MIDASim: a fast and simple simulator for realistic microbiome data.

Background: Advances in sequencing technology has led to the discovery of associations between the human microbiota and many diseases, conditions, and traits. With the increasing availability of microbiome data, many statistical methods have been developed for studying these associations. The growing number of newly developed methods highlights the need for simple, rapid, and reliable methods to simulate realistic microbiome data, which is essential for validating and evaluating the performance of these methods. However, generating realistic microbiome data is challenging due to the complex nature of microbiome data, which feature correlation between taxa, sparsity, overdispersion, and compositionality. Current methods for simulating microbiome data are deficient in their ability to capture these important features of microbiome data, or can require exorbitant computational time. Methods: We develop MIDASim ( MI crobiome DA ta Sim ulator), a fast and simple approach for simulating realistic microbiome data that reproduces the distributional and correlation structure of a template microbiome dataset. MIDASim is a two-step approach. The first step generates correlated binary indicators that represent the presence-absence status of all taxa, and the second step generates relative abundances and counts for the taxa that are considered to be present in step 1, utilizing a Gaussian copula to account for the taxon-taxon correlations. In the second step, MIDASim can operate in both a nonparametric and parametric mode. In the nonparametric mode, the Gaussian copula uses the empirical distribution of relative abundances for the marginal distributions. In the parametric mode, an inverse generalized gamma distribution is used in place of the empirical distribution. Results: We demonstrate improved performance of MIDASim relative to other existing methods using gut and vaginal data. MIDASim showed superior performance by PER-MANOVA and in terms of alpha diversity and beta dispersion in either parametric or nonparametric mode. We also show how MIDASim in parametric mode can be used to assess the performance of methods for finding differentially abundant taxa in a compositional model. Conclusions: MIDASim is easy to implement, flexible and suitable for most microbiome data simulation situations. MIDASim has three major advantages. First, MIDASim performs better in reproducing the distributional features of real data compared to other methods at both presence-absence level and relative-abundance level. MIDASim-simulated data are more similar to the template data than competing methods, as quantified using a variety of measures. Second, MIDASim makes few distributional assumptions for the relative abundances, and thus can easily accommodate complex distributional features in real data. Third, MIDASim is computationally efficient and can be used to simulate large microbiome datasets.

MERIT: Controlling Monte-Carlo error rate in large-scale Monte-Carlo hypothesis testing.

The use of Monte-Carlo (MC) p $$ p $$ -values when testing the significance of a large number of hypotheses is now commonplace. In large-scale hypothesis testing, we will typically encounter at least some p $$ p $$ -values near the threshold of significance, which require a larger number of MC replicates than p $$ p $$ -values that are far from the threshold. As a result, some incorrect conclusions can be reached due to MC error alone; for hypotheses near the threshold, even a very large number (eg, 1 0 6 $$ 1{0}^6 $$ ) of MC replicates may not be enough to guarantee conclusions reached using MC p $$ p $$ -values. Gandy and Hahn (GH)6-8 have developed the only method that directly addresses this problem. They defined a Monte-Carlo error rate (MCER) to be the probability that any decisions on accepting or rejecting a hypothesis based on MC p $$ p $$ -values are different from decisions based on ideal p $$ p $$ -values; their method then makes decisions by controlling the MCER. Unfortunately, the GH method is frequently very conservative, often making no rejections at all and leaving a large number of hypotheses "undecided". In this article, we propose MERIT, a method for large-scale MC hypothesis testing that also controls the MCER but is more statistically efficient than the GH method. Through extensive simulation studies, we demonstrate that MERIT controls the MCER while making more decisions that agree with the ideal p $$ p $$ -values than GH does. We also illustrate our method by an analysis of gene expression data from a prostate cancer study.

Ranked severe maternal morbidity index for population-level surveillance at delivery hospitalization based on hospital discharge data.

