# World Federation of Societies of Biological Psychiatry (WFSBP) consensus statement on candidate biomarkers for anorexia nervosa.

Hubertus Himmerich, Johanna Louise Keeler, Joseph King, Stefan Ehrlich, Lisa-Katrin Kaufmann, Cynthia Bulik, Sarah Cohen-Woods, Tracey Wade, Howard Steiger, Linda Booij, et al.

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## To cite this version:

Hubertus Himmerich, Johanna Louise Keeler, Joseph King, Stefan Ehrlich, Lisa-Katrin Kaufmann, et al. World Federation of Societies of Biological Psychiatry (WFSBP) consensus statement on candidate biomarkers for anorexia nervosa. World Journal of Biological Psychiatry, 2026, pp.1-92. 10.1080/15622975.2026.2626934. hal-05534289

HAL Id: hal-05534289

https://normandie-univ.hal.science/hal-05534289v1

Submitted on 3 Mar 2026

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# World Federation of Societies of Biological Psychiatry (WFSBP) consensus statement on candidate biomarkers for anorexia nervosa

Hubertus Himmerich, Johanna Louise Keeler, Joseph A. King, Stefan Ehrlich, Lisa-Katrin Kaufmann, Cynthia M. Bulik, Sarah Cohen-Woods, Tracey Wade, Howard Steiger, Linda Booij, Palmiero Monteleone, Giammarco Cascino, Alessio Maria Monteleone, Ulrich Cuntz, Ulrich Voderholzer, Selamawit Alemayehu Tessema, Yael D. Lewis, Magnus Sjögren, Johannes Hebebrand, Jochen Seitz, Marta Tyszkiewicz-Nwafor, Olga Karpenko, Hiba Mutwalli, Sergueï O. Fetissov, Isabelle Mack, Namrata Dhopatkar, Sabrina Mörkl, Carol Kan, Maria M. Uribe, Kazuhiro Yoshiuchi, Nadine Abuobeid, Dimitrios Kapogiannis, Daniel Stein, Sevgi Bektas, Daniel J. Müller, Philip Gorwood, Philibert Duriez, Chloé Tezenas du Montcel, Elżbieta Paszyńska, Susan L. McElroy, Wiesława Dominika Wranik, Fernando Fernandez-Aranda, Philip S. Mehler, Hana Papežová, Radka Roubalová, Petra Procházková, Rebecca Morris, Piotr Lewczuk, Christoph Hiemke, Michael Berk, Andreas Karwautz, Janet Treasure, Siegfried Kasper, the WFSBP Task Force on Biological Markers & the WFSBP Task Force on Eating Disorders

**To cite this article:** Hubertus Himmerich, Johanna Louise Keeler, Joseph A. King, Stefan Ehrlich, Lisa-Katrin Kaufmann, Cynthia M. Bulik, Sarah Cohen-Woods, Tracey Wade, Howard Steiger, Linda Booij, Palmiero Monteleone, Giammarco Cascino, Alessio Maria Monteleone, Ulrich Cuntz, Ulrich Voderholzer, Selamawit Alemayehu Tessema, Yael D. Lewis, Magnus Sjögren, Johannes Hebebrand, Jochen Seitz, Marta Tyszkiewicz-Nwafor, Olga Karpenko, Hiba Mutwalli, Sergueï O. Fetissov, Isabelle Mack, Namrata Dhopatkar, Sabrina Mörkl, Carol Kan, Maria M. Uribe, Kazuhiro Yoshiuchi, Nadine Abuobeid, Dimitrios Kapogiannis, Daniel Stein, Sevgi Bektas, Daniel J. Müller, Philip Gorwood, Philibert Duriez, Chloé Tezenas du Montcel, Elżbieta Paszyńska, Susan L. McElroy, Wiesława Dominika Wranik, Fernando Fernandez-Aranda, Philip S. Mehler, Hana Papežová, Radka Roubalová, Petra Procházková, Rebecca Morris, Piotr Lewczuk, Christoph Hiemke, Michael Berk, Andreas Karwautz, Janet Treasure, Siegfried Kasper, the WFSBP Task Force on Biological Markers & the WFSBP Task Force on Eating Disorders (01 Mar 2026): World Federation of Societies of Biological Psychiatry (WFSBP) consensus statement on candidate biomarkers for anorexia nervosa, The World Journal of Biological Psychiatry, DOI: 10.1080/15622975.2026.2626934

**To link to this article:** https://doi.org/10.1080/15622975.2026.2626934

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# World Federation of Societies of Biological Psychiatry (WFSBP) consensus statement on candidate biomarkers for anorexia nervosa

Hubertus Himmerich<sup>a,b</sup>, Johanna Louise Keeler<sup>a</sup>, Joseph A. King<sup>c</sup>, Stefan Ehrlich<sup>c,d</sup>, Lisa-Katrin Kaufmann<sup>e,f</sup>, Cynthia M. Bulik<sup>g,h</sup>, Sarah Cohen-Woods<sup>i</sup>, Tracey Wade<sup>j</sup>, Howard Steiger<sup>j,k</sup>, Linda Boojij<sup>j,k</sup>, Palmiero Monteleone<sup>l</sup>, Giammarco Cascino<sup>l</sup>, Alessio Maria Monteleone<sup>m</sup>, Ulrich Cuntz<sup>n,o</sup>, Ulrich Voderholzer<sup>n,p</sup>, Selamawit Alemayehu Tessema<sup>q</sup>, Yael D. Lewis<sup>r,s</sup>, Magnus Sjögren<sup>t</sup>, Johannes Hebebrand<sup>u</sup>, Jochen Seitz<sup>u</sup>, Marta Tyszkiewicz-Nwafor<sup>v</sup>, Olga Karpenko<sup>w</sup>, Hiba Mutwalli<sup>a,x</sup>, Sergueï O. Fetissov<sup>y</sup>, Isabelle Mack<sup>z,aa</sup>, Namrata Dhopatkar<sup>b</sup>, Sabrina Mörkl<sup>ab</sup>, Carol Kana<sup>a,ac</sup>, Maria M. Uribe<sup>ad</sup>, Kazuhiro Yoshiuchi<sup>ae</sup>, Nadine Abuobeid<sup>a</sup>, Dimitrios Kapogiannis<sup>af,ag</sup>, Daniel Stein<sup>5,ah</sup>, Sevgi Bektas<sup>a,ai</sup>, Daniel J. Müller<sup>aj,ak</sup>, Philip Gorwood<sup>al,am</sup>, Philibert Duriez<sup>am</sup>, Chloé Tezenas du Montcel<sup>al,am</sup>, Elżbieta Paszyńska<sup>an</sup>, Susan L. McElroy<sup>ao,ap</sup>, Wiesława Dominika Wranik<sup>aq</sup>, Fernando Fernandez-Aranda<sup>ar,as</sup>, Philip S. Mehler<sup>at,au</sup>, Hana Papežová<sup>av</sup>, Radka Roubalová<sup>aw</sup>, Petra Procházková<sup>aw</sup>, Rebecca Morris<sup>a</sup>, Piotr Lewczuk<sup>ax,ay</sup>, Christoph Hiemke<sup>az,ba</sup>, Michael Berk<sup>bb</sup>, Andreas Karwautz<sup>bc</sup>, Janet Treasure<sup>a,b</sup> and Siegfried Kasper<sup>bd</sup>, the WFSBP Task Force on Biological Markers<sup>**</sup> the WFSBP Task Force on Eating Disorders<sup>***</sup>

<sup>a</sup>Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, Centre for Research in Eating and Weight Disorders (CREW), London, UK; <sup>b</sup>South London and Maudsley NHS Foundation Trust, London, UK; <sup>c</sup>Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Dresden, Germany; <sup>d</sup>Eating Disorder Treatment and Research Center, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany; <sup>e</sup>Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands; <sup>f</sup>Experimental Psychopathology and Psychotherapy, Department of Psychology, University of Zurich, Zurich, Switzerland; <sup>g</sup>Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; <sup>h</sup>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden; <sup>i</sup>Flinders University Institute for Mental Health and Wellbeing, Flinders University, Adelaide, Australia; <sup>j</sup>Eating Disorders Continuum, Douglas Mental Health University Institute, Montreal, Canada; <sup>k</sup>Department of Psychiatry, McGill University, Montreal, Canada; <sup>l</sup>Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Salerno, Italy; <sup>m</sup>Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy; <sup>n</sup>Center for Psychosomatic Medicine, Schönen Klinik Rosenbeck, Prien am Chiemsee, Germany; <sup>o</sup>Forschungsprogramm für Psychotherapieevaluation im Komplexen Therapiesetting, Paracelsus Medizinische Universität, Salzburg, Austria; <sup>p</sup>Department of Psychiatry and Psychotherapy, Ludwig Maximilians Universität, Munich, Germany; <sup>q</sup>Psychiatry Department, St Paul Hospital Millennium Medical College, Addis Ababa, Ethiopia; <sup>r</sup>Hadarim Eating Disorders Unit, Shalvata Mental Health Center, Hod HaSharon, Israel; <sup>s</sup>Gray Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; <sup>t</sup>Institute for Clinical Science, Umeå University, Umeå, Sweden; <sup>u</sup>Department of Child and Adolescent Psychiatry, Psychotherapy, and Psychosomatics, and Center for Translational Neuro and Behavioural Sciences, University Hospital Essen, University of Duisburg-Essen, Essen, Germany; <sup>v</sup>Department of Adult Psychiatry, Poznan University of Medical Sciences, Poznan, Poland; <sup>w</sup>Mental-health Clinic No. 1 Named after N. A. Alexeev, Moscow, Russia; <sup>x</sup>Department of Clinical Nutrition, College of Applied Medical Sciences, Imam Abdulrahman Bin Faisal University, Damam, Saudi Arabia; <sup>y</sup>Neuroendocrine, Endocrine and Germalin Differentiation and Communication Laboratory, University of Rouen Normandie, Rouen, France; <sup>z</sup>Department of Psychosomatic Medicine and Psychotherapy, University Hospital Tübingen, Tübingen, Germany; <sup>aa</sup>Centre of Excellence for Eating Disorders (KOMET), Tübingen, Germany; <sup>ab</sup>Department of Medical Psychology, Psychosomatics and Psychotherapy, Medical University of Graz, Graz, Austria; <sup>ac</sup>Vincent Square Eating Disorder Service, Central and North West London NHS Foundation Trust, UK; <sup>ad</sup>Universidad Nacional de Colombia, Bogota, Colombia; <sup>ae</sup>Department of Stress Sciences and Psychosomatic Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; <sup>af</sup>National Institute on Aging, Baltimore, MD, USA; <sup>ag</sup>Department of Neurology, Johns Hopkins University, Baltimore, MD, USA; <sup>ah</sup>Safra Children's Hospital, Sheba Medical Center, Tel Hashomer, Israel; <sup>ai</sup>Department of Psychology, Hacettepe University, Ankara, Türkiye; <sup>aj</sup>Department of Psychiatry, University of Toronto, Toronto, Canada; <sup>ak</sup>Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of

CONTACT Hubertus Himmerich

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hubertus.himmerich@kcl.ac.uk

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Centre for Research in Eating and Weight Disorders (CREW), Department of

Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.

**Ana Andreazza (Canada), Bernhard Baune (Germany), Michael Berk (Australia), Brisa Fernandes (Australia), Carla Gallo (Peru), Manfred Gerlach (Germany), Christoph Hiemke (Germany), Marek Jarema (Poland), Dimitrios Kapogiannis (USA), Sidney Kennedy (Canada) Yong-Ku Kim (Korea), Piotr Lewczuk (Germany), Andrea Lopez Mato (Argentina), Peter Riederer (Germany), Mohamed Saoud (France), Martin Schalling (Sweden), Florence Thibaut (France), Marat Galievich Uzbekov (Russia), Lakshmi Yatham (Canada), Moussa B. H. Youdim (Israel).

***Ursula Bailer, (Germany), Sevgi Bektas (Türkiye), Cynthia M. Bulik (USA), Chiara Conti (Italy), Martina De Zwaan (Germany), Angela Favaro (Italy), Fernando Fernandez-Aranda (Spain), Serguei Fetissov (France), Anja Hilbert (Germany), Hubertus Himmerich (UK/Germany), Hans Hoek (Netherlands), Carol Kan (UK), Olga Karpenko (Russia), Andreas Karwautz (Austria), Siegfried Kasper (Austria), Walter H. Kaye (USA), Cheyenne Lee (Singapore), Yael D. Lewis (Israel), Susan L. McElroy (USA), James Mitchell (USA), Palmiero Monteleone (Italy), Sabrina Mörkl (Austria), Daniel J. Müller (Canada), Hiba Mutwalli (Saudi Arabia), Bruno P. Nazar (Brazil), Hana Papežová (Czech Republic), Jan M. Sjögren (Sweden), Howard Steiger (Canada), Daniel Stein (Israel), Christopher P. Szabo (South Africa), Marta Tyszkiewicz-Nwafor (Poland), Ulrich Voderholzer (Germany), María Mercedes Uribe Isaza (Colombia), Tracey Wade (Australia), Wiesława D. Wranik (Canada), See Heng Yim (Hong Kong, China), Kazuhiro Yoshiuchi (Japan).

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Supplemental data for this article is available online at https://doi.org/10.1080/15622975.2026.2626934.

© 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
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Würzburg, Würzburg, Germany; aInstitut de Psychiatrie et Neurosciences de Paris, Université Paris Cité, Paris, France; aMHU PARIS
Psychiatrie & Neurosciences, Hôpital Sainte-Anne, Paris, France; aDepartment of Integrated Dentistry, Community Dentistry Section,
Poznan University of Medical Sciences, Poznan, Poland; aoLindner Center of HOPE, Mason, OH, USA; aPUniversity of Cincinnati College of
Medicine, Cincinnati, OH, USA; aDepartment of Community Health, and Epidemiology, Dalhousie University, Halifax, Canada; aDepartment
of Clinical Psychology, University Hospital of Bellvitge-IDIBELL and University of Barcelona, Barcelona, Spain; aCIBER Physiopathology of
Obesity and Nutrition (CIBERobn), ISCIII, Barcelona, Spain; atACUTE Center for Eating Disorders and Severe Malnutrition at Denver Health,
Denver, CO, USA; auDepartment of Medicine, University of Colorado School of Medicine, Aurora, CO, USA; avDepartment of Psychiatry, First
Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; awInstitute of Microbiology of the Czech
Academy of Sciences, Prague, Czech Republic; axDepartment of Psychiatry and Psychotherapy, Universitätsklinikum Erlangen and
Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; ayDepartment of Neurodegeneration Diagnostics, Medical University
of Bialystok, Bialystok, Poland; azDepartment of Psychiatry and Psychotherapy, University Medical Center of Mainz, Mainz, Germany;
baInstitute of Clinical Chemistry and Laboratory Medicine, University Medical Center of Mainz, Mainz, Germany; bbInstitute for Mental and
Physical Health and Clinical Translation (IMPACT), Deakin University, Geelong, Australia; beEating Disorders Care & Research Unit,
Department of Child and Adolescent Psychiatry, Medical University of Vienna, Vienna, Austria; bdCenter for Brain Research, Medical
University of Vienna, Vienna, Austria

ABSTRACT

**Objectives:** This World Federation of Societies of Biological Psychiatry (WFSBP) consensus paper aims to summarise and evaluate the published study results on objectively measurable biological markers associated with anorexia nervosa (AN).

**Methods:** The relevant literature was reviewed by the WFSBP Task Forces on Eating Disorders and on Biological Markers, and a consensus regarding the significance of the published evidence was reached.

**Results:** Candidate biological markers that have been associated with AN include clinical (e.g. body weight), molecular (e.g. genetic, epigenetic, hormonal, immunological, metabolomic), cellular (e.g. leukocytes), neuroimaging (e.g. structure, function, connectivity), digital, cardiac and neurophysiological parameters. Some clinical and laboratory parameters are risk markers in clinical practice. Biological markers have pathophysiological relevance in understanding the biological and metabolic pathophysiology of AN and its physical health consequences. Few studies have examined pharmacogenetics or therapeutic drug monitoring as tools to monitor and guide the treatment of AN.

**Conclusions:** Biological markers will hopefully soon enable clinicians to intervene earlier in a more targeted manner to mitigate treatment resistance. However, the current scientific basis for most biological markers are group comparisons only. Studies on sensitivity, specificity and the prognostic value of these markers are lacking.

ARTICLE HISTORY

Received 10 October 2025

Revised 1 February 2026

Accepted 1 February 2026

KEYWORDS

Anorexia nervosa;

biomarkers; genetics;

epigenetics;

neuroimaging;

metabolomics;

microbiome; digital

markers

# 1. Introduction

Genetic findings have recently suggested reframing anorexia nervosa (AN) as a metabo-psychiatric disorder (Bulik, Carroll, et al. 2021). Further evidence of the significance of biological factors in the pathophysiology of AN has arisen from metabolic and endocrinological (Minor et al. 2009; Galusca et al. 2015; Duncan et al. 2017), immunological (Dalton et al. 2018; Escelsior et al. 2022), and neuroimaging (Seitz et al. 2016, 2018; Walton et al. 2022; Kaufmann et al. 2023) research. Examples of clinical indications for a strong contribution of biological factors are the female predominance of the disorder and the onset age between 10 and 15 years, the association with starvation and the strong correlation between premorbid body weight and weight loss (Keys et al. 1950; Hebebrand, Hildebrandt, et al. 2022; Hebebrand, Seitz, et al. 2024). AN can result in physical health consequences such as bradycardia, hypotension, hair loss, and endocrine dysregulation including amenorrhoea. These changes reflect starvation which is a key symptom of AN. Thus, there

is an overlap between the physical and psychological symptoms of starvation (Keys et al. 1950) and the symptoms of AN (Hebebrand, Hildebrandt, et al. 2022). During starvation all tissues adapt to a reduced energy intake to enhance the probability of survival (Hebebrand, Hildebrandt, et al. 2022; Hebebrand, Plieger, et al. 2024). Those changes can entail reduced energy expenditure, the emergence of hyperactivity which may be interpreted as food foraging behaviour (Hebebrand, Hildebrandt, et al. 2022) and/or thermo-regulatory adaptation (Gutierrez et al. 2024). Apart from the physical adaptation, profound psychological changes are associated with starvation, including pre-occupation with food, altered eating behaviour, depression, insomnia and increased rigidity (Keys et al. 1950; Hebebrand, Hildebrandt, et al. 2022).

As the largest endocrine organ, the adipose tissue secretes hundreds of adipokines (Kershaw and Flier 2004; Lehr et al. 2012). Thus, loss of adipose tissue results in dysregulation of adipokine secretion, which in turn mediates the adaptation to starvation. The
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hormone leptin figures prominently in this adaptation
process. Hypoleptinaemia (absolute or adjusted for
body weight) represents a core endocrine finding in
AN, which underlies the neuroendocrine adaptation to
starvation (Hebebrand, Hildebrandt, et al. 2022;
Hebebrand, Plieger, et al. 2024).

Brain imaging in AN has consistently shown reduc-
tions in the volume of grey (GM) and white (WM) mat-
ter, as well as reduced connectivity between different
brain areas (Seitz et al. 2016, 2018; Walton et al. 2022;
Kaufmann et al. 2023). These changes may be the bio-
logical correlates of cognitive problems associated
with AN, such as deficiencies of central coherence,
cognitive flexibility, memory and visuospatial process-
ing (Reville et al. 2016; Stedal et al. 2021; Keeler et al.
2022b). The underlying molecular changes of reduced
brain matter appear to be a consequence of starvation
potentially mediated by the downregulation of neuro-
trophic factors like leptin (Wronski et al. 2024, 2023a,
2023b) and brain-derived neurotrophic factor (BDNF)
(Keeler et al. 2022c; Hebebrand, Plieger, et al. 2024).
These alterations may be linked to decreased neuro-
plasticity and neuronal damage, suggested by research
indicating increases in markers of neuronal injury such
as neurofilament light chain (NfL) in AN (Nilsson et al.
2019; Hellerhoff et al. 2021). In addition to the molec-
ular, cellular, tissue or organ signals, digital biomarkers
have recently emerged, that aim to capture human
behaviour and symptoms (Zierer et al. 2024).

Such structural, molecular or behavioural markers
may be used as indicators for biological processes
underlying the development of AN, the manifestation
of its subtypes, its prognosis, or response to an inter-
vention (FDA-NIH Biomarker Working Group 2016).
These indicators might therefore have value as bio-
markers for diagnosis, monitoring, pharmacodynamic/
response, prediction, prognosis, safety, and susceptibil-
ity/risk. A single biomarker may qualify for more
than one of these categories and one biomarker in
isolation would likely be difficult to interpret due to
interrelationships with other biomarkers (Califf 2018).
Complicating this, biomarkers are not a unitary con-
struct and encompass biomarkers of risk, diagnosis or
trait, state, stage, response and prognosis (Davis
et al. 2015).

To be used as biomarkers in psychiatry, these sig-
nals should be specifically associated with a mental
disorder and should help to facilitate diagnosis and/or
contribute to the determination of prognosis, risk or
treatment efficacy (Charney et al. 2002). This leads to a
very important issue in biomarker research, namely a
clear definition of the Context of Use (COU) (Cummings

et al. 2025). What is the role of a given biomarker: is it
a diagnostic biomarker? Is it a prognostic biomarker?
Is it a biomarker useful for differential diagnosis, or
perhaps in screening? Alternatively, perhaps we expect
it to predict therapy response or perhaps risk of dis-
ease recurrence? All these questions need to be criti-
cally addressed before evaluation of any biomarker in
any medical condition, as they have consequences for
decision whether to accept a candidate for routine
application (in a given COU) or not (Cummings et al.
2025). For example, entirely different sensitivities and
specificities are expected from biomarkers to be
applied for diagnostic or screening purposes. In the
context of AN, such biomarkers may be useful for
future subtyping, assessing and/or predicting the
development of the eating disorder (ED) or personality
and behavioural traits, assessing cognitive and emo-
tional capacity, and importantly, informing treatment
decisions.

Technical innovations have made the exploration of
novel biomarkers possible. These technological
advances include epigenetics (Booij and Steiger 2020),
metabolomics (Jia et al. 2023), the microbiome (Fan
et al. 2023), and multiplex measurement of inflamma-
tory biomarkers (Breithaupt et al. 2024). Additionally,
some of the molecules that have been identified as
altered in AN have been identified as potential drug
targets. For example, several case reports on the use
of metreleptin – a recombinant analogue of the human
hormone leptin – in AN have been published with
positive results (Milos et al. 2020; Gradl-Dietsch et al.
2023). The Task Force EDs of the World Federation of
Societies of Biological Psychiatry (WFSBP) recognises
the new technical opportunities, the increased under-
standing of the biological factors of AN and the novel
conceptualisation of AN as a metabo-psychiatric disor-
der. It is also mindful of the plethora of newly discov-
ered findings and the heterogeneity of the quality of
published studies which makes guidance for clinicians
and researchers desirable. This consensus paper on
biomarkers in AN summarises published study results
on molecular, electrophysiological, neuroimaging, digi-
tal and other objectively measurable markers that
have been reported to be associated with AN, the
health risks associated with AN, and the treat-
ment of AN.

