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  • 1.
    Bai, Ge
    et al.
    Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden..
    Szwajda, Agnieszka
    Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden..
    Wang, Yunzhang
    Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden..
    Li, Xia
    Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden..
    Bower, Hannah
    Karolinska Inst, Dept Med Solna, Clin Epidemiol Div, Stockholm, Sweden..
    Karlsson, Ida K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden.
    Johansson, Boo
    Univ Gothenburg, Ctr Ageing & Hlth AgeCap, Dept Psychol, Gothenburg, Sweden..
    Aslan, Anna K. Dahl
    Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden.;Univ Skovde, Sch Hlth Sci, Skovde, Sweden..
    Pedersen, Nancy L.
    Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden..
    Hagg, Sara
    Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden..
    Jylhava, Juulia
    Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden..
    Frailty trajectories in three longitudinal studies of aging: Is the level or the rate of change more predictive of mortality?2021In: Age and Ageing, ISSN 0002-0729, E-ISSN 1468-2834, Vol. 50, no 6, p. 2174-2182Article in journal (Refereed)
    Abstract [en]

    Background: frailty shows an upward trajectory with age, and higher levels increase the risk of mortality. However, it is less known whether the shape of frailty trajectories differs by age at death or whether the rate of change in frailty is associated with mortality. Objectives: to assess population frailty trajectories by age at death and to analyse whether the current level of the frailty index (FI) i.e. the most recent measurement or the person-specific rate of change is more predictive of mortality. Methods: 3,689 individuals from three population-based cohorts with up to 15 repeated measurements of the Rockwood frailty index were analysed. The FI trajectories were assessed by stratifying the sample into four age-at-death groups: <70, 70-80, 80-90 and >90 years. Generalised survival models were used in the survival analysis. Results: the FI trajectories by age at death showed that those who died at <70 years had a steadily increasing trajectory throughout the 40 years before death, whereas those who died at the oldest ages only accrued deficits from age similar to 75 onwards. Higher level of FI was independently associated with increased risk of mortality (hazard ratio 1.68, 95% confidence interval 1.47-1.91), whereas the rate of change was no longer significant after accounting for the current FI level. The effect of the FI level did not weaken with time elapsed since the last measurement. Conclusions: Frailty trajectories differ as a function of age-at-death category. The current level of FI is a stronger marker for risk stratification than the rate of change.

  • 2.
    Bai, Ge
    et al.
    Karolinska Inst, Dept Med Epidemiol & Biostat, Nobels Vag 12A, S-17165 Stockholm, Sweden..
    Wang, Yunzhang
    Karolinska Inst, Dept Med Epidemiol & Biostat, Nobels Vag 12A, S-17165 Stockholm, Sweden..
    Kuja-Halkola, Ralf
    Karolinska Inst, Dept Med Epidemiol & Biostat, Nobels Vag 12A, S-17165 Stockholm, Sweden..
    Li, Xia
    Karolinska Inst, Dept Med Epidemiol & Biostat, Nobels Vag 12A, S-17165 Stockholm, Sweden..
    Tomata, Yasutake
    Karolinska Inst, Dept Med Epidemiol & Biostat, Nobels Vag 12A, S-17165 Stockholm, Sweden.;Kanagawa Univ Human Serv, Fac Hlth & Social Serv, Sch Nutr & Dietet, Yokosuka, Kanagawa, Japan..
    Karlsson, Ida K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Karolinska Inst, Dept Med Epidemiol & Biostat, Nobels Vag 12A, S-17165 Stockholm, Sweden.;Jonkoping Univ, Sch Hlth & Welf, Inst Gerontol & Aging Res Network Jonkoping ARN J, Jonkoping, Sweden..
    Pedersen, Nancy L.
    Karolinska Inst, Dept Med Epidemiol & Biostat, Nobels Vag 12A, S-17165 Stockholm, Sweden..
    Hagg, Sara
    Karolinska Inst, Dept Med Epidemiol & Biostat, Nobels Vag 12A, S-17165 Stockholm, Sweden..
    Jylhava, Juulia
    Karolinska Inst, Dept Med Epidemiol & Biostat, Nobels Vag 12A, S-17165 Stockholm, Sweden.;Univ Tampere, Fac Social Sci Hlth Sci, Tampere, Finland.;Univ Tampere, Gerontol Res Ctr GEREC, Tampere, Finland..
