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The A1330V polymorphism of the low-density lipoprotein receptor-related protein 5 gene (LRP5) associates with low peak bone mass in young healthy men.

https://arctichealth.org/en/permalink/ahliterature165637
Source
Bone. 2007 Apr;40(4):1006-12
Publication Type
Article
Date
Apr-2007
Author
Anne Saarinen
Ville-Valtteri Välimäki
Matti J Välimäki
Eliisa Löyttyniemi
Kirsi Auro
Piia Uusen
Mairi Kuris
Anna-Elina Lehesjoki
Outi Mäkitie
Author Affiliation
Folkhälsan Institute of Genetics and Department of Medical Genetics, University of Helsinki, Helsinki, Finland.
Source
Bone. 2007 Apr;40(4):1006-12
Date
Apr-2007
Language
English
Publication Type
Article
Keywords
Adolescent
Adult
Alleles
Bone Density - genetics
Calcifediol - blood
Finland
Fractures, Bone - etiology - genetics
Gene Frequency
Humans
LDL-Receptor Related Proteins - genetics
Low Density Lipoprotein Receptor-Related Protein-5
Male
Military Personnel
Osteoporosis - etiology - genetics
Parathyroid Hormone - blood
Polymorphism, Single Nucleotide
Risk factors
Abstract
Polymorphisms in the gene coding for low-density lipoprotein receptor-related protein 5 (LRP5) contribute to variation in bone mass in the general population. Whether this is due to influence on bone mass acquisition or on bone loss thereafter has not been established.
We studied the association of LRP5 polymorphisms with peak bone mass in young men. The study included 235 Finnish men, aged 18.3 to 20.6 years. Lifestyle factors and fracture history were recorded. Bone mineral content (BMC), density (BMD) and scan area were measured for the lumbar spine and proximal femur by dual energy X-ray absorptiometry (DXA). Blood and urine were collected for determination of bone turnover markers, serum 25-OHD and PTH. Genomic DNA was extracted from peripheral blood for genetic analysis of LRP5. Ten single nucleotide polymorphisms in LRP5 were analyzed and correlated with bone parameters.
Only the A1330V polymorphism of LRP5 significantly associated with bone parameters. In comparison with subjects with the AlaAla genotype (n=215), those with AlaVal genotype (n=20) had lower femoral neck BMC (P=0.029) and BMD (P=0.012), trochanter BMC (P=0.0067) and BMD (P=0.015), and total hip BMC (P=0.0044) and BMD (P=0.0089). Fracture history was similar for the genotypes.
The polymorphic valine variant at position 1330 of LRP5 was significantly associated with reduced BMC and BMD values in healthy young Finnish men. The results provide evidence for the crucial role of LRP5 in peak bone mass acquisition.
PubMed ID
17223614 View in PubMed
Less detail

Effects of hormonal contraception on systemic metabolism: cross-sectional and longitudinal evidence.

