/adult (Tanner stage 5) (n = 38). Due to the small number of participants in the pre/early pubertal group, we decided to combine these individuals with those in the midpubertal group, creating two groups: early-midpubertal group (Tanner stages 1–4) (n = 18) and late-postpubertal/adult (Tanner stages 5) (n = 38).

2.5. Data analysis

Self-report data, white matter tract variables, and fasting plasma ghrelin levels were analyzed using R (R Core Team, 2018). Between-group differences were assessed using independent samples t-test, X2 tests, or overall analysis of variance (ANOVA). Plasma fasting ghrelin levels were log-transformed to correct for unequal variances and inspected for outliers. One subject was removed from data analyses because she was an outlier (confirmed with statistical methods; at least 3 S.D.s from the mean; Supplementary Figure 1 shows ghrelin distribution by group after removal of the outlier).

For the second aim of the study, we explored the relationship between fasting total ghrelin (both acyl and des-acyl ghrelin) and white matter microstructure in the fornix and the hippocampal portion of the cingulum as well as the control tract - the gyrus portion of the cingulum - according to disease state. Partial correlations were used to quantify relations between FA-t and plasma fasting ghrelin levels, controlling for Tanner stage in each group. These separate within-group correlations were compared using Fisher’s z transformation to verify significant between-group differences in correlations. Given the a priori nature of our regions of interest we considered the results to be significant at p < 0.05, uncorrected, two-tailed. Finally, within AN and atypical AN, we assessed the association of ghrelin levels on white matter microstructure connecting the hippocampus and how these relationships may differ across pubertal status as measured by Tanner staging.

We conducted multiple regression analysis on white matter microstructure in AN to evaluate the extent to which puberty status accounted for ghrelin relationships with white matter within AN using the processR package (Moon, 2019) which is based on the Hayes PROCESS macros for SPSS and SAS (Hayes, 2016). In the model, significant white matter microstructure tracts served as the dependent variable and ghrelin as the independent variable. Pubertal status (categorical variable; early-midpubertal group and late-postpubertal) served as the moderator in the model. Both percent expected body weight and length of illness were included as covariates in the model, as both body weight and length of illness have been shown to be associated with FA (Vogel et al. 2016; Gaudio et al. 2019). Percent expected body weight was determined by the growth charts available from the Center for Disease Control and Prevention (CDC), and 20 was the reference age for any participant older than 20 years. Regression based moderation analyses were conducted using the Model 1 in this macro to refer to simple moderation. Predictors that built the interaction term were mean centered. We calculated the moderation effect and the proportion of variance explained by the moderating effect of each moderator (R2 increases due to the interaction). For any moderation analysis in which the interaction term was statistically significant, the model was graphed in order to confirm whether the slopes from the interaction were in the hypothesized direction. To identify points along the continuum of the moderator where the effect of the predictor transitioned from being statistically significant to nonsignificant, conditional effects were tested using the Johnson-Neyman procedure.

In supplementary analyses, we excluded the participants with atypical AN and repeated all analyses with only the AN and HC groups to ensure that our findings were not driven by the atypical AN group as differences in WM integrity between AN and atypical AN have been previously reported (Olivo et a., 2019).

3. Results

3.1. Participant characteristics

Groups did not differ for age, race, and ethnicity (Table 1). As expected, females with AN/atypical AN had a significantly lower body mass index (BMI) and percent expected body weight compared to the HC group. Exercise patterns between AN and HC did not differ (Supplementary Table 2). Further, individuals with atypical AN had a significantly higher percent expected body weight (mean = 90.0, standard deviation = 2.9) compared to individuals with AN (mean = 78.1, standard deviation = 5.4) (T = −7.5, p <0.001). However, no other differences were evident between the AN and atypical AN groups (see Supplemental Table 1) and thus we analyzed the combined AN/atypical AN group together moving forward. As expected, individuals in the early-mid pubertal group (N = 13) were significantly younger than individuals in the late-postpubertal group (N = 23), (age early-mid pubertal group = 17.5, age late-postpubertal group = 20.5, T = −3.2, p< 0.005). There were no differences in the distribution of pubertal groupings between AN/atypical AN (64% late-postpubertal) and HC (75% late-postpubertal). Within the AN/atypical AN group, the early-midpubertal group and late-postpubertal group did not differ on any measure of eating disorder pathology, number of missed periods in the past 6 months, age of onset of eating disorder or duration of illness.

