HIV-1 subtype distribution and its demographic determinants in newly diagnosed patients in Europe suggest highly compartmentalized epidemics
- Ana B Abecasis1, 2Email author,
- Annemarie MJ Wensing3,
- Dimitris Paraskevis4,
- Jurgen Vercauteren5, 6,
- Kristof Theys5,
- David AMC Van de Vijver7,
- Jan Albert8, 9,
- Birgitta Asjö10,
- Claudia Balotta11,
- Danail Beshkov12,
- Ricardo J Camacho2, 13,
- Bonaventura Clotet14,
- Cillian De Gascun15,
- Algis Griskevicius16,
- Zehava Grossman17,
- Osamah Hamouda18,
- Andrzej Horban19,
- Tatjana Kolupajeva20,
- Klaus Korn21,
- Leon G Kostrikis22,
- Claudia Kücherer18,
- Kirsi Liitsola23,
- Marek Linka24,
- Claus Nielsen25,
- Dan Otelea26,
- Roger Paredes14,
- Mario Poljak27,
- Elisabeth Puchhammer-Stöckl28,
- Jean-Claude Schmit29, 30,
- Anders Sönnerborg31,
- Danika Stanekova32,
- Maja Stanojevic33,
- Daniel Struck29,
- Charles AB Boucher7 and
- Anne-Mieke Vandamme2, 5
© Abecasis et al.; licensee BioMed Central Ltd. 2013
Received: 17 July 2012
Accepted: 21 December 2012
Published: 14 January 2013
Understanding HIV-1 subtype distribution and epidemiology can assist preventive measures and clinical decisions. Sequence variation may affect antiviral drug resistance development, disease progression, evolutionary rates and transmission routes.
We investigated the subtype distribution of HIV-1 in Europe and Israel in a representative sample of patients diagnosed between 2002 and 2005 and related it to the demographic data available. 2793 PRO-RT sequences were subtyped either with the REGA Subtyping tool or by a manual procedure that included phylogenetic tree and recombination analysis. The most prevalent subtypes/CRFs in our dataset were subtype B (66.1%), followed by sub-subtype A1 (6.9%), subtype C (6.8%) and CRF02_AG (4.7%). Substantial differences in the proportion of new diagnoses with distinct subtypes were found between European countries: the lowest proportion of subtype B was found in Israel (27.9%) and Portugal (39.2%), while the highest was observed in Poland (96.2%) and Slovenia (93.6%). Other subtypes were significantly more diagnosed in immigrant populations. Subtype B was significantly more diagnosed in men than in women and in MSM > IDUs > heterosexuals. Furthermore, the subtype distribution according to continent of origin of the patients suggests they acquired their infection there or in Europe from compatriots.
The association of subtype with demographic parameters suggests highly compartmentalized epidemics, determined by social and behavioural characteristics of the patients.
Human immunodeficiency virus type 1 (HIV-1) is characterized by extensive genetic diversity. HIV-1 strains are divided in four groups (M, N, O and P), originating from four separate cross-species transmissions from chimpanzees and/or gorillas to humans. While HIV-1 groups O, N and P are mainly restricted to Central Africa, group M has caused the HIV pandemic [1–4]. HIV-1 group M has been further classified into 9 distinct subtypes, sub-subtypes and inter-subtype circulating recombinant forms (CRFs). Subtypes and sub-subtypes arose from founder effects at different time points in the past, and inter-subtype recombinants can arise in patients co-infected with strains from two different subtypes. If these newly recombined strains have a significant epidemic spread, they are called Circulating Recombinant Forms (CRFs) .
The spread of HIV-1 subtypes is important for epidemiological purposes but can also be of relevance in clinical settings. Some biological properties differ between subtypes. They have different rates of evolution and their sequence variation may affect antiviral drug resistance development [6–12], but overall limited differences are found in the genetic barrier to drug resistance development between subtypes . Other studies suggested differences in disease progression: subtype D seems to have a faster disease progression than subtypes A or C [14, 15]. In the absence of antiretroviral prophylaxis, subtype C is transmitted from mother-to-child more frequently compared to subtype D, which in turn is more frequently transmitted than subtype A [16, 17]. Some studies suggest that sexual transmission of subtype C is also more likely than of subtypes A and D [18, 19]. In addition, it is still not well understood how to cope with the genetic variability of HIV-1 for the development of an efficient HIV-1 vaccine [20–22].
