Depression, obesity, and smoking were independently associated with inadequate glycemic control in patients with type 1 diabetes
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to explore the associations between inadequate glycemic control of diabetes and psychological, anthropometric, and lifestyle variables in a population-based cohort of type 1 diabetes patients. DESIGN: Cross-sectional study. METHODS: In this study, 292 patients with type 1 diabetes, aged 1859 years, participated. psychological data were assessed by self-report instruments: Hospital Anxiety and Depression Scale and Toronto Alexithymia Scale-20. Anthropometrics, blood analyses, data from medical records, and data from the Swedish National Diabetes Registry were collected. RESULTS: Self-reported depression (adjusted odds ratio (AOR) 4.8), obesity (AOR 4.3), and smoking (AOR 3.0) were independently associated with inadequate glycemic control of diabetes (HbA1c>8.6%). Gender-stratified analyses showed that self-reported depression (AOR 19.8) and obesity (AOR 7.0) in women and smoking in men (AOR 4.2) were associated with HbA1c>8.6%. Alexithymia, antidepressant medication, and physical inactivity were associated with HbA1c>8.6% only in bivariate analyses. Alexithymia, self-rated anxiety, physical inactivity, and absence of abdominal obesity were associated with self-reported depression. CONCLUSIONS: Depression was the only psychological factor independently associated with HbA1c>8.6%. The association was of comparable importance as obesity and smoking, well-known risk factors for inadequate glycemic control and diabetes complications. The association between depression and HbA1c>8.6% was particularly strong for women. Alexithymia, which is a relatively stable personality trait, was associated with depression. In the future care of patients with diabetes, psychological aspects should be considered alongside anthropometrics and lifestyle factors in order to achieve the goals for HbA1c.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".