A pan-cancer analysis of the microbiome in metastatic cancer.

Microbial communities are resident to multiple niches of the human body and are important modulators of the host immune system and responses to anticancer therapies. Recent studies have shown that complex microbial communities are present within primary tumors. To investigate the presence and relevance of the microbiome in metastases, we integrated mapping and assembly-based metagenomics, genomics, transcriptomics, and clinical data of 4,160 metastatic tumor biopsies. We identified organ-specific tropisms of microbes, enrichments of anaerobic bacteria in hypoxic tumors, associations between microbial diversity and tumor-infiltrating neutrophils, and the association of Fusobacterium with resistance to immune checkpoint blockade (ICB) in lung cancer. Furthermore, longitudinal tumor sampling revealed temporal evolution of the microbial communities and identified bacteria depleted upon ICB. Together, we generated a pan-cancer resource of the metastatic tumor microbiome that may contribute to advancing treatment strategies.

The long-term genetic stability and individual specificity of the human gut microbiome.

By following up the gut microbiome, 51 human phenotypes and plasma levels of 1,183 metabolites in 338 individuals after 4 years, we characterize microbial stability and variation in relation to host physiology. Using these individual-specific and temporally stable microbial profiles, including bacterial SNPs and structural variations, we develop a microbial fingerprinting method that shows up to 85% accuracy in classifying metagenomic samples taken 4 years apart. Application of our fingerprinting method to the independent HMP cohort results in 95% accuracy for samples taken 1 year apart. We further observe temporal changes in the abundance of multiple bacterial species, metabolic pathways, and structural variation, as well as strain replacement. We report 190 longitudinal microbial associations with host phenotypes and 519 associations with plasma metabolites. These associations are enriched for cardiometabolic traits, vitamin B, and uremic toxins. Finally, mediation analysis suggests that the gut microbiome may influence cardiometabolic health through its metabolites.

Metabolic Dynamics and Prediction of Gestational Age and Time to Delivery in Pregnant Women.

Metabolism during pregnancy is a dynamic and precisely programmed process, the failure of which can bring devastating consequences to the mother and fetus. To define a high-resolution temporal profile of metabolites during healthy pregnancy, we analyzed the untargeted metabolome of 784 weekly blood samples from 30 pregnant women. Broad changes and a highly choreographed profile were revealed: 4,995 metabolic features (of 9,651 total), 460 annotated compounds (of 687 total), and 34 human metabolic pathways (of 48 total) were significantly changed during pregnancy. Using linear models, we built a metabolic clock with five metabolites that time gestational age in high accordance with ultrasound (R = 0.92). Furthermore, two to three metabolites can identify when labor occurs (time to delivery within two, four, and eight weeks, AUROC ≥ 0.85). Our study represents a weekly characterization of the human pregnancy metabolome, providing a high-resolution landscape for understanding pregnancy with potential clinical utilities.

SARS-CoV-2-infection- and vaccine-induced antibody responses are long lasting with an initial waning phase followed by a stabilization phase.

It is thought that mRNA-based vaccine-induced immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) wanes quickly, based mostly on short-term studies. Here, we analyzed the kinetics and durability of the humoral responses to SARS-CoV-2 infection and vaccination using >8,000 longitudinal samples collected over a 3-year period in New York City. Upon primary immunization, participants with pre-existing immunity mounted higher antibody responses faster and achieved higher steady-state antibody titers than naive individuals. Antibody kinetics were characterized by two phases: an initial rapid decay, followed by a stabilization phase with very slow decay. Booster vaccination equalized the differences in antibody concentration between participants with and without hybrid immunity, but the peak antibody titers decreased with each successive antigen exposure. Breakthrough infections increased antibodies to similar titers as an additional vaccine dose in naive individuals. Our study provides strong evidence that SARS-CoV-2 antibody responses are long lasting, with initial waning followed by stabilization.

Smartphones and the Neuroscience of Mental Health.

Improvements in understanding the neurobiological basis of mental illness have unfortunately not translated into major advances in treatment. At this point, it is clear that psychiatric disorders are exceedingly complex and that, in order to account for and leverage this complexity, we need to collect longitudinal data sets from much larger and more diverse samples than is practical using traditional methods. We discuss how smartphone-based research methods have the potential to dramatically advance our understanding of the neuroscience of mental health. This, we expect, will take the form of complementing lab-based hard neuroscience research with dense sampling of cognitive tests, clinical questionnaires, passive data from smartphone sensors, and experience-sampling data as people go about their daily lives. Theory- and data-driven approaches can help make sense of these rich data sets, and the combination of computational tools and the big data that smartphones make possible has great potential value for researchers wishing to understand how aspects of brain function give rise to, or emerge from, states of mental health and illness.

