Tracking symptom dynamics in people living with HIV on different treatment: a longitudinal cross-lagged panel network analysis
Objective To characterize dynamic interrelationships among physical, cognitive, and psychological symptoms in people living with HIV and identify bridge symptoms between subgroups receiving distinct antiretroviral therapy regimens.Methods A longitudinal design was employed in this study. A total of...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
|
| Series: | Annals of Medicine |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/07853890.2025.2541090 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1839608213485387776 |
|---|---|
| author | Meilian Xie Xiaoyu Liu Yanping Yu Zhiyun Zhang Li Zhang Jieli Zhang Dongxia Wu |
| author_facet | Meilian Xie Xiaoyu Liu Yanping Yu Zhiyun Zhang Li Zhang Jieli Zhang Dongxia Wu |
| author_sort | Meilian Xie |
| collection | DOAJ |
| description | Objective To characterize dynamic interrelationships among physical, cognitive, and psychological symptoms in people living with HIV and identify bridge symptoms between subgroups receiving distinct antiretroviral therapy regimens.Methods A longitudinal design was employed in this study. A total of 676 individuals diagnosed with HIV filled out the Self-Report Symptom Checklist (HIV-SRSC) at baseline, 3-month, and 6-month follow-ups in the clinic. We evaluated bridge symptoms—those that connect various communities—within longitudinal networks utilizing cross-lagged panel network analyses (CLPN).Results The longitudinal networks presented differences and similarities between the Traditional Medication Regimen Group (TMR group) and the Novel Medication Regimen Group (NMR group). The expected influence of bridging symptoms in the CLPN predominantly centers on the cognitive symptom clusters, both in the TMR andNMR groups. However, the TMR group has stronger directional connections compared to the NMR group. ‘Memory loss’ (COGS3) demonstrates a bridging influence 1.62 (T1→T2) versus 1.51 (T2→T3) in the NMR group, and ‘Having difficulty in concentrating’ (COGS1) demonstrates a bridging influence 1.65 (T1→T2) versus 1.47 (T2→T3) in the TMR group. Nevertheless, physical symptoms, such as clusters related to gastrointestinal issues (PHYS12, PHYS13 and PHYS14), showed stable connections across both T1→T2 and T2→T3.Conclusion While TMR amplified cognitive-to-psychological symptom cascades, NMR fragmented cross-domain connectivity. These findings necessitate regimen-personalized interventions that preemptively target cognitive symptoms in TMR recipients while stabilizing bridge symptoms in NMR patients. Furthermore, cognitive dysfunction is a high-priority therapeutic target across all ART classes, which provides mechanistic evidence for ART-specific symptom management frameworks. |
| format | Article |
| id | doaj-art-cb7a6f77e7e3475f972d0a112e8f0096 |
| institution | Matheson Library |
| issn | 0785-3890 1365-2060 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Annals of Medicine |
| spelling | doaj-art-cb7a6f77e7e3475f972d0a112e8f00962025-07-31T14:57:24ZengTaylor & Francis GroupAnnals of Medicine0785-38901365-20602025-12-0157110.1080/07853890.2025.2541090Tracking symptom dynamics in people living with HIV on different treatment: a longitudinal cross-lagged panel network analysisMeilian Xie0Xiaoyu Liu1Yanping Yu2Zhiyun Zhang3Li Zhang4Jieli Zhang5Dongxia Wu6Nursing Management Department, Beijing Ditan Hospital Capital Medical University, Beijing, ChinaSchool of Statistics, Capital University of Economics and Business, Beijing, ChinaBeijing Home of Red Ribbon, Beijing Ditan Hospital Capital Medical University, Beijing, ChinaNursing Management Department, Beijing Ditan Hospital Capital Medical University, Beijing, ChinaInfection and Immunization Clinic, Beijing Ditan Hospital Capital Medical University, Beijing, ChinaDepartment of Infectious Disease Medicine, Fifth Medical Center of Chinese, PLA General Hospital, Beijing, ChinaDepartment of Infection and Immunology, Beijing Youan Hospital Capital Medical University, Beijing, ChinaObjective To characterize dynamic interrelationships