Spiral.imperial.ac.uk
Word Counts: 3282IntroductionGenetic clustering methods have been widely used to identify characteristics of specific populations who are at heightened risk of transmitting HIV to uninfected individuals PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CZXplbWVyPC9BdXRob3I+PFllYXI+MjAxNDwvWWVhcj48
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ADDIN EN.CITE.DATA [12-15] has shown that highly similar virus strains can be inferred as being connected by a short chain of transmission ADDIN EN.CITE <EndNote><Cite><Author>Volz</Author><Year>2012</Year><RecNum>128</RecNum><DisplayText>[16]</DisplayText><record><rec-number>128</rec-number><foreign-keys><key app="EN" db-id="w0xdrevxhtetfheszd7px0dqtewvd5092vvw" timestamp="1533859568">128</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Volz, E. M.</author><author>Koopman, J. S.</author><author>Ward, M. J.</author><author>Brown, A. L.</author><author>Frost, S. D.</author></authors></contributors><auth-address>Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America. erikvolz@umich.edu</auth-address><titles><title>Simple epidemiological dynamics explain phylogenetic clustering of HIV from patients with recent infection</title><secondary-title>PLoS Comput Biol</secondary-title></titles><periodical><full-title>PLoS Comput Biol</full-title></periodical><pages>e1002552</pages><volume>8</volume><number>6</number><edition>2012/07/05</edition><keywords><keyword>Cluster Analysis</keyword><keyword>Computational Biology</keyword><keyword>Computer Simulation</keyword><keyword>Epidemics/statistics & numerical data</keyword><keyword>Epidemiologic Factors</keyword><keyword>Genes, pol</keyword><keyword>HIV Infections/epidemiology/transmission/*virology</keyword><keyword>HIV-1/*classification/*genetics</keyword><keyword>Homosexuality, Male</keyword><keyword>Humans</keyword><keyword>Male</keyword><keyword>Michigan/epidemiology</keyword><keyword>*Models, Biological</keyword><keyword>Phylogeny</keyword><keyword>Time Factors</keyword></keywords><dates><year>2012</year></dates><isbn>1553-7358 (Electronic)
1553-734X (Linking)</isbn><accession-num>22761556</accession-num><urls><related-urls><url>;[16] and these clustered sequences may be linked as transmission pairs or belong to a larger local transmission network ADDIN EN.CITE <EndNote><Cite><Author>Frost</Author><Year>2015</Year><RecNum>136</RecNum><DisplayText>[7]</DisplayText><record><rec-number>136</rec-number><foreign-keys><key app="EN" db-id="w0xdrevxhtetfheszd7px0dqtewvd5092vvw" timestamp="1534128488">136</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Frost, S. D.</author><author>Pillay, D.</author></authors></contributors><auth-address>Department of Veterinary Medicine and Institute of Public Health, University of Cambridge.
University College London, United Kingdom Africa Centre for Health and Population Studies, University of KwaZulu Natal, Durban, South Africa.</auth-address><titles><title>Understanding drivers of phylogenetic clustering in molecular epidemiological studies of HIV</title><secondary-title>J Infect Dis</secondary-title></titles><periodical><full-title>J Infect Dis</full-title></periodical><pages>856-8</pages><volume>211</volume><number>6</number><edition>2014/10/15</edition><keywords><keyword>Female</keyword><keyword>HIV/*genetics</keyword><keyword>HIV Infections/*transmission</keyword><keyword>Humans</keyword><keyword>Male</keyword><keyword>Hiv</keyword><keyword>clustering</keyword><keyword>epidemiology</keyword><keyword>phylogenetics</keyword></keywords><dates><year>2015</year><pub-dates><date>Mar 15</date></pub-dates></dates><isbn>1537-6613 (Electronic)
0022-1899 (Linking)</isbn><accession-num>25312038</accession-num><urls><related-urls><url>;[7]. However, factors such as the recency of infection, sampling fractions (i.e. the intensity of sampling of local transmissions), survivor bias (i.e. younger individuals are more likely to cluster), and migration status (i.e. immigrants are more likely to be infected while abroad) may also contribute to any observed phylogenetic linkages ADDIN EN.CITE <EndNote><Cite><Author>Frost</Author><Year>2015</Year><RecNum>136</RecNum><DisplayText>[7]</DisplayText><record><rec-number>136</rec-number><foreign-keys><key app="EN" db-id="w0xdrevxhtetfheszd7px0dqtewvd5092vvw" timestamp="1534128488">136</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Frost, S. D.</author><author>Pillay, D.</author></authors></contributors><auth-address>Department of Veterinary Medicine and Institute of Public Health, University of Cambridge.
