Introduction of the NIH-funded population-based longitudinal subcohorts at Des Moines University
Description
Background: The Principal Investigator (PI), as the sole author, has generated high-throughput data from three NIH-funded population-based longitudinal subcohort studies examining epigenetic links between environmental exposures and cardiometabolic diseases. These subcohorts were drawn from the National Heart, Lung, and Blood Institute Twin Study (NTS), the community-based Atherosclerosis Risk in Communities (ARIC) Study, and the Multi-Ethnic Study of Subclinical Atherosclerosis (MESA), with additional access to extensive data in the NTS parent cohort.
Objectives: To leverage existing data effectively and creatively through collaborative research, aiming for competitive grants and high-impact publications.
Method: Studies 1 and 2 included twins with buffy coat DNA samples from the NTS. Study 1 included 40 white male twin pairs discordant for cardiovascular death. Study 2 included 22 pairs discordant for incident obesity, and 26 pairs discordant for incident type 2 diabetes. Study 1 measured methylation (Illumina 450K), while studies 1 and 2 measured hydroxymethylation with hMe-Seal-seq. Study 3 was a two-cohort, individually matched 1:2 nested case-control study: 140 incident coronary heart disease (CHD) cases and 280 controls from ARIC, and 60 incident CHD cases and 120 controls from MESA, and measured plasma miRs using next-generation sequencing. Rich phenomics data were available for these studies.
Results: Limitations of current biostatistical and bioinformatic analytic methods constrain the ability to fully extract rich information from high-throughput and extensive phenotypic data generated from small-scale cohorts.
Conclusion: The data are underutilized, supporting the need for innovative strategies, including new/advanced statistical and bioinformatic approaches, AI integration, and effective research collaborations.
Citation Information
Dai, Jun, "Introduction of the NIH-funded population-based longitudinal subcohorts at Des Moines University" (2026). Office of Research DMU Research Symposium. 16.
https://digitalcommons.dmu.edu/researchsymposium/2025rs/2025abstracts/16
Introduction of the NIH-funded population-based longitudinal subcohorts at Des Moines University
Background: The Principal Investigator (PI), as the sole author, has generated high-throughput data from three NIH-funded population-based longitudinal subcohort studies examining epigenetic links between environmental exposures and cardiometabolic diseases. These subcohorts were drawn from the National Heart, Lung, and Blood Institute Twin Study (NTS), the community-based Atherosclerosis Risk in Communities (ARIC) Study, and the Multi-Ethnic Study of Subclinical Atherosclerosis (MESA), with additional access to extensive data in the NTS parent cohort.
Objectives: To leverage existing data effectively and creatively through collaborative research, aiming for competitive grants and high-impact publications.
Method: Studies 1 and 2 included twins with buffy coat DNA samples from the NTS. Study 1 included 40 white male twin pairs discordant for cardiovascular death. Study 2 included 22 pairs discordant for incident obesity, and 26 pairs discordant for incident type 2 diabetes. Study 1 measured methylation (Illumina 450K), while studies 1 and 2 measured hydroxymethylation with hMe-Seal-seq. Study 3 was a two-cohort, individually matched 1:2 nested case-control study: 140 incident coronary heart disease (CHD) cases and 280 controls from ARIC, and 60 incident CHD cases and 120 controls from MESA, and measured plasma miRs using next-generation sequencing. Rich phenomics data were available for these studies.
Results: Limitations of current biostatistical and bioinformatic analytic methods constrain the ability to fully extract rich information from high-throughput and extensive phenotypic data generated from small-scale cohorts.
Conclusion: The data are underutilized, supporting the need for innovative strategies, including new/advanced statistical and bioinformatic approaches, AI integration, and effective research collaborations.
