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Prough MB, Zaman A, Caywood LJ, Clouse JE, Herington SD, Slifer SH, Dorfsman DA, Adams LA, Laux RA, Song YE, Lynn A, Fuzzell D, Fuzzell SL, Miller SD, Miskimen K, Main LR, Osterman MD, Ogrocki P, Lerner AJ, Vance JM, Haines JL, Scott WK, Pericak-Vance M, Cuccaro ML. Visuospatial and Verbal Memory Differences in Amish Individuals With Alzheimer Disease and Related Dementias. Alzheimer Dis Assoc Disord 2023; 37:195-199. [PMID: 37561946 PMCID: PMC10529392 DOI: 10.1097/wad.0000000000000570] [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: 02/07/2023] [Accepted: 06/13/2023] [Indexed: 08/12/2023]
Abstract
BACKGROUND Verbal and visuospatial memory impairments are common to Alzheimer disease and Related Dementias (ADRD), but the patterns of decline in these domains may reflect genetic and lifestyle influences. The latter may be pertinent to populations such as the Amish who have unique lifestyle experiences. METHODS Our data set included 420 Amish and 401 CERAD individuals. Sex-adjusted, age-adjusted, and education-adjusted Z-scores were calculated for the recall portions of the Constructional Praxis Delay (CPD) and Word List Delay (WLD). ANOVAs were then used to examine the main and interaction effects of cohort (Amish, CERAD), cognitive status (case, control), and sex on CPD and WLD Z-scores. RESULTS The Amish performed better on the CPD than the CERAD cohort. In addition, the difference between cases and controls on the CPD and WLD were smaller in the Amish and Amish female cases performed better on the WLD than the CERAD female cases. DISCUSSION The Amish performed better on the CPD task, and ADRD-related declines in CPD and WLD were less severe in the Amish. In addition, Amish females with ADRD may have preferential preservation of WLD. This study provides evidence that the Amish exhibit distinct patterns of verbal and visuospatial memory loss associated with aging and ADRD.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Reneé A Laux
- Department of Population and Quantitative Health Sciences
| | - Yeunjoo E Song
- Department of Population and Quantitative Health Sciences
| | - Audrey Lynn
- Department of Population and Quantitative Health Sciences
| | - Denise Fuzzell
- Department of Population and Quantitative Health Sciences
| | | | | | | | - Leighanne R Main
- Department of Genetics and Genome Sciences
- Cleveland Institute for Computational Biology, Case Western Reserve University
| | - Michael D Osterman
- Department of Population and Quantitative Health Sciences
- Cleveland Institute for Computational Biology, Case Western Reserve University
| | - Paula Ogrocki
- Department of Neurology, Case Western Reserve University School of Medicine
- Department of Neurology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Alan J Lerner
- Department of Neurology, Case Western Reserve University School of Medicine
- Department of Neurology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Jeffery M Vance
- John P. Hussman Institute for Human Genomics
- The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL
| | - Jonathan L Haines
- Department of Population and Quantitative Health Sciences
- Cleveland Institute for Computational Biology, Case Western Reserve University
| | - William K Scott
- John P. Hussman Institute for Human Genomics
- The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL
| | - Margaret Pericak-Vance
- John P. Hussman Institute for Human Genomics
- The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL
| | - Michael L Cuccaro
- John P. Hussman Institute for Human Genomics
- The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL
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Ramos J, Caywood LJ, Prough MB, Clouse JE, Herington SD, Slifer SH, Fuzzell MD, Fuzzell SL, Hochstetler SD, Miskimen KL, Main LR, Osterman MD, Zaman AF, Whitehead PL, Adams LD, Laux RA, Song YE, Foroud TM, Mayeux RP, George-Hyslop PS, Ogrocki PK, Lerner AJ, Vance JM, Cuccaro ML, Haines JL, Pericak-Vance MA, Scott WK. Genetic variants in the SHISA6 gene are associated with delayed cognitive impairment in two family datasets. Alzheimers Dement 2023; 19:611-620. [PMID: 35490390 PMCID: PMC9622429 DOI: 10.1002/alz.12686] [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: 09/23/2021] [Revised: 03/08/2022] [Accepted: 03/28/2022] [Indexed: 11/12/2022]
Abstract
INTRODUCTION Studies of cognitive impairment (CI) in Amish communities have identified sibships containing CI and cognitively unimpaired (CU) individuals. We hypothesize that CU individuals may carry protective alleles delaying age at onset (AAO) of CI. METHODS A total of 1522 individuals screened for CI were genotyped. The outcome studied was AAO for CI individuals or age at last normal exam for CU individuals. Cox mixed-effects models examined association between age and single nucleotide variants (SNVs). RESULTS Three SNVs were significantly associated (P < 5 × 10-8 ) with AAO on chromosomes 6 (rs14538074; hazard ratio [HR] = 3.35), 9 (rs534551495; HR = 2.82), and 17 (rs146729640; HR = 6.38). The chromosome 17 association was replicated in the independent National Institute on Aging Genetics Initiative for Late-Onset Alzheimer's Disease dataset. DISCUSSION The replicated genome-wide significant association with AAO on chromosome 17 is located in the SHISA6 gene, which is involved in post-synaptic transmission in the hippocampus and is a biologically plausible candidate gene for Alzheimer's disease.
