Perspectives

When metabolic strategies fall short: The unaddressed role of food addiction

Vera I. Tarman

Received: 12 Sept. 2025; Accepted: 18 Dec. 2025; Published: 31 Jan. 2026

Copyright: © 2026. The Author. Licensee: AOSIS.
This work is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0/).

Abstract

Metabolic syndrome and food addiction frequently co-occur in clinical settings and mutually reinforce each other through overlapping biological pathways. Compulsive overeating in metabolic syndrome is associated with worsening insulin resistance and lipid profiles, while food addiction exacerbates metabolic dysfunction. Converging evidence highlights the roles of insulin and leptin resistance, endocannabinoids, inflammation and altered gut–brain signalling in sensitising reward circuits. Despite validated tools, few studies examine food addiction symptoms and detailed metabolic profiles together. Targeted interventions may improve both conditions. These shared pathways suggest new avenues for individualised treatment and risk assessment.

Keywords: metabolic syndrome; food addiction; ultra processed food addiction; treatment interventions; obesity; eating disorders.

Introduction

Metabolic syndrome describes a cluster of risk factors, including central obesity, impaired glucose regulation, hypertension, dyslipidaemia and fatty liver disease, that substantially increase the likelihood of cardiovascular morbidity and type 2 diabetes.¹ Lifestyle modification and pharmacotherapy remain the cornerstones of treatment, yet long-term outcomes are often disappointing. Many patients struggle to maintain dietary changes despite initial success and relapse is common even after interventions as intensive as bariatric surgery.²

A parallel body of research has developed around the concept of food addiction. Drawing from substance use disorder frameworks, food addiction highlights a subset of individuals who experience intense cravings, loss of control, compulsive consumption and continued use of ultra-processed foods despite negative consequences.³ Importantly, neural and behavioural studies confirm that these symptoms are not merely a byproduct of obesity.³ The Yale Food Addiction Scale (YFAS) provides a validated way to measure these patterns,⁴ while neuroimaging reveals distinct activation in reward-related brain circuits when people with food addiction are exposed to food cues.⁵

Despite their clear overlap, metabolic research and food addiction studies have remained largely separate fields.⁴ Clinicians treating metabolic syndrome have not yet routinely assessed for food addiction symptoms and addiction researchers rarely provide detailed metabolic profiling. Yet the evidence increasingly suggests that shared biological mechanisms link these two conditions. Understanding this integration may help explain why metabolic treatments sometimes fail and may allow for the development of more tailored and effective therapeutic strategies.⁶ To further address these issues, a more integrated approach may prove useful.

Why an integrated perspective is needed

The interaction between metabolic dysfunction and food addiction appears to be circular. On the one hand, metabolic disturbances alter the brain’s regulation of reward and appetite, making individuals more susceptible to compulsive eating.^1,3 On the other hand, compulsive and addictive intake of ultra-processed foods further worsens metabolic parameters, creating a vicious cycle that is difficult to disrupt.^7,8 This bidirectional relationship helps explain why patients with metabolic syndrome often relapse despite strong willpower and intensive interventions and why some individuals are much more prone to treatment failure than others.

An integrated perspective acknowledges that, despite recognition that compulsive eating has biological components, metabolic syndrome treatment protocols rarely incorporate validated food addiction screening tools or addiction-informed interventions. This disconnect between knowledge and practice represents a missed opportunity for patient care. Screening for food addiction in patients with metabolic syndrome can therefore be viewed as a way to identify an important risk factor that contributes directly to treatment failure or treatment resistance.⁹ By understanding and measuring these connections, clinicians can both anticipate challenges and design strategies that address the biological vulnerabilities that underlie compulsive eating. The preliminary evidence from the TOWARD (Text-based communications, Online interactions, Wellness coaching, Asynchronous education and community support, Real-time biofeedback, Dietary monitoring) study suggests potential benefits of comprehensive metabolic interventions on eating behaviour symptoms in some patients with metabolic syndrome.⁶ However, this multi-component intervention included wellness coaching and dietary counselling alongside metabolic strategies, making it impossible to isolate which elements drove improvements in addictive eating patterns. Additionally, with only a minority of participants meeting food addiction criteria at baseline and mixed outcomes (including symptom worsening in approximately 13.5% of participants), these findings should be interpreted as hypothesis-generating rather than definitive evidence. Next, I explore the potential biological links between concepts related to metabolism and food addictions.