University College London, United Kingdom Africa Centre for Health and Population Studies, University of KwaZulu Natal, Durban, South Africa.</auth-address><titles><title>Understanding drivers of phylogenetic clustering in molecular epidemiological studies of HIV</title><secondary-title>J Infect Dis</secondary-title></titles><periodical><full-title>J Infect Dis</full-title></periodical><pages>856-8</pages><volume>211</volume><number>6</number><edition>2014/10/15</edition><keywords><keyword>Female</keyword><keyword>HIV/*genetics</keyword><keyword>HIV Infections/*transmission</keyword><keyword>Humans</keyword><keyword>Male</keyword><keyword>Hiv</keyword><keyword>clustering</keyword><keyword>epidemiology</keyword><keyword>phylogenetics</keyword></keywords><dates><year>2015</year><pub-dates><date>Mar 15</date></pub-dates></dates><isbn>1537-6613 (Electronic)
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AG==
ADDIN EN.CITE.DATA [3, 11, 13, 17-23] in high income countries have combined epidemiological data and genetic clustering patterns to examine factors that were associated with cluster size and membership, including demographic characteristics, geographical variables and risk behaviors. For example, transmission related to same-sex practices among menPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5DaGFsbWV0PC9BdXRob3I+PFllYXI+MjAxMDwvWWVhcj48
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ADDIN EN.CITE.DATA [6]. This finding informed the DREAMS (Determined, Resilient, Empowered, AIDS-free, Mentored and Safe) initiative, a global partnership to reduce HIV infections among adolescent girls and young women in 10 sub-Saharan African countries ADDIN EN.CITE <EndNote><Cite><Author>Fleischman</Author><Year>2015</Year><RecNum>218</RecNum><DisplayText>[24]</DisplayText><record><rec-number>218</rec-number><foreign-keys><key app="EN" db-id="w0xdrevxhtetfheszd7px0dqtewvd5092vvw" timestamp="1544732754">218</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Fleischman, Janet</author><author>Peck, Katherine</author></authors></contributors><titles><title>Addressing HIV risk in adolescent girls and young women</title><secondary-title>CSIS Global Health Policy Center</secondary-title></titles><periodical><full-title>CSIS Global Health Policy Center</full-title></periodical><dates><year>2015</year></dates><urls></urls></record></Cite></EndNote>[24]. In a mixed HIV epidemic like Nigeria, where HIV will continue to spread across different populations if transmission in any one risk group proceeds uninterrupted, there is a need to document the characteristics of genetic clusters and examine factors that determine the formation of clusters to provide information for the development of interventions that decrease HIV transmission in high risk populations. Moreover, men who have sex with men (MSM) have been shown to bear among the highest incidence rates of HIV infection in Nigeria ADDIN EN.CITE <EndNote><Cite><Author>Nowak</Author><Year>2019</Year><RecNum>308</RecNum><DisplayText>[25]</DisplayText><record><rec-number>308</rec-number><foreign-keys><key app="EN" db-id="w0xdrevxhtetfheszd7px0dqtewvd5092vvw" timestamp="1549211807">308</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Nowak, Rebecca G</author><author>Mitchell, Andrew</author><author>Crowell, Trevor A</author><author>Liu, Hongjie</author><author>Ketende, Sosthenes</author><author>Ramadhani, Habib O</author><author>Ndembi, Nicaise</author><author>Adebajo, Sylvia</author><author>Ake, Julie</author><author>Michael, Nelson L</author></authors></contributors><titles><title>Individual and sexual network predictors of HIV incidence among men who have sex with men in Nigeria</title><secondary-title>JAIDS Journal of Acquired Immune Deficiency Syndromes</secondary-title></titles><periodical><full-title>JAIDS Journal of Acquired Immune Deficiency Syndromes</full-title></periodical><dates><year>2019</year></dates><isbn>1525-4135</isbn><urls></urls></record></Cite></EndNote>[25]. Using a dynamic infectious disease model fitted to time-scaled phylogenies, we previously estimated transmission patterns of MSM and a representative sample of newly enrolled treatment-na?ve HIV individuals without recorded HIV acquisition risk PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Wb2x6PC9BdXRob3I+PFllYXI+MjAxNzwvWWVhcj48UmVj
