Patients with HIV are subject to development of HIV metabolic syndrome characterized by dyslipidemia, lipodystrophy and insulin resistance secondary to highly active antiretroviral therapy (HAART). Rosuvastatin is a highly potent HMG-CoA reductase inhibitor. Rosuvastatin is effective at lowering LDL and poses a low risk for drug-drug interaction as it does not share the same metabolic pathway as HAART drugs. This study sought to determine the efficacy of rosuvastatin on lipid parameters in HIV positive patients with HIV metabolic syndrome.Mean TC decreased from 6.54 to 4.89 mmol/L . Mean LDL-C decreased from 3.39 to 2.24 mmol/L . Mean HDL rose from 1.04 to 1.06 mmol/L . Mean triglycerides decreased from 5.26 to 3.68 mmol/L . Secondary analysis examining the effectiveness of rosuvastatin monotherapy (n = 70) vs. rosuvastatin plus fenofibrate (n = 43) showed an improvement of 21.3% in TG and a decrease of 4.1% in HDL-C in the monotherapy group. The rosuvastatin plus fenofibrate showed a greater drop in triglycerides  and an increase in HDL of 7.6% (p = 0.08).This study found that rosuvastatin is effective at improving potentially atherogenic lipid parameters in HIV-positive patients. The lipid changes we observed were of a smaller magnitude compared to non-HIV subjects. Our results are further supported by a small, pilot trial examining rosuvastatin effectiveness in HIV who reported similar median changes from baseline of -21.7% (TC), -22.4% (LDL-C), -30.1% (TG) with the exception of a 28.5% median increase in HDL. In light of the results revealed by this pilot study, clinicians may want to consider a possible resistance to statin therapy when treating patients with HIV metabolic syndrome. Patients with HIV are subject to dyslipidemia and other complications secondary to their highly active antiretroviral therapy (HAART) that are often classified as HIV metabolic syndrome -3. As a Rosuvastatin is a highly potent 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) inhibitor and is not metabolised by the Cytochrome P3A4 (CYP3A4) enzyme system, which is frequently inhibited by certain HAART therapies. The lack of requirement of CYP3A4 metabolism for rosuvastatin makes it an attractive lipid-lowering drug to treat the dyslipidemia associated with HAART . To dateElucidating any resistance to this new, potent statin would be of great clinical importance given the potential for wide use of rosuvastatin in this population, which is considered to be at moderate risk for cardiovascular disease.The overall effect of rosuvastatin on lipid parameters was as follows: TC decreased from 6.54 to 4.89 mmol/L . Mean triglycerides decreased from 5.26 to 3.68 mmol/L . Mean LDL decreased from 3.39 to 2.34 mmol/L . Mean HDL rose from 1.04 to 1.06 mmol/L . Mean TC:HDL decreased from 7.22 to 5.03 . Mean apolipoprotein B decreased from 1.30 to 0.97 g/L . The serum triglycerides of 33 patients were significantly elevated thus precluding calculation of serum LDL-C concentration. Five (5) patients experienced adverse events that ultimately led to discontinuation of the medication but were included in the study as they remained on the medication for longer than 4 weeks prior to being discontinued. Three (3) patients were discontinued due to elevated liver enzymes and 2 patients were discontinued due to complaints of muscle soreness.A dose-response analysis was conducted wherein patients on rosuvastatin monotherapy (n = 68) were subdivided based on current rosuvastatin dosage. The 10 mg subgroup (n = 45) showed a greater improvement across all lipid parameters relative to the 20 mg subgroup (n = 23) with the exception of HDL  vs. patients in whom rosuvastatin has been added onto their fenofibrate regime   . By compThe only other study examining the effects of rosuvastatin in an HIV+ population also showed improvements that were not as significant as would be expected from this highly potent statin. Calza et al randomised HIV+ patients to rosuvastatin or placebo for 24 weeks (n = 16) and reported median changes from baseline of -21.7% (TC), -22.4% (LDL-C) and -30.1% (TG) in atherogenic lipid lowering parameters, along with a 28.5% median increase in HDL . These rOur dose response analysis Fig.  showed vResistance to statin therapy is not unique to rosuvastatin as evidenced by studies examining the effects of pravastatin in the HIV population. These studies observed no better than a 20.4% decrease in LDL-C compared to a 32.7% decrease in the general population. In each case the treatment group was given pravastatin 40 mg -23.Protease inhibitor (PI)-containing HAART is often cited as a cause dyslipidemia and lipodystrophy in HIV patients and could possibly be the underlying cause of resistance to therapy observed in this study ,2,4. In The current published guidelines recommend diet and exercise counselling, alteration of ARV regimen or adding lipid lowering medications for dyslipidemia in patients on ARV therapy . A statiThe resistance to therapy evidenced by the primary lipid analysis and the dose-response analysis of this study is of importance to clinicians treating HIV dyslipidemia as it demonstrates that simply increasing the dosage of rosuvastatin may not be the most effective way to improve lipid parameters in this population. Rosuvastatin is well suited for the treatment of HIV dyslipidemia as it is not metabolised by the CYP3A4 pathway, therefore, we suggest that clinicians consider a combination approach involving low dose rosuvastatin (i.e. 10 mg) in combination with either a fibrate or ezetimibe as data presented here and previously published data suggest that these combinations are effective ,24.A retrospective analysis was undertaken to determine the effect of rosuvastatin on the lipid profile of patients with HIV.All patients were seen at the Immunodeficiency Clinic/HIV Metabolic Clinic (IDC/HIVMC) at St. Paul's Hospital, Vancouver, BC, Canada. This is a tertiary referral center where over 700 patients receive care for HIV associated metabolic disorders. Patients who were treated with rosuvastatin between January 2003 and July 2006 were included in the analysis. Rosuvastatin therapy was initiated either as an alternative to a previously prescribed statin that proved ineffective, either alone or in combination with other lipid lowering therapy, as an add-on to the patient's current lipid lowering therapy or as monotherapy to statin-naïve patients.The effect of rosuvastatin on serum lipid concentrations of TC, LDL-C, HDL-C, TG and apolipoprotein B (Apo B) was analysed. Adverse events, as indicated by ALT or AST five times upper limit of normal (ULN) or CK ten times ULN as well as any condition requiring discontinuation of the drug, were documented.161 patients seen in the IDC/HIVMC had been prescribed rosuvastatin in dosages of 5,10,20 or 40 mg per day orally. 14 were excluded because exact start date of rosuvastatin could not be determined and an additional 17 were excluded because they lacked sufficient data for analysis and 130 patients remained for analysis. Five patients discontinued rosuvastatin therapy due to adverse events. For these patients their most recent lipid parameters prior to discontinuation were used in study analyses. Concomitant medications including other lipid lowering therapy, namely cholesterol transport blockers (ezetimibe) and fibrates (fenofibrate), and previous use of statins were documented as well as each patient's current antiretroviral regimen, simplified to either regimens containing protease inhibitors (PI) or those not containing protease inhibitors (Non-PI).Mean patient age was 52.6 ± 8.29 years, 128 (98.5%) patients were male and 20 (15.4%) had diabetes mellitus. The mean duration of rosuvastatin therapy was 140.8 ± 112.84 days. 78 (60.0%) patients had previously taken a statin  before being switched to rosuvastatin. 109 (83.8%) patients were on PI ARV regimens, 14 (10.8%) were on non-PI ARV regimens and 7 (5.4%) were taking no ARV therapy.Student's T-test for both paired and unpaired samples was used to compare pre and post treatment differences and subgroup differences, respectively.Ethical approval was granted by the Research Ethics Board at St. Paul's Hospital.The author(s) declare that they have no competing interests.KJ was the principle author of the paper, participated in design of the project, performed statistical analysis and was the primary collector of data by means of chart review. MB participated in design of the project and edited the manuscript. GB designed the project, aided in data acquisition and was the principal editor of the manuscript. All authors read and approved the final manuscript.