https://arctichealth.org/en/permalink/ahliterature289536
Source
Int J Epidemiol. 2016 10; 45(5):1445-1457
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Date
10-2016
Author
Qin Wang
Peter Würtz
Kirsi Auro
Laure Morin-Papunen
Antti J Kangas
Pasi Soininen
Mika Tiainen
Tuulia Tynkkynen
Anni Joensuu
Aki S Havulinna
Kristiina Aalto
Marko Salmi
Stefan Blankenberg
Tanja Zeller
Jorma Viikari
Mika Kähönen
Terho Lehtimäki
Veikko Salomaa
Sirpa Jalkanen
Marjo-Riitta Järvelin
Markus Perola
Olli T Raitakari
Debbie A Lawlor
Johannes Kettunen
Mika Ala-Korpela
Author Affiliation
Computational Medicine, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu, Finland.
Source
Int J Epidemiol. 2016 10; 45(5):1445-1457
Date
10-2016
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Keywords
Adult
Cholesterol, HDL - blood
Contraceptives, Oral, Hormonal - pharmacology
Cross-Sectional Studies
Cytokines - blood
Fatty Acids - blood
Female
Finland
Humans
Linear Models
Longitudinal Studies
Metabolome - drug effects
Metabolomics
Progestins - pharmacology
Risk factors
Triglycerides - blood
Young Adult
Abstract
Hormonal contraception is commonly used worldwide, but its systemic effects across lipoprotein subclasses, fatty acids, circulating metabolites and cytokines remain poorly understood.
A comprehensive molecular profile (75 metabolic measures and 37 cytokines) was measured for up to 5841 women (age range 24-49 years) from three population-based cohorts. Women using combined oral contraceptive pills (COCPs) or progestin-only contraceptives (POCs) were compared with those who did not use hormonal contraception. Metabolomics profiles were reassessed for 869 women after 6 years to uncover the metabolic effects of starting, stopping and persistently using hormonal contraception.
The comprehensive molecular profiling allowed multiple new findings on the metabolic associations with the use of COCPs. They were positively associated with lipoprotein subclasses, including all high-density lipoprotein (HDL) subclasses. The associations with fatty acids and amino acids were strong and variable in direction. COCP use was negatively associated with albumin and positively associated with creatinine and inflammatory markers, including glycoprotein acetyls and several growth factors and interleukins. Our findings also confirmed previous results e.g. for increased circulating triglycerides and HDL cholesterol. Starting COCPs caused similar metabolic changes to those observed cross-sectionally: the changes were maintained in consistent users and normalized in those who stopped using. In contrast, POCs were only weakly associated with metabolic and inflammatory markers. Results were consistent across all cohorts and for different COCP preparations and different types of POC delivery.
Use of COCPs causes widespread metabolic and inflammatory effects. However, persistent use does not appear to accumulate the effects over time and the metabolic perturbations are reversed upon discontinuation. POCs have little effect on systemic metabolism and inflammation.
Notes
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Cites: Int J Epidemiol. 2008 Dec;37(6):1220-6 PMID 18263651
Cites: Diabetes Care. 2013 Mar;36(3):648-55 PMID 23129134
Cites: Diabetes Care. 2013 Nov;36(11):3732-8 PMID 24026559
Cites: Best Pract Res Clin Endocrinol Metab. 2013 Feb;27(1):13-24 PMID 23384742
Cites: Contraception. 2004 Nov;70(5):365-70 PMID 15504374
Cites: Circ Cardiovasc Genet. 2015 Feb;8(1):192-206 PMID 25691689
Cites: N Engl J Med. 2012 Jul 5;367(1):20-9 PMID 22762315
Cites: Nat Genet. 2013 Nov;45(11):1345-52 PMID 24097064
Cites: Mol Syst Biol. 2010 Dec 21;6:441 PMID 21179014
Cites: Proc Natl Acad Sci U S A. 2000 Feb 1;97(3):1242-6 PMID 10655515
Cites: PLoS Med. 2014 Dec 09;11(12):e1001765 PMID 25490400
Cites: Science. 2016 Mar 11;351(6278):1166-71 PMID 26965621
Cites: Blood. 2004 Feb 1;103(3):927-33 PMID 14551147
Cites: J Am Coll Cardiol. 2013 Jan 29;61(4):427-36 PMID 23265341
Cites: Endocrine. 2015 Aug;49(3):820-7 PMID 25539793
Cites: Contraception. 2012 May;85(5):446-52 PMID 22078632
Cites: Contraception. 2003 Jun;67(6):423-9 PMID 12814810
Cites: Analyst. 2009 Sep;134(9):1781-5 PMID 19684899
Cites: BMJ. 2015 May 26;350:h2135 PMID 26013557
Cites: Circulation. 2015 Feb 3;131(5):451-8 PMID 25623155
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Cites: BMJ. 2014 Nov 18;349:g6330 PMID 25406188
Cites: Scand J Clin Lab Invest. 2009;69(2):168-74 PMID 18937150
Cites: Eur J Contracept Reprod Health Care. 2010 Dec;15 Suppl 2:S42-53 PMID 21091166
Cites: Nat Genet. 2012 Jan 29;44(3):269-76 PMID 22286219
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Cites: J Clin Endocrinol Metab. 1995 Jun;80(6):1816-21 PMID 7775629
Cites: Lancet. 2012 Aug 11;380(9841):572-80 PMID 22607825
Cites: Contraception. 2001 Jul;64(1):11-6 PMID 11535207
Cites: Hypertension. 2004 Dec;44(6):838-46 PMID 15520301
Cites: PLoS Med. 2014 Feb 25;11(2):e1001606 PMID 24586121
Cites: Eur J Clin Pharmacol. 1996;50(3):179-84 PMID 8737756
Cites: Int J Epidemiol. 2015 Apr;44(2):623-37 PMID 26050255
Cites: J Thromb Haemost. 2006 Jan;4(1):77-82 PMID 16409455
Cites: Am J Transl Res. 2014 Oct 11;6(5):614-24 PMID 25360225
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Cites: Epidemiol Rev. 2014;36:57-70 PMID 24025350
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Cites: Natl Health Stat Report. 2013 Feb 14;(62):1-15 PMID 24988816
Cites: Nat Commun. 2014 Aug 21;5:4708 PMID 25144627
Cites: N Engl J Med. 2008 Oct 30;359(18):1897-908 PMID 18971492
Cites: Best Pract Res Clin Endocrinol Metab. 2013 Feb;27(1):25-34 PMID 23384743
Cites: Ann Intern Med. 2014 Mar 18;160(6):398-406 PMID 24723079
Cites: Contraception. 2004 Feb;69(2):105-13 PMID 14759614
Cites: JAMA. 2009 Jul 1;302(1):37-48 PMID 19567438
Cites: Best Pract Res Clin Endocrinol Metab. 2013 Feb;27(1):35-45 PMID 23384744
Cites: N Engl J Med. 2012 Jun 14;366(24):2257-66 PMID 22693997
Cites: Lancet. 2013 Jul 27;382(9889):339-52 PMID 23727170
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Cites: Lancet. 2014 Aug 16;384(9943):626-35 PMID 25131982
Cites: Contraception. 2005 Feb;71(2):118-21 PMID 15707561
PubMed ID
27538888 View in PubMed
Less detail

Effects of hormonal contraception on systemic metabolism: cross-sectional and longitudinal evidence.

