Endocrine Abstracts

Background: Metabolic syndrome (MS) is associated with increased mortality, and the key factors predictive of its development among patients with obesity are still unclear. We recently demonstrated with a machine learning approach that Insulin-like Growth Factor 1 (IGF-1) is a novel marker of metabolic health and that in individuals with obesity, lower IGF-1 levels are associated with increased metabolic deterioration. However, the interpretation of IGF-1 serum measurement is limited by a poor standardization of its normal values, as they vary significantly with sex, age and BMI.

Objective: To calculate a surrogate marker of serum IGF-1 concentration, normalized for age and sex, expressed as IGF-1 standard deviation score (IGF1-zSDS), in a cohort of 2032 individuals with obesity and to investigate its association with the presence of MS.

Methods: We conducted a cross-sectional study on adult Caucasian patients entering our third-tier obesity centre from 2010 to 2022. Anthropometric parameters, routine laboratory assessments, markers of glycolipid metabolism and serum IGF-1 levels were obtained. Adult treatment panel III criteria were adopted for the clinical diagnosis of MS. IGF1-zSDS was calculated both in men and in women age-grouped as follows: 18–22; 23–30; 31–50; 51–65; >65 years, according to the equation IGF-1 zSDS=(IGF-1 – mean)/SD. Student’s t-test was used to assess differences between patients with MS and groups of obese patients without MS (noMS) matched for age. A multinomial logistic analysis (MLA) was performed to assess the association between IGF1-zSDS and the probability of being diagnosed with MS.

Results: A total of 2032 patients (1551 females and 481 males) were enrolled. IGF-1 means and SDs obtained were specific for the obese population. Overall, male subjects had both a higher BMI (39.1±7.2 vs 37.8±7.1, P=0.001) and a higher prevalence of MS (64.7% vs 45.7%, P<0.0001) than their female counterpart, suggesting that women may seek medical attention earlier or may be less likely to develop MS. The IGF1-zSDS in the overall population was 0.13±0.8 and was significantly lower in patients with MS than in noMS. A MLA showed that for each decrease in IGF1-zSDS units, the chance of having MUO increased by 30%.

Conclsion: In a large population with obesity, lower IGF1-zSDS is associated with a higher chance of suffering from MS. Our results obtained with classical statistical analysis confirm preliminary results proposed by artificial intelligence. We suggest to use specific IGF-1 reference values to calculate IGF1-zSDS in obese Caucasic patients

Keywords: metabolic syndrome, insulin-like growth factor 1