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1,041 results on '"Hurme, M"'

1. Self-rated health in individuals with and without disease is associated with multiple biomarkers representing multiple biological domains

Author

Kananen, L., Enroth, L., Raitanen, J., Jylhävä, J., Bürkle, A., Moreno-Villanueva, M., Bernhardt, J., Toussaint, O., Grubeck-Loebenstein, B., Malavolta, M., Basso, A., Piacenza, F., Collino, S., Gonos, E. S., Sikora, E., Gradinaru, D., Jansen, E. H. J. M., Dollé, M. E. T., Salmon, M., Stuetz, W., Weber, D., Grune, T., Breusing, N., Simm, A., Capri, M., Franceschi, C., Slagboom, P. E., Talbot, D. C. S., Libert, C., Koskinen, S., Bruunsgaard, H., Hansen, ÅM., Lund, R., Hurme, M., and Jylhä, M.

Published
2021
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3. Neurologically healthy humans’ ability to make saccades toward unseen targets

Author

Olkoniemi, H. (Henri), Hurme, M. (Mikko), Railo, H. (Henry), Olkoniemi, H. (Henri), Hurme, M. (Mikko), and Railo, H. (Henry)

Abstract

Some patients with a visual field loss due to a lesion in the primary visual cortex (V1) can shift their gaze to stimuli presented in their blind visual field. The extent to which a similar “blindsight” capacity is present in neurologically healthy individuals remains unknown. Using retinotopically navigated transcranial magnetic stimulation (TMS) of V1 (Experiment 1) and metacontrast masking (Experiment 2) to suppress conscious vision, we examined neurologically healthy humans’ ability to make saccadic eye movements toward visual targets that they reported not seeing. In the TMS experiment, the participants were more likely to initiate a saccade when a stimulus was presented, and they reported not seeing it, than in trials which no stimulus was presented. However, this happened only in a very small proportion (∼8%) of unseen trials, suggesting that saccadic reactions were largely based on conscious perception. In both experiments, saccade landing location was influenced by unconscious information: When the participants denied seeing the target but made a saccade, the saccade was made toward the correct location (TMS: 68%, metacontrast: 63%) more often than predicted by chance. Signal detection theoretic measures suggested that in the TMS experiment, saccades toward unseen targets may have been based on weak conscious experiences. In both experiments, reduced visibility of the target stimulus was associated with slower and less precise gaze shifts. These results suggest that saccades made by neurologically healthy humans may be influenced by unconscious information, although the initiation of saccades is largely based on conscious vision.

Published
2023

4. The relationship of trait-like compassion with epigenetic aging:the population-based prospective Young Finns Study

Author

Dobewall, H. (Henrik), Keltikangas-Järvinen, L. (Liisa), Marttila, S. (Saara), Mishra, P. P. (Pashupati P.), Saarinen, A. (Aino), Cloninger, C. R. (C. Robert), Zwir, I. (Igor), Kähönen, M. (Mika), Hurme, M. (Mikko), Raitakari, O. (Olli), Lehtimäki, T. (Terho), Hintsanen, M. (Mirka), Dobewall, H. (Henrik), Keltikangas-Järvinen, L. (Liisa), Marttila, S. (Saara), Mishra, P. P. (Pashupati P.), Saarinen, A. (Aino), Cloninger, C. R. (C. Robert), Zwir, I. (Igor), Kähönen, M. (Mika), Hurme, M. (Mikko), Raitakari, O. (Olli), Lehtimäki, T. (Terho), and Hintsanen, M. (Mirka)

Abstract

Introduction: Helping others within and beyond the family has been related to living a healthy and long life. Compassion is a prosocial personality trait characterized by concern for another person who is suffering and the motivation to help. The current study examines whether epigenetic aging is a potential biological mechanism that explains the link between prosociality and longevity. Methods: We used data from the Young Finns Study that follows six birth-cohorts from age 3–18 to 19–49. Trait-like compassion for others was measured with the Temperament and Character Inventory in the years 1997 and 2001. Epigenetic age acceleration and telomere length were measured with five DNA methylation (DNAm) indicators (DNAmAgeHorvath, IEAA_Hannum, EEAA_Hannum, DNAmPhenoAge, and DNAmTL) based on blood drawn in 2011. We controlled for sex, socioeconomic status in childhood and adulthood, and body-mass index. Results and discussion: An association between higher compassion in 1997 and a less accelerated DNAmPhenoAge, which builds on previous work on phenotypic aging, approached statistical significance in a sex-adjusted model (n = 1,030; b = −0.34; p = 0.050). Compassion in 1997 predicted less accelerated epigenetic aging over and above the control variables (n = 843; b = −0.47; p = 0.016). There was no relationship between compassion in 2001 (n = 1108/910) and any of the other four studied epigenetic aging indicators. High compassion for others might indeed influence whether an individual’s biological age is lower than their chronological age. The conducted robustness checks partially support this conclusion, yet cannot rule out that there might be a broader prosocial trait behind the findings. The observed associations are interesting but should be interpreted as weak requiring replication.

