Longitudinal Progression of Estimated GFR in HIV-1-Infected Patients with Normal Renal Function on Tenofovir-Based Therapy in China

Figures & data

Figure 1 Study flow diagram.

Table 1 The Difference of Slopes Before and After Cutoff Times and Comparison of One-Linear and Piecewise-Linear Models

Without TDF		With TDF
Model 1
Comparison of slopes	Exp(β)^a (95% CI)	Comparison of slopes	Exp(β)^a (95% CI)
<2.55 y	−4.79 (−5.84, −3.74) <0.001	<1.40 y	−8.47 (−11.56, −5.37) <0.001
≥2.55 y		≥1.40 y, < 3.20 y
Comparison of models	Log likelihood ratio test^b	Comparison of slopes
One-linear model	<0.001	≥1.40 y, <3.20 y	−9.22 (−12.52, −5.92) <0.001
Non-linear model		≥3.20 y
		Comparison of models	Log likelihood ratio test^b
		One-linear model	<0.001
		Non-linear model
Model 2
Comparison of slopes	Exp(β)^a (95% CI)	Comparison of slopes	Exp(β)^a (95% CI)
<2.15 y	−5.43 (−6.47, −4.40) <0.001	<1.40 y	−10.14 (−14.44, −5.85) <0.001
≥2.15 y		≥1.40 y, <2.30 y
Comparison of models	Log likelihood ratio test^b	Comparison of slopes
One-linear model	<0.001	≥1.40 y, <2.30 y	−8.54 (−12.67, −4.41) <0.0001
Non-linear model		≥2.30 y
		Comparison of models	Log likelihood ratio test^b
		One-linear model	<0.001
		Non-linear model
Model 3
Comparison of slopes	Exp(β)^a (95% CI)	Comparison of slopes	Exp(β)^a (95% CI)
<2.15 y	−4.28 (−6.24, −2.33) <0.001	<1.30 y	−7.09 (−13.99, −0.20) 0.044
≥2.15 y		≥1.30 y, <2.10 y
Comparison of models	Log likelihood ratio test^b	Comparison of slopes
One-linear model	<0.001	≥1.30 y, <2.10 y	−8.82 (−14.89, −2.76) 0.004
Non-linear model		≥2.10 y
		Comparison of models	Log likelihood ratio test^b
		One-linear model	<0.001
		Non-linear model

Notes: ^aExp(β) represents the difference of segmented slopes (mL/min/1.73 m²/year), along with a p value from Wald test. ^bLog likelihood ratio test was used to compare one-linear regression model with two-piecewise regression model, below 0.05 indicates two-piecewise regression model was a better fit to the data than the one-linear model that assumed a single slope across the entire period of observation. Model 1: unadjusted for any variables at baseline. Model 2: adjusted for age, sex, weight, height, body mass index (BMI), CD4 count, eGFR, dyslipidemia, HIV/AIDS risk factors (sexual orientation and intravenous drug use), WHO stage III/IV HIV/AIDS, hepatitis B positivity, hepatitis C positivity, anemia, diabetes, and HIV-1 RNA viral load at baseline. Model 3: propensity score-matched sample.

Table 2 Characteristics at Cohort Entry Stratified by Tenofovir Disoproxil Fumarate Before and After Propensity Score Matching

