We obtained routinely collected DTG based regimen treatment patient-level data for people receiving HIV care in EGPAF-supported ART facilities in Malawi. We used two sources: the Malawi District Health Information Software version 2 (DHIS2) and facility-based ART electronic medical records systems (EMRS). Strategic information and evaluation (SI&E) officers extracted the data using a Microsoft Excel tool.

Data collection tools, developed in accordance with the study protocol, were digitized and deployed online for use across the sampled health facilities. Twenty-one nurses and clinicians were recruited to collect the data using tablets configured with ODK-X software for quick and secure online data transfer. Data were synchronized to a central server every 2 days after verification and proofreading by field supervisors. Quality control measures included: embedded logic checks during data entry and random sampling of data by supervisors to ensure accuracy, completeness, and protocol adherence. A standardized data extraction was designed and administered to ensure consistent and comprehensive data captured across all 40 targeted health facilities.

The study sample comprised 3,525 adult HIV-positive patients receiving DTG-based ART. Eligibility criteria required participants to be aged 18 years or older and have at least three recorded body weight measurements during the study period. Two groups were compared in this study: patients who switched to DTG from another regimen, ART-experienced and patients who initiated treatment with DTG as their first regimen, ART-naïve patients: Clinical parameters were extracted from medical records, including patient cards, ART registers, CD4 count logbooks, and laboratory registers. “Time 0” was defined as the point of regimen change to DTG for ART-experienced patients or the initiation of DTG-based ART for ART-naïve patients. The parameters retrieved included: demographics: gender, and date of birth. Anthropometrics: weight (kg) at ART initiation and during follow-up, height (cm) at ART initiation date, ART Data: regimen type, regimen start and stop dates, ART start date, and treatment outcome, HIV-Related Data: date of confirmed HIV diagnosis, date of treatment initiation, viral load results and viral load capture date, CD4 cell counts, and duration of viral suppression until Time 0.

Data were obtained from ART registers, patient cards, laboratory logbooks, HIV Testing and Counseling (HTC) registers, viral load registers, CD4 count registers, and electronic medical records (EMR). The facilities were sampled from districts in the central region (Mchinji, Dedza, Ntcheu) and southern region (Blantyre, Neno, Thyolo, Zomba, Chiradzulu).

Patients were included into the study if they were on ART and alive between 2018 and 2021 and data values for the following key variables were available at all data capture points: weight, regimen type, age, and sex. Patients were excluded from this study if they enrolled on ART after November 2020, had missing data values on the key variables (weight, regimen type, age, and sex) at any data capture point.

A single-stage sampling technique using probability proportional to size (PPS) was employed to select 40 health facilities from the 179 supported by EGPAF in eight districts and a sample size of at least 3,500 was determined.

The study employed four latent variable models; LPA, LCGA, LGCM, and GMM to identify heterogeneity in body weight gain trajectories. Model performance was evaluated using statistical fit indices (Akaike Information Criterion [AIC], Bayesian Information Criterion [BIC], sample-size adjusted BIC [SSBIC], log-likelihood, and entropy), classification quality (posterior probabilities), and interpretability of identified trajectories. Descriptive statistics summarized patient characteristics, with means, medians, and standard deviations (SDs) for continuous variables, and proportions for categorical variables. Independent t-tests compared mean weight gain by gender, ART status (naïve vs. experienced), and age categories. A significance threshold of p < 0.05 was applied. All models were estimated in R (10) using the tidyLPA (11), lcmm (12), and lavaan (13) packages. Missing data were addressed using Full Information Maximum Likelihood (FIML) under the assumption of Missing at Random (MAR) (27).

Figure 1 provides a conceptual visualization of LPA, LCGA, GCM, and GMM and how they differ in capturing heterogeneity ranging from static profiles to longitudinal trajectories with or without within-class variability.

Below are the representative model equations for Latent Profile Analysis (LPA), Latent Class Growth Analysis (LCGA), Growth Curve Modeling (GCM) and Growth Mixture Modeling (GMM).

Latent Profile Analysis (LPA) is a person-centered statistical technique that is used to identify unobserved subgroups within a population based on continuous observed variables. It classifies individuals into distinct profiles that share similar patterns of responses (25). LPA uncovers hidden heterogeneity, enabling more precise interpretation and targeted insights from complex data. Below is a standard LPA notation equation, illustrating how the model represents these latent profiles.