Published
2023

5. Circulating cell-free DNA in health and disease:the relationship to health behaviours, ageing phenotypes and metabolomics

Author

Kananen, L. (Laura), Hurme, M. (Mikko), Buerkle, A. (Alexander), Moreno-Villanueva, M. (Maria), Bernhardt, J. (Jurgen), Debacq-Chainiaux, F. (Florence), Grubeck-Loebenstein, B. (Beatrix), Malavolta, M. (Marco), Basso, A. (Andrea), Piacenza, F. (Francesco), Collino, S. (Sebastiano), Gonos, E. S. (Efstathios S.), Sikora, E. (Ewa), Gradinaru, D. (Daniela), Jansen, E. H. (Eugene H. J. M.), Dolle, M. E. (Martijn E. T.), Salmon, M. (Michel), Stuetz, W. (Wolfgang), Weber, D. (Daniela), Grune, T. (Tilman), Breusing, N. (Nicolle), Simm, A. (Andreas), Capri, M. (Miriam), Franceschi, C. (Claudio), Slagboom, E. (Eline), Talbot, D. (Duncan), Libert, C. (Claude), Raitanen, J. (Jani), Koskinen, S. (Seppo), Härkänen, T. (Tommi), Stenholm, S. (Sari), Ala-Korpela, M. (Mika), Lehtimäki, T. (Terho), Raitakari, O. T. (Olli T.), Ukkola, O. (Olavi), Kähönen, M. (Mika), Jylhä, M. (Marja), Jylhävä, J. (Juulia), Kananen, L. (Laura), Hurme, M. (Mikko), Buerkle, A. (Alexander), Moreno-Villanueva, M. (Maria), Bernhardt, J. (Jurgen), Debacq-Chainiaux, F. (Florence), Grubeck-Loebenstein, B. (Beatrix), Malavolta, M. (Marco), Basso, A. (Andrea), Piacenza, F. (Francesco), Collino, S. (Sebastiano), Gonos, E. S. (Efstathios S.), Sikora, E. (Ewa), Gradinaru, D. (Daniela), Jansen, E. H. (Eugene H. J. M.), Dolle, M. E. (Martijn E. T.), Salmon, M. (Michel), Stuetz, W. (Wolfgang), Weber, D. (Daniela), Grune, T. (Tilman), Breusing, N. (Nicolle), Simm, A. (Andreas), Capri, M. (Miriam), Franceschi, C. (Claudio), Slagboom, E. (Eline), Talbot, D. (Duncan), Libert, C. (Claude), Raitanen, J. (Jani), Koskinen, S. (Seppo), Härkänen, T. (Tommi), Stenholm, S. (Sari), Ala-Korpela, M. (Mika), Lehtimäki, T. (Terho), Raitakari, O. T. (Olli T.), Ukkola, O. (Olavi), Kähönen, M. (Mika), Jylhä, M. (Marja), and Jylhävä, J. (Juulia)

Abstract

Circulating cell-free DNA (cf-DNA) has emerged as a promising biomarker of ageing, tissue damage and cellular stress. However, less is known about health behaviours, ageing phenotypes and metabolic processes that lead to elevated cf-DNA levels. We sought to analyse the relationship of circulating cf-DNA level to age, sex, smoking, physical activity, vegetable consumption, ageing phenotypes (physical functioning, the number of diseases, frailty) and an extensive panel of biomarkers including blood and urine metabolites and inflammatory markers in three human cohorts (N = 5385; 17‐82 years). The relationships were assessed using correlation statistics, and linear and penalised regressions (the Lasso), also stratified by sex. cf-DNA levels were significantly higher in men than in women, and especially in middle-aged men and women who smoke, and in older more frail individuals. Correlation statistics of biomarker data showed that cf-DNA level was higher with elevated inflammation (C-reactive protein, interleukin-6), and higher levels of homocysteine, and proportion of red blood cells and lower levels of ascorbic acid. Inflammation (C-reactive protein, glycoprotein acetylation), amino acids (isoleucine, leucine, tyrosine), and ketogenesis (3-hydroxybutyrate) were included in the cf-DNA level-related biomarker profiles in at least two of the cohorts. In conclusion, circulating cf-DNA level is different by sex, and related to health behaviour, health decline and metabolic processes common in health and disease. These results can inform future studies where epidemiological and biological pathways of cf-DNA are to be analysed in details, and for studies evaluating cf-DNA as a potential clinical marker.

Published
2023

7. An epigenome-wide association study meta-analysis of educational attainment

Author

Linnér, R Karlsson, Marioni, R E, Rietveld, C A, Simpkin, A J, Davies, N M, Watanabe, K, Armstrong, N J, Auro, K, Baumbach, C, Bonder, M J, Buchwald, J, Fiorito, G, Ismail, K, Iurato, S, Joensuu, A, Karell, P, Kasela, S, Lahti, J, McRae, A F, Mandaviya, P R, Seppälä, I, Wang, Y, Baglietto, L, Binder, E B, Harris, S E, Hodge, A M, Horvath, S, Hurme, M, Johannesson, M, Latvala, A, Mather, K A, Medland, S E, Metspalu, A, Milani, L, Milne, R L, Pattie, A, Pedersen, N L, Peters, A, Polidoro, S, Räikkönen, K, Severi, G, Starr, J M, Stolk, L, Waldenberger, M, Consortium, B IOS, Eriksson, J G, Esko, T, Franke, L, Gieger, C, Giles, G G, Hägg, S, Jousilahti, P, Kaprio, J, Kähönen, M, Lehtimäki, T, Martin, N G, van Meurs, J BC, Ollikainen, M, Perola, M, Posthuma, D, Raitakari, O T, Sachdev, P S, Taskesen, E, Uitterlinden, A G, Vineis, P, Wijmenga, C, Wright, M J, Relton, C, Smith, G Davey, Deary, I J, Koellinger, P D, and Benjamin, D J

Published
2017
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8. DNA methylation signature of chronic low-grade inflammation and its role in cardio-respiratory diseases

