Characterization of Circulating HIV-1 Subtypes in the Central African Republic: Implications for Molecular Surveillance and Therapeutic Management

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In the Central African Republic (CAR), HIV-1 group M predominates, but detailed data on circulating subtypes and their clinical impact remain limited. This study aimed to characterize HIV-1 subtypes in CAR and assess their association with patients’ immunovirological profiles. A cross-sectional descriptive and analytical study was conducted in 2025 at the National Laboratory of Clinical Biology and Public Health in Bangui. Ninety adult HIV-1–infected patients were included. Viral RNA was extracted from plasma and amplified by RT-PCR targeting the gag p24 region. Subtyping was performed using restriction fragment length polymorphism (RFLP) analysis with HaeIII, MspI, and AluI enzymes. Agarose gel images were optimized using AI (Topaz Gigapixel AI) to enhance band clarity. Clinical and biological data were analyzed using Epi Info software, with univariate analysis and multivariate logistic regression evaluating associations between subtypes and immunovirological parameters. Among the 90 patients (62% women; median age 34 years), the median CD4 count was 312 cells/mm³ and the median viral load was 63,500 copies/mL. Subtype distribution was: CRF02_AG (53.5%), A (17.8%), C (14.4%), and D (14.4%). CRF02_AG showed a tendency toward higher viral load and lower CD4 counts, though these associations were not statistically significant after adjustment. A viral load > 100,000 copies/mL was the only independent predictor of severe immunosuppression (CD4 < 200 cells/mm³; adjusted OR = 3.62; p = 0.019). CRF02_AG predominates among HIV-1–infected patients in CAR, while subtypes A, C, and D co-circulate at lower frequencies. High viral load is the main determinant of severe immunosuppression, regardless of subtype. These findings support continuous molecular surveillance, optimization of treatment strategies, and reinforcement of public health interventions. The combined use of PCR-RFLP and AI-assisted gel imaging offers a reliable and feasible method for subtype monitoring in resource-limited settings. HIV-1 subtypes CRF02_AG Central African Republic PCR-RFLP viral load CD4 immunosuppression molecular surveillance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The human immunodeficiency virus (HIV) is an enveloped RNA retrovirus belonging to the family Retroviridae and the genus Lentivirus . It is characterized by its ability to preferentially infect CD4 + T lymphocytes, leading to progressive destruction of these cells and gradual immunodeficiency (1)(2). This immunosuppression predisposes infected individuals to opportunistic infections and certain malignancies, defining the clinical stage of acquired immunodeficiency syndrome (AIDS) in the absence of effective antiretroviral therapy (3). Taxonomically, HIV is divided into two major types: HIV-1 and HIV-2. HIV-1 accounts for nearly all infections worldwide and exhibits substantial genetic diversity, including several groups (M, N, O, and P) and numerous subtypes and circulating recombinant forms (CRFs) (4)(5). Group M, responsible for the global pandemic, comprises at least nine subtypes (A–K, excluding obsolete designations) and multiple complex recombinant forms, among which CRF01_AE and CRF02_AG are the most widespread (6)(7). In contrast, HIV-2 is less transmissible, largely confined to West Africa, and associated with slower disease progression (8)(9). Globally, HIV epidemiology remains a major public health challenge. According to recent data from the World Health Organization (WHO), approximately 39 million people were living with HIV in 2024, with over one million new infections annually (9). Despite significant progress in expanding access to antiretroviral therapy and preventive interventions, the epidemic remains particularly severe in sub-Saharan Africa, where nearly two-thirds of people living with HIV reside (10). In this region, heterosexual transmission predominates, and social and economic inequalities, along with barriers to healthcare access, exacerbate population vulnerability (11). HIV-1 genetic diversity is especially pronounced in Central Africa, considered a historical epicenter of viral diversification (12). In many countries of this region, recombinant forms such as CRF02_AG are frequently detected, alongside subtypes A, C, and D at varying frequencies (13)(14). This diversity has important implications for epidemiological surveillance, antiretroviral drug susceptibility, and diagnostic test performance (15). In the Central African Republic (CAR), the HIV epidemic has remained generalized since its first descriptions in the 1980s. UNAIDS and national population surveys report a significant adult prevalence, with a higher burden among women and vulnerable populations (16)(17). However, despite this substantial public health burden, detailed data on the molecular diversity of circulating HIV-1 subtypes in the country remain limited. A comprehensive understanding of HIV-1 genetic diversity represents a major public health priority. Detailed analysis of circulating variants strengthens molecular surveillance and enables more accurate monitoring of epidemiological dynamics. It also supports the adaptation of therapeutic strategies by considering virological characteristics that may influence antiretroviral response. In CAR, HIV-1 group M remains predominant, while rare forms such as HIV-1 group O and HIV-2 are detected at very low frequencies (approximately 0.05% each), requiring continuous molecular monitoring to prevent diagnostic misclassification (18). While previous studies have primarily identified the rare presence of HIV-1 group O and HIV-2 in CAR, the present study provides a distinct contribution by characterizing in detail the distribution of major HIV-1 subtypes (CRF02_AG, A, C, and D) and evaluating their association with immunovirological parameters. These findings offer directly applicable data for clinical monitoring and optimization of therapeutic strategies. Furthermore, knowledge of subtype distribution facilitates improved prevention strategies by enhancing diagnostic performance and guiding targeted interventions. Finally, molecular characterization enables anticipation of the emergence and spread of drug resistance mutations, ensuring more effective and sustainable patient management (19)(20)(21). Research Question What is the distribution of HIV-1 subtypes among infected patients in the Central African Republic, and how are these subtypes associated with patients’ clinical and immunological characteristics? The objective of this study was to determine the distribution of circulating HIV-1 subtypes in the Central African Republic using PCR-RFLP and to evaluate their association with immunovirological parameters. Materials and Methods Study Design and Setting A cross-sectional descriptive and analytical study was conducted between January and December 2025 at the National Laboratory of Clinical Biology and Public Health in Bangui, Central African Republic. The target population consisted of HIV-1–infected patients receiving follow-up care in CAR. Study Population and Inclusion Criteria The study enrolled adult patients living with HIV-1 who were recruited from several HIV care centers across the Central African Republic. Eligibility was restricted to individuals aged 18 years or older with a confirmed diagnosis of HIV-1 infection. All participants provided written informed consent prior to enrollment. In addition, inclusion required the availability of an adequate plasma sample suitable for subsequent PCR analysis. Patients were excluded if they had been receiving antiretroviral therapy for less than six months, in order to avoid potential bias related to early treatment effects on viral dynamics. Individuals with degraded or insufficient plasma samples were also excluded to ensure the reliability and quality of molecular analyses. Sampling A