Prevalence of HIV-1 subtypes among men who have sex with men in China: a systematic review

Abstract

The distribution of prevalent HIV-1 strains are still complex in China. Men who have sex with men (MSM) play an important bridging role in spreading HIV. The aim of our study was to quantitatively evaluate the prevalence of HIV-1 subtypes among the MSM population in China from published studies. Relevant studies were searched by selection criteria from CNKI, CBM, Pubmed, etc. We computed the estimates of the pooled proportion of HIV-1 subtypes. Heterogeneity between studies was investigated and measured using Cochran’s Q statistic and the I² statistic. All analyses were conducted by the R statistical package version 2.13.1. A meta-analysis was performed, which included 19 articles. For comprehensive analysis of env, gag and pol genes, the pooled estimates for the prevalence of subtype B was 28.25% (95% CI: 18.10–39.66%), CRF01_AE was 53.46% (95% CI: 46.11–60.74%), CRF07_BC was 18.66% (95% CI: 13.06–25.01%) and CRF08_BC was 5.85% (95% CI: 2.73–10.07%), respectively. In subgroup analysis, the proportion of subtype B decreased, while the proportion of CRF01_AE and CRF07_BC showed an increasing tendency. Beijing, Guangdong and Henan provinces had high proportions of subtype CRF01_AE while Guangdong and Hebei provinces had the highest proportions of subtype B and CRF07_BC, respectively. A high genetic variability of HIV-1 presents a serious challenge for HIV prevention and treatment strategies among MSM in China.

Keywords

HIV AIDS epidemiology clade strain prevalence men HIV-1 subtype MSM China

Introduction

Human immunodeficiency virus (HIV), which is a member of the retroviruses,¹ has a high genetic variability and includes three basic structural genes – gag, pol and env genes.^2,3 HIV can be divided into HIV-1 and HIV-2 subtypes by serological reaction and virus nucleic acid sequence.⁴ The HIV-1 subtype accounts for the main global HIV/AIDS (Acquired Immune Deficiency Syndrome) pandemic. The HIV-1 progenitor probably was passed from chimpanzees to human hunters through blood-borne transmission.⁵ So far, HIV-1 has evolved to assume many forms and can be divided into three groups: M (major), O (outlier) and N (non-major and non-outlier).⁶ Additionally, the M group accounts for more than 90% of the reported HIV/AIDS cases around the world.⁷ HIV-1 has been classified into nine major subtypes (A–D, F–H, J and K), and at least 60 major circulating recombinant forms (CRFs) designated by a number and the genetic subtypes are present in their genome and multiple unique recombinant forms (URFs).^8–12 URFs are widely distributed throughout the world with recombination breakpoints different from those found in CRFs.¹³

The distribution of HIV-1 subtype shows large geographical restriction on the global map, i.e. subtype B is most prevalent in North America, Western Europe, Australia and Japan; subtype C is found in the Southern African region and the Indian sub-continent.^14–17 In China, the distribution of prevalent HIV-1 subtypes is also complex. Data from two national molecular epidemic surveys indicates that HIV-1 prevalent strains in China have A, B′ (Thailand B), B (Euro-American), C, D, F, G, J, CRF01_AE, CRF07_BC, CRF08_BC and CRF02_AG.^18,19 In addition, CRF01_AE, B′ and CRF_BC (CRF07_BC, CRF08_BC) were the major prevalent forms.^20,21

Men who have sex with men (MSM) play an important bridging role in spreading HIV and other sexually transmitted infections due to unprotected sexual behaviours and multiple sexual partners.^22–24 An estimated 2% to 5% of Chinese adult men are homosexual/bisexual and the potential number of MSM may be enormous.²⁵ In 1981, the first patient diagnosed with HIV/AIDS was MSM who emerged in the United States.²⁶ In China, the first HIV/AIDS patient was also MSM in 1989, but the case did not capture the attention of the government.²⁷ So far, sexual transmission has become the major route of HIV transmission in China.^28,29 It is estimated that of the 780,000 people who were living with HIV in China in 2011, 71.4% were men and 17.4% had been infected through homosexual transmission. Meanwhile, the MSM population was becoming one of the most important populations affected by HIV.^30–32 Most of the studies on MSM describe HIV sero-prevalence, social aspects and risk behaviours in China. But studies concerning the prevalence of diverse HIV-1 subtypes are limited and should be given attention.

