Objective. To investigate the relationship between insulin resistance and viral load decay in nondiabetic and noncirrhotic genotype 1 chronic HCV patients during peginterferon and ribavirin treatment and the possible influence of BMI and leptin as metabolic confounders. Methods. 75 consecutive noncirrhotic, nonobese, and nondiabetic patients with genotype 1 chronic hepatitis C treated with peginterferon alpha 2a plus ribavirin were evaluated. HOMA-IR, serum leptin, and BMI were measured in all patients at baseline and at weeks 12 and 48, whereas viral load was measured at the same time points and then 24 weeks after the end of treatment. Results. HOMA-IR was significantly associated with both BMI and leptin at baseline. During peginterferon plus ribavirin treatment, there was a significant reduction of HOMA-IR at weeks 12 and 48 from baseline  in patients who achieved an early viral load decay (EVR), a trend not observed in patients who not achieved EVR. No variations during treatment were observed regarding BMI and leptin irrespective of EVR. Conclusion. The early reduction of HOMA-IR but not of BMI and leptin during antiviral treatment in noncirrhotic, chronic hepatitis C genotype 1 patients who achieved EVR suggests a viral genesis of insulin resistance in patients with nonmetabolic phenotype. The standard treatment of genotype 1 chronic hepatitis C with pegylated-interferon plus ribavirin is associated with a 40–50% rate of sustained viral response (SVR) .A number of pretreatment variables related to the virus  and to the host , as well as on-treatment variables  have been demonstrated to affect the response to anti-viral therapy in patients with chronic hepatitis C .Among host factors, overweight and insulin resistance are the most commonly studied since they are a possible target of intervention. One of the open issues today is the mutual influence of insulin resistance and viral load variations during antiviral treatment.The relationship between insulin resistance and hepatitis C virus (HCV) is complex and still not completely elucidated. HCV genotype 1 is able to directly induce insulin resistance  by interThere is robust evidence that antiviral treatment influences insulin resistance. Patients with SVR showed a significant reduction of insulin resistance from baseline to the end of follow-up compared with patients who did not achieve SVR , 12. TheOn the other hand, insulin resistance can influence antiviral response. Patients with high insulin resistance have a slower decay of HCV viral load than patients with low insulin resistance already in the early phase of treatment, suggesting that hyperinsulinaemia reduces the cellular response to pegylated-interferon . FurtherAnother question is whether the insulin resistance variations during treatment are the result of a selective direct antiviral effect  or rather an indirect consequence of antiviral treatment on combined metabolic factors . It has been observed that, in patients with HCV genotype 1 infections, therapy with peginterferon and ribavirin is associated with a decrease of body weight and insulin resistance . ElevateWith this in mind, the aim of our study was to evaluate the relationship between insulin resistance and viral load modifications as well as the possible influence of body mass index (BMI) and serum leptin as metabolic confounders on insulin resistance kinetics in nondiabetic and noncirrhotic genotype 1 chronic hepatitis C patients during peginterferon alpha and ribavirin treatment.The current study was designed to analyse data from 75 consecutive, nondiabetic patients with genotype 1 chronic HCV without cirrhosis who had not previously been treated with interferon alpha and/or ribavirin and who were recruited from two referral liver units .Seventy-five consecutive, nondiabetic, and noncirrhotic patients with genotype 1 chronic HCV who had not previously been treated with interferon alpha and/or ribavirin were included in this study. There were 45 men (60%) and 30 women (40%), with median age of 49 years (range 26–67). All patients had HCV-RNA levels higher than 1000 IU/mL, increased serum alanine aminotransferase (ALT) levels at screening, and a liver biopsy specimen taken in the 18 months prior to study entry showing chronic hepatitis. Patients were excluded if they had HCV genotype other than type 1 infection , biopsy-proven cirrhosis, other causes of liver disease, hepatitis B virus infection, human immunodeficiency virus infection, autoimmune disorders, clinically significant cardiac or cardiovascular abnormalities, organ grafts, systemic infections, clinically significant bleeding disorders, evidence of malignant neoplastic diseases, concomitant immunosuppressive medication, fasting glucose levels >7 mmol/L or glucose levels >11.1 mmol/L at 2 hours after an oral intake of 75 grams of glucose  or glycosylated hemoglobin > 48 mmol/mol (6.5%) or antidiabetic treatment, or any alcohol intake or drug abuse within the six months prior to study entry. Patients with a BMI ≥ 30 were also excluded from the study.μg/kg/week and ribavirin ) either 1000 mg/day (body weight < 75 kg) or 1200 mg/day (body weight > 75 kg). During treatment, patients were evaluated at monthly intervals  to monitor compliance and side effects. Patients who showed a <2log10 drop in viral load at week 12 as compared to baseline or those whose HCV-RNA level was positive at week 24 discontinued treatment on the basis of the international guidelines. Conversely, patients who had a ≥2log10 drop in viral load at week 12 as compared to baseline and whose HCV-RNA level was negative at week 24 continued the treatment schedule for a total of 48 weeks. The study protocol was approved by the local Ethics Committees and patients gave written informed consent to participate.All 75 patients were treated with the same initial schedule of peginterferon alpha-2a . Serum HCV-RNA levels were quantified in all patients at the beginning of treatment and then at weeks 4, 12, 24, 48, and 72 by using a real-time polymerase chain reaction (PCR) (COBAS TaqMan). HCV genotype was determined using the INNO-LiPA HCV II kit .10 drop in viral load at week 12 as compared to baseline and whose HCV-RNA level was negative at week 24 were defined as early virologic responders (EVR). All EVRs also had end-of-treatment response (EOTR) defined as having negative HCV-RNA levels at week 48. Sustained virological response was defined as undetectable HCV-RNA levels in the serum at week 72 . Patients whose viral load declined more slowly (<2log10 drop at week 12 as compared to baseline), those whose viral load dropped >2log10 at week 12 as compared to baseline and who still had positive HCV-RNA at week 24, those who became HCV-RNA positive after negativization before the end of treatment (breakthrough response), and those who became HCV-RNA positive after negativization at the end of treatment (relapsers) were all considered non-SVRs.Patients who had a ≥2logAll patients underwent liver biopsy within the 18 months prior to treatment. Paraffin embedded biopsies were analyzed by a single pathologist unaware of the clinical and biological data, except for the presence of chronic hepatitis C. This analysis was performed after hematoxylin-phloxin-safran, Perls, and picrosirius red staining. Liver biopsy specimens no less than 15 mm long or the presence of at least 10 complete portal tracts were required. Necroinflammatory activity was graded and fibrosis was staged according to Ishak's scoring system ), insulin ), adoponectin, and leptin  were all measured after overnight fasting. Glucose levels were also determined at 2 hours after an oral glucose intake of 75 grams .Insulin resistance was determined by the homeostasis model assessment (HOMA) method using the following equation: HOMA-IR = fasting insulin (mIU/L) × fasting glucose (mmol/L)/22.5 . HOMA-IRt-test or Pearson's correlation test (for continuous variables) and X2 test  were used. Pearson's correlation test was also used to assess the association among metabolic variables. Logistic regression was carried out for multivariate analysis and for the definition of relative risk of single variables. A P value less than 0.05 was considered statistically significant.Database management and statistical analyses were performed using a statistical software package (SPSS 10.0.5). Univariate analysis was carried out on metabolic variables for their possible association with SVR. Of 75 patients , median age 48 years (range 26–67), 26 experienced SVR (34.7%). P = 0.010) and leptin (P = 0.000), but not between HOMA-IR and age (P = 0.59) or baseline adiponectin (P = 0.53). The same associations are shown in When we evaluated the influence of each variable against each one of the others, we found a significant association between HOMA-IR and both BMI , although the significance of age is weak.P = 0.81).We also assessed the quartile distribution of HOMA-IR, assuming the 75th percentile of HOMA-IR value, equal to 3.20, as the cut-off value of insulin resistance in our population. The patients in the top quartile of HOMA-IR distribution values who were classified as having insulin resistance showed no difference in SVR rates (36.8%) compared with patients in the other three quartiles of HOMA-IR  but not in the non-EVR groups  and diabetes. Insulin resistance occurs very early in transgenic mice expressing the HCV core protein (8), and this is similarly observed in humans . HCV is Since 