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ADDIN EN.CITE.DATA [27, 28]. In brief, TRUST/RV368 cohort is a multicenter prospective observational study of MSM in Abuja and Lagos, Nigeria. Participants were recruited using respondent-driven sampling (RDS) between 2013 and 2017. Eligible participants included those who were male at birth, aged 16 and older in Abuja or 18 and older in Lagos, and reported receptive or insertive anal intercourse in the previous 12 months. Demographic and behavioral data were obtained from structured interviews. Upon enrollment, participants were screened for HIV using finger stick blood samples using a parallel testing algorithm with Determine (Alere, Watham, MA, USA) and Uni-gold (Trinity Biotech, Co-Wicklow, Ireland) with HIV-1/2 Stat-Pak (Chembio Diagnostics, Medford, NY) used as a tie-breaker for discrepant results.All participant plasma samples with HIV RNA ≥1,000 copies/mL at the baseline visit were genetically sequenced for clustering analysis. Samples with HIV RNA below this cut-off were excluded because of poor amplification. Participants who were HIV uninfected at enrollment were followed for up to 18 months and incident infections were sequenced at the time of seroconversion. Ethical ConsiderationsThe institutional review boards at the Nigerian Federal Capital Territory Health Research Ethics Committee; the Nigerian Ministry of Defense in Nigeria, the University of Maryland Baltimore, and the Walter Reed Army Institute of Research reviewed and approved the research protocol. All participants provided informed consent and data was de-identified. Genetic Sequencing and Pairwise Calculation Sequences of the HIV-1 pol gene (corresponding to HXB2 positions 2273-3869 or 2108-3308) were generated using methods as previously described PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5IZWlwZXJ0eiBKcjwvQXV0aG9yPjxZZWFyPjIwMTM8L1ll
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ADDIN EN.CITE.DATA [18, 26, 29]. Pairwise genetic distances, corresponding to HXB2 positions 2317-3249, were calculated using the TN93 model ADDIN EN.CITE <EndNote><Cite><Author>Tamura</Author><Year>1993</Year><RecNum>153</RecNum><DisplayText>[30]</DisplayText><record><rec-number>153</rec-number><foreign-keys><key app="EN" db-id="w0xdrevxhtetfheszd7px0dqtewvd5092vvw" timestamp="1536264368">153</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Tamura, Koichiro</author><author>Nei, Masatoshi</author></authors></contributors><titles><title>Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees</title><secondary-title>Molecular biology and evolution</secondary-title></titles><periodical><full-title>Molecular biology and evolution</full-title></periodical><pages>512-526</pages><volume>10</volume><number>3</number><dates><year>1993</year></dates><isbn>1537-1719</isbn><urls></urls></record></Cite></EndNote>[30]. Genetic clusters were defined among individuals whose sequences had a genetic distance of ≤1.5% PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Qb29uPC9BdXRob3I+PFllYXI+MjAxNjwvWWVhcj48UmVj
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ADDIN EN.CITE.DATA [32].HIV-1 Genotyping Tool at the National Center for Biotechnology information, Jumping Profile HMM Tool at GLOBICS, REGA HIV Subtyping Tool at BIOAFRICA, and the NCBI BLASTn tool were used along with the HIV BLAST tool at the Los Alamos HIV sequence database website (). The results from these tools were compared to determine the subtype(s) of each sequence. If the subtype(s) of a sequence from all tools were in agreement a final subtype result was assigned. If the results were different, neighbor joining trees of the sequences along with relevant HIV-1 reference subtypes or CRFs were made at various breakpoints and over the span of the whole sequence to determine the genetic relatedness of the sample to reference sequences ADDIN EN.CITE <EndNote><Cite><Author>Delatorre</Author><Year>2014</Year><RecNum>178</RecNum><DisplayText>[33]</DisplayText><record><rec-number>178</rec-number><foreign-keys><key app="EN" db-id="w0xdrevxhtetfheszd7px0dqtewvd5092vvw" timestamp="1539661375">178</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Delatorre, E.