Molecular epidemiology investigation can be a powerful tool in developing sensitive diagnostic tools, in the management of individual infections, tracking transmission patterns and the spread and evolution of the epidemic.³³ The distribution of HIV-1 in various populations is different due to the different regions, nationalities, habits and beliefs. The different distribution of HIV-1 subtypes enables us to track the route of transmission of HIV and understand characteristics of distribution in various locations and different populations. For example, for subtype C, the genetic characteristics and evolutionary history of its epidemiology in east Africa can be known by molecular epidemiology investigation. Subtype C most probably originated in Burundi around the early 1960s, and later spread to Ethiopia, Kenya, Tanzania and Uganda, giving rise to major country-specific monophyletic sub-clusters between the early 1970s and early 1980s.³⁴

The purpose of our study is to summarise the published evidence on the prevalence of different HIV-1 subtypes among MSM populations in China, understand the range of HIV-1 genetic evolution among MSM populations and provide valuable information for the development of effective prevention and intervention strategies for this population.

Methods

Literature search strategy

The data source for this review was the literature published until 25 December 2013, searched by two investigators independently in the following two databases: Chinese databases (Wanfang, Weipu, China Biological Medicine and China National Knowledge Infrastructure) and English database (Pubmed). The searches were conducted using key words for screening potential articles: ‘MSM’ or ‘homosexual’ or ‘gay’ and HIV-1 and ‘China’ and ‘Chinese’. The search was conducted with restriction on language in English in the Pubmed database. A manual check on each of the articles included in the analysis was also performed to identify additional potentially relevant studies. Other relevant articles and reports released by World Health Organization (WHO) and the Chinese Ministry of Health were utilised.

Selection criteria

Articles were eligible for inclusion in this systematic review if they met the following criteria. Inclusion criteria were: (1) the eligible articles were HIV-1 molecular epidemic studies; (2) the studies described HIV-1-positive MSM in the mainland of China; (3) the clear method to classify HIV-1 subtypes was described (all the studies used standard methodologies in their molecular analyses, consisting of the amplification of specific genomic regions [mostly env, gag and/or pol] using PCR, followed by sequencing of the PCR products and phylogenetic tree analysis); (4) the results provided the prevalence of different HIV-1 subtypes among MSM; (5) the studies were published as full papers; (6) study published in Chinese or English language. The two authors independently selected the studies. All disagreements were resolved by consensus.

Exclusion criteria were: (1) review papers, conference abstracts and presentations, case reports, letters; (2) studies with overlapping time intervals of sample or collection from the same origin; (3) dissertations; (4) studies presenting confusing data or probable errors; (5) the number of cases is less than 10. If the same study data were published in both English and Chinese sources, the articles published in Chinese language were excluded from this study.

Data extraction

According to the inclusion criteria and exclusion criteria, all data were extracted independently by reviewers. The following data were extracted from the included studies: first author, published year, study year, geographic location, population recruit, method to classify the HIV-1 subtypes, demographics (age, number of HIV-1 positive samples, marital status, education and number of sex partners) and the prevalence of different HIV-1 subtypes. Any differences were resolved by consensus.

Statistical analysis

We computed the estimates of the pooled proportion of HIV-1 subtypes using Freeman–Tukey double arcsine transformation.^35,36 Heterogeneity between studies was investigated and measured using Cochran’s Q statistic and the I² statistic.^37,38 I² ranges from 0 to 100%, with values of 25%, 50% and 75% suggested to representing respectively low, moderate and high levels of heterogeneity.³⁷ A random-effects model accounts for heterogeneity across studies, and this model assumes different true effect sizes and estimates the average effect from a distribution with a mean and variance.³⁹ Analyses of subgroups were conducted based on the study year, location and recruitment settings. All analyses were carried out by the statistical software package R version 2.13.1 (R Foundation for Statistical Computing, Beijing1, China) with functions such as metaprop, metabin, metabias, etc. (R package: meta).

Results

Titles and abstracts from a total of 286 articles were screened and 247 articles were excluded. The reasons for exclusion were data not being available for HIV-1 molecular epidemic analysis among MSM in China and duplicate publications. After thorough reviewing of the remaining 39 full-text articles, 20 articles were excluded and a total of 19 articles met our selection criteria.^40–58 Figure 1 shows additional details on each step.

