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Nurkkala J, Vaura F, Toivonen J, Niiranen T. Genetics of hypertension-related sex differences and hypertensive disorders of pregnancy. Blood Press 2024; 33:2408574. [PMID: 39371034 DOI: 10.1080/08037051.2024.2408574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Revised: 09/16/2024] [Accepted: 09/19/2024] [Indexed: 10/08/2024]
Abstract
Background: Hypertension and hypertensive disorders of pregnancy (HDP) cause a significant burden of disease on societies and individuals by increasing cardiovascular disease risk. Environmental risk factors alone do not explain the observed sexual dimorphism in lifetime blood pressure (BP) trajectories nor inter-individual variation in HDP risk. Methods: In this short review, we focus on the genetics of hypertension-related sex differences and HDP and discuss the importance of genetics utilization for sex-specific hypertension risk prediction. Results: Population and twin studies estimate that 28-66% of variation in BP levels and HDP is explained by genetic variation, while genomic wide association studies suggest that BP traits and HDP partly share a common genetic background. Moreover, environmental and epigenetic regulation of these genes differ by sex and oestrogen receptors in particular are shown to convey cardio- and vasculoprotective effects through epigenetic regulation of DNA. The majority of known genetic variation in hypertension and HDP is polygenic. Polygenic risk scores for BP display stronger associations with hypertension risk in women than in men and are associated with sex-specific age of hypertension onset. Monogenic forms of hypertension are rare and mostly present equally in both sexes. Conclusion: Despite recent genetic discoveries providing new insights into HDP and sex differences in BP traits, further research is needed to elucidate the underlying biology. Emphasis should be placed on demonstrating the added clinical value of these genetic discoveries, which may eventually facilitate genomics-based personalized treatments for hypertension and HDP.
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Affiliation(s)
- Jouko Nurkkala
- Division of Perioperative Services, Intensive Care and Pain Medicine, Turku University Hospital, Turku, Finland
- Department of Anesthesiology and Intensive Care, University of Turku, Turku, Finland
| | - Felix Vaura
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Jenni Toivonen
- Division of Perioperative Services, Intensive Care and Pain Medicine, Turku University Hospital, Turku, Finland
- Department of Anesthesiology and Intensive Care, University of Turku, Turku, Finland
| | - Teemu Niiranen
- Department of Internal Medicine, University of Turku, Turku, Finland
- Division of Medicine, Turku University Hospital, Turku, Finland
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Turku, Finland
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2
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Fujii R. En route to conquer the silent killer "hypertension": Integration of polygenic risk score with non-genetic determinants. Hypertens Res 2024; 47:3079-3081. [PMID: 39090181 DOI: 10.1038/s41440-024-01826-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2024] [Accepted: 07/03/2024] [Indexed: 08/04/2024]
Affiliation(s)
- Ryosuke Fujii
- Department of Preventive Medical Sciences, Fujita Health University School of Medical Sciences, Toyoake, 470-1192, Japan.
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3
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Vaura F. Blood pressure polygenic risk scores tackle hard endpoints. Hypertens Res 2024; 47:3273-3274. [PMID: 39261705 DOI: 10.1038/s41440-024-01876-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2024] [Accepted: 08/13/2024] [Indexed: 09/13/2024]
Affiliation(s)
- Felix Vaura
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland.
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Bae E, Ji Y, Jo J, Kim Y, Lee JP, Won S, Lee J. Effects of polygenic risk score and sodium and potassium intake on hypertension in Asians: A nationwide prospective cohort study. Hypertens Res 2024; 47:3045-3055. [PMID: 38982292 PMCID: PMC11534693 DOI: 10.1038/s41440-024-01784-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 06/11/2024] [Accepted: 06/15/2024] [Indexed: 07/11/2024]
Abstract
Genetic factors, lifestyle, and diet have been shown to play important roles in the development of hypertension. Increased salt intake is an important risk factor for hypertension. However, research on the involvement of genetic factors in the relationship between salt intake and hypertension in Asians is lacking. We aimed to investigate the risk of hypertension in relation to sodium and potassium intake and the effects of genetic factors on their interactions. We used Korean Genome and Epidemiology Study data and calculated the polygenic risk score (PRS) for the effect of systolic and diastolic blood pressure (SBP and DBP). We also conducted multivariable logistic modeling to evaluate associations among incident hypertension, PRSSBP, PRSDBP, and sodium and potassium intake. In total, 41,351 subjects were included in the test set. The top 10% PRSSBP group was the youngest of the three groups (bottom 10%, middle, top 10%), had the highest proportion of women, and had the highest body mass index, baseline BP, red meat intake, and alcohol consumption. The multivariable logistic regression model revealed the risk of hypertension was significantly associated with higher PRSSBP, higher sodium intake, and lower potassium intake. There was significant interaction between sodium intake and PRSSBP for incident hypertension especially in sodium intake ≥2.0 g/day and PRSSBP top 10% group (OR 1.27 (1.07-1.51), P = 0.007). Among patients at a high risk of incident hypertension due to sodium intake, lifestyle modifications and sodium restriction were especially important to prevent hypertension.
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Affiliation(s)
- Eunjin Bae
- Department of Internal Medicine, Gyeongsang National University Changwon Hospital, Changwon, Republic of Korea
- Department of Internal Medicine, College of Medicine, Gyeongsang National University, Jinju, Republic of Korea
- Institute of Medical Science, College of Medicine, Gyeongsang National University, Jinju, Republic of Korea
| | - Yunmi Ji
- College of Natural Sciences, Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Jinyeon Jo
- Department of Public Health Sciences, Institute of Health & Environment, Seoul National University, Seoul, Republic of Korea
| | - Yaerim Kim
- Department of Internal Medicine, College of Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Jung Pyo Lee
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sungho Won
- Department of Public Health Sciences, Institute of Health & Environment, Seoul National University, Seoul, Republic of Korea.
- Interdisciplinary Program for Bioinformatics, College of Natural Science, Seoul National University, Seoul, Republic of Korea.
- RexSoft Corps, Seoul, Republic of Korea.
| | - Jeonghwan Lee
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea.
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
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5
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Lu L, Gu X, Yang D, Wang B, Long G. Circulating fatty acids, genetic susceptibility and hypertension: a prospective cohort study. Front Nutr 2024; 11:1454364. [PMID: 39545052 PMCID: PMC11562856 DOI: 10.3389/fnut.2024.1454364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Accepted: 10/07/2024] [Indexed: 11/17/2024] Open
Abstract
Background Combining genetic risk factors and plasma fatty acids (FAs) can be used as an effective method of precision medicine to prevent hypertension risk. Methods A total of 195,250 participants in the UK Biobank cohort were included in this study from 2006-2010. Polygenic risk scores (PRSs) were calculated for hypertension using single-nucleotide polymorphisms (SNPs). Concentrations of plasma FAs, including polyunsaturated fatty acids (PUFAs), monounsaturated fatty acids (MUFAs) and saturated fatty acids (SFAs), were tested by nuclear magnetic resonance. The Cox model was used to test for the main effects of PRS, different plasma FAs and their joint effects on hypertension. Relative excess risk due to interaction (RERI) and the attributable proportion due to interaction (AP) were used to test the additive interaction. Results Plasma PUFAs, n-3 PUFAs, MUFAs and SFAs were related to the risk of hypertension (PUFAs: HR, 0.878; 95% CI, 0.868-0.888; MUFAs: HR, 1.13; 95% CI, 1.123-1.150; SFAs: HR, 1.086; 95% CI, 1.074-1.098; n-3 PUFAs: HR, 0.984; 95% CI, 0.973-0.995). Moreover, an additive interaction was found between PRS and plasma FAs, which could contribute to an approximately 10-18% risk of hypertension, and the associations between high plasma MUFAs and a high PRS of hypertension were the strongest positive [RERI: 0.178 (95% CI: 0.062, 0.294), AP: 0.079 (95% CI: 0.027, 0.130)]. Conclusion Increased plasma MUFAs or SFAs and decreased plasma PUFAs or n-3 PUFAs were associated with hypertension risk, especially among people at high genetic risk.
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Affiliation(s)
- Lingling Lu
- Department of Infectious Disease, Children’s Hospital of Nanjing Medical University, Nanjing, China
| | - Xiaoli Gu
- Department of Party and Government Office, Children’s Hospital of Nanjing Medical University, Nanjing, China
| | - Daheng Yang
- Department of Clinical Laboratory, Children’s Hospital of Nanjing Medical University, Nanjing, China
| | - Bingjian Wang
- Department of Cardiology, Huai’an First People’s Hospital Affiliated with Nanjing Medical University, Huai’an, China
| | - Guangfeng Long
- Department of Clinical Laboratory, Children’s Hospital of Nanjing Medical University, Nanjing, China
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6
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Armstrong ND, Srinivasasainagendra V, Patki A, Jones AC, Parcha V, Pampana A, Broeckel U, Lange LA, Arora P, Limdi NA, Tiwari HK, Irvin MR. Utility of a Systolic Blood Pressure Polygenic Risk Score With Chlorthalidone Response. JAMA Cardiol 2024:2824907. [PMID: 39441603 DOI: 10.1001/jamacardio.2024.3649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/25/2024]
Abstract
Importance The clinical utility of polygenic risk scores (PRS) for blood pressure (BP) response to antihypertensive treatment (AHT) has not been elucidated. Objective To investigate the ability of a systolic BP (SBP) PRS to predict AHT response and apparent treatment-resistant hypertension (aTRH). Design, Setting, and Participants The Genetics of Hypertension Associated Treatments (GenHAT) study was an ancillary pharmacogenomic study to the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT). ALLHAT, which enrolled participants aged 55 years or older with hypertension (HTN) starting in February 1994, completed follow-up in March 2002. The current study was conducted from a subset of Black GenHAT participants randomized to the treatment groups of either chlorthalidone (n = 3745) or lisinopril (n = 2294), with genetic data available from a prior genetic association study. The current study's objective was to examine the association of the SBP PRS to AHT response over 6 months, as well as to examine the predictive accuracy of the SBP PRS with aTRH. The current analysis took place in February 2023, with additional analyses conducted in July 2024. Exposure An SBP PRS (comprising 1 084 157 genetic variants) stratified as quintiles and per SD. Main Outcomes and Measures The primary outcome was change in SBP (ΔSBP) and diastolic BP (ΔDBP) over 6 months. aTRH was defined as the use of 3 AHTs with uncontrolled HTN at year 3 of follow-up or taking 4 or more AHTs at year 3 of follow-up, regardless of BP. Baseline demographics were compared across PRS quintiles using Kruskal-Wallis or χ2 tests as appropriate. The least-square means of BP response were calculated through multivariable adjusted linear regression, and multivariable adjusted logistic regression was used to calculate the odds ratios and 95% confidence intervals for aTRH. Results Among 3745 Black GenHAT participants randomized to chlorthalidone treatment, median (IQR) participant age was 65 (60-71) years, and 2064 participants (55.1%) were female. Each increasing quintile of the SBP PRS from 1 to 5 was associated with a reduced BP response to treatment over 6 months. Participants in the lowest quintile experienced a mean ΔSBP of -10.01 mm Hg (95% CI, -11.11 to -8.90) compared to -6.57 mm Hg (95% CI, -7.67 to -5.48) for participants in the median quintile. No associations were observed between the SBP PRS and BP response to lisinopril. Participants in the highest PRS quintile had 67% higher odds of aTRH compared to those in the median quintile (odds ratio, 1.67; 95% CI, 1.19-2.36). These associations were independently validated. Conclusions and Relevance In this genetic association study, Black individuals with HTN at a lower genetic risk of elevated BP experienced an approximately 3.5 mm Hg-greater response to chlorthalidone compared with those at an intermediate genetic risk of elevated BP. SBP PRS may also identify individuals with HTN harboring a higher risk of treatment-resistant HTN. Overall, SBP PRS demonstrates potential to identify those who may have greater benefit from chlorthalidone, but future research is needed to determine if PRS can inform initiation and choice of treatment among individuals with HTN.
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Affiliation(s)
| | | | - Amit Patki
- Department of Biostatistics, University of Alabama at Birmingham
| | - Alana C Jones
- Department of Epidemiology, University of Alabama at Birmingham
| | - Vibhu Parcha
- Division of Cardiovascular Disease, University of Alabama at Birmingham
| | - Akhil Pampana
- Division of Cardiovascular Disease, University of Alabama at Birmingham
| | - Ulrich Broeckel
- Section of Genomic Pediatrics, Children's Research Institute, Department of Pediatrics, The Medical College of Wisconsin, Milwaukee
- RPRD Diagnostics, Milwaukee, Wisconsin
| | - Leslie A Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Denver
| | - Pankaj Arora
- Division of Cardiovascular Disease, University of Alabama at Birmingham
- Section of Cardiology, Birmingham Veterans Affairs Medical Center, Birmingham, Alabama
| | - Nita A Limdi
- Department of Neurology, Heersink School of Medicine, University of Alabama at Birmingham
| | - Hemant K Tiwari
- Department of Biostatistics, University of Alabama at Birmingham
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Jia Z, Zhang H, Lv Y, Yu L, Cui Y, Zhang L, Yang C, Liu H, Zheng T, Xia W, Xu S, Li Y. Intrauterine chromium exposure and cognitive developmental delay: The modifying effect of genetic predisposition. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 946:174350. [PMID: 38960203 DOI: 10.1016/j.scitotenv.2024.174350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 06/04/2024] [Accepted: 06/26/2024] [Indexed: 07/05/2024]
Abstract
There is limited evidence on the effects of intrauterine chromium (Cr) exposure on children's cognitive developmental delay (CDD). Further, little is known about the genetic factors in modifying the association between intrauterine Cr exposure and CDD. The present study involved 2361 mother-child pairs, in which maternal plasma Cr concentrations were assessed, a polygenic risk score for the child was constructed, and the child's cognitive development was evaluated using the Bayley Scales of Infant Development. The risks of CDD conferred by intrauterine Cr exposure in children with different genetic backgrounds were evaluated by logistic regression. The additive interaction between intrauterine Cr exposure and genetic factors was evaluated by calculating the relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and synergy index (SI). According to present study, higher intrauterine Cr exposure was significantly associated with increased CDD risk [each unit increase in ln-transformed maternal plasma Cr concentration (ln-Cr): adjusted OR (95 % CI), 1.18 (1.04-1.35); highest vs lowest quartile: adjusted OR (95 % CI), 1.57 (1.10-2.23)]. The dose-response relationship of intrauterine Cr exposure and CDD for children with high genetic risk was more prominent [each unit increased ln-Cr: adjusted OR (95 % CI), 1.36 (1.09-1.70)]. Joint effects between intrauterine Cr exposure and genetic factors were found. Specifically, for high genetic risk carriers, the association between intrauterine Cr exposure and CDD was more evident [highest vs lowest quartile: adjusted OR (95 % CI), 2.33 (1.43-3.80)]. For those children with high intrauterine Cr exposure and high genetic risk, the adjusted AP was 0.39 (95 % CI, 0.07-0.72). Conclusively, intrauterine Cr exposure was a high-risk factor for CDD in children, particularly for those with high genetic risk. Intrauterine Cr exposure and one's adverse genetic background jointly contribute to an increased risk of CDD in children.
