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. Author manuscript; available in PMC: 2025 Mar 20.
Published in final edited form as: Neurogastroenterol Motil. 2023 Nov 5;36(1):e14695. doi: 10.1111/nmo.14695

Neurohormonal response patterns to hunger, satiation, and postprandial fullness in normal weight, anorexia nervosa, and obesity

Alejandro Campos 1, Tomas Marek 2,3, Gerardo Calderon 1, Wissam Ghusn 1, Lizeth Cifuentes 1, Leslie A Sim 2, Michael Camilleri 1, Barham Abu Dayyeh 1, John D Port 2,4, Andres Acosta 1
PMCID: PMC11925049  NIHMSID: NIHMS2061268  PMID: 37926943

Abstract

Background:

Food intake is regulated by homeostatic and hedonic systems that interact in a complex neuro-hormonal network. Dysregulation in energy intake can lead to obesity (OB) or anorexia nervosa (AN). However, little is known about the neurohormonal response patterns to food intake in normal weight (NW), OB, and AN.

Material & Methods:

During an ad libitum nutrient drink (Ensure®) test (NDT), participants underwent three pseudo-continuous arterial spin labeling (pCASL) MRI scans. The first scan was performed before starting the NDT after a > 12 h overnight fast (Hunger), the second after reaching maximal fullness (Satiation), and the third 30-min after satiation (postprandial fullness). We measured blood levels of ghrelin, cholecystokinin (CCK), glucagon-like peptide (GLP-1), and peptide YY (PYY) with every pCASL-MRI scan. Semiquantitative cerebral blood flow (CBF) maps in mL/100 gr brain/min were calculated and normalized (nCBF) with the CBF in the frontoparietal white matter. The hypothalamus (HT), nucleus accumbens [NAc] and dorsal striatum [DS] were selected as regions of interest (ROIs).

Results:

A total of 53 participants, 7 with AN, 17 with NW (body-mass index [BMI] 18.5–24.9 kg/m2) and 29 with OB (BMI ≥30 kg/m2) completed the study. The NW group had a progressive decrease in all five ROIs during the three stages of food intake (hunger, satiation, and post-prandial fullness). In contrast, participants with OB showed a minimal change from hunger to postprandial fullness in all five ROIs. The AN group had a sustained nCBF in the HT and DS, from hunger to satiation, with a subsequent decrease in nCBF from satiation to postprandial fullness. All three groups had similar hormonal response patterns with a decrease in ghrelin, an increase in GLP-1 and PYY, and no change in CCK.

Conclusion:

Conditions of regulated (NW) and dysregulated (OB and AN) energy intake are associated with distinctive neurohormonal activity patterns in response to hunger, satiation, and postprandial fullness.

Keywords: anorexia nervosa, food intake regulation, neuroimaging, obesity, pCASL-MRI

1 |. INTRODUCTION

Food intake in humans is a complex process divided into hunger, satiation, and postprandial fullness. Hunger is defined as the drive to consume, satiation as the process that brings an eating episode to an end (intra-meal inhibition), and postprandial fullness to the process that inhibits eating in the postprandial period (inter-meal inhibition). This dynamic process is regulated by a dynamic interaction between the homeostatic and hedonic systems.1–3 The homeostatic regulation consists of a continuous synchronization of short-term signals secreted in response to the arrival of nutrients to the gut lumen (e.g., peptide YY [PYY], glucagon-like peptide 1 [GLP-1], cholecystokinin [CCK])4 or the need for energy (e.g., ghrelin).5 These short-term signals coordinate the stages of food intake and their effects are modulated by long-term metabolic signals secreted by the adipose tissue (e.g., leptin).6 On the other hand, the hedonic system is regulated through neurotransmitter pathways in the central nervous system like the dopaminergic mesolimbic pathway and by serotoninergic, opioidergic, and endocannabinoid systems that control conscious processes such as “liking” and strong unconscious ones such as “wanting” (i.e., incentive salience).7–11 The reciprocal influence between the homeostatic and hedonic systems is integrated by higher-order neural circuitries that coordinate and shape human eating behavior.