</author><author>Mir, D.</author><author>Bello, G.</author></authors></contributors><auth-address>Laboratorio de AIDS & Imunologia Molecular, Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro, Brazil.</auth-address><titles><title>Spatiotemporal dynamics of the HIV-1 subtype G epidemic in West and Central Africa</title><secondary-title>PLoS One</secondary-title></titles><periodical><full-title>PLoS One</full-title></periodical><pages>e98908</pages><volume>9</volume><number>2</number><edition>2014/06/12</edition><keywords><keyword>Africa, Central/epidemiology</keyword><keyword>Africa, Western/epidemiology</keyword><keyword>Bayes Theorem</keyword><keyword>HIV Infections/*epidemiology</keyword><keyword>HIV-1/classification/*isolation & purification</keyword><keyword>Humans</keyword><keyword>Phylogeography</keyword></keywords><dates><year>2014</year></dates><isbn>1932-6203 (Electronic)
1932-6203 (Linking)</isbn><accession-num>24918930</accession-num><urls><related-urls><url>;[33]. Sequence Quality Control Obtained sequencing electropherograms were visually inspected using Sequencher 5.4 (Gene Codes Corp., Ann Arbor, Michigan, USA) at two independent laboratories (the Institute of Human Virology Nigeria, Abuja, Nigeria and the U.S. Military HIV Research Program in Bethesda, Maryland, USA) to verify that each nucleotide base was covered by at least three reads, one of which had to be in the opposite direction as the other two. Sequences were first aligned using HIV Align () and the alignments were manually edited using Geneious (). Sequence genetic relatedness was assessed in MEGA version 5.2.2. Samples whose sequences were <1.0% different and had been processed on the same day were re-processed and re-sequenced to rule out cross-sample contamination. Dependent Variables:Cluster membership: if a participant had a genetic distance less than 1.5% with another participant in the sample, the participant was classified as part of a genetic cluster. The size of a genetic cluster equaled the number of total participants in a cluster. We categorized the cluster membership in two ways: 1) 0, if a participant was not in a genetic cluster, and 1, if a participant was in a genetic cluster; 2) 0, if a participant was not in any genetic cluster, 1, if a participant was in a small genetic cluster, including 2-3 participants, and 2, if a participant was in a genetic cluster with 4or more participants.Independent Variables:ART uptake: A comprehensive chart review was performed to determine ART uptake among the participants. A participant was considered ART experienced if he initiated ART more than 28 days before the genotype was drawn.Consistent Condom Use: The participants were asked to report the average frequency of condom use during vaginal sex, receptive anal sex and insertive anal sex in the previous 12 months. The frequencies were measured on a 5-point Likert scale: never, almost never, about half of the time, almost always, and always. Participants were classified as using condoms consistently if they reported almost always or always using condoms for all three types of sexual intercourse. Sexually Transmitted Infection (STI) Status: Participants were asked to provide samples for testing of STIs other than HIV (refer as “other STIs”). If they were diagnosed with syphilis, gonorrhea, chlamydia, or presented with symptoms consistent with an otherwise undiagnosed STI syndrome, they were classified as having an STI. Syphilis was diagnosed using blood specimens. Testing for other bacterial STIs was performed using urine, oropharyngeal