A toolbox of machine learning software to support microbiome analysis.

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.

Overview of data preprocessing for machine learning applications in human microbiome research.

Although metagenomic sequencing is now the preferred technique to study microbiome-host interactions, analyzing and interpreting microbiome sequencing data presents challenges primarily attributed to the statistical specificities of the data (e.g., sparse, over-dispersed, compositional, inter-variable dependency). This mini review explores preprocessing and transformation methods applied in recent human microbiome studies to address microbiome data analysis challenges. Our results indicate a limited adoption of transformation methods targeting the statistical characteristics of microbiome sequencing data. Instead, there is a prevalent usage of relative and normalization-based transformations that do not specifically account for the specific attributes of microbiome data. The information on preprocessing and transformations applied to the data before analysis was incomplete or missing in many publications, leading to reproducibility concerns, comparability issues, and questionable results. We hope this mini review will provide researchers and newcomers to the field of human microbiome research with an up-to-date point of reference for various data transformation tools and assist them in choosing the most suitable transformation method based on their research questions, objectives, and data characteristics.

Machine learning approaches in microbiome research: challenges and best practices.

Microbiome data predictive analysis within a machine learning (ML) workflow presents numerous domain-specific challenges involving preprocessing, feature selection, predictive modeling, performance estimation, model interpretation, and the extraction of biological information from the results. To assist decision-making, we offer a set of recommendations on algorithm selection, pipeline creation and evaluation, stemming from the COST Action ML4Microbiome. We compared the suggested approaches on a multi-cohort shotgun metagenomics dataset of colorectal cancer patients, focusing on their performance in disease diagnosis and biomarker discovery. It is demonstrated that the use of compositional transformations and filtering methods as part of data preprocessing does not always improve the predictive performance of a model. In contrast, the multivariate feature selection, such as the Statistically Equivalent Signatures algorithm, was effective in reducing the classification error. When validated on a separate test dataset, this algorithm in combination with random forest modeling, provided the most accurate performance estimates. Lastly, we showed how linear modeling by logistic regression coupled with visualization techniques such as Individual Conditional Expectation (ICE) plots can yield interpretable results and offer biological insights. These findings are significant for clinicians and non-experts alike in translational applications.

Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action.

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.

Microbiome-based risk prediction in incident heart failure: a community challenge.

Heart failure (HF) is a major public health problem. Early identification of at-risk individuals could allow for interventions that reduce morbidity or mortality. The community-based FINRISK Microbiome DREAM challenge (synapse.org/finrisk) evaluated the use of machine learning approaches on shotgun metagenomics data obtained from fecal samples to predict incident HF risk over 15 years in a population cohort of 7231 Finnish adults (FINRISK 2002, n=559 incident HF cases). Challenge participants used synthetic data for model training and testing. Final models submitted by seven teams were evaluated in the real data. The two highest-scoring models were both based on Cox regression but used different feature selection approaches. We aggregated their predictions to create an ensemble model. Additionally, we refined the models after the DREAM challenge by eliminating phylum information. Models were also evaluated at intermediate timepoints and they predicted 10-year incident HF more accurately than models for 5- or 15-year incidence. We found that bacterial species, especially those linked to inflammation, are predictive of incident HF. This highlights the role of the gut microbiome as a potential driver of inflammation in HF pathophysiology. Our results provide insights into potential modeling strategies of microbiome data in prospective cohort studies. Overall, this study provides evidence that incorporating microbiome information into incident risk models can provide important biological insights into the pathogenesis of HF.

Indoor Airborne Microbiome and Endotoxin: Meteorological Events and Occupant Characteristics Are Important Determinants.