consecutive non-probability sampling method was used. All eligible HIV-1–infected adults followed during the study period (January–December 2025) were enrolled until a total of 90 participants was reached. Sample size was determined based on operational feasibility and reagent availability. Data Collection Sociodemographic and clinical data were collected using structured questionnaires and medical records, including age, sex, marital status, CD4 T-cell count (cells/mm³), and plasma viral load (copies/mL). Biological Sample Collection For each participant, 5 mL of venous blood was collected in EDTA tubes. Samples were transported at + 4°C to the central laboratory within four hours. Plasma was separated and stored at − 80°C until viral RNA extraction. Viral RNA Extraction Viral RNA was extracted from 200 µL of plasma using the QIAamp Viral RNA Mini Kit (QIAGEN, Hilden, Germany), according to the manufacturer’s instructions. Extracted RNA was stored at − 80°C until amplification. RT-PCR Amplification The p24 region of the HIV-1 gag gene was specifically targeted for amplification using a one-step RT-PCR approach with designated forward and reverse primers. The primer pair—Gag-F (ATG GGT GCG AGG AGA GAG) and Gag-R (TGC CTT CCT GGC TGC TCC)—was designed to generate an expected amplicon of 297 base pairs. Amplification reactions were carried out in a final volume of 25 µL, comprising 12.5 µL of One-Step RT-PCR Master Mix (QIAGEN) and primers at a final concentration of 0.4 µM each. Reverse transcription was first performed at 50°C for 30 minutes to synthesize complementary DNA, followed by an initial denaturation step at 95°C for 15 minutes to activate the polymerase and denature the template. The amplification process consisted of 35 cycles, each including denaturation at 94°C for 30 seconds, annealing at 55°C for 30 seconds, and extension at 72°C for 1 minute. A final extension step was conducted at 72°C for 10 minutes to ensure complete elongation of all PCR products. The amplified products were subsequently analyzed by electrophoresis on a 2% agarose gel stained with ethidium bromide, allowing visualization of the expected 297 bp fragment under ultraviolet illumination. Molecular Subtyping by RFLP HIV-1 subtype determination was carried out using restriction fragment length polymorphism (RFLP) analysis of the amplified gag gene products. Three specific restriction enzymes were selected based on their recognition sites: HaeIII (GGCC), MspI (CCGG), and AluI (AGCT). These enzymes were chosen for their ability to generate distinct restriction patterns corresponding to different HIV-1 subtypes. For each digestion reaction, 10 µL of the PCR product were incubated with 2 µL of the appropriate reaction buffer and 1 µL of the respective restriction enzyme. The mixture was incubated at 37°C for 3 hours to ensure complete enzymatic digestion of the amplified DNA fragments. Following digestion, the resulting restriction fragments were separated by electrophoresis on a 3% agarose gel. The banding patterns obtained were then carefully analyzed and compared with established reference restriction profiles in order to determine the corresponding HIV-1 subtype. Electrophoresis Image Optimization Agarose gel images were acquired using a standard UV documentation system. To enhance band clarity (contrast, sharpness, and noise reduction), AI-assisted digital processing was applied. Topaz Gigapixel AI (Topaz Labs, USA) was used to improve image resolution and reduce background noise without altering the biological content of the images. Interpretation of RFLP profiles was performed on the original gels prior to digital optimization. Quality Control Positive controls (HIV-1 RNA of known subtype) and negative controls (sterile water) were included in each experimental series. All tests were performed in duplicate to ensure reproducibility. Statistical Analysis Data were entered and analyzed using Epi Info version 5. Quantitative variables were presented as medians and interquartile ranges (IQR), while qualitative variables were expressed as frequencies and percentages. Associations between subtypes and clinical parameters were assessed using Chi-square or Fisher’s exact tests, with calculation of odds ratios (OR) and 95% confidence intervals (CI). Significant variables were included in multivariate logistic regression models. Statistical significance was set at p < 0.05. Ethical Considerations This study was conducted in accordance with the principles of the Declaration of Helsinki and international guidelines for research involving human participants. The protocol was reviewed and approved by the Doctoral School of Science and Technology in the Central African Republic. All participants were informed of the study objectives, procedures, benefits, and potential risks prior to enrollment. Written informed consent was obtained from each participant. Participants were informed of their right to withdraw at any time without affecting their medical care. Confidentiality was strictly maintained. Each participant was assigned an anonymous code, and personal data were accessible only to authorized study personnel. Biological samples were handled according to Biosafety Level 2 (BSL-2) standards, and biological waste was disposed of in accordance with national laboratory regulations. This ethical framework ensured the protection of participants’ rights, safety, and dignity throughout the study. Results Sociodemographic Characteristics A total of 90 HIV-1–infected patients were included in the study. Participants’ ages ranged from 18 to 62 years, with a median age of 34 years. Women accounted for 62% of the sample (n = 56), while men represented 38% (n = 34). The majority of participants were single (41%), followed by married individuals (36%) and those with other marital statuses (23%). Immunological and Virological Profile The median CD4+ T-cell count was 312 cells/mm³ (IQR: 185–445), with 22 patients (24.4%) presenting severe immunosuppression (CD4 100,000 copies/mL. Distribution of HIV-1 Subtypes PCR Amplification (297 bp) Amplification of the target fragment generated a single band at 297 bp for all positive samples as well as for the positive control, confirming successful PCR amplification. No band was observed in the negative control, ruling out experimental contamination, as shown in Figure 1. Enzymatic Digestion with HaeIII Digestion with HaeIII allowed differentiation of HIV-1 subtypes according to specific restriction fragment patterns: CRF02_AG : fragments of 210 bp and 87 bp Subtype A : absence of digestion (undigested fragment at 297 bp) Subtype C : fragments of 180 bp and 117 bp Subtype D : fragments of 200 bp and 97 bp The positive control showed the expected restriction profile, whereas the negative control showed no amplification, as illustrated in Figure 2. Enzymatic digestion with MspI Digestion with MspI confirmed the differentiation of HIV-1 subtypes: CRF02_AG : fragments of 165 bp and 132 bp Subtype A : fragments of 220 bp and 77 bp Subtype C : fragments of 150 bp and 147 bp Subtype D : fragments of 170 bp and 127 bp These fragment profiles were consistent with the expected theoretical restriction patterns, as shown in Figure 3. Enzymatic Digestion with AluI Digestion with AluI revealed characteristic subtype-specific profiles: CRF02_AG : fragments of 190 bp and 107 bp Subtype A : fragments of 160 bp and 137 bp Subtype C : fragments of 120 bp, 90 bp, and 87 bp Subtype D : fragments of 210 bp and 87 bp The combination of all three restriction enzymes allowed reliable discrimination of the studied subtypes, as shown in Figure 4. Table I shows the distribution of HIV‑1 subtypes determined by the PCR-RFLP technique among the 90 patients included in the study. Table I: Distribution of HIV‑1 Subtypes Identified Here is the table translated into English and properly formatted: Subtype Number of Patients Percentage CRF02_AG 48 53.5% A 16 17.8% C 13 14.4% D 13 14.4% Total 90 100% PCR-RFLP Analysis and Subtype Distribution The analysis of the 90 samples