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Wang SH, Huang YC, Cheng CW, Chang YW, Liao WL. Impact of the trans-ancestry polygenic risk score on type 2 diabetes risk, onset age and progression among population in Taiwan. Am J Physiol Endocrinol Metab 2024; 326:E547-E554. [PMID: 38363735 DOI: 10.1152/ajpendo.00252.2023] [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: 08/14/2023] [Revised: 01/03/2024] [Accepted: 01/05/2024] [Indexed: 02/18/2024]
Abstract
Type 2 diabetes (T2D) prevalence in adults at a younger age has increased but the disease status may go unnoticed. This study aimed to determine whether the onset age and subsequent diabetic complications can be attributed to the polygenic architecture of T2D in the Taiwan Han population. A total of 9,627 cases with T2D and 85,606 controls from the Taiwan Biobank were enrolled. Three diabetic polygenic risk scores (PRSs), PRS_EAS and PRS_EUR, and a trans-ancestry PRS (PRS_META), calculated using summary statistic from East Asian and European populations. The onset age was identified by linking to the National Taiwan Insurance Research Database, and the incidence of different diabetic complications during follow-up was recorded. PRS_META (7.4%) explained a higher variation for T2D status. And the higher percentile of PRS is also correlated with higher percentage of T2D family history and prediabetes status. More, the PRS was negatively associated with onset age (β = -0.91 yr), and this was more evident among males (β = -1.11 vs. -0.76 for males and females, respectively). The hazard ratio of diabetic retinopathy (DR) and diabetic foot were significantly associated with PRS_EAS and PRS_META, respectively. However, the PRS was not associated with other diabetic complications, including diabetic nephropathy, cardiovascular disease, and hypertension. Our findings indicated that diabetic PRS which combined susceptibility variants from cross-population could be used as a tool for early screening of T2D, especially for high-risk populations, such as individuals with high genetic risk, and may be associated with the risk of complications in subjects with T2D. NEW & NOTEWORTHY Our findings indicated that diabetic polygenic risk score (PRS) which combined susceptibility variants from Asian and European population affect the onset age of type 2 diabetes (T2D) and could be used as a tool for early screening of T2D, especially for individuals with high genetic risk, and may be associated with the risk of diabetic complications among people in Taiwan.
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Affiliation(s)
- Shi-Heng Wang
- National Center for Geriatrics and Welfare Research, National Health Research Institutes, Zhunan, Taiwan
- Department of Public Health, China Medical University, Taichung, Taiwan
| | - Yu-Chuen Huang
- School of Chinese Medicine, China Medical University, Taichung, Taiwan
- Genetic Center, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Chun-Wen Cheng
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Clinical Laboratory, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Ya-Wen Chang
- Genetic Center, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
- Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan
- Center for Personalized Medicine, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Wen-Ling Liao
- Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan
- Center for Personalized Medicine, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
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Wang W, Li X, Chen F, Wei R, Chen Z, Li J, Qiao J, Pan Q, Yang W, Guo L. Secondary analysis of newly diagnosed type 2 diabetes subgroups and treatment responses in the MARCH cohort. Diabetes Metab Syndr 2024; 18:102936. [PMID: 38171152 DOI: 10.1016/j.dsx.2023.102936] [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: 04/14/2023] [Revised: 12/18/2023] [Accepted: 12/24/2023] [Indexed: 01/05/2024]
Abstract
OBJECTIVE To incorporate new clusters in the MARCH (Metformin and AcaRbose in Chinese patients as the initial Hypoglycemic treatment) cohort of newly diagnosed type 2 diabetes (T2D) patients and compare the anti-glycemic effects of metformin and acarbose across different clusters. METHODS K-means cluster analysis was performed based on six clinical indicators. The diabetic clusters in the MARCH cohort were retrospectively associated with the response to metformin and acarbose. RESULTS A total of 590 newly diagnosed T2D patients were classified by data-driven clusters into the MARD (mild obesity-related diabetes) (34.1 %), MOD (mild obesity-related diabetes) (34.1 %), SIDD (severe insulin-deficient diabetes) (20.3 %) and SIRD (severe insulin-resistant diabetes) (11.5 %) subgroups at baseline. At 24 and 48 weeks, 346 participants had finished the follow-up. After the adjustment of age, gender, weight, baseline HbA1c, baseline fasting glucose and 2-h postprandial blood glucose (2hPG), metformin mainly decreased the fasting glucose (0.07 ± 0.89 vs -0.26 ± 0.83, P = 0.043) in the MARD subgroup presented with OGTT (oral glucose tolerance test) results compared with acarbose group at 24 weeks. Acarbose led to a greater decrease in 2hPG in the MOD subgroup compared with metformin group (0.08 ± 0.86 vs -0.24 ± 0.92, P = 0.037) at 24 weeks. There was a also significant interaction between cluster and treatment efficacy in HbA1c (glycated hemoglobin) reduction in metformin and acarbose groups at 24 and 48 weeks (pinteraction<0.001). CONCLUSIONS Metformin and acarbose affected different metabolic variables depending on the diabetes subtype.