These complex homeostatic and hedonic interactions are dysregulated at the neural and/or hormonal levels in conditions like obesity (OB), eating disorders (e.g., anorexia nervosa [AN], bulimia, binge eating disorder), and cancer.12,13 Hence, identifying the distinct neurohormonal patterns of physiological (i.e., normal weight [NW]) and pathophysiological states (e.g., AN, OB) is critical for understanding the mechanisms that regulate food intake in these states.

In this study, we aim to characterize the distinctive neural and hormonal patterns in three main stages of food intake (i.e., hunger, satiation, and postprandial fullness) in participants with NW, AN, and OB.

2 |. METHODS

2.1 |. Participants

We included men and women between 18 and 65 years with stable body weight (≤3% change of total body weight in the previous 3 months). Participants were recruited from the local community via electronic advertising and invitation from previous studies. Participants were divided into three groups: participants with AN diagnosed by The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V)14 criteria were included in the AN group, participants with a body-mass index (BMI) of 18.5–24.9 kg/m2 were included in the NW group, and participants with a BMI of ≥30 kg/m2 were included in the OB group. We excluded participants with (a) history of bariatric procedures (surgical or endoscopic), (b) use of medications that may alter gastrointestinal motility, body weight, appetite, or nutrient absorption within the last 3 months (including antidiabetic medications), (c) history of hypersensitivity to the components of the study materials and/or meals, and (d) contraindications for MRI.

2.2 |. Study overview

The study was approved by the Mayo Clinic Institutional Review Board of Mayo Clinic and all participants gave written informed consent following a thorough explanation of the experimental protocol. The study was performed over a seven-year period from April 2013 through July 2020. Participants arrived at the Mayo Clinic MRI Unit at 8 a.m. after an overnight fast (>12-h). Upon arrival, participants completed an MRI safety questionnaire followed by anthropometric measurements (height and weight) and placement of an antecubital vein access for blood sample collection. Women of childbearing potential had a negative pregnancy test before the study visit. The study consisted of three pseudo-continuous arterial spin labeling (pCASL) MRI scans in a 3 T MRI scanner paired with blood draws to measure appetite hormones (GLP-1, CCK, PYY, ghrelin) during a validated ad libitum nutrient drink test (NDT) (Figure 1).

FIGURE 1.

FIGURE 1

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Study day overview.

2.3 |. Imaging processing and analysis

The study was performed in the MRI unit at Mayo Clinic, Rochester, MN. All imaging was performed on a single 3 T MRI scanner (DV750 running 24.0 software, GE Medical Systems, Milwaukee, Wisconsin). Participants were placed supine on the MR table in a standard 8-channel head coil, and the head was positioned such that the corpus callosum would be in the true axial plane and the falx was aligned with the static magnetic field (B0). Small marks were made on the scalp with a sharpie pen such that the head could be repositioned precisely for subsequent scans. Next, three plane localizer and calibration scans were performed, then an axial 3D pseudo continuous arterial spin label sequence was prescribed (3D pCASL,15,16 TR = 4546 ms, TE = 10.5 ms, 512×512, 8 arms, NEX = 3, 1525 ms post label delay, FOV = 24, slice thickness = 3 mm, 50 slices). The duration of the pCASL acquisition was 4 min. Finally, a standard T2-weighted scan was performed using the exact same slice prescription as for the ASL scan (FRFSE-XL, TR = 6194 ms, TE = 100.3 ms, ETL = 12, NEX = 2, 256×256, FOV = 24, slice thickness = 3 mm, 50 slices). Scans were acquired for each subject at the three stages: hunger, satiation, and postprandial fullness.