Its use is limited by high costs, long turn-around time, and inability to test patients with very low or undetectable viremia. We aimed at assessing the efficiency of population genotypic assays based on gp120 V3-loop sequencing for the determination of tropism in plasma viral RNA and in whole-blood viral DNA. Contemporary and follow-up plasma and whole-blood samples from patients undergoing tropism testing via the enhanced sensitivity Trofile® (ESTA) were collected. Clinical and clonal geno2pheno[coreceptor] (G2P) models at 10% and at optimised 5.7% false positive rate cutoff were evaluated using viral DNA and RNA samples, compared against each other and ESTA, using Cohen's kappa, phylogenetic analysis, and area under the receiver operating characteristic (AUROC).TrofileBoth clinical and clonal G2P  showed good performances in predicting the ESTA outcome . The rate of agreement between DNA- and RNA-based clinical G2P was fair , and DNA-based clinical G2P accurately predicted the plasma ESTA (AUROC = 0.86). Significant differences in the viral populations were detected when comparing inter/intra patient diversity of viral DNA with RNA sequences.Plasma HIV RNA or whole-blood HIV DNA V3-loop sequencing interpreted with clinical G2P is cheap and can be a good surrogate for ESTA. Although there may be differences among viral RNA and DNA populations in the same host, DNA-based G2P may be used as an indication of viral tropism in patients with undetectable plasma viremia. Anotherh as 91% . Variablh as 91% -26.® test in predicting the virological response to a short-term maraviroc exposure in HIV-infected patients  in-silico genotypic tropism prediction system was employed, using both clinical  and clonal interpretations at 10% false positive rate (FPR)  from 55 patients. By stratifying for contemporary VL, the rate of successful sequencing from RNA was 40%, 88.2%, and 96.2% at < = 50 cp/ml, 51-500 cp/ml, and >500 cp/ml respectively. The rate of successful sequencing from DNA was 95.1%, 100%, and 100% at < = 50 cp/ml, 50-500 cp/ml, and >500 cp/ml, respectively.Fifty-one of the 55 patients had their baseline plasma tested by ESTA, and 28 were subsequently administered MVC. Patients' baseline characteristics are summarized in Table In a cross sectional crude analysis, using dichotomized ESTA results , we did not find any significant association with contemporary patients characteristics, except for the time from HIV-1 diagnosis , and nadir CD4 count [median (IQR) 138 cells/mm3 (31-200) for R5 and 14 cells/mm3 (6-41) for X4 or dual/mixed-tropic isolates p = 0.05].We first compared the prediction performance of geno2pheno versus the ESTA result. For this analysis, we excluded samples not reportable by ESTA. To match geno2pheno categories, the dual/mixed virus classification by ESTA was pooled with the X4 virus classification. Figure As a second performance test, we obtained geno2pheno tropism predictions for V3 sequences obtained from DNA samples . The resulting prediction of ESTA X4-D/M tropism showed an AUROC of 0.86 using geno2pheno clinical interpretation, 0.69 using the geno2pheno clonal interpretation at 10% FPR, and 0.76 using the optimised 5.75% FPR cutoff  concordant predictions, 3/40 (7.5%) false positives, and 2/40 (5%) false negatives, using RNA-predicted X4-tropism as the reference outcome. The kappa statistics yielded a strength of agreement of 0.74 (95% CI: 0.53-0.95), with a Fisher's p-value < 0.0001. The AUROC obtained by predicting the geno2pheno RNA tropism from contemporary DNA sequences was 0.875.Out of 28 patients with an R5-tropic virus by ESTA subsequently starting MVC, 22 had an available virological follow up at 12 weeks. All patients had a VL below 50 cp/ml, except two patients with 120 and 292 cp/ml. Virological follow-up at 24 weeks was available for 19 patients. All of these had a VL below 50 cp/ml, except two patients (different from those at three months) with a VL of 2,003 and 88 cp/ml.Immunological follow-up at 12 weeks was available for 20 out of the 28 patients that started MVC. The median (IQR) CD4 increase was 84 (range -9 to 165) cells/mm3. A Wilcoxon test showed that this increase from baseline was statistically significant (p = 0.019). After 24 weeks of therapy, immunological follow-up was available for 19 patients. The median (IQR) CD4 increase was 46 (-9 to 143) cells/mm3(p = 0.053).Finally, we executed statistical tests for difference in proportions by looking at the whole set of mutations retrieved in the RNA samples with respect to HIV-1 HXB2 envelope reference, grouping sequences in pre- and post-MVC initiation . Of the 9 pre-MVC sequences , 4 (44%) were classified as X4 by clinical geno2pheno interpretation, with FPR of 4.8%, 7.8%, 0.2%, and 0.8%. Tropism prediction did not change for any patient when considering post-MVC samples. No substitution was significantly associated with MVC exposure by correcting test statistics with Benjamini Hochberg  nor retaining raw unadjusted p-values; nonetheless a deletion at position 354, mutation 355K, 369L, and 259Q  showed an increase in prevalence after MVC initiation. Figure [coreceptor] interpretation in treatment-experienced subjects proved to be a valid alternative to ESTA tropism, yielding an AUROC of 0.83 using the clinical interpretation. The clonal interpretation showed a lower AUROC, although the difference with the clinical interpretation showed only a trend, probably due to the limited sample size. The geno2pheno[coreceptor] website suggests that only the clonal interpretation should be used for treatment-experienced patients, since the clinical system was trained only on treatment-naïve patients. However, our results show that the clinical interpretation is better (at least not inferior) than the clonal interpretation even in treatment-experienced patients. It is important to highlight the fact that the clinical interpretation needs contemporary VL, nadir CD4 and CD8 information in order to work properly. Interestingly, the clinical mode improved sensitivity much more than specificity with respect to the clonal mode. The sensitivity of V3 RNA genotyping followed by clonal geno2pheno[coreceptor] interpretation has been recently shown to increase by testing multiple aliquots of the plasma RNA extract  as a cut-off for classification of R5 and X4-D/M viruses. Recent retrospective analysis of the MERIT trial data indicated that the most accurate FPR cutoff may be in the range from 2% to 5.75%, when triplicate PCR testing and fully automated sequencing is used [[coreceptor] system . Using One major advantage with genotyping is that even patients with non-reportable ESTA can be given prediction of tropism. By definition, ESTA is subject to a larger proportion of failures with respect to genotype due to virus polymorphisms invalidating the cloning procedure and an inherently lower rate of reverse transcription of a far larger virus genome region. Moreover, our efficiency of RNA genotyping at VL between 50 and 500 cp/ml (where ESTA is not even attempted) was 88%, whilst that of DNA genotyping was 100% at these viral loads.Interestingly, in-silico tropism prediction using whole-blood DNA genotyping may be a solution when considering treatment switch to a CCR5-antagonists for patients with undetectable viral load. The perspective for treatment switches in these patients may be attractive, because of the good tolerability of MVC and because patients that are not at a late stage of disease are more likely to harbour a CCR5- rather than a CXCR4-tropic virus [As expected, we found some differences when comparing paired DNA and RNA sequences, consistent with the notion that the archived population may not correspond to the most prevalent virus in plasma, whose source are the productively infected cells. However, when comparing DNA and RNA tropism prediction by looking at contemporary samples, the degree of agreement was good, implying that such minor differences may not commonly translate into inappropriate indications. It remains to be established whether an X4 virus population detected in PBMC DNA in the context of R5 virus in plasma RNA can impair response to maraviroc. Although reported on a limited number of cases, this did not appear to be the case in the French GenoTropism study ,42.® [HIV-1 tropism determination via plasma viral V3 RNA genotyping coupled with geno2pheno interpretation may represent a valid alternative to ESTA. The clinical validation of genotypic determination of viral tropism has been recently performed using retrospective samples from the MOTIVATE study  as well ® ,42. As s® ,44. MostMaurizio Zazzi has received recent research funding from Pfizer; served as a consultant for Abbott Molecular, Boehringer Ingelheim, Gilead Sciences, and Janssen; and served on speakers' bureaus for Abbott, Bristol-Myers Squibb, Merck, and Pfizer.Andrea De Luca received speakers honoraria, served as consultant or participated in advisory boards for GlaxoSmithKline, Gilead, Bristol-Myers Squibb, Abbott Virology, Tibotec-Janssen, Siemens Diagnostics and Monogram Biosciences.Roberto Cauda has attended advisory boards or has been a consultant for Glaxo-SmithKline, Gilead, Bristol-Myers Squibb, Boehringer Ingelheim, Abbott Virology, Novartis, Pfizer, Schering-plough, and Merck Sharp & Dohme.All other authors declare no competing interests.MCFP assisted with manuscript writing and statistical analyses; LB, MF, SDG and MC assisted with patients' care and data acquisition; FR, GM and AM assisted with laboratory assays; RC, MZ and ADL assisted with manuscript revision and research group leading. All authors read and approved the final manuscript.Evolutionary relationships of 155 V3 sequences obtained from 51 patients at different time points joining RNA and DNA samples + 1 outgroup . Sequences are labelled by DNA/RNA type, by sampling date (the number before the type), and by patient's identifier (first one or two numbers). When considering all the 155 viral DNA/RNA V3 sequences obtained from all the 55 patients, the median (IQR) distance among all samples was 0.126 (0.101-0.159). The median (IQR) interpatient RNA-RNA distance was 0.133 (0.110-0.166)  and DNA-DNA  distance was 0.121 (0.095-0.152). The median (IQR) intrapatient RNA-RNA (n = 56), DNA-DNA (n = 52) and paired RNA-DNA (n = 120) distances were 0.007 (0.000-0.017), 0.015 (0.007-0.031) and 0.023 (0.015-0.031), respectively, with a Kruskal's p-value < 0.0001.Click here for fileDetailed classifications of tropism by different geno2pheno modes and ESTA on our data sets.Click here for file


The aim of this study was to: 1) Evaluate the prognostic value of the new suPARnostic® assay and 2) Determine whether polymorphisms in the active promoter of uPAR influences survival and/or suPAR values in HIV-1 patients who are antiretroviral therapy (ART) naive.High blood levels of soluble urokinase Plasminogen Activator Receptor (suPAR) are associated with poor outcomes in human immunodeficiency-1 (HIV-1) infected individuals. Research on the clinical value of suPAR in HIV-1 infection led to the development of the suPARnosticDNA samples were collected retrospectively from 145 Danes infected with HIV-1 with known seroconversion times. In addition, plasma was collected retrospectively from 81 of these participants for use in the suPAR analysis. Survival was analysed using Kaplan Meier analysis.Survival was strongly correlated to suPAR levels (p < 0.001). Levels at or above 6 ng/ml were associated with death in 13 of 27 patients within a two-years period; whereas only one of 54 patients with suPAR levels below 6 ng/ml died during this period. We identified two common uPAR promoter polymorphisms: a G to A transition at -118 and an A to G transition at -465 comparative to the transcription start site. These promoter transitions influenced neither suPAR levels nor patient survival.