|Year : 2020 | Volume
| Issue : 1 | Page : 8-12
Genetic diversity of human immunodeficiency virus-1 in Nigeria: 2002–2017 – systematic review and meta-analysis
Mohammed Ibrahim Tahir1, Maryam Aminu2, Babangida Ahmed Suleiman2, Ahmed Saraja Opaluwa3, Abdurrahman El-Fulaty Ahmad1
1 Department of Medical Laboratory Science, Ahmadu Bello University, Zaria, Nigeria
2 Department of Microbiology, Ahmadu Bello University, Zaria, Nigeria
3 Department of Medical Microbiology, Ahmadu Bello University, Zaria, Nigeria
|Date of Submission||19-Apr-2019|
|Date of Acceptance||21-Nov-2019|
|Date of Web Publication||14-Jan-2020|
Dr. Mohammed Ibrahim Tahir
Department of Medical Laboratory Science, Ahmadu Bello University, Zaria
Source of Support: None, Conflict of Interest: None
Nigeria was ranked second highest country with human immunodeficiency virus (HIV) burden worldwide. HIV-1 subtypes and circulating recombinant forms genetic variability affect the protease and reverse transcriptase genes which code for viral enzymes and are the main targets for antiretroviral drugs. Therefore, this study was aimed at reviewing and pooling such HIV-1 subtypes in Nigeria to represent the collective prevalence of each subtype. Studies of HIV-1 subtypes in Nigeria published from 2002 to 2017 were retrieved and synthesised from different sources electronically. Sixteen studies were included for random effect meta-analysis for various subtypes in each study. The pooled prevalence was charted in forest plot and effect estimates from individual studies against some measure of study size or precision were presented in funnel plots. The pooled prevalence of Subtype G, CRF02_AG, CRF06_cpx, Subtype A and Subtype C were 38.27% (95% Confidence Interval [CI]: 21.27%- 55.98%), 37.81% (95% CI: 20.37%- 55.25%), 6.6% (95% CI: 7.10%-7.10%), 14.05% (95% CI: 9.06% - 19.04%) and 2.80% (95% CI: 2.70%- 8.30%) respectively. This study suggests HIV-1 subtypes G, CRF02_AG and A are the most prevalent in Nigeria.
Keywords: Human immunodeficiency virus-1 subtypes, meta-analysis, molecular diversity, Nigeria
|How to cite this article:|
Tahir MI, Aminu M, Suleiman BA, Opaluwa AS, Ahmad AE. Genetic diversity of human immunodeficiency virus-1 in Nigeria: 2002–2017 – systematic review and meta-analysis. Niger Postgrad Med J 2020;27:8-12
|How to cite this URL:|
Tahir MI, Aminu M, Suleiman BA, Opaluwa AS, Ahmad AE. Genetic diversity of human immunodeficiency virus-1 in Nigeria: 2002–2017 – systematic review and meta-analysis. Niger Postgrad Med J [serial online] 2020 [cited 2020 May 29];27:8-12. Available from: http://www.npmj.org/text.asp?2020/27/1/8/275811
| Introduction|| |
Nigeria, the most populous country in Africa, was ranked second highest with Human Immunodeficiency Virus (HIV)-1 burden worldwide. The first case of acquired immune deficiency syndrome (AIDS) in Nigeria was reported in 1986, in response to which antenatal clinics sentinel surveillance was set up by the Nigerian government to serve as a system for assessing HIV epidemic. The survey results showed an increase in the prevalence of HIV from 1.2% in 1991 to 5.8% in 2001. However, the prevalence rate declined to 4.4% in 2005 and thereafter increased to 4.6% in 2008. Results from 2010 round of sentinel survey show that the national prevalence was 4.1%. This trend indicates that the epidemic is stabilising at about 4% from 2005 to 2014. Estimates of 2013 projected about 3 million people live with HIV.
HIV-1 is exemplified by broad genetic variability determined by several factors, such as the lack of proofreading ability of the reverse transcriptase (RT), the rapid turnover of HIV-1 in vivo, host selective immune pressures and recombination events during replication.
Transmission and diverse distribution of HIV-1 subtype and its circulating recombinant forms (CRFs) in African subregions may be attributed to spatial accessibility; human migrations and movements through the available transportation system. The diversity poses a serious challenge for viral load determination, drug resistance testing and HIV vaccine development.
