Comparative Metabolic Effects of Integrase Inhibitor and Emtricitabine-Containing Tenofovir Derivatives in People Living with HIV: A Multi-Center Retrospective Analysis

doi:10.21203/rs.3.rs-4698296/v1

Download PDF

Research Article

Comparative Metabolic Effects of Integrase Inhibitor and Emtricitabine-Containing Tenofovir Derivatives in People Living with HIV: A Multi-Center Retrospective Analysis

https://doi.org/10.21203/rs.3.rs-4698296/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

This retrospective, multi-center cohort study aimed to compare the effects of Integrase Strand Transfer Inhibitor (INSTI) based therapies containing tenofovir disoproxil fumarate (TDF)/ Emstristabin (FTC) /Dolutegravir (DTG), TDF/FTC/Cobicistat (C)/Elvitegravir(EVG), TDF/FTC/Raltegravir (RAL) and tenofovir alafenamide (TAF)/FTC/Bictegravir (BIC), TAF/FTC/c/EVG) combinations on bone metabolism, lipid profile, and renal function in people living with HIV (PLWH).

Methods

Adults aged ≥ 18 years receiving antiretroviral therapy (ART) for ≥ 12 months were followed for ≥ 24 months. Data were obtained from HIV/AIDS clinic records and hospital databases, including demographics, laboratory values (HIV RNA, CD4 + T lymphocyte count, creatinine, eGFR, ALT), lipid profiles (total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides), and DEXA results. Statistical significance was defined as a p-value less than 0.05.

Findings:

The treatment outcomes of 901 HIV-infected individuals from 9 centers in Türkiye were evaluated. After applying exclusion criteria, data from 845 individuals were included: 462 in the TDF group (mean age 43.25 ± 12.35) and 383 in the TAF group (mean age 41.75 ± 12.08) (p = 0.082). The proportion of female patients was 18.4% in the TDF group and 13.6% in the TAF group (p = 0.058). In the TDF group, 77.2% were treatment-naïve, compared to 52.2% in the TAF group (p < 0.001). At 24 months, HIV RNA levels were 14218.43 ± 233947.64 IU/ml in the TDF group and 3247.15 ± 55371.53 IU/ml in the TAF group (p < 0.001). Although CD4 levels were higher at baseline in the TAF group (p < 0.001), the TDF group showed a greater increase at 48 months (p = 0.013). ALT normalization was better in the TDF group, while total and non-HDL cholesterol levels were higher in the TAF group.

Conclusion

TDF and TAF-containing INSTI-based regimens exhibit distinct impacts on lipid profiles and immune function in PLWH. The early advantages of TAF in viral load reduction diminish over time, contrasting with the long-term benefits of TDF in terms of CD4 counts, ALT levels, and non-HDL cholesterol. These findings highlight the necessity of individualized treatment in selecting ART regimens.

HIV

integrase inhibitors

tenofovir disoproxil fumarate

tenofovir alafenamide

metabolic effects

lipid profile

renal function

HIV (Human Immunodeficiency Virus) infection continues to be a serious global health issue. Antiretroviral therapies (ART) used in the treatment of HIV-infected individuals play a critical role in suppressing viral load, halting disease progression, and preventing its transmission [1]. While ART has significantly extended the lifespan of people living with HIV (PLWH), concerns arise over the metabolic effects and side effects associated with long-term drug use [1].

ART involves different combinations of drugs commonly used in the treatment of HIV infection, such as Tenofovir Disoproxil Fumarate (TDF), Tenofovir Alafenamide Fumarate (TAF), and Emtricitabine (FTC). These drugs belong to nucleoside reverse transcriptase inhibitors (NRTIs), which inhibit the replication of HIV [1, 2].

Typically, these are combined with a third drug, like integrase inhibitors, offering high efficacy, a favorable side effect profile, and ease of use. Moreover, these combinations help prevent the development of resistance and improve patients' quality of life. Regular and consistent use of ART is crucial for maintaining HIV suppression and preventing AIDS progression. However, these combinations also come with disadvantages. Treatment selection should be based on individual patient needs, health status, and tolerance to side effects. For instance, compared to TDF, TAF tends to cause fewer renal and bone-related side effects [2, 3]. While TDF may adversely affect kidney function in some patients, TAF can mitigate this risk. TDF may lead to bone mineral density loss, whereas TAF carries less risk. Additionally, TDF has been shown to have less impact on lipid profiles, which could positively impact cardiovascular health. However, TAF is generally more expensive, which may present a financial disadvantage [3, 4].

Metabolic side effects associated with long-term ART use are significant for people living with HIV. Potential side effects of antiretroviral therapy include impaired kidney function, decreased bone density, and lipid metabolism disorders. Therefore, comparing the metabolic effects of TDF, TAF, FTC, and integrase inhibitor combinations is crucial for developing treatment protocols and optimizing clinical practices [5]. This multi-center retrospective study compares TDF and TAF-containing treatment regimens' metabolic effects and side effects, examining differences between patients using TDF/FTC/RALTEGRAVIR (RAL), TDF/FTC/ Cobicistat (c)/ ELVITEGRAVIR (EVG), and TDF/FTC/DOLUTEGRAVIR (DTG) regimens. It also examines those using TAF/FTC/c/EVG and TAF/FTC/ BICTEGRAVIR (BIC) regimens.

Study Population and Design

This study was designed as a retrospective, multi-center cohort study. A total of 901 HIV-infected patients receiving integrase-based TDF regimens (TDF/FTC + DTG, TDF/FTC/c/EVG, TDF/FTC + RAL) and integrase-based TAF regimens (TAF/FTC/BIC, TAF/FTC/c/EVG) from 9 different centers in Türkiye were included. Patient records were retrospectively reviewed.

Inclusion Criteria

PWLH's included in the study were aged 18 years or older, confirmed HIV/AIDS diagnosis by ELISA and Western Blot tests, received specified ART for at least 12 months, and were followed for at least 24 months from the start of treatment.

Exclusion Criteria

PLWHs who have yet to receive ART for at least 12 months.
PLWHs followed for less than 24 months from the start of treatment.
PLWHs under 18 years of age were excluded.

Data Collection

Data were collected from outpatient follow-up forms for HIV/AIDS patients and hospital databases. Demographic, laboratory, and clinical information of patients were recorded. Variables collected included age, gender, weight, height, body mass index (BMI), comorbidities (diabetes mellitus, hypertension, chronic kidney disease, coronary artery disease, hyperlipidemia), medications used, and initiated ART regimens.

Measurements such as weight, HIV RNA, CD4 + T lymphocyte count, creatinine, estimated glomerular filtration rate (eGFR), alanine aminotransferase (ALT), total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglyceride values were recorded at 0 (baseline), 12, 24, 36, and 48 months. Reasons for ART regimen changes were obtained from patient follow-up forms.

Definitions

Staging of renal impairment was based on the 2012 Chronic Kidney Disease Guidelines by the National Kidney Foundation Kidney Disease Outcome Quality Initiative. Glomerular filtration rate (GFR) was categorized as follows: 60–90 ml/min/1.73m2, mild; 30–59 ml/min/1.73m2, moderate; 15–29 ml/min/1.73m2, severe renal impairment. GFR < 15 ml/min/1.73m2 was considered as renal failure.

Lipid profile definitions included fasting measurements: total cholesterol > 200 mg/dl indicating cholesterol increase; LDL > 130 mg/dl indicating LDL increase; HDL ≤ 40 mg/dl indicating HDL decrease; triglyceride > 150 mg/dl indicating triglyceride increase.

Osteoporosis diagnosis was based on the European AIDS Clinical Society (EACS) guidelines version 11.1. A BMD T-score ≤ -2.5 in postmenopausal women and men aged 50 years and older at the femoral neck or lumbar spine indicated osteoporosis; a BMD Z-score ≤ -2 in premenopausal women and men under 50 years old stated osteoporosis.

Primary Endpoint

Comparison of the effects of integrase-based TDF or TAF and FTC combinations on bone metabolism, lipid profile, renal function, and weight gain in patients.

Secondary Endpoints

Comparison of the frequency of adverse effects associated with different combinations between groups.

