<figure><img src="image_52.png" /></figure>

found to be elevated in patients with acute AN. Some of these omega 3 fatty acids, such as DHA, were associated with a higher risk of developing AN by the age of 18 (Shih et al. 2016; Nguyen et al. 2019; Santos Ferreira et al. 2019). Recovery from AN normalised levels of omega-3 fatty acids, including SDA (Shih et al. 2016). However, individuals with recovered AN had higher levels of omega-3 fatty acids compared to HCs, particularly DPA (Shih et al. 2016).

One study found increased levels of omega-6 fatty acids in individuals with AN in comparison to HCs. This includes gamma-linolenic acid (GLA), dihomo-gamma-linolenic acid (DGLA) and octadecadienoic acid (OBA) (Shih et al. 2016). When compared to those who recovered from AN, levels of OBA and SDA were higher in acute AN. Similar to omega-3 fatty acids, the comparison between recovered AN and HCs showed that recovered AN patients have higher levels of the omega-6 fatty acid, OBA (Shih et al. 2016). One study found that increased levels of the monounsaturated fatty acid (FA) ratio at age seven years were associated with a lower risk of developing AN at age 18 (Santos Ferreira et al. 2019).

SCFAs have been found to be higher in acute AN (Monteleone et al. 2021a). A recent study found that valeric acid was the only SCFA associated with BMI (Castellini et al. 2023). Similar to most fatty acid findings, faecal levels of valeric and propionic acid were increased in individuals with AN, which remained high even after weight restoration (Monteleone et al. 2021c). On the contrary, acetate, butyric, isobutyric and isovaleric acids were significantly lower in acute AN and people who recovered from AN (Monteleone et al. 2021c; Xu et al. 2022). After nourishment, acetate and propionic acid were lower in AN (Prochazkova et al. 2021). Additionally, studies have shown that SCFAs and BMI were significantly correlated, suggesting a crucial role of SCFAs in weight management (Xu et al. 2022). See also paragraph 3.11.1.1. on SCFAs as microbial metabolites.

Carnitine derivatives are crucial molecules involved in the beta-oxidation process of fatty acid metabolism. These molecules are sensitive to nourishment states. Studies have found reduced concentrations of butyrylcarnitine, O-acetylcarnitine, octanoylcarnitine, acetyl-L-carnitine and palmitoylcarnitine in people with acute AN (Föcker et al. 2012; Miyata et al. 2021).

Research has linked these disturbed metabolomic profiles with extreme low intake of calories, which could be partially modified after nourishment. Therefore, it might be premature to draw a conclusion on whether the use of this category of metabolites is helpful as a biomarker for patients with AN. However,

meta-analytical evidence shows increased levels of total cholesterol, HDL, low density lipoprotein (LDL), triglycerides and free fatty acids in patients with acute AN compared to HCs (Hussain et al. 2019). These changes remain after weight restoration which suggests a persistence of metabolic dysregulation after weight restoration (Hussain et al. 2024).

### 3.10.4. Vitamins and coenzymes

A few studies have examined vitamins and coenzymes in people with AN. Increased levels of choline (B4) were reported in 10 patients with acute AN, compared to HCs (Miyata et al. 2021). In addition, increased total concentration of choline in GM has been reported in 30 predominantly adolescent patients with AN, compared to age-matched female HCs (Doose et al. 2023), indicating lipid catabolism. No significant difference in Cobalamin (B12) was noted between AN-subtypes, although lower BMI was associated with higher level of cobalamin (B12) in acute phase of illness (Burdo et al. 2020). Small sample sizes and multiple testing render it difficult for a firm conclusion to be drawn. The former concern was raised by one study regarding the finding of raised folate (B9) in 53 patients with acute AN and 40 patients in remission from AN, compared to 36 HCs (Burdo et al. 2020). Lower levels of nicotinamide (B3) and nicotinate (Vitamin E) have been reported in faecal samples of patients with AN compared to HCs, but the difference in nicotinamide (B3) became statistically insignificant following refeeding (Tomášová et al. 2022).

### 3.10.5. Steroids

Steroids are hormones that are made naturally in the human body and that are chemically characterised by four fused carbon atom rings. Cholesterol is the precursor molecule to many steroid hormones, including sex hormones and hormones necessary for metabolism, water and electrolyte balance in the body.

The acute phase of AN is associated with a disruption of steroid metabolism. Compared to HCs, people with acute AN show higher concentrations of cholesterol and other metabolites of the lecithin-cholesterol acyl transferase (LCAT) pathway that persist after a short-term weight restoration refeeding program (Tam et al. 2021). The effect of refeeding on steroid metabolism in people with AN has been also investigated by Bulant et al. (2020) who showed that, after treatment, plasma levels of pregnenolone sulphate and 20α-dehydropregnenolone sulphate increased, whereas 7β-hydroxy-DHEA, 5-androstene-3β,7β,17βtriol, epipregnanolone, and epietiocholanolone levels significantly decreased. Notably, higher DHEAS
<figure><img src="image_53.png" /></figure>

blood levels in people with AN predicted weight gain at 3- and 6-months and increase in fat mass and appendicular lean mass (Kimball et al. 2022) Increased serum levels of cholesterol typically occurring in the acute phase of AN could also explain the enhanced concentrations of faecal coprosterol (or coprostanol) of patient with AN, since coprosterol is a cholesterol derivative formed from the biohydrogenation of cholesterol in the gut likely by intestinal bacterial flora (Monteleone et al. 2021c). The pathophysiology of increased cholesterol concentration in the blood of people with AN is still not fully understood. People with AN often show decreased blood levels of the thyroid hormone triiodothyronine (T3). T3 regulates the cholesterol ester transfer protein (CETP) which affects cholesterol metabolism. People with AN have been reported to have increased CETP activity and, therefore, increased cholesterol (Ohwada et al. 2006).

### 3.10.6. Organic acids

Organic acids are organic compounds with acidic properties such as carboxylic, sulphonic lactic and pyruvic acid. Some short organic acids are important metabolites produced by the gut microbiome. Therefore, additional information on these specific organic acids is available in section 3.11. of this article.

Increased lactate and pyruvate serum concentrations have been found in acute and recovered patients with AN (Salehi et al. 2021; Tomášová et al. 2022). These metabolites are involved in metabolic pathways such as glycolysis and gluconeogenesis, providing energy under aerobic and anaerobic conditions. Concentrations of branched-chain keto acids have been found to be lower in serum of people with acute AN compared to HCs (Tomášová et al. 2022), while higher in faecal samples, persisting after weight restoration (Monteleone et al. 2021a). Since branched-chain keto acids inhibit glucose production in hepatocytes suppressing liver mitochondrial pyruvate carrier activity and pyruvate-supported respiration (Nishi et al. 2023), it has been hypothesised that, in addition to being linked to the state of emaciation, lower circulating levels of branched-chain keto acids may constitute a compensatory mechanism to maintain pyruvate-/lactate-driven glucose production.

Concentrations of several other organic acids (such as succinate, malate, fumarate and citrate) have been found, even if not consistently, reduced in serum and stool samples from people with AN compared with HCs (Miyata et al. 2021; Salehi et al. 2021; Tomášová et al. 2022). A preliminary study showed that faecal concentration of acetic, succinic, malic, sebacic and p-hydroxyphenylacetic acid differed between AN-R and

AN-BP. Indeed, compared to HCs, p-hydroxyphenylacetic, acetic and succinic acid were higher in AN-BP and lower in AN-R, while malic acid concentration was lower in AN-R and AN-BP (Monteleone et al. 2021b).

Serum levels of uraemic toxin-related compounds p-cresyl sulphate, indole-3-acetic acid and phenylacetic acid were found to be higher in acute AN than HCs (Miyata et al. 2021). Moreover, concentrations of p-cresyl sulphate were positively associated with abundance of *Clostridium coccoides* group and *Clostridium leptum* subgroup in people with AN, suggesting that changes in uraemic toxins are potentially linked to the gut dysbiosis occurring in acute AN. Lower serum methanol concentration in acute and weight-restored AN compared with HCs has also been associated with altered microbiome metabolism due to lower gut microbiota diversity (Salehi et al. 2021).

In summary, tentative evidence suggests a potential role of metabolomics in understanding the metabolic disturbances associated with AN. Patients with AN may exhibit distinct metabolic differences compared to HCs. Understanding these differences between AN-subtypes and stages of illness can provide important insights into the underlying mechanisms and inform the development of potential therapeutic interventions for AN.

### 3.11. Microbiome and markers of the gut-brain-axis

In patients with AN, perturbations in gut microbiota composition have been reported to be associated with gastrointestinal (GI) symptoms (Riedlinger et al. 2020; Di Lodovico et al. 2021; Anton-Păduraru et al. 2023; Garcia and Gutierrez 2023). For instance, constipation emerges as a prevalent issue in AN, attributable to enduring food restriction. Similarly, patients with AN frequently report sensations of abdominal fullness, which are often cited as a reason for food refusal. Interestingly, this symptomatology aligns with objectively assessed delays in gastric emptying time, a phenomenon that tends to ameliorate during the refeeding process. During the process of refeeding and rehabilitation, involving normalisation of eating patterns and/or attaining a healthy body weight where applicable, a notable transformation in GI symptoms unfolds. This metamorphosis entails the persistence of certain complaints, the disappearance of others, and the emergence of new ones (Riedlinger et al. 2020; Riedlinger et al. 2022). Notably, overall improvement in lower GI symptoms surpasses that observed in the upper GI tract throughout refeeding (Riedlinger et al. 2020; Riedlinger et al. 2022).
<figure><img src="image_54.png" /></figure>

GI symptoms, such as constipation, intertwined with disordered eating patterns and aberrant food intake behaviours, potentially compounded by compensatory measures like vomiting or laxative abuse, may contribute to a distinct microbial environment within the colon compared to individuals with healthy GI function (Mack et al. 2018). Consequently, it is not surprising that GI microbiota composition between individuals with AN and their healthy counterparts is often different, as highlighted by recent reviews (Di Lodovico et al. 2021; Nikolova et al. 2021; Anton-Păduraru et al. 2023; Garcia and Gutierrez 2023).

Human studies analysing the GI microbiota in AN cross-sectionally compared to HCs and/or longitudinally are listed in Table S9. The microbiome is typically characterised by its richness, diversity, and community structure. Richness represents a simple census of the unique species present within a sample, independent of their individual population sizes. Diversity is categorised into two primary scales: α-diversity, which measures the variety of species or strains within a single sample, and β-diversity, which quantifies the variation in microbial composition between different samples or over time. The community structure dictates the microbiome's functional capacity. Even if two microbial communities share the same total bacterial count, differences in their structural organisation will lead to distinct outcomes in nutrient metabolism, pathogen defence, and vitamin synthesis. A structure characterised by high diversity and evenness is generally more resilient, possessing a superior ability to recover its functional equilibrium following a disturbance, such as a course of antibiotics (Qian et al. 2020).

When comparing AN with HCs, eight studies reported no difference, three studies showed a decrease, and one study showed an increase in microbial richness, whereas in five studies, this was not reported or was not applicable. Similarly, regarding microbial diversity, 11 studies reported no difference, one study a decrease and five studies did not assess microbial diversity when comparing patients with AN and HCs. However, regarding the microbial community structure, differences were observed in 10 studies, no differences in two and in five studies, the microbial community structure was not assessed. Thus, α-diversity appears unaffected, but differences in the microbial community structure are likely. There is only one meta-analysis that was able to pool the data from four studies, which is why conclusions can hardly be drawn. It found a slight increase in α-diversity in AN versus HCs but comparable β-diversity (Di Lodovico et al. 2021). However, most studies report inconsistent

changes in certain taxa. Nevertheless, there may be tendencies towards a reduction in butyrate-producing species and an increase in mucin-degrading species (Di Lodovico et al. 2021).

There are some indications that gut microbiota composition in acute AN may contribute to maintaining low weight or continued weight loss. Fan et al. (2023) showed that faecal transplantation from patients with AN into a classical model of germ-free mice led to reduced weight gain compared to mice receiving faecal transplants from healthy, normal-weight donors when combined with food restriction (Fan et al. 2023). They also found hypothalamic gene expression of anorexigenic genes like BDNF to be increased and that of orexigenic genes like ghrelin to be reduced, potentially explaining their findings. These results corroborate findings from an earlier study, where mothers of mice pups received faecal transplants from healthy donors and patients with AN. The pups of AN-transplanted mothers had reduced weight gain, decreased appetite and reduced energy efficiency as well as increased anxiety and obsessive-compulsive traits (Hata et al. 2019).

However, Glenny et al. (2021) performed faecal microbiota transplants into germ-free mice with ad libitum access to food and water. The mice were colonised with samples from three distinct donor groups: patients with AN prior to treatment, the same patients following renourishment, and age- and sex-matched HCs. Despite the different origins of the microbiota, the mice receiving AN-derived samples exhibited no significant differences in body weight compared to those receiving HC samples; after four weeks, body weights across all groups remained comparable (Glenny et al. 2021). A detailed overview of studies analysing gut microbiota before and after weight rehabilitation in human AN is displayed in Table S8. For both microbial richness and community structure, study findings were inconsistent, whereas findings were consistent for microbial β-diversity which remained stable within the individuals across time, if reported.

Studies including adolescents or children with AN are still in the minority (Andreani et al. 2024) and their results remain difficult to interpret. However, research this early in the disease course might be an essential step to help discern the chicken-and-egg question of causality regarding the microbiome-gut-brain axis in patients with AN.

Gut microbiota in AN may affect psychological processes via the gut-brain-axis. Kleiman et al. (2015) found a negative link between depression levels and microbial richness, contrasting with the observations
<figure><img src="image_55.png" /></figure>

of Schulz et al. (2021) of no correlation between bacterial richness/diversity and depression/anxiety symptoms. Borgo et al. (2017) noted a negative relationship between depression scores and *Clostridium* species as well as between faecal butyrate concentrations and symptoms of depressive/anxiety disorders. Similarly, Castellini et al. (2023) identified a connection between the bacterial community, butyric acid, and anxiety in patients with AN. However, the above studies had small sample sizes. Fan et al. (2023) discerned numerous further associations, including 11 taxa with EDI-3 related ED symptoms and seven taxa with BMI. The degree of depression symptoms was not explanatory for the microbiota composition in a large cohort of adolescents and young adults (Andreani et al. 2024).

Notably, most studies highlighted a significant correlation between depression (Borgo et al. 2017) and anxiety (Borgo et al. 2017; Castellini et al. 2023) and BMI in their cohorts. A detailed investigation and the only study consequently controlling for patients BMI was a re-analysis of the MICROBIAN study (Mack et al. 2016) conducted by Ketel et al. (Ketel et al. 2024). Several significant associations between psychological parameters and gastrointestinal microbiota were attenuated after controlling for patients' BMI. Post-rehabilitation, positive links were noted between species of *Blautia* and *Ruminococcus* and depressive symptoms. Additionally, during inpatient treatment, propionate and acetate levels exhibited a positive correlation with reductions in depression severity (Ketel et al. 2024). Overall, the situation is unclear.

### 3.11.1. Microbial metabolites

Although composition of gut microbiota in patients with AN varies across studies, a common factor could be the metabolic potential of the microbiome. More important than the presence of specific bacteria seems to be the molecules that the microorganisms produce and with which they can affect the physiology of their host.

#### 3.11.1.1. Short-chain fatty acids.
Several studies examining gut microbiome composition of patients with AN have shown a lower proportion of bacteria producing SCFAs, mainly acetate, propionate, and butyrate, compared to HCs (Morita et al. 2015; Borgo et al. 2017; Prochazkova et al. 2021). These molecules are mainly produced by microbial fermentation of dietary fibre. SCFAs are an important source of energy for colonocytes, as well as for distant organs such as liver, muscles and brain. In addition, they play an important role in gut-brain signalling, including influencing the expression of peripheral appetite-regulating peptides such as PYY and

GLP-1 by enteroendocrine L cells *via* the GPR41 and GPR43 (Chambers et al. 2015; Yu et al. 2024). SCFAs can also regulate leptin levels by activating the free fatty acid binding receptors (FFARs) in adipocytes (Gabriel and Fantuzzi 2019). At the same time, SCFAs modulate the production of central appetite-regulating peptides in the hypothalamus, such as the orexigenic NPY and AgRP or the anorexigenic POMC (Roubalová et al. 2024; Yu et al. 2024).

SCFAs are also important modulators of immune response that regulate mucosal barrier integrity, mucosal immunity and susceptibility to intestinal immune disorders. They signal to cells *via* GPRs expressed on immune cells such as neutrophils, macrophages, dendritic cells and lymphocytes in the gut. Butyrate and propionate can also get into cells by passive diffusion and affect gene expression by inhibition of histone deacetylase. In the gut, SCFAs affect different pathways such as the NF-κB signalling pathway that mediates transcription of various cytokines and chemokines, e.g. IL-1β, TNF-α, IL-2 (Liu et al. 2023). Recent meta-analyses suggest AN may be associated with elevated levels the proinflammatory cytokines IL-1β, TNF-α, and IL-6 (Solmi et al. 2015; Dalton et al. 2018). Thus, in patients with AN, decreased levels of SCFA-producing gut bacteria may contribute to a shift in the immune response towards a pro-inflammatory state and an increased risk of immune-mediated inflammatory diseases such as type 1 diabetes, rheumatoid arthritis and inflammatory-bowel disease (IBD) (Mann et al. 2024). A Danish population-based study found that a prior AN diagnosis was associated with the development of IBD later in life (although the reverse was not observed) (Larsen et al. 2021). Therefore, the therapeutic potential of SCFAs is now being explored.

