Would sickle cell trait influence the metabolic control in sub‐Saharan individuals with type 2 diabetes?
Bibliographic record
Abstract
AIMS: To determine the prevalence and effects of sickle cell trait on metabolic control in a Cameroonian diabetic population in a tertiary care setup. METHODS: This was a cross-sectional study involving 73 consecutive outpatients with Type 2 diabetes recruited from the Yaounde National Diabetes and Obesity Centre. Sickle cell trait status was based on haemoglobin electrophoresis. Metabolic control was assessed by plasma glucose and HbA(1c), and comparisons made between participants with and without sickle cell trait, with adjustment for confounders through linear regressions models. RESULTS: The prevalence of sickle cell trait was 19%, without sex difference, and comparable with figures in individuals without diabetes in this setting. Participants with diabetes and sickle cell trait were older than the non-trait participants (66 vs. 58 years, P = 0.02). Otherwise, clinical and biological profile including indicators of metabolic control were similarly distributed between trait and non-trait participants (all P >0.08). After adjustment for confounders, sickle cell trait was unrelated to fasting glucose (β = 0.02; 95% confidence interval -37.68-43.30) and HbA(1c) (β = -0.03, 95% confidence interval -1.18-0.93), and did not affect the relationship between the two markers of diabetes control (β = -0.03, 95% confidence interval -1.18-0.89). CONCLUSIONS: Sickle cell trait was as frequent in this subgroup of patients with Type 2 diabetes as in the general population, suggesting no specific association with diabetes. It does not affect the metabolic control of diabetes. However, how this translates into long-term outcome needs to be fully elucidated in this setting, with an increasing population with both sickle cell trait and diabetes mellitus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".