Biological links between metabolism and food addiction

The biological mechanisms connecting food addiction and metabolic syndrome converge on several well-studied systems. These are as follows:

Insulin resistance disrupts reward regulation by weakening the ability of insulin to facilitate dopamine release in the brain after food consumption. The resulting decrease in dopamine leads to a loss of reinforcement which may paradoxically heighten cravings for palatable foods.¹⁰

Leptin resistance produces a similar effect. Although leptin normally suppresses appetite and dampens the brain’s responsiveness to food cues, in resistant states the brain continually responds to hunger and reward signals despite sufficient energy reserves.¹¹ Food cravings persist.

The endocannabinoid system also plays a central role. Elevated circulating endocannabinoids such as anandamide and 2-arachidonoylglycerol (2-AG) stimulate appetite and enhance food-related reward while also interfering with glucose and lipid metabolism. These elevated signals thus link overeating and metabolic disease in a mechanistic way.¹²

Chronic low-grade inflammation, mediated by cytokines such as interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α), contributes both to insulin resistance and to altered neural signalling in brain regions that govern appetite and impulse control.¹³ This leads to heightened reward and cravings for sugary foods.

Finally, the gut–brain axis represents a critical bridge between metabolism and behaviour. Hormones such as Glucagon-like peptide-1 (GLP-1), ghrelin and peptide YY (PYY) regulate feelings of hunger and satiety, while gut-derived metabolites and microbiome composition shape the reward value assigned to food.^14,15 Disturbances in this communication system increase cue-driven eating and reduce the effectiveness of natural satiety signals.

Interesting work is being done on how the gut microbiota composition alters dopaminergic and opioid signalling in the nucleus accumbens through neuroinflammatory processes, linking dysbiosis to both metabolic dysfunction and compulsive eating.¹⁶ Similarly, microbial metabolites have been shown to regulate dopamine and serotonin turnover in reward circuits while affecting glucose homeostasis.¹⁷ The gut-brain–microbiome interface promises to be fertile ground for understanding shared mechanisms between metabolism and addictive eating.

Together, all these processes illustrate how metabolic dysfunction primes the brain for compulsive eating, while compulsive eating feeds back to worsen metabolic markers, amplifying and perpetuating the cycle.

Biological markers

Several classes of biological markers appear promising for identifying food addiction in metabolic syndrome. Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) provides a measure of insulin sensitivity, while leptin levels relative to fat mass indicate the efficacy of leptin signalling. Inflammatory markers such as C-reactive protein (CRP), IL-6 and TNF-α correlate with altered circulating endocannabinoid levels, reflecting the interaction between inflammatory and endocannabinoid signalling pathways. Finally, gut hormones, including GLP-1, ghrelin and PYY, offer additional measures of metabolic-reward pathway integration.^11,12,13,14

In addition to these peripheral markers, brain imaging provides important complementary data. Functional MRI studies consistently demonstrate hyperactivation of the nucleus accumbens and other reward-related regions in individuals with food addiction, particularly when they are responding to visual or olfactory food cues.^5,18 This ‘neural fingerprint’ differentiates food addiction from obesity alone and offers an additional measurable dimension of food addiction. Future research should determine if corresponding MRI findings correlate with the severity of metabolic syndrome. Additionally, reviewing current evidence from a variety of perspectives can further expand our understanding of important relationships.

Current evidence across domains

In population-based studies, between 15% and 20% of individuals meet criteria for food addiction based on validated scales, with even higher rates reported among people with obesity or type 2 diabetes.^3,4 Importantly, those with food addiction symptoms often report greater difficulty maintaining weight loss and poorer glycaemic control, suggesting that addictive-like eating compounds metabolic treatment challenges.^19,20

Clinical evidence from bariatric surgery also supports this view. Patients who exhibit food addiction symptoms preoperatively are more likely to experience weight regain and metabolic relapse following surgery.² Likewise, individuals with diabetes who screen positive for food addiction often show more variable adherence to dietary recommendations and medications.⁹

Numerous studies document that food addiction, as measured by validated screening tools, is closely associated with greater obesity severity and a higher risk of metabolic dysregulation, particularly in populations experiencing obesity and diabetes.^21,22

Meta-analyses have shown that food addiction symptoms are more frequent in bulimia nervosa than in binge eating disorder or controls, suggesting that it represents a more severe or distinctive variant of eating pathology.²³ Food addiction also demonstrates substantial psychiatric comorbidity: in general population studies, 75% of individuals with food addiction met the criteria for depression and 77% for anxiety, compared to approximately 25% and 23%, respectively.⁸

Neuroimaging research further strengthens the link. Studies reveal heightened mesolimbic activation in food addiction populations compared to weight-matched controls.²⁴ This point is illustrated given the vast popularity of GLP-a medications. Pharmacological interventions such as GLP-1 receptor agonists not only improve metabolic outcomes but also appear to normalise activation patterns in neural reward circuits, illustrating how treatment can simultaneously modulate neural-addictive and metabolic pathways.²⁵ Based on this review of relevant literature, it is now possible to propose strategies to address potential research directions on this topic for future clinical applications.