Semiquantitative cerebral blood flow (CBF) maps calibrated in mL/100 gr of tissue/min were calculated from the pCASL data on the MRI scanner16 and transferred along with the T2-weighted images to an Advantage Windows workstation (GE Medical Systems, Waukesha, WI). The T2-weighted images were interpolated to a 512×512 matrix to match the ASL images and placed side-by-side on the workstation. We selected subcortical brain structures associated with homeostatic (hypothalamus [HT]) and hedonic (the nucleus accumbens [NAc] and dorsal striatum [DS] [formed by the caudate and putamen nuclei]) food intake regulation as regions of interest (ROIs). We selected the frontoparietal white matter as a control (not involved in food intake regulation) ROI. ROIs sampling the CBF in these structures were manually drawn as pairs (i.e., left and right hemisphere) on the T2-weighted images, then transferred to the CBF maps, where the average regional blood flow in each ROI (in mL/100 g of tissue/min) was recorded in a spreadsheet. The data from the hypothalamic ROIs were combined and analyzed as a single ROI due to their small size. We hypothesized that because most subjects had never undergone head MRI, subjects would have various levels of anxiety during their scans: initially high with relatively increased cardiac output, and decreasing as they became accustomed to the MR scans with relatively decreased cardiac output. To control for such physiologic flow variations over time, the CBF data from these five regions (HT, left and right NAc [lNAc, rNAc], and left and right DS [lDS, rDS]) were normalized for each timepoint by the corresponding average CBF from the left and right frontoparietal white matter (control ROIs) to create normalized CBF (nCBF) measurements. In this way, we would mitigate effects due to variable cardiac output due to anxiety. Finally, the total change in nCBF was calculated as the percentage of change (ΔnCBF%) from hunger (i.e., first stage) to postprandial fullness (i.e., third/last stage) for each ROI.

2.4 |. NDT

Each patient completed the validated NDT17,18 that consisted of ingesting a nutrient drink (Ensure® 1.05 kcal/mL [65% carbohydrates, 21% fat, 14% protein]) at a constant rate of 7.5 mL/min (0.25 FL OZ/min) until reaching satiation. The nutrient drink was provided at 2–4°C (35.6–39.2 F) temperature and rate was regulated by refilling a 30 mL (1 FL OZ) cup with Ensure® every 4 minutes using a constant-rate perfusion pump. Fullness was assessed every 5 mins using a graphic rating scale graded from 0 to 5 (0 = no symptoms; 1 = first sensation of fullness; 2 = mild; 3 = moderate; 4 = severe; 5 = maximum fullness). The NDT started after completion of baseline questionnaires, anthropometric measurements and placement of IV access. Study participants confirmed a > 12 h fasting before starting the NDT. Upon reaching maximal fullness, participants stopped drinking the nutrient drink and were instructed to remain for another 30 min to assess postprandial fullness. During the NDT, participants were isolated from each other, and external food cues were minimized (i.e., no access to television, cellphone, other food items).

2.5 |. Appetite hormones measurements

We collected blood samples in parallel with each MRI scan (hunger, satiation, and postprandial fullness stages). Plasma from blood samples was used to measure appetite hormones. GLP-1 (cat#RAB0039, Millipore) and CCK (Cat#CCKT-36HK, Millipore) were measured by enzyme-linked immunoassay (ELISA), active ghrelin (Cat#GHRA-88HK, Millipore) and PYY (Cat#PYYT-66HK, Millipore) were measured using radio-immunosorbent assay. We calculated the area under the curve (AUC) for each hormone using the trapezoid method.

