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The geometry of clinical labs and wellness states from deeply phenotyped humans.

ABSTRACT: Longitudinal multi-omics measurements are highly valuable in studying heterogeneity in health and disease phenotypes. For thousands of people, we have collected longitudinal multi-omics data. To analyze, interpret and visualize this extremely high-dimensional data, we use the Pareto Task Inference (ParTI) method. We find that the clinical labs data fall within a tetrahedron. We then use all other data types to characterize the four archetypes. We find that the tetrahedron comprises three wellness states, defining a wellness triangular plane, and one aberrant health state that captures aspects of commonality in movement away from wellness. We reveal the tradeoffs that shape the data and their hierarchy, and use longitudinal data to observe individual trajectories. We then demonstrate how the movement on the tetrahedron can be used for detecting unexpected trajectories, which might indicate transitions from health to disease and reveal abnormal conditions, even when all individual blood measurements are in the norm.

SUBMITTER: Zimmer A

PROVIDER: S-EPMC8196202 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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The geometry of clinical labs and wellness states from deeply phenotyped humans.

Zimmer Anat A Korem Yael Y Rappaport Noa N Wilmanski Tomasz T Baloni Priyanka P Jade Kathleen K Robinson Max M Magis Andrew T AT Lovejoy Jennifer J Gibbons Sean M SM Hood Leroy L Price Nathan D ND

Nature communications 20210611 1

Longitudinal multi-omics measurements are highly valuable in studying heterogeneity in health and disease phenotypes. For thousands of people, we have collected longitudinal multi-omics data. To analyze, interpret and visualize this extremely high-dimensional data, we use the Pareto Task Inference (ParTI) method. We find that the clinical labs data fall within a tetrahedron. We then use all other data types to characterize the four archetypes. We find that the tetrahedron comprises three wellnes ...[more]

PMID: 34117230

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Project description:BackgroundDNA copy number variants (CNVs) are found in 15% of subjects with ID but their association with phenotypic abnormalities has been predominantly studied in smaller cohorts of subjects with detailed yet non-systematically categorized phenotypes, or larger cohorts (thousands of cases) with smaller number of generalized phenotypes.MethodsWe evaluated the association of de novo, familial and common CNVs detected in 78 ID subjects with phenotypic abnormalities classified using the Winter-Baraitser Dysmorphology Database (WBDD) (formerly the London Dysmorphology Database). Terminology for 34 primary (coarse) and 169 secondary (fine) phenotype features were used to categorize the abnormal phenotypes and determine the prevalence of each phenotype in patients grouped by the type of CNV they had.ResultsIn our cohort more than 50% of cases had abnormalities in primary categories related to head (cranium, forehead, ears, eye globes, eye associated structures, nose) as well as hands and feet. The median number of primary and secondary abnormalities was 12 and 18 per subject, respectively, indicating that the cohort consisted of subjects with a high number of phenotypic abnormalities (median De Vries score for the cohort was 5). The prevalence of each phenotypic abnormality was comparable in patients with de novo or familial CNVs in comparison to those with only common CNVs, although a trend for increased frequency of cranial and forehead abnormalities was noted in subjects with rare de novo and familial CNVs. Two clusters of subjects were identified based on the prevalence of each fine phenotypic feature, with an average of 28.3 and 13.5 abnormal phenotypes/subject in the two clusters respectively (P?<?0.05).ConclusionsOur study is a rare example of using standardized, deep morphologic phenotype clustering with phenotype/CNV correlation in a cohort of subjects with ID. The composition of the cohort inevitably influences the phenotype/genotype association, and our studies show that the influence of the de novo CNVs on the phenotype is less obvious in cohorts consisting of subjects with a high number of phenotypic abnormalities. The outcome of phenotype/genotype analysis also depends on the choice of phenotypes assessed and standardized phenotyping is required to minimize variability.

Project description:The etiology of severe forms of COVID19, especially in young patients, remains a salient unanswered question. Here we build built on a 3-tier cohort where all individuals/patients were strictly below 50 years of age and where a number of comorbidities were excluded at study onset. Besides healthy controls (N=22), these included patients in the intensive care unit with Acute Respiratory Distress Syndrome (ARDS) (“critical group”; N=47), and those in a non-critical care ward under supplemental oxygen (“non-critical group”, N=25). This highly curated cohort allowed us to perform a deep multi-omics approach, which included whole genome sequencing, whole blood RNA-sequencing, plasma and peripheral-blood mononuclear cells proteomics, multiplex cytokine profiling, mass-cytometry-based immune cell profiling in conjunction with viral parameters i.e. anti-SARS-Cov-2 neutralizing antibodies and multi-target antiviral serology. Critical patients were characterized by an exacerbated inflammatory state, perturbed lymphoid and myeloid cell compartments, signatures of dysregulated blood coagulation and active regulation of viral entry into the cells. A unique gene signature that differentiates critical from non-critical patients was identified by an ensemble of machine learning, deep learning and quantum annealing approachmethods. Within this gene networksignature, Sstructural Causal causal Modeling modeling identified several genes that may potentially drivepromote ARDS driver genes etiology, among which the up-regulated metalloprotease ADAM9 seems to be a key driver. Inhibition of ADAM9 ex vivo interfered with SARS-Cov-2 uptake and replication in human epithelial cells. In brief, Hence we applyied an advanced integrated machine learning approach and probabilistic programming strategy to identify causal molecular driver geness for of severe forms of COVID-19 in a small, uncluttered tightly controlled cohort of patients, the importance of which were then validated with experiments.

Project description:An increasing number of large-scale multi-modal research initiatives has been conducted in the typically developing population, e.g. Dev. Cogn. Neur. 32:43-54, 2018; PLoS Med. 12(3):e1001779, 2015; Elam and Van Essen, Enc. Comp. Neur., 2013, as well as in psychiatric cohorts, e.g. Trans. Psych. 10(1):100, 2020; Mol. Psych. 19:659–667, 2014; Mol. Aut. 8:24, 2017; Eur. Child and Adol. Psych. 24(3):265–281, 2015. Missing data is a common problem in such datasets due to the difficulty of assessing multiple measures on a large number of participants. The consequences of missing data accumulate when researchers aim to integrate relationships across multiple measures. Here we aim to evaluate different imputation strategies to fill in missing values in clinical data from a large (total N = 764) and deeply phenotyped (i.e. range of clinical and cognitive instruments administered) sample of N = 453 autistic individuals and N = 311 control individuals recruited as part of the EU-AIMS Longitudinal European Autism Project (LEAP) consortium. In particular, we consider a total of 160 clinical measures divided in 15 overlapping subsets of participants. We use two simple but common univariate strategies—mean and median imputation—as well as a Round Robin regression approach involving four independent multivariate regression models including Bayesian Ridge regression, as well as several non-linear models: Decision Trees (Extra Trees., and Nearest Neighbours regression. We evaluate the models using the traditional mean square error towards removed available data, and also consider the Kullback–Leibler divergence between the observed and the imputed distributions. We show that all of the multivariate approaches tested provide a substantial improvement compared to typical univariate approaches. Further, our analyses reveal that across all 15 data-subsets tested, an Extra Trees regression approach provided the best global results. This not only allows the selection of a unique model to impute missing data for the LEAP project and delivers a fixed set of imputed clinical data to be used by researchers working with the LEAP dataset in the future, but provides more general guidelines for data imputation in large scale epidemiological studies. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-022-01656-z.

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The geometry of clinical labs and wellness states from deeply phenotyped humans.

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The geometry of clinical labs and wellness states from deeply phenotyped humans.

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