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Affiliation(s)
- Zhenxian Jia
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Hongling Zhang
- Wuchang University of Technology, Wuhan, Hubei, People's Republic of China
| | - Yiqing Lv
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Ling Yu
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Yuan Cui
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Liping Zhang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Chenhui Yang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Hongxiu Liu
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Tongzhang Zheng
- Department of Epidemiology, School of Public Health, Brown University, Providence, RI 02912, United States
| | - Wei Xia
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Shunqing Xu
- School of Environmental Science and Engineering, Hainan University, Haikou 570228, People's Republic of China.
| | - Yuanyuan Li
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China.
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8
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Hrytsenko Y, Spitzer BW, Wang H, Bertisch SM, Taylor K, Garcia-Bedoya O, Ramos AR, Daviglus ML, Gallo LC, Isasi C, Cai J, Qi Q, Alcantara C, Redline S, Sofer T. Obstructive sleep apnea mediates genetic risk of Diabetes Mellitus: The Hispanic Community Health Study/Study of Latinos. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.09.10.24313336. [PMID: 39314966 PMCID: PMC11419195 DOI: 10.1101/2024.09.10.24313336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
Objective We sought to evaluate whether obstructive sleep apnea (OSA), and other sleep disorders, increase genetic risk of developing diabetes mellitus (DM). Research Design and Methods Using GWAS summary statistics from the DIAGRAM consortium and Million Veteran Program, we developed multi-ancestry Type 2 Diabetes (T2D) polygenic risk scores (T2D-PRSs) useful in admixed Hispanic/Latino individuals. We estimated the association of the T2D-PRS with cross-sectional and incident DM in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). We conducted a mediation analysis with T2D-PRSs as an exposure, incident DM as an outcome, and OSA as a mediator. Additionally, we performed Mendelian randomization (MR) analysis to assess the causal relationship between T2D and OSA. Results Of 12,342 HCHS/SOL participants, at baseline, 48.4% were normoglycemic, 36.6% were hyperglycemic, and 15% had diabetes, and 50.9% identified as female. Mean age was 41.5, and mean BMI was 29.4. T2D-PRSs was strongly associated with baseline DM and with incident DM. At baseline, a 1 SD increase in the primary T2D-PRS had DM adjusted odds ratio (OR) = 2.67, 95% CI [2.40; 2.97] and a higher incident DM rate (incident rate ratio (IRR) = 2.02, 95% CI [1.75; 2.33]). In a stratified analysis based on OSA severity categories the associations were stronger in individuals with mild OSA compared to those with moderate to severe OSA. Mediation analysis suggested that OSA mediates the T2D-PRS association with DM. In two-sample MR analysis, T2D-PRS had a causal effect on OSA, OR = 1.03, 95% CI [1.01; 1.05], and OSA had a causal effect on T2D, with OR = 2.34, 95% CI [1.59; 3.44]. Conclusions OSA likely mediates genetic effects on T2D.
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Affiliation(s)
- Yana Hrytsenko
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Brian W. Spitzer
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Heming Wang
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Suzanne M. Bertisch
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kent Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Olga Garcia-Bedoya
- Division of Academic Internal Medicine and Geriatrics, College of Medicine, University of Illinois Chicago, Chicago, Illinois, USA
| | - Alberto R Ramos
- Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Martha L. Daviglus
- DInsititute for Minority Health Research, Department of Medicine, College of Medicine University of Illinois Chicago, Chicago, IL, USA
| | - Linda C Gallo
- Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Carmen Isasi
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Qibin Qi
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, New York, USA
| | | | - Susan Redline
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Tamar Sofer
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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9
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Kämpe A, Suvisaari J, Lähteenvuo M, Singh T, Ahola-Olli A, Urpa L, Haaki W, Hietala J, Isometsä E, Jukuri T, Kampman O, Kieseppä T, Lahdensuo K, Lönnqvist J, Männynsalo T, Paunio T, Niemi-Pynttäri J, Suokas K, Tuulio-Henriksson A, Veijola J, Wegelius A, Daly M, Taylor J, Kendler KS, Palotie A, Pietiläinen O. Genetic contribution to disease-course severity and progression in the SUPER-Finland study, a cohort of 10,403 individuals with psychotic disorders. Mol Psychiatry 2024; 29:2733-2741. [PMID: 38556557 PMCID: PMC11420086 DOI: 10.1038/s41380-024-02516-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 02/28/2024] [Accepted: 03/04/2024] [Indexed: 04/02/2024]
Abstract
Genetic factors contribute to the susceptibility of psychotic disorders, but less is known how they affect psychotic disease-course development. Utilizing polygenic scores (PGSs) in combination with longitudinal healthcare data with decades of follow-up we investigated the contributing genetics to psychotic disease-course severity and diagnostic shifts in the SUPER-Finland study, encompassing 10 403 genotyped individuals with a psychotic disorder. To longitudinally track the study participants' past disease-course severity, we created a psychiatric hospitalization burden metric using the full-coverage and nation-wide Finnish in-hospital registry (data from 1969 and onwards). Using a hierarchical model, ranking the psychotic diagnoses according to clinical severity, we show that high schizophrenia PGS (SZ-PGS) was associated with progression from lower ranked psychotic disorders to schizophrenia (OR = 1.32 [1.23-1.43], p = 1.26e-12). This development manifested already at psychotic illness onset as a higher psychiatric hospitalization burden, the proxy for disease-course severity. In schizophrenia (n = 5 479), both a high SZ-PGS and a low educational attainment PGS (EA-PGS) were associated with increased psychiatric hospitalization burden (p = 1.00e-04 and p = 4.53e-10). The SZ-PGS and the EA-PGS associated with distinct patterns of hospital usage. In individuals with high SZ-PGS, the increased hospitalization burden was composed of longer individual hospital stays, while low EA-PGS associated with shorter but more frequent hospital visits. The negative effect of a low EA-PGS was found to be partly mediated via substance use disorder, a major risk factor for hospitalizations. In conclusion, we show that high SZ-PGS and low EA-PGS both impacted psychotic disease-course development negatively but resulted in different disease-course trajectories.
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Affiliation(s)
- Anders Kämpe
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
- Department of Molecular Medicine and surgery (MMK), Karolinska Institutet, Stockholm, Sweden.
| | - Jaana Suvisaari
- National Institute for Health and Welfare, Department of Mental Health and Substance Abuse Services, Helsinki, Finland
| | - Markku Lähteenvuo
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Department of Forensic Psychiatry, University of Eastern Finland School of Medicine, Niuvanniemi hospital, Kuopio, Finland
| | - Tarjinder Singh
- Broad Institute, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Massachusetts General Hospital, Analytic and Translational Genetics Unit, Boston, MA, USA
| | - Ari Ahola-Olli
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Lea Urpa
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Willehard Haaki
- Department of Psychiatry, University of Turku, Turku, Finland
| | - Jarmo Hietala
- Department of Psychiatry, University of Turku, Turku, Finland
| | - Erkki Isometsä
- Department of Psychiatry, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Tuomas Jukuri
- Department of Psychiatry, Oulu University Hospital, Oulu, Finland
| | - Olli Kampman
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Psychiatry, The Wellbeing Services County of Ostrobothnia, Ostrobothnia, Finland
- Department of Clinical Sciences, Psychiatry, Umeå University, Umeå, Sweden
- Department of Clinical Medicine (Psychiatry), Faculty of Medicine, University of Turku, Turku, Finland
| | - Tuula Kieseppä
- Hospital District of Helsinki and Uusimaa, Helsinki, Finland
| | | | - Jouko Lönnqvist
- Department of Psychiatry, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- National Institute for Health and Welfare, Helsinki, Finland
| | - Teemu Männynsalo
- Department of Psychiatry, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Tiina Paunio
- Department of Psychiatry, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- National Institute for Health and Welfare, Helsinki, Finland
- SleepWell Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jussi Niemi-Pynttäri
- Department of Psychiatry, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Kimmo Suokas
- Tampere University, Tampere, Finland
- Department of Psychiatry, Tampere University Hospital, Tampere, Finland
| | | | - Juha Veijola
- Department of Psychiatry, Oulu University Hospital, Oulu, Finland
- Department of Psychiatry, Research Unit of Clinical Neuroscience, University of Oulu, Oulu, Finland
| | - Asko Wegelius
- Department of Psychiatry, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Mark Daly
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Broad Institute, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Massachusetts General Hospital, Analytic and Translational Genetics Unit, Boston, MA, USA
- Broad Institute Harvard, Program in Medical and Population Genetics, Cambridge, MA, USA
| | - Jacob Taylor
- Harvard Medical School, Department of Medicine, Boston, USA
| | - Kenneth S Kendler
- Virginia Institute of Psychiatric and Behavioral Genetics, Richmond, VA, USA
- Medical College of Virginia/Virginia Commonwealth University, Department of Psychiatry, Richmond, VA, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Broad Institute, Stanley Center for Psychiatric Research, Cambridge, MA, USA
| | - Olli Pietiläinen
- Broad Institute, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
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10
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Fujii R, Hishida A, Nakatochi M, Okumiyama H, Takashima N, Tsuboi Y, Suzuki K, Ikezaki H, Shimanoe C, Kato Y, Tamura T, Ito H, Michihata N, Tanoue S, Suzuki S, Kuriki K, Kadota A, Watanabe T, Momozawa Y, Wakai K, Matsuo K. Polygenic risk score for blood pressure and lifestyle factors with overall and CVD mortality: a prospective cohort study in a Japanese population. Hypertens Res 2024; 47:2284-2294. [PMID: 38961281 DOI: 10.1038/s41440-024-01766-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 05/29/2024] [Accepted: 06/06/2024] [Indexed: 07/05/2024]
Abstract
Although previous polygenic risk score (PRS) studies for cardiovascular disease (CVD) focused on incidence, few studies addressed CVD mortality and quantified risks by environmental exposures in different genetic liability groups. This prospective study aimed to examine the associations of blood pressure PRS with all-cause and CVD mortality and to quantify the attributable risk by modifiable lifestyles across different PRS strata. 9,296 participants in the Japan Multi-Institutional Collaborative Cohort Study without hypertension at baseline were analyzed in this analysis. PRS for systolic blood pressure and diastolic blood pressure (PRSSBP and PRSDBP) were developed using publicly available Biobank Japan GWAS summary statistics. CVD-related mortality was defined by the International Classification of Diseases 10th version (I00-I99). Cox-proportional hazard model was used to examine associations of PRSs and lifestyle variables (smoking, drinking, and dietary sodium intake) with mortality. During a median 12.6-year follow-up period, we observed 273 all-cause and 41 CVD mortality cases. Compared to the middle PRS group (20-80th percentile), adjusted hazard ratios for CVD mortality at the top PRS group ( > 90th percentile) were 3.67 for PRSSBP and 2.92 for PRSDBP. Attributable risks of CVD mortality by modifiable lifestyles were higher in the high PRS group ( > 80th percentile) compared with the low PRS group (0-80th percentile). In summary, blood pressure PRS is associated with CVD mortality in the general Japanese population. Our study implies that integrating PRS with lifestyle could contribute to identify target populations for lifestyle intervention even though improvement of discriminatory ability by PRS alone is limited.
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Affiliation(s)
- Ryosuke Fujii
- Department of Preventive Medical Sciences, Fujita Health University School of Medical Sciences, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, 470-1192, Japan.
- Institute for Biomedicine, Eurac Research, Via A.Volta 21, Bolzano/Bozen, 39100, Italy.