Statistical Analysis

Descriptive statistics were used to summarize the characteristics of the study population. The Shapiro-Wilk test was used to assess the normality of data distribution. The Mann-Whitney U test compared patient characteristics between TAF and TDF groups. The Kruskal-Wallis test compared numerical data among TAF and TDF subgroups. Post hoc multiple comparisons were performed using Tamhane tests. The Friedman test was used to compare patient characteristics among different time points for TAF and TDF groups. Repeated measures of two-way ANOVA were used to analyze the differences in patient characteristics between TAF and TDF groups. Continuous variables were presented as mean ± standard deviation, and categorical variables were compared using the Chi-square test. Statistical significance accepted as a p value < 0.05. Data analyses were conducted using IBM SPSS Statistics, Version 23.0 (IBM Corp., Armonk, NY).

This study examined retrospective data from 901 PLWH collected at nine distinct centers in Türkiye. The study didn't include 56 patients (6.2% Consequently, data from 845 HIV-positive individuals were included in the analysis; 462 were in the TDF group (mean age 43.25 ± 12.35 years), and 383 were in the TAF group (mean age 41.75 ± 12.08 years) (p = 0.082). Analysis of gender distribution revealed that the proportion of female patients was 18.4% in the TDF group compared to 13.6% in the TAF group (p = 0.058). Evaluation of treatment experience showed that 77.2% of patients in the TDF group were treatment-naive, whereas this proportion was 52.2% in the TAF group (p < 0.001). There was no significant difference between groups regarding time since diagnosis (p = 0.192) (Table 1).

When examining viral loads, initial HIV RNA levels were 134,145,346.281,701,499,958.36 copies/mL in the TDF group and 812,026.562,192,237.36 copies/mL in the TAF group, which was not statistically significant. At 24 months post-treatment, HIV RNA levels were 14,218.43233,947.64 copies/mL in the TDF group and 3,247.1555,371.53 < 0.001). However, there were no significant differences between the groups at 36 and 48 months (p > 0.05) (Table 2, Fig. 1).

Table 1. Comparison results of the general characteristics for patients who used TDF and TAF.

Parameter		ANTIRETROVIRAL REGIMENS		p-values
		TDF (n=462)	TAF (n=383)
Age		43,25±12,35	41,75±12,08	0,082
Gender	Woman	85 (18.4%)	52 (13.6%)	0,058
	Male	377 (81.6%)	331 (86.4%)
Naive / Experienced	Naive	355 (77.2%)	200 (52.2%)	<0,001
	Experienced	105 (22.8%)	183 (47.8%)
Duration of Diagnosis		5,9±3,2	5,8±3,34	0,192

TDF: Tenofovir Disoproxil Fumarate

TAF: Tenofovir Alafenamide Fumarate (TAF)

Table 2

Comparison results of the laboratory findings for patients who used TDF and TAF.
		ANTIRETROVIRAL REGIMENS		² p-values	³ p-values
		TDF (n = 462)	TAF (n = 383)	² p-values	³ p-values
HIV RNA	Baseline	134145346,28 ± 1701499958,36	812026,56 ± 2192237,36	0,139	0,198
	12th Months	5051,03 ± 60162,25	4325,08 ± 55665,01	0,144
	24th Months	14218,43 ± 233947,64	3247,15 ± 55371,53	0,007
	36th Months	13979,2 ± 220910,49	184,99 ± 1858,73	0,669
	48th Months	4,94 ± 34,44	1,94 ± 9,37	0,794
	¹ p-values	< 0,001 (n = 163)	< 0,001 (n = 87)
CD4	Baseline	393,06 ± 236,19	482,07 ± 236,64	< 0,001	0,013
	12th Months	549,21 ± 235,04	577,1 ± 217,79	0,171
	24th Months	588,55 ± 225,61	638,27 ± 224,14	0,015
	36th Months	626,06 ± 228,1	650,64 ± 208,35	0,339
	48th Months	625,51 ± 208,46	688,89 ± 200,45	0,021
	¹ p-values	0,001 (n = 92)	< 0,001 (n = 47)
Creatinine	Baseline	0,85 ± 0,16	0,87 ± 0,2	0,328	0,856
	12th Months	0,91 ± 0,18	0,92 ± 0,26	0,746
	24th Months	0,92 ± 0,19	0,93 ± 0,19	0,596
	36th Months	0,94 ± 0,22	0,93 ± 0,19	0,519
	48th Months	1 ± 0,07	1,01 ± 0,08	0,831
	¹ p-values	< 0,001 (n = 185)	< 0,001 (n = 126)
GFR	Baseline	101,48 ± 19,32	103,9 ± 20,97	0,018	0,711
	12th Months	98,17 ± 17,2	98,9 ± 18,66	0,211
	24th Months	96,32 ± 16,76	98,03 ± 18,77	0,176
	36th Months	93,38 ± 18,12	95,73 ± 17,39	0,074
	48th Months	91,22 ± 17,69	93,1 ± 15,69	0,364
	¹ p-values	< 0,001 (n = 155)	< 0,001 (n = 122)
ALT	Baseline	32,2 ± 36,7	25,35 ± 17,67	0,018	0,026
	12th Months	27,11 ± 23,16	23,72 ± 15,51	0,029
	24th Months	25,59 ± 18,89	24,26 ± 14,48	0,587
	36th Months	26,55 ± 21,37	25,5 ± 17,91	0,322
	48th Months	27,03 ± 21,88	23,08 ± 15,66	0,090
	¹ p-values	0,480 (n = 185)	0,758 (n = 125)
Gamma-glutamyl transferaz	Baseline	37,56 ± 47,13	30,39 ± 38,08	0,041	0,338
	12th Months	31,03 ± 33,65	28,3 ± 29,58	0,641
	24th Months	29,51 ± 30,12	31,25 ± 37	0,488
	36th Months	31,94 ± 32,34	30,57 ± 26,11	0,425
	48th Months	30,1 ± 25,18	26,91 ± 16,41	0,846
	¹ p-values	0,124 (n = 155)	0,068 (n = 72)
Phosphor	Baseline	3,98 ± 11,8	3,99 ± 12,03	0,284	0,403
	12th Months	3,28 ± 0,59	3,31 ± 0,54	0,449
	24th Months	3,25 ± 0,57	5,12 ± 22,35	0,120
	36th Months	3,34 ± 1,54	4,1 ± 12,83	0,945
	48th Months	3,26 ± 2,21	3,28 ± 0,57	0,027
	¹ p-values	0,006 (n = 134)	0,987 (n = 86)
Total Cholesterol	Baseline	166,13 ± 45,07	177,46 ± 44,69	< 0,001	0,008
	12th Months	170,52 ± 42,73	193,27 ± 45,95	< 0,001
	24th Months	174,54 ± 41,36	191,76 ± 43,87	< 0,001
	36th Months	179,67 ± 46,1	195,39 ± 42,34	< 0,001
	48th Months	182,06 ± 42,86	189,89 ± 43,7	0,082
	¹ p-values	0,001 (n = 111)	0,002 (n = 88)
High-density lipoprotein (HDL)	Baseline	38,25 ± 12,86	40,71 ± 11,29	0,002	0,210
	12th Months	42,05 ± 15,39	43,69 ± 10,45	< 0,001
	24th Months	40,95 ± 9,71	44,8 ± 10,08	< 0,001
	36th Months	41,97 ± 12,41	45,18 ± 12,22	0,001
	48th Months	43,41 ± 17,83	44,58 ± 10,54	0,033
	¹ p-values	0,002 (n = 113)	0,026 (n = 86)
T Cholesterol/HDL	Baseline	4,63 ± 1,56	4,56 ± 1,27	0,965	0,975
	12th Months	4,31 ± 1,28	4,57 ± 1,22	0,002
	24th Months	4,45 ± 1,35	4,43 ± 1,17	0,487
	36th Months	4,47 ± 1,31	4,57 ± 1,33	0,237
	48th Months	4,49 ± 1,43	4,44 ± 1,32	0,981
	¹ p-values	0,202 (n = 112)	0,546 (n = 86)
Non-HDL Cholesterol	Baseline	127,88 ± 42,36	136,94 ± 41,18	0,002	0,018
	12th Months	128,87 ± 40,4	149,57 ± 44,01	< 0,001
	24th Months	133,7 ± 39,68	146,95 ± 42,84	< 0,001
	36th Months	137,68 ± 42,64	150,2 ± 43,41	< 0,001
	48th Months	138,27 ± 44,9	145,27 ± 42,3	0,148
	¹ p-values	0,021 (n = 111)	0,002 (n = 86)
low-density lipoprotein (LDL)	Baseline	101,58 ± 36,04	112,13 ± 44,55	0,001	0,050
	12th Months	104,35 ± 34,3	123,16 ± 52,64	< 0,001
	24th Months	106,69 ± 34,18	120,63 ± 50,25	< 0,001
	36th Months	114,3 ± 48,37	128,94 ± 60,95	0,001
	48th Months	113,12 ± 45,21	116,62 ± 35,34	0,095
	¹ p-values	0,019 (n = 104)	0,024 (n = 79)
Trigliserit	Baseline	143,5 ± 79,61	151,54 ± 89,92	0,562	0,330
	12th Months	139,65 ± 86,91	169,1 ± 104,83	< 0,001
	24th Months	145,93 ± 90,77	161,22 ± 92,11	0,004
	36th Months	143,82 ± 93,45	171,69 ± 107,92	< 0,001
	48th Months	151,87 ± 104,69	155,45 ± 91,29	0,179
	¹ p-values	0,259 (n = 110)	0,042 (n = 79)
Spine dual-energy X-ray absorptiometry (Dexa)	Baseline	0,26 ± 1,32	-0,06 ± 1,49	0,069	0,114
	24th Months	0,24 ± 1,29	-0,26 ± 1,36	0,071	0,114
	¹ p-values	< 0,001 (n = 67)	0,710 (n = 26)
Femur Dexa	Baseline	0,35 ± 1,07	0,16 ± 1,25	0,281	0,813
	24th Months	0,31 ± 0,98	-0,21 ± 1,29	0,041	0,813
	¹ p-values	< 0,001 (n = 67)	0,016 (n = 28)
Data were shown as mean ± standard deviation. ¹: p-values for comparison results among several periods. The sample size and data in all periods are given along with p-values. ²: p-values for comparison results between TAF and TDF groups. ²: p-values for comparison of the alteration between TAF and TDF groups.
TDF: Tenofovir Disoproxil Fumarate TAF: Tenofovir Alafenamide Fumarate (TAF)