2005, low SVR rates in patients with chronic hepatitis C and insulin resistance (defined as HOMA-IR ≥ 2) have been reported, and two meta-analyses reinforced this observation , 24. HowIn our study, however, HOMA-IR was not associated with SVR. One reason could be related to patient selection. Our cohort did not include patients with advanced fibrosis nor those with high HOMA-IR values (median 1.9) or high BMI median 23.6), a feature that is in line with the observation by Eslam and colleagues. In fact, of all the studies in their meta-analysis which failed to find an association between insulin resistance and SVR, the mean baseline HOMA-IR was <3 and the prevalence of advanced fibrosis or cirrhosis was also low or even absent . This ob.6, a feaWhen we evaluated the difference in HOMA-IR values at baseline, at week 12 and at the end of treatment on the basis of viral kinetics, we observed a rapid decrease (week 12) of HOMA-IR value followed by a slight increase at the end of treatment (week 48) only in the group of patients with early viral abatement (EVR). This observation suggests an imbalance of the metabolic pathway in the early phase of treatment in patients who are unable to suppress viral replication. Many studies seem to support this hypothesis. Bortoletto and colleagues demonstrated that insulin resistance exerts its negative influence on viral decay very early, already in the first 24-hours after starting antiviral treatment . On the The possible link between HCV infection, insulin resistance, and the early phase of viral kinetics during antiviral treatment involves the downregulation of IRS1/2 and upregulation of the suppressor of cytokine signaling-3 (SOCS-3) genes in liver tissue , 22. SOCγ (PPAR-γ) pathway or via the metformin-induced hepatic AMP-activated protein kinase.Many studies have tried to demonstrate the beneficial effect of adding insulin sensitiser agents to peginterferon and ribavirin in order to enhance viral clearance in genotype 1 chronic hepatitis C with insulin resistance. Neither metformin nor pioglitazone proved the validity of this therapeutic strategy in the attempt to achieve SVR, although higher early viral response (at 4 and 12 weeks) was observed in the insulin sensitizer plus peginterferon and ribavirin groups compared with the control groups (peginterferon plus ribavirin) –32. The One of the goals of this study was to explore the association of some metabolic variables with insulin resistance. First, we found a different association with gender among various metabolic variables. No differences in HOMA-IR were observed between males and females, whereas BMI was higher in males. Although we expected higher leptin levels in males based on BMI, we found higher levels of both leptin and adiponectin in females. This observation was already reported by Lo Iacono and colleagues  and was Furthermore, our study is aimed at exploring whether HOMA-IR variations during antiviral treatment are associated with similar variations in leptin and BMI or not. Although we assessed both leptin and adiponectin at baseline, we chose to compare only leptin variations during treatment because only leptin and BMI, but not adiponectin, were associated with HOMA-IR.α, and insulin, whereas its secretion, a prerogative of adipocytes alone, is proportional to the fat mass and provides antiobesity signals. Nonetheless, obese patients have elevated levels of leptin, suggesting that they are resistant to its action. One of the mechanisms of leptin resistance is an increase in SOCS3 levels, which impairs postreceptor signaling and leads to reduced adenosine monophosphate-activated protein kinase (AMPK) activation [Leptin is a 16-kDa polypeptide that is synthesized by mature adipocytes, skeletal muscle cells, and culture-activated hepatic stellate cells. Leptin expression is regulated by IL-1, TNF-tivation .In our study, we observed no significant variations in either BMI or in leptin levels between baseline and weeks 12 and 48, unlike HOMA-IR. This issue seems to further suggest a selective effect of treatment on HCV-related insulin resistance. More information needs to be obtained by comparing insulin resistance to leptin levels during long-term follow-up after treatment.Furthermore, unlike what was reported by Conjeevaram and colleagues , we did In summary, in our selected population of noncirrhotic, nonobese, and nondiabetic HCV chronic patients, HOMA-IR, as expression of insulin resistance, is not a predictor of SVR but rather a dynamic parameter influenced by viral load. In fact, its significant reduction in parallel with a consistent abatement of viral load during antiviral treatment (EVR patients) compared with patients who did not achieve significant drop in viral load (non-EVR) suggests that the viral load has a role in determining a state of “inflammatory” insulin resistance, irrespective of BMI and leptin levels.In conclusion, our data support the hypothesis of a viral-related insulin resistance in chronic hepatitis C genotype 1 patients without a metabolic phenotype.


Syphilis remains a global public health threat and can lead to severe complications. In addition to resolution of clinical manifestations, a reduction in nontreponemal antibody titers after treatment is regarded as “proof of cure.” However, some patients manifest < 4-fold decline  or persistently positive nontreponemal titers despite an appropriate decline (“serofast”) that may represent treatment failure, reinfection, or a benign immune response. To delineate these treatment phenomena, we conducted a systematic review of the literature regarding serological outcomes and associated factors among HIV-infected and -uninfected subjects.Six databases  were searched with no date restrictions. Relevant articles that evaluated serological treatment responses and correlates of serological cure  were included.We identified 1693 reports in the literature, of which 20 studies met selection criteria. The median proportion of patients who had serological non-response was 12.1 % overall , but varied depending on the time points after therapy. The serofast proportion could only be estimated from 2 studies, which ranged from 35.2–44.4 %. Serological cure was primarily associated with younger age, higher baseline nontreponemal titers, and earlier syphilis stage. The relationship between serological cure and HIV status was inconsistent; among HIV-infected patients, CD4 count and HIV viral load was not associated with serological cure.Serological non-response and the serofast state are common syphilis treatment outcomes, highlighting the importance of determining the immunological and clinical significance of persistent nontreponemal antibody titers after therapy.The online version of this article (doi:10.1186/s12879-015-1209-0) contains supplementary material, which is available to authorized users. Syphilis is an ancient infectious disease, yet modern efforts to control the disease remain ineffective in many countries, especially among high risk populations like men who have sex with men (MSM) –3. The Win vitro, most testing for syphilis relies on measurement of immune responses rather than on direct tests for Treponema pallidum : 4.9 – 25.6). At six months, the median proportion of patients who were serological non-responders following therapy was 20.5 % (IQR: 8.2 - 25.6). This proportion decreased to 11.2 % (IQR: 5.8 – 31.9) at ≥ 12 months for all stages of syphilis. Using data from the 10 studies that included only ES patients, the median proportion of serological non-response was 9.4 % (IQR: 4.8 – 19.1); only one study by Fiumara  was restFive studies reported treatment failures, which met the definitions for a ≥ four-fold rise in nontreponemal titers after treatment in the absence of reinfection; the proportion reported from these studies ranged from 0–24 % , 33, 37.Ten studies assessed the association of patient age with serological response to therapy Table . Five foThirteen studies evaluated the association of baseline nontreponemal antibody titers with serological treatment response. Of these, ten found that higher baseline titers  were significantly associated with a greater likelihood of or time to serological cure in both HIV-infected and HIV-uninfected patients  among both HIV-infected and HIV-uninfected patients with PS or SS. In this study, serological cure rates among HIV-infected patients were reported to be 88 % with one PCN dose compared to 97 % with three doses, but the difference was not found to be statistically significant (p = 0.18) [Nine studies evaluated the effect of treatment regimens on serological outcomes, but only two found any significant associations. Yang, et. al.  reported = 0.18) .p = 0.03). The RCT by Rolfs, et. al. [Nine studies evaluated the association of HIV status with serological outcomes after treatment of syphilis by comparing patients with and without HIV infection. Of these studies, only two reported that HIV-infected patients were less likely to achieve serological cure , 37; theSix studies analyzed the serological response after treatment solely among HIV-infected persons Table . Ghanem,Additional factors have been evaluated for their associations with serological outcomes following syphilis treatment Table . Seven sOnly one study performed lumbar punctures among individuals with serological non-response to therapy . FiumaraT. pallidum cannot be cultured in vitro, thus forcing reliance on serological tests for