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan.
| | - Asahi Hishida
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Masahiro Nakatochi
- Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, 1-1-20 Daikominami, Higashi-ku, Nagoya, 461-8673, Japan
| | - Hiroshi Okumiyama
- Department of Preventive Medical Sciences, Fujita Health University School of Medical Sciences, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, 470-1192, Japan
| | - Naoyuki Takashima
- Department of Epidemiology for Community Health and Medicine, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kamigyo-ku, Kyoto, 602-8566, Japan
- NCD Epidemiology Research Center, Shiga University of Medical Science, Tsukiwacho, Seta, Otsu, 520-2192, Japan
| | - Yoshiki Tsuboi
- Department of Preventive Medical Sciences, Fujita Health University School of Medical Sciences, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, 470-1192, Japan
| | - Koji Suzuki
- Department of Preventive Medical Sciences, Fujita Health University School of Medical Sciences, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, 470-1192, Japan
| | - Hiroaki Ikezaki
- Department of Comprehensive General Internal Medicine, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
- Department of General Internal Medicine, Kyushu University Hospital, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Chisato Shimanoe
- Department of Pharmacy, Saga University Hospital, 5-1-1 Nabeshima, Saga, 849-8501, Japan
| | - Yasufumi Kato
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Takashi Tamura
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Hidemi Ito
- Division of Cancer Information and Control, Aichi Cancer Center Research Institute, 1-1 Kanokoden, Chikusa-ku, Nagoya, 464-8681, Japan
- Division of Descriptive Cancer Epidemiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Nobuaki Michihata
- Cancer Prevention Center, Chiba Cancer Center Research Institute, 666-2 Nitona-cho, Chuo-ku, Chiba, 260-8717, Japan
| | - Shiroh Tanoue
- Department of Epidemiology and Preventive Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Sadao Suzuki
- Department of Public Health, Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya, 467-8601, Japan
| | - Kiyonori Kuriki
- Laboratory of Public Health, Division of Nutritional Sciences, School of Food and Nutritional Sciences, University of Shizuoka, 52-1 Yada, Suruga-ku, Shizuoka, 422-8526, Japan
| | - Aya Kadota
- NCD Epidemiology Research Center, Shiga University of Medical Science, Tsukiwacho, Seta, Otsu, 520-2192, Japan
- Faculty of Nursing Science, Tsuruga Nursing University, 78-2 Kizaki, Tsuruga, 914-0814, Japan
| | - Takeshi Watanabe
- Department of Preventive Medicine, Tokushima University Graduate School of Biomedical Sciences, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, Center for Integrative Medical Sciences, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
| | - Kenji Wakai
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Keitaro Matsuo
- Division of Cancer Information and Control, Aichi Cancer Center Research Institute, 1-1 Kanokoden, Chikusa-ku, Nagoya, 464-8681, Japan
- Division of Descriptive Cancer Epidemiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
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11
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Rabby MG, Suzauddula M, Hasan MS, Dewan MA, Islam MN. In-silico identification and functional characterization of common genes associated with type 2 diabetes and hypertension. Heliyon 2024; 10:e36546. [PMID: 39262940 PMCID: PMC11388505 DOI: 10.1016/j.heliyon.2024.e36546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/12/2024] [Accepted: 08/19/2024] [Indexed: 09/13/2024] Open
Abstract
Type 2 diabetes (T2D) and hypertension are global public health concerns and major metabolic disorders in humans. Experimental evidence indicates considerable hereditary influences on the etiology of T2D and hypertension, but the molecular basis of these diseases is still limited. Thus, the current study analyzed 185 (132 T2D and 53 hypertension) GWAS catalog datasets and identified 83 common genes linked to T2D and hypertension pathogenesis. These genes were further examined using various bioinformatics approaches to elucidate their molecular mechanisms underlying the pathophysiology of T2D and hypertension. Gene ontology (GO) analysis revealed the biological, cellular, and molecular functions of these genes, which were also linked to different T2D and hypertension pathways. Specifically, seven genes were found to be crucial for T2D, and nine were directly associated with hypertension. Protein-protein interaction (PPI) analysis identified 28 candidate genes and seven hub genes through 11 topological methods. Among 231 miRNAs, seven were significant in interacting with the hub genes, and nine transcription factors (TFs) out of 36 were linked to these hub genes. Additionally, two of the seven hub genes were downregulated by 43 FDA-approved drugs. These findings elucidate the molecular processes underlying T2D and hypertension, suggesting that targeting these genes could lead to future drug development and therapeutic strategies to treat T2D and hypertension.
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Affiliation(s)
- Md Golam Rabby
- Department of Nutrition and Food Technology, Jashore University of Science and Technology, Jashore, 7408, Bangladesh
| | - Md Suzauddula
- College of Agriculture and Natural Resources, National Chung Hsing University, Taichung City, 40227, Taiwan
| | - Md Sakib Hasan
- Department of Nutrition and Food Technology, Jashore University of Science and Technology, Jashore, 7408, Bangladesh
| | - Mahbubur Alam Dewan
- Department of Nutrition and Food Technology, Jashore University of Science and Technology, Jashore, 7408, Bangladesh
| | - Md Numan Islam
- Department of Nutrition and Food Technology, Jashore University of Science and Technology, Jashore, 7408, Bangladesh
- Department of Food Science and Technology, University of Nebraska Lincoln, USA
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12
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Takase M, Hirata T, Nakaya N, Nakamura T, Kogure M, Hatanaka R, Nakaya K, Chiba I, Kanno I, Nochioka K, Tsuchiya N, Narita A, Metoki H, Satoh M, Obara T, Ishikuro M, Ohseto H, Uruno A, Kobayashi T, Kodama EN, Hamanaka Y, Orui M, Ogishima S, Nagaie S, Fuse N, Sugawara J, Kuriyama S, Tamiya G, Hozawa A, Yamamoto M. Associations of combined genetic and lifestyle risks with hypertension and home hypertension. Hypertens Res 2024; 47:2064-2074. [PMID: 38914703 PMCID: PMC11298407 DOI: 10.1038/s41440-024-01705-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/09/2024] [Accepted: 04/12/2024] [Indexed: 06/26/2024]
Abstract
No study, to our knowledge, has constructed a polygenic risk score based on clinical blood pressure and investigated the association of genetic and lifestyle risks with home hypertension. We examined the associations of combined genetic and lifestyle risks with hypertension and home hypertension. In a cross-sectional study of 7027 Japanese individuals aged ≥20 years, we developed a lifestyle score based on body mass index, alcohol consumption, physical activity, and sodium-to-potassium ratio, categorized into ideal, intermediate, and poor lifestyles. A polygenic risk score was constructed with the target data (n = 1405) using publicly available genome-wide association study summary statistics from BioBank Japan. Using the test data (n = 5622), we evaluated polygenic risk score performance and examined the associations of combined genetic and lifestyle risks with hypertension and home hypertension. Hypertension and home hypertension were defined as blood pressure measured at a community-support center ≥140/90 mmHg or at home ≥135/85 mmHg, respectively, or self-reported treatment for hypertension. In the test data, 2294 and 2322 participants had hypertension and home hypertension, respectively. Both polygenic risk and lifestyle scores were independently associated with hypertension and home hypertension. Compared with those of participants with low genetic risk and an ideal lifestyle, the odds ratios for hypertension and home hypertension in the low genetic risk and poor lifestyle group were 1.94 (95% confidence interval, 1.34-2.80) and 2.15 (1.60-2.90), respectively. In summary, lifestyle is important to prevent hypertension; nevertheless, participants with high genetic risk should carefully monitor their blood pressure despite a healthy lifestyle.
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Affiliation(s)
- Masato Takase
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Takumi Hirata
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Human Care Research Team, Tokyo metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan
| | - Naoki Nakaya
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Tomohiro Nakamura
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Kyoto Women's University, Kyoto, Japan
| | - Mana Kogure
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Rieko Hatanaka
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Kumi Nakaya
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Ippei Chiba
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Ikumi Kanno
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Kotaro Nochioka
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku University Hospital, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Naho Tsuchiya
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Akira Narita
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Hirohito Metoki
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical and Pharmaceutical University, Miyagino-ku, Sendai, Japan
| | - Michihiro Satoh
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical and Pharmaceutical University, Miyagino-ku, Sendai, Japan
| | - Taku Obara
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Mami Ishikuro
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Hisashi Ohseto
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Akira Uruno
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Tomoko Kobayashi
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku University Hospital, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Eiichi N Kodama
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- International Research Institute of Disaster Science, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Yohei Hamanaka
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Masatsugu Orui
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Soichi Ogishima
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Satoshi Nagaie
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Nobuo Fuse
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Junichi Sugawara
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku University Hospital, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Suzuki Memorial Hospital, Satonomori, Iwanumashi, Miyagi, Japan
| | - Shinichi Kuriyama
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- International Research Institute of Disaster Science, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Gen Tamiya
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Atsushi Hozawa
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan.
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan.
| | - Masayuki Yamamoto
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
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13
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Jo J, Ha N, Ji Y, Do A, Seo JH, Oh B, Choi S, Choe EK, Lee W, Son JW, Won S. Genetic determinants of obesity in Korean populations: exploring genome-wide associations and polygenic risk scores. Brief Bioinform 2024; 25:bbae389. [PMID: 39207728 PMCID: PMC11359806 DOI: 10.1093/bib/bbae389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 06/24/2024] [Indexed: 09/04/2024] Open
Abstract
East Asian populations exhibit a genetic predisposition to obesity, yet comprehensive research on these traits is limited. We conducted a genome-wide association study (GWAS) with 93,673 Korean subjects to uncover novel genetic loci linked to obesity, examining metrics such as body mass index, waist circumference, body fat ratio, and abdominal fat ratio. Participants were categorized into non-obese, metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO) groups. Using advanced computational methods, we developed a multifaceted polygenic risk scores (PRS) model to predict obesity. Our GWAS identified significant genetic effects with distinct sizes and directions within the MHO and MUO groups compared with the non-obese group. Gene-based and gene-set analyses, along with cluster analysis, revealed heterogeneous patterns of significant genes on chromosomes 3 (MUO group) and 11 (MHO group). In analyses targeting genetic predisposition differences based on metabolic health, odds ratios of high PRS compared with medium PRS showed significant differences between non-obese and MUO, and non-obese and MHO. Similar patterns were seen for low PRS compared with medium PRS. These findings were supported by the estimated genetic correlation (0.89 from bivariate GREML). Regional analyses highlighted significant local genetic correlations on chromosome 11, while single variant approaches suggested widespread pleiotropic effects, especially on chromosome 11. In conclusion, our study identifies specific genetic loci and risks associated with obesity in the Korean population, emphasizing the heterogeneous genetic factors contributing to MHO and MUO.
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Affiliation(s)
- Jinyeon Jo
- Department of Public Health Sciences, Graduate school of Public Health, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
| | - Nayoung Ha
- Department of Public Health Sciences, Graduate school of Public Health, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
| | - Yunmi Ji
- Interdisciplinary Program in Bioinformatics, College of Natural Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
| | - Ahra Do
- Interdisciplinary Program in Bioinformatics, College of Natural Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
| | - Je Hyun Seo
- Veterans Health Service Medical Center, Veterans Medical Research Institute, 53, Jinhwangdo-ro 61-gil, Gangdong-gu, Seoul, 05368, South Korea
| | - Bumjo Oh
- Department of Family Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, 20, Boramae-ro 5-gil, Dongjak-gu, Seoul, 07061, South Korea
| | - Sungkyoung Choi
- Department of Applied Mathematics, Hanyang University (ERICA), 55, Hanyang-deahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, South Korea
| | - Eun Kyung Choe
- Division of Colorectal Surgery, Department of Surgery, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
- Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, 39FL, 152, Teheran-ro, Gangnam-gu, Seoul, 06236, South Korea
| | - Woojoo Lee
- Department of Public Health Sciences, Graduate school of Public Health, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
- Institute of Health and Environment, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
| | - Jang Won Son
- Division of Endocrinology, Department of Internal Medicine, Bucheon St. Mary's hospital, The Catholic University of Korea, 327, Sosa-ro, Bucheon-si, Gyeonggi-do, Bucheon, 14647, South Korea
| | - Sungho Won
- Department of Public Health Sciences, Graduate school of Public Health, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
- Interdisciplinary Program in Bioinformatics, College of Natural Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
- Institute of Health and Environment, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
- RexSoft Corps, Seoul National University Administration Building, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
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14
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Forer L, Taliun D, LeFaive J, Smith AV, Boughton A, Coassin S, Lamina C, Kronenberg F, Fuchsberger C, Schönherr S. Imputation Server PGS: an automated approach to calculate polygenic risk scores on imputation servers. Nucleic Acids Res 2024; 52:W70-W77. [PMID: 38709879 PMCID: PMC11223871 DOI: 10.1093/nar/gkae331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 04/02/2024] [Accepted: 04/15/2024] [Indexed: 05/08/2024] Open
Abstract
Polygenic scores (PGS) enable the prediction of genetic predisposition for a wide range of traits and diseases by calculating the weighted sum of allele dosages for genetic variants associated with the trait or disease in question. Present approaches for calculating PGS from genotypes are often inefficient and labor-intensive, limiting transferability into clinical applications. Here, we present 'Imputation Server PGS', an extension of the Michigan Imputation Server designed to automate a standardized calculation of polygenic scores based on imputed genotypes. This extends the widely used Michigan Imputation Server with new functionality, bringing the simplicity and efficiency of modern imputation to the PGS field. The service currently supports over 4489 published polygenic scores from publicly available repositories and provides extensive quality control, including ancestry estimation to report population stratification. An interactive report empowers users to screen and compare thousands of scores in a fast and intuitive way. Imputation Server PGS provides a user-friendly web service, facilitating the application of polygenic scores to a wide range of genetic studies and is freely available at https://imputationserver.sph.umich.edu.
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Affiliation(s)
- Lukas Forer
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Daniel Taliun
- Canada Excellence Research Chair in Genomic Medicine, McGill University, Montreal, Québec, Canada
- Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montréal, Québec, Canada
| | - Jonathon LeFaive
- Department of Biostatistics and the Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Albert V Smith
- Department of Biostatistics and the Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Andrew P Boughton
- Department of Biostatistics and the Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Stefan Coassin
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Claudia Lamina
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Christian Fuchsberger
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
- Department of Biostatistics and the Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
- Institute for Biomedicine, Eurac Research, Bolzano, Italy
| | - Sebastian Schönherr
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
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15
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Hrytsenko Y, Shea B, Elgart M, Kurniansyah N, Lyons G, Morrison AC, Carson AP, Haring B, Mitchell BD, Psaty BM, Jaeger BC, Gu CC, Kooperberg C, Levy D, Lloyd-Jones D, Choi E, Brody JA, Smith JA, Rotter JI, Moll M, Fornage M, Simon N, Castaldi P, Casanova R, Chung RH, Kaplan R, Loos RJF, Kardia SLR, Rich SS, Redline S, Kelly T, O'Connor T, Zhao W, Kim W, Guo X, Ida Chen YD, Sofer T. Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores. Sci Rep 2024; 14:12436. [PMID: 38816422 PMCID: PMC11139858 DOI: 10.1038/s41598-024-62945-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 05/22/2024] [Indexed: 06/01/2024] Open
Abstract
We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1% (SBP) and 4.7 to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.
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Affiliation(s)
- Yana Hrytsenko
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Benjamin Shea
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Michael Elgart
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | | | - Genevieve Lyons
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alanna C Morrison
- Department of Epidemiology, School of Public Health, Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Bernhard Haring
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Medicine III, Saarland University, Homburg, Saarland, Germany
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Bruce M Psaty
- Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Byron C Jaeger
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - C Charles Gu
- The Center for Biostatistics and Data Science, Washington University, St. Louis, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Daniel Levy
- The Population Sciences Branch of the National Heart, Lung and Blood Institute, Bethesda, MD, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Donald Lloyd-Jones
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Eunhee Choi
- Columbia Hypertension Laboratory, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Jennifer A Brody
- Department of Medicine, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Jerome I Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Matthew Moll
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, West Roxbury, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA
| | - Myriam Fornage
- Department of Epidemiology, School of Public Health, Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Noah Simon
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Peter Castaldi
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Ramon Casanova
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Ren-Hua Chung
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Taipei City, Taiwan
| | - Robert Kaplan
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty for Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Susan Redline
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Tanika Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Timothy O'Connor
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Program in Health Equity and Population Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Wonji Kim
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA
| | - Xiuqing Guo
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yii-Der Ida Chen
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Tamar Sofer
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Center for Life Sciences CLS-934, 3 Blackfan St., Boston, MA, 02115, USA.