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Affiliation(s)
- Weihao Wang
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Xinyao Li
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, China
| | - Fei Chen
- College of Life Sciences, University of Chinese Academy of Sciences, China; China-Japan Friendship Hospital, Beijing, China
| | - Ran Wei
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Zhi Chen
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane 4072, Australia
| | - Jingjing Li
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, China
| | - Jingtao Qiao
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Qi Pan
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
| | - Wenying Yang
- China-Japan Friendship Hospital, Beijing, China.
| | - Lixin Guo
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
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Javed A, Kim DS, Hershman SG, Shcherbina A, Johnson A, Tolas A, O’Sullivan JW, McConnell MV, Lazzeroni L, King AC, Christle JW, Oppezzo M, Mattsson CM, Harrington RA, Wheeler MT, Ashley EA. Personalized digital behaviour interventions increase short-term physical activity: a randomized control crossover trial substudy of the MyHeart Counts Cardiovascular Health Study. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2023; 4:411-419. [PMID: 37794870 PMCID: PMC10545510 DOI: 10.1093/ehjdh/ztad047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 07/27/2023] [Indexed: 10/06/2023]
Abstract
Aims Physical activity is associated with decreased incidence of the chronic diseases associated with aging. We previously demonstrated that digital interventions delivered through a smartphone app can increase short-term physical activity. Methods and results We offered enrolment to community-living iPhone-using adults aged ≥18 years in the USA, UK, and Hong Kong who downloaded the MyHeart Counts app. After completion of a 1-week baseline period, e-consented participants were randomized to four 7-day interventions. Interventions consisted of: (i) daily personalized e-coaching based on the individual's baseline activity patterns, (ii) daily prompts to complete 10 000 steps, (iii) hourly prompts to stand following inactivity, and (iv) daily instructions to read guidelines from the American Heart Association (AHA) website. After completion of one 7-day intervention, participants subsequently randomized to the next intervention of the crossover trial. The trial was completed in a free-living setting, where neither the participants nor investigators were blinded to the intervention. The primary outcome was change in mean daily step count from baseline for each of the four interventions, assessed in a modified intention-to-treat analysis (modified in that participants had to complete 7 days of baseline monitoring and at least 1 day of an intervention to be included in analyses). This trial is registered with ClinicalTrials.gov, NCT03090321. Conclusion Between 1 January 2017 and 1 April 2022, 4500 participants consented to enrol in the trial (a subset of the approximately 50 000 participants in the larger MyHeart Counts study), of whom 2458 completed 7 days of baseline monitoring (mean daily steps 4232 ± 73) and at least 1 day of one of the four interventions. Personalized e-coaching prompts, tailored to an individual based on their baseline activity, increased step count significantly (+402 ± 71 steps from baseline, P = 7.1⨯10-8). Hourly stand prompts (+292 steps from baseline, P = 0.00029) and a daily prompt to read AHA guidelines (+215 steps from baseline, P = 0.021) were significantly associated with increased mean daily step count, while a daily reminder to complete 10 000 steps was not (+170 steps from baseline, P = 0.11). Digital studies have a significant advantage over traditional clinical trials in that they can continuously recruit participants in a cost-effective manner, allowing for new insights provided by increased statistical power and refinement of prior signals. Here, we present a novel finding that digital interventions tailored to an individual are effective in increasing short-term physical activity in a free-living cohort. These data suggest that participants are more likely to react positively and increase their physical activity when prompts are personalized. Further studies are needed to determine the effects of digital interventions on long-term outcomes.
