Waist Circumference Is the Best Index for Obesity-Related Cardiovascular Disease Risk in Individuals with Spinal Cord Injury
Bibliographic record
Abstract
Obesity is an important identifier of cardiovascular disease (CVD) risk, but is challenging to determine accurately in individuals with spinal cord injury (SCI). Body mass index (BMI) is used worldwide as a simple indicator of obesity, but is difficult to measure in individuals with SCI. Furthermore, standard BMI cutoffs underestimate obesity in this population. Therefore, we aimed to identify the best marker of obesity in individuals with SCI, considering both practicality, and ability to detect adiposity and CVD risk. Five anthropometric measures were evaluated: BMI; waist circumference (WC); waist-to-height ratio (WHtR); waist-to-hip ratio; and neck circumference. We evaluated relationships between these measures and abdominal and total body-fat percentage, seven cardiovascular metabolic risk factors (fasting insulin, glucose, glucose tolerance, triglycerides, high-density lipoprotein, low-density lipoprotein, and total cholesterol), and the Framingham risk score. BMI, WC, and WHtR were correlated with abdominal fat percentage. WC and WHtR were correlated with five metabolic risk factors as well as the Framingham risk score. WC is a more practical measure for an SCI population. The optimal cutoff for identifying adverse CVD risk in individuals with SCI was identified as WC ≥94 cm, with 100% sensitivity and 79% specificity. We propose that WC is a simple, more sensitive alternative to BMI in this population that is easy to use in multiple settings. The cutoff provides a simple tool to predict adverse CVD risk profiles that can be used to guide risk management, as well as as a practical aid for individuals with SCI to maintain a healthy body composition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".