2.6 |. Statistical analysis

We used SPSS Statistics for Windows, version 28.0.0.0 (SPSS Inc., Chicago, Ill., USA) to perform the statistical analyses. We used two-way repeated-measure analysis of variance (ANOVA) to test for group × stage interaction on the primary outcome (nCBF) in each ROI (HT, lNAc, rNAc, lDS, and rDS). Sphericity (i.e., condition where the variances of the differences between all combinations of related groups are equal was met) (p > 0.2) in all five ROIs. Fishers least square difference test was used to assess for post hoc multiple comparisons between the groups at each stage in each ROI and for every hormone and their respective AUC. This test does not correct for multiple comparisons. We used Welch t test to analyze the difference in ΔnCBF% between the groups. Categorical data were analyzed using Fisher’s exact test. Results are presented as mean and standard deviation (±SD) with their respective 95% confidence interval (CI). All tests were two-tailed, and p < 0.05 was considered statistically significant.

3 |. RESULTS

3.1 |. Patient characteristics

A total of 53 participants (18 NW, 7 AN, and 29 OB) were enrolled and completed the study (Table 1). Participants in the AN group were younger (mean age 26.6 ± 10.5 years) and only female. The OB group consumed significantly more calories to reach satiation (p < 0.0001) than the NW group and the AN group, with the latter consuming the least calories.

TABLE 1.

Baseline characteristics. Values are presented as mean and standard deviation (SD) or counts and percentages (%).

All (n = 53) Normal weight (n = 17) Obesity (n = 29) Anorexia nervosa (n = 7) p Value
Demographics
 Age, years 33.5 (9.7) 32.9 (9.1) 35.5 (9.3) 26.6 (10.5) 0.05
 Sex, female (%) 44 (83) 12 (70.6) 27 (93) 7 (100) 0.07
 Racea, white (%) 45 (85) 13 (76.5) 27 (93) 5 (71) 0.19
Anthropometrics
 Weight, kg 83.7 (22.1) 68 (6.6) 100.1 (13.5) 53.5 (13.6) <0.0001
 BMI, kg/m2 28.5 (6.6) 23.1 (1.2) 33.9 (2.6) 19.1 (3.4) <0.0001
Nutrient drink test
 Calories to satiationb, kcalc 1100 (466) 986.6 (359) 1306 (426) 522.5 (248) <0.0001
 Time to satiationb, min 34.7 (14.7) 31 (11) 41 (13) 16 (7.8) <0.0001
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Abbreviation: BMI, body mass index.

a

Self-reported.

b

Until termination of the nutrient drink test.

c

65% carbohydrates, 21% fat, 14% protein per calorie.

3.2 |. Blood flow changes in the HT, NAc, and DS during hunger, satiation, and postprandial fullness stages

The changes in nCBF in the HT, left and right NAc, and left and right DS during hunger, satiation, and postprandial fullness among the three groups are presented in Figure 2 and corresponding data is presented in the Supplementary Materials.

FIGURE 2.

FIGURE 2

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Changes in nCBF in the hypothalamus, right and left NAc, and right and left DS among normal weight (NW; black solid lines and squares), obesity (OB; blue solid lines and circles), and anorexia nervosa (AN; yellow solid lines and triangles) groups. Data presented as means and standard error of the mean (SEM). Symbols represent p values <0.05 from pairwise comparisons. *: NW versus OB; #: NW versus AN; &: OB versus AN.

3.3 |. HT

There was no significant stage × group interaction in the HT (F (4, 100) = 1.619; p = 0.18). During the hunger stage, the AN group had a lower mean (SD) nCBF in the HT (3.0 [0.13] nCBF) compared with the NW (3.5 [0.64] nCBF; p = 0.03) and OB (3.3 [0.42] nCBF; p = 0.09) groups, however, this difference was not statistically significant in the OB group. There were no differences in nCBF in the HT during satiation among the groups. During postprandial fullness, the OB group had a significantly higher nCBF in the HT (3.3 [0.46] nCBF) compared with the AN (2.8 [0.41] nCBF; p = 0.02) and NW (3 [0.41] nCBF; p = 0.07) groups, this difference was not statistically significant in the NW group. There was a strong negative correlation between the number of calories consumed and the ΔnCBF% in the HT in the NW group (R:0.595, p = 0.0116), while no correlations were observed in the OB and AN group (Figure 3).