Airborne bacteria and endotoxin may affect asthma and allergies. However, there is limited understanding of the environmental determinants that influence them. This study investigated the airborne microbiomes in the homes of 1038 participants from five cities in Northern Europe: Aarhus, Bergen, Reykjavik, Tartu, and Uppsala. Airborne dust particles were sampled with electrostatic dust fall collectors (EDCs) from the participants' bedrooms. The dust washed from the EDCs' clothes was used to extract DNA and endotoxin. The DNA extracts were used for quantitative polymerase chain (qPCR) measurement and 16S rRNA gene sequencing, while endotoxin was measured using the kinetic chromogenic limulus amoebocyte lysate (LAL) assay. The results showed that households in Tartu and Aarhus had a higher bacterial load and diversity than those in Bergen and Reykjavik, possibly due to elevated concentrations of outdoor bacterial taxa associated with low precipitation and high wind speeds. Bergen-Tartu had the highest difference (ANOSIM R = 0.203) in ß diversity. Multivariate regression models showed that α diversity indices and bacterial and endotoxin loads were positively associated with the occupants' age, number of occupants, cleaning frequency, presence of dogs, and age of the house. Further studies are needed to understand how meteorological factors influence the indoor bacterial community in light of climate change.

Oral bacterial composition associated with lung function and lung inflammation in a community-based Norwegian population.

BACKGROUND: The oral cavity is the gateway to the bacteria community in the lung. Disruption of the symbiotic balance of the oral microbiota has been associated with respiratory diseases. However, little is known about the relationship between oral bacteria and respiratory outcomes in the general population. We aimed to describe the associations between oral bacteria, lung function, and lung inflammation in a community-based population. METHODS: Oral (gingival) samples were collected concurrently with spirometry tests in 477 adults (47% males, median age 28 years) from the RHINESSA study in Bergen, Norway. Bacterial DNA from the 16S rRNA gene from gingival fluid were sequenced by Illumina®MiSeq. Lung function was measured using spirometry and measurement of fractional exhaled nitric oxide (FeNO) were performed to examine airway inflammation. Differential abundance analysis was performed using ANCOM-BC, adjusting for weight, education, and smoking. RESULTS: The abundance of the genera Clostridiales, Achromobacter, Moraxella, Flavitalea and Helicobacter were significantly different among those with low FEV1 (< lower limit of normal (LLN)) as compared to normal FEV1 i.e. ≥ LLN. Twenty-three genera differed in abundance between among those with low FVC < LLN as compared to normal FEV1 ≥ LLN. The abundance of 27 genera from phyla Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria and Sacchribacteria differed significantly between elevated FeNO levels (≥ 50 ppb) compared to FeNO ≤ 25 ppb. CONCLUSION: Oral bacterial composition was significantly different for those with low FEV or FVC as compared to those with normal lung function equal to or higher than LLN. Differential bacterial composition was also observed for elevated FeNO levels.

Previous tuberculosis infection associated with increased frequency of asthma and respiratory symptoms in a Nordic-Baltic multicentre population study.

Background: Tuberculosis (TB) infection induces profound local and systemic, immunological and inflammatory changes that could influence the development of other respiratory diseases; however, the association between TB and asthma is only partly understood. Our objective was to study the association of TB with asthma and respiratory symptoms in a Nordic-Baltic population-based study. Methods: We included data from the Respiratory Health in Northern Europe (RHINE) study, in which information on general characteristics, TB infection, asthma and asthma-like symptoms were collected using standardised postal questionnaires. Asthma was defined based on asthma medication usage and/or asthma attacks 12 months prior to the study, and/or by a report of ≥three out of five respiratory symptoms in the last 12 months. Allergic/nonallergic asthma were defined as asthma with/without nasal allergy. The associations of TB with asthma outcomes were analysed using logistic regressions with adjustments for age, sex, smoking, body mass index and parental education. Results: We included 8379 study participants aged 50-75 years, 61 of whom reported having had TB. In adjusted analyses, participants with a history of TB had higher odds of asthma (OR 1.99, 95% CI 1.13-3.47). The associations were consistent for nonallergic asthma (OR 2.17, 95% CI 1.16-4.07), but not for allergic asthma (OR 1.20, 95% CI 0.53-2.71). Conclusion: We found that in a large Northern European population-based cohort, persons with a history of TB infection more frequently had asthma and asthma symptoms. We speculate that this may reflect long-term effects of TB, including direct damage to the airways and lungs, as well as inflammatory responses.

Exposure to Antibacterial Chemicals Is Associated With Altered Composition of Oral Microbiome.