Hemelaar et al. documented the molecular epidemiology of HIV-1 in the world in 2011 using convenience sampling and a literature review. Subtype C was described as the most prevalent globally, representing 48% of the infections, while subtypes A, B, CRF02_AG, CRF01_AE, subtype G and D accounted for 12, 11, 8, 5, 5 and 2% of the infections, respectively. In this study, subtype B accounted for 85% of HIV-1 infections in Western and Central Europe, while subtype A, C and G followed, with 2-3% of infections . Another manuscript by the EuroSIDA study group also based on analysis of HIV-1 genomic sequences from 939 HIV-1 patients from Europe, Israel and Argentina followed from May 1994 onwards, documented a subtype B prevalence of 86%, 2% of subtype A, 4% of subtype C and 7% of other subtypes .
We had access to sequences from the SPREAD (Strategy to Control Spread of HIV Drug Resistance) surveillance programme, which is coordinated by the European Society for Antiviral Resistance (ESAR). This programme was initiated with the objective of reliably determining the prevalence of transmission of drug resistance within the different patient risk-groups and to identify risk factors enhancing the risk of transmission of drug resistance. A second objective was to characterize the epidemiological and sequence diversity of HIV-1 in Europe. Different than in previous approaches, in this study the samples were collected in a representative way from newly diagnosed patients (http://www.esar-society.eu/). In this paper, we describe the subtype distribution of HIV-1 in Europe and Israel, based on the SPREAD sequences of three collection periods from patients newly diagnosed between 2002 and 2005 [25, 26].
Subtype B accounts for 70% of HIV-1 infections in newly diagnosed patients living in Europe
Percent subtypes for the complete set of patients and only for patients originating from SPREAD countries and 95% confidence intervals
Patients originating from SPREAD countries
HIV-1 molecular epidemiology is highly heterogeneous between European countries
New diagnoses with subtype B occurred in 79% of patients originating from and living in Europe
The country of origin corresponds to the country where the patient was born. When re-analyzing the distribution of subtypes including only patients who originated from SPREAD countries (n = 2225), the proportion of newly diagnosed patients infected with subtype B increased significantly from 66.1% to 79.5%. Subtypes or CRFs A1, CRF01_AE, CRF02_AG and C decreased significantly from 6.9%, 4.0%, 4.7% and 6.8% to 5.2%, 2.6%, 1.7% and 2.5% respectively, while the proportion of subtype G remained approximately stable (3.8% to 3.3%) (Table 1). A significant rise in proportion of newly diagnosed in this analysis means that the respective subtype is less found among the immigrant population.
The proportion of new diagnoses with different subtypes among native populations is country-specific
Proportion of new diagnoses with the 1 st , 2 nd and 3 rd most observed subtypes in each country of sampling compared to 1 st , 2 nd and 3 rd most observed subtypes among natives (unadjusted values only)
CRF01_AE and A1
AE and G
A1, C, F, G, AE, AG
AG and A1
G and U/URFs
CRF02_AG and C
C and AG
CRF01_AE and C and F
CRF01_AE, A1, C and F
CRF01_AE and C
CRF01_AE and CRF02_AG and A1
CRF01_AE and C
The most extreme case of discrepancy in infecting subtypes between natives and immigrants is Israel, where natives are exclusively infected with subtype B and the majority of infected immigrants - mainly from Ethiopia – are infected with subtype C.
The HIV-1 subtypes infecting immigrant patients living in Europe are mostly similar to the HIV-1 subtypes causing epidemics in their country/continent of origin
When analyzing the distribution of subtypes of the patients originating from countries other than SPREAD countries, we found results consistent with our current HIV-1 molecular epidemiological knowledge in those countries, suggesting that the country where the patient originates is also the country where the infection was acquired or that they acquired their infection in Europe from compatriots (Additional file 5: Figure S4; Additional file 4: Table S2). The logistic regression results were consistent with these results by indicating continent of origin and countries of origin as significantly associated with the proportion of new diagnoses with different subtypes (see Additional file 4: Table S3 for details) while country of sampling was rarely associated with it.
HIV-1 molecular epidemiology in Europe is highly stratified according to gender and risk group
Logistic regression indicated risk group MSM as a positive predictor of infection by subtype B, while heterosexual risk group is linked to infection with sub-subtype A1 and subtype C. Risk group MSM also indicates lower risk of infection with CRF01_AE, CRF02_AG and subtypes C and F. See Additional file 4: Table S3 for Odds Ratios.
