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The risk of dyslipidemia on PLHIV associated with different antiretroviral regimens in Huzhou
The risk of dyslipidemia on PLHIV associated with different antiretroviral regimens in Huzhou
https://orcid.org/0000-0001-9697-2645
Wang Yanan Conceptualization Data curation Formal analysis Methodology Writing – original draft 1 2 3
Yang Zhongrong Conceptualization Funding acquisition Supervision Writing – review & editing 1
Li Jing Conceptualization Funding acquisition Supervision Writing – review & editing 1
Wu Zhenqian Data curation Investigation 1
Liu Xiaoqi Conceptualization Funding acquisition Supervision Writing – review & editing 1
Wang Hui Data curation Formal analysis Methodology 2 3
Chen Yuxin Data curation Formal analysis Methodology 2 3
Wang Ziyi Data curation Formal analysis Methodology 2 3
Tong Zhaowei Data curation Investigation 4
Li Xiaofeng Data curation Investigation 4
Ren Feilin Conceptualization Funding acquisition Supervision Writing – review & editing 1
Jin Meihua Conceptualization Funding acquisition Supervision Writing – review & editing 1 *
Mao Guangyun Conceptualization Data curation Formal analysis Methodology Supervision Writing – review & editing 2 3 5 *
1 Huzhou Center for Disease Control and Prevention, Huzhou, Zhejiang, China
2 Division of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
3 Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
4 Department of Infectious Diseases, Huzhou Central Hospital, Huzhou, Zhejiang, China
5 National Clinical Research Center for Ocular Diseases, Wenzhou, Zhejiang, China
Bekolo Cavin Epie Editor
University of Dschang, CAMEROON
Competing Interests: The authors have declared that no competing interests exist.

* E-mail: huzhoujmh6821@163.com (MJ); mgy@wmu.edu.cn (GM)
20 9 2024
2024
19 9 e030546128 2 2023
30 5 2024
© 2024 Wang et al
2024
Wang et al
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Background

Dyslipidemia 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.

Method

PLHIV 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.

Result

The 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.

Conclusion

The 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.

the Huzhou Medical Key Supporting Discipline (Epidemiology) Jin Meihua http://dx.doi.org/10.13039/501100017531 Medical and Health Research Project of Zhejiang Province 2022KY369 王 亚楠 Huzhou science and technology research plan project 2022GYB13 Liu Xiaoqi the Key Laboratory of Emergency detection for Public Health of Huzhou Yang Zhongrong This work was supported by Medical and Health Research Project of Zhejiang Province (2022KY369 to JMH), Huzhou science and technology research plan project (2022GYB13 to LXQ), the Huzhou Medical Key Supporting Discipline (Epidemiology, to JMH), and the Key Laboratory of Emergency detection for Public Health of Huzhou (to JMH). Data AvailabilityData cannot be shared publicly because of the data involves information of people living with HIV, which is a sensitive topic in Chinese culture. Data are available from the Ethics Committee of Huzhou center for disease control and prevention (contact via Zhongrong Yang with email: yzhr91@126.com) for researchers who meet the criteria for access to confidential data.
Data Availability

Data cannot be shared publicly because of the data involves information of people living with HIV, which is a sensitive topic in Chinese culture. Data are available from the Ethics Committee of Huzhou center for disease control and prevention (contact via Zhongrong Yang with email: yzhr91@126.com) for researchers who meet the criteria for access to confidential data.
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pmcIntroduction

The widespread use of HAART has led to an extended life expectancy for PLHIV. However, the burden of non-AIDS-related diseases, such as dyslipidemia, type 2 diabetes, and cardiovascular disease (CVD), steadily increasing annually [1]. CVD complications have become a significant cause of death among HIV-infected patients [2]. Previous studies have demonstrated that people newly diagnosed with HIV have a higher risk of cardiovascular disease compared to the general population [3, 4]. Following prolonged exposure to ART, the drugs are likely to further increase the risk of CVD by exacerbating mitochondrial toxicity accumulation, enhancing lipid biosynthesis function, and reducing the liver’s capacity to clear lipids, among other mechanisms [5]. Furthermore, with exposure to different combinations of antiretroviral drugs, the risk may vary widely [6].

In developing countries, first-line HAART regimens typically comprises a combination of at least three drugs, primarily two nucleoside reverse transcriptase inhibitors (NRTI) and one non-nucleoside reverse transcriptase inhibitors (NNRTI) or integrase inhibitor. The selection of the appropriate drug combination is contingent on a range of factors, including HIV viral load, CD4 and CD8 counts, drug interactions and more [7]. At present, the preferred combination of free first-line treatment for adults in China is 3TC+EFV+TDF. Other free alternative first-line treatment regimens include 3TC+AZT+EFV, 3TC+AZT+nevirapine (NVP) and TDF+3TC/ NVP/emtricitabine (FTC). As an important modifiable risk factor for PLHIV atherosclerotic cardiovascular disease (ASCVD) [8], dyslipidemia also influences the selection and implementation of HAART.

However, the current research data on abnormal blood lipid metabolism in HIV-infected patients primarily originates from abroad. China’s first-line ART program differs from those of developed countries, and the sociodemographic characteristics are also distinct from those of foreign nations. The existing domestic studies have a short follow-up period, a limited sample size, and the changes in blood lipid metabolism are not fully elucidated. This study used comprehensive clinical research data on HIV/AIDS patients in Huzhou from July 2005 to June 2022 to estimate the prevalence of dyslipidemia among PLHIV before and after long-term ART regimens. The objective was to assess the trend of dyslipidemia and evaluate the association between different HAART regimens and dyslipidemia in PLHIV. Early intervention for disorders of lipid metabolism can potentially prevent possible CVD events, thereby providing clinical decision-making guidance throughout the entire HIV management process.

