class: center, middle, inverse, title-slide # MELD Project: <em>M</em>etabolic consequences of <em>E</em>arly <em>L</em>ife adversity and risk for type 2 <em>D</em>iabetes ### Luke W. Johnston --- layout: true <div class="my-footer"> <span> <img src="../../common/dda-logo.png" alt="DDA", width="75"> <img src="../../common/sdca-logo.png" alt="SDCA", width="55"> <a href="https://slides.lwjohnst.com/steno/2020-02-06/">slides.lwjohnst.com/steno/2020-02-06</a> </span> </div> --- # Purpose: - Seek feedback - Show my project - Share about: - Early life influences - Analytic technique - Open science practices --- class: center, middle # 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 -- - Easier now with: - 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 --- ## 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 .pull-left[ **Adversity of trauma**: - Refugees - Immigration - Childhood abuse - Poor or unsafe neighbourhood ] -- .pull-right[ **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. -- - *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 <img src="../../dda/2020-02-04/images/lexis-diagram.svg" width="70%" style="display: block; margin: auto;" /> --- ## Identifying variables with DAG <img src="../../dda/2020-02-04/images/dag.svg" width="85%" style="display: block; margin: auto;" /> --- ## For objectives, need to analyze data like this... <img src="../../au-ph/2019-08-15/images/network.png" width="75%" style="display: block; margin: auto;" /> --- class: center, middle # Current Analytic Problem --- ## Modern studies generate lots of metabolic data .pull-left[ - -omics type data - Metabolic biomarkers ] .pull-right[ - High dimensionality - Complex networks ] --- ## "Traditional" analysis may use: Dimensionality reduction <div class="figure" style="text-align: center"> <img src="../../au-ph/2019-08-15/images/pca.png" alt="Reducing number of variables with PCA" width="280" /> <p class="caption">Reducing number of variables with PCA</p> </div> ??? 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 .center[ ``` 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 <img src="index_files/figure-html/img-traditional-network-analysis-1.png" width="50%" height="50%" style="display: block; margin: auto;" /> ??? 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? -- - 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? <img src="../../au-ph/2019-08-15/images/network.png" width="75%" style="display: block; margin: auto;" /> --- ## Potential solution: NetCoupler .pull-left[ **Causal structure learning** that ... - Finds most likely network structure - Allows inclusion of exposure and outcome - Identifies causal links between and within network ] .pull-right[  ] .footnote[NetCoupler algorithm was developed by Clemens Wittenbecher.] --- ## NetCoupler algorithm overview <img src="../../dda/2020-02-04/images/netcoupler-process.svg" width="75%" style="display: block; margin: auto;" /> --- ## Infer (potentially) causal pathways with graphical model <img src="../../au-ph/2019-08-15/images/nc-causal-pathways.png" width="85%" style="display: block; margin: auto;" /> --- ## Overall analysis with NetCoupler <img src="../../dda/2020-02-04/images/methods.svg" width="65%" style="display: block; margin: auto;" /> --- # Current state of project: Just started **(Trying to) Adhere to open scientific practices**: - Project proposal: <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/) -- - Developing NetCoupler: <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) -- - Developing protocol to register study: <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/meld-protocol](https://lwjohnst.gitlab.io/meld-protocol) ??? About transparency of process to making claim. --- class: center, middle ## Question: ## How many know or have heard or know about <u>open science</u>? --- class: center, middle ## Question: ## ...or <u>open access, open methods, open data, or open source</u>? --- ## Open science: Terms and meanings .center[ | **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 | ] --- class: middle, center # Thanks for listening 😁 --- class: middle, center # Algorithm (steps) used in NetCoupler --- ### 1. DAG skeleton of network is estimated <img src="../../au-ph/2019-08-15/images/nc-step-1.png" width="1151" height="100%" style="display: block; margin: auto;" /> --- ### 2. Initial links formed on exposure-side <img src="../../au-ph/2019-08-15/images/nc-step-2.png" width="65%" style="display: block; margin: auto;" /> --- ### 3. Direct effects estimated between exposure and network <img src="../../au-ph/2019-08-15/images/nc-step-3.png" width="55%" style="display: block; margin: auto;" /> --- ### 4. Repeat identifying links, find ambiguous, delete indirect links <img src="../../au-ph/2019-08-15/images/nc-step-4.png" width="55%" style="display: block; margin: auto;" /> ??? If you were interested in linkages with an exposure on the network, you could stop here. --- ### 5. Initial links formed with outcome-side <img src="../../au-ph/2019-08-15/images/nc-step-5.png" width="65%" style="display: block; margin: auto;" /> --- ### 6. Direct effects estimated between network and outcome <img src="../../au-ph/2019-08-15/images/nc-step-6.png" width="65%" style="display: block; margin: auto;" /> --- ### 7. Repeat identifying links, delete indirect links, find ambiguous <img src="../../au-ph/2019-08-15/images/nc-step-7.png" width="65%" style="display: block; margin: auto;" /> ??? You could stop here if you were interested in linkages with the network and an outcome. --- ### 8. (optional) Combine both estimations into joint model <img src="../../au-ph/2019-08-15/images/nc-step-8.png" width="85%" style="display: block; margin: auto;" />