Continent and country of origin and risk factor are the main determinants of subtype distribution
Multinomial logistic regression indicated that gender, risk group and continent of origin were the main determinants of subtype distribution. Country of sampling was also indicated as a determinant of subtype distribution by the regression model, but the p-value (p>0.05) for this association was not significant (Additional file 4: Table S4).
The proportion of newly diagnosed with CRF02_AG and subtype F increased significantly between 2002 and 2005
In this paper, we have described the HIV-1 subtype distribution and its associated socio-demographic factors in newly diagnosed patients in West-Central Europe using sequences from the SPREAD programme (http://www.esar-society.eu/). Samples were collected between 2002 and 2005 from drug-naïve patients diagnosed not earlier than 6 months before sampling, together with clinical and epidemiological information. The sampling strategy was based on representative sampling over countries and risk groups, allowing a more accurate picture than in previous studies that were either based on convenience sampling  or focused solely on one country [28, 29]. In addition, our study is unique since it permitted to combine for the first time molecular data of a ‘continent’-scaled sample, together with demographic and behaviour information of the patients. The primary objective of the SPREAD study was to measure the extent of transmission of drug-resistant HIV, and an additional objective was to characterize the genetic diversity of the epidemic in Europe. For this second objective, we subtyped all samples from three inclusion rounds of the SPREAD programme (Sept 2002-Dec 2005), and analyzed the geographical spread and the factors associated with this subtype distribution. Although SPREAD sampling strategy was carefully designed to avoid sampling bias, we noticed that not all countries were sampled at the same density. Given the differences found in the sampling rate of newly diagnosed patients especially in small countries, we performed a weighted analysis to account for such differences. Such a strategy has, however, its own limits since diagnosis rates may differ among risk groups  or the proportion of infected patients that are undiagnosed may differ between countries. Although we found some significant differences with the weighted analysis, this difference was not substantial and did not result in different conclusions.
Because of its high specificity, even though at the cost of sensitivity [31, 32], we only used the Rega subtyping tool in our study, and complemented it with manual analysis for unclassified sequences. Although our subtyping methodology was extremely meticulous, a limitation of any study using only one genetic region is the fact that no claims can be made with regard to the absence of recombination breakpoints outside of the sequenced genomic regions.
Similar as in the UNAIDS report for Western Europe , we found subtype B to be the most prevalent subtype but lower than reported before: 66.1% compared to the 85% reported by UNAIDS. In the subset of patients not only diagnosed but also originating from Europe the proportion of subtype B among newly diagnosed patients was significantly higher (79.5%) resembling the UNAIDS data. These differences are thus likely explained by the very different sampling strategies between the studies: UNAIDS used country of origin of the patient, while we used country of sampling; and UNAIDS used a convenience sample of data collected from patients diagnosed at any time and we used a representative sample of newly diagnosed patients. Subtypes A1 (6.9%), C (6.8%) and CRF02_AG (4.7%) are the other most prevalent subtypes in patients diagnosed in Europe. Again here the results are not entirely consistent with the UNAIDS report, where the most prevalent non-B subtypes in Western and Central Europe were CRF02_AG (2000–2003: 2.94%; 2004–2007: 4.50%) and subtype C (2000–2003: 2.9%; 2004–2007: 1.91%). As in our study only newly diagnosed patients were included in the sample, a major advantage of our sampling strategy is that it maps the more recent past compared to previous studies. Our findings may therefore be more relevant for the epidemic in the near future.
The information collected allowed us to make a detailed analysis of the relationship between the socio-demographic and other epidemiological indicators of the patient and the HIV subtype. Despite the increasing spread of different non-B subtypes, the continent of origin and the risk group of the patient are still good predictors of the subtype. While for now, we are still able to attribute the proportion of new diagnoses with many non-B subtypes in Europe to certain demographic groups; this might change in the future. For example, subtype G has already a well-established epidemic in Portugal even within natives and in both heterosexual and IDU risk groups. From our results, it is clear that some non-B subtypes are still being imported into Europe and remain largely limited to migrant populations, such as CRF02_AG in Belgium; while some have established themselves in the native European population, like subtype G in Portugal and subtype A1 in Greece. Similar observations can be made for the risk group analysis, where subtypes are clearly compartmentalized in different transmission groups. This suggests highly stratified epidemics occurring in each country and risk group. HIV infection is still determined by social, behavioural and demographic characteristics of the patients, and we should thus target preventive measures to specific populations.