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Affiliation(s)
- Jairo Ramos
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Laura J. Caywood
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Michael B. Prough
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Jason E. Clouse
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Sharlene D. Herington
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Susan H. Slifer
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - M. Denise Fuzzell
- Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Sarada L. Fuzzell
- Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | | | | | - Leighanne R. Main
- Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Michael D. Osterman
- Case Western Reserve University School of Medicine, Cleveland, OH, USA
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
| | - Andrew F. Zaman
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Patrice L. Whitehead
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Larry D. Adams
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Renee A. Laux
- Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Yeunjoo E. Song
- Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Tatiana M. Foroud
- Indiana Alzheimer’s Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Richard P. Mayeux
- Taub Institute on Alzheimer’s Disease and the Aging Brain, Department of Neurology, Columbia University, New York, NY, USA
- Gertrude H. Sergievsky Center, Columbia University, New York, NY, USA
- Department of Neurology, Columbia University, New York, NY, USA
| | | | - Paula K. Ogrocki
- University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Alan J. Lerner
- University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Jeffery M. Vance
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
- The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Michael L. Cuccaro
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
- The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Jonathan L. Haines
- Case Western Reserve University School of Medicine, Cleveland, OH, USA
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
| | - Margaret A. Pericak-Vance
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
- The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - William K. Scott
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
- The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
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3
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Osterman MD, Song YE, Adams LD, Laux RA, Caywood LJ, Prough MB, Clouse JE, Herington SD, Slifer SH, Lynn A, Fuzzell MD, Fuzzell SL, Hochstetler SD, Miskimen K, Main LR, Dorfsman DA, Ogrocki P, Lerner AJ, Ramos J, Vance JM, Cuccaro ML, Scott WK, Pericak-Vance MA, Haines JL. The genetic architecture of Alzheimer disease risk in the Ohio and Indiana Amish. HGG ADVANCES 2022; 3:100114. [PMID: 35599847 PMCID: PMC9114685 DOI: 10.1016/j.xhgg.2022.100114] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 04/22/2022] [Indexed: 11/26/2022] Open
Abstract
Alzheimer disease (AD) is the most common type of dementia and is currently estimated to affect 6.2 million Americans. It ranks as the sixth leading cause of death in the United States, and the proportion of deaths due to AD has been increasing since 2000, while the proportion of many other leading causes of deaths have decreased or remained constant. The risk for AD is multifactorial, including genetic and environmental risk factors. Although APOE ε4 remains the largest genetic risk factor for AD, more than 26 other loci have been associated with AD risk. Here, we recruited Amish adults from Ohio and Indiana to investigate AD risk and protective genetic effects. As a founder population that typically practices endogamy, variants that are rare in the general population may be of a higher frequency in the Amish population. Since the Amish have a slightly lower incidence and later age of onset of disease, they represent an excellent and unique population for research on protective genetic variants. We compared AD risk in the Amish and to a non-Amish population through APOE genotype, a non-APOE genetic risk score of genome-wide significant variants, and a non-APOE polygenic risk score considering all of the variants. Our results highlight the lesser relative impact of APOE and differing genetic architecture of AD risk in the Amish compared to a non-Amish, general European ancestry population.