Regarding CD4 levels, baseline values were 393.06 ± 236.19 cells/mm³ in the TDF group and 482.07 ± 236.64 cells/mm³ in the TAF group (p < 0.001). However, over the 48-month follow-up period, CD4 increases were significantly higher in TDF patients than those using TAF. Meanwhile, the TAF group has higher total and non-HDL cholesterol levels over the 48-month period (Fig. 1).

The comparison results of different integrase inhibitor-based combinations containing TDF and TAF in HIV-infected individuals in Türkiye are presented in Tables 3 and 4. In subgroup analysis, the mean age in the TDF/FTC/BIC subgroup (39.2611.92) was significantly lower compared to other subgroups. When analyzing gender distribution among subgroups, the female proportion in the TDF/FTC/RAL subgroup (35.1%) was significantly higher compared to other subgroups (p = 0.006). Regarding experience status, the proportion of inexperienced patients was significantly higher in the TDF/FTC/DTG subgroup (80.9%) compared to other subgroups (p < 0.001). Significant differences were also found among subgroups regarding duration since diagnosis and height parameters (p < 0.001). However, differences in weight parameters were not statistically significant (p > 0.05) (Table 3).

Table 3

Comparison results of the general characteristics for patients who used TDF and TAF subtypes.
		Antiretroviral Regimens					² p-values
		TDF/FTC/RAL (n = 37)	TDF/FTC/c/EVG (n = 69)	TDF/FTC/DTG (n = 293)	TAF/FTC/c/EVG (n = 194)	TAF/FTC/BIC (n = 139)	² p-values
Age		46,84 ± 12,74	43,48 ± 11,69	42,43 ± 12,19	42,88 ± 11,72	39,26 ± 11,92	0,003
Gender	Female	13 (35.1%)	6 (8.7%)	52 (17.7%)	27 (13.9%)	19 (13.7%)	0,006
Gender	Male	24 (64.9%)	63 (91.3%)	241 (82.3%)	167 (86.1%)	120 (86.3%)	0,006
Naive /experienced	Naive	23 (63.9%)	41 (59.4%)	237 (80.9%)	85 (43.8%)	73 (52,5%)	< 0,001
Naive /experienced	Experienced	13 (36.1%)	28 (40.6%)	56 (19.1%)	109 (56.2%)	66 (47.5%)	< 0,001
Time of diagnosis (years)		7,32 ± 4,75	8,16 ± 2	5,45 ± 3,08	7,15 ± 3,45	4,13 ± 2,71	< 0,001
Data were shown as mean ± standard deviation. ¹: p-values for comparison results among several periods. ²: p-values for comparison results among TAF and TDF subgroups.