Where:

y i is the vector of observed indicators for individual i.

c is the number of latent classes,

π c is the probability of membership in latent class cc (mixing proportion),

y ij is the observed value of indicator j for individual i,

θ jc are the class-specific parameters for indicator j in class c.

f ( y ij ∣ θ jc ) is the probability density function of the observed indicator y_ij given the parameters θ_jc.

Latent Class Growth Analysis (LCGA) is a person-centered method that identifies unobserved subgroups based on longitudinal trajectories. Unlike LPA, LCGA focuses on patterns of change over time rather than cross-sectional profiles. LCGA fixes the within-class variances of growth factors (intercepts and slopes) to zero (24). The following standard LCGA equation illustrates how these latent trajectory classes are modeled.

Where:

y it k is an outcome for individual ii at time t in latent class k.

β 0 k is a class-specific intercept.

β 1 k t is a class-specific linear slope.

β 2 k t 2 is a class-specific quadratic slope.

Growth Curve Modeling (GCM) examines individual trajectories of change over time. Unlike LCGA, LGCM models continuous variation across individuals rather than classifying them into discrete latent classes (20, 22). The following equation illustrates how these individual growth trajectories are represented.

Where:

Y it is outcome for individual i at time t.

β 0 , β 1 are the fixed (population-level) intercept and slope.

β 2 is a quadratic slope, optional term for acceleration/deceleration in growth.

u 0 i . u 1 i ∼ N ( 0 , Ψ ) = random effects for individual deviations.

ε it ∼ N ( 0 , σ 2 ) Residual error at time t for individual i, assumed normally distributed.

Growth Mixture Modeling (GMM) identifies latent subgroups with distinct growth trajectories while allowing for individual variation within each class (23). Unlike LCGA, GMM models both between-class differences and within-class heterogeneity change over time (19). The following standard GMM equation illustrates how these latent trajectory classes and individual variations are represented:

Where:

Y it ( c ) is the observed continuous outcome for individual i at time t in class c.

u 0 ic , u 1 ic ∼ N ( 0 , Ψ c ) =  individual-level random effects (intercept and slope deviations) within class c.

β 2 c t 2 = class specific quadratic slope (optional).

ε it c ~ N ( 0 , σ c 2 )  = residual error for class c.

Constraining class-specific residuals stabilizes standard errors, particularly for intercept estimates, allowing a clearer assessment of how adding quadratic terms affects growth trajectories Class-specific residual variances were held constant across the linear and quadratic models to simplify estimation, ensure comparability of parameter estimates, and maintain interpretability of growth trajectories. This was done to stabilize standard errors and ensure that differences between models reflect changes in trajectory shape rather than residual variability.

To ensure the robustness of the findings, sensitivity analyses were conducted. These included varying the initial starting values for each model to minimize the risk of converging on local maxima. Additionally, model stability was assessed by systematically altering the number of latent classes to evaluate the consistency and reliability of the results across different model specifications. Furthermore, alternative model specifications were tested to assess the robustness of the identified trajectories. These included allowing for class-specific variances in the intercept and slope terms (i.e., relaxing the assumption of homoscedasticity across classes), testing models with and without quadratic growth terms, and comparing LCGA models with growth mixture models (GMM) that permit within-class variability. Additionally, the inclusion of covariates predicting class membership was examined to determine the influence of external variables on classification. The consistency of class membership probabilities and model fit indices across these specifications supported the stability and validity of the final model solution.

Ethical approval for the study was obtained from the National Health Sciences Research Committee (NHSRC) of Malawi’s Ministry of Health, (protocol V2.09 # 18/09/2139), and registered with USA’s Office of the Human Research Protections (OHRP) IRB # IRB00003509, FWA 99999576. Ethical review authorities in 40 of the participating health facilities independently reviewed and approved the data collection protocols from their facilities. No Individual written consent was required because this was a retrospective data collection process where there was no direct contact with patients. No identifiers (ART numbers, names, home addresses, or phone numbers) were extracted from any of the data sources used. Data was collected anonymously and solely from hospital records.

Table 1 shows that male patients gained an average of 1.23 kg, while females gained 1.65 kg at 12 months from DTG initiation or switch (p = 0.446). ART-naïve patients experienced a higher mean weight gain of 2.29 kg compared to 1.39 kg for ART-experienced patients. Among males, the mean weight gain was significantly different between ART-experienced (1.02 kg) and ART-naïve (3.66 kg) individuals (p = 0.012). However, for females, the difference between ART-experienced (1.71 kg) and ART-naïve (1.19 kg) patients was not statistically significant (p = 0.6172).