## 2. Methods

The WFSBP Task Force EDs reached consensus on the
relevant biological markers that should be included in
this consensus statement. The authors organised
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themselves within workstreams that covered: genetics and epigenetics; neuroimaging; molecular markers of brain plasticity and damage; appetite regulators, gastrointestinal signalling molecules and adipokines; stress markers; sexual and social hormones; immunological markers; metabolomics; the microbiome and markers of the gut-brain axis; salivary markers; neurophysiological markers; digital biomarkers; physical health risk and refeeding markers; therapeutic drug monitoring and pharmacogenetics.

Each workstream decided on the specific search strategy for each biomarker depending on the available evidence. Literature searches included the databases PubMed, ISI Web of Science and PsycInfo, supplemented by hand-searches through the reference lists of included papers and relevant reviews. Individual searches were conducted per biomarker, with search terms relating to that biomarker in combination with 'anorexia nervosa'.

Identified meta-analyses were included in the results section. Evidence from lower-level research such as pilot, cross-sectional and longitudinal studies was also reported if necessary for the assessment of the biomarker in question or if no meta-analyses were published.

# 3. Results

## 3.1. Genetics and epigenetics

### 3.1.1. Genetic research in anorexia nervosa

The recognition that genetics play a role in EDs, and in behavioural conditions more broadly, has been said to be 'one of the most dramatic shifts in the modern history of the behavioural sciences' (Plomin 2000). Indeed, the nature vs. nurture debate has moved on to a nature and nurture discourse, where researchers are determining the extent of contribution from both, and how nurture (environmental factors) may influence our nature (genetic predisposition), and in turn how our nature may predict nurture (such as dietary selection) (Dick et al. 2010).

Twin studies represent one of the most critical tools for elucidation of genetic and environmental factors in the development of mental health conditions. Simply put, twin studies are leveraged through the comparison of concordance rates between monozygotic (identical) twins – who share ~100% of their assorting alleles and dizygotic (non-identical) twins – who share ~50% of their assorting alleles. This enables researchers to establish the extent of genetic contribution to variance in the conditions observed. In EDs, twin-based heritability ($h^2_{twin}$) estimates demonstrate moderate to

high genetic contributions to AN (28–88%), bulimia nervosa ([BN] 54–83%), binge eating disorder ([BED] 39–57%) (Thornton et al. 2011; Bulik et al. 2019) and avoidant restrictive food intake disorder ([ARFID] 79%) (Dinkler et al. 2023). With the establishment that genetic variation contributes to the risk of developing these conditions, there is a drive to elucidate the underlying molecular genetic aetiology of EDs – to identify specific genetic risk variants. We note, however, this does not preclude identifying environmental contributions, which will be addressed later in this section.

### 3.1.2. Risk genes: genome-wide association studies (GWAS)

In the 1990s and early 2000s, genetic studies used a candidate gene approach, which involves identifying an *a priori* hypothesised gene (or groups of genes) based commonly on the function of that gene and evaluating if genetic variation within that gene differs between those affected by an ED, and those not. These focused on monoaminergic, appetite, and weight-regulation systems (e.g. serotonin, leptin, ghrelin, fat mass and obesity-associated protein), and did not identify replicable genetic risk loci (positions of variation on the deoxyribonucleic acid [DNA]) (Pinheiro et al. 2010). Biotechnology and statistical genetic methods have progressed since this time, with the advent of genome-wide association studies (GWAS) (Loos 2020). GWAS allow researchers to interrogate millions of genetic markers across the entire genome, without relying on *a priori* assumed functions and mechanisms as was necessary in candidate gene studies.

While a paradigm-shifting biotechnological advancement, GWAS does come with its own challenges, one of the most widely recognised being the need for exceptionally large sample sizes of tens, or even hundreds of thousands of cases and controls (Wray et al. 2018). The genetic architecture of complex disorders such as AN, BN, BED, and ARFID mean that thousands of genetic variants each individually contribute an extremely small effect size to increase risk for developing these conditions (Fu et al. 2013). The issue of small effect sizes is then combined with the need for hundreds of thousands of statistical tests to analyse each genetic variant captured individually, in relation to the outcome of interest. This requires statistical correction to avoid false positives, meaning the standard threshold for significance ($p<0.05$) must be substantially lower ($p<5 \times 10^8$) to obtain robust and reliable results and to ensure that subsequent interrogation of identified loci are wise investments in effort and money.
<figure><img src="image_22.png" /></figure>

To achieve such substantial numbers the Psychiatric Genetics Consortium (PGC) was created in 2007 establishing a global infrastructure for the investigation of the genetic architecture of multiple psychiatric disorders (Sullivan et al. 2018). In 2013, the ED working group was established, led by Cynthia Bulik, with an initial focus on AN. Through the determination and collaboration of hundreds of researchers worldwide, replicable genetic risk variants are now being identified for AN (Boraska et al. 2014; Duncan et al. 2017; Watson et al. 2019, 2022), and studies are underway identifying risk variants for binge-eating behaviour, BN, BED, and ARFID (Bulik, Thornton, et al. 2021; Bulik et al. 2023; Monssen et al. 2024).

The first GWAS meta-analysis was published in 2012 and investigated six ED related phenotypes: drive for thinness, body dissatisfaction, bulimia, weight fluctuation symptoms, breakfast skipping, and childhood obsessive-compulsive personality disorder trait (Boraska et al. 2012). This meta-analysis included three studies and in its largest analysis (weight fluctuation) included 2,976 individuals (807 cases and 2,169 healthy controls [HCs]). These analyses identified no genome-wide significant loci, reflecting the pattern seen regularly in psychiatric disorders where much larger sample sizes are required to identify robust replicable genetic loci. Following this, analyses have been mostly focused on AN and phenotypes within this condition.

The first global GWAS meta-analysis involved a collaborative effort between the genetic consortium for AN (GCAN) and the Wellcome Trust Case Control Consortium 3 (WTCCC3) in 2014, and identified no genome-wide significant associations with 17,767 individuals (2,907 cases and 14,860 HCs) (Boraska et al. 2014). As shown in Table 1 initial findings in meta-analyses proved inconsistent (further detail can be found in Table S1). In 2017, the PGC Eating Disorders Working Group (PGC-ED) carried forward the GCAN/WTCCC3 and combined them with genotypes from other samples collected globally to publish their first large-scale meta-analysis in AN (Freeze I). This meta-analysis identified a single genome-wide significant signal on chromosome 12q13.2 spanning six genes, with the top variant (rs4622308) being in high linkage disequilibrium (and thus closely associated) with already identified risk variants for type I diabetes, rheumatoid arthritis, and other autoimmune conditions (Duncan et al. 2017).

The second PGC-ED meta-analysis substantially increased numbers to include 16,992 AN cases and 55,525 HCs from 15 countries (Freeze II), and identified eight genome-wide significant loci, which highlighted

potential roles of many brain-expressed genes (Watson et al. 2019) (see Table 1). The previously identified top 'hit' from the 2017 meta-analysis did not achieve genome-wide significance in this analysis but remained close to significance. Such fluctuation is not uncommon in early phases of genetic discovery as samples sizes remain low and variability in phenotyping across contributing samples exist. A further analysis was undertaken by the PGC-ED to investigate if early-onset AN (defined as onset prior to 13 years) and typical-onset AN present with differential genomic risk loci; with these analyses also limited by lower sample numbers (1,269 early-onset, 6,998 typical-onset, and 25,042 HCs) no early-onset AN loci were identified.

In addition to estimating heritability from twin studies, it is also possible to estimate single nucleotide polymorphism-based heritability ($h^2_{SNP}$) via GWAS. Single nucleotide polymorphisms (SNPs) are the genetic variants measured in a GWAS, and SNP-based heritability refers to the heritability that is captured within these studies at the molecular genetic level. Importantly, since GWAS only capture common genetic variation (and not rare variants), it is expected that $h^2_{SNP}$ will be lower than $h^2_{twin}$. The $h^2_{SNP}$ of AN reported in the aforementioned AN GWAS range from 0.17 to 0.25 (Duncan et al. 2017; Watson et al. 2019) and did not appear to differ markedly between early- and typical-onset AN (Watson et al. 2022).

While GWAS have firmly centred on AN, a recent meta-analysis was published investigating an approximate BED phenotype (Burstein et al. 2023). Allocation of caseness for BED was not based on clinical diagnoses. A supervised machine-learning (ML) method was applied in the Million Veterans Program cohort, which then underwent cross-ancestral GWAS ($n=822$ cases). This analysis identified three genome-wide significant loci in three genes (*HFE*, *MCHR2*, *LRP11*), which did not overlap with AN risk loci identified to date; however, the findings are very early in the stage of BED molecular genetic risk identification and larger analyses with rigorous phenotyping will be required to ascertain the robustness of these loci.

In addition to the focus on AN, research has also centred on single nucleotide polymorphisms, a specific type of genetic variant characterised by the substitution of a single nucleotide (or base) in the DNA sequence. Copy number variation is another form of genetic variation which involves regions of DNA that are repeated, with the number of copies varying between individuals. Copy number variation can influence gene transcription and translation and has been associated with a range of complex diseases, including
<table><caption>Table 1. Summary table reporting genome-wide association meta-analyses of eating disorder phenotypes in reverse chronological order.</caption><thead><tr><th rowspan="2">Authors (year)</th><th rowspan="2">Condition</th><th rowspan="2">N</th><th colspan="2">Discovery stage</th><th rowspan="2">Results: GWAS loci pathway/network analyses/functional characterisation PRS (variance captured)</th></tr><tr><th>n (patients / controls)</th><th>Ancestry</th></tr></thead><tbody><tr><td>Walker et al. (2025)</td><td>AN (Replication only)</td><td>2</td><td>12,458<br>Anorexia Nervosa Genetics Initiative (ANGI; 7,414/5,044)<br>Replication in 387,190 UK Biobank (1,260/385,930)</td><td>European</td><td><strong>Pleiotropic loci</strong><br>In 178 copy number variants reported to be pleiotropic in 54 complex and Mendelian traits/disorders no copy number variants reached significance following correction for multiple testing.</td></tr><tr><td>Burstein et al. (2023)</td><td>BED</td><td>2</td><td>362,712<br>(African GWAS n=77,574; European GWAS n=285,138)</td><td>African and European</td><td><strong>GWAS loci</strong><br>Two genome-wide significant loci from European GWAS in HFE and near MCHR2. One further genome-wide significant locus in LRP11 in cross-ancestral meta-analysis.<br><strong>Pathway / Network analysis</strong><br>In addition to neuropsychiatric, obesity-related, autoimmune, and cancer traits, enrichment was found in gene sets related to haem and uric acid metabolism.<br><strong>PRS (variance captured)</strong><br>European-ancestry PRS significantly predicted BED in two independent cohorts, but not in a third smaller cohort. Variance captured not reported. Also predicted UKBB iron overload and iron deficiency (β=-0.03; p=0.01).<br><strong>Functional characterisation</strong><br>Phenome-wide association studies (PheWAS) of lead SNPs from GWAS implication in iron dysregulation, iron metabolism and iron deficiency anaemia disorders.</td></tr><tr><td rowspan="2">Watson et al. (2022)</td><td rowspan="2">AN (age of onset, early-onset and typical-onset)</td><td rowspan="2">13</td><td rowspan="2">AN age of onset: 13 cohorts<br>41,316 (9335/31,981); early-onset AN: 5 cohorts<br>26,311 (1269/25,042)<br>typical-onset AN: 5 cohorts<br>32,040 (6998/25,042)</td><td rowspan="2">European</td><td><strong>GWAS loci</strong><br>No genome-wide significant loci in GWAS of early-onset AN. Two genome-wide significant loci in GWAS of typical-onset AN (rs3821875, rs4641158).<br>Heritability estimates (h<sup>2</sup><sub>SNP</sub>) were 0.01-0.04 for AN age of onset, 0.16-0.25 for early-onset AN, and 0.17-0.25 for typical-onset AN.<br><strong>PRS (variance captured)</strong><br>PRS for age of onset explained 0.13% (liability scale R<sup>2</sup>) of variance, for early-onset explained 0.85%, and for typical-onset 0.25% of the variance.</td></tr><tr><td><strong>Functional characterisation</strong><br>Gene expression was most enriched in brain tissues although not significant. Gene sets were also not significant.</td></tr><tr><td>Watson et al. (2019)</td><td>AN</td><td>33</td><td>72,517 (16,992/55,525)</td><td>European</td><td><strong>GWAS loci</strong><br>Eight genome-wide significant loci located near genes NCKIPSD, CADM1, ASB3/ERLEC1, MGMT, FOXP1, PTBP2, CDH10, NSUN3.<br>Genome-wide common variant heritability (h<sup>2</sup><sub>SNP</sub>) was 0.11-0.17 (s.e.=0.01).<br><strong>Pathway / Network analysis</strong><br>Gene-wise analysis identified 79 significant genes (majority chromosome 3). A significant biological pathway linked to regulation of embryonic development.<br><strong>Functional characterisation</strong><br>Significant loci annotated to 121 brain-expressed genes; enrichment of genes highly expressed in brain (including cerebellum and neurons) and linked to feeding behaviours.<br><strong>PRS (variance captured)</strong><br>PRS explained ~1.7% of phenotypic variance (liability scale R<sup>2</sup>)</td></tr></tbody></table>

(Continued)
<figure><img src="image_23.png" /><figcaption>Table 1. Continued.</figcaption></figure>

<table><thead><tr><th rowspan="2">Authors (year)</th><th rowspan="2">Condition</th><th colspan="3">Discovery stage</th><th rowspan="2">Results: GWAS loci pathway/network analyses/functional characterisation PRS (variance captured)</th></tr><tr><th>N</th><th>n (patients / controls)</th><th>Ancestry</th></tr></thead><tbody><tr><td>Huckins et al. (2018)</td><td>AN<br/>Replication:</td><td>9<br/>2</td><td>17,643 (2,158/15,485)<br/>in silico n=1,033/3,733;<br/>de novo n=261/1,500</td><td>European</td><td>No genome-wide significant associations.</td></tr><tr><td>Duncan et al. (2017)</td><td>AN</td><td>12</td><td>14,477 (3,495/10,982)</td><td>European</td><td><strong>GWAS loci</strong><br/>One genome-wide significant locus (12q13.2; top variant in region - rs4622308)<br/>Genome-wide common variant heritability (h<sup>2</sup><sub>SNP</sub>) of AN was 0.20 (SE = 0.021).<br/><strong>Pathway / Network analysis</strong><br/>Multiple genes reached gene-based significance due to high LD in region, most located in region around top SNP (rs4622308). No pathways were significant.<br/><strong>Functional characterisation:</strong> GTEx search did not identify any patterns of gene expression for top genes in region.</td></tr><tr><td>Boraska et al. (2014)</td><td>AN</td><td>15<br/>15</td><td>17,767 (2,907/14,860)<br/>European<br/>11,306 (2,677/8,629)<br/>Japanese<br/>879 (458/421)</td><td>European<br/>European and Japanese</td><td><strong>GWAS loci</strong><br/>One genome-wide significant SNP (rs4957798) in the discovery meta-analysis, but not replicated in the global meta-analysis, with no genome-wide significant SNPs identified.</td></tr><tr><td>Boraska et al. (2012)</td><td>Six eating disorder phenotypes: DT, BD, WF, BS, OCPD, Bulimia</td><td>3</td><td>DT n=2,680<br/>BD n=2,789 (821/1,968)<br/>WF n=2,976 (807/2,169)<br/>BS n=2,967 (798/2,169)<br/>OCPD n=2,773<br/>(761/2,012)<br/>Bulimia n=1,360<br/>(633/727)</td><td>European</td><td>No genome-wide significant associations.</td></tr></tbody></table>

Abbreviations: AN = Anorexia Nervosa; BED = Binge Eating Disorder; BD = body dissatisfaction; BS = breakfast skipping behaviour; CI = confidence interval; CNV = copy number variant; DT = Drive for Thinness; GWAS = Genome wide association studies; h<sup>2</sup><sub>SNP</sub> = single nucleotide polymorphism-based heritability; LD = linkage disequilibrium; OCPD = Childhood Obsessive-Compulsive Personality Disorder trait; OR = odds ratio; PheWAS = Phenome-wide association studies; PRS = polygenic risk scores; SE = standard error; SNP = single nucleotide polymorphism; UKBB = UK Biobank; WF = weight fluctuation symptoms. Detailed table available in Supplementary Materials (Table S1).

psychiatric conditions (Mollon et al. 2023). To date, only one large-scale investigation of rare copy number variants has been published in EDs, and specifically AN (Walker et al. 2025), leveraging data from the Anorexia Nervosa Genetics Initiative (ANGI) and the UK Biobank. This study was limited due to differing genomic arrays used across cohorts and variation in cohort characteristics such as age and how diagnoses were established, requiring cautious interpretation of results. None of the 15 tested loci, including 16p11.2, NRXN1, 22q11.2 and 3q29 deletions, displayed significant associations with AN (Walker et al. 2025), even though the 3q29 deletion, for example, had previously been associated with several relevant conditions, including failure to thrive, mild to moderate intellectual disability (Yilmaz et al. 2017; Chang et al. 2019), feeding and weight gain problems (Wang et al. 2011), bipolar disorder (Sudmant et al. 2015), and schizophrenia (Cheng et al. 2005).

As demonstrated in Table 1, as sample size increases, power improves for these analyses. This work is ongoing through the PGC-ED and global collaboration of scientists. It is likely that larger meta-analyses will

identify a greater number of variants, as has been the pattern of discovery in other psychiatric disorders. Through further identification, biological networks and pathways implicated in AN will be uncovered and improve our understanding of how genetic and biological factors influence AN risk and potentially inform the development of biologically informed pharmacotherapeutics. Though current understanding of the genetic aetiology of BN, BED, and ARFID is more limited, large global efforts are underway to advance understanding of both genetic and environmental risk for all EDs.

### 3.1.3. Genetic correlations

GWAS also enable the calculation of genetic correlations (SNP-r<sub>g</sub>) across disorders or traits using summary statistics (Bulik-Sullivan et al. 2015). SNP-r<sub>g</sub> reveal the extent to which two traits are influenced by the same genetic factors and can be positive (the same genetic factors influence both traits in the same direction; e.g. either increasing or decreasing risk for both traits) or negative (the same genetic factors influence both traits but in opposite directions; e.g. increasing risk for
<figure><img src="image_24.png" /></figure>

one trait while decreasing risk for the other). Genetic correlations can inform the underlying genetic aetiology of disorders and can be highly informative with respect to shared mechanisms underlying disorders. We did not conduct a systematic review of the vast genetic correlation literature, but rather briefly summarise the over-arching findings. A more detailed review can be found in Watson et al. (2023).

Unsurprisingly, AN has strong positive genetic correlations with other psychiatric disorders and traits (SNP-$r_g$ = 0.17–0.49) including obsessive-compulsive disorder, depression, anxiety, and schizophrenia. Less anticipated are the significant genetic correlations between AN and a range of metabolic, anthropometric, and glycaemic traits (SNP-$r_g$ = -0.22 to -0.32). Negative genetic correlations are observed between an array of anthropometric traits such as body mass index (BMI), obesity and waist to hip ratio and metabolic traits such as fasting insulin, insulin resistance, leptin, and type 2 diabetes, with the single positive genetic correlation being with high-density lipoprotein (HDL) cholesterol (Duncan et al. 2017; Watson et al. 2019). Adjusting for BMI did not significantly alter correlations with non-BMI traits, indicating that these correlations are not driven by genetic influences for BMI. The interpretation of these results is that some of the same genetic factors that increase risk for AN, decrease risk for developing traits such as obesity and type 2 diabetes. Furthermore, these results encourage the consideration of both psychiatric and metabolic/anthropometric factors in understanding the biology of AN. Sample sizes are still too small to determine whether the same patterns hold for early and typical onset AN or across genders. However, an initial glimpse into differences across EDs suggest that while AN, BN, and BED show similar genetic correlations with psychiatric traits, they diverge markedly on anthropometric and metabolic traits, with the two binge-type EDs showing positive (and not negative) genetic correlations with metabolic and anthropometric phenotypes (Hübel et al. 2021).

### 3.1.4. Polygenic risk scores

GWAS also enable the calculation of a metric called a polygenic risk score (PRS) also known as polygenic score or genetic risk score. A PRS captures the magnitude of the genetic load an individual carries for a particular disease or trait by creating a weighted sum of all known risk alleles that an individual carries (Figure 1) (Wray et al. 2021). The purple curve represents a theoretical PRS distribution for individuals with AN and the green curve represents the PRS

<figure><img src="image_25.png" /><figcaption>Figure 1. Theoretical distribution of a polygenic risk score for anorexia nervosa. AN = anorexia nervosa. Graph A depicts the counts of cases with AN, graph C of controls without AN.</figcaption></figure>

distribution for HCs (i.e. individuals without AN). Although in general, individuals with AN have higher PRS for the disease than HCs, the curves overlap considerably. The individual demarcated by the purple circle 'A' represents an individual with AN who has a relatively low polygenic risk for AN, whereas the person depicted by the circle marked C is someone with high genetic risk, who has nonetheless never developed the illness. This drawing clearly illustrates why PRS cannot be utilised as a genetic test for AN or other studied traits. There are many individuals with a high AN PRS who never develop the illness as well as many individuals with low polygenic risk who do develop AN (Bulik 2024). The huge overlap of such curves representing the small differences between distributions are a general problem in biomarker research that causes false positivity or false negativity.

Importantly, the robustness of a PRS for any illness depends on the genetic architecture of the target trait as well as the size of the GWAS from which it is derived. Larger samples of heritable traits yield more robust PRS and confidence in the ability of PRS to index disease risk. As stated unequivocally by Wray et al. (2021) 'PRS are not and never will be stand-alone predictors of common diseases'.

