An accurate aging clock developed from large-scale gut microbiome and human gene expression data.

Accurate measurement of the biological markers of the aging process could provide an "aging clock" measuring predicted longevity and enable the quantification of the effects of specific lifestyle choices on healthy aging. Using machine learning techniques, we demonstrate that chronological age can be predicted accurately from (1) the expression level of human genes in capillary blood and (2) the expression level of microbial genes in stool samples. The latter uses a very large metatranscriptomic dataset, stool samples from 90,303 individuals, which arguably results in a higher quality microbiome-aging model than prior work. Our analysis suggests associations between biological age and lifestyle/health factors, e.g., people on a paleo diet or with IBS tend to have higher model-predicted ages and people on a vegetarian diet tend to have lower model-predicted ages. We delineate the key pathways of systems-level biological decline based on the age-specific features of our model.

Gut Microbiome Activity Contributes to Prediction of Individual Variation in Glycemic Response in Adults.

Limiting postprandial glycemic response (PPGR) is an important intervention in reducing the risk of chronic metabolic diseases and has been shown to impart significant health benefits in people with elevated levels of blood sugar. In this study, we collected gut microbiome activity data by assessing the metatranscriptome, and we measured the glycemic responses of 550 adults who consumed more than 30,000 meals, collectively, from omnivore or vegetarian/gluten-free diets. We demonstrate that gut microbiome activity, anthropometric factors, and food macronutrients modulate individual variation in glycemic response. We employ two predictive models, including a mixed-effects linear regression model (R = 0.77) and a gradient boosting machine model (Rtrain = 0.80/R2train = 0.64; Rtest = 0.64/R2test = 0.40), which demonstrate variation in PPGR between individuals when ingesting the same foods. All features in the final mixed-effects linear regression model were significant (p < 0.05) except for two features which were retained as suggestive: glutamine production pathways (p = 0.08) and the interaction between tyrosine metabolizers and carbs (p = 0.06). We introduce molecular functions as features in these two models, aggregated from microbial activity data, and show their statistically significant contributions to glycemic control. In summary, we demonstrate for the first time that metatranscriptomic activity of the gut microbiome is correlated with PPGR among adults.

Blood sugar dysregulation is caused by various underlying conditions, including type 2 diabetes, and this may lead to extended periods of hypoglycemia or hyperglycemia, which can be harmful or deadly. Clinically, glycemic control is a primary therapeutic target for dysglycemia, and food and nutrition are frequent interventions used to reduce postprandial blood glucose excursions. Primary determinants of postprandial glycemic response (PPGR) include dietary carbohydrates, individual phenotypes, and individual molecular characteristics which include the gut microbiome. Typical investigations of gut microbiomes depend on analysis methods which have poor taxonomic resolution, cannot identify certain microorganisms, and are prone to errors. In this study, each RNA molecule was identified and counted, allowing quantitative strain-level taxonomic classification and molecular pathway analysis. The primary goal of the study was to assess the impact of microbial functional activity on PPGR. The study was conducted in the USA and involved a multiethnic population of healthy adults with HbA1c levels below 6.5. All participants received 14-day omnivore diets or vegetarian/gluten-free diets, depending on nutritional requirements (omnivore diets include meat while vegetarian/gluten-free diets exclude both gluten and meat). Over this timeframe, blood glucose levels were measured in 15-min intervals, 24 h per day, capturing postprandial responses for more than 27,000 meals, including more than 18,000 provided meals which spanned a wide range of foods and macronutrient characteristics. Computational modeling demonstrated the statistical significance of all features and identified new features which may be relevant to glycemic control. These results show, for the first time, that a person's glycemic response depends on individual traits, including both their anthropometrics and their gut metatranscriptome, representing the activity of gut microbiomes.

A clinically validated human capillary blood transcriptome test for global systems biology studies.

To prevent and treat chronic diseases, including cancer, a global application of systems biology is needed. We report here a whole blood transcriptome test that needs only 50 µl of capillary (fingerprick) blood. This test is suitable for global applications because the samples are preserved at ambient temperature for up to 4 weeks and the RNA preservative inactivates all pathogens, enabling safe transportation. Both the laboratory and bioinformatic steps are automated and performed in a clinical lab, which minimizes batch effects and creates unbiased datasets. Given its clinical testing performance and accessibility to traditionally underrepresented and diverse populations, this test offers a unique ability to reveal molecular mechanisms of disease and enable longitudinal, population-scale studies.