Does nocturnal blood pressure fall in people of African and South Asian descent differ from that in European white populations? A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To assess whether nocturnal blood pressure fall in people of African (Black) and South Asian descent differs from that of the European origin white populations (White). METHODS: A systematic literature review was carried out using Medline 1966-2003 and Embase 1980-2003, and citations from references. The meta-analysis was performed using Cochrane review manager software (RevMan version 4.2; The Cochrane Collaboration, Oxford, UK). RESULTS: Seventeen studies were identified; 11 studies from the USA, one from the USA and Canada, and six studies from the United Kingdom. The mean percentage systolic blood pressure (SBP) nocturnal fall was below 10% (non-dipping) in 10 of 17 studies (59%) and the diastolic blood pressure (DBP) nocturnal fall was below 10% in four of 16 studies (25%) in Blacks compared with four of 17 studies (24%) in SBP and none in DBP nocturnal falls in Whites. Compared with Whites, Blacks had a significantly lower mean percentage nocturnal fall; the overall weighted mean difference in SBP was -3.07 (95% confidence interval, -3.81, -2.33; P < 0.00001) and in DBP was -2.98 (95% confidence interval, -3.97, -2.00; P < 0.00001). Two studies on South Asians showed a higher SBP but a similar mean DBP nocturnal fall compared with Whites. CONCLUSION: Smaller nocturnal blood pressure falls and a higher prevalence of non-dipping may contribute to the higher levels of hypertension complications seen in Black people. No such phenomenon was seen in South Asians but more research is needed to explore their higher stroke mortality.
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".