Abstract
Background
Despite effective antiretroviral therapy (ART), a subset of people living with HIV(PLWH)experiences low-level viremia(LLV), yet its impact on subsequent virologic failure (VF) and immunologic failure (IF) remains unclear. This study aims to elucidate the effect of LLV on the risk of VF and IF.
Methods
This retrospective cohort study included PLWH aged ≥ 18 years who initiated ART in Qinzhou City, Guangxi, China, from 2005 to 2023. Participants were categorized into mutually exclusive viral load categories: undetectable viral load (≤ 50 copies/mL) and LLV (51–999 copies/mL). Time-updated Cox proportional hazards models were used to evaluate the association between LLV and the risk of VF and IF.
Results
A total of 4,274 participants were followed for 12,288.35 person-years, with a median follow-up of 3.03 years. Among these participants, 11.16% experienced at least one LLV event. Compared to those with an undetectable viral load, individuals with LLV had a significantly higher risk of VF (adjusted hazard ratio [aHR] = 5.23, 95% CI: 3.48–7.85), with intermittent low-level viremia (ILLV) showing the strongest association (aHR = 7.34, 95% CI: 4.92–10.94). Conversely, LLV was associated with a lower risk of IF compared with an undetectable viral load (aHR = 0.59, 95% CI: 0.37–0.92), a finding consistent with the reduced risk observed in the ILLV subgroup (aHR = 0.56, 95% CI: 0.33–0.96).
Conclusions
Patients with LLV had an increased risk of subsequent VF, emphasizing the need for close monitoring of viral load and potential treatment adjustments to optimize long-term ART outcomes.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s12981-026-00911-3.
Keywords: HIV, Immunologic failure, Low-level viremia, Virological failure, Antiretroviral therapy
Introduction
The widespread implementation of antiretroviral therapy (ART) has significantly reduced HIV-related morbidity and mortality, with most individuals achieving sustained viral suppression (< 50 copies/mL) and maintaining normal immune function [1–4]. However, a subset of people living with HIV (PLWH) experiences low-level viremia (LLV), typically defined as detectable viremia below the threshold for virologic failure (VF) [16]. Accumulating evidence suggests that LLV may contribute to adverse clinical outcomes, including virologic nonsuppression, VF, viral genotype resistance, chronic immune activation, non-AIDS-defining events, and premature mortality, ultimately diminishing quality of life and survival among PLWH [5, 6].
Globally, the prevalence of LLV among PLWH on ART ranges from 3% to 26% [7–10]. Accumulating but inconsistent evidence suggested that LLV was associated with an increased risk of VF [11, 12]. A study conducted in Yunnan, China, reported that LLV with a viral load (VL ≥ 200 copies/mL) was associated with an increased risk of VF, whereas this association was not significant in patients with LLV between 50 and 199 copies/mL [13]. Conversely, other cohort studies demonstrated that persistent low-level viremia (PLLV) or frequent LLV at 50–200 copies/ml may elevate the risk of VF [14, 15]. These discrepancies may arise from variations in study designs, populations [16], and inconsistent definitions and thresholds of LLV [2, 17]. Additionally, most existing research has treated LLV as a static baseline exposure, thereby failing to capture the dynamic fluctuations of viral load during long-term ART.
Whether patterns of LLV increase the risk for subsequent immunologic failure (IF) is equally uncertain. IF is typically defined as an inadequate CD4+ T-cell response or a progressive decline in CD4+ count [18]. Even at low levels of LLV, the presence of the virus may trigger persistent immune activation, potentially accelerating CD4 + T cell depletion and impairing immune reconstitution [19–21]. A recent review reported that individuals with PLLV continued to exhibit elevated proinflammatory cytokine levels and a higher risk of IF compared to those with undetectable viral load [5]. Yet, a study from Taipei, China, found that the duration of HIV plasma VL of 50 to 1000 copies/mL did not influence the mean CD4+ T cell count [22]. Effective immune reconstitution is vital for reducing HIV-related morbidity and mortality, making it essential to evaluate whether LLV poses a significant threat to immunologic outcomes.
Guangxi Zhuang Autonomous Region, located in southwestern China, is a resource-limited area bearing one of the country’s heaviest HIV/AIDS burdens [23, 24]. Within this region, Qinzhou City ranks third in cumulative reported cases among the 14 prefecture-level divisions [25]. Although free ART has been widely accessible in Qinzhou since 2005 in accordance with national guidelines, the clinical characteristics and impact of LLV remain unexplored despite nearly two decades of treatment. Therefore, this study aims to evaluate the impact of LLV on both VF and IF among PLWH undergoing ART in Qinzhou, Guangxi, China. By assessing the relationship between LLV and clinical outcomes, this study seeks to provide scientific evidence to inform treatment strategies and optimize HIV management in resource-limited settings.