Table 4

Comparison of laboratory findings results for patients who used TDF and TAF subtypes.
		ART Regimens					² P-values	³ P-values
		TDF/FTC/RAL (n = 37)	TDF/FTC/c/EVG (n = 69)	TDF/FTC/DTG (n = 293)	TAF/FTC/c/EVG (n = 194)	TAF/FTC/BIC (n = 139)	² P-values	³ P-values
HIV RNA	Baseline	40252728,76 ± 199948111,39	6055220,72 ± 30479285,72	195084467,56 ± 2082998467,86	907609,16 ± 2392262,36	467308,62 ± 1095397,84	0,042	0,228
	12th Months	1798,67 ± 10639,7	176,9 ± 1406,08	6176,94 ± 71302,54	2041,79 ± 15388,13	9147,38 ± 90662,56	0,117
	24th Months	15 ± 54,83	27,91 ± 191,31	23483,02 ± 301035,67	7040,21 ± 81990,16	92,05 ± 729,31	0,051
	36th Months	4,48 ± 18,95	372,23 ± 2589,25	23964,9 ± 289794,46	336,2 ± 2506,52	3,55 ± 27,94	0,047
	48th Months	0 ± 0	4,09 ± 15,11	6,93 ± 43,06	3,13 ± 11,77	0 ± 0	0,318
	¹ p-values	< 0,001 (n = 15)	< 0,001 (n = 27)	< 0,001 (n = 97)	< 0,001 (n = 41)	< 0,001 (n = 18)
CD4	Baseline	417,26 ± 247,35	479,35 ± 211,21	376,48 ± 234,61	543,27 ± 226,27	435,8 ± 242,43	< 0,001	0,213
	12th Months	586,44 ± 243,59	610,19 ± 206,21	545,73 ± 230,43	576,17 ± 210,15	582,89 ± 239,74	0,374
	24th Months	631,04 ± 211,81	642,69 ± 189,34	591,38 ± 230,68	633,61 ± 235,08	611,66 ± 239,04	0,582
	36th Months	625,15 ± 199,64	656,67 ± 196,49	632,5 ± 238,23	640,46 ± 204,01	592,05 ± 248,18	0,926
	48th Months	646,47 ± 181,77	623,41 ± 207,33	634,62 ± 212,24	684,86 ± 217,71	742,17 ± 123,8	0,335
	¹ p-values	0,002 (n = 10)	< 0,001 (n = 17)	< 0,001 (n = 49)	0,002 (n = 30)	N/A (n = 2)
Creatinine	Baseline	0,84 ± 0,18	0,85 ± 0,14	0,85 ± 0,17	0,86 ± 0,18	0,88 ± 0,22	0,929	0,933
	12th Months	0,83 ± 0,17	0,89 ± 0,16	0,93 ± 0,19	0,89 ± 0,17	0,96 ± 0,38	0,002
	24th Months	0,84 ± 0,18	0,87 ± 0,16	0,93 ± 0,19	0,91 ± 0,2	0,93 ± 0,17	0,002
	36th Months	0,89 ± 0,19	0,91 ± 0,29	0,95 ± 0,21	0,92 ± 0,18	0,93 ± 0,19	0,067
	48th Months	1 ± 0	1 ± 0	1 ± 0	1,01 ± 0,1	1 ± 0	0,715
	¹ p-values	< 0,001 (n = 22)	< 0,001 (n = 31)	< 0,001 (n = 110)	< 0,001 (n = 79)	0,002 (n = 20)
GFR	Baseline	100,32 ± 21,32	100,72 ± 15,57	101,4 ± 19,54	103,27 ± 20,09	105,16 ± 23,16	0,051	0,169
	12th Months	105,44 ± 15,61	99,18 ± 13,43	96,93 ± 17,7	99,1 ± 18,51	99,77 ± 17,52	0,026
	24th Months	101,79 ± 15,98	100,16 ± 16,18	94,84 ± 16,98	98 ± 20,25	99,06 ± 17,1	0,034
	36th Months	95,57 ± 16,47	98,3 ± 11,57	91,61 ± 19,24	94,85 ± 17,54	99,07 ± 18,4	0,005
	48th Months	91,21 ± 18,1	96,17 ± 14,28	90,49 ± 17,19	91,85 ± 16,2	97,77 ± 15,68	0,101
	¹ p-values	0,001 (n = 17)	0,229 (n = 28)	< 0,001 (n = 92)	< 0,001 (n = 75)	0,001 (n = 20)
ALT	Baseline	33,97 ± 55,64	26,23 ± 19,66	33,64 ± 37,89	25,41 ± 18,39	25,55 ± 16,02	0,062	0,074
	12th Months	29,03 ± 16,57	25,97 ± 16,76	27,7 ± 25,72	24 ± 15,47	23,82 ± 16,82	0,109
	24th Months	26,83 ± 16,93	24,02 ± 12,93	25,66 ± 20,42	24,81 ± 14,53	23,96 ± 14,91	0,776
	36th Months	26,44 ± 16,73	29,82 ± 24,46	26,56 ± 23,27	24,28 ± 13,96	29,63 ± 26,18	0,612
	48th Months	33,96 ± 38,82	24,77 ± 14,07	28,22 ± 20,95	21,69 ± 11,29	24,08 ± 13,02	0,071
	¹ p-values	0,182 (n = 22)	0,283 (n = 31)	0,288 (n = 110)	0,880 (n = 78)	0,051 (n = 20)
Gamma-glutamyl transferaz	Baseline	31 ± 27,14	38,91 ± 67,9	37,48 ± 43,88	30,66 ± 37,92	29,63 ± 40,92	0,157	0,505
	12th Months	27,69 ± 14,78	35,61 ± 53,72	29,3 ± 27,03	30,64 ± 34,23	24,08 ± 18,82	0,072
	24th Months	25,76 ± 16,99	30,24 ± 24,99	29,3 ± 31,87	32,27 ± 36,28	27,07 ± 29,65	0,018
	36th Months	26,87 ± 17,45	36,34 ± 39,49	31,5 ± 31,31	30,93 ± 27,31	27,54 ± 19,46	0,790
	48th Months	24,82 ± 12,25	31,69 ± 22,49	31,25 ± 27,65	27,07 ± 16,77	26 ± 14,28	0,840
	¹ p-values	0,984 (n = 20)	0,015 (n = 29)	0,146 (n = 102)	0,111 (n = 64)	0,541 (n = 8)
Fosfor	Baseline	3,25 ± 0,54	3,32 ± 0,51	4,32 ± 14,59	3,27 ± 0,55	5,57 ± 21,1	0,503		0,060
	12th Months	3,29 ± 0,65	3,15 ± 0,55	3,27 ± 0,59	3,34 ± 0,53	3,34 ± 0,58	0,193
	24th Months	3,25 ± 0,61	3,15 ± 0,64	3,23 ± 0,57	4,45 ± 13,26	7,15 ± 35,46	0,094
	36th Months	3,1 ± 0,62	3,05 ± 0,52	3,45 ± 1,95	4,82 ± 16,87	3,15 ± 0,53	0,105
	48th Months	2,88 ± 0,74	2,91 ± 0,82	3,44 ± 2,73	3,31 ± 0,55	3,35 ± 0,61	0,015
	¹ p-values	0,009 (n = 14)	0,108 (n = 25)	0,634 (n = 75)	0,782(n = 53)	0,314 (n = 6)
T.Cholesterol	Baseline	174,79 ± 58,05	179,63 ± 43,41	163,3 ± 44,24	185,48 ± 41,34	171,57 ± 50,79	< 0,001		0,014*
	12th Months	168,43 ± 40,81	178,55 ± 42,68	168,05 ± 43,88	201,23 ± 45,67	181,2 ± 44,64	< 0,001
	24th Months	181,2 ± 38,32	184,23 ± 40,52	171,34 ± 42,07	201,19 ± 44,38	181,53 ± 39,66	< 0,001
	36th Months	181,62 ± 46,57	191,19 ± 44,98	176,21 ± 46,9	198,6 ± 42,66	190,52 ± 47,54	< 0,001
	48th Months	187,94 ± 43,17	192,21 ± 50,02	179,68 ± 40,56	196,06 ± 42,77	183,38 ± 48,33	0,024
	¹ p-values	0,612 (n = 10)	0,008 (n = 28)	< 0,001 (n = 67)	0,055 (n = 72)	0,542 (n = 9)
HDL	Baseline	37,85 ± 11,03	40,57 ± 10,7	38,18 ± 13,26	42,03 ± 10,65	39,97 ± 11,89	0,002		0,359
	12th Months	41,77 ± 10,05	46,71 ± 23,99	41,08 ± 12,47	43,47 ± 8,78	44,46 ± 11,66	0,004
	24th Months	40,92 ± 11,94	42,69 ± 8,9	40,53 ± 9,69	44,96 ± 9,47	44,98 ± 11,23	< 0,001
	36th Months	45,65 ± 24,57	43,44 ± 9,9	40,97 ± 10,71	45,37 ± 12,77	44,15 ± 8,39	0,014
	48th Months	41,76 ± 12,25	48,44 ± 33,89	42,42 ± 11,73	45,45 ± 10,71	42,72 ± 10,22	0,170
	¹ p-values	0,434 (n = 10)	0,309 (n = 29)	0,002 (n = 68)	0,473 (n = 72)	0,036 (n = 7)
T.Cholesterol/HDL	Baseline	4,92 ± 1,98	4,68 ± 1,48	4,53 ± 1,27	4,59 ± 1,19	4,52 ± 1,44	0,614		0,421
	12th Months	4,17 ± 1,14	4,24 ± 1,37	4,31 ± 1,28	4,72 ± 1,06	4,28 ± 1,41	< 0,001
	24th Months	4,62 ± 1,23	4,47 ± 1,19	4,42 ± 1,39	4,61 ± 1,14	4,21 ± 1,17	0,017
	36th Months	4,36 ± 1,28	4,51 ± 1,05	4,5 ± 1,45	4,66 ± 1,4	4,41 ± 1,14	0,644
	48th Months	5,02 ± 1,44	4,45 ± 1,17	4,4 ± 1,53	4,44 ± 1,4	4,24 ± 1,22	0,408
	¹ p-values	0,395 (n = 10)	0,017 (n = 29)	0,042 (n = 67)	0,791 (n = 72)	0,368 (n = 7)
Non-HDL Cholesterol	Baseline	136,94 ± 53,86	139,06 ± 43,28	125,13 ± 41,64	143,55 ± 39,23	131,84 ± 46,04	< 0,001			0,035**
	12th Months	126,67 ± 38,71	131,83 ± 41,24	127,53 ± 41,74	157,7 ± 42,8	136,76 ± 43,8	< 0,001
	24th Months	140,28 ± 34,12	141,69 ± 39,32	130,76 ± 40,37	156,23 ± 43,49	136,55 ± 38,77	< 0,001
	36th Months	135,96 ± 37,4	147,74 ± 41,04	135,23 ± 44,48	153,23 ± 44,71	146,35 ± 45,79	< 0,001
	48th Months	146,18 ± 37,58	143,42 ± 68,09	137,26 ± 38,74	150,61 ± 42,2	140,5 ± 45,23	0,049
	¹ p-values	0,345 (n = 10)	0,002 (n = 28)	0,074 (n = 67)	0,033 (n = 72)	0,680 (n = 7)
LDL	Baseline	111,7 ± 42,87	110,72 ± 38,99	99,59 ± 34,45	117,03 ± 39,89	110,01 ± 55,06	< 0,001			0,096
	12th Months	102,7 ± 39,96	105,41 ± 35,56	103,59 ± 32,96	133,01 ± 51,05	110,35 ± 55,41	< 0,001
	24th Months	109,76 ± 26,69	113,63 ± 37,53	104,73 ± 32,9	130,55 ± 54,33	106,94 ± 34,43	< 0,001
	36th Months	121,36 ± 67,28	125,22 ± 54,05	108,83 ± 37,36	131,75 ± 59,58	115,53 ± 37,54	0,001
	48th Months	113,18 ± 34,9	126,82 ± 63,7	111,34 ± 41,48	122,56 ± 34,57	109,82 ± 41,19	0,024
	¹ p-values	0,631 (n = 9)	0,011 (n = 25)	0,024 (n = 65)	0,088 (n = 67)	0,172 (n = 6)
Trigliserit	Baseline	145,39 ± 85,2	163,02 ± 90,73	138,82 ± 78,47	151,37 ± 87,12	153,86 ± 96,11	0,368			0,592
	12th Months	136,43 ± 69,57	141 ± 62,98	138,97 ± 93,48	171,59 ± 113,25	167,04 ± 91,22	< 0,001
	24th Months	150,72 ± 72,97	145,32 ± 75,81	143,25 ± 85,53	169,54 ± 105,39	146,51 ± 66,91	0,060
	36th Months	155,92 ± 96,25	138,57 ± 70,71	144,28 ± 100,38	172,39 ± 115,42	172,34 ± 95,23	0,019
	48th Months	163,71 ± 69,91	150,38 ± 78,55	152,1 ± 117,42	158,18 ± 90,43	169,79 ± 110,87	0,154
	¹ p-values	0,155 (n = 10)	0,365 (n = 27)	0,653 (n = 67)	0,080 (n = 66)	0,137 (n = 7)
Spine dual-energy X-ray absorptiometry (Dexa)	Baseline	0,57 ± 1,76	0,67 ± 1,11	0,24 ± 1,26	0,54 ± 1,59	-0,31 ± 1,4	0,004			0,041
	24th Months	0,6 ± 0,57	-0,48 ± 1,48	0,55 ± 1,08	0,34 ± 1,18	-0,39 ± 1,44	0,013			0,041
	¹ p-values	N/A (n = 2)	0,027 (n = 6)	< 0,001 (n = 59)	0,136(n = 8)	0,756 (n = 18)
Femur Dexa	Baseline	0,38 ± 1,12	0,8 ± 0,95	0,34 ± 1,03	0,53 ± 1,34	0,03 ± 1,15	0,049			0,086
	24th Months	0,3 ± 0,99	-0,17 ± 1,07	0,51 ± 0,87	-0,03 ± 1,18	-0,28 ± 1,46	0,103			0,086
	¹ p-values	N/A (n = 2)	0,043(n = 6)	< 0,001 (n = 59)	0,043 (n = 8)	0,084 (n = 20)
Data were shown as mean ± standard deviation. 1: p-values for comparison results among several periods. The sample size and data in all periods are given along with p-values. 2: p-values for comparison results among TAF and TDF subgroups. 2: p-values for comparison results of the alteration among TAF and TDF subgroups. : According to multiple comparison tests, there was a statistically significant difference between TDF/FTC/RAL and TDF/FTC/DTG and between TDF/FTC/DTG and TAF/FTC/c/EVG subgroups. *: Multiple comparison tests showed a statistically significant difference between TDF/FTC/RAL and TAF/FTC/c/EVG subgroups.