#### 3.11.1.2. Neurotransmitters.
Multiple neurotransmitters are secreted by the gut microbiome. They can affect metabolism, appetite, immunity, and anxiety and stress behaviour (Neuman et al. 2015). In some studies, faecal levels of neurotransmitters serotonin, dopamine, and GABA were decreased in patients with AN compared to HCs (Prochazkova et al. 2021). Microorganisms not only produce neurotransmitters but also influence their synthesis *via* enteroendocrine cells of the intestine. In addition, their metabolic activity can reduce the bioavailability of their precursors such as tryptophan, tyrosine or glutamine ingested with food. Several studies showed a higher abundance of bacterial genus Alistipes in patients with AN compared to HCs (Di Lodovico et al. 2021). Members of this genus can hydrolyse serotonin precursor tryptophan to indole and thus decrease serotonin bioavailability and may be involved in anxiety and depression. Animal studies using the ABA model have shown that dysregulation
<figure><img src="image_56.png" /></figure>

of the GABAergic system, particularly involving GABA<sub>A</sub> receptors, is associated with anxiety-like behaviour and reduced food intake. Also, modulation of hypothalamic GABAergic signalling has been shown to enhance food intake in rodents (Spadini et al. 2021). Further, evidence points to alterations in dopamine levels and signalling in individuals with AN. Specifically, functional MRI studies have observed altered activation in brain regions associated with reward processing, suggesting a dysfunctional striatal dopamine system in AN (Gorwood et al. 2016).

### 3.11.1.3. Bile acids.
Bile acids can significantly influence the composition and function of the microbial community through their antimicrobial properties. At the same time, bacteria encode many enzymes that can metabolise these acids and change their composition. This microbial manipulation of the bile acid pool contributes to the maintenance of intestinal homeostasis, while imbalances in this system are associated with various diseases. Studies using metabolomics profiling showed higher concentrations of both primary and secondary bile acids in the serum of patients with AN compared to HCs (Perino et al. 2021; Fan et al. 2023).

### 3.11.2. Manipulation of the gut microbiota

Interest in employing therapeutics targeting the gut microbiota in AN stems in part from the association of AN with GI and weight issues. Currently, treatment with probiotics (Solis et al. 2002; Nova et al. 2006; Žaja et al. 2021) or parabiotics/postbiotics (inactivated bacteria or fractions) and stool transplantation (faecal microbiota transplantation; FMT) (Aira et al. 2020; Wilson et al. 2023) are undergoing study (Socała et al. 2021).

FMT may be a complementary treatment strategy for AN. The introduction of FMT from a healthy donor may help to restore the integrity of the intestinal barrier, support immune homeostasis and improve the regulation of satiety and hunger. A recent study showed a weight gain of 13.8% over a 36-week period in patients with AN diagnosed two years prior to FMT (de Clercq et al. 2019). Another study explored changes in the gut microbiome, microbial metabolites and clinical parameters in patient with severe and enduring AN and small intestinal bacterial overgrowth syndrome (SIBO). Post-treatment analyses showed an improvement in gut barrier function, increased bacterial diversity, and elevated levels of SCFAs. However, no significant clinical improvement in AN symptoms was observed (Prochazkova et al. 2019). The results of the studies suggest that earlier intervention might be more beneficial for patients with AN.

The effect of probiotic or prebiotic supplementation was determined in several studies. A recent case-control study involving 100 children with AN demonstrated beneficial effects of a four-strain probiotic supplementation, including improved gut-brain communication, restoration of the gut microbiome, and enhanced clinical outcomes (Lu et al. 2025). In another randomised, placebo-controlled study, 31 female paediatric patients with AN and constipation received 10<sup>8</sup> CFU of *L. reuteri* DSM17938 per dose for 3 months. The probiotic supplementation resulted in improved gastrointestinal motility and weight restoration (Žaja et al. 2021).

## 3.12. Salivary markers

Saliva is a valuable but underused matrix in human biomonitoring. By focusing on this accessible and clinically relevant fluid, new tools for early health risk assessment and non-invasive diagnostics can be obtained. From the oral perspective AN impact on salivary biomarkers is linked to hyposalivation, inflammation, oxidative stress, and microbiota disruption. A general meta-analysis and separate studies have shown decreased salivary pH (MD ≈ -0.39) and salivary flow (MD ≈ -0.27 ml/min) in AN patients. In a retrospective study, patients with AN were affected by higher levels of albumin, inorganic phosphate, aspartate aminotransferase (ASAT), chloride, magnesium, and total protein. ASAT and total protein were found to be good predictors of AN disease (sensitivity approximately 65%, specificity approximately 67%) (Niederau et al. 2025).

### 3.12.1. Salivary microbiota

The relationship between oral microbiota and purging behaviours (such as self-induced vomiting) is an emerging area of research with increasing clinical relevance. Purging – especially vomiting – can significantly alter the oral environment, which in turn affects the microbiota. In a recent case-control study in patients with purging subtype of AN, BN or another specified ED vs. participants without EDs or any type of purging behaviour (Diaz-Perdigones et al. Submitted) oral microbiota populations between vomiting and control groups differed. *Actinomyces* and *Bulleidia* were more abundant in the vomiting group, while *Veillonella* and *Oribacterium* were less abundant compared to the control group. More importantly, vomiting frequency was related to *Actinomyces* and *Bulleidia* abundance; the more vomiting behaviour, the more *Actinomyces* and *Bulleidia* abundance, establishing these bacteria as possible markers of vomiting behaviour. However,
<figure><img src="image_57.png" /></figure>

there is a paucity of literature on this topic and further evidence is needed.

### 3.12.2. Salivary HPA-axis markers

One hypothesis of the pathogenesis of AN considers altered functioning of the HPA axis (see section 3.7), autonomic nervous system (ANS) and immune system due to chronic mental stress. Biological markers in saliva available for measuring alterations of this system include alpha amylase, salivary immunoglobulin A (slgA), cortisol and opiorphin. These stress markers can be measured in saliva where their levels correspond with plasma levels (Kirschbaum and Hellhammer 1989). Depending on their molecular weight there are three pathways that systemic constituents may pass from blood vessels to salivary gland epithelium: passing through the space between the salivary acinar cells, filtration through pores of the cell membranes or selected transport across the cell membrane (Špilak et al. 2025).

#### 3.12.2.1. Salivary alpha amylase.
Alpha-amylase in saliva (salivary alpha-amylase, SAA) is produced by the parotid gland and regulated by sympathetic nervous system (SNS). It can be used as an indicator of SNS activity and peripheral marker of central norepinephrine release. SAA level depends on the function of the parotid glands, presence of carbohydrates in food and time of day. Levels of SAA decrease after waking up and then slowly increase with a peak in the late afternoon and evening.

Hypercortisolaemia due to prolonged mental stress can lead to the alterations in SAA levels. Based on this finding it was suggested that both restrictive and binge-purging types of AN may lead to increase of SAA activity. Cross-sectional case-control studies of restrictive AN failed to show any alterations in the level of SAA compared to HCs (Paszynska et al. 2016, 2020b). However, there was no comparison with other AN subtypes, weight-restored AN individuals or 24-h measurement protocols (Paszynska et al. 2016, 2020b). A recently published meta-analysis of SAA levels showed that the mean difference between AN patients and controls was +6.32 units per litre (U/l), yet it was not statistically significant ($p=0.32$), (Niederau et al. 2025). Under conditions of stress, SAA secretion may have additional peaks that suggests an asymmetry between HPA axis and SNS as an occurrence of HPA axis hyperactivity (Paszynska et al. 2022).

All available studies on SAA in AN have a cross-sectional approach, assessing patients at a specific point in time (e.g. during hospitalisation), without further monitoring of treatment effects.

#### 3.12.2.2. Secretory IgA.
Immunoglobulins (IgA, IgG, IgM) in saliva are auto-antibodies of regulatory peptides, significant anti-inflammatory factors and their role is not limited only to protection against infections. Stress can influence the levels of immunoglobulins in different manners. For example, chronic academic stress, passive coping, and feelings of disgust have been reported to reduce slgA levels, while acute academic and naturalistic stress have been found to increase slgA production (Brandtzaeg 2007). SlgA is the major secretory immunoglobulin present on the oral mucosa. The level of slgA in saliva correlates positively with the plasma cortisol level and fluctuates synchronously over 24 h (Hucklebridge et al. 1998). It has been shown that the slgA concentration is related to the degree of stress and can be used for the assessment of HPA axis function (Brandtzaeg 2007). SlgA activity may influence appetite and lead to decreased food intake (Acres et al. 2012). Cross-sectional case-control studies of individuals with restrictive acute AN revealed significantly higher slgA levels compared to HCs. Furthermore, a significant positive correlation between slgA and cortisol concentrations was observed within the AN group. (Paszynska et al. 2016, 2020b).

It can be assumed that patients with AN often experience chronic inflammation, discomfort, decreased salivary flow and composition, as well as increased bacterial colonisation due to poor oral hygiene. These factors might lead to microinflammation and stress, which can increase the secretion of immunoglobulins in the oral cavity which might also reflect an immune system dysfunction in AN patients.

#### 3.12.2.3. Salivary cortisol.
Cortisol reflects HPA axis reactivity and in AN has been measured in various ways including free cortisol levels in saliva (Paszynska et al. 2016, 2020b; Westwater et al. 2021). Meta-analyses of studies performed among people with AN indicated higher morning serum, urine, and salivary cortisol levels than HCs (Monteleone et al. 2018; Bailly et al. 2021b; Thavaraputta et al. 2023). Interestingly, increased cortisol levels in underweight patients with AN normalise after weight gain (Lo Sauro et al. 2008; Culbert et al. 2016). Given this preliminary evidence, it can be argued that hypercortisolism in individuals with acute AN is a phenomenon mediated by the effects of starvation or malnutrition on the HPA axis *via* increased release of CRH (Gold et al. 1986). HPA axis function in individuals who have recovered from AN is still inadequate. Repeated saliva collection can be used to assess free cortisol and SAA over a long period of time.

#### 3.12.2.4. Salivary opiorphin.
Opiorphin is a salivary peptide produced by the submandibular glands that
<figure><img src="image_58.png" /></figure>

possesses analgesic and antidepressant properties. Its biological action relates to the longer bioavailability of enkephalins and activation of μ- and δ-opioid receptors (Javelot et al. 2010; Yang et al. 2011). A case-control study did not find significant differences in opiorphin levels between patients with AN and HCs (Paszynska et al. 2020a). However, Paszynska et al. (2020b) suggested that level of salivary opiorphin may correlate with levels of stress biomarkers in AN and therefore its measurement could be used as a non-invasive method of disease assessment. The case control study of restrictive acute AN patients noted variability in opiorphin levels and lack of relationship with basic stress biomarkers (cortisol, SAA, slgA). Opiorphin was associated with AN disease duration, Beck Depression Inventory (BDI) score, low body weight, and dental biofilm accumulation. These findings suggest that opiorphin might be a useful biomarker in monitoring of AN disease with severe malnutrition and also reflect immune conditions and inflammation (Paszynska et al. 2020b). Despite knowledge of salivary opiorphin's physiological properties its role in AN remains unexplored. There is still a lack of longitudinal and before-after weight restoration studies, and more research is needed on this topic.

### 3.13. Neurophysiological markers

#### 3.13.1. Square wave jerks

Square wave jerks (SWJs) are involuntary saccades away and back to fixation. They have been observed to occur at abnormally high rates in many neurodegenerative and some psychiatric disorders. In a cross-sectional study of 23 female participants with AN and 22 matched HCs, people with AN exhibited SWJs at a significantly higher rate than HCs (Phillipou et al. 2014). The study authors suggested these findings were due to underlying changes in the GABA system.

#### 3.13.2. Electroencephalography and nerve conduction velocity

Electroencephalography (EEG) measures electrical activity of the brain's cortex, thus enabling testing of various brain functions. The starvation of people with AN affects the brain, producing distorted thinking and a particular neurocognitive profile (Berchio et al. 2022). Testing can be performed in resting state or under certain emotional stimuli or cognitive tasks – both of which have been carried out in small studies of AN, usually involving 10-30 participants (see Tables S10 and S11). Studies have described differences in resting state EEG between AN and control participants, with microstates – which are spatial patterns of electrical voltage that remain relatively stable for short periods

– in AN that are associated with internal/external signal processing (Berchio et al. 2024) and repeatedly abnormalities in posterior theta power, reflecting reduced top-down control. Evoked potential studies have yielded findings suggestive of specific patterns of information processing in AN (Karavia et al. 2024), altered attention level and attention bias to food cues, negative emotion and facial expressions (Wolz et al. 2015; Susta et al. 2022; Eichin et al. 2023), as well as a possible atypical neural mechanism underlying changes in cognitive flexibility (Berchio et al. 2023). The overall paucity of studies, with different tasks involved, different EEG measure parameters taken for analysis and various analysis methods precludes drawing robust conclusions regarding the patterns described.

If epileptic seizures occur in a person with AN, they can be a consequence of starvation and electrolyte disturbances (e.g. hyponatraemia, hypomagnesemia), dehydration, hypoglycaemia, refeeding syndrome, the use of alcohol, drugs or medications that have been prescribed to treat comorbid health problems such as depression or taken without prescription to lose weight (e.g. bupropion) (Gibson et al. 2023; Himmerich and Treasure 2024). EEG signs of epileptic seizures include spikes, sharp waves, and spike-and-wave patterns.

### 3.14. Digital biomarkers

The intersection of digital technology and healthcare has elicited hopes for new avenues for managing complex health conditions like AN (see Table S12). Digital biomarkers have emerged as a promising area of research and development which may lead to novel insights into the diagnosis, monitoring, and management of AN which has traditionally been assessed and monitored through clinical interviews, physical examinations, and psychological assessments.

Wearable inertial sensors, such as accelerometers and actigraphy devices, have emerged as tools for objectively quantifying human motion and physical activity (PA) (O'Neil et al. 2016). These devices operate by capturing acceleration data, which can then be processed to derive a variety of PA metrics. They provide date-time stamped information regarding body acceleration, enabling real-time estimations of PA frequency, intensity, and duration with minimal burden on participants (O'Neil et al. 2016). Widely utilised research-grade devices include ActiGraph models (e.g. GT9X Link, GT3X) and Actiheart, with ActiGraph often considered a 'gold standard' for validating other consumer-based activity monitors (O'Neil et al. 2016; Suau et al. 2024).
<figure><img src="image_59.png" /></figure>

The measurement capabilities of these devices are extensive, encompassing various aspects of physical activity and sleep. They can accurately assess step counts, estimate energy expenditure in kilocalories, and quantify time spent across different PA intensities, including sedentary, very light, light, moderate, and vigorous activity (Alberti et al. 2013; Karr 2017; Lehmann et al. 2018; Suau et al. 2024). Actigraphy appears well-suited for measuring sleep patterns, offering insights into sleep fragmentation, sleep efficiency, and napping sessions (Lim et al. 2023).

A significant advantage of objective assessment methods is their ability to overcome the inherent limitations of subjective self-report tools, such as questionnaires like the International Physical Activity Questionnaire (IPAQ) (Alberti et al. 2013; Lehmann et al. 2018). Research indicates that subjective methods frequently overestimate PA in patient populations, including those with AN, when compared to objective measurements (Alberti et al. 2013; Lehmann et al. 2018). This discrepancy underscores a crucial point: for a condition like AN, where distorted body perception and denial are core symptoms, self-report can be unreliable for behaviours such as physical activity. Therefore, objective measures from accelerometers and actigraphy become not merely supplementary but fundamental for accurate assessment of PA patterns, energy expenditure, and sleep, providing a more accurate representation of a patient's behaviour. This capability makes these devices invaluable for both clinical assessment and research in the future, as they bypass the cognitive biases often present in self-reported data.

Effective utilisation of these devices requires careful consideration of several factors. Device placement on the body (e.g. wrist, ankle, hip, or chest) can influence the validity and accuracy of measurements (Alberti et al. 2013; Karr 2017; O'Neil et al. 2016; Suau et al. 2024). Furthermore, the validity and accuracy of data can be affected by factors such as gait speed, the specific filtering processes applied during data analysis, and the monitoring conditions (e.g. controlled laboratory settings versus free-living environments) (Suau et al. 2024).

Altered physical activity and sleep disturbances are clinically significant features of AN. Excessive physical activity, often described as 'driven exercise', is a common compensatory behaviour reported in 30-80% of AN patients (Karr 2017; Lehmann et al. 2018). This elevated activity is associated with a longer duration of inpatient treatment, higher rates of chronic illness outcomes, and increased risk of treatment dropout (Lehmann et al. 2018). Beyond physical activity, individuals with EDs, including AN, commonly report

poorer sleep quality (Lim et al. 2023). Specific sleep disturbances identified in AN patients include insomnia, sleep fragmentation, low sleep efficiency, and an increased propensity for napping (Lim et al. 2023). Interestingly, AN has also been linked to an 'early riser' chronotype and an elevated risk of insomnia, which contrasts with the evening-based patterns often seen in many other psychiatric conditions like mood disorders or schizophrenia (Wilcox et al. 2024).