Author

Wielscher, M. (Matthias), Mandaviya, P. R. (Pooja R.), Kuehnel, B. (Brigitte), Joehanes, R. (Roby), Mustafa, R. (Rima), Robinson, O. (Oliver), Zhang, Y. (Yan), Bodinier, B. (Barbara), Walton, E. (Esther), Mishra, P. P. (Pashupati P.), Schlosser, P. (Pascal), Wilson, R. (Rory), Tsai, P.-C. (Pei-Chien), Palaniswamy, S. (Saranya), Marioni, R. E. (Riccardo E.), Fiorito, G. (Giovanni), Cugliari, G. (Giovanni), Karhunen, V. (Ville), Ghanbari, M. (Mohsen), Psaty, B. M. (Bruce M.), Loh, M. (Marie), Bis, J. C. (Joshua C.), Lehne, B. (Benjamin), Sotoodehnia, N. (Nona), Deary, I. J. (Ian J.), Chadeau-Hyam, M. (Marc), Brody, J. A. (Jennifer A.), Cardona, A. (Alexia), Selvin, E. (Elizabeth), Smith, A. K. (Alicia K.), Miller, A. H. (Andrew H.), Torres, M. A. (Mylin A.), Marouli, E. (Eirini), Gao, X. (Xin), van Meurs, J. B. (Joyce B. J.), Graf-Schindler, J. (Johanna), Rathmann, W. (Wolfgang), Koenig, W. (Wolfgang), Peters, A. (Annette), Weninger, W. (Wolfgang), Farlik, M. (Matthias), Zhang, T. (Tao), Chen, W. (Wei), Xia, Y. (Yujing), Teumer, A. (Alexander), Nauck, M. (Matthias), Grabe, H. J. (Hans J.), Doerr, M. (Macus), Lehtimaki, T. (Terho), Guan, W. (Weihua), Milani, L. (Lili), Tanaka, T. (Toshiko), Fisher, K. (Krista), Waite, L. L. (Lindsay L.), Kasela, S. (Silva), Vineis, P. (Paolo), Verweij, N. (Niek), van der Harst, P. (Pim), Iacoviello, L. (Licia), Sacerdote, C. (Carlotta), Panico, S. (Salvatore), Krogh, V. (Vittorio), Tumino, R. (Rosario), Tzala, E. (Evangelia), Matullo, G. (Giuseppe), Hurme, M. A. (Mikko A.), Raitakari, O. T. (Olli T.), Colicino, E. (Elena), Baccarelli, A. A. (Andrea A.), Kahonen, M. (Mika), Herzig, K.-H. (Karl-Heinz), Li, S. (Shengxu), BIOS consortium, Conneely, K. N. (Karen N.), Kooner, J. S. (Jaspal S.), Kottgen, A. (Anna), Heijmans, B. T. (Bastiaan T.), Deloukas, P. (Panos), Relton, C. (Caroline), Ong, K. K. (Ken K.), Bell, J. T. (Jordana T.), Boerwinkle, E. (Eric), Elliott, P. (Paul), Brenner, H. (Hermann), Beekman, M. (Marian), Levy, D. (Daniel), Waldenberger, M. (Melanie), Chambers, J. C. (John C.), Dehghan, A. (Abbas), Järvelin, M.-R. (Marjo-Riitta), Wielscher, M. (Matthias), Mandaviya, P. R. (Pooja R.), Kuehnel, B. (Brigitte), Joehanes, R. (Roby), Mustafa, R. (Rima), Robinson, O. (Oliver), Zhang, Y. (Yan), Bodinier, B. (Barbara), Walton, E. (Esther), Mishra, P. P. (Pashupati P.), Schlosser, P. (Pascal), Wilson, R. (Rory), Tsai, P.-C. (Pei-Chien), Palaniswamy, S. (Saranya), Marioni, R. E. (Riccardo E.), Fiorito, G. (Giovanni), Cugliari, G. (Giovanni), Karhunen, V. (Ville), Ghanbari, M. (Mohsen), Psaty, B. M. (Bruce M.), Loh, M. (Marie), Bis, J. C. (Joshua C.), Lehne, B. (Benjamin), Sotoodehnia, N. (Nona), Deary, I. J. (Ian J.), Chadeau-Hyam, M. (Marc), Brody, J. A. (Jennifer A.), Cardona, A. (Alexia), Selvin, E. (Elizabeth), Smith, A. K. (Alicia K.), Miller, A. H. (Andrew H.), Torres, M. A. (Mylin A.), Marouli, E. (Eirini), Gao, X. (Xin), van Meurs, J. B. (Joyce B. J.), Graf-Schindler, J. (Johanna), Rathmann, W. (Wolfgang), Koenig, W. (Wolfgang), Peters, A. (Annette), Weninger, W. (Wolfgang), Farlik, M. (Matthias), Zhang, T. (Tao), Chen, W. (Wei), Xia, Y. (Yujing), Teumer, A. (Alexander), Nauck, M. (Matthias), Grabe, H. J. (Hans J.), Doerr, M. (Macus), Lehtimaki, T. (Terho), Guan, W. (Weihua), Milani, L. (Lili), Tanaka, T. (Toshiko), Fisher, K. (Krista), Waite, L. L. (Lindsay L.), Kasela, S. (Silva), Vineis, P. (Paolo), Verweij, N. (Niek), van der Harst, P. (Pim), Iacoviello, L. (Licia), Sacerdote, C. (Carlotta), Panico, S. (Salvatore), Krogh, V. (Vittorio), Tumino, R. (Rosario), Tzala, E. (Evangelia), Matullo, G. (Giuseppe), Hurme, M. A. (Mikko A.), Raitakari, O. T. (Olli T.), Colicino, E. (Elena), Baccarelli, A. A. (Andrea A.), Kahonen, M. (Mika), Herzig, K.-H. (Karl-Heinz), Li, S. (Shengxu), BIOS consortium, Conneely, K. N. (Karen N.), Kooner, J. S. (Jaspal S.), Kottgen, A. (Anna), Heijmans, B. T. (Bastiaan T.), Deloukas, P. (Panos), Relton, C. (Caroline), Ong, K. K. (Ken K.), Bell, J. T. (Jordana T.), Boerwinkle, E. (Eric), Elliott, P. (Paul), Brenner, H. (Hermann), Beekman, M. (Marian), Levy, D. (Daniel), Waldenberger, M. (Melanie), Chambers, J. C. (John C.), Dehghan, A. (Abbas), and Järvelin, M.-R. (Marjo-Riitta)