Materials and methods

Study participants

This study was a retrospective cohort study involving 1,876 PLHIV who sought treatment from the China Disease Prevention and Control Information System (CDPCIS) in the Huzhou area, China. The inclusion criteria were as follows: over 18 years of age; having no received HAART; diagnosed or identified from July, 2005 to June, 2022; living in the Huzhou area containing temporary residents. The exclusion criteria were having no baseline and at least one follow-up measurements of blood lipids; presence of liver disease (baseline aminotransferases are elevated more than 3 times the upper limit or bilirubin levels are elevated more than 2.5 times above the upper limit) or chronic kidney disease (Serum creatinine levels exceed the upper limit by 1.5 times). Since the number of participants receiving other ART regimens except for 3TC+EFV+AZT and 3TC+EFV+TDF was small and scattered, these individuals were also excluded from the final analysis to facilitate the interpretation of the results. The data analyses of the study were conducted from July to December 2022. We first accessed the patient data in July 2022.

Study design

The overview of the study workflow is shown in the supporting information of S1 Fig. In the primary analysis, we enrolled 532 participants, of which 391 were on an initial ART regimen of 3TC+EFV+TDF,while 141 were on 3TC+EFV+AZT at the baseline. These patients were guaranteed to have complete baseline data and at least one follow-up visit data. In the secondary analysis, we further selected 476 participants from the 532 PLHIV who did not change their HAART regimen at any follow-up. Due to the irregularfollow-up time for these patients, we fixed four time points at 1-year intervals (baseline, 12 months, 24 months, 36months, using a +/- 3 months window). To enhance the accuracy of the dynamic analysis of lipids, we retained 376 PLHIV who had at least two follow-up lipid data. Subsequently, PLHIV receiving two HAART regimens were matched by age, BMI, CD4 count, CD4/CD8 ratio, and baseline lipid level at a ratio of 1:1, using a propensity score matching approach.Ultimately, 77 patients were included in each of the 3TC+EFV+TDF and 3TC+EFV+AZT two regimens, and their longitudinal lipid data were analysed.

Covariates

The baseline data of the participants, serving as covariates, were extracted from the CDPCIS, encompassingdemographic and clinicalcharacteristics, as well as laboratory results. The baseline was defined as the date when the participants first sought care for HAART at the local health system. The demographic and clinical data, collected through a face-to-face interview and physical examination, included age, gender, weight, height, initial HAART regimens, route of transmission, education level, WHO clinical stage, and others. BMI was calculated as the weight (kg) divided by the square of height (m2). Information on laboratory tests, strictly measured by local AIDS-designated treatment hospitals, included total cholesterol (TC), triglyceride (TG), CD4 cell count (CD4), CD8 cell count (CD8), serum hemoglobin, platelet (PLT), white blood cell (WBC), alanine aminotransferase (ALT), aspartate transaminase (AST), serum creatinine, total bilirubin (TBIL), and fasting plasma glucose (FPG). The diagnosis criterion fora history of diabetes mellitus in PLHIV wasa baseline FPG of ≥ 7.0 mmol/L or having received treatment for diabetes.

Definition of dyslipidemia

Dyslipidemia was determined according to the Guidelines for the Prevention and Treatment of Dyslipidemia in Adults in China (2016 Revision).Participants were categorized as having dyslipidemia if they exhibited one or more of the following conditions: TC ≥ 5.2mmol/L or TG ≥ 1.7mmol/L or low-density lipoprotein cholesterol (LDL-C)≥ 3.37mmol/L or high-density lipoprotein cholesterol (HDL-C) ≤ 1.04mmol/L. The survey lacked indicators for both HDL_C and LDL_C, therefore, dyslipidemia was considered to be present as long as there was a marginal elevation in lipid profiles during the follow-up period in order to avoid underestimation the outcomes.

Ethical approval

The study was approved by the Research Ethics Committee of Huzhou Center for Disease Control and Prevention. Written informed consent was given by all participants. All procedures performed were carried out in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.

Statistical analysis

We described continuous variables using mean ± standard deviation (SD) or median and interquartile range (IQR), and the Student t-test or Wilcoxon rank sum test was used to compare differences. Categorical variables were presented as proportions, and chi-square or Fisher’s exact tests were used for their comparisons. The prevalence of dyslipidemia and its components in PLHIV with varying characteristics were caculated. Missing values were imputed using a 5-fold multiple imputation approach, and a sensitivity analysis was conducted on the comparison of pre- and post-imputation to validate the stability of the imputations (the supporting information of S1 Table).

We used two analytical methods to investigate how the probability of experiencing dyslipidemia depends on major HAART regimens. Firstly, a multivariate logistic regression model was employed to determine the statistical association of two, adjusting for potential confounders, including age, BMI, ALT, AST, PLT, CD4/CD8 and history of diabetes (p-value <0.1in the univariate analysis) at baseline. Then, to address the concern that lipid profiles may change over time and the measurement at a single time point measurement may not be sufficient, we examined the association between lipid status over time and two HAART regimens that never change using a generalized estimating equation (GEE) model. Here, an exchange correlation structure was used as the GEE model’s working correlation matrix.

All data management and statistical analyses were conducted using R Version 4.2.0 (Copyright© 2022 The R Foundation for Statistical Computing). All tests were two-side and P ≤ 0.05 was set as the significant level.