Antimicrobial chemicals are used as preservatives in cosmetics, pharmaceuticals, and food to prevent the growth of bacteria and fungi in the products. Unintentional exposure in humans to such chemicals is well documented, but whether they also interfere with human oral microbiome composition is largely unexplored. In this study, we explored whether the oral bacterial composition is affected by exposure to antibacterial and environmental chemicals. Gingival fluid, urine, and interview data were collected from 477 adults (18-47 years) from the RHINESSA study in Bergen, Norway. Urine biomarkers of triclosan, triclocarban, parabens, benzophenone-3, bisphenols, and 2,4- and 2,5-dichlorophenols (DCPs) were quantified (by mass spectrometry). Microbiome analysis was based on 16S amplicon sequencing. Diversity and differential abundance analyses were performed to identify how microbial communities may change when comparing groups of different chemical exposure. We identified that high urine levels (>75th percentile) of propyl parabens were associated with a lower abundance of bacteria genera TM7 [G-3], Helicobacter, Megasphaera, Mitsuokella, Tannerella, Propionibacteriaceae [G-2], and Dermabacter, as compared with low propylparaben levels (<25th percentile). High exposure to ethylparaben was associated with a higher abundance of Paracoccus. High urine levels of bisphenol A were associated with a lower abundance of Streptococcus and exposure to another environmental chemical, 2,4-DCP, was associated with a lower abundance of Treponema, Fretibacterium, and Bacteroidales [G-2]. High exposure to antibacterial and environmental chemicals was associated with an altered composition of gingiva bacteria; mostly commensal bacteria in the oral cavity. Our results highlight a need for a better understanding of how antimicrobial chemical exposure influences the human microbiome.

Association of oral bacteria with oral hygiene habits and self-reported gingival bleeding.

AIM: To describe associations of gingival bacterial composition and diversity with self-reported gingival bleeding and oral hygiene habits in a Norwegian regional-based population. MATERIALS AND METHODS: We examined the microbiome composition of the gingival fluid (16S amplicon sequencing) in 484 adult participants (47% females; median age 28 years) in the Respiratory Health in Northern Europe, Spain and Australia (RHINESSA) study in Bergen, Norway. We explored bacterial diversity and abundance differences by the community periodontal index score, self-reported frequency of gingival bleeding, and oral hygiene habits. RESULTS: Gingival bacterial diversity increased with increasing frequency of self-reported gingival bleeding, with higher Shannon diversity index for "always" ß = 0.51 and "often" ß = 0.75 (p < .001) compared to "never" gingival bleeding. Frequent gingival bleeding was associated with higher abundance of several bacteria such as Porphyromonas endodontalis, Treponema denticola, and Fretibacterium spp., but lower abundance of bacteria within the gram-positive phyla Firmicutes and Actinobacteria. Flossing and rinsing with mouthwash twice daily were associated with higher total abundance of bacteria in the Proteobacteria phylum but with lower bacterial diversity compared to those who never flossed or never used mouthwash. CONCLUSIONS: A high frequency of self-reported gingival bleeding was associated with higher bacterial diversity than found in participants reporting no gingival bleeding and with higher total abundance of known periodontal pathogens such as Porphyromonas spp., Treponema spp., and Bacteroides spp.

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions.

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

Cross-sectional and longitudinal association of non-exercise estimated cardiorespiratory fitness with depression and anxiety in the general population: The HUNT study.

BACKGROUND: Cardiorespiratory fitness may help to prevent depression and anxiety. A paucity of literature has considered the relationship between cardiorespiratory fitness (CRF) and the incidence of depression and anxiety. The objective of this study was to investigate cross-sectional and longitudinal associations of estimated cardiorespiratory fitness (CRF) with depression and anxiety. METHODS: This study included middle-aged and older participants from the second (HUNT 2, 1995-1997) and third (HUNT3, 2006-2008) survery of the Nord-Trøndelag Health Study (HUNT). Baseline non-exercise estimated CRF (eCRF) was determined using standardized algorithms. Depression and anxiety were measured using the Hospital Anxiety and Depression Scale. Logistic regression models were used to evaluate the cross-sectional and longitudinal associations between eCRF and depression and anxiety. RESULTS: In cross-sectional adjusted analysis including those who participated in HUNT2 (n = 26,615 mean age 55.7 years), those with medium and high level of eCRF had 21% (OR, 0.79; 95% CI, 0.71-0.89) and 26% (OR, 0.74; 95% CI, 0.66-0.83) lower odds of depression compared to those with low eCRF level, respectively. Longitudinal analysis including those who participated in both HUNT2 and HUNT3 (n = 14,020 mean age 52.2 years) found that medium and level of eCRF was associated with 22% (OR, 0.78; 95% CI, 0.64-0.96) and 19% (OR, 0.81; 95% CI, 0.66-0.99) lower odds of depression compared to those with low eCRF level, respectively. CRF was not associated with anxiety, either cross-sectionally or longitudinally. CONCLUSION: Our data suggest that a medium and high level of eCRF during late middle age is cross-sectionally and prospectively associated with lower odds of depression. However, our data do not support that eCRF is associated with anxiety. Further studies are warranted to conclude a causal relationship between eCRF and depression.

