https://orcid.org/0000-0001-8757-6587
Abstract
Objectives
Real-world data evaluating weight changes in people living with HIV (PLWH) following switch to integrase strand transfer inhibitor (INSTI), specifically bictegravir (BIC), are limited. This retrospective cohort study analyzed weight changes upon switching to INSTI from non-nucleoside reverse transcriptase inhibitor (NNRTI) or protease inhibitor (PI) in treatment-experienced PLWH.
Methods
Adult PLWH (≥18 years) treated with NNRTI or PI (non-switch cohorts) and those switching to INSTI (switch cohorts) between January 1, 2014 and August 31, 2019 were identified using IQVIA’s Ambulatory Electronic Medical Records linked to a prescription drug claims database. The associations of switching to INSTI and individual INSTI agents with having ≥5% weight gain at 12 months of follow-up were evaluated, adjusting for demographics and baseline clinical characteristics.
Results
At 12 months of follow-up, PLWH in the NNRTI–INSTI switch cohort (n = 508) were more likely to have ≥5% weight gain over 12 months compared to the NNRTI non-switch cohort (n = 614; odds ratio, OR [95% CI], 1.7 [1.2–2.4]). Switching from NNRTI to dolutegravir (DTG: OR [95% CI], 2.1 [1.4–3.0]) or BIC (2.0 [1.0–4.2]) resulted in significantly higher odds of ≥5% weight gain. PI–INSTI switch (n = 295) and non-switch (n = 228) cohorts had similar proportions of PLWH with ≥5% (21.1–23.4%) or ≥10% (7.8–7.9%) weight gain, and no significant association was found between switching from PI to INSTI and weight gain.
Conclusion
Weight gain and related metabolic health of PLWH switching from NNRTI to DTG or BIC should be closely monitored by clinicians. Further research is needed to assess other metabolic outcomes in PLWH remaining on PI and those who switch from PI to INSTI.
Introduction
Human immunodeficiency virus (HIV) affects approximately 1.2 million people in the United StatesCitation1. Although there is no effective cure for HIV, antiretroviral therapy (ART) has significantly reduced HIV transmission and HIV-associated morbidity and mortalityCitation2. Current treatment guidelines published by the United States (US) Department of Health and Human Services (DHHS) recommend a three-drug regimen of two nucleoside/nucleotide reverse transcriptase inhibitors (NRTIs) in combination with a third active drug (anchor agent) from one of the following classes: non-nucleoside reverse transcriptase inhibitor (NNRTI), protease inhibitor (PI), or integrase strand transfer inhibitor (INSTI)Citation2. Two-drug regimens that consist of an anchor agent and a NRTI (e.g. dolutegravir/lamivudine [DTG/3TC]) are also available for selected groups of PLWH (e.g. PLWH without hepatitis B virus [HBV] coinfection)Citation2–4. The approved INSTI drugs for treatment-experienced PLWH include raltegravir (RAL), elvitegravir (EVG), dolutegravir (DTG), and bictegravir (BIC)Citation2. Compared to older NNRTI- or PI-based ART regimens, INSTI-based regimens, as a group, are considered to be well tolerated with fewer side-effectsCitation5; treatment with BIC and DTG also provide a lower pill burden due to the availability of single tablet regimens (STRs) and/or lower risk of treatment-emergent resistanceCitation2. To that end, many patients are switching from NNRTI or PI to INSTI-based regimensCitation6. However, excess weight gain and/or an increase in body mass index (BMI) has been reported in most studies of treatment-experienced PLWH switching to INSTI-based regimensCitation7–11. DTG has been reported with greater increases in weight after switching as compared to RAL and EVG in some studiesCitation7,Citation10.