Figure 1.

Selection process for study inclusion in the meta-analysis.

Study characteristics

Characteristics of the 19 eligible articles are presented in Table 1.^40–58 The overall published year of each study ranged from 2007 to 2013. The geographical locations of the studies included Beijing, Guangdong, Jiangsu, Hebei, Henan, Shanghai, Shanxi, Anhui and Fujian provinces. Four studies were published in English. Nine studies reported the information on MSM recruitment from hospital, bars, baths, bathhouses and HIV/AIDS sentinel surveillance. The sample sizes of HIV-1 positive that had amplified successfully ranged from 11 to 131.

Table 1.

Study characteristics of the prevalence of different HIV-1 subtypes in China.

First author, published year	Location, study year	Population recruitment site	Method used to classify HIV-1 subtypes and genetic regions of detection	Characteristics of HIV-1 positive samples	HIV-1 subtype classification
Zhang et al. 2007⁴⁰	Beijing, 2005–2007	Bars, clubs, parks and bathhouses	RT-PCR for the amplification of env, gag and pol gene region; C2-V5 region of env, the P17 and p24 region of gag region were coded	N = 54 Age: 19–54, mean 31 Lifetime number of male sex partners: 1–10: 45.5% 11 or more: 54.5%	Gag gene: n = 45 B: 32/45 (71.1%) CRF01_AE: 11/45 (24.4%) CRF07_BC: 2/45 (4.4%) Env gene: n = 29 B: 25/29 (86.2%) CRF01_AE: 1/29 (3.4%) CRF07_BC: 2/29 (6.9%) Thailand B: 1/29 (3.4%) Pol gene: n = 40 B: 37/40 (92.5%) CRF01_AE: 3/40 (7.5%)
Zhao et al. 2008⁴¹	Guangdong, 2006	NR	Nested multiple PCR for the amplification of env and gag gene region; C2-V3 region of env, P17 region of gag region were coded	N = 18 Age: 19–45, mean 27.1	Env gene: n = 15 B: 3/15 (20.0%) CRF01_AE: 9/15 (60.0%) CRF07_BC: 2/15 (13.3%) CRF08_BC: 1/15 (6.7%) Gag gene: n = 18 B: 4/18 (22.2%) CRF01_AE: 10/18 (55.6%) CRF07_BC: 3/18 (16.7%) CRF08_BC: 1/18 (5.6%)
Guo et al. 2009⁴²	Jiangsu, 2006–2007	Baths	Nested PCR for the env and gag gene region; P17 and C2V3 were coded	N = 25	Gag gene: n = 14 B: 5/14 (35.7%) CRF01_AE: 5/14 (35.7%) CRF07_BC: 4/14 (28.6%) Env gene: n = 11 B: 5/11 (45.5%) CRF01_AE: 4/11 (36.4%) BC recombinant: 2/11 (18.2%)
Han et al. 2009⁴³	Guangdong, 2006–2007	NR	Nested multiple PCR for the amplification of env and gag gene region; C2V3V4 region of env, P17/P24 of gag region were coded	N = 19 Age: 19–48	Env gene: n = 13 B: 3/13 (23.1%) CRF01_AE: 8/13 (61.5%) CRF07_BC: 2/13 (15.4%) Gag gene: n = 17 B: 4/17 (23.5%) CRF01_AE: 11/17 (64.7%) CRF07_BC: 2/17 (11.8%)
Li et al. 2009⁴⁴	Hebei, 2004–2006	NR	Nested PCR for the env gene region; V3-V4 region of env, neighboring gene (668 bp) were coded	N = 36 Age: 18–42, mean 28.8 Marital status: never married: 72.2% ever married: 27.8% Education: over senior middle school: 88.9%	Env gene: n = 36 B: 21/36 (58.3%) CRF01_AE: 14/36 (38.9%) CRF08_BC: 1/36 (2.8%)
Liu et al. 2009⁴⁵	Beijing, 2007–2008	Hospital	Nested multiple PCR for the amplification of env and gag gene region; C2-V5 region of env, the P17-P24 region of gag region were coded	N = 17 Age: 18–40	Gene: n = 17 B: 5/17 (31.3%) CRF01_AE: 9/17 (56.3%) CRF07_BC: 3/17 (12.5%) Env gene: n = 16 B: 5/16 (31.3%) CRF01_AE: 9/16 (56.3%) CRF07_BC: 2/16 (12.5%) Gag gene: n = 11 B: 3/11 (27.3%) CRF01_AE: 7/11 (63.6%) CRF07_BC: 1/11 (9.1%)
Liu et al. 2010⁴⁶	Hebei, 2006–2008	NR	Nested PCR for the env gene region; C3-V4 region of env, neighbouring gene (668 bp) were coded	N = 51 Age: 18–50, mean 26	Env gene: n = 51 B: 32/51 (62.7%) CRF01_AE: 16/51 (31.4%) CRF07_BC: 3/51 (5.9%)
Zhang et al. 2010⁴⁷	Zhejiang, 2008–2009	NR	Nested PCR for the env gene region; C2-V3 region of env were coded	N = 13 Age: 21–40, mean 30 Marital status: never married: 61.5% ever married: 38.5% Education: primary school: 18.2% junior middle school: 54.5% senior middle school: 9.1% over college: 18.2% Lifetime number of sex partners: <1: 10%, 1–5: 60%, 6–20: 20%, >21: 10%	Env gene: n = 11 B: 1/11 (9.1%) CRF01_AE: 4/11 (36.4%) CRF07_BC: 6/11 (54.5%)
