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The absolute risk of recurrent myocardial infarction is similar amongst both sexes: MONICA Iceland Study 1981-1999.

https://arctichealth.org/en/permalink/ahliterature51997
Source
Eur J Cardiovasc Prev Rehabil. 2004 Apr;11(2):121-4
Publication Type
Article
Date
Apr-2004
Author
Gunnar Sigurdsson
Nikulas Sigfusson
Inga Ingibjörg Gudmundsdottir
Uggi Agnarsson
Helgi Sigvaldason
Vilmundur Gudnason
Author Affiliation
Icelandic Heart Association, and Department of Internal Medicine, Landspitali University Hospital, Reykjavik, Iceland. gunnars@lanspital.is
Source
Eur J Cardiovasc Prev Rehabil. 2004 Apr;11(2):121-4
Date
Apr-2004
Language
English
Publication Type
Article
Keywords
Adult
Aged
Comparative Study
Female
Humans
Iceland - epidemiology
Incidence
Male
Middle Aged
Myocardial Infarction - epidemiology - etiology
Recurrence
Risk assessment
Sex Factors
Time Factors
Abstract
BACKGROUND: To do a gender comparison of absolute risk of recurrent myocardial infarction (MI). DESIGN: Registration of all first and second MI amongst Icelandic males and females 1981-1999. METHODS: The whole of Icelandic population, 40-74 years of age. RESULTS: The mean recurrence rate (second attack) for men was 45.7/1000 MI survivors/year and for women 39.0/1000 per year. The male/female (M/F) ratio was 1.17, 95% confidence interval 1.00-1.37, P = 0.05 and did not change significantly with age. The M/F ratio for first MI in comparison was two to seven, lowest in the oldest group. The recurrence rate decreased significantly and similarly in both sexes during the observation period. CONCLUSION: The absolute risk of MI is closely similar amongst both sexes and has decreased similarly suggesting that the same kind of secondary intervention is effective amongst both sexes in a general population.
PubMed ID
15187815 View in PubMed
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Adipose tissue density, a novel biomarker predicting mortality risk in older adults.

https://arctichealth.org/en/permalink/ahliterature113601
Source
J Gerontol A Biol Sci Med Sci. 2014 Jan;69(1):109-17
Publication Type
Article
Date
Jan-2014
Author
Rachel A Murphy
Thomas C Register
Carol A Shively
J Jeffrey Carr
Yaorong Ge
Marta E Heilbrun
Steven R Cummings
Annemarie Koster
Michael C Nevitt
Suzanne Satterfield
Frances A Tylvasky
Elsa S Strotmeyer
Anne B Newman
Eleanor M Simonsick
Ann Scherzinger
Bret H Goodpaster
Lenore J Launer
Gudny Eiriksdottir
Sigurdur Sigurdsson
Gunnar Sigurdsson
Vilmundur Gudnason
Thomas F Lang
Stephen B Kritchevsky
Tamara B Harris
Author Affiliation
Laboratory of Population Science, National Institute on Aging, 7201 Wisconsin Ave, 3C-309 Bethesda, MD 20814. rachel.murphy@nih.gov.
Source
J Gerontol A Biol Sci Med Sci. 2014 Jan;69(1):109-17
Date
Jan-2014
Language
English
Publication Type
Article
Keywords
Absorptiometry, Photon
Adiponectin - metabolism
Adipose Tissue - metabolism - radiography
Aged
Aged, 80 and over
Aging - physiology
Animals
Biological Markers - metabolism
Body mass index
Female
Follow-Up Studies
Humans
Leptin - metabolism
Macaca fascicularis
Male
Obesity - metabolism - mortality - radiography
Prognosis
Prospective Studies
Risk factors
Survival Rate - trends
Abstract
Knowledge of adipose composition in relation to mortality may help delineate inconsistent relationships between obesity and mortality in old age. We evaluated relationships between abdominal visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) density, mortality, biomarkers, and characteristics.
VAT and SAT density were determined from computed tomography scans in persons aged 65 and older, Health ABC (n = 2,735) and AGES-Reykjavik (n = 5,131), and 24 nonhuman primates (NHPs). Associations between adipose density and mortality (4-13 years follow-up) were assessed with Cox proportional hazards models. In NHPs, adipose density was related to serum markers and tissue characteristics.