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16
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Ghouse J, Sveinbjörnsson G, Vujkovic M, Seidelin AS, Gellert-Kristensen H, Ahlberg G, Tragante V, Rand SA, Brancale J, Vilarinho S, Lundegaard PR, Sørensen E, Erikstrup C, Bruun MT, Jensen BA, Brunak S, Banasik K, Ullum H, Verweij N, Lotta L, Baras A, Mirshahi T, Carey DJ, Kaplan DE, Lynch J, Morgan T, Schwantes-An TH, Dochtermann DR, Pyarajan S, Tsao PS, Laisk T, Mägi R, Kozlitina J, Tybjærg-Hansen A, Jones D, Knowlton KU, Nadauld L, Ferkingstad E, Björnsson ES, Ulfarsson MO, Sturluson Á, Sulem P, Pedersen OB, Ostrowski SR, Gudbjartsson DF, Stefansson K, Olesen MS, Chang KM, Holm H, Bundgaard H, Stender S. Integrative common and rare variant analyses provide insights into the genetic architecture of liver cirrhosis. Nat Genet 2024; 56:827-837. [PMID: 38632349 PMCID: PMC11096111 DOI: 10.1038/s41588-024-01720-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 03/18/2024] [Indexed: 04/19/2024]
Abstract
We report a multi-ancestry genome-wide association study on liver cirrhosis and its associated endophenotypes, alanine aminotransferase (ALT) and γ-glutamyl transferase. Using data from 12 cohorts, including 18,265 cases with cirrhosis, 1,782,047 controls, up to 1 million individuals with liver function tests and a validation cohort of 21,689 cases and 617,729 controls, we identify and validate 14 risk associations for cirrhosis. Many variants are located near genes involved in hepatic lipid metabolism. One of these, PNPLA3 p.Ile148Met, interacts with alcohol intake, obesity and diabetes on the risk of cirrhosis and hepatocellular carcinoma (HCC). We develop a polygenic risk score that associates with the progression from cirrhosis to HCC. By focusing on prioritized genes from common variant analyses, we find that rare coding variants in GPAM associate with lower ALT, supporting GPAM as a potential target for therapeutic inhibition. In conclusion, this study provides insights into the genetic underpinnings of cirrhosis.
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Affiliation(s)
- Jonas Ghouse
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | | | - Marijana Vujkovic
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anne-Sofie Seidelin
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Helene Gellert-Kristensen
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Gustav Ahlberg
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Søren A Rand
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Joseph Brancale
- Section of Digestive Diseases, Department of Internal Medicine, and Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Silvia Vilarinho
- Section of Digestive Diseases, Department of Internal Medicine, and Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Pia Rengtved Lundegaard
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Erik Sørensen
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
| | - Mie Topholm Bruun
- Department of Clinical Immunology, Odense University Hospital, Odense, Denmark
| | | | - Søren Brunak
- Translational Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Karina Banasik
- Department of Obstetrics and Gynaecology, Copenhagen University Hospital Hvidovre, Copenhagen, Denmark
| | | | - Niek Verweij
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Luca Lotta
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Aris Baras
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Tooraj Mirshahi
- Department of Molecular and Functional Genomics, Geisinger Health System, Danville, PA, USA
| | - David J Carey
- Department of Molecular and Functional Genomics, Geisinger Health System, Danville, PA, USA
| | - David E Kaplan
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Julie Lynch
- VA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Timothy Morgan
- Gastroenterology Section, Veterans Affairs Long Beach Healthcare System, Long Beach, CA, USA
- Department of Medicine, University of California, Irvine, CA, USA
| | - Tae-Hwi Schwantes-An
- Gastroenterology Section, Veterans Affairs Long Beach Healthcare System, Long Beach, CA, USA
- Department of Medical and Molecular Genetics, Indiana University, Indianapolis, IN, USA
| | - Daniel R Dochtermann
- Center for Data and Computational Sciences, VA Boston Healthcare System, Boston, MA, USA
| | - Saiju Pyarajan
- Center for Data and Computational Sciences, VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Philip S Tsao
- Palo Alto Epidemiology Research and Information Center for Genomics, VA Palo Alto, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Triin Laisk
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Julia Kozlitina
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Anne Tybjærg-Hansen
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - David Jones
- Precision Genomics, Intermountain Healthcare, Saint George, UT, USA
| | - Kirk U Knowlton
- Intermountain Medical Center, Intermountain Heart Institute, Salt Lake City, UT, USA
- University of Utah, School of Medicine, Salt Lake City, UT, USA
| | - Lincoln Nadauld
- Precision Genomics, Intermountain Healthcare, Saint George, UT, USA
- Stanford University, School of Medicine, Stanford, CA, USA
| | | | - Einar S Björnsson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Internal Medicine and Emergency Services, Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland
| | - Magnus O Ulfarsson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland
| | | | | | - Ole B Pedersen
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark
| | - Sisse R Ostrowski
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Daniel F Gudbjartsson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Morten Salling Olesen
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kyong-Mi Chang
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Hilma Holm
- deCODE Genetics/Amgen, Reykjavik, Iceland
| | - Henning Bundgaard
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Stefan Stender
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
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17
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Øvretveit K, Ingeström EML, Spitieris M, Tragante V, Wade KH, Thomas LF, Wolford BN, Wisløff U, Gudbjartsson DF, Holm H, Stefansson K, Brumpton BM, Hveem K. Polygenic risk scores associate with blood pressure traits across the lifespan. Eur J Prev Cardiol 2024; 31:644-654. [PMID: 38007706 PMCID: PMC11025038 DOI: 10.1093/eurjpc/zwad365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 10/18/2023] [Accepted: 11/02/2023] [Indexed: 11/28/2023]
Abstract
AIMS Hypertension is a major modifiable cause of morbidity and mortality that affects over 1 billion people worldwide. Blood pressure (BP) traits have a strong genetic component that can be quantified with polygenic risk scores (PRSs). To date, the performance of BP PRSs has mainly been assessed in adults, and less is known about polygenic hypertension risk in childhood. METHODS AND RESULTS Multiple PRSs for systolic BP (SBP), diastolic BP (DBP), and pulse pressure were developed using either genome-wide significant weights, pruning and thresholding, or Bayesian regression. Among 87 total PRSs, the top performer for each trait was applied in independent cohorts of children and adult to assess genotype-phenotype associations and disease risk across the lifespan. Differences between those with low (1st decile), average (2nd-9th decile), and high (10th decile) PRS emerge in the first years of life and are maintained throughout adulthood. These diverging BP trajectories also seem to affect cardiovascular and renal disease risk, with increased risk observed among those in the top decile and reduced risk among those in the bottom decile of the polygenic risk distribution compared with the rest of the population. CONCLUSION Genetic risk factors are associated with BP traits across the lifespan, beginning in the first years of life. Given the importance of exposure time in disease pathogenesis and the early rise in BP levels among those genetically susceptible, PRSs may help identify high-risk individuals prior to hypertension onset, facilitate primordial prevention, and reduce the burden of this public health challenge.
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Affiliation(s)
- Karsten Øvretveit
- K.G. Jebsen Centre for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, N-7491 Trondheim, Norway
| | - Emma M L Ingeström
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Michail Spitieris
- K.G. Jebsen Centre for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, N-7491 Trondheim, Norway
- Department of Mathematical Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | | | - Kaitlin H Wade
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 1TH, UK
- Population Health Science, Bristol Medical School, Bristol BS8 1TH, UK
- Avon Longitudinal Study of Parents and Children, Bristol BS8 1TH, UK
| | - Laurent F Thomas
- K.G. Jebsen Centre for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, N-7491 Trondheim, Norway
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Brooke N Wolford
- K.G. Jebsen Centre for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, N-7491 Trondheim, Norway
| | - Ulrik Wisløff
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Daniel F Gudbjartsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Hilma Holm
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
| | - Kari Stefansson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Ben M Brumpton
- K.G. Jebsen Centre for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, N-7491 Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, Norwegian University of Science and Technology, Levanger, Norway
| | - Kristian Hveem
- K.G. Jebsen Centre for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, N-7491 Trondheim, Norway
- Department of Innovation and Research, St. Olavs Hospital, Trondheim, Norway
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18
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Dzau VJ, Hodgkinson CP. Precision Hypertension. Hypertension 2024; 81:702-708. [PMID: 38112080 DOI: 10.1161/hypertensionaha.123.21710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Hypertension affects >1 billion people worldwide. Complications of hypertension include stroke, renal failure, cardiac hypertrophy, myocardial infarction, and cardiac failure. Despite the development of various antihypertensive drugs, the number of people with uncontrolled hypertension continues to rise. While the lack of compliance associated with frequent side effects to medication is a contributory issue, there has been a failure to consider the diverse nature of hypertensive populations. Instead, we propose that hypertension can only be truly managed by precision. A precision medicine approach would consider each patient's unique factors. In this review, we discuss the progress toward precision medicine for hypertension with more predictiveness and individualization of treatment. We will highlight the advances in data science, omics (genomics, metabolomics, proteomics, etc), artificial intelligence, gene therapy, and gene editing and their application to precision hypertension.
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Affiliation(s)
- Victor J Dzau
- Mandel Center for Hypertension and Atherosclerosis, the Duke Cardiovascular Research Center, Duke University Medical Center, Durham, NC (V.J.D., C.P.H.)
- National Academy of Medicine, Washington, DC (V.J.D.)
| | - Conrad P Hodgkinson
- Mandel Center for Hypertension and Atherosclerosis, the Duke Cardiovascular Research Center, Duke University Medical Center, Durham, NC (V.J.D., C.P.H.)
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19
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Reay WR, Clarke E, Eslick S, Riveros C, Holliday EG, McEvoy MA, Peel R, Hancock S, Scott RJ, Attia JR, Collins CE, Cairns MJ. Using Genetics to Inform Interventions Related to Sodium and Potassium in Hypertension. Circulation 2024; 149:1019-1032. [PMID: 38131187 PMCID: PMC10962430 DOI: 10.1161/circulationaha.123.065394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 11/28/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Hypertension is a key risk factor for major adverse cardiovascular events but remains difficult to treat in many individuals. Dietary interventions are an effective approach to lower blood pressure (BP) but are not equally effective across all individuals. BP is heritable, and genetics may be a useful tool to overcome treatment response heterogeneity. We investigated whether the genetics of BP could be used to identify individuals with hypertension who may receive a particular benefit from lowering sodium intake and boosting potassium levels. METHODS In this observational genetic study, we leveraged cross-sectional data from up to 296 475 genotyped individuals drawn from the UK Biobank cohort for whom BP and urinary electrolytes (sodium and potassium), biomarkers of sodium and potassium intake, were measured. Biologically directed genetic scores for BP were constructed specifically among pathways related to sodium and potassium biology (pharmagenic enrichment scores), as well as unannotated genome-wide scores (conventional polygenic scores). We then tested whether there was a gene-by-environment interaction between urinary electrolytes and these genetic scores on BP. RESULTS Genetic risk and urinary electrolytes both independently correlated with BP. However, urinary sodium was associated with a larger BP increase among individuals with higher genetic risk in sodium- and potassium-related pathways than in those with comparatively lower genetic risk. For example, each SD in urinary sodium was associated with a 1.47-mm Hg increase in systolic BP for those in the top 10% of the distribution of genetic risk in sodium and potassium transport pathways versus a 0.97-mm Hg systolic BP increase in the lowest 10% (P=1.95×10-3). This interaction with urinary sodium remained when considering estimated glomerular filtration rate and indexing sodium to urinary creatinine. There was no strong evidence of an interaction between urinary sodium and a standard genome-wide polygenic score of BP. CONCLUSIONS The data suggest that genetic risk in sodium and potassium pathways could be used in a precision medicine model to direct interventions more specifically in the management of hypertension. Intervention studies are warranted.