FIGURE 3.

FIGURE 3

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Correlations between calories consumed and ΔnCBF% in the hypothalamus in the NW and OB groups.

3.4 |. NAc

A significant group × stage interaction on nCBF was observed in the left NAc (F [4, 104] = 2.49; p = 0.04). In both left and right NAc, the OB group had a significantly lower nCBF during the hunger stage (1.9 [0.23] nCBF in the right and left) compared to the AN (2.2 [0.37] nCBF in the right; p = 0.02 and 2.1 [0.27] nCBF in the left; p = 0.09) and NW (2.2 [0.37] nCBF in the right; p = 0.03 and 2.1 [0.27] nCBF in the left; p = 0.04) groups, this difference was not statistically significant in the AN group. There were no differences in nCBF in the right or left NAc during the satiation and postprandial fullness stages between the three groups. However, the change in nCBF in the OB group was minimal compared to the progressive decrease throughout the three stages in the AN and NW groups, which was demonstrated by the significant main effect of stage in the left (F (1.87, 97.26) = 3.52; p = 0.04) and right NAc (F (1.99, 99.46) = 5.16; p = 0.007).

3.5 |. DS

There was a significant group × stage interaction on nCBF in the right DS (F [4, 100] = 2.91; p = 0.02) and significant differences in nCBF across the three stages in the left (F [1.89, 94.97] = 3.83; p = 0.03) and right DS (F [2, 100] = 5.92; p = 0.04). However, there were no significant differences in stage post hoc analysis in the left and right DS among the three groups.

3.6 |. Total change in blood flow during food intake in the HT, NAc, and DS

There were significant changes throughout the three stages among the groups in the five ROIs (i.e., HT, left and right NAc, and left and right DS). The ΔnCBF% showed that the NW group had an extensive decrease of >9.5% in nCBF in the five ROIs. Interestingly, in the AN group, the ΔnCBF% was >5.8% in the HT and right-sided ROIs (NAc and DS), while the ΔnCBF% in the left-sided ROIs was <4.1%. In contrast, the OB group had a negligible mean ΔnCBF% the five ROIs, with the highest ΔnCBF% in the left (1.09% [20.80]) and right NAc (1.09% [15.2]). The differences in ΔnCBF% were statistically significant between the NW and OB groups in the HT (p = 0.017), left (p = 0.015) and right NAc (p = 0.012), and left (p = 0.029) and right DS (p = 0.004) (Figure 4). Corresponding data for the pairwise comparisons can be found in the Supplementary Material.

FIGURE 4.

FIGURE 4

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Percentage of total change in nCBF (ΔnCBF%) from hunger to postprandial fullness. Data presented as mean and standard error of the mean (SEM).

3.7 |. Changes in Ghrelin, CCK, GLP-1, and PYY

The changes in appetite hormones among the three groups are displayed in Figure 5 and corresponding data is presented in the Supplementary Material. The AN group had significantly higher levels of ghrelin during the hunger stage compared to the OB (p = 0.04) and the NW (p = 0.04) groups. The AUC levels of ghrelin were significantly higher compared to the NW and OB groups (p = 0.055), however, these differences did not reach statistical significance. The OB groups characterized by higher AUC levels of the satiation hormone CCK compared to the NW and AN group (p = 0.0002), and during hunger (p = 0.02), satiation (p = 0.006), and postprandial fullness (p = 0.041) compared with the NW group. PYY AUC levels were different among the groups (p = 0.021), where the AN had higher AUC levels of PYY, however, there were no differences at each stage. There were no differences in GLP-1 levels between the groups.

FIGURE 5.