Of the 16 identified studies that investigated AN PRS and ED phenotypes, 12 analysed clinical diagnoses as the outcome (AN or ARFID), while four investigated ED symptoms, three in the same cohort (Avon Longitudinal Study of Parents and Children; ALSPAC) and one a global disordered eating outcome in addition to disordered eating behaviours such as purging, binging, and restriction ((Curtis et al. 2024); see Table S2). Overall, it is evident that AN PRS is associated relatively consistently with AN diagnosis, with one study reporting no relationship with ARFID (Koomar et al. 2021). This is the first and currently only study
<figure><img src="image_26.png" /></figure>

correlating AN PRS with a clinical ED outside of AN, but it represents a well-powered study for PRS analyses in terms of the target sample size (n > 3,000 cases) and therefore suggests that different genetic drivers may contribute to these two pathologies. It is important, however, to further explore the relationship of AN PRS with other ED phenotypes such as BED, BN, and other specified feeding and eating disorders (OSFED) especially atypical AN (AAN) as there is evidence that AN PRS predicts a measure of global disordered eating, and symptoms and behaviours of EDs, in adolescents in the general population (Curtis et al. 2024; Herle et al. 2021; Yilmaz et al. 2023) and not just clinical diagnoses. PRSs could also be improved in the future to be more specific for different sub-types or phenotypes within clinical diagnoses as evidenced by distinct PRSs generated for early and later ages of onset for AN (Watson et al. 2022). There is some indication that the current AN PRS may be more highly associated with outcomes in females (Papini et al. 2024; Yilmaz et al. 2023), and may also interact with other AN risk factors such as maternal genitourinary tract infection, age at birth, method of delivery, parental education, income, and psychiatric history (Papini et al. 2024).

In addition to AN, many other psychological and psychiatric phenotypes have been correlated with AN PRS. Of these many reported no significant effects with psychiatric disorders such as major depressive disorder, bipolar disorder, panic disorder, attention deficit hyperactivity disorder, alcohol dependence or suicidal behaviours. However, a few studies did report positive associations with suicide related behaviours such as suicide attempts and non-suicidal self-injury, and cannabis use disorder (Table S3). Relationships that were reported were nominal in effect size, and suggest that while AN PRS may influence some other psychological or psychiatric traits these appear small or context dependent, for example with gender differences observed in neuroticism where AN PRS was a predictor only in men (Gale et al. 2016).

These conflicting findings may in part be driven by the relatively early nature of PRS investigations in AN relative to some other psychiatric disorders. As previously described, the discovery cohort determines the power of the PRS as a tool to predict outcomes: at this stage, AN PRS studies are relatively small and so the PRSs derived on these discovery cohorts may still have relatively low power. Over time, as the discovery cohort increases, the field can further explore relationships between AN PRS and other psychological and psychiatric outcomes with greater certainty. At this stage relationships appear to be weak, and

inconsistent outside of AN diagnosis and ED phenotypes themselves.

Similarly inconsistent effects are reported across the few studies that investigated a range of brain structure and function where sex-specific relationships are reported (e.g. (Leehr et al. 2019)) or no associations identified at all (e.g. (Taquet et al. 2021)). Studies have also investigated reproductive timing (Ni et al. 2019) with evidence for a relationship, and inflammatory bowel disease or other medical conditions with no evidence (Table S4). Finally, AN PRS has been associated with urbanicity, nutrient intake, academic performance, and other established risk factors for AN.

More consistent findings, like the relationship between AN PRS and AN diagnosis, are observed with metabolic phenotypes (Table S5). Most of these studies indicate higher AN PRS is associated with lower, or slower growth, and reduced body mass indicators such as weight, BMI, bone mineral density, and weight trajectory. In line with some observations in other phenotypes, there is some evidence that these effects are limited to or more pronounced in females (see Table S5).

Overall, AN PRS appears to predict AN diagnosis, some ED symptoms, metabolic phenotypes, and some established risk factors for AN. There is also some evidence that these relationships may be particularly evident in women or girls. It is too early to be certain what the relationship is between AN PRS and the multiple other phenotypes that have been explored, in part likely due to the restricted power of our current AN PRS tool. The observation that effects may be more pronounced, or restricted to, women or girls may also be driven by the fact that the discovery cohorts upon which the PRS has been trained upon possess a majority of female cases with only ~3% male cases with AN (Watson et al. 2022). This may restrict the applicability of the AN PRS based on the current GWAS discovery cohorts to populations including males.

In addition to the limited number of men with AN included in the original discovery GWAS, it is notable that most studies used European-ancestry cohorts. Critically this is the case for the discovery GWAS and so generalisability of the PRS may not be transferable across ancestries. To date, target cohorts have also been predominantly European ancestry and so we do not know how the present AN PRS may perform in other ancestry populations.

We have focused on how AN PRS may correlate with a range of phenotypes and outcomes. Other PRSs have also been correlated with AN as the outcome, for example the PRS for BMI. While this is outside the scope of this review, it is worth noting that the
<figure><img src="image_27.png" /></figure>

strongest PRS predictors beyond the AN PRS for AN are those for metabolic traits such as BMI (Hübel et al. 2021). The mechanism for this relationship is not yet clear with some evidence that this may be due to differences in BMI predicting ED risk, rather than the biological drivers for BMI also driving ED risk (Watson et al. 2019); but this remains a highly active area of research. As mentioned above when discussing genetic correlations, while there is evidence for genetic correlations across EDs and other psychiatric traits, there appears to be marked differences on genetic correlations across EDs with anthropometric and metabolic traits, with the two EDs defined by binge eating (BN and BED) showing positive genetic correlations with metabolic and anthropometric phenotypes – the opposite to that seen with restrictive EDs (Hübel et al. 2021).

### 3.1.5. Epigenetics

Epigenetic mechanisms influence gene expression (and corresponding phenotypic variations) through molecular modifications that regulate gene activity in the absence of physical changes to the DNA code (Li 2021). Epigenetic changes, mainly involving DNA methylation, histone modifications and noncoding ribonucleic acid ([RNAs] (Peixoto et al. 2020), appear to occur in an environmentally responsive and site-specific fashion – meaning that, through epigenetic programming, environmental exposures occurring throughout the life span can influence phenotypic manifestations by enabling or silencing the expression of specific genetic tendencies (e.g. (Szyf 2015)). Consequently, with respect to many physical- and mental-illness phenotypes, epigenetic effects have the potential to influence the progression (depending upon an individual's life history) from genetic susceptibility to full-blown illness and back (Recillas-Targa 2022; Steiger et al. 2023). Data have implicated epigenetic processes in diverse medical and psychiatric problems – including EDs (see (Booij and Steiger 2020; Campbell et al. 2011; Hübel et al. 2021; Steiger and Booij 2020)).

3.1.5.1. **DNA methylation.** DNA methylation, the best-studied of epigenetic mechanisms, involves the addition of methyl groups to DNA sites at which a cytosine is followed by a guanine - called CpGs. DNA methylation acts in a site-specific fashion, so as to selectively block or suppress the expression of specific genes (Fujita et al. 1999; Inamdar et al. 1991). Diverse environmental factors influence the methylation of DNA: Prenatal influences, acting through maternal placental signalling or paternal epigenetic marks in sperm, have been documented (Chan et al. 2018), as

have various effects associated with perinatal, childhood and adulthood exposures to stress (Steiger and Booij 2020; Steiger and Thaler 2016). In addition, because certain micronutrients (e.g. folate, choline, betaine, methionine) participate in methyl-transfer reactions upon which DNA methylation depends (Anderson et al. 2012), nutritional factors also have an influence (Guarasci et al. 2018; Koklesova et al. 2021). Given the preceding, DNA methylation constitutes a potential 'platform' upon which effects of heredity, nutritional status and life experiences (during perinatal, developmental and later-life phases) converge. DNA methylation may therefore have intriguing potentials as a biomarker for a disorder in which life stresses and nutritional triggers are so obviously implicated (Steiger and Booij 2020).

The literature on DNA methylation in EDs includes three non-systematic reviews (Booij and Steiger 2020; Hirtz and Hinney 2020; Steiger and Booij 2020), one systematic review of the literature on AN, BN and BED up to 2017 (Hübel, Marzi, et al. 2019), and another covering the literature on AN up to 2023 (Käver et al. 2024). We conducted an updated literature search on May 5, 2025 using Pubmed, Google Scholar and Web of Science literature databases and identified no relevant additional papers that had not been treated in the previous reviews cited. Given the preceding, rather than providing a systematic review here, we instead summarise findings that indicate replicable methylation changes at specific genes in participants with AN. Interested readers are referred to the previous reviews cited above for comprehensive treatments.

3.1.5.2. **Methylation at candidate genes.** Like candidate-gene studies in the field of psychiatric genetics, candidate-gene studies on DNA methylation in AN have produced unstable results. Käver et al. (2024) reviewed findings (available up to 2023) from 17 systematically selected case-control studies targeting candidate-gene methylation in AN. In short, these studies inconsistently pointed to altered methylation of genes involved in neurotransmitter function, neural plasticity, social attachment, and appetite and energy regulation – including genes regulating dopamine, serotonin, oxytocin, BDNF, leptin, ghrelin, among others.

3.1.5.3. **Epigenome-wide association studies.** There are five published reports, conducted by three different research groups, documenting results of epigenome-wide methylation studies (EWAS) in people with AN – all using Illumina platforms to assay DNA obtained from whole blood or leukocytes (Booij et al. 2015; Iranzo-Tatay et al. 2022; Kesselmeier et al. 2018; Steiger et al. 2019; Steiger et al. 2023) (see Table 2). Broadly,
<figure><img src="image_28.png" /><figcaption>Table 2. Summary of the methodology and findings of the five EWAS studies in individuals with anorexia nervosa.</figcaption></figure>

<table><thead><tr><th>Study</th><th>Booij et al. (2015)</th><th>Kesselmeier et al. (2018)</th><th>Steiger et al. (2019)</th><th>Iranzo-Tatay et al. (2022)</th><th>Steiger et al. (2023)</th></tr></thead><tbody><tr><td>Sample</td><td>All females<br>29 active AN<br>15 NED</td><td>All females<br>47 active AN<br>47 NED, lean<br>100 NED,<br>population-based<br>5 MZ pairs discordant for<br>active-AN</td><td>All females<br>75 active AN,<br>31 in stable remission<br>41 NED</td><td>All females<br>7 MZ twin pairs<br>7 active AN (singletons)<br>7 NED (singletons)</td><td>All females<br>145 active AN<br>49 in remission <sup>3</sup> 1 year<br>64 NED</td></tr><tr><td>Cell type<br>DNA methylation<br>method</td><td>Leukocytes<br>Infinium Human<br>Methylation 450<br>BeadChip Kit</td><td>Whole blood<br>Infinium Human<br>Methylation 450<br>BeadChip Kit</td><td>Leukocytes<br>Infinium Human<br>Methylation 450<br>BeadChip Kit /<br>Infinium<br>MethylationEPIC 850<br>BeadChip Kit <sup>1</sup></td><td>Whole blood<br>Infinium MethylationEPIC<br>850 BeadChip Kit</td><td>Leukocytes<br>Infinium Human<br>Methylation 450<br>BeadChip Kit /<br>Infinium<br>MethylationEPIC 850<br>BeadChip Kit<sup>1</sup></td></tr><tr><td># CGs</td><td>AN vs. NED:<br>14 probes <i>q</i> <.05</td><td>An vs. lean NED: 51 CGs<br>AN vs. population-based<br>NED: 81 CGs.<br>All based on 2 different<br>reference free<br>methods, <i>p</i> <.01<br>MZ sample: 26/51 CGs<br>and 54/81 CGs were in<br>same direction as<br>results from the AN vs.<br>lean vs. population<br>NED comparisons, all<br><i>p</i> > .01</td><td>AN vs. NED: 58 CGs; AN<br>vs. remitted: 265 CGs;<br>remitted vs. NED: 0<br>CGs.<br>All <i>q</i> <.01</td><td>MZ twin with AN vs.<br>healthy co-twin: 9<br>CGs (<i>q</i> <.05). 6 CGs<br>were replicated in a<br>AN vs NED<br>comparison<br>(singletons) (<i>p</i> <.05)</td><td>AN vs. NED: 205 CGs; AN<br>vs. remitted: 162 CGs;<br>remitted vs. NED:<br>0 CGs. All <i>q</i> <.01</td></tr><tr><td colspan="6">Genes identified as being differentially methylated in AN in 2 or more EWAS studies</td></tr><tr><td>NR1H3</td><td>X</td><td>X</td><td>X</td><td></td><td>X</td></tr><tr><td>TNXB</td><td>X</td><td>X</td><td>X</td><td></td><td>X</td></tr><tr><td>SYNJ2</td><td></td><td></td><td>X</td><td>X</td><td>X</td></tr><tr><td>PRDM16</td><td>X</td><td>X</td><td></td><td></td><td></td></tr><tr><td>HDAC4</td><td>X</td><td>X</td><td></td><td></td><td></td></tr></tbody></table>

Abbreviations: AN = anorexia nervosa; MZ = monozygotic; NED = no eating disorder.¹ Only CGs common to both the 450 BeadChip Kit and the Infinium Methylation EPIC 850K BeadChip Kit were analysed.

comparisons across individuals with active AN or those who never had an ED reveal differential methylation at multiple CpG sites. The number of identified CpGs varied across studies, with studies with larger samples reporting larger numbers of significant probes. Across studies, DNA methylation levels were reported to be altered at CpG sites mapping primarily onto genes involved with mental, metabolic and immune functions.

To date, there have been no meta-analyses on findings from EWAS studies in AN – and we feel it premature to attempt to do so given that the available studies implicate heterogeneous methods, and that three out of the five studies in AN, involve overlapping samples and are published by the same group. This being the case, rather than a systematic review, we conducted a qualitative analysis of findings from the five available EWAS studies in AN aimed at identifying reproducible epigenetic biomarkers. For these comparisons, we applied two criteria: To qualify as a possible biomarker, an identified gene (irrespective of the CpG site) had to have been found to be (i) differentially methylated in at least two EWAS studies conducted by independent research groups, or (ii) differentially methylated in at least one EWAS study and, as well,

associated with AN at genome-wide significance in at least one of the available case-control GWAS studies on AN. Stringent application of the preceding criteria isolated five genes of interest: *NR1H3*, *TNXB*, *SYNJ2*, *PRDM16* and *HDAC4* (see Table S6).

*NR1H3* was found to be differentially methylated in AN in four of the five EWAS studies, three conducted by one group (Booij et al. 2015; Steiger et al. 2019; Steiger et al. 2023) and one by another (Kesselmeier et al. 2018). *NR1H3* is involved in lipid metabolism, energy homeostasis, and inflammation. Three EWAS studies conducted by two different research groups found hypermethylation at the *TNXB* gene (Booij et al. 2015; Kesselmeier et al. 2018; Steiger et al. 2019). Involved in connective tissue formation, *TNXB* has been implicated in the development of syndromes characterised by hyperflexibility (like Ehlers-Danlos) that have also been linked to AN (Baeza-Velasco 2021). Three EWAS studies from two independent groups found differential methylation at the *SYNJ2* gene (Iranzo-Tatay et al. 2022; Steiger et al. 2019; Steiger et al. 2023), which is responsible for nerve-cell communications. In addition, the *PRDM16* gene, associated with thermogenesis, was associated with AN in two independent studies (Booij et al. 2015; Kesselmeier
<figure><img src="image_29.png" /></figure>

et al. 2018). The same two EWAS studies reported altered methylation levels at the *HDAC4* gene, which regulates histone deacetylation.

Applying our second criterion for establishing epi-
genetic biomarkers, we compared results from the five
available EWASs to findings published in eight GWAS
studies comparing individuals with AN to those with
no ED (Wang et al. 2011; Nakabayashi et al. 2009;
Wade et al. 2013a; Boraska et al. 2014; Duncan et al.
2017; Li et al. 2017; Huckins et al. 2018; Watson et al.
2019). Interestingly, none of the genes previously asso-
ciated with AN in GWASs (whether at genome-wide
significance or as ‘top hits’) showed differential meth-
ylation levels in an EWAS study. We speculate that the
latter tendency may differentiate pleiotropic genes for
AN that, because they do not depend upon environ-
mental factors, are strong candidates for direct
genome-wide association (in GWASs) from genes
whose phenotypic manifestations depend greatly upon
epistatic (gene-environment) effects that are isolated
in EWASs.

Some EWAS results have associated illness duration
and lower BMI with more pronounced DNA methyla-
tion alterations, and disease remission (as indicated by
full or partial weight restoration) with normalisation of
methylation levels (see (Booij et al. 2015; Steiger et al.
2019; Steiger et al. 2023)) – as would suggest revers-
ible alterations that correspond to illness activity and
severity. The preceding implies that methylation levels
have potential as markers of disease staging or thera-
peutic response.

**3.2. Neuroimaging**

**3.2.1. Structural neuroimaging**

Neuroimaging encompasses a set of methods utilised
to investigate the structure and function of the brain.
Over the last 30 years, a variety of neuroimaging meth-
ods have been used to study the brains of individuals
with AN and help better understand the underlying
biological mechanisms of the condition (King et al.
2018). This section provides a selective update on
structural neuroimaging in AN, highlighting the most
significant recent findings, challenges to scientific
progress, and promising avenues for future research.

Modern non-invasive magnetic resonance imaging (MRI) techniques, including diffusion tensor imaging (DTI) and network-based approaches, have driven a recent surge in research on brain structure in AN. These efforts have resulted in several comprehensive meta-analyses and dedicated review articles (Barona et al. 2019; Bracké et al. 2023; Gaudio et al. 2019;

Kappou et al. 2021; King et al. 2018; Meneguzzo et al. 2019; Monzon et al. 2017; Seitz et al. 2016; Su et al. 2021; Walton et al. 2022), as well as the establishment of an international consortium aimed at advancing our understanding of structural brain changes in EDs including AN (https://enigma.ini.usc.edu/ongoing/enigma-eating-disorders/).

The most applied structural neuroimaging method in AN research involves analysis of T1-weighted MRI brain scans, which allows for assessment of various macrostructural features of the brain by distinguishing between GM – primarily consisting of neuronal cell bodies, dendrites, and axon terminals – and WM, which comprises myelinated axonal pathways linking different brain regions. While most studies in AN samples have used voxel-based morphometry to investigate cortical and/or subcortical GM volume (Seitz et al. 2016; Su et al. 2021), there is an increasing trend towards surface-based approaches, which separate GM volume into its underlying components: cortical thickness and surface area (King et al. 2018). In recent years, a few studies in individuals with AN have begun exploring other metrics such as subcortical shape, cortical complexity and gyrification, (Bernardoni et al. 2018; Collantoni et al. 2020; Collantoni et al. 2025; Leppanen et al. 2019) conducting fine-grained analysis of subcortical substructures.

In acutely underweight individuals with AN, reductions in brain mass and enlarged ventricles are often readily visible in raw MRI scans and numerous case-control studies using the above methods have reported widespread reductions in GM in both adolescents and adult patients with AN relative to HCs (King et al. 2018). Recent mega- and meta-analyses in the largest samples to date indicate that the magnitude of GM reduction observed in AN in cortical thickness, subcortical volume and, to a lesser extent, surface area surpasses structural changes observed in any other psychiatric disorders (Bahnsen et al. 2022; Walton et al. 2022). Importantly, however, these deficits are closely linked to nutritional status and are significantly less pronounced after partial weight restoration (at least in younger patients with a shorter duration of illness (Kaufmann et al. 2020) and may not be detectable after long-term weight recovery. Together, the available data indicate that macrostructural GM alterations in AN may be a largely reversible consequence of underweight rather than a trait biomarker. However, the underlying cellular mechanisms remain unclear. There is also preliminary evidence that leptin levels are associated with structural alterations in specific brain areas (Wronski et al. 2025).
<figure><img src="image_30.png" /></figure>

The integration of peripheral biomarkers into the analysis of dynamic brain structural alterations in AN, combined with the application of advanced multi-modal methods – such as approaches rooted in artificial intelligence – promises new insight that may eventually help enhance clinical decision-making (Arold et al. 2023; Doose et al. 2023; Keeler et al. 2024).

In contrast to the amount of research focused on GM alterations in AN, relatively few studies have reported regional and/or global measures of WM volume. Similar to the findings in GM discussed above, meta-analytic data suggest that reduced WM volume in acutely underweight individuals with AN might be reversible following weight restoration (Seitz et al. 2016). However, more recent studies found no alterations in WM volume in AN relative to HCs (Geisler et al. 2022). Thus, the effect size of WM volume reduction in AN is likely to be considerably smaller than that of GM (Walton et al. 2022).

Research utilising DTI has provided more detailed insights into WM microstructure and connectivity in individuals with EDs (Barona et al. 2019; Gaudio et al. 2019; Zhang et al. 2020). DTI allows for the assessment of WM tract microstructural integrity by measuring water diffusivity along axons. Many studies investigating WM microstructure in AN have focused on group differences in diffusion strength at the voxel or tract level. In acutely underweight individuals with AN, several studies have reported altered WM matter microstructure, with evidence for both decreased and increased WM integrity (e.g. fractional anisotropy) in key tracts such as interhemispheric connections and limbic association fibres including the cingulum, corpus callosum, and fornix, as well as the superior longitudinal fasciculus and thalamo-cortical tracts (Barona et al. 2019; King et al. 2018). However, most studies did not address potential biases arising from partial volume effects (Kaufmann et al. 2017) and more research in larger (longitudinal) samples is needed to better understand dynamic changes in WM microstructure and connectivity in the disorder (Lloyd 2022).

A limited number of studies have utilised WM fibre tractography methods to assess structural connectivity in AN, yielding mixed results (Gaudio et al. 2019). While most of these studies have focused on specific tracts or connectivity from selected regions of interest, a few recent studies with robust statistical power have applied network-based graph-theoretic techniques to model whole-brain structural architecture. In a large sample of acutely underweight individuals with AN, Geisler et al. (2022) observed significant alterations in the WM connectome, including increased fractional anisotropy and reduced radial diffusivity, particularly in

occipitoparietal regions. Similarly, a recent study in an even larger sample of acutely ill patients with AN by Lloyd et al. (2023) found reduced structural connectivity in subcortical networks and enhanced cortical network connectivity when compared to HCs. Collectively, these studies suggest that individuals with AN may have disruptions in the structural connections between brain regions.

### 3.2.2. Functional connectivity

Intrinsic neural activation and intrinsic functional connectivity describe spontaneous neural activity and functional interactions between brain regions while a person is at rest, without external tasks or stimuli (Van Den Heuvel and Pol 2010). These measures are assessed using functional MRI (fMRI), positron emission tomography (PET), single photon emission computed tomography (SPECT), and arterial spin labelling (ASL). Intrinsic functional connectivity can be assessed at both global (across the whole brain) and local levels (for single brain regions), using network connectivity measures to evaluate the overall connection strength or to identify altered long- and short-range connections (Jiang and Zuo 2016; Van Den Heuvel and Pol 2010). Graph-theoretical measures further characterise the brain's network topology and organisational properties (Rubinov and Sporns 2010). Interindividual variation in intrinsic functional connectivity has been shown to allow individual identification with 78-95% accuracy and holds high predictive value for behavioural outcomes, such as cognitive performance (Mansour L et al. 2021). This section offers a focused overview of functional neuroimaging in AN, summarising recent advances and key findings across the acute phase, weight-related changes, and long-term recovery.