Zheng et al. 2010⁴⁸	Tianjin, 2008	NR	Nested multiple PCR for the amplification of gag and pol gene region; gag and pol genes were coded	N = 67 Age: mean 29 Marital status: never married:67.2% Education: over senior middle school: 86.6%	Gag gene: n = 50 B: 8/50 (16.0%) CRF01_AE: 36/50 (72.0%) CRF07_BC: 6/50 (12.0%) Pol gene: n = 50 B: 8/50 (16.0%) CRF01_AE: 36/50 (72.0%) CRF07_BC: 6/50 (12.0%)
Li et al. 2011⁴⁹	Hebei, 2008–2009	The local HIV/AIDS sentinel surveillance	RT-PCR for the env, gag, and pol gene region; gag, pol and partial env genes (C2-V3 region) were coded	N = 21 Age: 23–57, mean: 33.2	Gene: n = 17 B: 6/31 (35.3%) CRF01_AE: 9/17 (42.9%) CRF07_BC: 2/17 (11.8%) Env gene: n = 16 B: 6/16 (37.5%) CRF01_AE: 8/16 (50.0%) CRF07_BC: 2/16 (12.5%) Gag gene: n = 15 B: 4/15 (26.7%) CRF01_AE: 9/15 (60.0%) CRF07_BC: 2/15 (13.3%) Pol gene: n = 15 B: 6/15 (40.0%) CRF01_AE: 7/15 (46.7%) CRF07_BC: 2/15 (13.3%)
Yang et al. 2011⁵⁰	Yunnan, 2008	CDC	Nested PCR for the env gene region; gp41 fragments (about 461 bases) were coded	N = 56	Env gene: n = 48 CRF01_AE: 38/48 (79.2%) CRF07_BC: 4/48 (8.3%) CRF08_BC: 5/48 (10.4%) URF B/C: 1/48 (2.1%)
Wang et al. 2012⁵¹	Henan, 2010	The local HIV/AIDS sentinel surveillance	RT-PCR for the env, gag and pol gene region; gag, pol (1584 bp, 3147 bp, respectively) and C2-V3 region (558 bp) of env were coded	N = 31 Age: 21–59, mean: 33.2	Gene: n = 31 B: 12/31 (38.1%) CRF01_AE: 14/31 (42.9%) CRF07_BC: 5/31 (19.0%) Env gene: n = 21 B: 8/21 (38.1%) CRF01_AE: 9/21 (42.9%) CRF07_BC: 4/21 (19.0%) Gag gene: n = 29 B: 12/29 (41.4%) CRF01_AE: 12/29 (41.4%) CRF07_BC: 5/29 (17.2%) Pol gene: n = 14 B: 6/14 (42.9%) CRF01_AE: 4/14 (28.6%) CRF07_BC: 4/14 (28.6%)
Hu et al. 2013⁵²	Beijing, 2009	Hospital	Nested PCR for the amplification of gag and env gene region; the P17-P24 region of gag region were coded	N = 43	Gene: n = 43 B: 10/43 (23.3%) CRF01_AE: 29/43 (67.4%) C: 1/43 (2.3%) B/C: 3/43 (7.0%)
Wu et al. 2012⁵³	Shanghai, 2010	CDC	RT-PCR and nested RT-PCR for the amplification of gag gene region; pol region (parts of PR and of RT gene) were coded	N = 26	Gag gene: n = 26 B: 3/26 (11.5%) CRF01_AE: 20/26 (76.9%) CRF07_BC: 3/26 (11.5%)
Xing et al. 2012⁵⁴	Shanxi, 2004–2011	NR	RT-PCR for the amplification of env, pol and gag gene region; gag (1081 bp), env (647 bp) and pol (1180 bp) were coded	N = 71 Age: 21–69, mean: 33.0 Marital status: never married: 48/71 ever married: 23/71 Education: primary school: 1 junior middle school: 7 senior middle school: 23 over college: 35 Mean of sexual partners: 17 (1–300)	Gene: n = 51 B: 3/51 (5.9%) CRF01_AE: 28/51 (54.9%) CRF07_BC: 20/51 (39.2%)
Song et al. 2012⁵⁵	Henan, 2011	CDC	RT-PCR for the amplification of gag gene region; the outside nest genes of gag were coded	N = 24 Age: 21–58, mean: 29.3	Gag gene: n = 24 B: 8/24 (33.3%) CRF01_AE: 10/24 (41.7%) CRF07_BC: 6/24 (25.0%)
Wu et al. 2013⁵⁶	Anhui, 2011	NR	RT-PCR for the amplification of gag and pol gene region; p24 and p17 of gag were coded	N = 138	Pol gene: n = 131 B: 6/131 (4.5%) CRF01_AE: 72/131 (55.0%) CRF07_BC: 44/131 (33.5%) New recombinant strains: 9/131 (7.0%)
Xei et al. 2013⁵⁷	FuJian, 2007–2010	NR	Nested RT-PCR for the amplification of pol gene region; full sequences (pol region) and partial reverse transcriptase sequences were coded	N = 50 Age: 16–50, mean: 27 ± 6.88	Pol gene: n = 50 B: 9/50 (18.0%) CRF01_AE: 30/50 (60.0%) CRF07_BC: 10/50 (20.0%) CRF08_BC: 1/50 (2.0%).
Wang 2012⁵⁸	Beijing, 2010–2011	NR	RT-PCR and nested RT-PCR for the amplification of gag gene region; genetic fragments of gag were coded	N = 30	Gag gene: n = 30 B: 7/30 (23.3%) CRF01_AE: 18/30 (60.0%) CRF07_BC: 4/30 (13.3%) CRF15_01B:1/30 (3.33%)

