The role of the intestinal microbiota in eating disorders - bulimia nervosa and binge eating disorder

Causal effects of gut microbiota, metabolites, immune cells, liposomes, and inflammatory proteins on anorexia nervosa: A mediation joint multi-omics Mendelian randomization analysis

BACKGROUND

Anorexia nervosa (AN) is a significant psychological disorder influenced by environmental and genetic elements. Emerging research highlights the pivotal role of the gut microbiome in the development of diverse mental health conditions. This study aims to explore the causal effects and interactions of the gut microbiome, metabolites, immune cells, lipids, and inflammatory proteins on the risk of anorexia nervosa through mediation and multi-omics Mendelian Randomization (MR) analysis.

METHODS

This study used data from the FinnGen genome-wide association study (GWAS) of AN (N = 402,625), integrated with GWAS data on 473 of gut microbiota (N = 5959), 233 metabolites (N = 136,016), 731 immune cells (N = 3757), 179 lipids (N = 7174), and 91 inflammatory proteins (N = 14,824). This study used the univariate MR (UVMR), mediation MR analysis, and sensitivity analysis to assess the potential causal associations between these biomarkers and AN.

RESULTS

The inverse variance weighted (IVW) results suggest that 25 gut microbiota have causal effects on AN. Firmicutes E (OR: 0.294, 95 % CI: 0.107-0.806, P = 0.017), RUG147 (OR: 0.386, 95 % CI: 0.151-0.990, P = 0.048), CAG-977 (OR: 0.562, 95 % CI: 0.378-0.837, P = 0.005), Desulfobacterota A (OR: 0.651, 95 % CI: 0.466-0.909, P = 0.012), CAG-269 sp002372935 (OR: 0.673, 95 % CI: 0.483-0.937, P = 0.019), Klebsiella (OR: 0.684, 95 % CI: 0.566-0.827, P = 0.00009), Desulfovibrionia (OR: 0.706, 95 % CI: 0.538-0.926, P = 0.012), Klebsiella pneumoniae (OR: 0.737, 95 % CI: 0.600-0.906, P = 0.004), Desulfovibrionales (OR: 0.786, 95 % CI: 0.631-0.979, P = 0.031), CAG-776 (OR: 0.787, 95 % CI: 0.632-0.980, P = 0.032), Desulfovibrionaceae (OR: 0.788, 95 % CI: 0.635-0.978, P = 0.030). 13 gut microbiota were risk factors for AN, including Parachlamydiales (OR: 3.134，95%CI: 1.185-8.287, P = 0.021), Paenibacillus J (OR: 2.366，95%CI: 1.305-4.29, P = 0.005), Gillisia (OR: 1.947，95%CI: 1.135-3.339, P = 0.016), UBA1191 (OR: 1.856，95%CI: 1.221-2.822, P = 0.004), UBA7703 (OR: 1.843，95%CI: 1.032-3.289, P = 0.039), Faecalicatena sp002161355 (OR: 1.788，95%CI: 1.114-2.870, P = 0.016), Johnsonella ignava (OR: 1.742，95%CI: 1.031-2.944, P = 0.038), Staphylococcus aureus (OR: 1.614, 95%CI: 1.007-2.588, P = 0.047), Comamonas (OR: 1.522，95%CI: 1.004-2.307, P = 0.048), Ruminococcus D (OR: 1.24，95%CI: 1.050-1.464, P = 0.011), CAG-349 (OR: 1.198，95%CI: 1.048-1.370, P = 0.008), Ruminococcus D bicirculans (OR: 1.175，95%CI: 1.001-1.379, P = 0.048), CAG-177 (OR: 1.272，95%CI: 1.077-1.503, P = 0.005). Reverse MR analysis showed that causal effect of AN on 18 gut microbiota, but to a lesser extent. 12 metabolites have causal effects on AN. There are 7 protective factors, including glucose levels (OR: 0.700, 95%CI: 0.550-0.893, P = 0.004), isoleucine levels (OR: 0.769, 95%CI: 0.602-0.983, P = 0.036), phospholipids in large VLDL (OR: 0.856, 95%CI: 0.736-0.996, P = 0.044), total lipids in large VLDL (OR: 0.860, 95%CI: 0.740-0.999, P = 0.049), total lipids in small VLDL (OR: 0.863, 95%CI: 0.751-0.992, P = 0.038), free cholesterol in small VLDL (OR: 0.86, 95%CI: 0.752-0.996, P = 0.044), and free cholesterol in medium VLDL (OR: 0.866, 95%CI: 0.752-0.998, P = 0.047). There are 5 risk factors, including estimated degree of unsaturation (OR: 1.174, 95%CI: 1.009-1.367, P = 0.039), free cholesterol to total lipids ratio in small VLDL (OR: 1.199, 95%CI: 1.017-1.414, P = 0.031), phospholipids to total lipids ratio in small VLDL (OR: 1.216, 95%CI: 1.008-1.467, P = 0.041), total cholesterol levels in small HDL (OR: 1.241, 95%CI: 1.008-1.530, P = 0.042), and phospholipids to total lipids ratio in medium VLDL (OR: 1.280, 95%CI: 1.055-1.553, P = 0.012). Reverse MR analysis showed that AN had a causal effect on 15 metabolites. Mediation analysis reveals that the estimated degree of unsaturation mediates 0.69 % of the effect of Klebsiella pneumoniae on AN. Total lipids in small VLDL mediate 0.358 % of the effect of CAG-177 on AN, with a mediated proportion of 1.490 %. The mediation proportions for Estimated degree of unsaturation and Total lipids in small VLDL are relatively small. 36 immune cells have causal effects on AN. There are 7 protective factors, including Switched memory B cells %B cell (OR: 0.892，95%CI: 0.801-0.994, P = 0.038), CD127-CD8+ T cell absolute count (OR: 0.888，95%CI: 0.789-1.000, P = 0.049), IgD + CD24- B cell (OR: 0.917，95%CI: 0.862-0.975, P = 0.006), HVEM+ T cell (OR: 0.945，95%CI: 0.894-0.999, P = 0.045), CD40 + CD14 + CD16- monocyte (OR: 0.937，95%CI: 0.882-0.996, P = 0.038), CD64 + CD14 + CD16- monocyte (OR: 0.966，95%CI: 0.939-0.993, P = 0.016), CD8+ natural killer T cells (OR: 0.911，95%CI: 0.836-0.992, P = 0.032), HLA-DR+ T cells (OR: 0.921，95%CI: 0.866-0.980, P = 0.010), CD28-CD8+ T cells (OR: 0.886，95%CI: 0.792-0.991, P = 0.034). There are 26 risk factors. Reverse MR analysis showed that AN had a causal effect on 31 immune cells. AN increases the expression levels of five types of immune cells, including CD40 + CD14-CD16+ monocytes (OR: 1.087，95%CI: 1.004-1.177, P = 0.041), PDL-1+ CD14-CD16+ monocytes (OR: 1.082，95%CI: 1.002-1.168, P = 0.046), CD45+ CD33dim HLA-DR+ cells (OR: 1.145，95%CI: 1.019-1.287, P = 0.023), CD45+ basophils (OR: 1.164，95%CI: 1.036-1.307, P = 0.011), CD8+ natural killer T cells (OR: 1.102, 95%CI: 1.015-1.196, P = 0.020), and also decreases the expression levels of 26 immune cells. 6 liposomes showed exhibit protective effects against AN, including phosphatidylcholine (18:0_20:3) levels (OR: 0.852, 95%CI: 0.740-0.981, P = 0.026), phosphatidylcholine (O-18:2_18:1) levels (OR: 0.800, 95%CI: 0.672-0.952, P = 0.012), phosphatidylinositol (18:0_18:1) levels (OR: 0.873, 95%CI: 0.773-0.986, P = 0.029), phosphatidylinositol (18:1_18:2) levels (OR: 0.844, 95%CI: 0.734-0.971, P = 0.018), sphingomyelin (d38:1) levels (OR: 0.903，95%CI: 0.820-0.995, P = 0.039), and triacylglycerol (56:4) levels (OR: 0.786, 95%CI: 0.660-0.936, P = 0.007). There are 3 risk factors, including diacylglycerol (16:1_18:1) levels (OR: 1.208, 95%CI: 1.040-1.404, P = 0.014), phosphatidylcholine (18:1_18:3) levels (OR: 1.237, 95%CI: 1.003-1.526, P = 0.047), and phosphatidylinositol (16:0_20:4) levels (OR: 1.148, 95%CI: 1.003-1.314, P = 0.045). Reverse MR analysis showed that AN had a causal effect on 3 phosphatidylcholine (15:0_18:2) levels (OR: 1.075, 95%CI: 1.001-1.154, P = 0.048), phosphatidylcholine (O-16:2_18:0) levels (OR: 1.078, 95%CI: 1.002-1.159, P = 0.043), and triacylglycerol (51:1) levels (OR: 0.919, 95%CI: 0.850-0.994, P = 0.035). 6 inflammatory proteins have causal effects on AN, with protective factors including Glial cell line-derived neurotrophic factor levels (OR: 0.822，95%CI: 0.692-0.978, P = 0.027) and Interleukin-15 receptor subunit alpha levels (OR: 0.886, 95%CI: 0.789-0.995, P = 0.041) and risk factors including CC motif chemokine 4 levels (OR: 1.126, 95%CI: 1.011-1.254, P = 0.031), Interleukin-12 subunit beta levels (OR: 1.135, 95%CI: 1.033-1.248, P = 0.008), Monocyte chemoattractant protein-1 levels (OR: 1.152, 95%CI: 1.010-1.314, P = 0.035), and Sulfotransferase 1A1 levels (OR: 1.166, 95%CI: 1.006-1.351, P = 0.042). Reverse MR analysis showed that AN had a causal effect on Transforming growth factor-alpha (OR: 1.054，95%CI: 1.010-1.101, P = 0.016).