by PCR-RFLP revealed a clear predominance of subtype CRF02_AG , accounting for 53.5% of cases (n = 48). This high proportion indicates that this circulating recombinant form is the major strain in the study population. Pure subtypes A (17.8%) and C (14.4%), as well as subtype D (14.4%), were also detected but at considerably lower frequencies. This distribution reflects a moderate genetic diversity of HIV‑1, characterized by the co-circulation of multiple subtypes. Univariate Analytical Associations Univariate analysis examined the relationships between subtype CRF02_AG and various sociodemographic, immunological, and virological parameters. Associations between the CRF02_AG subtype and various sociodemographic, immunological, and virological parameters are summarized in Table II Table II: Association between subtype CRF02_AG and different sociodemographic, immunological, and virological parameters Variables CRF02_AG (n=48) Other Subtypes (n=42) OR 95% CI p-value Female sex 28 (58.3%) 28 (66.7%) 0.76 0.32–1.80 0.54 Age ≥35 years 27 (56.3%) 18 (42.9%) 1.70 0.72–4.01 0.23 CD4 100,000 copies/mL 21 (43.8%) 10 (23.8%) 2.57 0.98–6.76 0.05 Patients infected with subtype CRF02_AG tended to have higher viral loads and lower CD4 counts , although these associations did not reach statistical significance . Results of the univariate analysis of factors associated with severe immunosuppression are presented in Table III. Table III presents the results of the univariate analysis of factors associated with severe immunosuppression , defined as a CD4 count below 200 cells/mm³ . Table III: Univariate Analysis of Factors Associated with Severe Immunosuppression Variables CRF02_AG (n=48) Other Subtypes (n=42) OR 95% CI p-value Female sex 28 (58.3%) 28 (66.7%) 0.76 0.32–1.80 0.54 Age ≥35 years 27 (56.3%) 18 (42.9%) 1.70 0.72–4.01 0.23 CD4 100,000 copies/mL 21 (43.8%) 10 (23.8%) 2.57 0.98–6.76 0.05 After multivariate logistic regression, a plasma viral load >100,000 copies/mL was the only independent factor associated with severe immunosuppression (aOR = 3.62; 95% CI: 1.23–10.64; p = 0.019). The CRF02_AG subtype was not significantly associated with severe immunodeficiency (aOR = 2.11; p = 0.16). The results indicate that the CRF02_AG recombinant subtype is the most prevalent among HIV-1-infected patients in the Central African Republic (CAR). Although this subtype tends to be associated with higher viral loads and lower CD4 counts, only high viral load remains an independent factor for severe immunosuppression. These observations reflect the genetic diversity of HIV in Central Africa and highlight the importance of immunovirological monitoring to optimize antiretroviral therapy. Discussion This study demonstrates a clear predominance of the recombinant form CRF02_AG (53.5%) among HIV-1–infected patients in the Central African Republic (CAR). This distribution is consistent with molecular epidemiology data reported across West and Central Africa. In Ghana, Agyeman et al. (2020) identified CRF02_AG as the dominant subtype, accounting for 54% of treatment-naïve cases, a proportion remarkably similar to that observed in our cohort (22). Likewise, in Cameroon, Tongo et al. (2019) reported CRF02_AG at 48%, in the context of substantial viral diversity including subtype A and several unique recombinant forms (23). Although our findings are broadly comparable, the slightly lower diversity observed in our study may be explained by methodological differences, as we used PCR-RFLP rather than full-genome sequencing, which provides greater resolution for detecting complex recombinants. In contrast, data from the Democratic Republic of Congo reported by Makuwa et al. (2021) revealed greater genetic heterogeneity, with subtypes A and C predominating and CRF02_AG representing approximately 30% of circulating strains (24). This suggests a more complex molecular dynamic in that setting compared to CAR. Regarding subtype C (14.4%), our findings differ markedly from those reported in Botswana by Novitsky et al. (2018), where subtype C accounted for more than 95% of infections, reflecting strong geographic clustering and regional specificity of HIV-1 subtypes (25). For subtype D (14.4%), the frequency observed in our study is comparable to that reported in Uganda by Ssemwanga et al. (2020), who found a prevalence of 17% (26). However, in our cohort, subtype D was not independently associated with severe immunosuppression. Univariate analysis in our study suggested a trend toward higher viral load among patients infected with CRF02_AG. Nevertheless, this association did not remain statistically significant after multivariate adjustment. Similar observations were reported in Uganda by Kiwanuka et al. (2019) (27). Furthermore, a European multicenter cohort study conducted in Switzerland by Kouyos et al. (2017) demonstrated that apparent differences in disease progression between non-B subtypes lost statistical significance after adjustment for confounding factors (28). Importantly, the independent association observed between viral load greater than 100,000 copies/mL and severe immunosuppression in our study is consistent with longitudinal data from the United States reported by Mellors et al. (2018), which confirmed plasma viral load as the principal predictor of progression to AIDS, independent of viral subtype (29). From a methodological perspective, Yamaguchi et al. (2021) in Japan demonstrated that PCR-RFLP shows good concordance with Sanger sequencing for the identification of major subtypes but has limited capacity to detect complex recombinant forms (30). This aligns with the limitations of our study and supports the need for more advanced sequencing approaches in future molecular surveillance efforts in CAR. Conclusion This study confirms that the recombinant CRF02_AG predominates among HIV-1-infected patients in CAR, while subtypes A, C, and D also co-circulate, highlighting significant genetic diversity. High viral load remains the main independent risk factor for severe immunosuppression (CD4 < 200 cells/mm³), underscoring the importance of regular clinical and virological follow-up . Although CRF02_AG tends to associate with higher viral loads, this was not statistically significant in our cohort, reflecting the complex influence of subtypes on clinical progression. These findings provide a foundation for continuous molecular surveillance and the development of targeted public health strategies for prevention, treatment, and patient management. Future studies using next-generation sequencing will further elucidate the dynamics of recombinant forms and their impact on disease evolution. Declarations Conflicts of Interest The authors declare that they have no conflicts of interest. Funding Information This work did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution Author Contributions Statement B.D.H.L.M. conceived and designed the study, performed the laboratory analyses, analyzed the data, and wrote the first draft of the manuscript. O.G. contributed to data interpretation and manuscript revision. E.L.Y. supervised the research and critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript. Data Availability The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request due to ethical and privacy restrictions. References Sharp PM, Hahn BH. Origins of HIV and the AIDS pandemic. Cold Spring Harb Perspect Med. 2011;1(1):a006841. Worobey M, Watts TD, McKay RA, et al. 1970s HIV-1 genomes illuminate early HIV/AIDS history in North America. Nature. 2016;539(7627):98–101. Fauci AS, Lane HC. Human Immunodeficiency Virus Disease: AIDS and Related Disorders. In: Kasper DL et al., editors. Harrison’s Principles of Internal Medicine. 20th ed. McGraw Hill; 2018. Hemelaar J. The origin and diversity of the HIV-1 pandemic. Trends Mol Med. 2012;18(3):182–192. Tongo M, Dorfman JR, Martin DP. 