FIGURE 5

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Changes in gut hormones across the three stages of food intake in participants with normal weight (NW; black dashed lines and squares), obesity (OB; blue dashed lines and circles), and anorexia nervosa (AN; yellow dashed lines and triangles. Data presented as means and standard error of the mean (SEM). Symbols represent p values < 0.05 from pairwise comparisons. *: NW versus OB; **: p values < 0.001; #: NW versus AN; &: OB versus AN.

4 |. DISCUSSION

In this study we investigated the neurohormonal response during the three main stages of food intake using a validated ad libitum NDT in participants with NW, OB, and AN (Graphical abstract). Our results showed that NW participants display a progressive nCBF decrease from hunger to postprandial fullness in the HT, left and right NAc, and left and right DS, essential brain areas of homeostatic and hedonic food intake regulation. Participants with AN showed a decrease in nCBF in the HT and rDS only after satiation, with significantly lower blood flow to the HT during all three stages. Interestingly, participants with OB had a sustained “hunger-like” nCBF that extended until postprandial fullness in these three brain areas, with a distinctive low activity in the NAc throughout the three stages of food intake compared to NW and OB. The levels of appetite hormones followed a similar response pattern in all three groups, although levels were different among the groups.

Understanding how the central nervous system integrates homeostatic and hedonic signals is essential to advance our knowledge in energy balance and food intake regulation in health and disease. Thus, it is crucial to identify the distinctive neurohormonal response patterns during the food intake stages in a physiological state (NW) and contrast the neurohormonal differences with other disorders characterized by dysregulation of energy balance such as OB and AN.

Studying food intake regulation through neuroimaging is an intricate task due to the complexity and heterogeneity of this process in humans. Moreover, this complexity represents a crucial challenge when interpreting results and drawing conclusions.19–21 This limitation depends largely on the neuroimaging technique used (ASL, blood oxygenation level dependent [BOLD] MRI, positron emission tomography [PET], single-photon emission computerized tomography [SPECT]), individual characteristics (age, gender, and eating habits), the type (ingestive, visual, hormonal) and via of delivery (oral, intragastric, infused) of the investigated stimulus, the palatability and macronutrient composition of study food, and the stage assessed (hunger, satiation, or postprandial fullness).19,22–24 This is the first pCASL study to assess these neurohormonal changes during the three main stages of food intake, in three distinct groups of participants, using a standardized and validated NDT that induced maximal fullness.

Our findings demonstrated that participants with NW had an increased nCBF in the HT, NAc, and DS during hunger which decreases gradually throughout satiation and postprandial fullness. Previous studies have demonstrated a decrease in hypothalamic activity in NW participants in response to a food stimulus, which might reflect the effects of peripheral signals secreted in response to food intake that result in a decrease in hunger and meal termination. One PASL study in 11 NW participants showed a significant decrease in hypothalamic CBF after ingestion of 500 mL of high-fat (8%) compared to low-fat (<0.1%) yogurt.25 Another BOLD-MRI study showed a progressive and sustained (30 min after ingestion) decrease in hypothalamic activity in five NW participants after 75 g/300 mL of an oral glucose solution compared to 300 mL of tap water or 300 mL of sucrose solution.26 Similarly, another BOLD-fMRI study in 20 NW participants showed a lower CBF in the HT 15 min after 75 g/300 mL glucose (but not with fructose) solution ingestion.27 These findings suggest that the type of stimulus plays a key role in the neural response to food intake. Other studies have also demonstrated that NW (n = 11) participants have a decrease in regional CBF (rCBF) (using PET) in the striatum after reaching satiation with a nutrient drink.28 However, in the latter study, participants were scanned under extreme fasting conditions (>36 h) and satiation was assessed using a volume of Ensure© corresponding to 50% of each individual’s calculated daily energy requirement. Here we observed in the NW group, the number of calories consumed during the NDT correlated with the decrease in hypothalamic blood flow. This suggests that the metabolic feedback in response to food intake, deactivates the HT from the “hunger-like” state.