Higher density adipose tissue was associated with mortality in both studies with adjustment for risk factors including adipose area, total fat, and body mass index. In women, hazard ratio and 95% CI for the densest quintile (Q5) versus least dense (Q1) for VAT density were 1.95 (1.36-2.80; Health ABC) and 1.88 (1.31-2.69; AGES-Reykjavik) and for SAT density, 1.76 (1.35-2.28; Health ABC) and 1.56 (1.15-2.11; AGES-Reykjavik). In men, VAT density was associated with mortality in Health ABC, 1.52 (1.12-2.08), whereas SAT density was associated with mortality in both Health ABC, 1.58 (1.21-2.07), and AGES-Reykjavik, 1.43 (1.07-1.91). Higher density adipose tissue was associated with smaller adipocytes in NHPs. There were no consistent associations with inflammation in any group. Higher density adipose tissue was associated with lower serum leptin in Health ABC and NHPs, lower leptin mRNA expression in NHPs, and higher serum adiponectin in Health ABC and NHPs.
VAT and SAT density provide a unique marker of mortality risk that does not appear to be inflammation related.
PubMed ID
23707956 View in PubMed
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Adipose Tissue, Muscle, and Function: Potential Mediators of Associations Between Body Weight and Mortality in Older Adults With Type 2 Diabetes.

https://arctichealth.org/en/permalink/ahliterature257280
Source
Diabetes Care. 2014 Oct 14;
Publication Type
Article
Date
Oct-14-2014
Author
Rachel A Murphy
Ilse Reinders
Melissa E Garcia
Gudny Eiriksdottir
Lenore J Launer
Rafn Benediktsson
Vilmundur Gudnason
Palmi V Jonsson
Tamara B Harris
Author Affiliation
Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Bethesda, MD rachel.murphy@nih.gov.
Source
Diabetes Care. 2014 Oct 14;
Date
Oct-14-2014
Language
English
Publication Type
Article
Abstract
Studies in type 2 diabetes report both increased mortality for normal weight and no evidence of an obesity paradox. We aimed to examine whether adipose tissue, muscle size, and physical function, which are known to vary by weight, mediate associations between BMI and mortality.
The AGES-Reykjavik cohort comprised participants aged 66-96 years with diabetes defined by fasting glucose, medications, or self-report. BMI was determined from measured height and weight and classified as normal (18.5-24.9 kg/m(2), n = 117), overweight (25.0-29.9 kg/m(2), n = 293, referent group) or obese (=30.0 kg/m(2), n = 227). Thigh muscle area and intermuscular, visceral, and subcutaneous adipose tissues were assessed with computed tomography. Function was assessed from gait speed and knee extensor strength. Hazard ratios (HRs) and 95% CIs were estimated by Cox proportional hazards regression adjusted for demographics and diabetes-related risk factors.
The median follow-up was 6.66 years, and there were 85, 59, and 44 deaths among normal weight, overweight, and obese participants, respectively. There was no mortality risk for obese participants and an increased risk among normal weight compared with overweight participants (HR 1.72 [95% CI 1.12-2.64]). Associations remained with adjustment for adipose tissues and knee extensor strength; however, mortality risk for normal weight was attenuated following adjustment for thigh muscle (HR 1.36 [95% CI 0.87-2.11]) and gait speed (HR 1.44 [95% CI 0.91-2.27]). Linear regression confirmed with bootstrapping indicated that thigh muscle size mediated 46% of the relationship between normal weight and mortality.
Normal weight participants had elevated mortality risk compared with overweight participants. This paradoxical association was mediated in part by muscle size.
PubMed ID
25315206 View in PubMed
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Advanced quantitative methods in correlating sarcopenic muscle degeneration with lower extremity function biometrics and comorbidities.

https://arctichealth.org/en/permalink/ahliterature290186
Source
PLoS One. 2018; 13(3):e0193241
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Date
2018
Author
Kyle Edmunds
Magnús Gíslason
Sigurður Sigurðsson
Vilmundur Guðnason
Tamara Harris
Ugo Carraro
Paolo Gargiulo
Author Affiliation
Institute for Biomedical and Neural Engineering, Reykjavík University, Reykjavík, Iceland.