RT-PCR: reverse transcriptase polymerase chain reaction; CDC: Centers for Disease Control.

All studies used nested polymerase chain reaction (PCR), reverse transcription-polymerase chain reaction (RT-PCR), or nested RT-PCR to determine different HIV-1 subtypes. Additionally, most of studies used the combination of env, gag and pol genes for amplification and analysis. The coded region mainly comprised C2-V5 region of env, the P17 or P24 region of gag and pol region in the included studies. HIV-1 prevalent subtypes were commonly subtype B, CRF01_AE, CRF07_BC and CRF08_BC, and were rarely Thailand B, CRF15_01B and unique recombinant forms (URFB/C) in the included studies.

Comprehensive analysis of env, gag and pol genes indicated that the proportion of subtype B was from 4.58% to 71.11%, CRF01_AE was from 24.44% to 79.17%, CRF07_BC was from 4.44% to 54.55% and CRF08_BC was from 2.00% to 10.42%. For gag gene region, the proportion of subtype B was from 16.00% to 71.11%, CRF01_AE was from 24.44% to 72.00% and CRF07_BC was from 4.44% to 28.57%. In addition, for env gene region, the proportion of subtype B ranged from 9.09% to 86.21%, CRF01_AE from 3.45% to 79.17%, CRF07_BC from 5.88% to 54.55%, CRF08_BC from 2.78% to 10.42%. In addition for pol gene region, the proportion of subtype B ranged from 4.58% to 92.50%, CRF01_AE from 7.50% to 76.92% and CRF07_BC from 11.54% to 33.59%.

