Our Science2020-06-19T15:45:56-07:00

Our Science

Our Science

Understanding Systemic Chronic Inflammation

When we hear the word “inflammation”, we think of a red, warm, swollen cut. These are indeed clinical signs of acute inflammation, which is the body’s response to an infection or trauma, and is short-term in nature.

Systemic chronic inflammation (SCI) is very different from acute inflammation. It is a life-long process and is the result of pro-inflammatory cytokines released from immune cells and the chronic activation of the innate immune system, the arm of immunity that dictates the response to pathogens and is involved in a wide range of non-communicable diseases of aging.

Environmental and social triggers of chronic inflammation and its consequences on human health

Systemic chronic inflammation accumulates with time and is induced by environment lifestyle insults, ultimately causing collateral damage to tissues and organs. This damage is the cause of age-related diseases including cancer, cardiovascular disease, autoimmune disease and many more.

Contrary to acute inflammation that is measured by standard blood tests; given the complexity of age-related SCI, at present, there are no standard biomarkers for this type of inflammation.

Based on the Stanford 1,000 Immunomes research, Edifice Health’s iAge® test is the first scientifically-backed test to quantify SCI and Immune Health.

Immunological determinants of human health and disease

The Stanford 1000 Immunomes Project is a collaborative ongoing study at Stanford University that aims to define the biological basis of aging and disease using state-of-the-art ‘omics’ platforms and advanced artificial intelligence (AI) methods.

The main focus of the Stanford Project is to establish biomarkers for healthy versus sub-functional immune systems. The Stanford team identified interaction between genetic and environmental factors, which contribute to the observed heterogeneity of biological responses in human beings. To this end, 1000 individuals of different age groups (9-96 years old) were recruited between 2008-2018. Their blood samples were screened using multiple state-of-the-art technologies at a single facility, the Human Immune Monitoring Center (HIMC) to measure circulating proteins, cell types, cellular functions, whole-genome blood gene expression and subjects’ haplotypes using deep sequencing technologies.

The research identified blood biomarkers of age-related chronic inflammation and derived a ‘metric’ for age-related multi-morbidity, which is the result of cumulative damage, as measured by the accumulation of up to 10 diseases of aging (cancer, cardiovascular, respiratory, gastrointestinal, urologic, neurologic, endocrine metabolic, musculoskeletal, genital-reproductive and psychiatric) and can also be used for early detection of immune decline and cardiovascular aging. The result was the derivation of two important health measures:

  1. Systemic Chronic Inflammation Index (SCI Index®) – derived by comparing immune protein levels to people with the same chronological age from the 1000 Immunomes Project at Stanford University; and
  2. Inflammatory clock of aging (iAge®) – showing how much older/younger a person appears with respect to his(her) chronological age.

iAge® and the SCI index® are predictors of multi-morbidity and immunosenescence.

The dataset has enabled us for the first time to identify reliable biomarkers of aging and disease in a longitudinal population-based study of immunology and aging. The Stanford Project provides reference values for thousands of immune variables and identifies clusters of individuals sharing similar health versus disease immune profiles.

Using AI

A deep neural network, non-linear model, was used to capture the network structure of inflammatory molecules and to predict calendar age in an unbiased fashion.

Slide Multidimensional Data Deep Neural Network (A.I.) Systemic Chronic Inflammation metric

Using deep learning to derive inflammatory clock

Cross-validation of the data was conducted to ensure statistical robustness and generalization. A model was ultimately derived to predict the sum of age-related disease and was adjusted to account for calendar age, cholesterol, gender, medication history, and more.

On-going Research Studies

Disease-specific proteomic signatures for major chronic diseases

We are actively running studies in collaboration with big pharma, food companies and academic research groups to explore the impact of systemic chronic inflammation on our health in the space of neurodegenerative diseases, psychiatric diseases, cancer, allergies, infectious diseases and infertility. We are also assessing how taking certain supplements and food impacts systemic chronic inflammation and our health.

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