The BMI of people living with HIV (PLWH) at ART initiation has steadily increased over the past two decadesCitation12. Hence, the US DHHS treatment guidelines recommend monitoring body weight when selecting the initial ART regimen and regimen switchCitation2. A multisite study conducted in the US showed that about one-fourth of PLWH with normal BMI at ART initiation were overweight or obese after 3 years of ARTCitation12. Furthermore, one study reported that PLWH on ART gained weight faster than HIV-negative individuals of a similar ageCitation13. Increased BMI and weight can lead to a higher risk of developing diabetes and cardiovascular diseasesCitation14. To reduce the burden of cardiometabolic comorbidities on PLWH and healthcare systems, it is imperative to identify patient- and/or ART-related risk factors of weight gain among PLWH.
The risk factors associated with a significant weight gain reported for treatment-experienced PLWH include undetectable plasma RNA levels, female sex, African-American race, older age (e.g. ≥60 years), and having tenofovir alafenamide (TAF) in the regimenCitation7,Citation15. Furthermore, an analysis of the US OPERA cohort found significant weight gain following switch from tenofovir disoproxil fumarate (TDF)- to a TAF-based regimen, regardless of the anchor agentCitation16. Increased weight and/or BMI were also reported in studies of PLWH switching from older NNRTI- or PI-based ART regimens to INSTI-based regimensCitation17,Citation18. Although the association between INSTI and weight gain in treatment-experienced PLWH has been examined in clinical trials and observational studies in multiple countries, studies using real-world data of US PLWH, especially those switching from NNRTI or PI to BIC (approved in February 2018), remain scant. Furthermore, many studies that assessed weight changes following switch to INSTI-based ART lack a comparison group of PLWH who did not switch to INSTI and continued the baseline NNRTI- or PI-based ARTCitation7–11. This retrospective database study was conducted to assess weight changes in treatment-experienced PLWH who switched to INSTI-based ART compared to those who remained on NNRTI- or PI-based ART using real-world data.
Methods
This retrospective observational cohort study utilized IQVIA’s Ambulatory Electronic Medical Records (AEMR) database linked to the prescription drug claims database (LRx) from January 1, 2013 to February 29, 2020 (study period). The study period ended on February 29, 2020 to avoid any impact of the Coronavirus 2019 (COVID-19) pandemic on study findings.
Data sources
The AEMR database consists of around 72 million patient records that are sourced from an “opt-in” provider research network. The database includes records collected across >100,000 physicians from >800 large practices and physician networks across the US. LRx contains information on dispensed prescriptions, with 92%, 72%, and 76% coverage of prescriptions from the retail channel, standard mail service, and long-term care facilities in the US, respectively. All data were compliant with the Health Insurance Portability and Accountability Act (HIPAA), and measures were taken to protect patient privacyCitation19,Citation20. This study was conducted using de-identified HIPAA-compliant data and, therefore, Institutional Review Board (IRB) review was not applicable.
Patient selection and study cohorts
This study included four mutually exclusive study cohorts: PLWH who switched from NNRTI to INSTI (NNRTI–INSTI switch cohort), who remained on NNRTI (NNRTI nonswitch cohort), who switched from PI to INSTI (PI–INSTI switch cohort), and those PLWH who remained on PI (PI nonswitch cohort). The switch cohorts were further stratified based on the INSTI agent used (BIC, DTG, EVG, and RAL). Inclusion and exclusion criteria for patient selection are detailed in Supplementary Figure S1. Briefly, PLWH were selected into the NNRTI or PI switch cohorts based on ≥1 prescription for INSTI from January 1, 2014 to August 31, 2019 (selection window). The date of the first INSTI prescription was defined as the index date (i.e. switch date). All PLWH in the NNRTI–INSTI and PI–INSTI switch cohorts had ≥12 months of AEMR and LRx data prior to the index date (baseline) and ≥6 months of continuous treatment with NNRTI/PI during the baseline period. Similarly, PLWH were selected into the NNRTI or PI nonswitch cohorts based on ≥1 prescription for NNRTI or PI and not having any INSTI prescription during the selection window. All PLWH in the nonswitch cohorts had ≥12 months of continuous treatment with NNRTI or PI during the study period. The index date (i.e. sham-switch date) was randomly selected from dates of AEMR prescription dates and LRx claim