    Frailty and the risk of dementia: is the association explained by shared environmental and genetic factors?2021In: BMC Medicine, E-ISSN 1741-7015, Vol. 19, no 1, article id 248Article in journal (Refereed)
    Abstract [en]

    Background Frailty has been identified as a risk factor for cognitive impairment and dementia. However, it is not known whether familial factors, such as genetics and shared environmental factors, underlie this association. We analyzed the association between frailty and the risk of dementia in a large twin cohort and examined the role of familial factors in the association. Methods The Rockwood frailty index (FI) based on 44 health deficits was used to assess frailty. The population-level association between FI and the risk of all-cause dementia was analyzed in 41,550 participants of the Screening Across the Lifespan Twin (SALT) study (full sample, aged 41-97 years at baseline), using Cox and competing risk models. A subsample of 10,487 SALT participants aged 65 and older who received a cognitive assessment (cognitive sample) was used in a sensitivity analysis to assess the effect of baseline cognitive level on the FI-dementia association. To analyze the influence of familial effects on the FI-dementia association, a within-pair analysis was performed. The within-pair model was also used to assess whether the risk conferred by frailty varies by age at FI assessment. Results A total of 3183 individuals were diagnosed with dementia during the 19-year follow-up. A 10% increase in FI was associated with an increased risk of dementia (hazard ratio [HR] 1.17 (95% confidence interval [CI] 1.07, 1.18)) in the full sample adjusted for age, sex, education, and tobacco use. A significant association was likewise found in the cognitive sample, with an HR of 1.13 (95% CI 1.09, 1.20), adjusted for age, sex, and cognitive level at baseline. The associations were not attenuated when adjusted for APOE e4 carrier status or considering the competing risk of death. After adjusting for familial effects, we found no evidence for statistically significant attenuation of the effect. The risk conferred by higher FI on dementia was constant after age 50 until very old age. Conclusions A higher level of frailty predicts the risk of dementia and the association appears independent of familial factors. Targeting frailty might thus contribute to preventing or delaying dementia.

  • 3.
    Bokenberger, K.
    et al.
    Karolinska Institutet, Department of Medical Epidemiology & Biostatistics, Stockholm, Sweden.
    Sjölander, A.
    Karolinska Institutet, Department of Medical Epidemiology & Biostatistics, Stockholm, Sweden.
    Dahl Aslan, Anna K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Karolinska Institutet, Department of Medical Epidemiology & Biostatistics, Stockholm, Sweden.
    Karlsson, Ida K.
    Karolinska Institutet, Department of Medical Epidemiology & Biostatistics, Stockholm, Sweden.
    Akerstedt, T.
    Stockholm University, Stress Research Institute, Stockholm, Sweden.
    Pedersen, N. L.
    Karolinska Institutet, Department of Medical Epidemiology & Biostatistics, Stockholm, Sweden.
    Midlife shift work and risk of incident dementia2017In: Sleep, ISSN 0161-8105, E-ISSN 1550-9109, Vol. 40, p. A425-A425Article in journal (Refereed)
  • 4.
    Bokenberger, Kathleen
    et al.
    Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Stockholm, Sweden.
    Sjölander, Arvid
    Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Stockholm, Sweden.
    Dahl Aslan, Anna K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Stockholm, Sweden.
    Karlsson, Ida K.
    Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Stockholm, Sweden.
    Åkerstedt, Torbjörn
    Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
    Pedersen, Nancy Lee
    Department of Psychology, University of Southern California, Los Angeles, United States.
    Shift work and risk of incident dementia: a study of two population-based cohorts2018In: European Journal of Epidemiology, ISSN 0393-2990, E-ISSN 1573-7284, Vol. 33, no 10, p. 977-987Article in journal (Refereed)
    Abstract [en]

    This study aimed to investigate the association between shift work and incident dementia in two population-based cohorts from the Swedish Twin Registry (STR). The STR-1973 sample included 13,283 participants born 1926–1943 who received a mailed questionnaire in 1973 that asked about status (ever/never) and duration (years) of shift work employment. The Screening Across the Lifespan Twin (SALT) sample included 41,199 participants born 1900–1958 who participated in a telephone interview in 1998–2002 that asked about night work status and duration. Dementia diagnoses came from Swedish patient registers. Cox proportional-hazards regression was used to estimate hazard ratios (HR) with 95% confidence intervals (CI). Potential confounders such as age, sex, education, diabetes, cardiovascular disease and stroke were included in adjusted models. In genotyped subsamples (n = 2977 in STR-1973; n = 10,366 in SALT), APOE ε4 status was considered in models. A total of 983 (7.4%) and 1979 (4.8%) dementia cases were identified after a median of 41.2 and 14.1 years follow-up in the STR-1973 and SALT sample, respectively. Ever shift work (HR 1.36, 95% CI 1.15–1.60) and night work (HR 1.12, 95% CI 1.01–1.23) were associated with higher dementia incidence. Modest dose-response associations were observed, where longer duration shift work and night work predicted increased dementia risk. Among APOE ε4 carriers, individuals exposed to ≥ 20 years of shift work and night work had increased dementia risk compared to day workers. Findings indicate that shift work, including night shift work, compared to non-shift jobs is associated with increased dementia incidence. Confirmation of findings is needed. 

