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:BackgroundProtein intake plays an important role in maintaining the health status of older adults. However, few epidemiologic studies examined midlife protein intake in relation to healthy aging.ObjectivesThe objective of this study was to evaluate the long-term role of dietary protein intake in healthy aging among female participants in the prospective Nurses' Health Study (NHS) cohort.MethodsWe included 48,762 NHS participants aged <60 y in 1984. Total protein, animal protein, dairy protein (a subset of animal protein), and plant protein were derived from validated food frequency questionnaires. Healthy aging was defined as being free from 11 major chronic diseases, having good mental health, and not having impairments in either cognitive or physical function, as assessed in the 2014 or 2016 NHS participant questionnaires. We used multivariate logistic regression adjusted for lifestyle, demographics, and health status to estimate the odds ratios (ORs) and 95% confidence intervals for protein intake in relation to healthy aging.ResultsA total of 3721 (7.6%) NHS participants met our healthy aging definition. Protein intake was significantly associated with higher odds of healthy aging. The ORs (95% confidence intervals) per 3%-energy increment with healthy aging were 1.05 (1.01, 1.10) for total protein, 1.07 (1.02, 1.11) for animal protein, 1.14 (1.06, 1.23) for dairy protein, and 1.38 (1.24, 1.54) for plant protein. Plant protein was also associated with higher odds of absence of physical function limitations and good mental status. In substitution analyses, we observed significant positive associations for the isocaloric replacement of animal or dairy protein, carbohydrate, or fat with plant protein (ORs for healthy aging: 1.22-1.58 for 3% energy replacement with plant protein).ConclusionsDietary protein intake, especially plant protein, in midlife, is associated with higher odds of healthy aging and with several domains of positive health status in a large cohort of female nurses.

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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