Hepatic steatosis is associated with anthropometry, cardio‐metabolic disease risk, sex, age and urbanisation, but not with ethnicity in adult Kenyans
Bibliographic record
Abstract
OBJECTIVE: We aimed to determine the associations of non-alcoholic fatty liver disease (NAFLD) with cardio-metabolic risk factors for diabetes in adult Kenyans. METHODS: A cross-sectional study was undertaken among rural and urban Kenyans of different ethnic origin. Ultrasonography scanning (USS) methods were used for the assessment of hepatic fat accumulation for NAFLD assessment and abdominal fat distribution, and simple anthropometry measurements were performed. All participants underwent a 2-h oral glucose tolerance test, and biochemical, haemodynamic and lifestyle data were obtained. Multivariate logistic regression analyses were used to assess sex, age, residency and ethnic differences in the association between NAFLD and various metabolic parameters. RESULTS: In total, 743 individuals (59.1% women) with a mean age of 38.0 (range 18-68) years participated in the study. Overall, 118 individuals (15.9%) had NAFLD, of whom 94.1% had mild steatosis. Age >40 years was significantly associated with having NAFLD compared with <30 years of age with no difference found in NAFLD between ethnic groups (Luo, Kamba, Maasai). All body composition and clinical measurements were associated with NAFLD (p < 0.045 for OR). CONCLUSION: Finding lower odds for NAFLD in men was unexpected, as was the lack of differences in NAFLD among the ethnic groups, while higher odds for NAFLD with increasing age and in urban vs. rural populations was expected. Especially the sex-specific results warrant further studies in black African populations on biology of body composition for having NAFLD, and whether this translates into insulin resistance and higher risk of diabetes and consequently cardiovascular disease in black African women.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".