This study compared changes in total cholesterol, non-HDL cholesterol, and Spine Dx levels among patients using different subtypes of TDF and TAF. Statistically significant differences were found between TDF/FTC/RAL and TDF/FTC/DTG and between TDF/FTC/DTG and TAF/FTC/c/EVG. Significant differences were also observed between TDF/FTC/RAL and TAF/FTC/c/EVG subgroups. These findings suggest that different subtypes of TDF and TAF may have varying effects on cholesterol profiles (Table 4, Fig. 2).

When evaluating the 24-month follow-up period between those taking TDF and those taking TAF, it was found that the rate of CD4 increase was superior in the TDF group. At the 48-month follow-up, ALT levels were observed to normalize more favorably in the TDF group. Total cholesterol levels were higher in the TAF group at the 48-month follow-up, and non-HDL cholesterol levels were noted to have unfavorable outcomes in the TAF group.

This study was conducted based on retrospective data from PWLH followed in various centers in Türkiye. Our findings indicate the advantages of TDF-containing regimens in terms of CD4 cell count increase and normalization of ALT levels. In contrast, during the initial 24 months, TAF regimens were advantageous regarding HIV RNA reduction and phosphorus metabolism. Evaluation of lipid profiles revealed disadvantages in non-HDL cholesterol, total cholesterol, and triglyceride parameters among patients using TAF. These findings suggest that TDF and TAF treatment regimens have different effects, emphasizing the importance of considering individual patient characteristics and needs in treatment selection.

Our study showed no significant difference in HIV RNA levels between TDF and TAF groups at baseline and within the first 24 months of treatment [6, 7]. However, at the 24-month follow-up, we observed higher HIV RNA levels in the TDF group compared to the TAF group (14218.43 ± 233947.64 vs. 3247.15 ± 55371.53, p < 0.001), but this difference was not significant at 36 and 48 months (P > 0.05). Various studies have reported no significant differences in viral load reduction between TDF and TAF groups [8]. However, some studies have indicated significant differences in HIV RNA reduction with TAF regimens [8]. These discrepancies may stem from differences in study methodologies, patient populations, and treatment protocols. Our findings suggest that while TDF and TAF exhibit similar virologic efficacy, treatment duration, and patient populations may influence the observed differences at specific time points.

In our study, over the 48-month follow-up period, TDF participants demonstrated a more significant increase in CD4 levels than those using TAF. Despite lower baseline CD4 levels in the TDF group, improvement in CD4 levels over the 48-month follow-up was superior to that in the TAF group. A literature review reveals insufficient direct comparison data on CD4 level increases between TDF and TAF. However, some studies have yielded different results in evaluations of CD4 increases. For instance, in a study comparing Elvitegravir/Cobicistat/FTC/TAF regimen with Elvitegravir/Cobicistat/FTC/TDF regimen, the increase per microliter based on initial CD4 + cell counts varied [9].

It is known that TAF use maintains plasma tenofovir levels 91% lower, while intracellular tenofovir levels are four times higher compared to TDF. This suggests that the concentration of plasma tenofovir may be higher with TDF use. Some studies have found that, although not statistically significant, groups using TDF showed faster CD4 increases than those using TAF [10] [11]. Meta-analyses have not found significant differences in CD4 levels between TDF and TAF groups. However, these contradictory findings in the literature may be attributed to different pharmacokinetic properties of the drugs. For example, a substantial decrease in plasma tenofovir concentrations has been observed when switching from TDF to TAF. This factor could be significant in explaining their effects on CD4 levels [12]. These findings appear consistent with the knowledge that plasma concentrations of tenofovir are significantly higher with TDF than with TAF. For instance, a transition from TDF to TAF showed a 90% reduction in plasma tenofovir concentration. Plasma tenofovir concentrations in TDF users averaged 99.98 (2.24) ng/mL, whereas in TAF users, it was 10.2 (1.6) ng/mL [13]. In a recent study investigating the effectiveness of Pre-Exposure Prophylaxis (PREP), it was demonstrated that plasma tenofovir concentration is closely associated with the risk of HIV infection development [14]. These findings suggest higher plasma tenofovir concentrations may confer an advantage regarding CD4 level improvement. However, it should be emphasized that new research is needed to understand better the role of plasma tenofovir concentrations in the effectiveness of HIV prevention strategies.

Our study observed that the mean age in the TDF/FTC/BIC subgroup (39.2611.92) was significantly lower compared to other subgroups. This could be attributed to the fact that this drug is the most recently introduced medication in our country, and it is predominantly initiated in younger patients newly diagnosed with HIV. When analyzing the gender distribution among subgroups, it was determined that the proportion of females in the TDF/FTC/RAL subgroup (35.1%) was significantly higher compared to other subgroups. The TDF/FTC/RAL combination is known to be a reliable option, especially during pregnancy, and it may be associated with its initiation in women with childbearing potential.

In our study, weight gain associated with antiretroviral therapy (ART), metabolic disturbances, and cardiovascular diseases remains a significant concern. It has been previously reported that switching from TDF to TAF may lead to the development of hyperlipidemia. Studies suggest that transitioning to TAF may be associated with a higher incidence of dyslipidemia [3, 15]. Our research observed more favorable outcomes in cholesterol and non-HDL cholesterol levels among the TDF group. One possible explanation for this finding could be the self-statin-like effect of TDF, which has been shown to reduce blood lipid concentrations directly [16]. However, other studies suggest that this effect may stem directly from TAF use. A recent study found that transitioning to TAF resulted in increased cholesterol and LDL levels, which play a crucial role in the etiology of atherosclerosis [17]. The increase in LDL-C and TC due to TAF is particularly significant, as LDL cholesterol is considered a primary driver of cardiovascular risk [18]. Changes in cardiovascular risk are directly proportional to changes in LDL cholesterol levels and the achieved LDL cholesterol level [19].