### 3.14.1. Actigraphy in physical activity monitoring

Recent research has highlighted the critical role of physical activity patterns in individuals with AN. Lehmann et al. (2018) employed the SenseWear™ armband to objectively assess physical activity levels in hospitalised AN patients compared to HCs. They found that AN patients engaged more in very light and light-intensity activities while exhibiting less moderate to vigorous activities (Lehmann et al. 2018). Grosser et al. (2020) explored the association between physical activity, nutritional status, and psychological parameters in AN patients, and found an inverse relationship between time spent in sedentary behaviour and BMI outcomes (Grosser et al. 2020). Similarly, Langlet et al. (2021) quantified physical activity and sleeping behaviour during the initial week of inpatient care for AN, uncovering a significant amount of time spent inactive and variations in sleep quality among patients (Grosser et al. 2020). In another study, Sauchelli et al. (2015) used actigraphy to monitor physical and sleep activity with the Actiwatch. Although AN patients and HCs did not differ in mean time spent in moderate-to-vigorous physical activity (MVPA), fewer AN patients had a high physical activity profile compared to HCs. Both lower levels of MVPA and greater ED severity had a direct effect on poor treatment outcome. The poor physical condition of the patients during the evaluation and the restrictions of movement during inpatient and/or day hospital treatment may be attributed to the contradictory results obtained in the literature. These studies underscore the altered physical activity patterns in AN, suggesting a nuanced approach to integrating physical activity monitoring into treatment plans.

### 3.14.2. Smartwatch and smartphone applications in psychological response monitoring

The evolution of smartwatches and smartphones has enabled real-time monitoring of psychological responses, offering a promising tool for managing AN. Vinusha et al. (2023) developed a smartwatch with features for monitoring heart rate, finger movements, and
<figure><img src="image_60.png" /></figure>

emotional states, aimed at assisting individuals with EDs in their recovery journey. This innovation facilitates timely interventions by alerting healthcare providers based on predetermined thresholds of heart rate changes. Additionally, the integration of a metaverse platform allows for virtual engagement between patients and caregivers, enhancing the support system available for recovery (Vinusha et al. 2023).

Complementing smartwatch innovations, smartphone applications have been developed to facilitate self-monitoring and engagement among individuals with AN. Tregarthen et al. (2015) introduced a smartphone app enabling users to self-monitor meals, emotions, behaviours, and thoughts, reaching over 100,000 users within two years. This app demonstrated the potential to engage underserved populations not receiving clinical treatment (Tregarthen et al. 2015). Keshen et al. (2020) compared electronic self-monitoring via Recovery Record to traditional paper records, although finding no significant differences in effectiveness, highlighted the varying preferences and contexts in which digital tools may serve as adjunct or alternative monitoring methods (Keshen et al. 2020).

### 3.14.3. Sensing technologies for comprehensive healthcare

The application of sensing technologies, inspired by the Interpersonal Neurobiology Theory (IPNB), promotes a holistic approach to healthcare for individuals with AN. Almenara et al. (2022) suggested an approach using various sensors, including body, sociometric, and ambient sensors, to evaluate bodily processes, social interactions, and environmental influences. This comprehensive monitoring has been suggested to be able to provide deeper insights into the physiological, psychological, and environmental factors impacting AN, advocating for smarter healthcare solutions that consider the complex interplay of these elements (Almenara et al. 2022).

### 3.14.4. Smartphone-based ecological momentary assessment (EMA)

Several studies have utilised smartphone-based ecological momentary assessment (EMA) to investigate the dynamic interplay of affect and behaviour in AN. Kolar et al. (2016) conducted an EMA study using smartphones with 20 female adolescents with AN and 20 HCs. The AN group showed significantly higher mean and maximum levels of aversive tension. Crucially, in the AN group, reported food intake was associated with higher levels of aversive tension, whereas school or sport-related events were not

specifically linked to aversive tension in this group. This study highlights how EMA can pinpoint specific contextual triggers for negative affective states in AN. Using smartphone-based EMA in an epidemiological cohort, findings suggested that experiential avoidance was temporally linked to greater engagement in disordered eating behaviours (DEBs), supporting theories that DEBs may serve an avoidance function for unpleasant inner experiences (Peschel et al. 2023). This type of research demonstrates EMA's capacity to examine the micro-processes and maintaining mechanisms of ED symptoms relevant to AN. The general utility of EMA lies in its ability to capture momentary snapshots of daily life, providing rich, contextualised data on the antecedents and consequences of ED behaviours and associated affective states (Shiffman et al. 2008; Goldschmidt et al. 2018; Drexl et al. 2025). These methods can inform 'just-in-time adaptive interventions' (JITAlS) (Smith et al. 2019; Linardon and Torous 2025; Pavlacic et al. 2025).

### 3.15. Markers of physical health risk and refeeding markers

AN is a chronic disease with numerous psychiatric and medical comorbidities. It can affect all organ systems and thus leads to more serious physical harm than any other mental illness. The 2011 meta-analysis by Arcelus et al. (2011) provided an overview of the results of 36 studies. The mortality rate indicates the relative risk of a patient group compared to a similar population group. The studies reported outcomes of AN during 166 642 person-years, and the weighted mortality rates (i.e. deaths per 1000 person-years) was 5.1 for AN. The standardised mortality ratio for AN was 5.86. One in 5 individuals with AN who died had committed suicide (Arcelus et al. 2011).

This section explains the biological markers of the physical health risk associated with AN. Table 4 summarises these markers, the specific risk that they indicate and the direction of change.

#### 3.15.1. Body measures as risk markers

Low weight has been a core feature of the definition of AN since its first modern-age description in the late nineteenth century (Gull 1997). A meta-analysis by Hübel, Yilmaz, et al. (2019) showed marked differences in weight and BMI between persons with AN and HCs. Low weight remains an essential diagnostic criterion for AN (APA 2013; World Health Organization 2024). In the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), while the diagnosis of
<figure><img src="image_61.png" /><figcaption>Table 4. Overview of biological risk markers.</figcaption></figure>

<table><thead><tr><th>Parameter</th><th>Direction of change</th><th>Risk factor for</th><th>Comments</th><th>Citation</th></tr></thead><tbody><tr><td><strong>Body weight and composition</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>BMI</td><td>↓</td><td>General morbidity, mortality</td><td>Risk increases continuously with lower BMI</td><td>(Cuntz et al. 2023; Gibson et al. 2024)</td></tr><tr><td>Recent weight loss</td><td>↑</td><td>Low prealbumin and reduced hand grip strength; bradycardia, lower pulse rate nadir</td><td></td><td>(Whitelaw et al. 2018; Gibson et al. 2024)</td></tr><tr><td>Weight suppression (discrepancy between highest adult weight and current weight)</td><td>↑</td><td>Increased likelihood of amenorrhoea, reduced systolic blood pressure, (nadir) haemoglobin; lower pulse rate nadir, bradycardia</td><td></td><td>(Whitelaw et al. 2018; Gibson et al. 2024)</td></tr><tr><td>BMI after treatment</td><td>↓</td><td>Relapse</td><td>A higher BMI/weight/body fat was associated with lower odds of relapse when it was assessed at discharge than when assessed at admission.</td><td>(Sala et al. 2023)</td></tr><tr><td>Rapidity of weight loss</td><td>↑</td><td>Electrolyte abnormalities, dehydration, cardiovascular complications (such as bradycardia) and endocrine disturbances</td><td>Likelihood of an admission increases with increasing rate of weight loss and lower energy intake, independent of underweight.</td><td>(Brennan et al. 2024)</td></tr><tr><td>Weight loss rate</td><td>↑</td><td></td><td></td><td></td></tr><tr><td>Fat mass</td><td>↓</td><td>Amenorrhoea (specifically associated with lower extremity fat mass)</td><td>Posttreatment fat mass and gain in fat mass during treatment are negatively associated with presence of amenorrhoea.</td><td>(Hübel, Yilmaz, et al. 2019)</td></tr><tr><td>% body fat</td><td>↓</td><td>Amenorrhoea</td><td>Fat percentage is a positive predictor of resumption of menses (but not superior measure to BMI).</td><td>(Winkler et al. 2017)</td></tr><tr><td><strong>Metabolism</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Gonadal axis</td><td>↓</td><td>Amenorrhoea, osteopenia</td><td>caused by decreased energy availability in patients with AN</td><td>(Jada et al. 2021)</td></tr><tr><td>Adrenocorticotropic hormone (ACTH), cortisol, aldosterone</td><td>↑</td><td>Impaired glucose tolerance, elevated blood lipid levels, increased release of mineralocorticoids (=> hypokalaemia, increased water retention)</td><td>Indicators of activated HPA axis. HPA axis is activated to provide energy for the organism when energy intake is too low.</td><td>(Misra and Klibanski 2014; Schorr and Miller 2017; Thavaraputta et al. 2023)</td></tr><tr><td>Cholesterol</td><td>↑</td><td>The increased cholesterol is not the main reason for an increased cardiovascular risk in AN</td><td>Consequence of the activated HPA axis and the low T3 levels.</td><td>(Ohwada et al. 2006; Affaticati et al. 2023)</td></tr><tr><td>Thyroid hormones (T3, T4)</td><td>↓</td><td>Hypothermia, bradycardia, constipation</td><td></td><td>(Wu et al. 2024)</td></tr><tr><td>Body temperature</td><td>↓</td><td>Unclear, whether independent risk</td><td></td><td>(Brennan et al. 2023; Ralph-Nearman et al. 2024)</td></tr><tr><td>Resting metabolic rate</td><td>↓</td><td>Unclear, whether independent risk</td><td>Resting energy expenditure increases with increased energy intake and body weight.</td><td>(de Zwaan et al. 2002; Reed et al. 2024)</td></tr><tr><td>Blood glucose</td><td>↓</td><td>Hypoglycaemia, coma</td><td></td><td>(Gibson et al. 2020)</td></tr><tr><td>Glucose tolerance</td><td>↓</td><td>Impaired glucose tolerance predicts poorer refeeding outcome, (slower weight gain)</td><td></td><td>(Casper 1996; Yasuhara et al. 2005)</td></tr><tr><td><strong>Cardiovascular system</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Heart rate</td><td>↓</td><td>Bradycardia is a risk factor for orthostatic collapse and syncope</td><td>Unclear whether dangerous</td><td>(Assalone et al. 2022; Jomah et al. 2024)</td></tr><tr><td>Tachycardia</td><td>↑</td><td>Heart failure</td><td>Seems to indicate high risk</td><td>(Krantz and Mehler 2004)</td></tr><tr><td>Blood pressure</td><td>↓</td><td>Orthostatic collapse</td><td></td><td>(Sekaninova et al. 2020; Jenkins et al. 2022)</td></tr><tr><td>QRS amplitude</td><td>↓</td><td>Indicates low left ventricular mass</td><td></td><td></td></tr><tr><td>QT Interval*</td><td>↑</td><td>Potential risk indicator for ventricular arrhythmia</td><td>Explained by hypokalaemia or psychotropic medication</td><td>(Krantz et al. 2020)</td></tr></tbody></table>

(Continued)
<figure><img src="image_62.png" /><figcaption>Table 4. Continued.</figcaption></figure>

<table><thead><tr><th>Parameter</th><th>Direction of change</th><th>Risk factor for</th><th>Comments</th><th>Citation</th></tr></thead><tbody><tr><td>Left ventricular mass</td><td>↓</td><td>might indicate risk of heart failure</td><td></td><td>(Kuwabara et al. 2018)</td></tr><tr><td>Pericardial effusion</td><td>↑</td><td>Pericardial tamponade</td><td>Mostly asymptomatic</td><td>(Kuwabara et al. 2018)</td></tr><tr><td><strong>Water balance, electrolytes and hepatic markers</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Sodium</td><td>↓</td><td>Adynamia, coma, pontine myelinolysis</td><td>Mostly indicative for polydipsia</td><td>(Santonastaso et al. 1998; Lawson et al. 2012; Boyd and Durant 2018; Che Mokhtar et al. 2022)</td></tr><tr><td>Potassium</td><td>↓</td><td>Nephropathy, seizures, arrhythmias, TdP (Torsades de pointes)</td><td>More frequent in AN-BP</td><td>(Puckett 2023)</td></tr><tr><td>Phosphate</td><td>↓</td><td>Rhabdomyolysis<br>Marker for refeeding syndrome – coma, death</td><td></td><td>(O'Connor and Nicholls 2013)</td></tr><tr><td>Creatin kinase</td><td>↑</td><td>Rhabdomyolysis<br>Refeeding syndrome</td><td></td><td>(Stheneur et al. 2014; Riva et al. 2021; Tseng et al. 2023)</td></tr><tr><td>Creatinine clearance</td><td>↓</td><td>Renal failure</td><td></td><td>(Bouquegneau et al. 2012)</td></tr><tr><td>GFR</td><td>↓</td><td>Renal failure</td><td></td><td>(Bouquegneau et al. 2012)</td></tr><tr><td>Albumin</td><td>↓</td><td>Oedema</td><td></td><td>(Wu et al. 2024)</td></tr><tr><td>ALT/AST</td><td>↑</td><td>Coagulopathy, hepatic failure, death</td><td></td><td>(Kheloufi et al. 2014; Rosen et al. 2017; Cuntz and Voderholzer 2022)</td></tr><tr><td><strong>Micronutrients/Vitamins</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Zinc</td><td>↓</td><td>Iron deficiency anaemia</td><td></td><td>(Winston 2012)</td></tr><tr><td>Iron</td><td>↓</td><td>Macrocytic anaemia,</td><td></td><td>(Winston 2012)</td></tr><tr><td>Folate</td><td>↓</td><td></td><td></td><td>(Patrick 2002; Castro et al. 2004; Winston 2012)</td></tr><tr><td>Vitamin B1</td><td>↓</td><td>Wernicke's encephalopathy</td><td></td><td>(Patrick 2002; Tam et al. 2022; Affaticati et al. 2023)</td></tr><tr><td>Vitamin B12</td><td>↑↓</td><td>Macrocytic anaemia, funicular myelosis</td><td>Several studies found increased vitamin B12 mean blood concentrations in people with AN, but vitamin B12 deficiency has also been reported.</td><td>(Hadigan et al. 2000; Tam et al. 2022; Affaticati et al. 2023)</td></tr><tr><td>Vitamin K</td><td>↓</td><td>Osteoporosis</td><td></td><td>(Urano et al. 2015)</td></tr><tr><td>Vitamin D</td><td>↓</td><td>Osteoporosis, fractures<br>Bone density</td><td></td><td>(Mehler et al. 2018)</td></tr><tr><td><strong>Hematologic parameters</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Erythrocytes</td><td>↓</td><td>Anaemia, weakness</td><td></td><td>(Hütter et al. 2009)</td></tr><tr><td>Haemoglobin</td><td>↓</td><td>Anaemia, weakness</td><td></td><td>(Walsh et al. 2020)</td></tr><tr><td>Haematocrit</td><td>↓</td><td>Water overload, oedema, anaemia</td><td></td><td>(Walsh et al. 2020)</td></tr><tr><td>Leucocytes</td><td>↓</td><td>Bacterial infections</td><td>Gelatinous bone marrow transformation</td><td>(Bowers and Eckert 1978; Schafernak 2016; Walsh et al. 2020; Keeler et al. 2025)</td></tr><tr><td>Thrombocytes (platelets)</td><td>↓</td><td>Coagulopathy</td><td></td><td>(Walsh et al. 2020)</td></tr><tr><td>NLR</td><td>↓</td><td>No specific risk known</td><td></td><td>(Bou Khalil et al. 2022; Caldiroli et al. 2023)</td></tr><tr><td>Ferritin</td><td>↑</td><td>No specific risk known</td><td></td><td>(Wanby et al. 2016)</td></tr><tr><td><strong>Neurological (selected)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>EEG</td><td>↓</td><td>No specific risk known</td><td></td><td></td></tr><tr><td>Nerve conduction velocity</td><td>↓</td><td>No specific risk known</td><td></td><td></td></tr><tr><td><strong>Bone markers</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>BMD</td><td>↓</td><td>High risk of bone fracture</td><td>Fat mass is associated with total BMD.<br>Duration of amenorrhoea, age and illness duration are not significant predictors of BMD.</td><td>(Lucas et al. 1999)</td></tr><tr><td>IGF-I</td><td>↓</td><td>Independent predictor of BMD</td><td></td><td>(Barrios et al. 2024; Fazeli and Klibanski 2014)</td></tr><tr><td>Androgen</td><td>↓</td><td>Independent predictor of BMD</td><td></td><td>(Fazeli and Klibanski 2014; Thavaraputta et al. 2023)</td></tr></tbody></table>

Abbreviations: ACTH = adrenocorticotropic hormone; ALT = alanine aminotransferase; AN = anorexia nervosa; AN-BP = anorexia nervosa binge-purge subtype; AST = aspartate aminotransferase; BMD = bone mass density; BMI = body mass index; EEG = electroencephalogram; GFR = glomerular filtration rate; HPA = hypothalamic-pituitary-adrenal axis; IGF-1 = insulin-like growth factor 1; NLR = neutrophil-lymphocyte ratio; TdP = Torsades de pointes; ↑ = higher values or an increase indicate an increased risk, ↓ = lower values or a decrease indicate an increased risk, ↑↓ = increased or decreased are found in AN, and are both associated with a specific risk.
<figure><img src="image_63.png" /></figure>

AAN is applied when all AN criteria are met except for the weight criterion, i.e. weight is within or above normal range. Regarding medical status, low weight is clearly associated with medical complications with markedly high risk in cases of BMI < 13 kg/m² (Cuntz et al. 2023). A recent retrospective study of an adult AN population (n=761), found that while recent weight loss and weight suppression were associated with markers of malnutrition including low prealbumin, reduced hand grip strength, amenorrhoea and reduced systolic blood pressure, low BMI on admission was predictive of all markers (Gibson et al. 2024). Additionally, a recent meta-analysis found that low discharge weight was a good predictor of symptom recurrence and relapse. Stopping treatment too early has a poor prognosis. Admission weight did not play a significant role (Sala et al. 2023). For a synopsis of meta-analytic results for physical health risk and refeeding markers of AN see Tables S13 and S14, and for a synopsis of included studies on anthropometric biomarkers for AN see Table S15.