® assay, were strongly predictive of survival in ART-naïve HIV-1 infected patients. Furthermore, plasma suPAR levels were not influenced by uPAR promoter polymorphisms.Plasma suPAR levels, as measured by the suPARnostic Patients with HIV infection receive regular clinical follow-up to assess the need for more invasive treatment regimens such as initiation of ART. Currently, this assessment relies on the evaluation of HIV RNA viral load and CD4 cell count; the former of which requires expensive, high technology laboratory facilities. Thus, there is a need for robust, reliable prognostic markers for HIV infection that can also be used in resource-limited settings. Inflammation increases HIV replication and CD4 T cell depletion  and markAn elevated level of suPAR is predictive of negative clinical outcome in a number of different diseases such as HIV-1, tubercuuPAR is a glycosylphosphatidylinositol (GPI)-linked 3-domain receptor expressed on monocytes, macrophages, activated T-lymphocytes, natural killer cells and neutrophils . uPAR inThe gene for uPAR maps to chromosome 19q13.2 and consists of 7 exons and 6 introns ,16. In v® assay are prognostic for HIV-1 disease progression and whether suPAR levels and survival are influenced by common genetic polymorphisms in the uPAR promoter region.The present study investigates whether plasma suPAR levels measured by the suPARnosticth, 1996 was measured for suPAR . Of these 81 patients, 61 patients had a CD4 count included in their medical records within three months of the blood sample date.The seroconverter cohort consist of 145 HIV-1 infected Caucasians with documented time for seroconversion. DNA was isolated from these individuals using QIAamp Blood Mini Kit according to the manufacturer's protocol  and stored at -20°C. The cohort comprised 122 males with a mean seroconvertion age of 32.0 years and 23 females who seroconverted at a mean age of 40.2 years. Seroconversion was defined as a seronegative and a seropositive HIV antibody test within a 12 months period. The time of seroconvertion is calculated as the midpoint in time between the last seronegative sample and the first seropositive sample. Patients were followed prospectively every 3–6 months. In this cross-sectional study, one plasma sample from each of the 81 patients taken before the introduction of ART on May 15The AccuPrime PCR  was performed according to the manufactures instructions using 3 μL DNA template and a primermix of 10 μM sense primer (uPARpr_s) 5'-ACTACGCCCGGCTAATTTTT-3' and antisense (uPARpr_as) 5'-CCCCCTCAATTAGACCCTGT-3' . PCR-program 94°C for 2 minutes; 30*. All PCR-products were run on a 2% volume agarose gel  stained by ethidium bromide with a 100 bp ladder .DNA from 52 individuals was used for sequencing. Prior to sequencing the PCR product was purified using a PCR clean-up-system  according to the manufacturers protocol. Sequencing was carried out using 0,5 μL Terminator Ready Reaction Mix , 3 μL PCR-product, 3pmol Primer (either uPARpr_s or uPARpr_as), 4 μL 5× Sequencing buffer and sterile water to 20 μL. PCR-program: 96°C for 60 seconds; 25* followed by a sodium precipitation and sequencing on an ABI PRISM 3100 Genetic Analyzer (Applied Biosystems).2), 200 μM of each dNTP, 0,2 μM of each primer (uPARpr_s and uPARpr_as), 2.5 units HotStarTaq and 2 μl template DNA per reaction under a PCR-program of 95°C for 15 minutes; 35*; 72°C for 8 minutes.A hot start PCR was performed according to manufactures instructions (Qiagen), using 10 μL 10*PCR-buffer  in a total of 15 μL pr. reaction and a control without Eci-1. MspA1 l restriction analysis was done using 2 units MspA1 l to 10 μL PCR-product with 1*NEBuffer4 and 100 μg/ml BSA in a total of 15 μL per reaction and a control without MspA1 l (New England Biolabs). Following incubation at 37°C for 2 hours, the RFLP product was analysed on 2% agarosegel  using ethidium bromide staining and a 100 bp ladder (New England Biolabs).® assay  according to the manufactures instructions. Samples were labelled with a number and the technician was blinded to the identity of the patient sample. Briefly, a mastermix of 25 ul plasma and 225 ul assay buffer (containing HRP-labelled secondary antibody) was prepared and 100 ul was added in duplicates onto an ELISA plate (pre-coated from manufactures with catching antibody). After one hour of incubation, the plate was washed, substrate added for 20 minutes and reaction was stopped with 50 ul 0.5 M H2SO4. Plates were measured at 450 nM with reference 630 nM. One kit measured 39 samples in duplicates in less than two hours. The measured sample intra-assay variation was 3.0 % and inter-assay variation was 10.0 %. This is similar to the inter- and intra-assay variations described in the manufactures kit protocol . The kit standard curve is validated to measure suPAR levels between 0.6 to 22.0 ng/ml with a detection limit (LLOD) of 0.1 ng/ml.SuPAR was measured using the suPARnosticth, 1996 when HAART was introduced to the cohort. The level of significance was set to 0.05. Data were analyzed using the SPSS software, version 13.0.Comparisons were done using the nonparametric Mann-Whitney U test and Pearson Rank test. Heredity of Mentel was tested by the Hardy-Weinberg Equilibrium, and survival of the seroconverters was analyzed using Kaplain-Meier. For the survival analysis, patient follow-up was censored on May 15® ranged between 1.22 and 12.16 ng/ml. SuPAR correlated to age, and death during follow-up (table SuPAR levels N = 81) measured by suPARnostic measuredFor the Kaplan Meier analysis, patients were divided into three equally sized groups of 27 patients based on their respective suPAR levels. As shown in figure The uPAR promoter was sequenced in 52 individuals covering the active promoter from -643 bp upstream to +159 bp downstream of the transcription start site. Two mutations were detected: a G to A transition at -118 and an A to G transition at -465 upstream of the transcription start site. Of the 52 individuals, 43 (≈ 83%)were found to be -118 G/G, 8 (≈ 15%) to be G/A and 1 (≈ 2%) to be A/A. For the -465 transition, 31 (≈ 60%) were A/A, 20 (≈ 38%) were A/G and 1 (≈ 2%) was G/G. It was possible to develop a RFLP assay based on the sequencing results. By Eci-1 endonuclease fragmentation the -118 mutant A can be separated from the wildtype G by cutting of the A allele between base pair -106 and -107. The wildtype A and the mutant G at -465 can be differentiated by MspA1 l, due to a restriction site between base pair -466 and -467 in the mutant allele  was analyzed for the promoter transitions using the RFLP assay. In this cohort, 130 (≈ 90%) were -118 G/G carriers and 15 (≈ 10%) patients were G/A heterozygotes while no homozygous A/A carriers were found. With regard to the -465A/G transition, 84 (≈ 58%) were A/A, 55 (≈ 38%) were A/G heterozygotes and 6 (≈ 4%) were G/G homozygotes. The mutations were in Hardy-Weinberg Equilibrium. No individuals were homozygotes for one of the transitions and heterozygote for the other transition, whereas a total of six individuals were heterozygotes for both transitions. By RFLP with MspA1 l and Eci-1 on PCR-products from these six individuals, fragments matching a restriction pattern of 709, 348, 184/177 and 85 bp were identified. This is consistent with one of the alleles not to be restricted by Eci-1, while the other allele additionally is restricted by MspA1 l, indicating that the two polymorphism are not travelling on the same allele and thereby are in genetic disequilibrium . Similar, the distribution of individuals with different genotypes according to median suPAR was not significant table . Finally® assay in a cohort of HIV infected patients who were ART naïve. Furthermore, this study was designed to assess whether suPAR values, as measured by the new assay, are influenced by polymorphisms in the active region of the uPAR promoter.The aim of the current study was to evaluate the prognostic value of the newly available suPARnosticWe hypothesize that elevated plasma suPAR levels indicate a heightened inflammatory state. SuPAR correlates to C-reactive protein , which aIn agreement with the inflammation hypothesis, previous studies have shown a negative correlation between suPAR levels and CD4 T-cell counts and a positive correlation between suPAR and HIV viral load ,9 LikewiTo further explore the combined prognostic value of suPAR and CD4, we plotted patients who died within a two-year follow-up period in a CD4/suPAR graph. Patients with a low CD4 count (defined as below 200) and high suPAR levels (defined as above 6 ng/ml) had high two-year mortality while those with low CD4 and low suPAR levels were still alive after two years. Thus, suPAR and CD4 both added clinical value on the risk of mortality in this cohort of non-ART-treated HIV-1 patients.A cut-off limit for suPAR has yet to be defined. In this study, only 1 of 54 patients with plasma suPAR levels below 6 ng/ml died within two years following blood sampling, while 13 of 27 patients with suPAR at or above 6 ng/ml died in the same period (figure Two common mutations were defined in this study: a G to A transition at -118 and an A to G transition at -465 upstream of the transcription start site. Neither of these mutations had any influence on survival nor suPAR plasma levels. Previous studies have shown that in vitro regulation of the PEA3/etc site at -247 to -241 could influence the promoter activity , however® assay to non-ART HIV disease progression. A resent South African study employed the suPARnostic® assay to individuals initiating ART [This is the first paper that applies the suPARnosticting ART . In 293 ting ART .® assay is an independent prognostic marker of mortality in HIV-1 patients who are not on ART treatment. Moreover, these results suggest that patients with suPAR levels at or above 6 ng/ml should be considered for initiation of ART, in particular in cases with CD4 between 350 and 200 cells per ul. Finally, this study found that the value of suPAR levels as a biomarker for survival in HIV patients is not affected by uPAR promoter polymorphisms.This study confirms that the suPAR level measured using the suPARnosticJEO is founder of, and shareholder in, ViroGates. ViroGates IP includes patents on using suPAR for diagnostic and prognostic purposes.UVS, RLN and JEO designed the study and analysis. UVS and RLN did the laboratory work. CP established the HIV-1 cohort. UVS and JEO wrote the first draft. All authors read and approved the final manuscriptThe pre-publication history for this paper can be accessed here:


Our intention was to compare the rate of immunological progression prior to antiretroviral therapy (ART) and the virological response to ART in patients infected with subtype B and four non-B HIV-1 subtypes  in an ethnically diverse population of HIV-1-infected patients in south London.env sequencing. Subtypes were compared on the rate of CD4 cell decline using a multi-level random effects model. Virological response to ART was compared using the time to virological suppression (< 400 copies/ml) and rate of virological rebound (> 400 copies/ml) following initial suppression.A random sample of 861 HIV-1-infected patients attending HIV clinics at King's and St Thomas' hospitals' were subtyped using an in-house enzyme-linked immunoassay and Complete subtype and epidemiological data were available for 679 patients, of whom 357 (52.6%) were white and 230 (33.9%) were black African. Subtype B (n = 394) accounted for the majority of infections, followed by subtypes C (n = 125), A (n = 84), D (n = 51) and CRF02-AG (n = 25). There were no significant differences in rate of CD4 cell decline, initial response to highly active antiretroviral therapy and subsequent rate of virological rebound for subtypes B, A, C and CRF02-AG. However, a statistically significant four-fold faster rate of CD4 decline  was observed for subtype D. In addition, subtype D infections showed a higher rate of virological rebound at six months (70%) compared with subtypes B , A  and C This is the first study from an industrialized country to show a faster CD4 cell decline and higher rate of subsequent virological failure with subtype D infection. Further studies are needed to identify the molecular mechanisms responsible for the greater virulence of subtype D. The world-wide HIV epidemic has been characterized by increasing genetic diversity, with multiple distinct viral subtypes, as well as sub-subtypes, and circulating recombinant forms (CRFs) -3. At prAlthough subtype B dominates in North America, western Europe and Australia, the recent epidemiology of HIV-1 infection in the UK and many western European countries has been characterized by a marked increase in the prevalence of non-B subtypes and several CRFs -9. In thGiven the increasing subtype diversity in various sub-populations, the potential for the emergence of novel genetic variants, and the increasing availability of antiretroviral therapy (ART) worldwide, it has become even more important to establish the clinical implications of subtype variation -14. LimiHowever, several recent studies from sub-Saharan Africa have found higher rates of disease progression in individuals infected with subtype D virus ,22-25. TKing's College Hospital and St Thomas' Hospital HIV clinics are based in the inner London boroughs of Lambeth, Southwark and Lewisham. In addition to containing a large migrant population from sub-Saharan Africa and a significant black Caribbean community, these areas have the highest rate of new HIV diagnoses in the UK. The two clinics care for a heterogeneous population of almost 3000 HIV-1-infected patients, a large proportion of whom originate from sub-Saharan Africa. We selected an approximately 50% random sample of 861 patients  based on all adult (≥ 18 years) HIV-1-infected patients who had attended the HIV clinic at either site over a one-year period between