Development of an algorithm to predict appendicular lean mass (ALM) from regional spine and hip DXA scans
| dc.contributor.author | Rossum, Krista | |
| dc.contributor.examiningcommittee | Leslie, William (Internal Medicine) | en_US |
| dc.contributor.examiningcommittee | Lix, Lisa (Community Health Sciences) | en_US |
| dc.contributor.supervisor | Tangri, Navdeep | |
| dc.contributor.supervisor | Bohm, Clara | |
| dc.date.accessioned | 2023-01-10T17:01:18Z | |
| dc.date.available | 2023-01-10T17:01:18Z | |
| dc.date.copyright | 2023-01-04 | |
| dc.date.issued | 2023-01-04 | |
| dc.date.submitted | 2023-01-05T03:31:19Z | en_US |
| dc.degree.discipline | Community Health Sciences | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | Background: Sarcopenia is characterized by progressive muscle loss with reduced physical function and/or reduced muscle strength. Sarcopenia is common in older individuals and negatively impacts quality of life. It is associated with several adverse health outcomes, including but not limited to falls, reduced mobility, and increased mortality. All current operational definitions of sarcopenia include a measurement of muscle mass, most often Dual-energy X-ray Absorptiometry (DXA)-derived appendicular lean mass (ALM). ALM can only be derived from whole-body DXA scans. However, whole-body DXA scans are performed less commonly than hip and spine DXA scans as part of clinical care. The primary objective of our study was to develop an algorithm to predict ALM from regional spine and hip DXA scan. The exploratory objective of this study was to determine if self-reported history of falls is associated with sarcopenia, as determined using predicted ALM. Methods: We performed a retrospective cross-sectional study using a subset of patients from the Manitoba BMD clinical database who had whole-body DXA scans and hip and spine DXA scans at the same visit. We developed the algorithm using backward stepwise multiple linear regression and report the proportion of variation explained (i.e., R2), adjusted for the covariates age, sex, height, weight, spine and hip fat fraction, spine and hip tissue thickness. We internally validated the algorithm using the bootstrap method. Mean bootstrap parameter estimates were used as the final equation. We evaluated the relationship between sarcopenia, defined as low predicted-ALM/height2, and self-reported falls using logistic regression; odds ratios (OR), area under the curve (AUC) and 95% confidence intervals (CI) are reported. Results: There were 678 patients with both whole-body and hip and spine DXA scans included in our dataset. Mean age was 52.6 (standard deviation [SD] 21.0) and 77.0% identified as female. Mean ALM was 18.0 kg (SD 5.0 kg). The final predictive model included sex, age, log of weight, spine average fat fraction and hip average fat fraction; it had an adjusted R2 of 0.891 (95% CI 0.876 – 0.906). Sarcopenia, defined as low predicted-ALM/ht2, was not associated with increased odds of falls (OR 0.92, 95% CI 0.31 – 2.74, p=0.88). Conclusion: Our evidence supports that hip and spine DXA scans can be used to predict ALM, but that low ALM index is not associated with increased odds of falls. | en_US |
| dc.description.note | February 2023 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/37079 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Appendicular Lean Mass | en_US |
| dc.subject | DXA | en_US |
| dc.subject | Sarcopenia | en_US |
| dc.title | Development of an algorithm to predict appendicular lean mass (ALM) from regional spine and hip DXA scans | en_US |
| dc.type | master thesis | en_US |
| local.subject.manitoba | yes | en_US |