MELD Project: The Metabolic consequences of adverse Early Life conditions and subsequent risk for adult type 2 Diabetes

# MELD Project: The <em>M</em>etabolic consequences of adverse <em>E</em>arly <em>L</em>ife conditions and subsequent risk for adult type 2 <em>D</em>iabetes
## DDA Start-up Presentation
### Luke W. Johnston

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# Outline:

- Project description

- Plans during funding

- Future perspectives

- Challenges

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# MELD Project Description

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## Brief introduction

- Early life influences risk for diabetes

- How metabolic processes mediate link? 🤷🤷

- Difficult to study due to long timespans

- Easier now with:
    - Long term registers
    - Cohort <svg style="height:0.8em;top:.04em;position:relative;fill:#214c78;" viewBox="0 0 512 512"><path d="M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z"/></svg> with registers
    - Powerful mediation methods

.footnote[<svg style="height:0.8em;top:.04em;position:relative;fill:#214c78;" viewBox="0 0 384 512"><path d="M224 136V0H24C10.7 0 0 10.7 0 24v464c0 13.3 10.7 24 24 24h336c13.3 0 24-10.7 24-24V160H248c-13.2 0-24-10.8-24-24zm64 236c0 6.6-5.4 12-12 12H108c-6.6 0-12-5.4-12-12v-8c0-6.6 5.4-12 12-12h168c6.6 0 12 5.4 12 12v8zm0-64c0 6.6-5.4 12-12 12H108c-6.6 0-12-5.4-12-12v-8c0-6.6 5.4-12 12-12h168c6.6 0 12 5.4 12 12v8zm0-72v8c0 6.6-5.4 12-12 12H108c-6.6 0-12-5.4-12-12v-8c0-6.6 5.4-12 12-12h168c6.6 0 12 5.4 12 12zm96-114.1v6.1H256V0h6.1c6.4 0 12.5 2.5 17 7l97.9 98c4.5 4.5 7 10.6 7 16.9z"/></svg>: [lwjohnst.gitlab.io/dda-pdf](https://lwjohnst.gitlab.io/dda-pdf/)]

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## Research questions:

- *Aim*: To quantify and understand impact of early life conditions on adult
metabolic capacity and risk for T2D.

### Objectives

1. Investigate how specific early life conditions affect risk of T2D nationally
1. Extend and generalise NetCoupler algorithm 
([github.com/NetCoupler](https://github.com/NetCoupler/NetCoupler))
    - Estimates direct and mediating pathways in high dimensional metabolic data
1. Identify mediating pathways of metabolic capacity between early
life adversity and T2D using NetCoupler

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## Methods: Population

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## Methods: Variables

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## Methods: Analysis process

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## Analyzing data that looks like:

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## NetCoupler: A causal structure learning algorithm that...

- Allows inclusion of exposure and outcome

- Identifies causal links between and within network
]

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## NetCoupler procedure

.footnote[<svg style="height:0.8em;top:.04em;position:relative;fill:#214c78;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> 📦 [github.com/NetCoupler](https://github.com/NetCoupler)]

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## Infer (potentially) causal pathways with graphical model

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## General timeline

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# (Some) Plans <svg style="height:0.8em;top:.04em;position:relative;fill:#214c78;" viewBox="0 0 512 512"><path d="M139.61 35.5a12 12 0 0 0-17 0L58.93 98.81l-22.7-22.12a12 12 0 0 0-17 0L3.53 92.41a12 12 0 0 0 0 17l47.59 47.4a12.78 12.78 0 0 0 17.61 0l15.59-15.62L156.52 69a12.09 12.09 0 0 0 .09-17zm0 159.19a12 12 0 0 0-17 0l-63.68 63.72-22.7-22.1a12 12 0 0 0-17 0L3.53 252a12 12 0 0 0 0 17L51 316.5a12.77 12.77 0 0 0 17.6 0l15.7-15.69 72.2-72.22a12 12 0 0 0 .09-16.9zM64 368c-26.49 0-48.59 21.5-48.59 48S37.53 464 64 464a48 48 0 0 0 0-96zm432 16H208a16 16 0 0 0-16 16v32a16 16 0 0 0 16 16h288a16 16 0 0 0 16-16v-32a16 16 0 0 0-16-16zm0-320H208a16 16 0 0 0-16 16v32a16 16 0 0 0 16 16h288a16 16 0 0 0 16-16V80a16 16 0 0 0-16-16zm0 160H208a16 16 0 0 0-16 16v32a16 16 0 0 0 16 16h288a16 16 0 0 0 16-16v-32a16 16 0 0 0-16-16z"/></svg>