dates during the period of ≥6 to 12 months of continuous treatment with the index anchor agent to ensure that the duration of the ART with the same anchor agent before the index date (baseline ART) was similar between the switch and nonswitch cohorts. All PLWH were required to have ≥6 months of continuous ART after the index date (treated follow-up). PLWH were censored in the case of virologic failure (plasma HIV RNA ≥200 copies/mL) after 6 months of ART, switch to a different anchor agent class or a different INSTI agent, switch between a TAF and a non-TAF NRTI (TDF/abacavir [ABC]), the last AEMR or LRx record in the study period, or the end of the study period (February 29, 2020). Continuous treatment was defined as having no gap of >90 days in medication supply for NNRTI/PI or the index INSTI agent. All PLWH were ≥18 years of age as of the index date and had ≥1 diagnosis code for HIV (International Classification of Diseases, Ninth and Tenth Revision, Clinical Modification [ICD-9-CM and ICD-10-CM] codes of 042, V08, B20, and Z21) during the study period. To ensure consistency with treatment recommendations, all PLWH were also required to have ≥2 different NRTI backbones during the baseline and follow-up ART. PLWH were excluded if there was evidence (in AEMR or LRx data) for baseline treatment with INSTI, TAF during baseline ART, concurrent treatment with multiple anchor agents during the treated follow-up period, a large gap (>45 days) between the end of medication supply in baseline ART and the index date, or any diagnosis for malignancies, pregnancy, HIV-2, or gastric bypass surgery during the study period. PLWH were also required to have ≥1 weight measurement during baseline ART and at 12 ± 6 months of treatment follow-up to assess weight changes.
Study outcomes
The proportion of PLWH with ≥5% or ≥10% weight gain from baseline was assessed at 12 months of treated follow-up in the switch and nonswitch cohorts as well as for PLWH switching to each INSTI agent (BIC, DTG, EVG, RAL) within the switch cohorts. Due to the small sample size (n < 10), these outcomes were not assessed in PLWH switching from NNRTI to RAL. The weight closest to the 12 months of follow-up and was measured within 12 ± 6 months of treated follow-up was used to compared with the baseline weight (weight measurement closest to the index date).
Covariates
Patient demographic characteristics (age, sex, race/ethnicity, and geographic region), payer type, provider specialty for the index ART, and plasma HIV RNA level were reported from data on or closest to the index date. Clinical characteristics including Quan’s Charlson Comorbidity Index score (CCI; modified to exclude HIV)Citation21 and the presence of selected comorbidities of interest were reported from the 12-month baseline period. ART treatment patterns (type of anchor agent and NRTI backbone) and use of other medications associated with weight gain or weight loss were described during baseline and follow-up (Supplementary Table S1).
Statistical analysis
Baseline demographic and clinical characteristics, baseline and follow-up ART treatment patterns, and the proportion of PLWH with ≥5% or ≥10% weight gain at 12 (± 6) months of treated follow-up were compared between the switch and nonswitch cohorts, using Pearson’s chi-square or Fisher’s exact test for categorical variables and independent t-test (for means) or Wilcoxon rank sum test (for medians) for continuous variables. All tests were conducted assuming a two-tailed test of significance and with alpha level set a priori at 5%.
Multivariable logistic regression models were used to estimate the association between switching from NNRTI- or PI-based ART to INSTI-based ART versus remaining on NNRTI- or PI-based ART and having ≥5% weight gain at 12 (± 6) months of follow-up. Models were adjusted for important patient demographic and ART characteristics, guided by observed differences between study cohorts based on bivariate analysis and literature on potential risk factors of weight gain among PLWH. Baseline CD4 count and plasma HIV RNA level were missing for most patients in the cohorts and therefore not adjusted in the model (). Analyses evaluating the association of switching to specific INSTIs with weight gain was also conducted by including the comparison of switching to BIC, DTG, or EVG versus nonswitch in the regression model (switching to RAL was only included in the analyses involving the PI-INSTI switch cohort). Given that TAF use is correlated with BIC due to its single-tablet formulation with TAF, a sensitivity analysis was conducted by excluding follow-up TAF usage in the model. Analyses estimating the odds of having ≥10% weight gain were not conducted due to limited sample sizes.
