Highly Active Antiretroviral Therapy-Associated Metabolic Syndrome and Lipodystrophy: Pathophysiology and Current Therapeutic Interventions
Bibliographic record
Abstract
The use of highly active antiretroviral therapy (HAART) has extremely enhanced the clinical outcome of HIV disease with a decrease in mortality and morbidity. However, the inclusion of protease inhibitors (PIs) and nucleoside reverse transcriptase inhibitors (tNRTIs) has strongly been linked to the development of metabolic abnormalities and lipodystrophy. Lipodystrophy is defined by the loss of peripheral subcutaneous fat and central adiposity, mainly in the abdomen, breast and dorsocervical region. These disorders are reported to be cosmetically distressing and socially stigmatizing to many patients leading to decreased adherence to antiretroviral therapy. Metabolic syndrome precedes lipodystrophy leading to increased risk of diabetes and cardiovascular diseases. With a shifted trajectory of HIV/AIDS morbidity from immunodeficiency and opportunistic infections to metabolic complications, clinical management of these patients has therefore become more complex. Currently there are no evidence-based standard guidelines for the management of HIV-associated lipodystrophy. Several pharmacological interventions such as using anti-diabetic, anti-dyslipidemic drugs or hormone replacement therapy have been tried to effectively improve metabolic syndrome and lipodystrophy but have been hampered by low efficacy, drug interactions, or unwanted side-effects. Non-pharmacological interventions including surgical manipulations, dietary and lifestyle modifications have also been tried with limited success. This review focuses on the proposed mechanisms involved in the development of metabolic syndrome and lipodystrophy, and highlights suggested potential therapeutic interventions to prevent lipodystrophy associated with HAART. J Endocrinol Metab. 2017;7(4):103-116 doi: https://doi.org/10.14740/jem364w
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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) | 0.001 | 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".