May 1999 and May 2000.3 or the presence of symptomatic HIV disease. CD4 cell counts were determined with a FACScount apparatus (Becton Dickinson) in freshly collected whole blood.HIV-1-infected patients receiving antiretroviral therapy are seen routinely at three- to four-month intervals for clinical evaluation, monitoring of CD4 count, viral load, haematology and biochemistry. Those not on antiretroviral therapy are reviewed every three to six months. The criteria for initiation of ART are the presence of a CD4 cell count of < 350 cells/mmThe local HIV clinic databases and the patients' medical records were used to obtain demographic data , and clinical and laboratory data . Ethnic group was based on self-reported ethnicity on the clinic registration form and country of birth.At one of the two clinic sites , adherence to the antiretroviral drug regimen is assessed routinely at each clinic visit by documenting the number of times a specific drug dose had been missed, as well as the number of times it had been taken more than two hours late over the preceding 30-day period.Env sequencing was performed to assign a subtype to samples identified as non-B-using EIA (n = 124) and in samples with mixed reactivity (n = 19) or non-reactivity (n = 71) on serology. In addition, env sequencing was performed to validate EIA-determined subtype B infections in all black Africans, the majority of black Caribbeans, and in a sample of white patients with a subtype B infection (n = 30); previous studies have shown that serological typing can discriminate between B and non-B subtypes with a high degree of specificity in populations with predominantly subtype B infections  of unambiguous sequence were available) and reference sequences were generated using the latest version of CLUSTAL W http://www.ebi.ac.uk/clustalw/ within Bioedit v4.8.5. The parameters of the optimal model of evolution were estimated using Modeltest v3.0 within the phylogenetic analysis package Phylogenetic Analysis Using Parsimony (Paup*), and neighbor-joining trees (bootstrapped × 1000) were generated. The tree topology was used to assign subtypes based on a high level of bootstrap support (> 70%) for each subtype or CRF cluster.Samples were assigned a subtype by phylogenetic analysis of http://www.ncbi.nlm.nih.gov/projects/genotyping/ with a window size of 100 bp and an increment step of 50 bp; and the Basic Local Alignment Search Tool (BLAST 2.0) search tool at Los Alamos database http://www.hiv.lanl.gov/content/sequence/BASIC_BLAST/basic_blast.html.When a phylogenetically determined subtype was available, this was used to assign a definitive subtype. Two further subtyping methods were applied as a validation of the phylogenetic analysis results and to enable a subtype to be assigned to samples for which phylogenetic analysis was not possible: the National Center for Biotechnology Information Retrovirus Genotyping Tool , this was used to assign subtype. Where an HIV-1 sequence subtype was not available, as was the case with most B subtypes, an EIA-defined subtype B infection was assigned . Because serology is unreliable for discriminating between B and non-B subtype in low subtype B prevalence populations such as black Africans [We compared the demographic, clinical and laboratory characteristics at HIV diagnosis among patients infected with subtype B and the four main non-B subtypes , using chi-squared tests for categorical variables, and either Kruskal-Wallis or Wilcoxon rank-sum tests for continuous variables. We first compared subtype B with all non-B infections combined, and then conducted a series of pair-wise comparisons for the individual non-B subtypes.We first compared the rate of CD4 cell decline prior to starting antiretroviral therapy in patients infected with B and non-B subtypes  based on their pre-therapy longitudinal CD4 cell count profiles. The rate of decline in square root transformed CD4 cell count for each subtype was estimated and compared using two-level random effects multiple regression models, and for four other variables . This model recognizes that the data are series of CD4 cell counts from the same individual over time, and allows each individual to have his or her own estimated intercept and rate of decline by introducing patient-specific elements (random effects). Multivariable regression analysis was used to examine for independent predictors of rate of CD4 decline using a backward elimination procedure. As the date of infection was not known in the majority of cases, all multivariate analyses were adjusted for the baseline CD4 cell count.We also compared the time to virological suppression < 400 copies/ml following initiation of highly active antiretroviral therapy (HAART) and the time to virological rebound  after initial suppression, using Kaplan-Meier estimation. Log-rank tests were used to analyze pair-wise differences between subtypes, and all analyses were adjusted for percent adherence. Percent adherence was calculated for each patient visit by dividing the total number of missed or late doses by the total number of doses prescribed over a 30-day period, multiplied by 100. Percent adherence was analyzed as a binary variable (100% adherent vs. < 100% adherent); less than 100% adherence is associated with a significant reduction in attainment and maintenance of viral load suppression .Univariate logistic regression analysis was used to examine the association between adherence and subtype, as well as other variables, including gender, risk groups and ethnicity, for each visit up to three visits. A generalized estimating equation (GEE) model for binary outcome with an exchangeable correlation matrix was also used to examine the relationship between adherence and subtype, incorporating up to three adherence assessments. Patients with more than one visit were used to examine changes in adherence overtime. A further analysis using a GEE model with an exchangeable correlation matrix was also performed for the subgroup of 94 patients with at least two adherence assessments. All data were analyzed using Stata 7.5 .Of 861 patients, 182 patients were excluded from the analysis because their subtype could not be determined due to mixed reactivity or non reactivity (n = 109); unspecified non-B subtype (n = 16); less common non-B subtypes (n = 34); and incomplete epidemiological data . Table S1, Additional file Fifty-eight of 357 whites (16.2%) were infected with non-B subtypes . Of the 230 black Africans, only 11 (4.8%) were infected with B subtype, and the most common non-B subtypes were C , A , D , and CRF02-AG . Of the 51 black Caribbeans, 38 (74.5%) were infected with B subtypes, and 13 (25.5%) with non-B subtypes .3 versus 250 (IQR = 100-449) in subtype A (p = 0.02), 250 (IQR = 141-413) in subtype C (p = 0.01), 249 (IQR = 30-508) in subtype D (p = 0.04), and 297 (IQR = 113-386) with CRF02-AG (p = 0.06). There were no statistically significant differences in the median age or viral load at HIV diagnosis, or in the type of ART regimen between B and any of the non-B subtypes  on pair-wise comparisons.There were no statistically significant differences between the non-B subtypes in demographic characteristics or stage of disease at presentation. However, compared to those infected with any of the four non-B subtypes, patients with subtype B were more than twice as likely to be male (89.1% vs. 35.3% to 48%) , to be white (78.9% vs. 2.8% to 22.6%) , and to be homosexual or bisexual versus heterosexual (74.4% vs. 6.4% to 15.5%) . The median CD4 cell count at HIV diagnosis was significantly lower in patients infected with non-B versus B subtype: 331 (IQR = 196-501) cells/mmWe analyzed 2778 CD4 cell counts in 627 patients, after exclusion of 52 patients who had only a baseline CD4 count available. A median of six serial CD4 cell counts were available in 627 patients prior to the initiation of any antiretroviral therapy . The median six-monthly decline in the square root transformed CD4 cell count was -0.22  for subtype B and -0.27  for all non-B subtypes combined. However, there were non-B subtype-specific differences in the rate of CD4 change: -0.22  for subtype A; -0.21  for subtype C; -0.80  for subtype D; and -0.01  for CRF02-AG.There were no statistically significant differences in the rate of CD4 decline between B versus A (p = 0.24) or B versus C (p = 0.99). However, subtype D-infected patients had a four-fold more rapid rate of CD4 decline compared with subtypes B, A and C . There was no association between rate of square root CD4 cell decline and ethnicity, gender risk group or age. The faster rate of CD4 decline in subtype D compared to other major subtypes remained significant after adjustment for ethnicity, gender and baseline CD4 cell count .Overall, 374 of 679 study patients commenced antiretroviral therapy; 217 were subtype B, 46 were subtype A, 68 were subtype C, 29 were subtype D, and 14 were CRF02-AG. Of these, 141 received a protease inhibitor (PI) based combination, 109 a non-nucleoside reverse transcriptase (NNRTI) based regimen, 98 a triple nucleoside reverse transcriptase (NRTI) regimen, and 26 an NNRTI and PI combination. There were no differences in the type of regimen across subtypes, although a higher proportion of CRF02-AG patients received a PI-based regimen. We found no significant differences between subtypes B, A, C, D and CRF02-AG in the time to achieve viral load suppression (< 400 copies/ml) after initiation of HAART . The findings were similar after adjusting for important confounders, such as HIV risk group, baseline viral load and CD4 cell count, type of HAART regimen, and levels of adherence.Of the 374 patients who received HAART, an adherence assessment was available for at least one visit in 148 patients (this was 66/217 (30.4%) for B; 11/46 (23.9%) for A; 25/68 (36.8%) for C; 15/29 (51.7%) for D; and 5/14 (35.7%) for CRFO2-AG), for two visits in 94 and three visits in 53 patients. In this subgroup, the percentage of patients with 100% adherence at the first, second and third adherence assessments across all subtypes was 73 (49.3%), 57 (60.6%), and 32 (60.4%), respectively. We found no statistically significant differences across subtypes in the percentage with 100% adherence at the three visits, and no association between 100% adherence and subtype (p > 0.5), ethnicity, gender or risk group. The findings were similar when the analysis was repeated using a GEE model with an exchangeable correlation matrix incorporating all three adherence assessments.Of the 133 subtype B patients who attained an initial viral load of < 400 copies/ml after initiation of HAART, 67 (45%) experienced subsequent viral load rebound by 12 months (defined as two consecutive counts of > 400 copies/ml)  after adjustment for baseline CD4 count and viral load, risk group and ethnicity. In contrast, subtype A versus B infection was associated with a reduced risk of viral rebound .Our study was based on a large, well-characterized and ethnically diverse HIV-1-infected cohort in south London, half of whom were infected with the four main non-B subtypes , and with similar access to HIV care and monitoring and antiretroviral therapy. Although we found no clinically or statistically important differences in either the rate of immunological progression prior to antiretroviral therapy or in the initial virological response to antiretroviral therapy between subtype B and all non-B subtypes combined, certain non-B subtype-specific differences were observed. In particular, subtype D infection was associated with both a statistically significant four-fold faster rate of CD4 decline and a higher rate of virological rebound on ART compared with subtype B and the other main non-B subtypes, A and C.We considered carefully whether the more rapid immunological progression in subtype D-infected patients than in patients with other subtypes could be explained by the shorter follow up, fewer CD4 cell count measurements and other differences in demographic characteristics. However, the faster rate of CD4 decline among subtype D patients remained statistically significant even after adjustment for all potentially important confounding factors, including gender, risk group, ethnic group and age. A further limitation of this and other studies on the impact of subtype on disease progression is that date of HIV infection was unknown. However, the baseline CD4 cell count, a surrogate for duration of infection, was similar across the non-B subtypes, but lower compared to B subtype infection , consistOur study is the first from an industrialized country and including white patients infected with non-B subtypes to show a faster rate of disease progression with subtype D infection. This is consistent with the findings from five sub-Saharan African cohort studies ,22-25. IOur findings of a similar rate of disease progression between subtypes