Table 1.

Demographic and clinical characteristics of females with anorexia nervosa/atypical anorexia and healthy controls.

Anorexia nervosa/atypical anorexia nervosa n = 36 Healthy controls n = 20 Between group comparison 95% confidence inteval
Age (years) 19.4 (2.6) 18.4 (2.4) −0.4 – 2.5
% ideal body weight 82.8 (7.4) 102.3 (7.5) −23.7 – −15.4*
Missed periods in past 6 mo 3.3 (2.4) 0.7 (1.6) −5.1 – −2.0*
Tanner stage 4.5 (0.98) 4.6 (0.63) −0.5 – 0.3
Eating Disorder Examination Global 3.2 (1.3) 0.0 (0.2) 2.6 – 3.6*
Current psychotropic medication, no (%) 23 (63.8) - -
Current comorbid diagnosis, no (%) 25 (69.4) - -
Duration of illness (years) 2.5 (2.4) - -
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*

= p <0.001

3.2. AN/atypical AN vs. HC: FA-t

Figure 1a depicts the white matter ROI tracts. We found no significant differences in FA-t between the AN/atypical AN and HC groups in the fornix, right or left cingulum-hippocampal region, or right or left cingulum-cingulate gyrus region (Table 2). Further, in supplemental analyses, no significant differences were evident in FA-t between the AN and HC groups in the fornix, right or left cingulum-hippocampal region, or right or left cingulum-cingulate gyrus region (Supplementary Table 3).

Figure 1.

Figure 1.

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Significant interaction between pubertal status and fasting plasma ghrelin in free-water corrected fractional anisotropy (FA-t) (right – 1a). Individuals with AN/atypical AN in the late-postpubertal group had lower FA-t in the fornix than individuals in the early-midpubertal group. Further, the interaction between pubertal status and fasting ghrelin was significant such that higher levels of fasting ghrelin were associated with lower FA-t in the fornix only for females with AN/atypical AN in the late-postpubertal group. The results from the puberty status x fasting plasma ghrelin interaction are shown in purple (1b) and superimposed on the fornix tract (1b).

Table 2.

FA-t in white matter tracts with hippocampal projections in participants with AN/atypical AN vs. healthy controls.

Major bundles Baseline FA-t
Healthy controls AN/atypical AN
n=20 n=36 p
Mean SD Mean SD
Fornix 0.62 0.08 0.62 0.07 0.95
Left cingulum - hippocampal region 0.51 0.02 0.52 0.03 0.31
Left cingulum - cingulate gyrus region 0.66 0.02 0.66 0.03 0.84
Right cingulum - hippocampal region 0.54 0.03 0.55 0.03 0.46
Right cingulum - cingulate gyrus region 0.62 0.03 0.63 0.04 0.71
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FA-t: free-water corrected fractional anisotropy, AN: anorexia nervosa, SD: standard deviation

3.3. AN/atypical AN vs. HC: Fasting plasma ghrelin levels

As expected, fasting plasma ghrelin levels differed between the two groups (T(47) = −2.49, p = 0.02). Individuals with AN/atypical AN had higher fasting ghrelin (mean = 816.5 pg/mL, st. dev. = 217.5 pg/mL) compared with the HC group (mean = 678.3 pg/mL, st. dev = 284.8 pg/mL). Fasting ghrelin was positively correlated with Tanner stage in individuals with AN/atypical AN (r = 0.52, p < 0.005) and was not correlated with Tanner stage in the HC group (r = −0.33, p > 0.05); these correlations were significantly different between AN/atypical AN and HC groups (z = −2.91, p < 0.001). In supplemental analyses, we excluded participants with atypical AN. Fasting plasma ghrelin levels were higher in individuals with AN compared to healthy controls. Fasting plasma ghrelin levels did not differ between individuals with AN and atypical AN (Supplementary 1.4).