Additionally, similar cardiovascular risk reductions have been observed regardless of the method used to lower LDL cholesterol (lifestyle changes, diet, or lipid-lowering drugs) [20]. Our study results indicate a significant increase in both cholesterol and non-HDL cholesterol levels among patients using TAF [16, 19]. In a recent study, it was observed that transitioning from TDF to TAF in individuals living with HIV resulted in a significant increase in BMI, total cholesterol, and LDL values [20]. In the same study, the median Atherosclerotic Cardiovascular Disease (ASCVD) risk score increased from 6.9–8.1% after switching to TAF (P < 0.01). Adjustments made for age, gender, race, other medications that may contribute to weight gain, and duration of living with HIV showed that transitioning from TDF to TAF was associated with an average 13% increase in ASCVD risk score (95% CI, 4–23%). Following the transition to TAF, the average 13% increase in ASCVD risk score led to 50.7% of participants surpassing the 7.5% statin initiation threshold. Considering the increases in BMI and cholesterol levels, changes in ASCVD risk scores may be associated with an increased risk of cardiovascular disease in individuals transitioning from TDF to TAF. Therefore, we believe that evaluating non-TAF options may be safer for patients with a history of cardiovascular risk factors [21].

LDL cholesterol has traditionally been widely used as a marker for cardiovascular risk. However, non-HDL cholesterol has been suggested to be more helpful in assessing atherogenic risk due to its inclusion of proatherogenic triglyceride-rich lipoproteins (TRL) and apolipoprotein B (apoB) particles [22]. Our study observed that non-HDL cholesterol levels were significantly higher in patients using TAF than those using TDF. Similarly, a study monitoring lipid parameters after switching from TDF to TAF showed a significant increase in non-HDL cholesterol post-TAF [23]. In our cohort, we observed cholesterol elevation due to TAF use persisted for up to 24 months and then tended to decrease, possibly attributed to lifestyle changes and statin use associated with hyperlipidemia.

Furthermore, Silva et al. demonstrated that patients using TAF/FTC/c/EVG required lipid-lowering therapy twice as often as those using TDF/FTC/c/EVG [19]. When examining TAF subgroups in our study, we found that patients using bictegravir-based combinations had a higher increase in non-HDL cholesterol than those using c/EVG. Previous studies have shown that bictegravir, compared to EVG/c, presents a better lipid profile. However, these studies primarily reflect changes in patients undergoing treatment switches rather than direct head-to-head comparisons [24]. Long-term follow-up data from TAF/FTC/BIC combinations also showed total cholesterol and LDL increases, with lipid-lowering therapy initiated in ≤ 2% of patients annually after week 48 [25]. Based on these findings, lipid values should be closely monitored in patients using TAF, especially those at higher cardiovascular risk, and alternative options to TAF should be considered. Traditional risk factors for dyslipidemia, such as diabetes, high BMI, advanced age, and low CD4 + cell counts (< 350 cells/µl), should prompt close monitoring and counseling on lifestyle changes in HIV-positive individuals.

In individuals living with HIV, the infection itself, along with aging, comorbidities, and certain antiretroviral (ARV) medications, increases the risk of osteoporosis. Long-term use of TDF medications is mainly associated with reduced bone mineral density and osteoporosis [26]. Conversely, TAF is associated with lower plasma tenofovir levels, resulting in fewer bone-related side effects. When phosphorus levels were examined, TAF users were found to have significantly higher levels at 48 months compared to TDF users, which may indicate a potential positive effect of TAF on bone health. Studies have shown improvement in bone mineral density when switching from TDF to TAF [27, 28]. However, in a randomized open-label study, no change in bone mineral density was observed after 48 weeks of switching from TDF/FTC/RPV to TAF/FTC/RPV [29].

In our study, when examining TDF subgroups, we observed significant deterioration in spine DEXA results at 24 months, particularly in the TDF/FTC/c/EVG group compared to other groups. This raises the possibility that the boosting effect of cobicistat may increase TDF plasma levels and thereby contribute to bone toxicity. Similarly, another study demonstrated increased bone and kidney toxicity when TDF and cobicistat were administered together, with an area under the curve (AUC) 23% higher [30]. According to a recent meta-analysis, the risk of bone and kidney-related toxicity with TDF was associated with the concurrent use of pharmacological enhancers like cobicistat and ritonavir [30]. When TDF was used without enhancers, there was no significant difference in grade 1–4 adverse events or rates of discontinuation due to renal or bone-related treatment compared to TAF. Therefore, when conducting bone and kidney research, data should be carefully evaluated separately for enhancer use, and further studies in this area are warranted.

Our study observed that ALT levels were significantly higher in patients starting on TDF than those starting on TAF. Additionally, among patients starting on TDF, we noted that the increase in CD4 levels and the decrease in ALT levels at 48 months were better than the TAF group. In a study evaluating treatment-naive HIV patients, it was found that ALT or AST levels were > 1.25 times higher in patients with low baseline CD4 levels [31]. Interestingly, despite being taken at lower doses, TAF can achieve better concentrations in the liver. TDF, particularly effective in reducing lipids, especially in older people, may have a lesser effect on hepatic steatosis, leading to better ALT outcomes. The dyslipidemia observed in TAF users may indirectly relate to elevated liver enzymes. A study examining determinants of liver steatosis in individuals living with HIV participating in the Swiss HIV Cohort Study identified a significant relationship between BMI ≥ 25 kg/m², age 50 and older, and the use of TAF with liver steatosis [32].

In our study, subgroup analyses revealed that treatments with the most detrimental effects on cholesterol levels were TAF and BIC. DOL and TAF were independently associated with a higher likelihood of weight gain. Integrase inhibitors like efavirenz have been suggested to disrupt adipocyte function and increase proinflammatory cytokines. Our research observed that lipid profile deterioration was particularly pronounced in measurements taken within the first 24 months. The study's subgroup analyses showed that TAF and BIC therapies had the most detrimental impact on cholesterol levels [33]. Our study also indicates that lipid deterioration was particularly pronounced in measurements taken within the first 24 months. Another study highlighted that over the years, switching from TDF to TAF and especially using INSTI combinations could lead to a cascade of metabolic disorders, including metabolic syndrome, fatty liver, and eventually insulin resistance [34]. The elevation in ALT observed in cases using TAF-based combinations could be associated with hepatosteatosis. However, this issue should be investigated further with well-designed randomized controlled trials.

Our study has several limitations that need to be acknowledged. Despite having a relatively large case series, it is designed retrospectively. Significant differences were observed among patient groups evaluated in terms of drug experience. In the TDF group, the percentage of inexperienced patients was 77.2%, whereas in the TAF group, this percentage was 52.2% (p < 0.001). This finding is attributable to the retrospective nature of our study. The observed difference in drug experience in our retrospective study is associated with the clinical use durations of TDF (since 2001) and TAF (since 2015), as well as the characteristics of these drugs. Having been used for years, TDF has been more commonly preferred among inexperienced patient groups. In contrast, as a newer component, TAF has been chosen by patients who have previously experienced different ART combinations. This factor is crucial for interpreting our study's findings and understanding its conclusions.

Our study, based on retrospective data analysis from various centers in Türkiye, demonstrates that increases in CD4 levels and normalization of ALT levels have a significant impact on the effectiveness of HIV treatment. However, adverse lipid profiles were identified in patients receiving TAF. These findings underscore that TDF and TAF regimens have distinct effects, highlighting the importance of considering individual patient characteristics (such as viral load, CD4 levels, renal and hepatic function, and lipid profile) and need in treatment selection. This approach can contribute to personalized treatment strategies and identifying more suitable therapies for patients' health conditions. Individual patient status and other clinical factors should also be considered when making treatment decisions.