Source
PLoS One. 2018; 13(3):e0193241
Date
2018
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Abstract
Sarcopenic muscular degeneration has been consistently identified as an independent risk factor for mortality in aging populations. Recent investigations have realized the quantitative potential of computed tomography (CT) image analysis to describe skeletal muscle volume and composition; however, the optimum approach to assessing these data remains debated. Current literature reports average Hounsfield unit (HU) values and/or segmented soft tissue cross-sectional areas to investigate muscle quality. However, standardized methods for CT analyses and their utility as a comorbidity index remain undefined, and no existing studies compare these methods to the assessment of entire radiodensitometric distributions. The primary aim of this study was to present a comparison of nonlinear trimodal regression analysis (NTRA) parameters of entire radiodensitometric muscle distributions against extant CT metrics and their correlation with lower extremity function (LEF) biometrics (normal/fast gait speed, timed up-and-go, and isometric leg strength) and biochemical and nutritional parameters, such as total solubilized cholesterol (SCHOL) and body mass index (BMI). Data were obtained from 3,162 subjects, aged 66-96 years, from the population-based AGES-Reykjavik Study. 1-D k-means clustering was employed to discretize each biometric and comorbidity dataset into twelve subpopulations, in accordance with Sturges' Formula for Class Selection. Dataset linear regressions were performed against eleven NTRA distribution parameters and standard CT analyses (fat/muscle cross-sectional area and average HU value). Parameters from NTRA and CT standards were analogously assembled by age and sex. Analysis of specific NTRA parameters with standard CT results showed linear correlation coefficients greater than 0.85, but multiple regression analysis of correlative NTRA parameters yielded a correlation coefficient of 0.99 (P
Notes
Cites: J Gerontol A Biol Sci Med Sci. 1999 Apr;54(4):M172-6 PMID 10219007
Cites: J Gerontol A Biol Sci Med Sci. 2006 Oct;61(10):1059-64 PMID 17077199
Cites: Curr Opin Clin Nutr Metab Care. 2004 Jul;7(4):405-10 PMID 15192443
Cites: J Am Geriatr Soc. 2002 May;50(5):889-96 PMID 12028177
Cites: Am J Epidemiol. 2007 May 1;165(9):1076-87 PMID 17351290
Cites: J Am Geriatr Soc. 2004 Jan;52(1):80-5 PMID 14687319
Cites: Calcif Tissue Int. 2012 May;90(5):354-64 PMID 22451219
Cites: JAMA Neurol. 2015 Jun;72(6):682-8 PMID 25867544
Cites: Lancet Diabetes Endocrinol. 2014 Oct;2(10):819-29 PMID 24731660
Cites: Metabolism. 2000 Apr;49(4):467-72 PMID 10778870
Cites: Acta Physiol Scand. 1978 May;103(1):31-9 PMID 208350
Cites: Artif Organs. 2005 Jun;29(6):440-3 PMID 15926978
Cites: Eur J Transl Myol. 2015 Mar 11;25(2):4847 PMID 26913149
Cites: Osteoporos Int. 2010 Apr;21(4):543-59 PMID 19779761
Cites: Dentomaxillofac Radiol. 2010 Sep;39(6):323-35 PMID 20729181
Cites: Eur J Transl Myol. 2015 Mar 17;25(2):5133 PMID 26913154
Cites: Eur J Transl Myol. 2016 Jun 22;26(2):6015 PMID 27478562
Cites: J Am Geriatr Soc. 1991 Jun;39(6):555-61 PMID 1805811
Cites: Bone. 2008 Apr;42(4):798-805 PMID 18234578
Cites: J Gerontol. 1992 Nov;47(6):M204-10 PMID 1430855
Cites: J Am Med Inform Assoc. 2013 May 1;20(3):544-53 PMID 23059731
Cites: J Appl Physiol (1985). 2001 Jun;90(6):2157-65 PMID 11356778
Cites: Age Ageing. 2013 Mar;42(2):145-50 PMID 23315797
Cites: J Physiol. 1983 May;338:37-49 PMID 6875963
Cites: J Gerontol A Biol Sci Med Sci. 2006 Jan;61(1):72-7 PMID 16456196
Cites: J Am Geriatr Soc. 2013 Feb;61(2):237-42 PMID 23320618
Cites: Comput Math Methods Med. 2016;2016:8932950 PMID 28115982
Cites: Exerc Sport Sci Rev. 2001 Oct;29(4):141-8 PMID 11688785