Wearable Devices: Implications for Precision Medicine and the Future of Health Care.

Wearable devices are integrated analytical units equipped with sensitive physical, chemical, and biological sensors capable of noninvasive and continuous monitoring of vital physiological parameters. Recent advances in disciplines including electronics, computation, and material science have resulted in affordable and highly sensitive wearable devices that are routinely used for tracking and managing health and well-being. Combined with longitudinal monitoring of physiological parameters, wearables are poised to transform the early detection, diagnosis, and treatment/management of a range of clinical conditions. Smartwatches are the most commonly used wearable devices and have already demonstrated valuable biomedical potential in detecting clinical conditions such as arrhythmias, Lyme disease, inflammation, and, more recently, COVID-19 infection. Despite significant clinical promise shown in research settings, there remain major hurdles in translating the medical uses of wearables to the clinic. There is a clear need for more effective collaboration among stakeholders, including users, data scientists, clinicians, payers, and governments, to improve device security, user privacy, data standardization, regulatory approval, and clinical validity. This review examines the potential of wearables to offer affordable and reliable measures of physiological status that are on par with FDA-approved specialized medical devices. We briefly examine studies where wearables proved critical for the early detection of acute and chronic clinical conditions with a particular focus on cardiovascular disease, viral infections, and mental health. Finally, we discuss current obstacles to the clinical implementation of wearables and provide perspectives on their potential to deliver increasingly personalized proactive health care across a wide variety of conditions.

The dynamic nature of student discipline and discipline disparities.

Researchers have long used end-of-year discipline rates to identify punitive schools, explore sources of inequitable treatment, and evaluate interventions designed to stem both discipline and racial disparities in discipline. Yet, this approach leaves us with a "static view"-with no sense of how disciplinary responses fluctuate throughout the year. What if daily discipline rates, and daily discipline disparities, shift over the school year in ways that could inform when and where to intervene? This research takes a "dynamic view" of discipline. It leverages 4 years of atypically detailed data regarding the daily disciplinary experiences of 46,964 students from 61 middle schools in one of the nation's largest school districts. Reviewing these data, we find that discipline rates are indeed dynamic. For all student groups, the daily discipline rate grows from the beginning of the school year to the weeks leading up to the Thanksgiving break, falls before major breaks, and grows following major breaks. During periods of escalation, the daily discipline rate for Black students grows significantly faster than the rate for White students-widening racial disparities. Given this, districts hoping to stem discipline and disparities may benefit from timing interventions to precede these disciplinary spikes. In addition, early-year Black-White disparities can be used to identify the schools in which Black-White disparities are most likely to emerge by the end of the school year. Thus, the results reported here provide insights regarding not only when to intervene, but where to intervene to reduce discipline rates and disparities.

Mapping human brain charts cross-sectionally and longitudinally.

Brain scans acquired across large, age-diverse cohorts have facilitated recent progress in establishing normative brain aging charts. Here, we ask the critical question of whether cross-sectional estimates of age-related brain trajectories resemble those directly measured from longitudinal data. We show that age-related brain changes inferred from cross-sectionally mapped brain charts can substantially underestimate actual changes measured longitudinally. We further find that brain aging trajectories vary markedly between individuals and are difficult to predict with population-level age trends estimated cross-sectionally. Prediction errors relate modestly to neuroimaging confounds and lifestyle factors. Our findings provide explicit evidence for the importance of longitudinal measurements in ascertaining brain development and aging trajectories.

Inferring the T cell repertoire dynamics of healthy individuals.

The adaptive immune system is a diverse ecosystem that responds to pathogens by selecting cells with specific receptors. While clonal expansion in response to particular immune challenges has been extensively studied, we do not know the neutral dynamics that drive the immune system in the absence of strong stimuli. Here, we learn the parameters that underlie the clonal dynamics of the T cell repertoire in healthy individuals of different ages, by applying Bayesian inference to longitudinal immune repertoire sequencing (RepSeq) data. Quantifying the experimental noise accurately for a given RepSeq technique allows us to disentangle real changes in clonal frequencies from noise. We find that the data are consistent with clone sizes following a geometric Brownian motion and show that its predicted steady state is in quantitative agreement with the observed power-law behavior of the clone-size distribution. The inferred turnover time scale of the repertoire increases with patient age and depends on the clone size in some individuals.

Generational differences in mental health trends in the twenty-first century.