Methods
Patients and study setting
This was a retrospective cohort study. We collected baseline and follow-up data from the National Free Antiretroviral Treatment Program (NFATP) database, a surveillance system that records follow-up data for PLWH receiving free ART in China. We included all the PLWH aged 18 years or older who initiated antiretroviral treatment between June 10, 2005, and July 25, 2023, and were residents of Qinzhou City, Guangxi Province, China. Within the NFATP database, the baseline socio-demographic characteristics (age, sex, ethnicity, marital status, region, occupation, HIV transmission route), laboratory tests (HIV viral loads [VLs], CD4+ T cell counts, HBsAg, Anti-HCV) and clinical characteristics (sexually transmitted infections, WHO clinical stage, ART regimen) were recorded. Every 6 months or 12 months, they performed HIV RNA and CD4+ T cell testing in accordance with local guidelines [26]. ART regimens were classified according to the core drug class combined with two nucleoside reverse transcriptase inhibitors (NRTIs): efavirenz (EFV)-based, nevirapine (NVP)-based, or protease inhibitors (PIs)-based regimens; regimens containing integrase strand transfer inhibitors (INSTIs) or mixed core drugs were grouped as Other. Patients who switched their core drug during follow-up were noted as having switched regimens.
The inclusion criteria for patients in this study were as follows: (1) age ≥ 18 years old; (2) currently receiving ART for HIV infection for ≥ 6 months. Patients who met any of the following criteria were excluded from the study: (1) lack of baseline or follow-up viral load data; (2) missing records for CD4+ T cell counts at baseline or follow-up; (3) VF occurring before any episode of LLV or virologic suppression; (4) only a single follow-up visit recorded.
Definition of exposure and outcome variables
Primary exposure
The main exposure of interest was the pattern of viremia during ART. Patients were classified into two groups: (1) Undetectable viral load, defined as 2 consecutive VLs of ≤ 50 copies/mL within ≥ 30 days; (2) LLV was subdivided into three groups: PLLV, blips, and intermittent low-level viremia (ILLV). PLLV was defined as ≥ 2 consecutive VLs of 51–199 copies/mL taken ≥ 30 days apart. Blips was defined as one VL between 51 and 999 copies/mL, with VLs before and after at or below 50 copies/mL. ILLV was defined as intermittent episodes of VLs at levels ≤ 50, 51–199, or 200–999 copies/mL that did not fulfill the criteria for PLLV, blips, or VF. Follow-up for patients commenced at the second viral load measurement and continued until the study endpoint or the last available observation.
Outcomes
The primary outcome was VF; the secondary outcomes were IF. VF was defined as 2 consecutive VLs of ≥ 200 copies/mL or a single VL of ≥ 1000 copies/mL. Patients meeting any of the following criteria were classified as experiencing IF in this study, regardless of whether their VL was fully suppressed, as defined by the WHO [27] and previous studies [28, 29]: (1) CD4+ T cell counts were consistently lower than the baseline level before ART initiation on two consecutive tests after six months of ART ; (2) CD4+ T cell counts were consistently below 100 cells/µL on two consecutive tests after six months of ART.
Statistical analysis
Descriptive statistics were presented as medians with interquartile ranges (IQR) for continuous variables and counts with proportions for categorical variables. Time-updated Cox proportional hazards models were used to evaluate the association between LLV and the risk of VF and IF. To account for within-individual correlation, robust standard errors clustered by patient ID were used. We conducted several sensitivity analyses to ensure the robustness of our findings. First, we performed a subgroup analysis in which we categorized LLV as PLLV, blips and ILLV. Second, competing-risk analyses were performed using the Fine-Gray model, treating death as a competing event for VF or IF, and cumulative incidence function (CIF) curves were plotted to visualize the risk over time. In addition, given the major changes in China’s National Free ART policy in 2016 [30], we further performed stratified analyses by calendar period to assess whether the association between LLV and VF varied across policy eras. Two-sided P values < 0.05 were considered statistically significant. All statistical analyses and visualizations were conducted using R version 4.3.1(R Core Team, 2023; RRID: SCR 001905).