Characteristics	Before Matching			After Matching
	Without TDF	With TDF	P value	Without TDF	With TDF	P value
Overall	(n=524,63.7%)	(n=299,36.3%)		(n=260,66.7%)	(n=130,33.3%)
Age (years)	27 (24–32)	30 (25–36)	<0.001	27 (25–32)	27 (25–33)	0.638
Female	20 (3.8%)	18 (6.0%)	0.147	5 (1.9%)	5 (3.8%)	0.428
Weight (kg)	63 (57–70)	63 (56–67)	0.185	62 (57–70)	63 (58–68)	0.810
Height (cm)	172 (169–175)	172 (169–175)	0.546	172 (170–175)	172 (170–175)	0.790
BMI (kg/m^2)	21.1 (19.5–23.1)	21.0 (19.4–22.7)	0.240	21.0 (19.4–23.1)	21.2 (19.5–22.9)	0.637
CD4 (cells/μL)	323 (246–423)	247 (117–359)	<0.001	326 (262–420)	335 (246–414)	0.988
Triglycerides (mmol/L)	1.1 (0.8–1.7)	1.3 (0.9–1.7)	0.715	1.2 (0.8–1.7)	1.2 (0.8–1.7)	0.739
Total cholesterol (mmol/L)	4.0 (3.5–4.5)	3.8 (3.3–4.4)	0.006	4.0 (3.5–4.5)	3.9 (3.5–4.4)	0.676
eGFR (mL/min per1.73m^2)	111 (102–121)	112 (103–126)	0.426	112 (101–122)	111 (103–120)	0.767
Dyslipidemia	72 (13.7%)	42 (14.0%)	0.879	34 (13.1%)	17 (13.1%)	1.000
Risk Factors			0.005			0.689
Homosexual	413 (78.8%)	203 (67.9%)		205 (78.8%)	100 (76.9%)
Heterosexual	69 (13.2%)	60 (20.1%)		31 (11.9%)	20 (15.4%)
Injection drug user	1 (0.2%)	0 (0.0%)		1 (0.4%)	0 (0.0%)
Other	41 (7.8%)	36 (12.0%)		23 (8.8%)	10 (7.7%)
WHO stage III/IV	84 (16.0%)	103 (34.4%)	<0.001	31 (11.9%)	13 (10.0%)	0.692
Hepatitis B Status			<0.001			NA
Positive	9 (1.7%)	43 (14.4%)		0 (0.0%)	0 (0.0%)
Negative	476 (90.8%)	236 (78.9%)		260 (100.0%)	130 (100.0%)
Unknown	39 (7.4%)	20 (6.7%)		0 (0.0%)	0 (0.0%)
Hepatitis C Status			0.003			NA
Positive	3 (0.6%)	11 (3.7%)		0 (0.0%)	0 (0.0%)
Negative	472 (90.1%)	255 (85.3%)		260 (100.0%)	130 (100.0%)
Unknown	49 (9.4%)	33 (11.0%)		0 (0.0%)	0 (0.0%)
Anaemia	11 (2.1%)	40 (13.4%)	<0.001	3 (1.2%)	0 (0.0%)	0.539
Diabetes	16 (3.1%)	10 (3.3%)	0.818	8 (3.1%)	2 (1.5%)	0.571
Viral Load (Copies per mL)			0.012			0.712
<400	39 (7.4%)	17 (5.7%)		26 (10.0%)	9 (6.9%)
≥400, <10,000	127 (24.2%)	45 (15.1%)		65 (25.0%)	29 (22.3%)
≥10,000, <100,000	92 (17.6%)	56 (18.7%)		49 (18.8%)	23 (17.7%)
≥100,000	29 (5.5%)	15 (5.0%)		15 (5.8%)	8 (6.2%)
Unknown	237 (45.2%)	166 (55.5%)		105 (40.4%)	61 (46.9%)
Protease inhibitors	8 (1.5%)	34 (11.4%)	<0.001	2 (0.1%)	1 (0.1%)	1.000

Notes: Data are n (%) or median (IQR) unless otherwise indicated. Baseline was defined as the date of starting antiretroviral therapy on or after January 2010. After matching, P value > 0.05 indicates a relatively small baseline imbalance between TDF and non-TDF users. Diabetes and dyslipidemia defined by the diagnosis or related medication. Anemia was defined as hemoglobin <12.0 g/dL in women and <13.0 g/dL in men. Coinfection with hepatitis B defined by positive hepatitis B surface antigen, coinfection with hepatitis C defined by positive HCV viral load.

Table 3 Predicted eGFR Change Rates in the Piecewise-Linear Mixed Effects Model

Without TDF			With TDF
	Exp(β) (95% CI)	P value		Exp(β) (95% CI)	P value
Model 1 (n=823 Patients, 11,422 Measurements)
Time as linear trend	−1.29 (−1.58, −1.00)	<0.001	Time as linear trend	−1.46 (−1.94, −0.98)	<0.001
Fitted Groups			Fitted Groups
<2.55 y (n=6098 measurements)	0.74 (0.21, 1.28)	0.006	<1.40 y (n=3172 measurements)	−4.73 (−6.09, −3.37)	<0.001
≥2.55 y (n=900 measurements)	−4.04 (−4.72, −3.37)	<0.001	≥1.40 y, <3.20 y (n=996 measurements)	3.74 (1.64, 5.84)	0.004
–	–	–	≥3.20 y (n=256 measurements)	−5.48 (−8.03, −2.93)	<0.001
Model 2 (n=707 Patients, 8507 Measurements)
Time as linear trend	−1.20 (−1.54, −0.85)	<0.001	Time as linear trend	−2.56 (−3.19, −1.94)	<0.001
Fitted Groups			Fitted Groups
<2.15 y (n=4857 measurements)	0.47 (0.00, 0.94)	0.049	<1.40 y (n=2395 measurements)	−5.31 (−6.57, −4.06)	<0.001
≥2.15 y (n=492 measurements)	−4.96 (−5.76, −4.17)	<0.001	≥1.40 y, <2.30 y (n=551 measurements)	4.83 (1.38, 8.28)	0.006
–	–	–	≥2.30 y (n=212 measurements)	−3.71 (−5.97, −1.45)	0.001
Model 3 (n=390 Patients, 4663 Measurements)
Time as linear trend	−0.47 (−1.09, 0.15)	0.139	Time as linear trend	−1.77 (−2.60, −0.94)	<0.001
Fitted Groups			Fitted Groups
<2.15 y (n=2794 measurements)	0.77 (−0.07, 1.60)	0.072	<1.30 y (n=1124 measurements)	−2.78 (−4.73, −0.83)	0.005
≥2.15 y (n=271 measurements)	−3.51 (−5.04, −1.99)	<0.001	≥1.30 y, <2.10 y (n=306 measurements)	4.31 (−1.28, 9.90)	0.131
–	–	–	≥2.10 y (n=168 measurements)	−4.51 (−6.86, −2.17)	<0.001