swabs and rectal swabs. Number of Male Partners: Participants were asked to report the average number of male partners for anal sex in the past 12 months. Since we observed a discrepancy among the reported number of insertive, receptive and anal sex partners, we dichotomized responses as either less than or equal to 1 male partner or more than 1 male partner to minimize measurement error.Sexual Positions: The sexual positions of the participants were inferred from the self-reported number of partners for insertive or receptive anal sex in the past 12 months. Participants were categorized as insertive or receptive if they only had insertive or receptive sex respectively. If participants had both, they were classified as versatile. All the behavioral questionnaire and clinical evaluations were completed at the baseline visit concurrent with the collection of blood for sequencing.Statistical Analysis We used multivariable logistic regression to analyze different characteristics of the clustered HIV-infected participants and multinomial logistic regression to assess the factors associated with the magnitude of the cluster. The primary dependent variables were a binary category of any clustering and a multinomial category for none, small (size 2-3) and large (size 4 and above) genetic clusters. The independent demographic variables were age, education, gender identity, employment status, religion and sexual orientation. We also assessed the association between clustering membership and HIV risk behaviors, including current ART status, consistent condom use, current STI status, sexual position and the number of male partners. Viral load and CD4 counts at the time of sequencing and the stage of HIV infection (prevalent or incident) were included as confounding variables ADDIN EN.CITE <EndNote><Cite><Author>Frost</Author><Year>2015</Year><RecNum>136</RecNum><DisplayText>[7]</DisplayText><record><rec-number>136</rec-number><foreign-keys><key app="EN" db-id="w0xdrevxhtetfheszd7px0dqtewvd5092vvw" timestamp="1534128488">136</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Frost, S. D.</author><author>Pillay, D.</author></authors></contributors><auth-address>Department of Veterinary Medicine and Institute of Public Health, University of Cambridge.
University College London, United Kingdom Africa Centre for Health and Population Studies, University of KwaZulu Natal, Durban, South Africa.</auth-address><titles><title>Understanding drivers of phylogenetic clustering in molecular epidemiological studies of HIV</title><secondary-title>J Infect Dis</secondary-title></titles><periodical><full-title>J Infect Dis</full-title></periodical><pages>856-8</pages><volume>211</volume><number>6</number><edition>2014/10/15</edition><keywords><keyword>Female</keyword><keyword>HIV/*genetics</keyword><keyword>HIV Infections/*transmission</keyword><keyword>Humans</keyword><keyword>Male</keyword><keyword>Hiv</keyword><keyword>clustering</keyword><keyword>epidemiology</keyword><keyword>phylogenetics</keyword></keywords><dates><year>2015</year><pub-dates><date>Mar 15</date></pub-dates></dates><isbn>1537-6613 (Electronic)
0022-1899 (Linking)</isbn><accession-num>25312038</accession-num><urls><related-urls><url>;[7]. Because viral load was positively skewed, log10 transformations were used in the final models. Adjusted odds ratio (AOR) and 95% confidence intervals (CIs) were reported. The regression analyses were adjusted for non-random sampling method by including RDS weights. Sensitivity analyses were conducted to test the odds of clustering at different genetic distances, 1%, 2%, and 4.5%.A correlation permutation test was also performed to evaluate the likelihood of observing any clustering for certain behaviors. We calculated the Pearson’s correlation of ART use, STI status and the number of male partners between every pair of genetically linked participants and compared with the null distribution of correlation obtained from randomly permuting observed data. The achieved significance level (ASL) and null distribution were reported. Although the TRUST/RV368 cohort is a longitudinal study, this analysis was done cross-sectionally because the variables were collected at the same time as sequencing. R package, igraph, and SAS 9.4 were used in data management and analysis. ResultsDescriptive analysisA total of 417 HIV-infected participants were eligible for virus sequencing and included in the analysis. 