Diabetes In Asians at Risk in Youth

Overarching aim: To improve the case detection rate of T2D in young Singaporeans adults aged 21-40y.

Specific Aim 1: To conduct a case-finding study for T2D and prediabetes through age- and BMI-stratified sampling in a contemporary population of young Singaporean adults aged 21-40y.

Specific Aim 2: To identify novel risk factors and develop a composite score to improve the detection rate for Asian YOD and prediabetes. These include non-traditional predictors such as glucometrics from continuous glucose monitoring (CGM), body composition measures, and socio-behavioural analyses.

Specific Aim 3: To identify the contribution of genomic differences to the risk of Asian Young Onset Diabetes

To fulfil Aim 1 within the first 1 to 2 years, the investigators plan to perform island-wide screening for diabetes or prediabetes amongst people aged 21-40y (n=3000). Recruitment will be stratified firstly by age categories (21-25y, 25-30y, 30-35y, 35-40y), aiming for n=750 per age category, followed by BMI categories of normal, overweight or obese by Asian thresholds (<23kgm2, 23-27.5kg/m2 and >=27.5kg/m2) within each age group. Participants will be recruited through online and print media including social media advertisements, word of mouth and /or referrals. A study poster advert will be posted online and in print, which will then direct participants to a webpage www.thdiary.sg which will explain the study design further and eligibility requirements and additional study details. The partiipants can proceed to register themselves via FORMSG on this webpage and if they meet the criteria they will be directed to CALSG to book an appointment with the study team.

Upon consent and recruitment, the following will be done:

1. Anthropometric measurements alongside traditional risk factors including blood pressure (BP), and waist-hip-ratio.
2. Body Impedance Analysis. This is a painless way to measure what the body is made of and involves standing on a special scale and holding onto handles.
3. Venipuncture will be conducted for an oral glucose tolerance test (OGTT) for 0 min, 1h and 2h glucose readings and HbA1c and storage.
4. Download of selected social media data (not directly containing personal data or posts) for up to 1 year preceding the visit from facebook, instagram and tiktok.
5. Continuous glucose monitor (CGM) sensor insertion. On the day of CGM insertion, a pre-insertion questionnaire (using form.sg, DIARY skin reaction form 1 before sensor wear) will be filled in which includes a picture of the site before sensor insertion.
6. A survey will also be conducted to ascertain sociodemographic (employment, type of residence) and educational status, family history of diabetes and comorbid conditions (if any), the individual's dietary intake, physical activity levels, and health-seeking behaviour. This survey will also include a section to fill in their average daily step count for 7/30 days. The participant will be guided to look for their step count data over 7 or 30 days (depending on what is available) from their smartphone activity tracker (this includes Apple Health, Samsung Health or Google Fit or any other activity tracker). This information will then be entered into the survey form on form.sg. No screenshots or data export is required.
7. Handgrip measurements
8. Second and third venipunctures for second part of OGTT.
9. Collection of dietary data via a digital food log

In Aim 2, the investigators aim to identify novel contemporary risk markers associated with T2D or prediabetes in young adults. This aim will not require another study visit from participants. Stored samples from Aim 1 baseline OGTT venipuncture will be used as well as the data retrieved from the study procedures in aim 1. Those who are diagnosed with T2D and prediabetes (estimated n=200 each), together with a normoglycaemic group (n=200) from Aim 1 will have the following done to enable diabetes subtyping: lipids, C-peptide, Insulin levels, Glutamic Acid Decarboxylase (GAD), Islet Antigen 2 (IA-2) Antibodies (Ab) and Zinc Transport 8 (ZnT8) Ab.

A composite score will then be developed for the diagnosis of prediabetes and diabetes. The dataset will be split into development and validation sets. Logistic regression modelling and Receiver Operating Characteristics (ROC) curve analysis will be employed to develop a composite score in identifying people with T2D or prediabetes, based on the sample with 200 newly diagnosed T2D, 200 prediabetes and 200 normoglycaemic participants. With the development set, two models will be fitted to predict a subject belongs to T2D, prediabetes or normoglycaemia: (a) candidate predictor variables of all abovementioned indicators and their derivatives, including sociodemographic, family history of diabetes and comorbid conditions, dietary intake, physical activity levels, body composition metrics, health-seeking behaviours, as well as CGM biomarkers, and (b) candidate predictor variables of all these variables except those extracted / derived from CGM. Variable selection methods will be employed to determine final models with optimal predictability, parsimony and model fit indices. Two composite scores, including and not including CGM biomarkers, will then be defined by the final selected models.

To fulfil aim 3, stored blood from participants will be used in ongoing large scale genomic studies such as the National Precision Medicine's Phase 3 programme. In this program, participants will have whole genome sequencing performed under research, with a cluster specific centralized genomics team made up of clinical geneticists, bioinformaticians, variant curators, and genetic counsellors available to process the genetic specimens end to end (genomics innovation hub). This would involve curating genes relevant to the disease subtype, in this case, diabetes, but also returning incidental findings that are Tier 1 American College of Medical Genetics and Genomics (ACMG) conditions. VCF files from the participants of this study will be returned to this study team so that further research can be done to determine if genomic information can further help risk stratify Asian YOD.