Results
Participant characteristics
We identified 4,274 HIV-positive patients who met the inclusion and exclusion criteria. The flow of patients included in the analysis is shown in Fig. 1. The total follow-up time amounted to 12,288.35 person-years; the median follow-up time was 3.03 years (95% CI: 2.99–3.15) among patients with VF and 2.94 years (95% CI: 2.88–2.96) among those with IF. During follow-up, 11.16% of participants experienced at least one LLV event. Regarding demographic characteristics, 43.3% of the cohort were ≥ 50 years, and the majority were male (66.2%) and of Han ethnicity (92.4%). Heterosexual transmission was the most common route of transmission, accounting for 85.6%. At baseline, 31.8% had CD4+ T cell counts of 350–499 cells/µL. The baseline ART regimen for most PLWH was the EFV-based regimen (66.4%). (Table 1).
Fig. 1.
Patient enrollment flowchart. VL, viral load
Table 1.
Socio-demographic and clinical characteristics of patients
| Characteristics | All | Undetectable | LLV | χ² | P |
|---|---|---|---|---|---|
| (N = 4274) | (N = 3797) | (N = 477) | |||
| Age (year) | 12.960 | 0.002 | |||
| 18–34 | 882 (20.6%) | 787 (20.7%) | 95 (19.9%) | ||
| 35–49 | 1542 (36.1%) | 1401 (36.9%) | 141 (29.6%) | ||
| Over 50 | 1850 (43.3%) | 1609 (42.4%) | 241 (50.5%) | ||
| Sex | 3.629 | 0.057 | |||
| Female | 1443 (33.8%) | 1301 (34.3%) | 142 (29.8%) | ||
| Male | 2831 (66.2%) | 2496 (65.7%) | 335 (70.2%) | ||
| Ethnicity | 0.669 | 0.413 | |||
| Han | 3951 (92.4%) | 3515 (92.6%) | 436 (91.4%) | ||
| Other | 323 (7.6%) | 282 (7.4%) | 41 (8.6%) | ||
| Education | 1.587 | 0.662 | |||
| Primary or below | 2072 (48.5%) | 1831 (48.2%) | 241 (50.5%) | ||
| Junior high school | 1779 (41.6%) | 1593 (42.0%) | 186 (39.0%) | ||
| Senior high school | 325 (7.6%) | 286 (7.5%) | 39 (8.2%) | ||
| College or above | 98 (2.3%) | 87 (2.3%) | 11 (2.3%) | ||
| Marital status | 8.748 | 0.013 | |||
| Single | 607 (14.2%) | 548 (14.4%) | 59 (12.4%) | ||
| Married | 3051 (71.4%) | 2684 (70.7%) | 367 (76.9%) | ||
| Other | 616 (14.4%) | 565 (14.9%) | 51 (10.7%) | ||
| Occupation | 0.069 | 0.793 | |||
| Farmer | 3047 (71.3%) | 2704 (71.2%) | 343 (71.9%) | ||
| Other | 1227 (28.7%) | 1093 (28.8%) | 134 (28.1%) | ||
| ART regimen at baseline | 25.269 | <0.001 | |||
| NVP-based | 808 (18.9%) | 708 (18.6%) | 100 (21.0%) | ||
| EFV-based | 2839 (66.4%) | 2565 (67.6%) | 274 (57.4%) | ||
| PIs-based/Other | 627 (14.7%) | 524 (13.8%) | 103 (21.6%) | ||
| HIV transmission route | 2.517 | 0.284 | |||
| Heterosexual intercourse | 3658 (85.6%) | 3261 (85.9%) | 397 (83.2%) | ||
| Injecting drug use | 407 (9.5%) | 353 (9.3%) | 54 (11.3%) | ||
| Other | 209 (4.9%) | 183 (4.8%) | 26 (5.5%) | ||
| WHO clinical stage | 21.021 | <0.001 | |||
| I | 1986 (46.5%) | 1810 (47.7%) | 176 (36.9%) | ||
| II | 607 (14.2%) | 530 (14.0%) | 77 (16.1%) | ||
| III | 873 (20.4%) | 750 (19.7%) | 123 (25.8%) | ||
| IV | 808 (18.9%) | 707 (18.6%) | 101 (21.2%) | ||
| Time from HIV diagnosis to ART treatment (days) | 4.761 | 0.029 | |||
| ≤ 30 | 2479 (58.0%) | 2225 (58.6%) | 254 (53.2%) | ||
| > 30 | 1795 (42.0%) | 1572 (41.4%) | 223 (46.8%) | ||
| HBV infection at baseline | 5.119 | 0.077 | |||
| Negative | 3243 (75.9%) | 2899 (76.3%) | 344 (72.1%) | ||
| Positive | 527 (12.3%) | 464 (12.2%) | 63 (13.2%) | ||
| Missing | 504 (11.8%) | 434 (11.5%) | 70 (14.7%) | ||
| HCV infection at baseline | 5.734 | 0.057 | |||
