Osteosarcopenic obesity syndrome (OSO) is a disease that seriously endangers the health of older people. The rational classification of the disease can guide the clinical diagnosis and treatment. Therefore, classifying OSO based on inter-correlations of its diagnostic variables and exploring its structural properties may offer insights into clinical prevention and treatment of OSO.

To explore the structural properties of OSO, providing a theoretical basis for individualized diagnosis and treatment of the disease.

A cross-sectional study was conducted with a random sample of OSO patients (≥60 years old) who underwent physical examination in Physical Examination Center, the 2nd Affiliated Hospital of Harbin Medical University from January 2018 to December 2020. The data collected include 9 diagnostic variables for OSO〔skeletal muscle index, grip strength, body fat percentage, BMD of the lumbar spine (L1-L4), hip and femoral neck, BMI, waist circumference, walking pace〕, sociodemographic characteristics, lifestyle and prevalence of common chronic diseases. KMO test and Bartlett's test of sphericity were used to evaluate the suitability of diagnostic variables for factor analysis. The components with an eigenvalue equal to or greater than 1.000 were extracted by principal component analysis, and the varimax orthogonal rotation matrix was obtained by the varimax orthogonal rotation method. The common factors were named according to the orthogonal rotation matrix of factors. On the basis of factor analysis, thesum of squares and systematic cluster analysis were used to develop a dendrogram for classifying patients. The structural properties of OSO were analyzed by comparing the values of diagnostic variables and clinical features among patients of different categories.

A total of 107 cases were included. The KMO value (0.688) and the result of Bartlett's test of sphericity (χ²=492.374, P<0.001) indicated that the data of diagnostic variables were suitable for factor analysis. Three common factors (osteoporosis factor, muscle + body fat factor and obesity factor) with an eigenvalue greater than 1.000 were extracted, explaining 81.408% variance of the total. The load value of each diagnostic variable on its common factor ranged from 0.770 to 0.918. The patients were divided into 3 categoriesby cluster analysis using the common factors. The skeletal muscle index, grip strength, body fat percentage, BMD of L1-L4, hip and femoral neck, BMI and waist circumference varied significantly across patients of different categories (P<0.05). The values of BMD of L1-L4, hip and femoral neck of OSO patients in the first category were significantly lower than those of the other two categories (P<0.05). The BMI and waist circumference values of OSO patients in the second category were lower than those of the other two categories (P<0.05). OSO patients in the third category had higher values of skeletal muscle index, grip strength and BMD of L1-L4, hip and femoral neck, but lower body fat percentage than those of the other two categories (P<0.05). There were statistically significant differences in sex ratio, distribution of education level and total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), uric acidand creatinine in the serum among different categories of patients (P<0.05). OSO patients in the first category had higher prevalence of below the undergraduate education level than those in the third category (P<0.017). OSO patients in the second category had higher level of TC than those in the third category (P<0.05). In comparison with those in other two categories, OSO patients in the third category had higher personal monthly income equal to or greater than 5 000 yuan, and lower female ratio (P<0.017). Moreover, OSO patients in the third category also demonstrated higher levels of uric acid and creatinine in the serum (P<0.05) .

OSO diagnostic variables can be generalized and interpreted in terms of osteoporosis, muscle and body fat, and obesity. And OSO patients have different structural properties. The application of multivariate statistical methods to study the structural properties of OSO patients will contribute to the individualized management of such patients.