153 (36.7%) infected MSM were genetically clustered. The demographic characteristics of the sample stratified by cluster status are presented in Table 1. More than half of participants reported inconsistent condom use and having an STI in the past 12 months regardless of clustering. MSM whose HIV did not fall within a genetic cluster were more likely to have heterosexual relationships in the past 12 months (50.4%) than MSM (37.3%) whose HIV clustered. The uptake of ART was low, only 6.5% for those with clustering and 12.1% for those without clustering.Sequences were obtained from 376 (90.2%) prevalent and 41 (9.8%) incident HIV infections. The average genetic distance between sequences was 8.3% with a range of 0.00%-15.37%. A total of 207 genetically-linked ties presented in 46 genetically-linked clusters (Figure 1). Among the genetically-linked clusters, 30 (65.3%) were comprised of pairs and 10 (21.7%) included 3-5 participants. 319(76.5%) sequenced participants were infected with HIV subtype CRF02_AG and its associated recombinants. The largest cluster included 15 participants whose HIV was a mix of subtype A1 and A1/U recombinant. As depicted in Figure 1, 50% (8) of the incident cases were concentrated in the two largest clusters and not evenly distributed throughout the clusters. Participants within the same cluster were more likely to have the same STI and report similar frequencies of condom use, even though it was low among all participants. Model Results:Logistic regression was used to predict any clustering by demographic and sexual behaviors. As depicted in Table 2, compared to participants who self-identified as bigender, male participants were 2.42 times more likely to be in a cluster (95% CI: 1.08-10.78). Compared to MSM with heterosexual relationships, MSM with no female partners were 2.66 times more likely to be in a cluster (95% CI: 1.97-6.80). Viral load was also significantly higher (AOR=1.72; 95% CI: 1.04-2.86) in clustered participants. As documented in Table 3, being ART inexperienced and having other STIs were positively associated with being in a genetic cluster (AOR= 2.82; 95 % CI: 0.79-3.65; AOR=1.16; 95% CI: 0.31-4.35; respectively), though the association was not significant. In multinomial logistic regressions, participants with more than a high school education were more likely to be in a large cluster (size 4 and above vs. un-clustered: AOR = 23.84; 95% CI: 2.66-213.49), but less likely to be in a smaller cluster (size 2-3 vs. un-clustered: AOR=0.34; 95% CI: 0.08-1.51) compared to un-linked participants. Participants who self-identified as male were more likely to be in a large cluster (size 4 and above vs. un-clustered: AOR = 17.28; 95% CI: 2.93-102.12) than participants who self-identified as bigender. Compared to MSM who had heterosexual relationships in the past 12 months, MSM with no heterosexual relationships were 4.67 (95% CI: 2.05-10.64) times more likely to be in a large cluster (size 4 and above) and 3.17(95% CI: 1.89-5.32) times more likely to be in a smaller cluster (size 2-3). As documented in Table 3, those who were ART inexperienced were significantly more likely to be in a larger cluster (≥4 cluster size vs no cluster: AOR = 6.61; 95% CI: 2.25-19.37). Compared to non-linked participants, those within a large cluster (≥4) were more likely to have other STIs (AOR = 3.98; 95% CI: 0.89-17.22), and those within a smaller cluster (2-3 cluster size) were more likely to engage in receptive sex (AOR=3.48; 95% CI: 2.03-5.92) and versatile in their sexual position (AOR=2.87; 95% CI: 2.41-3.41). The results of risk behavior variables were