BACKGROUND: Severe maternal morbidity (SMM) is broadly defined as an unexpected and potentially life-threatening event associated with labor and delivery. The Centers for Disease Control and Prevention (CDC) produced 21 different indicators based on International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) hospital diagnostic and procedure codes to identify cases of SMM. OBJECTIVES: To examine existing SMM indicators and determine which indicators identified the most in-hospital mortality at delivery hospitalization. METHODS: Data from the 1993-2015 and 2017-2019 Healthcare Cost and Utilization Project's National Inpatient Sample were used to report SMM indicator-specific prevalences, in-hospital mortality rates, and population attributable fractions (PAF) of mortality. We hierarchically ranked indicators by their overall PAF of in-hospital mortality. Predictive modeling determined if SMM prevalence remained comparable after transition to ICD-10-CM coding. RESULTS: The study population consisted of 18,198,934 hospitalizations representing 87,864,173 US delivery hospitalizations. The 15 top ranked indicators identified 80% of in-hospital mortality; the proportion identified by the remaining indicators was negligible (2%). The top 15 indicators were: restoration of cardiac rhythm; cardiac arrest; mechanical ventilation; tracheostomy; amniotic fluid embolism; aneurysm; acute respiratory distress syndrome; acute myocardial infarction; shock; thromboembolism, pulmonary embolism; cerebrovascular disorders; sepsis; both DIC and blood transfusion; acute renal failure; and hysterectomy. The overall prevalence of the top 15 ranked SMM indicators (~22,000 SMM cases per year) was comparable after transition to ICD-10-CM coding. CONCLUSIONS: We determined the 15 indicators that identified the most in-hospital mortality at delivery hospitalization in the US. Continued testing of SMM indicators can improve measurement and surveillance of the most severe maternal complications at the population level.

Compositional analysis of microbiome data using the linear decomposition model (LDM).

SUMMARY: There are compelling reasons to test compositional hypotheses about microbiome data. We present here linear decomposition model-centered log ratio (LDM-clr), an extension of our LDM approach to allow fitting linear models to centered-log-ratio-transformed taxa count data. As LDM-clr is implemented within the existing LDM program, this extension enjoys all the features supported by LDM, including a compositional analysis of differential abundance at both the taxon and community levels, while allowing for a wide range of covariates and study designs for either association or mediation analysis. AVAILABILITY AND IMPLEMENTATION: LDM-clr has been added to the R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM.

Integrative analysis of microbial 16S gene and shotgun metagenomic sequencing data improves statistical efficiency.

Background: The most widely used technologies for profiling microbial communities are 16S marker-gene sequencing and shotgun metagenomic sequencing. Interestingly, many microbiome studies have performed both sequencing experiments on the same cohort of samples. The two sequencing datasets often reveal consistent patterns of microbial signatures, highlighting the potential for an integrative analysis to improve power of testing these signatures. However, differential experimental biases, partially overlapping samples, and differential library sizes pose tremendous challenges when combining the two datasets. Currently, researchers either discard one dataset entirely or use different datasets for different objectives. Methods: In this article, we introduce the first method of this kind, named Com-2seq, that combines the two sequencing datasets for testing differential abundance at the genus and community levels while overcoming these difficulties. The new method is based on our LOCOM model (Hu et al., 2022), which employs logistic regression for testing taxon differential abundance while remaining robust to experimental bias. To benchmark the performance of Com-2seq, we introduce two ad hoc approaches: applying LOCOM to pooled taxa count data and combining LOCOM p-values from analyzing each dataset separately. Results: Our simulation studies indicate that Com-2seq substantially improves statistical efficiency over analysis of either dataset alone and works better than the two ad hoc approaches. An application of Com-2seq to two real microbiome studies uncovered scientifically plausible findings that would have been missed by analyzing individual datasets. Conclusions: Com-2seq performs integrative analysis of 16S and metagenomic sequencing data, which improves statistical efficiency and has the potential to accelerate the search of microbial communities and taxa that are involved in human health and diseases.

Impact of Experimental Bias on Compositional Analysis of Microbiome Data.

Microbiome data are subject to experimental bias that is caused by DNA extraction and PCR amplification, among other sources, but this important feature is often ignored when developing statistical methods for analyzing microbiome data. McLaren, Willis, and Callahan (2019) proposed a model for how such biases affect the observed taxonomic profiles; this model assumes the main effects of bias without taxon-taxon interactions. Our newly developed method for testing the differential abundance of taxa, LOCOM, is the first method to account for experimental bias and is robust to the main effect biases. However, there is also evidence for taxon-taxon interactions. In this report, we formulated a model for interaction biases and used simulations based on this model to evaluate the impact of interaction biases on the performance of LOCOM as well as other available compositional analysis methods. Our simulation results indicate that LOCOM remained robust to a reasonable range of interaction biases. The other methods tend to have an inflated FDR even when there were only main effect biases. LOCOM maintained the highest sensitivity even when the other methods could not control the FDR. We thus conclude that LOCOM outperforms the other methods for compositional analysis of microbiome data considered here.