diagnosis and proof of cure. This practice has been in place for nearly a century due to the absence of novel technologies to confirm eradication of the organism. Unfortunately, our systematic review of 20 studies conducted worldwide among HIV-infected and HIV-uninfected patients with syphilis demonstrates that a substantial proportion (12.1 %) of patients exhibit serological non-response after treatment, which is time-dependent. The median proportion of serological non-response patients was 20.5 % at six months, which decreased to 11.2 % at ≥ 12 months for all stages of syphilis. Our findings highlight the importance of developing a consistent definition of serological non-response, which is not an absolute or definite condition but should be thought of as a continuum with serological cure and the serofast state over time. The presence of persistent nontreponemal antibodies may represent treatment failure, reinfection, or another undefined immune phenomenon; however, none of the studies in our review could distinguish among these possibilities when neither a four-fold rise in nontreponemal antibody titers nor a history of re-exposure was evident. The appropriate time to determine falling titers, and the correlates that affect the time when titers fall, remains unclear. Serological cure was less likely in older individuals, patients with lower baseline nontreponemal antibody titers, and patients with later stages of syphilis. The clinical or biological explanations for these observations have not been well established. Perhaps more importantly, there are no consistent criteria by which patients can be stratified to determine their need for additional evaluation or syphilis therapy when serological non-response exists.T. pallidum. Other investigators soon discovered that antibodies from syphilis patients reacted not just with antigens from spirochetes but also with cardiolipin found in normal nonsyphilitic tissues. Pangborn subsequently purified cardiolipin from beef heart extract [T. pallidum has been identified as 13 % cardiolipin [Nontreponemal antibodies have played a pivotal role in syphilis screening and monitoring of treatment outcomes; yet the mechanism by which they arise is poorly understood. The first serological test for syphilis was developed in 1906 as a complement fixation assay by Wasserman,et. al. , using a extract , which idiolipin , 44. Howdiolipin . Nontrepdiolipin –48, and T. pallidum was raised by early investigators, based on findings from animal and human studies conducted in the 1960s [T. pallidum can disseminate to multiple organs in humans within hours of inoculation [T. pallidum is then known to infect various anatomical sites, including the central nervous system and eye as “immune privileged” areas where the spirochetes can slowly replicate and evade the immune response [The possibility that persistent nontreponemal antibodies after treatment represent persistence of he 1960s , 50. Thehe 1960s . Howeverculation . T. pallresponse . Symptomresponse ; howeverresponse .T. pallidum would be essential for analyzing patient specimens before and after treatment. Unfortunately, a meta-analysis of studies evaluating the performance of T. pallidum PCR on samples taken during different stages of infection reported pooled sensitivities as low as 31.2 % and 47.4 % in blood and CSF specimens, respectively [T. pallidum in serological non-responders or serofast patients. This suggests that other means, such as analysis of antigen-specific immune responses that occur during infection and after therapy, may be a more pragmatic approach to investigating these conditions.In order to address the question of persisting spirochetes after therapy, highly sensitive direct detection methods for ectively , illustrT. pallidum is extremely sensitive to minute amounts of PCN in vitro and in vivo if maintained for sufficient periods of time [The hypothesis that a lingering immune response is responsible for persistent nontreponemal antibodies can be argued based on a number of key observations. We found that 11 % of patients still exhibit serological non-response at ≥ 12 months after therapy; yet reported tertiary syphilis cases are still relatively uncommon worldwide. Thus, the observed proportion of serological non-response is not likely to be associated with a high frequency of cardiovascular or neurological complications as might be expected, given the global rate of syphilis. PCN has been considered to be highly effective since it was first reported for treatment of syphilis . T. pall of time , 55, and of time . These fT. pallidum specific antibodies. This kind of pathogen-specific memory response is seen after both infections and vaccinations. Long-term study of human volunteers has shown that pathogen-specific memory responses have very slow decay rates with apparent half-lives ranging from years to millennia [Treponemal IgM and IgG antibodies have already been demonstrated to persist in syphilis patients after therapy –59, indiillennia . TherefoT. pallidum. These antibodies are autoreactive since cardiolipin, the main antigenic component that binds to nontreponemal antibodies, is present in mitochondrial membranes. Disappearance of these autoreactive antibodies after syphilis therapy leading to seroreversion suggests the involvement of tolerance control. In serological non-response and the serofast state, there is an apparent failure of the expected process for down-regulation. Persisting nontreponemal antibodies after effective therapy may thus represent failure of immune tolerance rather than lack of pathogen clearance. Recent work on HIV has shown that many antibodies against the virus are autoreactive and under tolerance controls [In contrast, nontreponemal antibodies decline after therapy in the majority of syphilis patients, indicating that they are down-regulated after clearance of controls –67, and T. pallidum in HIV-infected persons. The epidemiological connection between HIV and syphilis is well-established [Our review found conflicting evidence on the relationship between demographic, clinical factors, HIV status and serological response. The associations of syphilis stage and baseline nontreponemal antibody titers with serological treatment outcomes appear to be the most consistent among the studies in this review. Only two out of nine studies reported that HIV-infected status decreased the likelihood and time to serological cure , 37. Marablished , 71, butSeveral limitations are evident from this systematic review. First, there was no standard definition for serological non-response that was consistent. There was heterogeneity in the classifications of serological outcomes that did not meet the criteria for serological cure, which is partly related to the lack of consensus of the U.S. and European Union guideline definitions , 72. ThiT. pallidum infections that has prevailed over decades. Clinicians have been taught to expect at least a four-fold decline in nontreponemal antibody titers or seroreversion to following therapy; however, serological non-response occurs in nearly one out of every eight patients with syphilis after treatment. This significant proportion and our clinical observations regarding serological non-response and the serofast state suggest the following plausible scenarios that patients may eventually exhibit serological response or serorevert over time (e.g. as in LLS); patients may be getting reinfected at a high rate; patients may not be achieving 100 % cure from PCN; or that patients with persistent nontreponemal antibody titers have neither reinfection nor persistent T. pallidum but instead, an altered immune process in which their anticardiolipin antibodies are not down-regulated. Given the diversity of data presented here, it is conceivable that each of these possibilities holds true for a subset of patients, and we presently lack sufficient information to differentiate among these or other scenarios. There have been vast improvements in methods for detection of microorganisms, and in understanding the host response to different infections. Unfortunately, modern innovative technologies have not yet been applied to the study of syphilis. Based on the data from our systematic review, we do not believe that further retrospective studies of serological response to recommended therapies will be useful or informative. Rather, coordinated investigations involving novel B cell methods, direct detection assays for T. pallidum, and correlations with nontreponemal and treponemal antibody tests in syphilis-infected patients are essential to advancing clinical management of this formidable pathogen.Our results highlight a fundamental problem in the management of Syphilis remains a global public health threat, whose clinical management is highly dependent on nontreponemal antibody titers. Unfortunately, the basis for the cardiolipin antibodies detected during syphilis infection is poorly understood, and our systematic review affirms that a significant proportion of both HIV-infected and HIV-uninfected patients will have serological non-response after therapy. These patients may have reinfection, treatment failure, or a benign immune response. In order to explore these possibilities and inform clinical practice, efforts must be directed towards designing prospective syphilis studies and utilizing modern technologies to determine the microbiological and immunological basis for these antibodies.