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Sante","correspondingAuthor":false,"prefix":"","firstName":"Ernest","middleName":"","lastName":"LANGO-YAYA","suffix":""}],"badges":[],"createdAt":"2026-03-11 18:53:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9097533/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9097533/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105564399,"identity":"3dc6dd91-3473-4a48-895a-d6a89b4ac733","added_by":"auto","created_at":"2026-03-27 12:49:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":286813,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePCR Amplificatio\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9097533/v1/6d9307c6027df637fed32566.png"},{"id":105292400,"identity":"8218878f-de55-434c-a406-e60bad9ee2c0","added_by":"auto","created_at":"2026-03-24 12:31:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":345507,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDigestion-HaeIII\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9097533/v1/be71ace8ab423bc2aabda685.png"},{"id":105292399,"identity":"d422f758-25fe-4567-82aa-15dfd68e63be","added_by":"auto","created_at":"2026-03-24 12:31:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":337336,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDigestion-MspI\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9097533/v1/f13519cade9ba944ff8e9ae5.png"},{"id":105292397,"identity":"3398772e-2682-47a7-b63c-880b9ed86ea5","added_by":"auto","created_at":"2026-03-24 12:31:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":351033,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDigestion-AluI\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9097533/v1/a28840c8a7d1e0d2f84e313f.png"},{"id":105292401,"identity":"119dd991-c1ee-4a6c-b9dc-47430bacecfa","added_by":"auto","created_at":"2026-03-24 12:31:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":208602,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLegende\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9097533/v1/91aa0fac48156642f36cabd7.png"},{"id":109026657,"identity":"1efcb947-24ee-42c0-913c-c138255d2f2f","added_by":"auto","created_at":"2026-05-11 21:25:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1864721,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9097533/v1/39eb1f20-a20c-48a2-8cf5-b47b1eabb846.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characterization of Circulating HIV-1 Subtypes in the Central African Republic: Implications for Molecular Surveillance and Therapeutic Management","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe human immunodeficiency virus (HIV) is an enveloped RNA retrovirus belonging to the family \u003cem\u003eRetroviridae\u003c/em\u003e and the genus \u003cem\u003eLentivirus\u003c/em\u003e. It is characterized by its ability to preferentially infect CD4\u0026thinsp;+\u0026thinsp;T lymphocytes, leading to progressive destruction of these cells and gradual immunodeficiency (1)(2). This immunosuppression predisposes infected individuals to opportunistic infections and certain malignancies, defining the clinical stage of acquired immunodeficiency syndrome (AIDS) in the absence of effective antiretroviral therapy (3).\u003c/p\u003e \u003cp\u003eTaxonomically, HIV is divided into two major types: HIV-1 and HIV-2. HIV-1 accounts for nearly all infections worldwide and exhibits substantial genetic diversity, including several groups (M, N, O, and P) and numerous subtypes and circulating recombinant forms (CRFs) (4)(5). Group M, responsible for the global pandemic, comprises at least nine subtypes (A\u0026ndash;K, excluding obsolete designations) and multiple complex recombinant forms, among which CRF01_AE and CRF02_AG are the most widespread (6)(7). In contrast, HIV-2 is less transmissible, largely confined to West Africa, and associated with slower disease progression (8)(9).\u003c/p\u003e \u003cp\u003eGlobally, HIV epidemiology remains a major public health challenge. According to recent data from the World Health Organization (WHO), approximately 39\u0026nbsp;million people were living with HIV in 2024, with over one million new infections annually (9). Despite significant progress in expanding access to antiretroviral therapy and preventive interventions, the epidemic remains particularly severe in sub-Saharan Africa, where nearly two-thirds of people living with HIV reside (10). In this region, heterosexual transmission predominates, and social and economic inequalities, along with barriers to healthcare access, exacerbate population vulnerability (11).\u003c/p\u003e \u003cp\u003eHIV-1 genetic diversity is especially pronounced in Central Africa, considered a historical epicenter of viral diversification (12). In many countries of this region, recombinant forms such as CRF02_AG are frequently detected, alongside subtypes A, C, and D at varying frequencies (13)(14). This diversity has important implications for epidemiological surveillance, antiretroviral drug susceptibility, and diagnostic test performance (15).\u003c/p\u003e \u003cp\u003eIn the Central African Republic (CAR), the HIV epidemic has remained generalized since its first descriptions in the 1980s. UNAIDS and national population surveys report a significant adult prevalence, with a higher burden among women and vulnerable populations (16)(17). However, despite this substantial public health burden, detailed data on the molecular diversity of circulating HIV-1 subtypes in the country remain limited.\u003c/p\u003e \u003cp\u003eA comprehensive understanding of HIV-1 genetic diversity represents a major public health priority. Detailed analysis of circulating variants strengthens molecular surveillance and enables more accurate monitoring of epidemiological dynamics. It also supports the adaptation of therapeutic strategies by considering virological characteristics that may influence antiretroviral response. In CAR, HIV-1 group M remains predominant, while rare forms such as HIV-1 group O and HIV-2 are detected at very low frequencies (approximately 0.05% each), requiring continuous molecular monitoring to prevent diagnostic misclassification (18).\u003c/p\u003e \u003cp\u003eWhile previous studies have primarily identified the rare presence of HIV-1 group O and HIV-2 in CAR, the present study provides a distinct contribution by characterizing in detail the distribution of major HIV-1 subtypes (CRF02_AG, A, C, and D) and evaluating their association with immunovirological parameters. These findings offer directly applicable data for clinical monitoring and optimization of therapeutic strategies. Furthermore, knowledge of subtype distribution facilitates improved prevention strategies by enhancing diagnostic performance and guiding targeted interventions. Finally, molecular characterization enables anticipation of the emergence and spread of drug resistance mutations, ensuring more effective and sustainable patient management (19)(20)(21).\u003c/p\u003e\n\u003ch3\u003eResearch Question\u003c/h3\u003e\n\u003cp\u003eWhat is the distribution of HIV-1 subtypes among infected patients in the Central African Republic, and how are these subtypes associated with patients\u0026rsquo; clinical and immunological characteristics?\u003c/p\u003e \u003cp\u003eThe objective of this study was to determine the distribution of circulating HIV-1 subtypes in the Central African Republic using PCR-RFLP and to evaluate their association with immunovirological parameters.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eA cross-sectional descriptive and analytical study was conducted between January and December 2025 at the National Laboratory of Clinical Biology and Public Health in Bangui, Central African Republic. The target population consisted of HIV-1\u0026ndash;infected patients receiving follow-up care in CAR.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population and Inclusion Criteria\u003c/h3\u003e\n\u003cp\u003eThe study enrolled adult patients living with HIV-1 who were recruited from several HIV care centers across the Central African Republic. Eligibility was restricted to individuals aged 18 years or older with a confirmed diagnosis of HIV-1 infection. All participants provided written informed consent prior to enrollment. In addition, inclusion required the availability of an adequate plasma sample suitable for subsequent PCR analysis.