Determinants of bone quality: heritability versus lifestyle factors
Bibliographic record
Abstract
INTRODUCTION One in three women in Canada will suffer an osteoporotic fracture in their lifetime, having serious consequences on quality of life and the Canadian Health Care system. Osteoporosis is a multifactorial disease compromised of genetic, environmental and lifestyle influences. Studies suggest heritability of skeletal traits in parental-offspring pairs is apparent by adolescence or early adulthood. Approximately 40-62% of bone mineral density (BMD) may be determined by genetics [1], but calcium intake and high-impact physical activity have also been shown to increase bone density [2][3]. Therefore, apart from inherited factors, such lifestyle influences may be significant contributors to bone quality [4]. To date, the research on familial bone health and fracture risk has been acquired using dual x-Ray absorptiometry (DXA). However, this two-dimensional measurement technique cannot account for the structural properties of bone, and over half of fractures occur in women above the osteoporosis threshold for DXA [5]. Recently, high-resolution peripheral quantitative computed tomography (HR-pQCT) has been used in conjunction with finite element analysis (FEA) to estimate the bone’s resistance to fracture. These techniques make it possible to explore the familial association of bone microarchitecture and bone strength. Therefore, the purpose of this study was to assess the similarities of lifestyle and bone architecture between mothers and daughters using both DXA and HR-pQCT to better understand the interactions of these elements, and to compare the two scanning modalities. METHODS The study included 29 healthy pairs of mature mothers and daughters. HR-pQCT (Scanco Medical) scans at the radius and tibia and DXA (Discovery W, Hologic) scans of the hip and spine were obtained for each participant, and analyzed to determine areal and volumetric BMD, geometric and microstructural indices. FEA was performed to calculate an estimate of bone strength. Information on diet, exercise and health history was collected through questionnaires and a total body DXA scan assessed body composition. T-tests, Chi square and ANCOVAs were performed to compare groups, and ICC was used to quality similarities between mothers and daughters. Linear regression assessed the variance between bone quality, heredity and lifestyle factors. RESULTS Initial differences in bone parameters (DXA and HR-pQCT) between mothers and daughters disappear after adjusting for the effects of age, height and weight (p > 0.05). ICC reveal low to moderate correlations between mothers and daughters with similar relationships observed by DXA (r = 0.37 to 0.38, p <0.05) and HR-pQCT (r = 0.36 to 0.51, p<0.05). ICC were similar between the radius (r = 0.374 to 0.402) and tibia (r = 0.37 to 0.51). Cortical porosity (r = 0.51) and area (r = 0.43) at the tibia were closely related between mothers and daughters, so too was percent body fat (r = 0.56). Total calcium intake and the history of physical activity did not correlate. Following linear regression, 75% of tibia cortical area can be explained by lean mass, height, age and physical activity (p<0.05). DISCUSSION AND CONCLUSIONS Overall, there was a low to moderate correlation between mothers and daughters, with cortical bone at the tibia being most similar. We found mothers and daughters have comparable body composition; however, calcium intake and physical activity did not correlate. Our preliminary analysis reveal 75% of cortical bone area can be explained by lifestyle factors. This suggests differences in lifestyle may have an impact on the daughter’s bones, separate from their mother’s influence. However, there are many factors that were not accounted for at the moment, and this merits further investigation. In conclusion, DXA and HR-pQCT gave comparable correlations in bone factors between mothers and daughters irrespective of skeletal site, and while heredity relationships existed between mothers and daughters, lifestyle factors appear to be stronger influences in tibia bone quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".