Microbiome can predict a person’s chronological age

Microbiome can predict a person’s chronological age

Publication date: Feb 12, 2020

Given a microbiome sample (skin, mouth or fecal swab), researchers have demonstrated they can now use machine learning to predict a person’s chronological age, with a varying degree of accuracy.

“This new ability to correlate microbes with age will help us advance future studies of the roles microbes play in the aging process and age-related diseases, and allow us to better test potential therapeutic interventions that target microbiomes,” said co-senior author Zhenjiang Zech Xu, PhD, who was at the time of the study a postdoctoral researcher in the UC San Diego School of Medicine lab of co-senior author Rob Knight, PhD, professor and director of the UC San Diego Center for Microbiome Innovation.

In a 2014 study, Washington University researchers compared “microbial age” -; age as predicted by the fecal microbiome -; and actual chronological age in the context of malnourished infants during the first months of life.

“This was the most comprehensive investigation of microbiome and age to date,” said first author Shi Huang, PhD, a postdoctoral researcher in Knight’s lab and the UC San Diego Center for Microbiome Innovation.

“The accuracy of our results demonstrate the potential for applying machine learning and artificial intelligence techniques to better understand human microbiomes,” said co-author Ho-Cheol Kim, PhD, program director of the Artificial Intelligence for Healthy Living Program, a collaboration between IBM Research and UC San Diego under the IBM AI Horizons Network.

According to co-author Yoshiki VcE1zquez-Baeza, PhD, associate director of bioinformatic integration at the UC San Diego Center for Microbiome Innovation, age prediction is a particularly attractive method for training predictive models because participants don’t need to meet special criteria in order to become a sample donor, and assessing age typically does not require a visit to a hospital.

Concepts Keywords
Aging Artificial intelligence
Antibiotics Human Microbiome Project
Artificial Intelligence Pharmacomicrobiomics
Autism Inflammatory bowel disease
Autoimmune Human gastrointestinal microbiota
Autoimmune Disease Rob Knight
Bacteria Environmental microbiology
Baeza Microbiology
Bioinformatic Bacteriology
Biotechnological Medical specialties
Body Mass Microbiomes
Bowel Branches of biology
California Antibiotics
Canada Conditions inflammation
Cardiovascular Inflammatory bowel diabetes
China Predictive tool
Correlation
Diabetes
Gender
Hospital
IBM
Immune Health
Inflammation
Inflammatory Bowel Disease
Mail
Microbes
Microbiome
Neurological
Neurological Disorders
Obesity
Oral Cavity
PhD
Physiology
Saliva
San Diego
Scientific Community
Sequencing
Serum
Tanzania
Washington
Yoshiki
Zhenjiang

Semantics

Type Source Name
disease MESH neurological disorders
disease MESH autism
disease MESH obesity
disease MESH inflammatory bowel disease
disease MESH autoimmune disease
disease MESH communities
disease MESH habits
drug DRUGBANK (S)-Des-Me-Ampa
disease MESH critically ill
disease MESH inflammation
drug DRUGBANK Tropicamide
disease MESH aging

Original Article

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