Compositional analysis of microbiome data using the linear decomposition model (LDM).

Summary: There are compelling reasons to test compositional hypotheses about microbiome data. We present here LDM-clr, an extension of our linear decomposition model (LDM) approach to allow fitting linear models to centered-log-ratio-transformed taxa count data. As LDM-clr is implemented within the existing LDM program, it enjoys all the features supported by LDM, including a compositional analysis of differential abundance at both the taxon and community levels, while allowing for a wide range of covariates and study designs for either association or mediation analysis. Availability and Implementation: LDM-clr has been added to the R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM . Contact: yijuan.hu@emory.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Integrative analysis of microbial 16S gene and shotgun metagenomic sequencing data improves statistical efficiency.

The most widely used technologies for profiling microbial communities are 16S marker-gene sequencing and shotgun metagenomic sequencing. Interestingly, many microbiome studies have performed both sequencing experiments on the same cohort of samples. The two sequencing datasets often reveal consistent patterns of microbial signatures, highlighting the potential for an integrative analysis to improve power of testing these signatures. However, differential experimental biases, partially overlapping samples, and differential library sizes pose tremendous challenges when combining the two datasets. Currently, researchers either discard one dataset entirely or use different datasets for different objectives. In this article, we introduce the first method of this kind, named Com-2seq, that combines the two sequencing datasets for the objective of testing differential abundance at the genus and community levels while overcoming these difficulties. We demonstrate that Com-2seq substantially improves statistical efficiency over analysis of either dataset alone and works better than two ad hoc approaches.

Impact of experimental bias on compositional analysis of microbiome data.

Microbiome data are subject to experimental bias that is caused by DNA extraction, PCR amplification among other sources, but this important feature is often ignored when developing statistical methods for analyzing microbiome data. McLaren, Willis and Callahan (2019) proposed a model for how such bias affects the observed taxonomic profiles, which assumes main effects of bias without taxon-taxon interactions. Our newly developed method, LOCOM (logistic regression for compositional analysis) for testing differential abundance of taxa, is the first method that accounted for experimental bias and is robust to the main effect biases. However, there is also evidence for taxon-taxon interactions. In this report, we formulated a model for interaction biases and used simulations based on this model to evaluate the impact of interaction biases on the performance of LOCOM as well as other available compositional analysis methods. Our simulation results indicated that LOCOM remained robust to a reasonable range of interaction biases. The other methods tended to have inflated FDR even when there were only main effect biases. LOCOM maintained the highest sensitivity even when the other methods cannot control the FDR. We thus conclude that LOCOM outperforms the other methods for compositional analysis of microbiome data considered here.

Fresh vs. frozen embryo transfer: new approach to minimize the limitations of using national surveillance data for clinical research.

OBJECTIVE: To assess the benefit of frozen vs. fresh elective single embryo transfer using traditional and novel methods of controlling for confounding. DESIGN: Retrospective cohort study using data from the National Assisted Reproductive Technology Surveillance System. SETTING: Not applicable. PATIENT(S): A total of 44,750 women aged 20-35 years undergoing their first lifetime oocyte retrieval and embryo transfer in 2016-2017, who had ≥4 embryos cryopreserved. INTERVENTION(S): Fresh elective single embryo transfer and frozen elective single embryo transfer. MAIN OUTCOME MEASURE(S): The primary outcome was a singleton live birth. Secondary outcomes included rates of total live birth (singleton plus multiple gestations), twin live birth, clinical intrauterine gestation, total pregnancy loss, biochemical pregnancy, and ectopic pregnancy. Outcomes for infants included gestational age at delivery, birth weight, and being small for gestational age. RESULT(S): The eligibility criteria were met by 6,324 fresh and 2,318 frozen cycles. Patients undergoing fresh and frozen transfer had comparable mean age (30.69 [standard deviation {SD} 0.08] years vs. 31.06 [SD 0.08] years) and body mass index (24.76 [SD 0.20] vs. 25.65 [SD 0.15]); however, women in the frozen cohort created more embryos (8.1 [SD 0.12] vs. 6.8 [SD 0.08]). Singleton live birth rates in the fresh vs. frozen groups were 51.4% vs. 48.8% (risk ratio 1.05; 95% confidence interval [CI], 1.00-1.10). After adjustment with a log-linear regression model and propensity score analysis, the difference in singleton live birth rates remained nonsignificant (adjusted risk ratio, 1.05; 95% CI, 0.97-1.14 and 1.02; 95% CI, 0.96-1.08, respectively). A novel dynamical model confirmed inherent fertility (probability of ever achieving a pregnancy) was balanced between groups (odds ratio, 1.23; 95% CI 0.78-1.95]). The per-cycle probability of singleton live birth was not different between groups (odds ratio 1.11 [95% CI 0.94-1.3]). CONCLUSION(S): In this retrospective cohort study of fresh vs. frozen elective single embryo transfer, there was no statistically significant difference in singleton live birth rate after adjustment using log-linear models and propensity score analysis. The successful application of a novel dynamical model, which incorporates multiple assisted reproductive technology cycles from the same woman as a surrogate for inherent fertility, offers a novel and complementary perspective for assessing interventions using national surveillance data.