Younger patients demonstrated greater weight gain, with those aged 0–17 years gaining an average of 3.45 kg, followed by 18–34 years (2.00 kg), 35–49 years (0.68 kg), and the lowest weight gain observed in the 50 + years group. When comparing weight gain between ART-naïve and ART-experienced patients within these age categories, no significant differences were found, as p-values across all groups ranged from 0.2002 to 0.8094, indicating that treatment experience did not significantly influence weight gain across different age groups.

Table 2 presents model fit statistics for four latent modeling approaches: Latent Profile Analysis (LPA), Latent Class Growth Analysis (LCGA), Latent Growth Curve Modeling (LGCM), and Growth Mixture Modeling (GMM) examining weight gain trajectories among patients on DTG-based ART. Across all approaches, models with more than one class generally improved fit as indicated by lower AIC values. For LPA, the 2-class model demonstrated the best fit (AIC = 34,683.88), suggesting two distinct weight gain profiles. In LCGA, the 2-class solution was optimal (AIC = 42,422.59), capturing heterogeneity in growth trajectories. LGCM indicated that a 3-class model provided the best balance between fit and complexity (AIC = 44,674.91), reflecting variation in continuous weight trajectories over time. For GMM, the 1-class model showed marginally lower AIC (42,886.66), although additional classes captured potential subgroup patterns. Overall, these results indicate that different modeling approaches reveal distinct patterns of weight gain, with the 2-class LPA and LCGA models and 3-class LGCM model providing the most parsimonious representations of heterogeneity in patient body weight responses.

Table 4 summarizes class proportions and corresponding sample sizes for the Latent Class Growth Analysis (LCGA) model. Class 1 (Moderate Growth) represents the largest group, comprising 97.62% of the sample (n = 3,441). Class 2 (Rapid Growth) accounts for 0.54% (n = 19), while Class 3 (Minimal/No Growth) includes 1.84% of the sample (n = 65). These results indicate that the majority of individuals experienced moderate gain, with smaller proportions showing rapid or minimal/no growth. The majority of individuals fall into Class 1, indicating moderate body weight gain is the most common trend. Class 2 (Rapid body weight gain) and Class 3 (Minimal/No gain) are much smaller groups, but their needs may differ and require targeted interventions. Understanding the characteristics of these smaller groups could help improve personalized care strategies.

Table 6 shows that the LCGA fixed effects differed across the three body weight gain classes. In Class 1 (Moderate Gain), linear and quadratic intercepts were nearly identical (53.05 vs. 53.04), indicating that the quadratic term adds little to model fit. In Class 2 (Rapid Gain), the linear intercept (89.37) slightly exceeded the quadratic (89.12), both significant (p < 0.001). In Class 3 (Minimal/No Gain), the quadratic intercept (13.32) slightly exceeded the linear (12.72), showing small differences. These results highlight the varying contributions of linear and quadratic terms across classes.

Random-effects analysis showed similar variability in intercepts (linear 110.51 vs. quadratic 110.57) and identical residual standard errors (3.68 ± 0.05), indicating that differences stem mainly from fixed effects. The largest fixed-effects differences occurred in Classes 2 and 3. Including covariates reduced the variance of the linear intercept (57.23), suggesting substantial influence on linear growth, while the quadratic intercept variance remained stable (110.58), indicating limited impact on non-linear patterns.

Table 7 presents the distribution of participants across weight gain classes and the corresponding posterior classification probabilities. The table shows that most individuals (93.22%) belong to Class 1, representing “Moderate Weight Gain.” Class 2 (5.59%) reflects “Rapid Weight Gain,” a smaller but clinically relevant subgroup requiring closer monitoring. Class 3 (1.13%) includes those with “Minimal or No Weight Gain,” highlighting the need for targeted interventions, while Class 4 (0.06%) represents rare outliers, possibly exceptional responses, or data anomalies. These classifications provide insights into weight gain patterns and can guide personalized care and interventions for different subgroups.

The latent class growth analysis models showed increasing classification precision with more classes. The 1-class model had an entropy of 0.00, indicating no meaningful separation, while the 2-class model improved slightly to 0.03. The 3-class model achieved substantially better classification with an entropy of 0.63, suggesting moderate certainty in assigning individuals to distinct trajectories. Overall, the results indicate that moving from one to three classes provides clearer differentiation of growth patterns in the data. Refer to Figure 2.