Results

Characteristics of participants

A total of 532 PLHIV (458 males and 74 females) aged 18–82 years were included in our study. Of these, 391 received the HAART regimen of 3TC+EFV+TDF at baseline, while 373 developed dyslipidemia during follow-up. The characteristics of the study participants, according to their dyslipidemia status, are shown in Table 1. Compared to the non-dyslipidemia participants, those with dyslipidemia were more likely to be older, have a higher mean level of BMI, and a history of diabetes. They also tended to have liver dysfunction, with higher TC, TG, and PLT levels at baseline.

10.1371/journal.pone.0305461.t001 Table 1 Basic characteristics of participants.

Baseline variables	Non-dyslipidemia(N = 159)	Dyslipidemia(N = 373)	P-value	
Age, years	35.0(25.0,48.0)	40.0(28.0,53.0)	0.029	
Gender			0.181	
    Male	132(83.0)	326(87.4)		
    Female	27(17.0)	47(12.6)		
BMI, kg/m2	21.5±2.7	22.4±2.9	0.003	
Initial HAART regimens			0.001	
    3TC+EFV+TDF	132(83.0)	259(69.4)		
    3TC+EFV+AZT	27(17.0)	114(30.6)		
Infection pathway			0.160	
    Heterosexual transmission	92(57.9)	245(65.7)		
    MSM	64(40.3)	125(33.5)		
    Others	3(1.9)	3(0.8)		
WHO clinical stage			0.319	
    Ⅰ or Ⅱ	157(98.7)	361(96.8)		
    Ⅲ or Ⅳ	2(1.3)	12(3.2)		
Education level			0.261	
    Illiterate or elementary school	45(28.3)	114(30.6)		
    Junior or high or secondary school	78(49.1)	197(52.8)		
    College and above	36(22.6)	62(16.6)		
With diabetes			0.009	
    No	153(97.5)	337(91.1)		
    Yes	4(2.5)	33(8.9)		
CD4, cells/L	270.0(180.0,400.0)	285.8(180.3,435.0)	0.379	
CD4/CD8	0.4(0.2,0.5)	0.4(0.2,0.5)	0.209	
TC, mmol/L	3.8(3.4,4.2)	4.3(3.8,4.8)	<0.001	
TG, mmol/L	1.0(0.7,1.3)	1.5(1.1,2.1)	<0.001	
WBC, 109/L	5.3(4.2,6.6)	5.3(4.3,6.4)	0.725	
Platelet, 109/L	178.0(148.0,218.0)	195.0(159.0,234.0)	0.007	
Hemoglobin, g/L	146.0(133.0,154.0)	149.0(134.0,158.0)	0.153	
ALT, U/L	21.0(15.0,30.0)	22.2(15.7,33.0)	0.098	
AST, U/L	21.0(18.0,25.1)	22.0(19.0,29.0)	0.006	
Creatinine, mmol/L	72.0(64.0,82.2)	73.0(64.9,82.5)	0.393	
TBIL, mmol/L	10.5(6.8,14.6)	9.8(7.3,13.6)	0.420	
Abbreviations:BMI: Body mass index; MSM: men who have sex with men; CD4: CD4+ T-lymphocyte count; CD8: CD8+ T-lymphocyte count; TC: total cholesterol; TG: triglycerides; WBC: White blood cell; ALT: Alanine aminotransferase; AST: Aspartate transaminase; TBIL: Total bilirubin; 3TC: lamivudine; EFV: efavirenz; TDF: tenofovir disoproxil fumarate; AZT: Zidovudine.

Prevalence of dyslipidemia

After HAART, the lipid profiles of PLHIV, including TC and TG, and the prevalence of dyslipidemia, further increased, as shown in the supporting information of S2 Fig. Table 2 presented the prevalence of dyslipidemia among various subpopulations. The total prevalence of dyslipidemia reached 70.11%, with the prevalence of marginal elevated TC and marginal elevated TG being 36.28% and 62.78%, respectively. The prevalence of dyslipidemia was consistent across both sexes, but we observed a higher prevalence of marginal TG elevation in men compared to women. The prevalence of dyslipidemia generally increased with increasing BMI, particularly at the marginal elevated TG level. Participants with diabetes had a higher prevalence of dyslipidemia and abnormal lipid levels. The prevalence of dyslipidemia varied among different HAART regimens. In comparison to PLHIV who received 3TC+EFV+AZT, those receiving 3TC+EFV+TDF had a lower prevalence of dyslipidemia (80.9% vs 66.2%, p-value < 0.01), and a lower prevalence of marginal elevated TG (75.9% vs 58.1%, p-value < 0.01).

10.1371/journal.pone.0305461.t002 Table 2 Prevalence of dyslipidemia by subpopulation.