Cites: J Gerontol. 1994 Mar;49(2):M85-94 PMID 8126356
Cites: Med J Aust. 2016 Oct 3;205(7):329-33 PMID 27681976
Cites: J Gerontol A Biol Sci Med Sci. 2000 Mar;55(3):M168-73 PMID 10795731
Cites: Pflugers Arch. 1997 Jul;434(3):246-53 PMID 9178622
Cites: Age (Dordr). 2013 Aug;35(4):1401-9 PMID 22588637
Cites: Neurology. 2008 Sep 2;71(10):758-65 PMID 18765652
Cites: Neurol Res. 2010 Feb;32(1):13-9 PMID 20092691
Cites: Am J Epidemiol. 1998 Apr 15;147(8):755-63 PMID 9554417
Cites: J Biomed Inform. 2002 Oct-Dec;35(5-6):352-9 PMID 12968784
Cites: J Appl Physiol (1985). 2000 Jul;89(1):104-10 PMID 10904041
Cites: J Am Geriatr Soc. 1991 Feb;39(2):142-8 PMID 1991946
Cites: Magn Reson Imaging. 2012 Nov;30(9):1234-48 PMID 22898692
Cites: Med Sci Sports Exerc. 1994 Apr;26(4):432-9 PMID 8201898
Cites: Am J Physiol. 1997 Feb;272(2 Pt 1):C638-49 PMID 9124308
Cites: Comput Math Methods Med. 2015 ;2015 :162481 PMID 26417376
Cites: J Am Geriatr Soc. 2003 Nov;51(11):1602-9 PMID 14687390
Cites: R J. 2011 Dec;3(2):29-33 PMID 27942416
Cites: J Am Med Inform Assoc. 2007 Jan-Feb;14(1):1-9 PMID 17077452
Cites: N Engl J Med. 1995 Mar 2;332(9):556-61 PMID 7838189
Cites: Int J Med Inform. 2008 Feb;77(2):81-97 PMID 17188928
Cites: J Bone Miner Res. 2010 Mar;25(3):513-9 PMID 20422623
Cites: Eur Heart J. 1993 May;14(5):584-91 PMID 8508850
Cites: Neuromuscul Disord. 2002 Oct;12(7-8):631-8 PMID 12207930
Cites: Ann Surg Oncol. 2015 Nov;22(12 ):3897-904 PMID 26242367
Cites: Eur J Transl Myol. 2015 Mar 11;25(2):4832 PMID 26913148
Cites: BMJ. 2010 Sep 09;341:c4467 PMID 20829298
Cites: Muscle Nerve. 1995 Jul;18(7):708-14 PMID 7783760
Cites: J Am Geriatr Soc. 2005 Oct;53(10):1675-80 PMID 16181165
Cites: J Appl Physiol (1985). 2000 Sep;89(3):1072-8 PMID 10956353
Cites: J Am Geriatr Soc. 2003 Mar;51(3):314-22 PMID 12588574
Cites: Bone. 2006 Sep;39(3):644-51 PMID 16790372
Cites: J Gerontol A Biol Sci Med Sci. 2002 Oct;57(10):B359-65 PMID 12242311
Cites: Am J Epidemiol. 2016 Jan 1;183(1):53-60 PMID 26643983
Cites: Exp Gerontol. 2012 Jan;47(1):38-44 PMID 22015325
Cites: Diabetologia. 2005 Feb;48(2):301-8 PMID 15660262
Cites: Ecol Lett. 2008 Jan;11(1):1-15 PMID 18047588
Cites: J Gerontol A Biol Sci Med Sci. 2005 Jul;60(7):882-7 PMID 16079212
Cites: Eur J Clin Invest. 1984 Aug;14(4):282-7 PMID 6434323
Cites: Am J Clin Nutr. 2000 Apr;71(4):885-92 PMID 10731493
PubMed ID
29513690 View in PubMed
Less detail

Advanced quantitative methods in correlating sarcopenic muscle degeneration with lower extremity function biometrics and comorbidities.

https://arctichealth.org/en/permalink/ahliterature292572
Source
PLoS One. 2018; 13(3):e0193241
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Date
2018
Author
Kyle Edmunds
Magnús Gíslason
Sigurður Sigurðsson
Vilmundur Guðnason
Tamara Harris
Ugo Carraro
Paolo Gargiulo
Author Affiliation
Institute for Biomedical and Neural Engineering, Reykjavík University, Reykjavík, Iceland.
Source
PLoS One. 2018; 13(3):e0193241
Date
2018
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Keywords
Age Factors
Aged
Aged, 80 and over
Body mass index
Cluster analysis
Comorbidity
Disability Evaluation
Female
Follow-Up Studies
Humans
Lower Extremity - diagnostic imaging - physiopathology
Male
Muscle, Skeletal - diagnostic imaging - physiopathology
Nonlinear Dynamics
Prospective Studies
Regression Analysis
Sarcopenia - complications - diagnostic imaging - epidemiology - physiopathology
Sex Factors
Tomography, X-Ray Computed
Abstract
