Evidence map›Paper›PMID 39302927›Full record

ArticlePloS one2024

The risk of dyslipidemia on PLHIV associated with different antiretroviral regimens in Huzhou.

Yanan Wang, Zhongrong Yang, Jing Li, Zhenqian Wu, Xiaoqi Liu, Hui Wang, Yuxin Chen, Ziyi Wang, Zhaowei Tong, Xiaofeng Li and 3 more

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Yanan WangHuzhou Center for Disease Control and Prevention, Huzhou, Zhejiang, China.ORCID 0000-0001-9697-2645
Zhongrong YangHuzhou Center for Disease Control and Prevention, Huzhou, Zhejiang, China.
Jing LiHuzhou Center for Disease Control and Prevention, Huzhou, Zhejiang, China.
Zhenqian WuHuzhou Center for Disease Control and Prevention, Huzhou, Zhejiang, China.
Xiaoqi LiuHuzhou Center for Disease Control and Prevention, Huzhou, Zhejiang, China.
Hui WangDivision of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Yuxin ChenDivision of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Ziyi WangDivision of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Zhaowei TongDepartment of Infectious Diseases, Huzhou Central Hospital, Huzhou, Zhejiang, China.
Xiaofeng LiDepartment of Infectious Diseases, Huzhou Central Hospital, Huzhou, Zhejiang, China.
Feilin RenHuzhou Center for Disease Control and Prevention, Huzhou, Zhejiang, China.
Meihua JinHuzhou Center for Disease Control and Prevention, Huzhou, Zhejiang, China.
Guangyun MaoDivision of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDyslipidemia is increasingly common in people living with HIV (PLHIV), thereby increasing the risk of cardiovascular events and diminishing the quality of life for these individuals. The study of blood lipid metabolism of PLHIV has great clinical significance in predicting the risk of cardiovascular disease. Therefore, this study aims to examine the blood lipid metabolism status of HIV-infected patients in Huzhou before and after receiving highly active antiretroviral therapy (HAART) and to explore the impact of different HAART regimens on dyslipidemia.

methodPLHIV confirmed in Huzhou from June 2010 to June 2022 was included. The baseline characteristics and clinical data during the follow-up period were collected, including some blood lipid indicators (total cholesterol and triglycerides) and HAART regimens. A multivariate logistic regression model and the generalized estimating equation model were used to analyze the independent effects of treatment regimens on the risk of dyslipidemia.

resultThe overall prevalence of dyslipidemia among PLHIV after HAART was 70.11%. PLHIV receiving lamivudine (3TC) + efavirenz (EFV) + zidovudine (AZT) had a higher prevalence of dyslipidemia compared to those receiving 3TC+EFV+tenofovir disoproxil fumarate (TDF). In a logistic analysis adjusted for important covariates such as BMI, age, diabetes status, etc., we found that the risks of dyslipidemia were higher with 3TC+EFV+AZT (dyslipidemia: odds ratio [OR] = 2.09, 95% confidence interval [Cl]: 1.28-3.41; TG ≥1.7: OR = 2.40, 95%Cl:1.50-3.84) than with 3TC+EFV+TDF. Furthermore, on PLHIV that was matched 1:1 by the HAART regimens, the results of the generalized estimation equation again showed that 3TC+EFV+AZT (TG ≥1.7: OR = 1.84, 95%Cl: 1.10-3.07) is higher for the risk of marginal elevations of TG than 3TC+EFV+TDF.

conclusionThe prevalence of dyslipidemia varies according to different antiretroviral regimens. Using both horizontal and longitudinal data, we have repeatedly demonstrated that AZT has a more adverse effect on blood lipids than TDF from two perspectives. Therefore, we recommend caution in using the 3TC+EFV+AZT regimen for people at clinical risk of co-occurring cardiovascular disease.

Indexed as

Antiretroviral Therapy, Highly ActiveBenzoxazinesDyslipidemiasHIV InfectionsLamivudineAdultAlkynesAnti-HIV AgentsChinaCyclopropanesFemaleHumansMaleMiddle AgedPrevalenceRisk FactorsAlkynesAnti-HIV AgentsBenzoxazinesCyclopropanesefavirenzLamivudineTenofovirZidovudine

Identifiers

PMID39302927
PMCPMC11414983

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.