Baseline variables	N	Dyslipidemia(%)	TC≥5.3mmol/L (%)	TG≥1.7mmol/L (%)	
Total	532	373(70.11)	193(36.28)	334(62.78)	
Gender					
    Male	458	326(71.2)	163(35.6)	298(65.1)	
    Female	74	47(63.5)	30(40.5)	36(48.6)	
				**	
Age					
    18–29	160	103(64.4)	55(34.4)	93(58.1)	
    30–39	115	82(71.3)	36(31.3)	77(67.0)	
    40–49	101	69(68.3)	35(34.7)	65(64.4)	
    ≥50	156	119(76.3)	67(42.9)	99(63.5)	
BMI					
    <18.5	44	28(63.6)	17(38.6)	26(59.1)	
    18.5–24	271	192(70.8)	99(36.5)	167(61.6)	
    ≥24	124	101(81.5)	53(42.7)	95(76.6)	
		*		**	
Initial ART regimens					
    3TC+TDF+EFV	391	259(66.2)	133(34.0)	227(58.1)	
    3TC+AZT+EFV	141	114(80.9)	60(42.6)	107(75.9)	
		**		**	
With diabetes					
    Yes	37	33(89.2)	15(40.5)	32(86.5)	
    No	490	337(68.8)	176(35.9)	300(61.2)	
		**		**	
CD4					
    <200	115	75(65.2)	37(32.2)	62(53.9)	
    200–350	131	82(62.6)	40(30.5)	74(56.5)	
    ≥350	141	102(72.3)	53(37.6)	93(66.0)	
CD4/CD8					
    <0.4	215	137(63.7)	64(29.8)	124(57.7)	
    0.4–1.0	152	110(72.4)	61(40.1)	95(62.5)	
    ≥1.0	20	12(60.0)	5(25.0)	10(50.0)	
WHO clinical stage					
    Ⅰor Ⅱ	518	361(69.7)	190(36.7)	322(62.2)	
    Ⅲ or Ⅳ	14	12(85.7)	3(21.4)	12(85.7)	
*: P-value <0.05

**: P-value < 0.01

Abbreviations: BMI: Body mass index; CD4: CD4+ T-lymphocyte count; CD8: CD8+T-lymphocyte count; 3TC: lamivudine; EFV: efavirenz; TDF: tenofovir disoproxil fumarate; AZT: Zidovudine.

Evolution of dyslipidemia in PLHIV receiving different ART

486 PLHIV, none of them changed HAART during the follow-up period. The baseline rates of dyslipodemia, marginal elevated TC, and marginal elevated TG were similar in those receiving 3TC+EFV+AZT (dyslipodemia: 38.18%, TC ≥ 5.3 mmol/L: 11.82, TG ≥ 1.7mmol/L: 32.73%) and those receiving 3TC+EFV+TDF (dyslipodemia: 37.77%, TC ≥ 5.3 mmol/L: 12.5, TG ≥ 1.7mmol/L: 29.79%), as shown in the supporting information of S2 Table. After a prolonged period of HAART, the prevalence of dyslipodemia increased in these PLHIV, regardless of the regimen. However, the changes indyslipodemia varied among PLHIV receiving different regimens, as evident in Fig 1. The rate of dyslipidemia in PLHIV receiving 3TC+EFV+AZT increased more dramatically, particularly in TC, with a percentage difference of 269.2%. Although the proportion of censoring at 3-year follow-up of these 486 patients was substantial, it is evident from the supporting information of S3 Fig that PLHIV who received 3TC+EFV+AZT consistently had a higher prevalence of dyslipodemia than those receiving the other regimen throughout the entire follow-up period. Within one year after HAART initiation, the prevalence of dyslipidemia in patients receiving 3TC+EFV+TDF even showed a gradual decreasing trend. The number of participants in the cohort at various follow-up points stratified by regimen is presented in the supporting information of S3 Table.

10.1371/journal.pone.0305461.g001 Fig 1 Changes in the prevalence of dyslipidemia in PLHIV receiving different HAART regimens.

Association of different combination antiretroviral therapies with dyslipidemia

Multivariate analysis revealed that the risk of dyslipidemia varied according to different HAART regimens. As shown in Table 3, the proportion of dyslipidemia in PLHIV receiving 3TC+EFV+AZT was obviously higher than in those receiving 3TC+EFV+TDF (80.9% vs 66.2%). The same trend was observed for marginal elevated TC (42.6% vs 34.0%) and marginal elevated TG (75.9% vs 58.1%). Compared to patients treated with 3TC+EFV+TDF, the risk of dyslipidemia for those under the treatment of a combination antiretroviral drug consisting of 3TC, EFV and AZT was significantly increased by 1.09 (OR = 2.09, 95%CI: 1.28–3.41), after adjusting for BMI, age, CD4/CD8, ALT, AST, platelet, and history of diabetes. Meanwhile, after adjusting for the corresponding covariates,theOR (95%CI) of an elevated TG level and an elevated TC level for those receiving 3TC+EFV+AZT were 2.40 (1.50–3.84) and 1.39 (0.93–2.09), respectively, when compared with patients receiving the other regimen. After considering the repeated measurements of blood lipids in the GEE model, PLHIV receiving 3TC+EFV+AZT consistently positive association with marginal elevated TG risk, with an OR value of 1.84 (95%CI: 1.10–3.07, the supporting information of S4 Table, the supporting information of S4 Fig). These results proved from multiple dimensions that the adverse effect on blood lipid metabolism of PLHIV who use the 3TC+EFV+AZT regimen is significantly greater than that of 3TC+EFV+TDF.

10.1371/journal.pone.0305461.t003 Table 3 Association of two initial HAART regimens with different lipid types.

Variables	N	#(%)	Crude	Adjusted	
OR (95%CI)	p-value	OR (95%CI)	p-value	
Dyslipidemia							
3TC +EFV+TDF	391	259(66.2)	Ref	Ref	Ref	Ref	
3TC +EFV+AZT	141	114(80.9)	2.15(1.35,3.44)	<0.01	2.09(1.28,3.41)	<0.01	
TC ≥ 5.3							
3TC +EFV+TDF	391	133(34.0)	Ref	Ref	Ref	Ref	
3TC +EFV+AZT	141	60(42.6)	1.44(0.97,2.13)	0.07	1.39(0.93,2.09)	0.11	
TG ≥ 1.7							
3TC +EFV+TDF	391	227(58.1)	Ref	Ref	Ref	Ref	
3TC +EFV+AZT	141	107(75.9)	2.27(1.47,3.51)	<0.01	2.40(1.50,3.84)	<0.01	
Note: Dyslipidemia: adjusted for BMI, age, glu, cd4/cd8, alt, ast and plt; TC ≥ 5.3: adjusted for BMI, age, glu, route of transmission and education; TG ≥ 1.7: adjusted for bmi, age, sex, cd4, glu, scr, ast and platelet.