Nevertheless, accumulating data points to the dangers which may be overlooked in normal-weight restrictive disorders. A systematic review and meta-analysis of 8 studies including 2,331 participants with AN or AAN, found between 29 and 42% of participants presented with medical instability requiring hospitalisation in the absence of low BMI (Brennan et al. 2023). Another study following 205 AN/AAN patients in London clinics identified weight loss rate and low mean energy intake in patients requiring admission independent of underweight (Brennan et al. 2024). This is in line with previous studies of adolescents admitted for EDs, where about half of cases exhibited the atypical weight presentation (Fisher et al. 2015; Garber et al. 2019), and to weight loss again being associated with heart rate, a malnutrition marker (Whitelaw et al. 2018; Garber et al. 2019). Considering these data, it has been suggested that the straightforward weight criterion be omitted, and an evaluation of weight loss be considered (Phillipou and Beilharz 2019). In addition, the recently updated clinical description of AN in the International Classification of Diseases, 11th Revision (ICD-11) refers to patients' weight history and adds that rapid weight loss, such as loss of more than 20% of total body weight in 6 months, may replace the underweight criterion, provided all other criteria are met (World Health Organization 2024).

A recent retrospective study in 545 patients with severe AN or avoidant restrictive food intake disorder hospitalised in a medical stabilisation unit between 2018 and 2021 found a mean BMI of 13 kg/m² or less

in 46% of patients with AN-R and in 39% with AN-BP. Hypoglycaemia was common and correlated with elevated liver function tests and low prealbumin. Refeeding hypophosphataemia developed in 26% of patients starting day 2 and was associated with lower BMI. Hypokalaemia appeared on admission among 39%, twice as common in patients diagnosed with AN-BP (Leach et al. 2024)

AN is a complex mental health and metabolic disorder. According to EWAS studies, for example, its biological features refer to cholesterol metabolism, lipid synthesis, the structure and stability of connective tissues, membrane transport and cell motility and the differentiation of brown adipose tissue and lipid metabolism (Booij et al. 2015; Kesselmeier et al. 2018; Steiger et al. 2019; Iranzo-Tatay et al. 2022; Steiger et al. 2023). In clinical settings, however, the attention is often focussed on underweight and malnutrition and their ramifications. With the obesity epidemic in the background in recent years data have been accumulating regarding AAN, which emerges as a comparable entity to AN, clinically and medically. This has led to suggestions that additional markers, including degree of weight loss and rate of weight loss, may be superior to BMI alone.

Of note, BMI alone, as a measure of weight in relation to height, is not sufficient to assess the individual risk of patients. In fact, the variance in morbidity and mortality is too large and it is necessary to identify other criteria that help to assess individual risk. AN can affect all organ systems, and individual risk markers should quantify the degree of organ involvement that contributes to individual risk.

### 3.15.2. Body composition, fat content

The meta-analysis by Hübel, Yilmaz, et al. (2019) provides a comprehensive evaluation of body composition changes in individuals with AN, highlighting significant reductions in fat mass and fat-free mass compared to HCs. Patients with AN had 50% lower fat mass, with a MD of -8.8 kg (95% confidence intervals [CI]: -9.81 to -7.79), and 4.98 kg lower fat-free mass (95% CI: -5.85 to -4.12) compared to HCs. During early weight restoration, fat mass was preferentially stored in the trunk region, differing from patterns in HCs. Furthermore, fat mass remained altered in AN, significantly lower than HC, even after weight restoration. Altered fat mass distribution has been described in AN with higher marrow adipose tissue and lower subcutaneous (SAT) and visceral (VAT) adipose tissue compared to HC (Fazeli et al. 2019). See sections 3.15.9 and 3.15.10 on bone marrow and bone markers.
<figure><img src="image_64.png" /></figure>

Comparing constitutional thinness with AN, Bailly et al. (2021a) found that normal range fat percentages (around 18.9%) were retained in the former, whereas AN patients had significantly lower body fat percentages (~11.4%). However, fat free mass was similar between the two groups.

AN-associated alterations in body composition and fat distribution are linked to clinical outcomes like menstrual recovery, where a BMI of 19kg/m² or a fat percentage of 23% predicts a 50% likelihood of resuming menses (Winkler et al. 2017). In contrast, constitutionally thin individuals do not show such disruptions, suggesting that hormonal and metabolic factors unique to AN play a pivotal role in these outcomes. Additionally, constitutionally thin individuals have not been reported to show alterations in thyroid parameters or bone density parameters.

### 3.15.3. Metabolic changes

Metabolism in AN is characterised by inadequate energy intake and the consequent need to conserve energy and to mobilise remaining energy reserves and make them available to the vital organs. This metabolic adaptation can be measured and verified on the one hand by a reduction in thyroid activity and the associated low T3 constellation and an increase in reverse T3 levels (metabolically inactive form of T3) as part of the euthyroid sick syndrome (ESS). ESS is a condition where thyroid hormone levels are abnormal in individuals with nonthyroidal illnesses, but the thyroid gland itself is functioning normally. This hormonal constellation leads to reduced energy consumption (bradycardia) and reduced production of body heat (hypothermia) (Misra and Klibanski 2014; Wu et al. 2024).

Mobilisation of energy reserves is triggered by activation of the HPA axis, as measured by increased basal cortisol levels. Consequences include impaired glucose tolerance and elevated blood lipid levels. In addition, increased HPA axis activity leads to release of mineralocorticoids and thus a tendency for hypokalaemia and increased water retention (Tey et al. 2005; Yücel et al. 2005; Ehrlich et al. 2006).

Thus, low T3, high basal cortisol levels, bradycardia, hypothermia, hypercholesterolaemia, a tendency to oedema and elevated postprandial glucose levels are all adaptive to ensure functioning of the organism in conditions of energy deprivation. On the one hand, they merely reflect the poor nutritional status and are not independent risk markers. On the other hand, these metabolic adaptations lead to dangerous changes in cardiac rhythm, electrolyte and water

balance and glucose metabolism, which most likely contribute to the increased mortality in this group of individuals.

AN is associated with pathological glucose metabolism, including impaired glucose tolerance, stemming from HPA axis abnormalities and hypercortisolism (de Rosa et al. 1983; Casper 1996; Himmerich et al. 2010; Thavaraputta et al. 2023). For example, a case-control study by de Rosa et al. (1983) in patients with AN showed reduced basal glucose levels with a flat glucose curve and reduced insulin levels with a slight increase after glucose administration. A similar case-control study conducted by Fukushima et al. (1993) showed that patients with AN had lower fasting plasma glucose and insulin concentrations characterised by reduced insulin secretion, increased insulin sensitivity, and reduced glucose effectiveness (Fukushima et al. 1993). An observational study of interstitial glycaemic cycles showed that patients with AN had chronic, prolonged, mild hypoglycaemia over 24h, including day and night, despite normal fasting glycaemia. This might be linked to the adaptive increase in counter-regulatory hormones to protect patients with AN from deeper hypoglycaemia (Germain et al. 2023). Furthermore, serum glucose levels increased significantly from admission to discharge (Puckett et al. 2024). During refeeding, the rapid shift from a starvation-adapted, catabolic state to a carbohydrate-rich, anabolic state can lead to paradoxically elevated insulin levels. This initial surge in insulin, is a key driver of refeeding syndrome and its characteristic electrolyte imbalances (Ponzo et al. 2021).

Refeeding syndrome is a critical metabolic complication characterised by severe electrolyte and fluid shifts following the reintroduction of nutrition in chronically malnourished patients, such as people with AN. Upon carbohydrate reintroduction, the subsequent glycaemic load triggers a rapid surge in endogenous insulin secretion. This anabolic stimulus drives the intracellular uptake of glucose alongside the electrolytes phosphate, magnesium, and potassium which can result in acute, life-threatening hypophosphataemia and associated systemic consequences, including cardiac arrhythmias and multi-organ failure (Gallagher et al. 2022).

Importantly, a single blood glucose measurement may miss ongoing hypoglycaemia. Thus, it has been suggested that haemoglobin A1C (HbA1c) (in addition to serum glucose) be measured as it reflects average blood glucose levels over the preceding 2 to 3 months (Puckett et al. 2024). Hypoglycaemia has been suggested to be a major factor in bradycardia, sinus arrest
<figure><img src="image_65.png" /></figure>

and sudden death of patients with AN (Mehler et al. 2022). Since hypoglycaemia can be life-threatening, one of the tasks of treatment is to keep energy intake high enough to prevent hypoglycaemia.

### 3.15.4. Cardiovascular system

The severe energy deprivation associated with AN produces massive change in the cardiovascular system. Blood flow is centralised, arterial blood supply is reduced, and heart rate and blood pressure are decreased. The consequences of these changes can be inferred from the functional and morphological changes in the circulatory system, whereby, in terms of physical risk, a distinction must be made between changes that are merely adaptive, reducing energy expenditure, and thus only indicating the degree of underweight or weight loss, and those that indicate harmful consequences and thus are risk markers in the true sense. In severe cases, physical restraint, placement of a temporary pacemaker, or use of ionotropic or chronotropic medication might be indicated.

#### 3.15.4.1. Functional cardiovascular changes.
The resting electrocardiogram (ECG) in AN shows some abnormalities due to centralisation of circulation and smaller left ventricular muscle mass. As the pulmonary circulation remains unchanged in contrast to the systemic circulation, the ECG shows signs of relative right heart strain, i.e. a steep or right-sided type, more frequent incomplete right bundle branch block and a shift of the R/S transition to the left. Peripheral low voltage and a decrease in QRS amplitude indicate reduced left ventricular muscle mass. All these changes are adaptive and do not represent an independent risk.

Recurrent vomiting and misuse of diuretics and laxatives may lead to hypokalaemia, which can cause a significant risk of arrhythmias. Co-occurring hypercortisolism and hyperaldosteronism further intensify this tendency.

The risk of arrhythmia can be read from the ECG using the typical signs of hypokalaemia: T-wave flattening, preterminal T-wave negativisation, U-waves and QTc prolongation. These ECG changes make it possible to estimate the potassium voltage between extracellular and intracellular spaces. The higher the concentration gradient between cell and intracellular space, the higher the risk of cardiac arrhythmias, which can range from simple premature beats to ventricular fibrillation. These ECG changes therefore indicate a high cardiac risk.

Prolongation of the QT interval can lead to increased susceptibility to ventricular arrhythmias, including

Torsade de Pointe (TdP) arrhythmias. For a long time, the high mortality rate in patients with AN was thought to be caused by fatal ventricular arrhythmias, which in turn were caused by the increased QT-dispersion associated with underweight (Cooke et al. 1994; Swenne and Larsson 1999). However, the relationship between underweight or weight loss and the QT interval is increasingly being questioned: in a 2008 meta-analysis, although a prolonged QT interval was found in AN compared with HCs, the QTc remained within the normal range and showed no pathological prolongation (Lesinskiene et al. 2008). It is possible that the finding of a relative prolongation of the QT interval is due to the correction formulae used, which do not sufficiently consider the high tendency to bradycardia in AN (Walter et al. 2015).

It is still considered likely that the increased mortality in AN is also caused by the occurrence of fatal ventricular arrhythmias (Mehler et al. 2022). It has been suggested that the QT interval may be responsible for some of these events. It has also been observed that in individuals with AN, the QTc interval fails to shorten with exercise and increased heart rate, contrasting significantly with the typical physiological response seen in HCs (Janzen et al. 2020). Prolongation of the QT interval is more common with medication (Gibson et al. 2020), and electrolyte imbalances (Bomba et al. 2018), may also contribute (Geer et al. 2019). Another theory is that low basal metabolic rate and increased vagal tone contribute to increased QT variability, which ultimately increases the risk of ventricular arrhythmias (Koschke et al. 2010).

Ultimately, the QT interval is obviously not only prolonged by malnutrition. However, since other factors accompanying AN can prolong the QTc time and this prolongation is associated with an arrhythmia risk, the QTc should be determined in patients with AN.

As an adaptation to low body weight and low Triiodothyronine levels, adolescent girls with AN showed significantly lower heart rates, lower systolic blood pressure, and lower body temperature compared with control subjects (Misra et al. 2004). Underweight adolescents with AN are significantly more likely to have lower blood pressure and bradycardia (Brennan et al. 2023). A systematic review showed lower heart rate (HR) and higher heart rate variability (HRV) in AN compared to HCs (Ralph-Nearman et al. 2024).

On the ECG, bradycardia usually appears as sinus bradycardia (Brennan et al. 2023; Jomah et al. 2024). However, supraventricular replacement rhythms, sinoatrial blocks and atrioventricular block patterns are
<figure><img src="image_66.png" /></figure>

also rarely seen (Kanbur et al. 2009; Cussen and Harding 2021). In most cases, these bradycardic rhythm disturbances do not result in syncope; usually there is only mild orthostatic dysregulation. With exercise, the heart rate increases, and the block patterns are reversible. A pacemaker is therefore rarely required (Sall and Timperley 2015). Nevertheless, bradycardic arrhythmias are thought to possibly contribute to the mortality of patients.

### 3.15.4.2. Structural heart changes.
Malnutrition, low weight, metabolic changes and centralisation of the circulation also lead to morphological changes in the heart that can be detected by echocardiography in patients with severe forms of AN (Smythe et al. 2021).

Essentially, these changes consist of

1. The muscle mass of the heart, particularly of the left ventricle, is reduced.

2. This is associated with changes in the diastolic and systolic function of the heart.

3. Mitral and tricuspid regurgitation are more common.

4. In more severe underweight, pericardial effusions are regularly observed.

The lower the weight, the lower the muscle thickness of the left ventricle. The left ventricular mass is significantly reduced, and septum and posterior wall are significantly thinner. Biopsy of the myocardium shows increased connective tissue transformation in severe AN (Lamzabi et al. 2015; Kuwabara et al. 2018; Smythe et al. 2021).

It has been shown that the ejection fraction of the left ventricle usually remains normal. Recent studies using global longitudinal strain to measure left ventricular systolic function have yielded conflicting results. The presence of global systolic dysfunction in AN and reduced left ventricular muscle cannot be confirmed by this method (Justine et al. 2022; Gherbesi et al. 2024). On the other hand, diastolic dysfunction can be regularly detected (Jomah et al. 2024).

There is an increased incidence of discrete mitral and tricuspid valve regurgitation in AN. Cases in which these insufficiencies require intervention are extremely rare (Borgia et al. 2021; Santangelo et al. 2022).

The lower the weight, the more common pericardial effusions are in patients with AN. These disappear with weight gain and are usually without haemodynamic significance (Docx et al. 2010).

These echocardiographic findings suggest that the ability of the myocardium to compensate for increased stress is reduced. There are few mechanisms available

to adapt to the stress caused by a decrease in intravascular volume. Circulation is already largely centralised. Myocardial contractility is very limited. The only way to maintain adequate circulation is to increase heart rate. Tachycardia is therefore a risk marker for impending heart failure (Krantz and Mehler 2004).

### 3.15.5. Micronutrient deficiency

AN is associated with deficiencies in micronutrients, such as zinc, copper, vitamin C, riboflavin, and vitamin B6 (Winston 2012). A retrospective study of the medical records from 1,026 adult patients with EDs who were newly admitted to inpatient or residential care, found that 30.0% of patients had vitamin D deficiency (Mehler et al. 2018). In an observational study of 29 patients, 14 had developed vitamin A deficiency and 12 hypercarotenaemia (Silverman 1974). Plasma vitamin B12 levels are often elevated in more severe forms of AN. It has been suggested that there is a need for future investigation of vitamin B12 as a potential biomarker of severity, probably due to liver involvement (Tam et al. 2022; Affaticati et al. 2023).

Another study found zinc, copper, and iron binding protein deficiencies, and suggested hypogeusia may result from self-imposed nutritional restriction and lack of zinc (Casper et al. 1980). Yet another study found that adolescent patients with AN presented with nutritional abnormalities such as folate and zinc deficiencies, but none had severe vitamin deficiencies (Castro et al. 2004). Most deficiencies persisted after short-term weight recovery. Furthermore, folic acid deficiency may account for the persistence of anaemia (Castro et al. 2004). A cohort study in patients with untreated AN showed vitamin D deficiency was present in over one-third of patients and a strong relationship between vitamin D status and hip bone mineral density (BMD) values with benefits for those with 25OHD levels above 20ng/ml (Gatti et al. 2015). The majority of patients with EDs had at least one micronutrient deficiency. The most common micronutrient deficiency was zinc (64.3%), followed by copper (37%) and selenium (21%) (Hanachi et al. 2019). The two most frequent vitamin deficiencies were vitamin D and vitamin B1 for 54.2% and 15% of patients, respectively. When comparing the two subtypes of AN, selenium and vitamin B12 levels were lower in AN-BP patients, while copper level was lower in AN-R (Hanachi et al. 2019).

One review suggested that, despite controversy, elevated serum vitamin A levels were attributed to inadequate intake of other nutrients required for vitamin A metabolism (should this be catabolism). Furthermore, food restriction in AN can lead to low
<figure><img src="image_67.png" /></figure>

plasma thiamine levels. Vitamin B9, folic acid and Vitamin B12 deficiency have also been reported in patients with ED (Patrick 2002; Díaz-Marsá et al. 2017).

