M84. METFORMIN FOR EARLY CO-MORBID PREDIABETES OR DIABETES IN SCHIZOPHRENIA SPECTRUM DISORDERS: A DOUBLE BLIND RANDOMIZED PILOT STUDY
Bibliographic record
Abstract
Abstract Background Patients with severe mental illness (SMI) loose 15–20 years of life due to cardiovascular disease. Much of the metabolic risk, including high rates of type 2 diabetes (T2D) is accrued early on in the illness, highlighting the need for early intervention strategies to target modifiable cardiovascular risk factors. Beyond cardiovascular (CV) risk, metabolic complications have wide-ranging detrimental effects on cognitive performance, medication compliance, and quality of life. There is however an astounding paucity of studies in SMI examining metabolic interventions outside of weight loss. Furthermore, patients with SMI are typically systematically excluded from trials investigating anti-diabetic agents resulting in lack of evidence to guide treatment. Methods Thirty participants with schizophrenia spectrum disorders and co-morbid prediabetes or type 2 diabetes were randomly assigned, in a double-blind fashion to 1500mg/ day of metformin or placebo (2:1 ratio; n=21 metformin and n=9 placebo). Patients had to be overweight or obese, within 5 years of psychosis onset or under the age of 40, and receiving a stable dose of antipsychotics. The primary outcome measures were improvements in glycemia (HbA1c, fasting glucose), and insulin resistance index (Matsuda-derived from glucose tolerance tests and the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR)). Secondary outcome measures included changes in weight, fat mass (MRI quantification of hepatic and visceral fat), improvements in cognition, and hippocampal volume (MRI). Data were analyzed using mixed-models methods, and intention to treat analysis. Results Twenty-two patients (n=14 metformin; n=8 placebo) completed the 4-month trial. The metformin group had a significant decrease over time in the HOMA-IR (p=0.043), and fasting blood glucose (p=0.007) vs. placebo. There were no differences between treatment groups in the Matsuda index or HBA1c or any secondary outcome measures. Interestingly, weight loss in both groups correlated significantly with decreases in subcutaneous, but not visceral adipose tissue measured by MRI. Controlling for baseline BMI and fasting blood glucose did not change any study findings. Exploratory correlations between change in metabolic indices and change in clinical and cognitive parameters did not reveal any significant associations. Discussion Independently of weight loss, metformin is effective in improving dysglycemia and insulin sensitivity in a young, severely mentally ill population at very high risk for early CV mortality. Our preliminary findings, however, fail to find an effect of metformin on weight reduction or tissue specific adiposity measures (which in themselves represent key CV risk factors). Notably, patients in this study had overt glucose dysregulation, a sample routinely excluded in studies examining weight loss interventions in SMI. Moreover, meta-analyses of metformin studies in SMI note greater efficacy early in the illness; possibly weight loss effects are blunted once patients develop prediabetes/T2D. It is also possible that this subgroup of patients may benefit from alternate or combined antidiabetic/obesity therapies. In conclusion, our findings support glucose lowering effects of metformin in SMI patients with early onset prediabetes/T2D, but raise the issue of treatment initiation before the development of overt glucose dysregulation to obtain maximum benefits on adiposity reduction. Given the extremely high rates of T2D in patients with SMI, future adequately powered trials are required to examine metabolic interventions in relation to CV risk factors, and also other related domains such as cognitive function in this highly under-researched population.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".