Future directions

Despite growing evidence linking metabolic dysfunction and addictive eating patterns, comprehensive studies examining these relationships with detailed biological profiling remain scarce. We need long-term studies that can determine whether metabolic dysfunction predicts food addiction development and tracks its progression over time. These studies should measure when metabolic changes (insulin resistance, leptin dysfunction and endocannabinoid dysregulation) appear relative to when food addiction symptoms first emerge. Establishing metabolic markers as early indicators of food addiction would enable clinicians to recognise this condition before severe complications develop, leading to earlier addiction-focused interventions.

Comprehensive phenotyping studies could integrate validated food addiction screening with detailed metabolic profiling and neuroimaging. Such multi-level characterisation would identify biological signatures linking metabolic dysfunction to addictive eating patterns and reveal which metabolic profiles predict treatment resistance to standard metabolic interventions. Understanding these relationships would alert clinicians to address underlying addictive processes, prompting consideration of addiction-specific treatments rather than continuing ineffective metabolic interventions alone.

Intervention research is critically needed to test whether metabolic treatments can reduce food addiction symptoms. Such clinical trials would provide causal evidence that correcting metabolic dysfunction reduces food addiction symptoms. However, these studies must use designs that separate biological effects from behavioural support; this is a methodological gap in existing multi-component interventions. Successfully demonstrating that metabolic markers can identify food addiction and track treatment response would transform clinical practice by providing objective criteria for when patients require addiction-focused care.

Clinical applications

While the research priorities described above will strengthen the evidence base, several clinical applications can be implemented immediately based on existing evidence. There are already current and evidence-based frameworks such as the TOWARD programme and the low-carb and psychoeducational food addiction programme initiative that have attempted to address both dimensions.^6,26 Screening for food addiction should be incorporated into obesity, diabetes and metabolic clinics using brief validated tools such as the Modified YFAS,⁴ the Craved Scale²⁶ and the Uncope when applied to sugar.²⁷ Identifying patients with addictive eating patterns can help clinicians explain patient challenges, lapses and treatment resistance without resorting to a narrative of poor willpower, thereby reducing shame and stigmatisation.

Based on the evidence reviewed here, treatment strategies can then be tailored for improved patient adherence and clinical outcomes. Coordinated treatment applications of pharmacological agents already used in metabolic care, such as GLP-1 receptor agonists, may also reduce cravings and cue-driven eating patterns, providing a dual benefit.²⁴ Behavioural treatments can include not only dietary counselling but also techniques from addiction medicine, such as trigger management, craving regulation and support to maintain a changed lifestyle of eating patterns.^6,26 For more severe cases, psychotherapy informed by addiction models may complement metabolic care for enhanced integration of treatment approaches.²⁸ This integrated framework establishes weight regain and dietary relapse as partially biologically driven phenomena as well as social and psychological factors, paving the way for more effective interventions.

Conclusion

Metabolic syndrome and food addiction represent interlocking conditions that reinforce each other through shared biological mechanisms. Insulin resistance, leptin dysfunction, endocannabinoid activation, inflammatory signalling and gut–brain disruptions all promote compulsive eating behaviours, which in turn worsen metabolic outcomes. Failure to recognise this overlap risks misinterpreting relapse as personal weakness rather than as a biologically supported pattern. Integrating assessments of food addiction into metabolic research and clinical care offers a powerful opportunity to improve patient outcomes through biologically informed screening, risk stratification and individualised treatment. Far from derailing metabolic care, acknowledging food addiction equips clinicians with more robust treatment interventions and offers a stronger foundation for long-term success.

Acknowledgements

The author declares that no financial or personal relationships inappropriately influenced the writing of this article.

Vera I. Tarman: Conceptualisation; Writing – original draft; Writing – review & editing. The author confirms that this work is entirely their own, has reviewed the article, approved the final version for submission and publication, and takes full responsibility for the integrity of its findings.

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

The author declares that all data that support this research article and findings are available in the article and its references.

The views and opinions expressed in this article are those of the author and are the product of professional research. They do not necessarily reflect the official policy or position of any affiliated institution, funder, agency or that of the publisher. The author is responsible for this article’s results, findings and content.

References