Although it is still unclear whether the above described vitamin and trace element deficiencies have any clinical significance, experts agree that the prophylactic administration of thiamine is useful, at least during the refeeding phase (da Silva et al. 2020; Preiser et al. 2021). Thiamine plays an important key role in carbohydrate metabolism and is needed to produce energy *via* the Krebs cycle.

### 3.15.6. Water and electrolyte balance

Considerable shifts in water and electrolyte balance occur in the context of EDs, especially AN and BN. Abnormal eating habits often also involve drinking behaviour, with polydipsia (to suppress hunger or increase weight) and water restriction (to decrease weight). Recurrent vomiting leads to volume loss, loss of chloride and other electrolytes and metabolic alkalosis. Abuse of diuretics and laxatives also leads to volume deprivation and shifts in electrolyte balance, particularly hypokalaemia and sodium loss, but not hypochloraemia. While disturbed regulation of electrolyte balance in EDs also affects other electrolytes such as magnesium, chloride and calcium, potassium, sodium and phosphate are particularly important for assessing risk. Additionally, in general, it is important to recognise that electrolyte aberrations are much more common in those with purging behaviours versus those with restrictive eating.

Physical exercise can additionally exacerbate water and electrolyte disturbances in people with AN by water and electrolyte loss through sweat, dehydration, hyponatraemia and hypokalaemia, and rhabdomyolysis (El Ghoch et al. 2016; Quesnel et al. 2023).

#### 3.15.6.1. Potassium.
Hypokalaemia in the context of EDs is most frequently caused by recurrent vomiting, which leads to hypokalaemia *via* renal compensation of the associated alkalosis (Mehler and Walsh 2016). Diuretics and laxatives can also lead to a direct loss of potassium. In cases of severe purging behaviour, the associated hyperaldosteronism plays a major role (Lai et al. 2017).

The loss of potassium initially affects the extracellular potassium concentration, while the intracellular potassium concentration is less affected. This causes an increase in the potassium voltage of muscle and nerve cells and thus an increased electrophysiological excitability of these cells. The rate of potassium loss is decisive for the level of the resulting potassium voltage. Chronic potassium loss also leads to a drop in

intracellular potassium concentration and thus to a less pronounced change in potassium voltage. The risk of cardiac arrhythmia is therefore less pronounced with chronic potassium loss than with acute potassium loss. The ECG changes associated with hypokalaemia (Khan et al. 2007) allow estimation the resulting potassium voltage and thus the arrhythmogenic risk.

Hypokalaemia in the context of AN (Sugimoto et al. 2003) or in the context of diuretic abuse (Copeland 1989) is thought to account for case reports for the severe complication of pontine myelinolysis, even if there was no concomitant hyponatraemia. There are also case reports of rhabdomyolysis (Dive et al. 1991). Chronic hypokalaemia combined with volume and substrate deficiency are the conditions for the development of hypokalaemia nephropathy (Bouquegneau et al. 2012; Stheneur et al. 2014). This disease is characterised by tubulointerstitial fibrosis, which is clinically expressed as polyuria and metabolic alkalosis, and can result in end-stage renal disease and the need for haemodialysis.

In patients with AN-BP, recurrent or chronic hypokalaemia may eventually lead to hypokalaemic nephropathy and the development of end stage renal disease (ESRD). Because of the deleterious effect of prolonged hypokalaemia on the kidneys, patients with AN-BP and AN patients who regularly use over-the-counter laxatives have an elevated risk of poor renal outcomes (Khatri et al. 2023).

#### 3.15.6.2. Sodium.
A relative excess of water in relation to sodium manifests itself in laboratory values as hyponatraemia. It is caused by significantly increased water intake (primary polydipsia) and/or impaired water excretion, e.g. due to advanced renal insufficiency, excessive release of antidiuretic hormone (ADH), or inability to renally clear free water due to severe weight loss.

The more acute the hyponatraemia, the greater the risk of complications and the greater the need for aggressive therapy. Serum sodium levels above 130 millimoles per litre (mmol/l) (or 130 milliequivalents per litre [mEq/L]) are considered 'mild hyponatraemia', levels between 120 mmol/ and 130 mmol 'intermediate'; and levels below 120 mmol/l (or 120 mEq/L) are considered severe. Clinical symptoms include nausea, malaise, headache, lethargy and possibly seizures. In extreme cases of hyponatraemia, coma and respiratory arrest (< 115 to 120 mEq/L) and non-cardiogenic pulmonary oedema may occur. Extreme hyponatraemia can lead to herniation of the brain stem, which is the most feared complication of hyponatraemia.
<figure><img src="image_68.png" /></figure>

The urgency and aggressiveness of treatment depend on symptom severity, the acuity of the hyponatraemia, the level of serum sodium concentration and the patient's underlying disease. Care must be taken not to correct hyponatraemia too quickly, as this can lead to significant neurological damage (osmotic demyelination syndrome and pontine myelinolysis), especially in patients with chronic hyponatraemia.

3.15.6.3. **Phosphate.** One of the most feared complications of refeeding is refeeding syndrome. To understand what happens during refeeding, intracellular phosphate concentration is crucial (Cuntz et al. 2022; Medicine 2022). Much of the aetiology of refeeding hypophosphataemia is due to increased glucose supply causing insulin secretion which, in turn, drives phosphorus to move intracellularly (Medicine 2022).

However, intracellular phosphate deficiency might play an additional relevant role. The intracellular phosphate concentration is normally about 14 times higher than in serum. The concentration gradient is maintained by an active transport mechanism. The recovery of cellular metabolism during refeeding requires the availability of intracellular phosphate to produce energy-rich phosphates. In severe cachexia, the concentration gradient between serum and cells is significantly lower, i.e. serum levels provide an inadequate estimate of available phosphate.

Intracellular phosphate deficiency has detrimental effects on all organs and systems (Subramanian and Khardori 2000). In the heart, impaired contractility and reduced cardiac output lead to ventricular arrhythmias and heart failure (Kohn et al. 1998). Muscle dysfunction in various tissues may result in ophthalmoplegia, dysphagia or ileus; rhabdomyolysis may cause severe muscle pain and weakness or tubular necrosis along with impaired diaphragmatic contractility and respiratory paralysis. Neurological symptoms may include confusion, delirium, convulsions, tetany or coma (Dive et al. 1991; Wada et al. 1992; Walder and Baumann 2008). At the latest when symptoms occur or when creatine kinase rises, as an indication of rhabdomyolysis, phosphate should be substituted orally or, if necessary, parenterally. It is better to supplement oral phosphate prophylactically at the start of refeeding (Gallagher et al. 2022).

3.15.6.4. **Water balance.** When nutrition begins to normalise as part of treatment, many patients with AN show a pronounced tendency to oedema, characterised by a disproportionate increase in extracellular volume (Ehrlich et al. 2006; Rigaud et al. 2010; Fortune and

Kaplan 2012). The weight gain due to oedema formation can be up to 10 kg and often unsettles patients and therapists alike and must therefore be included in the assessment of weight progression.

In principle, one can distinguish between oedema formation with refeeding and oedema due to Pseudo Bartter syndrome (PBS) which is defined as hypokalaemic hypochloraemic metabolic alkalosis in the absence of renal tubular pathology. PBS is a result of hyperaldosteronism secondary to volume depletion from purging behaviours (Thavaraputta et al. 2023). The activation of this mechanism leads to significantly increased water retention and a tendency to hypokalaemia under conditions of normalisation of the diet. In less pronounced cases, informing the patient of the benign nature of the oedema formation and a wait-and-see approach are sufficient, as the PBS normalises completely within the first few weeks. If oedema is pronounced, diuretic treatment, in this case with aldosterone antagonists, should be considered. However, diuretic treatment contributes to a delay in normalisation of renal function.

Pathophysiologically, other factors have been implicated in oedema formation. These include the reintroduction of insulin secretion combined with increased insulin sensitivity, leading to sodium retention in the distal tubule (Yücel et al. 2005). In cases with hyponatraemia, the syndrome of inappropriate ADH secretion (SIADH) due to pharmacological treatment should also be considered – SIADH is also thought to occur more frequently in AN (Challier and Cabrol 1995).

### 3.15.7. Renal function

The basic function of the kidneys is to remove harmful water-soluble substances from the body while maintaining electrolyte and water balance and regulating pH. The kidneys are very sensitive to malnutrition, partly because they are a highly active organ with a high oxygen demand and therefore a high energy requirement. Blood flow to the kidneys is very high compared to other organs, accounting for about 20-25% of cardiac output to support the task of filtering urine. Therefore, in addition to the direct damage to the kidneys from malnutrition, the volume depletion seen in ED (e.g. due to oligodipsia or repeated vomiting) also threatens renal function.

Renal dysfunction is common in ED patients who are admitted to hospital. Approximately 70% of patients show signs of acute renal failure, including the above-mentioned electrolyte disturbances and impaired osmoregulation. Clinically, this manifests as a decreased filtration fraction and impaired ability to
<figure><img src="image_69.png" /></figure>

concentrate urine (Aperia et al. 1978). Renal function is usually assessed clinically by glomerular filtration rate (GFR), which is low in approximately one third of hospitalised patients with AN (Gurevich et al. 2021; Riva et al. 2021). However, GFR is calculated from creatinine, which is low relative to the loss of skeletal muscle mass in AN and is therefore likely to underestimate the degree of renal insufficiency.

Chronic renal failure is not uncommon in patients with AN (Puckett 2023; Tseng et al. 2023). A study conducted a 21-year follow-up of 84 patients after their first hospitalisation for AN and reported that 5.2% of the patients had chronic renal failure requiring haemodialysis (Zipfel et al. 2000). The most likely cause of chronic renal failure in AN is chronic hypokalaemia associated with volume and substrate depletion, which sets the stage for the development of hypokalaemia nephropathy. This disease is characterised by tubulointerstitial fibrosis, and clinically manifested by polyuria, metabolic alkalosis, proteinuria and consequent progressive renal failure (Bouquegneau et al. 2012). On the other hand, renal function is also affected by the occurrence of nephrolithiasis and, more rarely, nephrocalcinosis (Roberts et al. 2005; Denburg et al. 2017).

### 3.15.8. Liver function

Elevated transaminases are a common laboratory finding in patients with AN, with a correlation between the degree of malnutrition and transaminase levels (Rosen et al. 2017; Cuntz and Voderholzer 2022). Extremely underweight patients with AN are at risk of developing life-threatening liver failure (Furuta et al. 1999; Dowman et al. 2010; Chen et al. 2023). Such cases are additionally characterised by coagulopathy (International Normalised Ratio [INR]>1.5) and/or hepatic encephalopathy, which are present alongside significantly elevated liver values. These symptoms are initially mild and manifest as behavioural changes, slight confusion, slurred speech and sleep disturbances. However, they can progress to coma. An improvement in the nutritional situation and provision of sufficient energy has been shown to lead to a rapid improvement of liver function. When the transaminases return to normal levels, INR and encephalopathy severity also improve.

The mechanism behind the observed abnormalities in liver function tests appears to be hepatic autophagy. The underlying cause of autophagy and the subsequent elevation of transaminases is the energy deficiency caused by AN, which forces liver cells to obtain energy through self-digestion. Histological

examination typically shows the absence of portal fibrosis or periportal inflammatory infiltrates, while ultrasonography shows a normal echogenicity pattern without anomalies. Conversely, microscopic analysis reveals the presence of autophagosomes in vesicular intracellular compartments containing secreted material from cell organelles such as mitochondria or endoplasmic reticulum (Kheloufi et al. 2014).

Hypoglycaemia may be a sensitive indirect marker of impending hepatic damage as individuals with AN and starvation-induced hepatitis are at increased risk of hypoglycaemia due to depleted glycogen stores and impaired gluconeogenesis (Rosen et al. 2017).

### 3.15.9. Bone marrow suppression

The energy requirements of the bone marrow for production and ongoing turnover of cells in the circulating blood is high. It is therefore not surprising that the chronic energy deficiency associated with AN leads to a significant reduction in the regenerative activity of bone marrow cells, affecting all cell lines (Walsh et al. 2020). These findings are more pronounced the greater the degree of underweight. The bone marrow is converted into a thick mucopolysaccharide with serous fat atrophy, a process known as gelatinous bone marrow transformation (Schafernak 2016; Shergill et al. 2017).

Anaemia is frequently present, especially in severely underweight patients, and can be very pronounced (Cuntz et al. 2022). Consistent with the reduced cell proliferation rate of the bone marrow, the anaemia is usually aplastic. Iron deficiency is rarely a cause, although many of the patients eat a vegetarian diet and therefore consume less iron. As many patients are amenorrhoeic, the iron loss is lower. It should be noted that ferritin is paradoxically elevated in many cases and does not allow a reliable estimate of the iron supply (Wanby et al. 2016).

Among the frequent changes in blood values in patients with AN are reductions in cell counts, especially leukocytes (Keeler et al. 2025), which are often reduced depending on the severity of the underweight. Leukocytes, especially granulocytes, are necessary for defence against bacterial infections. Infection represents a significant health risk. Therefore, leukocytes should be regularly monitored. However, the actual risk of infection has not been systematically investigated, so only case reports can be referred to (Birmingham et al. 2003; Brown et al. 2005).

Typically, leukocytopenia is the most noted blood count abnormality, followed by anaemia and thrombocytopenia (Walsh et al. 2020).
<figure><img src="image_70.png" /></figure>

### 3.15.10. Bone markers in an

In AN, a state of chronic energy deprivation leads to compromised bone health and alterations in bone markers which may persist after treatment and nutritional stabilisation (Puckett 2023). BMD, the main indicator of bone health is measured by dual-energy X-ray absorptiometry (DXA) and is described in Z scores compared to age and sex matched HCs, with osteopenia defined as a BMD between -1.5 and -2.5, while osteoporosis is defined as a BMD < -2.5.

Several meta-analyses have shown that individuals with AN have significantly lower BMD compared to HCs (Robinson et al. 2016; Solmi et al. 2016; Lopes et al. 2022). In a meta-analysis of eight studies (Solmi et al. 2016) reported that 21.6% of individuals with AN had osteoporosis, while 45% had osteopenia. For a synopsis of meta-analytic results see Table S16. More recent large cohort studies with at least 300 patients with AN have found low BMD rates of 40-70% (see Table S17; (Workman et al. 2020; Leach et al. 2024)). BMD has been found to be lower in AAN than HCs, and though results have been inconsistent, larger studies have found higher BMD in AAN compared to AN (Nagata et al. 2019; Tuli et al. 2023; Davis et al. 2024). Low BMI, long illness duration, longer periods of amenorrhoea, late menarche age and body composition were all found to predict low BMD in many, but not all studies (Solmi et al. 2016; Workman et al. 2020; Bemer et al. 2021; Lopes et al. 2022; Tuli et al. 2023). Follow-up studies have described low BMD in some AN patients five-20years after onset of AN, but these studies are few and small in scale (Jagielska et al. 2017; Mumford et al. 2019). Risk of osteopenia and osteoporosis is also increased by comorbid depression (Ji et al. 2024) and potentially psychotropic medications like selective serotonin reuptake inhibitors (SSRI) (Zhou et al. 2018) and antipsychotics (Al Jumaili and Jain 2025).

The risk of fractures is another critical concern in AN. In a meta-analysis of six studies, (Solmi et al. 2016) found that AN patients had a significantly higher overall fracture risk compared to HCs, with an odds ratio (OR) of 1.84 (p=0.008) across all skeletal sites. A large retrospective cohort study from Denmark found that risk of fractures was highest in patients with active AN compared with recovered patients, who had a higher risk than that of HCs, but no association between fracture risk and BMD (Frølich et al. 2020a, 2020b).

Vitamin D is essential for bone health, and its deficiency can exacerbate bone loss. A meta-analysis by found individuals with AN that did not receive supplementation had significantly lower levels of (Veronese

et al. 2015) 25OH-D compared to HCs. In contrast, patients with AN who received Vitamin D supplementation had higher levels compared to HCs. Interestingly, there was no significant difference in dietary Vitamin D intake between AN patients and HCs.

Bone turnover markers in patients with AN are disturbed, showing different levels than HCs and altered relationships with other bone markers or related hormones or regulatory factors. Reductions in bone formation i.e. dickkopf-1 protein (DKK-1) or IGF-1 have been demonstrated, along with increased bone resorption (Maïmoun et al. 2020; Maïmoun et al. 2014). Weight restoration has been associated with increase in bone formation markers i.e. bone-specific alkaline phosphatase (BALP) and N-terminal propeptide of type 1 procollagen (P1NP) and decreased resorption markers such as C-terminal collagen cross-links (CTX)(Giollo et al. 2017).

Adipose Tissue and Bone Marrow Fat have been explored with altered fat distribution described, including higher marrow adipose tissue and lower SAT and VAT compared to HC (Fazeli et al. 2019).

In terms of clinical management, the primary approach involves nutritional rehabilitation and weight restoration. Additionally, there may be a role for use of long-term bisphosphonate use in adult women with AN, and hormone replacement therapy (HRT) but more research is required (Robinson et al. 2016).

### 3.16. Therapeutic drug monitoring and pharmacogenetics

Therapeutic drug monitoring (TDM) is an important measure for quality assurance in adolescent and adult patients receiving psychoactive drugs like antipsychotics, antidepressants or mood stabilisers (Egberts et al. 2015; Gerlach et al. 2016; Hiemke et al. 2018; Unterecker et al. 2019; Eap et al. 2021; Schoretsanitis et al. 2021; Wesner et al. 2023). TDM uses rigorous measures of therapy surveillance including reliable measures of adverse events and taking serum levels (and interpreting them) of the drugs in use in the individual patient in routine treatment for maximising safety and quality assurance (Egberts et al. 2020). In particular in minors, TDM is helpful and recommended as most treatments are off-label (Egberts et al. 2022) and there is only preliminary data on age-specific reference ranges for serum levels in minors in general and in EDs in particular (Fekete et al. 2020).