https://arctichealth.org/en/permalink/ahliterature289694
Source
Int J Epidemiol. 2016 10; 45(5):1445-1457
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Date
10-2016
Author
Qin Wang
Peter Würtz
Kirsi Auro
Laure Morin-Papunen
Antti J Kangas
Pasi Soininen
Mika Tiainen
Tuulia Tynkkynen
Anni Joensuu
Aki S Havulinna
Kristiina Aalto
Marko Salmi
Stefan Blankenberg
Tanja Zeller
Jorma Viikari
Mika Kähönen
Terho Lehtimäki
Veikko Salomaa
Sirpa Jalkanen
Marjo-Riitta Järvelin
Markus Perola
Olli T Raitakari
Debbie A Lawlor
Johannes Kettunen
Mika Ala-Korpela
Author Affiliation
Computational Medicine, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu, Finland.
Source
Int J Epidemiol. 2016 10; 45(5):1445-1457
Date
10-2016
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Keywords
Adult
Cholesterol, HDL - blood
Contraceptives, Oral, Hormonal - pharmacology
Cross-Sectional Studies
Cytokines - blood
Fatty Acids - blood
Female
Finland
Humans
Linear Models
Longitudinal Studies
Metabolome - drug effects
Metabolomics
Progestins - pharmacology
Risk factors
Triglycerides - blood
Young Adult
Abstract
Hormonal contraception is commonly used worldwide, but its systemic effects across lipoprotein subclasses, fatty acids, circulating metabolites and cytokines remain poorly understood.
A comprehensive molecular profile (75 metabolic measures and 37 cytokines) was measured for up to 5841 women (age range 24-49 years) from three population-based cohorts. Women using combined oral contraceptive pills (COCPs) or progestin-only contraceptives (POCs) were compared with those who did not use hormonal contraception. Metabolomics profiles were reassessed for 869 women after 6 years to uncover the metabolic effects of starting, stopping and persistently using hormonal contraception.
The comprehensive molecular profiling allowed multiple new findings on the metabolic associations with the use of COCPs. They were positively associated with lipoprotein subclasses, including all high-density lipoprotein (HDL) subclasses. The associations with fatty acids and amino acids were strong and variable in direction. COCP use was negatively associated with albumin and positively associated with creatinine and inflammatory markers, including glycoprotein acetyls and several growth factors and interleukins. Our findings also confirmed previous results e.g. for increased circulating triglycerides and HDL cholesterol. Starting COCPs caused similar metabolic changes to those observed cross-sectionally: the changes were maintained in consistent users and normalized in those who stopped using. In contrast, POCs were only weakly associated with metabolic and inflammatory markers. Results were consistent across all cohorts and for different COCP preparations and different types of POC delivery.
Use of COCPs causes widespread metabolic and inflammatory effects. However, persistent use does not appear to accumulate the effects over time and the metabolic perturbations are reversed upon discontinuation. POCs have little effect on systemic metabolism and inflammation.
Notes
Cites: J Obstet Gynaecol Can. 2009 Jul;31(7):627-40 PMID 19761636
Cites: Clin Sci. 1971 Oct;41(4):301-7 PMID 5097474
Cites: Int J Epidemiol. 2008 Dec;37(6):1220-6 PMID 18263651
Cites: Diabetes Care. 2013 Mar;36(3):648-55 PMID 23129134
Cites: Diabetes Care. 2013 Nov;36(11):3732-8 PMID 24026559
Cites: Best Pract Res Clin Endocrinol Metab. 2013 Feb;27(1):13-24 PMID 23384742
Cites: Contraception. 2004 Nov;70(5):365-70 PMID 15504374
Cites: Circ Cardiovasc Genet. 2015 Feb;8(1):192-206 PMID 25691689