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Affiliation(s)
- Michael D. Osterman
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
| | - Yeunjoo E. Song
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Larry D. Adams
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Renee A. Laux
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Laura J. Caywood
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Michael B. Prough
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Jason E. Clouse
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Sharlene D. Herington
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Susan H. Slifer
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Audrey Lynn
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - M. Denise Fuzzell
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Sarada L. Fuzzell
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Sherri D. Hochstetler
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kristy Miskimen
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Leighanne R. Main
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Daniel A. Dorfsman
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
- Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Paula Ogrocki
- Department of Neurology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
- Department of Neurology, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Alan J. Lerner
- Department of Neurology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
- Department of Neurology, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Jairo Ramos
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Jeffery M. Vance
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
- Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Michael L. Cuccaro
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
- Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - William K. Scott
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
- Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Margaret A. Pericak-Vance
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
- Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Jonathan L. Haines
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
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Chen X, Yin J, Cao D, Xiao D, Zhou Z, Liu Y, Shou W. The Emerging Roles of the RNA Binding Protein QKI in Cardiovascular Development and Function. Front Cell Dev Biol 2021; 9:668659. [PMID: 34222237 PMCID: PMC8242579 DOI: 10.3389/fcell.2021.668659] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 05/10/2021] [Indexed: 12/30/2022] Open
Abstract
RNA binding proteins (RBPs) have a broad biological and physiological function and are critical in regulating pre-mRNA posttranscriptional processing, intracellular migration, and mRNA stability. QKI, also known as Quaking, is a member of the signal transduction and activation of RNA (STAR) family, which also belongs to the heterogeneous nuclear ribonucleoprotein K- (hnRNP K-) homology domain protein family. There are three major alternatively spliced isoforms, QKI-5, QKI-6, and QKI-7, differing in carboxy-terminal domains. They share a common RNA binding property, but each isoform can regulate pre-mRNA splicing, transportation or stability differently in a unique cell type-specific manner. Previously, QKI has been known for its important role in contributing to neurological disorders. A series of recent work has further demonstrated that QKI has important roles in much broader biological systems, such as cardiovascular development, monocyte to macrophage differentiation, bone metabolism, and cancer progression. In this mini-review, we will focus on discussing the emerging roles of QKI in regulating cardiac and vascular development and function and its potential link to cardiovascular pathophysiology.
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Affiliation(s)
- Xinyun Chen
- Department of Pediatrics, Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, United States
- Guangdong Key Laboratory for Genome Stability and Human Disease Prevention, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Shenzhen University, Shenzhen, China
| | - Jianwen Yin
- Department of Foot, Ankle and Hand Surgery, Shenzhen Second People’s Hospital, First Affiliated Hospital of Shenzhen University, Shenzhen, China
| | - Dayan Cao
- Department of Pediatrics, Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Deyong Xiao
- Department of Pediatrics, Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Zhongjun Zhou
- Faculty of Medicine, School of Biomedical Sciences, The University of Hong Kong, Hong Kong
| | - Ying Liu
- Department of Pediatrics, Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Weinian Shou
- Department of Pediatrics, Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, United States
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5
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The GGLEAM Study: Understanding Glaucoma in the Ohio Amish. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18041551. [PMID: 33561996 PMCID: PMC7915874 DOI: 10.3390/ijerph18041551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 02/02/2021] [Accepted: 02/03/2021] [Indexed: 11/17/2022]
Abstract
Glaucoma leads to millions of cases of visual impairment and blindness around the world. Its susceptibility is shaped by both environmental and genetic risk factors. Although over 120 risk loci have been identified for glaucoma, a large portion of its heritability is still unexplained. Here we describe the foundation of the Genetics of GLaucoma Evaluation in the AMish (GGLEAM) study to investigate the genetic architecture of glaucoma in the Ohio Amish, which exhibits lower genetic and environmental heterogeneity compared to the general population. To date, we have enrolled 81 Amish individuals in our study from Holmes County, Ohio. As a part of our enrollment process, 62 GGLEAM study participants (42 glaucoma-affected and 20 unaffected individuals) received comprehensive eye examinations and glaucoma evaluations. Using the data from the Anabaptist Genealogy Database, we found that 80 of the GGLEAM study participants were related to one another through a large, multigenerational pedigree containing 1586 people. We plan to integrate the health and kinship data obtained for the GGLEAM study to interrogate glaucoma genetics and pathophysiology in this unique population.
