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Record W97829983 · doi:10.1139/jpn.0648

Metabolic syndrome: relevance to antidepressant treatment

2006· article· en· W97829983 on OpenAlexaffvenueabout
Pratap Chokka, Manuel E. Tancer, Vikram K. Yeragani

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2006
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInsulin resistanceMedicineMetabolic syndromeDiabetes mellitusInternal medicineDyslipidemiaHyperinsulinemiaObesityType 2 diabetesPopulationAntidepressantEndocrinology

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.286
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations34
Published2006
Admission routes3
Has abstractyes

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