Investigating the Link Between Advanced Glycation End Products (AGEs) and Muscle Wasting in Sarcobesity

The study in question is of a cross-sectional observational type. The reference population is defined by patients with obesity and a diagnosis of T2D within 15 years of entry into the study or patients with concomitant type 2 diabetes and lipodystrophy syndrome. This population was chosen because they are at high risk of sarcopenia.

Lipodystrophy includes a heterogeneous spectrum of genetic and acquired diseases characterized by loss of subcutaneous adipose tissue, ectopic fat accumulation, insulin resistance, metabolic and cardiovascular diseases, premature aging, sarcopenia, muscle pain, high-grade inflammation, epigenetic dysregulation, and mitochondrial dysfunction. Therefore, patients with T2D and lipodystrophy are highly inflamed as they generally present with a more severe T2D phenotype, presumed sarcopenic, and with a high rate of endogenous AGE production. Patients with concurrent lipodystrophy and T2D will be recruited as sarcopenic and obese subjects, representing an excellent strategy for comparison with diabetic individuals without lipodystrophy.

SUBJECTS AND METHODS A total of 195 consecutive subjects will be enrolled in the study from the Endocrinology Unit of the University of Eastern Piedmont between April 2024 and April 2026, who meet the inclusion criteria.

Study duration:

The study will last for two years corresponding to the enrollment period given the cross-sectional nature of the study.

Statistical Analysis

Descriptive statistics will be used to summarize sociodemographic, anthropometric, clinical, and lifestyle-related information collected. Categorical variables will be summarized using absolute frequencies and percentages, while numerical variables will be summarized using mean and standard deviation or median and interquartile range if not normally distributed according to the Shapiro-Wilk test and after observation of Q-Q plots (quantile-quantile plot).

The Pearson correlation coefficient or the corresponding non-parametric Spearman rank correlation coefficient and confidence intervals will be initially calculated to assess the correlation between the levels of individual AGEs and skeletal muscle mass (SMM), handgrip strength (HGS), parameters of body composition, and functional parameters of skeletal muscle. Subsequently, linear regression models will be used to evaluate the relationship between AGEs and sarcopenia-defining indices adjusted for age, sex, duration of diabetes, and other potential confounding factors such as inflammation, adherence to the Western diet, and levels of physical activity. The LASSO method will be used for variable selection in multivariable regression models.

Univariable and multivariable Poisson regression models with robust variance will be used to estimate relative prevalence risks for the association between AGEs and patient characteristics with sarcopenia presence and the corresponding confidence intervals.

An integration of clinical data, biochemical data, AGE levels, and patient omic signatures will be performed to develop a multifactorial diagnostic model using multivariate statistical analysis (e.g., factor analysis, principal component analysis, cluster analysis, discriminant analysis, partial least squares analysis, logistic regression) and data-driven approaches. Machine learning algorithms will be applied to prioritize and weigh risk factors. These analyses will be conducted with internal statistical consultation already utilized by the group.

Expected Results

With this study, the investigators expect to obtain further information and correlations between nutritional assessment and its impact on inflammation, sarcopenia definition, and progression, obesity, and T2D, based on body measurements and clinical parameters. Through biochemical, hormonal, and metabolomic analyses conducted on biological samples, te investigators expect to identify possible markers related to the presence of AGEs. In conclusion, the primary expected outcome would be to identify a positive correlation between AGE accumulation in at least one compartment (skin, plasma, urine) and the severity of sarcopenia, thus obtaining a rapid and non-invasive method to identify individuals at high risk of developing muscle wasting (MW) and identify correlations between AGE levels and other metabolic characteristics, even in lipodystrophic pathology.

Inclusion criteria

• Patients of both sexes.
• Adults.
• BMI compatible with obesity and a diagnosis of type 2 diabetes under good metabolic control (HbA1c < 7.5%) within 15 years of entry into the study or diagnosis of lipodystrophy (included in the European Consortium of Lipodystrophies (ECLip) Registry (eclip-web.org))

Exclusion criteria

• Age under 18 years.
• Secondary obesity or genetic diseases (Prader-Willi Syndrome, Down Syndrome); metabolic and endocrine disorders (Cushing's syndrome, hypothyroidism).
• Subjects with: Inflammatory Bowel Disease (IBD), cancer.
• Confirmed or planned pregnancy during the study participation months.