There are substantive evidence that different HIV types, groups and subtypes harbour different biological properties, as well as response and susceptibility to antiretrovirals. HIV-1 subtypes and CRFs genetic variability affect the protease and RT genes which code for viral enzymes and these are the main targets for antiretroviral drugs. The frequency and pattern by which HIV-1 subtype polymorphisms induce resistance or lead to enhanced emergence of drug resistance under pharmacological pressure have been reported in several studies.
Sequence diversity has resulted in identification of many subtypes of HIV-1 M group, as well as CRF and a number of other unique recombinant forms of HIV-1. As of October, 2017, 90 CRFs have been identified, and more than 80 of them were published and are at public domain in the Los Alamos HIV database. This diversity may obscure diagnosis and treatment modalities for HIV infection and represents a challenge for vaccine design.
Several studies have reported different prevalent subtypes from different regions of Nigeria. However, there is no study that represents the entire country on prevalent subtypes of HIV-1. Therefore, this review was aimed at pooling such HIV-1 subtypes in Nigeria to represent collective prevalence of each subtype. This study will provide synthesised information to the body of knowledge on the genetic diversity of HIV-1 in Nigeria useful for HIV intervention programs. This review will go a long way in providing a bearing and exclusive scope in the prevalent subtypes in Nigeria.
| Materials and Methods|| |
Data were synthesised from different sources that included PubMed, Institute of Science Information, Google Scholar and African Journal Online. The keywords used to retrieve data were HIV-1 subtypes, HIV-1 Molecular epidemiology, HIV Nigeria and other related terms were cross-referenced for search. Authorities in the field were contacted on grey areas for clarification. The last search was on 4 October 2017.
Studies on the prevalence of HIV-1 subtypes among HIV-infected patients, reported in English language and published from 2002 to 2017 were included. This study did not restrict inclusion to age, method of HIV-1 characterisation and type and length of gene studied [Figure 1].
|Figure 1: The flow plan of the studies reviewed for inclusion into the study|
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Data extraction and quality assessment
A total of 73 citations were located and 16 met the inclusion criteria and hence included. The criteria for inclusion were studies published between 2002 and 2017 and reported on HIV-1 genetic diversity in Nigeria. The investigators, in parallel used the established inclusion criteria to extract and select relevant studies. Data were organised based on study year, study area, studied population, sample size, gene targeted and the study outcome (prevalence of various HIV-1 subtypes) [Table 1].
|Table 1: Outline of studies on human immunodeficiency virus-1 subtypes in Nigeria|
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Random effects meta-analysis was employed to estimate overall prevalence, and heterogeneity was analysed by Cochran's Q test and I2 as developed by Fuchs et al. Negative I2 was considered 0% and indicates no heterogeneity, while 25%, 50% and 75% represent low, moderate and high heterogeneity, respectively. Publication bias was evaluated using Egger's regression and was determined and graphically presented as Funnel plot. Level of significance was set at P < 0.05.
| Results|| |
The pooled prevalence of Subtype G, CRF02_AG, CRF06_cpx, Subtype A and Subtype C were 38.27% (95% Confidence Interval [CI]: 21.27%- 55.98%), 37.81% (95% CI: 20.37%- 55.25%), 6.6% (95% CI: 7.10%-7.10%), 14.05% (95% CI: 9.06% - 19.04%) and 2.80% (95% CI: 2.70%- 8.30%), respectively. The forest plots for the included studies were represented in [Figure 2] and [Figure 3] as pooled by two HIV-1 subtypes among others. The Cochran's Q and I2 random-effects meta-analysis were depicted in [Table 2]. Few studies reported low prevalence of other uncommon subtypes. These subtypes include CRF43_02G, A/CRF36_cpx, CRF25_cpx, CRF25_cpx, CRF36_cpx, A/CRF02_AG, CRF15-01B/G, CRF43-02G/G, G/CRF02_AG, CRF01_A/E, CRF09_cpx, CRF-18_cpx, J, URF AD, A3, CRF22 and URF AG.
|Figure 2: Forest plot of human immunodeficiency virus-1 Subtype G pooled prevalence in Nigeria from 2002 to 2017|
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|Figure 3: Forest plot of human immunodeficiency virus-1 Subtype CRF02_AG pooled prevalence in Nigeria from 2002 to 2017|
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|Table 2: Human immunodeficiency virus-1 subtypes pooled prevalence and study heterogeneity|
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The pooled prevalence of HIV-1 subtypes indicated no heterogeneity except for Subtype A, whereby the pooled studies showed moderate heterogeneity [Table 2].
