Analysis of Biological Aging - Online Course
A 3-Day Livestream Seminar Taught by
Lauren Gaydosh10:00am-12:30pm (convert to your local time)
1:30pm-3:30pm
Chronological time passes at the same pace for all of us. However, the rate at which our biology ages differs, with some aging biologically more slowly and others more rapidly. Biological aging refers to the gradual decline in integrity across biological systems that occurs with advancing chronological age, and is the cause of age-related chronic disease and disability. With advances in the collection of biological data, there are now many approaches to measuring biological age, including leading edge methods such as epigenetic clocks. The implementation of established methods for measuring and analyzing biological age is the focus of this course.
The goal of this seminar is to provide a thorough conceptual understanding of biological aging and the geroscience hypothesis (i.e., aging is the primary cause chronic diseases, and thus, targeting biological aging can delay or prevent a range of chronic diseases simultaneously). Additionally, the course will equip you with the technical skills to estimate and analyze biological aging. Topics covered include: identifying biomarkers of aging, using DNA methylation data to train machine learning algorithms to predict biological age, blood-protein based measures of biological age, innovations in the measurement of biological age using omics data, the management and manipulation of high-dimension omics datasets, and an overview of the key software tools and packages.
Starting March 14, we are offering this seminar as a 3-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.
*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
Closed captioning is available for all live and recorded sessions. Live captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
More details about the course content
The integration of biological data into the study of health and health disparities provides exciting opportunities to evaluate models of biological embedding, better understand the role of social influences, and test interventions designed to improve health. Many social and behavioral studies, such as the National Health and Nutrition Examination Survey (NHANES), National Longitudinal Study of Adolescent to Adult Health (Add Health), and the Health and Retirement Study (HRS), now include the collection of biomarkers that allow for the measurement of biological age. This includes new “omics” data on the epigenome, transcriptome, microbiome, proteome, and metabolome. Yet the size and complexity of “omics” data can be challenging for new users.
This course will provide you with methods for managing, cleaning, and modeling “omics” data, and applying the leading algorithms for the estimation of biological age. After completing the course, you will be able to construct measures of biological age across different data sources and understand how to analyze these measures.
This seminar will introduce biological aging and the geroscience hypothesis, considering their conceptualization and measurement with respect to reference populations and the selection of biomarkers.
The seminar will provide an overview of common machine learning techniques used to train measures of biological age, including lasso and ridge regression. We will then discuss a large group of measures of biological age derived from DNA methylation data, referred to as epigenetic clocks. After a brief overview of DNA methylation, we will cover the first iteration of epigenetic clocks that were trained to predict chronological age. Then, we will discuss new advances in the field that train epigenetic clocks using phenotypic measures of biological risk or function. We will close our discussion of epigenetic clocks with an exercise creating clocks from randomly selected sites in the epigenome. Finally, we will move beyond epigenetic clocks to consider other sources of data used in the construction of biological age, including blood-based protein markers and RNA. We will also consider measures of biological age that focus on specific age-related changes of senescence and inflammation. Throughout the course, you will gain experience with these methods through hands-on exercises.
The integration of biological data into the study of health and health disparities provides exciting opportunities to evaluate models of biological embedding, better understand the role of social influences, and test interventions designed to improve health. Many social and behavioral studies, such as the National Health and Nutrition Examination Survey (NHANES), National Longitudinal Study of Adolescent to Adult Health (Add Health), and the Health and Retirement Study (HRS), now include the collection of biomarkers that allow for the measurement of biological age. This includes new “omics” data on the epigenome, transcriptome, microbiome, proteome, and metabolome. Yet the size and complexity of “omics” data can be challenging for new users.
This course will provide you with methods for managing, cleaning, and modeling “omics” data, and applying the leading algorithms for the estimation of biological age. After completing the course, you will be able to construct measures of biological age across different data sources and understand how to analyze these measures.
This seminar will introduce biological aging and the geroscience hypothesis, considering their conceptualization and measurement with respect to reference populations and the selection of biomarkers.
The seminar will provide an overview of common machine learning techniques used to train measures of biological age, including lasso and ridge regression. We will then discuss a large group of measures of biological age derived from DNA methylation data, referred to as epigenetic clocks. After a brief overview of DNA methylation, we will cover the first iteration of epigenetic clocks that were trained to predict chronological age. Then, we will discuss new advances in the field that train epigenetic clocks using phenotypic measures of biological risk or function. We will close our discussion of epigenetic clocks with an exercise creating clocks from randomly selected sites in the epigenome. Finally, we will move beyond epigenetic clocks to consider other sources of data used in the construction of biological age, including blood-based protein markers and RNA. We will also consider measures of biological age that focus on specific age-related changes of senescence and inflammation. Throughout the course, you will gain experience with these methods through hands-on exercises.
Computing
We will use the computing environment of R and RStudio , as well as multiple packages available from scientists who developed algorithms for biological age, including methylCIPHER, PCClocks, and BioAge. All software and the datasets used for exercises will be distributed as an easy to install virtual machine. This will spare you the effort of manually installing the various software used in the course.
Basic familiarity with R is highly desirable, but even novice R coders should be able to follow the presentation and do the exercises.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.
We will use the computing environment of R and RStudio , as well as multiple packages available from scientists who developed algorithms for biological age, including methylCIPHER, PCClocks, and BioAge. All software and the datasets used for exercises will be distributed as an easy to install virtual machine. This will spare you the effort of manually installing the various software used in the course.
Basic familiarity with R is highly desirable, but even novice R coders should be able to follow the presentation and do the exercises.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.
Who should register?
If you want to learn about the conceptualization and measurement of biological age, to construct measures in your own data, or use measures in available datasets, this course is for you. This course is intended for an interdisciplinary audience from the social and behavioral sciences, as well as public and population health and medicine. Knowledge of linear regression and experience with statistical computing would be beneficial.
If you want to learn about the conceptualization and measurement of biological age, to construct measures in your own data, or use measures in available datasets, this course is for you. This course is intended for an interdisciplinary audience from the social and behavioral sciences, as well as public and population health and medicine. Knowledge of linear regression and experience with statistical computing would be beneficial.
Seminar outline
Day 1: Introduction to biological aging
-
- The geroscience hypothesis
- The search for a biomarker of biological age
- Intro to computing environment, R and RStudio
- Machine learning primer – LASSO and ridge regression
Day 2: DNA methylation-based measures of biological age
-
- DNA methylation primer
- Chronological age epigenetic clocks – Horvath, Hannum
- Phenotype-based epigenetic clocks – PhenoAge, GrimAge, Dunedin PACE
- Generating epigenetic clocks with randomly selected CpG sites
Day 3: Measurement of biological age using other data
-
- Blood protein measures – Klemera-Doubal method, homeostatic dysregulation, PhenoAge
- RNA measures of transcriptomic age
- Senescence
- Inflammaging
Day 1: Introduction to biological aging
-
- The geroscience hypothesis
- The search for a biomarker of biological age
- Intro to computing environment, R and RStudio
- Machine learning primer – LASSO and ridge regression
Day 2: DNA methylation-based measures of biological age
-
- DNA methylation primer
- Chronological age epigenetic clocks – Horvath, Hannum
- Phenotype-based epigenetic clocks – PhenoAge, GrimAge, Dunedin PACE
- Generating epigenetic clocks with randomly selected CpG sites
Day 3: Measurement of biological age using other data
-
- Blood protein measures – Klemera-Doubal method, homeostatic dysregulation, PhenoAge
- RNA measures of transcriptomic age
- Senescence
- Inflammaging
Payment information
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.