What Can We Learn about the Bias of Microbiome Studies from Analyzing Data from Mock Communities?

It is known that data from both 16S and shotgun metagenomics studies are subject to biases that cause the observed relative abundances of taxa to differ from their true values. Model community analyses, in which the relative abundances of all taxa in the sample are known by construction, seem to offer the hope that these biases can be measured. However, it is unclear whether the bias we measure in a mock community analysis is the same as we measure in a sample in which taxa are spiked in at known relative abundance, or if the biases we measure in spike-in samples is the same as the bias we would measure in a real (e.g., biological) sample. Here, we consider these questions in the context of 16S rRNA measurements on three sets of samples: the commercially available Zymo cells model community; the Zymo model community mixed with Swedish Snus, a smokeless tobacco product that is virtually bacteria-free; and a set of commercially available smokeless tobacco products. Each set of samples was subject to four different extraction protocols. The goal of our analysis is to determine whether the patterns of bias observed in each set of samples are the same, i.e., can we learn about the bias in the commercially available smokeless tobacco products by studying the Zymo cells model community?

Testing microbiome associations with survival times at both the community and individual taxon levels.

BACKGROUND: Finding microbiome associations with possibly censored survival times is an important problem, especially as specific taxa could serve as biomarkers for disease prognosis or as targets for therapeutic interventions. The two existing methods for survival outcomes, MiRKAT-S and OMiSA, are restricted to testing associations at the community level and do not provide results at the individual taxon level. An ad hoc approach testing each taxon with a survival outcome using the Cox proportional hazard model may not perform well in the microbiome setting with sparse count data and small sample sizes. METHODS: We have previously developed the linear decomposition model (LDM) for testing continuous or discrete outcomes that unifies community-level and taxon-level tests into one framework. Here we extend the LDM to test survival outcomes. We propose to use the Martingale residuals or the deviance residuals obtained from the Cox model as continuous covariates in the LDM. We further construct tests that combine the results of analyzing each set of residuals separately. Finally, we extend PERMANOVA, the most commonly used distance-based method for testing community-level hypotheses, to handle survival outcomes in a similar manner. RESULTS: Using simulated data, we showed that the LDM-based tests preserved the false discovery rate for testing individual taxa and had good sensitivity. The LDM-based community-level tests and PERMANOVA-based tests had comparable or better power than MiRKAT-S and OMiSA. An analysis of data on the association of the gut microbiome and the time to acute graft-versus-host disease revealed several dozen associated taxa that would not have been achievable by any community-level test, as well as improved community-level tests by the LDM and PERMANOVA over those obtained using MiRKAT-S and OMiSA. CONCLUSIONS: Unlike existing methods, our new methods are capable of discovering individual taxa that are associated with survival times, which could be of important use in clinical settings.

A Bottom-up Approach to Testing Hypotheses That Have a Branching Tree Dependence Structure, with Error Rate Control.

Modern statistical analyses often involve testing large numbers of hypotheses. In many situations, these hypotheses may have an underlying tree structure that both helps determine the order that tests should be conducted but also imposes a dependency between tests that must be accounted for. Our motivating example comes from testing the association between a trait of interest and groups of microbes that have been organized into operational taxonomic units (OTUs) or amplicon sequence variants (ASVs). Given p-values from association tests for each individual OTU or ASV, we would like to know if we can declare a certain species, genus, or higher taxonomic group to be associated with the trait. For this problem, a bottom-up testing algorithm that starts at the lowest level of the tree (OTUs or ASVs) and proceeds upward through successively higher taxonomic groupings (species, genus, family etc.) is required. We develop such a bottom-up testing algorithm that controls a novel error rate that we call the false selection rate. By simulation, we also show that our approach is better at finding driver taxa, the highest level taxa below which there are dense association signals. We illustrate our approach using data from a study of the microbiome among patients with ulcerative colitis and healthy controls.

LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control.

Compositional analysis is based on the premise that a relatively small proportion of taxa are differentially abundant, while the ratios of the relative abundances of the remaining taxa remain unchanged. Most existing methods use log-transformed data, but log-transformation of data with pervasive zero counts is problematic, and these methods cannot always control the false discovery rate (FDR). Further, high-throughput microbiome data such as 16S amplicon or metagenomic sequencing are subject to experimental biases that are introduced in every step of the experimental workflow. McLaren et al. [eLife 8, e46923 (2019)] have recently proposed a model for how these biases affect relative abundance data. Motivated by this model, we show that the odds ratios in a logistic regression comparing counts in two taxa are invariant to experimental biases. With this motivation, we propose logistic compositional analysis (LOCOM), a robust logistic regression approach to compositional analysis, that does not require pseudocounts. Inference is based on permutation to account for overdispersion and small sample sizes. Traits can be either binary or continuous, and adjustment for confounders is supported. Our simulations indicate that LOCOM always preserved FDR and had much improved sensitivity over existing methods. In contrast, analysis of composition of microbiomes (ANCOM) and ANCOM with bias correction (ANCOM-BC)/ANOVA-Like Differential Expression tool (ALDEx2) had inflated FDR when the effect sizes were small and large, respectively. Only LOCOM was robust to experimental biases in every situation. The flexibility of our method for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. Our R package LOCOM is publicly available.

A rarefaction-without-resampling extension of PERMANOVA for testing presence-absence associations in the microbiome.

MOTIVATION: PERMANOVA is currently the most commonly used method for testing community-level hypotheses about microbiome associations with covariates of interest. PERMANOVA can test for associations that result from changes in which taxa are present or absent by using the Jaccard or unweighted UniFrac distance. However, such presence-absence analyses face a unique challenge: confounding by library size (total sample read count), which occurs when library size is associated with covariates in the analysis. It is known that rarefaction (subsampling to a common library size) controls this bias but at the potential costs of information loss and the introduction of a stochastic component into the analysis. RESULTS: Here, we develop a non-stochastic approach to PERMANOVA presence-absence analyses that aggregates information over all potential rarefaction replicates without actual resampling, when the Jaccard or unweighted UniFrac distance is used. We compare this new approach to three possible ways of aggregating PERMANOVA over multiple rarefactions obtained from resampling: averaging the distance matrix, averaging the (element-wise) squared distance matrix and averaging the F-statistic. Our simulations indicate that our non-stochastic approach is robust to confounding by library size and outperforms each of the stochastic resampling approaches. We also show that, when overdispersion is low, averaging the (element-wise) squared distance outperforms averaging the unsquared distance, currently implemented in the R package vegan. We illustrate our methods using an analysis of data on inflammatory bowel disease in which samples from case participants have systematically smaller library sizes than samples from control participants. AVAILABILITY AND IMPLEMENTATION: We have implemented all the approaches described above, including the function for calculating the analytical average of the squared or unsquared distance matrix, in our R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integrative analysis of relative abundance data and presence-absence data of the microbiome using the LDM.

SUMMARY: We previously developed the LDM for testing hypotheses about the microbiome that performs the test at both the community level and the individual taxon level. The LDM can be applied to relative abundance data and presence-absence data separately, which work well when associated taxa are abundant and rare, respectively. Here, we propose LDM-omni3 that combines LDM analyses at the relative abundance and presence-absence data scales, thereby offering optimal power across scenarios with different association mechanisms. The new LDM-omni3 test is available for the wide range of data types and analyses that are supported by the LDM. AVAILABILITY AND IMPLEMENTATION: The LDM-omni3 test has been added to the R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Associations between microbial communities and key chemical constituents in U.S. domestic moist snuff.

