| Literature DB >> 32368103 |
Mei Ye1,2, Xin Chen1,3, Yu Wang4, Yan-Heng Zhou5, Wei Pang1, Chiyu Zhang6, Yong-Tang Zheng1,4.
Abstract
BACKGROUND: Estimating the prevalence and characterizing the transmission of HIV-1 drug resistance in treatment-naïve individuals are very important in the prevention and control of HIV/AIDS. As one of the areas most affected by HIV/AIDS, few data are currently available for HIV-1 drug resistance in antiretroviral therapy (ART)-naïve individuals in Myanmar, which borders Yunnan, China.Entities:
Keywords: ART-naïve; HIV-1; Myanmar; Yunnan; antiretroviral therapy; drug-resistance mutations; transmission cluster
Year: 2020 PMID: 32368103 PMCID: PMC7182463 DOI: 10.2147/IDR.S246462
Source DB: PubMed Journal: Infect Drug Resist ISSN: 1178-6973 Impact factor: 4.003
Figure 1The qualified individuals and their geographic distribution. (A) Flow chart shows the individuals meeting inclusion criteria. (B) The prevalence of TDR among ART-naïve HIV-1 infected individuals residing in Myanmar between 2008 and 2014. The triangle shadow indicates the well-known illicit drug-producing region “Golden Triangle”.
Social-Demographic Characteristics of Study Subjects
| Variables | HIV-1 Positive | ART-Naïve Burmese Having | Individuals Within a Cluster |
|---|---|---|---|
| n (%) | n (%) | n (%) | |
| Sampling years | |||
| 2008–2010 | 201 (53.9) | 146 (86.4) | 41 (28.1) |
| 2011–2014 | 172 (46.1) | 23 (13.6) | 14 (60.9) |
| Risk groups | |||
| IDUs | 268 (71.8) | 92 (54.4) | 34 (37.0) |
| LDTDs | 105 (28.2) | 77 (45.6) | 21 (27.3) |
| Residence | |||
| Kachin | 104 (27.9) | 78 (46.2) | 22 (28.2) |
| Shan State | 55 (14.7) | 35 (20.7) | 17 (48.6) |
| Mandalay | 66 (17.7) | 49 (29.0) | 16 (32.7) |
| Sagaing | 2 (0.5) | 2 (1.2) | 0 (0) |
| Yangon | 4 (1.1) | 4 (2.4) | 0 (0) |
| Dehong | 139 (37.3) | 0 (0) | 0 (0) |
| NAa | 3 (0.8) | 1 (0.6) | 0 (0) |
| Gender | |||
| Male | 365 (97.9) | 166 (98.2) | 55 (33.1) |
| Female | 7 (1.9) | 3 (1.8) | 0 (0) |
| NAa | 1 (0.3) | 0 (0) | 0 (0) |
| Age | |||
| ≤25 | 56 (15.0) | 26 (15.4) | 11 (42.3) |
| 26–30 | 102 (27.3) | 50 (29.6) | 17 (34.0) |
| 31–35 | 81 (21.7) | 30 (17.8) | 5 (16.7) |
| 36–40 | 76 (20.4) | 43 (25.4) | 13 (30.2) |
| ≥41 | 55 (14.7) | 20 (11.8) | 9 (45.0) |
| NAa | 3 (0.8) | 0 (0) | 0 (0) |
| Occupation | |||
| Jobless | 27 (7.2) | 10 (5.9) | 4 (40.0) |
| Driver | 107 (28.7) | 79 (46.7) | 21 (26.6) |
| Farmer | 170 (45.6) | 42 (24.9) | 20 (47.6) |
| Othersb | 64 (17.2) | 37 (21.9) | 10 (27.0) |
| NAa | 5 (1.3) | 1 (0.6) | 0 (0) |
| Total | 373 (100) | 169 (100) | 55 (32.5) |
Notes: aNA: not available; bBusinessman, worker, soldier, entertainment, waiter, government staff, and other occupations.
Figure 2Maximum likelihood tree based on the pol fragment among ART-naïve individuals in Myanmar. The red asterisk and spots indicate the drug-resistance mutation with clinical significance and other DRMs, respectively. Drug-resistance mutations with clinical significance lead to low levels and above drug resistance. Mutations associated with resistance to PI, NRTI and NNRTI are indicated with magenta, red and blue, respectively. The brackets indicate transmission clusters that were identified by Cluster Picker.
Figure 3Characteristics of HIV-1 drug resistance among ART-naïve individuals in Myanmar, 2008–2014. (A) The number of drug-resistance mutation sequences in different subtypes. (B) The distribution of subtypes of drug-resistance mutations. (C) The level of resistance predicted by the Stanford HIV Drug Resistance Database.
Figure 4Maximum likelihood tree of the drug-resistance sequences among ART-naïve individuals in the China–Myanmar border region. The sequences from the Burmese IDUs,28,33,34 Burmese LDTDs,30 the Burmese staying in Yunnan29,32 and newly diagnosed HIV-1 infections in Dehong, Yunnan province37,38 are highlighted by green spots, green triangles, green diamonds and red stars, respectively. Mutations associated with resistance to PI, NRTI and NNRTI are indicated with magenta, red and blue, respectively. The brackets indicate transmission clusters that were identified by Cluster Picker.