Impact of appendicular skeletal muscle mass on effect of intensive blood pressure control
Bibliographic record
Abstract
Abstract Background Appendicular skeletal muscle mass (ASM) is associated with cardiovascular diseases (CVDs) and chronic kidney diseases (CKDs). However, whether low ASM affects the efficacy of intensive systolic blood pressure control is currently uncertain. Purpose To examine the impact of low ASM on the effects of intensive SBP control on cardiovascular and kidney outcomes. Methods Data from the Systolic Blood Pressure Intervention Trial (SPRINT) and the Action to Control Cardiovascular Risk in Diabetes Blood Pressure (ACCORD-BP) trials was used. The primary outcome was a composite of myocardial infarction, acute coronary syndrome without myocardial infarction, stroke, heart failure, and cardiovascular death. The prespecified incident CKD was defined as a >30% decrease in estimated glomerular filtration rate to a value <60 mL/min/1.73 m2. Results A total of 14,017 patients were included in this study, 2203 (15.7%) of whom had low ASM. Over a median follow-up of 3.26 years, 1,032 primary outcomes and 460 incident CKD were observed. Patients with low ASM had a significantly higher risk of primary outcome and CKD. Intensive SBP control effects on cardiovascular outcomes were not significantly different on a relative scale or absolute scale. But low ASM significantly amplified the increased risk of CKD associated with intensive SBP control, on an absolute scale (P for interaction = 0.04). Conclusions Low ASM is one of the markers of increased risk of CVD and CKD, and it did not affect the cardiovascular benefits of intensive treatment, but significantly amplified the increased risk of CKD associated with intensive SBP control.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".