In the acute stage of AN, intrinsic neural activation has been examined in a meta-analysis comparing individuals with AN and HCs, revealing reductions in activity in the anterior and middle cingulate cortex, alongside increases in activity in the right parahippocampal gyrus (Su et al. 2021). While meta-analyses investigating intrinsic connectivity are currently lacking, a systematic review suggests overlapping alterations across studies within corticolimbic regions (Gaudio et al. 2016), with mixed directionality. Whole-brain analyses of long-range connections consistently report wide-spread weakened functional connectivity (Ehrlich et al. 2015; Kaufmann et al. 2023; Lotter et al. 2021; Sudo et al. 2024) and altered network topology (Geisler et al. 2016; Kaufmann et al. 2023) in the acute stage of AN. Additionally, some studies report elevated connectivity in regions such as
<figure><img src="image_31.png" /></figure>

the dorsolateral prefrontal cortex (Steward et al. 2022; Sudo et al. 2024) and the striatum (Membrives et al. 2021; Muratore et al. 2024; Uniacke et al. 2019; Via et al. 2021). However, it should be noted that there is considerable heterogeneity across studies in terms of connectivity measures and brain networks examined.

Weight-related changes in intrinsic connectivity have been investigated in isolated longitudinal studies, with mixed findings. Increased long-range connectivity has been found to normalise during weight restoration (Cha et al. 2016; Uniacke et al. 2019; Via et al. 2021). However, analyses of weakened long-range connectivity suggest only partial normalisation (Kaufmann et al. 2023; Lotter et al. 2021), with persistent reductions in connectivity between regions of salience, fronto-parietal, and visual brain networks (Kaufmann et al. 2023; Uniacke et al. 2019). The degree of recovery may vary depending on patient characteristics, such as age (i.e. younger individuals may recover more quickly), and the specific connectivity measures analysed (i.e. short-range connections may recover more rapidly) (Seidel et al. 2024).

Among long-term weight-recovered individuals with AN, evidence remains mixed. Some studies report reduced connectivity in fronto-parietal, visual, and auditory networks (Boehm et al. 2016; Favaro et al. 2012; Scaife et al. 2017), while others describe increased connectivity in the default mode network (Cowdrey et al. 2014) or no residual alterations (Lotter et al. 2021), compared to HCs. Notably though, even in cases where no differences in network connectivity were detected, long-term recovered individuals could still be distinguished from HCs with high accuracy (>70%) based on intrinsic functional connectivity patterns (Geisler et al. 2020), suggesting a persistent aberrant brain network configuration in these individuals.

Overall, in the acute stage of AN, both intrinsic neural activity and functional connectivity appear to be weakened. Alterations in fronto-parietal and visual networks likely persist after weight restoration, potentially serving as trait markers of AN. These findings are consistent with evidence linking weaker intrinsic connectivity to more severe ED symptoms in healthy (self-reported absence of neurological or psychiatric disorders; no use of psychoactive medications; no other chronic diseases) young adults (Chen, Gao, et al. 2021), suggesting that disrupted intrinsic brain function may reflect a stable neurobiological marker of AN.

Table 3 shows the direction of change of all alterations of biological markers associated with AN in cross-sectional studies and with the changes of these markers with weight recovery.

### 3.3. Molecular markers of brain plasticity and damage

Neuronal biomarkers, encompassing entities like BDNF, tau, NfL, and synaptic biomarkers, have garnered significant attention due to their potential as indicators of neuronal damage, dysfunction, and plasticity alterations in neuropsychiatric disorders, including AN.

#### 3.3.1. Brain-derived neurotrophic factor and other neurotrophins

BDNF is a growth factor within the neurotrophin family, which also includes neurotrophin (NT)-3, -4 and -5. It is found both in brain and peripheral nervous system and can be quantified in humans peripherally in the serum and plasma, as well as in cerebral spinal fluid, usually using an enzyme-linked immunosorbent assay (ELISA). Peripheral levels of BDNF are often used as a proxy of central BDNF levels, given that BDNF is able to cross the blood-brain barrier bidirectionally, and that the two have been found to be associated (Klein et al. 2011; Sartorius et al. 2009). The precursor form of BDNF (proBDNF) is proteolytically cleaved into a mature form (mBDNF); however both forms are biologically-active molecules that act on different pathways (Koshimizu et al. 2010). BDNF binds to two types of receptor, producing downstream effects on synaptic plasticity (via the tropomyosin receptor kinase B [TrkB] receptor) and apoptosis (via the p75 neurotrophin receptor) (Podyma et al. 2021). BDNF promotes neuronal survival and growth and differentiation of both neurons and synapses within the central and peripheral nervous systems (Kowiański et al. 2018; Tolwani et al. 2004). In brain, BDNF has important roles in the hippocampus, hypothalamus, ventral tegmental area, cerebellum and cerebral cortex (Miranda et al. 2019). It is also expressed in other organ systems (e.g. kidneys, retina and heart) and peripheral tissues (e.g. skeletal muscle and adipose tissue) (Afsar and Afsar 2022; Kimura et al. 2016; Matthews et al. 2009; Pius-Sadowska and Machaliński 2017). BDNF synthesis is markedly increased by physical exercise, which is thought to at least partially mediate exercise-induced synaptic plasticity and hippocampal neurogenesis (Liu and Nusslock 2018; Loprinzi and Frith 2019). Additionally, BDNF expression in the hippocampus has been found to be induced by leptin (Li et al. 2021), see section 3.6.1. for a detailed overview of leptin.

Three meta-analyses have investigated differences in peripheral concentrations of BDNF between people with AN and HCs (Brandys et al. 2011; Keeler et al. 2022c; Shobeiri et al. 2022). All found moderate-large
<figure><img src="image_32.png" /><figcaption>Table 3. Biological markers in anorexia nervosa. For genetic and epigenetic markers see Tables 1–2 and S1–S5, for clinical risk markers see table 4, and for digital markers table 5.</figcaption></figure>

<table><thead><tr><th rowspan="2">Biological system</th><th rowspan="2">Biological marker</th><th colspan="3">AN vs. Controls</th><th colspan="3">Before vs. after weight recovery</th><th rowspan="2">Further information, references: See section</th></tr><tr><th>Method and/or specimen</th><th>Alteration</th><th>Highest evidence</th><th>Method and/or specimen</th><th>Change</th><th>Highest evidence</th><th>Relevance and comments</th></tr></thead><tbody><tr><td rowspan="10">Neuroimaging Markers</td><td rowspan="10">Brain</td><td>GM changes:</td><td rowspan="10">↓</td><td rowspan="10">↑</td><td rowspan="10">Meta-analyses</td><td rowspan="10">MRI; head</td><td rowspan="10">↑</td><td rowspan="10">Meta-analyses</td><td>Linked to nutritional status. Largely reversible after weight recovery.</td><td rowspan="10">3.2.1.</td></tr><tr><td>Cortical volume, thickness and surface area</td><td></td></tr><tr><td>Subcortical volume</td><td></td></tr><tr><td>WM changes</td><td rowspan="3">↑</td><td rowspan="3">↑</td><td rowspan="3">Cross-sectional studies</td><td rowspan="3">MRI; head</td><td rowspan="3">↑</td><td rowspan="3">Longitudinal studies</td><td>WM structural integrity is reduced. Connectivity findings are inconclusive and do not only show reduced connectivity.</td><td rowspan="3">3.2.1.</td></tr><tr><td>Microstructural integrity</td><td></td></tr><tr><td>Structural connectivity</td><td></td></tr><tr><td>Functional connectivity</td><td rowspan="4">↓</td><td rowspan="4">↓</td><td rowspan="4">Systematic reviews</td><td rowspan="4">MRI; head</td><td rowspan="4">↑</td><td rowspan="4">Longitudinal studies</td><td>Longitudinal studies show partial normalisation. Partially linked to nutritional status. Quicker recovery in younger people. Slower recovery of long-range connectivity.</td><td rowspan="4">3.2.2.</td></tr><tr><td>Long-range connectivity</td><td></td></tr><tr><td>Short-range connectivity</td><td></td></tr><tr><td>Brain</td><td>Molecular markers of brain plasticity and damage</td><td>BDNF</td><td>Plasma/Serum</td><td>↓</td><td>Meta-analyses</td><td>Plasma/Serum</td><td>↑</td><td>Meta-analysis</td><td>Consistently reported decreased BDNF serum concentrations in patients with AN in comparison to HC. Recovery to normal or potentially supranormal concentrations after weight restoration. State marker of AN. Not yet evaluated for clinical practice. Maintains the stability of microtubules in axons. Elevated levels can indicate neuronal damage. NFL is released into surrounding fluid when neurons are damaged.</td><td>3.3.1.</td></tr><tr><td rowspan="3">Tau protein</td><td rowspan="3">Serum</td><td rowspan="3">↑</td><td rowspan="3">↑</td><td rowspan="3">Cross-sectional studies</td><td rowspan="3">Meta-analysis</td><td rowspan="3">↓</td><td rowspan="3">↓</td><td rowspan="3">after full weight recovery</td><td rowspan="3">Longitudinal studies</td><td rowspan="3">3.3.2.</td></tr><tr><td rowspan="2">NfL</td><td rowspan="2">Serum</td><td rowspan="2">↑</td><td rowspan="2">↓</td><td rowspan="2">after partial and full weight recovery</td><td rowspan="2">Longitudinal studies</td><td rowspan="2">3.3.3.</td></tr><tr><td rowspan="2">GFAP</td><td rowspan="2">Serum</td><td rowspan="2">↑</td><td rowspan="2">↓</td><td rowspan="2">after partial and full weight recovery</td><td rowspan="2">Longitudinal studies</td><td rowspan="2">3.3.4.</td></tr></tbody></table>

(Continued)
<figure><img src="image_33.png" /><figcaption>Table 3. Continued.</figcaption></figure>

<table><thead><tr><th rowspan="2">Biological system</th><th rowspan="2">Biological marker</th><th colspan="4">AN vs. Controls</th><th colspan="4">Before vs. after weight recovery</th><th rowspan="2">Relevance and comments</th><th rowspan="2">Further information, references; See section</th></tr><tr><th>Method and/or specimen</th><th>Alteration</th><th>Highest evidence</th><th>Method and/or specimen</th><th>Change</th><th>Highest evidence</th><th>Cross-sectional studies</th><th>↑ NPY in CSF.<br>Leptin inhibits NPY release.</th><th></th></tr></thead><tbody><tr><td colspan="12" style="text-align: left; font-weight: bold;">Signalling molecules associated with the regulation of appetite and metabolism</td></tr><tr><td rowspan="2">Hypothalamus</td><td>Plasma, serum, CSF</td><td>↑↓</td><td>Cross-sectional studies</td><td>CSF</td><td>No change after short-term weight restoration</td><td>Serum</td><td>↑ with weight recovery</td><td>Longitudinal study</td><td>Inconsistent in serum/plasma.<br>↑ NPY in CSF.<br>Leptin inhibits NPY release.</td><td>3.4.1.</td></tr><tr><td>NPY</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="2">α-MSH</td><td>Plasma/serum</td><td>↑↓</td><td>Cross-sectional studies</td><td>Serum</td><td></td><td></td><td></td><td></td><td>Serum α-MSH correlates negatively with BMI-SDS in HCs but not in AN</td><td>3.4.2.</td></tr><tr><td>Serum</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Leptin stimulates α-MSH release, Ligand of GPR7/8.<br>Elevated in acute AN.<br>Decreases with refeeding.</td><td>3.4.3.</td></tr><tr><td rowspan="2">NPB</td><td>Serum</td><td>↑</td><td>Cross-sectional studies</td><td>Serum</td><td>↓ with weight recovery</td><td></td><td></td><td>Longitudinal study</td><td>Only two small studies available.<br>Ligand of GPR7/8; no differences in AN.</td><td>3.4.3.</td></tr><tr><td>Serum</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Unchanged with refeeding.<br>Only two small studies available.</td><td>3.4.4.</td></tr><tr><td rowspan="2">NPW</td><td>Serum</td><td>↔</td><td>Cross-sectional studies</td><td>Serum</td><td>↔</td><td></td><td></td><td>Longitudinal study</td><td>No significant difference between acutely ill inpatients with AN, partial weight normalisation or control group</td><td>3.4.5.</td></tr><tr><td>Serum</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Significantly lower in both acutely ill AN inpatients vs HC and after partial weight normalisation vs control</td><td>3.4.5.</td></tr><tr><td rowspan="2">KISS</td><td>Serum</td><td>↔</td><td>Longitudinal study</td><td>Serum</td><td>↔</td><td></td><td></td><td>Longitudinal study</td><td>No change of SPX during weight normalisation.</td><td>3.4.6.</td></tr><tr><td>Serum</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Significantly lower in acutely ill AN inpatients than HCs<br>Significantly lower in acutely ill AN inpatients than after partial weight normalisation</td><td>3.4.6.</td></tr><tr><td rowspan="2">PNX</td><td>Serum</td><td>↓</td><td>Longitudinal study</td><td>Serum</td><td>↑</td><td></td><td></td><td>Longitudinal study</td><td>Energy levels and psychological state influence orexin levels.</td><td>3.4.7.</td></tr><tr><td>Serum</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Orexin levels seem to normalise with weight recovery.</td><td>3.4.7.</td></tr><tr><td>Orexin</td><td>Plasma</td><td>↓↑</td><td>Cross-sectional studies</td><td>Plasma</td><td></td><td>↑</td><td></td><td>Longitudinal study</td><td></td><td></td></tr></tbody></table>

(Continued)
<figure><img src="image_34.png" /><figcaption>Table 3. Continued.</figcaption></figure>

<table><thead><tr><th rowspan="2">Biological system</th><th rowspan="2">Biological marker</th><th rowspan="2">Method and/or specimen</th><th colspan="2">AN vs. Controls</th><th colspan="4">Before vs. after weight recovery</th><th rowspan="2">Relevance and comments</th><th rowspan="2">Further information, references: See section</th></tr><tr><th>Alteration</th><th>Highest evidence</th><th>Method and/or specimen</th><th>Change</th><th>Highest evidence</th></tr></thead><tbody><tr><td>Gastrointestinal tract</td><td> signalling molecules</td><td>Plasma</td><td>↑</td><td>Meta-analysis</td><td>Plasma</td><td>↓</td><td>Meta-analysis</td><td>Ghrelin levels influenced by food intake, type of food and hydration level</td><td>3.5.1.</td></tr><tr><td>Gl tract</td><td>Total ghrelin</td><td>Plasma</td><td>↑</td><td>Meta-analysis</td><td>Plasma</td><td>↔</td><td>Meta-analysis</td><td>Acyl-ghrelin is the active form of ghrelin</td><td>3.5.1.</td></tr><tr><td>Acyl-ghrelin</td><td>Plasma</td><td>↑</td><td>Meta-analysis</td><td>Plasma</td><td>↓ after partial and full weight recovery</td><td>Longitudinal study</td><td>Desacyl-ghrelin is a form of ghrelin that lacks the acyl modification on its serine residue.</td><td>3.5.1.</td></tr><tr><td>Desacyl-ghrelin</td><td>Plasma</td><td>↑</td><td>Meta-analysis</td><td>Plasma</td><td>↓ after weight recovery</td><td>Longitudinal study</td><td>Patients with AN display higher LEAP-2 levels in undernourished compared to their refed state</td><td>3.5.2.</td></tr><tr><td>LEAP-2</td><td>Plasma</td><td>(↑) comparison with refed patients with AN, not HCs. ↓ following starvation in animals and humans</td><td>Longitudinal study; no cross-sectional comparison with HCs</td><td>Plasma</td><td>↓ after weight recovery</td><td>Longitudinal study</td><td>Abnormal LEAP-2 regulation during refeeding is associated with unstable remission</td><td>3.5.2.</td></tr><tr><td>GLP-1</td><td>Plasma</td><td>↑</td><td>Cross-sectional studies</td><td>Plasma</td><td>↑ after 2 weeks of refeeding</td><td>Longitudinal study</td><td>Cross-sectional studies in AN inconsistent.</td><td>3.5.3.</td></tr><tr><td>Cholecystokinin</td><td>Plasma</td><td>↑</td><td>Cross-sectional studies</td><td>N/A</td><td>N/A</td><td>N/A</td><td>In response to meals, especially those high in fat and protein content, cholecystokinin levels increase.</td><td>3.5.4.</td></tr><tr><td>PYY</td><td>Serum</td><td>↑</td><td>Cross-sectional studies</td><td>Serum</td><td>↔</td><td>Longitudinal studies</td><td>PYY reduces food intake. Findings inconsistent in AN. PYY concentrations have been found to either decrease or remain unchanged during nutritional rehabilitation</td><td>3.5.5.</td></tr><tr><td>Signalling molecules of the adipose tissue</td><td>Leptin</td><td>Plasma/Serum</td><td>↓</td><td>Meta-analysis</td><td>Plasma/Serum</td><td>↑</td><td>Longitudinal studies</td><td>Leptin reflects the amount of adipose tissue. Leptin is influenced hypothalamic appetite regulators (α-MSH, NPY, AgRP)</td><td>3.6.1.</td></tr><tr><td>Fatty tissue</td><td>Leptin</td><td>Plasma/Serum</td><td>↓</td><td>Meta-analysis</td><td>Plasma/Serum</td><td>↔</td><td>Longitudinal studies</td><td>IGF-1, GH, insulin and leptin influence adiponectin levels.</td><td>3.6.2.</td></tr><tr><td>Adiponectin</td><td>Plasma/Serum</td><td>↑</td><td>Meta-analysis</td><td>Plasma/Serum</td><td>↔</td><td>Longitudinal studies</td><td>N/A</td><td>3.6.3.</td></tr><tr><td>Resistin</td><td>Serum</td><td>↓</td><td>Meta-analysis</td><td>N/A</td><td>N/A</td><td>N/A</td><td>Longitudinal studies</td><td>3.6.4.</td></tr><tr><td>Vaspin</td><td>Serum</td><td>↑</td><td>Meta-analysis</td><td>Serum</td><td>↓</td><td>Longitudinal studies</td><td>Leptin stiumulates the HPA, HPT and HPG axis.</td><td>3.6.5.</td></tr><tr><td>Omentin</td><td>Serum</td><td>↑</td><td>Meta-analysis</td><td>Serum</td><td>Inconsistent</td><td>Longitudinal study</td><td>Body fat content influences visfatin levels.</td><td>3.6.6.</td></tr><tr><td>Visfatin</td><td>Serum and saliva</td><td>(↓)</td><td>Meta-analysis</td><td>Serum</td><td>Inconsistent</td><td>Longitudinal studies</td><td>Leptin stiumulates the HPA, HPT and HPG axis.</td><td>3.6.7.</td></tr><tr><td>Obestatin</td><td>Serum</td><td>↑</td><td>Cross-sectional studies</td><td>Serum</td><td>↓</td><td>Longitudinal studies</td><td>Leptin reflects the amount of adipose tissue. Leptin is influenced hypothalamic appetite regulators (α-MSH, NPY, AgRP)</td><td>3.6.8.</td></tr><tr><td>Apelin</td><td>Plasma/Serum</td><td>↑</td><td>Cross-sectional studies</td><td>Plasma/Serum</td><td>↓</td><td>Longitudinal studies</td><td>IGF-1, GH, insulin and leptin influence adiponectin levels.</td><td>3.6.8.</td></tr></tbody></table>
<table><caption>Table 3. Continued.</caption><thead><tr><th rowspan="2">Biological system</th><th rowspan="2">Biological marker</th><th colspan="3">AN vs. Controls</th><th colspan="4">Before vs. after weight recovery</th><th rowspan="2">Relevance and comments</th><th rowspan="2">Further information, references; See section</th></tr><tr><th>Method and/or specimen</th><th>Alteration</th><th>Highest evidence</th><th>Method and/or specimen</th><th>Change</th><th>Highest evidence</th></tr></thead><tbody><tr><td>Hypothalamus</td><td>CRH</td><td>CSF</td><td>↑</td><td>Cross-sectional studies</td><td>CSF</td><td>Normalise after body weight gain</td><td>One longitudinal study</td><td>Stimulates the release of ACTH from the pituitary gland.</td><td>3.7.1.</td></tr><tr><td>Pituitary gland</td><td>ACTH</td><td>Plasma/Serum</td><td>↑</td><td>Meta-analysis</td><td>Plasma/serum</td><td>Inconsistent</td><td>Cross-sectional studies</td><td>Produced by the pituitary gland and stimulates the adrenal glands to produce cortisol.</td><td>3.7.1.</td></tr><tr><td>Adrenal gland cortex</td><td>Cortisol</td><td>Plasma/Serum</td><td>↑</td><td>Meta-analyses</td><td>Plasma/serum</td><td>Normalise after body weight gain</td><td>Longitudinal studies</td><td>State-dependent phenomenon likely due to the effects of starvation or undernutrition.</td><td>3.7.2.</td></tr><tr><td rowspan="2">Sexual and social hormones</td><td rowspan="2">Hypothalamus</td><td rowspan="2">GnRH</td><td rowspan="2">↑</td><td rowspan="2">Meta-analyses</td><td rowspan="2">N/A</td><td rowspan="2">N/A</td><td rowspan="2">N/A</td><td rowspan="2">Stimulates LH and FSH production.</td><td rowspan="2">3.7.2.</td></tr><tr><td>Plasma/Serum</td><td>↑</td><td>Normalises with weight recovery</td><td>Longitudinal studies</td></tr><tr><td>Pituitary gland</td><td>LH</td><td>Plasma/Serum</td><td>↓</td><td>Cross-sectional studies</td><td>↑</td><td>Normalises with weight recovery</td><td>Longitudinal studies</td><td>In females, triggers ovulation and the production of oestrogen and progesterone. In males, stimulates the testes to produce testosterone.</td><td>3.8.1.</td></tr><tr><td>Pituitary gland</td><td>FSH</td><td>Plasma/Serum</td><td>↓</td><td>Cross-sectional studies</td><td>↑</td><td>Normalises with weight recovery</td><td>Longitudinal studies</td><td>Stimulates the ovaries to produce eggs and oestrogen (females) and the testes to produce sperms (males).</td><td>3.8.1.</td></tr><tr><td>Adrenal gland</td><td>DHEA</td><td>Serum</td><td>↔</td><td>Meta-analyses</td><td>N/A</td><td>N/A</td><td>N/A</td><td>Produced by the adrenal gland, helps production of testosterone and oestrogen.</td><td>3.8.2.</td></tr><tr><td rowspan="2">Adrenal gland</td><td rowspan="2">DHEA-S</td><td rowspan="2">Oestradiol</td><td rowspan="2">↓</td><td rowspan="2">Meta-analyses</td><td rowspan="2">N/A</td><td rowspan="2">N/A</td><td rowspan="2">N/A</td><td rowspan="2">Produced by the adrenal gland, helps production of testosterone and oestrogen.</td><td rowspan="2">3.8.2.</td></tr><tr><td>Cross-sectional studies</td><td>↑</td><td>Normalises with weight recovery</td><td>Longitudinal studies</td></tr><tr><td>Adrenal gland</td><td>♀: Ovaries, adipose tissue</td><td>↑</td><td>↑</td><td>Meta-analyses</td><td>↑</td><td>Normalises with weight recovery</td><td>Longitudinal studies</td><td>Produced by the adrenal gland, helps production of testosterone and oestrogen.</td><td>3.8.3.</td></tr><tr><td>Adrenal gland</td><td>♂: Ovaries, adipose tissue</td><td>↑</td><td>↑</td><td>Meta-analyses</td><td>↑</td><td>Normalises with weight recovery</td><td>Longitudinal studies</td><td>Produced by the adrenal gland, helps production of testosterone and oestrogen.</td><td>3.8.3.</td></tr><tr><td>Hypothalamus</td><td>Oxytocin</td><td>Plasma/Serum/ CSF</td><td>↓</td><td>Meta-analysis</td><td>Serum/CSF</td><td>Inconsistent</td><td>Longitudinal studies</td><td>An opposite trend with higher (↑) oxytocin levels reported in a sample of adolescents with AN.</td><td>3.8.4.</td></tr><tr><td rowspan="5">Immunological markers</td><td rowspan="5">Cytokines</td><td>TNF-α</td><td>↑</td><td>↔</td><td>Meta-analyses</td><td>Plasma/Serum</td><td>↔</td><td>Meta-analysis</td><td>3.9.1.</td></tr><tr><td>IL-1β</td><td>↑</td><td>↔</td><td>Meta-analyses</td><td>Plasma/Serum</td><td>↔</td><td>Meta-analysis</td><td>3.9.1.</td></tr><tr><td>IL-6</td><td>↑</td><td>↔</td><td>Meta-analyses</td><td>Plasma/Serum</td><td>↔</td><td>Meta-analysis</td><td>3.9.1.</td></tr><tr><td>IL-7</td><td>↑</td><td>↔</td><td>Meta-analyses</td><td>Plasma/Serum</td><td>↔</td><td>Meta-analysis</td><td>3.9.1.</td></tr><tr><td>IL-15</td><td>↑</td><td>↔</td><td>Meta-analyses</td><td>Plasma/Serum</td><td>↔</td><td>Meta-analysis</td><td>3.9.1.</td></tr></tbody></table>