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Affiliation(s)
- William R. Reay
- Schools of Biomedical Sciences and Pharmacy (W.R.R., R.J.S., M.J.C.), The University of Newcastle, Callaghan, NSW, Australia
- Precision Medicine Research Program (W.R.R., M.J.C.), New Lambton, NSW, Australia
| | - Erin Clarke
- Health Sciences (E.C., S.E., C.E.C.), The University of Newcastle, Callaghan, NSW, Australia
- Food and Nutrition Research Program (E.C., C.E.C.), New Lambton, NSW, Australia
| | - Shaun Eslick
- Health Sciences (E.C., S.E., C.E.C.), The University of Newcastle, Callaghan, NSW, Australia
| | - Carlos Riveros
- Hunter Medical Research Institute (C.R., E.G.H., J.R.A.), New Lambton, NSW, Australia
| | - Elizabeth G. Holliday
- Medicine and Public Health (E.G.H., R.P., S.H., J.R.A.), The University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute (C.R., E.G.H., J.R.A.), New Lambton, NSW, Australia
| | - Mark A. McEvoy
- Rural Health School, La Trobe University, Bendigo, Victoria, Australia (M.A.M.)
| | - Roseanne Peel
- Medicine and Public Health (E.G.H., R.P., S.H., J.R.A.), The University of Newcastle, Callaghan, NSW, Australia
| | - Stephen Hancock
- Medicine and Public Health (E.G.H., R.P., S.H., J.R.A.), The University of Newcastle, Callaghan, NSW, Australia
| | - Rodney J. Scott
- Schools of Biomedical Sciences and Pharmacy (W.R.R., R.J.S., M.J.C.), The University of Newcastle, Callaghan, NSW, Australia
- Cancer Detection and Therapy Research Program (R.J.S.), New Lambton, NSW, Australia
| | - John R. Attia
- Medicine and Public Health (E.G.H., R.P., S.H., J.R.A.), The University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute (C.R., E.G.H., J.R.A.), New Lambton, NSW, Australia
| | - Clare E. Collins
- Health Sciences (E.C., S.E., C.E.C.), The University of Newcastle, Callaghan, NSW, Australia
- Food and Nutrition Research Program (E.C., C.E.C.), New Lambton, NSW, Australia
| | - Murray J. Cairns
- Schools of Biomedical Sciences and Pharmacy (W.R.R., R.J.S., M.J.C.), The University of Newcastle, Callaghan, NSW, Australia
- Precision Medicine Research Program (W.R.R., M.J.C.), New Lambton, NSW, Australia
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Schjerven FE, Lindseth F, Steinsland I. Prognostic risk models for incident hypertension: A PRISMA systematic review and meta-analysis. PLoS One 2024; 19:e0294148. [PMID: 38466745 PMCID: PMC10927109 DOI: 10.1371/journal.pone.0294148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 10/26/2023] [Indexed: 03/13/2024] Open
Abstract
OBJECTIVE Our goal was to review the available literature on prognostic risk prediction for incident hypertension, synthesize performance, and provide suggestions for future work on the topic. METHODS A systematic search on PUBMED and Web of Science databases was conducted for studies on prognostic risk prediction models for incident hypertension in generally healthy individuals. Study-quality was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST) checklist. Three-level meta-analyses were used to obtain pooled AUC/C-statistic estimates. Heterogeneity was explored using study and cohort characteristics in meta-regressions. RESULTS From 5090 hits, we found 53 eligible studies, and included 47 in meta-analyses. Only four studies were assessed to have results with low risk of bias. Few models had been externally validated, with only the Framingham risk model validated more than thrice. The pooled AUC/C-statistics were 0.82 (0.77-0.86) for machine learning models and 0.78 (0.76-0.80) for traditional models, with high heterogeneity in both groups (I2 > 99%). Intra-class correlations within studies were 60% and 90%, respectively. Follow-up time (P = 0.0405) was significant for ML models and age (P = 0.0271) for traditional models in explaining heterogeneity. Validations of the Framingham risk model had high heterogeneity (I2 > 99%). CONCLUSION Overall, the quality of included studies was assessed as poor. AUC/C-statistic were mostly acceptable or good, and higher for ML models than traditional models. High heterogeneity implies large variability in the performance of new risk models. Further, large heterogeneity in validations of the Framingham risk model indicate variability in model performance on new populations. To enable researchers to assess hypertension risk models, we encourage adherence to existing guidelines for reporting and developing risk models, specifically reporting appropriate performance measures. Further, we recommend a stronger focus on validation of models by considering reasonable baseline models and performing external validations of existing models. Hence, developed risk models must be made available for external researchers.
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Affiliation(s)
- Filip Emil Schjerven
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Frank Lindseth
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ingelin Steinsland
- Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway
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21
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Dickie EW, Ameis SH, Boileau I, Diaconescu AO, Felsky D, Goldstein BI, Gonçalves V, Griffiths JD, Haltigan JD, Husain MO, Rubin-Kahana DS, Iftikhar M, Jani M, Lai MC, Lin HY, MacIntosh BJ, Wheeler AL, Vasdev N, Vieira E, Ahmadzadeh G, Heyland L, Mohan A, Ogunsanya F, Oliver LD, Zhu C, Wong JKY, Charlton C, Truong J, Yu L, Kelly R, Cleverley K, Courtney DB, Foussias G, Hawke LD, Hill S, Kozloff N, Polillo A, Rotenberg M, Quilty LC, Tempelaar W, Wang W, Nikolova YS, Voineskos AN. Neuroimaging and Biosample Collection in the Toronto Adolescent and Youth Cohort Study: Rationale, Methods, and Early Data. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:275-284. [PMID: 37979944 DOI: 10.1016/j.bpsc.2023.10.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 10/11/2023] [Accepted: 10/17/2023] [Indexed: 11/20/2023]
Abstract
BACKGROUND The Toronto Adolescent and Youth (TAY) Cohort Study will characterize the neurobiological trajectories of psychosis spectrum symptoms, functioning, and suicidality (i.e., suicidal thoughts and behaviors) in youth seeking mental health care. Here, we present the neuroimaging and biosample component of the protocol. We also present feasibility and quality control metrics for the baseline sample collected thus far. METHODS The current study includes youths (ages 11-24 years) who were referred to child and youth mental health services within a large tertiary care center in Toronto, Ontario, Canada, with target recruitment of 1500 participants. Participants were offered the opportunity to provide any or all of the following: 1) 1-hour magnetic resonance imaging (MRI) scan (electroencephalography if ineligible for or declined MRI), 2) blood sample for genomic and proteomic data (or saliva if blood collection was declined or not feasible) and urine sample, and 3) heart rate recording to assess respiratory sinus arrhythmia. RESULTS Of the first 417 participants who consented to participate between May 4, 2021, and February 2, 2023, 412 agreed to participate in the imaging and biosample protocol. Of these, 334 completed imaging, 341 provided a biosample, 338 completed respiratory sinus arrhythmia, and 316 completed all 3. Following quality control, data usability was high (MRI: T1-weighted 99%, diffusion-weighted imaging 99%, arterial spin labeling 90%, resting-state functional MRI 95%, task functional MRI 90%; electroencephalography: 83%; respiratory sinus arrhythmia: 99%). CONCLUSIONS The high consent rates, good completion rates, and high data usability reported here demonstrate the feasibility of collecting and using brain imaging and biosamples in a large clinical cohort of youths seeking mental health care.
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Affiliation(s)
- Erin W Dickie
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Stephanie H Ameis
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Isabelle Boileau
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Andreea O Diaconescu
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Daniel Felsky
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Benjamin I Goldstein
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Vanessa Gonçalves
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - John D Griffiths
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - John D Haltigan
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Muhammad O Husain
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Dafna S Rubin-Kahana
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Myera Iftikhar
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Melanie Jani
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Meng-Chuan Lai
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; National Taiwan University Hospital and College of Medicine, Taiwan
| | - Hsiang-Yuan Lin
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Bradley J MacIntosh
- Sunnybrook Research Institute, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Oslo University Hospital, Oslo, Norway
| | - Anne L Wheeler
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Hospital for Sick Children, Neurosciences and Mental Health, Toronto, Ontario, Canada; Department of Physiology, University of Toronto, Toronto, Ontario, Canada
| | - Neil Vasdev
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Erica Vieira
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Ghazaleh Ahmadzadeh
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Lindsay Heyland
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Acadia University, Wolfville, Nova Scotia, Canada
| | - Akshay Mohan
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Feyi Ogunsanya
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychology, Western University, London, Ontario, Canada
| | - Lindsay D Oliver
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Cherrie Zhu
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Lunenfeld-Tanenbaum Research Institute at Sinai Health, Toronto, Ontario, Canada
| | - Jimmy K Y Wong
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Colleen Charlton
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Jennifer Truong
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Lujia Yu
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Rachel Kelly
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Kristin Cleverley
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Darren B Courtney
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - George Foussias
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Lisa D Hawke
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Sean Hill
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Nicole Kozloff
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Alexia Polillo
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Martin Rotenberg
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Lena C Quilty
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Wanda Tempelaar
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Wei Wang
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Yuliya S Nikolova
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
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22
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Hardy D, Garvin J, Mersha T. Ancestry Specific Polygenic Risk Score, Dietary Patterns, Physical Activity, and Cardiovascular Disease. Nutrients 2024; 16:567. [PMID: 38398891 PMCID: PMC10893526 DOI: 10.3390/nu16040567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 02/14/2024] [Accepted: 02/15/2024] [Indexed: 02/25/2024] Open
Abstract
It is unknown whether the impact of high diet quality and physical activity depends on the level of polygenic risk score (PRS) in different ancestries. Our cross-sectional study utilized de-identified data from 1987-2010 for self-reported European Americans (n = 6575) and African Americans (n = 1606). The high-risk PRS increased ASCVD risk by 59% (Risk Ratio (RR) = 1.59; 95% Confidence Interval:1.16-2.17) in the highest tertile for African Americans and by 15% (RR = 1.15; 1.13-1.30) and 18% (RR = 1.18; 1.04-1.35) in the second and highest tertiles compared to the lowest tertile in European Americans. Within the highest PRS tertiles, high physical activity-diet combinations (Dietary Approaches to Stop High Blood Pressure (DASH), Mediterranean, or Southern) reduced ASCVD risks by 9% (RR = 0.91; 0.85-0.96) to 15% (RR = 0.85; 0.80-0.90) in European Americans; and by 13% (RR = 0.87; 0.78-0.97) and 18% (RR = 0.82; 0.72-0.95) for DASH and Mediterranean diets, respectively, in African Americans. Top molecular pathways included fructose metabolism and catabolism linked to obesity, insulin resistance, and type 2 diabetes. Additional molecular pathways for African Americans were Vitamin D linked to depression and aging acceleration and death signaling associated with cancer. Effects of high diet quality and high physical activity can counterbalance the influences of genetically high-risk PRSs on ASCVD risk, especially in African Americans.
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Affiliation(s)
- Dale Hardy
- Department of Internal Medicine, Morehouse School of Medicine, Atlanta, GA 30310, USA
| | - Jane Garvin
- College of Nursing, Walden University, Minneapolis, MN 55401, USA;
| | - Tesfaye Mersha
- Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA;
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23
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Chappell E, Arbour L, Laksman Z. The Inclusion of Underrepresented Populations in Cardiovascular Genetics and Epidemiology. J Cardiovasc Dev Dis 2024; 11:56. [PMID: 38392270 PMCID: PMC10888590 DOI: 10.3390/jcdd11020056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 01/25/2024] [Accepted: 02/02/2024] [Indexed: 02/24/2024] Open
Abstract
Novel genetic risk markers have helped us to advance the field of cardiovascular epidemiology and refine our current understanding and risk stratification paradigms. The discovery and analysis of variants can help us to tailor prognostication and management. However, populations underrepresented in cardiovascular epidemiology and cardiogenetics research may experience inequities in care if prediction tools are not applicable to them clinically. Therefore, the purpose of this article is to outline the barriers that underrepresented populations can face in participating in genetics research, to describe the current efforts to diversify cardiogenetics research, and to outline strategies that researchers in cardiovascular epidemiology can implement to include underrepresented populations. Mistrust, a lack of diverse research teams, the improper use of sensitive biodata, and the constraints of genetic analyses are all barriers for including diverse populations in genetics studies. The current work is beginning to address the paucity of ethnically diverse genetics research and has already begun to shed light on the potential benefits of including underrepresented and diverse populations. Reducing barriers for individuals, utilizing community-driven research processes, adopting novel recruitment strategies, and pushing for organizational support for diverse genetics research are key steps that clinicians and researchers can take to develop equitable risk stratification tools and improve patient care.
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Affiliation(s)
- Elias Chappell
- Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Laura Arbour
- Department of Medical Genetics, University of British Columbia, Vancouver, BC V6H 3N1, Canada
| | - Zachary Laksman
- Department of Medicine and the School of Biomedical Engineering, Centre for Heart Lung Innovation, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
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24
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Jefferis J, Hudson R, Lacaze P, Bakshi A, Hawley C, Patel C, Mallett A. Monogenic and polygenic concepts in chronic kidney disease (CKD). J Nephrol 2024; 37:7-21. [PMID: 37989975 PMCID: PMC10920206 DOI: 10.1007/s40620-023-01804-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 10/11/2023] [Indexed: 11/23/2023]
Abstract
Kidney function is strongly influenced by genetic factors with both monogenic and polygenic factors contributing to kidney function. Monogenic disorders with primarily autosomal dominant inheritance patterns account for 10% of adult and 50% of paediatric kidney diseases. However, kidney function is also a complex trait with polygenic architecture, where genetic factors interact with environment and lifestyle factors. Family studies suggest that kidney function has significant heritability at 35-69%, capturing complexities of the genome with shared environmental factors. Genome-wide association studies estimate the single nucleotide polymorphism-based heritability of kidney function between 7.1 and 20.3%. These heritability estimates, measuring the extent to which genetic variation contributes to CKD risk, indicate a strong genetic contribution. Polygenic Risk Scores have recently been developed for chronic kidney disease and kidney function, and validated in large populations. Polygenic Risk Scores show correlation with kidney function but lack the specificity to predict individual-level changes in kidney function. Certain kidney diseases, such as membranous nephropathy and IgA nephropathy that have significant genetic components, may benefit most from polygenic risk scores for improved risk stratification. Genetic studies of kidney function also provide a potential avenue for the development of more targeted therapies and interventions. Understanding the development and validation of genomic scores is required to guide their implementation and identify the most appropriate potential implications in clinical practice. In this review, we provide an overview of the heritability of kidney function traits in population studies, explore both monogenic and polygenic concepts in kidney disease, with a focus on recently developed polygenic risk scores in kidney function and chronic kidney disease, and review specific diseases which are most amenable to incorporation of genomic scores.
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Affiliation(s)
- Julia Jefferis
- Genetic Health Queensland, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia.
- Faculty of Medicine, University of Queensland, Brisbane, Australia.
- Kidney Health Service, Royal Brisbane and Women's Hospital, Brisbane, Australia.
| | - Rebecca Hudson
- Faculty of Medicine, University of Queensland, Brisbane, Australia
- Kidney Health Service, Royal Brisbane and Women's Hospital, Brisbane, Australia
| | - Paul Lacaze
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Andrew Bakshi
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Carmel Hawley
- Department of Nephrology, Princess Alexandra Hospital, Woolloongabba, QLD, Australia
- Australasian Kidney Trials Network, The University of Queensland, Brisbane, QLD, Australia
- Translational Research Institute, Brisbane, QLD, Australia
| | - Chirag Patel
- Genetic Health Queensland, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia
| | - Andrew Mallett
- Institutional for Molecular Bioscience and Faculty of Medicine, The University of Queensland, Saint Lucia, Australia.
- Department of Renal Medicine, Townsville University Hospital, Douglas, QLD, Australia.
- College of Medicine and Dentistry, James Cook University, Douglas, QLD, Australia.