Table 1. Demographic and baseline clinical characteristics of the NNRTI and PI switch and nonswitch cohorts.
Results
The study included 508 PLWH in the NNRTI–INSTI switch cohort, 614 in the NNRTI nonswitch cohort, 295 in the PI-INSTI switch cohort, and 228 PLWH in the PI nonswitch cohort (Supplementary Figure S1). In the NNRTI–INSTI switch cohort, there were 61, 207, and 233 PLWH who switched from NNRTI to BIC, DTG, and EVG, respectively. Within the PI–INSTI switch cohort, there were 11, 143, 122, and 19 PLWH who switched from PI to BIC, DTG, EVG, and RAL, respectively.
Demographic and baseline clinical characteristics
The demographic and baseline clinical characteristics of all cohorts are shown in . PLWH in the NNRTI switch and nonswitch cohorts were similar in age (median age: 51 years vs. 50 years); however, PLWH in the PI-INSTI switch cohort were significantly older than those in the PI nonswitch cohort (median age: 51 years vs. 48 years, p = .03). For both NNRTI and PI comparisons of the switch and non-switch cohorts, the switch and nonswitch cohorts had similar distributions of sex, racial/ethnic groups, payer type, and prescriber specialty for the index ART. The switch and nonswitch cohorts were predominantly male (78.0% vs. 78.3% for NNRTI comparison, 67.8% vs. 70.6% for PI comparison; both p >.05), with 36.1–43.9% of Black PLWH (36.1% vs. 37.8% for NNRTI comparison, 43.1% vs. 43.9% for PI comparison; both p >.05). In all cohorts, the majority of PLWH were covered by commercial insurance (71.5% vs. 71.5% for NNRTI comparison, 62.4% vs. 62.3% for PI comparison; both p >.05) and located in the southern US (67.3% vs. 58.6%, p = .03 for NNRTI comparison, 60.7% vs. 62.3% for PI comparison, p >.05). Infectious disease physicians prescribed the index ART for 40.3–47.8% of PLWH (40.3% vs. 43.2% for NNRTI comparison, 47.8% vs. 42.1% for PI comparison; both p >.05).
Compared to the NNRTI nonswitch cohort, the NNRTI–INSTI switch cohort had a higher mean Quan’s CCI score (.5 vs. .4) and a higher prevalence of chronic kidney disease/renal disease (9.4% vs. 3.9%), diabetes (13.6% vs. 8.6%), dyslipidemia/hyperlipidemia (39.9% vs. 27.9%), and esophageal reflux (11.2% vs. 6.2%; all p <.01). Although the mean CCI score was similar between the PI switch and nonswitch cohorts (.7 vs. .5, p >.05), the PI-INSTI switch cohort had a higher prevalence of diabetes (14.6% vs. 8.8%, p =.04) and dyslipidemia/hyperlipidemia (36.9% vs. 27.6%, p = .02).
For both NNRTI and PI comparisons (switch vs. non-switch), a higher proportion of PLWH in the switch cohort had ≥1 baseline CD4 cell count recorded in the AEMR database compared to the nonswitch cohort (NNRTI: 24.8% vs. 17.6%; PI: 30.5% vs. 14.5%; both p <.01). Similar proportions of PLWH in the switch and nonswitch cohorts had ≥1 baseline plasma HIV RNA level recorded in AEMR database (NNRTI: 5.6% vs. 4.9%; PI: 8.5% vs. 11.8%; both p >.05). Among PLWH with ≥1 baseline measure, the median CD4 cell count and plasma HIV RNA level were not significantly different between the switch and nonswitch cohorts.