Given the observed deterioration in mental health among Australians over the past decade, this study investigates to what extent this differs in people born in different decades-i.e., possible birth cohort differences in the mental health of Australians. Using 20 y of data from a large, nationally representative panel survey (N = 27,572), we find strong evidence that cohort effects are driving the increase in population-level mental ill-health. Deteriorating mental health is particularly pronounced among people born in the 1990s and seen to a lesser extent among the 1980s cohort. There is little evidence that mental health is worsening with age for people born prior to the 1980s. The findings from this study highlight that it is the poorer mental health of Millennials that is driving the apparent deterioration in population-level mental health. Understanding the context and changes in society that have differentially affected younger people may inform efforts to ameliorate this trend and prevent it continuing for emerging cohorts.

Childhood Non-HDL Cholesterol and LDL Cholesterol and Adult Atherosclerotic Cardiovascular Events.

BACKGROUND: Although low-density lipoprotein cholesterol (LDL-C) remains the primary cholesterol target in clinical practice in children and adults, non-high-density lipoprotein cholesterol (non-HDL-C) has been suggested as a more accurate measure of atherosclerotic cardiovascular disease (ASCVD) risk. We examined the associations of childhood non-HDL-C and LDL-C levels with adult ASCVD events and determined whether non-HDL-C has better utility than LDL-C in predicting adult ASCVD events. METHODS: This prospective cohort study included 21 126 participants from the i3C Consortium (International Childhood Cardiovascular Cohorts). Proportional hazards regressions were used to estimate the risk for incident fatal and fatal/nonfatal ASCVD events associated with childhood non-HDL-C and LDL-C levels (age- and sex-specific z scores; concordant/discordant categories defined by guideline-recommended cutoffs), adjusted for sex, Black race, cohort, age at and calendar year of child measurement, body mass index, and systolic blood pressure. Predictive utility was determined by the C index. RESULTS: After an average follow-up of 35 years, 153 fatal ASCVD events occurred in 21 126 participants (mean age at childhood visits, 11.9 years), and 352 fatal/nonfatal ASCVD events occurred in a subset of 11 296 participants who could be evaluated for this outcome. Childhood non-HDL-C and LDL-C levels were each associated with higher risk of fatal and fatal/nonfatal ASCVD events (hazard ratio ranged from 1.27 [95% CI, 1.14-1.41] to 1.35 [95% CI, 1.13-1.60] per unit increase in the risk factor z score). Non-HDL-C had better discriminative utility than LDL-C (difference in C index, 0.0054 [95% CI, 0.0006-0.0102] and 0.0038 [95% CI, 0.0008-0.0068] for fatal and fatal/nonfatal events, respectively). The discordant group with elevated non-HDL-C and normal LDL-C had a higher risk of ASCVD events compared with the concordant group with normal non-HDL-C and LDL-C (fatal events: hazard ratio, 1.90 [95% CI, 0.98-3.70]; fatal/nonfatal events: hazard ratio, 1.94 [95% CI, 1.23-3.06]). CONCLUSIONS: Childhood non-HDL-C and LDL-C levels are associated with ASCVD events in midlife. Non-HDL-C is better than LDL-C in predicting adult ASCVD events, particularly among individuals who had normal LDL-C but elevated non-HDL-C. These findings suggest that both non-HDL-C and LDL-C are useful in identifying children at higher risk of ASCVD events, but non-HDL-C may provide added prognostic information when it is discordantly higher than the corresponding LDL-C and has the practical advantage of being determined without a fasting sample.

GWAS of longitudinal trajectories at biobank scale.

Biobanks linked to massive, longitudinal electronic health record (EHR) data make numerous new genetic research questions feasible. One among these is the study of biomarker trajectories. For example, high blood pressure measurements over visits strongly predict stroke onset, and consistently high fasting glucose and Hb1Ac levels define diabetes. Recent research reveals that not only the mean level of biomarker trajectories but also their fluctuations, or within-subject (WS) variability, are risk factors for many diseases. Glycemic variation, for instance, is recently considered an important clinical metric in diabetes management. It is crucial to identify the genetic factors that shift the mean or alter the WS variability of a biomarker trajectory. Compared to traditional cross-sectional studies, trajectory analysis utilizes more data points and captures a complete picture of the impact of time-varying factors, including medication history and lifestyle. Currently, there are no efficient tools for genome-wide association studies (GWASs) of biomarker trajectories at the biobank scale, even for just mean effects. We propose TrajGWAS, a linear mixed effect model-based method for testing genetic effects that shift the mean or alter the WS variability of a biomarker trajectory. It is scalable to biobank data with 100,000 to 1,000,000 individuals and many longitudinal measurements and robust to distributional assumptions. Simulation studies corroborate that TrajGWAS controls the type I error rate and is powerful. Analysis of eleven biomarkers measured longitudinally and extracted from UK Biobank primary care data for more than 150,000 participants with 1,800,000 observations reveals loci that significantly alter the mean or WS variability.