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Ramos J, Chowdhury AR, Caywood LJ, Prough M, Denise Fuzzell M, Fuzzell S, Miskimen K, Whitehead PL, Adams LD, Laux R, Song Y, Ogrocki P, Lerner AJ, Vance JM, Haines JL, Scott WK, Pericak-Vance MA, Cuccaro ML. Lower Levels of Education Are Associated with Cognitive Impairment in the Old Order Amish. J Alzheimers Dis 2020; 79:451-458. [PMID: 33285633 DOI: 10.3233/jad-200909] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
BACKGROUND Lower education has been reported to be associated with dementia. However, many studies have been done in settings where 12 years of formal education is the standard. Formal schooling in the Old Order Amish communities (OOA) ends at 8th grade which, along with their genetic homogeneity, makes it an interesting population to study the effect of education on cognitive impairment. OBJECTIVE The objective of this study was to examine the association of education with cognitive function in individuals from the OOA. We hypothesized that small differences in educational attainment at lower levels of formal education were associated with risk for cognitive impairment. METHODS Data of 2,426 individuals from the OOA aged 54-99 were analyzed. The Modified Mini-Mental State Examination (3MS-R) was used to classify participants as CI or normal. Individuals were classified into three education categories: <8, 8, and >8 years of education. To measure the association of education with cognitive status, a logistic regression model was performed adding age and sex as covariates. RESULTS Our results showed that individuals who attained lowest levels of education (<8 and 8) had a higher probability of becoming cognitvely impaired compared with people attending >8 years (OR = 2.96 and 1.85). CONCLUSION Even within a setting of low levels of formal education, small differences in educational attainment can still be associated with the risk of cognitive impairment. Given the homogeneity of the OOA, these results are less likely to be biased by differences in socioeconomic backgrounds.
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Affiliation(s)
- Jairo Ramos
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Aneesa R Chowdhury
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Laura J Caywood
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Michael Prough
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - M Denise Fuzzell
- Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Sarada Fuzzell
- Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kristy Miskimen
- Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Patrice L Whitehead
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Larry D Adams
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Renee Laux
- Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Yeunjoo Song
- Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Paula Ogrocki
- University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Alan J Lerner
- University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Jeffery M Vance
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA.,The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Jonathan L Haines
- Case Western Reserve University School of Medicine, Cleveland, OH, USA.,Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
| | - William K Scott
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA.,The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Margaret A Pericak-Vance
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA.,The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Michael L Cuccaro
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA.,The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
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7
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Arabfard M, Ohadi M, Rezaei Tabar V, Delbari A, Kavousi K. Genome-wide prediction and prioritization of human aging genes by data fusion: a machine learning approach. BMC Genomics 2019; 20:832. [PMID: 31706268 PMCID: PMC6842548 DOI: 10.1186/s12864-019-6140-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 09/25/2019] [Indexed: 12/11/2022] Open
Abstract
Background Machine learning can effectively nominate novel genes for various research purposes in the laboratory. On a genome-wide scale, we implemented multiple databases and algorithms to predict and prioritize the human aging genes (PPHAGE). Results We fused data from 11 databases, and used Naïve Bayes classifier and positive unlabeled learning (PUL) methods, NB, Spy, and Rocchio-SVM, to rank human genes in respect with their implication in aging. The PUL methods enabled us to identify a list of negative (non-aging) genes to use alongside the seed (known age-related) genes in the ranking process. Comparison of the PUL algorithms revealed that none of the methods for identifying a negative sample were advantageous over other methods, and their simultaneous use in a form of fusion was critical for obtaining optimal results (PPHAGE is publicly available at https://cbb.ut.ac.ir/pphage). Conclusion We predict and prioritize over 3,000 candidate age-related genes in human, based on significant ranking scores. The identified candidate genes are associated with pathways, ontologies, and diseases that are linked to aging, such as cancer and diabetes. Our data offer a platform for future experimental research on the genetic and biological aspects of aging. Additionally, we demonstrate that fusion of PUL methods and data sources can be successfully used for aging and disease candidate gene prioritization.
