TY - JOUR
T1 - Development of genome-wide polygenic risk scores for lipid traits and clinical applications for dyslipidemia, subclinical atherosclerosis, and diabetes cardiovascular complications among East Asians
AU - Tam, Claudia H. T.
AU - Lim, Cadmon K. P.
AU - Luk, Andrea O. Y.
AU - Ng, Alex C. W.
AU - Lee, Heung-man
AU - Jiang, Guozhi
AU - Lau, Eric S. H.
AU - Fan, Baoqi
AU - Wan, Raymond
AU - Kong, Alice P. S.
AU - Tam, Wing-hung
AU - Ozaki, Risa
AU - Chow, Elaine Y. K.
AU - Lee, Ka-fai
AU - Siu, Shing-chung
AU - Hui, Grace
AU - Tsang, Chiu-chi
AU - Lau, Kam-piu
AU - Leung, Jenny Y. Y.
AU - Tsang, Man-wo
AU - Kam, Grace
AU - Lau, Ip-tim
AU - Li, June K. Y.
AU - Yeung, Vincent T. F.
AU - Lau, Emmy
AU - Lo, Stanley
AU - Fung, Samuel
AU - Cheng, Yuk-lun
AU - Chow, Chun-chung
AU - Hu, Miao
AU - Yu, Weichuan
AU - Tsui, Stephen K. W.
AU - Huang, Yu
AU - Lan, Huiyao
AU - Szeto, Cheuk-chun
AU - Tang, Nelson L. S.
AU - Ng, Maggie C. Y.
AU - So, Wing-yee
AU - Tomlinson, Brian
AU - Chan, Juliana C. N.
AU - Ma, Ronald C. W.
AU - The Hong Kong Diabetes Register TRS Study Group, including
AU - The Hong Kong Diabetes Biobank Study Group, including
PY - 2021
Y1 - 2021
N2 - Background: The clinical utility of personal genomic information in identifying individuals at increased risks for dyslipidemia and cardiovascular diseases remains unclear. Methods: We used data from Biobank Japan (n = 70,657–128,305) and developed novel East Asian-specific genome-wide polygenic risk scores (PRSs) for four lipid traits. We validated (n = 4271) and subsequently tested associations of these scores with 3-year lipid changes in adolescents (n = 620), carotid intima-media thickness (cIMT) in adult women (n = 781), dyslipidemia (n = 7723), and coronary heart disease (CHD) (n = 2374 cases and 6246 controls) in type 2 diabetes (T2D) patients. Results: Our PRSs aggregating 84–549 genetic variants (0.251 < correlation coefficients (r) < 0.272) had comparably stronger association with lipid variations than the typical PRSs derived based on the genome-wide significant variants (0.089 < r < 0.240). Our PRSs were robustly associated with their corresponding lipid levels (7.5 × 10− 103 < P < 1.3 × 10− 75) and 3-year lipid changes (1.4 × 10− 6 < P < 0.0130) which started to emerge in childhood and adolescence. With the adjustments for principal components (PCs), sex, age, and body mass index, there was an elevation of 5.3% in TC (β ± SE = 0.052 ± 0.002), 11.7% in TG (β ± SE = 0.111 ± 0.006), 5.8% in HDL-C (β ± SE = 0.057 ± 0.003), and 8.4% in LDL-C (β ± SE = 0.081 ± 0.004) per one standard deviation increase in the corresponding PRS. However, their predictive power was attenuated in T2D patients (0.183 < r < 0.231). When we included each PRS (for TC, TG, and LDL-C) in addition to the clinical factors and PCs, the AUC for dyslipidemia was significantly increased by 0.032–0.057 in the general population (7.5 × 10− 3 < P < 0.0400) and 0.029–0.069 in T2D patients (2.1 × 10− 10 < P < 0.0428). Moreover, the quintile of TC-related PRS was moderately associated with cIMT in adult women (β ± SE = 0.011 ± 0.005, Ptrend = 0.0182). Independent of conventional risk factors, the quintile of PRSs for TC [OR (95% CI) = 1.07 (1.03–1.11)], TG [OR (95% CI) = 1.05 (1.01–1.09)], and LDL-C [OR (95% CI) = 1.05 (1.01–1.09)] were significantly associated with increased risk of CHD in T2D patients (4.8 × 10− 4 < P < 0.0197). Further adjustment for baseline lipid drug use notably attenuated the CHD association. Conclusions: The PRSs derived and validated here highlight the potential for early genomic screening and personalized risk assessment for cardiovascular disease.
AB - Background: The clinical utility of personal genomic information in identifying individuals at increased risks for dyslipidemia and cardiovascular diseases remains unclear. Methods: We used data from Biobank Japan (n = 70,657–128,305) and developed novel East Asian-specific genome-wide polygenic risk scores (PRSs) for four lipid traits. We validated (n = 4271) and subsequently tested associations of these scores with 3-year lipid changes in adolescents (n = 620), carotid intima-media thickness (cIMT) in adult women (n = 781), dyslipidemia (n = 7723), and coronary heart disease (CHD) (n = 2374 cases and 6246 controls) in type 2 diabetes (T2D) patients. Results: Our PRSs aggregating 84–549 genetic variants (0.251 < correlation coefficients (r) < 0.272) had comparably stronger association with lipid variations than the typical PRSs derived based on the genome-wide significant variants (0.089 < r < 0.240). Our PRSs were robustly associated with their corresponding lipid levels (7.5 × 10− 103 < P < 1.3 × 10− 75) and 3-year lipid changes (1.4 × 10− 6 < P < 0.0130) which started to emerge in childhood and adolescence. With the adjustments for principal components (PCs), sex, age, and body mass index, there was an elevation of 5.3% in TC (β ± SE = 0.052 ± 0.002), 11.7% in TG (β ± SE = 0.111 ± 0.006), 5.8% in HDL-C (β ± SE = 0.057 ± 0.003), and 8.4% in LDL-C (β ± SE = 0.081 ± 0.004) per one standard deviation increase in the corresponding PRS. However, their predictive power was attenuated in T2D patients (0.183 < r < 0.231). When we included each PRS (for TC, TG, and LDL-C) in addition to the clinical factors and PCs, the AUC for dyslipidemia was significantly increased by 0.032–0.057 in the general population (7.5 × 10− 3 < P < 0.0400) and 0.029–0.069 in T2D patients (2.1 × 10− 10 < P < 0.0428). Moreover, the quintile of TC-related PRS was moderately associated with cIMT in adult women (β ± SE = 0.011 ± 0.005, Ptrend = 0.0182). Independent of conventional risk factors, the quintile of PRSs for TC [OR (95% CI) = 1.07 (1.03–1.11)], TG [OR (95% CI) = 1.05 (1.01–1.09)], and LDL-C [OR (95% CI) = 1.05 (1.01–1.09)] were significantly associated with increased risk of CHD in T2D patients (4.8 × 10− 4 < P < 0.0197). Further adjustment for baseline lipid drug use notably attenuated the CHD association. Conclusions: The PRSs derived and validated here highlight the potential for early genomic screening and personalized risk assessment for cardiovascular disease.
KW - Diabetes cardiovascular complications
KW - East Asians
KW - Lipid traits
KW - Polygenic risk scores
KW - Subclinical atherosclerosis
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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85101209469&origin=recordpage
U2 - 10.1186/s13073-021-00831-z
DO - 10.1186/s13073-021-00831-z
M3 - RGC 21 - Publication in refereed journal
C2 - 33608049
SN - 1756-994X
VL - 13
JO - Genome Medicine
JF - Genome Medicine
M1 - 29
ER -