scholarly journals Mid‐life epigenetic age, neuroimaging brain age, and cognitive decline: Coronary Artery Risk Development in Young Adults (CARDIA) Study

2021 ◽  
Vol 17 (S5) ◽  
Author(s):  
Yinan Zheng ◽  
Mohamad Habes ◽  
Mitzi M. Gonzales ◽  
Raymond Pomponio ◽  
Ilya M. Nasrallah ◽  
...  
2021 ◽  
Author(s):  
Kyeezu Kim ◽  
Brian T. Joyce ◽  
Yinan Zheng ◽  
Pamela J. Schreiner ◽  
David R. Jacobs Jr. ◽  
...  

DNA methylation-based biological age (epigenetic age) has been suggested as a useful biomarker of age-related conditions including type 2 diabetes (T2D), and its newest iterations (GrimAge measurements) have shown early promise. In this study, we explored the association between epigenetic age and incident T2D, in the context of their relationships with obesity. <p>A total of 1,057 participants in the Coronary Artery Risk Development in Young Adults (CARDIA) study were included in the current analyses. We stratified the participants into three groups; normal weight, overweight, and obese. A one-year increase of GrimAge was associated with higher 10-year (Y15 to Y25) incidence of T2D (OR=1.06, 95% CI=1.01-1.11). GrimAge acceleration, which represents the deviation of GrimAge from chronological age, was derived from the residuals of a model of GrimAge and chronological age, and any GrimAge acceleration (Positive GrimAA; having GrimAge older than chronological age) was associated with significantly higher odds of 10-year incidence of T2D in obese participants (OR=2.57, 95% CI=1.61-4.11). Cumulative obesity was estimated by years since obesity onset, and GrimAge partially mediated the statistical association between cumulative obesity and incident diabetes or prediabetes (proportion mediated = 8.0%). </p> In conclusion, both <a>older and accelerated GrimAge were associated with higher risk of T2D, particularly among obese participants. GrimAge also statistically mediated the associations between cumulative obesity and T2D. </a>Our findings suggest that epigenetic age measurements with DNA methylation can potentially be utilized as a risk factor or biomarker associated with T2D development.


2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Drew R. Nannini ◽  
Brian T. Joyce ◽  
Yinan Zheng ◽  
Tao Gao ◽  
Lei Liu ◽  
...  

Abstract Background The metabolic syndrome (MetS) is a collection of metabolic disturbances that can lead to various cardiovascular diseases. Previous studies have shown a more adverse metabolic risk profile is associated with more advanced biological aging. The associations between epigenetic biomarkers of age with MetS, however, are not well understood. We therefore investigated the associations between epigenetic age acceleration and MetS severity score and incident MetS. Results A subset of study participants with available whole blood at examination years 15 and 20 from the Coronary Artery Risk Development in Young Adults Study underwent epigenomic profiling using the Illumina MethylationEPIC Beadchip (~ 850,000 sites). Intrinsic and extrinsic epigenetic age acceleration (IEAA and EEAA) were calculated from DNA methylation levels. The MetS severity score was positively associated with IEAA at years 15 (P = 0.016) and 20 (P = 0.016) and EEAA at year 20 (P = 0.040) in cross-sectional analysis. IEAA at year 20 was significantly associated with incident MetS at year 30 (OR = 1.05 [95% CI 1.01, 1.10], P = 0.028). Conclusions To our knowledge, this is the first report of the longitudinal association between epigenetic age acceleration and MetS. These findings suggest that a higher MetS severity score is associated with accelerated epigenetic aging and such aging may play a role in the development of metabolic disorders, potentially serving as a useful biomarker of and early detection tool for future MetS.


Circulation ◽  
2019 ◽  
Vol 139 (Suppl_1) ◽  
Author(s):  
Yinan Zheng ◽  
Sadiya Khan ◽  
Tao Gao ◽  
Brian Joyce ◽  
Sanjiv Shah ◽  
...  

2021 ◽  
Author(s):  
Kyeezu Kim ◽  
Brian T. Joyce ◽  
Yinan Zheng ◽  
Pamela J. Schreiner ◽  
David R. Jacobs Jr. ◽  
...  

DNA methylation-based biological age (epigenetic age) has been suggested as a useful biomarker of age-related conditions including type 2 diabetes (T2D), and its newest iterations (GrimAge measurements) have shown early promise. In this study, we explored the association between epigenetic age and incident T2D, in the context of their relationships with obesity. <p>A total of 1,057 participants in the Coronary Artery Risk Development in Young Adults (CARDIA) study were included in the current analyses. We stratified the participants into three groups; normal weight, overweight, and obese. A one-year increase of GrimAge was associated with higher 10-year (Y15 to Y25) incidence of T2D (OR=1.06, 95% CI=1.01-1.11). GrimAge acceleration, which represents the deviation of GrimAge from chronological age, was derived from the residuals of a model of GrimAge and chronological age, and any GrimAge acceleration (Positive GrimAA; having GrimAge older than chronological age) was associated with significantly higher odds of 10-year incidence of T2D in obese participants (OR=2.57, 95% CI=1.61-4.11). Cumulative obesity was estimated by years since obesity onset, and GrimAge partially mediated the statistical association between cumulative obesity and incident diabetes or prediabetes (proportion mediated = 8.0%). </p> In conclusion, both <a>older and accelerated GrimAge were associated with higher risk of T2D, particularly among obese participants. GrimAge also statistically mediated the associations between cumulative obesity and T2D. </a>Our findings suggest that epigenetic age measurements with DNA methylation can potentially be utilized as a risk factor or biomarker associated with T2D development.


2016 ◽  
Vol 64 (S 01) ◽  
Author(s):  
B. Mayr ◽  
S. Buchholz ◽  
M. Lühr ◽  
C. Hagl ◽  
M. Pichlmaier

2010 ◽  
Vol 999 (999) ◽  
pp. 1-8 ◽  
Author(s):  
Luciana Moreira Lima ◽  
Maria das Gracas Carvalho ◽  
Claudia Natalia Ferreira ◽  
Ana Paula Fernandes ◽  
Cirilo Pereira da Fonseca Neto ◽  
...  

1987 ◽  
Vol 60 (16) ◽  
pp. 1269-1272 ◽  
Author(s):  
Lloyd W. Klein ◽  
Jai B. Agarwal ◽  
Michael B. Herlich ◽  
Therese M. Leary ◽  
Richard H. Helfant

2021 ◽  
Vol 77 (18) ◽  
pp. 65
Author(s):  
Maryam Saleem ◽  
Naveena Yanamala ◽  
Irfan Zeb ◽  
Brijesh Patel ◽  
Heenaben Patel ◽  
...  

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