relatively robust across different genetic cut-offs, 1%, 2%, 4.5% (Supplemental Table 1 and 2). Younger age (20-25 vs. 25 and above), no female partners, high viral load, ART inexperience, and having other STIs were positively associated with the likelihood of being in a cluster across different cut-offs, though not all the results were significant. Male gender identity was positively associated with clustering among lower cut-offs (1%, 2%) but the association was reversed in a less conservative cutoff of 4.5%. Higher CD4 count was positively associated with clustering probability in the 4.5% cutoff but not significant for the lower cutoffs. The permutation tests found no association between HIV genetic links and ART use (ρ= 0.12, ASL=0.20), STI status (ρ= 0.09, ASL = 0.31), the number of male partners (ρ= 0.02, ASL = 0.78) or consistent condom use (ρ= -0.04, ASL = 0.65). DiscussionIn a mixed epidemic setting of Nigeria, we found that one third of MSM living with HIV in the TRUST/RV368 were genetically clustered. MSM who were ART inexperienced and only involved in same gender sexual behaviors were more likely to belong to a genetic cluster and a large cluster (≥4). MSM who were employed, had a higher education, and had other STIs were more likely to be in a large genetic cluster (≥4). 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ADDIN EN.CITE.DATA [1, 8, 9]. Our largest reported cluster was composed of 15 individuals and 5 out of 46 clusters were composed of more than 6 individuals. If we increased the genetic distance to 4.5%, as used in de Oliverira et al.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5kZSBPbGl2ZWlyYTwvQXV0aG9yPjxZZWFyPjIwMTc8L1ll
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ADDIN EN.CITE.DATA [34] and Croatia ADDIN EN.CITE <EndNote><Cite><Author>Lepej</Author><Year>2009</Year><RecNum>205</RecNum><DisplayText>[20]</DisplayText><record><rec-number>205</rec-number><foreign-keys><key app="EN" db-id="w0xdrevxhtetfheszd7px0dqtewvd5092vvw" timestamp="1542053350">205</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Lepej, S. Z.</author><author>Vrakela, I. B.</author><author>Poljak, M.</author><author>Bozicevic, I.</author><author>Begovac, J.</author></authors></contributors><auth-address>University Hospital for Infectious Diseases, Zagreb, Croatia. Snjezana.Zidovec.Lepej@bfm.hr</auth-address><titles><title>Phylogenetic analysis of HIV sequences obtained in a respondent-driven sampling study of men who have sex with men</title><secondary-title>AIDS Res Hum Retroviruses</secondary-title></titles><periodical><full-title>AIDS Res Hum Retroviruses</full-title></periodical><pages>1335-8</pages><volume>25</volume><number>12</number><edition>2009/12/17</edition><keywords><keyword>Croatia/epidemiology</keyword><keyword>Drug Resistance, Viral/drug effects/genetics</keyword><keyword>HIV Infections/epidemiology/transmission/*virology</keyword><keyword>HIV-1/*classification/genetics</keyword><keyword>*Homosexuality, Male</keyword><keyword>Humans</keyword><keyword>Male</keyword><keyword>Mutation</keyword><keyword>Phylogeny</keyword><keyword>Viral Load</keyword><keyword>pol Gene Products, Human Immunodeficiency Virus/*classification/genetics</keyword></keywords><dates><year>2009</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>1931-8405 (Electronic)
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ADDIN EN.CITE.DATA [28, 37].There were several strengths in our study. First, the use of RDS recruitment strategy allowed us to sample a highly marginalized population of MSM in Nigeria. Second, the prospective cohort study design allowed us to include newly-diagnosed incident infections in the analysis. Third, HIV preventive behaviors were included in our analysis to inform targeted intervention strategies. However, this study had some limitations. First, we could only sequence individuals who had viral load greater than 1000 copies/ml, which may introduce bias. Among 757 HIV-infected participants in the cohort at the time of recruitment, only 50% had