Study design and population
This was a cross-sectional study. For this study, we examined IC among elderly patients ≥ 60 years at Beijing Hospital in China from November 2020 to December 2023. Initially, 745 participants were recruited. However, 284 participants were excluded based on the following criteria: (1) being under 60 years of age; (2) having incomplete intrinsic capacity data; and (3) lacking osteosarcopenia data. The final analysis included a total of 461 participants (Fig. 1). This study was conducted with the approval of the Medical Ethics Committee of Beijing Hospital (Approval Number: 2024BJYYEC-KY083-02) and was performed in accordance with the Declaration of Helsinki. Given the retrospective nature of this study and the maintenance of patient anonymity, a waiver for informed consent was granted.
Flowchart of Participant Selection
Assessment of osteosarcopenia
Sarcopenia was defined as the presence of both low appendicular skeletal muscle mass index (< 7.0 kg/m² for men and < 5.7 kg/m² for women) and low handgrip strength (< 28 kg for men and < 18 kg for women), and/or slow gait speed (< 1.0 m/s)[11]. These criteria were recommended by the Asian Working Group for Sarcopenia in 2019. Additionally, individuals with a self-reported history of sarcopenia were also included. Skeletal muscle mass was evaluated using bioelectrical impedance analysis with the Inbody S10 device (Korea). Grip strength was measured using the Leaping HealthTM WL-1000 dynamometer, with two trials conducted for each hand to determine the maximum grip strength. Gait speed was assessed by timing participants as they walked 6 m at their usual pace.
Osteosarcopenia was defined as the coexistence of osteoporosis and sarcopenia. Bone mineral density of the lumbar spine and bilateral femoral neck was measured using Dual-Energy X-ray Absorptiometry (GE Healthcare, Lunar iDXA). Osteoporosis was diagnosed based on a T-score below − 2.5, in accordance with World Health Organization criteria [12]or through self-reported history of osteoporosis.
Participants were categorized into four groups according to their osteoporosis and sarcopenia status. The normal group included individuals without either osteoporosis or sarcopenia. The sarcopenia group consisted of those with sarcopenia but without osteoporosis. The osteoporosis group included participants with osteoporosis but not sarcopenia. Finally, the osteosarcopenia group comprised individuals with both sarcopenia and osteoporosis.
Measurements of intrinsic capacity
IC framework was originally proposed by the World Health Organization [7]. Two experienced and rigorously trained nurses administered the IC assessment to all participants using a face-to-face questionnaire. The IC score was calculated based on the following method: each domain is assigned a maximum of 2 points. The total IC score is then obtained by summing the scores across the five domains (maximum score = 10), with higher scores reflecting stronger intrinsic capacity in older adults [13]. We used the Mini-Mental State Examination (MMSE) to evaluate cognitive status, with scores ≥ 27 assigned 2 points, ≥ 10 assigned 1 point, otherwise 0 points [14]. For evaluating vitality, the Mini Nutritional Assessment Short-Form (MNA-SF) was employed, with scores ≥ 12 indicating good nutritional health and earning 2 points, ≥ 8 assigned 1 points, otherwise 0 points [15]. Locomotion was assessed using the Short Physical Performance Battery (SPPB) [16]. Scores of ≥ 10 were awarded 2 points, while scores below 3 received 0 points, otherwise 1 point. Depressive symptoms were identified using the Geriatric Depression Scale-15 (GDS-15), with scores ≤ 4 indicating good psychological status and earning 2 points, ≥ 9 assigned 0 points, otherwise 1 point [17]. Sensory function was assessed based on auditory and visual impairments: 0 points for both impaired, 1 point for either vision or hearing impaired, and 2 points for both normal.
Data collection
Data were collected for the following variables: age, sex, education, body mass index (BMI), number of medications, Clinical Frailty Scale (CFS), blood pressure. Smoking status was assessed based on whether they currently smoke. Blood pressure was measured three times using a digital sphygmomanometer (Omron, Japan). Laboratory data were extracted from electronic medical records, including fasting plasma glucose (FPG), HbA1c, total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), albumin, 25-OH-VD3, and creatinine.
Statistical analysis
The statistical presentation of variables was conducted as follows: Normally distributed continuous data were summarized as mean values with standard deviations, whereas non-normally distributed continuous measures were reported using median and interquartile range values. Categorical data were presented as frequency percentages. Analytical comparisons employed different approaches based on variable types – continuous variables were analyzed through one-way ANOVA while categorical differences were examined using Pearson’s Chi-square tests. To address the issue of multiple comparisons, we employed the Bonferroni correction method. To investigate potential associations between IC domains and osteoporosis, sarcopenia, osteosarcopenia, multivariate logistic regression models were developed to assess relationships.
The analytical framework incorporated two hierarchical regression models to address potential confounding variables. The base model (model 1) maintained unadjusted estimates, with successive adjustments introduced in progressive specifications: Model 2 incorporated sex, education, smoking, and number of medications. Subpopulation analyses were conducted through stratification by seven key characteristics: age tertiles, biological sex, smoking status, drinking status, body mass index (BMI) categories, stroke event history, and diabetic status. Between-strata heterogeneity was evaluated using Breslow-Day tests for interaction, with effect estimates expressed as adjusted odds ratios (ORs) with 95% confidence intervals. All statistical computations were executed using R statistical environment (version 4.4.2), with hypothesis testing conducted at α = 0.05 threshold for statistical significance (two-tailed).