BACKGROUND: Smokeless tobacco (ST) products are widely used throughout the world and contribute to morbidity and mortality in users through an increased risk of cancers and oral diseases. Bacterial populations in ST contribute to taste, but their presence can also create carcinogenic, Tobacco-Specific N-nitrosamines (TSNAs). Previous studies of microbial communities in tobacco products lacked chemistry data (e.g. nicotine, TSNAs) to characterize the products and identify associations between carcinogen levels and taxonomic groups. This study uses statistical analysis to identify potential associations between microbial and chemical constituents in moist snuff products. METHODS: We quantitatively analyzed 38 smokeless tobacco products for TSNAs using liquid chromatography with tandem mass spectrometry (LC-MS/MS), and nicotine using gas chromatography with mass spectrometry (GC-MS). Moisture content determinations (by weight loss on drying), and pH measurements were also performed. We used 16S rRNA gene sequencing to characterize the microbial composition, and additionally measured total 16S bacterial counts using a quantitative PCR assay. RESULTS: Our findings link chemical constituents to their associated bacterial populations. We found core taxonomic groups often varied between manufacturers. When manufacturer and flavor were controlled for as confounding variables, the genus Lactobacillus was found to be positively associated with TSNAs. while the genera Enteractinococcus and Brevibacterium were negatively associated. Three genera (Corynebacterium, Brachybacterium, and Xanthomonas) were found to be negatively associated with nicotine concentrations. Associations were also investigated separately for products from each manufacturer. Products from one manufacturer had a positive association between TSNAs and bacteria in the genus Marinilactibacillus. Additionally, we found that TSNA levels in many products were lower compared with previously published chemical surveys. Finally, we observed consistent results when either relative or absolute abundance data were analyzed, while results from analyses of log-ratio-transformed abundances were divergent.

Efficient estimation of indirect effects in case-control studies using a unified likelihood framework.

Mediation models are a set of statistical techniques that investigate the mechanisms that produce an observed relationship between an exposure variable and an outcome variable in order to deduce the extent to which the relationship is influenced by intermediate mediator variables. For a case-control study, the most common mediation analysis strategy employs a counterfactual framework that permits estimation of indirect and direct effects on the odds ratio scale for dichotomous outcomes, assuming either binary or continuous mediators. While this framework has become an important tool for mediation analysis, we demonstrate that we can embed this approach in a unified likelihood framework for mediation analysis in case-control studies that leverages more features of the data (in particular, the relationship between exposure and mediator) to improve efficiency of indirect effect estimates. One important feature of our likelihood approach is that it naturally incorporates cases within the exposure-mediator model to improve efficiency. Our approach does not require knowledge of disease prevalence and can model confounders and exposure-mediator interactions, and is straightforward to implement in standard statistical software. We illustrate our approach using both simulated data and real data from a case-control genetic study of lung cancer.

The Effect of Antiretroviral Therapy for the Treatment of Human Immunodeficiency Virus (HIV)-1 in Pregnancy on Gestational Weight Gain.

BACKGROUND: Gestational weight gain above Institute of Medicine recommendations is associated with increased risk of pregnancy complications. The goal was to analyze the association between newer HIV antiretroviral regimens (ART) on gestational weight gain. METHODS: A retrospective cohort study of pregnant women with HIV-1 on ART. The primary outcome was incidence of excess gestational weight gain. Treatment effects were estimated by ART regimen type using log-linear models for relative risk (RR), adjusting for prepregnancy BMI and presence of detectable viral load at baseline. RESULTS: Three hundred three pregnant women were included in the analysis. Baseline characteristics, including prepregnancy BMI, viral load at prenatal care entry, and gestational age at delivery were similar by ART, including 53% of the entire cohort had initiated ART before pregnancy (P = nonsignificant). Excess gestational weight gain occurred in 29% of the cohort. Compared with non-integrase strand transfer inhibitor (-INSTI) or tenofovir alafenamide fumarate (TAF)-exposed persons, receipt of INSTI+TAF showed a 1.7-fold increased RR of excess gestational weight gain (95% CI: 1.18-2.68; P < .01), while women who received tenofovir disoproxil fumarate had a 0.64-fold decreased RR (95% CI: .41-.99; P = .047) of excess gestational weight gain. INSTI alone was not significantly associated with excess weight gain in this population. The effect of TAF without INSTI could not be inferred from our data. There was no difference in neonatal, obstetric, or maternal outcomes between the groups. CONCLUSIONS: Pregnant women receiving ART with a combined regimen of INSTI and TAF have increased risk of excess gestational weight gain.