- Adhere even more to open scientific practices
    - E.g. developing protocol to register study[1]

- Research stays in Boston to work with Clemens

.footnote[[1] MELD Project Protocol: [lwjohnst.gitlab.io/meld-protocol](https://lwjohnst.gitlab.io/meld-protocol)]

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# Future Perspectives

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## For project

- Potential for other research <svg style="height:0.8em;top:.04em;position:relative;fill:#214c78;" viewBox="0 0 384 512"><path d="M202.021 0C122.202 0 70.503 32.703 29.914 91.026c-7.363 10.58-5.093 25.086 5.178 32.874l43.138 32.709c10.373 7.865 25.132 6.026 33.253-4.148 25.049-31.381 43.63-49.449 82.757-49.449 30.764 0 68.816 19.799 68.816 49.631 0 22.552-18.617 34.134-48.993 51.164-35.423 19.86-82.299 44.576-82.299 106.405V320c0 13.255 10.745 24 24 24h72.471c13.255 0 24-10.745 24-24v-5.773c0-42.86 125.268-44.645 125.268-160.627C377.504 66.256 286.902 0 202.021 0zM192 373.459c-38.196 0-69.271 31.075-69.271 69.271 0 38.195 31.075 69.27 69.271 69.27s69.271-31.075 69.271-69.271-31.075-69.27-69.271-69.27z"/></svg>s
    - Cohort <svg style="height:0.8em;top:.04em;position:relative;fill:#214c78;" viewBox="0 0 512 512"><path d="M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z"/></svg> with registers
    - Application of causal learning algorithm

- Gain expertise in working with registers

- Potential to get <svg style="height:0.8em;top:.04em;position:relative;fill:#214c78;" viewBox="0 0 288 512"><path d="M209.2 233.4l-108-31.6C88.7 198.2 80 186.5 80 173.5c0-16.3 13.2-29.5 29.5-29.5h66.3c12.2 0 24.2 3.7 34.2 10.5 6.1 4.1 14.3 3.1 19.5-2l34.8-34c7.1-6.9 6.1-18.4-1.8-24.5C238 74.8 207.4 64.1 176 64V16c0-8.8-7.2-16-16-16h-32c-8.8 0-16 7.2-16 16v48h-2.5C45.8 64-5.4 118.7.5 183.6c4.2 46.1 39.4 83.6 83.8 96.6l102.5 30c12.5 3.7 21.2 15.3 21.2 28.3 0 16.3-13.2 29.5-29.5 29.5h-66.3C100 368 88 364.3 78 357.5c-6.1-4.1-14.3-3.1-19.5 2l-34.8 34c-7.1 6.9-6.1 18.4 1.8 24.5 24.5 19.2 55.1 29.9 86.5 30v48c0 8.8 7.2 16 16 16h32c8.8 0 16-7.2 16-16v-48.2c46.6-.9 90.3-28.6 105.7-72.7 21.5-61.6-14.6-124.8-72.5-141.7z"/></svg><svg style="height:0.8em;top:.04em;position:relative;fill:#214c78;" viewBox="0 0 288 512"><path d="M209.2 233.4l-108-31.6C88.7 198.2 80 186.5 80 173.5c0-16.3 13.2-29.5 29.5-29.5h66.3c12.2 0 24.2 3.7 34.2 10.5 6.1 4.1 14.3 3.1 19.5-2l34.8-34c7.1-6.9 6.1-18.4-1.8-24.5C238 74.8 207.4 64.1 176 64V16c0-8.8-7.2-16-16-16h-32c-8.8 0-16 7.2-16 16v48h-2.5C45.8 64-5.4 118.7.5 183.6c4.2 46.1 39.4 83.6 83.8 96.6l102.5 30c12.5 3.7 21.2 15.3 21.2 28.3 0 16.3-13.2 29.5-29.5 29.5h-66.3C100 368 88 364.3 78 357.5c-6.1-4.1-14.3-3.1-19.5 2l-34.8 34c-7.1 6.9-6.1 18.4 1.8 24.5 24.5 19.2 55.1 29.9 86.5 30v48c0 8.8 7.2 16 16 16h32c8.8 0 16-7.2 16-16v-48.2c46.6-.9 90.3-28.6 105.7-72.7 21.5-61.6-14.6-124.8-72.5-141.7z"/></svg> to expand project

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## For career

- Develop and expand my network in Europe

- Get more 👨‍💻👩‍💻 to work with

- Co-mentor PhD student

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# Challenges:

- Navigating Statistics Denmark website and documentation

- Translating Danish words for variables and registers 
    - ... and knowing what to look for

- Finding other datasets to use for linkage

- Balancing my time with other projects