Abstract

We performed a multi-ethnic Epigenome Wide Association study on 22,774 individuals to describe the DNA methylation signature of chronic low-grade inflammation as measured by C-Reactive protein (CRP). We find 1,511 independent differentially methylated loci associated with CRP. These CpG sites show correlation structures across chromosomes, and are primarily situated in euchromatin, depleted in CpG islands. These genomic loci are predominantly situated in transcription factor binding sites and genomic enhancer regions. Mendelian randomization analysis suggests altered CpG methylation is a consequence of increased blood CRP levels. Mediation analysis reveals obesity and smoking as important underlying driving factors for changed CpG methylation. Finally, we find that an activated CpG signature significantly increases the risk for cardiometabolic diseases and COPD.

Published
2022

10. Meta-analyses identify DNA methylation associated with kidney function and damage

Author

Schlosser, P. (Pascal), Tin, A. (Adrienne), Matias-Garcia, P. R. (Pamela R.), Thio, C. H. (Chris H. L.), Joehanes, R. (Roby), Liu, H. (Hongbo), Weihs, A. (Antoine), Yu, Z. (Zhi), Hoppmann, A. (Anselm), Grundner-Culemann, F. (Franziska), Min, J. L. (Josine L.), Adeyemo, A. A. (Adebowale A.), Agyemang, C. (Charles), Arnlov, J. (Johan), Aziz, N. A. (Nasir A.), Baccarelli, A. (Andrea), Bochud, M. (Murielle), Brenner, H. (Hermann), Breteler, M. M. (Monique M. B.), Carmeli, C. (Cristian), Chaker, L. (Layal), Chambers, J. C. (John C.), Cole, S. A. (Shelley A.), Coresh, J. (Josef), Corre, T. (Tanguy), Correa, A. (Adolfo), Cox, S. R. (Simon R.), de Klein, N. (Niek), Delgado, G. E. (Graciela E.), Domingo-Relloso, A. (Arce), Eckardt, K.-U. (Kai-Uwe), Ekici, A. B. (Arif B.), Endlich, K. (Karlhans), Evans, K. L. (Kathryn L.), Floyd, J. S. (James S.), Fornage, M. (Myriam), Franke, L. (Lude), Fraszczyk, E. (Eliza), Gao, X. (Xu), Gao, X. (Xin), Ghanbari, M. (Mohsen), Ghasemi, S. (Sahar), Gieger, C. (Christian), Greenland, P. (Philip), Grove, M. L. (Megan L.), Harris, S. E. (Sarah E.), Hemani, G. (Gibran), Henneman, P. (Peter), Herder, C. (Christian), Horvath, S. (Steve), Hou, L. (Lifang), Hurme, M. A. (Mikko A.), Hwang, S.-J. (Shih-Jen), Järvelin, M.-R. (Marjo-Riitta), Kardia, S. L. (Sharon L. R.), Kasela, S. (Silva), Kleber, M. E. (Marcus E.), Koenig, W. (Wolfgang), Kooner, J. S. (Jaspal S.), Kramer, H. (Holly), Kronenberg, F. (Florian), Kuhnel, B. (Brigitte), Lehtimaki, T. (Terho), Lind, L. (Lars), Liu, D. (Dan), Liu, Y. (Yongmei), Lloyd-Jones, D. M. (Donald M.), Lohman, K. (Kurt), Lorkowski, S. (Stefan), Lu, A. T. (Ake T.), Marioni, R. E. (Riccardo E.), Marz, W. (Winfried), McCartney, D. L. (Daniel L.), Meeks, K. A. (Karlijn A. C.), Milani, L. (Lili), Mishra, P. P. (Pashupati P.), Nauck, M. (Matthias), Navas-Acien, A. (Ana), Nowak, C. (Christoph), Peters, A. (Annette), Prokisch, H. (Holger), Psaty, B. M. (Bruce M.), Raitakari, O. T. (Olli T.), Ratliff, S. M. (Scott M.), Reiner, A. P. (Alex P.), Rosas, S. E. (Sylvia E.), Schottker, B. (Ben), Schwartz, J. (Joel), Sedaghat, S. (Sanaz), Smith, J. A. (Jennifer A.), Sotoodehnia, N. (Nona), Stocker, H. R. (Hannah R.), Stringhini, S. (Silvia), Sundstrom, J. (Johan), Swenson, B. R. (Brenton R.), Tellez-Plaza, M. (Maria), van Meurs, J. B. (Joyce B. J.), van Vliet-Ostaptchouk, J. V. (Jana V.), Venema, A. (Andrea), Verweij, N. (Niek), Walker, R. M. (Rosie M.), Wielscher, M. (Matthias), Winkelmann, J. (Juliane), Wolffenbuttel, B. H. (Bruce H. R.), Zhao, W. (Wei), Zheng, Y. (Yinan), Loh, M. (Marie), Snieder, H. (Harold), Levy, D. (Daniel), Waldenberger, M. (Melanie), Susztak, K. (Katalin), Kottgen, A. (Anna), Teumer, A. (Alexander), Schlosser, P. (Pascal), Tin, A. (Adrienne), Matias-Garcia, P. R. (Pamela R.), Thio, C. H. (Chris