Sarcopenic muscular degeneration has been consistently identified as an independent risk factor for mortality in aging populations. Recent investigations have realized the quantitative potential of computed tomography (CT) image analysis to describe skeletal muscle volume and composition; however, the optimum approach to assessing these data remains debated. Current literature reports average Hounsfield unit (HU) values and/or segmented soft tissue cross-sectional areas to investigate muscle quality. However, standardized methods for CT analyses and their utility as a comorbidity index remain undefined, and no existing studies compare these methods to the assessment of entire radiodensitometric distributions. The primary aim of this study was to present a comparison of nonlinear trimodal regression analysis (NTRA) parameters of entire radiodensitometric muscle distributions against extant CT metrics and their correlation with lower extremity function (LEF) biometrics (normal/fast gait speed, timed up-and-go, and isometric leg strength) and biochemical and nutritional parameters, such as total solubilized cholesterol (SCHOL) and body mass index (BMI). Data were obtained from 3,162 subjects, aged 66-96 years, from the population-based AGES-Reykjavik Study. 1-D k-means clustering was employed to discretize each biometric and comorbidity dataset into twelve subpopulations, in accordance with Sturges' Formula for Class Selection. Dataset linear regressions were performed against eleven NTRA distribution parameters and standard CT analyses (fat/muscle cross-sectional area and average HU value). Parameters from NTRA and CT standards were analogously assembled by age and sex. Analysis of specific NTRA parameters with standard CT results showed linear correlation coefficients greater than 0.85, but multiple regression analysis of correlative NTRA parameters yielded a correlation coefficient of 0.99 (P
Notes
Cites: J Gerontol A Biol Sci Med Sci. 1999 Apr;54(4):M172-6 PMID 10219007
Cites: J Gerontol A Biol Sci Med Sci. 2006 Oct;61(10):1059-64 PMID 17077199
Cites: Curr Opin Clin Nutr Metab Care. 2004 Jul;7(4):405-10 PMID 15192443
Cites: J Am Geriatr Soc. 2002 May;50(5):889-96 PMID 12028177
Cites: Am J Epidemiol. 2007 May 1;165(9):1076-87 PMID 17351290
Cites: J Am Geriatr Soc. 2004 Jan;52(1):80-5 PMID 14687319
Cites: Calcif Tissue Int. 2012 May;90(5):354-64 PMID 22451219
Cites: JAMA Neurol. 2015 Jun;72(6):682-8 PMID 25867544
Cites: Lancet Diabetes Endocrinol. 2014 Oct;2(10):819-29 PMID 24731660
Cites: Metabolism. 2000 Apr;49(4):467-72 PMID 10778870
Cites: Acta Physiol Scand. 1978 May;103(1):31-9 PMID 208350
Cites: Artif Organs. 2005 Jun;29(6):440-3 PMID 15926978
Cites: Eur J Transl Myol. 2015 Mar 11;25(2):4847 PMID 26913149
Cites: Osteoporos Int. 2010 Apr;21(4):543-59 PMID 19779761
Cites: Dentomaxillofac Radiol. 2010 Sep;39(6):323-35 PMID 20729181
Cites: Eur J Transl Myol. 2015 Mar 17;25(2):5133 PMID 26913154
Cites: Eur J Transl Myol. 2016 Jun 22;26(2):6015 PMID 27478562
Cites: J Am Geriatr Soc. 1991 Jun;39(6):555-61 PMID 1805811
Cites: Bone. 2008 Apr;42(4):798-805 PMID 18234578
Cites: J Gerontol. 1992 Nov;47(6):M204-10 PMID 1430855
Cites: J Am Med Inform Assoc. 2013 May 1;20(3):544-53 PMID 23059731
Cites: J Appl Physiol (1985). 2001 Jun;90(6):2157-65 PMID 11356778
Cites: Age Ageing. 2013 Mar;42(2):145-50 PMID 23315797
Cites: J Physiol. 1983 May;338:37-49 PMID 6875963
Cites: J Gerontol A Biol Sci Med Sci. 2006 Jan;61(1):72-7 PMID 16456196
Cites: J Am Geriatr Soc. 2013 Feb;61(2):237-42 PMID 23320618
Cites: Comput Math Methods Med. 2016;2016:8932950 PMID 28115982
Cites: Exerc Sport Sci Rev. 2001 Oct;29(4):141-8 PMID 11688785
Cites: J Gerontol. 1994 Mar;49(2):M85-94 PMID 8126356
Cites: Med J Aust. 2016 Oct 3;205(7):329-33 PMID 27681976
Cites: J Gerontol A Biol Sci Med Sci. 2000 Mar;55(3):M168-73 PMID 10795731
Cites: Pflugers Arch. 1997 Jul;434(3):246-53 PMID 9178622
Cites: Age (Dordr). 2013 Aug;35(4):1401-9 PMID 22588637
Cites: Neurology. 2008 Sep 2;71(10):758-65 PMID 18765652
Cites: Neurol Res. 2010 Feb;32(1):13-9 PMID 20092691