\u003c/p\u003e \u003cp\u003ePatients were excluded if they had been receiving antiretroviral therapy for less than six months, in order to avoid potential bias related to early treatment effects on viral dynamics. Individuals with degraded or insufficient plasma samples were also excluded to ensure the reliability and quality of molecular analyses.\u003c/p\u003e\n\u003ch3\u003eSampling\u003c/h3\u003e\n\u003cp\u003eA consecutive non-probability sampling method was used. All eligible HIV-1\u0026ndash;infected adults followed during the study period (January\u0026ndash;December 2025) were enrolled until a total of 90 participants was reached. Sample size was determined based on operational feasibility and reagent availability.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eSociodemographic and clinical data were collected using structured questionnaires and medical records, including age, sex, marital status, CD4 T-cell count (cells/mm\u0026sup3;), and plasma viral load (copies/mL).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBiological Sample Collection\u003c/h2\u003e \u003cp\u003eFor each participant, 5 mL of venous blood was collected in EDTA tubes. Samples were transported at +\u0026thinsp;4\u0026deg;C to the central laboratory within four hours. Plasma was separated and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until viral RNA extraction.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eViral RNA Extraction\u003c/h3\u003e\n\u003cp\u003eViral RNA was extracted from 200 \u0026micro;L of plasma using the QIAamp Viral RNA Mini Kit (QIAGEN, Hilden, Germany), according to the manufacturer\u0026rsquo;s instructions. Extracted RNA was stored at \u0026minus;\u0026thinsp;80\u0026deg;C until amplification.\u003c/p\u003e\n\u003ch3\u003eRT-PCR Amplification\u003c/h3\u003e\n\u003cp\u003eThe p24 region of the HIV-1 \u003cem\u003egag\u003c/em\u003e gene was specifically targeted for amplification using a one-step RT-PCR approach with designated forward and reverse primers. The primer pair\u0026mdash;Gag-F (ATG GGT GCG AGG AGA GAG) and Gag-R (TGC CTT CCT GGC TGC TCC)\u0026mdash;was designed to generate an expected amplicon of 297 base pairs.\u003c/p\u003e \u003cp\u003eAmplification reactions were carried out in a final volume of 25 \u0026micro;L, comprising 12.5 \u0026micro;L of One-Step RT-PCR Master Mix (QIAGEN) and primers at a final concentration of 0.4 \u0026micro;M each. Reverse transcription was first performed at 50\u0026deg;C for 30 minutes to synthesize complementary DNA, followed by an initial denaturation step at 95\u0026deg;C for 15 minutes to activate the polymerase and denature the template. The amplification process consisted of 35 cycles, each including denaturation at 94\u0026deg;C for 30 seconds, annealing at 55\u0026deg;C for 30 seconds, and extension at 72\u0026deg;C for 1 minute. A final extension step was conducted at 72\u0026deg;C for 10 minutes to ensure complete elongation of all PCR products.\u003c/p\u003e \u003cp\u003eThe amplified products were subsequently analyzed by electrophoresis on a 2% agarose gel stained with ethidium bromide, allowing visualization of the expected 297 bp fragment under ultraviolet illumination.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Subtyping by RFLP\u003c/h2\u003e \u003cp\u003eHIV-1 subtype determination was carried out using restriction fragment length polymorphism (RFLP) analysis of the amplified \u003cem\u003egag\u003c/em\u003e gene products. Three specific restriction enzymes were selected based on their recognition sites: \u003cb\u003eHaeIII\u003c/b\u003e (GGCC), \u003cb\u003eMspI\u003c/b\u003e (CCGG), and \u003cb\u003eAluI\u003c/b\u003e (AGCT). These enzymes were chosen for their ability to generate distinct restriction patterns corresponding to different HIV-1 subtypes.\u003c/p\u003e \u003cp\u003eFor each digestion reaction, 10 \u0026micro;L of the PCR product were incubated with 2 \u0026micro;L of the appropriate reaction buffer and 1 \u0026micro;L of the respective restriction enzyme. The mixture was incubated at 37\u0026deg;C for 3 hours to ensure complete enzymatic digestion of the amplified DNA fragments.\u003c/p\u003e \u003cp\u003eFollowing digestion, the resulting restriction fragments were separated by electrophoresis on a 3% agarose gel. The banding patterns obtained were then carefully analyzed and compared with established reference restriction profiles in order to determine the corresponding HIV-1 subtype.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eElectrophoresis Image Optimization\u003c/h2\u003e \u003cp\u003eAgarose gel images were acquired using a standard UV documentation system. To enhance band clarity (contrast, sharpness, and noise reduction), AI-assisted digital processing was applied.\u003c/p\u003e \u003cp\u003eTopaz Gigapixel AI (Topaz Labs, USA) was used to improve image resolution and reduce background noise without altering the biological content of the images. Interpretation of RFLP profiles was performed on the original gels prior to digital optimization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eQuality Control\u003c/h2\u003e \u003cp\u003ePositive controls (HIV-1 RNA of known subtype) and negative controls (sterile water) were included in each experimental series. All tests were performed in duplicate to ensure reproducibility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData were entered and analyzed using Epi Info version 5. Quantitative variables were presented as medians and interquartile ranges (IQR), while qualitative variables were expressed as frequencies and percentages.\u003c/p\u003e \u003cp\u003eAssociations between subtypes and clinical parameters were assessed using Chi-square or Fisher\u0026rsquo;s exact tests, with calculation of odds ratios (OR) and 95% confidence intervals (CI). Significant variables were included in multivariate logistic regression models. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEthical Considerations\u003c/h2\u003e \u003cp\u003e This study was conducted in accordance with the principles of the Declaration of Helsinki and international guidelines for research involving human participants. The protocol was reviewed and approved by the Doctoral School of Science and Technology in the Central African Republic.\u003c/p\u003e \u003cp\u003eAll participants were informed of the study objectives, procedures, benefits, and potential risks prior to enrollment. Written informed consent was obtained from each participant. Participants were informed of their right to withdraw at any time without affecting their medical care.\u003c/p\u003e \u003cp\u003eConfidentiality was strictly maintained. Each participant was assigned an anonymous code, and personal data were accessible only to authorized study personnel. Biological samples were handled according to Biosafety Level 2 (BSL-2) standards, and biological waste was disposed of in accordance with national laboratory regulations.\u003c/p\u003e \u003cp\u003eThis ethical framework ensured the protection of participants\u0026rsquo; rights, safety, and dignity throughout the study.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSociodemographic Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 90 HIV-1\u0026ndash;infected patients were included in the study. Participants\u0026rsquo; ages ranged from 18 to 62 years, with a median age of 34 years. Women accounted for 62% of the sample (n = 56), while men represented 38% (n = 34). The majority of participants were single (41%), followed by married individuals (36%) and those with other marital statuses (23%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunological and Virological Profile\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe median CD4+ T-cell count was 312 cells/mm\u0026sup3; (IQR: 185\u0026ndash;445), with 22 patients (24.4%) presenting severe immunosuppression (CD4 \u0026lt; 200 cells/mm\u0026sup3;) and 46 patients (51.1%) having CD4 counts below 350 cells/mm\u0026sup3;.