Discussion

This retrospective cohort study is one of the few reports in China that focuses on evaluating the prevalence of dyslipidemia and the impact of various drug combinations on dyslipidemia in PLHIV who are receiving the two most commonly used first-line free antiretroviral regimens.

The results of our study revealed that after antiretroviral treatment, the prevalence of dyslipidemia among HIV-infected individuals was 70.11%, which was significantly higher than the rate before HAART. Furthermore, the occurrence of increased TG levels was more common. The prevalence of hyperlipidemia among PLHIV across various studies ranges from 28% to 80%, with hypertriglyceridemia being the most common abnormality [9]. This wide range is understandable, given the intrinsic differences in study populations and the evolution of HIV drug treatments. A meta-analysis of 55 observational studies has reported that HIV-infected patients receiving antiretroviral therapy have significantly higher concentrations of total cholesterol and triglycerides compared to patients who never receive HAART [10], which is consistent with our findings.

Previous studies have reported that dyslipidemia in PLHIV is easily influenced by numerous factors. In addition to traditional factors such as age and genetic background, HIV infection-related chronic inflammation and abnormal immune activation, vascular endothelial cell dysfunction, and co-infection are also key factors affecting blood lipid metabolism [11–13]. Therefore, in order to comprehensively assess the actual impact of different HAART regimens on lipid profiles, we adjusted for various potential confounding factors.Our results clearly demonstrated that PLHIV receiving 3TC+EFV+AZT had a higher risk of dyslipidemia, particularly increased TG levels, compared to 3TC+EFV+TDF. This finding is broadly in line with two previous studies in Shenzhen, China,whichreported that 3TC+EFV+TDF has a lower risk of dyslipidemia than the other first-line free antiretroviral regimens available to PLHIV [14, 15].

Considering the limited drug resources, non-international first-line drugs, such as TDF, EFV and AZT, are still widely used in developing countries, as is the case in China. Moreover, the effects of various HAART drugs on lipid metabolism and related pathogenic mechanisms vary [16]. Sticprospective clinical studies have confirmed that the above-mentioned backbone drugs for free clinical application exhibit good virological and immunological effects [17]. In China, commonly used nucleoside reverse transcriptase inhibitors include 3TC, AZT, TDF, etc. Mitochondrial DNA replication ability and normal mitochondrial function in adipocytes can be inhibited by NRTIs, but the full mechanism of its effect on dyslipidemia remains unclear. Nucleoside drugs primarily cause lipoatrophy and hypertriglyceridemia [18]. AZT is more common. Interestingly, TDF has fewer adverse effects on lipid metabolism [19–21]. It can even activate the peroxisome proliferator-activated receptors expressed in the liver, upregulate the expression of CD36, and increase the uptake of free fatty acids in the circulation, which can reduce the concentration of TC, TG, LDL_C and Non_HDL_C in plasma. Commonly used non-nucleoside reverse transcriptase inhibitors include NVP, EFV and so on. They may affect blood lipid metabolism by regulating the expression of genes involved in cholesterol antiporters, but the specific pathogenesis remainsundetermined.Among them, EFV has a greater impact on blood lipids [22]. The ALTAIR study [23] showed that the use of EFV would cause a consistent increase in the concentration of different types of blood lipids. Nevertheless, since EFV was included in all HAART regimens in this study, its impact on outcomes was not considered. The above explanations support our hypothesis.

In HIV-infected patients, dyslipidemia is often observed and can significantly contribute to the elevated cardiovascular risk prevalent in this population [24]. Antiretroviral drug exposure appears to play a crucial role in this regard [25]. Consequently, optimizing the HAART regimen to enhance patient survival and prognosis is of considerable importance. Our findingsdemonstrated that 3TC+EFV+TDF is more suitable for managing blood lipids, provided there are no contraindications. Furthermore, a dynamic analysis of blood lipid metabolism reveals that the rate of dyslipidemia significantly changesin the early stages of treatment. This rate of change slows down as thetreatment time extends, supporting the early selection of appropriate antiviral treatment regimens to enhance the feasibility of lipid metabolism.

This study is the first study in Huzhou on the independent effect of HAART regimen on dyslipidemia in PLHIV. We conducted a comprehensive analysis of the initial treatment regimens and dyslipidemia during the follow-up period, and established a baseline-characteristically matched longitudinal cohort. The double-dimensional design significantly enhanced the credibility of our findings. However, the study also had some limitations. Firstly, the lack of blood lipid information was a significant concern. The current study included only 532 PLHIV, and selection bias wasunavoidable. Nevertheless, we established strict inclusion and exclusion criteria to ensure the homogeneity of participants as much as possible. Secondly, the absence of high-density lipoprotein cholesterol and low-density lipoprotein cholesterol in our database was a limitation. Tocircumventunderestimating the prevalence of dyslipidemia, we used the cut-off value of marginal elevated blood lipids to determine lipid status. Furthermore, when conducting longitudinal analysis, we reduced the sample size duetothe lack of regular blood testing for all patients. Despitethe loss to follow-up occurring at different time points, we observed significant differences between the two HAART regimens. Additionally, we lacked information on the use of lipid-modifying agents, which could have influenced our results to a certain extent.