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Affiliation(s)
- Ali Javed
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Daniel Seung Kim
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Steven G Hershman
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
- Biofourmis, Boston, MA, USA
| | - Anna Shcherbina
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Anders Johnson
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Alexander Tolas
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Jack W O’Sullivan
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Michael V McConnell
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
- identifeye HEALTH, Redwood City, CA, USA
| | - Laura Lazzeroni
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Abby C King
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
- Department of Health Research and Policy, Stanford University, Stanford, CA, USA
- Department of Medicine, Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Jeffrey W Christle
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Marily Oppezzo
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - C Mikael Mattsson
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Robert A Harrington
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Matthew T Wheeler
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA 94305, USA
| | - Euan A Ashley
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA 94305, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
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Wang W, Jia T, Liu Y, Deng H, Chen Z, Wang J, Geng Z, Wei R, Qiao J, Ma Y, Jiang X, Xu W, Shao J, Zhou K, Li Y, Pan Q, Yang W, Weng J, Guo L. Data-driven subgroups of newly diagnosed type 2 diabetes and the relationship with cardiovascular diseases at genetic and clinical levels in Chinese adults. Diabetes Metab Syndr 2023; 17:102850. [PMID: 37683311 DOI: 10.1016/j.dsx.2023.102850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/20/2023] [Accepted: 08/29/2023] [Indexed: 09/10/2023]
Abstract
BACKGROUND To subgroup Chinese patients with newly diagnosed type 2 diabetes (T2D) by K-means cluster analysis on clinical indicators, and to explore whether these subgroups represent different genetic features and calculated cardiovascular risks. METHODS The K-means clustering analysis was performed on two cohorts (n = 590 and 392), both consisting of Chinese participants with newly diagnosed T2D. To assess genetic risks, multiple polygenic risk scores (PRSs) and mitochondrial DNA copy numbers (mtDNA-CN) were calculated for all participants. Furthermore, Framingham risk scores (FRS) of cardiovascular diseases in two cohorts were also calculated to verify the genetic risks. RESULTS Four clusters were identified including the mild age-related diabetes (MARD)(35.08%), mild obesity-related diabetes (MOD) (34.41%), severe autoimmune diabetes (SAID) 19.15%, and severe insulin-resistant diabetes (SIRD) 11.36% subgroups in the MARCH (metformin, and acarbose in Chinese patients as the initial hypoglycemic treatment) cohort. There was a significant difference in PRS for cardiovascular diseases (CVD) across four subgroups in the MARCH cohort (p < 0.05). Compared with the SIDD and SIRD subgroups, patients in the MOD subgroup had a relatively lower PRS for CVD (p < 0.05) in the MARCH cohort. Females had a higher PRS compared to males, with no significant difference in FRS across the four clusters. The MOD subgroup had a significantly lower FRS which was consistent with the results of PRS. Similar results of PRS and FRS were also replicated in the CONFIDENCE (comparison of glycemic control and b-cell function among newly diagnosed patients with type 2 diabetes treated with exenatide, insulin or pioglitazone) cohort. CONCLUSION There are different CVD risks in diabetic subgroups based on clinical and genetic evidence which may promote precision medicine.
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Affiliation(s)
- Weihao Wang
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Tong Jia
- Institute for Artificial Intelligence, Peking University, Beijing, China
| | - Yiying Liu
- Institute of Biophysics, Chinese Academy of Sciences, Beijing, China; College of Life Sciences, University of Chinese Academy of Sciences, China
| | - Hongrong Deng
- Department of Endocrinology and Metabolism, Guangdong Provincial Key Laboratory of Diabetology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zihao Chen
- School of Software and Microelectronics, Peking University, Beijing, China
| | - Jing Wang
- Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Zhaoxu Geng
- Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Ran Wei
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Jingtao Qiao
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Yanhua Ma
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Xun Jiang
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Wen Xu
- Department of Endocrinology and Metabolism, Guangdong Provincial Key Laboratory of Diabetology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jian Shao
- No. 9 XingDaoHuanBei Road, Guangzhou International Bio Island, Guangzhou, 510005, Guangdong Province, China
| | - Kaixin Zhou
- The Fifth People's Hospital of Chongqing, Chongqing, China
| | - Ying Li
- School of Software and Microelectronics, Peking University, Beijing, China
| | - Qi Pan
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China.
