Association between abdominal obesity and some selected non-communicable diseases among adults in Calabar metropolis, Cross River State, Nigeria
Bibliographic record
Abstract
Background: Fat accumulation around the abdomen is an important consideration in obesity studies and management. Generalized obesity indices alone without considering abdominal obesity are not sufficient in the study and treatment of obesity and the related escalating cases of preventable non-communicable diseases globally. Objective: This study assessed the relationship between abdominal obesity and some selected non-communicable diseases such as generalized obesity, hypertension, and type 2 diabetes among adults in Calabar Metropolis. Materials and methods: A multistage sampling technique was used to select 500 participants (20-70 years). The cross-sectional descriptive study adapted the WHO STEPwise questionnaire for surveillance of non-communicable diseases. Data were analyzed using descriptive and inferential statistics including frequency, percentages, chi-square, correlation, and logistic regression; significant differences were established at p<0.05. Results: 51% of the participants were single while 44.8% were married. It was found that 27.8% and 56.9% of males, as well as 31.1% and 41.1% of females who were diagnosed with hypertension, had high and very high waist circumferences respectively compared to fewer males and females who were undiagnosed with hypertension. There was a correlation between waist circumference and diastolic blood pressure (r= -0.102, p=0.022) as well as with random blood glucose (r=0.123, p=0.006). The males had higher odds to develop hypertension (OR=2.754; CI=1.776-4.270) and fewer odds to develop abdominal obesity compared to the females. Conclusion: There were strong associations between hypertension, type 2 diabetes, generalized obesity, and waist circumference of the participants. More individuals with high waist circumferences had hypertension. Men were less likely to have abdominal obesity in relation to high blood pressure compared to women. There should be appropriate interventions for the individuals who were already diagnosed with the various NCDs while those at risk of developing the disorders should be identified within the population for proper counseling.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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 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".