The mean posterior probabilities indicate how confidently individuals are assigned to each latent class, with higher values reflecting clearer separation between classes. In our 3-class LCGA model, Class 1, Class 2, and Class 3 show mean posterior probabilities of 0.91, 0.97, and 0.90, respectively, suggesting good classification quality. These patterns are illustrated in Figure 3, which visualizes the distribution of posterior probabilities across the latent classes.

Latent Growth Curve Model (LGCM) was used to model individual body weight gain patterns over time by estimating both the initial status and the rate of growth of a given variable, Unlike Latent Class Growth Analysis (LCGA), which categorizes individuals into latent classes based on their growth trajectories, LGCM focuses on estimating continuous trajectories of growth for each participant.

Table 8 presents LGCM parameter estimates, highlighting distinct weight gain trajectories across the three classes. For fixed effects, Class 1 (Moderate Body Weight Gain) shows no significant linear effect (p = 0.859) but a significant quadratic effect (p < 0.001), indicating a curved trajectory over the 12 months on DTG-based regimens. Class 2 (Rapid Gain) has a significant negative linear intercept (p < 0.001) but a non-significant quadratic effect, reflecting a consistent trajectory from a lower starting point. Class 3 (Minimal/No Gain) shows no significant linear or quadratic effects, indicating a relatively flat trajectory.

Random-effects analysis reveals substantial variability in baseline weight, with intercept variances highest in Class 1 (0.00931) and lower in Classes 2 (0.00271). Residual standard errors are consistent between linear and quadratic models, highest in Class 1 (12.29 ± 0.24 linear; 12.51 ± 0.24 quadratic), moderate in Class 2 (9.32 ± 0.19; 9.45 ± 0.19), and lowest in Class 3 (7.46 ± 0.18; 7.58 ± 0.18), indicating that weight gain is least predictable in Class 1 and most predictable in Class 3. The slight increase in residual error with the quadratic model suggests minimal improvement in prediction. Overall, Class 1 exhibits curved growth and high individual variability, Class 2 shows moderate predictability with a consistent trajectory, and Class 3 remains stable and well-explained.

To identify multiple subgroups of weight gain trajectories while considering individual variabilities within those groups, we fitted unconditional Growth Mixture Model (GMM), combining the aspects of growth curve and mixture modeling to account for individual differences in patterns of change. This allowed for the analysis of heterogeneous developmental patterns, model complex, and non-linear changes across the 12 months. We compared its performance with LGCM in determining heterogeneous developmental patterns of body weight gain amongst patients placed on DGT. Table 9 presents the GMM fit statistics for three class model.

Table 9 presents the parameter estimates for both the 2-class and 3-class GMM models. Each model includes fixed effects (intercepts and slopes) that describe the average weight gain trajectory within each class, as well as random effects to account for individual-level variability.

The results indicate significant weight gain trajectories for both Class 2 and Class 3. For Class 2 (Rapid Weight Gain), the linear intercept coefficient is 52.65 (p = 0.00188), and the quadratic coefficient is 52.65 (p = 0.00084), suggesting both a significant baseline weight and a non-linear growth pattern over time. Similarly, Class 3 (Minimal/No Weight Gain) shows a significant linear intercept of 53.84 (p < 0.0001) and an identical quadratic coefficient, indicating a stable weight pattern with no substantial increase. These results suggest that individuals in Class 2 experience notable weight gain, whereas those in Class 3 exhibit minimal or no weight gain over time. The random effects demonstrate considerable variability in baseline weight across trajectories in comparison to classes1 and 2. The variance in intercepts is 143.78 for the linear model and 144.45 for the quadratic model in Class 2, suggesting substantial individual differences in initial weight. In contrast, Class 3 exhibits a lower variance in intercept (59.94), reflecting less variation in baseline weight. The residual standard errors are 3.86 ± 0.12 and 3.80 ± 0.13 for Class 2, and 3.61 ± 0.11 and 3.59 ± 0.11 for Class 3, indicating unexplained variability in weight gain patterns for the two models. These findings emphasize the importance of considering both linear and quadratic components when modeling weight trajectories, particularly in individuals on dolutegravir (DTG). The presence of significant variation in weight gain patterns highlights the need for individualized treatment strategies that account for different weight gain trajectories.