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Affiliation(s)
- Masoud Arabfard
- Department of Bioinformatics, Kish International Campus University of Tehran, Kish, Iran.,Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Mina Ohadi
- Iranian Research Center on Aging, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran.
| | - Vahid Rezaei Tabar
- Department of Statistics, Faculty of Mathematical Sciences and Computer, Allameh Tabataba'i University, Tehran, Iran
| | - Ahmad Delbari
- Iranian Research Center on Aging, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
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8
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Morris BJ, Willcox BJ, Donlon TA. Genetic and epigenetic regulation of human aging and longevity. Biochim Biophys Acta Mol Basis Dis 2019; 1865:1718-1744. [PMID: 31109447 PMCID: PMC7295568 DOI: 10.1016/j.bbadis.2018.08.039] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Revised: 08/02/2018] [Accepted: 08/28/2018] [Indexed: 02/06/2023]
Abstract
Here we summarize the latest data on genetic and epigenetic contributions to human aging and longevity. Whereas environmental and lifestyle factors are important at younger ages, the contribution of genetics appears more important in reaching extreme old age. Genome-wide studies have implicated ~57 gene loci in lifespan. Epigenomic changes during aging profoundly affect cellular function and stress resistance. Dysregulation of transcriptional and chromatin networks is likely a crucial component of aging. Large-scale bioinformatic analyses have revealed involvement of numerous interaction networks. As the young well-differentiated cell replicates into eventual senescence there is drift in the highly regulated chromatin marks towards an entropic middle-ground between repressed and active, such that genes that were previously inactive "leak". There is a breakdown in chromatin connectivity such that topologically associated domains and their insulators weaken, and well-defined blocks of constitutive heterochromatin give way to generalized, senescence-associated heterochromatin, foci. Together, these phenomena contribute to aging.
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Affiliation(s)
- Brian J Morris
- Basic & Clinical Genomics Laboratory, School of Medical Sciences and Bosch Institute, University of Sydney, New South Wales 2006, Australia; Honolulu Heart Program (HHP)/Honolulu-Asia Aging Study (HAAS), Department of Research, Kuakini Medical Center, Honolulu, HI 96817, United States; Department of Geriatric Medicine, John A. Burns School of Medicine, University of Hawaii, Kuakini Medical Center Campus, Honolulu, HI 96813, United States.
| | - Bradley J Willcox
- Honolulu Heart Program (HHP)/Honolulu-Asia Aging Study (HAAS), Department of Research, Kuakini Medical Center, Honolulu, HI 96817, United States; Department of Geriatric Medicine, John A. Burns School of Medicine, University of Hawaii, Kuakini Medical Center Campus, Honolulu, HI 96813, United States.
| | - Timothy A Donlon
- Honolulu Heart Program (HHP)/Honolulu-Asia Aging Study (HAAS), Department of Research, Kuakini Medical Center, Honolulu, HI 96817, United States; Departments of Cell & Molecular Biology and Pathology, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI 96813, United States.
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9
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Hook M, Roy S, Williams EG, Bou Sleiman M, Mozhui K, Nelson JF, Lu L, Auwerx J, Williams RW. Genetic cartography of longevity in humans and mice: Current landscape and horizons. Biochim Biophys Acta Mol Basis Dis 2018; 1864:2718-2732. [PMID: 29410319 PMCID: PMC6066442 DOI: 10.1016/j.bbadis.2018.01.026] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 01/24/2018] [Accepted: 01/28/2018] [Indexed: 12/14/2022]
Abstract
Aging is a complex and highly variable process. Heritability of longevity among humans and other species is low, and this finding has given rise to the idea that it may be futile to search for DNA variants that modulate aging. We argue that the problem in mapping longevity genes is mainly one of low power and the genetic and environmental complexity of aging. In this review we highlight progress made in mapping genes and molecular networks associated with longevity, paying special attention to work in mice and humans. We summarize 40 years of linkage studies using murine cohorts and 15 years of studies in human populations that have exploited candidate gene and genome-wide association methods. A small but growing number of gene variants contribute to known longevity mechanisms, but a much larger set have unknown functions. We outline these and other challenges and suggest some possible solutions, including more intense collaboration between research communities that use model organisms and human cohorts. Once hundreds of gene variants have been linked to differences in longevity in mammals, it will become feasible to systematically explore gene-by-environmental interactions, dissect mechanisms with more assurance, and evaluate the roles of epistasis and epigenetics in aging. A deeper understanding of complex networks-genetic, cellular, physiological, and social-should position us well to improve healthspan.
