Dataset Information

The Human Blood Transcriptome in a Large Population Cohort and Its Relation to Aging and Health.

ABSTRACT: Background: The blood transcriptome is expected to provide a detailed picture of an organism's physiological state with potential outcomes for applications in medical diagnostics and molecular and epidemiological research. We here present the analysis of blood specimens of 3,388 adult individuals, together with phenotype characteristics such as disease history, medication status, lifestyle factors, and body mass index (BMI). The size and heterogeneity of this data challenges analytics in terms of dimension reduction, knowledge mining, feature extraction, and data integration. Methods: Self-organizing maps (SOM)-machine learning was applied to study transcriptional states on a population-wide scale. This method permits a detailed description and visualization of the molecular heterogeneity of transcriptomes and of their association with different phenotypic features. Results: The diversity of transcriptomes is described by personalized SOM-portraits, which specify the samples in terms of modules of co-expressed genes of different functional context. We identified two major blood transcriptome types where type 1 was found more in men, the elderly, and overweight people and it upregulated genes associated with inflammation and increased heme metabolism, while type 2 was predominantly found in women, younger, and normal weight participants and it was associated with activated immune responses, transcriptional, ribosomal, mitochondrial, and telomere-maintenance cell-functions. We find a striking overlap of signatures shared by multiple diseases, aging, and obesity driven by an underlying common pattern, which was associated with the immune response and the increase of inflammatory processes. Conclusions: Machine learning applications for large and heterogeneous omics data provide a holistic view on the diversity of the human blood transcriptome. It provides a tool for comparative analyses of transcriptional signatures and of associated phenotypes in population studies and medical applications.

SUBMITTER: Schmidt M

PROVIDER: S-EPMC7931910 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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The Human Blood Transcriptome in a Large Population Cohort and Its Relation to Aging and Health.

Schmidt Maria M Hopp Lydia L Arakelyan Arsen A Kirsten Holger H Engel Christoph C Wirkner Kerstin K Krohn Knut K Burkhardt Ralph R Thiery Joachim J Loeffler Markus M Loeffler-Wirth Henry H Binder Hans H

Frontiers in big data 20201030

<b>Background:</b> The blood transcriptome is expected to provide a detailed picture of an organism's physiological state with potential outcomes for applications in medical diagnostics and molecular and epidemiological research. We here present the analysis of blood specimens of 3,388 adult individuals, together with phenotype characteristics such as disease history, medication status, lifestyle factors, and body mass index (BMI). The size and heterogeneity of this data challenges analytics in ...[more]

PMID: 33693414

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Project description:A number of prospective cohort studies are ongoing worldwide to investigate the impact of foetal and neonatal exposures to chemical substances on child health. To assess multiple exposure (mixture) effects and low prevalence health outcomes it is useful to pool data from several studies and conduct mega-data-analysis. To discuss a path towards data harmonization, representatives from several large-scale birth cohort studies and a biomonitoring programme formed a collaborative group, the Environment and Child Health International Birth Cohort Group (ECHIBCG). In this study, an intra-laboratory trial was performed to harmonize existing blood lead measurements within the groups' studies. Then, decentralized analyses were conducted in individual countries' laboratories to evaluate blood lead levels (BLL) in each study. The measurements of pooled BLL samples in French, German and three Japanese laboratories resulted in an overall mean blood lead concentration of 8.66??g?l-1 (95% confidence interval: 8.59-8.72??g?l-1) with 3.0% relative standard deviation. Except for China's samples, BLL from each study were comparable with mean concentrations below or close to 10??g?l-1. The decentralized multivariate analyses revealed that all models had coefficients of determination below 0.1. Determinants of BLL were current smoking, age >35 years and overweight or obese status. The three variables were associated with an increase in BLL in each of the five studies, most strongly in France by almost 80% and the weakest effect being in Norway with only 15%; for Japan, with the far largest sample (~18,000), the difference was 36%. This study successfully demonstrated that the laboratory analytical methods were sufficiently similar to allow direct comparison of data and showed that it is possible to harmonize the epidemiological data for joint analysis. This exercise showed the challenges in decentralized data analyses and reinforces the need for data harmonization among studies.

Dataset Information

The Human Blood Transcriptome in a Large Population Cohort and Its Relation to Aging and Health.

Publications

The Human Blood Transcriptome in a Large Population Cohort and Its Relation to Aging and Health.

Similar Datasets

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