In the early days of HIV treatment, drug resistance occurred rapidly and predictably in all patients, but under modern treatments, resistance arises slowly, if at all. The probability of resistance should be controlled by the rate of generation of resistance mutations. If many adaptive mutations arise simultaneously, then adaptation proceeds by soft selective sweeps in which multiple adaptive mutations spread concomitantly, but if adaptive mutations occur rarely in the population, then a single adaptive mutation should spread alone in a hard selective sweep. Here, we use 6717 HIV-1 consensus sequences from patients treated with first-line therapies between 1989 and 2013 to confirm that the transition from fast to slow evolution of drug resistance was indeed accompanied with the expected transition from soft to hard selective sweeps. This suggests more generally that evolution proceeds via hard sweeps if resistance is unlikely and via soft sweeps if it is likely.DOI:http://dx.doi.org/10.7554/eLife.10670.001 In the early days of HIV therapy, the strains of the virus that infected patients frequently evolved drug resistance and the therapies would often eventually fail. These treatments generally involved using a single anti-viral drug. Nowadays, better therapies involving combinations of several anti-viral drugs are available and drug resistance in HIV is a much rarer occurrence. This means that now a particular therapy may be an effective treatment for an HIV-infected individual over much longer periods of time.A theory of population genetics predicts that when it is easy for a population to acquire a beneficial genetic mutation – like one that provides drug resistance – multiple versions of that mutation may spread in the population at the same time. This is called a soft selective sweep. However, when beneficial mutations occur only rarely, it is expected that only one version of that mutation will take over in a population, which is known as a hard selective sweep.Here, Feder et al. test this theory using data from 6717 patients with HIV who were treated between 1989 and 2013 using a variety of different drug therapies. The experiments aimed to find out whether the transition from the older drug therapies –where the virus frequently acquired resistance – to the newer, more effective drugs was associated with a transition from soft to hard sweeps.Feder et al. find that HIV more often evolved drug resistance via soft sweeps in patients treated with the less effective drug combinations (like those given in the early days of HIV treatment), while hard sweeps were more common with the more effective drug combinations. This suggests that good drug combinations may allow fewer drug resistance mutations to occur in the HIV population within a patient. This may be because there are fewer virus particles in these patients, or because the specific combinations of mutations that provide resistance occur less often. Feder et al.’s findings are a step towards understanding why modern HIV treatments work so well, which will ultimately help us find better treatments for other infectious diseases.DOI:http://dx.doi.org/10.7554/eLife.10670.002 In the first two decades of the HIV epidemic, HIV became a prime example of fast evolutionary change, especially because of the evolution of drug resistance quickly after initiation of treatment. Nowadays, HIV treatments are more clinically effective and the evolution of drug resistance has become much slower and often does not occur for years if at all. The rate at which evolution occurs has been the subject of considerable recent interest in the evolutionary biology community. Although traditionally evolution was thought to be slow , there aPopulation genetic theory suggests that whether populations evolve slowly or quickly is driven by the availability of adaptive mutations. In a large population with a high mutation rate, mutations may be available as standing genetic variation (pre-existing variation) or be generated anew every generation, allowing the population to adapt to its environment rapidly. If adaptive mutations are rare, because the population is small, the mutation rate is low, or only few specific mutations (or combinations of mutations) can help a population adapt, the population will likely adapt to its environment much more slowly.The availability of adaptive mutations does not only change the rate of adaptation, it also changes how adaptation affects genetic diversity in a population. If adaptive mutations are rare, i.e., less than one adaptive mutation occurs per generation in the population, the first successful mutation is likely to rise to high frequency before any subsequent adaptive mutations reach appreciable frequencies see . This reIn contrast, when adaptive mutations are common, i.e., more than one occurs per generation in the population, the same adaptive mutation may occur several times in a very short time span on different genetic backgrounds. These adaptive mutations can increase in frequency virtually simultaneously  Penning and multIn HIV, the evolution of drug resistance was fast in patients on early anti-retroviral therapies , but curHowever, these predictions have not been tested in HIV or, in fact, in any natural population. In this study, we ask whether the transition from fast to slow evolution of drug resistance was indeed associated with a transition from soft to hard selective sweeps. If true, then in general, treatments in which resistance arises by soft sweeps might be predicted to have high rates of failure even before the rates of failure can be measured explicitly.https://hivdb.stanford.edu/). These sequences contain information about the number of DRMs and, as we will further explain in the next paragraph, they also contain information about genetic diversity in the viral population.To test whether a transition from soft to hard sweeps has occurred, we look at the relationship between fixed drug resistance mutations (DRMs) and genetic diversity across 29 common anti-retroviral drug regimens. The expectation is that when hard selective sweeps predominate, we will find a negative correlation between the number of DRMs and genetic diversity in a population. On the other hand, when soft selective sweeps predominate, we expect to find no such correlation. We use 6717 HIV sequences from the same number of patients from the Stanford HIV Drug Resistance Database  show strong reductions in diversity associated with each additional resistance mutation, a pattern more consistent with hard selective sweeps, whereas treatments that fail more often (such as regimens based only on NRTIs) show no reduction in diversity, a pattern consistent with soft selective sweeps. Although our results do not explain mechanistically how effective treatments lead to harder sweeps of drug resistance mutations, they suggest a more general principle: a lower rate of the production of adaptive mutations should be accompanied by harder sweeps.We collected sequences of reverse transcriptase and/or protease genes from 6717 patients from the Stanford HIV Drug Resistance Database . The seqAll 6717 patients received some type of therapy (between 1 and 4 drugs), with the majority (77%) receiving a regimen of three drugs. Nearly all patients received one or two nucleoside reverse transcriptase inhibitors (NRTI), usually paired with a non-nucleoside reverse transcriptase inhibitor (NNRTI) or a protease inhibitor (PI), which was boosted with a low dose of ritonavir in some patients. HIV subtypes were varied, with the majority being B (36%), C (34%) or CRF01_AE (13%). None of the remaining subtypes contributed more than 5% of the total sample.An additional dataset, which we call the clonal dataset, consisted of 11,653 sequences from 740 patients with multiple sequences per patient isolated through clonal amplification and subsequent Sanger sequencing. The clonal dataset was used for validation purposes only.r = 0.91, p<2.2 × 10−16). A similar pattern holds at the amino acid level . We therefore conclude that ambiguous calls are a reliable proxy for within-patient genetic diversity.We are interested in the effect of drug resistance evolution on within-patient genetic diversity, but in our main dataset, we only have one sequence per patient. To use this large dataset for our purposes, we therefore use ambiguous nucleotide calls as a proxy for within-patient genetic diversity. Although results from previous studies suggest that this approach is valid , we indeWe ask whether across all sequences, the presence of a drug resistance mutation (DRM) is associated with lower within-patient diversity, the classical signature of a selective sweep . For eacFirst, for the most common reverse transcriptase and protease DRMs, we compare sequences that have exactly one DRM with sequences that have zero DRMs . We plot the difference in within-patient diversity between the two groups with 95% confidence intervals in −4, between 1 and 2 is 1.6 × 10−5, between 2 and 3 is 1.1× 10−2, between 3 and 4 is 2.5 × 10−3). After 4 DRMs, subsequent DRMs do not significantly reduce diversity further. The observed pattern of DRMs associated with reduced diversity is mainly driven by the patients receiving NNRTI or boosted PI-based treatments, as can be seen when separating the above analysis by drug treatment category  are preferred to treatments based on unboosted PIs. These more and less effective groupings are the basis of comparisons in our parametric approach described below.To measure effectiveness quantitatively, we conducted a literature search to determine the percentage of patients who have remained virologically suppressed after one year of treatment  correspond well and clinical treatment effectiveness ranged widely, from very low effectiveness  to very high effectiveness  .10.7554/We hypothesize that effective treatments (such as those containing an NNRTI or boosted PI) likely make adaptation in viral populations limited by the generation of mutations and these populations should thus experience harder selective sweeps leading to a sharp reduction in diversity accompanying each additional DRM. Less effective treatments on the other hand (such as those containing only NRTIs or unboosted PIs) likely allow replication of fairly large HIV populations so that adaptation is not limited by the generation of mutations. They should thus experience soft selective sweeps and little or no reduction of diversity with each additional DRM. Below we test this hypothesis by assessing the reduction of diversity associated with the presence of a DRM among treatments that vary in clinical effectiveness.Before we are able to test this hypothesis, we have to deal with a peculiarity of our data. We found that even for sequences that carried no resistance mutations, more ambiguous calls were reported over time. This is likely due to increased awareness of genetic diversity in the HIV community, and not because diversity actually increased. We therefore employed a p-thinning routine to repeatedly subsample the data so that diversity measures would be comparable across years .To determine whether the effect of DRMs on within-patient diversity depends on clinical treatment effectiveness, we first fit a generalized