a high enough viral load to be sequenced. Individuals who were ART experienced and had suppressed viral load were not included in the study. Second, we could only assess the association between HIV risk behaviors with the cluster membership at the aggregated level. The variations of each cluster were not accounted for in the statistical analysis. Third, like all the other genetic clustering studies, we were unable to determine whether the results of genetic linkage were due to direct or indirect transmissions.ConclusionThe findings of this study make the following valuable contributions. First, it documents genetic clusters among MSM in Nigeria, where the epidemic is concentrated among key populations. Second, men who were in HIV genetic clusters were less likely to report ART use, had a high level of viral load due to recent infections, and did not consistently use condoms, all of which are important targets for combination interventions to prevent forward HIV transmissions. Third, the prevalence of STIs among clustered MSMs reinforces the need for behavioral interventions that promote safer sex and early diagnose and treatment of STIs. 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HIV Med 2009,10:432-438.Footnote PageAcknowledgmentsThe TRUST/RV368 Study Group includes Principal Investigators: Manhattan Charurat (IHV, University of Maryland, Baltimore, MD, USA), Julie Ake (MHRP, Walter Reed Army Institute of Research, Silver Spring, MD, USA); Co-Investigators: Sylvia Adebajo, Stefan Baral, Erik Billings, Trevor Crowell, George Eluwa, Abiola Fasina, Charlotte Gaydos, Sosthenes Ketende, Afoke Kokogho, Hongjie Liu, Jennifer Malia, Olumide Makanjuola, Nelson Michael, Nicaise Ndembi, Jean Njab, Rebecca Nowak, Oluwasolape Olawore, Zahra Parker, Sheila Peel, Habib Ramadhani, Merlin Robb, Cristina Rodriguez-Hart, Eric Sanders-Buell, Sodsai Tovanabutra, Erik Volz; Institutions: Institute of Human Virology at the University of Maryland School of Medicine (IHV-UMB), University of Maryland School of Public Health (UMD SPH), Johns Hopkins Bloomberg School of Public Health (JHSPH), Johns Hopkins University School of Medicine (JHUSOM), U.S. Military HIV Research Program (MHRP), Walter Reed Army Institute of Research (WRAIR), Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF), Henry M. Jackson Foundation Medical Research International (HJFMRI), Institute of Human Virology Nigeria (IHVN), International Centre for Advocacy for the Right to Health (ICARH), The Initiative for Equal Rights (TIERS), Population Council (Pop Council) Nigeria, Imperial College London. Accession numbers:The HIV-1 sequences described in this study are available under GenBank accession numbers: MK784335 to MK787541 and MH654824-MH654973.Funding StatementThis work was supported by a cooperative agreement between the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., and the U.S. Department of Defense [W81XWH-11-2-0174]; the National Institutes of Health [R01 MH099001, R01 AI120913, R01 MH110358]; Fogarty Epidemiology Research Training for Public Health Impact in Nigeria program [D43TW010051]; and the President’s Emergency Plan for AIDS Relief through a cooperative agreement between the Department of Health and Human Services/Centers for Disease Control and Prevention, and the Institute for Human Virology-Nigeria [NU2GGH002099]. DisclaimerThe views expressed are those of the authors and should not be construed to represent the positions of the U.S. Army, the Department of Defense, or the Department of Health and Human Services. The investigators have adhered to the policies for protection of human subjects as prescribed in AR-70. Gustavo Kijak is currently employed by GSK Vaccines but his participation in this study precedes the current employment.This manuscript was presented at the poster section in International AIDS Conference(IAS) 2018.Corresponding author contact informationYuruo Li, Email:yuruo.li12@Phone number: 404-539-6249 ................
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