| Negative | 2849 (66.7%) | 2540 (66.9%) | 309 (64.8%) | ||
| Positive | 369 (8.6%) | 337 (8.9%) | 32 (6.7%) | ||
| Missing | 1056 (24.7%) | 920 (24.2%) | 136 (28.5%) | ||
| STIs at baseline | 6.007 | 0.050 | |||
| Positive | 270 (6.3%) | 236 (6.2%) | 34 (7.1%) | ||
| Negative | 3676 (86%) | 3282 (86.5%) | 394 (82.6%) | ||
| Missing | 328 (7.7%) | 279 (7.3%) | 49 (10.3%) | ||
| CD4+ T cell counts at baseline, cells/µL | 5.525 | 0.137 | |||
| 0-199 | 1053 (24.6%) | 954 (25.1%) | 99 (20.8%) | ||
| 200–349 | 1074 (25.1%) | 940 (24.8%) | 134 (28.1%) | ||
| 350–499 | 1361 (31.8%) | 1210 (31.9%) | 151 (31.7%) | ||
| Over 500 | 786 (18.4%) | 693 (18.2%) | 93 (19.5%) | ||
Categorical variables are presented as counts and percentages. Percentages were calculated based on available data and may not sum to 100% or may slightly exceed 100% due to rounding. WHO World Health Organization; ART Antiretroviral Therapy; NVP Nevirapine; EFV Efavirenz; PIs protease inhibitors; LLV low-level viraemia; HBV Hepatitis B Virus; HCV Hepatitis C Virus; STIs Sexually Transmitted Infections
Association between viral load categories and incident VF
Table 2 showed that LLV was statistically significantly associated with an elevated risk of VF (aHR = 5.23, 95% CI: 3.48–7.85). Male patients were at increased risk of VF compared with females (aHR = 1.85, 95% CI: 1.19–2.87). Compared with heterosexual intercourse, injecting drug use was associated with an increased risk of VF (aHR = 2.44, 95% CI: 1.34–4.45). Patients with WHO clinical stage IV had a statistically lower risk of VF than those with WHO clinical stage I (aHR = 0.47, 95% CI: 0.28–0.78). A lower baseline CD4+ T cell count was associated with an increased risk of VF. Specifically, patients with baseline CD4+ T cell counts of 350–499 cells/µL (aHR = 1.92, 95% CI: 1.06–3.45), 200–349 cells/µL (aHR = 3.17, 95% CI: 1.78–5.62) and 0-199 cells/µL (aHR = 7.30, 95% CI: 3.89–13.68) had a higher risk of VF compared to those with CD4+ T cell counts at baseline above 500 cells/µL.
Table 2.
Time-updated cox proportional hazards model for the association between LLV and Virological Failure in HIV patients on ART, viral load categories identified two categories: Undetectable, LLV
| Characteristics | Univariable | Multivariable | ||||
|---|---|---|---|---|---|---|
| cHR | 95% CI | P | aHR | 95% CI | P | |
| Viral load categories | ||||||
| Undetectable | – | – | – | – | – | – |
| LLV | 6.37 | (4.31–9.42) | <0.001 | 5.23 | (3.48–7.85) | <0.001 |
| Age (years old) | ||||||
| 18–34 | – | – | – | – | – | – |
| 35–49 | 1.03 | (0.64–1.65) | 0.907 | – | – | – |
| Over 50 | 0.85 | (0.53–1.36) | 0.499 | – | – | – |
| Sex | ||||||
| Female | – | – | – | – | – | – |
| Male | 2.31 | (1.52–3.50) | <0.001 | 1.85 | (1.19–2.87) | 0.006 |
| Ethnicity | ||||||
| Han | – | – | – | – | – | – |
| Other | 0.68 | (0.33–1.40) | 0.299 | – | – | – |
| Education | ||||||
| Primary or below | – | – | – | – | – | – |
| Junior high school | 0.95 | (0.66–1.35) | 0.764 | – | – | – |
| Senior high school | 1.14 | (0.64–2.03) | 0.664 | – | – | – |
| College or above | 0.52 | (0.12–2.16) | 0.366 | – | – | – |
| Marital status | ||||||
| Single | – | – | – | – | – | – |
| Married | 0.71 | (0.45–1.13) | 0.151 | – | – | – |
| Other | 0.62 | (0.32–1.21) | 0.163 | – | – | – |
| Occupation | ||||||
| Farmer | – | – | – | – | – | – |
| Other | 0.89 | (0.61–1.29) | 0.528 | – | – | – |