(Continued)
<figure><img src="image_35.png" /><figcaption>Table 3. Continued.</figcaption></figure>

<table><thead><tr><th rowspan="2">Biological system</th><th rowspan="2">Biological marker</th><th rowspan="2">Method and/or specimen</th><th colspan="2">AN vs. Controls</th><th colspan="4">Before vs. after weight recovery</th><th rowspan="2">Further information, references: See section</th></tr><tr><th>Alteration</th><th>Highest evidence</th><th>Method and/or specimen</th><th>Change</th><th>Highest evidence</th><th>Relevance and comments</th></tr></thead><tbody><tr><td rowspan="2">Immunoglobulins</td><td>Blood</td><td>α-MSH IgG antibodies</td><td>↓ levels but ↑ biological signalling efficiency of α-MSH in acute stage</td><td>Cross-sectional</td><td>Serum</td><td>↑ levels with weight recovery</td><td>Longitudinal in adolescents</td><td>Correlate negatively with BMI-SDS in both HCs and AN, ↑ affinity enhances carrier role of IgG for α-MSH</td><td>3.9.2.</td></tr><tr><td>Ghrelin IgG antibodies</td><td>Plasma</td><td>↓ levels and affinity</td><td>Cross-sectional</td><td>Plasma</td><td>↑ levels with weight recovery</td><td>Longitudinal</td><td>↓ affinity reduces carrier role of IgG for ghrelin</td><td>3.9.2.</td></tr><tr><td rowspan="7">Immune cells</td><td>Blood, bone marrow</td><td>slgA</td><td>↑</td><td>Cross-sectional studies</td><td>N/A</td><td>N/A</td><td>N/A</td><td></td><td>3.12.2.</td></tr><tr><td>Leukocytes</td><td>Blood</td><td>↓</td><td>Meta-analysis</td><td>Blood</td><td>↑</td><td>Meta-analysis</td><td></td><td>3.9.3.</td></tr><tr><td>Lymphocytes</td><td>Blood</td><td>↓</td><td>Meta-analyses</td><td>Blood</td><td>↑</td><td>Longitudinal studies</td><td></td><td>3.9.3.</td></tr><tr><td>Eosinophils</td><td>Blood</td><td>↓</td><td>Meta-analysis</td><td>Blood</td><td>↑</td><td>Longitudinal studies</td><td></td><td>3.9.3.</td></tr><tr><td>Basophils</td><td>Blood</td><td>↓</td><td>Meta-analysis</td><td>Blood</td><td>↑</td><td>Longitudinal studies</td><td></td><td>3.9.3.</td></tr><tr><td>Monocytes</td><td>Blood</td><td>↓</td><td>Meta-analysis</td><td>Blood</td><td>↑90% IBW</td><td>Longitudinal studies</td><td></td><td>3.9.3.</td></tr><tr><td>Neutrophils</td><td>Blood</td><td>↓</td><td>Meta-analysis</td><td>Blood</td><td>↑</td><td>Longitudinal studies</td><td></td><td>3.9.3.</td></tr><tr><td rowspan="3">Acute-phase protein</td><td>Blood, liver</td><td>CRP</td><td>↓</td><td>Meta-analyses</td><td>N/A</td><td>N/A</td><td>N/A</td><td>Triglycerides higher in AN relative to HCs.</td><td>3.9.4.</td></tr><tr><td>Metabolomic markers</td><td>Plasma/Serum</td><td>↑</td><td>Meta-analysis</td><td>Plasma</td><td>↑</td><td>Longitudinal study</td><td>Triglycerides higher in AN relative to HCs.</td><td>3.10.3.</td></tr><tr><td>Blood</td><td>Triglycerides</td><td>↑</td><td>Meta-analysis</td><td>Plasma</td><td>↑</td><td>Longitudinal study</td><td>Triglycerides higher in AN relative to HCs.</td><td>3.10.3.</td></tr><tr><td rowspan="2">VLDL</td><td>Total cholesterol</td><td>Plasma/Serum</td><td>↑</td><td>Meta-analysis</td><td>Plasma/serum</td><td>↓</td><td>Meta-analysis</td><td>Increased cholesterol levels in AN are a result of HPA axis activation and low T3 levels.</td><td>3.10.3</td></tr><tr><td>VLDL</td><td>Plasma/serum</td><td>↑</td><td>Cross-sectional studies</td><td>Plasma/serum</td><td>↑</td><td>Naturalistic longitudinal study</td><td>Increased VLDL in acute AN relative to HC.</td><td>3.10.3</td></tr><tr><td rowspan="2">LDL</td><td>LDL</td><td>Plasma/serum</td><td>↑</td><td>Meta-analysis</td><td>Plasma/serum</td><td>↔</td><td>Meta-analysis</td><td>VLDL levels remain high despite weight recovery.</td><td>3.10.3</td></tr><tr><td>LDL</td><td>Plasma/serum</td><td>↑</td><td>Meta-analysis</td><td>Plasma/serum</td><td>↔</td><td>Meta-analysis</td><td>Increased in AN relative to HC. Higher LDL in partially weight restored patients with AN compared to HC.</td><td>3.10.3</td></tr><tr><td rowspan="2">HDL</td><td>HDL</td><td>Plasma/serum</td><td>↑</td><td>Meta-analysis</td><td>Plasma/serum</td><td>↔</td><td>Meta-analysis</td><td>Increased HDL at 7 years associated with a higher risk of developing AN.</td><td>3.10.3</td></tr><tr><td>HDL</td><td>Plasma/serum</td><td>↑</td><td>Meta-analysis</td><td>Plasma/serum</td><td>↔</td><td>Meta-analysis</td><td>Increased HDL at 7 years associated with a higher risk of developing AN.</td><td>3.10.3</td></tr></tbody></table>

(Continued)
<table><caption>Table 3. Continued.</caption><thead><tr><th rowspan="2">Biological system</th><th rowspan="2">Biological marker</th><th colspan="4">AN vs. Controls</th><th colspan="4">Before vs. after weight recovery</th><th rowspan="2">Further information, references; See section</th></tr><tr><th>Method and/or specimen</th><th>Alteration</th><th>Highest evidence</th><th>Method and/or specimen</th><th>Change</th><th>Highest evidence</th><th>Relevance and comments</th></tr></thead><tbody><tr><td rowspan="5">Microbiome markers</td><td>Alpha diversity</td><td>Faeces</td><td>↔↑↓</td><td>Cross-sectional studies</td><td>Faeces</td><td>↔↑↓</td><td>Longitudinal studies</td><td></td><td>3.11.</td></tr><tr><td>– Richness</td><td>Faeces</td><td>↔↑↓</td><td>Meta-analysis</td><td>Faeces</td><td>↔</td><td>Longitudinal studies</td><td></td><td>3.11</td></tr><tr><td>Alpha diversity – Diversity</td><td>Faeces</td><td>↑↓</td><td>Meta-analysis</td><td>Faeces</td><td>↔↑↓</td><td>Longitudinal studies</td><td>Direction of differences inconsistent.</td><td>3.11</td></tr><tr><td>Community Structure</td><td>Faeces</td><td>↑↓</td><td>Meta-analysis</td><td>Faeces</td><td>↔</td><td>Longitudinal studies</td><td>Tendencies towards ↓ butyrate-producing species and ↑ mucin-degrading species.</td><td>3.11</td></tr><tr><td>Taxa differences</td><td>Faeces</td><td>↑↓</td><td>Meta-analysis</td><td>Faeces</td><td>↔</td><td>Longitudinal studies</td><td>Nominally but not significantly higher α-amylase levels in AN compared to HC.</td><td>3.12.2.1.</td></tr><tr><td rowspan="2">Salivary markers</td><td>α-Amylase</td><td>Saliva</td><td>↔</td><td>Meta-analysis</td><td>N/A</td><td>N/A</td><td>N/A</td><td></td><td>3.12.2.4.</td></tr><tr><td>Opirophin</td><td>Saliva</td><td>↔</td><td>Cross-sectional studies</td><td>N/A</td><td>N/A</td><td>N/A</td><td></td><td>3.13.1.</td></tr><tr><td>Neurophysiological markers</td><td>Eye movements</td><td>Square wave jerks</td><td>Oculography</td><td>↑</td><td>Cross-sectional studies</td><td>N/A</td><td>N/A</td><td>N/A</td><td></td></tr><tr><td colspan="10">Abbreviations: α-MSH=alpha-melanocyte stimulating hormone; ACTH=adrenocorticotrophic hormone; BDNF=brain derived neurotrophic factor; BMI-SDS=body mass index standard deviation score; CRH=corticotropin releasing hormone; CRP=C-reactive protein; CSF=cerebrospinal fluid; DHEA=dehydroepiandrosterone; DHEA-S=dehydroepiandrosterone sulphate; FSH=follicle-stimulating hormone; GFAP=glial fibrillary acidic protein; GH=growth hormone; GLP-1=glucagon-like peptide 1; GM=grey matter; GnRH=gonadotropin-releasing hormone; HC=healthy controls; HDL=high density lipoprotein; HPA=hypothalamus pituitary adrenal; HPT=hypothalamus pituitary thyroid; HPG=hypothalamus-pituitary-gonadal; IGF-1=insulin-like growth factor 1; IgG=immunoglobulin G; IL=interleukin; KISS=kisseptin; LDL=low density lipoprotein; LEAP-2=liver expressed antimicrobial peptide 2; LH=luteinising hormone; MRI=magnetic resonance imaging; N/A=not assessed or not applicable; NFL=neurofilament light chain; NPB=neuropeptide b; NPW=neuropeptide w; NPY=neuropeptide y; PNX=phoenixin; PYY=Peptide YY; sigA=salivary immunoglobulin A; SPX=spxin; T3=triliodothyronine; TNF-α=tumour necrosis factor-alpha; VLDL=very low density lipoprotein; WM=white matter; ↑=significantly higher values or increase, ↓=significantly lower values or decrease, ↑↓=inconsistent results, ↔=no significant change.</td></tr></tbody></table>
<figure><img src="image_36.png" /></figure>

state-related reductions in BDNF during the acute stage of AN. Over the course of weight restoration, increases in BDNF have been found, with trends towards supranormal levels following longer-term weight restoration (Keeler et al. 2022c). This has been confirmed in the largest longitudinal study to date on adolescent patients with AN, which demonstrated that BDNF levels increased continuously during recovery and reached supranormal levels at 2.5-year follow-up (Borsdorf et al. 2021). These findings provide stronger evidence that elevated BDNF may be a risk factor for relapse given its anorexigenic effects. Subgroup analyses revealed that BDNF was lower only in the restricting subtype of AN (AN-R), and not the binge-purging subtype (AN-BP) (Keeler et al. 2022c). Additionally, it is possible that the reduction in BDNF may only exist in adults; whilst there were insufficient studies to perform subgroup analyses by age category in the aforementioned meta-analysis (Keeler et al. 2022c), other studies have failed to find state-related reductions in BDNF in adolescent populations (e.g. (Steinhäuser et al. 2021)). In terms of genetic risk for impairments in BDNF synthesis, a meta-analysis examining the Val66Met polymorphism (rs6265) of the BDNF gene found no greater association in people with AN than HCs (Brandys et al. 2013). However, recent reviews have provided more nuanced perspectives on this polymorphism's role in AN susceptibility, prognosis, and treatment response (Cao et al. 2024). Current evidence suggests the Val66Met polymorphism may interact with stress-related events and metabolic disturbances in modulating AN risk, representing a more complex relationship than previously understood.

BDNF is most likely to represent a state marker of AN, at least in AN-R, which is thought to be indicative of nutritional status rather than merely low weight (Keeler et al. 2022c). It has been suggested that reductions in BDNF may be a physiological adaptation to increase food intake, since BDNF has anorexigenic appetite-suppressing effects (Brandys et al. 2011). However, there has been some debate in the discussion of BDNF as a biomarker for AN, as whilst it demonstrates association with the illness and test-retest reliability, it may not have high enough specificity to distinguish AN from other forms of psychopathology (Brandys et al. 2011). For example, meta-analytic results suggest decreased BDNF in major depressive disorder (MDD), across most studies (Brunoni et al. 2008). It is possible that decreases in BDNF may represent a biomarker for general psychopathological processes, such as alterations in neuroplasticity, which may be present transdiagnostically. However, it should be noted that although treatment with antidepressants normalises

BDNF levels in people with MDD (Brunoni et al. 2008), traditional antidepressants are largely ineffective in AN. Furthermore, Keeler et al. (2022c) found that AN-BP does not show decreases in BDNF despite presumably still having comorbid depressive features. There is mixed evidence for an association between depression symptoms and BDNF in people with AN (Mercader et al. 2007; Nakazato et al. 2009; Steinhäuser et al. 2021; Keeler et al. 2022a). Further investigations are necessary in order to ascertain whether reductions in BDNF may meet the criteria for classification as a biomarker of nutritional status or neuroplasticity for AN during the acute stages, which may in future aid clinicians to offer personalised BDNF-promoting treatments (e.g. ketamine, psilocybin; (Keeler, Kan, et al. 2024; Keeler et al. 2021)).

Additional neurotrophins of interest include vascular endothelial growth factor (VEGF), crucial for vasculogenesis and angiogenesis, glial cell-derived neurotrophic factor (GDNF), which promotes neuronal survival, and neurotrophin (NT)-3 and -4, both of which support the development of sensory neurons. A systematic review found that peripheral concentrations of VEGF-A and NT-4 were decreased in AN relative to HCs, with no differences in NT-3 or GDNF, although evidence is preliminary (Keeler et al. 2022c).

### 3.3.2. Tau protein

Tau is a microtubule-associated protein predominantly expressed in neurons of the CNS, playing a crucial role in stabilising microtubules and thereby influencing axonal transport and neuronal plasticity (Barbier et al. 2019; Mandelkow and Mandelkow 2012). Structurally, tau is an intrinsically disordered protein, because it lacks a stable structure and exists in a relatively unfolded state, allowing it to interact with various cellular components and adopt multiple conformations (Barbier et al. 2019; Michalicova et al. 2020; Mueller et al. 2021). Its function is finely regulated by post-translational modifications, notably phosphorylation, which modulates its affinity for microtubules and other interacting partners (Barbier et al. 2019; Mandelkow and Mandelkow 2012). Alterations in tau, such as hyperphosphorylation and aggregation, are hallmark features of several neurodegenerative diseases, including Alzheimer's disease (AD), where they form neurofibrillary tangles contributing to neuronal dysfunction and cell death (Barbier et al. 2019; Mandelkow and Mandelkow 2012).

Neurodegeneration processes can be detected and quantified using various biomarkers in cerebrospinal fluid (CSF) and blood, of which total tau is a non-specific biomarker, making it a very useful
<figure><img src="image_37.png" /></figure>

screening tool and has been routinely used for decades
in diagnostics of AD and prion diseases (Lewczuk et al.
2018b). In contrast, hyperphosphorylated Tau (p-tau)
isoforms (like p-tau181 or p-tau217), seem to be more
specific for AD, obviously reflecting AD-specific
processes.

Recent advancements have enabled the measure-
ment of specific tau species, such as p-tau and tau
fragments (Gonzalez-Ortiz et al. 2023a, 2023b; Horie
et al. 2023). These biomarkers offer insights into the
disease stage, neuroprogression, and response to ther-
apy (Gonzalez-Ortiz et al. 2023a, 2023b; Horie et al.
2023). For instance, CSF levels of p-tau181 and
p-tau217 have been associated with amyloid patho-
logy and tau PET measures, suggesting their utility in
diagnosing AD and distinguishing it from other tauop-
athies (Gonzalez-Ortiz et al. 2023b). Moreover, novel
blood-based biomarkers, such as plasma p-tau181,
have shown promise in reflecting CNS tau pathology,
potentially serving as accessible and cost-effective
tools for early diagnosis and monitoring of disease
progression (Gonzalez-Ortiz et al. 2023a).

Recent research has highlighted the relevance of tau in psychiatric conditions, including EDs. Elevated levels of tau have been observed in plasma of both patients with AN and those recovered from AN (Doose et al. 2022), suggesting potential neuronal injury or degeneration in this disorder. A longitudinal study found increased tau protein in acutely underweight patients with AN compared to HC participants but no changes in tau protein levels upon short-term partial weight restoration (Hellerhoff et al. 2021). This finding contrasts with the normalisation of other neuronal damage markers like NfL and glial fibrillary acidic protein (GFAP) after weight gain, suggesting persistent neuronal damage processes specifically in thin non-myelinated axons of cortical interneurons, where tau is abundantly found.

Recent research has further elucidated the rela-
tionship between tau protein levels and structural
brain changes in AN. Unlike NfL, which shows signif-
icant associations with cortical thickness in several
brain regions, tau protein levels do not demonstrate
such direct relationships with cortical thinning (Doose
et al. 2022). This differential pattern suggests that
various neuronal damage markers in AN might reflect
distinct pathophysiological processes affecting differ-
ent neuronal populations and brain structures. The
persistent elevation of tau protein despite weight res-
toration warrants further investigation into its
long-term implications for cognitive function and
recovery in AN.

3.3.3. *Neurofilament proteins*

Neurofilaments are cytoskeletal components predomi-
nantly expressed in long myelinated axons that are
released into the CSF and peripheral blood in response
to pathological processes involving axonal integrity
(Bavato et al. 2024). Changes in neurofilament levels
can be detected even in the presence of subclinical
damage such as asymptomatic strokes or WM hyperin-
tensities linked to silent cerebrovascular disease
(Lewczuk et al. 2018a; Bavato et al. 2024; Rabl et al.
2024). Over the past years, research studies have
assessed blood concentrations of NfL, the smallest and
most abundant neurofilament subunit, in different clin-
ical conditions (Barro et al. 2020; Rosengren et al.
1999; Sjögren et al. 2001). Promising findings in inflam-
matory, neurodegenerative, traumatic, and cerebrovas-
cular diseases suggest a clinical application of NfL as a
non-specific, all-around tool to assess the extent of
brain damage (Barro et al. 2020). NfL measures in neu-
rological conditions may allow for early pathological
process detection, quantification of active brain patho-
logy, monitoring treatment response, and predicting
clinical outcome. On the other hand, NfL seems to be
strongly influenced by age, which is a limiting factor
when it comes to its routine application (age-related
reference ranges will be probably needed).

There is growing evidence on NfL in AN, with mul-
tiple studies consistently demonstrating elevated
plasma/serum concentrations, indicating ongoing neu-
ronal injury and disruptions in axonal integrity. Nilsson
et al. (2019) provided the first clear evidence of
increased plasma NfL levels in AN compared to both
HCs and recovered individuals with AN, with levels
negatively associated with BMI. Across studies, despite
some variability, results suggest that neurofilament
levels are generally elevated in AN patients compared
to HCs. The study by Nilsson et al. (2019) showed the
largest effect size, while a study by Wentz et al. (2021)
showed the smallest effect size. Hellerhoff et al. (2021)
demonstrated that elevated NfL levels in acutely
underweight AN patients decrease with weight resto-
ration, and another study found no damage to neu-
rons in individuals recovered from AN (Doose et al.
2022). These findings suggest that neuronal damage
processes may partially normalise with nutritional
rehabilitation, which aligns with imaging studies show-
ing reversibility of certain brain changes after weight
gain. In contrast to these results, one study investi-
gated the relation to age and found increased NFL
levels with age in people with AN and recovered from
AN (AN-rec) implying that a permanent brain damage
is induced by the disorder (Nilsson et al. 2019). This
<figure><img src="image_38.png" /></figure>

was also corroborated by findings in a follow-up study in middle aged women that had suffered from AN (Wentz et al. 2021). Overall, the findings indicate that AN may be associated with increased neuronal injury, as reflected by elevated neurofilament levels. However, these are still findings that need to be validated in follow-up studies and therefore must be interpreted with caution.

Recent research has established important neuro-anatomical correlations with NfL levels in AN. Hellerhoff et al. (2023) found that higher baseline levels of NfL were significantly associated with lower cortical thickness in several brain regions, with the most prominent clusters located in bilateral temporal lobes. Importantly, this association was specific to AN and not detected in HCs, providing the first evidence linking a serum marker of axonal damage directly to structural brain alterations in AN. Links between NfL and structural brain changes had already been reported in neurodegenerative diseases such as Parkinson's and AD (Khalil et al. 2024).