ART treatment patterns during baseline and follow-up
The ART treatment patterns during baseline in the NNRTI and PI switch and nonswitch cohorts are shown in . Prior to the index date, there were differences in the NNRTI anchor agent used in the NNRTI switch and nonswitch cohorts. Compared to the nonswitch cohort, the switch cohort had a higher proportion of PLWH using efavirenz (85.4% vs. 69.2%) and a lower proportion of PLWH using rilpivirine (9.6% vs. 27.5%; both p <.01). In both cohorts, the majority of patients had a TDF-based NRTI backbone during baseline (91.4–93.8%). The most commonly used anchor agents in the PI–INSTI switch and nonswitch cohorts were atazanavir (38.3% vs. 48.7%) and darunavir (36.3% vs. 41.2%). Significantly fewer patients in the PI–INSTI switch cohort used a TDF-based NRTI backbone compared to the PI nonswitch cohort (73.2% vs. 81.6%, p = .02). Both NNRTI and PI comparisons had similar proportions of PLWH in the switch and nonswitch cohorts who used medications associated with weight gain (NNRTI: 40.9% vs. 38.9%; PI: 47.5% vs. 46.9%; both p >.05) or weight loss (NNRTI: 21.2% vs. 19.1%; PI: 25.4% vs. 18.4%; both p >.05).
Table 2. Treatment patterns during baseline and follow-up of the NNRTI and PI switch and nonswitch cohorts.
During follow-up, the switch cohorts had a significantly higher proportion of PLWH with a TAF-based NRTI backbone compared to the nonswitch cohorts (NNRTI: 49.9% vs. 1.8%; PI: 33.9% vs. 2.2%; both p <.01). Similar proportions of PLWH in the switch and nonswitch cohorts used non-ART medications that are associated with weight gain (NNRTI: 55.5% vs. 54.9%; PI: 60.7% vs. 59.2%; both p >.05) and weight loss (NNRTI: 32.1% vs. 27.2%; PI: 36.9% vs. 29.4%; both p >.05).
Changes in weight from baseline to 12 months of follow-up
At baseline, the median weight was similar between the switch and nonswitch cohorts of both NNRTI (83.0 kg vs. 82.0 kg) and PI (81.6 kg vs. 82.6 kg) comparisons (). The proportion of PLWH in the switch and nonswitch cohorts who experienced ≥5% or ≥10% weight gain at 12 months of follow-up is shown in . At 12 months of follow-up, there was a higher proportion of PLWH with ≥5% weight gain (33.5% vs. 20.7%) and those with ≥10% weight gain (11.4% vs. 5.2%; both p <.01) in the NNRTI–INSTI switch cohort compared to the nonswitch cohort (). Within the NNRTI–INSTI switch cohort, the proportion of PLWH with ≥5% or ≥10% weight gain was the highest for BIC, followed by DTG and EVG (). There was no significant difference in the proportion of PLWH with ≥5% (23.6% vs. 21.1%) or ≥10% (7.2% vs. 7.9%) weight gain between the PI-INSTI switch and nonswitch cohorts (). Also, no significant difference was observed between PLWH who switched to different INSTI agents from PI and PLWH in the PI nonswitch cohort ().
Figure 1. Weight change during treated follow-up in the switch and nonswitch cohorts: (a) NNRTI comparison;a (b) PI comparison.
Abbreviations: BIC, bictegravir; DTG, dolutegravir; EVG, elvitegravir; NNRTI, non-nucleoside reverse transcriptase inhibitor; PI, protease inhibitor; PLWH, people living with HIV.
* p <.05 and *** p <.001 for comparisons between respective switch and nonswitch cohorts.
a NNRTI switch cohort excludes PLWH treated with RAL due to low sample size (n = 7).