| ART regimen at baseline | ||||||
| NVP-based | – | – | – | – | – | – |
| EFV-based | 0.70 | (0.46–1.05) | 0.087 | – | – | – |
| PIs-based/Other | 1.12 | (0.70–1.81) | 0.636 | – | – | – |
| HIV transmission route | ||||||
| Heterosexual intercourse | – | – | – | – | – | – |
| Injecting drug use | 2.89 | (1.86–4.49) | <0.001 | 2.44 | (1.34–4.45) | 0.003 |
| Other | 1.56 | (0.78–3.12) | 0.205 | 1.37 | (0.60–3.14) | 0.451 |
| WHO clinical stage | ||||||
| I | – | – | – | – | – | – |
| II | 1.31 | (0.81–2.13) | 0.272 | 1.18 | (0.73–1.93) | 0.494 |
| III | 1.34 | (0.88–2.03) | 0.172 | 0.77 | (0.48–1.23) | 0.281 |
| IV | 0.88 | (0.54–1.44) | 0.622 | 0.47 | (0.28–0.78) | 0.003 |
| Days from HIV diagnosis to ART treatment | ||||||
| ≤ 30 | – | – | – | – | – | – |
| > 30 | 1.18 | (0.85–1.65) | 0.327 | – | – | – |
| HBV infection at baseline | ||||||
| Negative | – | – | – | – | – | – |
| Positive | 1.15 | (0.69–1.89) | 0.593 | – | – | – |
| Missing | 1.20 | (0.74–1.94) | 0.465 | – | – | – |
| HCV infection at baseline | ||||||
| Negative | – | – | – | – | – | – |
| Positive | 1.77 | (1.01–3.12) | 0.047 | – | – | – |
| Missing | 1.28 | (0.88–1.86) | 0.202 | – | – | – |
| STIs infection at baseline | ||||||
| Positive | – | – | – | – | – | – |
| Negative | 1.10 | (0.55–2.19) | 0.783 | – | – | – |
| Missing | 1.83 | (0.81–4.15) | 0.149 | – | – | – |
| Switched ART regimen during follow-up | ||||||
| No | – | – | – | – | – | – |
| Yes | 1.72 | (0.77–3.86) | 0.185 | – | – | – |
| CD4+ T cell counts during follow-up, cells/µL | ||||||
| Over 500 | – | – | – | – | – | – |
| 350–499 | 1.91 | (1.08–3.37) | 0.026 | 1.92 | (1.06–3.45) | 0.031 |
| 200–349 | 2.79 | (1.63–4.77) | <0.001 | 3.17 | (1.78–5.62) | <0.001 |
| 0-199 | 6.38 | (3.65–11.13) | <0.001 | 7.30 | (3.89–13.68) | <0.001 |
WHO World Health Organization; ART Antiretroviral Therapy; NVP Nevirapine; EFV Efavirenz; PIs protease inhibitors; LLV low-level viraemia; HBV Hepatitis B Virus; HCV Hepatitis C Virus; STIs Sexually Transmitted Infections; cHR crude Hazard Ratio; aHR adjusted Hazard Ratio; CI confidence interval
Association between viral load categories and incident IF
Table 3 showed that LLV was associated with a significantly lower risk of IF (aHR = 0.59, 95% CI: 0.37–0.92). Being married was associated with a reduced risk of LLV (aHR = 0.65, 95% CI: 0.46–0.91) compared to being single. Compared to farmers, individuals in other occupations were less likely to experience IF (aHR = 0.69, 95% CI: 0.52–0.92). The EFV-based regimen was significantly associated with an increased risk of IF compared to the NVP-based regimen (aHR = 1.74, 95% CI: 1.28–2.37). Compared to WHO clinical stage I, those in WHO clinical stage II (aHR = 0.61, 95% CI: 0.44–0.84), WHO clinical stage III (aHR = 0.26, 95% CI: 0.18–0.36) and WHO clinical stage IV (aHR = 0.24, 95% CI: 0.17–0.34) were associated with a lower risk of IF. Delayed initiation of ART (> 30 days) was associated with an increased risk of IF (aHR = 1.29, 95% CI: 1.03–1.61). A lower baseline CD4+ T cell count was associated with an increased risk of IF. Specifically, patients with baseline CD4+ T cell counts of 350–499 cells/µL (aHR = 1.72, 95% CI: 1.20–2.46), 200–349 cells/µL (aHR = 2.76, 95% CI: 1.96-3,90) and 0-199 cells/µL (aHR = 6.97, 95% CI: 4.89–9.91) had a higher risk of IF compared to those with CD4+ T cell counts at baseline above 500 cells/µL.
Table 3.