linear mixed model (GLMM) using the number of DRMs and sequence length to predict diversity as measured by the number of ambiguous calls. Because we found the subsampled number of ambiguous calls to be distributed according to a negative binomial distribution, we used a negative binomial error distribution . For each treatment, we report the total effect of the number of DRMs on diversity . We also excluded 45 sequences sampled before 1995 which had an extreme influence on the p-thinning routine . The analysis that includes these sequences leads to qualitatively similar results (see the supplement). We also used only sequences with at most 4 DRMs. This captures the initial change in diversity due to the fixation of DRMs, and allows the We find that most of our  below 0 . This paWe found a slightly positive value of  failure . Among t the USA   with negative binomial error distributions to each of the 1000 datasets. For each subsample, we separately fit the effect of increased numbers of DRMs on diversity for inferior and superior RTI treatments  and inferior and superior PI-based treatments (2NRTIs + PI and 2NRTIs + PI boosted with ritonavir).We first use this model to assess the difference between inferior and superior RTI treatments . We find that among sequences from patients receiving highly effective treatments with NNRTIs each fixed DRM is associated with a mean additional 13.6% reduction in diversity as compared to populations with no DRMs . Among sequences from patients receiving less effective treatments with only NRTIs, each fixed DRM is associated with a mean additional 4.3% reduction in diversity compared to populations with no DRMs  . The rel−16). In fact, in every one of the 1000 subsampled datasets, the NNRTI category had a more negative slope than the NRTI category.To assess the difference in effect of DRMs on diversity between NRTIs and NNRTIs, we performed a matched Wilcoxon sign-rank test comparing the slopes of the model fits to the 1000 subsampled datasets. We found treatments containing NNRTIs had significantly lower slopes than those treatments containing only NRTIs (p<2.2 × 10−16). The relative effect of these Among PI-based treatments, we found that sequences from effective treatments based on boosted PIs showed a mean additional reduction in diversity of 30.3% with each fixed DRM as compared to populations with no DRMs . In contrast, sequences from less effective treatments based on unboosted PIs showed a smaller decrease in diversity of 4.3%  with each fixed DRM . The effIn both treatment classes (RTI-based and PI-based), we find that the fixation of a DRM under more effective treatments leads to a much stronger reduction of diversity than the fixation of a DRM under less effective treatments.As a second, non-parametric approach, we did a test with the ability to compare the effects of all treatments (RTIs and PIs) directly. In order to do this, we fit a linear regression among the 20 treatments for which we had information about effectiveness to predict the reduction of diversity per DRM (taken as the random effects fit by treatment in the GLMM above) from treatment effectiveness. We observe a negative relationship between tiveness . We findWithin this analysis, we find that among regimens with >30% effectiveness, those with the highest We find a similar negative relationship between ore 1995  and inclore 1995 .Treatment for HIV-1 represents an enormous success of modern medicine. Whereas early antiretroviral treatments were associated with fast evolution of drug resistance and high rates of treatment failure, there are currently many combinations of drugs that are successful at keeping HIV-1 at low or undetectable levels for many years, preventing the evolution of resistance and the progression to AIDS. Indeed, the evolution of drug resistance has become fairly uncommon . In thisWe want to study how the evolutionary dynamics of selective sweeps have changed in the evolution of drug resistance over the past two and a half decades. Because it would be unethical to give subpar treatment to HIV infected patients, we can only investigate this question using historical data. The only type of data that is available for a wide range of treatments and time points is Sanger sequencing data, due to its importance in HIV research and diagnostic testing. Although we often have only one sequence per patient, it is important to note that there is still information present in these sequences about genetic diversity and selective sweeps. First, we used ambiguous nucleotide calls as a proxy for genetic diversity. Although we are not the first study to do so , as far Examining the relationship between DRMs and diversity recapitulates expected results: we first find that across the entire dataset, sequences with a single DRM have lower genetic diversity than sequences without any DRMs. This result confirms a finding from a previous much smaller study looking at patients on NNRTI based treatments . In addiThe key result of this paper . The DRMs that are observed may be associated with variable selection coefficients and may therefore differ in their rate of expansion in the population and thus the associated signature of the selective sweep. Additionally, DRMs occurring later in the course of an infection may have smaller beneficial effects as compared to early DRMs. Different patients may have varying profiles of diversity at the time of diagnosis, which could also effect whether sweeps will be hard or soft. All of these items could serve to obscure the relationship between DRM fixation and changes in diversity, but our results appear robust despite the substantial noise.Although our results appear robust, certain systematic biases could also affect our results. First, we observe an increase in the number of ambiguous calls through our study period. This is likely due to a greater awareness of within-patient diversity and improvements in sequencing technology. Having more ambiguous reads called in later years gives us more power to detect large decreases in diversity as compared to earlier years. As effective treatments come from later in the study period, this could lead to more observed hard sweeps among effective treatments. However, we believe that our conclusion is robust to this systematic bias as we used a conservative Time can also factor into our analysis in other ways that are harder to control. Improved disease monitoring may mean that modern patients have a shorter time between the appearance of a DRM and its sequencing and discovery. This could result in hard selective sweep signatures that are more pronounced in later years because they are detected before they have had time to erode. However, Pennings et al. estimate that sweep signatures require a substantial amount of time to erode (on the order of months) , so we dFinally, because our primary measure to understand the effect of the fixation of a drug resistance mutation is an internal comparison with other patients on the same treatment, we do not believe that our results could be generated solely by data heterogeneity. Further, that our results are robust to different measurements and filtering approaches suggests DRMs do indeed sweep differently in HIV populations subject to more or less effective treatments.Drug resistance evolution is no longer the threat to HIV patients it once was and treatments exist that almost never lead to resistance. However, our observation that effective drug combinations lead to hard selective sweeps could be useful for improving treatments for other pathogens. Even among relatively small samples, if patients who acquire drug resistance mutations do not have a significant decrease in diversity relative to those who did not acquire drug resistance mutations, this might suggest that the fixing mutations are occurring via soft selective sweeps, and the treatment brings patients into a dangerous regime for drug resistance. Alternatively, if patients acquire drug resistance mutations, but those patients also have very little diversity, it may be that this is a safer treatment and that the emergence and fixation of drug resistance mutations was a relatively uncommon occurrence.Looking at changes in diversity following a sweep in order to assess the mode of adaptation could be particularly well-suited to looking at evolution of cancer. While single cell sequences isolated from tumors have yielded promising insights about evolutionary dynamics, the process is invasive and relatively difficult. Sequencing tumor-free cancerous cells circulating in the blood provides less information, but can be done serially and provides a good measure of tumor heterogeneity. Applying a method such as ours to monitor changes in cell-free DNA diversity over time may allow us to determine if certain treatments reproducibly lead to soft sweeps and thus are very likely to fail in general.Comparing treatment effectiveness with the occurrence of soft and hard selective sweeps may also provide supplementary information about additional risk factors among patients. In the case of HIV, we find that high effectiveness is associated with hard selective sweeps, which suggests that the the virus has a hard time evolving drug resistance, and the patients in whom resistance evolved are merely the unlucky ones. However, there may be cases where failure is rare, but associated with soft selective sweeps. Such a situation thus reflects a discordance between what happens within patients and what we see at a population level. This, in turn, may be indicative of a behavioral, genetic or virologic difference among the groups of patients and efforts should be made to find out how failing patients are different from non-failing patients.In conclusion, we find that the study of diversity in viral populations with resistance can show differences in evolutionary pathways of adapting pathogenic populations and provides a concrete example of how population genetics theory can make substantive predictions about medically relevant problems. Next generation and single molecule sequencing have the capacity to bring much more precision in determining the dynamics of within-patient populations. However, we also urge researchers and clinicians to report more information concerning the diversity of pathogen populations, even in the form of minor allele frequency cut offs for calling ambiguous calls or raw sequencing data, as this might allow new insight from data that might be otherwise overlooked.We collected one consensus sequence of the HIV-1 reverse transcriptase gene per patient from 6717 patients across 120 different studies from the Stanford HIV Drug Resistance Database . These pNearly all patients received treatment that included one or more nucleoside reverse transcriptase inhibitors (NRTI). In many patients, the NRTIs were paired with either a non-nucleoside reverse transcriptase inhibitor (NNRTI) or a protease inhibitor (PI), which was in some patients boosted with a low dose of ritonavir, a second PI, in order to boost drug levels and improve effectiveness. Among patients receiving PIs, only patients with both protease and reverse transcriptase sequences were included. All sequence information in the HIV database is recorded as aligned consensus