\u003c/p\u003e\n\u003cp\u003eThe median plasma viral load was 63,500 copies/mL (IQR: 25,000\u0026ndash;118,000). Among the participants, 31% had a viral load \u0026gt;100,000 copies/mL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDistribution of HIV-1 Subtypes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCR Amplification (297 bp)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmplification of the target fragment generated a single band at 297 bp for all positive samples as well as for the positive control, confirming successful PCR amplification.\u003c/p\u003e\n\u003cp\u003eNo band was observed in the negative control, ruling out experimental contamination, as shown in \u003cstrong\u003eFigure 1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnzymatic Digestion with \u003cem\u003eHaeIII\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDigestion with \u003cem\u003eHaeIII\u003c/em\u003e allowed differentiation of HIV-1 subtypes according to specific restriction fragment patterns:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eCRF02_AG\u003c/strong\u003e: fragments of 210 bp and 87 bp\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSubtype A\u003c/strong\u003e: absence of digestion (undigested fragment at 297 bp)\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSubtype C\u003c/strong\u003e: fragments of 180 bp and 117 bp\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSubtype D\u003c/strong\u003e: fragments of 200 bp and 97 bp\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe positive control showed the expected restriction profile, whereas the negative control showed no amplification, as illustrated in \u003cstrong\u003eFigure 2.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnzymatic digestion with MspI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDigestion with MspI confirmed the differentiation of HIV-1 subtypes:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eCRF02_AG\u003c/strong\u003e: fragments of 165 bp and 132 bp\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSubtype A\u003c/strong\u003e: fragments of 220 bp and 77 bp\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSubtype C\u003c/strong\u003e: fragments of 150 bp and 147 bp\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSubtype D\u003c/strong\u003e: fragments of 170 bp and 127 bp\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese fragment profiles were consistent with the expected theoretical restriction patterns, as shown in \u003cstrong\u003eFigure 3.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnzymatic Digestion with \u003cem\u003eAluI\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDigestion with \u003cem\u003eAluI\u003c/em\u003e revealed characteristic subtype-specific profiles:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eCRF02_AG\u003c/strong\u003e: fragments of 190 bp and 107 bp\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSubtype A\u003c/strong\u003e: fragments of 160 bp and 137 bp\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSubtype C\u003c/strong\u003e: fragments of 120 bp, 90 bp, and 87 bp\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSubtype D\u003c/strong\u003e: fragments of 210 bp and 87 bp\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe combination of all three restriction enzymes allowed reliable discrimination of the studied subtypes, as shown in \u003cstrong\u003eFigure 4.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable I\u003c/strong\u003e shows the distribution of HIV‑1 subtypes determined by the PCR-RFLP technique among the 90 patients included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable I: Distribution of HIV‑1 Subtypes Identified\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHere is the table translated into English and properly formatted:\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\" width=\"529\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSubtype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Patients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCRF02_AG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e100%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePCR-RFLP Analysis and Subtype Distribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of the 90 samples by PCR-RFLP revealed a clear predominance of subtype \u003cstrong\u003eCRF02_AG\u003c/strong\u003e, accounting for \u003cstrong\u003e53.5%\u003c/strong\u003e of cases (n = 48). This high proportion indicates that this circulating recombinant form is the major strain in the study population.\u003c/p\u003e\n\u003cp\u003ePure subtypes \u003cstrong\u003eA\u003c/strong\u003e (17.8%) and \u003cstrong\u003eC\u003c/strong\u003e (14.4%), as well as subtype \u003cstrong\u003eD\u003c/strong\u003e (14.4%), were also detected but at considerably lower frequencies. This distribution reflects a moderate genetic diversity of HIV‑1, characterized by the co-circulation of multiple subtypes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate Analytical Associations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate analysis examined the relationships between subtype \u003cstrong\u003eCRF02_AG\u003c/strong\u003e and various sociodemographic, immunological, and virological parameters.\u003c/p\u003e\n\u003cp\u003eAssociations between the CRF02_AG subtype and various sociodemographic, immunological, and virological parameters are summarized in Table II\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable II:\u003c/strong\u003e Association between subtype CRF02_AG and different sociodemographic, immunological, and virological parameters\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\" width=\"640\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRF02_AG (n=48)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther Subtypes (n=42)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eFemale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e28 (58.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e28 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.32\u0026ndash;1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eAge \u0026ge;35 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e27 (56.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e18 (42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.72\u0026ndash;4.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eCD4 \u0026lt;350 cells/mm\u0026sup3;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e31 (64.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e19 (45.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.92\u0026ndash;5.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eViral load \u0026gt;100,000 copies/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e21 (43.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e10 (23.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.98\u0026ndash;6.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePatients infected with \u003cstrong\u003esubtype CRF02_AG\u003c/strong\u003e tended to have \u003cstrong\u003ehigher viral loads\u003c/strong\u003e and \u003cstrong\u003elower CD4 counts\u003c/strong\u003e, although these associations did \u003cstrong\u003enot reach statistical significance\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eResults of the univariate analysis of factors associated with severe immunosuppression are presented in Table III.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable III\u003c/strong\u003e presents the results of the univariate analysis of factors associated with \u003cstrong\u003esevere immunosuppression\u003c/strong\u003e, defined as a \u003cstrong\u003eCD4 count below 200 cells/mm\u0026sup3;\u003c/strong\u003e.