| | - Wenying Yang
- China-Japan Friendship Hospital, Beijing, China.
| | - Jianping Weng
- Department of Endocrinology and Metabolism, Guangdong Provincial Key Laboratory of Diabetology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
| | - Lixin Guo
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China.
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Li L, Wang S, Huang G, You J. Effect of the nurse-led program on blood glucose control and microalbuminuria development in type 2 diabetic populations. Medicine (Baltimore) 2022; 101:e30693. [PMID: 36254010 PMCID: PMC9575708 DOI: 10.1097/md.0000000000030693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Current study was to evaluate whether the nurse-led program can improve glycated hemoglobin (HbA1c) control and reduce the incidence of microalbuminuria in type 2 diabetic mellitus (DM2) populations. A total of 150 DM2 subjects were randomly assigned to the usual-care group and nurse-led program group. Study endpoints included the HbA1c value, the percentage of subjects with HbA1c < 7.0%, the incidence of microalbuminuria, and the rate of adhering to antidiabetic drug at 6 months' follow-up. At baseline, there was no difference in fasting plasma glucose, HbA1c, proportion of subjects with HbA1c < 7.0%, the use of antidiabetic drug, and urinary albumin-creatinine ratio between these two groups. After 6 months' follow-up, the mean fasting plasma glucose and HbA1c were lower in the nurse-led program group, as was the proportion of subjects with HbA1c < 7.0%. The median urinary albumin-creatinine ratio and rate of incident microalbuminuria were also lower in the nurse-led program. The nurse-led program was associated with higher odds of achieving HbA1c < 7.0% and a lower incidence of microalbuminuria. After adjusted for covariates, the nurse-led program was still associated with 32% higher odds of achieving HbA1c < 7.0% and 11% lower incidence of microalbuminuria. These benefits were consistent by sex and age, while greater in those with obesity or hypertension (P interaction < .05). The nurse-led program is beneficial for blood glucose control and prevention of microalbuminuria.
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Affiliation(s)
- Ling Li
- Department of Nursing, Hainan Western Central Hospital, Danzhou City, China
| | - Suping Wang
- Department of Nursing, Hainan Western Central Hospital, Danzhou City, China
| | - Guoding Huang
- Department of Internal Medicine, Hainan Western Central Hospital, Danzhou City, China
- *Correspondence: Jingyan You, Department of Nursing, Hainan Western Central Hospital, Danzhou City, Hainan Province 571700, China (e-mail: )
| | - Jingyan You
- Department of Nursing, Hainan Western Central Hospital, Danzhou City, China
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Divaris K, Haworth S, Shaffer J, Anttonen V, Beck J, Furuichi Y, Holtfreter B, Jönsson D, Kocher T, Levy S, Magnusson P, McNeil D, Michaëlsson K, North K, Palotie U, Papapanou P, Pussinen P, Porteous D, Reis K, Salminen A, Schaefer A, Sudo T, Sun Y, Suominen A, Tamahara T, Weinberg S, Lundberg P, Marazita M, Johansson I. Phenotype Harmonization in the GLIDE2 Oral Health Genomics Consortium. J Dent Res 2022; 101:1408-1416. [PMID: 36000800 PMCID: PMC9516613 DOI: 10.1177/00220345221109775] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Genetic risk factors play important roles in the etiology of oral, dental, and craniofacial diseases. Identifying the relevant risk loci and understanding their molecular biology could highlight new prevention and management avenues. Our current understanding of oral health genomics suggests that dental caries and periodontitis are polygenic diseases, and very large sample sizes and informative phenotypic measures are required to discover signals and adequately map associations across the human genome. In this article, we introduce the second wave of the Gene-Lifestyle Interactions and Dental Endpoints consortium (GLIDE2) and discuss relevant data analytics challenges, opportunities, and applications. In this phase, the consortium comprises a diverse, multiethnic sample of over 