Cites: N Engl J Med. 2012 Jul 5;367(1):20-9 PMID 22762315
Cites: Nat Genet. 2013 Nov;45(11):1345-52 PMID 24097064
Cites: Mol Syst Biol. 2010 Dec 21;6:441 PMID 21179014
Cites: Proc Natl Acad Sci U S A. 2000 Feb 1;97(3):1242-6 PMID 10655515
Cites: PLoS Med. 2014 Dec 09;11(12):e1001765 PMID 25490400
Cites: Science. 2016 Mar 11;351(6278):1166-71 PMID 26965621
Cites: Blood. 2004 Feb 1;103(3):927-33 PMID 14551147
Cites: J Am Coll Cardiol. 2013 Jan 29;61(4):427-36 PMID 23265341
Cites: Endocrine. 2015 Aug;49(3):820-7 PMID 25539793
Cites: Contraception. 2012 May;85(5):446-52 PMID 22078632
Cites: Contraception. 2003 Jun;67(6):423-9 PMID 12814810
Cites: Analyst. 2009 Sep;134(9):1781-5 PMID 19684899
Cites: BMJ. 2015 May 26;350:h2135 PMID 26013557
Cites: Circulation. 2015 Feb 3;131(5):451-8 PMID 25623155
Cites: PLoS One. 2012;7(6):e37815 PMID 22675492
Cites: Am J Clin Nutr. 2014 Sep;100(3):746-55 PMID 25057156
Cites: Circulation. 2010 Jun 8;121(22):2388-97 PMID 20497981
Cites: BMJ. 2013 Sep 12;347:f5298 PMID 24030561
Cites: Am J Clin Nutr. 1978 May;31(5):794-8 PMID 645627
Cites: J Thromb Haemost. 2013 Jan;11(1):203-5 PMID 23122048
Cites: Drugs. 2000 Oct;60(4):721-869 PMID 11085198
Cites: BMJ. 2014 Nov 18;349:g6330 PMID 25406188
Cites: Scand J Clin Lab Invest. 2009;69(2):168-74 PMID 18937150
Cites: Eur J Contracept Reprod Health Care. 2010 Dec;15 Suppl 2:S42-53 PMID 21091166
Cites: Nat Genet. 2012 Jan 29;44(3):269-76 PMID 22286219
Cites: Mol Syst Biol. 2008;4:167 PMID 18277383
Cites: Kidney Int. 2004 Aug;66(2):591-6 PMID 15253711
Cites: J Nutr. 2007 Jun;137(6 Suppl 1):1586S-1590S; discussion 1597S-1598S PMID 17513431
Cites: N Engl J Med. 1990 Nov 15;323(20):1375-81 PMID 2146499
Cites: N Engl J Med. 2012 Nov 29;367(22):2089-99 PMID 23126252
Cites: BMJ. 2011 Oct 25;343:d6423 PMID 22027398
Cites: Eur J Hum Genet. 2016 Feb;24(2):284-90 PMID 26014426
Cites: J Clin Endocrinol Metab. 1995 Jun;80(6):1816-21 PMID 7775629
Cites: Lancet. 2012 Aug 11;380(9841):572-80 PMID 22607825
Cites: Contraception. 2001 Jul;64(1):11-6 PMID 11535207
Cites: Hypertension. 2004 Dec;44(6):838-46 PMID 15520301
Cites: PLoS Med. 2014 Feb 25;11(2):e1001606 PMID 24586121
Cites: Eur J Clin Pharmacol. 1996;50(3):179-84 PMID 8737756
Cites: Int J Epidemiol. 2015 Apr;44(2):623-37 PMID 26050255
Cites: J Thromb Haemost. 2006 Jan;4(1):77-82 PMID 16409455
Cites: Am J Transl Res. 2014 Oct 11;6(5):614-24 PMID 25360225
Cites: Life Sci. 1995;56(9):687-95 PMID 7869850
Cites: Epidemiol Rev. 2014;36:57-70 PMID 24025350
Cites: Am J Obstet Gynecol. 2008 Nov;199(5):529.e1-529.e10 PMID 18533124
Cites: J Clin Endocrinol Metab. 2007 Jun;92(6):2074-9 PMID 17374706
Cites: Circulation. 2015 Mar 3;131(9):774-85 PMID 25573147
Cites: Natl Health Stat Report. 2013 Feb 14;(62):1-15 PMID 24988816
Cites: Nat Commun. 2014 Aug 21;5:4708 PMID 25144627
Cites: N Engl J Med. 2008 Oct 30;359(18):1897-908 PMID 18971492
Cites: Best Pract Res Clin Endocrinol Metab. 2013 Feb;27(1):25-34 PMID 23384743
Cites: Ann Intern Med. 2014 Mar 18;160(6):398-406 PMID 24723079
Cites: Contraception. 2004 Feb;69(2):105-13 PMID 14759614
Cites: JAMA. 2009 Jul 1;302(1):37-48 PMID 19567438
Cites: Best Pract Res Clin Endocrinol Metab. 2013 Feb;27(1):35-45 PMID 23384744
Cites: N Engl J Med. 2012 Jun 14;366(24):2257-66 PMID 22693997
Cites: Lancet. 2013 Jul 27;382(9889):339-52 PMID 23727170
Cites: Circulation. 2013 Jan 22;127(3):340-8 PMID 23258601
Cites: Diabetes. 2012 Jun;61(6):1372-80 PMID 22511205
Cites: Lancet. 2014 Aug 16;384(9943):626-35 PMID 25131982
Cites: Contraception. 2005 Feb;71(2):118-21 PMID 15707561
PubMed ID
27538888 View in PubMed
Less detail