Cites: Am J Epidemiol. 1998 Apr 15;147(8):755-63 PMID 9554417
Cites: J Biomed Inform. 2002 Oct-Dec;35(5-6):352-9 PMID 12968784
Cites: J Appl Physiol (1985). 2000 Jul;89(1):104-10 PMID 10904041
Cites: J Am Geriatr Soc. 1991 Feb;39(2):142-8 PMID 1991946
Cites: Magn Reson Imaging. 2012 Nov;30(9):1234-48 PMID 22898692
Cites: Med Sci Sports Exerc. 1994 Apr;26(4):432-9 PMID 8201898
Cites: Am J Physiol. 1997 Feb;272(2 Pt 1):C638-49 PMID 9124308
Cites: Comput Math Methods Med. 2015 ;2015 :162481 PMID 26417376
Cites: J Am Geriatr Soc. 2003 Nov;51(11):1602-9 PMID 14687390
Cites: R J. 2011 Dec;3(2):29-33 PMID 27942416
Cites: J Am Med Inform Assoc. 2007 Jan-Feb;14(1):1-9 PMID 17077452
Cites: N Engl J Med. 1995 Mar 2;332(9):556-61 PMID 7838189
Cites: Int J Med Inform. 2008 Feb;77(2):81-97 PMID 17188928
Cites: J Bone Miner Res. 2010 Mar;25(3):513-9 PMID 20422623
Cites: Eur Heart J. 1993 May;14(5):584-91 PMID 8508850
Cites: Neuromuscul Disord. 2002 Oct;12(7-8):631-8 PMID 12207930
Cites: Ann Surg Oncol. 2015 Nov;22(12 ):3897-904 PMID 26242367
Cites: Eur J Transl Myol. 2015 Mar 11;25(2):4832 PMID 26913148
Cites: BMJ. 2010 Sep 09;341:c4467 PMID 20829298
Cites: Muscle Nerve. 1995 Jul;18(7):708-14 PMID 7783760
Cites: J Am Geriatr Soc. 2005 Oct;53(10):1675-80 PMID 16181165
Cites: J Appl Physiol (1985). 2000 Sep;89(3):1072-8 PMID 10956353
Cites: J Am Geriatr Soc. 2003 Mar;51(3):314-22 PMID 12588574
Cites: Bone. 2006 Sep;39(3):644-51 PMID 16790372
Cites: J Gerontol A Biol Sci Med Sci. 2002 Oct;57(10):B359-65 PMID 12242311
Cites: Am J Epidemiol. 2016 Jan 1;183(1):53-60 PMID 26643983
Cites: Exp Gerontol. 2012 Jan;47(1):38-44 PMID 22015325
Cites: Diabetologia. 2005 Feb;48(2):301-8 PMID 15660262
Cites: Ecol Lett. 2008 Jan;11(1):1-15 PMID 18047588
Cites: J Gerontol A Biol Sci Med Sci. 2005 Jul;60(7):882-7 PMID 16079212
Cites: Eur J Clin Invest. 1984 Aug;14(4):282-7 PMID 6434323
Cites: Am J Clin Nutr. 2000 Apr;71(4):885-92 PMID 10731493
PubMed ID
29513690 View in PubMed
Less detail

Age-related macular degeneration and mortality in community-dwelling elders: the age, gene/environment susceptibility Reykjavik study.

https://arctichealth.org/en/permalink/ahliterature261803
Source
Ophthalmology. 2015 Feb;122(2):382-90
Publication Type
Article
Date
Feb-2015
Author
Diana E Fisher
Fridbert Jonasson
Gudny Eiriksdottir
Sigurdur Sigurdsson
Ronald Klein
Lenore J Launer
Vilmundur Gudnason
Mary Frances Cotch
Source
Ophthalmology. 2015 Feb;122(2):382-90
Date
Feb-2015
Language
English
Publication Type
Article
Keywords
Age Factors
Aged
Aged, 80 and over
Cause of Death
Cohort Studies
Disease Susceptibility
Female
Follow-Up Studies
Gene-Environment Interaction
Humans
Iceland - epidemiology
Incidence
Macular Degeneration - mortality
Male
Proportional Hazards Models
Prospective Studies
Risk factors
Abstract
To investigate the association between age-related macular degeneration (AMD) and mortality in older persons.
Population-based prospective cohort study.
Participants 67 to 96 years of age (43.1% male) enrolled between 2002 and 2006 in the Age, Gene/Environment Susceptibility-Reykjavik Study.
Retinal photographs of the macula were acquired digitally and evaluated for the presence of AMD lesions using the Wisconsin Age-Related Maculopathy grading scheme. Mortality was assessed prospectively through 2013 with cause of death available through 2009. The association between AMD and death, resulting from any cause and specifically cardiovascular disease (CVD), was examined using Cox proportional hazards regression with age as the time scale, adjusted for significant risk factors and comorbid conditions. To address a violation in the proportional hazards assumption, analyses were stratified into 2 groups based on the mean age at death (83 years).
Mortality resulting from all causes and CVD.
