There is increasing recognition of the importance of both the microbiome and vitamin D in states of health and disease. Microbiome studies have already demonstrated unique microbial patterns in systemic autoimmune diseases such as inflammatory bowel disease, rheumatoid arthritis, and systemic lupus erythematosus. Dysbiosis also seems to be associated with allergies, in particular asthma, atopic dermatitis, and food allergy. Even though the effect of vitamin D supplementation on these pathologies is still unknown, vitamin D deficiency deeply influences the microbiome by altering the microbiome composition and the integrity of the gut epithelial barrier. It also influences the immune system mainly through the vitamin D receptor (VDR). In this review, we summarize the influence of the microbiome and vitamin D on the immune system with a particular focus on allergic diseases and we discuss the necessity of further studies on the use of probiotics and of a correct intake of vitamin D.

Vitamin D (VD) and micronutrients, including folic acid, are able to modulate both the innate and the adaptive immune responses. Low VD and folic acid levels appear to promote cognitive decline as in Alzheimer’s disease (AD). A machine learning approach was applied to analyze the impact of various compounds, drawn from the blood of AD patients, including VD and folic acid levels, on the Mini-Mental State Exam (MMSE) in a cohort of 108 patients with AD. The first analysis was aimed at predicting the MMSE at recruitment, whereas a second investigation sought to predict the MMSE after a 4 year follow-up. The simultaneous presence of low levels of VD and folic acid allow to predict MMSE, suggestive of poorer cognitive function. Such results suggest that the low levels of VD and folic acid could be associated with more severe cases of cognitive impairment in AD. It could be hypothesized that simultaneous supplementation of VD and folic acid could slow down the progression of cerebral degeneration at least in a subset of AD individuals.

Vascular dementia (VD) is a cognitive impairment typical of advanced age with vascular etiology. It results from several vascular micro-accidents involving brain vessels carrying less oxygen and nutrients than it needs. This being a degenerative disease, the diagnosis often arrives too late, when the brain tissue is already damaged. Thus, prevention is the best solution to avoid irreversible cognitive impairment in patients with specific risk factors. Using the machine learning (ML) approach, our group evaluated Mini-Mental State Examination (MMSE) changes in patients affected by Alzheimer’s disease by considering different clinical parameters. We decided to apply a similar ML scheme to VD due to the consistent data obtained from the first work, including the assessment of various ML models (LASSO, RIDGE, Elastic Net, CART, Random Forest) for the outcome prediction (i.e., the MMSE modification throughout time). MMSE at recruitment, folate, MCV, PTH, creatinine, vitamin B12, TSH, and hemoglobinwere the best predictive parameters individuated by the best ML model: Random Forest. ML results can be useful inidentify predictive biomarkers for cognitive worsening in VD early and also for focusing on necessary examinations at the first visits to draw the most predictive features, saving time and money and reducethe burden on the patients themselves. Such results should be integrated with brain imaging, physiological signal measurements, and sensory patterns, particularly forthose senses already demonstrated to have a significant link with neurodegeneration. Adjusting compound deficit by administering nutraceuticals could support treatment effectiveness and lead to a better quality of life for patients, families, and caregivers, with a consistent impact on the national health systems load.