nucleotide sequences. The data are taken across many studies, each with their own procedures and cutoffs for calling positions ambiguous. No electropherogram data were available and the ambiguous calls reported were taken as submitted to the database. The sequences from these patients were labeled the direct PCR (D-PCR) dataset.For each sequence we counted the number of relevant drug resistance mutations . For a portion of the analysis, we rely on examining many HIV-1 sequences from patients being treated with the same drug regimen. Because we measure the association between the number of DRMs and genetic diversity by examining a slope (n = 174) with much less variety in their given treatments (the mean number of sequences per patient was 58 with an interquartile range of 16 to 64). These sequences were derived from taking a within-patient population of HIV-1 and attempting to isolate, clone and sequence single strains. We used these reads  to validate ambiguous calls as a within-patient diversity measure.We supplemented our analysis with an additional dataset of patients from which multiple HIV-1 strains were sampled. This dataset comprised 10,235 sequences, but from a relatively small number of patients  a, t, c and g calls with their capital counterparts. Nucleotides called as W, S, M, K, R, Y (ambiguity between two nucleotides) and B, D, H, V (ambiguity between three nucleotides) and their lowercase counterparts were included as ambiguous calls. Ns and Xs (indicating no information about the identity of the position) were excluded.We also examined ambiguities on the amino acid level by using nucleotide level information. If, for example, a nucleotide triplet was recorded as AAM, where M indicates an adenine/cytosine ambiguity, the amino acid at that position was ambiguous between AAA  and AAC . The amino acid for that position would then be recorded K/N. All ambiguous calls at the nucleotide level were translated into ambiguous calls at the amino acid level, including if the ambiguous call reflected synonymous encodings . The number of ambiguous amino acids was recorded for each sequence.We determine whether mutations are associated with drug resistance based on the 2009 update of DRMs for the surveillance of transmitted HIV-1 drug resistance adopted by the World Health Organization , which l5163 patients in the D-PCR dataset had a sequenced protease gene available in the database (77% of patients). Not all patients had entire reverse transcriptase and protease genes sequenced, but only 1% of sequences had fewer than 500 basepairs sequenced of 1680 possible in reverse transcriptase and only 1% of available protease sequences had fewer than 210 of 300 bases in protease.In order to validate the appropriateness of ambiguous calls as a proxy for genetic diversity, we computed diversity using an ambiguous call measure and compared it to a diversity measure that did not use the ambiguous call data. For each site, we computed genetic diversity using the ambiguous calls as the proportion of all D-PCR HIV-1 sequences that had an ambiguous basepair call at that site. This approximates the percentage of patients with within-patient diversity by site.In order to test if this measure of diversity based on ambiguous calls correlates well with other measures of diversity that don’t depend on ambiguous calls, we used the clonal dataset to compute the average sitewise We expect that clinical effectiveness of an HIV treatment affects the probability of the virus undergoing a soft or hard selective sweep. All HIV treatment regimens that occurred at least 50 times in the D-PCR dataset were evaluated for treatment effectiveness based on a literature review. As a measure for treatment effectiveness, we recorded the proportion of patients whose treatment was still successful after a year of treatment, as indicated by a viral load of ≤ 50 copies of HIV-1 RNA/mL or less after 48 or 52 weeks in an on-treatment analysis. Our literature review was mostly based on the papers reviewed in Lee et al . Because−8). That the difference in effect can be seen in the zero DRM class but not among sequences with any number of DRMs underscores the strong interaction between low diversity and multiple DRMs among modern treatments.Because the drug regimens systematically changed over time , it is eBecause the probability of calling a nucleotide as ambiguous has increased over time, we have potentially greater power to detect changes in the number of ambiguous reads with increasing number of DRMs. To account for this change in power, we used a p-thinning procedure  to contrWe first measured the effect of year on diversity by fitting a linear model to the number of ambiguous calls in each year among all sequences with zero observed DRMs. By limiting our analysis to only sequences without DRMs, we remove the hypothesized change in diversity expected following a sweep. We observed the following relationship between year and the number of ambiguous calls:1989,i) is calculated as According to this relationship, we computed probabilities of calling ambiguous reads in different years relative to the first year sampled . We subsampled our data using these probabilities so that late and early years had comparable numbers of ambiguous calls. For example, in 2000, nearly twice as many ambiguous calls were reported as compared to 1989. Therefore, for each ambiguous call observed in a sequence from 2000, we include it in our sample with probability ∼1/2. Intuitively, this translates observations from later years into units of ambiguous calls that are comparable to those observed in 1989. The subsample effect for year (i) see . The fitBecause very few ambiguous calls were made in 1989, p-thinning all read counts to be comparable to sequences from 1989 lowers the resolution of our data. We therefore also performed subsampling using only data after and including 1995, which retained more ambiguous calls. This particular cutoff was chosen for two reasons. First, ambiguous calls were not reliably recorded until several landmark papers studying within-patient diversity were published in 1993 . Second,Using data only after and including 1995, the subsample effect for year vere see . This isIn including each ambiguous call with probability relative to the subsample effect, for each sequence thinning , and is ing (see ). These We estimate the relationship between the number of DRMs and genetic diversity by fitting a generalized linear mixed model (GLMM) with a negative binomial error distribution for our 29 abundant treatments. We found that the subsampled data visually fit a negative binomial distribution much more closely than a Poisson distribution, which is often used for count data See . In thisThis model was fit to 1000 datasets created by p-thinning the number of ambiguous calls. The overall effect of a DRM on diversity was fit by the We performed the above procedure three times: our main analysis used the To quantify the effect of treatment effectiveness on To compare how the effect of DRMs on genetic diversity varied between two groups of treatments, we fit generalized linear models (GLMs) with a negative binomial error distribution in R  using thThese models were parametrized to fit separate slopes for the four different types of treatments . To do this, we used an indicator variable for membership in each of the four groups and fit the following model:The four Apart from discriminating between two treatment categories, we also tested the association between our continuous measure of treatment effectiveness and the change in diversity associated with a DRM on a given treatment  lead to an increased anticorrelation between diversity and the number of drug resistance mutations (DRMs). This is interpreted as evidence that more efficacious drugs lead to harder selective sweeps and hence greater reductions in diversity per DRM. Overall this is an interesting manuscript that does its best to use data collected for another purpose to study an interesting issue in evolutionary dynamics.Specific points:eLife.The following points need to be addressed, however, before the paper can be considered for publication in 1) The Introduction would benefit from emphasizing the following points more carefully. The motivation for distinguishing between soft and hard selective sweeps is not described adequately . It is also important to note in the very first paragraph that this paper is restricted to drug resistance mutations in treated populations. In untreated populations, the story is very different. The fact that more efficacious treatments lead to fewer resistant mutations is almost tautological. The connection between this effectiveness and a signature of the evolutionary dynamics of resistance is nice, and that is the point that needs to be emphasized.2) Ambiguous calls from Sanger sequencing are used as a proxy for diversity because this is the best that can be done given the available data. A convincing argument is made suggesting that the ambiguous calls are at least a reasonable proxy, based on earlier work and analysis of data from multiple clones sequenced from individual patients. However, there could be additional factors correlated with treatment efficacy that also affect the fraction of ambiguous calls. For example, there may be an effect of the timing of the sample relative to the progression of the disease (e.g. relative to treatment failure). Since there is only one sample per individual, it is easy to imagine that sampling is done more often at treatment failure for less efficacious drugs, but not for more efficacious drugs. This doesn't necessarily confound the analysis, but it could potentially lead to a correlation between efficacy and ambiguous calls that does not have to do with the hardness of the sweeps. These sorts of caveats are unavoidable in this kind of study, but the potential problems should be acknowledged.3) In  The following points need to be addressed, however, before the paper can be considered for publication in eLife. 