3.4. AN/atypical AN vs. HC: Relationships between FA-t and ghrelin

The correlation between ghrelin and FA-t in the fornix in females with AN/atypical AN was significantly different from the relationship observed in the HC group (Table 3). In AN/atypical AN, fasting ghrelin levels were negatively associated with FA-t in the fornix (r = −0.65, p < 0.001). While nonsignificant, the correlation in the HC group between fasting ghrelin and FA-t in the fornix was in the positive direction. No other significant relations of FA-t with ghrelin were evident in either group. In supplemental analyses, we excluded the participants with atypical AN and observed similar patterns, in the same direction between fasting plasma ghrelin and FA-t in the fornix in the AN only group (Supplemental 1.5, Supplemental Table 4).

Table 3.

Correlations between ghrelin levels and FA-t white matter tracts with hippocampal projections in participants with anorexia nervosa/atypical anorexia nervosa and healthy controls.

AN/atypical AN n = 36 Healthy controls n = 20 Between group comparison N = 56
r p r p z p
Fornix −0.65 0.00 0.23 0.10 −1.64 0.05
Cingulum hippocampal portion
Left 0.15 0.43 −0.2 0.40 1.08 0.14
Right 0.93 0.62 −0.10 0.70 0.68 0.25
Control region – Cingulum gyrus portion
Left 0.13 0.49 −0.21 0.34 1.07 0.14
Right 0.11 0.56 −0.09 0.70 0.67 0.25
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FA-t: free-water corrected fractional anisotropy, AN: anorexia nervosa

3.5. Relationship between white matter microstructure, ghrelin, and pubertal status in AN

Multiple regression analyses examining relationships between white matter microstructure, ghrelin, and pubertal status were computed separately for the fornix, as the fornix was the only ROI that showed a significantly different relationship with ghrelin between AN/atypical AN and HC. All models included one moderator variable (pubertal status), two covariates (percent expected body weight, length of illness) and the interaction term (ghrelin*pubertal status).

Results revealed a significant main effect of pubertal status on the relationship between FA-t in the fornix and ghrelin levels in AN/atypical AN (b = 1.39). Individuals with AN/atypical AN in the late-postpubertal group had lower FA-t in the fornix than individuals in the early-midpubertal group (t(23) = 2.67, p < 0.01). Fasting ghrelin and percent expected body weight were not significant predictors of FA-t in AN/atypical AN (Table 4). However, the length of illness was a significant predictor in the overall model (Table 4). Longer duration of illness was associated with lower FA-t (Table 4). Further, the interaction between pubertal status and fasting ghrelin was significant such that higher levels of fasting ghrelin were associated with lower FA-t in the fornix only for females with AN/atypical AN in the late-postpubertal group (b = −0.47), t(23) = −2.26, p <0.0 1). For females with AN/atypical AN in early to mid-stages of puberty, fasting ghrelin was not significantly associated with FA-t (Figure 1 - b). Our sub-group analysis excluding participants with atypical AN replicated these results (Supplementary 1.6, Supplementary Table 5, Supplementary Figure 2).

Table 4.

Summary of coefficients in moderation analysis within participants with anorexia nervosa/atypical anorexia nervosa.

Antecedent Consequent
Fornix(Y)
Coefficient SE t p
Fasting ghrelina c 1 −0.075 0.120 −0.626 .538
Pubertal status c 2 1.396 0.520 2.682 .013
% ideal body weight g 1 −0.002 0.002 −1.100 .283
Length of illness g 2 −0.012 0.005 −2.485 .021
Fasting ghrelin × Puberty status c 3 −0.474 0.180 −2.629 .015
Constant i Y 1.029 0.400 2.569 .017
Observations 29
R2 0.523
Adjusted R2 0.419
Residual SE 0.055 (df = 23)
F statistic F(5,23) = 5.035, p = .003
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a

Log transformed fasting ghrelin.

