Effectiveness and health benefits of a nutritional, chronobiological and physical exercise primary care intervention in fibromyalgia and chronic fatigue syndrome: SYNCHRONIZE + mixed-methods study protocol

Neuronutritional Approach to Fibromyalgia Management: A Narrative Review

Fibromyalgia (FM) is a complex and common syndrome characterized by chronic widespread pain, fatigue, sleep disturbances, and various functional symptoms without clear structural or pathological causes. Affecting approximately 1-5% of the global population, with a higher prevalence in women, FM significantly impacts patients' quality of life, often leading to considerable healthcare costs and loss of productivity. Despite its prevalence, the etiology of FM remains elusive, with genetic, environmental, and psychological factors, including nutrition, being implicated. Currently, no universally accepted treatment guidelines exist, and management strategies are often symptomatic. This narrative review explores the potential of a neuronutritional approach to FM management. It synthesizes existing research on the relationship between FM and nutrition, suggesting that dietary interventions could be a promising complementary treatment strategy. Various nutritional interventions, including vitamin D, magnesium, iron, and probiotics supplementation, have shown potential in reducing FM symptoms, such as chronic pain, anxiety, depression, cognitive dysfunction, sleep disturbances, and gastrointestinal issues. Additionally, weight loss has been associated with reduced inflammation and improved quality of life in FM patients. The review highlights the anti-inflammatory benefits of plant-based diets and the low-FODMAPs diet, which have shown promise in managing FM symptoms and related gastrointestinal disorders. Supplements such as vitamin D, magnesium, vitamin B12, coenzyme Q10, probiotics, omega-3 fatty acids, melatonin, S-adenosylmethionine, and acetyl-L-carnitine are discussed for their potential benefits in FM management through various mechanisms, including anti-inflammatory effects, modulation of neurotransmitters, and improvement of mitochondrial function. In conclusion, this review underscores the importance of considering neuronutrition as a holistic approach to FM treatment, advocating for further research and clinical trials to establish comprehensive dietary guidelines and to optimize management strategies for FM patients.

[The role of diet in multicomponent interaction for people with fibromyalgia, a current challenge]

Introduction

Nutrition and Chronobiology as Key Components of Multidisciplinary Therapeutic Interventions for Fibromyalgia and Associated Chronic Fatigue Syndrome: A Narrative and Critical Review

Fibromyalgia (FM) is often accompanied by chronic fatigue syndrome (CFS). It is a poorly understood disorder that mainly affects women and leads to chronic pain, fatigue, and insomnia, among other symptoms, which decrease quality of life. Due to the inefficiency of current pharmacological treatments, increasing interest is being directed towards non-pharmacological multicomponent therapies. However, nutrition and chronobiology are often overlooked when developing multicomponent therapies. This narrative and critical review explore the relevance of nutritional and chronobiological strategies in the therapeutic management of FM and the often-associated CFS. Reviewed literature offers scientific evidence for the association of dietary habits, nutrient levels, body composition, gut microbiota imbalance, chronobiological alterations, and their interrelation with the development and severity of symptoms. This review highlights the key role of nutrition and chronobiology as relevant and indispensable components in a multidisciplinary approach to FM and CFS.

Exploring Depression and Nutritional Covariates Amongst US Adults using Shapely Additive Explanations

Background

Depression affects personal and public well-being and identification of natural therapeutics such as nutrition is necessary to help alleviate this public health concern.

Objective

The study aimed to identify feature importance in a machine learning model using solely nutrition covariates.

Methods

A retrospective analysis was conducted using a modern, nationally representative cohort, the National Health and Nutrition Examination Surveys (NHANES 2017-2020). Depressive symptoms were evaluated using the validated 9-item Patient Health Questionnaire (PHQ-9), and all adult patients (total of 7929 individuals) who completed the PHQ-9 and total nutritional intake questionnaire were included in the study. Univariable regression was used to identify significant nutritional covariates to be included in a machine learning model and feature importance was reported. The acquisition and analysis of the data were authorized by the National Center for Health Statistics Ethics Review Board.

Results

7929 patients met the inclusion criteria in this study. The machine learning model had 24 out of a total of 60 features that were found to be significant on univariate analysis (p < 0.01 used). In the XGBoost model the model had an Area Under the Receiver Operator Characteristic Curve (AUROC) = 0.603, Sensitivity = 0.943, Specificity = 0.163. The top four highest ranked features by gain, a measure of the percentage contribution of the covariate to the overall model prediction, were Potassium Intake (Gain = 6.8%), Vitamin E Intake (Gain = 5.7%), Number of Foods and Beverages Reported (Gain = 5.7%), and Vitamin K Intake (Gain 5.6%).

Conclusion

Machine learning models with feature importance can be utilized to identify nutritional covariates for further study in patients with clinical symptoms of depression.