ALT: Alanine aminotransferase

APO B: Apolipoprotein B

ART: Antiretroviral therapy

ASCVD: Atherosclerotic Cardiovascular Disease

AUC: Under the curve

BIC: Bictegravir

BMI: Body mass index

C: Cobicistat

DTG: Dolutegravir

EACS: European AIDS Clinical Society

eGFR: Estimated glomerular filtration rate

EVG: Elvitegravir

FTC: Emtricitabine

HDL: High-density lipoprotein

LDL: Low-density lipoprotein

NRTI: Nucleoside reverse transcriptase inhibitors

PLWH: People living with HIV

PREP: Pre-Exposure Prophylaxis

RAL: Raltegravir

TAF: Tenofovir alafenamide

TDF: Tenofovir disoproxil fumarate

Ethics approval: This study titled "Comparative Metabolic Effects of Integrase Inhibitor and Emtricitabine-Containing Tenofovir Derivatives in People Living with HIV: A Multi-Center Retrospective Analysis" received ethical approval from the Sakarya University Non-Interventional Clinical Research Ethics Committee. The application was thoroughly reviewed, and the committee determined that the study adhered to ethical standards and posed no scientific concerns. The ethical approval was granted on January 31, 2023, under the reference number E-71522473-050.01.04-216291-46.

Availability of data and material:

The data supporting this study's findings are available upon reasonable request from the corresponding author, Oguz Karabay. Additional data related to this study can be provided by contacting the corresponding author.

Competing interests: The authors declare that they have no competing interests related to this study.

Funding: This research did not receive any specific grant from public, commercial, or non-profit funding agencies. However, literature support was provided by İLKO İlaç during the design of the article.

Acknowledgments

We would like to thank Yaşariye Bayazıt for her scientific contributions to this study.

Authors' contributions: All authors contributed to the study conception and design. The conception and design of the study were led by OK, AV. Supervision was provided by OK and EG. Material support were secured by OK. Data collection, processing, and analysis were carried out by AV, İY, NCB and supported by the other authors (AK, RD, Y Ç, H Ç, MTŞ, ZAO, UB, SŞ, SA, MD, DY, Nİ, BT, TD Ç, AB, AU, ÜE, Y Ç, AU, ÜE) through patient referrals and clinical data provision. Literature review and writing responsibilities were shared among OK, AV, AK, RD, Y Ç, H Ç, MTŞ, ZAO, UB, SŞ, SA, MD, DY, Nİ, BT, TD Ç, AB, AU, ÜE, Y Ç, AU, ÜE, EG with critical review conducted before submission to ensure intellectual content and technical accuracy.

Auhors' information:

Center number	Name Sur name	Afiliation	ORCID
1	Oğuz Karabay	Sakarya University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Sakarya, Turkiye	0000-0003-1514-1685-
1	Aslı Vatan	Sakarya University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Sakarya, Turkiye	0000-0003-4157-9226
1	İlknur Yılmaz	Sakarya University Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Sakarya, Turkiye	0009-0007-1369-4936
1	Nurselin Can Balta	Sakarya University Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Sakarya, Turkiye	0000-0001-9491-4572
2	Arzu Kantürk	Health Science University Faculty of Medicine, Prof.Dr. Cemil Taşcıoğlu City Hospital, Infectious Disease and Clinical Microbiology, Turkiye	0000-0002-8397-6089
3	Rıdvan Dumlu	Medipol University Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Istanbul, Turkiye	0000-0001-8213-6064
4	Yasemin Cağ	Istanbul Medeniyet University Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Istanbul, Turkiye	0000-0002-9983-0308
4	Hülya Çaşkurlu	Istanbul Medeniyet University Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Istanbul, Turkiye	0000-0002-6760-2052
4	Merve Tokgöz Şık	Istanbul Medeniyet University Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Istanbul, Turkiye	0009-0002-8494-4705
5	Zahide Aşık Otman	University of Health Sciences, Antalya Training and Research Hospital, Department of Infectious Diseases and Clinical Microbiology, Antalya, Turkiye	0000-0002-1729-2922
5	Umay Balcı	University of Health Sciences, Antalya Training and Research Hospital, Department of Infectious Diseases and Clinical Microbiology, Antalya, Turkiye	0000-0003-2369-0692
6	Seniha Şenbayrak	University of Health Sciences, Haydarpasa Numune Training and Research Hospital, Department of Infectious Diseases and Clinical Microbiology, Istanbul	0000-0002-4983-6613
7	Sıla Akhan	Kocaeli University, Department of Infectious Diseases and Clinical Microbiology, Kocaeli, Turkiye	0000-0002-2540-2060
7	Müge Toygar Deniz	Kocaeli University, Department of Infectious Diseases and Clinical Microbiology, Kocaeli, Turkiye	0000-0002-6946-2727
8	Dilek Yekenkurul	Duzce University, Department of Infectious Diseases and Clinical Microbiology, Düzce, Turkiye	0000-0002-4456-7485
8	Nevin Ince	Duzce University, Department of Infectious Diseases and Clinical Microbiology, Düzce, Turkiye	0000-0002-0129-4536
8	Bekir Tunca	Düzce University, Department of Infectious Diseases and Clinical Microbiology,Düzce, Turkey	0000-0003-4756-5968
9	Tuba Damar Çakırca	Şanlıurfa Training and Research Hospital, Department of Infectious Diseases and Clinical Microbiology, Şanlıurfa, Türkiye	0000-0002-1706-230X
10	Aliye Baştuğ	Health Science University Infectious Disease and Clinical Microbiology, Ankara Bilkent City Hospital, Ankara, Turkiye	0000-0002-8831-4877
11	Abdullah Ucar	Sakarya University, Faculty of Medicine, Public Health Department.	0000-0002-0220-3720
12	Unal Erkorkmaz	Sakarya University School of Medicine, Department of Biostatistics, Sakarya, Turkiye	0000-0002-8497-4704
1	Ertuğrul Güçlü	Sakarya University, Department of Infectious Diseases and Clinical Microbiology, Sakarya, Turkiye	0000-0003-2860-2831