  • 5. Davies, G.
    et al.
    Lam, M.
    Harris, S. E.
    Trampush, J. W.
    Luciano, M.
    Hill, W. D.
    Hagenaars, S. P.
    Ritchie, S. J.
    Marioni, R. E.
    Fawns-Ritchie, C.
    Liewald, D. C. M.
    Okely, J. A.
    Ahola-Olli, A. V.
    Barnes, C. L. K.
    Bertram, L.
    Bis, J. C.
    Burdick, K. E.
    Christoforou, A.
    Derosse, P.
    Djurovic, S.
    Espeseth, T.
    Giakoumaki, S.
    Giddaluru, S.
    Gustavson, D. E.
    Hayward, C.
    Hofer, E.
    Ikram, M. A.
    Karlsson, R.
    Knowles, E.
    Lahti, J.
    Leber, M.
    Li, S.
    Mather, K. A.
    Melle, I.
    Morris, D.
    Oldmeadow, C.
    Palviainen, T.
    Payton, A.
    Pazoki, R.
    Petrovic, K.
    Reynolds, C. A.
    Sargurupremraj, M.
    Scholz, M.
    Smith, J. A.
    Smith, A. V.
    Terzikhan, N.
    Thalamuthu, A.
    Trompet, S.
    Van Der Lee, S. J.
    Ware, E. B.
    Windham, B. G.
    Wright, M. J.
    Yang, J.
    Yu, J.
    Ames, D.
    Amin, N.
    Amouyel, P.
    Andreassen, O. A.
    Armstrong, N. J.
    Assareh, A. A.
    Attia, J. R.
    Attix, D.
    Avramopoulos, D.
    Bennett, D. A.
    Böhmer, A. C.
    Boyle, P. A.
    Brodaty, H.
    Campbell, H.
    Cannon, T. D.
    Cirulli, E. T.
    Congdon, E.
    Conley, E. D.
    Corley, J.
    Cox, S. R.
    Dale, A. M.
    Dehghan, A.
    Dick, D.
    Dickinson, D.
    Eriksson, J. G.
    Evangelou, E.
    Faul, J. D.
    Ford, I.
    Freimer, N. A.
    Gao, H.
    Giegling, I.
    Gillespie, N. A.
    Gordon, S. D.
    Gottesman, R. F.
    Griswold, M. E.
    Gudnason, V.
    Harris, T. B.
    Hartmann, A. M.
    Hatzimanolis, A.
    Heiss, G.
    Holliday, E. G.
    Joshi, P. K.
    Kähönen, M.
    Kardia, S. L. R.
    Karlsson, Ida K.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Kleineidam, L.
    Knopman, D. S.
    Kochan, N. A.
    Konte, B.
    Kwok, J. B.
    Le Hellard, S.
    Lee, T.
    Lehtimäki, T.
    Li, S. -C
    Liu, T.
    Koini, M.
    London, E.
    Longstreth, W.T., Jr.
    Lopez, O. L.
    Loukola, A.
    Luck, T.
    Lundervold, A. J.
    Lundquist, A.
    Lyytikäinen, L. -P
    Martin, N. G.
    Montgomery, G. W.
    Murray, A. D.
    Need, A. C.
    Noordam, R.
    Nyberg, L.
    Ollier, W.
    Papenberg, G.
    Pattie, A.
    Polasek, O.
    Poldrack, R. A.
    Psaty, B. M.
    Reppermund, S.
    Riedel-Heller, S. G.
    Rose, R. J.
    Rotter, J. I.
    Roussos, P.
    Rovio, S. P.
    Saba, Y.
    Sabb, F. W.
    Sachdev, P. S.
    Satizabal, C. L.
    Schmid, M.
    Scott, R. J.
    Scult, M. A.
    Simino, J.
    Slagboom, P. E.
    Smyrnis, N.
    Soumaré, A.
    Stefanis, N. C.
    Stott, D. J.
    Straub, R. E.