Metabolic profiling of pregnancy: cross-sectional and longitudinal evidence.

https://arctichealth.org/en/permalink/ahliterature282956
Source
BMC Med. 2016 Dec 13;14(1):205
Publication Type
Article
Date
Dec-13-2016
Author
Qin Wang
Peter Würtz
Kirsi Auro
Ville-Petteri Mäkinen
Antti J Kangas
Pasi Soininen
Mika Tiainen
Tuulia Tynkkynen
Jari Jokelainen
Kristiina Santalahti
Marko Salmi
Stefan Blankenberg
Tanja Zeller
Jorma Viikari
Mika Kähönen
Terho Lehtimäki
Veikko Salomaa
Markus Perola
Sirpa Jalkanen
Marjo-Riitta Järvelin
Olli T Raitakari
Johannes Kettunen
Debbie A Lawlor
Mika Ala-Korpela
Source
BMC Med. 2016 Dec 13;14(1):205
Date
Dec-13-2016
Language
English
Publication Type
Article
Keywords
Adult
Cross-Sectional Studies
Female
Finland
Humans
Metabolomics - methods
Middle Aged
Pregnancy - metabolism
Young Adult
Abstract
Pregnancy triggers well-known alterations in maternal glucose and lipid balance but its overall effects on systemic metabolism remain incompletely understood.
Detailed molecular profiles (87 metabolic measures and 37 cytokines) were measured for up to 4260 women (24-49 years, 322 pregnant) from three population-based cohorts in Finland. Circulating molecular concentrations in pregnant women were compared to those in non-pregnant women. Metabolic profiles were also reassessed for 583 women 6 years later to uncover the longitudinal metabolic changes in response to change in the pregnancy status.
Compared to non-pregnant women, all lipoprotein subclasses and lipids were markedly increased in pregnant women. The most pronounced differences were observed for the intermediate-density, low-density and high-density lipoprotein triglyceride concentrations. Large differences were also seen for many fatty acids and amino acids. Pregnant women also had higher concentrations of low-grade inflammatory marker glycoprotein acetyls, higher concentrations of interleukin-18 and lower concentrations of interleukin-12p70. The changes in metabolic concentrations for women who were not pregnant at baseline but pregnant 6 years later (or vice versa) matched (or were mirror-images of) the cross-sectional association pattern. Cross-sectional results were consistent across the three cohorts and similar longitudinal changes were seen for 653 women in 4-year and 497 women in 10-year follow-up. For multiple metabolic measures, the changes increased in magnitude across the three trimesters.
Pregnancy initiates substantial metabolic and inflammatory changes in the mothers. Comprehensive characterisation of normal pregnancy is important for gaining understanding of the key nutrients for fetal growth and development. These findings also provide a valuable molecular reference in relation to studies of adverse pregnancy outcomes.
Notes
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PubMed ID
27955712 View in PubMed
Less detail