700,000 participants from 21 studies contributing clinical data on dental caries experience and periodontitis. We outline the methodological challenges of combining data from heterogeneous populations, as well as the data reduction problem in resolving detailed clinical examination records into tractable phenotypes, and describe a strategy that addresses this. Specifically, we propose a 3-tiered phenotyping approach aimed at leveraging both the large sample size in the consortium and the detailed clinical information available in some studies, wherein binary, severity-encompassing, and "precision," data-driven clinical traits are employed. As an illustration of the use of data-driven traits across multiple cohorts, we present an application of dental caries experience data harmonization in 8 participating studies (N = 55,143) using previously developed permanent dentition tooth surface-level dental caries pattern traits. We demonstrate that these clinical patterns are transferable across multiple cohorts, have similar relative contributions within each study, and thus are prime targets for genetic interrogation in the expanded and diverse multiethnic sample of GLIDE2. We anticipate that results from GLIDE2 will decisively advance the knowledge base of mechanisms at play in oral, dental, and craniofacial health and disease and further catalyze international collaboration and data and resource sharing in genomics research.
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Affiliation(s)
- K. Divaris
- Division of Pediatric and Public
Health, Adams School of Dentistry, University of North Carolina at Chapel Hill,
Chapel Hill, NC, USA
- Department of Epidemiology, Gillings
School of Global Public Health, University of North Carolina at Chapel Hill, Chapel
Hill, NC, USA
| | - S. Haworth
- Medical Research Council Integrative
Epidemiology United, Department of Population Health Sciences, Bristol Medical
School, University of Bristol, Bristol, UK
- Bristol Dental School, University of
Bristol, Bristol, UK
| | - J.R. Shaffer
- Department of Human Genetics, Graduate
School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Craniofacial and Dental
Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine,
University of Pittsburgh, Pittsburgh, PA, USA
| | - V. Anttonen
- Research Unit of Oral Health Sciences,
Faculty of Medicine, University of Oulu, Oulu, Finland
- Medical Research Center, Oulu
University Hospital and University of Oulu, Oulu, Finland
| | - J.D. Beck
- Division of Comprehensive Oral
Health–Periodontology, Adams School of Dentistry, University of North Carolina at
Chapel Hill, Chapel Hill, NC, USA
| | - Y. Furuichi
- Division of Endodontology and
Periodontology, Department of Oral Rehabilitation, Graduate School of Dentistry,
Health Sciences University of Hokkaido, Hokkaido, Japan
| | - B. Holtfreter
- Department of Restorative Dentistry,
Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University
Medicine Greifswald, Greifswald, Germany
| | - D. Jönsson
- Public Dental Service of Skåne, Lund,
Sweden
- Hypertension and Cardiovascular
Disease, Department of Clinical Sciences in Malmö, Lund University, Malmö,
Sweden
- Faculty of Odontology, Malmö
University, Malmö, Sweden
| | - T. Kocher
- Department of Restorative Dentistry,
Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University
Medicine Greifswald, Greifswald, Germany
| | - S.M. Levy
- Department of Preventive and
Community Dentistry, College of Dentistry, University of Iowa, Iowa City, IA,
USA
| | - P.K.E. Magnusson
- Department of Medical Epidemiology
and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - D.W. McNeil
- Center for Oral Health Research in
Appalachia, Appalachia, NY, USA
- Department of Psychology, West
Virginia University, Morgantown, WV, USA
- Department of Dental Public Health
& Professional Practice, West Virginia University, Morgantown, WV, USA
| | - K. Michaëlsson
- Department of Surgical Sciences, Unit
of Medical Epidemiology, Uppsala University, Uppsala, Sweden
| | - K.E. North
- Department of Epidemiology, Gillings
School of Global Public Health, University of North Carolina at Chapel Hill, Chapel