    Sundet, K.
    Taylor, A. M.
    Taylor, K. D.
    Tzoulaki, I.
    Tzourio, C.
    Uitterlinden, A.
    Vitart, V.
    Voineskos, A. N.
    Kaprio, J.
    Wagner, M.
    Wagner, H.
    Weinhold, L.
    Wen, K. H.
    Widen, E.
    Yang, Q.
    Zhao, W.
    Adams, H. H. H.
    Arking, D. E.
    Bilder, R. M.
    Bitsios, P.
    Boerwinkle, E.
    Chiba-Falek, O.
    Corvin, A.
    De Jager, P. L.
    Debette, S.
    Donohoe, G.
    Elliott, P.
    Fitzpatrick, A. L.
    Gill, M.
    Glahn, D. C.
    Hägg, S.
    Hansell, N. K.
    Hariri, A. R.
    Ikram, M. K.
    Jukema, J. W.
    Vuoksimaa, E.
    Keller, M. C.
    Kremen, W. S.
    Launer, L.
    Lindenberger, U.
    Palotie, A.
    Pedersen, N. L.
    Pendleton, N.
    Porteous, D. J.
    Räikkönen, K.
    Raitakari, O. T.
    Ramirez, A.
    Reinvang, I.
    Rudan, I.
    Rujescu, D.
    Schmidt, R.
    Schmidt, H.
    Schofield, P. W.
    Schofield, P. R.
    Starr, J. M.
    Steen, V. M.
    Trollor, J. N.
    Turner, S. T.
    Van Duijn, C. M.
    Villringer, A.
    Weinberger, D. R.
    Weir, D. R.
    Wilson, J. F.
    Malhotra, A.
    McIntosh, A. M.
    Gale, C. R.
    Seshadri, S.
    Mosley, T.H., Jr.
    Bressler, J.
    Lencz, T.
    Deary, I. J.
    Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function2018In: Nature Communications, E-ISSN 2041-1723, Vol. 9, no 1, article id 2098Article in journal (Refereed)
    Abstract [en]

    General cognitive function is a prominent and relatively stable human trait that is associated with many important life outcomes. We combine cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N = 300,486; age 16-102) and find 148 genome-wide significant independent loci (P &lt; 5 × 10-8) associated with general cognitive function. Within the novel genetic loci are variants associated with neurodegenerative and neurodevelopmental disorders, physical and psychiatric illnesses, and brain structure. Gene-based analyses find 709 genes associated with general cognitive function. Expression levels across the cortex are associated with general cognitive function. Using polygenic scores, up to 4.3% of variance in general cognitive function is predicted in independent samples. We detect significant genetic overlap between general cognitive function, reaction time, and many health variables including eyesight, hypertension, and longevity. In conclusion we identify novel genetic loci and pathways contributing to the heritability of general cognitive function.

  • 6. de Rojas, I.
    et al.
    Moreno-Grau, S.
    Tesi, N.
    Grenier-Boley, B.
    Andrade, V.
    Jansen, I. E.
    Pedersen, N. L.
    Stringa, N.
    Zettergren, A.
    Hernández, I.
    Montrreal, L.
    Antúnez, C.
    Antonell, A.
    Tankard, R. M.
    Bis, J. C.
    Sims, R.
    Bellenguez, C.
    Quintela, I.
    González-Perez, A.
    Calero, M.
    Franco-Macías, E.
    Macías, J.
    Blesa, R.
    Cervera-Carles, L.
    Menéndez-González, M.
    Frank-García, A.
    Royo, J. L.
    Moreno, F.
    Huerto Vilas, R.
    Baquero, M.
    Diez-Fairen, M.
    Lage, C.
    García-Madrona, S.
    García-González, P.
    Alarcón-Martín, E.
    Valero, S.
    Sotolongo-Grau, O.
    Ullgren, A.
    Naj, A. C.
    Lemstra, A. W.
    Benaque, A.
    Pérez-Cordón, A.
    Benussi, A.
    Rábano, A.
    Padovani, A.
    Squassina, A.
    de Mendonça, A.
    Arias Pastor, A.
    Kok, A. A. L.
    Meggy, A.
    Pastor, A. B.
    Espinosa, A.
    Corma-Gómez, A.
    Martín Montes, A.
    Sanabria, Á.
    DeStefano, A. L.
    Schneider, A.
    Haapasalo, A.
    Kinhult Ståhlbom, A.
    Tybjærg-Hansen, A.
    Hartmann, A. M.
    Spottke, A.