1) The Introduction would benefit from emphasizing the following points more carefully. The motivation for distinguishing between soft and hard selective sweeps is not described adequately . It is also important to note in the very first paragraph that this paper is restricted to drug resistance mutations in treated populations. In untreated populations, the story is very different. The fact that more efficacious treatments lead to fewer resistant mutations is almost tautological. The connection between this effectiveness and a signature of the evolutionary dynamics of resistance is nice, and that is the point that needs to be emphasized.We thank the reviewers for noting ways in which the most important points of the paper can be better highlighted and motivated. To this end, we have expanded the discussion of several things that were unclear in the Introduction, and most substantively, expanded the discussion concerning the importance of connecting evolutionary dynamics to the probability of eventual failure .2) Ambiguous calls from Sanger sequencing are used as a proxy for diversity because this is the best that can be done given the available data. A convincing argument is made suggesting that the ambiguous calls are at least a reasonable proxy, based on earlier work and analysis of data from multiple clones sequenced from individual patients. However, there could be additional factors correlated with treatment efficacy that also affect the fraction of ambiguous calls. For example, there may be an effect of the timing of the sample relative to the progression of the disease (e.g. relative to treatment failure). Since there is only one sample per individual, it is easy to imagine that sampling is done more often at treatment failure for less efficacious drugs, but not for more efficacious drugs. This doesn't necessarily confound the analysis, but it could potentially lead to a correlation between efficacy and ambiguous calls that does not have to do with the hardness of the sweeps. These sorts of caveats are unavoidable in this kind of study, but the potential problems should be acknowledged.We appreciate that we are working with a very noisy dataset with many potential confounding factors and it is important to be direct about the ways in which they might influence our results. While much of the information concerning caution about our analysis was present in the original draft, we have consolidated the information into an expanded set of paragraphs in the Discussion. We have further grouped these caveats into separate categories: 1) data-specific noise factors that should obscure the signal, 2) properties of the data that might contribute to our observed signal that we control for within the analysis and 3) properties of the data that might contribute to our observed signal that are not possible to control. For each of these potential biases, we discuss why we do not believe that they generate the patterns seen in our data. The particular issue related to treatment monitoring described by the reviewers is included in this third category. We believe that this new format should make it more apparent to readers the types of factors that must be considered in interpreting our results.3) In In investigating this issue, we found that the y-intercept was indeed higher for these more efficacious treatments. This effect is caused by the positive relationship between year and the probability of calling ambiguous reads. Therefore, better treatments (from later years) have a higher 0 DRM category than worse treatments (from earlier years). We controlled for the effect of year in the model, but the particular way in which that graph was plotted did not (and cannot) properly display the effect of year across the different treatments (as each treatment is comprised of patients from across multiple years). The more parsimonious solution to display this effect would be to rescale the number of ambiguous reads so that they are comparable across years.p-thinning procedure, in which the number of ambiguous reads per sequence was subsampled so that year effect was controlled for explicitly. Switching to this approach did not change any of our results in a substantive manner, and offered several advantages over our original approach:We therefore modified our analysis slightly in order to account for this effect through employing a A) We now examine ambiguous read data as counts instead of proportions, which better preserves the variance structure inherent in our data;B) Since the number of ambiguous calls does not increase over time, we have comparable power to detect changes in the number of ambiguous calls with different numbers of DRMs among early treatments and late treatments;C) Confidence intervals and summary statistics can be constructed from the different datasets of subsampled ambiguous reads, which allows for the results to be more interpretable.To ensure that this approach was statistically sound, we had extensive conversation with Susan P Holmes of the Department of Statistics at Stanford University. We further believe that her contributions to improving the work merits authorship, and the author list has been updated accordingly.In our new updated version of p-thinning process and updated analysis in full detail. Although p-thinning makes the manuscript slightly more complicated, we believe that this approach is the best way to account for year changing throughout our sample period, and ultimately the fairest way to compare early and late treatments.The majority of the updated text in our resubmitted manuscript describes the


The goal of antiretroviral treatment (ART) in HIV-1 infection is the permanent suppression of plasma viral load (pVL) below the currently existing limit of detection of 50 copies/mL (DAIG HIV-therapy guidelines). Therefore, treatment effectiveness is based on pVL. pVL measurements however do not give any information about the viral reservoir in peripheral blood mononuclear cells (PBMCs). Therefore, the proviral DNA of HIV-1 could be a useful marker for the investigation of viral reservoirs and monitoring ART in patients showing undetectable pVL.Seventy-seven treatment-experienced HIV-1 infected patients with pVL <50 copies/mL were randomly selected. pVL and proviral DNA load were measured using the Roche COBAS TaqMan HIV-1 v2.0 assay. Additionally, CD4+ cell count per mL and the total white blood cell (wbc) count per mL were determined for each patient. Follow-up data were collected 24 weeks after the time point of the first measurement. Proviral DNA load per mL, CD4+ cell count per mL as well as wbc count per mL were observed over time and differences were estimated using the Mann–Whitney U test. Additionally, correlations between the clinical parameters were analyzed using the two-sided Pearson correlation analysis.Out of the 77 patients, 38 show a significant increase in proviral load per mL over time , whereas 39 patients show a significant decrease . No differences became visible in the CD4+ cell count per mL comparing week 0 and week 24 data. Thirty five patients show a significant increase in wbc count per mL over time , while 42 patients show a significant decrease . A significant correlation of increasing proviral load per mL and wbc count per mL for data of the first (p<0.001) and the second measurement (p<0.001) can be detected, while there are no correlations found between proviral load per mL and CD4+ cell count per mL.Our data show that the presence of viral reservoirs in other cell types and not only CD4+ cells is most probable. HIV-1 proviral DNA seems to be an interesting marker in patients with undetectable pVL and allows the assessment of replication under ART. Nevertheless, further longitudinal studies are needed to assess the usefulness and the clinical significance of this marker.


Adherence to antiretroviral therapy (ART) plays an important role in treatment outcomes. It is crucial to identify factors influencing adherence in order to optimize treatment responses. The aim of this study was to assess the rates of, and factors associated with, suboptimal adherence (SubAdh) in the first 24 months of ART in an Asian HIV cohort.As part of a prospective resistance monitoring study, the TREAT Asia Studies to Evaluate Resistance Monitoring Study (TASER-M) collected patients’ adherence based on the World Health Organization-validated Adherence Visual Analogue Scale. SubAdh was defined in two ways: (i) <100% and (ii) <95%. Follow-up time started from ART initiation and was censored at 24 months, loss to follow-up, death, treatment switch, or treatment cessation for >14 days. Time was divided into four intervals: 0–6, 6–12, 12–18 and 18–24 months. Factors associated with SubAdh were analysed using generalized estimating equations.p=0.006), compared to sites with ≤2 assessments per patient per year. Compared to heterosexual exposure, SubAdh was higher in injecting drug users (IDUs) , p=0.004) and lower in homosexual exposure , p<0.001). Patients taking a nucleoside transcriptase inhibitor and protease inhibitor (NRTI+PI) combination were less likely to report adherence <100% , p=0.001) compared to patients taking an NRTI and non-nucleoside transcriptase inhibitor (NRTI+NNRTI) combination. SubAdh decreased with increasing time on ART . Similar associations were found with adherence <95% as the outcome.Out of 1316 patients, 32% ever reported <100% adherence and 17% ever reported <95%. Defining the outcome as SubAdh <100%, the rates of SubAdh for the four time intervals were 26%, 17%, 12% and 10%. Sites with an average of >2 assessments per patient per year had an odds ratio (OR)=0.7  (0.55 to 0.90), We found that SubAdh, defined as either <100% and <95%, was associated with mode of HIV exposure, ART regimen, time on ART and frequency of adherence measurement. The more frequently sites assessed patients, the lower the SubAdh, possibly reflecting site resourcing for patient counselling. Although social desirability bias could not be excluded, a greater emphasis on more frequent adherence counselling immediately following ART initiation and through the first six months may be valuable in promoting treatment and programme retention. Adherence to antiretroviral therapy (ART) plays a critical role in optimizing HIV treatment outcomes. Poor or suboptimal adherence (SubAdh) has been associated with virological failure, poor CD4 response as well as an increased risk of developing drug resistance –3. To maPrevious studies have shown that factors such as developing side effects, having been in clinical stage B/C, being in a younger age group and using alcohol were significant contributors to SubAdh , 5. In rThere are many methods available to monitor ART adherence. Self-reported assessments have been shown to be an appropriate tool for measuring adherence in resource-limited settings due to their ease of use and low cost. Studies suggest that although self-reported methods tend to overestimate actual adherence, they comprise a valid tool that can be used to measure levels of antiretroviral adherence, especially in low-income countries –11. MoreThe objectives of this study were to investigate the rates of SubAdh and the effects of demographic and clinical parameters on the association with SubAdh in the first 24 months after ART initiation, based on the 30-day self-reported adherence assessments in an Asian adult HIV cohort.The Therapeutics, Research, Education and AIDS Training in Asia (TREAT Asia) Studies to Evaluate Resistance Monitoring (TASER-M), began recruitment in 2007 and included 12 clinical sites from Thailand, Hong Kong, Malaysia, the Philippines and Indonesia. The primary goal of TASER-M was to monitor HIV drug resistance mutations in Asia. Patients were enrolled as either treatment naïve (those initiating ART) or treatment experienced (those switching to second-line therapy) . TASER-MData transfers from clinical sites were aggregated at The Kirby Institute, University of New South Wales, Australia, at six-monthly intervals in March and September. Ethics approvals were obtained from the University of New South Wales Ethics Committee and institutional review boards at the participating clinical sites and coordinating centre (TREAT Asia/The Foundation for AIDS Research (amfAR), Bangkok, Thailand). Written informed consent was obtained from participants prior to enrolment.SubAdh was defined in two ways: (i) adherence <100% and (ii) adherence <95%, in the past 30 days according to