Among 4910 participants, after a median follow-up of 8.6 years, 1742 died (35.5%), of whom 614 (35.2%) had signs of AMD at baseline. Cardiovascular disease was the cause of death for 357 people who died before the end of 2009, of whom 144 (40%) had AMD (101 with early disease and 43 with late disease). After considering covariates, including comorbid conditions, having early AMD at any age or having late AMD in individuals younger than 83 years (n = 4179) were not associated with all-cause or CVD mortality. In individuals 83 years of age and older (n = 731), late AMD was associated significantly with increased risk of all-cause mortality (hazard ratio [HR], 1.76; 95% confidence interval [CI], 1.20-2.57) and CVD-related mortality (HR, 2.37; 95% CI, 1.41-3.98). In addition to having AMD, older individuals who died were more likely to be male and to have low body mass index, impaired cognition, and microalbuminuria.
Competing risk factors and concomitant conditions are important in determining mortality risk resulting from AMD. Individuals with early AMD are not more likely to die than peers of comparable age. Late AMD becomes a predictor of mortality by the mid-octogenarian years.
Notes
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PubMed ID
25264026 View in PubMed
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The AGES-Reykjavik study atlases: Non-linear multi-spectral template and atlases for studies of the ageing brain.

https://arctichealth.org/en/permalink/ahliterature292006
Source
Med Image Anal. 2017 Jul; 39:133-144
Publication Type
Journal Article
Date
Jul-2017
Author
Lars Forsberg
Sigurdur Sigurdsson
Jesper Fredriksson
Asdis Egilsdottir
Bryndis Oskarsdottir
Olafur Kjartansson
Mark A van Buchem
Lenore J Launer
Vilmundur Gudnason
Alex Zijdenbos
Author Affiliation
The Icelandic Heart Association, Kopavogur, Iceland; Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden. Electronic address: larsef@me.com.
Source
Med Image Anal. 2017 Jul; 39:133-144
Date
Jul-2017
Language
English
Publication Type
Journal Article
Keywords
Aged
Aging
Algorithms
Anatomy, Artistic
Atlases as Topic
Brain - diagnostic imaging
Female
Humans
Image Processing, Computer-Assisted - methods
Magnetic Resonance Imaging - methods
Male
Abstract
Quantitative analyses of brain structures from Magnetic Resonance (MR) image data are often performed using automatic segmentation algorithms. Many of these algorithms rely on templates and atlases in a common coordinate space. Most freely available brain atlases are generated from relatively young individuals and not always derived from well-defined cohort studies. In this paper, we introduce a publicly available multi-spectral template with corresponding tissue probability atlases and regional atlases, optimised to use in studies of ageing cohorts (mean age 75 ± 5 years). Furthermore, we provide validation data from a regional segmentation pipeline to assure the integrity of the dataset.
Notes
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PubMed ID
28501699 View in PubMed
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The AGES-Reykjavik study atlases: Non-linear multi-spectral template and atlases for studies of the ageing brain.

https://arctichealth.org/en/permalink/ahliterature282555
Source
Med Image Anal. 2017 May 06;39:133-144
Publication Type
Article
Date
May-06-2017
Author
Lars Forsberg
Sigurdur Sigurdsson
Jesper Fredriksson
Asdis Egilsdottir
Bryndis Oskarsdottir
Olafur Kjartansson
Mark A van Buchem
Lenore J Launer
Vilmundur Gudnason
Alex Zijdenbos
Source
Med Image Anal. 2017 May 06;39:133-144
Date
May-06-2017
Language
English
Publication Type
Article
Abstract
Quantitative analyses of brain structures from Magnetic Resonance (MR) image data are often performed using automatic segmentation algorithms. Many of these algorithms rely on templates and atlases in a common coordinate space. Most freely available brain atlases are generated from relatively young individuals and not always derived from well-defined cohort studies. In this paper, we introduce a publicly available multi-spectral template with corresponding tissue probability atlases and regional atlases, optimised to use in studies of ageing cohorts (mean age 75 ± 5 years). Furthermore, we provide validation data from a regional segmentation pipeline to assure the integrity of the dataset.
PubMed ID
28501699 View in PubMed
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The AGES-Reykjavik Study suggests that change in kidney measures is associated with subclinical brain pathology in older community-dwelling persons.