4. Discussion

Study findings suggest that ghrelin may affect the white matter microstructure in the fornix in those with low-weight eating disorders including AN and atypical AN in a developmental stage-dependent manner. As hypothesized, the relationship between levels of the orexigenic hormone ghrelin and FA-t differed between groups. In adolescents with low-weight AN and atypical AN, but not in HC, higher levels of fasting ghrelin were associated with lower FA-t in the fornix, the main fiber tract connecting the hippocampus with key energy balance and reward brain areas. Furthermore, within the AN/atypical AN group, pubertal status moderated this relationship such that higher levels of fasting ghrelin were associated with lower FA-t in the fornix only for those in the late-postpubertal group. By contrast, the relationship between ghrelin and FA-t in the fornix was non-significant for those with AN/atypical AN in the early to mid-stages of puberty, as it was for all HC.

Ghrelin is a hormone that stimulates appetite and is reported to exert anti-inflammatory and protective effects systemically in the central nervous system (Baldanzi et al., 2002; Bayliss and Andrews, 2013; Chung et al., 2007; Yamashita et al., 2019). Our study, and prior research, demonstrates that in active AN, ghrelin levels are abnormally high despite a reported lack of appetite, suggesting an ineffective compensatory mechanism during starvation (Hübel et al., 2019). Furthermore, our data demonstrate that ghrelin levels were modulated by pubertal status in AN/atypical AN. During puberty, the brain undergoes important neurodevelopmental growth. White matter begins to mature early in development and continues to undergo ongoing refinement, throughout the entire brain and particularly in the hippocampus (Benes et al., 1994; Luna, 2009). The onset of AN may disrupt typical patterns of white matter development, particularly in regions that undergo maturation during this period.

Across all stages of puberty, ghrelin levels are higher in AN compared to HC. We believe that in early-mid puberty, the observed higher levels of ghrelin in AN/atypical AN could represent an appropriate adaptive response to starvation and may contribute to the preservation of white matter. Our data demonstrate that in late post-pubertal adolescents with AN/atypical AN ghrelin levels are even higher and higher ghrelin levels are associated with decreased white matter in the fornix, suggesting possible dysregulation of the neuroprotective effects of ghrelin. Importantly, the moderation effect of pubertal status on the relationship between ghrelin and FA-t was evident even after controlling for the duration of illness, suggesting a role for pubertal development. The length of illness or age of onset did not significantly differ between the early-mid pubertal group and the late-post pubertal group.

Our data analytic approach and findings are strengthened by examining the impact of pubertal status, rather than age alone. Indeed, adolescence is not strictly defined by age and varies in great part due to the influence of puberty and sex-based differences (Dahl, 2004; Spear, 2000); as such, the relationship between white matter integrity and age is nonlinear. There is a steep increase in FA from childhood to adolescence and a less steep progression from adolescence to adulthood (Lebel et al., 2008). Importantly, research suggests that pubertal status, classified by Tanner stage, is more tightly coupled to white matter maturation than chronological age (Asato et al., 2010). Specifically, once healthy individuals reach pubertal maturation (Tanner stage 5), white matter maturation may occur at a slower rate. Future research should explore how ghrelin may influence white matter microstructure in the fornix following weight restoration across stages of pubertal development.

Although the main focus of this investigation was the relationship between FA-t and ghrelin, examination of FA-t in the fornix revealed no group differences. One possible explanation for the lack of difference in FA-t between groups is that our sample includes adolescents and young adults with AN/atypical AN and could be underpowered to detect subtle differences in FA-t, due to more pronounced differences in older patients. Another explanation is that prior studies reporting group differences in FA-t have not corrected for PVE. Our results showing no differences in white matter between AN and controls are consistent with one other study similar to ours that used free-water imaging to correct for PVE (Kaufmann et al. 2017). On the other hand, several studies have shown differences in white matter between AN and controls (see Gaudio et al. 2019) for a review). Studies which have reported group differences in fornix between AN and HC are more likely related to PVE rather than microstructural abnormalities as suggested by Kaufman et al. This is based on the observation that FA differences between AN and control groups in the fornix are no longer present when using the free-water-corrected FA-t maps. A recent meta-analysis of white matter in AN also stresses the importance of accounting for PVE in AN research (Meneguzzo et al., 2019). This method provides information that is expected to represent more specifically the biological characteristics of white matter, as it aims to exclude extracellular components such as CSF, which is affected by brain volume changes that are expected in AN. Additional studies are needed with larger sample sizes, across age groups, and which correct for PVE.

