Opportunistic measurement of sagittal abdominal diameter with bone densitometry predicts death and cardiovascular events
Bibliographic record
Abstract
Abstract Supine sagittal abdominal diameter (SAD), also known as abdominal height, has been proposed as a simple measure for assessing abdominal adiposity. We aimed to determine whether SAD from DXA performed for osteoporosis assessment predicts major adverse cardiovascular events (MACEs) using the population-based DXA registry for the Province of Manitoba, Canada. The study population comprised 72 974 individuals aged 40 yr and older with baseline DXA assessment between February 1999 and March 2018. Incident MACE (composite of all-cause mortality, acute myocardial infarction [MI], non-hemorrhagic stroke) was ascertained from linked healthcare databases. During mean 8.4 yr follow-up (611 862 person-years), 14 457 (18.8%) individuals experienced incident MACE. Risk stratification was greatest with SAD/weight ratio, with area under the curve (AUC) for MACE and its components ranging from 0.582 for acute MI to 0.620 for death (all p < .001), all significantly better than with BMI (p < .001). In multivariable-adjusted models, each SD increase in SAD/weight was associated with increased risk for MACE (hazards ratio [HR] 1.20, 95% CI 1.18–1.22), death (HR 1.22, 95% CI 1.20–1.25), acute MI (HR 1.19, 95% CI 1.14–1.24), and stroke (HR 1.17, 95% CI 1.12–1.22). A linear gradient was seen across SAD/weight quintiles (all p-trend < .001), with adjusted HR for MACE 1.61 (95% CI 1.50–1.72) for highest vs lowest quintile. Results were similar when further adjusted for BMI in non-obese and obese individuals (p-interaction for obesity = .141) and in both women and men (p-interaction for sex = .471). In conclusion, SAD measured opportunistically at the time of DXA testing is predictive of death and major cardiovascular events in individuals undergoing osteoporosis assessment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".