    Corbatón-Anchuelo, A.
    Rongve, A.
    Borroni, B.
    Arosio, B.
    Nacmias, B.
    Nordestgaard, B. G.
    Kunkle, B. W.
    Charbonnier, C.
    Abdelnour, C.
    Masullo, C.
    Martínez Rodríguez, C.
    Muñoz-Fernandez, C.
    Dufouil, C.
    Graff, C.
    Ferreira, C. B.
    Chillotti, C.
    Reynolds, C. A.
    Fenoglio, C.
    Van Broeckhoven, C.
    Clark, C.
    Pisanu, C.
    Satizabal, C. L.
    Holmes, C.
    Buiza-Rueda, D.
    Aarsland, D.
    Rujescu, D.
    Alcolea, D.
    Galimberti, D.
    Wallon, D.
    Seripa, D.
    Grünblatt, E.
    Dardiotis, E.
    Düzel, E.
    Scarpini, E.
    Conti, E.
    Rubino, E.
    Gelpi, E.
    Rodriguez-Rodriguez, E.
    Duron, E.
    Boerwinkle, E.
    Ferri, E.
    Tagliavini, F.
    Küçükali, F.
    Pasquier, F.
    Sanchez-Garcia, F.
    Mangialasche, F.
    Jessen, F.
    Nicolas, G.
    Selbæk, G.
    Ortega, G.
    Chêne, G.
    Hadjigeorgiou, G.
    Rossi, G.
    Spalletta, G.
    Giaccone, G.
    Grande, G.
    Binetti, G.
    Papenberg, G.
    Hampel, H.
    Bailly, H.
    Zetterberg, H.
    Soininen, H.
    Karlsson, Ida K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Alvarez, I.
    Appollonio, I.
    Giegling, I.
    Skoog, I.
    Saltvedt, I.
    Rainero, I.
    Rosas Allende, I.
    Hort, J.
    Diehl-Schmid, J.
    Van Dongen, J.
    Vidal, J. -S
    Lehtisalo, J.
    Wiltfang, J.
    Thomassen, J. Q.
    Kornhuber, J.
    Haines, J. L.
    Vogelgsang, J.
    Pineda, J. A.
    Fortea, J.
    Popp, J.
    Deckert, J.
    Buerger, K.
    Morgan, K.
    Fließbach, K.
    Sleegers, K.
    Molina-Porcel, L.
    Kilander, L.
    Weinhold, L.
    Farrer, L. A.
    Wang, L. -S
    Kleineidam, L.
    Farotti, L.
    Parnetti, L.
    Tremolizzo, L.
    Hausner, L.
    Benussi, L.
    Froelich, L.
    Ikram, M. A.
    Deniz-Naranjo, M. C.
    Tsolaki, M.
    Rosende-Roca, M.
    Löwenmark, M.
    Hulsman, M.
    Spallazzi, M.
    Pericak-Vance, M. A.
    Esiri, M.
    Bernal Sánchez-Arjona, M.
    Dalmasso, M. C.
    Martínez-Larrad, M. T.
    Arcaro, M.
    Nöthen, M. M.
    Fernández-Fuertes, M.
    Dichgans, M.
    Ingelsson, M.
    Herrmann, M. J.
    Scherer, M.
    Vyhnalek, M.
    Kosmidis, M. H.
    Yannakoulia, M.
    Schmid, M.
    Ewers, M.
    Heneka, M. T.
    Wagner, M.
    Scamosci, M.
    Kivipelto, M.
    Hiltunen, M.
    Zulaica, M.
    Alegret, M.
    Fornage, M.
    Roberto, N.
    van Schoor, N. M.
    Seidu, N. M.
    Banaj, N.
    Armstrong, N. J.
    Scarmeas, N.
    Scherbaum, N.
    Goldhardt, O.
    Hanon, O.
    Peters, O.
    Skrobot, O. A.
    Quenez, O.
    Lerch, O.
    Bossù, P.
    Caffarra, P.
    Dionigi Rossi, P.
    Sakka, P.
    Hoffmann, P.
    Holmans, P. A.
    Fischer, P.
    Riederer, P.
    Yang, Q.
    Marshall, R.
    Kalaria, R. N.
    Mayeux, R.
    Vandenberghe, R.
    Cecchetti, R.
    Ghidoni, R.
    Frikke-Schmidt, R.
    Sorbi, S.
    Hägg, S.
    Engelborghs, S.
    Helisalmi, S.
    Botne Sando, S.
    Kern, S.
    Archetti, S.
    Boschi, S.