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Affiliation(s)
- Michael Hook
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Suheeta Roy
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Evan G Williams
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich CH-8093, Switzerland
| | - Maroun Bou Sleiman
- Interfaculty Institute of Bioengineering, Laboratory of Integrative and Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland
| | - Khyobeni Mozhui
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - James F Nelson
- Department of Cellular and Integrative Physiology and Barshop Institute for Longevity and Aging Studies, The University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Lu Lu
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Johan Auwerx
- Interfaculty Institute of Bioengineering, Laboratory of Integrative and Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland
| | - Robert W Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA.
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10
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Johnson SC. Nutrient Sensing, Signaling and Ageing: The Role of IGF-1 and mTOR in Ageing and Age-Related Disease. Subcell Biochem 2018; 90:49-97. [PMID: 30779006 DOI: 10.1007/978-981-13-2835-0_3] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Nutrient signaling through insulin/IGF-1 was the first pathway demonstrated to regulate ageing and age-related disease in model organisms. Pharmacological or dietary interventions targeting nutrient signaling pathways have been shown to robustly attenuate ageing in many organisms. Caloric restriction, the most widely studied longevity promoting intervention, works through multiple nutrient signaling pathways, while inhibition of mTOR through treatment with rapamycin reproducibly delays ageing and disease through specific inhibition of the mTOR complexes. Although the benefits of reduced insulin/IGF-1 in lifespan and health are well documented in model organisms, defining the precise role of the IGF-1 in human ageing and age-related disease has proven more difficult. Association studies provide some insight but also reveal paradoxes. Low serum IGF-1 predicts longevity, but IGF-1 decreases with age and IGF-1 therapy benefits some of age-related pathologies. Circulating IGF-1 has been associated both positively and negatively with risk of age-related diseases in humans, and in some cases both activation and inhibition of IGF-1 signaling have provided benefit in animal models of the same diseases. Interventions designed modulate the nutrient sensing signaling pathways positively or negatively are already available for clinical use, highlighting the need for a clear understanding of the role of nutrient signaling in ageing and age-related disease. This chapter examines data from model organisms and human genetic association studies, with a special emphasis on IGF-1 and mTOR, and discusses potential models for resolving the paradoxes surrounding IGF-1 data.
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Affiliation(s)
- Simon C Johnson
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, WA, USA.
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11
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Kim S, Welsh DA, Myers L, Cherry KE, Wyckoff J, Jazwinski SM. Non-coding genomic regions possessing enhancer and silencer potential are associated with healthy aging and exceptional survival. Oncotarget 2016; 6:3600-12. [PMID: 25682868 PMCID: PMC4414140 DOI: 10.18632/oncotarget.2877] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2014] [Accepted: 12/08/2014] [Indexed: 01/04/2023] Open
Abstract
We have completed a genome-wide linkage scan for healthy aging using data collected from a family study, followed by fine-mapping by association in a separate population, the first such attempt reported. The family cohort consisted of parents of age 90 or above and their children ranging in age from 50 to 80. As a quantitative measure of healthy aging, we used a frailty index, called FI34, based on 34 health and function variables. The linkage scan found a single significant linkage peak on chromosome 12. Using an independent cohort of unrelated nonagenarians, we carried out a fine-scale association mapping of the region suggestive of linkage and identified three sites associated with healthy aging. These healthy-aging sites (HASs) are located in intergenic regions at 12q13-14. HAS-1 has been previously associated with multiple diseases, and an enhancer was recently mapped and experimentally validated within the site. HAS-2 is a previously uncharacterized site possessing genomic features suggestive of enhancer activity. HAS-3 contains features associated with Polycomb repression. The HASs also contain variants associated with exceptional longevity, based on a separate analysis. Our results provide insight into functional genomic networks involving non-coding regulatory elements that are involved in healthy aging and longevity.