Meta-analysis of summary estimates

For comprehensive analysis of env, gag and pol genes, the pooled estimates for the prevalence of subtype B was 28.25% (95% CI: 18.10–39.66%), CRF01_AE was 53.46% (95% CI: 46.11–60.74%), CRF07_BC was 18.66% (95% CI: 13.06–25.01%) and CRF08_BC was 5.85% (95% CI: 2.73–10.07%), respectively. Strong evidence of heterogeneity was observed for overall estimates of subtype B (p < 0.05, I²= 89.7% (95% CI: 85.3–92.8%)), CRF01_AE (p < 0.05, I²= 73.5% (95% CI: 58.2–83.1%)), CRF07_BC (p < 0.05, I²= 72.3% (95% CI: 55.1–83.0%)) while homogeneity was observed for estimates of CRF08_BC (p = 0.3384, I²= 10.9% (95% CI: 0.0–86.4%)). The detailed information is shown in Figures 2 to 5. No obvious publication bias was found for these overall estimates (p > 0.05).

Figure 2.

Overall proportion of subtype B among the Chinese MSM population.

Figure 3.

Overall proportion of subtype CRF07_AE among the Chinese MSM population.

Figure 4.

Overall proportion of subtype CRF08_BC among the Chinese MSM population.

Figure 5.

Overall proportion of subtype CRF07_BC among the Chinese MSM population.

In the env gene region, the pooled estimates for the prevalence of subtype B was 43.08% (95 CI: 28.76–58.01%), CRF01_AE was 44.25% (95% CI: 29.42–59.62%), CRF07_BC was 13.09% (95% CI: 9.13–17.63%) and CRF08_BC was 7.89% (95% CI: 3.48–13.87%). In the gag gene region, the overall prevalence of subtype B was 32.90% (95% CI: 21.48–0.45.46%), CRF01_AE was 51.36% (95% CI: 40.14–62.51%) and CRF07_BC was 14.52% (95% CI: 10.53–19.02%). In the pol gene region, the pooled estimates for the prevalence of subtype B was 29.98% (95% CI: 8.63–57.50%), CRF01_AE was 49.23% (95% CI: 31.05–67.52%) and CRF07_BC was 20.88% (95% CI: 12.85–30.27%).

Subgroup analysis

As shown in Table 2, stratified analyses were conducted according to study year and geographical location. At least in part, heterogeneity in the studies was reduced in subgroup analysis. For comprehensive analysis of env, gag and pol genes, results indicated that with the increase in study year, the proportion of subtype B decreased, while the proportion of CRF01_AE and CRF07_BC showed an increasing tendency. Subtype B ranged from 43.81% (95% CI%: 24.84–63.76%) (2004–2007) to 20.65% (95% CI: 10.86–32.60%) (2008–2011). CRF01_AE ranged from 42.33% (95% CI: 28.58–56.72%) (2004–2007) to 59.47% (95% CI: 49.93–68.66%) (2008–2011). CRF07_BC ranged from 12.03% (95% CI: 6.37–19.17%) (2004–2007) to 19.58% (95% CI: 12.13–28.31%) (2008–2011). Stratified analysis also showed that Hebei province had a high proportion of subtype B (56.57% (95% CI: 47.11–65.79%)); however, Beijing (50.71% (95% CI: 29.79–71.50%)), Guangdong (52.75% (95% CI: 39.61–65.69%)) and Henan (43.85% (95% CI: 31.32–56.80%)) province had high proportions of subtype CRF01_AE. The highest proportion of CRF07_BC was found Guangdong province (21.17% (95% CI: 11.50–32.84%)).

Table 2.

Pooled prevalence of different HIV-1 subtypes stratified by study year, location.