H. L.), Joehanes, R. (Roby), Liu, H. (Hongbo), Weihs, A. (Antoine), Yu, Z. (Zhi), Hoppmann, A. (Anselm), Grundner-Culemann, F. (Franziska), Min, J. L. (Josine L.), Adeyemo, A. A. (Adebowale A.), Agyemang, C. (Charles), Arnlov, J. (Johan), Aziz, N. A. (Nasir A.), Baccarelli, A. (Andrea), Bochud, M. (Murielle), Brenner, H. (Hermann), Breteler, M. M. (Monique M. B.), Carmeli, C. (Cristian), Chaker, L. (Layal), Chambers, J. C. (John C.), Cole, S. A. (Shelley A.), Coresh, J. (Josef), Corre, T. (Tanguy), Correa, A. (Adolfo), Cox, S. R. (Simon R.), de Klein, N. (Niek), Delgado, G. E. (Graciela E.), Domingo-Relloso, A. (Arce), Eckardt, K.-U. (Kai-Uwe), Ekici, A. B. (Arif B.), Endlich, K. (Karlhans), Evans, K. L. (Kathryn L.), Floyd, J. S. (James S.), Fornage, M. (Myriam), Franke, L. (Lude), Fraszczyk, E. (Eliza), Gao, X. (Xu), Gao, X. (Xin), Ghanbari, M. (Mohsen), Ghasemi, S. (Sahar), Gieger, C. (Christian), Greenland, P. (Philip), Grove, M. L. (Megan L.), Harris, S. E. (Sarah E.), Hemani, G. (Gibran), Henneman, P. (Peter), Herder, C. (Christian), Horvath, S. (Steve), Hou, L. (Lifang), Hurme, M. A. (Mikko A.), Hwang, S.-J. (Shih-Jen), Järvelin, M.-R. (Marjo-Riitta), Kardia, S. L. (Sharon L. R.), Kasela, S. (Silva), Kleber, M. E. (Marcus E.), Koenig, W. (Wolfgang), Kooner, J. S. (Jaspal S.), Kramer, H. (Holly), Kronenberg, F. (Florian), Kuhnel, B. (Brigitte), Lehtimaki, T. (Terho), Lind, L. (Lars), Liu, D. (Dan), Liu, Y. (Yongmei), Lloyd-Jones, D. M. (Donald M.), Lohman, K. (Kurt), Lorkowski, S. (Stefan), Lu, A. T. (Ake T.), Marioni, R. E. (Riccardo E.), Marz, W. (Winfried), McCartney, D. L. (Daniel L.), Meeks, K. A. (Karlijn A. C.), Milani, L. (Lili), Mishra, P. P. (Pashupati P.), Nauck, M. (Matthias), Navas-Acien, A. (Ana), Nowak, C. (Christoph), Peters, A. (Annette), Prokisch, H. (Holger), Psaty, B. M. (Bruce M.), Raitakari, O. T. (Olli T.), Ratliff, S. M. (Scott M.), Reiner, A. P. (Alex P.), Rosas, S. E. (Sylvia E.), Schottker, B. (Ben), Schwartz, J. (Joel), Sedaghat, S. (Sanaz), Smith, J. A. (Jennifer A.), Sotoodehnia, N. (Nona), Stocker, H. R. (Hannah R.), Stringhini, S. (Silvia), Sundstrom, J. (Johan), Swenson, B. R. (Brenton R.), Tellez-Plaza, M. (Maria), van Meurs, J. B. (Joyce B. J.), van Vliet-Ostaptchouk, J. V. (Jana V.), Venema, A. (Andrea), Verweij, N. (Niek), Walker, R. M. (Rosie M.), Wielscher, M. (Matthias), Winkelmann, J. (Juliane), Wolffenbuttel, B. H. (Bruce H. R.), Zhao, W. (Wei), Zheng, Y. (Yinan), Loh, M. (Marie), Snieder, H. (Harold), Levy, D. (Daniel), Waldenberger, M. (Melanie), Susztak, K. (Katalin), Kottgen, A. (Anna), and Teumer, A. (Alexander)

Abstract

Chronic kidney disease is a major public health burden. Elevated urinary albumin-to-creatinine ratio is a measure of kidney damage, and used to diagnose and stage chronic kidney disease. To extend the knowledge on regulatory mechanisms related to kidney function and disease, we conducted a blood-based epigenome-wide association study for estimated glomerular filtration rate (n = 33,605) and urinary albumin-to-creatinine ratio (n = 15,068) and detected 69 and seven CpG sites where DNA methylation was associated with the respective trait. The majority of these findings showed directionally consistent associations with the respective clinical outcomes chronic kidney disease and moderately increased albuminuria. Associations of DNA methylation with kidney function, such as CpGs at JAZF1, PELI1 and CHD2 were validated in kidney tissue. Methylation at PHRF1, LDB2, CSRNP1 and IRF5 indicated causal effects on kidney function. Enrichment analyses revealed pathways related to hemostasis and blood cell migration for estimated glomerular filtration rate, and immune cell activation and response for urinary albumin-to-creatinineratio-associated CpGs.