CONCLUSIONS

This study used large-scale and novel GWAS data, for the first time reveals through mediation analysis and multi-omics MR analysis the roles of gut microbiota, metabolites, immune cells, lipids, and inflammatory proteins in the pathogenesis of AN. These findings provide new biomarkers and targets for further prevention and treatment strategies.

Relationship of Genetic Polymorphisms and Microbial Composition with Binge Eating Disorder: A Systematic Review

Humans are the result of an evolutionary process, and because of this, many biological processes are interconnected with each other. The intestine-brain axis consists of an intricately connected neuronal-neuroendocrine circuit that regulates the sensation of hunger and satiety. Genetic variations and the consumption of unnatural diets (ultra-processed foods, high contents of sugars, etc.) can override this circuit and cause addiction to certain foods and/or the inability to feel satiety in certain situations. The patients who come to consultations (mainly psychology or nutrition) in an attempt to resolve this problem sometimes fail, which leads to them looking for new strategies based on biological predisposition. This investigation aims to evaluate the genetic studies regarding the microbiota carried out in the last 12 years in humans to try to determine which genes and microbes that have been recently studied are related to patients diagnosed with binge eating disorder or compulsive eating (presenting obesity or not). The protocol followed the PRISMA statement, and the following databases were searched from 2012 until the present day: PubMed, PsycINFO, SCOPUS, and Web of Science. Twenty-four international articles were analyzed, including cross-sectional or exploratory studies; five of them referred to the microbial composition, and in nineteen, the existence of genetic polymorphisms present in binge eating disorder or in compulsive eating could be observed: DRD2, OPRM1, COMT, MC4R, BNDF, FTO, SLC6A3, GHRL, CARTPT, MCHR2, and LRP11. Even though there is still much to investigate on the subject, it must be highlighted that, in the last 4 years, a two-fold increase has been observed in potential markers and in studies related to the matter, also highlighting the importance of different analyses in relation to psychosocial factors and their interaction with the genetic and microbial factors, for which research on the matter must be continued.

From gut microbiota to brain: implications on binge eating disorders

The prevalence of eating disorders has been increasing over the last 50 years. Binge eating disorder (BED) and bulimia nervosa (BN) are two typical disabling, costly and life-threatening eating disorders that substantially compromise the physical well-being of individuals while undermining their psychological functioning. The distressing and recurrent episodes of binge eating are commonly observed in both BED and BN; however, they diverge as BN often involves the adoption of inappropriate compensatory behaviors aimed at averting weight gain. Normal eating behavior is coordinated by a well-regulated trade-off between intestinal and central ingestive mechanism. Conversely, despite the fact that the etiology of BED and BN remains incompletely resolved, emerging evidence corroborates the notion that dysbiosis of gastrointestinal microbiome and its metabolites, alteration of gut-brain axis, as well as malfunctioning central circuitry regulating motivation, execution and reward all contribute to the pathology of binge eating. In this review, we aim to outline the current state of knowledge pertaining to the potential mechanisms through which each component of the gut-brain axis participates in binge eating behaviors, and provide insight for the development of microbiome-based therapeutic interventions that hold promise in ameliorating patients afflicted with binge eating disorders.

The intersection between eating disorders and gastrointestinal disorders: a narrative review and practical guide

Eating disorders include a spectrum of disordered thinking patterns and behaviours around eating. There is increasing recognition of the bi-directional relationship between eating disorders and gastrointestinal disease. Gastrointestinal symptoms and structural issues might arise from eating disorders, and gastrointestinal disease might be a risk factor for eating disorder development. Cross-sectional research suggests that individuals with eating disorders are disproportionately represented among people seeking care for gastrointestinal symptoms, with avoidant-restrictive food intake disorder in particular garnering attention for high rates among individuals with functional gastrointestinal disorders. This Review aims to describe the research to date on the relationship between gastrointestinal disorders and eating disorders, highlight research gaps, and provide brief, practical guidance for the gastroenterology provider in detecting, potentially preventing, and treating gastrointestinal symptoms in eating disorders.