Time-updated Cox proportional hazards model for the association between LLV and Immunological Failure in HIV patients on ART, viral load categories identified two categories: Undetectable, LLV
| Characteristics | Univariable | Multivariable | ||||
|---|---|---|---|---|---|---|
| cHR | 95% CI | P | aHR | 95% CI | P | |
| Viral load categories | ||||||
| Undetectable | – | – | – | – | – | – |
| LLV | 0.62 | (0.40–0.95) | 0.029 | 0.59 | (0.37–0.92) | 0.022 |
| Age (years old) | ||||||
| 18–34 | – | – | – | – | – | – |
| 35–49 | 1.17 | (0.86–1.60) | 0.311 | – | – | – |
| Over 50 | 1.58 | (1.18–2.11) | 0.002 | – | – | – |
| Sex | ||||||
| Female | – | – | – | – | – | – |
| Male | 1.22 | (0.98–1.52) | 0.074 | – | – | – |
| Ethnicity | ||||||
| Han | – | – | – | – | – | – |
| Other | 0.52 | (0.32–0.84) | 0.007 | – | – | – |
| Education | ||||||
| Primary or below | – | – | – | – | – | – |
| Junior high school | 0.79 | (0.64–0.98) | 0.035 | – | – | – |
| Senior high school | 0.88 | (0.60–1.29) | 0.501 | – | – | – |
| College or above | 0.44 | (0.18–1.09) | 0.076 | – | – | – |
| Marital status | ||||||
| Single | – | – | – | – | – | – |
| Married | 0.74 | (0.56–0.98) | 0.035 | 0.65 | (0.46–0.91) | 0.011 |
| Other | 0.89 | (0.62–1.29) | 0.548 | 0.70 | (0.45–1.08) | 0.104 |
| Occupation | ||||||
| Farmer | – | – | – | – | – | – |
| Other | 0.64 | (0.50–0.82) | <0.001 | 0.69 | (0.52–0.92) | 0.011 |
| ART regimen at baseline | ||||||
| NVP-based | – | – | – | – | – | – |
| EFV-based | 1.43 | (1.08–1.89) | 0.013 | 1.74 | (1.28–2.37) | <0.001 |
| PIs-based/Other | 1.05 | (0.72–1.55) | 0.793 | 1.25 | (0.81–1.91) | 0.313 |
| HIV transmission route | ||||||
|
Heterosexual intercourse |
– | – | – | – | – | – |
| Injecting drug use | 1.47 | (1.07–2.02) | 0.018 | – | – | – |
| Other | 0.98 | (0.61–1.58) | 0.941 | – | – | – |
| WHO clinical stage | ||||||
| I | – | – | – | – | – | – |
| II | 0.67 | (0.49–0.91) | <0.001 | 0.61 | (0.44–0.84) | 0.003 |
| III | 0.41 | (0.30–0.56) | <0.001 | 0.26 | (0.18–0.36) | <0.001 |
| IV | 0.48 | (0.35–0.66) | <0.001 | 0.24 | (0.17–0.34) | <0.001 |
| Days from HIV diagnosis to ART treatment | ||||||
| ≤ 30 | – | – | – | – | – | – |
| > 30 | 1.10 | (0.89–1.34) | 0.378 | 1.29 | (1.03–1.61) | 0.029 |
| HBV infection at baseline | ||||||
| Negative | – | – | – | – | – | – |
| Positive | 1.09 | (0.81–1.46) | 0.586 | – | – | – |
| Missing | 1.07 | (0.78–1.46) | 0.692 | – | – | – |
| HCV infection at baseline | ||||||
| Negative | – | – | – | – | – | – |
| Positive | 1.35 | (0.95–1.92) | 0.089 | – | – | – |
| Missing | 1.07 | (0.84–1.35) | 0.591 | – | – | – |
| STIs infection at baseline | ||||||
| Positive | – | – | – | – | – | – |
| Negative | 1.02 | (0.67–1.57) | 0.910 | – | – | – |
| Missing | 1.17 | (0.69–1.99) | 0.569 | – | – | – |
| Switched ART regimen during follow-up | ||||||
| No | – | – | – | – | – | – |
| Yes | 1.58 | (0.88–2.84) | 0.123 | – | – | – |
| CD4+ T cell counts during follow-up, cells/µL | ||||||
| Over 500 | – | – | – | – | – | – |
| 350–499 | 1.41 | (1.00–2.00) | 0.051 | 1.72 | (1.20–2.46) | 0.003 |
| 200–349 | 1.82 | (1.32–2.50) | <0.001 | 2.76 | (1.96–3.90) | <0.001 |
| 0-199 | 3.74 | (2.71–5.16) | <0.001 | 6.97 | (4.89–9.91) | <0.001 |
WHO World Health Organization; ART Antiretroviral Therapy; NVP Nevirapine; EFV Efavirenz; PIs protease inhibitors; LLV low-level viraemia; HBV Hepatitis B Virus; HCV Hepatitis C Virus; STIs Sexually Transmitted Infections; cHR crude Hazard Ratio; aHR adjusted Hazard Ratio; CI confidence interval
Sensitivity analyses
In the sensitivity analysis, LLV was further subdivided into PLLV, blips, and ILLV. Of note, ILLV had a significantly higher risk of VF (aHR = 7.34, 95%CI: 4.92–10.94) (Supplementary Table S1). When stratified by ART initiation period, LLV was significantly associated with a higher risk of VF among patients who initiated ART during 2005–2015 (aHR = 3.09, 95% CI: 1.78–5.35) and those who initiated during 2016–2023 (aHR = 12.74, 95% CI: 6.37–25.45) (Supplementary Table S2). In the competing risks model, LLV was significantly associated with an elevated risk of VF (aSHR = 2.86, 95% CI: 1.91–4.30) (Supplementary Table S3). CIF curves in Figure S1 further illustrated a notably higher cumulative incidence of VF among patients with LLV. Supplementary Table S4 showed that participants with ILLV had a significantly lower risk of IF (aHR = 0.56, 95% CI: 0.33–0.96). In the competing risks model, LLV was not associated with the risk of IF (Supplementary Table S5).