In patients with AN all phases of pharmacokinetics may be affected. Changes in gastrointestinal tract function, such as delayed gastric emptying and altered
<figure><img src="image_71.png" /></figure>

intestinal motility, can lead to unpredictable absorp-
tion of oral medications. Laxative use (which is com-
mon among adolescents with AN) may intensify
abnormalities of drug absorption (Turner et al. 2000).
Loss of body fat and muscle mass alters the volume
of distribution (Vd) of medications. With reduced fat
stores, lipophilic drugs will have a smaller volume of
distribution and increased concentrations in blood
(van den Berg et al. 2023). Liver function, in particular
the activity of cytochrome P450 enzymes (CYP), can
be impaired in AN. In 24 patients with AN, the meta-
bolic activity of CYP3A4 was decreased, whereas the
metabolic activity of CYP1A2 was increased (Sandvik
et al. 2020). Such changes will affect drug concentra-
tions and may change the effectiveness and tolerabil-
ity of drugs. Altered pharmacokinetics require
individual dose adjustment when treating patients
with AN, best done in conjunction with TDM (Hiemke
et al. 2018).

However, to date, TDM has been rarely used in AN or other EDs. Regarding olanzapine, most studies have been small (Fekete et al. 2017), AN: n=37, BN: n=2; (Bachmann et al. 2008) AN: n=5; (Theisen et al. 2006) AN: n=13). A recent larger study proved the potential of TDM of olanzapine in adolescents with AN. In this study, Karwautz et al. (2024) found a high correlation (0.72) of dosage and serum blood level, relatively few adverse events (6.3%), clinical improvement in 75% (Clinical Global Impressions [CGI]), and BMI-improvements (1.5 kg/m²), and was able to define a preliminary disorder- and age-specific therapeutic reference range for olanzapine in adolescent AN (11.9–39.9 ng/mL). In an additional publication (Krauss et al. Submitted), that used the TDM-sample (n=47) on olanzapine reported by Karwautz et al. (2024), the olanzapine group was compared with a sample receiving no olanzapine (n=47). Patients in the olanzapine group (mean 9 mg/day) achieved greater weekly weight gain (0.898 vs. 0.677 kg, p=0.004) compared to the group without olanzapine. Both groups showed similar reductions in ED pathology. The only study in adults that measured serum levels of olanzapine was an RCT where 75 participants were randomly assigned to receive up to 10 mg/day olanzapine, and 77 participants to receive placebo (Attia et al. 2019). This RCT found mean plasma levels of 21.0 ng/mL (standard deviation [SD]=12.8) at week 8 and of 22.0 ng/mL (SD = 18.2) at week 16.

To date, only a single study used TDM for fluoxetine
in minors (n=138) including AN patients (Frey et al.
2023): n=13). However, diagnosis-specific data on ED
subsamples were lacking.

When serum levels vary among patients receiving
the same drug dosage, investigating the genetics of
the hepatic CYP system can clarify this variance. Such
analysis helps identify why certain patients fail to
achieve therapeutic thresholds – often due to rapid or
ultrarapid metabolism or non-adherence – while others
experience increased toxicity due to poor
metabolism.

Despite its potential, pharmacogenetic research
remains virtually non-existent in EDs including AN. By
investigating specific genes within the CYP system
that govern the metabolism of medications, researchers
and clinicians could better understand individual
pharmacokinetic profiles. Consequently, these insights
could guide clinical decision-making to determine the
optimal dosage for patients with AN.

**4. Discussion**

**4.1. Summary and general comments**

The biological markers of AN include genetic and epi-
genetic markers; neuroimaging markers of the brain;
molecular markers of brain plasticity and brain dam-
age; hypothalamic, gastrointestinal and adipose
tissue-related signalling molecules associated with the
regulation of appetite and metabolism; stress hor-
mones; sexual and social hormones; immunological
markers; metabolic and gut microbiome markers; neu-
rophysiological and digital markers. Additionally, AN is
associated with various physical health risks for which
specific risk markers are in clinical use, for example
body and body composition markers; metabolic, car-
diovascular and nutritional markers; markers of water
and electrolyte balance, renal, hepatic, bone and bone
marrow markers as well as neurological signs and
symptoms. Therapeutic drug monitoring and pharma-
cogenetics testing may identify markers related to the
treatment of AN. Methods for the determination of
these markers include neuroimaging, genetics and epi-
genetics, molecular diagnostics using blood or serum,
saliva, CSF, faeces or urine samples, clinical and neuro-
physiological testing as well as digital methods such
as actigraphy.

Only few of these biological markers seem to be
causally linked to AN, such as genetic markers that
convey a degree of risk of the metabolic and psychiat-
ric features of AN (Bulik, Carroll, et al. 2021) as well as
immunological markers such as auto-antibodies against
α-MSH, ghrelin and leptin (Terashi et al. 2011;
Espinoza-García et al. 2022; Seitz et al. 2024). Most of
the biological markers including neuroimaging,
<figure><img src="image_72.png" /></figure>

hormones, metabolomic, microbiome, neurophysiolog-
ical or digital markers are consequences of
malnutrition.

Overall, the markers associated with AN are not
used in clinical practice yet, and they are mostly not
specific to AN. Only the physical risk markers in Section
3.15 are already in clinical use. However, they are not
specific for AN, and rather indicate a specific physical
risk as explained in Section 3.15.

For most of the markers there is a considerable
overlap between the values found in people with AN
and HC. Thus, none of these single markers can distin-
guish between AN and healthy people or between AN
and patients with other diseases that might also influ-
ence these markers. A pattern of several markers in
combination might be able to provide this distinction;
however, such patterns have not been established. It is
a challenge to ascertain whether biological markers
are state or trait markers as we see some improve-
ment after weight recovery, but a scar seems to remain.

Even though the neuroimaging, neuroplasticity,
appetite-regulating, stress-related, metabolic, micro-
biomic and digital biomarkers are potentially not
related to the causal factors of AN, they capture many
aspects of the psychosocial, metabolic and behavioural
symptomatology of AN. The reduction of GM volume
and connectivity together with the impairments in
neuroplasticity and neurogenesis have been sug-
gested to be related to the difficulties in memory,
bigger picture thinking and cognitive flexibility in
people with AN (Keeler et al. 2021). The disturbances
in hormones related to stress (e.g. cortisol), social
bonding (e.g. oxytocin) and sexuality (e.g. LH) are
linked to the relational difficulties, the social isolation
and the loneliness that people with AN are experienc-
ing (Sánchez-Sánchez and Rolland 2023). The micro-
biomic and metabolomic changes reflect the restricted
diet and its limited variety. Additionally, the low leptin
levels which induce an urge to exercise (Hebebrand
et al. 2003) and the increased level of physical activity
(Favaro et al. 2000) that can be measured using digi-
tal devices link to the over-exercise that people with
AN often display. Even though these biomarkers are
not specific to AN, they might inform the develop-
ment of future biological therapies. The fact that
these changes are rather consequences then causal
factors in the pathophysiology of AN does not neces-
sarily mean that they cannot serve as treatment tar-
gets. For example, several case reports have
demonstrated that metreleptin might help to break
the vicious cycle of malnutrition and the urge to exer-
cise (Hebebrand, Plieger, et al. 2024).

Research on biological markers for the diagnosis
and the treatment of EDs seems to be lagging other
areas of psychiatry as it appears to have a large vari-
ance of psychological risks and drivers. In AD, the CSF
biomarkers β-amyloid-42 (Aβ-42), P-tau, and Amyloid β
42/40 ratio are routinely used, particularly in the con-
text of early diagnosis and the assessment of the risk
of cognitive decline in subjects with mild cognitive
impairment. Therefore, it is worth emphasising that
the Neurochemical Dementia Diagnostics in AD is an
example of a very well defined COU discussed briefly
in the Introduction (Hansson et al. 2019; Dulewicz
et al. 2022). Similarly, TDM and pharmacogenetic test-
ing are already in clinical use in the pharmacological
treatment affective and psychotic disorders (Hiemke
et al. 2018), but the body of evidence in EDs is scarce.

There is no singular and generally accepted way to
classify biomarkers. They could be classified based on
how they are measured (e.g. neuroimaging, laboratory,
electrophysiological, digital), based on the biological
system level (molecular, cellular or tissue biomarkers)
or where they are used in the diagnostic or therapeu-
tic process (e.g. diagnostic, monitoring, outcome bio-
markers). They can also be classified as biomarkers of
risk, diagnosis or trait, state, stage, response and prog-
nosis (Berk 2015; Davis et al. 2015). However, the cur-
rent classifications use overlapping categories.

## 4.2. Genetics and epigenetics

EDs are finally coming to the fore in the genetic anal-
ysis, with increasingly large samples for meta-analyses
enabling improvement in the understanding of the
underlying genetic (and biologic) aetiology of AN, and
in due course other EDs also. At this stage, much evi-
dence supports the reconceptualization of AN both as
a disorder of the mind/brain and the body i.e. a
metabo-psychiatric disorder (Bulik et al. 2019; Watson
et al. 2019).

It is also critical to highlight that genetic studies to
date have focused on women (both twin and molec-
ular). How genetic risk may differentially impact men
is not yet clear. In the current GWASs only approxi-
mately 3% of the cases are men (Watson et al. 2022).
There is some evidence, for example, that AN poly-
genic risk correlates with percent body fat differen-
tially between men and women (Hübel, Gaspar, et
al. 2019), as well as anthropometric genetic correlations
being stronger in women (Duncan et al. 2017; Watson
et al. 2023). With women dominating samples at this
time in ED research, there is a need to expand to
large male case cohorts, and as much as possible to
<figure><img src="image_73.png" /></figure>

other gender groups, to establish if risk variants vary or are the same, and/or how polygenic predictors behave in these populations. This criticism also applies to gender and ancestry, with analyses overwhelmingly based on European-ancestry samples. The danger of lack of diversity in genetics is assuming that the same genetic factors influence disease risk across populations and developing therapeutics based on non-diverse GWAS that will ultimately increase rather than decrease health disparities (Huckins 2022). The PGC-ED is actively collecting genetic samples from individuals across ancestries to address this imbalance, and extending to BN, BED, ARFID, and OSFED. This initiative will help to identify genes and pathways that are shared across conditions as well as those that are specific, further clarifying the aetiology of EDs.

Regarding epigenetics, accumulating literature on DNA methylation in AN points to the intriguing possibility that several aspects of phenomenology in AN may be epigenetically regulated. With some consistency, available EWAS studies have documented probe-specific differences as to DNA methylation levels observed in individuals with active AN when compared to people who have either recovered from AN or who have never been ill with it. Differentially methylated regions identified in the studies in question tend to implicate genes involved in brain function, metabolism and immune response. Together, these results support an understanding of AN as having multiple (psychiatric, metabolic and immune) determinants, as would be consistent with the recently proposed conceptualisation of AN as being a 'metabo-psychiatric disorder' (Duncan et al. 2017; Watson et al. 2019). In addition, findings from available EWAS studies have revealed that differential methylation findings at some DNA sites appear to be replicable. The preceding is striking, given the low probability with which chance repeated identifications of the same genes (among tens of thousands of genes interrogated using high throughput microarray technologies) would emerge across independent studies.

Studies emerging from the Steiger/Booij group point to an additional possibility that altered DNA methylation levels may be most pronounced in individuals with lowest BMIs and longest durations of illness, and reversible with weight restoration. Together, such observations suggest potentials of DNA methylation changes as markers of illness staging, entrenchment and recovery. Should such effects be corroborated in future longitudinal studies, findings have potential to isolate genes (and biological systems they

represent) that are involved in the development of and, more importantly, remission from AN symptoms.

We add a comment on limitations of the epigenetic literature in AN. Samples are invariably small, which calls the stability of findings into question and heightens risk of spurious observations. Also, given tissue specificity of DNA methylation, findings obtained using peripheral tissues have uncertain relevance to brain function (Edgar et al. 2017). Finally, none of the available EWAS studies apply measures of gene expression, without which functional significance of altered methylation levels observed remains uncertain.

As a clinician, it is key not to assume EDs are entirely genetically based, nor environmentally driven, but a complex, nuanced, interplay between the two that changes over critical developmental periods, including entry to puberty, entry into young adulthood, and in women, across the perimenopausal period (Klump et al. 2007; Klump et al. 2012; Wade et al. 2013b; Fairweather-Schmidt and Wade 2015). Twin studies indicate that interactions between genetic and environmental factors are important, with peer weight-related teasing posing a critical trigger of increased genetic variance (Fairweather-Schmidt and Wade 2017). In time, environmental contributors will be integrated into predictive models that also include polygenic risk, to better elucidate interactions, and the additive genetic and environmental contributions to EDs.

Molecular gene-environment interaction studies are yet to be meaningfully explored in EDs and will necessarily initially focus on the context of PRS. Ultimately, the hope is that PRS will have clinical applicability, most likely in combination with other risk factors, not only predicting risk of developing a condition, but also for screening, clarifying diagnoses, optimising treatment allocation, predicting treatment response and adverse reactions (Murray et al. 2021; Wray et al. 2021). Given the nature of PRS and clear limits in their utility to predict eating or other psychiatric disorders, they should never be considered tests to be used for such procedures as embryonic screening as this represents a serious misuse of science. The inappropriate commercialisation of PRS is not only a misapplication of the science but can also convey false promise and assurance to consumers who are seeking certainty in a domain where complexity is the rule. Nonetheless great promise stands in the integration of PRS into models with other risk factors to better identify those at risk, and target interventions in the future, and crucially to better understand the underlying pathogenesis of these conditions.
<figure><img src="image_74.png" /></figure>

Ultimately, the promise of GWAS may lie in: 1) informing EDs nosology based on genetic factors; 2) understanding ways in which the various EDs are situated biologically vis a vis other psychiatric and metabolic conditions; 3) clinical application of PRS to inform treatment optimisation and personalisation; 4) provide an empirical approach to understand how genetic factors can render some individuals more vulnerable to environmental risk factors, i.e. genetics offering the best strategy to understand environment.

### 4.3. Neuroimaging

In summary, structural neuroimaging has revealed substantial and widespread alterations in the brain associated with AN particularly in the acutely underweight state. The available data point to significant reductions in measures of GM and alterations in the WM connectome. While neuroimaging shows promise in elucidating the neurobiology underlying AN, several limitations remain. Reliable relationships between structural measures and ED psychopathology, other than those linked to weight status, remain elusive. Future research incorporating multivariate analysis methods may offer more comprehensive and reliable representations of brain-behaviour relationships (Marek et al. 2022). The influence of important clinical variables, such as illness duration and clinical subtype, also warrants further investigation. Despite these challenges, neuroimaging research has provided vital insights into how the brain is affected by acute AN and dynamically changes following weight restoration. As neuroimaging methods and analyses continue to evolve, these techniques hold promise for uncovering brain processes that can be targeted in treatments to promote more sustained recovery from AN. Future research, especially in longitudinal and larger samples, is essential for clarifying the dynamic nature of both GM and WM changes and their relationship to clinical outcomes.

The same applies to functional neuroimaging and connectivity. Large-scale longitudinal studies are needed to identify robust neural functional markers associated with the development and maintenance of AN. Such studies will also help clarify the roles of factors such as sex, age, and illness severity.

### 4.4. Markers of neuronal and synaptic plasticity

Understanding the role of neuronal biomarkers, including tau, NFP/NFL, and synaptic markers, in EDs holds immense promise for advancing diagnostic accuracy, prognostic assessment, and treatment development in these challenging conditions. By elucidating the neurobiological alterations associated with AN and BN,

researchers can pave the way for targeted interventions that address underlying neuronal dysfunction and promote recovery.

The investigation of neuronal biomarkers in EDs represents a cutting-edge area of research that offers profound insights into the intricate interplay between neurobiology and psychopathology. By harnessing the power of advanced neuroimaging techniques, biomarker analyses, and translational studies, scientists are poised to revolutionise our understanding and management of AN and BN, ultimately improving outcomes for individuals grappling with these debilitating disorders.

### 4.5. Connections and mutual influences of hormonal and metabolic markers

Even though we structured this consensus statement into different categories of biological markers, this is a somewhat artificial approach that does not match the pathophysiological reality where the regulatory and effector molecules of various systems interact.

For example, the restricted eating and increased physical activity lead to nutritional deficiency and, consequently, to reduced adipose tissue. As a result of the reduced fatty tissue, leptin production is reduced leading to a decreased production of FSH and LH with the consequence of amenorrhoea due to reduced follicle growth and ovulation. This mechanism is reversible along with weight restoration (Ballauff et al. 1999). Additionally, restricted eating leads to low blood glucose which activates the glucocorticoid cortisol, because cortisol release is part of the body's counter-regulatory response to maintain blood glucose levels (Lee et al. 2023). Together with the lack of the gonadotropic hormones FSH and LH, the increased cortisol production leads to osteoporosis with a loss of bone mass and an increased risk of fracture (Fazeli and Klibanski 2018). Additionally, a reduced sexual desire and sexual dysfunction in AN may be accounted for by low concentrations of gonadotrophins, hypoestrogenism, and elevated cortisol levels (Fichter et al. 1982).