Figure 4 illustrates class-specific model performance for LCGA, LGCM, and GMM using Log-Likelihood, AIC, and BIC, presented on a dual y-axis to retain the metrics in their original scales. Log-Likelihood is shown with solid lines, while AIC (dashed) and BIC (dot-dash) are plotted on the primary axis.

LCGA demonstrated the best overall fit, with progressively improved performance across classes and achieving the lowest AIC (42,562.61), lowest BIC (42,624.29), and highest Log-Likelihood (−21,271.31) at Class 3. In contrast, LGCM consistently underperformed, displaying higher AIC (44,674.91), higher BIC (44,736.59), and lower Log-Likelihood (−22,327.45) across all class structures. GMM showed competitive but relatively stable performance, with minimal variation in AIC (42,898.65), BIC (42,972.66), and Log-Likelihood (−21,437.32) across classes.

Overall, LCGA offered the most optimal balance between model fit and interpretability, followed by GMM, while LGCM was the least optimal. The dual-axis plot enables clear visualization of these trends, facilitating comparison across Log-Likelihood, AIC, and BIC. These results underscore LCGA—and to a lesser extent GMM as the preferred approaches for capturing heterogeneity in longitudinal weight-gain trajectories while minimizing the risk of overfitting (Figure 5).

These results underscore the need to balance model fit and interpretability. LPA may be sufficient for broad classification, but LCGA and GMM provide richer insights for more complex, time-dependent data. As Nagin (7) noted, model selection should consider both statistical adequacy and theoretical clarity. Masyn (15) warned of the potential for overfitting in highly flexible models like GMM, while Muthén (16) suggested validating latent classes using external samples to enhance generalizability. In conclusion, GMM emerged as the most informative model for capturing diverse body weight gain patterns among DTG-treated patients, followed by LCGA. However, researchers must carefully weigh the trade-offs between parsimony and explanatory depth when choosing an appropriate model for longitudinal analysis.

Our analyses identified distinct body weight gain trajectories among patients on DTG-based regimens, revealing clinically relevant subgroups. The majority of patients fell into the moderate weight gain trajectory (Class 1, ~93%), exhibiting predictable and steady body weight changes. In contrast, smaller subgroups demonstrated rapid body weight gain (Class 2, ~5–6%) or minimal/no body weight gain (Class 3, ~1%), highlighting heterogeneity in DTG treatment response. Rapid gainers were more likely to be younger and ART-naïve, suggesting a heightened metabolic response to DTG initiation, while minimal gainers were older or ART-experienced, possibly reflecting metabolic resistance or prior treatment effects. These patterns were consistent across multiple modeling approaches (LPA, LCGA, LGCM, GMM), emphasizing the robustness of the findings. Clinicians can use these insights to stratify patients by risk of atypical weight changes and tailor monitoring protocols accordingly, particularly during the first 6–12 months of therapy when deviations from expected trajectories are most pronounced.

From a management perspective, patients identified in the rapid gain trajectory may benefit from proactive interventions such as dietary counselling, physical activity guidance, or early pharmacologic strategies (e.g., metformin in high-risk cases) to mitigate cardiometabolic complications. Conversely, patients in the minimal/no gain group may require assessment for underlying nutritional or metabolic concerns, with consideration of therapy adjustments if clinically warranted. Incorporating routine weight monitoring, stratified by baseline characteristics such as age, sex, and prior ART experience, can facilitate early identification of patients at risk of atypical weight trajectories. Future research should explore the mechanistic drivers of these differential responses and evaluate targeted intervention strategies to optimize outcomes, supporting a more personalized approach to DTG-based treatment.

This study has several limitations. First, it was based on a single cohort using routine program data, which may limit generalizability and introduces potential measurement error, particularly in weight assessments. Second, the analysis relied on secondary data, restricting control over variable availability and measurement precision, and the models did not account for time-varying covariates such as dietary changes, comorbidities, or treatment adherence, which may influence weight gain trajectories. Third, although model fit indices guided the selection of latent classes, some subjective judgment was required in determining the optimal number and interpretation of classes, introducing potential model selection bias. Quadratic random intercepts were not supported by our data, as the estimated variance converged to zero, indicating that individual differences in quadratic growth were negligible in this sample. Finally, while distinct weight gain patterns were identified, biological, genetic, or behavioural predictors that could explain subgroup membership were not integrated. Future research should validate these trajectory classes in independent cohorts, incorporate metabolic and genetic biomarkers, and explore behavioural and contextual factors influencing weight outcomes. Such work would enhance external validity and support more precise targeting of interventions in HIV care.