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Quantified self-tracking diabetes study

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DIYgenomics Diabesity Quantified Self-Tracking Study

DIYgenomics wiki homepage


Title: Diabesity 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]
  • Other diabetes and obesity SNPs from 23andMe


Phenotype data:


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: (see Ethical Review Q&A)

Alexander Gerlyand, 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/





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