In the future, we strongly recommend that staff incorporate all critical lipid profiles into the health system and diligently prepare to conduct prospective cohort studies. These studies will evaluate longitudinal changes in these parameters in PLHIV receiving various HAART regimens. The laws of blood lipid metabolism in HIV-infected patients will be more accurately described, and the association between different HAART regimens and dyslipidemia will also be assessed in greater detail.

Conclusions

The prevalence of blood lipid abnormalities varied among different classes of drugs. When compared to 3TC+EFV+AZT, 3TC+EFV+TDF have a lower risk of dyslipidemia. For patients who are potentiallyatrisk for cardiovascular disease, we recommend that when receiving HAART in the early stages, they should choose a HAART regimen that is beneficial for blood lipids.

Supporting information

S1 Fig The study workflow.

(TIF)

S2 Fig Changes of blood lipid and prevalence of dyslipidemia in PLHIV after HAART.

TG and TC with obviously skewed distribution, they are expressed logarithmically.

(TIF)

S3 Fig Evolution of dyslipidemia in PLHIV receiving different HAART regimens during 3 years of follow-up.

(TIF)

S4 Fig Repeat measurements of blood lipids in PLHIV receiving different HAART regimens.

(TIF)

S1 Table Comparison of pre- and post-imputations by 5-fold multiple imputation.

Continuous variables was described as median (1st quartile, 3rd quartile) as its distribution was skewed and Mann-Whitney U test was applied to compare the difference between two groups; Categorical data were presented with number (%) and chi-square tests or Fisher’s exact test were used to compare the differences between pre- and post-imputations data. Abbreviations: CD4: CD4+ T-lymphocyte count; CD8: CD8+ T-lymphocyte count; WBC: White blood cell; ALT: Alanine aminotransferase; AST: Aspartate transaminase; TBIL: Total bilirubin; FPG: Fast plasma glucose.

(DOCX)

S2 Table Changes in the prevalence of dyslipidemia in PLHIV receiving different HAART regimens.

Percentage of change = (prevalence of dyslipidemia after HAART- prevalence of dyslipidemia before HAART)/ prevalence of dyslipidemia before HAART.

(DOCX)

S3 Table Numbers of patients in the cohort at various follow-up points stratified by regimens.

(DOCX)

S4 Table The gee model of effect of two HAART regimens on the risk of dyslipidemia of PLHIV.

77 pairs of PLHIV were included the gee model, in which one case (PLHIV receiving 3TC+EFV+AZT) was matched by age, BMI, cd4, cd8, TG and TC at baseline with one control (PLHIV receiving 3TC+EFV+TDF).

(DOCX)

The authors would like to thank all participants for their valuable contributions to this study.

10.1371/journal.pone.0305461.r001
Decision Letter 0
Ceccarelli Manuela Academic Editor
© 2024 Manuela Ceccarelli
2024
Manuela Ceccarelli
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version0
10 Aug 2023

PONE-D-23-04082The Risk of Dyslipidemia on PLHIV Associated with Different Antiretroviral Regimens in HuzhouPLOS ONE

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #2: Yes

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I read this manuscript with great interest and want to acknowledge the authors for their excellent work. My main concern in this manuscript is data management.

I’m wondering about the data analysis of this study is logistic regression. I expected a time-to-event analysis or joint models since the data has a time component, repeated measurement of outcome variables (lipid profiles), and comparing the two HARRT regimens (3TC-EFV-TDF and 3TC-EFV-AZT). The epidemiological measures for cohort study with an open population are incidence density and measures of association again be relative risk or hazard ratio, but this paper reported prevalence rate and odds ratio for retrospective cohort study. I will be happy if the authors of this manuscript address these issues.

Reviewer #2: 1) There are some typos in the lines 203-214 where dyslipidemia is written as "dyslipodemia", please correct it.

2) In line 32, method section of abstract, the authors wrote "from June 2010 to June 2022", while in all the other section that period seems to be from July 2005 to June 2022 (lines 80-81 and 91), please correct it;

3) Data about the use of lowering triglycerides/cholesterol drugs are missing in the analysis. It would be interesting to know if the reduction in lipid profile was observed in people treated or not with those drugs. Could the authors provide this information about the participants, possibly making a sub-analysis that include this variable?

**********

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Reviewer #1: No

Reviewer #2: No

**********

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10.1371/journal.pone.0305461.r002
Author response to Decision Letter 0
Submission Version1
4 Sep 2023

COMMENTS TO THE AUTHOR:

Reviewer#1:

Question1. I’m wondering about the data analysis of this study is logistic regression. I expected a time-to-event analysis or joint models since the data has a time component, repeated measurement of outcome variables (lipid profiles), and comparing the two HARRT regimens (3TC-EFV-TDF and 3TC-EFV-AZT). The epidemiological measures for cohort study with an open population are incidence density and measures of association again be relative risk or hazard ratio, but this paper reported prevalence rate and odds ratio for retrospective cohort study. I will be happy if the authors of this manuscript address these issues.