The subtype B epidemic was firstly described in the MSM population , but was found in IDUs soon afterwards . Interestingly, the prevalence of subtype B is still higher in the MSM risk group. This could either indicate a higher transmission potential of subtype B in MSM, or could just be a reflection of the long-term establishment of subtype B infection in this risk group, that may be more compartmentalized than IDUs. Even though some reports are consistent with the hypothesis of a biological difference between subtypes with regard to transmission rates and routes [36, 37], with respect to our findings, more data are needed to exclude simple epidemiological circumstances. In this study, we find that heterosexuals are more frequently infected with non-B subtypes, and this is reflected in the higher proportion of women – and consequently probably children - infected with such strains. Finally, we find that the MSM risk group presents a recent rise of proportion of sub-subtype A1 infections (4.9%), mostly caused by an epidemic among Greek MSMs.
The proportion of new diagnoses with HIV-1 CRF02_AG and subtype F increased significantly between 2002 and 2005, while for subtype C we saw a significant decrease. No other significant time trends were found, indicating stable epidemics of most HIV-1 subtypes. However, given the short time period studied and the fact that most of the patients have an unknown date of infection, no firm conclusions should be made in this respect.
Although different subtypes of HIV-1 represent different epidemics, they have been dealt as a single epidemic in UNAIDS and ECDC reports. Herein, we present the social, behavioural and demographic determinants of HIV-1 subtype distribution in Europe. Stratifying results by subtypes allows a better understanding of changing prevalence and mobility of the virus.
Sequences included in the study were from 20 European countries - Austria (AT), Belgium (BE), Cyprus (CY), Denmark (DK), Finland (FI), Germany (DE), Greece (GR), Ireland (IE), Italy (IT), Luxembourg (LU), Netherlands (NL), Norway (NO), Poland (PL), Portugal (PT), Slovenia (SI), Spain (ES), Sweden (SE), Serbia (CS), Czech Republic (CZ), Slovakia (SK) - and Israel (IL). Samples were collected from HIV-1 infected individuals in whom infection was newly diagnosed between September 2002-December 2005, no longer than 6 months before sampling. To guarantee representativeness in each country, individuals were selected according to the national distribution of transmission risk groups and the geographical distribution of patients with new diagnoses of HIV-1 infection. The strategies used to achieve this were: in countries where more than 80% of all newly diagnosed individuals were expected to be covered by the participating centers, a random sample from all newly identified individuals was taken. In other countries, stratified sampling weighted for the proportion of newly diagnosed patients among different risk groups and among different geographical areas was performed or a consecutive number of patients up to a predefined number per geographic region were included. All recruited patients were antiretroviral drug naive at the time of sampling, and drug resistance genotyping was performed in the national reference laboratories as described before [25, 26]. Details about the study design were reported previously and can be found on the website (http://www.esar-society.eu/) [25, 26].
Since the % of infected inhabitants sampled was variable between countries, the counts of subtypes of each country were weighted accordingly in the determination of the proportion of newly diagnosed patients with different subtypes in Europe. Although there were some significant differences, the overall conclusions of our analysis did not change. Therefore, the unweighted proportion is given, unless specified, and all weighted analyses can be found in supplementary material.
Sequences were subtyped using the REGA Subtyping tool version 2 (http://jose.med.kuleuven.ac.be/genotypetool/html/subtypinghiv.html) [31, 38]. Detailed information about the algorithm of the REGA Subtyping tool version 2 are available on the website: http://www.bioafrica.net/subtypetool/html/.
Reports generated by the subtyping tool were individually viewed and a csv formatted file with the results was downloaded.
For sequences that were too complex for the REGA subtyping tool to assign it to a subtype or CRF automatically and that were therefore classified as ‘Unassigned’, a manual subtyping procedure was used. In this procedure, the sampled sequences were aligned against reference sequences of all pure subtypes and the reference sequences of the first 14 CRFs, using the reference set as described in the Los Alamos database. Although 51 CRFs have been described, CRFs 15 to 51 are not responsible for important epidemics and a BLAST search indicated none were present among our data. Therefore, we decided to leave them out from the phylogenetic analysis. The multiple alignment was generated using ClustalW  and manually edited with Se-Al v2.0 . The sequences were then tested for evidence of recombination using the bootscan plot as implemented in Simplot v3.5.1. Phylogenetic analyses were performed with and without including CRF reference sequences in the datasets. The putative recombination pattern was confirmed by separate phylogenetic analysis in the fragments with different evolutionary history. Any genomic region was assigned to a certain HIV subtype if it clustered with reference sequences of this subtype and this clustering was supported by bootstrap values higher than 70%.