### 3.3.4. Glial fibrillary acidic protein

GFAP is a type III intermediate filament protein that plays a crucial role in the cytoskeleton structure of glial cells and supports neighbouring neurons and the blood-brain barrier (BBB) (de Reus et al. 2024). GFAP is encoded by a single gene mapped to human chromosome 17q21 and is highly regulated by protein kinases such as protein kinase-A, calmodulin-dependent kinase II, and proteinkinase C (PK-C) (de Reus et al. 2024). GFAP is expressed by numerous cell types in the CNS, including astrocytes and ependymal cells during development.

GFAP has been studied in the context of brain disorders, such as multiple sclerosis and neuromyelitis optica, which target astrocytes. Elevated blood levels of GFAP have been found in neuroinflammatory diseases (Shaygannejad et al. 2024). GFAP has also been identified as a potential biomarker protein for neurodegenerative diseases, including Parkinson's disease (de Reus et al. 2024).

In AN, GFAP levels have been found to be significantly increased in acutely underweight patients compared to HCs, with subsequent normalisation after partial weight restoration (Hellerhoff et al. 2021). Interestingly, GFAP showed a negative association with age in AN patients-a finding contrary to the typical positive correlation observed in healthy populations-potentially indicating disrupted maturational processes in astrocytes during severe malnutrition. The decrease in GFAP levels with weight gain correlates with improvements in BMI,

suggesting that astroglial damage processes may be reversible with nutritional rehabilitation. Since there has been only one study, this finding must be interpreted with caution.

## 3.4. Hypothalamic signalling molecules associated with the regulation of appetite and metabolism

The Y neuropeptide family, comprising neuropeptide Y (NPY) and peptide YY (PYY), has been hypothesised to play a significant role in the aetiology of EDs. These peptides are key regulators of food intake and metabolic processes, acting at both central and peripheral levels, and are involved in gut-brain signalling pathways.

### 3.4.1. Neuropeptide Y

NPY is primarily synthesised and functions in the brain, where it plays a crucial role in regulating appetite and energy homeostasis. It stimulates hunger, decreases energy expenditure, and promotes weight gain. During periods of food deprivation, NPY secretion is elevated, which leads to increased food intake and preferential carbohydrate consumption to restore energy balance. Leptin, a critical regulator of energy homeostasis, is a potent inhibitor of NPY secretion. Studies examining fasting serum NPY concentrations in patients with AN have yielded inconsistent results: some found decreased levels (Baranowska et al. 2001; Tyszkiewicz-Nwafor et al. 2021), while others showed elevated levels (Jagielska et al. 2013; Sedlackova et al. 2012; Sedlackova et al. 2011), and yet others found normal levels (Baranowska et al. 1997; Galusca et al. 2015). Furthermore, several studies have observed no alterations in NPY concentrations postprandially (Sedlackova et al. 2012; Sedlackova et al. 2011). In contrast, early cross-sectional studies assessing NPY in CSF consistently reported elevated levels in patients with AN compared to controls, with no normalisation after partial weight restoration, suggesting a possible central dysregulation of NPY (Kaye et al. 1990).

### 3.4.2. alpha-MSH

Alpha-Melanocyte Stimulating Hormone (α-MSH) is a neuropeptide derived from proopiomelanocrotine (POMC) that activates the melanocortin 4 receptor. Beyond its action on inducing melanin synthesis, α-MSH is known for its anorexigenic action and is usually decreased during undernutrition (Fan et al. 1997) as a consequence of a reduced leptin signalling (Seeley et al. 1997). Two studies found lower α-MSH levels in AN compared to HCs (Galusca et al. 2015; Roubalova
<figure><img src="image_39.png" /></figure>

et al. 2021). However, two other studies reported no differences (Moriya et al. 2006). According to a longitudinal study in 40 patients, α-MSH levels could vary depending on patient nutritional status and be lower in acute stages but restored after weight regain (Seitz et al. 2024). Two studies identified increased levels of antibodies reactive to α-MSH in patients with AN (Fetissov et al. 2002; Escelsior et al. 2022). For more detailed information on the influence of immunoglobulins on α-MSH signalling see Section 3.9.2.

Interestingly a preclinical study showed that activation of the melanocortin-4-receptor (MC4R) with α-MSH could increase running wheel activity and decrease food intake in animal model of AN offering a potential mechanistic underpinning to its role in AN (Hillebrand et al. 2005).

### 3.4.3. Neuropeptide B (NPB) and neuropeptide W (NPW)

Neuropeptide B (NPB) and neuropeptide W (NPW) are endogenous ligands of the G protein-coupled receptors (GPR) GPR7 and GPR8, but their precise roles in humans are not fully understood. NPB is produced in amygdala, hypothalamic nuclei, and pancreatic islets; NPW is produced in neurons within the hypothalamus and brain-stem. Animal studies suggest that these neuropeptides may be involved in energy homeostasis and regulation of the hypothalamic-pituitary-adrenal (HPA) axis. To date, only two studies have investigated NPB and NPW in patients with acute AN. These studies reported elevated levels of NPB and normal levels of NPW (Grzelak et al. 2018a). Additionally, during nutritional rehabilitation, NPB concentrations decreased, while NPW levels remained unchanged (Grzelak et al. 2018b).

### 3.4.4. Kisspeptin (KISS)

Kisspeptin (KISS) is primarily produced in the arcuate nucleus and the anterior ventral periventricular nucleus of the hypothalamus. It is a ligand for the KISS1R receptor, which plays a crucial role in regulating the ovarian cycle and the gonadal axis. It is suggested that KISS neurons project axons to the arcuate nucleus, where they directly stimulate POMC/CART neurons and indirectly inhibit NPY/ agouti-related protein (AgRP) neurons. This in turn suggests that KISS neurons integrate peripheral metabolic signals, influencing the reproductive hormone axis, and may also serve as natural satiety signals. In studies of people with AN, some show normal KISS levels (Pałasz et al. 2021; Podfigurna et al. 2018), while others report lower serum levels in typical AN and higher levels in AAN (Bacopoulou et al. 2017). Pałasz et al. (2021) showed

no significant differences between malnourished AN patients, partially weight restored AN patients, or HC. Also in patients with AN, KISS has been positively correlated with BMI and, inversely, with physical activity (Bacopoulou et al. 2017).

### 3.4.5. Spexin (SPX)

Spexin (SPX), a highly conserved peptide from the galanin/KISS family, regulates reproductive processes, gastrointestinal activity, and cardiovascular function. Animal studies have shown that exogenous SPX reduces food intake and body weight, possibly inducing satiety by inhibiting NPY and orexin. SPX also increases locomotion and lipid oxidation and improves glucose tolerance (Kumar et al. 2021; Türkel et al. 2022).

### 3.4.6. Phoenixin (PNX)

PNX is a reproductive hormone involved in regulating the hypothalamus-pituitary-gonadal (HPG) axis, partly through its interactions with KISS. Its expression in several nuclei related to food intake regulation suggests its involvement in these processes. PNX is secreted in the gastrointestinal tract, potentially playing a role in the gut-brain axis. In AN patients, PNX concentrations were lower in malnourished adolescents and increased during weight normalisation (Pałasz et al. 2021).

### 3.4.7. Orexin

Orexins/hypocretins are neuropeptides implicated in numerous processes, including food intake, sleep quality and cognition. Findings on plasma orexin levels are inconsistent (Bronsky et al. 2011; Steward et al. 2019). In a longitudinal study with 36 girls with AN and 14 HCs, where orexin levels were elevated in girls with AN compared to HCs, orexin levels of girls with AN were comparable to those of HCs after 8 weeks of re-alimentation (Bronsky et al. 2011).

There is general agreement that orexins, especially Orexin-A, have a role in arousal and the sleep-wake cycle, and their involvement in narcolepsy was well documented (Mogavero et al. 2023; Mohammadi et al. 2021), even as replacement therapy strategy (Thomaz et al. 2024). People with AN experience poorer sleep quality than in HC (Sauchelli et al. 2016), and this was found to be associated with concentrations of orexin-A; both were found to be predictors of poorer treatment outcome.

### 3.4.8. Endocannabinoid system

The endocannabinoid system is a complex signalling system that modulates the central nervous system
<figure><img src="image_40.png" /></figure>

(CNS). It is made up of endogenous ligands (endocannabinoids, or eCBs), specific receptors, and the enzymatic machinery responsible for metabolising them. The two eCBs that have been most extensively studied are anandamide (AEA) and 2-arachidonoylglycerol (2-AG). These are lipids that are derived from polyunsaturated fatty acids and synthesised from membrane phospholipid precursors. Their production is triggered in an activity-dependent manner, typically in response to intracellular increases in calcium ions ($Ca^{2+}$) associated with the activation of metabotropic or ionotropic receptors. The synthesis of AEA and 2-AG is mediated, respectively, by N-acylphosphatidylethanolamine phospholipase D (NAPE-PLD) and diacylglycerol lipase (DAGL). In the CNS, eCBs are synthesised postsynaptically and released locally in response to physiological or pathological stimuli, acting retrogradely on presynaptic terminals. Brain concentrations of 2-AG are significantly higher than those of AEA. These compounds are mainly degraded by fatty acid amide hydrolase (FAAH), which hydrolyses AEA, and monoacylglycerol lipase (MAGL), which degrades 2-AG.

The two main receptors of the eCB system are cannabinoid receptor type 1 (CB1R) and cannabinoid receptor type 2 (CB2R). Both are coupled to G proteins. CB1R is predominantly found in the CNS, particularly in GABAergic and glutamatergic neurons. It is involved in processes such as motor control, emotion, cognition, body temperature regulation, appetite, and energy homeostasis. CB1Rs are also found in peripheral tissues such as skeletal muscle, adipose tissue, adrenal glands, gastrointestinal tract, gonads, and immune cells.

The CB2 receptor is predominantly localised in immune system cells and haematopoietic tissues. Although AEA and 2-AG share receptors, they are not functionally equivalent and can induce different biological responses. AEA acts as a partial agonist of CB1R and a weak agonist of CB2R, whereas 2-AG behaves as a full agonist of both. In addition to specific enzymes and receptors, both compounds can be metabolised by other enzymes, such as cyclooxygenase-2 (COX-2) and cytochrome P450, and other non-cannabinoid receptors that are potentially involved in signalling these ligands have also been proposed. These include the orexin type 1 receptor (OX1R) and the serotonin 5-HT2A receptor (5-HT2R), which broadens the functional spectrum of the system.

The eCB system acts as a key modulator of neurotransmission. By acting on presynaptic receptors, it inhibits the release of both excitatory (glutamate) and inhibitory (γ-aminobutyric acid; GABA) neurotransmitters in a retrograde manner. Activation of CB1R triggers

several intracellular cascades, resulting in hyperpolarisation of the presynaptic neuron and inhibition of neurotransmitter exocytosis. The functional translation of this system involves its participation in a wide range of physiological and pathological processes at both the central and peripheral levels, including neurodevelopment, synaptic plasticity, nociception, thermoregulation, neuroprotection, immunomodulation, inflammation, the regulation of emotions and cognition, motor activity and cell proliferation, as well as the control of energy intake and homeostasis. At a central level, the eCB system modulates the circuits that regulate appetite, including the homeostatic pathway at the hypothalamic level and the hedonic pathway associated with the dopaminergic reward system, which has a predominantly orexigenic function. At a peripheral level, the eCB system regulates energy storage and mobilisation by acting on metabolically active tissues, such as adipose tissue, skeletal muscle, and the gastrointestinal tract. This contributes to the neuroendocrine integration of energy balance.

The involvement of the eCB system in extreme weight and feeding situations (from obesity to AN) has aroused great interest, given its physiological role in modulating feeding behaviour and energy metabolism (D'Addario et al. 2014; Solinas et al. 2008). The seminal study in rodents by Kirkham et al. (2002) demonstrated that under normal conditions there is an increase in fasting brain AEA and 2-GA concentrations, with a subsequent return to basal levels during feeding and in the postprandial state. In humans, studies investigating the functionality of the eCB system in non-clinical populations have shown an increase in plasma concentrations of 2-AG and AEA prior to food intake (Monteleone et al. 2012), particularly with exposure to highly palatable foods (Monteleone et al. 2012). In the postprandial phase, a reduction in circulating concentrations of AEA and 2-AG has been observed, although it varies depending on the palatability of the food and the type of eCB (Monteleone et al. 2015). In this regard, a positive correlation has been described between limbic concentrations of dopamine and eCBs and craving for highly palatable foods, reinforcing their involvement in food-related reward seeking behaviour (DiPatrizio 2014).

Several studies suggest the hypothesis that dysfunction of the eCB system could underlie maladaptive behaviours related to food intake, related cognitive aspects and body fat mass distribution (Monteleone et al. 2005; Pastor et al. 2016). Higher basal plasma concentrations of AEA and/or 2-AG have been observed in people with obesity, and it has been suggested that the increase in these concentrations may even precede the onset of obesity, being higher than those observed
<figure><img src="image_41.png" /></figure>

in participants with lower BMI, such as HC and AN (Baenas et al. 2023).

In a pioneering study in EDs, Monteleone et al. (2005) described an increase in fasting circulating AEA in AN and BED. However, no differences in plasma 2-AG levels were found compared to HCs. Changes in peripheral AEA levels were positively correlated with subjects' ratings of the urge to eat and enjoyment of the food presented. Again, plasma 2-AG levels did not differ significantly, regardless of meal type. Contrary to what happens in obesity, hypoactivation of the eCB system has been suggested to occur in AN (Baenas et al. 2023; Monteleone et al. 2005), when compared to the HC group, with lower levels of AEA during fast-ing. When considering clinical and psychopathological symptoms in AN (Baenas et al. 2023), AEA was associ-ated with emotional dysregulation, whereas 2-AG was associated with general psychopathology. However, some results in the literature show discrepancies and opposite findings (Piccolo et al. 2020).

In this regard, neuroimaging studies have analysed the association between circulating levels of eCBs and brain functional connectivity, specifically in the nucleus accumbens (NAcc) region (Miranda-Olivos et al. 2023). They found that functional connectivity in the NAcc insula mediated the association between AEA concen-trations and BMI in the AN group.

## 3.5. Gastrointestinal signalling molecules associated with regulation of appetite and metabolism

### 3.5.1. Ghrelin

Ghrelin is an appetite regulatory hormone that sig-nals the hypothalamus to increase appetite and stim-ulates the pituitary gland to produce and release crucial hormones. Ghrelin levels undergo an ultradian secretion pattern increasing during fasting and decreasing after a meal. Meta-analytical evidence sug-gests ghrelin levels may be higher in patients with AN, including ghrelin's acyl-ghrelin and desacyl-ghrelin forms (Seidel et al. 2021). The same meta-analysis shows that total ghrelin levels decreased with follow-up in longitudinal studies. Similar observations have been made for desacyl-ghrelin with higher lev-els in patients with AN. Longitudinal studies show a decrease of desacyl-ghrelin but not acyl ghrelin between inclusion and follow-up (Duriez et al. 2020; Grigioni et al. 2022; Koyama et al. 2010; Uehara et al. 2005). In Seidel et al. (2021), pooled data from five studies showed no significant difference in acyl ghrelin between pre-treatment and follow-up.

Other studies have shown that acyl-ghrelin was increased in AN individuals compared to HCs which they linked with the acute stress induced to partici-pants (Westwater et al. 2021). More studies have con-firmed these findings, in both fasting and postprandial states (Dutkiewicz et al. 2022; Mancuso et al. 2020). Ghrelin levels were also correlated with the type and preference of food consumed (i.e. satiated acute AN had lower ghrelin levels after food consumption; while satiated recovered AN had higher ghrelin levels after consuming preferred food but lower ghrelin levels after consuming non-preferred food) (Monteleone et al. 2016a). Therefore, ghrelin has different qualities making it a possible biomarker assessing the success of treatment, though more research is needed

Ghrelin may have a role beyond appetite regulation, particularly in the modulation of reward-oriented behaviour (Al Massadi et al. 2019). Ghrelin is rec-ognised as a proimpulsive peptide increasing impul-sive and compulsive-like behaviour in rodents. Interestingly, patients with acute AN exhibit a negative correlation between ghrelin levels and sensitivity to delayed gratification, a core feature of reward alter-ations in AN, whereas a positive correlation was found in remitted AN and HCs (Bernardoni et al. 2020). Therefore, the normalisation of the interplay between metabolic markers of appetite and reward-oriented behaviour could be one of the footprints of remis-sion in AN.

Finally, an association between lower ghrelin levels, poor inhibitory control and higher ED-related psycho-pathology has been described in patients with AN. In their findings, Paslakis et al. (2019) further describe that although they did not find a direct association between ghrelin levels and executive dysfunction, both variables may act as mediators in the pathogen-esis of AN, as patients with AN and lower ghrelin lev-els appear to be more severely ill and have more impaired decision making (Paslakis et al. 2019).

Measuring ghrelin can be challenging in clinical set-tings as acyl ghrelin is rapidly degraded into desacyl form by several plasmatic esterase and specific treat-ment conditions are necessary to ensure stability (Theofanopoulou et al. 2022). Moreover, both nutri-tional state (i.e. BMI) and feeding state (sated of fasted condition, duration of overnight fast, meal composi-tion) influence plasma ghrelin accounting for high het-erogeneity between studies. The dynamic analysis of the kinetics of regulation of ghrelin before and after a meal could help overcome these limitations. Therefore, ghrelin has different qualities making it a possible bio-marker assessing the success of treatment, while there
is still a need for additional investigations to support
its informativity in different settings and subtypes of
the disorder.

3.5.2. LEAP-2

Liver Expressing Antimicrobial Peptide 2 (LEAP-2) has recently been identified as a new actor in the ghrelin system with antagonistic action on the ghrelin receptor, counteracting the orexigenic effect of ghrelin in mice and humans (Ge et al. 2018; M'Kadmi et al. 2019). Plasma levels of LEAP-2 are elevated in overweight and obese adults, and correlate with BMI, suggesting opposite regulation between LEAP-2 and ghrelin (Andreoli et al. 2024; Mani et al. 2019). With its antagonistic action on the ghrelin system, LEAP-2 is now identified as an anorexigenic peptide. Indeed, central and peripheral infusions of LEAP-2 lead to an inhibition of ghrelin-induced food intake in mice (Ge et al. 2018; Hagemann et al. 2022; Mani et al. 2019). Similarly, LEAP-2 infusion induces a spontaneous decrease of caloric intake during free meals in healthy males (Hagemann et al. 2022) and post prandial increase of LEAP2 has been implicated in eating behaviour suppression (Bhargava et al. 2023). Recent evidence showed that central LEAP-2 injections lead to the reduction of reward-oriented behaviour towards food cues, decreasing food palatability through dopamine modulation (Tufvesson-Alm et al. 2024).

So far, one study has explored the regulation of LEAP-2 in a longitudinal evaluation of 30 patients with AN at various nutritional levels (i.e. undernourished and refed after a four-month inpatient program). Results showed unexpectedly higher LEAP-2 levels in undernourished AN patients compared to those in the refed state, suggesting a potential abnormal regulation of LEAP-2 with energy state in AN (Du Montcel et al. 2023). Interestingly, patients exhibiting this dysregulation of LEAP-2 had an increased risk of relapse at six months (i.e. BMI< 18.5 kilograms per square metre [kg/m²]). With effects of regulating appetite and food reward, disrupted regulation of LEAP-2 could be a risk factor of relapse through inappropriate signalling of the energy state that would increase the rapid weight loss. These results have not yet been replicated, but identifying patients exhibiting such dysregulated profiles could be considered as a potential promising biomarker.

3.5.3. GLP-1

Glucagon-Like Peptide-1 (GLP-1) acts as an appetite suppressor that releases signalling through the vagal nerve and is negatively correlated with body weight

(Smith and Moran 2021). No difference in GLP-1 levels was detected between testing groups and AN individuals (Stengel et al. 2014; Westwater et al. 2021). This was also supported by a study that showed no correlation between GLP-1 levels and body weight (Stengel et al. 2014). Other evidence has shown an increase in GLP-1 in patients with AN compared to constitutionally thin subjects and HCs (Germain et al. 2007; Heruc et al. 2018b). After refeeding, GLP-1 levels were significantly higher when compared to baseline (Heruc et al. 2018b). Both the levels of GLP-1 in AN individuals and the response of food consumption and weight restoration on GLP-1 levels warrant more investigations. Of note, GLP-1 agonist treatment of individuals with obesity and a history of AN has been associated with relapse of the AN (Guerdjikova et al. 2024).

3.5.4. Cholecystokinin

Cholecystokinin is a hormone secreted by cells in the small intestine and is responsible for the stimulation of release of bile acids to promote fat digestion as well as stimulation of secretion of pancreatic enzymes. Its role in appetite regulation includes acting as a satiety signal hormone. Most evidence suggests cholecystokinin is increased in individuals with AN relative to HCs (Philipp et al. 1991; Geracioti et al. 1992; Tamai et al. 1993; Fujimoto et al. 1997; Tomasik et al. 2005). Although cholecystokinin is considered a biomarker of satiation in healthy individuals, its use as a biomarker for AN needs further investigations.

3.5.5. Peptide YY

Primarily secreted in the gastrointestinal system, PYY plays a key role in central and peripheral appetite regulation. PYY signals meal termination, reduces food intake, and slows gastrointestinal motility. It functions as a satiety signal by inhibiting the production of ghrelin. Studies of fasting serum PYY concentrations in patients with AN exhibit mixed results. Some studies report no significant differences compared to HCs (Otto et al. 2007; Sedlackova et al. 2012; Tam et al. 2020; Tyszkiewicz-Nwafor et al. 2021), while others document elevated levels (Misra et al. 2006; Nakahara et al. 2007; Pfluger et al. 2007; Lawson et al. 2011; Heruc et al. 2018a; Mancuso et al. 2020). During nutritional rehabilitation, PYY concentrations have been found to either remain unchanged (Tam et al. 2020) or decreased (Heruc et al. 2018a). Postprandially, PYY levels increased in some studies (Nakahara et al. 2007; Otto et al. 2007; Sedlackova et al. 2012), while others reported stable levels (Stock et al. 2005). In addition, in
<figure><img src="image_42.png" /></figure>

adults with AN, higher circulating PYY has been asso-
ciated with increased HPA drive and with greater
disordered-eating psychopathology, independent of
BMI (Lawson et al. 2011).

PYY has also been linked to the capacity for smell and taste, and has been suggested to modulate taste responsiveness (La Sala et al. 2013). In a study analysing differences in smell and taste capacity between woman with extreme weight conditions, higher odour threshold scores (i.e. greater olfactory capacity) were associated with higher PYY levels in a subsample of patients with AN (Fernández-Aranda et al. 2016). Interestingly, results also showed that this greater smell capacity was an indirect indicator of ED severity (Fernández-Aranda et al. 2016).