Thus, there is, for example, an interaction between sexual and metabolic hormonal signals which explains the pathophysiological chain reaction from nutritional deficiency to physical health consequences such as amenorrhoea and osteoporosis.

### 4.6. Digital biomarkers

#### 4.6.1. Actigraphy and accelerometers as digital biomarkers in an

The application of actigraphy and accelerometers as digital biomarkers in AN holds promise across several
<figure><img src="image_75.png" /></figure>

clinical domains as diagnostic, monitoring and prognostic biomarkers (see Table 5).

While not primary diagnostic tools, objective physical activity and sleep patterns could serve as adjunctive diagnostic indicators or phenotypic markers. For instance, a distinct pattern of light activity (Lehmann

et al. 2018) or a specific sleep chronotype (Wilcox et al. 2024) could contribute to a more nuanced diagnostic specification, especially in challenging cases or for differential diagnosis from other conditions that cause weight loss (Himmerich and Treasure 2024). These objective behavioural signatures could provide

<table><caption>Table 5. Candidates for digital biomarkers for anorexia nervosa.</caption><thead><tr><th>Device</th><th>Index</th><th>Metric description</th><th>Findings in AN</th></tr></thead><tbody><tr><td rowspan="5">Actigraphy<br>(Accelerometers)</td><td rowspan="3">Physical Activity<br>(Frequency, Intensity,<br>Duration)</td><td>Objectively quantify human motion, providing date-time stamped information on how often, how intensely, and how long physical activity occurs.</td><td>Excessive physical activity, often termed 'driven exercise', is reported in 30-80%.</td></tr><tr><td>Metrics include step counts, energy expenditure, and time spent in sedentary, very light, light, moderate, and vigorous activity.</td><td>High levels of light activity early in treatment may indicate a higher risk for slower recovery or treatment resistance.</td></tr><tr><td>Objective measures overcome the tendency for AN patients to overestimate PA in self-reports.</td><td>Acute activity urges have been linked to lower early weight gain during inpatient treatment.</td></tr><tr><td>Sleep Patterns</td><td>Actigraphy is particularly suitable for measuring various aspects of sleep, including sleep fragmentation, sleep efficiency, and napping sessions.</td><td>Specific sleep disturbances identified including insomnia, sleep fragmentation, low sleep efficiency, and an increased propensity for napping, an 'early riser' chronotype.</td></tr><tr><td>Total Screen Time</td><td>Objective tracking of the overall duration of smartphone use via built-in applications.</td><td>Higher smartphone screen time was found and positively correlated with overall ED psychopathology and body dissatisfaction</td></tr><tr><td rowspan="7">Smartphones</td><td rowspan="3">Diet and Fitness App Use</td><td>Engagement with applications designed for tracking diet (e.g. calorie counting) and fitness (e.g. weight tracking).</td><td>These apps can trigger or exacerbate ED symptoms and may reinforce unhealthy obsessions.</td></tr><tr><td>Interaction data from apps designed for ED treatment support (e.g. Recovery Record), including frequency of meal logging, consistency of self-monitoring, types of emotions recorded, and engagement with coping skills modules.</td><td>App-augmented treatment associated with significantly larger weight gain for patients in low-weight categories</td></tr><tr><td>Real-time self-reports of mood, thoughts, behaviours, and context delivered via the smartphone interface, capturing momentary snapshots of daily life.</td><td>Usage logs generated by these apps hold potential as dynamic indicators of treatment engagement, symptom fluctuation, or relapse risk.</td></tr><tr><td rowspan="2">Treatment Support App<br>Patterns</td><td>Engagement with applications designed for tracking diet (e.g. calorie counting) and fitness (e.g. weight tracking).</td><td>Significantly higher mean and maximum levels of aversive tension.</td></tr><tr><td>Interaction data from apps designed for ED treatment support (e.g. Recovery Record), including frequency of meal logging, consistency of self-monitoring, types of emotions recorded, and engagement with coping skills modules.</td><td>App-augmented treatment associated with significantly larger weight gain for patients in low-weight categories</td></tr><tr><td>EMA: Affective and<br>Behavioural Dynamics</td><td>Real-time self-reports of mood, thoughts, behaviours, and context delivered via the smartphone interface, capturing momentary snapshots of daily life.</td><td>Reported food intake was associated with higher levels of aversive tension.</td></tr><tr><td>Passive Sensing: Mobility<br>Patterns (via GPS)</td><td>Data collected from the phone's GPS sensor to track changes in daily movement range, time spent at home versus other locations, and patterns potentially indicative of social avoidance or excessive exercise routines. GPS is frequently used in general mental health digital phenotyping.</td><td>Disordered eating behaviours may serve an avoidance function for unpleasant inner experiences.</td></tr><tr><td>Passive Sensing: Social<br>Interaction Proxies (via<br>call/text logs,<br>microphone)</td><td>Data from call/text logs and ambient sound captured by the microphone to infer changes in communication frequency or patterns that might reflect social withdrawal or isolation.</td><td>Changes in daily movement range, time spent at home versus other locations, avoidance of social situations (especially those involving food), or patterns indicative of excessive exercise routines.</td></tr><tr><td>Passive Sensing: Sleep<br>Proxies (via light sensor,<br>phone non-usage)</td><td>Inferences about sleep patterns drawn from data such as light sensor readings (reflecting light exposure patterns) and periods of smartphone non-usage, which can provide objective data on sleep patterns, potentially complementing actigraphy</td><td>Changes in communication frequency or patterns that might reflect social withdrawal or isolation.</td></tr><tr><td>Passive Sensing: Device<br>Usage Patterns (e.g. app<br>switching frequency)</td><td>Beyond total screen time, patterns such as how frequently a user switches between different applications, which could offer subtle behavioural clues</td><td>Disturbances like insomnia or altered chronotypes.</td></tr></tbody></table>

Abbreviations: AN = anorexia nervosa; ED = eating disorder; EMA = ecological momentary assessment; GPS = global positioning system.
<figure><img src="image_76.png" /></figure>

additional evidence to support a clinical diagnosis or characterise specific AN subtypes.

Continuous monitoring of PA levels (intensity, duration, frequency) and sleep parameters (efficiency, fragmentation) offers an objective means to track disease progression and response to treatment. Changes in these objective metrics could signal improvement or deterioration, allowing clinicians to make timely adjustments to therapeutic strategies. For example, a sustained reduction in problematic light PA or an improvement in sleep efficiency could objectively indicate a positive treatment response.

The observed negative association between light PA and psychopathological improvement (Alberti et al. 2013) suggests that specific objective PA patterns could serve as prognostic indicators for treatment outcome. Patients exhibiting certain activity profiles, particularly high levels of light activity early in treatment, might be at higher risk for slower recovery or treatment resistance. Similarly, the presence of acute activity urges has been linked to lower early weight gain during inpatient treatment (Halbeisen et al. 2024). These objective behavioural markers could help identify individuals who may require more intensive or tailored interventions.

Despite their promising potential, several challenges must be addressed for the widespread adoption and optimisation of these technologies. A significant hurdle is the methodological heterogeneity across studies, including variability in device models, placement, data processing algorithms, and outcome definitions (Alberti et al. 2013; Lehmann et al. 2018; de Rijk et al. 2024; Suau et al. 2024). This inconsistency limits the comparability and generalisability of findings, underscoring the need for greater standardisation in research protocols. Many existing studies, particularly pilot investigations, suffer from small sample sizes (Karr 2017; Lim et al. 2023), which limits statistical power and the generalisability of findings (de Rijk et al. 2024). Larger, well-powered studies are therefore imperative.

### 4.6.2. The potential of smartphone technology

Smartphones, with their widespread adoption and sophisticated capabilities, offer a unique platform for collecting diverse data streams relevant to an individual's behaviour and psychological state (Melbye et al. 2020; Almenara et al. 2022). Beyond dedicated accelerometry for physical activity (which was the focus of the above part), smartphones can provide data through:

*   **Usage Logs:** Screen time, specific application usage patterns (Rozgonjuk et al. 2023).

*   **EMA:** Real-time self-reports of mood, thoughts, behaviours, and context, delivered via the smartphone interface (Shiffman et al. 2008; Kolar et al. 2016; Peschel et al. 2023; Drexl et al. 2025).

*   **Passive Sensing:** Data from inbuilt sensors such as global positioning system (GPS; location and mobility), microphone (ambient sound, social interaction proxies), light sensors (circadian rhythm proxies), and call/text logs (social connectivity) (Tregarthen et al. 2015; Harari et al. 2016; Insel 2017; Melbye et al. 2020; MacLeod et al. 2021; Onnela 2021; Almenara et al. 2022; Kilshaw et al. 2022; Aledavood et al. 2025; Linardon and Torous 2025).

This approach aligns with the broader concept of digital phenotyping, which aims to use data from personal digital devices to construct an individual's 'digital phenotype', offering insights into their real-world functioning and mental state (Tregarthen et al. 2015; Insel 2017; MacLeod et al. 2021; Onnela 2021; Almenara et al. 2022; Kilshaw et al. 2022; Rozgonjuk et al. 2023; Stierle et al. 2023; Linardon and Torous 2025).

#### 4.6.2.1. Objectively tracked total screen time.
A notable area of investigation involves the relationship between objectively tracked total smartphone screen time and ED psychopathology. A study by Rozgonjuk et al. (2023) examined 119 women, including 29 with a self-reported history of AN diagnosis. Using built-in smartphone applications like iOS Screen Time and Android Digital Wellbeing to track usage, the researchers found that objectively measured total smartphone screen time was positively correlated with overall ED psychopathology (measured by the Eating Disorder Examination Questionnaire, EDE-Q) and body dissatisfaction (measured by the Body Shape Questionnaire, BSQ) in the total sample (Rozgonjuk et al. 2023). Women with a history of an ED diagnosis also reported higher smartphone screen time compared to those with no such history (Rozgonjuk et al. 2023). Interestingly, while total smartphone use showed these correlations, objectively tracked Instagram use time did not show a statistically significant difference between those with and without an ED diagnosis history, and its link to ED symptoms was less consistent than that of total screen time (Rozgonjuk et al. 2023). This suggests that general problematic smartphone use, rather than engagement with a specific platform alone, might be more indicative of ED-related concerns (Rozgonjuk et al. 2023). However, it is important to note that specific subgroup analyses for the AN participants concerning their tracked overall smartphone screen time and its correlation with ED
<figure><img src="image_77.png" /></figure>

symptomatology or body dissatisfaction were not detailed in the available documentation. The study also highlighted discrepancies between self-reported and objectively tracked technology use, emphasising the value of objective data (Rozgonjuk et al. 2023).

#### 4.6.2.2. Impact of diet and fitness apps.
The use of diet and fitness applications has also been identified as potentially problematic. Qualitative research suggests that these apps can trigger or exacerbate ED symptoms by heavily focusing on quantification (e.g. calorie counting, weight tracking), promoting overuse, and providing feedback that may reinforce unhealthy obsessions (Simpson and Mazzeo 2017). The constant availability and discreet nature of these apps on smartphones can facilitate continuous engagement with potentially harmful content (Simpson and Mazzeo 2017). One study found that college students who reported using diet and fitness apps had higher levels of ED symptoms, though this relied on self-reported app use rather than objective tracking (Simpson and Mazzeo 2017).

#### 4.6.2.3. Usage patterns of treatment support apps.
Smartphone applications designed to support ED treatment, such as Recovery Record, offer another avenue for data collection. These apps often allow users to self-monitor meals, emotions, thoughts, and behaviours (Fairburn and Rothwell 2015; Tregarthen et al. 2015). Research on Recovery Record has indicated that app-augmented treatment was associated with significantly larger weight gain for patients in low-weight categories (including AN) and a greater percentage of patients moving into a higher BMI class (Tregarthen et al. 2015). While these studies primarily evaluate the app as an intervention tool, the usage logs generated (e.g. frequency of meal logging, consistency of self-monitoring, types of emotions recorded before/after meals, engagement with coping skills modules) hold potential as dynamic indicators of treatment engagement, symptom fluctuation, or relapse risk. However, detailed analyses correlating specific patterns of app usage with clinical outcomes in AN are an area for future development. The concept of 'closed-loop interventions', where app data could trigger personalised support, is also relevant here (Smith et al. 2019; Linardon and Torous 2025).

#### 4.6.2.4. Digital phenotyping and its potential for AN.
Digital phenotyping aims to use these passively collected digital traces to draw inferences about an individual's psychological state and behavioural patterns (Tregarthen et al. 2015; Insel 2017; Smith et al. 2019; MacLeod et al. 2021; Onnela 2021; Almenara et al. 2022; Kilshaw et al. 2022; Rozgonjuk et al. 2023; Aledavood et al. 2025; Linardon and Torous 2025).

For AN, potentially relevant data types could include:

* Mobility Patterns (via GPS): Changes in daily movement range, time spent at home versus other locations, avoidance of social situations (especially those involving food), or patterns indicative of excessive exercise routines (Huckins et al. 2019; MacLeod et al. 2021; Onnela 2021; Kilshaw et al. 2022; Almenara et al. 2022; Cho et al. 2024; Linardon et al. 2025). GPS is one of the most frequently used sensors in general mental health digital phenotyping (Onnela 2021; Linardon et al. 2025)

* Social Interaction Proxies (via call/text logs, microphone for ambient sound): Changes in communication frequency or patterns that might reflect social withdrawal or isolation (Harari et al. 2016; Kilshaw et al. 2022; Aledavood et al. 2025)

* Sleep Proxies (via light sensor, phone non-usage patterns).

* Device Usage Patterns: Beyond total screen time, patterns like app switching frequency or keyboard dynamics (though research on keyboard dynamics for AN is not apparent in the provided material) could offer subtle behavioural clues (Bangamuarachchi 2023; Stierle et al. 2023)

* Artificial intelligence (AI) and ML are considered essential for analysing these complex data (D'mello and Kory 2015; Huckins et al. 2019; Smith et al. 2019; MacLeod et al. 2021; Almenara et al. 2022; Bangamuarachchi 2023; Aledavood et al. 2025; Linardon and Torous 2025; Woll et al. 2025)

High-quality original research studies using passively collected smartphone sensor data (excluding primary accelerometry) to derive AN-specific indicators are currently very limited (Juarascio et al. 2015; Linardon et al. 2025; Linardon and Torous 2025). Reviews often highlight the potential but note a lack of empirical studies in AN populations (Almenara et al. 2022; Linardon et al. 2025; Linardon and Torous 2025).

Objectively collected data such as actigraphy and smartphone-derived data might be useful candidates for digital biomarkers for AN. Future progress depends on multimodal data integration, advanced AI/ML application, longitudinal AN-specific studies, methodological standardisation, and robust ethical frameworks, all developed with patient-centred perspectives.
<figure><img src="image_78.png" /></figure>

### 4.6.3. The combination of digital technologies for research in an

The evolving landscape of digital biomarkers and wearable technologies heralds a promising future for the diagnosis, monitoring, and treatment of AN. As we advance, the integration of these technologies with AI and ML algorithms will likely become more refined, offering unprecedented precision in understanding and predicting individual patient trajectories. Digital biomarkers are transforming the AN care landscape by allowing real-time personalised insights into the patient's behavioural and physiological patterns. Ranging from smartphone-based monitoring to virtual reality (VR) therapy and AI-driven interventions, such tools target AN core challenges such as social withdrawal, disgust and self-disgust emotional dysregulation and distorted body image. VR enables clinicians to provide complex exposure scenarios tailored for AN patients. These include engaging with avatars, practicing social eating, eliciting and measuring disgust and experiencing larger body sizes to manage weight-related distress (Keizer et al. 2016; Porras-Garcia et al. 2021; Bektas et al. 2023).

Smartphone-based digital phenotyping utilises passive data such as geolocation, screentime, and heart rate variability, and has shown promising value in detecting early warning signs factors links to the relapse of AN patients, specifically social withdrawal and psychological distress. When paired with EMA, such technology allows real-time tracking of behaviour fluctuations and mood, supporting clinicians in identifying when patients start to disengage from daily functioning or portray signs of cognitive rigidity around social settings and food. One example is Porras-Segovia et al. (2020), who highlighted how passive sensing and EMA could identify precursors to withdrawal and suicidal ideation, which are highly relevant given the risk of emotional dysregulation and suicidality in AN patients (Porras-Segovia et al. 2020).

In parallel, VR therapies have shown promise in treating AN-specific symptoms, specifically in addressing fear of weight gain, body image disturbances and cognitive distortions around self-perception. Behrens et al. (2023) discovered that repeated exposure to healthy-weight avatars in VR triggered strong emotional responses and reduced fear of weight gain in AN patients gradually. This suggests the ability of VR to desensitise patients to anxiety-provoking stimuli in a controlled immersive way (Behrens et al. 2023). Similarly, Brizzi et al. (2025) utilised VR body-swapping techniques to allow a hospitalised patient to experience embodiment in a health-weight body, which

resulted in immediate reductions in negative self-beliefs and body dissatisfaction; common features in restrictive AN (Brizzi et al. 2025). Additionally, Ascione (2024) integrated attentional bias modification along with VR-based mirror exposure, leading to sustained improvements in anxiety, drive for thinness and body-checking behaviours at six-month follow up (Ascione 2024). These interventions represent a new frontier in exposure-based treatments and directly target cognitive-affective mechanisms specific to AN, such as anxiety-driven avoidance, perceptual body image distortion, and weight phobia.