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25
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Singh S, Choudhury A, Hazelhurst S, Crowther NJ, Boua PR, Sorgho H, Agongo G, Nonterah EA, Micklesfield LK, Norris SA, Kisiangani I, Mohamed S, Gómez-Olivé FX, Tollman SM, Choma S, Brandenburg JT, Ramsay M. Genome-wide association study meta-analysis of blood pressure traits and hypertension in sub-Saharan African populations: an AWI-Gen study. Nat Commun 2023; 14:8376. [PMID: 38104120 PMCID: PMC10725455 DOI: 10.1038/s41467-023-44079-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 11/29/2023] [Indexed: 12/19/2023] Open
Abstract
Most hypertension-related genome-wide association studies (GWASs) focus on non-African populations, despite hypertension (a major risk factor for cardiovascular disease) being highly prevalent in Africa. The AWI-Gen study GWAS meta-analysis for blood pressure (BP)-related traits (systolic and diastolic BP, pulse pressure, mean-arterial pressure and hypertension) from three sub-Saharan African geographic regions (N = 10,775), identifies two novel genome-wide significant signals (p < 5E-08): systolic BP near P2RY1 (rs77846204; intergenic variant, p = 4.95E-08) and pulse pressure near LINC01256 (rs80141533; intergenic variant, p = 1.76E-08). No genome-wide signals are detected for the AWI-Gen GWAS meta-analysis with previous African-ancestry GWASs (UK Biobank (African), Uganda Genome Resource). Suggestive signals (p < 5E-06) are observed for all traits, with 29 SNPs associating with more than one trait and several replicating known associations. Polygenic risk scores (PRSs) developed from studies on different ancestries have limited transferability, with multi-ancestry PRS providing better prediction. This study provides insights into the genetics of BP variation in African populations.
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Affiliation(s)
- Surina Singh
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
| | - Ananyo Choudhury
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Scott Hazelhurst
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
| | - Nigel J Crowther
- Department of Chemical Pathology, National Health Laboratory Service, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Palwendé R Boua
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Clinical Research Unit of Nanoro, Institut de Recherche en Sciences de la Sante, Ouagadougou, Burkina Faso
| | - Hermann Sorgho
- Clinical Research Unit of Nanoro, Institut de Recherche en Sciences de la Sante, Ouagadougou, Burkina Faso
| | - Godfred Agongo
- Department of Biochemistry and Forensic Sciences, School of Chemical and Biochemical Sciences, C.K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana
- Navrongo Health Research Centre, Ghana Health Service, Navrongo, Ghana
| | - Engelbert A Nonterah
- Navrongo Health Research Centre, Ghana Health Service, Navrongo, Ghana
- Julius Global Health, Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Lisa K Micklesfield
- SAMRC Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Shane A Norris
- SAMRC Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- School of Health and Human Development, University of Southampton, Southampton, UK
| | | | - Shukri Mohamed
- African Population and Health Research Center, Nairobi, Kenya
| | - Francesc X Gómez-Olivé
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Stephen M Tollman
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Solomon Choma
- Department of Medical Science, Public Health and Health Promotion, School of Health Care Sciences, Faculty of Health Sciences, University of Limpopo, Polokwane, South Africa
| | - J-T Brandenburg
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Strengthening Oncology Services, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Michèle Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
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26
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Hrytsenko Y, Shea B, Elgart M, Kurniansyah N, Lyons G, Morrison AC, Carson AP, Haring B, Mitchel BD, Psaty BM, Jaeger BC, Gu CC, Kooperberg C, Levy D, Lloyd-Jones D, Choi E, Brody JA, Smith JA, Rotter JI, Moll M, Fornage M, Simon N, Castaldi P, Casanova R, Chung RH, Kaplan R, Loos RJ, Kardia SLR, Rich SS, Redline S, Kelly T, O’Connor T, Zhao W, Kim W, Guo X, Der Ida Chen Y, Sofer T. Machine learning models for blood pressure phenotypes combining multiple polygenic risk scores. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.13.23299909. [PMID: 38168328 PMCID: PMC10760279 DOI: 10.1101/2023.12.13.23299909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1% to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8% to 5.1% (SBP) and 4.7% to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs.
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Affiliation(s)
- Yana Hrytsenko
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
| | - Benjamin Shea
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
| | - Michael Elgart
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | | | - Genevieve Lyons
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Alanna C. Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - April P. Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Bernhard Haring
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Medicine III, Saarland University, Homburg, Saarland, Germany
| | - Braxton D. Mitchel
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Bruce M. Psaty
- Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Byron C. Jaeger
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - C Charles Gu
- The Center for Biostatistics and Data Science, Washington University, St. Louis, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Daniel Levy
- The Population Sciences Branch of the National Heart, Lung and Blood Institute, Bethesda, MD, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Donald Lloyd-Jones
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Eunhee Choi
- Columbia Hypertension Laboratory, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Jennifer A Brody
- Department of Medicine, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Matthew Moll
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- VA Boston Healthcare System, West Roxbury, MA, USA
| | - Myriam Fornage
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Noah Simon
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA
| | - Peter Castaldi
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Ramon Casanova
- Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Ren-Hua Chung
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Taipei City, Taiwan
| | - Robert Kaplan
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Ruth J.F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty for Health and Medical Sciences, University of Copenhagen, Denmark, DK
| | - Sharon L. R. Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Susan Redline
- Department of Medicine, Harvard Medical School, Boston, MA
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
| | - Tanika Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Timothy O’Connor
- Department of Medicine III, Saarland University, Homburg, Saarland, Germany
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Wonji Kim
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yii Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | - Tamar Sofer
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
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Hardy DS, Garvin JT, Mersha TB. Ancestry Specific Polygenic Risk Score, Dietary Patterns, Physical Activity, and Cardiovascular Disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.05.23299548. [PMID: 38106156 PMCID: PMC10723516 DOI: 10.1101/2023.12.05.23299548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Background It is unknown whether the impact of high diet-quality and physical activity (PA) depends on the level of polygenic risk score (PRS) in different ancestries. Objective Determine the associations and interactions between high-risk PRSs, dietary patterns, and high PA with atherosclerotic cardiovascular disease (ASCVD) in European Americans (EAs) and African Americans (AAs). Another aim determined the molecular pathways of PRS-mapped genes and their relationships with dietary intake. Methods Cross-sectional analyses utilized de-identified data from 1987-2010 from 7-National Heart, Lung, and Blood Institute Candidate Gene Association Resource studies from the Database of Genotypes and Phenotypes studies for EAs (n=6,575) and AAs (n=1,606). Results The high-risk PRS increased ASCVD risk by 59% (Risk Ratio=1.59;95% Confidence Interval:1.16-2.17) in the highest tertile for AAs and by 15% (RR=1.15;1.13-1.30) and 18% (RR=1.18;1.04-1.35) in the second and highest tertiles compared to the lowest tertile in EAs. Within the highest PRS tertiles, high PA-diet combinations (Dietary Approaches to Stop High Blood Pressure (DASH), or Mediterranean, or Southern) reduced ASCVD risks by 9% (RR=0.91;0.85-0.96) to 15% (RR=0.85;0.80-0.90) in EAs; and by 13% (RR=0.87;0.78-0.97) and 18% (RR=0.82;0.72-0.95) for the DASH and Mediterranean diets, respectively in AAs. Top molecular pathways included fructose metabolism and catabolism linked to obesity, insulin resistance, and type 2 diabetes in both ancestries. Additional molecular pathways for AAs were Vitamin D linked to depression and aging acceleration; and death signaling associated with cancer. Conclusions Effects of high diet-quality and high PA can counterbalance the influences of genetically high-risk PRSs on ASCVD risk, especially in AAs.
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Sofer T, Kurniansyah N, Granot-Hershkovitz E, Goodman MO, Tarraf W, Broce I, Lipton RB, Daviglus M, Lamar M, Wassertheil-Smoller S, Cai J, DeCarli CS, Gonzalez HM, Fornage M. A polygenic risk score for Alzheimer's disease constructed using APOE-region variants has stronger association than APOE alleles with mild cognitive impairment in Hispanic/Latino adults in the U.S. Alzheimers Res Ther 2023; 15:146. [PMID: 37649099 PMCID: PMC10469805 DOI: 10.1186/s13195-023-01298-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 08/24/2023] [Indexed: 09/01/2023]
Abstract
INTRODUCTION Polygenic Risk Scores (PRSs) are summaries of genetic risk alleles for an outcome. METHODS We used summary statistics from five GWASs of AD to construct PRSs in 4,189 diverse Hispanics/Latinos (mean age 63 years) from the Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA). We assessed the PRS associations with MCI in the combined set of people and in diverse subgroups, and when including and excluding the APOE gene region. We also assessed PRS associations with MCI in an independent dataset from the Mass General Brigham Biobank. RESULTS A simple sum of 5 PRSs ("PRSsum"), each constructed based on a different AD GWAS, was associated with MCI (OR = 1.28, 95% CI [1.14, 1.41]) in a model adjusted for counts of the APOE-[Formula: see text] and APOE-[Formula: see text] alleles. Associations of single-GWAS PRSs were weaker. When removing SNPs from the APOE region from the PRSs, the association of PRSsum with MCI was weaker (OR = 1.17, 95% CI [1.04,1.31] with adjustment for APOE alleles). In all association analyses, APOE-[Formula: see text] and APOE-[Formula: see text] alleles were not associated with MCI. DISCUSSION A sum of AD PRSs is associated with MCI in Hispanic/Latino older adults. Despite no association of APOE-[Formula: see text] and APOE-[Formula: see text] alleles with MCI, the association of the AD PRS with MCI is stronger when including the APOE region. Thus, APOE variants different than the classic APOE alleles may be important predictors of MCI in Hispanic/Latino adults.
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Affiliation(s)
- Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA.
| | - Nuzulul Kurniansyah
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Einat Granot-Hershkovitz
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Matthew O Goodman
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Wassim Tarraf
- Institute of Gerontology, Wayne State University, Detroit, MI, USA
| | - Iris Broce
- Department of Neurosciences, University of California San Diego, San Diego, CA, USA
| | | | - Martha Daviglus
- Department of Medicine, Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Melissa Lamar
- Department of Medicine, Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
- Rush Alzheimer's Disease Research Center, Rush University Medical Center, Chicago, IL, USA
| | - Sylvia Wassertheil-Smoller
- Department of Epidemiology & Population Health, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Charles S DeCarli
- Department of Neurology, University of California at Davis, Sacramento, CA, USA
| | - Hector M Gonzalez
- Department of Neurosciences, University of California San Diego, San Diego, CA, USA
- Shiley-Marcos Alzheimer's Disease Center, University of California San Diego, La Jolla, CA, USA
| | - Myriam Fornage
- Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA
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Tsegaselassie W, Jian Y, Berhanu GG, Tianyuan L, April M, Tali E, Fasil TA, Timothy TA, Jordana C, Marguerite IR, Robert SM, Michael VW, Kristine Y, Myriam F, Donald LJM, Mario S, Daichi S, Yuichiro Y, Paul M, Adam B. Associations of cardiometabolic polygenic risk scores with cardiovascular disease in African Americans. RESEARCH SQUARE 2023:rs.3.rs-3228815. [PMID: 37693576 PMCID: PMC10491340 DOI: 10.21203/rs.3.rs-3228815/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Background Cardiovascular disease (CVD) is a complex disease, and genetic factors contribute individually or cumulatively to CVD risk. While African American women and men are disproportionately affected by CVD, their lack of representation in genomic investigations may widen disparities in health. We investigated the associations of cardiometabolic polygenic risk scores (PRSs) with CVD risk in African Americans. Methods We used the Jackson Heart Study, a prospective cohort study of CVD in African American adults and the predicted atherosclerotic cardiovascular disease (ASCVD) 10-year risk. We included 40-79 years old adults without a history of coronary heart disease (CHD) or stroke at baseline. We derived genome-wide PRSs for systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol, LDL cholesterol, hemoglobin A1c (HbA1c), triglycerides, and C-reactive protein (CRP) separately for each of the participants, using African-origin UK Biobank participants' genome-wide association summary statistics. We estimated the associations between PRSs and 10-year predicted ASCVD risk adjusting for age, sex, study visit date, and genetic ancestry using linear and logistic regression models. Results Participants (n=2,077) were 63% female and 66% never-smokers. They had mean (SD) 56 (10) years of age, 127.8 (16.3) mmHg SBP, 76.3 (8.7) mmHg DBP, 200.4 (40.2) mg/dL total cholesterol, 51.7 (14.7) mg/dL HDL cholesterol, 127.2 (36.7) mg/dL LDL cholesterol, 6.0 (1.3) mmol/mol HbA1c, 108.9 (81.7) mg/dL triglycerides and 0.53 (1.1) CRP. Their median (interquartile range) predicted 10-year predicted ASCVD risk was 8.0 (4.0-15.0). Participants in the >75th percentile for HbA1c PRS had 1.42 percentage-point greater predicted 10-year ASCVD risk (1.42 [95% CI: 0.58-2.26]) and higher odds of ≥10% predicted 10-year ASCVD risk (OR: 1.46 [95% CI: 1.03-2.07]) compared with those in the <25th percentile for HbA1c PRS. Participants in the >75th percentile for SBP PRS had higher odds of ≥10% predicted 10-year ASCVD risk (OR: 1.52 [95% CI: 1.07-2.15]) compared with those in the <25th percentile for SBP PRS. Conclusion Among 40-79 years old African Americans without CHD and stroke, higher PRSs for HbA1c and SBP were associated with CVD risk. PRSs may help stratify individuals based on their clinical risk factors for CVD early prevention and clinical management.
