• If you are citizen of an European Union member nation, you may not use this service unless you are at least 16 years old.

  • Stop wasting time looking for files and revisions. Connect your Gmail, DriveDropbox, and Slack accounts and in less than 2 minutes, Dokkio will automatically organize all your file attachments. Learn more and claim your free account.


Quantified self-tracking diabetes study

This version was saved 8 years, 5 months ago View current version     Page history
Saved by Administrator
on May 18, 2012 at 7:08:34 pm

DIYgenomics Diabetes Quantified self-tracking Study

www.DIYgenomics.org web homepage

DIYgenomics wiki homepage

Contact @DIYgenomics or email "studies at DIYgenomics.org" for more information or to participate.


Title: Diabetes Quantified self-tracking Study


Summary: Quantified self tracking devices and smartphone health applications have been proliferating in the consumer sector for the measurement of a wide variety of health conditions. This study seeks to investigate the use of quantified self tracking devices in the health context and linkage between self tracking data and genomic profiles, physical biomarkers, behavioral change, and personalized intervention.


Hypothesis: Quantified self tracking devices may be leading to interesting new methods of discovery, empowerment, and health self-management amongst individuals. Specifically, quantified self tracking device data generates a large volume of attribute-rich data that could have novel correlations with both traditional and health 2.0 data streams such as genomic data, blood test data, online phenotypic surveys, and self-experimentation data. Baseline hemoglobin measures may already predict diabetes in high-risk individuals (Heianza 2012).


Genotype data: 

  • SNPs associated with resting heart rate: COMT Val158Met rs4680,[i] and GJA1 (rs9398652, rs11154022), MYH6 (rs452036, rs365990), MYH7 (rs223116), Intergenic (rs17287293), SLC35F1 (rs281868), SLC12A9 (rs314370), UfSp1 (rs12666989), FADS1 (rs174547), and CD34 (rs2745967).[ii]
  • SNPs associated with insulin resistance: TCF7L2 (rs7903146), PPARG (rs1801282, rs6802898, rs2197423 ), KCNJ11 (rs5219), IGF2BP2 (rs4402960), HHEX (rs1111875), CDKAL1 (rs4712523), SLC30A8 (rs13266634), WFS1 (rs10012946), CDKN2A/B (rs2383208), KCNQ1 (rs2237892), and MTNR1B (rs1387153).[iii]


Phenotype data:

Quantitative data: Hemoglobin & cholesterol profiles at baseline & after three months (HgbA1c + Chem 12 Panel + Lipid Profile - $69), and for intervention group B, quantitative data output from glucometer tracking devices. Ideally, an LDL particle size and an HS-CRP test (both must be doctor-ordered) would also be included to indicate diabetes risk and measure of overall current inflammation in the body

Qualitative data: standardized user survey instruments


Study protocol: Minimum n=100, goal n=300. Ongoing open enrollment in a study with one month duration. Participants randomized into three groups: 

A: Control group: No intervention

B: Experimental group 1: maintain a daily food consumption and exercise diary and conduct daily glucometer measurements (2x daily, fasting and postprandial (1-2 hrs after a meal)

C: Experimental group 2: maintain a daily food consumption and exercise diary


Study conduct: The study will be conducted in a healthy crowdsourced cohort. Enrollment will be open and ongoing, with at minimum 100 participants sought for the first phase of data analysis. Study recruitment and operation will be via the internet-based health collaboration community Genomera.com. The process is that participants will join the study, complete an informed consent process, agree to share personal genotyping data for the required variants, and complete the online survey instruments and other requirements. Personalized feedback will be provided to participants.


Potential study advisors:

Lyn Powel, PhD, Senior Research Scientist, Metabolic Disease Modeling Expert, Entelos

Nate Heintzman, PhD, UCSD, SweetSpot Diabetes Care

Ben van Ommen, PhD, Head, Systems Biology, TNO Innovation for Life, the Netherlands


Potential citizen ethicist review:

Alexander Geryland, Biotechnology professional

Amanda Kahn-Kirby, Biotechnology professional

[i] Wacker J, Gatt JM. Resting posterior versus frontal delta/theta EEG activity is associated with extraversion and the COMT VAL(158)MET polymorphism. Neurosci Lett. 2010 Jul 5;478(2):88-92.

[ii] Eijgelsheim M, Newton-Cheh C, Sotoodehnia N, et al. Genome-wide association analysis identifies multiple loci related to resting heart rate. Hum Mol Genet. 2010 Oct 1;19(19):3885-94.

[iii] 23andMe. Type 2 Diabetes. Available at: https://www.23andme.com/health/Type-2-Diabetes/techreport/





Comments (0)

You don't have permission to comment on this page.