B, A, C and CRF02-AG, are also consistent with those epidemiological studies that have examined for differences between these subtypes ,17,19,20Subtype-specific differences in virological and immunological characteristics may account for a faster rate of CD4 decline in subtype D-infected patients through an impact on viral load, viral tropism, syncytia formation and fitness, or immune response ,12,14. AViral genetic variation can influence phenotypic properties, such as cell tropism, co-receptor usage and the ability to form syncytia, although these properties have only recently been correlated with viral subtype. There is some evidence that subtype C uses only the CCR5 co-receptor, and has a preponderance of R5 or NSI viruses and a relative lack of X4 or SI viruses ,38, whilenv sequencing, and an algorithm was devised to assign subtype. To clearly identify whether a sequence belonged to a subgroup representing a CRF within a certain subtype, phylogenetic analysis was done for each sequence individually. Significant misclassification in the assignment of subtype is unlikely as the majority of non-B subtype was assigned based on sequencing and phylogenetic analysis. The use of serologically defined subtype was mainly confined to subtype B infection among whites, as serotyping has been shown to be of good specificity for differentiating subtype B from non-B infections in populations predominantly infected with B subtype [env or gag sequencing does not fully represent all aspects of the genetic variability of HIV, particularly the relationship to phenotypic properties, and that there may be other virological strain differences not captured by HIV-1 subtype.In our study, HIV-1 subtype was determined by a combination of EIA and  subtype . HoweverWe found no differences in the initial virological response to HAART, and in the proportion achieving a suppressed viral load six months after initiation of HAART according to subtype. This is consistent with the findings from three other cohort studies that compared the virological response to antiretroviral therapy based on HIV-1 subtype -29. In aOur study is the first to find a higher rate of rebound for subtype D , although this was based on only 11 patients with subtype D infection. Differences in antiretroviral compliance may also have contributed, although in our study, where adherence data was available on a subgroup of patients, we found no difference in levels of adherence according to either ethnic group or subtype.In the much larger UK analysis , with ovThere is increasing data on subtype-specific variations in susceptibility to antiretroviral drugs ,14, withThere is now a clear consensus that there is a similar rate of progression and response to HAART for subtype B and the non-B subtypes, A and C, but that subtype D is associated with a faster rate of disease progression. Our study is the first to report a higher rate of treatment failure for subtype D infection, but this will require confirmation in a larger cohort of patients infected with different subtypes and receiving antiretroviral therapy, with longitudinal data on adherence. Other detailed virological and immunological studies are needed to provide insights into the molecular mechanisms accounting for the apparent greater virulence of subtype D infection and potential implications for clinical management.The authors declare that they have no competing interests.env sequencing. AMR contributed clinical data.PE coordinated the data collection and wrote the manuscript. MS and IT contributed to the preparation of the manuscript. MS, AMG, JM, SOS, IC, IT, NO and MZ performed the serotyping and All authors have read and approved the final manuscript.Table S1. Characteristic of 679 patients infected with subtypes B, A, C, D and CRF02-AG.Click here for file


Access to antiretroviral therapy has dramatically expanded in Africa in recent years, but there are no validated approaches to measure treatment adherence in these settings.In 16 health facilities, we observed a retrospective cohort of patients initiating antiretroviral therapy. We constructed eight indicators of adherence and visit attendance during the first 18 months of treatment from data in clinic and pharmacy records and attendance logs. We measured the correlation among these measures and assessed how well each predicted changes in weight and CD4 count.We followed 488 patients; 63.5% had 100% coverage of medicines during follow-up; 2.7% experienced a 30-day gap in treatment; 72.6% self-reported perfect adherence in all clinic visits; and 19.9% missed multiple clinic visits. After six months of treatment, mean weight gain was 3.9 kg and mean increase in CD4 count was 138.1 cells/mm3.Dispensing-based adherence, self-reported adherence, and consistent visit attendance were highly correlated. The first two types of adherence measure predicted gains in weight and CD4 count; consistent visit attendance was associated only with weight gain.This study demonstrates that routine data in African health facilities can be used to monitor antiretroviral adherence at the patient and system level. Global health initiatives have introduced antiretroviral therapy (ART) to ever-increasing numbers of HIV patients. Successful therapy depends on life-long adherence to these medications. Thus far, large-scale African ART program have reported mixed results on patient adherence to antiretrovirals (ARVs), with somAccurate assessment of adherence is critical to maximize clinical efficacy and minimize the potential population risks associated with drug resistance. However, no validated approaches exist to measure adherence, especially in low resource settings with potentially poor data availability.Patient self-report about recent adherence is a common assessment method due to its relative ease and low cost of data collection, but self-reports tend to overestimate adherence,6. In adPill counts, like self-reports, can overestimate adherence when compared with electronic medication monitoring,6. CollePharmacy refill records are commonly used in settings with electronic pharmacy data systems to calculate adherence indicators,,11 eitheConsistency of clinic attendance is potentially another way to assess continuity of care and risk for poor adherence. Because failure to attend clinic when expected is objective and easy to ascertain in most record systems, inconsistency of attendance may identify patients in need of outreach or adherence counseling.In 2006 the International Network for Rational Use of Drugs (INRUD) and national HIV/AIDS programs in five East African countries began the five-year Initiative on Antiretroviral Adherence (IAA) to develop practical interventions to improve adherence to ART in routine treatment settings. They found wide variations in definitions and practice in measuring and reporting adherence. To addrThis study was designed to assess the correlation between several of the INRUD-IAA measures: patient self-reports and pill counts ; pharmacy dispensing-based indicators; and attendance consistency. To validate these measures, we also assessed the extent to which they predict changes in weight and CD4 count 5-14 months after start of ART in treatment naïve patients.We conducted a retrospective cohort study in 16 HIV/AIDS treatment facilities in Ethiopia, Kenya, Rwanda, and Uganda using data from medical and pharmacy records on attendance, medication dispensing, pill counts or self-report adherence, demographic and clinical covariates, and clinical outcomes.From 80 facilities included in our pilot studies to test adherence indicators, [as referenced above] we selected four per country to represent the range of infrastructures and patient populations. Facilities that primarily conduct AIDS clinical research were excluded and no more than one referral center was included in each country.The study sample targeted 30 treatment naïve patients per facility who initiated ART 8-24 months prior to data collection and who met the following inclusion criteria: (a) ≥ 18 years old; (b) no previous exposure to ART except for prevention of mother to child transmission (PMTCT); (c) follow-up data for ≥ 6 months after ART initiation, as indicated by one or more recorded clinic visits in two consecutive 3-month periods after initiation; (d) at least one adherence self-report or pill count in medical or pharmacy records in these two quarters; (e) data available on prescribed ART regimen and quantity dispensed during the follow-up period; (f) at least one CD4 count recorded within 3 months prior to initiation and in the 18 months after initiation.Trained data collectors abstracted data from patient intake records, appointment schedules, and medical and pharmacy records for 18 months following ART initiation or until the date of data collection, whichever occurred first.All data for measuring patient adherence were extracted from routine clinic records, where they had been recorded by the treating clinician, nurse, or pharmacist during the clinic visit. Data for calculating adherence measures included: self-reported adherence; pill counts, documented as number of days of therapy remaining from previous dispensings; and type and days of antiretroviral medicines dispensed. Self-reported adherence was typically documented in medical records as perfect (which we coded as 1), good (2), or poor (3). In Ethiopia, self-reports were documented as good/fair vs. poor; because of the difference in coding, cases from Ethiopia were excluded from statistical analyses of self-reports. We calculated consistency of clinic attendance using scheduled and actual visit dates. Pill counts were recorded for <38% of clinic visits and the information recorded varied, so we dropped pill count adherence from the analysis.We constructed the following adherence measures at every follow-up visit after ART initiation: (a) average self-reported adherence from initiation to visit date; (b) whether the patient ever self-reported poor adherence; (c) percentage of days since ART initiation covered by dispensed medicines; (d) whether the patient ever experienced a gap of >30 days without ARVs, assuming daily consumption until available supply was exhausted; (e) percentage of visits to date that occurred on or before the scheduled date and the percentage within 3 days after the scheduled date; and (f) percentage of visits that occurred on or before the expected date of finishing the ARVs dispensed in the previous visit . The last indicator was developed as an alternative to the attendance-based indicators to accommodate systems where data on the dates of scheduled visits are not readily available.Health facility information collected included type , location , and management . Patient demographic information included age, gender, marital status, living situation, education, occupation, and presence of a treatment support partner. Clinical information included WHO stage of HIV/AIDS on ART initiation, evidence of ARVs to prevent mother to child transmission, evidence of tuberculosis at ART initiation, duration of ART, and ART regimen prescribed at each visit. We also extracted symptoms or diagnoses that might indicate possible side effects, opportunistic infections, or significant clinical events associated with HIV.Our primary outcomes were weight and CD4 counts. We recorded weight in kilograms at baseline and at every subsequent visit, and calculated weight gain (or loss) since the initiation of therapy at each visit. Treatment protocols typically call for patients to have a follow-up CD4 test within the first 6 months after initiating ART, although because of limitations in laboratory availability, this may stretch to a longer interval. We captured the dates and results of all CD4 tests from 3 months prior to initiating ART until the end of follow-up. For patients with at least one CD4 count recorded between 4 and 9 months after initiation of ART, we calculated changes between the baseline and the date of the follow-up CD4 count.Country data collectors entered data twice into standardized Excel spreadsheets; data entry differences and data errors were resolved by study staff. Using Pearson product moment coefficients (for continuous measures) and Kendall tau rank correlation coefficients (when one or both measures were binary), we estimated correlations among the different adherence and visit measures.We used generalized estimating equations (GEE) to predict weight change from baseline to each clinic visit during the first 270 days after ART initiation and ordinary least squares regression models to predict change in CD4 count from baseline to the follow-up CD4 test. Each adherence indicator was modeled separately for each clinical outcome; because of the difference in the way the indicator was reported, patients from Ethiopia were excluded from analyses modeling average self-reported adherence. All models