among physical, cognitive, and psychological symptoms in people living with HIV and identify bridge symptoms between subgroups receiving distinct antiretroviral therapy regimens.Methods A longitudinal design was employed in this study. A total of 676 individuals diagnosed with HIV filled out the Self-Report Symptom Checklist (HIV-SRSC) at baseline, 3-month, and 6-month follow-ups in the clinic. We evaluated bridge symptoms—those that connect various communities—within longitudinal networks utilizing cross-lagged panel network analyses (CLPN).Results The longitudinal networks presented differences and similarities between the Traditional Medication Regimen Group (TMR group) and the Novel Medication Regimen Group (NMR group). The expected influence of bridging symptoms in the CLPN predominantly centers on the cognitive symptom clusters, both in the TMR andNMR groups. However, the TMR group has stronger directional connections compared to the NMR group. ‘Memory loss’ (COGS3) demonstrates a bridging influence 1.62 (T1→T2) versus 1.51 (T2→T3) in the NMR group, and ‘Having difficulty in concentrating’ (COGS1) demonstrates a bridging influence 1.65 (T1→T2) versus 1.47 (T2→T3) in the TMR group. Nevertheless, physical symptoms, such as clusters related to gastrointestinal issues (PHYS12, PHYS13 and PHYS14), showed stable connections across both T1→T2 and T2→T3.Conclusion While TMR amplified cognitive-to-psychological symptom cascades, NMR fragmented cross-domain connectivity. These findings necessitate regimen-personalized interventions that preemptively target cognitive symptoms in TMR recipients while stabilizing bridge symptoms in NMR patients. Furthermore, cognitive dysfunction is a high-priority therapeutic target across all ART classes, which provides mechanistic evidence for ART-specific symptom management frameworks.https://www.tandfonline.com/doi/10.1080/07853890.2025.2541090HIV/AIDSsymptomsnetwork analysislongitudinal cohort |
| spellingShingle | Meilian Xie Xiaoyu Liu Yanping Yu Zhiyun Zhang Li Zhang Jieli Zhang Dongxia Wu Tracking symptom dynamics in people living with HIV on different treatment: a longitudinal cross-lagged panel network analysis Annals of Medicine HIV/AIDS symptoms network analysis longitudinal cohort |
| title | Tracking symptom dynamics in people living with HIV on different treatment: a longitudinal cross-lagged panel network analysis |
| title_full | Tracking symptom dynamics in people living with HIV on different treatment: a longitudinal cross-lagged panel network analysis |
| title_fullStr | Tracking symptom dynamics in people living with HIV on different treatment: a longitudinal cross-lagged panel network analysis |
| title_full_unstemmed | Tracking symptom dynamics in people living with HIV on different treatment: a longitudinal cross-lagged panel network analysis |
| title_short | Tracking symptom dynamics in people living with HIV on different treatment: a longitudinal cross-lagged panel network analysis |
| title_sort | tracking symptom dynamics in people living with hiv on different treatment a longitudinal cross lagged panel network analysis |
| topic | HIV/AIDS symptoms network analysis longitudinal cohort |
| url | https://www.tandfonline.com/doi/10.1080/07853890.2025.2541090 |
| work_keys_str_mv | AT meilianxie trackingsymptomdynamicsinpeoplelivingwithhivondifferenttreatmentalongitudinalcrosslaggedpanelnetworkanalysis AT xiaoyuliu trackingsymptomdynamicsinpeoplelivingwithhivondifferenttreatmentalongitudinalcrosslaggedpanelnetworkanalysis AT yanpingyu trackingsymptomdynamicsinpeoplelivingwithhivondifferenttreatmentalongitudinalcrosslaggedpanelnetworkanalysis AT zhiyunzhang trackingsymptomdynamicsinpeoplelivingwithhivondifferenttreatmentalongitudinalcrosslaggedpanelnetworkanalysis AT lizhang trackingsymptomdynamicsinpeoplelivingwithhivondifferenttreatmentalongitudinalcrosslaggedpanelnetworkanalysis AT jielizhang trackingsymptomdynamicsinpeoplelivingwithhivondifferenttreatmentalongitudinalcrosslaggedpanelnetworkanalysis AT dongxiawu trackingsymptomdynamicsinpeoplelivingwithhivondifferenttreatmentalongitudinalcrosslaggedpanelnetworkanalysis |