Discussion
This study addressed a critical gap in understanding the clinical implications of LLV in Guangxi, a high-HIV-burden region of southern China, by presenting a longitudinal cohort analysis of the association between LLV and subsequent VF and IF from 2005 to 2023. By employing time-dependent models, we accounted for the dynamic nature of viral replication over time. Compared with sustained undetectable viral load, LLV was associated with an elevated risk of VF, and ILLV conferred an even greater risk, suggesting potential harm of intermittent viral replication if left unaddressed. These findings have immediate clinical relevance for resource-limited settings, advocating for intensified viral load monitoring and drug resistance detection, proactive ART regimen adjustments, and tailored public health strategies to mitigate long-term treatment failure in high-burden regions.
This study identified LLV in 11.16% of participants, a prevalence comparable to the 11.1% reported in Hangzhou, China [31]. Significantly, LLV was independently associated with an elevated risk of VF, and this association remained robust across multiple sensitivity analyses. These findings are consistent with previous studies and underscore the importance of routine viral load monitoring for all PLWH initiating ART [16]. Several potential explanations may underlie the observed association between LLV and VF. First, persistent low-level HIV replication during LLV may facilitate the accumulation of drug resistance mutations, compromising ART efficacy and predisposing individuals to VF [32, 33]. Second, suboptimal adherence to ART, a well-documented driver of LLV, may result in subtherapeutic drug concentrations, thereby enabling intermittent viral replication [34].
In our study, patients with ILLV showed a substantially elevated risk of VF, whereas persistent PLLV and blips appeared to have limited prognostic relevance. These findings are consistent with prior studies reporting no significant association between isolated blips and VF, which attributed such fluctuations to assay variability or transient biological noise with limited prognostic significance [35, 36]. In contrast, previous studies reported that PLLV was associated with an increased risk of VF [15, 37]. In low- and middle-income countries, infrequent VL testing (e.g., 1–2 measurements per year in this cohort) and limited therapeutic options may exacerbate the consequences of unrecognized PLLV. The number of patients with PLLV in our cohort was relatively small (n = 25), which may have limited the statistical power to detect a significant association. Notably, the heightened risk associated with ILLV suggests that the pattern of viremia, not just its magnitude, may be meaningful. Low adherence and HIV drug resistance have been associated with the presence of LLV [38, 39]. This suggests that the “intermittent” nature of VL fluctuations may reflect unstable viral replication or early signs of emerging resistance, serving as a critical warning signal that warrants attention. However, we observed that patients with LLV did not undergo more frequent follow-up testing than those with sustained virological suppression. Consequently, more frequent clinical follow-up and shorter intervals for viral load monitoring may be particularly beneficial. To mitigate this, we propose intensifying VL monitoring to 2–4 tests per year for patients with PLLV and to biannual testing for those with recurrent blips, ILLV, or high adherence risk profiles. Comprehensive assessment of adherence, pharmacokinetic interactions, and emerging drug resistance should be an integral part of clinical management, and regimen optimization is warranted once viremia or confirmed resistance is identified [40].