    Fostinelli, S.
    Gil, S.
    Mendoza, S.
    Mead, S.
    Ciccone, S.
    Djurovic, S.
    Heilmann-Heimbach, S.
    Riedel-Heller, S.
    Kuulasmaa, T.
    del Ser, T.
    Lebouvier, T.
    Polak, T.
    Ngandu, T.
    Grimmer, T.
    Bessi, V.
    Escott-Price, V.
    Giedraitis, V.
    Deramecourt, V.
    Maier, W.
    Jian, X.
    Pijnenburg, Y. A. L.
    Smith, A. D.
    Saenz, A.
    Bizzarro, A.
    Lauria, A.
    Vacca, A.
    Solomon, A.
    Anastasiou, A.
    Richardson, A.
    Boland, A.
    Koivisto, A.
    Daniele, A.
    Greco, A.
    Marianthi, A.
    McGuinness, B.
    Fin, B.
    Ferrari, C.
    Custodero, C.
    Ferrarese, C.
    Ingino, C.
    Mangone, C.
    Reyes Toso, C.
    Martínez, C.
    Cuesta, C.
    Muchnik, C.
    Joachim, C.
    Ortiz, C.
    Besse, C.
    Johansson, C.
    Zoia, C. P.
    Laske, C.
    Anastasiou, C.
    Palacio, D. L.
    Politis, D. G.
    Janowitz, D.
    Craig, D.
    Mann, D. M.
    Neary, D.
    Jürgen, D.
    Daian, D.
    Belezhanska, D.
    Kohler, E.
    Castaño, E. M.
    Koutsouraki, E.
    Chipi, E.
    De Roeck, E.
    Costantini, E.
    Vardy, E. R. L. C.
    Piras, F.
    Roveta, F.
    Prestia, F. A.
    Assogna, F.
    Salani, F.
    Sala, G.
    Lacidogna, G.
    Novack, G.
    Wilcock, G.
    Thonberg, H.
    Kölsch, H.
    Weber, H.
    Boecker, H.
    Etchepareborda, I.
    Piaceri, I.
    Tuomilehto, J.
    Lindström, J.
    Laczo, J.
    Johnston, J.
    Deleuze, J. -F
    Harris, J.
    Schott, J. M.
    Priller, J.
    Bacha, J. I.
    Snowden, J.
    Lisso, J.
    Mihova, K. Y.
    Traykov, L.
    Morelli, L.
    Brusco, L. I.
    Rainer, M.
    Takalo, M.
    Bjerke, M.
    Del Zompo, M.
    Serpente, M.
    Sanchez Abalos, M.
    Rios, M.
    Peltonen, M.
    Herrman, M. J.
    Kohler, M.
    Rojo, M.
    Jones, M.
    Orsini, M.
    Medel, N.
    Olivar, N.
    Fox, N. C.
    Salvadori, N.
    Hooper, N. M.
    Galeano, P.
    Solis, P.
    Bastiani, P.
    Mecocci, P.
    Passmore, P.
    Heun, R.
    Antikainen, R.
    Olaso, R.
    Perneczky, R.
    Germani, S.
    López-García, S.
    Love, S.
    Mehrabian, S.
    Bagnoli, S.
    Kochen, S.
    Andreoni, S.
    Teipel, S.
    Todd, S.
    Pickering-Brown, S.
    Natunen, T.
    Tegos, T.
    Laatikainen, T.
    Strandberg, T.
    Polvikoski, T. M.
    Matoska, V.
    Ciullo, V.
    Cores, V.
    Solfrizzi, V.
    Lisetti, V.
    Sevillano, Z.
    Aguilera, N.
    Alarcon, E.
    Boada, M.
    Buendia, M.
    Cañabate, P.
    Carracedo, A.
    Diego, S.
    Gailhajenet, A.
    Guitart, M.
    González-Pérez, A.
    Ibarria, M.
    Lafuente, A.
    Macias, J.
    Maroñas, O.
    Martín, E.
    Martínez, M. T.
    Marquié, M.
    Mauleón, A.
    Moreno, M.
    Orellana, A.
    Pancho, A.
    Pelejá, E.
    Pérez-Cordon, A.
    Preckler, S.
    Real, L. M.
    Ruiz, A.
    Sáez, M. E.
    Sanabria, A.
    Serrano-Rios, M.
    Tárraga, L.
    Vargas, L.
    Adarmes-Gómez, A. D.
    Alonso, M. D.
    Álvarez, I.
    Álvarez, V.