Furthermore, AI-enabled digital health tools are advancing behaviour interventions by reducing avoidance, enhancing treatment engagement, and supporting day-to-day symptom regulation. For example, Recovery Record, is a mobile app facilitating real-time food and symptom logging, which supports in disrupting restrictive eating patterns by introducing real-time accountability and clinician feedback. In outpatient settings, it addressed comorbid affective symptoms and physical recovery by resulting in fewer emergency visits, decreases in depressive symptoms and higher rates of weight gain in AN patients (Palacios et al. 2024). Likewise, Therabot, a generative AI chatbot offered 24/7 conversational support for individuals at high risk of EDs and led to significant reductions in ED symptoms including shame, perfectionism, and rigidity. Hence, the ability to access therapeutic dialogue during moments of distress can reduce urges to restrict or engage in compensatory behaviours, reinforcing recovery motivation between therapy sessions and emotional safety (Heinz et al. 2024).

Nevertheless, the promise of such technologies must be tempered with caution. The failure of the National Eating Disorders Association's chatbot Tessa, which provided harmful advice by encouraging contradicting AN treatment protocols such as weight loss strategies and calorie restriction (500-1000 calorie deficit), highlights the ethical risks of integrating AI in sensitive clinical contexts without proper oversight. The suspension and backlash of the tool emphasise the critical need for continuous monitoring, clinical validation and ethical guardrails while developing AI-driven mental health support systems. As Torous and Nebeker (2017) argued, without such safeguards, digital tools can unintentionally reinforce disordered behaviours like compulsive exercise, food restriction, or body image distortion, thus worsening the conditions they aim to treat (Torous and Nebeker 2017). The iatrogenic risks of AI are being described in several psychiatric disorders (Østergaard 2025), but AN is foremost.
<figure><img src="image_79.png" /></figure>

As future research continues to refine personalised, AI-driven interventions that adapt in real time to the needs of AN patients, the ethical, privacy, and data security aspects of digital health technologies will remain paramount. Robust frameworks are essential to safeguard patient information while enabling the innovations' transformative benefit. Furthermore, interdisciplinary collaboration between clinicians, technologists, patients, and policymakers will be crucial to ensure these solutions are accessible, equitable, and integrated into existing treatment ecosystems. Ultimately, the promise of digital biomarkers in transforming the care of individuals with AN lies in their ability to bridge gaps in current treatment paradigms facilitating early detection, personalised care, real-time support, and sustained recovery. By addressing the specific symptoms and treatment barriers of AN, such technologies have the potential to significantly enhance outcomes and long-term quality of life for those affected by this complex disorder.

## 4.7. Innovative biomarkers and research questions for future research

EWAS found methylation differences in the TNXB gene between people with AN and HCs (Booij et al. 2015; Kesselmeier et al. 2018; Steiger et al. 2019). These results indicate a crucial role of the structure and stability of connective tissues in the development of AN. Additionally, connective tissue changes were found in the myocardium of patients with severe AN (Lamzabi et al. 2015; Kuwabara et al. 2018; Smythe et al. 2021). Moreover, clinical observations suggest that individuals with connective tissue syndromes manifest with clinical symptoms that overlap with AN, e.g. gastrointestinal problems and lower BMI (Baeza-Velasco et al. 2021). Therefore, future research might elucidate the relationship between abnormal connective tissue and AN, the impact of starvation towards the development of abnormal connective tissue and how the sensory signals sent from potentially abnormal connective tissue to the CNS impact interoception in AN (Gibson and Mehler 2024).

Synaptic biomarkers, crucial for assessing synaptic function and plasticity, have emerged as key players in unravelling the neurobiological underpinnings of neurological disorders. Although studies have suggested alterations in synaptic proteins like synaptophysin, postsynaptic density protein 95 (PSD-95), and SNAP-25 in individuals with various disorders implicating synaptic dysfunction such as dementia (Leuba et al. 2008) there are as yet no studies in AN or other EDs.

Other innovative biological markers could be markers of oxidative stress such as plasma advanced oxidation protein products and advanced glycation end products or activity markers like α4βδ-GABAAR (Aoki et al. 2014). In addition to blood-, saliva-, CSF, urine-, and faeces-based biomarkers, there are breath- or skin-based biomarkers available to reflect on the gastrointestinal, skin or ear-nose-throat microbiome. Rather than relying on a single marker, integrating signal patterns that reflect the status of the brain, appetite regulation, and physical activity may provide greater specificity. However, the use of AI might be necessary to calculate those patterns.

### 4.7.1. The economics of biomarkers

In addition to the many clinical benefits outlined, the advances in the use of biomarkers in health care present new opportunities in health adjacent disciplines, including epidemiology, health services research, and health economics. Widespread use of biomarker testing will allow for better disease classification and epidemiologic descriptive studies of prevalence or disease progression, as well as predictive modelling (Hay et al. 2023). This knowledge can lead to improved estimations of the burden of disease and associated health care resource needs at the system level (Davillas and Pudney 2020).

Questions remain regarding the economics of biomarker use, specifically the cost-effectiveness of biomarker testing, and the cost-effectiveness of biomarker-guided or pharmacogenetic therapies. Most interesting to health care systems is the question whether reliance on biomarkers and biomarker-guided therapy improves overall health outcomes sufficiently to justify the costs of testing. Studies in this area have focused primarily on oncologic treatments, and we did not identify any cost-effectiveness studies related to biomarkers in AN. Cost-effectiveness research of AN treatments is overall relatively sparse (Faller et al. 2024). In general, the estimation of the cost-effectiveness of biomarker testing is challenging because their impact on health outcomes is indirect and difficult to isolate (Kramer et al. 2025). There is early evidence that biomarker guided treatment has the potential to improve the allocation of resources by improving disease prevention and/or better matching patients with the most appropriate treatments (Verbelen et al. 2017).

## 4.8. Markers of physical health risk and refeeding markers

Several laboratory markers (e.g. electrolytes, liver enzymes, blood count) are crucial to monitor physical
health and to ensure the safety of people with AN during the refeeding process (Himmerich et al. 2010; Psychiatrists 2022). National clinical and scientific ED societies have established guidance for general and specialised clinicians based on these parameters. Those guidelines like the Medical Emergencies in Eating Disorders (MEED)(Psychiatrists 2022) indicate the physical risk level for patients with AN based on abnormalities of laboratory parameters or results of the physical examination.

Clinicians have also started to develop an overall risk score for underweight patients with AN based on clinically relevant laboratory parameters (Cuntz et al. 2023), and to develop algorithms about how to use these risk parameters to aid diagnostic and therapeutic processes at specific steps during the AN treatment cycle (Himmerich and Treasure 2024). These efforts will

hopefully enable clinicians to intervene earlier, to discover and apply more targeted and individually-tailored therapies and to change or escalate treatment where necessary (Himmerich et al. 2024) in order to avoid treatment resistance leading to a chronic course of AN.

## 4.9. Synopsis

In brief, biomarkers can serve as indicators of the biological vulnerability to AN, reflect its metabolic and psychiatric symptoms, indicate physical health consequences and medical risk, and offer utility in monitoring nutritional, pharmacological or psychotherapeutic treatment strategies. This is summarised in Figure 2. There is no clear-cut distinction between markers related to the metabolic symptoms of AN and markers of its physical health consequences, because the physical health consequences

<figure><img src="image_80.png" /></figure>

**Figure 2.** Overview of biological candidate markers of anorexia nervosa. For details see text. The arrows indicate the direction of influence. The markers of biological vulnerability convey the risk for the manifestation of the metabo-psychiatric signature of AN. The metabo-psychiatric markers are involved in the development of the physical health consequences and health risks. Treatment monitoring markers are supposed to aid the treatment which alleviates AN and reduces its biological correlates and health consequences. Abbreviations: α-MSH = α-melanocyte-stimulating hormone; 2-AG = 2-arachidonoylglycerol; ACTH = adrenocorticotropic hormone; AEA = anandamide; AN = anorexia nervosa; BDNF = brain-derived neurotrophic factor; BMI = body mass index; CB1R = Cannabinoid Receptor Type 1; ClpB = caseinolytic protease B; CRH = corticotropin-releasing hormone; CRP = C-reactive protein; CYP = cytochrome P450; DNA = deoxyribonucleic acid; EEG = electroencephalogram; EMA = ecological momentary assessment; EWAS = epigenome-wide association study; FSH = follicle-stimulating hormone; GFAP = glial fibrillary acidic protein; GLP-1 = glucagon-like peptide-1; GWAS = genome-wide association study; HDAC4 = histone deacetylase 4; IgG = immunoglobulin G; IgM = immunoglobulin M; IL = Interleukin; LEAP-2 = liver-expressed antimicrobial peptide 2; LH = luteinizing hormone; NCV = nerve conduction velocity; NfL = neurofilament light chain; NPY = neuropeptide Y; NR1H3 = nuclear receptor subfamily 1 group H member 3; PRDM16 = PR domain containing 16 (protein that tells a precursor cell to turn into a brown fat cell); SCFAs = short-chain fatty acids; slgA = salivary immunoglobulin A; SYNJ2 = synaptojanin 2; TDM = therapeutic drug monitoring; TNXB = tenascin-X; VR = virtual reality.
<figure><img src="image_81.png" /></figure>

of AN – such as GM and functional connectivity loss – perpetuate the illness and worsen its psychiatric and metabolic symptoms. Figure 2 is therefore not meant to visualise clear categories of biological markers but to provide an overview of the various types of biological candidate markers that have been associated with AN.

Alternative categorizations are possible, such as grouping biomarkers into clinical, molecular, cellular, neuroimaging, digital, cardiac, and neurophysiological parameters based on their respective measurement modalities, or into brain, CSF, saliva, plasma, serum or faecal biomarkers according to the location where the sample was taken.

## 4.10. Limitations

Our consensus statement on biological markers in AN has several limitations. As it focuses on the markers themselves, it did not explain the underpinning pathophysiology. Not all molecules tested in AN could be reported in this consensus paper. Therefore, the task force selected the most promising candidate biomarkers and those with the highest level of evidence. Examples of molecular markers not mentioned in the results section are myostatin which is a myokine that acts as an inhibitor of muscle growth and which has been found to have lower concentrations in the plasma of women with AN compared to controls (Maïmoun et al. 2022) and somatostatin, a polypeptide hormone with numerous inhibitory roles in the body, which, for example, shows a higher plasma level response to a test meal in people with AN compared with HCs and weight-recovered patients with AN (Pirke et al. 1994).

We mainly reported differences between people with AN and HCs. Differences between AN and other disorders associated with low body weight or starvation such as ARFID, constitutional thinness, hyperthyroidism, cancer cachexia, other psychiatric disorders, depression with appetite loss, or schizophrenia with delusion of poisoning were not incorporated into the paper. Similarly, we did not include AAN even though many studies have been unable to find psychological or medical differences in patients of both disorders. This indicates that both disorders represent a single restrictive ED (Golden and Walsh 2024), and a homogenous illness conceptualisation with diagnostic subcategorizations may be more appropriate (Hebebrand, Seitz, et al. 2024). Additionally, AAN has become an increasingly important ED diagnosis in clinical practice. However, biological research into AAN has been scant and unlike AN, which is listed as one of the main Feeding and EDs in, AAN is summarised under OFSED (American Psychiatric Association 2022). Therefore, this consensus paper focused on AN.

Overall, the scientific basis for the biological markers discussed in this article are group comparisons. Studies on the sensitivity, the specificity and the predictive value of these markers for AN are lacking. At present, biomarkers cannot be used as decision aids for diagnosis and treatment. Our review of the literature corroborates the view that currently biological makers only reflect an indication of AN, biological or behavioural features associated with AN, or a physical health risk as a consequence of AN. There are no biological markers available to monitor treatment success or to monitor recovery from physical and mental health consequences. However, TDM might help to increase safety during the pharmacological treatment of AN.

Existing research is not yet at the stage where we can make definitive conclusions about the distinctiveness or the specificity of different biomarkers, or categorise them as risk, diagnosis, trait, state, stage, response or prognosis markers. These are scientific questions for future research in biomarkers of AN.

## Acknowledgements

None.

## Statement of interest

Hubertus Himmerich, Johanna Louise Keeler, and Janet Treasure report funding from the UK Medical Research Council (MRC) for a double-blinded randomised controlled feasibility trial to test oral ketamine in people with AN and depression. Hubertus Himmerich has been a principal investigator of a proof-of-concept study testing psilocybin in AN; this study was an industry cooperation between the South London and Maudsley NHS Foundation Trust (SLaM), COMPASS Pathfinder Limited and Worldwide Clinical Trials Limited. Andreas Karwautz is vice-president of the scientific network society 'Therapeutic Drug Monitoring in Child and Adolescent Psychiatry, e.V.' based in Würzburg, Germany, and vice-president of the 'Austrian Society on Eating Disorders'. Susan L. McElroy has been a consultant to or member of the scientific advisory boards of Allergan, Avanir, Brackett, F. Hoffmann-La Roche Ltd. Idorsia, Mitsubishi Tanabe Pharma America, Myriad, Novo Nordisk, Opiant, Otsuka, Sipnose, Sunovion and Takeda. She has been a principal or co-investigator on studies sponsored by Allergan, Avanir, Brainsway, Idorsia, Janssen, Marriott Foundation, Medibio, Myriad, National Institute of Mental Health, Neurocrine, Novo Nordisk, Otsuka, and Sunovion. She is also an inventor on United States Patent No. 6,323,236 B2, Use of Sulfamate Derivatives for Treating Impulse Control Disorders, and along with the patent's assignee, University of Cincinnati, Cincinnati, Ohio, has received payments from Johnson & Johnson, which has exclusive rights under the patent. Michael Berk is supported by a NHMRC Senior Principal Research Fellowship and Leadership 3 Investigator grant (1156072 and 2017131). He received grant funding from Wellcome Trust, MRFF, Victorian Government Department of
<figure><img src="image_82.png" /></figure>

Jobs, Precincts and Regions, Cooper University USA, Janssen Lundbeckfonden Copenhagen, St. Biopharma, Psychscene. com, WFSBP, NeuroSAS, CINP, Shanghai Mental Health Centre, Penn State College of Medicine, Precision Psych Fondamental, ISBD, Milken Baszucki Brain Research Fund, Stanley Medical Research Institute, Danmarks Frie Forskningsfond Psykiatrisk Centre Kovenhavn, Patient-Centred Outcomes Research Institute (PCORI), Australian Eating Disorders Research and Translation Centre AEDRTC, USA Department of Defense Office of the Congressionally Directed Medical Research Programs (CDMRP) and Equity Trustees Limited. He received honoraria for lectures from Global Congress of Biological Psychiatry India, Otsuka CNS, RANZCP New Zealand, Eisai Australia, Sandoz, Allori, Lundbeck, World Congress of Psychiatry, African College of Neuropsychopharmacology, SVI Inaugural Health Matters Webinar Series, Argentine Association of Psychiatrists Congress of Psychiatry and Mental Health and Global Bipolar Cohort (GBC). Siegfried Kasper received grants/ research support, consulting fees and/or honoraria from Angelini, AOP Orphan Pharmaceuticals AG, Celgene GmbH, Janssen-Cilag Pharma GmbH, KRKA-Pharma, Lundbeck A/S, Mundipharma, Neuraxpharm, Pfizer, Sage, Sanofi, Schwabe, Servier, Shire, Sumitomo Dainippon Pharma Co. Ltd., Sun Pharmaceutical Industries Ltd. and Takeda. Fernando Fernandez-Aranda received consultation fees from Novo Nordisk. Philip Gorwood received speaker and advisory board honoraria from Johnson & Johnson, Lundbeck, MindMed and Newron & Otsuka. Johannes Hebebrand received speaker honoraria from Amryt Pharmaceuticals and Novo-Nordisk; he was named as inventor in two patent applications that the University of Duisburg-Essen had filed on the use of leptin analogues for treating anorexia nervosa and atypical anorexia nervosa. All other authors report no conflict of interest.

## Funding

Hubertus Himmerich and Janet Treasure receive salary support from the National Institute for Health and Care Research (NIHR) Biomedical Research Centre (BRC) for Mental Health, South London, UK, and Maudsley NHS Foundation Trust and the Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK. Rebecca Morris receives a PhD scholarship from the NIHR Maudsley BRC. Hiba Mutwalli receives a sponsorship from the Saudi Arabian government for higher education. Andreas Karwautz has received salary support from the Medical University of Vienna. Fernando Fernandez-Aranda reports that his contribution was partially supported by the Instituto de Salud Carlos III (FORT23/00032; AC24/00148), AGAUR-Generalitat de Catalunya (2021-SGR-00824), EU Grants (eprObes Ref. 101080219-2 and Biorexia [EPPermed 2024-618]), CIBEROBN which is an initiative of the Instituto de Salud Carlos III (ISCIII), the CERCA Programme/ Generalitat de Catalunya for institutional support, und by the Catalan Institution for Research and Advanced Studies (ICREA). This research was additionally supported by the Intramural Research Program of the National Institutes of Health (NIH). The contributions of the NIH authors (Dimitrios Kapogiannis) are considered Works of the United States Government. The findings and conclusions expressed in this paper are those of the authors and do not necessarily reflect the views of the NIH or the U.S. Department of Health and Human Services. Lisa-Katrin Kaufmann was supported by the

Swiss National Science Foundation (Grants 202966 and 225519) and by a FAN "Academic Excellence Fellowship" from the University of Zurich.

## ORCID

Hubertus Himmerich http://orcid.org/0000-0003-1209-6073

Sabrina Mörkl http://orcid.org/0000-0001-5034-6358

Elżbieta Paszyńska http://orcid.org/0000-0001-7135-6264

Michael Berk http://orcid.org/0000-0002-5554-6946

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