Metabolic signatures of birthweight in 18 288 adolescents and adults.

https://arctichealth.org/en/permalink/ahliterature289671
Source
Int J Epidemiol. 2016 10; 45(5):1539-1550
Publication Type
Journal Article
Meta-Analysis
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Date
10-2016
Author
Peter Würtz
Qin Wang
Marjo Niironen
Tuulia Tynkkynen
Mika Tiainen
Fotios Drenos
Antti J Kangas
Pasi Soininen
Michael R Skilton
Kauko Heikkilä
Anneli Pouta
Mika Kähönen
Terho Lehtimäki
Richard J Rose
Eero Kajantie
Markus Perola
Jaakko Kaprio
Johan G Eriksson
Olli T Raitakari
Debbie A Lawlor
George Davey Smith
Marjo-Riitta Järvelin
Mika Ala-Korpela
Kirsi Auro
Author Affiliation
Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland peter.wurtz@computationalmedicine.fi.
Source
Int J Epidemiol. 2016 10; 45(5):1539-1550
Date
10-2016
Language
English
Publication Type
Journal Article
Meta-Analysis
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Keywords
Adiposity
Adolescent
Adult
Aged
Amino Acids - blood
Biomarkers - blood
Body mass index
Disease Susceptibility - blood - metabolism
Fatty Acids - blood
Female
Finland
Gestational Age
High-Throughput Screening Assays
Humans
Infant, Low Birth Weight - blood - metabolism
Infant, Newborn
Lipoproteins - blood
Male
Metabolomics
Middle Aged
Risk factors
United Kingdom
Young Adult
Abstract
Lower birthweight is associated with increased susceptibility to cardiometabolic diseases in adulthood, but the underlying molecular pathways are incompletely understood. We examined associations of birthweight with a comprehensive metabolic profile measured in adolescents and adults.
High-throughput nuclear magnetic resonance metabolomics and biochemical assays were used to quantify 87 circulating metabolic measures in seven cohorts from Finland and the UK, comprising altogether 18 288 individuals (mean age 26 years, range 15-75). Metabolic associations with birthweight were assessed by linear regression models adjusted for sex, gestational age and age at blood sampling. The metabolic associations with birthweight were compared with the corresponding associations with adult body mass index (BMI).
Lower birthweight adjusted for gestational age was adversely associated with cardiometabolic biomarkers, including lipoprotein subclasses, fatty acids, amino acids and markers of inflammation and impaired liver function (P
Notes
Cites: BMJ. 1989 Mar 4;298(6673):564-7 PMID 2495113
Cites: Int J Epidemiol. 2013 Feb;42(1):111-27 PMID 22507743
Cites: Diabetes. 2012 Jul;61(7):1895-902 PMID 22553379
Cites: PLoS Med. 2007 Aug;4(8):e263 PMID 17760500
Cites: Diabetes Care. 2013 Nov;36(11):3732-8 PMID 24026559
Cites: Diabetologia. 2011 Aug;54(8):2016-24 PMID 21487729
Cites: BMJ. 1998 Jul 25;317(7153):241-5 PMID 9677213
Cites: Int J Epidemiol. 2015 Apr;44(2):578-86 PMID 26016847
Cites: JAMA. 2004 Dec 8;292(22):2755-64 PMID 15585736
Cites: Am J Clin Nutr. 2011 Dec;94(6 Suppl):1799S-1802S PMID 21613556
Cites: Circ Cardiovasc Genet. 2015 Feb;8(1):192-206 PMID 25691689
Cites: Ann Epidemiol. 2006 Jan;16(1):19-25 PMID 16039874
Cites: Hum Mol Genet. 2012 Dec 15;21(24):5344-58 PMID 22956269
Cites: Nat Genet. 2013 Nov;45(11):1345-52 PMID 24097064
Cites: PLoS Med. 2014 Dec 09;11(12):e1001765 PMID 25490400
Cites: Pediatr Res. 2013 Apr;73(4 Pt 2):570-6 PMID 23314292
Cites: Pediatrics. 2003 May;111(5 Pt 1):1081-9 PMID 12728092
Cites: Int J Epidemiol. 2016 Oct;45(5):1493-1506 PMID 27494945
Cites: Am J Hum Genet. 2014 Feb 6;94(2):198-208 PMID 24462370
Cites: Analyst. 2009 Sep;134(9):1781-5 PMID 19684899
Cites: Int J Epidemiol. 2011 Jun;40(3):647-61 PMID 21324938
Cites: Diabetologia. 1998 Oct;41(10):1133-8 PMID 9794098
Cites: BMJ. 1996 Feb 17;312(7028):406-10 PMID 8601111
Cites: Am J Hum Biol. 2013 Jul-Aug;25(4):465-72 PMID 23649903
Cites: BMJ. 1995 Jul 15;311(6998):171-4 PMID 7613432
Cites: BMJ. 1999 Jul 24;319(7204):245-9 PMID 10417093
Cites: JAMA. 2008 Dec 24;300(24):2886-97 PMID 19109117
Cites: Diabetologia. 1992 Jul;35(7):595-601 PMID 1644236
Cites: Scand J Public Health. 2014 Nov;42(7):563-71 PMID 25053467
Cites: Lipids Health Dis. 2013 Apr 30;12:57 PMID 23631373
Cites: Am J Epidemiol. 2007 Sep 15;166(6):634-45 PMID 17456478
Cites: Eur Heart J. 2008 Apr;29(8):1049-56 PMID 18403494
Cites: Proc Natl Acad Sci U S A. 2013 Jan 29;110(5):1917-22 PMID 23277558
Cites: BMJ. 1995 Feb 18;310(6977):432-6 PMID 7873948
Cites: Hypertension. 2004 Dec;44(6):838-46 PMID 15520301
Cites: PLoS Med. 2014 Feb 25;11(2):e1001606 PMID 24586121
Cites: Nat Med. 2011 Apr;17(4):448-53 PMID 21423183
Cites: JAMA. 2016 Mar 15;315(11):1129-40 PMID 26978208
Cites: Lancet. 2002 Aug 31;360(9334):659-65 PMID 12241871
Cites: PLoS Med. 2012;9(5):e1001212 PMID 22563304
Cites: Int J Epidemiol. 2003 Oct;32(5):862-76 PMID 14559765
Cites: N Engl J Med. 2008 Jul 3;359(1):61-73 PMID 18596274
Cites: Circulation. 2015 Mar 3;131(9):774-85 PMID 25573147
Cites: Diabetes Care. 2009 Apr;32(4):741-50 PMID 19131466
Cites: PLoS One. 2016 Feb 10;11(2):e0148361 PMID 26863521
Cites: Nat Genet. 2013 Jan;45(1):76-82 PMID 23202124
Cites: Nat Commun. 2014 Aug 21;5:4708 PMID 25144627
Cites: Biomed Res Int. 2013;2013:720514 PMID 23841090
Cites: BMJ. 1996 Feb 17;312(7028):401-6 PMID 8601110
Cites: Am J Clin Nutr. 2007 May;85(5):1244-50 PMID 17490959
Cites: PLoS Med. 2013;10 (6):e1001474 PMID 23824655
Cites: Nature. 2004 Jan 29;427(6973):411-2 PMID 14749819
Cites: Nutrition. 2016 Jul-Aug;32(7-8):725-31 PMID 27025974
Cites: Int J Epidemiol. 2002 Dec;31(6):1235-9 PMID 12540728
Cites: Nature. 2004 Jul 22;430(6998):419-21 PMID 15269759
Cites: Diabetologia. 2015 May;58(5):968-79 PMID 25693751
PubMed ID
27892411 View in PubMed
Less detail