Published
2021

11. DNA methylation and lipid metabolism:an EWAS of 226 metabolic measures

Author

Gomez-Alonso, M. d. (Monica del C.), Kretschmer, A. (Anja), Wilson, R. (Rory), Pfeiffer, L. (Liliane), Karhunen, V. (Ville), Seppala, I. (Ilkka), Zhang, W. (Weihua), Mittelstrass, K. (Kirstin), Wahl, S. (Simone), Matias-Garcia, P. R. (Pamela R.), Prokisch, H. (Holger), Horn, S. (Sacha), Meitinger, T. (Thomas), Serrano-Garcia, L. R. (Luis R.), Sebert, S. (Sylvain), Raitakari, O. (Olli), Loh, M. (Marie), Rathmann, W. (Wolfgang), Mueller-Nurasyid, M. (Martina), Herder, C. (Christian), Roden, M. (Michael), Hurme, M. (Mikko), Jarvelin, M.-R. (Marjo-Riitta), Ala-Korpela, M. (Mika), Kooner, J. S. (Jaspal S.), Peters, A. (Annette), Lehtimaki, T. (Terho), Chambers, J. C. (John C.), Gieger, C. (Christian), Kettunen, J. (Johannes), Waldenberger, M. (Melanie), Gomez-Alonso, M. d. (Monica del C.), Kretschmer, A. (Anja), Wilson, R. (Rory), Pfeiffer, L. (Liliane), Karhunen, V. (Ville), Seppala, I. (Ilkka), Zhang, W. (Weihua), Mittelstrass, K. (Kirstin), Wahl, S. (Simone), Matias-Garcia, P. R. (Pamela R.), Prokisch, H. (Holger), Horn, S. (Sacha), Meitinger, T. (Thomas), Serrano-Garcia, L. R. (Luis R.), Sebert, S. (Sylvain), Raitakari, O. (Olli), Loh, M. (Marie), Rathmann, W. (Wolfgang), Mueller-Nurasyid, M. (Martina), Herder, C. (Christian), Roden, M. (Michael), Hurme, M. (Mikko), Jarvelin, M.-R. (Marjo-Riitta), Ala-Korpela, M. (Mika), Kooner, J. S. (Jaspal S.), Peters, A. (Annette), Lehtimaki, T. (Terho), Chambers, J. C. (John C.), Gieger, C. (Christian), Kettunen, J. (Johannes), and Waldenberger, M. (Melanie)

Abstract

Background: The discovery of robust and trans-ethnically replicated DNA methylation markers of metabolic phenotypes, has hinted at a potential role of epigenetic mechanisms in lipid metabolism. However, DNA methylation and the lipid compositions and lipid concentrations of lipoprotein sizes have been scarcely studied. Here, we present an epigenome-wide association study (EWAS) (N = 5414 total) of mostly lipid-related metabolic measures, including a fine profiling of lipoproteins. As lipoproteins are the main players in the different stages of lipid metabolism, examination of epigenetic markers of detailed lipoprotein features might improve the diagnosis, prognosis, and treatment of metabolic disturbances. Results: We conducted an EWAS of leukocyte DNA methylation and 226 metabolic measurements determined by nuclear magnetic resonance spectroscopy in the population-based KORA F4 study (N = 1662) and replicated the results in the LOLIPOP, NFBC1966, and YFS cohorts (N = 3752). Follow-up analyses in the discovery cohort included investigations into gene transcripts, metabolic-measure ratios for pathway analysis, and disease endpoints. We identified 161 associations (p value < 4.7 × 10⁻¹⁰), covering 16 CpG sites at 11 loci and 57 metabolic measures. Identified metabolic measures were primarily medium and small lipoproteins, and fatty acids. For apolipoprotein B-containing lipoproteins, the associations mainly involved triglyceride composition and concentrations of cholesterol esters, triglycerides, free cholesterol, and phospholipids. All associations for HDL lipoproteins involved triglyceride measures only. Associated metabolic measure ratios, proxies of enzymatic activity, highlight amino acid, glucose, and lipid pathways as being potentially epigenetically implicated. Five CpG sites in four genes were associated with differential expression of transcripts in blood or adipose tissue. CpG sites in ABCG1 and PHGDH showed associations with metabolic measures

Published
2021

17. HDL cholesterol efflux capacity is inversely associated with subclinical cardiovascular risk markers in young adults:the cardiovascular risk in Young Finns study

Author

Hunjadi, M. (Monika), Lamina, C. (Claudia), Kahler, P. (Patrick), Bernscherer, T. (Tamara), Viikari, J. (Jorma), Lehtimäki, T. (Terho), Kähönen, M. (Mika), Hurme, M. (Mikko), Juonala, M. (Markus), Taittonen, L. (Leena), Laitinen, T. (Tomi), Jokinen, E. (Eero), Tossavainen, P. (Päivi), Hutri-Kähönen, N. (Nina), Raitakari, O. (Olli), Ritsch, A. (Andreas), Hunjadi, M. (Monika), Lamina, C. (Claudia), Kahler, P. (Patrick), Bernscherer, T. (Tamara), Viikari, J. (Jorma), Lehtimäki, T. (Terho), Kähönen, M. (Mika), Hurme, M. (Mikko), Juonala, M. (Markus), Taittonen, L. (Leena), Laitinen, T. (Tomi), Jokinen, E. (Eero), Tossavainen, P. (Päivi), Hutri-Kähönen, N. (Nina), Raitakari, O. (Olli), and Ritsch, A. (Andreas)