Bekker LG, Beyrer C, Mgodi N, Lewin SR, Delany-Moretlwe S, Taiwo B, et al. HIV infection. Nat Rev Dis Primers. 2023 Aug 17;9(1):42.
Ke S, Fang Q, Lan J, Qiao N, Zhang X, Xie C, et al. Survival times of HIV/AIDS in different AIDS Diagnostic and Treatment Guidelines from 2006 to 2020 in Liuzhou, China. BMC Public Health. 2023 Sep 7;23(1):1745.
Kauppinen KJ, Kivelä P, Sutinen J. Switching from Tenofovir Disoproxil Fumarate to Tenofovir Alafenamide Significantly Worsens the Lipid Profile in a Real-World Setting. AIDS Patient Care STDS. 2019 Dec;33(12):500–6.
Ruderman SA, Crane HM, Nance RM, Whitney BM, Harding BN, Mayer KH, et al. Brief Report: Weight Gain Following ART Initiation in ART-Naïve People Living With HIV in the Current Treatment Era. J Acquir Immune Defic Syndr. 2021 Mar 1;86(3):339–43.
Hodder S, Squires K, Kityo C, Hagins D, Avihingsanon A, Kido A, et al. Brief Report: Efficacy and Safety of Switching to Coformulated Elvitegravir, Cobicistat, Emtricitabine, and Tenofovir Alafenamide (E/C/F/TAF) in Virologically Suppressed Women. J Acquir Immune Defic Syndr. 2018 Jun 1;78(2):209–13.
Post FA, Yazdanpanah Y, Schembri G, Lazzarin A, Reynes J, Maggiolo F, et al. Efficacy and safety of emtricitabine/tenofovir alafenamide (FTC/TAF) vs. emtricitabine/tenofovir disoproxil fumarate (FTC/TDF) as a backbone for treatment of HIV-1 infection in virologically suppressed adults: subgroup analysis by third agent of a randomized, double-blind, active-controlled phase 3 trial<sup/>. HIV Clin Trials. 2017 May;18(3):135–40.
DeJesus E, Haas B, Segal-Maurer S, Ramgopal MN, Mills A, Margot N, et al. Superior Efficacy and Improved Renal and Bone Safety After Switching from a Tenofovir Disoproxil Fumarate- to a Tenofovir Alafenamide-Based Regimen Through 96 Weeks of Treatment. AIDS Res Hum Retroviruses. 2018 Apr;34(4):337–42.
Sax PE, Zolopa A, Brar I, Elion R, Ortiz R, Post F, et al. Tenofovir alafenamide vs. tenofovir disoproxil fumarate in single tablet regimens for initial HIV-1 therapy: a randomized phase 2 study. J Acquir Immune Defic Syndr. 2014 Sep 1;67(1):52–8.
Venter WDF, Moorhouse M, Sokhela S, Fairlie L, Mashabane N, Masenya M, et al. Dolutegravir plus Two Different Prodrugs of Tenofovir to Treat HIV. N Engl J Med. 2019 Aug 29;381(9):803–15.
Arribas JR, Thompson M, Sax PE, Haas B, McDonald C, Wohl DA, et al. Brief Report: Randomized, Double-Blind Comparison of Tenofovir Alafenamide (TAF) vs Tenofovir Disoproxil Fumarate (TDF), Each Coformulated With Elvitegravir, Cobicistat, and Emtricitabine (E/C/F) for Initial HIV-1 Treatment: Week 144 Results. J Acquir Immune Defic Syndr. 2017 Jun 1;75(2):211–8.
Tao X, Lu Y, Zhou Y, Huang Y, Chen Y. Virologically suppressed HIV-infected patients on TDF-containing regimens significantly benefit from switching to TAF-containing regimens: A meta-analysis of randomized controlled trials. Int J Infect Dis. 2019 Oct;87:43–53.
Podany AT, Bares SH, Havens J, Dyavar SR, O’Neill J, Lee S, et al. Plasma and intracellular pharmacokinetics of tenofovir in patients switched from tenofovir disoproxil fumarate to tenofovir alafenamide. AIDS. 2018 Mar 27;32(6):761–5.
Von Kleist M, Anderson PL, Garcia-Lerma G, Liu A. Pharmacokinetics and Pharmacodynamics of Pre-Exposure Prophylaxis Against HIV. Frontiers Media SA; 2020. 91 p.
Gazzola L, Tagliaferri G, De Bona A, Mondatore D, Borsino C, Bini T, et al. Dyslipidaemia after switch to tenofovir alafenamide (TAF)-based cART regimens in a cohort of HIV-positive patients: what clinical relevance? HIV Med. 2021 Feb;22(2):140–5.
Santos JR, Saumoy M, Curran A, Bravo I, Llibre JM, Navarro J, et al. The lipid-lowering effect of tenofovir/emtricitabine: a randomized, crossover, double-blind, placebo-controlled trial. Clin Infect Dis. 2015 Aug 1;61(3):403–8.
Ference BA, Ginsberg HN, Graham I, Ray KK, Packard CJ, Bruckert E, et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel. Eur Heart J. 2017 Aug 21;38(32):2459–72.
Borén J, Chapman MJ, Krauss RM, Packard CJ, Bentzon JF, Binder CJ, et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease: pathophysiological, genetic, and therapeutic insights: a consensus statement from the European Atherosclerosis Society Consensus Panel. Eur Heart J. 2020 Jun 21;41(24):2313–30.
Cid-Silva P, Fernández-Bargiela N, Margusino-Framiñán L, Balboa-Barreiro V, Mena-De-Cea Á, López-Calvo S, et al. Treatment with tenofovir alafenamide fumarate worsens the lipid profile of HIV-infected patients versus treatment with tenofovir disoproxil fumarate, each coformulated with elvitegravir, cobicistat, and emtricitabine. Basic Clin Pharmacol Toxicol. 2019 Apr;124(4):479–90.
Schafer JJ, Sassa KN, O’Connor JR, Shimada A, Keith SW, DeSimone JA. Changes in Body Mass Index and Atherosclerotic Disease Risk Score After Switching From Tenofovir Disoproxil Fumarate to Tenofovir Alafenamide. Open Forum Infect Dis. 2019 Oct;6(10):ofz414.
Martini S, Maggi P, Gervasoni C, Onorato L, Ferrara S, Alessio L, et al. Dynamics of Lipid Profile in Antiretroviral-Naïve HIV-Infected Patients, Treated with TAF-Based Regimens: A Multicenter Observational Study. Biomedicines [Internet]. 2022 Dec 7;10(12). Available from: http://dx.doi.org/10.3390/biomedicines10123164
Arsenault BJ, Rana JS, Stroes ESG, Després JP, Shah PK, Kastelein JJP, et al. Beyond low-density lipoprotein cholesterol: respective contributions of non-high-density lipoprotein cholesterol levels, triglycerides, and the total cholesterol/high-density lipoprotein cholesterol ratio to coronary heart disease risk in apparently healthy men and women. J Am Coll Cardiol. 2009 Dec 29;55(1):35–41.
Plum PE, Maes N, Sauvage AS, Frippiat F, Meuris C, Uurlings F, et al. Impact of switch from tenofovir disoproxil fumarate-based regimens to tenofovir alafenamide-based regimens on lipid profile, weight gain and cardiovascular risk score in people living with HIV. BMC Infect Dis. 2021 Sep 6;21(1):910.
Saumoy M, Sanchez-Quesada JL, Ordoñez-Llanos J, Podzamczer D. Do All Integrase Strand Transfer Inhibitors Have the Same Lipid Profile? Review of Randomised Controlled Trials in Naïve and Switch Scenarios in HIV-Infected Patients. J Clin Med Res [Internet]. 2021 Aug 4;10(16). Available from: http://dx.doi.org/10.3390/jcm10163456
Sax PE, Arribas JR, Orkin C, Lazzarin A, Pozniak A, DeJesus E, et al. Bictegravir/emtricitabine/tenofovir alafenamide as initial treatment for HIV-1: five-year follow-up from two randomized trials. EClinicalMedicine. 2023 May;59:101991.
Han WM, Wattanachanya L, Apornpong T, Jantrapakde J, Avihingsanon A, Kerr SJ, et al. Bone mineral density changes among people living with HIV who have started with TDF-containing regimen: A five-year prospective study. PLoS One. 2020 Mar 25;15(3):e0230368.
Mills A, Arribas JR, Andrade-Villanueva J, DiPerri G, Van Lunzen J, Koenig E, et al. Switching from tenofovir disoproxil fumarate to tenofovir alafenamide in antiretroviral regimens for virologically suppressed adults with HIV-1 infection: a randomised, active-controlled, multicentre, open-label, phase 3, non-inferiority study. Lancet Infect Dis. 2016 Jan 1;16(1):43–52.
Kanda N, Okamoto K, Okumura H, Mieno M, Sakashita K, Sasahara T, et al. Outcomes associated with treatment change from tenofovir disoproxil fumarate to tenofovir alafenamide in HIV-1-infected patients: a real-world study in Japan. HIV Med. 2021 Jul;22(6):457–66.
Moore AEB, Burns JE, Sally D, Milinkovic A, Krokos G, John J, et al. Bone turnover change after randomised switch from tenofovir disoproxil to tenofovir alafenamide fumarate in men with HIV. AIDS. 2024 Mar 15;38(4):521–9.
Hill A, Hughes SL, Gotham D, Pozniak AL. Tenofovir alafenamide versus tenofovir disoproxil fumarate: is there a true difference in efficacy and safety? J Virus Erad. 2018 Apr 1;4(2):72–9.
Dusingize JC, Hoover DR, Shi Q, Mutimura E, Rudakemwa E, Ndacyayisenga V, et al. Association of Abnormal Liver Function Parameters with HIV Serostatus and CD4 Count in Antiretroviral-Naive Rwandan Women. AIDS Res Hum Retroviruses. 2015 Jul;31(7):723–30.
Riebensahm C, Berzigotti A, Surial B, Günthard HF, Tarr PE, Furrer H, et al. Factors Associated With Liver Steatosis in People With Human Immunodeficiency Virus on Contemporary Antiretroviral Therapy. Open Forum Infect Dis. 2022 Nov;9(11):ofac538.
Litwack G. Aquaporin Regulation. Academic Press; 2020. 374 p.
Biały M, Czarnecki M, Inglot M. Impact of Combination Antiretroviral Treatment on Liver Metabolic Health in HIV-Infected Persons. Viruses [Internet]. 2023 Dec 15;15(12). Available from: http://dx.doi.org/10.3390/v15122432
Biały M, Czarnecki M, Inglot M. Impact of Combination Antiretroviral Treatment on Liver Metabolic Health in HIV-Infected Persons. Viruses [Internet]. 2023 Dec 15;15(12). Available from: http://dx.doi.org/10.3390/v15122432

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Comparative Metabolic Effects of Integrase Inhibitor and Emtricitabine-Containing Tenofovir Derivatives in People Living with HIV: A Multi-Center Retrospective Analysis

Status:

Version 1

Abstract

Background

Methods

Findings:

Conclusion

Figures

INTRODUCTION

METHOD

Statistical Analysis

RESULTS

DISCUSSION

Limitations of the study

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1