Abdominal Fat from Spine Dual-Energy X-Ray Absorptiometry and Risk for Subsequent Diabetes
Bibliographic record
Abstract
CONTEXT: Abdominal obesity is a major risk factor for diabetes. Dual-energy x-ray absorptiometry (DXA) of the lumbar spine provides an index of abdominal fat. OBJECTIVE: Our objective was to examine the hypothesis that DXA-derived abdominal fat measurement in women undergoing osteoporosis investigation predicts risk for subsequent diagnosis of diabetes. DESIGN: This historical cohort study was derived from the Manitoba Bone Density Program Database for the Province of Manitoba, Canada. SETTING AND PATIENTS: 30,252 nondiabetic women aged 40 yr and older were referred for baseline osteoporosis assessment with DXA between January 1990 and March 2007. MAIN OUTCOME MEASURES: Each woman's longitudinal provincial health service record was assessed for the presence of diabetes diagnosis codes after DXA testing. RESULTS: During 5.2 + or - 2.6 yr of observation, 1252 (4.1%) women met the case definition for diabetes. A greater proportion of abdominal fat from spine DXA was strongly related to subsequent diabetes diagnosis in models adjusted for age, body mass index, and other comorbidities. Those in the highest quintile had 3.56 (95% confidence interval = 2.67-4.75) times the risk for subsequent diabetes diagnosis compared with those in the lowest (reference) quintile. Fat from hip DXA was not predictive of subsequent diabetes after adjustment for the same variables (1.00, 95% confidence interval = 0.79-1.26). CONCLUSIONS: Predictive information about diabetes risk can be obtained from spine DXA scans performed for osteoporosis risk assessment. This is consistent with evidence linking abdominal fat with insulin resistance and the metabolic syndrome.
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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 | 0.000 | 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 | 0.000 | 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".