	B			CRF01_AE			CRF07_BC
	n	Proportion,% (95% CI)	p, I²	n	Proportion,% (95% CI)	p, I²	n	Proportion,% (95% CI)	p, I²
Study year
≤2007	5	43.81 (24.84–63.76)	<0.01, 81.2%	5	42.33 (28.58–56.72)	0.03, 63.1%	4	12.03 (6.37–19.17)	0.10, 52.60%
>2007	8	20.65 (10.86–32.60)	<0.01, 81.2%	9	59.47 (49.93–68.66)	<0.01, 67.6%	9	19.58 (12.13–28.31)	<0.01, 71.80%
Location
Beijing	4	37.15 (14.93–62.73)	<0.01, 89.20%	4	50.71 (29.79–71.50)	<0.01, 84.30%	3	10.32 (5.05–17.20]	0.19, 39.00%
Guangdong	3	27.68 (16.49–40.51)	0.68, 0%	4	52.75 (39.61–65.69)	0.46, 0%	4	21.17 (11.50–32.84)	0.47, 0%
Henan	2	36.81 (24.88–49.64)	0.69, 0%	2	43.85 (31.32–56.80)	0.80, 0%	2	20.86 (11.41–32.27)	0.42, 0%
Hebei	3	56.57 (47.11–65.79)	0.14, 48.40%	3	37.73 (28.82–47.08)	0.28, 20.60%	2	8.30 (3.04–15.84)	0.39, 0%

Discussion

Prevalence of HIV-1 has increased rapidly among MSM.⁵⁹ Our research provided the quantitative synthesis of the prevalence of HIV-1 subtypes among MSM in China. Nineteen eligible articles published from 2007 to 2013 were included, and the sample size of HIV-1 positives that was amplified successfully ranged from 11 to 131. The results of meta-analysis showed that HIV-1 prevalent strains were subtype B, CRF01_AE, CRF07_BC and CRF08_BC among MSM. National survey of HIV molecular epidemiology in China evidenced that the majority of HIV-1 subtype was CRF01_AE among MSM in China.^20,21 Nevertheless, several studies revealed that the major subtype of HIV-1 among MSM is subtype B in South America, Korean, South America, Cuba and Slovakia,^60–63 which differ from our analysis.

The method of HIV-1 classification include sequence-based typing, heteroduplex mobility assay (HMA), restriction fragment long polymorphism (RFLP), oligonucleotide probe hybridisation assay and genechip.⁶⁴ In the method of sequence-based typing, special HIV segments are amplified using nested or RT-nested PCR, and then according to HIV Sequences Database and programs of professional software, the epidemic strains are sequenced to determine the subtypes.⁶⁵ Compared to other methods, sequence-based typing and HMA may be simpler, faster, and more practical for use in developing countries. Moreover, this method is able to identify a new subtype accurately and is called the ‘Gold Standard’. Local strain match, low sensitivity, low specificity and high cost may be the barriers for the application of other methods. All of the included studies used sequence-based typing to classify the HIV-1 subtypes in our meta-analysis.

HIV-1 viruses have three basic structural genes, including gag, pol and env gene. They encode core protein, polymerase and outer membrane protein, respectively. Louwagie et al.⁶⁶ showed that the classification of HIV-1 based on the env gene region was similar to gag. However, Gao et al.⁶⁷ pointed out the difference between env and gag/pol gene regions to ascertain the HIV-1 prevalent strains, particular to subtype E and A. Different gene regions of HIV-1 strains have significant difference in aberration rates. Moreover, the env gene has higher nucleic acid sequence variation rates (30%), while the gag (6%) and pol (3%) gene have lower rates.⁶⁸ Hence, for avoiding potential error classification was done by env, gag and pol genes to estimate the prevalence of HIV-1 subtypes.

CRF01_AE, mainly through heterosexual transmission, was originally discovered in Thailand and later spread all over Southeast Asia.^69–73 In China, CRF01_AE was first detected among the intravenous drug users (IDUs) in Yunnan province in 1989.⁷⁴ It was introduced by commercial sex workers from Thailand. Now, it mainly occurs in the southwest border and the southeast coastal areas of China.^75,76 Subtype B strain was believed to have been introduced via Haiti in the 1970s, which was first identified among MSM in the United States, and for many years dominated the HIV-1 epidemic in Europe and America. It was first isolated from IDUs in Yunnan Province in China.^20,77–79 In our study, it is important to note that the dominant genetic forms of HIV-1 changed from subtype B to CRF01_AE among MSM populations in China. This was consistent with a previous study in Hong Kong, which proposed the recent increasing number of people infected with HIV-1 subtype CRF01_AE and the decreasing HIV-1 subtype B in the MSM population after 2005.⁸⁰ Several studies indicated that subtype B and CRF01_AE occurred through homosexual transmission.^81–83 CRF01_AE was the most prevalent strain in our study. The most likely reason might be the transmission of restructuring of subtype being wider and easier to change. CRF01_AE can be subjected to evolution mutation and recombinant variation. This may be accounted by variation of viral genetic evolutionary rate among different subtypes. However, further study will be necessary to clarify this claim.⁸⁰ Besides, CRF01_AE might also be transmitted through other routes among MSM. Our analysis implies that MSM populations may also have heterosexual behaviours, because with the study year increasing, the proportion of CRF01_AE was also increasing among MSM. Besides, Sun et al.⁸⁴ also indicated that CRF01-AE was the major epidemic strain to spread HIV through sexual behaviours and drug intake. That is, it displayed dual characteristics of drug abuse and sexual transmission. Therefore, this population needs be educated on safe sexual behaviours in order to decrease the proportion of the CRF01_AE prevalent strain.

