Heterogeneity workshop / journal club
Heterogeneity Workshop and Journal Club
This file presents an overview of the talking points for the workshop, including relevant articles, measurement discussions, ideas, and suggestions.
Draft Agenda
| Time | Topic | Presenter |
|---|---|---|
| 11:00-12:00 | Diabetes Heterogeneity Overview | Daniel |
| Introduction to the Workshop and Journal Club | Daniel | |
| Conceptual background: Different types of heterogeneity | Daniel | |
| Overview: Current approaches to heterogeneity in T2D and pre-diabetes | Christian / Mikkel S | |
| 12:00-12:30 | Lunch Break | |
| 12:30-14:00 | Measurement Selection and Discussion | Kristina / David / Mikkel K / Jonas |
| 14:00-15:30 | Statistical Analysis and Group Work | Christian / Daniel |
| 15:30-16:00 | Plan for Journal Club series: dates, leads, topics | All |
Session Descriptions
11:00-12:00 | Diabetes Heterogeneity Overview
A general introduction to diabetes heterogeneity and current discussions within the field, followed by an overview of three clustering methods: K-means, hierarchical clustering, and latent class analysis (LCA).
12:00-12:30 | Lunch Break
Sandwiches from the cafeteria
12:30-14:00 | Measurement Selection and Discussion
In this session we will review a proposed measurement list and discuss which measurements add important scientific value, consider participant burden, and assess feasibility across the different Steno Diabetes Centers.
14:00-15:30 | Statistical Analysis and Group Work
In this session we will review statistical methods and how their algorithms work to explore heterogeneity. This will include a short presentation / some code-along / group exercises in R using a simple dataset to explore different clustering approaches.
15:30-16:00 | Plan for Journal Club
To conclude we will discuss the best way to organise the Heterogeneity Journal club: Best day/time, which topics will we cover, who will take the lead on what.
Articles for discussion:
Ahlqvist et al. “Novel subgroups of adult-onset diabetes and their association with outcomes : a data-driven cluster analysis of six variables”
Wagner et al. “Beyond Glucose—Rethinking Prediabetes for Precision Prevention”
Dennis et al. “Precision medicine in type 2 diabetes: using individualized prediction models to optimize selection of treatment.”
Udler et al. “Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: a soft clustering analysis.”
Deutsch et al. “Phenotypic and genetic classification of diabetes”
Dennis et al. “Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial data”
Potential talking and discussion points:
Hard or soft clustering
Limitations of clustering and potential solutions
Temporal drift
- Wagner: approximately 20% of type-2 diabetic patients change subgroup over a 5-year period.
What to do with people who don’t fit clearly into one subtype?
Uncertainty about whether subgroups capture discrete pathologies
Clustering versus other approaches to map heterogeneity
Clustering using dynamic data (e.g. OGTT responses, continuous glucose monitoring, or physical activity monitoring)
Clustering using dynamic data (e.g. OGTT responses, continuous glucose monitoring, or physical activity monitoring)