Update wp3.qmd

Small edits and added links.

wp3.qmd

@@ -4,10 +4,10 @@ title: "Work Package 3: Heterogeneity"
 
 WP3 will characterise heterogeneity among people in early middle age with HbA1c defined pre-diabetes. The aim is to identify a set of easily obtainable biomarkers that can optimally distinguish those with high probability of stable pre-diabetes/remission from those at highest risk of progression to diabetes.
 ## WP 3.1 Risk clustering and long-term prediction
-WP3.1 will investigate to which degree detailed biological data (e.g. genetics, omics, health behavioural data) can improve risk prediction within a Danish ([ADDITION-PRO13](https://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-12-1078)) and a Greenlandic (Greenland Health Surveys) context. Both cohorts recruited participants more than a decade ago and examined a wide set of cardiometabolic and genetic risk factors, health behaviours and biomarkers, including fat distribution measures and physical activity measures from week long combined heart rate/accelerometer readings. We will link these cohorts to the register-based risk predictions from WP2 (backdated to each cohort baseline), and examine the added predictive value of individual biological risk indicators and of risk factor clusters. The availability of more than a decade of accrued follow-up for diabetes incidence in these cohorts will enable us to obtain a global indication of diabetes risk clusters within the first year of DP-Next.
+WP3.1 will investigate to which degree detailed biological data (e.g. genetics, omics, health behavioural data) can improve risk prediction within a Danish ([ADDITION-PRO](https://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-12-1078)) and a Greenlandic ([Greenland Health Surveys](https://www.tandfonline.com/doi/full/10.1080/22423982.2019.1709257)) context. Both cohorts recruited participants more than a decade ago and examined a wide set of cardiometabolic and genetic risk factors, health behaviours and biomarkers, including fat distribution measures and physical activity measures from week long combined heart rate/accelerometer readings. We will link these cohorts to the register-based risk predictions from WP2 (backdated to each cohort baseline), and examine the added predictive value of individual biological risk indicators and of risk factor clusters. The availability of more than a decade of accrued follow-up for diabetes incidence in these cohorts will enable us to obtain a global indication of diabetes risk clusters within the first year of DP-Next.
 
-##WP 3.2 Deep Phenotyping of HbA1c defined pre-diabetes
-###Hypothesis: Among individuals with routinely identified prediabetes based on HbA1c, deep phenotyping with non-invasive methods will identify subgroups at the highest risk of progression to T2D and subgroups likely to maintain prediabetes or achieve remission; beyond the predictive ability of age, sex, HbA1c and BMI.
+## WP 3.2 Deep Phenotyping of HbA1c defined pre-diabetes
+### Hypothesis: Among individuals with routinely identified prediabetes based on HbA1c, deep phenotyping with non-invasive methods will identify subgroups at the highest risk of progression to T2D and subgroups likely to maintain prediabetes or achieve remission; beyond the predictive ability of age, sex, HbA1c and BMI.
 
 ### Background: 
 Although elevated blood glucose precedes T2D onset by over a decade14, many people with slightly elevated HbA1c do not progress to diabetes15. Depending on the criteria for prediabetes, 10-40% develop T2D within 1-5 years16–18 and 17-45% revert to normoglycaemia15, pointing to significant heterogeneity in diabetes risk among individuals with prediabetes19–21. A relatively recent cluster-driven diabetes subclassification delineated two subgroups with relatively young age of diabetes onset and absence of marked obesity and low insulin secretion22, which map to genetic, metabolic and phenotypic characteristics already present in pre-diabetes. Notably, a recent study identified elevated liver fat and diminished beta-cell function as the most predictive factors for progressing to T2D in people with pre-diabetes23, highlighting the need to extend precision approaches for diabetes prevention beyond genetic predisposition and insulin resistance. 
@@ -26,7 +26,7 @@ In the core protocol we will identify a cohort of 1000 individuals aged 40 to 55
 Participants will be recruited in Odense (350), Aarhus (350), Aalborg (150), Greenland (50) and the Faroe Islands (50). We intend to include 100 individuals of Greenlandic ancestry, 50 living in Denmark and 50 living in Greenland. Participants will be recruited based on data from routine HbA1c checks, accessed through the LABKA registers. Consequently the clinical decisions prompting a HbA1c measurement will form part of the selection process, reflecting current clinical practice.
 
 ### Core Protocol:
-Participants will be invited to answer an online questionnaire (medical and family history, sociodemographic data, health literacy, food habits24 and cravings25 , personality traits such as willingness to take risks, quality of life, self-perceived mental stress26, depression, anxiety, sleep apnea scores27) followed by a 4-hour clinical examination at one of the Steno Diabetes Centers. The programme includes: anthropometric measures (height, weight, waist-hip ratio, and DEXA-scan for body composition), a 5-point oral glucose tolerance test to estimate insulin sensitivity and beta-cell function, liver elastography to estimate liver fat and liver stiffness, blood pressure, heart rate variability and pulse-wave velocity for arterial stiffness estimation. Participants will measure physical activity with a combined heart rate monitor / accelerometer during 7 days following the visit. Blood samples will be analysed for lipids and HbA1c and processed for biobanking of plasma, serum and DNA. The biobank will further include urine, saliva and faeces samples. All further measurements will be in biobank samples as part of the extended protocol.
+Participants will be invited to answer an online questionnaire (medical and family history, sociodemographic data, health literacy, food habits and cravings , personality traits such as willingness to take risks, quality of life, self-perceived mental stress, depression, anxiety, sleep apnea scores) followed by a 4-hour clinical examination at one of the Steno Diabetes Centers. The programme includes: anthropometric measures (height, weight, waist-hip ratio, and DEXA-scan for body composition), a 5-point oral glucose tolerance test to estimate insulin sensitivity and beta-cell function, liver elastography to estimate liver fat and liver stiffness, blood pressure, heart rate variability and pulse-wave velocity for arterial stiffness estimation. Participants will measure physical activity with a combined heart rate monitor / accelerometer during 7 days following the visit. Blood samples will be analysed for lipids and HbA1c and processed for biobanking of plasma, serum and DNA. The biobank will further include urine, saliva and faeces samples. All further measurements will be in biobank samples as part of the extended protocol.
 
 The primary outcome for the core protocol is incident Type 2 Diabetes. The LABKA register will identify incident T2D (based on routine HbA1c) supplemented with a study HbA1c measurement at the end of the DP-Next project. The primary statistical analysis will use unsupervised Latent Class Analysis28 to map the heterogeneity across all measured variables. LCA will be carried out at two levels: a full model including all available data and a minimal model using as few data as possible with limited loss of strength. Modelling studies show that our sample of 1000 is sufficient to identify patterns and clusters in most data sets.