Obesity is associated with a poor COVID-19 prognosis, and it seems associated with reduced humoral response to vaccination. Public health campaigns have advocated for weight loss in subjects with obesity, hoping to eliminate this risk. However, no evidence proves that weight loss leads to a better prognosis or a stronger immune response to vaccination. We aimed to investigate the impact of rapid weight loss on the adaptive immune response in subjects with morbid obesity. Twenty-one patients followed a hypocaloric, very-low-carbohydrate diet one week before to one week after the two mRNA vaccine doses. The diet’s safety and efficacy were assessed, and the adaptive humoral (anti-SARS CoV-2 S antibodies, Abs) and cell-mediated responses (IFNγ secretion on stimulation with two different SARS CoV-2 peptide mixes, IFNγ-1 and IFNγ-2) were evaluated. The patients lost ~10% of their body weight with metabolic improvement. A high baseline BMI correlated with a poor immune response (R −0.558, p = 0.013 for IFNγ-1; R −0.581, p = 0.009 for IFNγ-2; R −0.512, p = 0.018 for Abs). Furthermore, there was a correlation between weight loss and higher IFNγ-2 (R 0.471, p = 0.042), and between blood glucose reduction and higher IFNγ-1 (R 0.534, p = 0.019), maintained after weight loss and waist circumference reduction adjustment. Urate reduction correlated with higher Abs (R 0.552, p = 0.033). In conclusion, obesity is associated with a reduced adaptive response to a COVID-19 mRNA vaccine, and weight loss and metabolic improvement may reverse the effect.

The key factors playing a role in the pathogenesis of metabolic alterations observed in many patients with obesity have not been fully characterized. Their identification is crucial, and it would represent a fundamental step towards better management of this urgent public health issue. This aim could be accomplished by exploiting the potential of machine learning (ML) technology. In a single-centre study (n = 2567), we used an ML analysis to cluster patients with metabolically healthy (MHO) or metabolically unhealthy (MUO) obesity, based on several clinical and biochemical variables. The first model provided by ML was able to predict the presence/absence of MHO with an accuracy of 66.67% and 72.15%, respectively, and included the following parameters: HOMA-IR, upper body fat/lower body fat, glycosylated haemoglobin, red blood cells, age, alanine aminotransferase, uric acid, white blood cells, insulin-like growth factor 1 (IGF-1) and gamma-glutamyl transferase. For each of these parameters, ML provided threshold values identifying either MUO or MHO. A second model including IGF-1 zSDS, a surrogate marker of IGF-1 normalized by age and sex, was even more accurate with a 71.84% and 72.3% precision, respectively. Our results demonstrated high IGF-1 levels in MHO patients, thus highlighting a possible role of IGF-1 as a novel metabolic health parameter to effectively predict the development of MUO using ML technology.