Prevalence and risk factors for metabolic syndrome in schizophrenia, schizoaffective, and bipolar disorder
Bibliographic record
Abstract
BACKGROUND: Metabolic Syndrome (MetS) is a risk for developing cardiovascular diseases and its prevalence is especially high in psychiatric patients. To date, there is limited data from the United Arab Emirates (UAE) on the prevalence of MetS. Therefore, we aimed to investigate its prevalence and possible risk factors in a large sample of psychiatric patients in the UAE. METHODS: A cross-sectional study was conducted at Al-Ain Hospital, in Al-Ain City, UAE. We collected demographic and clinical data on patients diagnosed with schizophrenia, schizoaffective, and bipolar affective disorder in the period between January 2017 and December 2020. This included their secondary diagnosis (psychiatric or medical), vital signs (heart rate, systolic and diastolic blood pressure, Body Mass Index [BMI]), metabolic parameters (fasting blood glucose, cholesterol, triglycerides, low-density lipoprotein, high-density lipoproteins), and prescribed medications. We used the American Association of Clinical Endocrinology (AACE) criteria to diagnose MetS. RESULTS: = 250) had MetS with no statistical difference between the three groups. Fasting blood glucose levels and abnormally elevated triglycerides were significant predictors for MetS. CONCLUSION: Our study found that around one in three patients had MetS irrespective of the three diagnoses. Some variables were significant predictors for MetS. Our findings were consistent with other studies and warrant the need for regular screening and management of abnormal metabolic parameters.
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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 | 0.000 | 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".