**3.6. Signalling molecules from adipose tissue**

3.6.1. Leptin

Leptin was discovered in 1994 (Zhang et al. 1994) as
the first out of currently more than 600 known adi-
pokines (Kirichenko et al. 2022), which underscores
adipose tissue's role as the largest endocrine organ.
The clinical phenotypes of the *ob/ob* mouse and of
humans harbouring mutations in the leptin gene pre-
cluding the expression of functional leptin are charac-
terised by a voracious appetite, early onset extreme
obesity, hypothalamic hypogonadism and impaired
immunity (Farooqi and O'Rahilly 2009; Friedman 2019).

In ob/ob mice, all psychological and somatic symp-
toms of congenital leptin deficiency can be treated
successfully by application of exogenous leptin (Farooqi
and O'Rahilly 2009; Friedman 2019). In humans, recom-
binant human leptin (metreleptin) normalises hunger
and satiety within hours to days after its first applica-
tion; long-term supplementation entails significant
weight loss and achievement of adult reproductive
status (Farooqi and O'Rahilly 2009). Systematic psycho-
logical studies on the effect of treatment with recom-
binant human leptin have not been conducted;
preliminary evidence suggests that mood may improve
(Hebebrand, Zorn, et al. 2022; von Schnurbein et al.
2023). In adults, treatment with recombinant leptin
has led to volume increments of specific brain regions
(Matochik et al. 2005).

Circulating leptin levels correlate with BMI (kg/m²) and, even more so, with adipose tissue mass (Considine et al. 1996; Terra et al. 2013). Persons with obesity have high serum leptin levels, whereas lean people have low leptin levels. Indeed, there is substantial variation in leptin secretion for any percentage of body fat. Throughout adolescence serum leptin increases in females only; adult females have two to three times

higher leptin levels than males (Hebebrand, Plieger, et al. 2024). As demonstrated in meta-analyses, individuals with AN have sub-physiological levels (hypoleptinaemia) (Cassioli et al. 2025; Karageorgiou et al. 2020; Wu et al. 2024). In contrast, the soluble leptin receptor, which binds leptin in serum, is increased (Wu et al. 2024), thus further reducing the bioavailability of leptin in AN (Hebebrand, Plieger, et al. 2024).

In the early 2000s, clinical trials failed to show a substantial weight reducing effect of metreleptin in patients with multifactorial obesity revealing that an elevation of an already high leptin secretion in such patients does not reduce appetite or body weight; only patients with the lowest leptin levels lost more than ten percent of their weight (Depaoli et al. 2018). Accordingly, the primary role of leptin as an anorexigenic/satiety hormone has been questioned (Flier and Maratos-Flier 2017). Instead, evidence has accumulated that leptin is the key hormone to both centrally and peripherally regulate the adaptation to starvation based on a simple feedback mechanism (Ahima et al. 1996; Hebebrand, Hildebrandt, et al. 2022). The reduction of adipose tissue mass induced by a prolonged reduced energy intake results in hypoleptinaemia, thereby altering the function of both peripheral and central tissues to increase the likelihood of survival mostly by reducing energy expenditure. All hypothalamic-pituitary-end organ axes are up- or down-regulated by hypoleptinaemia (Ahima et al. 1996).

Examples of somatic functional implications of reduced central and/or peripheral leptin signalling include amenorrhoea, bradycardia, hypotonia, hair loss, and reduced bone density and haematopoiesis (Hebebrand, Plieger, et al. 2024). As leptin interacts in concert with gonadotropins and the growth hormone axis to initiate the physiological changes of puberty, decrease in leptin may account for the delay or interruption of sexual maturation characteristic of adolescent AN. Additionally, leptin contributes to preserve adequate reproductive functions by stimulating the activity of the gonadotropic axis (Ahima et al. 1996).

Apart from reducing energy expenditure, hypolepti-
naemia increases the sensitivity of sweet receptors in
the tongue and slows gastrointestinal motility entail-
ing both a rapid feeling of fullness upon food intake
and constipation (Hebebrand, Hildebrandt, et al. 2022).
Increased running wheel activity in food-restricted rats,
potentially reflecting food foraging behaviour, has
causally been linked to hypoleptinaemia (Hebebrand
et al. 2019). In patients with AN, the relationship
between hyperactivity and leptin might correspond to
an inverted U with its occurrence dependent on serum
levels in the range of approximately 0.5 to 2.0
<figure><img src="image_43.png" /></figure>

nanograms per millilitre (ng/ml) (Holtkamp et al. 2006; Hebebrand et al. 2019).

Evidence from both rodents and humans suggests that psychological symptoms of starvation such as fatigue, insomnia, depressed mood, reduced libido, and constant preoccupation with food are mediated by hypoleptinaemia (Hebebrand, Hildebrandt, et al. 2022). In patients with AN, premorbid BMI strongly predicts weight loss, suggesting that 'entrapment' in this disorder may be triggered by an amount of weight loss sufficient to induce hypoleptinaemia (Hebebrand, Seitz, et al. 2024). Off-label metreleptin treatment of patients with AN and AAN has been found to result in a reduction of starvation psychological symptoms within one to five days upon dosages ≥ 6 milligrams per litre (mg/l), the response varying between patients (Hebebrand, Hildebrandt, et al. 2022; Hebebrand, Plieger, et al. 2024). In some patients, weight phobia also declined. Clinical trials are required to validate these findings and to determine if leptin substitution will, in the medium term, facilitate weight gain.

Leptin is also a potent neurotrophic factor (Bouret 2010). It mediates volume increases of amygdala and hippocampal and thalamic nuclei during weight gain in patients with AN (Wronski et al. 2023a, 2023b, 2024; Bahnsen et al. 2024; Wronski et al. 2025). In both mice (Ahima et al. 1999) and humans (Matochik et al. 2005) with congenital leptin deficiency, treatment with recombinant leptin increases volumes of specific brain regions as well as whole brain volume.

To summarise, leptin (perhaps in combination with other adipokines) may prove to be a crucial endocrine mediator of 'entrapment' in AN (Hebebrand et al. 2023) caused by a sufficient amount of weight loss (Hebebrand, Plieger, et al. 2024; Hebebrand et al. 2025). Reduced leptin signalling in AN is causally involved in the emergence of somatic and psychological symptoms of starvation (Hebebrand, Plieger, et al. 2024). Considering the effect of food restriction induced hypoleptinaemia on the hypothalamus-pituitary-end organ axes (Ahima et al. 1999), leptin may act as a master switch underlying multiple endocrine alterations in patients with acute AN (Hebebrand, Plieger, et al. 2024). Vice versa, weight gain induced increments in leptin secretion might underlie the slow resolution of such symptoms. Indeed, complete resolution of mental symptoms requires normalisation of the size of particular brain nuclei (Wronski et al. 2023a, 2023, 2024, 2025; Bahnsen et al. 2024). Additionally, risk of relapse is decreased in patients with higher leptin levels after achievement of target weight (Sala et al. 2023). Measurement of leptin levels at baseline predicts fat mass (Mathiak et al. 1999). The level in acute AN may

help to differentiate constitutional thinness from AN with a leptin level < 2 ng/ml and > 4 ng/ml being consistent with acute AN and constitutional thinness, respectively (Föcker et al. 2011).

### 3.6.2. Adiponectins

Adiponectins are signalling proteins secreted by adipose tissue that play a crucial role in regulating metabolism, inflammation, and immunity. They have been evaluated in different study designs including meta-analyses (Karageorgiou et al. 2020; Tural and Iosifescu 2022). Two meta-analyses including AN and AN-BP and AN-R subtypes of AN, respectively, found greater adiponectin levels in people with AN compared to HCs (Karageorgiou et al. 2020; Tural and Iosifescu 2022). Other evidence from longitudinal studies accords with these meta-analytical findings (Andries et al. 2015; Tyszkiewicz-Nwafor et al. 2019; Calikoglu et al. 2024). However, some evidence revealed that weight restoration had no clear effect on the normalisation of adiponectin levels (Andries et al. 2015; Tyszkiewicz-Nwafor et al. 2019). The results suggest the role of adiponectin as a crucial molecule in balancing the metabolism, and a potential role of adiponectin to act as a biomarker of the disorder. However, many other molecules are found to be disturbed in AN and a full profile of the metabolic changes would aid in the diagnosis of the disorder. More evidence would be necessary to give solid conclusions.

### 3.6.3. Resistin

Resistin is a hormone that is produced by immune cells and associated with inflammation and a range of different diseases. A regression analysis showed a link between adiponectin and resistin where resistin acted like a significant modifier for adiponectin (Tural and Iosifescu 2022). The hormone resistin is showing some pre-elementary evidence to be utilised as a biomarker of the disorder. However, this hormone is found to be significantly lower in patients with AN when compared to HCs [−1.67 (−2.85, −0.48)] in a meta-analysis of 20 studies (Karageorgiou et al. 2020). Additional evidence has shown that it remained lower in recovered individuals with AN comparing to HCs (Tyszkiewicz-Nwafor et al. 2019) while some evidence shows that although plasma resistin was lower in AN individuals, the subcutaneous adipose tissue resistin was significantly higher in AN than HCs (Dostalova et al. 2006).

### 3.6.4. Vaspin

Vaspin is a regulatory hormone secreted by adipose tissue that is involved in insulin sensitivity.
<figure><img src="image_44.png" /></figure>

Meta-analytical evidence from 3 studies found a signif-
icant increase in serum vaspin levels in AN individuals
compared to HCs (Karageorgiou et al. 2020). Other
cross-sectional and longitudinal evidence supports the
findings of the meta-analysis and shows an increase in
both serum and salivary vaspin levels (Ostrowska et al.
2016; Oświęcimska et al. 2016; Grzelak et al. 2018b;
Paszynska et al. 2018). Serum vaspin normalised fol-
lowing treatment, suggesting endocrine system
involvement (Grzelak et al. 2018b).

3.6.5. Omentin

Omentin is an adipokine mainly produced by visceral
adipose tissue. Omentin is expressed in two isoforms,
omentin-1, which is the main circulating form in
human plasma, and omentin-2 which is released in the
intestinal lumen and has not been detected in plasma
(Tan et al. 2010). Higher omentin levels are seen in
individuals with AN while the lowest are seen in
patients with obesity (Calikoglu et al. 2024). Other
conflicting findings found no differences in omentin
levels in acute AN and partially recovered AN
(Tyszkiewicz-Nwafor et al. 2021). Therefore, available
evidence does not support its use as a biological
marker in AN.

3.6.6. Visfatin

Visfatin is a regulatory hormone that reduces glucose
release from the liver and triggers use of glucose
stored in fat and muscle cells. A meta-analysis of five
studies found lower serum/plasma levels of visfatin in
people with AN compared to HC, but this difference
was not significant, and there was high heterogeneity
across studies (Karageorgiou et al. 2020). Additional
studies have shown inconsistent findings that do not
accord with the above meta-analytical findings
(Dostálová et al. 2009; Ostrowska et al. 2014; Seidel
et al. 2015; Ostrowska et al. 2016; Baranowska-Bik
et al. 2017). Studies were also inconsistent in terms of
the effect of weight restoration and treatment on the
levels of visfatin in AN individuals (Dostálová et al.
2009; Seidel et al. 2015; Tyszkiewicz-Nwafor, Dutkiewicz,
et al. 2021).

3.6.7. Obestatin

The anorexigenic appetite regulatory hormone, obes-
tatin, which is produced in the gastrointestinal tract
was discovered in 2005 and therefore considered to be
a relatively recently discovered hormone (Zhang et al.
2005). It plays a crucial role in the course of AN as this
hormone is food consumption dependant. In acute

AN, the levels of obestatin were found to be higher
relative to partially treated individuals with AN
(Monteleone et al. 2008; Dutkiewicz et al. 2022). Weight
restoration showed an effect on the hormone levels.
However, supporting evidence is still needed to con-
firm the usefulness of this hormone as a bio-
marker for AN.

3.6.8. Apelin

Apelin has a crucial role in the activation of signal
transduction in human cells and in promoting food
intake (Yan et al. 2020). In the AN literature, apelin was
only looked at briefly and found to be significantly
higher than in individuals with obesity (Calikoglu et al.
2024). Apelin levels decreased after the initiation of
the treatment, which may be linked with adipose tis-
sue mass (Calikoglu et al. 2024).

3.6.9. Betatrophin

Betatrophin is a relatively recently described hormone
that is expressed in adipose tissue and liver and plays
a key role in glucose tolerance, lipid metabolism and
diabetes. The relationship between betatrophin and
obesity/metabolic disorders has been well documented
(Ye et al. 2019). The few studies that have examined it
in AN found that plasma betatrophin levels were ele-
vated compared to those in HCs, while in women with
morbid obesity, levels were significantly reduced
(Barja-Fernández et al. 2015). This suggests an inverse
regulatory effect between plasma betatrophin concen-
trations and BMI, and its possible involvement in lipid
metabolism.

3.6.10. Irisine

Irisine is a myokine (hormone released by muscles
during exercise and expressed in adipose tissue)
that plays a key role in the regulation of metabolism
and energy expenditure. It has been extensively
described in the obesity and cardiometabolic litera-
ture (Torabi et al. 2024). When it was analysed in
extreme weight conditions, including AN and obe-
sity, considering resting exercise expenditure (REE),
physical activity and irisin blood concentrations
(Stengel et al. 2013; Pardo et al. 2014), it was found
that AN showed similar levels than HC and lower
levels than participants with obesity. Among the
groups, irisin levels were inversely correlated with
daily physical activity and directly correlated with
REE. Particularly in AN, the results were not conclu-
sive and did not show clear correlations with activ-
ity levels (Hofmann et al. 2014).
<figure><img src="image_45.png" /></figure>

### 3.7. Stress-related hormones

The HPA axis is the main component of the endogenous stress response system responsible for the 'slow' response to real or perceived environmental stressors *via* the secretion of glucocorticoids (Tsigos and Chrousos 2002). The hypothalamic paraventricular nucleus (PVN) is a key component of the HPA axis, since PVN neurons release corticotropin-releasing hormone (CRH) (and arginine vasopressin) into the portal circulation. CRH stimulates corticotrophin cells in the anterior pituitary gland to synthesise and secrete adrenocorticotropic hormone (ACTH) into the systemic circulation. ACTH, in turn, stimulates secretion of cortisol from the adrenal cortex. Cortisol negatively regulates CRH and ACTH synthesis and secretion (De Kloet et al. 1998).

#### 3.7.1. CRH and ACTH

The HPA axis has been the focus of extensive research in people with AN with mixed findings (Lo Sauro et al. 2008; Schorr et al. 2015; Culbert et al. 2016).

Two initial studies reported increased levels of CRH in the spinal fluid of patients with AN in the acute phase of the illness but not after weight restoration (Kaye et al. 1987; Walsh et al. 1987). Regarding peripheral ACTH levels, a recent meta-analysis (Wu et al. 2024) confirmed that blood levels of ACTH are significantly higher in adult people with AN compared to HCs.

#### 3.7.2. Cortisol

Cortisol is the principal glucocorticoid secreted in response to a stressor in humans: it promotes gluconeogenesis and lipolysis in the liver and adipose tissue to provide the energy necessary to cope with the stressor. Following its secretion cortisol exerts negative feedback on HPA axis activity to stop its own secretion and terminate the stress response to minimise its catabolic, lipogenic, anti-reproductive and immunosuppressive actions, which have long-term deleterious effects on the organism (Adam and Epel 2007; Habib et al. 2001). Secretion of cortisol can be assessed in basal conditions and after stimulation or inhibition tests providing information on the tonic and dynamic functioning of the HPA axis, respectively. Baseline assessment of cortisol production can be performed by different techniques: a) measurement of the 24-hour pooled serum cortisol level, which provides an estimation of the total cortisol secreted over the 24-hour period; b) 24-hour urinary cortisol assessment, which measures the amount of free cortisol excreted in the urine over the 24-hour period; and c) measurement of

time-specific cortisol levels in blood or other biological fluids with serum morning cortisol assessment being the most common procedure. Dynamic evaluation of cortisol production is often obtained by measuring peripheral cortisol secretion levels after either pharmacologic tests or stress evoking laboratory paradigms. The most common pharmacological test is the dexamethasone suppression test (DST), which measures circulating cortisol levels in the morning after the administration of dexamethasone at 11:00 PM (individuals with HPA axis hyperactivity escape the suppression of morning cortisol levels). Among laboratory stress paradigms, the Trier Social Stress Test (TSST). Allen et al. (2017) measures cortisol response to an acute social challenge and is widely used. An intermediate strategy between basal and dynamic tests of HPA axis functioning is represented by the saliva Cortisol Awakening Response (CAR) (Stalder et al. 2016), which measures the increase in saliva cortisol levels occurring approximatively 30 min after awakening in the morning and can be considered either a basal measure or a measure of the HPA axis reactivity to awakening.

To date, three meta-analyses of studies assessing baseline cortisol levels (Bailly et al. 2021b; Thavaraputta et al. 2023; Wu et al. 2024) and one meta-analysis of studies measuring cortisol reactivity in response to an interpersonal stressor (Monteleone et al. 2018) have been conducted. Indeed, Bailly et al. (2021b) found that the 24-hour mean cortisol level was significantly higher in individuals with AN compared to both HCs and individuals with constitutional thinness. Thavaraputta et al. (2023) reported that in people with acute AN: 1) mean serum morning cortisol levels were higher than in HCs with a pooled mean difference (MD) of 4.58 milligrams per decilitre (mg/dL); 2) 12-hour and 24-hour pooled serum cortisol levels were significantly increased compared to HCs with pooled MD of 3.08 mg/dL and 7.23 mg/dL, respectively; 3) mean 24-hour urinary cortisol levels were significantly higher compared to HCs with a pooled MD of 30.06 milligrams per day (mg/day); 4) mean serum cortisol level measured in the morning after low-dose DST was significantly higher in AN than in HCs with a pooled MD of 5.4 mg/dL. Wu et al. (2024) confirmed that adults with AN had higher peripheral cortisol levels than HCs. All meta-analytic studies included patients in the acute phase of AN and meta-analyses of studies assessing baseline HPA axis functioning in individuals recovered from AN are lacking. This is an important limitation since several, although not all studies (Lo Sauro et al. 2008; Culbert et al. 2016), have found that
<figure><img src="image_46.png" /></figure>

increased cortisol levels in underweight patients with AN normalise after body weight gain. Given this preliminary evidence, it can be argued that the hypercor-tisolism of individuals with acute AN is a state-dependent phenomenon likely due to the effects of starvation or malnutrition on the HPA axis *via* an increased release of CRH (Gold et al. 1986).

As for dynamic assessment of cortisol production in AN, a meta-analysis of 16 studies assessing cortisol response to the TSST demonstrated no significant differences in either pre-task or post-task cortisol levels between a mixed group of people with AN, BN or BED and HCs (Monteleone et al. 2018). However, this result cannot be considered conclusive since the meta-analysis included individuals with different ED diagnoses in a combined group. Subsequent studies have suggested reduced cortisol reactivity to acute interpersonal (Monteleone et al. 2020; Schmalbach et al. 2020) and non-interpersonal stressors (Westwater et al. 2021) in individuals with AN and those with BN. This is consistent with some previous findings (Het et al. 2015; Vaz-Leal et al. 2018), and with the allostatic load theory (McEwen and Wingfield 2003) which claims that the stress induced prolonged HPA axis activity may result in a dampened functional reactivity. However, a dampened cortisol stress reactivity can only be hypothesised to occur in AN, and further studies are needed to investigate this field.

Some research has been published reporting on the CAR in individuals with AN, but meta-analyses are lacking. Briefly, most, but not all, studies support the CAR is enhanced in underweight individuals with AN, especially in those with AN-BP, but not in weight-restored individuals with AN (Monteleone et al. 2016b; Monteleone et al. 2017). These data further support the occurrence of HPA axis hyperactivity in the acute stage of AN, although this area deserves further exploration.

Interestingly, a recent review (Rossi et al. 2024) outlined that dampened HPA basal activity (namely, 24-h urinary free cortisol levels and morning plasmatic cortisol levels) and reduced CAR and stress reactivity characterise individuals with AN and childhood mal-treatment. Future studies will explore the hypothesis that HPA activity may represent a marker of early adverse experiences in individuals with AN.

To summarise, existing literature converges on the idea that HPA axis basal activity is enhanced in the acute stage of AN, which, in turn, implies increased secretion of cortisol in underweight individuals with AN. It has been proposed that such an increase in cortisol production may be a physiological adaptation to starvation and malnutrition (Fichter et al. 1986),

although its state-dependent nature has to be confirmed. It is worth noting that such an increase in HPA axis activity in acute AN does not result in the usual stigmata of Cushing's syndrome, including striae, central adiposity and weight gain (Lacroix et al. 2015). Thus, elevated cortisol levels may contribute to some clinical symptoms of acute AN such as hypogonadotropic hypogonadism and low bone mineral density, since elevated cortisol interferes with osteoblast proliferation, reduces calcium absorption in the gut, decreases gonadotropin secretion and impairs the synthesis of insulin-like growth factor-1 (IGF-1), a bone anabolic factor (McCarthy et al. 1990; Canalis 1996).

## 3.8. Sexual and social hormones

### 3.8.1. Luteinizing hormone (LH) and follicle-stimulating hormone (FSH)

Increased ghrelin and reduced leptin levels suppress the hypothalamic-pituitary-gonadal axis (HGA) by down-regulating gonadotropin-releasing hormone (GnRH) in the hypothalamus and luteinizing hormone (LH) and follicle-stimulating hormone (FSH) at the pituitary level (Haines 2023). Reduction of LH and FSH leads to a reduced oestrogen production and amenorrhoea in women with AN between puberty and menopause, and delayed puberty in adolescent females with AN (Himmerich et al. 2010).

### 3.8.2. Dehydroepiandrosterone (DHEA), dehydroepiandrosterone-sulphate (DHEA-S)

Dehydroepiandrosterone (DHEA) and Dehydroepiandrosterone - sulphate (DHEA-S), produced by the adrenal cortex, mainly serve as precursors for more potent androgens, which then exert their effects on peripheral tissues and as neurosteroids. In one meta-analysis (Thavaraputta et al. 2023), evaluating six studies including 83 individuals with AN and 76 HCs, the mean serum DHEA concentration was non-significantly higher in patients with AN compared with HCs. In a total of 15 studies with 584 individuals with AN and 251 HCs, mean serum DHEA-S was significantly lower in patients with AN vs. HCs. In another meta-analysis (Lin et al. 2022) of 15 cross-sectional studies including 1119 participants, patients with AN had significantly elevated serum DHEA levels and reduced DHEA-S levels compared with HCs. The differences between these meta-analyses in DHEA are not clear, related, perhaps, to different studies included in each meta-analysis. Due to these discrepancies, the significance of DHEA as a biomarker of AN is unclear.
<figure><img src="image_47.png" /></figure>

In the adrenal gland, DHEA converts to DHEA-S. In the peripheral tissues, DHEA is converted to oestrogens and androgens. It is suggested that during a major physiologic stress such as AN, there is a decreased conversion of DHEA to DHEA-S and to downstream sex hormones.