The impact of anorexia nervosa and BMI polygenic risk on childhood growth: A 20-year longitudinal population-based study.

Growth deviating from the norm during childhood has been associated with anorexia nervosa (AN) and obesity later in life. In this study, we examined whether polygenic scores (PGSs) for AN and BMI are associated with growth trajectories spanning the first two decades of life. AN PGSs and BMI PGSs were calculated for participants of the Avon Longitudinal Study of Parents and Children (ALSPAC; n = 8,654). Using generalized (mixed) linear models, we associated PGSs with trajectories of weight, height, body mass index (BMI), fat mass index (FMI), lean mass index (LMI), and bone mineral density (BMD). Female participants with AN PGSs one standard deviation (SD) higher had, on average, 0.004% slower growth in BMI between the ages 6.5 and 24 years and a 0.4% slower gain in BMD between the ages 10 and 24 years. Higher BMI PGSs were associated with faster growth for BMI, FMI, LMI, BMD, and weight trajectories in both sexes throughout childhood. Female participants with both a high AN PGS and a low BMI PGS showed slower growth compared to those with both a low AN PGS and a low BMI PGS. We conclude that AN PGSs and BMI PGSs have detectable sex-specific effects on growth trajectories. Female participants with a high AN PGS and low BMI PGS likely constitute a high-risk group for AN, as their growth was slower compared to their peers with high PGSs on both traits. Further research is needed to better understand how the AN PGS and the BMI PGS co-influence growth during childhood and whether a high BMI PGS can mitigate the effects of a high AN PGS.

A Bayesian approach for investigating the pharmacogenetics of combination antiretroviral therapy in people with HIV.

Combination antiretroviral therapy (ART) with at least three different drugs has become the standard of care for people with HIV (PWH) due to its exceptional effectiveness in viral suppression. However, many ART drugs have been reported to associate with neuropsychiatric adverse effects including depression, especially when certain genetic polymorphisms exist. Pharmacogenetics is an important consideration for administering combination ART as it may influence drug efficacy and increase risk for neuropsychiatric conditions. Large-scale longitudinal HIV databases provide researchers opportunities to investigate the pharmacogenetics of combination ART in a data-driven manner. However, with more than 30 FDA-approved ART drugs, the interplay between the large number of possible ART drug combinations and genetic polymorphisms imposes statistical modeling challenges. We develop a Bayesian approach to examine the longitudinal effects of combination ART and their interactions with genetic polymorphisms on depressive symptoms in PWH. The proposed method utilizes a Gaussian process with a composite kernel function to capture the longitudinal combination ART effects by directly incorporating individuals' treatment histories, and a Bayesian classification and regression tree to account for individual heterogeneity. Through both simulation studies and an application to a dataset from the Women's Interagency HIV Study, we demonstrate the clinical utility of the proposed approach in investigating the pharmacogenetics of combination ART and assisting physicians to make effective individualized treatment decisions that can improve health outcomes for PWH.

Fast and flexible inference for joint models of multivariate longitudinal and survival data using integrated nested Laplace approximations.

Modeling longitudinal and survival data jointly offers many advantages such as addressing measurement error and missing data in the longitudinal processes, understanding and quantifying the association between the longitudinal markers and the survival events, and predicting the risk of events based on the longitudinal markers. A joint model involves multiple submodels (one for each longitudinal/survival outcome) usually linked together through correlated or shared random effects. Their estimation is computationally expensive (particularly due to a multidimensional integration of the likelihood over the random effects distribution) so that inference methods become rapidly intractable, and restricts applications of joint models to a small number of longitudinal markers and/or random effects. We introduce a Bayesian approximation based on the integrated nested Laplace approximation algorithm implemented in the R package R-INLA to alleviate the computational burden and allow the estimation of multivariate joint models with fewer restrictions. Our simulation studies show that R-INLA substantially reduces the computation time and the variability of the parameter estimates compared with alternative estimation strategies. We further apply the methodology to analyze five longitudinal markers (3 continuous, 1 count, 1 binary, and 16 random effects) and competing risks of death and transplantation in a clinical trial on primary biliary cholangitis. R-INLA provides a fast and reliable inference technique for applying joint models to the complex multivariate data encountered in health research.