In the multivariable logistic regression model, PLWH in the NNRTI–INSTI switch cohort were 73% more likely to have ≥5% weight gain over 12 months compared to the NNRTI nonswitch cohort (OR [95% CI] = 1.73 [1.24–2.43]) ( and Supplementary Table S2). Other significant predictors of weight gain included lower baseline weight, younger age, and TAF use during follow-up. When comparing PLWH who switched from NNRTI to specific INSTI with PLWH in the NNRTI nonswitch cohort, PLWH who switched to DTG had higher odds of ≥5% weight gain (OR [95% CI] = 2.07 [1.43–2.98]) (Supplementary Table S3).
Figure 2. Logistic regression model for the association between switch to INSTI (excluding RAL) from NNRTI on ≥5% weight at 12 months of follow-up.
Abbreviations: CI, confidence interval; CCI, Charlson Comorbidity Index; INSTI, integrase strand transfer inhibitor; NNRTI, non-nucleoside reverse transcriptase inhibitor; TAF, tenofovir alafenamide.
* p <.05.
Weight loss medication includes drugs and medications that have been associated with weight loss.
In the sensitivity analysis of excluding follow-up TAF usage in the model, PLWH who switched from NNRTI to BIC had the highest odds of ≥5% weight gain (OR [95% CI] = 3.89 [2.19–6.88]), followed by DTG (OR [95% CI] = 2.20 [1.53–3.17]) and EVG (OR [95% CI] = 1.78 [1.24–2.55]) ( and Supplementary Table S3). No significant association was found between switching from PI to INSTI and having ≥5% weight gain over 12 months in the logistic regression model (Supplementary Table S4).
Figure 3. Logistic regression model for the association between switch to specific INSTI (excluding RAL) from NNRTI on ≥5% weight at 12 months of follow-up.
Abbreviations: CI, confidence interval; CCI, Charlson Comorbidity Index; BIC, bictegravir; DTG, dolutegravir; EVG, elvitegravir; INSTI, integrase strand transfer inhibitor; NNRTI, non-nucleoside reverse transcriptase inhibitor; OR, odds ratio; RAL, raltegravir.
* p <.05.
Weight loss medication includes drugs and medications that have been associated with weight loss.
TAF was not included in this model due to high correlation with BIC.
Discussion
This retrospective study analyzed real-world electronic medical records linked to prescription claims data and evaluated the association between switch to INSTI from NNRTI- or PI-based ART and weight gain in treatment-experienced PLWH. The study findings highlight the association between switching from NNRTI- or PI-based ART to BIC-based ART, the most recently approved INSTI-based regimen, and weight gain by comparing PLWH who switched to a INSTI-based regimen to those who remained on baseline NNRTI- or PI-based ART, which were not included in many previous studies that assessed weight changes following a switch to INSTI-based ARTCitation7–10,Citation22.
Among treatment-experienced PLWH in this study, significant (≥5%) weight gain at 12 months of follow-up was observed in PLWH who switched from NNRTI to INSTI compared to those who remained on NNRTI. In terms of specific INSTI agents, switching from NNRTI to DTG was associated with the highest odds of having ≥5% weight gain, regardless of TAF usage. These findings are consistent with a prior study that reported an average additional 2 kg weight gain after switching from a NNRTI- to an INSTI-based regimen compared with those remaining on a NNRTI-based regimen at 18 monthsCitation17. Furthermore, two prior studies have noted that greater weight gain associated with switching was primarily attributed to switches to DTGCitation7,Citation17. Consistent with prior studies, the current study found that TAF use during follow-up is associated with higher odds of significant weight gain in PLWH who switched from NNRTI to INSTI compared with those who remained on NNRTI; many studies have reported that the use of a TAF-based NRTI backbone, especially in combination with DTG or as part of BICCitation23–25, was associated with greater weight gainCitation26–29. Results of our regression analyses with and without adjustment for TAF usage indicate that a switch from non-TAF NRTI to TAF NRTI was the main driver of weight gain associated with the switch to BIC. A few studies suggest that weight gain associated with TAF based regimens may be caused by discontinuation of TDF, which has a weight-inhibiting effect, rather than by TAF itselfCitation30–32. Furthermore, a recent follow-up study of participants in the ADVANCE trial reported significant reduction in weight as well as improvement in lipid profile and blood glucose among PLWH switching from TAF/emtricitabine + DTG to TDF/lamivudine/DTGCitation33. Previous studies also reported weight gain following switching from efavirenz due to its weight-suppressive effectCitation17,Citation33,Citation34. Efavirenz was not included as a covariate in the regression model in this study as a relatively small difference was observed for patients using efavirenz at baseline between patients without and without ≥5% weight gain (81.0% and 74.9%, respectively). Furthermore, a recent review of 12 large clinical trials concluded that most weight gain following switching from efavirenz was modest and not associated with a long-term effectCitation34.