Metabolic signatures of birthweight in 18 288 adolescents and adults.

https://arctichealth.org/en/permalink/ahliterature289513
Source
Int J Epidemiol. 2016 10; 45(5):1539-1550
Publication Type
Journal Article
Meta-Analysis
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Date
10-2016
Author
Peter Würtz
Qin Wang
Marjo Niironen
Tuulia Tynkkynen
Mika Tiainen
Fotios Drenos
Antti J Kangas
Pasi Soininen
Michael R Skilton
Kauko Heikkilä
Anneli Pouta
Mika Kähönen
Terho Lehtimäki
Richard J Rose
Eero Kajantie
Markus Perola
Jaakko Kaprio
Johan G Eriksson
Olli T Raitakari
Debbie A Lawlor
George Davey Smith
Marjo-Riitta Järvelin
Mika Ala-Korpela
Kirsi Auro
Author Affiliation
Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland peter.wurtz@computationalmedicine.fi.
Source
Int J Epidemiol. 2016 10; 45(5):1539-1550
Date
10-2016
Language
English
Publication Type
Journal Article
Meta-Analysis
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Keywords
Adiposity
Adolescent
Adult
Aged
Amino Acids - blood
Biomarkers - blood
Body mass index
Disease Susceptibility - blood - metabolism
Fatty Acids - blood
Female
Finland
Gestational Age
High-Throughput Screening Assays
Humans
Infant, Low Birth Weight - blood - metabolism
Infant, Newborn
Lipoproteins - blood
Male
Metabolomics
Middle Aged
Risk factors
United Kingdom
Young Adult
Abstract
Lower birthweight is associated with increased susceptibility to cardiometabolic diseases in adulthood, but the underlying molecular pathways are incompletely understood. We examined associations of birthweight with a comprehensive metabolic profile measured in adolescents and adults.
High-throughput nuclear magnetic resonance metabolomics and biochemical assays were used to quantify 87 circulating metabolic measures in seven cohorts from Finland and the UK, comprising altogether 18 288 individuals (mean age 26 years, range 15-75). Metabolic associations with birthweight were assessed by linear regression models adjusted for sex, gestational age and age at blood sampling. The metabolic associations with birthweight were compared with the corresponding associations with adult body mass index (BMI).
Lower birthweight adjusted for gestational age was adversely associated with cardiometabolic biomarkers, including lipoprotein subclasses, fatty acids, amino acids and markers of inflammation and impaired liver function (P
Notes
Cites: BMJ. 1989 Mar 4;298(6673):564-7 PMID 2495113
Cites: Int J Epidemiol. 2013 Feb;42(1):111-27 PMID 22507743
Cites: Diabetes. 2012 Jul;61(7):1895-902 PMID 22553379
Cites: PLoS Med. 2007 Aug;4(8):e263 PMID 17760500
Cites: Diabetes Care. 2013 Nov;36(11):3732-8 PMID 24026559
Cites: Diabetologia. 2011 Aug;54(8):2016-24 PMID 21487729
Cites: BMJ. 1998 Jul 25;317(7153):241-5 PMID 9677213
Cites: Int J Epidemiol. 2015 Apr;44(2):578-86 PMID 26016847
Cites: JAMA. 2004 Dec 8;292(22):2755-64 PMID 15585736
Cites: Am J Clin Nutr. 2011 Dec;94(6 Suppl):1799S-1802S PMID 21613556
Cites: Circ Cardiovasc Genet. 2015 Feb;8(1):192-206 PMID 25691689
Cites: Ann Epidemiol. 2006 Jan;16(1):19-25 PMID 16039874
Cites: Hum Mol Genet. 2012 Dec 15;21(24):5344-58 PMID 22956269
Cites: Nat Genet. 2013 Nov;45(11):1345-52 PMID 24097064
Cites: PLoS Med. 2014 Dec 09;11(12):e1001765 PMID 25490400
Cites: Pediatr Res. 2013 Apr;73(4 Pt 2):570-6 PMID 23314292
Cites: Pediatrics. 2003 May;111(5 Pt 1):1081-9 PMID 12728092
Cites: Int J Epidemiol. 2016 Oct;45(5):1493-1506 PMID 27494945
Cites: Am J Hum Genet. 2014 Feb 6;94(2):198-208 PMID 24462370
Cites: Analyst. 2009 Sep;134(9):1781-5 PMID 19684899
Cites: Int J Epidemiol. 2011 Jun;40(3):647-61 PMID 21324938
Cites: Diabetologia. 1998 Oct;41(10):1133-8 PMID 9794098
Cites: BMJ. 1996 Feb 17;312(7028):406-10 PMID 8601111
Cites: Am J Hum Biol. 2013 Jul-Aug;25(4):465-72 PMID 23649903
Cites: BMJ. 1995 Jul 15;311(6998):171-4 PMID 7613432
Cites: BMJ. 1999 Jul 24;319(7204):245-9 PMID 10417093
Cites: JAMA. 2008 Dec 24;300(24):2886-97 PMID 19109117
Cites: Diabetologia. 1992 Jul;35(7):595-601 PMID 1644236
Cites: Scand J Public Health. 2014 Nov;42(7):563-71 PMID 25053467
Cites: Lipids Health Dis. 2013 Apr 30;12:57 PMID 23631373
Cites: Am J Epidemiol. 2007 Sep 15;166(6):634-45 PMID 17456478
Cites: Eur Heart J. 2008 Apr;29(8):1049-56 PMID 18403494
Cites: Proc Natl Acad Sci U S A. 2013 Jan 29;110(5):1917-22 PMID 23277558
Cites: BMJ. 1995 Feb 18;310(6977):432-6 PMID 7873948
Cites: Hypertension. 2004 Dec;44(6):838-46 PMID 15520301
Cites: PLoS Med. 2014 Feb 25;11(2):e1001606 PMID 24586121
Cites: Nat Med. 2011 Apr;17(4):448-53 PMID 21423183
Cites: JAMA. 2016 Mar 15;315(11):1129-40 PMID 26978208
Cites: Lancet. 2002 Aug 31;360(9334):659-65 PMID 12241871
Cites: PLoS Med. 2012;9(5):e1001212 PMID 22563304
Cites: Int J Epidemiol. 2003 Oct;32(5):862-76 PMID 14559765
Cites: N Engl J Med. 2008 Jul 3;359(1):61-73 PMID 18596274
Cites: Circulation. 2015 Mar 3;131(9):774-85 PMID 25573147
Cites: Diabetes Care. 2009 Apr;32(4):741-50 PMID 19131466
Cites: PLoS One. 2016 Feb 10;11(2):e0148361 PMID 26863521
Cites: Nat Genet. 2013 Jan;45(1):76-82 PMID 23202124
Cites: Nat Commun. 2014 Aug 21;5:4708 PMID 25144627
Cites: Biomed Res Int. 2013;2013:720514 PMID 23841090
Cites: BMJ. 1996 Feb 17;312(7028):401-6 PMID 8601110
Cites: Am J Clin Nutr. 2007 May;85(5):1244-50 PMID 17490959
Cites: PLoS Med. 2013;10 (6):e1001474 PMID 23824655
Cites: Nature. 2004 Jan 29;427(6973):411-2 PMID 14749819
Cites: Nutrition. 2016 Jul-Aug;32(7-8):725-31 PMID 27025974
Cites: Int J Epidemiol. 2002 Dec;31(6):1235-9 PMID 12540728
Cites: Nature. 2004 Jul 22;430(6998):419-21 PMID 15269759
Cites: Diabetologia. 2015 May;58(5):968-79 PMID 25693751
PubMed ID
27892411 View in PubMed
Less detail

A metabolic view on menopause and ageing.