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Affiliation(s)
| | | | | | - Lu Tianyuan
- Lady Davis Institute for Medical Research, Jewish General Hospital
| | | | | | | | | | | | | | | | | | | | | | | | - Sims Mario
- University of Mississippi Medical Center
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30
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Kurniansyah N, Goodman MO, Khan AT, Wang J, Feofanova E, Bis JC, Wiggins KL, Huffman JE, Kelly T, Elfassy T, Guo X, Palmas W, Lin HJ, Hwang SJ, Gao Y, Young K, Kinney GL, Smith JA, Yu B, Liu S, Wassertheil-Smoller S, Manson JE, Zhu X, Chen YDI, Lee IT, Gu CC, Lloyd-Jones DM, Zöllner S, Fornage M, Kooperberg C, Correa A, Psaty BM, Arnett DK, Isasi CR, Rich SS, Kaplan RC, Redline S, Mitchell BD, Franceschini N, Levy D, Rotter JI, Morrison AC, Sofer T. Evaluating the use of blood pressure polygenic risk scores across race/ethnic background groups. Nat Commun 2023; 14:3202. [PMID: 37268629 PMCID: PMC10238525 DOI: 10.1038/s41467-023-38990-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 05/24/2023] [Indexed: 06/04/2023] Open
Abstract
We assess performance and limitations of polygenic risk scores (PRSs) for multiple blood pressure (BP) phenotypes in diverse population groups. We compare "clumping-and-thresholding" (PRSice2) and LD-based (LDPred2) methods to construct PRSs from each of multiple GWAS, as well as multi-PRS approaches that sum PRSs with and without weights, including PRS-CSx. We use datasets from the MGB Biobank, TOPMed study, UK biobank, and from All of Us to train, assess, and validate PRSs in groups defined by self-reported race/ethnic background (Asian, Black, Hispanic/Latino, and White). For both SBP and DBP, the PRS-CSx based PRS, constructed as a weighted sum of PRSs developed from multiple independent GWAS, perform best across all race/ethnic backgrounds. Stratified analysis in All of Us shows that PRSs are better predictive of BP in females compared to males, individuals without obesity, and middle-aged (40-60 years) compared to older and younger individuals.
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Affiliation(s)
- Nuzulul Kurniansyah
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Matthew O Goodman
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
| | - Alyna T Khan
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jiongming Wang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Elena Feofanova
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Kerri L Wiggins
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Jennifer E Huffman
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Tanika Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Tali Elfassy
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Xiuqing Guo
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Walter Palmas
- Department of Medicine, Columbia University Medical Center, New York, NY, USA
| | - Henry J Lin
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Shih-Jen Hwang
- The Population Sciences Branch of the National Heart, Lung and Blood Institute, Bethesda, MD, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Yan Gao
- The Jackson Heart Study, University of Mississippi Medical Center, Jackson, MS, USA
| | - Kendra Young
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
| | - Gregory L Kinney
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
| | - Jennifer A Smith
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Bing Yu
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Simin Liu
- Center for Global Cardiometabolic Health, Department of Epidemiology, Brown University, Providence, RI, USA
- Department of Medicine, Brown University, Providence, RI, USA
| | - Sylvia Wassertheil-Smoller
- Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - JoAnn E Manson
- Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Yii-Der Ida Chen
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - I-Te Lee
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung City, 40705, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan
| | - C Charles Gu
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Sebastian Zöllner
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Myriam Fornage
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Adolfo Correa
- Departments of Medicine and Pediatrics, University of Mississippi Medical Center, Jackson, MS, USA
| | - Bruce M Psaty
- Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Donna K Arnett
- Office of the Provost, University of South Carolina, Columbia, SC, USA
| | - Carmen R Isasi
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Robert C Kaplan
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Daniel Levy
- The Population Sciences Branch of the National Heart, Lung and Blood Institute, Bethesda, MD, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Jerome I Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA.
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31
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Hardy DS, Garvin JT, Mersha TB. Analysis of ancestry-specific polygenic risk score and diet composition in type 2 diabetes. PLoS One 2023; 18:e0285827. [PMID: 37220136 PMCID: PMC10204962 DOI: 10.1371/journal.pone.0285827] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 05/02/2023] [Indexed: 05/25/2023] Open
Abstract
BACKGROUND Carbohydrate and protein dietary proportions have been debated as to whether higher or lower levels are optimal for diabetes metabolic control. OBJECTIVE The objective of this study was to investigate the associations, interactions, and mediational relationships between a polygenic risk score (PRS), carbohydrate and protein intake, and physical activity level on type 2 diabetes (T2DM) by genetic ancestry, in European Americans and African Americans. A secondary objective examined the biological pathways associated with the PRS-linked genes and their relationships to dietary intake. METHODS We performed a cross-sectional study in 9,393 participants: 83.3% European Americans and 16.7% African Americans from 7-NHLBI Care studies obtained from the database of Genotypes and Phenotypes. The main outcome was T2DM. Carbohydrate and protein intake derived from food frequency questionnaires were calculated as percent calories. Data were analyzed using multivariable generalized estimation equation models to derive odds ratios (OR) and 95% confidence intervals (CI). Ancestry-specific PRSs were constructed using joint-effects Summary Best Linear Unbiased Estimation in the train dataset and replicated in the test dataset. Mediation analysis was performed using VanderWeele's method. RESULTS The PRS in the highest tertile was associated with higher risk of T2DM in European Americans (OR = 1.25;CI = 1.03-1.51) and African Americans (OR = 1.54;1.14-2.09). High carbohydrate and low protein intake had lower risks of T2DM when combined with the PRS after adjusting for covariates. In African Americans, high physical activity combined with the high PRS and high protein diet was associated with a 28% lower incidence of T2DM when compared to low physical activity. In mediational models in African Americans, the PRS-T2DM association was mediated by protein intake in the highest tertile by 55%. The top PRS tertile had the highest magnitude of risks with metabolic factors that were significantly associated with T2DM, especially in European Americans. We found metabolic pathways associated with the PRS-linked genes that were related to insulin/IGF and ketogenesis/ketolysis that can be activated by moderate physical activity and intermittent fasting for better T2DM control. CONCLUSIONS Clinicians may want to consider diets with a higher portion of carbohydrates than protein, especially when the burden of high-risk alleles is great in patients with T2DM. In addition, clinicians and other medical professionals may want to emphasize the addition of physical activity as part of treatment regimen especially for African Americans. Given the metabolic pathways we identified, moderate physical activity and intermittent fasting should be explored. Researchers may want to consider longitudinal or randomized clinical trials to determine the predictive ability of different dietary patterns to inhibit T2DM in the presence of obesity and an elevated PRS.
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Affiliation(s)
- Dale S. Hardy
- Department of Internal Medicine, Morehouse School of Medicine, Atlanta, GA, United States of America
| | - Jane T. Garvin
- College of Nursing, Walden University, Minneapolis, MN, United States of America
| | - Tesfaye B. Mersha
- Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, United States of America
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32
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Aryal S, Manandhar I, Mei X, Yeoh BS, Tummala R, Saha P, Osman I, Zubcevic J, Durgan DJ, Vijay-Kumar M, Joe B. Combating hypertension beyond genome-wide association studies: Microbiome and artificial intelligence as opportunities for precision medicine. CAMBRIDGE PRISMS. PRECISION MEDICINE 2023; 1:e26. [PMID: 38550938 PMCID: PMC10953772 DOI: 10.1017/pcm.2023.13] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 03/15/2023] [Accepted: 04/04/2023] [Indexed: 11/03/2024]
Abstract
The single largest contributor to human mortality is cardiovascular disease, the top risk factor for which is hypertension (HTN). The last two decades have placed much emphasis on the identification of genetic factors contributing to HTN. As a result, over 1,500 genetic alleles have been associated with human HTN. Mapping studies using genetic models of HTN have yielded hundreds of blood pressure (BP) loci but their individual effects on BP are minor, which limits opportunities to target them in the clinic. The value of collecting genome-wide association data is evident in ongoing research, which is beginning to utilize these data at individual-level genetic disparities combined with artificial intelligence (AI) strategies to develop a polygenic risk score (PRS) for the prediction of HTN. However, PRS alone may or may not be sufficient to account for the incidence and progression of HTN because genetics is responsible for <30% of the risk factors influencing the etiology of HTN pathogenesis. Therefore, integrating data from other nongenetic factors influencing BP regulation will be important to enhance the power of PRS. One such factor is the composition of gut microbiota, which constitute a more recently discovered important contributor to HTN. Studies to-date have clearly demonstrated that the transition from normal BP homeostasis to a state of elevated BP is linked to compositional changes in gut microbiota and its interaction with the host. Here, we first document evidence from studies on gut dysbiosis in animal models and patients with HTN followed by a discussion on the prospects of using microbiota data to develop a metagenomic risk score (MRS) for HTN to be combined with PRS and a clinical risk score (CRS). Finally, we propose that integrating AI to learn from the combined PRS, MRS and CRS may further enhance predictive power for the susceptibility and progression of HTN.
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Affiliation(s)
- Sachin Aryal
- Center for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
| | - Ishan Manandhar
- Center for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
| | - Xue Mei
- Center for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
| | - Beng S. Yeoh
- Center for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
| | - Ramakumar Tummala
- Center for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
| | - Piu Saha
- Center for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
| | - Islam Osman
- Center for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
| | - Jasenka Zubcevic
- Center for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
| | - David J. Durgan
- Integrative Physiology & Anesthesiology, Baylor College of Medicine, Houston, TX, USA
| | - Matam Vijay-Kumar
- Center for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
| | - Bina Joe
- Center for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
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Park JK, Lu CY. Polygenic Scores in the Direct-to-Consumer Setting: Challenges and Opportunities for a New Era in Consumer Genetic Testing. J Pers Med 2023; 13:jpm13040573. [PMID: 37108959 PMCID: PMC10144199 DOI: 10.3390/jpm13040573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/15/2023] [Accepted: 03/21/2023] [Indexed: 04/29/2023] Open
Abstract
Direct-to-consumer (DTC) genetic tests have generated considerable scholarly attention and public intrigue. Although the current consumer genetic testing regime relies on the reporting of individual variants of interest to consumers, there has recently been interest in the possibility of integrating polygenic scores (PGS), which aggregate genetic liability for disease across the entire genome. While PGS have thus far been extensively explored as clinical and public health tools, the use of PGS in consumer genetic testing has not yet received systematic attention, even though they are already in use for some consumer genetic tests. In this narrative review, we highlight the ethical, legal, and social implications of the use of PGS in DTC genetic tests and synthesize existing solutions to these concerns. We organize these concerns into three domains: (1) industry variation; (2) privacy and commercialization; and (3) patient safety and risk. While previously expressed concerns in these domains will remain relevant, the emergence of PGS-based DTC genetic tests raises challenges that will require novel approaches.
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Affiliation(s)
- Jin K Park
- Harvard Medical School, Boston, MA 02115, USA
| | - Christine Y Lu
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02115, USA
- Kolling Institute, Faculty of Medicine and Health, The University of Sydney and the Northern Sydney Local Health District, Sydney, NSW 2077, Australia
- School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
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Yang G, Mishra M, Perera MA. Multi-Omics Studies in Historically Excluded Populations: The Road to Equity. Clin Pharmacol Ther 2023; 113:541-556. [PMID: 36495075 PMCID: PMC10323857 DOI: 10.1002/cpt.2818] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/01/2022] [Indexed: 12/14/2022]
Abstract
Over the past few decades, genomewide association studies (GWASs) have identified the specific genetics variants contributing to many complex diseases by testing millions of genetic variations across the human genome against a variety of phenotypes. However, GWASs are limited in their ability to uncover mechanistic insight given that most significant associations are found in non-coding region of the genome. Furthermore, the lack of diversity in studies has stymied the advance of precision medicine for many historically excluded populations. In this review, we summarize most popular multi-omics approaches (genomics, transcriptomics, proteomics, and metabolomics) related to precision medicine and highlight if diverse populations have been included and how their findings have advance biological understanding of disease and drug response. New methods that incorporate local ancestry have been to improve the power of GWASs for admixed populations (such as African Americans and Latinx). Because most signals from GWAS are in the non-coding region, other machine learning and omics approaches have been developed to identify the potential causative single-nucleotide polymorphisms and genes that explain these phenotypes. These include polygenic risk scores, expression quantitative trait locus mapping, and transcriptome-wide association studies. Analogous protein methods, such as proteins quantitative trait locus mapping, proteome-wide association studies, and metabolomic approaches provide insight into the consequences of genetic variation on protein abundance. Whereas, integrated multi-omics studies have improved our understanding of the mechanisms for genetic association, we still lack the datasets and cohorts for historically excluded populations to provide equity in precision medicine and pharmacogenomics.
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Affiliation(s)
- Guang Yang
- Department of Pharmacology, Center for Pharmacogenomics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Mrinal Mishra
- Department of Pharmacology, Center for Pharmacogenomics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Minoli A. Perera
- Department of Pharmacology, Center for Pharmacogenomics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
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Avvisato R, Jankauskas SS, Varzideh F, Kansakar U, Mone P, Santulli G. Sortilin and hypertension. Curr Opin Nephrol Hypertens 2023; 32:134-140. [PMID: 36683537 PMCID: PMC9976622 DOI: 10.1097/mnh.0000000000000866] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
PURPOSE OF REVIEW The current review aims to present the latest scientific updates on the role of Sortilin in the pathophysiology of hypertension. RECENT FINDINGS The main focus of this systematic overview is on the functional contribution of Sortilin to the pathogenesis of hypertension. Sortilin is a glycoprotein mostly known for its actions as a trafficking molecule directing proteins to specific secretory or endocytic compartments of the cell. Emerging evidence indicates that Sortilin is associated with pathological conditions, including inflammation, arteriosclerosis, dyslipidemia, insulin resistance, and vascular calcification. Most recently, Sortilin has been shown to finely control endothelial function and to drive hypertension by modulating sphingolipid/ceramide homeostasis and by triggering oxidative stress. SUMMARY The latest findings linking Sortilin and hypertension that are herein discussed can inspire novel areas of research which could eventually lead to the discovery of new therapeutic strategies in cardiovascular medicine.