Abstract

The atherogenic process begins already in childhood and progresses to symptomatic condition with age. We investigated the association of cholesterol efflux capacity (CEC) and vascular markers of subclinical atherosclerosis in healthy, young adults. CEC was determined in 2282 participants of the Young Finns study using cAMP treated ³H-cholesterol-labeled J774 cells. The CEC was correlated to baseline and 6-year follow-up data of cardiovascular risk factors and ultrasound measurements of arterial structure and function. CEC was higher in women, correlated with total cholesterol, HDL-C, and apolipoprotein A-I, but not with LDL-C or apolipoprotein B. Compared to the lowest CEC quartile, the highest CEC quartile was significantly associated with high CRP levels and inversely associated with adiponectin. At baseline, high CEC was associated with decreased flow-mediated dilation (FMD) and carotid artery distensibility, as well as an increased Young’s modulus of elasticity, indicating adverse changes in arterial structure, and function. The association reversed with follow-up FMD data, indicating the interaction of preclinical parameters over time. A higher CEC was directly associated with a lower risk of subclinical atherosclerosis at follow-up. In young and healthy subjects, CEC was associated with important lipid risk parameters at baseline, as in older patients and CAD patients, but inversely with early risk markers for subclinical atherosclerosis.

Published
2020

18. Circulating cell-free DNA level predicts all-cause mortality independent of other predictors in the Health 2000 survey

Author

Kananen, L. (L.), Hurme, M. (M.), Jylhä, M. (M.), Härkänen, T. (T.), Koskinen, S. (S.), Stenholm, S. (S.), Kähönen, M. (M.), Lehtimäki, T. (T.), Ukkola, O. (O.), Jylhävä, J. (J.), Kananen, L. (L.), Hurme, M. (M.), Jylhä, M. (M.), Härkänen, T. (T.), Koskinen, S. (S.), Stenholm, S. (S.), Kähönen, M. (M.), Lehtimäki, T. (T.), Ukkola, O. (O.), and Jylhävä, J. (J.)

Abstract

Increased levels of circulating cell-free DNA (cf-DNA) are associated with and predict poor health outcomes. However, its predictive ability for mortality in population-based samples remains understudied. We analysed the capability of cf-DNA to predict all-cause mortality and assessed whether it adds predictive value on top of the other risk factors in the Health 2000 survey (n = 1,257, 46–76 years of age, 15-years-follow-up, 18% deceased). When analysed in a multivariate model with the other factors that independently predicted mortality in the sample (age, gender, self-rated health, smoking and plasma levels of glucose and adiponectin), increases in cf-DNA levels were associated with increased risk of mortality (hazard ratio [HR] for 0.1 µg increase in cf-DNA: 1.017, 95% confidence interval [CI] 1.008–1.026, p = 0.0003). Inclusion of cf-DNA in the model improved the model fit and discrimination. Stratifying the analysis by cardiovascular disease (CVD) status indicated that cf-DNA predicted mortality equally well in individuals with (HR 1.018, 95% CI 1.008–1.026, p = 0.002) and without (HR 1.018, 95% CI 1.001–1.035, p = 0.033) CVD. In conclusion, our study indicates that cf-DNA level predicts mortality in middle-aged and older individuals, also among those with established CVD, and adds significant value to mortality prediction. Our results thus underscore the role of cf-DNA as a viable marker of health.

Published
2020

26. Design, recruitment, logistics, and data management of the GEHA (Genetics of Healthy Ageing) project

Author

Skytthe, A., Valensin, S., Jeune, B., Cevenini, E., Balard, F., Beekman, M., Bezrukov, V., Blanche, H., Bolund, L., Broczek, K., Carru, C., Christensen, K., Christiansen, L., Collerton, J. C., Cotichini, R., de Craen, A. J.M., Dato, S., Davies, K., De Benedictis, G., Deiana, L., Flachsbart, F., Gampe, J., Gilbault, C., Gonos, E. S., Haimes, E., Hervonen, A., Hurme, M. A., Janiszewska, D., Jylhä, M., Kirkwood, T. B.L., Kristensen, P., Laiho, P., Leon, A., Marchisio, A., Masciulli, R., Nebel, A., Passarino, G., Pelicci, G., Peltonen, L., Perola, M., Poulain, M., Rea, I. M., Remacle, J., Robine, J. M., Schreiber, S., Scurti, M., Sevini, F., Sikora, E., Skouteri, A., Slagboom, P. E., Spazzafumo, L., Stazi, M. A., Toccaceli, V., Toussaint, O., Törnwall, O., Vaupel, J. W., Voutetakis, K., and Franceschi, C.