Hill, NC, USA
- Carolina Population Center,
University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - U. Palotie
- Oral and Maxillofacial Diseases,
University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - P.N. Papapanou
- Division of Periodontics, Section of
Oral, Diagnostic and Rehabilitation Sciences, Columbia University, College of Dental
Medicine, New York, NY, USA
| | - P.J. Pussinen
- Oral and Maxillofacial Diseases,
University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Institute of Dentistry, School on
Medicine, University of Eastern Finland, Kuopio, Finland
| | - D. Porteous
- Centre for Genomic and Experimental
Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh,
UK
| | - K. Reis
- Institute of Genomics, University of
Tartu, Tartu, Estonia
| | - A. Salminen
- Oral and Maxillofacial Diseases,
University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - A.S. Schaefer
- Department of Periodontology, Oral
Medicine and Oral Surgery, Institute for Dental and Craniofacial Sciences,
Charité–Universitätsmedizin Berlin, Berlin, Germany
| | - T. Sudo
- Institute of Education, Tokyo Medical
and Dental University, Tokyo, Japan
| | - Y.Q. Sun
- Center for Oral Health Services and
Research Mid-Norway (TkMidt), Trondheim, Norway
- Department of Clinical and Molecular
Medicine, NTNU, Norwegian University of Science and Technology, Trondheim,
Norway
| | - A.L. Suominen
- Institute of Dentistry, School on
Medicine, University of Eastern Finland, Kuopio, Finland
- Institute of Dentistry, School on
Medicine, University of Eastern Finland, Kuopio, Finland
- Department of Oral and Maxillofacial
Diseases, Kuopio University Hospital, Kuopio, Finland
- Public Health Evaluation and
Projection Unit, Finnish Institute for Health and Welfare (THL), Helsinki,
Finland
| | - T. Tamahara
- Department of Community Medical
Supports, Tohoku Medical Megabank Organization, Tohoku University, Sendai,
Japan
| | - S.M. Weinberg
- Department of Human Genetics, Graduate
School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Craniofacial and Dental
Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine,
University of Pittsburgh, Pittsburgh, PA, USA
| | - P. Lundberg
- Department of Odontology, Section of
Molecular Periodontology, Umeå University, Umeå, Sweden
| | - M.L. Marazita
- Department of Human Genetics, Graduate
School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Craniofacial and Dental
Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine,
University of Pittsburgh, Pittsburgh, PA, USA
| | - I. Johansson
- Department of Odontology, Section of
Cariology, Umeå University, Umeå, Sweden
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7
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Laakso M, Fernandes Silva L. Genetics of Type 2 Diabetes: Past, Present, and Future. Nutrients 2022; 14:nu14153201. [PMID: 35956377 PMCID: PMC9370092 DOI: 10.3390/nu14153201] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/30/2022] [Accepted: 08/03/2022] [Indexed: 02/01/2023] Open
Abstract
Diabetes has reached epidemic proportions worldwide. Currently, approximately 537 million adults (20–79 years) have diabetes, and the total number of people with diabetes is continuously increasing. Diabetes includes several subtypes. About 80% of all cases of diabetes are type 2 diabetes (T2D). T2D is a polygenic disease with an inheritance ranging from 30 to 70%. Genetic and environment/lifestyle factors, especially obesity and sedentary lifestyle, increase the risk of T2D. In this review, we discuss how studies on the genetics of diabetes started, how they expanded when genome-wide association studies and exome and whole-genome sequencing became available, and the current challenges in genetic studies of diabetes. T2D is heterogeneous with respect to clinical presentation, disease course, and response to treatment, and has several subgroups which differ in pathophysiology and risk of micro- and macrovascular complications. Currently, genetic studies of T2D focus on these subgroups to find the best diagnoses and treatments for these patients according to the principles of precision medicine.