    Amer-Ferrer, G.
    Antequera, M.
    Bernal, M.
    Bullido, M. J.
    Burguera, J. A.
    Carrillo, F.
    Carrión-Claro, M.
    Casajeros, M. J.
    Clarimón, J.
    Cruz-Gamero, J. M.
    de Pancorbo, M. M.
    Escuela, R.
    Garrote-Espina, L.
    García-Alberca, J. M.
    Garcia Madrona, S.
    Garcia-Ribas, G.
    Gómez-Garre, P.
    Hevilla, S.
    Jesús, S.
    Labrador Espinosa, M. A.
    Legaz, A.
    Lleó, A.
    Lopez de Munain, A.
    Macias-García, D.
    Manzanares, S.
    Marín, M.
    Marín-Muñoz, J.
    Marín, T.
    Martínez, B.
    Martínez, V.
    Martínez-Lage Álvarez, P.
    Medina, M.
    Mendioroz Iriarte, M.
    Mir, P.
    Molinuevo, J. L.
    Pastor, P.
    Pérez Tur, J.
    Periñán-Tocino, T.
    Pineda-Sanchez, R.
    Piñol-Ripoll, G.
    Real de Asúa, D.
    Rodrigo, S.
    Rodríguez-Rodríguez, E.
    Sanchez del Valle Díaz, R.
    Sánchez-Juan, P.
    Sastre, I.
    Vicente, M. P.
    Vigo-Ortega, R.
    Vivancos, L.
    Macleod, C.
    McCracken, C.
    Brayne, C.
    Bresner, C.
    Grozeva, D.
    Bellou, E.
    Sommerville, E. W.
    Matthews, F.
    Leonenko, G.
    Menzies, G.
    Windle, G.
    Harwood, J.
    Phillips, J.
    Bennett, K.
    Luckuck, L.
    Clare, L.
    Woods, R.
    Saad, S.
    Burholt, V.
    Kehoe, P. G.
    Pérez-Tur, J.
    Scheltens, P.
    Holstege, H.
    Carracedo, Á.
    Amouyel, P.
    Schellenberg, G. D.
    Williams, J.
    Seshadri, S.
    van Duijn, C. M.
    Mather, K. A.
    Sánchez-Valle, R.
    Serrano-Ríos, M.
    Blennow, K.
    Huisman, M.
    Andreassen, O. A.
    Posthuma, D.
    van der Flier, W. M.
    Ramirez, A.
    Lambert, J. -C
    van der Lee, S. J.
    contributors, EADB
    group, The GR@ACE study
    consortium, DEGESCO
    IGAP (ADGC, CHARGE, EADI, GERAD)
    consortia, PGC-ALZ
    Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores2021In: Nature Communications, E-ISSN 2041-1723, Vol. 12, no 1, article id 3417Article in journal (Refereed)
    Abstract [en]

    Genetic discoveries of Alzheimer’s disease are the drivers of our understanding, and together with polygenetic risk stratification can contribute towards planning of feasible and efficient preventive and curative clinical trials. We first perform a large genetic association study by merging all available case-control datasets and by-proxy study results (discovery n = 409,435 and validation size n = 58,190). Here, we add six variants associated with Alzheimer’s disease risk (near APP, CHRNE, PRKD3/NDUFAF7, PLCG2 and two exonic variants in the SHARPIN gene). Assessment of the polygenic risk score and stratifying by APOE reveal a 4 to 5.5 years difference in median age at onset of Alzheimer’s disease patients in APOE ɛ4 carriers. Because of this study, the underlying mechanisms of APP can be studied to refine the amyloid cascade and the polygenic risk score provides a tool to select individuals at high risk of Alzheimer’s disease.

  • 7.
    Gatz, Margaret
    et al.
    Department of Psychology, University of Southern California, Los Angeles, USA.
    Jang, Jung Yun
    Department of Psychology, University of Southern California, Los Angeles, USA.
    Karlsson, Ida K.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Pedersen, Nancy L.
    Department of Psychology, University of Southern California, Los Angeles, USA.