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Affiliation(s)
- Sangkyu Kim
- Tulane Center for Aging and Department of Medicine, Tulane University Health Sciences Center, New Orleans, LA 70112, USA
| | - David A Welsh
- Department of Medicine, Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA
| | - Leann Myers
- Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University Health Sciences Center, New Orleans, LA 70112, USA
| | - Katie E Cherry
- Department of Psychology, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Jennifer Wyckoff
- Tulane Center for Aging and Department of Medicine, Tulane University Health Sciences Center, New Orleans, LA 70112, USA
| | - S Michal Jazwinski
- Tulane Center for Aging and Department of Medicine, Tulane University Health Sciences Center, New Orleans, LA 70112, USA
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12
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Abstract
Numerous genetic and non-genetic factors contribute to aging. To facilitate the study of these factors, various descriptors of biological aging, including 'successful aging' and 'frailty', have been put forth as integrative functional measures of aging. A separate but related quantitative approach is the 'frailty index', which has been operationalized and frequently used. Various frailty indices have been constructed. Although based on different numbers and types of health variables, frailty indices possess several common properties that make them useful across different studies. We have been using a frailty index termed FI34 based on 34 health variables. Like other frailty indices, FI34 increases non-linearly with advancing age and is a better indicator of biological aging than chronological age. FI34 has a substantial genetic basis. Using FI34, we found elevated levels of resting metabolic rate linked to declining health in nonagenarians. Using FI34 as a quantitative phenotype, we have also found a genomic region on chromosome 12 that is associated with healthy aging and longevity.
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Affiliation(s)
- Sangkyu Kim
- Tulane Center for Aging and Department of Medicine, Tulane University Health Sciences Center, New Orleans, LA, USA
| | - S. Michal Jazwinski
- Tulane Center for Aging and Department of Medicine, Tulane University Health Sciences Center, New Orleans, LA, USA
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13
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Haider SA, Faisal M. Human aging in the post-GWAS era: further insights reveal potential regulatory variants. Biogerontology 2015; 16:529-41. [PMID: 25895066 DOI: 10.1007/s10522-015-9575-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2015] [Accepted: 04/07/2015] [Indexed: 12/27/2022]
Abstract
Human aging involves a gradual decrease in cellular integrity that contributes to multiple complex disorders such as neurodegenerative disorders, cancer, diabetes, and cardiovascular diseases. Genome-wide association studies (GWAS) play a key role in discovering genetic variations that may contribute towards disease vulnerability. However, mostly disease-associated SNPs lie within non-coding part of the genome; majority of the variants are also present in linkage disequilibrium (LD) with the genome-wide significant SNPs (GWAS lead SNPs). Overall 600 SNPs were analyzed, out of which 291 returned RegulomeDB scores of 1-6. It was observed that just 4 out of those 291 SNPs show strong evidence of regulatory effects (RegulomeDB score <3), while none of them includes any GWAS lead SNP. Nevertheless, this study demonstrates that by combining ENCODE project data along with GWAS reported information will provide important insights on the impact of a genetic variant-moving from GWAS towards understanding disease pathways. It is noteworthy that both genome-wide significant SNPs as well as the SNPs in LD must be considered for future studies; this may prove to be crucial in deciphering the potential regulatory elements involved in complex disorders and aging in particular.
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Affiliation(s)
- Syed Aleem Haider
- National Center for Bioinformatics, Quaid-i-Azam University, Islamabad, 45320, Pakistan
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14
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Guerreiro R, Brás J, Hardy J, Singleton A. Next generation sequencing techniques in neurological diseases: redefining clinical and molecular associations. Hum Mol Genet 2014; 23:R47-53. [PMID: 24794858 PMCID: PMC4170717 DOI: 10.1093/hmg/ddu203] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The development of next-generation sequencing technologies has allowed for the identification of several new genes and genetic factors in human genetics. Common results from the application of these technologies have revealed unexpected presentations for mutations in known disease genes. In this review, we summarize the major contributions of exome sequencing to the study of neurodegenerative disorders and other neurological conditions and discuss the interface between Mendelian and complex neurological diseases with a particular focus on pleiotropic events.