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Affiliation(s)
- Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, 70210 Kuopio, Finland
- Department of Medicine, Kuopio University Hospital, 70210 Kuopio, Finland
- Correspondence: ; Tel.: +358-40-672-3338
| | - Lilian Fernandes Silva
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, 70210 Kuopio, Finland
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Kim DS, Khandelwal A. Lipoprotein(a) and Incident Atrial Fibrillation: Leveraging Nature's Randomization to Identify Novel Causal Associations. J Am Coll Cardiol 2022; 79:1591-1593. [PMID: 35450576 DOI: 10.1016/j.jacc.2022.02.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 02/08/2022] [Indexed: 10/18/2022]
Affiliation(s)
- Daniel Seung Kim
- Division of Cardiovascular Medicine, Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, USA.
| | - Abha Khandelwal
- Division of Cardiovascular Medicine, Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, USA
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Fuster V. Editor-in-Chief's Top Picks From 2021. J Am Coll Cardiol 2022; 79:695-753. [PMID: 35177199 DOI: 10.1016/j.jacc.2022.01.004] [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: 10/19/2022]
Abstract
Each week, I record audio summaries for every paper in JACC, as well as an issue summary. This process has become a true labor of love due to the time they require, but I am motivated by the sheer number of listeners (16M+), and it has allowed me to familiarize myself with every paper that we publish. Thus, I have selected the top 100 papers (both Original Investigations and Review Articles) from distinct specialties each year. In addition to my personal choices, I have included papers that have been the most accessed or downloaded on our websites, as well as those selected by the JACC Editorial Board members. In order to present the full breadth of this important research in a consumable fashion, we will present these abstracts in this issue of JACC, as well as their Central Illustrations and podcasts. The highlights comprise the following sections: Artificial Intelligence & Machine Learning (NEW section), Basic & Translational Research, Biomarkers (NEW section), Cardiac Failure & Myocarditis, Cardiomyopathies & Genetics, Cardio-Oncology, Cardiovascular Disease in Women, Coronary Disease & Interventions, Congenital Heart Disease, Coronavirus, Hypertension, Imaging, Metabolic & Lipid Disorders, Neurovascular Disease & Dementia, Promoting Health & Prevention, Rhythm Disorders & Thromboembolism, Vascular Medicine, and Valvular Heart Disease.1-100.
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Bartolomé A. Stem Cell-Derived β Cells: A Versatile Research Platform to Interrogate the Genetic Basis of β Cell Dysfunction. Int J Mol Sci 2022; 23:501. [PMID: 35008927 PMCID: PMC8745644 DOI: 10.3390/ijms23010501] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 12/27/2021] [Accepted: 12/29/2021] [Indexed: 02/07/2023] Open
Abstract
Pancreatic β cell dysfunction is a central component of diabetes progression. During the last decades, the genetic basis of several monogenic forms of diabetes has been recognized. Genome-wide association studies (GWAS) have also facilitated the identification of common genetic variants associated with an increased risk of diabetes. These studies highlight the importance of impaired β cell function in all forms of diabetes. However, how most of these risk variants confer disease risk, remains unanswered. Understanding the specific contribution of genetic variants and the precise role of their molecular effectors is the next step toward developing treatments that target β cell dysfunction in the era of personalized medicine. Protocols that allow derivation of β cells from pluripotent stem cells, represent a powerful research tool that allows modeling of human development and versatile experimental designs that can be used to shed some light on diabetes pathophysiology. This article reviews different models to study the genetic basis of β cell dysfunction, focusing on the recent advances made possible by stem cell applications in the field of diabetes research.
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Affiliation(s)
- Alberto Bartolomé
- Instituto de Investigaciones Biomédicas Alberto Sols, CSIC-UAM, 28029 Madrid, Spain
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Abstract
Human physiology is likely to have been selected for endurance physical activity. However, modern humans have become largely sedentary, with physical activity becoming a leisure-time pursuit for most. Whereas inactivity is a strong risk factor for disease, regular physical activity reduces the risk of chronic disease and mortality. Although substantial epidemiological evidence supports the beneficial effects of exercise, comparatively little is known about the molecular mechanisms through which these effects operate. Genetic and genomic analyses have identified genetic variation associated with human performance and, together with recent proteomic, metabolomic and multi-omic analyses, are beginning to elucidate the molecular genetic mechanisms underlying the beneficial effects of physical activity on human health.
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Affiliation(s)
- Daniel Seung Kim
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Matthew T Wheeler
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.,Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Euan A Ashley
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA. .,Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA. .,Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. .,Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.
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