included: gender, age , whether married, capital-urban-rural clinic location, baseline CD4 level, WHO stage at ART initiation (stage 1 or 2 vs. stage 3 or 4), time since ART initiation, evidence of TB at ART initiation, evidence of side effects or opportunistic infections since treatment initiation, number of different ART regimens, and whether the patient was ever treated with a protease inhibitor; weight change models also included baseline weight. All analyses were performed in SAS version 9.1 .This study was approved by the AIDS Control Programs of the participating countries and by the Harvard Pilgrim Health Care Human Studies Committee.The overall sample consisted of 488 patients. Of patients screened for sample inclusion, 7.4% were under age 18, 3.9% had evidence of previous ART; 4.8% did not have clinic visits in each of the first two quarters after initiating ART, and 8.6% did not have adherence self-reports in the first two post-initiation quarters. Gender, age, and WHO stage at ART initiation were similar between included and excluded patients.Table Table Patient self-reports mirror the positive results of the dispensing adherence measures. The average self-reported adherence score was 1.17 (out of 3). Nearly three-quarters of patients (72.6%) reported perfect adherence in all clinic visits; only 3% of patients reported poor adherence in any visit.Patients averaged more than one visit per month during follow-up and most attended a high percentage of visits as scheduled. About 20% of patients did not come on the day expected and only 6% failed to appear within 3 days for more than one in five of their visits. For the measure based on attending before all medicines dispensed in the previous visit were finished, consistency appears worse; 45% of patients did not come on or before the day on which medicines were finished for more than one in five of their visits. Since this measure does not account for medicines remaining from earlier visits, it may overestimate the frequency of running out of medicines.3 during that period.Finally, ART initiation is clearly associated with improvements in clinical outcomes. Patients gained an average of 3.9 kg (SD = 5.1) in the first six months of treatment. In the subgroup with a follow-up CD4 test, the average improvement in CD4 count was 138 (SD = 125) cells/mmTable The dispensing-based coverage measures were all highly inter-correlated, which is not surprising since they are derived from the same data source. The dispensing-based measures are also highly correlated with the attendance-based measures, suggesting that patients who present regularly for appointments maintain a more continuous supply of medicines. Self-report measures (average self-report in prior visits or ever reporting less than perfect adherence) are both significantly correlated with percent of days covered and coverage >95%, as well as with regularly attending clinic on the scheduled day or within 3 days.Table A total of 13 patients experienced a gap in therapy of >30 days during follow-up. Average medication coverage for these patients was 53% compared to 98% for the remaining cohort members. Based on the controlled GEE models, patients experiencing a 30-day gap in treatment gained an estimated 3.3 kg less  than those who did not experience such a gap, averaged across all visits during the follow-up period.A self-report of less than perfect adherence during any clinic visit since initiation of therapy was significantly associated with gaining 1.1 kg less  than those who always self-reported perfect adherence. Average self-reported adherence during all prior visits was also significantly related to weight gain. Compared to patients reporting perfect adherence in all visits, patients who averaged between good and perfect self-reported adherence gained 1.1 kg less , and patients who averaged good adherence or worse gained 1.2 kg less .All measures of consistent clinical attendance were significantly associated with weight gain. Based on GEE models, patients who attended <80% of their visits within 3 days of the scheduled day experienced a 1.4 kg lower  weight gain than those who attended a greater percentage of their visits on time.Table Average self-reported adherence recorded in medical records up to the date of the second CD4 test had an overall significant relationship with CD4 gain. Compared to patients who always reported perfect adherence, gains in CD4 counts among patients who averaged between good and perfect self-reported adherence were a non-significant 27.0 cells/mm3 less , while CD4 gains among patients who averaged good self-reported adherence or worse were 67.2 cells/mm3 less , a difference which was significant. Patients with any report of less than perfect adherence experienced gains in CD4 count that were 40.2 cells/mm3 lower  than those who always reported perfect adherence.The visit-based attendance measures were not significantly associated with improvements in CD4 counts.This validation study shows that adherence and attendance indicators measured using routine data that exist in typical African HIV/AIDS care settings were significantly associated with key clinical outcomes during the early treatment period. Consistency of clinic visits and dispensing-based adherence indicators were both moderately to highly associated with weight gain during the first nine months after ART initiation. In addition, both dispensing-based adherence measures and self-reported adherence were predictive of improvements in CD4 counts in that period. Given that the data were extracted from routine records and are likely to contain random errors, the strengths of these relationships are likely to be underestimated.In our study, the questions used to determine the adherence levels that were recorded in medical records were unknown and likely inconsistent across providers and treatment centers; in addition, self-reported adherence was recorded differently in Ethiopia than in the other three countries, requiring us to drop these cases from statistical analysis. Nevertheless, self-reported adherence measures showed promise. When measured in research settings, self-reported adherence has been reliably predictive of positive clinical outcomes-4,9. AttOur study has several notable limitations. First, we could not examine the validity of the INRUD-IAA indicators as predictors of patient dropout because study inclusion criteria required patients to remain in treatment for 4 to 6 months. This requirement was needed in order to obtain sufficient data to measure the dispensing-based and attendance-based measures, which depend on data from multiple visits. Most adherence failures occur early in the course of treatment. Our methods preclude examining the predictors and impacts of these early adherence failures.We measured the validity of the adherence measures in predicting clinical changes soon after the start of therapy. Further analyses would be needed to assess the predictive validity of these measures for patients in treatment for longer periods. We also were unable to assess body mass index as a measure of nutritional status at the initiation of therapy, a potential confounder for post-initiation weight gain, due to the lack of data on height in the clinical record. Although food supplements offered at clinics would be another potential confounder of the relationship between adherence and early weight gain, we are not aware of any supplements offered at study facilities during the study period.We did not assess the consistency of self-reported adherence in clinical and pharmacy records and as gathered by trained interviewers using a systematic method. These validation analyses will be part of future studies of the INRUD-IAA adherence indicators.Despite these limitations, our study is the first to demonstrate that standardized measures of treatment adherence and attendance derived from routine data in African ART treatment settings are valid predictors of clinical outcomes among newly treated patients. Because these data are already available in most settings, they should be more widely used for monitoring adherence to ART and evaluating interventions.This study demonstrates that adherence measures derived from dispensing data in pharmacy records, self-report data in medical records, and attendance logs predict key clinical outcomes related to individual patient success in treatment. However, a more important use would be as population measures to characterize the overall success of treatment programs or health facilities in maintaining patients on therapy. With such data, it would be possible to target quality improvement activities to programs, facilities, and ultimately patients that are in greatest need.The authors declare that they have no competing interests.DRD and AW designed and planned the research, participated in design of the questionnaires, selected facilities for the study, analyzed the data, and drafted the manuscript. JC designed and planned the research, and participated in design of the questionnaires. MPJ cleaned the data, and assisted in analyzing the data. FZ assisted in analyzing the data. TH, LG, JN, and RB participated in design of the questionnaires, trained data collectors in their respective countries, and collected and entered the data. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6963/10/42/prepub


Lamivudine is an oral nucleoside analogue widely used for the treatment of chronic hepatitis B. The main limitation of lamivudine use is the selection of resistant mutations that increases with time of utilization. Hepatitis B virus (HBV) isolates have been classified into eight genotypes (A to H) with distinct geographical distributions. HBV genotypes may also influence pathogenic properties and therapeutic features. Here, we analyzed the HBV genotype distribution and the nature and frequency of lamivudine resistant mutations among 36 patients submitted to lamivudine treatment for 12 to 84 months.7 and 6.9 × 107 copies/mL, respectively). Fifteen patients showed the L180M/M204V lamivudine resistant double mutation. The triple mutant rt173V/180M/204V, which acts as a vaccine escape mutant, was found in two individuals. The three isolates of genotype G were entirely sequenced. All three showed the double mutation L180M/M204V and displayed a large genetic divergence when compared with other full-length genotype G isolates.Half of the patients were homosexual men. Only 4/36 (11%) patients were HBV DNA negative. As expected for a Brazilian group, genotypes A , D  and F  were present. One sample was from genotype C, which is a genotype rarely found in Brazil. Three samples were from genotype G, which had not been previously detected in Brazil. Lamivudine resistance mutations were identified in 20/32 (62%) HBV DNA positive samples. Mean HBV loads of patients with and without lamivudine resistance mutations were not very different (2.7 × 10A high (55%) proportion of patients submitted to long term lamivudine therapy displayed resistant mutations, with elevated viral load. The potential of transmission of such HBV mutants should be monitored. The identification of genotypes C and G, rarely detected in South America, seems to indicate a genotype distribution different to that observed in non treated patients. Disparities in routes of transmission  and in pathogenic properties (genotype C is very aggressive) among HBV genotypes may explain the presence of rare genotypes in the present work. Hepatitis B virus (HBV) is an etiological agent of acute and chronic liver disease. Despite the introduction of HBV vaccination programs in the last decade, hepatitis B remains a largely disseminated disease, with an estimated 350 million people chronically infected around the world, and over 2 billion people who have already been infected with the virus. It is estimated that between 1 and 2 million people die annually as a consequence of the infection [e antigen (HBeAg) [Based on sequence divergence in the entire genome exceeding 8%, HBV is classified into eight genotypes (A to H) (see -4 for re (HBeAg) . This co (HBeAg) . This co (HBeAg) .In Brazil, genotypes A, D and F are the most prevalent ,17,18. I4 copies/mL [Lamivudine is an oral nucleoside analogue inhibiting the reverse transcriptase activity of HIV and HBV . This dropies/mL ,22. The opies/mL -24. The opies/mL ,25. In aopies/mL . This seopies/mL ,28. MoreHepatitis B acute exacerbation may occur after the appearance of YMDD mutants , accompaThe aim of the present study was to investigate the prevalence of HBV DNA and viral load variations in patients receiving lamivudine for a long period for HBV treatment, to characterize HBV genotypes, and to determine the frequency and nature of lamivudine resistance mutations. Furthermore the entire nucleotide sequences of three genotype G isolates recovered from this work were determined.Among 36 