Published
2011
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40. Socioeconomic position, lifestyle habits and biomarkers of epigenetic aging: a multi-cohort analysis

Author

Fiorito, G., Mccrory, C., Robinson, O., Carmeli, C., Rosales, C. O., Zhang, Y., Colicino, E., Dugue, P. -A., Artaud, F., Mckay, G. J., Jeong, A., Mishra, P. P., Nost, T. H., Krogh, V., Panico, S., Sacerdote, C., Tumino, R., Palli, D., Matullo, G., Guarrera, S., Gandini, M., Bochud, M., Dermitzakis, E., Muka, T., Schwartz, J., Vokonas, P. S., Just, A., Hodge, A. M., Giles, G. G., Southey, M. C., Hurme, M. A., Young, I., Mcknight, A. J., Kunze, S., Waldenberger, M., Peters, A., Schwettmann, L., Lund, E., Baccarelli, A., Milne, R. L., Kenny, R. A., Elbaz, A., Brenner, H., Kee, F., Voortman, T., Probst-Hensch, N., Lehtimaki, T., Elliot, P., Stringhini, S., Vineis, P., Polidoro, S., Alberts, J., Alenius, H., Avendano, M., Baltar, V., Bartley, M., Barros, H., Bellone, M., Berger, E., Blane, D., Candiani, G., Carra, L., Castagne, R., Chadeau-Hyam, M., Cima, S., Clavel-Chapelon, F., Costa, G., Courtin, E., Delpierre, C., D'Errico, A., Dermitzakis, M., Elovainio, M., Elliott, P., Fagherazzi, G., Fraga, S., Gares, V., Gerbouin-Rerolle, P., Giles, G., Goldberg, M., Greco, D., Guessous, I., Haba-Rubio, J., Heinzer, R., Hodge, A., Joost, S., Karimi, M., Kelly-Irving, M., Kahonen, M., Karisola, P., Khenissi, L., Kivimaki, M., Laine, J., Lang, T., Laurent, A., Layte, R., Lepage, B., Lorsch, D., Macguire, F., Machell, G., Mackenbach, J., Marmot, M., de Mestral, C., Miller, C., Milne, R., Muennig, P., Nusselder, W., Petrovic, D., Pilapil, L., Preisig, M., Pulkki-Raback, L., Raitakari, O., Ribeiro, A. I., Ricceri, F., Recalcati, P., Reinhard, E., Valverde, J. R., Saba, S., Santegoets, F., Satolli, R., Simmons, T., Severi, G., Shipley, M. J., Tabak, A., Terhi, V., Tieulent, J., Vaccarella, S., Vigna-Taglianti, F., Vollenweider, P., Vuilleumier, N., Zins, M., Medical Research Council (MRC), Commission of the European Communities, BIOS Consortium, Lifepath consortium, Epidemiology, Dermitzakis, Emmanouil, and Stringhini, Silvia

Subjects
Male, Aging, Geriatrics & Gerontology, Disease, epigenetic clocks, Bioinformatics, 0601 Biochemistry and Cell Biology, DISEASE, Epigenesis, Genetic, Cohort Studies, 0302 clinical medicine, Risk Factors, DNA METHYLATION, media_common, 0303 health sciences, education, Lifepath consortium, VDP::Medisinske Fag: 700::Helsefag: 800::Samfunnsmedisin, sosialmedisin: 801, CARDIOVASCULAR RISK, Aged, Aging/genetics, Aging/psychology, DNA Methylation, Educational Status, Female, Humans, Life Style, Mutation, Social Class, biological aging, socioeconomic position, Longevity, ASSOCIATION, Biological aging, Education, Epigenetic clocks, Socioeconomic position, 3. Good health, WIDE METHYLATION, Aging/genetics/psychology, DNA methylation, Biomarker (medicine), HEALTH, BIOS Consortium, Life Sciences & Biomedicine, Research Paper, Cohort study, VDP::Medisinske Fag: 700::Basale medisinske, odontologiske og veterinærmedisinske fag: 710::Medisinsk genetikk: 714, media_common.quotation_subject, CANCER-RISK, 610 Medicine & health, VDP::Medical disciplines: 700::Basic medical, dental and veterinary science disciplines: 710::Medical genetics: 714, Biology, PERIPHERAL-BLOOD, 03 medical and health sciences, SDG 3 - Good Health and Well-being, Genetic, 360 Social problems & social services, 1112 Oncology and Carcinogenesis, Epigenetics, ddc:613, 030304 developmental biology, Science & Technology, Mechanism (biology), MUTATIONS, dNaM, Socioeconomic Position, Biological Aging, Epigenetic Clocks, Cell Biology, 0606 Physiology, DRIFT, VDP::Medical disciplines: 700::Health sciences: 800::Community medicine, Social medicine: 801, 030217 neurology & neurosurgery, Epigenesis
Abstract

Source at https://doi.org/10.18632/aging.101900. Differences in health status by socioeconomic position (SEP) tend to be more evident at older ages, suggesting the involvement of a biological mechanism responsive to the accumulation of deleterious exposures across the lifespan. DNA methylation (DNAm) has been proposed as a biomarker of biological aging that conserves memory of endogenous and exogenous stress during life. We examined the association of education level, as an indicator of SEP, and lifestyle-related variables with four biomarkers of age-dependent DNAm dysregulation: the total number of stochastic epigenetic mutations (SEMs) and three epigenetic clocks (Horvath, Hannum and Levine), in 18 cohorts spanning 12 countries. The four biological aging biomarkers were associated with education and different sets of risk factors independently, and the magnitude of the effects differed depending on the biomarker and the predictor. On average, the effect of low education on epigenetic aging was comparable with those of other lifestyle-related risk factors (obesity, alcohol intake), with the exception of smoking, which had a significantly stronger effect. Our study shows that low education is an independent predictor of accelerated biological (epigenetic) aging and that epigenetic clocks appear to be good candidates for disentangling the biological pathways underlying social inequalities in healthy aging and longevity.

Published
2019

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