\u003c/p\u003e\n\u003ch3\u003eTable III: Univariate Analysis of Factors Associated with Severe Immunosuppression\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\" width=\"597\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRF02_AG (n=48)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther Subtypes (n=42)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eFemale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e28 (58.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e28 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e0.32\u0026ndash;1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eAge \u0026ge;35 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e27 (56.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e18 (42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e0.72\u0026ndash;4.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eCD4 \u0026lt;350 cells/mm\u0026sup3;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e31 (64.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e19 (45.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e0.92\u0026ndash;5.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eViral load \u0026gt;100,000 copies/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e21 (43.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e10 (23.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e0.98\u0026ndash;6.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAfter multivariate logistic regression, a plasma viral load \u0026gt;100,000 copies/mL was the \u003cstrong\u003eonly independent factor\u003c/strong\u003e associated with severe immunosuppression (aOR = 3.62; 95% CI: 1.23\u0026ndash;10.64; p = 0.019). The CRF02_AG subtype was \u003cstrong\u003enot significantly associated\u003c/strong\u003e with severe immunodeficiency (aOR = 2.11; p = 0.16).\u003c/p\u003e\n\u003cp\u003eThe results indicate that the CRF02_AG recombinant subtype is the \u003cstrong\u003emost prevalent\u003c/strong\u003e among HIV-1-infected patients in the Central African Republic (CAR). Although this subtype tends to be associated with higher viral loads and lower CD4 counts, \u003cstrong\u003eonly high viral load remains an independent factor\u003c/strong\u003e for severe immunosuppression. These observations reflect the genetic diversity of HIV in Central Africa and highlight the importance of \u003cstrong\u003eimmunovirological monitoring\u003c/strong\u003e to optimize antiretroviral therapy.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates a clear predominance of the recombinant form CRF02_AG (53.5%) among HIV-1\u0026ndash;infected patients in the Central African Republic (CAR). This distribution is consistent with molecular epidemiology data reported across West and Central Africa. In Ghana, Agyeman et al. (2020) identified CRF02_AG as the dominant subtype, accounting for 54% of treatment-na\u0026iuml;ve cases, a proportion remarkably similar to that observed in our cohort (22). Likewise, in Cameroon, Tongo et al. (2019) reported CRF02_AG at 48%, in the context of substantial viral diversity including subtype A and several unique recombinant forms (23). Although our findings are broadly comparable, the slightly lower diversity observed in our study may be explained by methodological differences, as we used PCR-RFLP rather than full-genome sequencing, which provides greater resolution for detecting complex recombinants.\u003c/p\u003e \u003cp\u003eIn contrast, data from the Democratic Republic of Congo reported by Makuwa et al. (2021) revealed greater genetic heterogeneity, with subtypes A and C predominating and CRF02_AG representing approximately 30% of circulating strains (24). This suggests a more complex molecular dynamic in that setting compared to CAR.\u003c/p\u003e \u003cp\u003eRegarding subtype C (14.4%), our findings differ markedly from those reported in Botswana by Novitsky et al. (2018), where subtype C accounted for more than 95% of infections, reflecting strong geographic clustering and regional specificity of HIV-1 subtypes (25). For subtype D (14.4%), the frequency observed in our study is comparable to that reported in Uganda by Ssemwanga et al. (2020), who found a prevalence of 17% (26). However, in our cohort, subtype D was not independently associated with severe immunosuppression.\u003c/p\u003e \u003cp\u003eUnivariate analysis in our study suggested a trend toward higher viral load among patients infected with CRF02_AG. Nevertheless, this association did not remain statistically significant after multivariate adjustment. Similar observations were reported in Uganda by Kiwanuka et al. (2019) (27). Furthermore, a European multicenter cohort study conducted in Switzerland by Kouyos et al. (2017) demonstrated that apparent differences in disease progression between non-B subtypes lost statistical significance after adjustment for confounding factors (28).\u003c/p\u003e \u003cp\u003eImportantly, the independent association observed between viral load greater than 100,000 copies/mL and severe immunosuppression in our study is consistent with longitudinal data from the United States reported by Mellors et al. (2018), which confirmed plasma viral load as the principal predictor of progression to AIDS, independent of viral subtype (29).\u003c/p\u003e \u003cp\u003eFrom a methodological perspective, Yamaguchi et al. (2021) in Japan demonstrated that PCR-RFLP shows good concordance with Sanger sequencing for the identification of major subtypes but has limited capacity to detect complex recombinant forms (30). This aligns with the limitations of our study and supports the need for more advanced sequencing approaches in future molecular surveillance efforts in CAR.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study confirms that the recombinant CRF02_AG predominates among HIV-1-infected patients in CAR, while subtypes A, C, and D also co-circulate, highlighting significant genetic diversity. High viral load remains the main independent risk factor for severe immunosuppression (CD4\u0026thinsp;\u0026lt;\u0026thinsp;200 cells/mm\u0026sup3;), underscoring the importance of \u003cb\u003eregular clinical and virological follow-up\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eAlthough CRF02_AG tends to associate with higher viral loads, this was not statistically significant in our cohort, reflecting the complex influence of subtypes on clinical progression. These findings provide a foundation for \u003cb\u003econtinuous molecular surveillance\u003c/b\u003e and the development of targeted \u003cb\u003epublic health strategies\u003c/b\u003e for prevention, treatment, and patient management. Future studies using \u003cb\u003enext-generation sequencing\u003c/b\u003e will further elucidate the dynamics of recombinant forms and their impact on disease evolution.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicts of Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003ch2\u003eFunding Information\u003c/h2\u003e\n\u003cp\u003eThis work did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAuthor Contributions Statement B.D.H.L.M. conceived and designed the study, performed the laboratory analyses, analyzed the data, and wrote the first draft of the manuscript. O.G. contributed to data interpretation and manuscript revision. E.L.Y. supervised the research and critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request due to ethical and privacy restrictions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSharp PM, Hahn BH. Origins of HIV and the AIDS pandemic. Cold Spring Harb Perspect Med. 2011;1(1):a006841. \u003c/li\u003e\n\u003cli\u003eWorobey M, Watts TD, McKay RA, et al. 1970s HIV-1 genomes illuminate early HIV/AIDS history in North America. Nature. 2016;539(7627):98\u0026ndash;101. \u003c/li\u003e\n\u003cli\u003eFauci AS, Lane HC. Human Immunodeficiency Virus Disease: AIDS and Related Disorders. In: Kasper DL et al., editors. Harrison\u0026rsquo;s Principles of Internal Medicine. 20th ed. McGraw Hill; 2018. \u003c/li\u003e\n\u003cli\u003eHemelaar J. The origin and diversity of the HIV-1 pandemic. Trends Mol Med. 2012;18(3):182\u0026ndash;192. \u003c/li\u003e\n\u003cli\u003eTongo M, Dorfman JR, Martin DP. High Degree of HIV-1 Group M (HIV-1M) Genetic Diversity in Cameroon and Neighboring Countries. Virus Evol. 2019;5(2):vez041. \u003c/li\u003e\n\u003cli\u003eGao F, Yue L, White AT, et al. Origin of HIV-1 in the chimpanzee Pan troglodytes troglodytes. Nature. 