With Egger's regression, there was no significant publication bias with respect to all the subtypes (P > 0.05) except Subtype C (P = 0.01951). The funnel plots showed symmetry of various reported prevalence. Prevalence observed in our reviewed studies were represented by unfilled circles, while imputed data point obtained based on the trim and fill method are represented by filled circles [Figure 4] and [Figure 5].
|Figure 4: Funnel plot of standard error by prevalence of human immunodeficiency virus-1 Subtype G|
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|Figure 5: Funnel plot of standard error by prevalence of human immunodeficiency virus-1 subtype CRF02_AG|
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| Discussion|| |
This meta-analysis showed slightly higher pooled prevalence of Subtype G in relation to CRF02_AG. The high prevalence of CRF02_AG can be attributed to the fact that it originated in Nigeria. This subtype emerged among several African countries and became epidemic in the African continent, mostly in West and West Central Africa, where it accounts for 50%–70% of the circulating strains. The high pooled prevalence of CRF02_AG in our study could reflect the recombination events between the “pure” Subtypes A and G in the region which had prevalence of 14.05% and 38.27%, respectively, it is reasonable to suggest that the CRF02_AG may have originated in this area of Africa.
Our report agrees with a review where Subtype A, G, CRF02_AG and CRF06_cpx have been reported to be most stable in Western Africa.
HIV epidemic in West Africa is reported to be dominated with subtype A and CRF02_AG. This was demonstrated in a study in Guinea Bissau. Although several epidemiological factors which could be spatial or/and temporal, invariably result in variations in the distribution pattern of the HIV subtypes and CRF across the countries.
HIV diversity poses a serious challenge for viral load determination, drug resistance testing and HIV vaccine development. With the introduction of ARV, emerging data indicate that viral subtypes may influence the effectiveness of antiretroviral treatment and that pre-existing mutations could reduce the effectiveness of antiretroviral drugs. Emerging HIV genetic variants result in adverse consequences related to pathogenesis, transmission, diagnosis, clinical management and vaccine production. Genetic variability of HIV-1 may pose significant problems for specificity and/or sensitivity of serological and molecular diagnostic tests which may represent a serious risk factor for the spreading of unidentified infections.
A study reported that disease progression of Subtypes A and G have been associated with longer AIDS-free survival period. Individuals infected with CRF02_AG or Subtype A have shown to have a slower disease progression relative to other non-A subtypes. Our report of subtype CRF02_AG as the second most prevalent is in contrast to where it was reported as the most prevalent genetic form in West and Central Africa. A study in South Africa has reported a relationship between HIV subtypes and mode of transmission as Subtype B viruses had association with male homosexual transmission and Subtype C viruses with heterosexual transmission.
The danger of designing a vaccine based on a unique isolate rather than the predominant circulating strain is eminent as the candidate vaccine may not protect against many other circulating variants in that locality. Obtaining comprehensive data on the pool of circulating strains is critical for designing vaccines based on local strains. A plausible approach to vaccine development is based on computer-derived sequences (“consensus sequences”) which are generated by aligning circulating primary isolates and selecting the most common nucleotide at each position. HIV-1 diversity influences antiretroviral therapies as subtypes differ from one another by 10%–12% of their nucleotides and 5%–6% of their amino acids in protease and RT which in turn can impact the spectrum of mutations caused by selective drug pressure.
The need for steady molecular surveillance program to detect new variant before becoming more prevalent cannot be overemphasised. This surveillance should be able to include travellers, expatriate, deportees, tourists and migrants through which infection with the possible new HIV variants can easily be spread.
Characterisation of the predominant HIV-1 subtypes, sub-subtypes and CRFs in a given population broadens our scope of viral diversity which will serve as platform to design templates for interventions, therapies and vaccines. Incorporation of population-based strains might maximise the efficacy of a potential vaccine candidate even though the significance of matching a vaccine candidate to regional circulating strains is yet unclear.
Understanding the prevalence of the HIV-1 will help to identify viral biological characteristics and the biologic consequences of these viral interactions. A number of studies have shown associations between viral genotype and disease phenotype.
Possible limitation of this review could be failure to access some studies that could have been included or were published in other languages. This might be inability to publicly produce research output by some researchers in the area of the scoping review.
This study shows HIV-1 Subtypes G, CRF02_AG and A are the most prevalent in Nigeria. This result will direct our quest for understanding the impact of genetic diversity on HIV drug resistance, the disease progress and treatment outcome.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
[Table 1], [Table 2]