https://arctichealth.org/en/permalink/ahliterature292670
Source
Kidney Int. 2018 Jun 27; :
Publication Type
Journal Article
Date
Jun-27-2018
Author
Sanaz Sedaghat
Jie Ding
Gudny Eiriksdottir
Mark A van Buchem
Sigurdur Sigurdsson
M Arfan Ikram
Osorio Meirelles
Vilmundur Gudnason
Andrew S Levey
Lenore J Launer
Author Affiliation
Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands; Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
Source
Kidney Int. 2018 Jun 27; :
Date
Jun-27-2018
Language
English
Publication Type
Journal Article
Abstract
Decreased glomerular filtration rate (GFR) and albuminuria may be accompanied by brain pathology. Here we investigated whether changes in these kidney measures are linked to development of new MRI-detected infarcts and microbleeds, and progression of white matter hyperintensity volume. The study included 2671 participants from the population-based AGES-Reykjavik Study (mean age 75, 58.7% women). GFR was estimated from serum creatinine, and albuminuria was assessed by urinary albumin-to-creatinine ratio. Brain MRI was acquired at baseline (2002-2006) and 5 years later (2007-2011). New MRI-detected infarcts and microbleeds were counted on the follow-up scans. White matter hyperintensity progression was estimated as percent change in white matter hyperintensity volumes between the two exams. Participants with a large eGFR decline (over 3 ml/min per 1.73m2 per year) had more incident subcortical infarcts (odds ratio 1.53; 95% confidence interval 1.05, 2.22), and more marked progression of white matter hyperintensity volume (difference: 8%; 95% confidence interval: 4%, 12%), compared to participants without a large decline. Participants with incident albuminuria (over 30 mg/g) had 21% more white matter hyperintensity volume progression (95% confidence interval: 14%, 29%) and 1.86 higher odds of developing new deep microbleeds (95% confidence interval 1.16, 2.98), compared to participants without incident albuminuria. The findings were independent of cardiovascular risk factors. Changes in kidney measures were not associated with occurrence of cortical infarcts. Thus, larger changes in eGFR and albuminuria are associated with increased risk for developing manifestations of cerebral small vessel disease. Individuals with larger changes in these kidney measures should be considered as a high risk population for accelerated brain pathology.
PubMed ID
29960746 View in PubMed
Less detail

The AGES-Reykjavik Study suggests that change in kidney measures is associated with subclinical brain pathology in older community-dwelling persons.

https://arctichealth.org/en/permalink/ahliterature300494
Source
Kidney Int. 2018 09; 94(3):608-615
Publication Type
Journal Article
Research Support, N.I.H., Extramural
Research Support, N.I.H., Intramural
Research Support, Non-U.S. Gov't
Date
09-2018
Author
Sanaz Sedaghat
Jie Ding
Gudny Eiriksdottir
Mark A van Buchem
Sigurdur Sigurdsson
M Arfan Ikram
Osorio Meirelles
Vilmundur Gudnason
Andrew S Levey
Lenore J Launer
Author Affiliation
Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands; Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
Source
Kidney Int. 2018 09; 94(3):608-615
Date
09-2018
Language
English
Publication Type
Journal Article
Research Support, N.I.H., Extramural
Research Support, N.I.H., Intramural
Research Support, Non-U.S. Gov't
Keywords
Aged
Albuminuria - physiopathology - urine
Cerebral Small Vessel Diseases - diagnosis - epidemiology
Creatinine - urine
Disease Progression
Female
Follow-Up Studies
Glomerular Filtration Rate - physiology
Humans
Incidence
Independent living
Kidney - physiopathology
Magnetic Resonance Imaging
Male
Prospective Studies
Renal Insufficiency, Chronic - physiopathology - urine
Risk factors
Serum Albumin
White Matter - diagnostic imaging - pathology
Abstract
Decreased glomerular filtration rate (GFR) and albuminuria may be accompanied by brain pathology. Here we investigated whether changes in these kidney measures are linked to development of new MRI-detected infarcts and microbleeds, and progression of white matter hyperintensity volume. The study included 2671 participants from the population-based AGES-Reykjavik Study (mean age 75, 58.7% women). GFR was estimated from serum creatinine, and albuminuria was assessed by urinary albumin-to-creatinine ratio. Brain MRI was acquired at baseline (2002-2006) and 5 years later (2007-2011). New MRI-detected infarcts and microbleeds were counted on the follow-up scans. White matter hyperintensity progression was estimated as percent change in white matter hyperintensity volumes between the two exams. Participants with a large eGFR decline (over 3 ml/min/1.73m2 per year) had more incident subcortical infarcts (odds ratio 1.53; 95% confidence interval 1.05, 2.22), and more marked progression of white matter hyperintensity volume (difference: 8%; 95% confidence interval: 4%, 12%), compared to participants without a large decline. Participants with incident albuminuria (over 30 mg/g) had 21% more white matter hyperintensity volume progression (95% confidence interval: 14%, 29%) and 1.86 higher odds of developing new deep microbleeds (95% confidence interval 1.16, 2.98), compared to participants without incident albuminuria. The findings were independent of cardiovascular risk factors. Changes in kidney measures were not associated with occurrence of cortical infarcts. Thus, larger changes in eGFR and albuminuria are associated with increased risk for developing manifestations of cerebral small vessel disease. Individuals with larger changes in these kidney measures should be considered as a high risk population for accelerated brain pathology.
PubMed ID
29960746 View in PubMed
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