A decrease in DHEA/DHEA-S levels is associated with sexual dysfunction and cognitive and mood impairment. Moreover, studies in females with AN suggest that there may be an association between low DHEA-S levels and low bone mineral density. This has led to assessment of the potential of DHEA to improve bone density in AN. The findings of these studies, including seven randomised controlled trials (RCTs) assessed in a systematic review (Lin et al. 2022), have found that, overall, DHEA does not improve bone mineral density compared with placebo after adjusting for weight gain, both as a monotherapy and when combined with oestrogen replacement. Moreover, in patients with open epiphyses, there is a risk of decreased bone mineral density and premature epiphysial closure in response to the combination therapy.

### 3.8.3. Sexuality, puberty, ovarian sex hormones and testosterone in AN

The main changes of sex hormones in people with AN are that the reduced production of GnRH, LH and FSH lead to decreased production of oestradiol in the ovaries in women and in the adipose tissue in men and to reduced testosterone secretion in the testes of men and the ovaries of women (Haines 2023).

Puberty is a significant risk period for development of EDs, especially in women. Recent research highlights the potential role of ovarian hormones in phenotypic and genetic risk for EDs during puberty. According to Klump (2013) both pubertal status and pubertal timing significantly increase the risk for most EDs in girls, such that advanced pubertal development and early pubertal timing are associated with increased rates of EDs. However, Castellini et al. (2016) have argued that the effect of early puberty may be less definite, as some studies indicate that early puberty increases the risk for the onset of EDs, while others have not found such an association. Nonetheless, girls who develop sexually early, are thought to be at particular risk, given that they experience pubertal-related physical changes (e.g. breast development, menarche, increased adiposity) earlier and may therefore experience more body dissatisfaction than their developmentally on-time peers.

The reduced libido of AN can be associated with both psychological mechanisms (reduced self-esteem,

body dissatisfaction) and low concentrations of circulating sex hormones. Indeed, Keys et al. (1950) showed that loss of libido is a cardinal symptom of starvation. Amenorrhoea in AN is related to hypoleptinaemia-induced low concentrations gonadotrophin-releasing hormones and thus a diminished release of luteinizing hormone.

In the meta-analysis by Thavaraputta et al. (2023), which evaluated 29 studies including 1029 individuals with AN and 574 HCs, significantly lower mean serum total testosterone was found in patients with AN compared with HCs. A meta-analysis (Lin et al. 2022) of seven studies found low serum testosterone in patients with AN vs. HCs. In females, androstenedione and testosterone are produced predominantly by the adrenal and ovary. Studies have suggested a potential association in AN between low testosterone levels and both low bone mineral density and symptoms of depression and anxiety. Nonetheless, recent RCTs in women with AN have shown no improvement with testosterone replacement in bone mineral density or in symptoms of anxiety, depression, and disordered eating (Sienkiewicz et al. 2011). Interestingly, long-term treatment with metreleptin has been reported to improve bone density of lean hypoleptinaemic women (Sienkiewicz et al. 2011). A case report indicates that treatment with metreleptin might also lead to a normalisation of testosterone levels (Antel et al. 2022).

Most male patients with acute AN have low testosterone levels (Cost et al. 2020). Low testosterone levels usually normalise with weight recovery (Wabitsch et al. 2001). Therefore, there is evidence for testosterone as a state marker of AN.

### 3.8.4. Oxytocin

Oxytocin, a centrally secreted nonapeptide produced by the paraventricular and supraoptic nuclei in the hypothalamus, plays a critical role in facilitating social bonding (Giel et al. 2018). Oxytocin also modulates a wide range of other functions, including metabolism, stress, appetite, eating behaviour, anxiety, and depressive symptoms (Giel et al. 2018). Animal studies indicate increased oxytocin secretion suppresses appetite in both lean and obese rodents (Skinner et al. 2019). Leptin may have an impact on oxytocin secretion in rodents (Perello and Raingo 2013). Peripheral oxytocin levels in plasma, serum, and CSF have been reported to be lower in adult people with AN, although findings are inconsistent, particularly regarding plasma and CSF levels (Maguire et al. 2013). A recent meta-analysis (Ferreira and Osório 2022) assessing how peripheral oxytocin levels among patients diagnosed with various psychiatric disorders differ from HCs has supported the
<figure><img src="image_48.png" /></figure>

presence of lower levels of oxytocin in patients with AN in comparison with HCs. A subgroup analysis in this meta-analysis revealed a more pronounced reduction in oxytocin levels among adult patients with restricting and binge-eating/purging AN subtypes. A later longitudinal study by Plessow et al. (2022) found that low basal oxytocin levels were more pronounced in AN-R compared to AN-BP. These findings highlight the importance of considering AN subtype in research on oxytocin. Studies examining oxytocin levels in individuals with partially or fully weight-recovered AN have yielded mixed results (Plessow et al. 2018) showing evidence of either normal or reduced endogenous oxytocin levels. These findings leave open the question of whether 1) altered oxytocin levels reflect the acute low-weight state of AN and normalise with recovery, or 2) alterations in the oxytocin system represent a residual 'scar' from the chronic starvation or a trait of AN. Age seems to be another critical factor. Adolescents with AN exhibit a distinct mechanism, with elevated oxytocin levels in malnourished patients that persist despite partial weight recovery (Tyszkiewicz-Nwafor et al. 2020). This longitudinal study suggests the altered oxytocin levels might contribute to ongoing symptoms. It is important to note that research on adolescents with AN is still scarce.

Overall, altered oxytocin levels have been suggested as a potential biomarker of AN-R and a possible treatment target for emerging treatments for AN (Stengel and Giel 2023). However, the potential role of intranasal oxytocin administration in AN treatment remains unclear, as current research lacks conclusive evidence of its efficacy (Leppanen et al. 2017; Hasselbalch et al. 2020; Chen, Chiang, et al. 2021; Maguire et al. 2024; Ahmed et al. 2025). Intranasal oxytocin administration may hold promise as an adjunct treatment when used in conjunction with nutritional rehabilitation or psychotherapy (Miller et al. 2023) although well-controlled and larger trials are needed.

## 3.9. Immunological markers

### 3.9.1. Cytokines

As mentioned in section 3.1., GWAS and EWAS results are suggestive of a potential role of the immune system in the pathophysiology of AN. Potential additional contributors to a dysregulated immune system in AN include increased oxidative stress, chronic physiological and psychological stress, changes in the intestinal microbiota (see section 3.11.), and an abnormal bone marrow microenvironment, which lead to alterations in cytokine signalling (Gibson and Mehler 2019).

Cytokines are messenger molecules that steer, amplify or reduce an immune response. They are produced primarily by immune cells, such as B and T lymphocytes, mast cells and macrophages, but also by a range of other cells, and by microglia and astrocytes in the brain. Cytokines have a complex and extensive range of effects and operate via various signalling pathways. They are most commonly quantified in the serum, plasma, and cerebral spinal fluid, with bioassays such as ELISA or polymerase chain reaction (PCR) (Liu et al. 2021). Some cytokines are broadly categorised as 'pro-inflammatory' (Himmerich et al. 2019), meaning that their main or most prominent function is to upregulate the immune response (N.B. these cytokines have multifaceted effects including importance for growth processes). The release of pro-inflammatory cytokines leads to sickness symptoms (i.e. fever, malaise, loss of appetite, fatigue), which in normal circumstances is adaptive. However, a chronic or prolonged release of pro-inflammatory cytokines can have negative consequences in the body including degenerative processes. This can also lead to neuroinflammation (inflammation of the CNS), which may occur as pro-cytokines are able to directly and quickly pass through the blood-brain barrier (Yarlagadda et al. 2009), amongst other mechanisms. The main pro-inflammatory cytokines that have been investigated in the pathophysiology of AN are tumour necrosis factor (TNF-α, interleukin (IL)-6, and -1β, and interferon-gamma (IFN-γ) (Khairova et al. 2009; Chang and Yen 2010).

Three meta-analyses of studies comparing cytokine concentrations between people with AN and HCs have been conducted to date (Solmi et al. 2015; Dalton et al. 2018; Keeler et al. Accepted.). The earlier meta-analysis found evidence for elevated concentrations of TNF-α, IL-1β, IL-6 and TNF-receptor-II in AN, together with decreases in the IL-6 receptor (Solmi et al. 2015). Weight gain was not associated with significant changes in TNF-α, IL-6 and IL-1β, although IL-6 was no longer significantly different from HCs following weight gain (Solmi et al. 2015). Notably, shorter AN illness duration was associated with greater concentrations of IL-6. The later meta-analysis by Dalton et al. (2018) had largely similar findings, where elevations in TNF-α and IL-6 were found in AN compared with HCs, although IL-1β was not elevated.

The latest and most comprehensive meta-analysis found that only concentrations of IL-6 were higher, with a small effect size, and although TNF-α was elevated in AN, this was not the case when study outliers were removed (Keeler et al. Accepted.). Findings were
<figure><img src="image_49.png" /></figure>

not different between AN subtypes and amongst several identified moderators including BMI and age, none were identified as significantly moderating the findings. Additionally, concentrations of IL-15 were elevated in AN compared to HCs, and levels of IL-7 were lower. Concentrations of IL-1β, IL-4, IL-8, IL-10, IFN-γ, monocyte chemoattractant protein (MCP) and transforming growth factor (TGF)-β were similar between AN and HCs. Longitudinally, normalisation over the course of weight restoration was only found for IL-6 but not TNF-α or IL-1β.

The heterogeneity across findings may be attributable to differences in methodology, sample characteristics or lifestyle factors (e.g. diet). Additionally, given EDs are a risk factor for the development of autoimmunity, and vice versa (Raevuori et al. 2014; Hedman et al. 2019; Sirufo et al. 2022) it is possible that there is a cohort of individuals with AN who present with a heightened inflammatory state. For example, one study found that despite the absence of differences between AN and HCs in IL-6 concentrations, when examining the distribution of IL-6 values, the majority of those in the highest 10% were AN cases (Nilsson et al. 2020). This finding supports the future investigation of a potential 'inflammatory subgroup' of AN.

Elevations in the pro-inflammatory cytokines TNF-α and IL-6 have been previously considered as potential biomarkers for AN, although they do not show specificity to AN as elevations are also observed in people with other psychiatric disorders such as major depressive disorder (Dowlati et al. 2010). Thus, they may be a generic marker of psychopathological severity or treatment response, although this should be explored further in well-designed studies accounting for confounding influences. Additionally, as aforementioned the presence of an inflammatory subgroup of people with AN is a possibility – a hypothesis that should be explored in studies with larger and more diverse samples. Studies with larger sample sizes and a range of cytokines and other immune cells are also warranted to investigate the possibility of dysfunctional networks between individual molecules, given the complexity of the signalling pathways, downstream effects and bidirectional influences between cytokines and their targets. There has been some discussion that reducing concentrations of pro-inflammatory cytokines might constitute a target for treatment in the future, for example with TNF-α blockers (Dalton et al. 2018). However, recommendations cannot be made given the heterogeneity of the evidence, and given that individuals with AN have a higher risk for bacterial infection (Birmingham and Treasure 2019; Keeler et al. 2023).

### 3.9.2. Immunoglobulins reactive with α-melanocyte-stimulating hormone (α-MSH)

As explained in section 3.4.2., α-MSH is an anorexigenic neuropeptide of the melanocortin system which plays a key role in the regulation of appetite and body weight (Anderson et al. 2016). α-MSH plasma levels are, however, highly variable, with different studies in AN reporting either lower or similar levels compared with HCs (Galusca et al. 2015; Seitz et al. 2024).

Caseinolytic protease B (ClpB), a protein produced by the Enterobacteriaceae family of gut bacteria, is an antigen-mimetic of α-MSH (Tennoune et al. 2014) which means that ClpB has structural similarity with α-MSH which is why the immune system produces antibodies that cross-react with both the bacterial protein ClpB and the endogenous appetite-reducing hormone α-MSH (Thomas et al. 2025). The relevance of α-MSH-reactive IgG to AN was first shown by positive correlations of its plasma levels with the Eating Disorder Inventory 2 (EDI-2) scores (Fetissov et al. 2005). This link appeared to be specific for α-MSH, since the same study did not find correlations for IgG reactive with other regulatory peptides such as oxytocin or corticotropin. In AN, the α-MSH IgG complexes have been suggested to lead to an increased biological activity compared to α-MSH alone by three different mechanisms: IGG antibodies act as a carrier for α-MSH and protect it from degradation by enzymes, the α-MSH IgG complexes trigger MC4R signalling at lower concentrations; and the α-MSH IgG complexes increase the rate at which the MC4R is internalised by cells which a marker of high receptor activity (Lucas et al. 2019).

Plasma levels of IgG reactive with α-MSH were therefore suggested as putative biomarkers of AN. However, no significant group differences between patients and HCs were found (Fetissov et al. 2005; Roubalova et al. 2021). Seitz et al. even reported low levels of α-MSH-reactive IgG in adolescents with AN at an early stage of disease coinciding with low total serum IgG concentration (Seitz et al. 2024). α-MSH-reactive IgG correlated positively with BMI-standard deviation score (BMI-SDS) in both patients with AN and in healthy adolescents (Seitz et al. 2024). The authors of this study discussed the high variability of α-MSH and of IgG-bound α-MSH, starvation-related downregulation of α-MSH and IgG production, counter-regulatory endocrine mechanisms, treatment effects and disease duration as potential influencing factors that might explain differences in study results (Seitz et al. 2024).

Bacterial ClpB is naturally present in the plasma of healthy subjects with levels correlating negatively
<figure><img src="image_50.png" /></figure>

with BMI suggesting an anorexigenic effect of ClpB. Reduced immune control of ClpB due to low plasma levels of anti-ClpB IgG in patients with AN were therefore suggested to contribute to the increased anorexigenic potency of this bacterial protein and facilitation of food restriction (Thomas et al. 2025). A recent preclinical study demonstrated that immunoneutralization of ClpB prevents development of activity-based anorexia (ABA) in mice (Thomas et al. 2024). In short, the current literature contains two different hypotheses how of ClpB, α-MSH and α-MSH-/anti-ClpB reactive IgG might influence the development of AN. One hypothesis is that anti-α-MSH IgG protect α-MSH from degradation and enhance its anorexigenic signalling at the MC4R (Fetissov et al. 2005; Lucas et al. 2019). However, cross-sectional studies did not find higher anti-α-MSH IgG levels in people with AN compared to HCs (Fetissov et al. 2005; Roubalova et al. 2021; Seitz et al. 2024). A different hypothesis is that ClpB itself has anorexigenic effects and that a reduced immune control of ClpB due to low plasma levels of anti-ClpB IgG increases the anorexigenic potency of ClpB (Thomas et al. 2025).

For future studies, it has been suggested that a combined analysis of α-MSH, α-MSH- and ClpB-binding IgG and ClpB protein could elucidate the combined effects of these molecules in the pathophysiology of AN (Fetissov and Hökfelt 2019).

IgG binding ghrelin has been shown to protect this orexigenic hormone from degradation in plasma and enhance its signalling upon the ghrelin receptor (Takagi et al. 2013). Low plasma levels of ghrelin-binding IgG were found in patients with AN and were increased after refeeding (Terashi et al. 2011). It is, hence, possible that the orexigenic effect of ghrelin is attenuated in AN because of its insufficient protection by IgG.

### 3.9.3. Leukocytes

Leukocytopenia (a low leukocyte count) is frequently encountered in people with AN where it is seen as a sign of self-starvation induced bone marrow suppression. A recent systematic review and meta-analysis of cross-sectional studies (Keeler et al. 2025) found that concentrations of lymphocytes, basophils, monocytes and neutrophils were lower in AN than in HCs. Additionally, the leukocytopenia of AN may be associated with an increased risk of severe infectious diseases (Himmerich et al. 2010). The cytopenias of AN are thought to be secondary to starvation and bone marrow insufficiency. These changes typically resolve with nutritional rehabilitation (Cleary et al. 2010).

### 3.9.4. C-reactive protein

A recently published cross-sectional meta-analysis found that C-reactive protein (CRP) levels were significantly lower in people with AN compared to HCs, potentially due to malnutrition. The authors suggested that because of reduced CRP levels, patients with AN might be at risk for bacterial infections or autoimmune diseases (Xu et al. 2025).

## 3.10. Metabolomics

Metabolomics, the identification of small molecules known as metabolites, is a promising area to explore for potential biological abnormalities in individuals with AN (Himmerich and Treasure 2024). Techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy are commonly used to quantify metabolites in human samples. This section examines how metabolomics differs between subtypes and stages of AN, and between AN and other psychiatric and metabolic disorders. A summary of studies on metabolomics in AN is presented in Table S7, and Table S8 provides a synopsis of meta-analytic results of these studies.

### 3.10.1. Amino acids and derivatives

Amino acids are organic compounds which are essential for the synthesis of proteins, neurotransmitters and other important nitrogen-containing compounds, such as peptide hormones. They are absorbed from the gastrointestinal tract following oral ingestion. Preliminary evidence suggests there may be altered levels of specific amino acids and their derivatives between patients with AN and HCs. Thus, reduced levels of essential amino acids for haemoglobin synthesis and energy regulation (Miyata et al. 2021; Monteleone et al. 2021b) and non-essential amino acids for protein synthesis (Miyata et al. 2021) have been found in patients with AN compared to HCs. However, findings are far from consistent and indicate the need for further study. For example, glutamate, an excitatory neurotransmitter involved in memory and mood regulation, has been observed to be reduced in faecal samples from acutely ill and recovered individuals with AN-R relative to HCs (Tomášová et al. 2022). However, in another study, no statistically significant difference in glutamate concentration in right inferior lateral prefrontal cortex and right occipital cortex was found between acute AN-BP and HCs (Westwater et al. 2022). The latter study acknowledged the potential impact of psychotropic medication on glutamate turnover.
<figure><img src="image_51.png" /></figure>

Changes in neurotransmitter levels and metabolites following weight restoration have been observed, suggesting the importance of considering the impact of nutritional status on metabolic profiles. For example, serum level of tyrosine, a precursor for various neurotransmitters, did not differ significantly between acute AN and HCs, but was significantly raised after weight restoration (Föcker et al. 2020). The picture is further complicated when exploring AN subtypes, with faecal tyrosine level being reduced in acute AN-R but raised in acute AN-BP (Monteleone et al. 2021b) compared to HCs (Miyata et al. 2021). More studies are needed to determine the role of neurotransmitters in the biomarker profile of AN.

### 3.10.2. Carbohydrates

Carbohydrates play a crucial role in energy production and are the main energy source for human metabolism. However, some patients with AN avoid consuming carbohydrate containing food like bread and starchy vegetables (Buck et al. 2022) in addition to the dietary restriction, which further affects the carbohydrate-related metabolite levels and other components necessary for normal metabolism.

Considerable research indicates that glucose, carbohydrates, sugars and their derivatives are generally lower in individuals with AN compared to those who are recovered from AN and HCs (Föcker et al. 2012; Monteleone et al. 2021a; Salehi et al. 2021). The malnutrition of AN contributes to disturbances in energy metabolism which, in turn, may affect most carbohydrate metabolites, if not all. However, studies investigating this subgroup of metabolites in human samples have found inconsistent results. Some studies found varying concentrations of hexoses, fucose, rhamnose, N-acetylglucosamine, tagatose and other types of sugars in AN while others suggested that levels may differentiate people with active AN from those with recovered AN and HCs (Monteleone et al. 2021b, 2021c). For the use of blood glucose concentration for clinical risk management, see section 3.15.3.

Sugar alcohol and its derivatives have received minimal attention in individuals with AN. Levels of scyllo-inositol and glycerol have been found to differ between AN subtypes (namely AN-R and AN-BP) and illness stages (namely acute AN, recovered AN and HCs) (Monteleone et al. 2021b). Both myo-inositol and meso-erythritol-1 were significantly reduced in patients with AN relative to HCs (Monteleone et al. 2021c; Westwater et al. 2022).

### 3.10.3. Lipids

Lipoproteins, saturated fatty acids, polyunsaturated fatty acids such as omega-3 and omega-6 fatty acids, short-chain fatty acids (SCFAs), carnitine derivatives might play a role as biomarkers in AN.

Lipoproteins are complex particles composed of lipids and proteins that transport lipids, like cholesterol and triglycerides, through the bloodstream. They can be measured in plasma using nuclear magnetic resonance spectroscopy. Lipoproteins have been investigated for their prognostic role in patients with AN. An increase in very low-density lipoprotein (VLDL) in childhood was associated with a lower risk of developing AN, while an increase in HDL at seven years was associated with a higher risk of developing AN at age 18 (Santos Ferreira et al. 2019). In individuals with AN, studies have found increased levels of lysophosphatidylcholine, lysophosphatidylethanolamine, phosphatidylinositol and sphingomyelin even after short term weight-restoration compared to HCs (Tam et al. 2021). Other studies have shown significant differences in lipid levels between AN and HCs, as well as between baseline and study timepoints. These lipids include glycerophospholipids, sphingolipids and lysophospholipids (Föcker et al. 2012, 2020; Tomášová et al. 2022).

Preliminary but consistent findings have been observed in the levels of saturated fatty acids in patients with AN. Faecal levels of stearic acid, lauric acid and its derivative hydroxystearic acid were all increased in patients with AN (Monteleone et al. 2021c), despite dietary restriction. The increase in saturated fatty acids remained high after weight restoration (Monteleone et al. 2021c). Additionally, when faecal levels of saturated fatty acids were tested acutely before nourishment, they remained high (Nguyen et al. 2019; Monteleone et al. 2021c). Comparisons among AN subtypes yielded consistent results, with the AN-BP subtype showing higher levels of faecal palmitic acid than the AN-R subtype (Monteleone et al. 2021b). Hussain et al. (2019) have conducted a meta-analysis finding increased concentrations of lipid metabolites in patients with acute AN and those who were partially weight restored.

Omega-3 fatty acids are polyunsaturated fatty acids that cannot be produced by the body and must therefore be obtained through diet. They are characterised by the location of their first double bond three carbons away from the methyl end of their carbon chain. Levels of docosahexaenoic acid (DHA), alpha linolenic acid (ALA), stearidonic acid (SDA), Eicosapentaenoic acid (EPA) and docosapentaenoic acid (DPA) were all