A critical analysis of eating disorders and the gut microbiome

The gut microbiota, also known as our "second brain" is an exciting frontier of research across a multitude of health domains. Gut microbes have been implicated in feeding behaviour and obesity, as well as mental health disorders including anxiety and depression, however their role in the development and maintenance of eating disorders (EDs) has only recently been considered. EDs are complex mental health conditions, shaped by a complicated interplay of factors. Perhaps due to an incomplete understanding of the etiology of EDs, treatment remains inadequate with affected individuals likely to face many relapses. The gut microbiota may be a missing piece in understanding the etiology of eating disorders, however more robust scientific inquiry is needed in the field before concrete conclusions can be made. In this spotlight paper, we critically evaluate what is known about the bi-directional relationship between gut microbes and biological processes that are implicated in the development and maintenance of EDs, including physiological functioning, hormones, neurotransmitters, the central nervous system, and the immune system. We outline limitations of current research, propose concrete steps to move the field forward and, hypothesize potential clinical implications of this research. Our gut is inhabited by millions of bacteria which have more recently been referred to as "our second brain". In fact, these microbes are thought to play a role in ED behaviour, associated anxiety and depression, and even affect our weight. Recent research has dove into this field with promising findings that have the potential to be applied clinically to improve ED recovery. The present paper discusses what is known about the gut microbiome in relation to EDs and the promising implications that leveraging this knowledge, through fecal microbiome transplants, probiotics, and microbiome-directed supplemental foods, could have on ED treatment.

Gut-brain mechanisms underlying changes in disordered eating behaviour after bariatric surgery: a review

Bariatric surgery results in long-term weight loss and an improved metabolic phenotype due to changes in the gut-brain axis regulating appetite and glycaemia. Neuroendocrine alterations associated with bariatric surgery may also influence hedonic aspects of eating by inducing changes in taste preferences and central reward reactivity towards palatable food. However, the impact of bariatric surgery on disordered eating behaviours (e.g.: binge eating, loss-of-control eating, emotional eating and 'addictive eating'), which are commonly present in people with obesity are not well understood. Increasing evidence suggests gut-derived signals, such as appetitive hormones, bile acid profiles, microbiota concentrations and associated neuromodulatory metabolites, can influence pathways in the brain implicated in food intake, including brain areas involved in sensorimotor, reward-motivational, emotional-arousal and executive control components of food intake. As disordered eating prevalence is a key mediator of weight-loss success and patient well-being after bariatric surgery, understanding how changes in the gut-brain axis contribute to disordered eating incidence and severity after bariatric surgery is crucial to better improve treatment outcomes in people with obesity.

Binge eating disorder

Binge eating disorder (BED) is characterized by regular binge eating episodes during which individuals ingest comparably large amounts of food and experience loss of control over their eating behaviour. The worldwide prevalence of BED for the years 2018-2020 is estimated to be 0.6-1.8% in adult women and 0.3-0.7% in adult men. BED is commonly associated with obesity and with somatic and mental health comorbidities. People with BED experience considerable burden and impairments in quality of life, and, at the same time, BED often goes undetected and untreated. The aetiology of BED is complex, including genetic and environmental factors as well as neuroendocrinological and neurobiological contributions. Neurobiological findings highlight impairments in reward processing, inhibitory control and emotion regulation in people with BED, and these neurobiological domains are targets for emerging treatment approaches. Psychotherapy is the first-line treatment for BED. Recognition and research on BED has increased since its inclusion into DSM-5; however, continuing efforts are needed to understand underlying mechanisms of BED and to improve prevention and treatment outcomes for this disorder. These efforts should also include screening, identification and implementation of evidence-based interventions in routine clinical practice settings such as primary care and mental health outpatient clinics.

Enhancing Human Superorganism Ecosystem Resilience by Holistically ‘Managing Our Microbes’

Microbes in the 21st century are understood as symbionts ‘completing’ the human ‘superorganism’ (Homo sapiens plus microbial partners-in-health). This paper addresses a significant paradox: despite the vast majority of our genes being microbial, the lack of routine safety testing for the microbiome has led to unintended collateral side effects from pharmaceuticals that can damage the microbiome and inhibit innate ‘colonization resistance’ against pathobionts. Examples are discussed in which a Microbiome First Medicine approach provides opportunities to ‘manage our microbes’ holistically, repair dysbiotic superorganisms, and restore health and resilience in the gut and throughout the body: namely, managing nosocomial infections for Clostridioides difficile and Staphylococcus aureus and managing the gut and neural systems (gut–brain axis) in autism spectrum disorder. We then introduce a risk analysis tool: the evidence map. This ‘mapping’ tool was recently applied by us to evaluate evidence for benefits, risks, and uncertainties pertaining to the breastmilk ecosystem. Here, we discuss the potential role of the evidence map as a risk analysis methodology to guide scientific and societal efforts to: (1) enhance ecosystem resilience, (2) ‘manage our microbes’, and (3) minimize the adverse effects of both acute and chronic diseases.