Further, consistent with findings of a study that assessed weight changes associated with switching to INSTI-based ART, younger age and lower weight at the time of switch was associated with a higher likelihood of significant weight gainCitation35. Although some studies show that older age is associated with significant weight gain following a switch to INSTI, these data have been inconsistentCitation7,Citation11,Citation27,Citation28. Previous studies have also reported that female gender and Black/African-American race/ethnicity were major risk factors for weight gainCitation7,Citation22,Citation27,Citation28,Citation36. However, small sample sizes in many of these studies limited the assessment of the association between switching to INSTI and weight gain in these patient subgroups. As obesity is associated with an increased risk of cardiovascular diseasesCitation37, future studies examining the association between switching to INSTI and weight gain in PLWH of different BMI categories are warranted.
No significant association was found between switching from PI to INSTI and having ≥5% weight gain at 12 months of follow-up. Other studies of treatment-experienced PLWH that compared weight changes between pre- and post-switch also reported weight gain among PLWH who switched from NNRTI, but not among those PLWH who switched from PICitation8,Citation11,Citation38. Specifically, it was reported that PLWH had gained weight at a slower rate following switching from PI-based ART to INSTI-based ARTCitation11.
Although this study and previous studies suggest INSTI-associated weight gain in treatment-experienced PLWH, further research is warranted to elucidate the underlying mechanisms for the observed weight gain and their long-term clinical implications. Currently, 5% weight loss is considered to be clinically significant in clinical trialsCitation39; however, there is no consensus on the definition of clinically significant weight gain, especially in PLWH treated with ART. Observational studies with long-term follow-up are needed to understand the association between magnitudes of weight gain and clinical implications (e.g. incident diabetes or dyslipidemia, elevated CV risk) in PLWH treated with ART. Also, not all fat contributes equally to increased risk of metabolic disorders; therefore, further studies that specifically assess lipodystrophy weight gain in PLWH switching from NNRTI- or PI-based ART to INSTI-based ART will be of significance. Although no significant difference in gaining ≥5% or ≥10% weight was found between PLWH switching from PI- to INSTI-based ART and those remaining on PI-based ART, it remains inconclusive whether other metabolic outcomes (e.g. insulin resistance) are also similar between the two groups.