A joint normal-ordinal (probit) model for ordinal and continuous longitudinal data.

In biomedical studies, continuous and ordinal longitudinal variables are frequently encountered. In many of these studies it is of interest to estimate the effect of one of these longitudinal variables on the other. Time-dependent covariates have, however, several limitations; they can, for example, not be included when the data is not collected at fixed intervals. The issues can be circumvented by implementing joint models, where two or more longitudinal variables are treated as a response and modeled with a correlated random effect. Next, by conditioning on these response(s), we can study the effect of one or more longitudinal variables on another. We propose a normal-ordinal(probit) joint model. First, we derive closed-form formulas to estimate the model-based correlations between the responses on their original scale. In addition, we derive the marginal model, where the interpretation is no longer conditional on the random effects. As a consequence, we can make predictions for a subvector of one response conditional on the other response and potentially a subvector of the history of the response. Next, we extend the approach to a high-dimensional case with more than two ordinal and/or continuous longitudinal variables. The methodology is applied to a case study where, among others, a longitudinal ordinal response is predicted with a longitudinal continuous variable.

Bayesian joint modeling of multivariate longitudinal and survival outcomes using Gaussian copulas.

There is an increasing interest in the use of joint models for the analysis of longitudinal and survival data. While random effects models have been extensively studied, these models can be hard to implement and the fixed effect regression parameters must be interpreted conditional on the random effects. Copulas provide a useful alternative framework for joint modeling. One advantage of using copulas is that practitioners can directly specify marginal models for the outcomes of interest. We develop a joint model using a Gaussian copula to characterize the association between multivariate longitudinal and survival outcomes. Rather than using an unstructured correlation matrix in the copula model to characterize dependence structure as is common, we propose a novel decomposition that allows practitioners to impose structure (e.g., auto-regressive) which provides efficiency gains in small to moderate sample sizes and reduces computational complexity. We develop a Markov chain Monte Carlo model fitting procedure for estimation. We illustrate the method's value using a simulation study and present a real data analysis of longitudinal quality of life and disease-free survival data from an International Breast Cancer Study Group trial.

Tree-based subgroup discovery using electronic health record data: heterogeneity of treatment effects for DTG-containing therapies.

The rich longitudinal individual level data available from electronic health records (EHRs) can be used to examine treatment effect heterogeneity. However, estimating treatment effects using EHR data poses several challenges, including time-varying confounding, repeated and temporally non-aligned measurements of covariates, treatment assignments and outcomes, and loss-to-follow-up due to dropout. Here, we develop the subgroup discovery for longitudinal data algorithm, a tree-based algorithm for discovering subgroups with heterogeneous treatment effects using longitudinal data by combining the generalized interaction tree algorithm, a general data-driven method for subgroup discovery, with longitudinal targeted maximum likelihood estimation. We apply the algorithm to EHR data to discover subgroups of people living with human immunodeficiency virus who are at higher risk of weight gain when receiving dolutegravir (DTG)-containing antiretroviral therapies (ARTs) versus when receiving non-DTG-containing ARTs.

Benchmarking differential abundance analysis methods for correlated microbiome sequencing data.

Differential abundance analysis (DAA) is one central statistical task in microbiome data analysis. A robust and powerful DAA tool can help identify highly confident microbial candidates for further biological validation. Current microbiome studies frequently generate correlated samples from different microbiome sampling schemes such as spatial and temporal sampling. In the past decade, a number of DAA tools for correlated microbiome data (DAA-c) have been proposed. Disturbingly, different DAA-c tools could sometimes produce quite discordant results. To recommend the best practice to the field, we performed the first comprehensive evaluation of existing DAA-c tools using real data-based simulations. Overall, the linear model-based methods LinDA, MaAsLin2 and LDM are more robust than methods based on generalized linear models. The LinDA method is the only method that maintains reasonable performance in the presence of strong compositional effects.

A review on longitudinal data analysis with random forest.

In longitudinal studies variables are measured repeatedly over time, leading to clustered and correlated observations. If the goal of the study is to develop prediction models, machine learning approaches such as the powerful random forest (RF) are often promising alternatives to standard statistical methods, especially in the context of high-dimensional data. In this paper, we review extensions of the standard RF method for the purpose of longitudinal data analysis. Extension methods are categorized according to the data structures for which they are designed. We consider both univariate and multivariate response longitudinal data and further categorize the repeated measurements according to whether the time effect is relevant. Even though most extensions are proposed for low-dimensional data, some can be applied to high-dimensional data. Information of available software implementations of the reviewed extensions is also given. We conclude with discussions on the limitations of our review and some future research directions.