| Literature DB >> 27742226 |
Nicholas T Funderburg1, Grace A McComsey2, Manjusha Kulkarni3, Tammy Bannerman3, Jessica Mantini3, Bernadette Thornton3, Hui C Liu4, Yafeng Zhang4, Qinghua Song4, Liang Fang4, Jason Dinoso4, Andrew Cheng4, Scott McCallister4, Marshall W Fordyce4, Moupali Das4.
Abstract
BACKGROUND: Initiation of antiretroviral therapy (ART) and subsequent virologic suppression reduces immune activation and systemic inflammation.Entities:
Keywords: Biomarkers; Immune activation; Inflammation; Tenofovir alafenamide
Mesh:
Substances:
Year: 2016 PMID: 27742226 PMCID: PMC5264242 DOI: 10.1016/j.ebiom.2016.10.009
Source DB: PubMed Journal: EBioMedicine ISSN: 2352-3964 Impact factor: 8.143
Demographics and baseline clinical characteristics.
| TAF ( | TDF ( | Total ( | ||
|---|---|---|---|---|
| Age, median (IQR) | 34 (26, 41) | 32 (27, 41) | 33 (26, 41) | 0.95 |
| Female, n (%) | 21 (21) | 16 (17) | 37 (19) | 0.40 |
| Black, n (%) | 19 (19) | 13 (14) | 32 (16) | 0.27 |
| BMI, median (IQR) | 24 (22, 27) | 25 (22, 29) | 24 (22, 28) | |
| US region, n (%) | 48 (49) | 54 (56) | 102 (53) | 0.31 |
| CD4 count, cells per μL, median (IQR) | 402 (254, 591) | 405 (297, 521) | 405 (274, 560) | 0.82 |
| CD4 < 200 cells per μL, n (%) | 16 (16) | 13 (14) | 29 (15) | |
| HIV RNA, log10 copies per mL, median (IQR) | 4.7 (4.1, 5.0) | 4.7 (4.3, 4.9) | 4.7 (4.2, 5.0) | 0.78 |
| HIV RNA < 100,000 copies per mL, n (%) | 72 (74) | 74 (77) | 146 (75) | 0.27 |
| Smoker, n (%) | 34 (35) | 21 (22) | 55 (28) | |
| Total cholesterol, mg per dL, median (IQR) | 163 (146, 184) | 167 (149,195) | 166 (147, 187) | 0.17 |
| LDL, mg per dL, median (IQR) | 106 (88,122) | 109 (91, 129) | 107 (89, 127) | 0.20 |
| HDL, mg per dL, median (IQR) | 46 (35, 54) | 46 (37, 54) | 46 (36, 54) | 0.67 |
| Triglycerides, mg per dL, median (IQR) | 106 (77, 153) | 108 (82, 137) | 107 (80, 145) | 0.70 |
| TC:HDL ratio, median (IQR) | 3.8 (3.1, 4.3) | 3.6 (3.2, 4.4) | 3.6 (3.2, 4.3) | 0.97 |
| eGFR by Cockcroft-Gault, median (IQR) | 120 (103, 135) | 116 (102, 139) | 118 (103, 137) | 0.95 |
| Hemoglobin, g per dL, median (IQR) | 14.3 (13.2, 15.3) | 14.6 (13.5, 15.2) | 14.4 (13.2, 15.2) | 0.39 |
| Diabetes mellitus, n (%) | 3 (3) | 3 (3) | 6 (3) | 0.98 |
| Hypertension, n (%) | 10 (10) | 16 (17) | 26 (13) | 0.19 |
| Hyperlipidemia, n (%) | 2 (2) | 4 (4) | 6 (3) | 0.39 |
Bold lettering denotes a p-value less than 0.05.
Fig. 1Fasting lipids at baseline and week 48.
P-values calculated using Wilcoxon rank-sum test for treatment comparison of change from baseline at Week 48.
Fig. 2Percent changes in markers of inflammation and immune activation.
Plasma samples were thawed and levels of A) soluble CD14 (sCD14), soluble CD163 (sCD163), B) C reactive protein (hsCRP), tumor necrosis factor receptor type 1 (TNFr-I), D-dimer, interleukin-6 (IL-6) and C) lipoprotein-associated phospholipase A2 (Lp-PLA2) were measured by ELISA. Differences in percent changes were assessed by Wilcoxon rank-sum test.
Fig. 3Changes in levels of biomarkers: equivalence by two one-sided test.
Using TOST, equivalence is established at α significance level if a (1–2α) ∗ 100% confidence interval (CI) for the ratio of treatment 1/treatment 2 is contained within a range around 100%, such as (80%–125%).
Fig. 4Changes in levels of biomarkers: equivalence by Random Forest.
Machine learning algorithm Random Forest with receiver operating characteristic (ROC) curve and variable importance was used to assess the biomarkers' ability to differentiate between arms (Breiman, 2001).
BL, baseline; Hb, hemoglobin; CLcr, creatinine clearance.