Response: Thank you very much for this valuable comment. Our primary statistical method of choice is logistic regression model, rather than the Cox proportional hazards model. The reason for this is that our outcome of interest is dyslipidemia rather than death. Although various laboratory measurements, including triglyceride levels, were periodically assessed during the patient’s follow-up, we do not have access to the exact timing of when lipid abnormalities occurred. In other words, we are unable to obtain the true survival time of the patients. Therefore, our outcome is defined as the occurrence of elevated lipid profiles at any point during the follow-up period, which does not allow for Cox regression analysis. Additionally, to avoid wasting the repeated measurements taken during the follow-up, we employed the generalized estimating equation (GEE) model in the second analysis to evaluate the association between treatment regimen and lipid abnormalities among patients who did not change their treatment during the follow-up. Moreover, this study is a retrospective cohort study, and retrospective studies cannot calculate incidence rates. Since the relative risk (RR) is based on rates, the measure of association in this article is presented as odds ratio (OR) values.

Reviewer#2:

Question1. There are some typos in the lines 203-214 where dyslipidemia is written as "dyslipodemia", please correct it.

Response: We are grateful for this kind reminder and sorry for this carelessness. We have completed the modification of “dyslipidemia”.

Question2. In line 32, method section of abstract, the authors wrote "from June 2010 to June 2022", while in all the other section that period seems to be from July 2005 to June 2022 (lines 80-81 and 91), please correct it

Response: Thank you again for your kind reminder and we are sorry for our carelessness. We have corrected the error in line 32.

Question3. Data about the use of lowering triglycerides/cholesterol drugs are missing in the analysis. It would be interesting to know if the reduction in lipid profile was observed in people treated or not with those drugs. Could the authors provide this information about the participants, possibly making a sub-analysis that include this variable?

Response: Many thanks for this important and interesting reminder. We completely agree with the reviewer. Unfortunately, there is no information in our data set on drug use other than antiviral regimens. We believe that if we obtain the relevant variables of lipid-lowering drugs, we may get some new results. In subsequent studies, we will recommend that CDC staff collect additional information about patients' drug use.

Attachment Submitted filename: Response to Reviewers.docx

10.1371/journal.pone.0305461.r003
Decision Letter 1
Bekolo Cavin Epie Academic Editor
© 2024 Cavin Epie Bekolo
2024
Cavin Epie Bekolo
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version1
22 Feb 2024

PONE-D-23-04082R1The risk of dyslipidemia associated with different antiretroviral therapy regimens in people living with HIV in Huzhou,ChinaPLOS ONE

Dear Dr. 王,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by the Apr 07 2024 11:59PM . If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter..

We look forward to receiving your revised manuscript.

Kind regards,

Cavin Epie Bekolo, MD, MSc

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #3: (No Response)

Reviewer #4: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #3: Yes

Reviewer #4: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #3: Yes

Reviewer #4: I Don't Know

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #3: No

Reviewer #4: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #3: Yes

Reviewer #4: No

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Sorry for this; the method of data analysis for this study doesn’t convince me. It is clear that survival analysis can apply to any outcome with a time component, not just death. For example, patients with longer follow-up times have a higher chance of developing dyslipidaemia than patients with shorter follow-up times. So, the analysis should consider follow-up time accounting interval censoring; otherwise, the result will be misleading. In the GEE analysis, the authors assessed those with unchanged HAART regimen, here what if the change is because of high lipid profile, does it mean you introducing bias in the analysis. Related question: how did you manage patients dead or lost in the follow-up period and having one or more lipid measurements? Did you assess the computing risks?

Reviewer #3: The authors have approached the subject in an easily understandable way. However, has the possibility of interference with other products taken by patients been taken into account? This could have been a factor influencing the advent of elevation of lipid profiles, can the authors give more details on this subject.

Reviewer #4: Dear Authors,

Thank you for the opportunity to review your manuscript. Your study addresses an important area of research, and I appreciate your efforts in exploring this subject. I have several comments and suggestions that I believe could enhance the clarity, accuracy, and impact of your paper:

1. I understand that English might not be the first language of the authors, but they might want to seek professional services to improve the presentation and grammar. This can remarkably improve the readability and professional presentation of your manuscript.

2. Research Question and Aims: The research question and specific aims of the study are not stated with sufficient clarity. Clearly articulating these elements is fundamental to guiding the reader through your research process and understanding the significance of your findings. I recommend revising the introduction to explicitly state the research question(s)/aims. For example, to estimate the prevalence of...., to assess the trend.... to evaluate the association of ART with dyslipedemia. When clearly stated readers expect to see results and discussion sections presented in alignment with the study aims. The current presentation is hard to follow.

3.Lines 269-272: The passage discussing the comparative adverse effects of different ART regimens on blood lipid metabolism seems more interpretative than is typical for a Results section. I recommend relocating or rephrasing this content to maintain a clear distinction between the presentation of findings (Results) and their interpretation (Discussion).

4. Including both unadjusted and adjusted regression analyses results for the covariates would offer a more comprehensive view of the data and the factors influencing dyslipidemia among PLHIV. Presenting the effect sizes of other potential determinants would also be beneficial in interpreting the results and understanding their broader implications. You could add it as supplementary data.

I believe that addressing these points will greatly improve the manuscript's quality and contribution to the field.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #3: No

Reviewer #4: No

**********

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While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

10.1371/journal.pone.0305461.r004
Author response to Decision Letter 1
Submission Version2
21 Apr 2024

Reviewer#1:

Question. It is clear that survival analysis can apply to any outcome with a time component, not just death. For example, patients with longer follow-up times have a higher chance of developing dyslipidaemia than patients with shorter follow-up times. So, the analysis should consider follow-up time accounting interval censoring; otherwise, the result will be misleading. In the GEE analysis, the authors assessed those with unchanged HAART regimen, here what if the change is because of high lipid profile, does it mean you introducing bias in the analysis. Related question: how did you manage patients dead or lost in the follow-up period and having one or more lipid measurements? Did you assess the computing risks?