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Affiliation(s)
- Rita Guerreiro
- Department of Molecular Neuroscience and Reta Lila Weston Laboratories, UCL Institute of Neurology, London WC1N 1PJ, UK Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD 20892, USA
| | - José Brás
- Department of Molecular Neuroscience and Reta Lila Weston Laboratories, UCL Institute of Neurology, London WC1N 1PJ, UK
| | - John Hardy
- Department of Molecular Neuroscience and Reta Lila Weston Laboratories, UCL Institute of Neurology, London WC1N 1PJ, UK
| | - Andrew Singleton
- Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD 20892, USA
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15
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Brooks-Wilson AR. Genetics of healthy aging and longevity. Hum Genet 2013; 132:1323-38. [PMID: 23925498 PMCID: PMC3898394 DOI: 10.1007/s00439-013-1342-z] [Citation(s) in RCA: 185] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2012] [Accepted: 07/15/2013] [Indexed: 12/17/2022]
Abstract
Longevity and healthy aging are among the most complex phenotypes studied to date. The heritability of age at death in adulthood is approximately 25 %. Studies of exceptionally long-lived individuals show that heritability is greatest at the oldest ages. Linkage studies of exceptionally long-lived families now support a longevity locus on chromosome 3; other putative longevity loci differ between studies. Candidate gene studies have identified variants at APOE and FOXO3A associated with longevity; other genes show inconsistent results. Genome-wide association scans (GWAS) of centenarians vs. younger controls reveal only APOE as achieving genome-wide significance (GWS); however, analyses of combinations of SNPs or genes represented among associations that do not reach GWS have identified pathways and signatures that converge upon genes and biological processes related to aging. The impact of these SNPs, which may exert joint effects, may be obscured by gene-environment interactions or inter-ethnic differences. GWAS and whole genome sequencing data both show that the risk alleles defined by GWAS of common complex diseases are, perhaps surprisingly, found in long-lived individuals, who may tolerate them by means of protective genetic factors. Such protective factors may ‘buffer’ the effects of specific risk alleles. Rare alleles are also likely to contribute to healthy aging and longevity. Epigenetics is quickly emerging as a critical aspect of aging and longevity. Centenarians delay age-related methylation changes, and they can pass this methylation preservation ability on to their offspring. Non-genetic factors, particularly lifestyle, clearly affect the development of age-related diseases and affect health and lifespan in the general population. To fully understand the desirable phenotypes of healthy aging and longevity, it will be necessary to examine whole genome data from large numbers of healthy long-lived individuals to look simultaneously at both common and rare alleles, with impeccable control for population stratification and consideration of non-genetic factors such as environment.
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Affiliation(s)
- Angela R Brooks-Wilson
- Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, 675 West 10th Avenue, Vancouver, BC, V5Z 1L3, Canada,
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16
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Kim JI. Social factors associated with centenarian rate (CR) in 32 OECD countries. BMC INTERNATIONAL HEALTH AND HUMAN RIGHTS 2013; 13:16. [PMID: 23497053 PMCID: PMC3599594 DOI: 10.1186/1472-698x-13-16] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2012] [Accepted: 02/26/2013] [Indexed: 12/29/2022]
Abstract
BACKGROUND Over the last fifty years, the number of centenarians has dramatically increased. The centenarian rate (CR) is representative of the general longevity prevalent in a nation; it indicates the number of individuals aged 100 years or above at a given date divided by the size of the corresponding cohort of a given age. Two important attributes of the CR (50-54) are that it reflects both unchanged age-specific fertility and the absence of migration in populations. It can generally be used in longevity-based evaluations of the broader concept of successful ageing. As such, this retrospective analysis of the social factors that contribute to the CR (50-54) may help to identify the factors associated with successful ageing.This study estimates the CR (50-54) and elucidates the influence of social factors on successful ageing and the CR (50-54), examining 32 member countries of the Organization for Economic Co-operation and Development (OECD). METHODS The social indicators for this study were obtained from the United Nations database. The data for the analysis of centenarians in the 32 OECD countries were obtained from the world population prospects conducted by the United Nations. Associations between social factors and CR (50-54) were assessed using Pearson correlation coefficients and regression models. RESULTS Significant positive correlations were found between the CR (50-54) and the social factors of expenditure on health as a percentage of gross domestic product (HEGDP: r = 0.411, p < 0.021), general government expenditure on health as a percentage of total government expenditure (GGEH: r = 0.474, p < 0.006), the proportion of fixed-telephone subscriptions in the population (FTS: r = 0.489, p < 0.005), and the human development index (HDI: r = 0.486, p < 0.005). Finally, these CR (50-54) predictors were used to form a model of successful ageing, with higher HEGDP and GGEH as health expenditure, higher FTS as standard of living, and higher HDI as social well-being (R2 = 0.573, P < 0.025). CONCLUSIONS The findings suggest that an increased CR (50-54) is affected by multiple social factors involved in successful ageing. Therefore, if they wish to improve their country's CR (50-54), governments must strengthen their existing support services for the elderly through making improvements to standards of living, social well-being and through increased financing of the health sector.
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Affiliation(s)
- Jong In Kim
- Division of Health and Welfare, Wonkwang University, Iksan, Republic of Korea.
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