https://arctichealth.org/en/permalink/ahliterature269922
Source
Nat Commun. 2014;5:4708
Publication Type
Article
Date
2014
Author
Kirsi Auro
Anni Joensuu
Krista Fischer
Johannes Kettunen
Perttu Salo
Hannele Mattsson
Marjo Niironen
Jaakko Kaprio
Johan G Eriksson
Terho Lehtimäki
Olli Raitakari
Antti Jula
Aila Tiitinen
Matti Jauhiainen
Pasi Soininen
Antti J Kangas
Mika Kähönen
Aki S Havulinna
Mika Ala-Korpela
Veikko Salomaa
Andres Metspalu
Markus Perola
Source
Nat Commun. 2014;5:4708
Date
2014
Language
English
Publication Type
Article
Keywords
Adolescent
Adult
Aged
Aged, 80 and over
Aging - physiology
Amino Acids - blood
Blood - metabolism
Cardiovascular Diseases - metabolism
Cohort Studies
Estonia
European Continental Ancestry Group
Female
Finland
Humans
Lipids - blood
Male
Menopause - metabolism
Middle Aged
Risk factors
Young Adult
Abstract
The ageing of the global population calls for a better understanding of age-related metabolic consequences. Here we report the effects of age, sex and menopause on serum metabolites in 26,065 individuals of Northern European ancestry. Age-specific metabolic fingerprints differ significantly by gender and, in females, a substantial atherogenic shift overlapping the time of menopausal transition is observed. In meta-analysis of 10,083 women, menopause status associates with amino acids glutamine, tyrosine and isoleucine, along with serum cholesterol measures and atherogenic lipoproteins. Among 3,204 women aged 40-55 years, menopause status associates additionally with glycine and total, monounsaturated, and omega-7 and -9 fatty acids. Our findings suggest that, in addition to lipid alterations, menopause may contribute to future metabolic and cardiovascular risk via influencing amino-acid concentrations, adding to the growing evidence of the importance of amino acids in metabolic disease progression. These observations shed light on the metabolic consequences of ageing, gender and menopause at the population level.
PubMed ID
25144627 View in PubMed
Less detail

Protective Low-Frequency Variants for Preeclampsia in the Fms Related Tyrosine Kinase 1 Gene in the Finnish Population.

https://arctichealth.org/en/permalink/ahliterature285323
Source
Hypertension. 2017 Aug;70(2):365-371
Publication Type
Article
Date
Aug-2017
Author
A Inkeri Lokki
Emma Daly
Michael Triebwasser
Mitja I Kurki
Elisha D O Roberson
Paavo Häppölä
Kirsi Auro
Markus Perola
Seppo Heinonen
Eero Kajantie
Juha Kere
Katja Kivinen
Anneli Pouta
Jane E Salmon
Seppo Meri
Mark Daly
John P Atkinson
Hannele Laivuori
Source
Hypertension. 2017 Aug;70(2):365-371
Date
Aug-2017
Language
English
Publication Type
Article
Keywords
Adult
Female
Finland - epidemiology
Genetic Variation
Humans
Hypertension - diagnosis - etiology
Pre-Eclampsia - epidemiology - genetics - physiopathology
Pregnancy
Protective factors
Vascular Endothelial Growth Factor Receptor-1 - genetics
Abstract
Preeclampsia is a common pregnancy-specific vascular disorder characterized by new-onset hypertension and proteinuria during the second half of pregnancy. Predisposition to preeclampsia is in part heritable. It is associated with an increased risk of cardiovascular disease later in life. We have sequenced 124 candidate genes implicated in preeclampsia to pinpoint genetic variants contributing to predisposition to or protection from preeclampsia. First, targeted exomic sequencing was performed in 500 preeclamptic women and 190 controls from the FINNPEC cohort (Finnish Genetics of Preeclampsia Consortium). Then 122 women with a history of preeclampsia and 1905 parous women with no such history from the National FINRISK Study (a large Finnish population survey on risk factors of chronic, noncommunicable diseases) were included in the analyses. We tested 146 rare and low-frequency variants and found an excess (observed 13 versus expected 7.3) nominally associated with preeclampsia (P
Notes
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PubMed ID
28652462 View in PubMed
Less detail

Variation in the selenoprotein S gene locus is associated with coronary heart disease and ischemic stroke in two independent Finnish cohorts.

https://arctichealth.org/en/permalink/ahliterature162357
Source
Hum Genet. 2007 Nov;122(3-4):355-65
Publication Type
Article
Date
Nov-2007
Author
Mervi Alanne
Kati Kristiansson
Kirsi Auro
Kaisa Silander
Kari Kuulasmaa
Leena Peltonen
Veikko Salomaa
Markus Perola
Author Affiliation
Department of Molecular Medicine, KTL-National Public Health Institute, Biomedicum, Helsinki, Finland. mervi.alanne@ktl.fi
Source
Hum Genet. 2007 Nov;122(3-4):355-65
Date
Nov-2007
Language
English
Publication Type
Article
Keywords
Aged
Alleles
Cohort Studies
Coronary Disease - genetics
Female
Finland
Genetic Variation
Haplotypes
Humans
Linkage Disequilibrium
Male
Membrane Proteins - genetics
Middle Aged
Polymorphism, Single Nucleotide
Prospective Studies
Risk factors
Selenoproteins - genetics
Sex Factors
Stroke - genetics
Abstract
Selenoprotein S (SEPS1) is a novel candidate gene involved in the regulation of inflammatory response and protection from oxidative damage. This study explored the genetic variation in the SEPS1 locus for an association with CVD as well as with quantitative phenotypes related to obesity and inflammation. We used the case-cohort design and time-to-event analysis in two separate prospectively followed population-based cohorts FINRISK 92 and 97 (n = 999 and 1,223 individuals, respectively) to study the associations of five single nucleotide polymorphisms with the risk for coronary heart disease (CHD) and ischemic stroke events. We found a significant association with increased CHD risk in females carrying the minor allele of rs8025174 in the combined analysis of both cohorts [hazard ratio (HR) 2.95 (95% confidence interval: 1.37-6.39)]. Another variant, rs7178239, increased the risk for ischemic stroke significantly in females [HR: 3.35 (1.66-6.76)] and in joint analysis of both sexes and both cohorts [HR: 1.75 (1.17-2.64)]. These results indicate that variation in the SEPS1 locus may have an effect on CVD morbidity, especially in females. This observation should stimulate further investigations of the role of this gene and protein in the pathogenesis of CVD.
PubMed ID
17641917 View in PubMed
Less detail

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