Notes: Exp(β), the rate of change in eGFR (mL/min/1.73 m²) per year, obtained with the interaction term between TDF using status and time since cART initiation. Model 1: unadjusted for any variables at baseline. Model 2: adjusted for age, sex, weight, height, body mass index (BMI), CD4 count, eGFR, dyslipidemia, HIV/AIDS risk factors (sexual orientation and intravenous drug use), WHO stage III/IV HIV/AIDS, hepatitis B positivity, hepatitis C positivity, anemia, diabetes, and HIV-1 RNA viral load at baseline. Model 3: propensity score matched sample.

Table 4 Association of Antiretroviral Exposure (in Different Time Ranges) with Risk of Renal Impairment Outcomes

Unmatched Sample^a
Without TDF			With TDF
	OR (95% CI)	P value		OR (95% CI)	P value
Reduced Kidney Function^b
Time as linear trend	1.67 (1.42, 1.98)	<0.001	Time as linear trend	1.80 (1.54, 2.09)	<0.001
Fitted Groups			Fitted Groups
<2.15 y	1.33 (0.97, 1.81)	0.074	<1.40 y	3.33 (2.34, 4.75)	<0.001
≥2.15 y	2.05 (1.54, 2.71)	<0.001	≥1.40 y, <2.30 y	0.59 (0.25, 1.39)	0.229
–	–	–	≥2.30 y	1.58 (1.03, 2.43)	0.035
Rapid Kidney Function Decline^c
Time as linear trend	0.91 (0.84, 0.98)	0.020	Time as linear trend	1.05 (0.93, 1.18)	0.418
Fitted Groups			Fitted Groups
<2.15 y	0.89 (0.80, 1.00)	0.048	<1.40 y	1.07 (0.87, 1.32)	0.512
≥2.15 y	0.94 (0.77, 1.14)	0.524	≥1.40 y, <2.30 y	0.22 (0.09, 0.51)	<0.001
–	–	–	≥2.30 y	2.80 (1.08, 7.27)	0.034
Matched Sample^d
Without TDF			With TDF
	OR (95% CI)	P value		OR (95% CI)	P value
Reduced Kidney Function^b
Time as linear trend	1.38 (1.12, 1.70)	0.003	Time as linear trend	1.49 (1.25, 1.78)	<0.001
Fitted Groups			Fitted Groups
<2.15 y	1.23 (0.84, 1.79)	0.287	<1.30 y	2.62 (1.50, 4.59)	<0.001
≥2.15 y	1.54 (1.08, 2.20)	0.017	≥1.30 y, <2.10 y	0.56 (0.14, 2.33)	0.429
–	–	–	≥2.10 y	1.34 (0.90, 1.99)	0.152
Rapid Kidney Function Decline^c
Time as linear trend	1.01 (0.92, 1.11)	0.834	Time as linear trend	1.15 (0.99, 1.34)	0.064
Fitted Groups			Fitted Groups
<2.15 y	0.94 (0.82, 1.08)	0.396	<1.30 y	1.19 (0.87, 1.62)	0.275
≥2.15 y	1.17 (0.94, 1.45)	0.171	≥1.30 y, <2.10 y	0.19 (0.07, 0.56)	0.002
–	–	–	≥2.10 y	12.43 (0.78, 197.43)	0.074

Notes: ^aRepresents the model adjusted for age, sex, weight, height, body mass index (BMI), CD4 count, eGFR, dyslipidemia, HIV/AIDS risk factors (sexual orientation and intravenous drug use), WHO stage III/IV HIV/AIDS, hepatitis B positivity, hepatitis C positivity, anemia, diabetes, and HIV-1 RNA viral load at baseline. ^bReduced kidney function was defined as the development of an eGFR< 90mL/min/1.73m² during follow-up among patients who had an eGFR greater than or equal to 90 mL/min/1.73m² at baseline. ^cRapid kidney function decline was defined as an annual decline of 3 mL/min/1.73m² or more. ^dRepresents the propensity score-matched model.

Figure 2 Nonlinear trajectory of eGFR among HIV-1-infected patients with or without TDF.

Notes: Nonlinear eGFR changes over time can be approximated with a piecewise-linear mixed effects model. (A)  and (B) show the adjusted smooth fit of eGFR data. (C) and (D) show the fit from the adjusted one linear and adjusted piecewise-linear mixed effects models. Models adjusted for age, sex, weight, height, BMI, CD4 count, eGFR, dyslipidemia, HIV/AIDS risk factors (sexual orientation and intravenous drug use), WHO stage III/IV HIV/AIDS, hepatitis B positivity, hepatitis C positivity, anemia, diabetes, and HIV-1 RNA viral load at baseline.