Metabolic phenotyping with computed tomography deep learning for metabolic syndrome, osteoporosis and sarcopenia predicts mortality in adults.

BACKGROUND: Computed tomography (CT) body compositions reflect age-related metabolic derangements. We aimed to develop a multi-outcome deep learning model using CT multi-level body composition parameters to detect metabolic syndrome (MS), osteoporosis and sarcopenia by identifying metabolic clusters simultaneously. We also investigated the prognostic value of metabolic phenotyping by CT model for long-term mortality. METHODS: The derivation set (n = 516; 75% train set, 25% internal test set) was constructed using age- and sex-stratified random sampling from two community-based cohorts. Data from participants in the individual health assessment programme (n = 380) were used as the external test set 1. Semi-automatic quantification of body compositions at multiple levels of abdominal CT scans was performed to train a multi-layer perceptron (MLP)-based multi-label classification model. External test set 2 to test the prognostic value of the model output for mortality was built using data from individuals who underwent abdominal CT in a tertiary-level institution (n = 10 141). RESULTS: The mean ages of the derivation and external sets were 62.8 and 59.7 years, respectively, without difference in sex distribution (women 50%) or body mass index (BMI; 23.9 kg/m2). Skeletal muscle density (SMD) and bone density (BD) showed a more linear decrement across age than skeletal muscle area. Alternatively, an increase in visceral fat area (VFA) was observed in both men and women. Hierarchical clustering based on multi-level CT body composition parameters revealed three distinctive phenotype clusters: normal, MS and osteosarcopenia clusters. The L3 CT-parameter-based model, with or without clinical variables (age, sex and BMI), outperformed clinical model predictions of all outcomes (area under the receiver operating characteristic curve: MS, 0.76 vs. 0.55; osteoporosis, 0.90 vs. 0.79; sarcopenia, 0.85 vs. 0.81 in external test set 1; P < 0.05 for all). VFA contributed the most to the MS predictions, whereas SMD, BD and subcutaneous fat area were features of high importance for detecting osteoporosis and sarcopenia. In external test set 2 (mean age 63.5 years, women 79%; median follow-up 4.9 years), a total of 907 individuals (8.9%) died during follow-up. Among model-predicted metabolic phenotypes, sarcopenia alone (adjusted hazard ratio [aHR] 1.55), MS + sarcopenia (aHR 1.65), osteoporosis + sarcopenia (aHR 1.83) and all three combined (aHR 1.87) remained robust predictors of mortality after adjustment for age, sex and comorbidities. CONCLUSIONS: A CT body composition-based MLP model detected MS, osteoporosis and sarcopenia simultaneously in community-dwelling and hospitalized adults. Metabolic phenotypes predicted by the CT MLP model were associated with long-term mortality, independent of covariates.

Low skull bone density is associated with poor motor prognosis in women with Parkinson's disease.

Parkinson's disease (PD) and osteoporosis are degenerative diseases that have shared pathomechanisms. To investigate the associations of skull bone density with nigrostriatal dopaminergic degeneration and longitudinal motor prognosis in female patients with PD. We analyzed the data of 260 drug-naïve female PD patients aged ≥50 years old who were followed-up for ≥3 years after their first visit to the clinic with baseline dopamine transporter (DAT) imaging. We measured skull bone density as a surrogate marker for systemic bone loss by calculating the Hounsfield unit (HU) in computed tomography scans. A Cox proportional hazard model was built to compare the rates of levodopa-induced dyskinesia (LID) or wearing-off according to skull HU. Longitudinal changes in levodopa-equivalent dose (LED) during a 3-year follow-up were assessed using a linear mixed model. A lower skull HU was associated with lower baseline DAT availability in striatal subregions; however, this relationship was not significant after adjusting for age, disease duration, body mass index, and white matter hyperintensities. After adjusting for confounding factors, a lower skull HU was significantly associated with an increased risk of LID development (hazard ratio = 1.660 per 1 standard deviation decrease, p = 0.007) and wearing-off (hazard ratio = 1.613, p = 0.016) in younger (<67 years) but not in older patients. Furthermore, a lower skull HU was associated with a steeper increase in LED during follow-up in younger patients only (ß = -21.99, p < 0.001). This study suggests that baseline skull bone density would be closely linked to motor prognosis in drug naïve women with PD.