OB has been associated with structural and connectivity alterations in the central and peripheral nervous system29–32 and with altered responsiveness to food cues, reward-related signals, and taste stimuli in cortical and subcortical areas.33 OB has also been linked to structural and functional hypothalamic alterations like34 that are presumed to be reversible after weight loss.35 Simon et al. showed that during a 26-min intragastric glucose infusion, participants with AN and OB had a sustained activity in the HT and NAc compared with a decreased activity in both areas observed in NW controls.36 As mentioned before, Gautier et al. showed an attenuated rCBF response in the HT after reaching satiation with a nutrient drink after >36 h fasting in participants with OB.28 Other studies have demonstrated similar findings of reduced activity in the DS in participants with OB. One study demonstrated that adolescent girls with OB (n = 33) had a decreased activity in the caudate nucleus in response to consumption of a milkshake.37 The available evidence suggests there is a discernible overlap in the dysregulated patterns within neurohormonal interactions among groups (NW, AN, and OB). This could represent a deeper effect that may be intrinsically tied to limitations in current experimental methodologies in food intake regulation studies. For example, alteration of blood flow to the HT could be driven by activity in specific nuclei. However, it is essential to underscore that a substantive mechanistic overlap in the pathophysiology is not merely a possibility but a credible hypothesis that cannot be dismissed when considering the indirect nature of these techniques assessments (i.e., blood flow as a surrogate for neural activity in neuroimaging studies.

Altered hormonal profiles have been described in OB.38,39 While there are some controversies, in general OB has been associated with decreased levels of GLP-1, PYY, and ghrelin compared with NW participants.38–42 Here we observed increased CCK levels in the OB group. Many homeostatic and hedonic alterations have been previously suggested in participants with OB, however, our findings suggest that participants with OB display a characteristic hypo-responsiveness in key brain areas. This sustained hunger-like neural activity seems to be independent of the changes in hormonal levels throughout the stages, which displayed a similar pattern to the NW group. This suggests that key brain areas in food intake regulation fail to respond to satiation and postprandial fullness signals produced by a food stimulus. Moreover, the OB group did not show the correlation observed in the NW group, where the number of calories consumed in the NDT correlated with a greater decrease in hypothalamic blood flow. Together this finding and the similar hormonal patterns observed in the three groups, suggest that despite having a hormonal response similar to the NW group, the HT in the OB group fails to respond to the metabolic feedback generated by food intake.

Many studies assessing neural activity or CBF in AN have focused on the changes after weight regain,43–47 with just a few investigating brain responses to food stimuli or the differences between other conditions (NW, OB, or others). One study demonstrated that compared with NW participants (without AN), active and weight recovered AN showed decreased activity in the HT, in response to visual cues of high-calorie foods during fasting conditions.48 Another study showed that under hunger conditions, participants with remitted AN showed decreased activity in key reward-related brain areas (ventral striatum, caudate, anterior cingulate cortex) compared with participants without AN.49 AN has been associated with altered hormonal profile50 with several studies showing increased ghrelin40,51,52 and PYY51 levels compared to NW. In our results, participants with AN had a distinctive decreased nCBF to the HT during all three stages, and a sustained nCBF from hunger to postprandial fullness. Interestingly, the AUC levels of ghrelin and PYY were significantly higher in the AN group, which might imply that the hypothalamic response is resistant to increased hunger and postprandial fullness signals.53 The hormonal alterations that result from hypogonadotropic hypogonadism in AN, particularly hypoestrogenism, seem to be key in the pathogenesis and involved complications of this disease.54 While variants in genes coding for estrogen receptors have been associated with restrictive AN, the mechanisms of a potential anorexigenic pathway are yet to be described but could provide crucial insight to the human food intake regulation framework.55