Cardiorespiratory Fitness and the Risk of First Acute Myocardial Infarction: The HUNT Study.

Background The majority of studies evaluating cardiorespiratory fitness ( CRF ) as a cardiovascular risk factor use cardiovascular mortality and not cardiovascular disease events as the primary end point, and generally do not include women. The aim of this study was to investigate the association of estimated CRF ( eCRF ) with the risk of first acute myocardial infarction ( AMI ). Methods and Results We included 26 163 participants (51.5% women) from the HUNT study (Nord-Trøndelag Health Study), with a mean age of 55.7 years, without cardiovascular disease at baseline. Baseline eCRF was grouped into tertiles. AMI was derived from hospital records and deaths from the Norwegian Cause of Death Registry. We used Fine and Gray regression modeling to estimate subdistribution hazards ratio ( SHR ) of AMI , accounting for competing risk of death. During a mean (range) follow-up of 13 (0.02-15.40) years (347 462 person-years), 1566 AMI events were recorded. In fully adjusted models men in the 2 highest eCRF had 4% ( SHR : 0.96, 95% CI : 0.83-1.11) and 10% ( SHR : 0.90, 95% CI : 0.77-1.05) lower SHR of AMI , respectively, when compared with men in the lowest tertile. The corresponding numbers in women were 12% ( SHR : 0.88, 95% CI : 0.72-1.08) and 25% ( SHR : 0.75, 95% CI : 0.60-0.95). Conclusions eCRF was inversely associated with risk of AMI event among women but not in men. Our data suggest that high eCRF may have substantial benefit in reducing the risk of AMI . Therefore, our data suggest that an increased focus on eCRF as a cardiovascular disease risk marker in middle-aged and older adults is warranted.

Determinants of Transitional Zone Area and Porosity of the Proximal Femur Quantified In Vivo in Postmenopausal Women.

Bone architecture as well as size and shape is important for bone strength and risk of fracture. Most bone loss is cortical and occurs by trabecularization of the inner part of the cortex. We therefore wanted to identify determinants of the bone architecture, especially the area and porosity of the transitional zone, an inner cortical region with a large surface/matrix volume available for intracortical remodeling. In 211 postmenopausal women aged 54 to 94 years with nonvertebral fractures and 232 controls from the Tromsø Study, Norway, we quantified femoral subtrochanteric architecture in CT images using StrAx1.0 software, and serum levels of bone turnover markers (BTM, procollagen type I N-terminal propeptide and C-terminal cross-linking telopeptide of type I collagen). Multivariable linear and logistic regression analyses were used to quantify associations of age, weight, height, and bone size with bone architecture and BTM, and odds ratio (OR) for fracture. Increasing age, height, and larger total cross-sectional area (TCSA) were associated with larger transitional zone CSA and transitional zone CSA/TCSA (standardized coefficients [STB] = 0.11 to 0.80, p ≤ 0.05). Increasing weight was associated with larger TCSA, but smaller transitional zone CSA/TCSA and thicker cortices (STB = 0.15 to 0.22, p < 0.01). Increasing height and TCSA were associated with higher porosity of the transitional zone (STB = 0.12 to 0.46, p < 0.05). Increasing BTM were associated with larger TCSA, larger transitional zone CSA/TCSA, and higher porosity of each of the cortical compartments (p < 0.01). Fracture cases exhibited larger transitional zone CSA and higher porosity than controls (p < 0.001). Per SD increasing CSA and porosity of the transitional zone, OR for fracture was 1.71 (95% CI, 1.37 to 2.14) and 1.51 (95% CI, 1.23 to 1.85), respectively. Cortical bone architecture is determined mainly by bone size as built during growth and is modified by lifestyle factors throughout life through bone turnover. Fracture cases exhibited larger transitional zone area and porosity, highlighting the importance of cortical bone architecture for fracture propensity.