Response: Thank you very much for your valuable comments. It is well known that survival analysis can be utilized for outcomes with a time component, provided that we have a relatively accurate survival time, that is, between the start of the study and the occurrence of the event of interest. Death is just one example we provide, as we can usually ascertain the actual time of death. In clinical practice, the time of visit is often used as a proxy for the time of occurrence of a disease, despite the potential for measurement errors. However, the survival analysis method is robust to this situation in the data. But I would like to say the outcomes of our study differ from those mentioned above. Lipids were measured at each follow-up, and as long as a patient had dyslipidemia, we considered that the end event of interest had occurred. Furthermore, our lipid data are incomplete. It is likely that patients already had dyslipidemia at the first follow-up, but were missed due to a lack of HDL_C or LDL_C data. These individuals were either not identified until later follow-ups based on other lipid data, or remained undetected. Consequently, the outcome was more suitable for analysis as a binary event rather than as an event that included time. In summary, the case is specific. Generally, survival analysis methods should be selected for data containing time, but for this study, we believe that logistic regression analysis is more suitable.

In the GEE analysis, we assessed those with unchanged HAART regimen. First of all, we cannot deny that patients may have some adverse reactions during treatment, such as metabolic disorders, and change the HAART regimen, which will cause bias in the study results. However, we were unable to ascertain the specific reasons for patients' treatment regimen changes, thereby precluding an estimation of this bias. This constitutes a significant limitation of our study. Nonetheless, 89.5% (476/532) of patients maintained their treatment regimen, suggesting a minimal bias. Secondly, the GEE analysis necessitated repeated measurements, hence we retained patients with at least two follow-up records.

Considering the interference caused by confounding factors such as age and CD4, the relationship between HAART and dyslipidemia was analyzed after 1:1 matching. Ultimately, GEE analysis was performed on only 77 pairs of patients after receiving matching, and none of these 77 pairs disappeared or died during follow-up. What I would like to emphasize is that, despite the fact that only 154 subjects remain after pairing, GEE analysis serves as an auxiliary analysis supporting the main research findings in this study. This part of the results is also included in a supplementary document.Thank you again for your comments.

Reviewer#3:

Question. The authors have approached the subject in an easily understandable way. However, has the possibility of interference with other products taken by patients been taken into account? This could have been a factor influencing the advent of elevation of lipid profiles, can the authors give more details on this subject.

Response: We are grateful for this kind reminder and we completely agree with your opinion. We must acknowledge that if patients take other medications, it may affect their blood lipid levels. Unfortunately, details of other drug use histories were missing from our database, which we will add to the limitations of the study.In subsequent studies, we will recommend that CDC staff collect additional information about patients' drug use.

Reviewer#4:

Question1. I understand that English might not be the first language of the authors, but they might want to seek professional services to improve the presentation and grammar. This can remarkably improve the readability and professional presentation of your manuscript.

Response: Thank you for your kind reminder and we will continue to polish our article to improve its readability.

Question2. Research Question and Aims: The research question and specific aims of the study are not stated with sufficient clarity. Clearly articulating these elements is fundamental to guiding the reader through your research process and understanding the significance of your findings. I recommend revising the introduction to explicitly state the research question(s)/aims. For example, to estimate the prevalence of...., to assess the trend.... to evaluate the association of ART with dyslipedemia. When clearly stated readers expect to see results and discussion sections presented in alignment with the study aims. The current presentation is hard to follow.

Response: We are grateful for this kind reminder. We have revised the research purpose of the introduction section to make it clearer and easier to understand.

Question3. Lines 269-272: The passage discussing the comparative adverse effects of different ART regimens on blood lipid metabolism seems more interpretative than is typical for a Results section. I recommend relocating or rephrasing this content to maintain a clear distinction between the presentation of findings (Results) and their interpretation (Discussion).

Response: Many thanks for this important and interesting reminder. We have deleted and modified the statements in lines 269-272 to make the description more logical.

Question4. Including both unadjusted and adjusted regression analyses results for the covariates would offer a more comprehensive view of the data and the factors influencing dyslipidemia among PLHIV. Presenting the effect sizes of other potential determinants would also be beneficial in interpreting the results and understanding their broader implications. You could add it as supplementary data.

Response: Many thanks for this kind reminder. However, we would like to say that this study is an association study, not an analysis of influencing factors. We had only one main independent variable, the HAART regimen, and the other variables in the multifactor model were corrected as confounders. And the effect sizes of HAART's effects on dyslipidemia, whether uncorrected or corrected, have been sorted out and placed in Table 3 of the paper. We do not think it is necessary to label the effect sizes of other confounding factors as this is not in line with the main idea of the article. Thank you again for your comments.

Attachment Submitted filename: Response to Reviewers.docx

10.1371/journal.pone.0305461.r005
Decision Letter 2
Bekolo Cavin Epie Academic Editor
© 2024 Cavin Epie Bekolo
2024
Cavin Epie Bekolo
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version2
31 May 2024

The Risk of Dyslipidemia on PLHIV Associated with Different Antiretroviral Regimens in Huzhou

PONE-D-23-04082R2

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10.1371/journal.pone.0305461.r006
Acceptance letter
Bekolo Cavin Epie Academic Editor
© 2024 Cavin Epie Bekolo
2024
Cavin Epie Bekolo
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
6 Aug 2024

PONE-D-23-04082R2

PLOS ONE

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