the VAS. The adherence percentage indicated in the two boxes to the right of the linear scale  was usedFollow-up time since start of ART was divided into four 6-monthly intervals: 0 to 6, 6 to 12, 12 to 18 and 18 to 24 months. If multiple adherence assessments were recorded in any given time interval, the lowest adherence level in that interval was used. Intervals missing an adherence record were kept as missing.We created a variable to represent site-level resources , 16 for Factors associated with SubAdh were analysed using generalized estimation equations (GEEs) with binomial distribution, logit link function, exchangeable correlation structure and robust standard errors, to account for repeated measures in different patients (GEE-logit). The exchangeable correlation matrix was chosen due to its simplicity, whereby only one correlation coefficient has to be estimated.p<0.05. CD4, AIDS diagnosis and adverse events were modelled as time-updated variables, lagged forward to the next time interval. For CD4 cell count, if more than one measurement was available during each of the six-month intervals, the average of the measurements was used for that interval. For AIDS diagnosis, once a patient was diagnosed as having an AIDS-defining illness, that patient would be coded in this category for the remaining follow-up time. Adverse events were coded as ever having events of grades 3 (severe), 4 (life-threatening) or 5 (causing death) in each of the time intervals. Multiple sensitivity analyses were performed:Variables included in the univariate analysis were age, sex, exposure category, CD4, initial ART category, hepatitis B and C co-infection, AIDS diagnosis, adverse events, site adherence frequency and time on ART. We did not adjust for viral load in the regression models due to concerns over causality between viral load and adherence. Variables significant in the univariate analysis at the 10% level were included in the multivariate model by a backward stepwise selection process. Factors were considered significant and adjusted for in the final multivariate model if The group of patients who were considered lost as of March 2012 (i.e. patients who had not been seen between March 2011 and March 2012) had theirfollow-up time extended to 24 months. Missing adherence, CD4, AIDS diagnosis and adverse events for these lost patients were imputed using last observation carried forward (LOCF) methods;All patient follow-up time was extended to 24 months regardless of their status. Any time interval without an adherence assessment, CD4, AIDS diagnosis and adverse events was imputed using LOCF. Adherence was also filled backwards to earlier time intervals, if missing, using the first assessment. As such, a patient with only one adherence assessment would have that adherence for the entire 24-month duration;The site adherence assessment variable was removed, and the model was adjusted by country income status [17];The site adherence assessment variable was removed, and the model was adjusted by individual countries; andThe analysis was performed using a GEE with Poisson distribution (GEE-Poisson), and the incidence rate ratios (IRRs) were reported.For all sensitivity analyses, SubAdh was defined as VAS self-reports <100%.All data management and statistical analyses were performed using SAS software version 9.2  and STATA software version 12.1 .A total of 1316 patients from 11 sites who were enrolled in TASER-M and initiated ART with at least one adherence assessment available within the first 24 months of ART, prior to the censoring date, were included. One site was excluded as adherence information was not collected. Within six months prior to ART initiation, the median viral load was 100,000 copies/mL . Seven hundred and eighteen patients (55%) contributed a complete 24-month follow-up time to the analysis, 137 (10%) switched treatment in the first 24 months, 21 (2%) ceased treatment for more than 14 days and 440 (33%) had follow-up times of less than 24 months due to death, loss to follow-up or being newly recruited. The median frequency of adherence measurements for sites ranged from one to six per patient per year. Five sites had frequency >2 per patient per year, and six sites were ≤2 per patient per year.p=0.014), exposure category (p<0.001), CD4 (p<0.001), initial ART category (p<0.001), hepatitis C co-infection (p=0.012), adverse events (p=0.090) and time on ART (p<0.001). The four covariates remaining significant in the multivariate model were site adherence assessments, exposure category, initial ART and time. After adjusting for the significant covariates, sites with average adherence measurements of >2 times per patient year had a 30% reduced odds for SubAdh <100% (odds ratio (OR)=0.7, 95% confidence interval (CI) (0.55 to 0.90), p=0.006). Homosexual mode of exposure was associated with an almost 50% reduction , p<0.001), while injecting drug users (IDUs) were almost twice as likely to report <100% adherence compared to the heterosexual exposure group , p=0.004). In terms of initial ART, patients who were put on NRTI+protease inhibitor (PI) combinations were less likely to report SubAdh , p=0.001). Increasing time on ART was also associated with decreasing odds of SubAdh  for time intervals 6 to 12, 12 to 18 and 18 to 24 months, respectively, compared to the initial six months. There were no significant differences by age, sex, CD4, hepatitis B and C co-infection, AIDS diagnosis and adverse events, after adjusting for the significant factors in the final model.The total numbers of patients with adherence assessments by time intervals were 1046 (0 to 6 months), 1051 (6 to 12 months), 841 (12 to 18 months) and 807 (18 to 24 months). The proportions of patients with self-reported adherence <100% in each of these intervals were 26%, 17%, 12% and 10% , with 32p<0.001) for SubAdh <95%. Those with homosexual HIV exposure were less likely to report SubAdh , p<0.001), while IDUs had more than double the odds , p<0.001), compared to the heterosexual exposure group. Patients who were prescribed NRTI+PI for their initial regimens showed decreased odds for SubAdh , p=0.001) compared to the NRTI+NNRTI group. Time on ART also showed similar effects to adherence <100% .The second section of p<0.001). All other covariates that were significant in p-values. The same can be said for sensitivity analysis (ii), where all patients had their follow-up time extended to 24 months regardless of their follow-up status. The results were somewhat attenuated with time ; 12 to 18 months: OR=0.59, 95% CI (0.52 to 0.68); and 18 to 24 months: OR=0.56, 95% CI (0.48 to 0.64), all p<0.001). This suggested that the improvement in adherence as time on ART increases was not entirely due to loss to follow-up and missing data.p=0.167). This indicates that there was no significant difference in adherence between the lower-middle countries and high+upper-middle countries. On the other hand, sensitivity analysis (iv) suggested that when categorizing by individual countries, the adherence effects for each country were not all the same . The significance and ORs of the other adjusted covariates in these two sensitivity models were similar to those in To investigate the effect of sites, we conducted sensitivity analyses (iii) and (iv), where the site adherence assessment variable was replaced by country income status and individual countries, respectively. In sensitivity analysis (iii), after controlling for exposure category, initial regimen and time on ART, the OR for having <100% adherence for sites in lower-middle-income countries was 0.71 compared to those from high-+upper-middle-income countries (95% CI (0.44 to 1.15), p=0.006; homosexual HIV exposure: IRR=0.59, 95% CI (0.45 to 0.76), p<0.001; IDUs: IRR=1.52, 95% CI (1.14 to 2.01), p=0.004; NRTI+PI initial regimen: IRR=0.42, 95% CI (0.24 to 0.73), p=0.002; time on ART 6 to 12 months: IRR=0.66, 95% CI (0.58 to 0.76), p<0.001; time on ART 12 to 18 months: IRR=0.48, 95% CI (0.40 to 0.58), p<0.001; and time on ART 18 to 24 months: IRR=0.42, 95% CI (0.34 to 0.51), p<0.001. The results suggested that both the GEE-logit model and the GEE-Poisson model are comparable when applied to our cohort data; therefore, we have chosen to maintain our original model in order to describe and interpret the effects of the covariates as ORs.In sensitivity analysis (v), we re-analysed the data using a GEE-Poisson model, rather than the GEE-logit model reported in Approximately 32% of patients reported at least one adherence measurement <100% and 17% reported at least one measurement <95% during the 24-month follow-up time. We found that SubAdh to first-line ART in this cohort, defined as either <100% and <95%, was associated with mode of HIV exposure, ART regimen at initiation, time on ART and frequency of adherence measurement. IDUs were more likely to report being less adherent to their ART regimen, while those with homosexual HIV exposure were less likely to report lower adherence. Patients who were put on PI-containing regimens were more likely to be adherent compared to those who were given NNRTI-based regimens. The effects of increasing adherence with increasing time on ART were similar from both the main analyses and sensitivity analyses.Clinical sites with an average adherence assessment of more than twice per patient per year had lower odds of SubAdh, indicating that the more frequently patients completed the VAS, the more adherent they became. We hypothesized that this could possibly be a reflection of the level of adherence counselling offered by clinical sites, that is, sites with a higher number of adherence measurement have more frequent patient contact, thus enabling low adherence to be identified early, and therefore allowing for more discussion and emphasis on the importance of adhering to the antiretroviral regimen. Patients’ level of engagement with healthcare providers has shown to be significantly associated with ART adherence in a multisite international study, which included sites from both resource-rich and resource-poor settings . TherefoThe proportion of SubAdh was highest in the first six months of ART, and decreased thereafter. It is commonly known that loss to follow-up in the early stages of ART under resource-limited settings is high , 19. OurOur study did not find a significant association among the commonly known predictors of SubAdh such as age, sex and side effects , 20–22. The major limitations of our study included only having adherence data in the form of the self-reported VAS. No pharmacy pick-up or pill count data were available. It could be argued that using self-reported adherence may introduce social desirability bias where individuals may overestimate adherence levels , 35. In In summary, suboptimal adherence was higher among IDUs and lower in patients with homosexual HIV exposure in our study cohort. The more frequently sites assessed their patients, the higher the adherence levels. Being on PI-based first-line regimens and longer times on ART was associated with improved adherence. A greater emphasis on more frequent adherence counselling immediately following ART initiation and through the first six months may be valuable in promoting treatment and programme retention.