4.1 |. Study limitations

This study has several limitations, including the small sample size of participants and lower number of participants in the AN group that were only females. Additionally, patients were not screened (and thus not excluded) for type 2 diabetes and early (i.e., polyphagia) and late (e.g., gastroparesis) manifestations could influence food intake. We did not standardize timing of studies in relation to phase of their menstrual cycle. In addition, the neuroimaging (pCASL-MRI) technology used in this study, represents an indirect estimate of neuronal activity through changes in blood flow, and it is not capable of differentiating between excitatory and inhibitory signals. Like all perfusion techniques, pCASL is limited due to its relatively low resolution and associated point spread function, making it less sensitive to blood flow changes in small structures such as the HT. Underlying (unknown) conditions can affect CBF and lead to a high inter- and intra-individual variability. Moreover, all the brain structures involved in food intake regulation regulate many other homeostatic and behavioral functions (e.g., mood, cognition, movement, etc.) and these simultaneous and dynamic regulatory processes might account, in part, for the observed variability in the current and most neuroimaging studies. Lastly, ROIs were selected prior to study initiation and not all the areas involved in food intake regulation were included and segmented. Importantly, there is a need to standardize the design, methodology, and data analysis and sharing of neuroimaging studies investigating food intake and eating behavior regulation in order to achieve the applicability of results.19,56

5 |. CONCLUSION

Participants with NW, OB, and AN have distinctive neurohormonal response patterns during food intake. Identifying these differences could lead to improved understanding, diagnosis, and treatment of conditions of dysregulated energy balance. Future studies are needed to replicate and expand these findings including more components of food intake regulation.

Supplementary Material

Supplemental table

Key points.

  1. Food intake in humans is regulated through a complex and multidirectional interaction of hormonal, visceral, and neural signals within the neuroenteroendocrine axis.

  2. The physiological response to food intake is characterized by a progressive deactivation of key brain areas in response to energy intake across the three main stages of food intake.

  3. Dysregulated food intake, as seen in conditions like obesity and anorexia nervosa, shows unique neurohormonal patterns. Obesity involves constant hunger-like activity in specific brain areas, while anorexia nervosa exhibits a lack of response to hunger signals in the hypothalamus.

ACKNOWLEDGMENTS

We thank the study participants and the Magnus Trust for supporting this study. We thank Mandie J. Maroney for her technical support.

Abbreviations:

ΔnCBF%

percentage of total change in normalized cerebral blood flow

ANOVA

analysis of variance

AUC

area under the curve

BMI

body-mass index

CBF

cerebral blood flow

CCK

cholecystokinin

CI

confidence interval

DS

dorsal striatum

DSM-V

Diagnostic and Statistical Manual of Mental Disorders Fifth Edition

GLP-1

glucagon-like peptide 1

HT

hypothalamus

MRI

magnetic resonance imaging

NAc

nucleus accumbens

nCBF

normalized cerebral blood flow

NDT

nutrient drink test

pCASL

pseudo-continuous arterial spin labeling

PYY

peptide YY

ROI

region of interest

SD

standard deviation

Footnotes

CONFLICT OF INTEREST STATEMENT

Andres Acosta is a stockholder in Gila Therapeutics, Phenomix Sciences; he served as a consultant for Rhythm Pharmaceuticals, General Mills. Michael Camilleri is a stockholder in Phenomix Sciences and Enterin and serves as a consultant to Kallyope with compensation to his employer, Mayo Clinic. Barham Abu Dayyeh serves a consultant to Endogenex, Endo-TAGSS, Metamodix, and BFKW; consultant and grant/research support from USGI, Cairn Diagnostics, Aspire Bariatrics, Boston Scientific; Barham Abu Dayyeh has Speaker roles with Olympus, Johnson and Johnson; speaker and grant/research support from Medtronic, Endogastric solutions; and research support from Apollo Endosurgery, and Spatz Medical.

CONSENT

Informed consent was obtained from all subjects involved in the study.

SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental table
NIHMS2061268-supplement-Supplemental_table.docx (36.4KB, docx)

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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