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Affiliation(s)
- Roberta Avvisato
- Department of Medicine, Division of Cardiology, Wilf Family Cardiovascular Research Institute, Einstein Institute for Aging Research and
- Department of Molecular Pharmacology, Einstein-Mount Sinai Diabetes Research Center (ES-DRC), Fleischer Institute for Diabetes and Metabolism (FIDAM), Institute for Neuroimmunology and Inflammation, Albert Einstein College of Medicine, New York, New York, USA
| | - Stanislovas S. Jankauskas
- Department of Medicine, Division of Cardiology, Wilf Family Cardiovascular Research Institute, Einstein Institute for Aging Research and
- Department of Molecular Pharmacology, Einstein-Mount Sinai Diabetes Research Center (ES-DRC), Fleischer Institute for Diabetes and Metabolism (FIDAM), Institute for Neuroimmunology and Inflammation, Albert Einstein College of Medicine, New York, New York, USA
| | - Fahimeh Varzideh
- Department of Medicine, Division of Cardiology, Wilf Family Cardiovascular Research Institute, Einstein Institute for Aging Research and
- Department of Molecular Pharmacology, Einstein-Mount Sinai Diabetes Research Center (ES-DRC), Fleischer Institute for Diabetes and Metabolism (FIDAM), Institute for Neuroimmunology and Inflammation, Albert Einstein College of Medicine, New York, New York, USA
| | - Urna Kansakar
- Department of Medicine, Division of Cardiology, Wilf Family Cardiovascular Research Institute, Einstein Institute for Aging Research and
- Department of Molecular Pharmacology, Einstein-Mount Sinai Diabetes Research Center (ES-DRC), Fleischer Institute for Diabetes and Metabolism (FIDAM), Institute for Neuroimmunology and Inflammation, Albert Einstein College of Medicine, New York, New York, USA
| | - Pasquale Mone
- Department of Medicine, Division of Cardiology, Wilf Family Cardiovascular Research Institute, Einstein Institute for Aging Research and
- Department of Molecular Pharmacology, Einstein-Mount Sinai Diabetes Research Center (ES-DRC), Fleischer Institute for Diabetes and Metabolism (FIDAM), Institute for Neuroimmunology and Inflammation, Albert Einstein College of Medicine, New York, New York, USA
| | - Gaetano Santulli
- Department of Medicine, Division of Cardiology, Wilf Family Cardiovascular Research Institute, Einstein Institute for Aging Research and
- Department of Molecular Pharmacology, Einstein-Mount Sinai Diabetes Research Center (ES-DRC), Fleischer Institute for Diabetes and Metabolism (FIDAM), Institute for Neuroimmunology and Inflammation, Albert Einstein College of Medicine, New York, New York, USA
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Andreassen OA, Hindley GFL, Frei O, Smeland OB. New insights from the last decade of research in psychiatric genetics: discoveries, challenges and clinical implications. World Psychiatry 2023; 22:4-24. [PMID: 36640404 PMCID: PMC9840515 DOI: 10.1002/wps.21034] [Citation(s) in RCA: 48] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/07/2022] [Indexed: 01/15/2023] Open
Abstract
Psychiatric genetics has made substantial progress in the last decade, providing new insights into the genetic etiology of psychiatric disorders, and paving the way for precision psychiatry, in which individual genetic profiles may be used to personalize risk assessment and inform clinical decision-making. Long recognized to be heritable, recent evidence shows that psychiatric disorders are influenced by thousands of genetic variants acting together. Most of these variants are commonly occurring, meaning that every individual has a genetic risk to each psychiatric disorder, from low to high. A series of large-scale genetic studies have discovered an increasing number of common and rare genetic variants robustly associated with major psychiatric disorders. The most convincing biological interpretation of the genetic findings implicates altered synaptic function in autism spectrum disorder and schizophrenia. However, the mechanistic understanding is still incomplete. In line with their extensive clinical and epidemiological overlap, psychiatric disorders appear to exist on genetic continua and share a large degree of genetic risk with one another. This provides further support to the notion that current psychiatric diagnoses do not represent distinct pathogenic entities, which may inform ongoing attempts to reconceptualize psychiatric nosology. Psychiatric disorders also share genetic influences with a range of behavioral and somatic traits and diseases, including brain structures, cognitive function, immunological phenotypes and cardiovascular disease, suggesting shared genetic etiology of potential clinical importance. Current polygenic risk score tools, which predict individual genetic susceptibility to illness, do not yet provide clinically actionable information. However, their precision is likely to improve in the coming years, and they may eventually become part of clinical practice, stressing the need to educate clinicians and patients about their potential use and misuse. This review discusses key recent insights from psychiatric genetics and their possible clinical applications, and suggests future directions.
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Affiliation(s)
- Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Guy F L Hindley
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Olav B Smeland
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
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Limonova AS, Ershova AI, Kiseleva AV, Ramensky VE, Vyatkin YV, Kutsenko VA, Meshkov AN, Drapkina OM. Assessment of polygenic risk of hypertension. КАРДИОВАСКУЛЯРНАЯ ТЕРАПИЯ И ПРОФИЛАКТИКА 2023. [DOI: 10.15829/1728-8800-2022-3464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Hypertension (HTN) is a leading risk factor for the development of cardiovascular diseases. In recent decades, the rapid development of genetic tests, in particular genome-wide association study (GWAS), has made it possible to identify hundreds of nucleotide sequence variants associated with the development of HTN. One approach to improve the predictive power of genetic testing is to combine information about many nucleotide sequence variants into a single risk assessment system, often referred to as a genetic risk score. Within the framework of this review, the most significant publications on the study of the genetic risk score for HTN will be considered, and the features of their development and application will be discussed.
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Affiliation(s)
- A. S. Limonova
- National Medical Research Center for Therapy and Preventive Medicine
| | - A. I. Ershova
- National Medical Research Center for Therapy and Preventive Medicine
| | - A. V. Kiseleva
- National Medical Research Center for Therapy and Preventive Medicine
| | - V. E. Ramensky
- National Medical Research Center for Therapy and Preventive Medicine; Lomonosov Moscow State University
| | - Yu. V. Vyatkin
- National Medical Research Center for Therapy and Preventive Medicine; Novosibirsk National Research State University
| | - V. A. Kutsenko
- National Medical Research Center for Therapy and Preventive Medicine; Faculty of Mechanics and Mathematics, Lomonosov Moscow State University
| | - A. N. Meshkov
- National Medical Research Center for Therapy and Preventive Medicine; Pirogov Russian National Research Medical University
| | - O. M. Drapkina
- National Medical Research Center for Therapy and Preventive Medicine
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Parcha V, Pampana A, Shetty NS, Irvin MR, Natarajan P, Lin HJ, Guo X, Rich SS, Rotter JI, Li P, Oparil S, Arora G, Arora P. Association of a Multiancestry Genome-Wide Blood Pressure Polygenic Risk Score With Adverse Cardiovascular Events. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2022; 15:e003946. [PMID: 36334310 PMCID: PMC9812363 DOI: 10.1161/circgen.122.003946] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 11/04/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND Traditional cardiovascular risk factors and the underlying genetic risk of elevated blood pressure (BP) determine an individual's composite risk of developing adverse cardiovascular events. We sought to evaluate the relative contributions of the traditional cardiovascular risk factors to the development of adverse cardiovascular events in the context of varying BP genetic risk profiles. METHODS Genome-wide polygenic risk score (PRS) was computed using multiancestry genome-wide association estimates among US adults who underwent whole-genome sequencing in the Trans-Omics for Precision program. Individuals were stratified into high, intermediate, and low genetic risk groups (>80th, 20-80th, and <20th centiles of systolic BP [SBP] PRS). Based on the ACC/AHA Pooled Cohort Equations, participants were stratified into low and high (10 year-atherosclerotic cardiovascular disease [CVD] risk: <10% or ≥10%) cardiovascular risk factor profile groups. The primary study outcome was incident cardiovascular event (composite of incident heart failure, incident stroke, and incident coronary heart disease). RESULTS Among 21 897 US adults (median age: 56 years; 56.0% women; 35.8% non-White race/ethnicity), 1 SD increase in the SBP PRS, computed using 1.08 million variants, was associated with SBP (β: 4.39 [95% CI, 4.13-4.65]) and hypertension (odds ratio, 1.50 [95% CI, 1.46-1.55]), respectively. This association was robustly seen across racial/ethnic groups. Each SD increase in SBP PRS was associated with a higher risk of the incident CVD (multivariable-adjusted hazards ratio, 1.07 [95% CI, 1.04-1.10]) after controlling for ACC/AHA Pooled Cohort Equations risk scores. Among individuals with a high SBP PRS, low atherosclerotic CVD risk was associated with a 58% lower hazard for incident CVD (multivariable-adjusted hazards ratio, 0.42 [95% CI, 0.36-0.50]) compared to those with high atherosclerotic CVD risk. A similar pattern was noted in intermediate and low genetic risk groups. CONCLUSIONS In a multiancestry cohort of >21 000 US adults, genome-wide SBP PRS was associated with BP traits and adverse cardiovascular events. Adequate control of modifiable cardiovascular risk factors may reduce the predisposition to adverse cardiovascular events among those with a high SBP PRS.
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Affiliation(s)
- Vibhu Parcha
- Division of Cardiovascular Disease, Univ of Alabama at Birmingham, Birmingham, AL
| | - Akhil Pampana
- Division of Cardiovascular Disease, Univ of Alabama at Birmingham, Birmingham, AL
| | - Naman S. Shetty
- Division of Cardiovascular Disease, Univ of Alabama at Birmingham, Birmingham, AL
| | - Marguerite R. Irvin
- Dept of Epidemiology, School of Public Health, Univ of Alabama at Birmingham, Birmingham, AL
| | - Pradeep Natarajan
- Cardiology Division, Dept of Medicine, Massachusetts General Hospital
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston
- Program in Medical & Population Genetics, Broad Institute of Harvard & MIT, Cambridge, MA
| | - Henry J. Lin
- The Institute for Translational Genomics & Population Sciences, Dept of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Xiuqing Guo
- The Institute for Translational Genomics & Population Sciences, Dept of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Stephen S. Rich
- Center for Public Health, Univ of Virginia, Charlottesville, VA
| | - Jerome I. Rotter
- The Institute for Translational Genomics & Population Sciences, Dept of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Peng Li
- School of Nursing, Univ of Alabama at Birmingham, Birmingham, AL
| | - Suzanne Oparil
- Division of Cardiovascular Disease, Univ of Alabama at Birmingham, Birmingham, AL
| | - Garima Arora
- Division of Cardiovascular Disease, Univ of Alabama at Birmingham, Birmingham, AL
| | - Pankaj Arora
- Division of Cardiovascular Disease, Univ of Alabama at Birmingham, Birmingham, AL
- Section of Cardiology, Birmingham Veterans Affairs Medical Center, Birmingham, AL
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Zhang Y, Elgart M, Kurniansyah N, Spitzer BW, Wang H, Kim D, Shah N, Daviglus M, Zee PC, Cai J, Gottlieb DJ, Cade BE, Redline S, Sofer T. Genetic determinants of cardiometabolic and pulmonary phenotypes and obstructive sleep apnoea in HCHS/SOL. EBioMedicine 2022; 84:104288. [PMID: 36174398 PMCID: PMC9515437 DOI: 10.1016/j.ebiom.2022.104288] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 08/24/2022] [Accepted: 09/08/2022] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Obstructive Sleep Apnoea (OSA) often co-occurs with cardiometabolic and pulmonary diseases. This study is to apply genetic analysis methods to explain the associations between OSA and related phenotypes. METHODS In the Hispanic Community Healthy Study/Study of Latinos, we estimated genetic correlations ρg between the respiratory event index (REI) and 54 anthropometric, glycemic, cardiometabolic, and pulmonary phenotypes. We used summary statistics from published genome-wide association studies to construct Polygenic Risk Scores (PRSs) representing the genetic basis of each correlated phenotype (ρg>0.2 and p-value<0.05), and of OSA. We studied the association of the PRSs of the correlated phenotypes with both REI and OSA (REI≥5), and the association of OSA PRS with the correlated phenotypes. Causal relationships were tested using Mendelian Randomization (MR) analysis. FINDINGS The dataset included 11,155 participants, 31.03% with OSA. 22 phenotypes were genetically correlated with REI. 10 PRSs covering obesity and fat distribution (BMI, WHR, WHRadjBMI), blood pressure (DBP, PP, MAP), glycaemic control (fasting insulin, HbA1c, HOMA-B) and insomnia were associated with REI and/or OSA. OSA PRS was associated with BMI, WHR, DBP and glycaemic traits (fasting insulin, HbA1c, HOMA-B and HOMA-IR). MR analysis identified robust causal effects of BMI and WHR on OSA, and probable causal effects of DBP, PP, and HbA1c on OSA/REI. INTERPRETATION There are shared genetic underpinnings of anthropometric, blood pressure, and glycaemic phenotypes with OSA, with evidence for causal relationships between some phenotypes. FUNDING Described in Acknowledgments.
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Affiliation(s)
- Yuan Zhang
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA,Department of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Michael Elgart
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Nuzulul Kurniansyah
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Brian W. Spitzer
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Heming Wang
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Doyoon Kim
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Neomi Shah
- Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Martha Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Phyllis C. Zee
- Center for Circadian and Sleep Medicine, Department of Neurology, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Daniel J. Gottlieb
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Brian E. Cade
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA,Corresponding author at: Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, 221 Longwood Avenue, Boston, MA 02115, USA.
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Life-Course Associations between Blood Pressure-Related Polygenic Risk Scores and Hypertension in the Bogalusa Heart Study. Genes (Basel) 2022; 13:genes13081473. [PMID: 36011384 PMCID: PMC9408577 DOI: 10.3390/genes13081473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 07/30/2022] [Accepted: 08/15/2022] [Indexed: 11/16/2022] Open
Abstract
Genetic information may help to identify individuals at increased risk for hypertension in early life, prior to the manifestation of elevated blood pressure (BP) values. We examined 369 Black and 832 White Bogalusa Heart Study (BHS) participants recruited in childhood and followed for approximately 37 years. The multi-ancestry genome-wide polygenic risk scores (PRSs) for systolic BP (SBP), diastolic BP (DBP), and hypertension were tested for an association with incident hypertension and stage 2 hypertension using Cox proportional hazards models. Race-stratified analyses were adjusted for baseline age, age2, sex, body mass index, genetic principal components, and BP. In Black participants, each standard deviation increase in SBP and DBP PRS conferred a 38% (p = 0.009) and 22% (p = 0.02) increased risk of hypertension and a 74% (p < 0.001) and 50% (p < 0.001) increased risk of stage 2 hypertension, respectively, while no association was observed with the hypertension PRSs. In Whites, each standard deviation increase in SBP, DBP, and hypertension PRS conferred a 24% (p < 0.05), 29% (p = 0.01), and 25% (p < 0.001) increased risk of hypertension, and a 27% (p = 0.08), 29% (0.01), and 42% (p < 0.001) increased risk of stage 2 hypertension, respectively. The addition of BP PRSs to the covariable-only models generally improved the C-statistics (p < 0.05). Multi-ancestry BP PRSs demonstrate the utility of genomic information in the early life prediction of hypertension.
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