Literature DB >> 31987713

Biomarkers panels can predict fatigue, depression and pain in persons living with HIV: A pilot study.

Julie A Zuñiga1, Michelle L Harrison2, Ashley Henneghan3, Alexandra A García3, Shelli Kesler4.   

Abstract

BACKGROUND: Persons living with HIV experience high symptom burden that can negatively impact medication adherence, work productivity, and quality of life. Symptoms are highly subjective, which can lead to under- or improper treatment. The purpose of this exploratory study was to examine relationships between circulating biomarkers representative of inflammatory, coagulation, and vascular function pathways and prevalent HIV symptoms. SETTING AND SAMPLE: Adults >18 years who were diagnosed with HIV and spoke English for this cross-sectional study were recruited from community clinics and organizations.
METHODS: Symptom burden was measured with the HIV Symptom Index; depression with the Patient Health Questionnaire. Human multiplex kits were used to determine serum concentrations of select biomarkers representing inflammatory, coagulation, and vascular function pathways. The biomarkers were included as features in machine learning models to determine which biomarkers predicted the most prevalent HIV symptoms (fatigue and muscle/joint pain) and the symptom of depression.
RESULTS: Participants (N = 32) were representative of the local population of people with HIV, being mostly Black (54.4%) and male (60.6%). Depression was predicted by age, gender, glucose, hemoglobin A1c, and inflammation. Muscle/joint pain was predicted by adiponectin, C-reactive protein, and serum amyloid A (SAA). Fatigue was predicted by adiponectin, SAA, and soluble interleukin-1 receptor type II (sIL-1RII).
CONCLUSION: Biomarker clusters can be a tool to monitor symptoms. Adding an objective measure to subjective patient experiences could improve management and monitoring of symptoms. Defining a biomarker cluster for the objective assessment of HIV symptoms warrants further investigation; however, the presence of comorbid conditions needs to be controlled.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Depression; Fatigue; HIV; Inflammation; Machine learning; Pain; Random Forest; Symptoms; Vascular

Mesh:

Substances:

Year:  2019        PMID: 31987713     DOI: 10.1016/j.apnr.2019.151224

Source DB:  PubMed          Journal:  Appl Nurs Res        ISSN: 0897-1897            Impact factor:   2.257


  6 in total

1.  Using Smartphone App Use and Lagged-Ensemble Machine Learning for the Prediction of Work Fatigue and Boredom.

Authors:  Damien Lekkas; George D Price; Nicholas C Jacobson
Journal:  Comput Human Behav       Date:  2021-09-24

Review 2.  Soluble Biomarkers of Cognition and Depression in Adults with HIV Infection in the Combination Therapy Era.

Authors:  Albert M Anderson; Qing Ma; Scott L Letendre; Jennifer Iudicello
Journal:  Curr HIV/AIDS Rep       Date:  2021-11-15       Impact factor: 5.071

Review 3.  Through the Looking-Glass: Psychoneuroimmunology and the Microbiome-Gut-Brain Axis in the Modern Antiretroviral Therapy Era.

Authors:  Adam W Carrico; Emily M Cherenack; Leah H Rubin; Roger McIntosh; Delaram Ghanooni; Jennifer V Chavez; Nichole R Klatt; Robert H Paul
Journal:  Psychosom Med       Date:  2022-08-28       Impact factor: 3.864

Review 4.  Neuroinflammation in HIV-associated depression: evidence and future perspectives.

Authors:  Arish Mudra Rakshasa-Loots; Heather C Whalley; Jaime H Vera; Simon R Cox
Journal:  Mol Psychiatry       Date:  2022-05-26       Impact factor: 13.437

5.  Fatigue is associated with worse cognitive and everyday functioning in older persons with HIV.

Authors:  Laura M Campbell; Ni Sun-Suslow; Anne Heaton; Robert K Heaton; Ronald J Ellis; David J Moore; Raeanne C Moore
Journal:  AIDS       Date:  2022-01-06       Impact factor: 4.632

Review 6.  Global Systematic Review of Common Mental Health Disorders in Adults Living with HIV.

Authors:  Jacqueline Hoare; Tatum Sevenoaks; Bulelwa Mtukushe; Taryn Williams; Sarah Heany; Nicole Phillips
Journal:  Curr HIV/AIDS Rep       Date:  2021-11-18       Impact factor: 5.071

  6 in total

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