MELD Project: Metabolic consequences of Early Life adversity and risk for type 2 DiabetesLuke W. Johnston

Purpose:

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Share about:
- Early life influences
- Analytic technique
- Open science practices

MELD Project Description

Background and rationale

Early life shown to influence risk of diabetes
How do metabolic processes mediate this link (in humans)? 🤷
Difficult to study due to long timespans

Background and rationale

Early life shown to influence risk of diabetes
How do metabolic processes mediate this link (in humans)? 🤷
Difficult to study due to long timespans
Easier now with:
- Registers
- Cohort with registers
- Powerful mediation methods

Some potential mechanisms

Impairs organ development (e.g cell numbers)
Affects insulin sensitivity pathways (e.g. in muscle)
Altered lifestyle behaviours
Excessive stress responses

Public health and clinical relevance

Adversity of trauma:

Refugees
Immigration
Childhood abuse
Poor or unsafe neighbourhood

Public health and clinical relevance

Adversity of trauma:

Refugees
Immigration
Childhood abuse
Poor or unsafe neighbourhood

Adversity of excess:

Childhood obesity
Low-nutrient dense foods
Lack of exercise

For my project, I won't be covering the adversity of excess.

Research questions:

Overall aim: Better quantify and understand impact of general early life (mainly childhood) conditions on adult metabolic capacity and risk for T2D.

Research questions:

Overall aim: Better quantify and understand impact of general early life (mainly childhood) conditions on adult metabolic capacity and risk for T2D.
Specific objectives:
1. How specific conditions affect risk of T2D
2. Find mediating pathways of metabolic capacity between adversity and T2D

Research questions:

Overall aim: Better quantify and understand impact of general early life (mainly childhood) conditions on adult metabolic capacity and risk for T2D.
Specific objectives:
1. How specific conditions affect risk of T2D
2. Find mediating pathways of metabolic capacity between adversity and T2D

To address 2., need to develop an analytic algorithm

I'm being intentionally vague about meaning of "early childhood conditions" because what conditions I use depends on the data that's available and what may better represent "adversity". This will require some exploratory work at objective 1. to then use in objective 2.

NetCoupler, which does direct and mediating pathway estimation

Identifying population with Lexis diagram

Identifying variables with DAG

For objectives, need to analyze data like this...

Current Analytic Problem

Modern studies generate lots of metabolic data

-omics type data
Metabolic biomarkers

High dimensionality
Complex networks

"Traditional" analysis may use: Dimensionality reduction

Reducing number of variables with PCA

This has the advantage of making things simpler while trying to maximize variance in the data. Afterward you can do modelling on each principal component. The disadvantage of this approach is that it loses a lot of information since the interdependence and connections between variables it not maintained.

"Traditional" analysis may use: Many regression-type models

O1 = M1 + covariates
O1 = M2 + covariates
...
O1 = M7 + covariates
O1 = M8 + covariates

Some ways you might go about analyzing this data is by running many regression models, one for each metabolic variable for instance.

This of course has problems since you're simply running a bunch of models and not taking account of the inherent interdependencies between variables.

"Traditional" analysis may use: Network analysis

This approach is nice in that you can extract information about the connection between metabolic variables. But there is no way to incorporate the disease outcome with this approach and in order to construct the network properly most methods require you provide a prespecified base network, which you might not know.

But, what if we...

want info about network structure?

But, what if we...

want info about network structure?
don't know the network structure?

But, what if we...

want info about network structure?
don't know the network structure?
have an exposure, metabolites, and outcome?

But, what if we...

want info about network structure?
don't know the network structure?
have an exposure, metabolites, and outcome?
are interested in causal links?

What if we want to know something like this?

Potential solution: NetCoupler

Causal structure learning that ...

Finds most likely network structure
Allows inclusion of exposure and outcome
Identifies causal links between and within network

Clemens Wittenbecher

NetCoupler algorithm was developed by Clemens Wittenbecher.

NetCoupler algorithm overview

Infer (potentially) causal pathways with graphical model

Overall analysis with NetCoupler

Current state of project: Just started

(Trying to) Adhere to open scientific practices:

Project proposal: lwjohnst.gitlab.io/dda-pdf

Current state of project: Just started

(Trying to) Adhere to open scientific practices:

Project proposal: lwjohnst.gitlab.io/dda-pdf
Developing NetCoupler: 📦 github.com/NetCoupler

Current state of project: Just started

(Trying to) Adhere to open scientific practices:

Project proposal: lwjohnst.gitlab.io/dda-pdf
Developing NetCoupler: 📦 github.com/NetCoupler
Developing protocol to register study: lwjohnst.gitlab.io/meld-protocol

About transparency of process to making claim.

Question:

How many know or have heard or know about open science?

Question:

...or open access, open methods, open data, or open source?

Open science: Terms and meanings

Term	Meaning
Open science	Freely available, openly licensed material for all things related to scientific activity
Open access	Free, unrestricted, publicly available published articles
Open data	Freely available, re-usable, openly licensed data
Open source/code	Freely available, re-usable, openly licensed scientific code used in generating results
Open methods/protocol	Freely available, re-usable, openly licensed methods and protocols used to create the data

Thanks for listening 😁

Algorithm (steps) used in NetCoupler

1. DAG skeleton of network is estimated

2. Initial links formed on exposure-side

3. Direct effects estimated between exposure and network

4. Repeat identifying links, find ambiguous, delete indirect links

If you were interested in linkages with an exposure on the network, you could stop here.

5. Initial links formed with outcome-side

6. Direct effects estimated between network and outcome

7. Repeat identifying links, delete indirect links, find ambiguous

You could stop here if you were interested in linkages with the network and an outcome.

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MELD Project: Metabolic consequences of Early Life adversity and risk for type 2 Diabetes

Luke W. Johnston

Purpose:

MELD Project Description

Background and rationale

Background and rationale

Some potential mechanisms

Public health and clinical relevance

Public health and clinical relevance

Research questions:

Research questions:

Research questions:

Identifying population with Lexis diagram

Identifying variables with DAG

For objectives, need to analyze data like this...

Current Analytic Problem

Modern studies generate lots of metabolic data

"Traditional" analysis may use: Dimensionality reduction

"Traditional" analysis may use: Many regression-type models

"Traditional" analysis may use: Network analysis

But, what if we...

But, what if we...

But, what if we...

But, what if we...

What if we want to know something like this?

Potential solution: NetCoupler

NetCoupler algorithm overview

Infer (potentially) causal pathways with graphical model

Overall analysis with NetCoupler

Current state of project: Just started

Current state of project: Just started

Current state of project: Just started

Question:

How many know or have heard or know about open science?

Question:

...or open access, open methods, open data, or open source?

Open science: Terms and meanings

Thanks for listening 😁

Algorithm (steps) used in NetCoupler

1. DAG skeleton of network is estimated

2. Initial links formed on exposure-side

3. Direct effects estimated between exposure and network

4. Repeat identifying links, find ambiguous, delete indirect links

5. Initial links formed with outcome-side

6. Direct effects estimated between network and outcome

7. Repeat identifying links, delete indirect links, find ambiguous

8. (optional) Combine both estimations into joint model

Purpose:

Help