HBsAg positive patients under long-term lamivudine treatment, only four  were tested negative for HBV DNA by nested PCR. All four were HIV negative, one was HBeAg positive and three were anti-HBe positive Table . ThirteeThe genotype/subgenotype distribution of the HBV isolates showed a predominance of genotype A, subgenotype A1 (Table The deduced amino acid sequences of the HBV DNA polymerase demonstrated that 20/32 (62%) isolates had lamivudine resistance mutations. The proportion of mutants varied from 60% to 100%, depending on the genotype, with the exception of genotype D, for which none of the three isolates was mutated. Fifteen patients showed the well-known double rtL180M/rtM204V mutation. Two others (03-hgg and 08-hgg) displayed a third mutation (rtV173L) related to lamivudine resistance [4 to 2 × 108 copies/mL . Interestingly, values not very different  were found in treated patients without mutations. Median values of virus load were significantly higher in patients with HBeAg (2 × 107copies/mL) than in patients with anti-HBe antibodies (1 × 106 copies/mL) (p < 0.05).Patients infected with isolates showing lamivudine resistance mutations had HBV loads varying from 8 × 10Failure to lamivudine treatment (phenotypic resistance) was evaluated by medical staff based in rapid increase of serum alanine aminotransferase (ALT) levels, sometimes accompanied by detection of a rebound of HBV load. There was a good correlation between phenotypic resistance and detection of lamivudine mutations. Indeed, 20/24 (83%) patients showing phenotypic resistance displayed lamivudine resistant mutations, whereas 8/8 100%) who had no medical signs did not . All three were men who reported sexual relationships with men. Two of them were co-infected with HIV and were HBeAg positive: patient 09-hgg had very high levels of HBeAg, and was negative for anti-HBe, whereas patient 05-hgg had low levels of HBeAg and anti-HBe. The third patient (06-hac) was anti-HBe positive Table .EcoRI restriction endonuclease  and the other with one EcoRI site . A similar experiment performed with BamHI restriction enzyme  were determined. All three had 3248 bp and showed a 36-nt insertion at the 5' end of the C gene, a 3-nt deletion in the pre-S1 region, and stop codons at positions 2 and 28 of the pre-core region, i.e. all features characteristics of genotype G [adw2 . Pre-S2 and S regions of isolates 06-hac and 09-hgg displayed several aminoacid variations that are present in the sequence consensus of genotypes H and F, whereas isolate 05-hgg showed aminoacid variations related to genotype A  or isoleucine (rtM204I), accompanied or not by a substitution at position rt180  and morent G145R ,35.Here, most patients under prolonged lamivudine treatment displayed HBV DNA in serum. Lamivudine resistant mutations were observed in 67% of the cases, while 33% did not display recognized resistance mutations. In a previous study, performed with HIV infected patients with occult HBV infection (HBsAg negative), the group of patients without resistance mutations had a lower mean viral load than patients with resistance mutations . HoweverRecent studies have suggested that the rate of lamivudine resistance was higher in patients infected with HBV genotype A than in those with genotype D ,38. HereThe three HBV/G isolates described here displayed lamivudine resistance mutations. In previous studies, no particular association has been made between HBV/G and lamivudine resistance. HBV/G was discovered in San Francisco , where aHBV/G displays two stop codons in the precore region, abolishing HBeAg synthesis . DespiteThe three HBV/G isolates characterized here had mean sequence variations (1.9 ± 0.2%) higher than those (0.4 ± 0.1%) observed between HBV/G isolates from other geographic regions. Phylogenetic trees based on pre-S/S, pre-core/core and X regions confirmed the classification of isolates 09-hgg, 05-hgg and 06-hac into genotype G. In the case of isolate 05-hgg, the presence of amino acids characteristics of genotype A in pre-S2 and S regions does not allow excluding the occurrence of a recombination event. Nucleotide sequencing of individual clones will be necessary to test this hypothesis.A high proportion (88%) of long term lamivudine treated patients were HBV DNA positive. Most samples displayed recognized lamivudine resistant mutations with viral loads sufficiently high to be transmissible. Genotypes C and G, rarely or never detected before in South America, were identified. Disparities in routes of transmission  and in pathogenic properties (genotype C is more aggressive than others) might explain the presence of these genotypes in the population studied. All three HBV/G isolates detected here displayed lamivudine mutations and showed large genetic variations between them. Large surveys conducted in the population of men who have sex with men should be useful to better characterize HBV genotype G.The study group consisted of 36 HBV chronically infected, ambulatory patients  who were submitted to long-term  lamivudine treatment at three public Brazilian hospitals  between 2002 and 2004. All the patients were HBsAg positive and anti-HBc positive. The protocol used was approved by the Fiocruz Ethical Committee. Thirteen patients were anti-HIV-1 positive by conventional serological tests, and lamivudine was administered in these patients as part of antiretroviral treatment. The 36 patients belonged to different risk groups for HBV and HIV infections. Twenty (56%) were homosexual or bisexual men. Six belonged to the blood transfusion risk group and three were infected through vertical transmission. No risk group was identified for seven patients.All HIV positive patients demonstrated clinical signs of lamivudine resistance, evaluated by medical staff. HBV monoinfected patients were selected randomly. Serum samples from all patients were initially tested for anti-HBc, HBsAg and anti-HBs in the hospitals. These samples were not available, and new blood samples were collected, retested for the presence of HBsAg and anti-HBc by enzyme-linked immunosorbent assays  and further used for genomic studies. Most HBsAg positive samples were submitted to HBeAg and anti-HBe antibodies detection. This was performed by using the commercially Axsym system .DNA was extracted from serum samples using phenol/chloroform after treatment of 250 μL of serum with 0.5 mg/mL of proteinase K in the presence of 0.2 M NaCl and 0.25% SDS for 4 hours at 37°C as reported previously . After pHBV DNAs were quantified by real-time using the TaqMan technology, according to  with somEF464070 to EF464099. Bioinformatics analysis of the sequences was performed applying the University of Wisconsin Genetic Computer Group package. Neighbor-joining phylogenetic trees were drawn and rearranged using the Mega program version 3 [PCR products of the partial S genomic region were directly sequenced. These products were obtained by semi-nested PCR amplification using primers PS1, S2 and S22 in the first round and PS4, S2 and S22 in the second round (Table ersion 3 . Represeersion 3 .A previously published PCR-RFLP genotyping method ,19, baseMB carried out the sequencing experiments. FJDS, KMRO, MA and CEB were involved in the clinical evaluation of the patients and supervised antiretroviral treatment. CN participated to the study design and revised the final version of the manuscript. SAG conceived and coordinated the study and wrote the manuscript. All authors read and approved the final version of the manuscript.


We examined the association between serum alpha-fetoprotein (AFP) level and sustained virological response (SVR) in 93 chronic hepatitis C patients. The SVR rate was much higher among patients with serum AFP levels below rather than above the median value (5.7 ng/ml) . Serum AFP should be added to the list of factors predictive of treatment response in chronic hepatitis C. Serum alpha-fetoprotein (AFP) is a fetal glycoprotein produced by the yolk sac and fetal liver In a previous study 3, neutrophil counts less than 1500 cells/mm3, and haemoglobin levels less than 10 g/dl. Child-Pugh C cirrhosis, and positive hepatitis B antigen were also excluded. Antiviral therapy included pegylated interferon alpha-2b  and ribavirin , initially given as a function of the patient's weight. The initial dosage was always higher than 1 µg/kg/week subcutaneously for pegylated interferon alpha-2b and 10.6 mg/kg/day orally for ribavirin. The duration of treatment was defined according to consensus recommendations according to genotype, HCV viral load, presence of cirrhosis and failure of previous treatment. All patients were followed for at least 6 months after treatment discontinuation in order to assess their SVR. HCV RNA was measured in the serum using the reverse PCR . Genotyping was performed using the Inno-Lipa assay . Hepatic necroinflammation and fibrosis were assessed by the METAVIR scoring system This retrospective study included patients with chronic hepatitis C who attended the Liver Unit at Necker Hospital, Paris, during 2002, and in whom antiviral therapy was initiated during this period. The inclusion criteria for antiviral therapy were: age over 18 years, positive serology for HCV antibodies, positive HCV RNA, and a pathological METAVIR score strictly higher than A1F1. Exclusion criteria were classic contraindications to interferon or ribavirin therapy, including autoimmune disorders, pregnancy, limiting psychiatric, ophthalmological or cardiological disorders, platelet count less than 75,000/mmThis study is based on the retrospective analysis (performed in 2007) of clinical and laboratory data collected in 2002 as part of the routine management of patients with chronic hepatitis C in the Liver Unit at Necker Hospital, Paris, France. A specific study code number was created for each patient so that no name or identifier other than age and sex appears in the database. Neither informed consent nor ethics approval was asked for this analysis, since it is exclusively retrospective and did not require any additional testing other than that performed as part of routine management of patients in 2002.The initial database contained 104 individuals. Those who were infected with genotype 4 (n = 4), and those with HIV co-infection (n = 7), were excluded from the analysis so that the assessment of AFP predictive value would be carried out only in HIV-negative non genotype 4-infected individuals. Most continuous variables were categorised according to medians or quartiles, except for body mass index (≤ or >25 kg/m2), platelets , AST  and ALT . Factors associated with SVR were examined in univariate and multivariate logistic regression. Factors retained in the final multivariate regression model all had P values less than 0.05, except for AFP which was forced into the model. Likelihood ratio tests were used to determine P values.All analyses were performed using Stata 9.0 statistical software .Baseline clinical, biological, virological and histological characteristics of the participants are given in The SVR rate was 36/93 (38.7%), and decreased with increasing levels of serum AFP: 69.6%, 47.8%, 17.4%, and 20.8%, for the 1st, 2nd, 3rd, and 4th quartiles, respectively (P = 0.001). Also, the SVR rate was 58.7% and 19.2% for those with serum AFP under and above the median value, respectively . For patients infected with genotype 1, SVR rates for patients with fibrosis scores of 0–2 (low to moderate) and 3–4 (severe) were 54.5% and 13.9%, respectively. For patients infected with other genotypes , the SVR rates for the same fibrosis categories were 84.2% and 18.8%, respectively.Other factors associated with SVR in univariate analysis were gender, haemoglobin level, serum AST, serum gamma-glutamyl transpeptidase (GGT), and platelet counts . In multThis study confirms our previous findings regarding the predictive role of AFP with regard to treatment response in chronic hepatitis C. In this study, the odds of treatment failure was six times higher for those with serum AFP above the median value (5.7 ng/ml), a figure identical to that observed in our previous study among Egyptian patients infected with HCV genotype 4  The multivariate analysis provides us with additional information on the mechanisms by which serum AFP might be associated with treatment response. One difference with our previous study is the disappearance of the association between AFP and SVR in multivariate analysis, after controlling for both serum GGT and liver fibrosis. These two factors have been shown associated with lower treatment response rates elsewhere In conclusion, this study confirms the value of serum AFP levels in predicting treatment outcome in patients with chronic hepatitis C, regardless of the infecting genotype. Higher levels of serum AFP may correspond to higher expression of HPC in individuals developing liver fibrosis.