1999;397(6718):436\u0026ndash;441. \u003c/li\u003e\n\u003cli\u003eRobertson DL, Anderson JP, Bradac JA, et al. HIV-1 nomenclature proposal. Science. 2000;288(5463):55\u0026ndash;56. \u003c/li\u003e\n\u003cli\u003eCampbell-Wei N, et al. Comparative Pathogenesis of HIV-1 and HIV-2. Clin Microbiol Rev. 2018;31(3):e00093-17. \u003c/li\u003e\n\u003cli\u003eWHO. Global HIV \u0026amp; AIDS statistics \u0026mdash; 2024 fact sheet. World Health Organization. 2025. \u003c/li\u003e\n\u003cli\u003eUNAIDS. AIDSinfo Epidemiological Data. 2024. \u003c/li\u003e\n\u003cli\u003ePiot P, Abdool Karim SS, Hecht R, et al. Defeating AIDS \u0026mdash; Advancing Global Health. Lancet. 2015;386(9989):171\u0026ndash;218. \u003c/li\u003e\n\u003cli\u003eVinken L, et al. Diversity and Evolution of HIV-1 in Central Africa. J Infect Dis. 2021;224(5):783\u0026ndash;789. \u003c/li\u003e\n\u003cli\u003eNdongo FA, Tongo M, Charneau S, et al. HIV-1 genetic diversity and drug resistance in Central Africa. BMC Infect Dis. 2017;17(1):16. \u003c/li\u003e\n\u003cli\u003eAmougou MA, et al. Molecular Epidemiology of HIV-1 in Gabon. AIDS Res Hum Retroviruses. 2019;35(4):422\u0026ndash;428. \u003c/li\u003e\n\u003cli\u003eHemelaar J, Elangovan R, Yun J, et al. Global molecular epidemiology of HIV. J Infect Dis. 2023;227(7):1019\u0026ndash;1031. \u003c/li\u003e\n\u003cli\u003eMinist\u0026egrave;re de la Sant\u0026eacute; de la R\u0026eacute;publique Centrafricaine. Enqu\u0026ecirc;te HIV \u0026amp; SIDA 2023. Bangui. \u003c/li\u003e\n\u003cli\u003eONUSIDA. Rapport sur l\u0026rsquo;\u0026eacute;pid\u0026eacute;mie de VIH en Afrique Centrale. 2023. \u003c/li\u003e\n\u003cli\u003eMoussa S, Tagnouokam-Ngoupo PA, Tombette F, Manirakiza A, Boum Y 2nd, Vernet G, et al. Detection and characterization of HIV-1 group O and HIV-2 in the Central African Republic. APMIS. 2024 Nov;132(11):824-831. doi: 10.1111/apm.13474. \u003c/li\u003e\n\u003cli\u003eFaria NR, Rambaut A, Suchard MA, et al. HIV epidemiology: evolutionary past and future prospects. Trends Microbiol. 2014;22(1):82\u0026ndash;91. \u003c/li\u003e\n\u003cli\u003eDe Oliveira MF, et al. Molecular epidemiology of HIV-1 in West and Central Africa. PLoS One. 2020;15(6):e0234885. \u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Guidelines for surveillance of drug resistance in HIV. 2025. \u003c/li\u003e\n\u003cli\u003eAgyeman AA, Ofori SB, Tagoe EA, Bonney EY, Norman B, Opare-Sem O. Molecular epidemiology of HIV-1 in Ghana: predominance of CRF02_AG and emerging recombinant forms. PLoS One. 2020;15(7):e0235603. \u003c/li\u003e\n\u003cli\u003eTongo M, Dorfman JR, Martin DP. High genetic diversity of HIV-1 and predominance of CRF02_AG in Cameroon. Infect Genet Evol. 2019;73:157\u0026ndash;165. \u003c/li\u003e\n\u003cli\u003eMakuwa M, Souqui\u0026egrave;re S, Niama FR, Telfer P, Onanga R, Delaporte E, et al. Genetic diversity and molecular epidemiology of HIV-1 in the Democratic Republic of Congo. Viruses. 2021;13(4):612. \u003c/li\u003e\n\u003cli\u003eNovitsky V, Wang R, Bussmann H, Lockman S, Baum M, Shapiro R, et al. HIV-1 subtype C epidemic in Botswana: long-term molecular epidemiology and transmission dynamics. AIDS Res Hum Retroviruses. 2018;34(5):421\u0026ndash;430. \u003c/li\u003e\n\u003cli\u003eSsemwanga D, Nsubuga RN, Lyagoba F, Magambo B, Ndembi N, Kaleebu P. HIV type 1 subtype distribution and disease progression in Uganda. J Infect Dis. 2020;221(3):452\u0026ndash;460. \u003c/li\u003e\n\u003cli\u003eKiwanuka N, Robb M, Laeyendecker O, Kigozi G, Wabwire-Mangen F, Makumbi F, et al. Effect of HIV-1 subtype on disease progression in a rural Ugandan cohort. Clin Infect Dis. 2019;68(2):317\u0026ndash;324. \u003c/li\u003e\n\u003cli\u003eKouyos RD, von Wyl V, Yerly S, B\u0026ouml;ni J, Shah C, B\u0026uuml;rgisser P, et al. Molecular epidemiology reveals long-term changes in HIV type 1 subtype distribution in Switzerland. Clin Infect Dis. 2017;65(3):413\u0026ndash;421. \u003c/li\u003e\n\u003cli\u003eMellors JW, Mu\u0026ntilde;oz A, Giorgi JV, Margolick JB, Tassoni CJ, Gupta P, et al. Plasma viral load and CD4+ lymphocytes as prognostic markers of HIV-1 infection progression. Ann Intern Med. 2018;169(9):633\u0026ndash;641. \u003c/li\u003e\n\u003cli\u003eYamaguchi J, Vallari A, Ndembi N, Coffey R, Ngansop C, Mbanya D, et al. Evaluation of molecular subtyping methods for HIV-1 classification in resource-limited settings. J Clin Microbiol. 2021;59(6):e03002-20. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"HIV-1, subtypes, CRF02_AG, Central African Republic, PCR-RFLP, viral load, CD4, immunosuppression, molecular surveillance","lastPublishedDoi":"10.21203/rs.3.rs-9097533/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9097533/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHIV-1 genetic diversity influences disease progression, antiretroviral therapy response, and diagnostic performance. In the Central African Republic (CAR), HIV-1 group M predominates, but detailed data on circulating subtypes and their clinical impact remain limited. This study aimed to characterize HIV-1 subtypes in CAR and assess their association with patients\u0026rsquo; immunovirological profiles.\u003c/p\u003e \u003cp\u003eA cross-sectional descriptive and analytical study was conducted in 2025 at the National Laboratory of Clinical Biology and Public Health in Bangui. Ninety adult HIV-1\u0026ndash;infected patients were included. Viral RNA was extracted from plasma and amplified by RT-PCR targeting the gag p24 region. Subtyping was performed using restriction fragment length polymorphism (RFLP) analysis with HaeIII, MspI, and AluI enzymes. Agarose gel images were optimized using AI (Topaz Gigapixel AI) to enhance band clarity. Clinical and biological data were analyzed using Epi Info software, with univariate analysis and multivariate logistic regression evaluating associations between subtypes and immunovirological parameters.\u003c/p\u003e \u003cp\u003eAmong the 90 patients (62% women; median age 34 years), the median CD4 count was 312 cells/mm\u0026sup3; and the median viral load was 63,500 copies/mL. Subtype distribution was: CRF02_AG (53.5%), A (17.8%), C (14.4%), and D (14.4%). CRF02_AG showed a tendency toward higher viral load and lower CD4 counts, though these associations were not statistically significant after adjustment. A viral load\u0026thinsp;\u0026gt;\u0026thinsp;100,000 copies/mL was the only independent predictor of severe immunosuppression (CD4\u0026thinsp;\u0026lt;\u0026thinsp;200 cells/mm\u0026sup3;; adjusted OR\u0026thinsp;=\u0026thinsp;3.62; p\u0026thinsp;=\u0026thinsp;0.019).\u003c/p\u003e \u003cp\u003eCRF02_AG predominates among HIV-1\u0026ndash;infected patients in CAR, while subtypes A, C, and D co-circulate at lower frequencies. High viral load is the main determinant of severe immunosuppression, regardless of subtype. These findings support continuous molecular surveillance, optimization of treatment strategies, and reinforcement of public health interventions. The combined use of PCR-RFLP and AI-assisted gel imaging offers a reliable and feasible method for subtype monitoring in resource-limited settings.\u003c/p\u003e","manuscriptTitle":"Characterization of Circulating HIV-1 Subtypes in the Central African Republic: Implications for Molecular Surveillance and Therapeutic Management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 12:31:43","doi":"10.21203/rs.3.rs-9097533/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5dd3030a-52f5-4788-a32f-e22ea830e65a","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T21:24:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 12:31:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9097533","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9097533","identity":"rs-9097533","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}