Potential associations between demographic and other parameters (area of transmission, ethnicity, etc.) and the distribution of B and non-B subtypes were statistically analysed. The SPREAD questionnaire included information about gender, age, risk factor, continent and country of origin, country of sampling and country where infection was obtained. The univariate analysis of association between these factors and proportion of B vs non-B, C vs non-C, A1 vs non-A1, G vs non-G, CRF01_AE vs non-CRF01AE, CRF02AG vs non-CRF02AG subtypes and URFs vs non-URFs was tested using the Chi-square test. The p-values of the chi-square test were calculated using the R package . The Holm-Bonferroni method was used to check whether multiple testing could lead to a false rejection of the null hypothesis (type I error). The odds ratio (OR) and the 95% confidence interval (CI) of the OR were calculated using a small script in Microsoft Excel. A multivariate analysis was also done with stepwise logistic regression, using as start variables: a) all variables; b) only the variables that were statistically significant in the previously described univariate analysis (p≤ 0.05). Both binary and multinomial logistic regression were performed with the R package.
Bayesian networks (BN) were run for variables that were significantly associated with the distribution of HIV-1 subtype/CRF using the univariate analysis. A BN is a probabilistic graphical model that illustrates the relationships among a set of variables. These relationships – dependencies - are defined by a set of nodes that represent the variables and a set of arcs that represent direct/unconditional dependencies between two variables in the dataset. The lack of an arc between two variables represents a conditional independency, meaning that these two variables are only dependent through another variable . This analysis allows to map the interdependence of the analysed parameters unveiling direct and indirect associations with the HIV-1 subtype/CRF. The best BN that models the observed correlations is determined by a scoring metric (trade-off between model complexity and accuracy), and we use a Bayesian metric that considers the most probable one as the best network (maximizing posterior probability of the model given the data). Since an exact search is computationally impossible, we use the search heuristic of simulated annealing. We then use non-parametric bootstrap resampling to assess how strongly the data support the most probable network. A bootstrap analysis with 100 replicates was then used to investigate the reproducibility of each arc of the BN. 70% bootstrap support was used as the cut-off to assign reliable arcs. To remove the bias caused by variable instances that are present in very few patients (less than 1%), we combined those instances together in a single instance called ‘Others’. This procedure was done using the preprocessing filter available in the WEKA software.
Finally, to test for time trends in subtype distribution, we used the Cochran-Armitage test as implemented in the prop.trend.test function in the stats package of the R package.
The overall work has been partially funded by the European Commission (grant QLK2-CT-2001-01344, fifth framework; grant LSHP-CT-2006-518211, sixth framework).
Investigators have been funded by: Fundação para a Ciência e Tecnologia (Portugal grant no. SFRH / BPD / 65605 / 2009), Research Fund (PDM) of the KU Leuven, Belgian AIDS Reference Laboratory Fund, Belgian Fonds voor Wetenschappelijk Onderzoek Vlaanderen (W.F.O. grant G.0611.09); Interuniversitaire Attractiepolen (Belgium; grant P6/41); Cyprus Research Promotion Foundation (grant Health/0104/22); Danish AIDS Foundation; Federal Ministry of Health (Germany; grant 1502-686-18); Federal Ministry of Education and Research (Germany; grant 01KI501); Fifth National Program on HIV/AIDS, Istituto Superiore di Sanità (Italy; grants N 40F.56 and 20D.1.6); Fondation Recherche sur le SiDA; Ministry of Health (Luxembourg); Ministry of Education and Science (Republic of Serbia; grant 175024); Slovak Ministry of Health ( Bratislava, grant 2005/37-SZU-15); Swedish Research Council; Maraton TV3 Fundation (Spain; grant 02–1730) and Collaborative HIV and Anti-HIV Drug Resistance Network (CHAIN, grant Health-F3-2009-223131, European Community’s Seventh Framework Programme FP7/ 2007–2013).
Publications costs have been supported by Collaborative HIV and Anti-HIV Drug Resistance Network (CHAIN, grant Health-F3-2009-223131, European Community’s Seventh Framework Programme FP7/ 2007–2013).
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