Metabolic syndrome: relevance to antidepressant treatment
Bibliographic record
Abstract
Antidepressant medications are often associated with weight gain and metabolic abnormalities in vulnerable patients. It is well known that obesity, insulin resistance and diabetes are associated with a large number of deaths all over the world. Patients with such psychiatric disorders as depression, anxiety and psychosis are at higher risk for cardiac mortality compared with the general population. This is compounded by the fact that many psychiatric drugs, such as antidepressants and antipsychotics, are associated with weight gain. Some drugs, such as tricyclic antidepressants, can cause insulin resistance and can increase serum lipids independent of their affect on weight. The original description of metabolic syndrome (MBS) comprised obesity, insulin resistance, hypertension, impaired glucose tolerance or diabetes, hyperinsulinemia and dyslipidemia, characterized by elevated triglycerides and low high-density lipoprotein (HDL) concentrations. All of the above are risk factors for atherosclerosis and thus pose a significant risk for coronary heart disease. Obesity/overweight and insulin resistance also present a significant risk for developing type-II diabetes. The risks for coronary heart disease and diabetes with metabolic syndrome are greater than those for simple obesity alone; thus an understanding of the pathogenesis of heart disease and diabetes and a rational approach to their therapy are of prime importance. There is a substantial amount of literature linking weight gain to the subsequent development of MBS related to psychotropic medication. Treatment with tricyclic antidepressants (TCAs), amitriptyline and doxepin can cause a substantial increase in weight—one of the main factors leading to treatment noncompliance, as shown in several previous studies. The newer antidepressant drugs, such as serotonin reuptake inhibitors, can lead to a small decrease in weight in the short-term and an increase during long-term treatment in some of these patients. Several factors need to be considered in this context. In some patients, depression itself is associated with weight gain. This is compounded by treatment side effects, which may include a decrease in basal metabolic rate in addition to an increase in appetite and carbohydrate craving. An increase in weight is associated with type-II diabetes and possible insulin resistance. Depression is reportedly associated with hypertension and atherosclerotic changes in some patients. Depression can also be associated with other comorbid disorders, such as anxiety, which may make these patients even more vulnerable to cardiac mortality. Recent literature has shown that some of the noninvasive measures, such as beat-beat heart rate and QT-interval variability on the surface electrocardiogram (ECG) provide valuable noninvasive measures to assess cardiac autonomic function. These measures are also useful in assessing prognosis in patients with cardiac illness and concomitant affective disorders. A decrease in vagal function and a relative increase in sympathetic function can be associated with the development of atherosclerosis and hypertension. In this regard, it is important to note that TCAs can be more cardiotoxic, and some of the serotonin reuptake inhibitors can have a neutral or a positive effect. For example, sertraline and paroxetine appear to be less toxic than TCAs. In addition to choosing an appropriate antidepressant, the clinician has to check the lipid profiles and blood glucose of these patients on a regular basis and, most importantly, use an appropriate antihypertensive medication in patients who need such treatment. This includes careful consideration of angiotensin converting enzyme (ACE) inhibitors, such as enalapril and lisinopril and their protective effect on type-II diabetes in some patients. Most importantly, regular physical exercise, which has several positive effects on weight gain, serum cholesterol levels, blood pressure and type-II diabetes, needs to be emphasized as a component of treatment. It will also be valuable to study the effects of the newer antidepressant drugs on cardiac autonomic function and also to understand the pharmacology of the antihypertensive drugs, which improve cardiovascular function in general. Pratap Chokka, MD Department of Psychiatry, University of Alberta, Edmonton, Alta. Manuel Tancer, MD Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, Mich. Vikram K. Yeragani, MD Department of Psychiatry, University of Alberta, Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Department of Cardiology, M. S. Ramaiah Memorial Hospital, Bangalore, India
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".