Despite evidence linking LLV to impaired immune reconstitution [41–44], its specific role in IF remains underexplored, as most research emphasizes its virologic consequences. Notably, our initial findings suggested that LLV acted as a protective factor against IF. However, we contend that this discrepancy likely arises from the misclassification of patients who deceased prior to meeting IF criteria, a phenomenon that can artifactually produce a deceptive protective effect. Our finding that this association vanished in the competing risk model further confirms that the observed benefit was a statistical artifact rather than a true biological phenomenon. Future research must therefore distinguish longitudinal LLV patterns, incorporate biomarkers of immune activation, and ensure rigorous competing risk analyses in well-defined cohorts to conclusively determine the impact of LLV on immunologic outcomes.
This study is subject to several limitations. First, the cohort only included PLWH in Qinzhou city, Guangxi, China, which may limit the generalizability to other areas with different demographic, clinical, or socioeconomic characteristics. Second, VL testing frequency in the region was limited to 1–2 tests annually, which may have underestimated LLV prevalence and obscured temporal associations between LLV and clinical outcomes. Third, the use of a lenient LLV threshold (51–999 copies/mL) may have diluted the risk estimates for VF, which are explicitly linked to lower LLV ranges (e.g., 51–200 copies/mL), warranting caution in interpreting the magnitude of risk. Our sensitivity analyses mitigated this limitation by evaluating narrower LLV categories. Fourth, critical confounders, such as ART adherence and drug resistance profiles, were not included as adjusted variables, potentially precluding a comprehensive assessment of factors driving LLV. Finally, the retrospective observational design inherently limits causal inference between LLV and virologic or immunologic outcomes. Future prospective studies incorporating standardized adherence metrics, resistance testing, and psychosocial variables are needed to clarify the mechanisms linking LLV to treatment failure and to validate these findings across diverse populations.
Conclusion
This study identified LLV as a critical predictor of subsequent virologic failure, though no significant association was found with immunologic failure or all-cause mortality. These findings underscore that LLV should be regarded as an early warning signal necessitating proactive clinical management. To mitigate VF risk, we recommend prioritizing drug resistance testing, considering ART regimen modification, and implementing enhanced viral load monitoring for patients with LLV.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We thank all the participants and the healthcare staff from the collaborating medical institutions for their support in data collection.
Abbreviations
- LLV
Low-level viremia
- ART
Antiretroviral therapy
- VF
Virologic failure
- IF
Immunologic failure
- PLLV
Persistent low-level viremia
- ILLV
Intermittent low-level viremia
- PLWH
People living with HIV
- VLs
Viral loads
- NFATP
National free antiretroviral treatment program
- IQR
Interquartile ranges
- NVP
Nevirapine
- EFV
Efavirenz
- PIs
Protease inhibitors
- INSTI
Integrase strand-transfer inhibitor
- CIF
Cumulative incidence function
Author contributions
LYL: contributed to the conception and design of the work, acquisition, analysis, and interpretation of data, and drafting the work; TH and SFD: contributed to the acquisition of data, analysis and interpretation of data, and drafting the work; CXT: contributed to the acquisition of data, analysis and interpretation of data, and review and editing of the manuscript; LJW, HYL, BLW, LDZ, YYL, JFQ, HSW, BYL, QJS, and LJH: contributed to the conception and design of the work and review and editing of the manuscript. All authors contributed to the manuscript’s revision and approved the final version. All authors read and approved the final manuscript.
Funding
This study was supported by the Guangxi Scientific and Technological Key Project (Grant No. GuikeAD23026340), Guangxi Natural Science Foundation (Grant 2023GXNSFAA026012), National Natural Science Foundation of China (Grant No. 82460658), the Guangxi Bagui Young Top Scholar (To Bingyu Liang), and the Thousands of Young and Middle Age Key Teachers Training Program in Guangxi Colleges and Universities (To Bingyu Liang).
Data availability
The datasets generated and/or analyzed during this study are not publicly accessible due to ethical and legal considerations. However, upon reasonable request, the corresponding author can be contacted for de-identified data.
Declarations
Ethics approval and consent to participate
This study was conducted following the Helsinki Declaration and was approved by the Human Research Ethics Committee of Guangxi Medical University (Ethical Review No. KY0294), which waived the requirement for informed consent because this was a retrospective analysis of anonymized data.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Long-yu Liao, Ting Huang and Shi-fu Deng have contributed equally to this work.
Contributor Information
Qi-jian Su, Email: agansue@163.com.
Bing-yu Liang, Email: liangbingyu@gxmu.edu.cn.
Li-jing Huang, Email: 984824352@qq.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and/or analyzed during this study are not publicly accessible due to ethical and legal considerations. However, upon reasonable request, the corresponding author can be contacted for de-identified data.