Bone turnover markers are associated with higher cortical porosity, thinner cortices, and larger size of the proximal femur and non-vertebral fractures.

Bone turnover markers (BTM) predict bone loss and fragility fracture. Although cortical porosity and cortical thinning are important determinants of bone strength, the relationship between BTM and cortical porosity has, however, remained elusive. We therefore wanted to examine the relationship of BTM with cortical porosity and risk of non-vertebral fracture. In 211 postmenopausal women aged 54-94 years with non-vertebral fractures and 232 age-matched fracture-free controls from the Tromsø Study, Norway, we quantified femoral neck areal bone mineral density (FN aBMD), femoral subtrochanteric bone architecture, and assessed serum levels of procollagen type I N-terminal propeptide (PINP) and C-terminal cross-linking telopeptide of type I collagen (CTX). Fracture cases exhibited higher PINP and CTX levels, lower FN aBMD, larger total and medullary cross-sectional area (CSA), thinner cortices, and higher cortical porosity of the femoral subtrochanter than controls (p≤0.01). Each SD increment in PINP and CTX was associated with 0.21-0.26 SD lower total volumetric BMD, 0.10-0.14 SD larger total CSA, 0.14-0.18 SD larger medullary CSA, 0.13-0.18 SD thinner cortices, and 0.27-0.33 SD higher porosity of the total cortex, compact cortex, and transitional zone (all p≤0.01). Moreover, each SD of higher PINP and CTX was associated with increased odds for fracture after adjustment for age, height, and weight (ORs 1.49; 95% CI, 1.20-1.85 and OR 1.22; 95% CI, 1.00-1.49, both p<0.05). PINP, but not CTX, remained associated with fracture after accounting for FN aBMD, cortical porosity or cortical thickness (OR ranging from 1.31 to 1.39, p ranging from 0.005 to 0.028). In summary, increased BTM levels are associated with higher cortical porosity, thinner cortices, larger bone size and higher odds for fracture. We infer that this is produced by increased periosteal apposition, intracortical and endocortical remodeling; and that these changes in bone architecture are predisposing to fracture.

Factors associated with adherence to antiretroviral therapy in HIV-infected patients in Kathmandu District, Nepal.

PURPOSE: There are a high number of HIV-infected patients receiving antiretroviral therapy (ART) in the Kathmandu District of Nepal, but information on adherence and factors influencing it are scarce in this population. The present study aimed to estimate ART adherence among HIV-infected patients in the Kathmandu District of Nepal, and to determine the factors associated with ART adherence. PATIENTS AND METHODS: This study included 316 HIV-infected patients attending three ART centers in the Kathmandu District. Information on sociodemographic characteristics, socioeconomic status, and ART use for the previous 7 days was collected via interview. Participants were considered adherent if they reported taking ≥95% of their ART as prescribed. The association between explanatory variables and ART adherence was measured using logistic regression and reported as odds ratios (OR) with 95% confidence intervals (CI). RESULTS: Male participants accounted for 64.6% (n=204). Overall ART adherence was 86.7%. ART adherence in men and women were 84.3% and 91.1%, respectively. Age (OR 1.04; 95% CI 1.00-1.09), travel time to ART centers (OR 1.38; 95% CI 1.12-1.71), history of illegal drug use (OR 3.98; 95% CI 1.71-9.24), and adverse effects (OR 4.88; 95% CI 1.09-21.8), were all independently and negatively associated with ART adherence. Use of reminder tools (OR 3.45; 95% CI 1.33-8.91) was independently and positively associated with ART adherence. CONCLUSION: The observed ART adherence in this study is encouraging. Travel time to ART centers, self-reported adverse effects, illegal drug use, and not using reminder tools were the major determinants of ART adherence. Interventions that take these factors into account could further improve ART adherence.