Strengths and Limitations

Our imaging method was a study strength. MRI measures can be affected by changes in nutrition and hydration, which are frequent among AN patients (Frank et al., 2018); modern neuroimaging can detect day-to-day changes in brain structure (Trefler et al., 2016) and diet can influence brain morphometry (McNamara et al., 2018). To diminish such nutritional status effects, study participants fasted for at least 8 hours prior to scanning, were scanned during the same time of day, and consumed a standardized meal prior to scanning. An additional strength was the heterogeneous sample of adolescents with low-weight eating disorders including AN and atypical AN, with both restricting and binge/purge type presentations, and a range of durations of illness (from less than 1 year to 8 years), increasing external validity. Atypical AN is more common than AN and thus our data—while not generalizable to individuals of all weights with atypical AN—increase external validity of findings to a broader range of patients with AN-like presentations. Prior studies using the FA-t method in the fornix have reported on a chronically ill sample of mostly adult patients with active AN (Kaufmann et al., 2017).

Study limitations also warrant acknowledgement. The sample size was not large enough to explore differences within the AN and atypical AN group or restricting vs. binge/purge types and despite being adequately powered, it was still of modest size. We were also not able to explore pharmacological treatments and psychiatric comorbidities which have been shown to influence white-matter microstructure (King et al. 2018). We report this information so that future pooled studies and meta-analyses can explore such effects. Other study limitations include the cross-sectional nature of the data, use of total plasma ghrelin (rather than the active form of ghrelin), lack of pre-pubertal subjects, and small sample of early-pubertal subjects. Finally, while the focus on ROI analyses increased power, more white matter structures in the brain are likely to be involved in the pathophysiology of AN.

Summary

In summary, we identified divergent relationships between levels of the neuroprotective appetite-regulating hormone ghrelin and FA-t of the fornix – a key hippocampal connection - in adolescent females with low-weight AN/atypical AN compared to healthy controls. Further, in AN/atypical AN, pubertal stage moderated this relationship. FA-t levels were lower in late-post pubertal group (vs. Early-mid pubertal group) in adolescents with AN/atypical AN and there was a negative relationship between ghrelin and FA-t in late-post pubertal adolescents only. These data suggest a lack of neuroprotective effects of ghrelin in females with AN/atypical AN in late-post puberty. Longitudinal studies will be important to determine whether white matter deterioration occurs later in development and/or improves with recovery and the impact of ghrelin.

Supplementary Material

1

Highlights.

  • Developmental stage-dependent associations between ghrelin and white-matter in AN.

  • No relationship between ghrelin and white-matter in healthy individuals, only in patients.

  • Higher level of ghrelin associated with lower white-matter microstructure in the fornix in AN.

Acknowledgments

This work was supported by the National Institutes of Health T32MH112485 (Breithaupt), R01MH103402 (Lawson, Misra, Eddy), K24MH120568 (Lawson), F32MH111172 (Becker), R01MH108595 (Thomas, Lawson, Micali), R01DK104772 (Holsen), R03MH110745 (Lyall), K01 MH115247-01A1 (Lyall), R21DA042271 (Makris), R01MH111917 (Makris), K24MH116366 (Makris), R01MH102377 (Kubicki), R01MH112748 (Kubicki), R01AG042512 (Kubicki), K24 MH110807(Kubicki), the Charles A. King Trust Postdoctoral Research Fellowship Program, Bank of America, N.A., Co-Trustees, through a Charles A. King Trust Fellowship (Plessow). EAL has a financial interest in OXT Therapeutics, a company developing an IN OXT and long-acting analogs of OXT to treat obesity and metabolic disease. EAL’s interests were reviewed and are managed by Massachusetts General Hospital and Partners HealthCare in accordance with their conflict of interest policies. This company was not involved in any way in this research.

Footnotes

Declaration of Interest

The other authors declare that they have no conflict of interest.

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