CRF07_BC and CRF08_BC, among the important CRFs in China, were first confirmed in IDUs. The two CRFs currently reported originated only in China.⁸⁵ CRF07_BC was found in northwest drug trafficking routes (Yunnan, Sichuan, Gansu, Ningxia and Xinjiang) in 1996;^68,86 and CRF07_BC was found in another trafficking route (from Yunnan to Guangxi) in 1996.⁸⁷ The detected proportion of these recombinant strains showed an increasing trend and became more prevalent among IDUs.²⁰ Then, the two CRFs spread across the country from high-risk groups (IDUs) to the general population through IDUs and heterosexual contact.^81,87 Our subgroup analysis indicated that the proportion of subtype CRF07_BC and CRF08_BC was increasing among MSM. One explanation for this was that MSM had more sexual behaviours with IDUs and may be more susceptible to infected B'/C recombinant strains. Another reason might be the increasing proportion of MSM who use drugs. The formation of CRF07_BC and CRF08_BC may be cross-fertilised by subtype B and subtype C among drug users. The MSM population including bisexuals, homosexuals and IDUs are more susceptible to infected B'/C recombinant strains.⁸¹ The drug used, specifically methamphetamine, possibly accelerated the spread of HIV infection among sexually active populations including MSM and woman sex workers.^88–90 Hence, it is important to implement new prevention strategies targeted toward MSM populations (who use new types of drugs) in the mainland of China.

The pooled prevalence estimates seem unlikely in different locations. A higher overall prevalence of subtype B is in Hebei province, CRF01_AE in Guangdong province and CRF07_BC in Guangdong province. In China, subtype CRF01_AE is the dominant prevalent HIV-1 strain in the maritime province.⁸² In addition, CRF01_AE spread from north-west border and south-east maritime areas to inland and north provinces (Jiangsu, Shanxi, Hunan, Tianjin).⁸¹ Subtype CEF07-BC is mainly prevalent in Xinjiang province and over 90% HIV-1 infection attributed to CRF07_BC,⁹¹ which is inconsistent with prevalent locations in our results. The reason might be the limited number of included studies and studies not originating from Xinjiang.

It is important to discuss some limitations in this meta-analysis. First of all, although careful search strategies were undertaken, overestimation or underestimation of the pooled prevalence cannot be excluded, mainly because data on the different HIV-1 subtypes among MSM populations in China is poorly indexed in databases. Our results lack sufficient information on HIV-1 prevalent strains, because the number of studies included in the subgroup analysis was small. Secondly, language bias may exist in our meta-analysis and the included studies were published in Chinese and English. Thirdly, significant heterogeneity was observed and still existed in some parts of subgroup analysis. Sample sizes of included studies were small and heterogeneity may be attributed to it. Lastly, our meta-analysis included the published studies and publication bias may exist despite passing the statistical tests.

In conclusion, the prevalent HIV-1 strains among MSM have evolved to complicated subtypes, and dominant subtypes included subtype B, CRF01_AE and CRF07_BC. The proportion of different HIV-1 subtypes changed constantly with different geographical location and study year. High rates of changing patterns of prevalent HIV-1 strains in China represent a serious challenge for effective HIV vaccines, treatment regimens and HIV prevention and control strategies.

Author contributions

Li Zhang and Yu-jie Wang contributed equally to this work and should be considered co-first authors.

Footnotes

Acknowledgements

This work was partly supported by grants from the Natural Science Foundation of Anhui Province in 2013 (Code: 1308085MH169) and the Academic Leader Foundation of Anhui Medical University, the Key Project of the Education Department of Anhui Province Natural Science Research (Code: KJ2012A165).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

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