Newer antiretroviral (ARV) agents have improved pharmacokinetics, potency, and tolerability and have enabled the design of regimens with improved virologic outcomes. Successful antiretroviral therapy is dependent on patient adherence. In previous research, we validated a subset of items from the ACTG adherence battery as prognostic of virologic suppression at 6 months and correlated with adherence estimates from the Medication Event Monitoring System (MEMS). The objective of the current study was to validate the longitudinal use of the Owen Clinic adherence index in analyses of time to initial virologic suppression and maintenance of suppression.score ≥ 5 (95% CI: 0.19–0.69) [reference: <5]. In the final generalized estimating equations (GEE) logistic regression model the adjusted odds ratio for time-updated adherence score was 0.17score ≥ 5 (0.05–0.66) [reference: <5].278 patients  met inclusion criteria. Median [range] time on the first regimen during the study period was 286 (30 – 1221) days. 217 patients (78%) achieved an undetectable plasma viral load (pVL) at median 63 days. 8.3% (18/217) of patients experienced viral rebound (pVL > 400) after initial suppression. Adherence scores varied from 0 – 25 . The lowest detectable adherence score cut point using this instrument was ≥ 5 for both initial suppression and maintenance of suppression. In the final Cox model of time to first undetectable pVL, controlling for prior treatment experience and baseline viral load, the adjusted hazard ratio for time updated adherence score was 0.36A brief, longitudinally administered self report adherence instrument predicted both initial virologic suppression and maintenance of suppression in patients using contemporary ARV regimens. The survey can be used for identification of sub-optimal adherence with subsequent appropriate intervention. In previous research, we validated a subset of items from the ACTG adherence battery as prognostic of virologic suppression at 6 months and moderately correlated with adherence estimates from the Medication Event Monitoring System (MEMS) . The obj10 transformed HIV plasma viral load were 173 and 5.0, respectively. Index antiretroviral regimens were distributed as follows: ≥ 2 nucleoside reverse transcriptase inhibitors (NRTIs) + 1 boosted protease inhibitor (PI/r) 73%, ≥ 2 NRTIs + 1 non-nucleoside reverse transcriptase inhibitor (NNRTI) 23%, and other regimens 4%. Enfuvirtide was included as part of the index regimen in only two patients. Median [IQR] days on the index regimen was 286 [115–566] overall. According to prior antiretroviral experience, the median [IQR] days on therapy was 285 [116–566] for treatment naïve patients and 286 [93–562] for treatment experienced patients. 217 patients (78%) achieved an undetectable pVL at median 63 days. 8.3% (18/217) of patients experienced viral rebound (pVL > 400) after initial suppression. The median number of per-patient administrations of the adherence instrument was 4, varying from 1 to 27 administrations. Adherence scores varied from 0 – 25 .Study eligibility criteria were met by 278 patients whose baseline characteristics are presented in Table Of the 1155 records in the final analysis dataset representing the longitudinal histories of 278 patients, HIV viral load and adherence were measured on the same date in 556 (48%) records. Of the 1155 records, 599 (52%) represented missing adherence scores at dates of viral load measurement. Of the 599 missing adherence scores, 426 were imputed using the last observation carried forward approach (LOCF) and 173 were imputed by backfilling values. Even though these missing adherence scores technically represent missing values at the time the viral load measures were taken, they conceptually represent values that were obtained at a different time point than the viral load measures. These instances typically represent patients for whom blood is drawn either before of after a clinic visit at which adherence assessment was conducted. The median (IQR) time between the regimen start date and date of the first recorded adherence score was 21 (13–60) days.10 HIV plasma viral load, prior antiretroviral treatment experience , index regimen type. Of these potential covariates, baseline HIV viral load and race were significantly (p < 0.05) associated with time to viral suppression. Table Because the distribution of adherence scores was highly skewed Figure  we modelTable In the developmental phase of adherence measurement in our clinic, we constructed a 5-item instrument whose individual items were selected from the 51-item ACTG adherence battery  on the bThese findings are not directly comparable to the effects demonstrated in our earlier study for several reasons including: (1) period effects (1998 – 1999 vs. 2003 – 2006) associated with changes in potency and simplicity of antiretroviral regimens; (2) differences in prior treatment experience ; (3) conditions of adherence measurement (written completion [earlier study] vs. computer assisted [current study]); and (4) differences in analytic approach . Nonetheless, the current results contribute to the predictive validation of the instrument as it has been used in routine clinical care of patients on antiretroviral therapy.In a recent review of the status of HIV adherence measurement, Chesney presented a conceptual model of adherence assessment and intervention, distinguishing research from clinical applications, and resource-rich from resource-poor settings. In discussing the "elusive gold standard" of adherence measurement, she emphasized that "efforts should continue to develop a portfolio of different valid and reliable self-report measures with varying strengths and weaknesses that can be optimally applied, depending on the situation ." In thaceiling effects [social desirability bias [First, adherence score distributions in the current Figure  and prev effects . Others  effects -8. The city bias . Simoni ity bias . Compariity bias ,12. WithSecond, the modeling of adherence score is not straightforward. As constructed its scale of measurement is discrete numerical with a possible range of 0 – 25 with skewness not amenable to a normalizing transformation. Although cut point selection for an underlying numerical measure may introduce bias in effect measurement  and may 10-HIV viral load, the adjusted odds ratio for viral suppression was 0.14  for a 6-month median adherence score greater than 5. Finally, in a third analysis of maintenance of longitudinal viral suppression, mean adherence scores were calculated for the period immediately prior to each viral load measurement, creating a score for each interval between viral load measurements. This operationalization of adherence was then fit in a GEE logit model for maintenance of viral suppression, again controlling for prior treatment experience and baseline log10-HIV viral load. The adjusted adherence odds ratio for maintaining viral suppression for a mean interval adherence score greater than 5 was 0.28  Therefore, although the adherence effect estimates were model dependent, the direction of effect was consistent and significant across models.Third, because of the observational nature of the data, measurements of adherence and HIV plasma viral load were not scheduled to occur simultaneously. Typically clinicians order viral loads every 3 – 6 months depending on clinical factors. Adherence in contrast is measured in our clinic at all routine visits. Conceptually, adherence is a construct representing a daily health behavior for which various self-report indicators have been developed and mapped to estimates of percentage adherence over a defined period or, as in the case of the Owen Clinic instrument, given interpretability primarily through demonstrated association with viral suppression. Because of the staggered nature of data accrual in the clinic, decisions must be made regarding how to line up sequential viral load and adherence measures. At a conceptual level, it is a non-trivial question to decide over how long a period an adherence measure based on a limited recall period (4 days in the case of our instrument) can be extrapolated with regard to preceding and future adherence behaviors for which the self-report data represents an imperfect indicator. In our primary analysis, we made the assumption that a given adherence assessment carried forward no longer than 90 days from the antecedent adherence measurement. Whether the observations that are not temporally matched represent truly missing observations is debatable since the very nature of the data accrual process in clinical care did not require temporal matching of adherence and viral load measurement. Because the LOCF principle has been criticized in recent years , we explDespite the limitations of self-report adherence measures, they are likely to remain the most frequent modality of adherence assessment in clinical settings. The brief self-report instrument examined in this study and in an earlier developmental study has been demonstrated to correlate with electronic drug monitoring and to be predictive of viral load responses both when administered at baseline and also when administered in longitudinal follow up of unselected patients in clinical care for HIV infection.all your pills?" (response options (numeric code): no days (0), one day (1), two days (2), three days (3), all four days (4)). Item 4 inquires "How closely did you follow your specific schedule over the last 4 days?" (response options (numeric code): never (4), some of the time (3), about half the time (2), most of the time (1), all of the time (0)). Item 5 deals with weekend adherence behavior asking "Did you skip any of the HIV medications last weekend – last Saturday or Sunday?" (response options (numeric code): no (0), yes (1)). The index score is the sum of responses to the four items with a possible range of 0 (best adherence) to 25 (poorest adherence) if each component of the regimen was dosed twice daily.A retrospective observational cohort study was conducted including all HIV-infected adults under care at the UCSD Owen Clinic between January 2003 and June 2006. Patients were included in the analyses reported here if they: 1) had at least one self report medication adherence score recorded; (2) either initiated antiretroviral therapy for the first time or began a new regimen during the study period; (3) had a plasma viral load ≥ 400 copies/ml prior to initiation of the index regimen; (4) had at least one post baseline plasma viral load; and (5) remained on the index regimen for at least 30 days. Only time on the first regimen during the study period (index regimen) is included in reported analyses. During the study period, patients on antiretroviral therapy were asked to complete, prior to meeting with their medical provider, a computer-assisted four item antiretroviral medication adherence survey [ had at lTwo outcome measures were operationally defined as: (1) time to first virologic suppression defined as HIV plasma viral load (pVL) ≤ 400 copies/ml after regimen initiation; and (2) maintenance of virologic suppression (pVL ≤ 400 copies/ml). Follow up time for each patient began with the date of initiation of the index antiretroviral regimen and ended with the earliest of the following events: (1) change or discontinuation of the index regimen; (2) last clinic visit date; or (3) end of the study period. Time to first virologic suppression on the index regimen was examined using extended Cox models incorporating time-updated adherence scores. It was confirmed that the proportional hazards assumption was met for all covariates included in the Cox models using log(t) by covariates interactions . MaintenBecause HIV plasma viral load and adherence score were not always measured on the same dates, records with missing values for adherence score after the first adherence measurement date were imputed using the last observation carried forward (LOCF) principle. Because the first adherence measurement date usually occurred after the regimen start date, records with missing early adherence scores were backfilled to the regimen start date using the score of the first adherence measurement. Adherence scores were carried forward and backfilled no more than 90 days from the temporally closest adherence measurement date. Viral load data were not carried forward.Statistical analyses were performed using Stata 10.0 . This research was approved by the University of California San Diego Human Subjects Committee (Project No. 040394)The authors declare that they have no competing interests.WCM designed the study, conducted the final analysis, and prepared the manuscript; EB and EW conducted extensive medical record review and prepared preliminary analysis of the data; CB and BC contributed to design of the study and manuscript preparation; SM advised on statistical analysis and contributed to the manuscript preparation. All authors read and approved the final manuscript.