The findings of this study should be interpreted in the context of limitations inherent to the data and study design. First, due to the open nature of the AEMR and LRx databases, continuous observation of PLWH and their treatments/diagnoses outside of the LRx and EMR system cannot be confirmed. To that end, the treatment pattern and comorbidities of PLWH may be underestimated. Furthermore, the full ART treatment history beyond 12 months prior to the index date cannot be ascertained in the study databases. The proportion of PLWH who were in their early journey of ART and experiencing “return to health” may differ between the switch and nonswitch cohorts, which could confound the study findings. Second, weight is inconsistently (e.g. provider variation, scale variation) measured and recorded in real-world data; PLWH with more weight measurements during the follow-up period may be closely monitored by their physicians for reasons (e.g. family history of diabetes) that may confound the association between switching to INSTI and weight change. Furthermore, weight change was assessed among patients with ≥1 weight measurement during baseline and within 12 ± 6 months of follow-up. Although the measurement that was closest to the index date and 12 months of follow-up was used to calculate weight change, there might be variations in the timing of measurement within each cohort and across different cohorts; such variation may also confound the study findings. The study findings may also be impacted by important unmeasured factors such as ART adherence, reasons for discontinuing ART (e.g. adverse effects), and lifestyle factors (e.g. diet, physical activity). Conditions (e.g. thyroid dysfunction, hypertension, diabetes, acquired immunodeficiency syndrome-related diseases) developed during the follow-up period were not examined in the study and may also contribute to the weight gain of PLWH. Another limitation is that treatment response was not captured by the study data. To that end, HIV-related laboratory data (i.e. plasma HIV RNA level, CD4 cell count) that could inform treatment response were only observed for a small proportion of the study cohorts (), probably due to a lack of integration of the EMR system with the testing centers. Incomplete capture of HIV-related laboratory data may lead to misclassification of virological suppression. While low CD4 cell count and high HIV-1 plasma RNA level at baseline were reported as predictors of significant weight gain in a post-hoc analysis of pooled data from three randomized clinical trials of treatment-naïve PLWHCitation40, the role of CD4 cell count, nadir CD4 cell count, and plasma HIV RNA level at switch in subsequent weight gain warrants further examination in future studies.
Conclusions
Switching from NNRTI- to INSTI-based ART, particularly BIC and DTG, is associated with significant (≥5%) weight gain over 12 months compared to remaining on NNRTI-based ART. Weight gain and associated metabolic health of PLWH switching from NNRTI- to INSTI-based ART should be closely monitored by clinicians, especially among those who switched to BIC or DTG. No significant association was observed between switching from PI- to INSTI-based ART and significant weight gain over 12 months. Given the different weight gain odds associated with switching from NNRTI- and PI-based ARTs, there is a need to identify patient subgroups that can benefit most from switching to INSTI-based ART. Further research is needed to assess other metabolic outcomes (e.g. insulin resistance) in PLWH remaining on PI and those who switched from PI to INSTI. Also, it is important to explore the clinical implications that are associated with different magnitudes of weight gain and identify patient subgroups that are at higher risk of gaining significant weight to help inform risk-reducing interventions.
Transparency
Author contributions
Conception and design of study: Girish Prajapati, Xiaohui Zhao, Jenny Tse, and Aimee M. Near. Acquisition of data, analysis and interpretation of data: Girish Prajapati, Xiaohui Zhao, Jenny Tse, Aimee M. Near, and Princy N. Kumar. Drafting and revising the manuscript: Girish Prajapati, Xiaohui Zhao, Jenny Tse, Aimee M. Near, and Princy N. Kumar. All authors approved the final manuscript version to be submitted.
Ethics statement
Not applicable. This was a retrospective database study using de-identified data compliant with the US Health Insurance Portability and Accountability Act of 1996. Therefore, ethics approval from the Institutional Review Board (IRB) was not required for this study.
Weight gain switching PLWH-CMRO_Supplemental material.docx
Download MS Word (238.8 KB)Acknowledgements
The authors thank Wei-Ti Huang and Hangcheng Liu of IQVIA for statistical expertise during the analysis of data of this study.
Declaration of financial/other relationships
Girish Prajapati is an employee of Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc, Rahway, NJ, USA. Aimee Near, Jenny Tse, and Xiaohui Zhao are employed by IQVIA, which received funding from Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc, Rahway, NJ, USA to conduct this study. Princy Kumar reports grant/research support from GSK, Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc, Rahway, NJ, USA, and Gilead; stock ownership with Merck & Co., Inc., Rahway, NJ, USA, Pfizer, Johnson & Johnson, GSK, and Gilead; and service as consultant/advisory board member with AMGEN, GSK, Merck & Co., Inc., Rahway, NJ, USA, and Gilead.
Results in part were presented at the 2022 AIDS meeting, Montreal, Canada, July 29–August 2, 2022.
Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.
Data availability statement
The data that support the findings of this study are available from IQVIA but restrictions apply to the availability of these data, which were used under license for the current study and so are not publicly available.
Additional information
Funding
References
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