    Dementia: Genes, environments, interactions2014In: Behavior genetics of cognition across the lifespan / [ed] D. Finkel & C. A. Reynolds, New York: Springer, 2014, p. 201-231Chapter in book (Refereed)
    Abstract [en]

    Dementia is an increasingly prevalent disorder as the world population ages, with Alzheimer disease the most common cause. Twin studies find concordance to be substantially higher among monozygotic as compared with dizygotic twin pairs. While a few genes have been identified that are responsible for early-onset Alzheimer disease, they account for well under 5 % of all cases. Among susceptibility genes for Alzheimer disease identified by association studies, population attributable fraction for the most prominent of these—apolipoprotein E—is estimated to be about 25 %, whereas other genes at best predict another 20 %. There are few strong environmental risk or protective factors, with vascular risks the best established, although with mechanisms still not fully understood. It seems likely that clusters of risk alleles, interactions between risk alleles and environmental exposures, and epigenetic mechanisms play a role in explaining Alzheimer disease, with different combinations of influences culminating in the observed pathophysiology. In particular, environmental exposures early in life could lead to deleterious changes in gene expression in late life.

  • 8. Hou, J.
    et al.
    Hess, J. L.
    Armstrong, N.
    Bis, J. C.
    Grenier-Boley, B.
    Karlsson, Ida K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping).
    Leonenko, G.
    Numbers, K.
    O’Brien, E. K.
    Shadrin, A.
    Thalamuthu, A.
    Yang, Q.
    Andreassen, O. A.
    Brodaty, H.
    Gatz, M.
    Kochan, N. A.
    Lambert, J. -C
    Laws, S. M.
    Masters, C. L.
    Mather, K. A.
    Pedersen, N. L.
    Posthuma, D.
    Sachdev, P. S.
    Williams, J.
    Fan, C. C.
    Faraone, S. V.
    Fennema-Notestine, C.
    Lin, S. -J
    Escott-Price, V.
    Holmans, P.
    Seshadri, S.
    Tsuang, M. T.
    Kremen, W. S.
    Glatt, S. J.
    Polygenic resilience scores capture protective genetic effects for Alzheimer’s disease2022In: Translational Psychiatry, E-ISSN 2158-3188, Vol. 12, no 1, article id 296Article in journal (Refereed)
    Abstract [en]

    Polygenic risk scores (PRSs) can boost risk prediction in late-onset Alzheimer’s disease (LOAD) beyond apolipoprotein E (APOE) but have not been leveraged to identify genetic resilience factors. Here, we sought to identify resilience-conferring common genetic variants in (1) unaffected individuals having high PRSs for LOAD, and (2) unaffected APOE-ε4 carriers also having high PRSs for LOAD. We used genome-wide association study (GWAS) to contrast “resilient” unaffected individuals at the highest genetic risk for LOAD with LOAD cases at comparable risk. From GWAS results, we constructed polygenic resilience scores to aggregate the addictive contributions of risk-orthogonal common variants that promote resilience to LOAD. Replication of resilience scores was undertaken in eight independent studies. We successfully replicated two polygenic resilience scores that reduce genetic risk penetrance for LOAD. We also showed that polygenic resilience scores positively correlate with polygenic risk scores in unaffected individuals, perhaps aiding in staving off disease. Our findings align with the hypothesis that a combination of risk-independent common variants mediates resilience to LOAD by moderating genetic disease risk.

  • 9. Jansen, I. E.
    et al.
    Savage, J. E.
    Watanabe, K.
    Bryois, J.
    Williams, D. M.
    Steinberg, S.
    Sealock, J.
    Karlsson, Ida K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Karolinska Institutet, Stockholm, Sweden.
    Hägg, S.
    Athanasiu, L.
    Voyle, N.
    Proitsi, P.
    Witoelar, A.
    Stringer, S.
    Aarsland, D.
    Almdahl, I. S.
    Andersen, F.
    Bergh, S.
    Bettella, F.
    Bjornsson, S.
    Brækhus, A.
    Bråthen, G.
    de Leeuw, C.
    Desikan, R. S.
    Djurovic, S.
    Dumitrescu, L.
    Fladby, T.
    Hohman, T. J.
    Jonsson, P. V.
    Kiddle, S. J.
    Rongve, A.
    Saltvedt, I.
    Sando, S. B.
    Selbæk, G.
    Shoai, M.
    Skene, N. G.
    Snaedal, J.
    Stordal, E.
    Ulstein, I. D.
    Wang, Y.
    White, L. R.
    Hardy, J.
    Hjerling-Leffler, J.
    Sullivan, P. F.
    van der Flier, W. M.
    Dobson, R.
    Davis, L. K.
    Stefansson, H.
    Stefansson, K.
    Pedersen, N. L.
    Ripke, S.
    Andreassen, O. A.
    Posthuma, D.
    Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk2019In: Nature Genetics, ISSN 1061-4036, E-ISSN 1546-1718, Vol. 51, no 3, p. 404-413Article in journal (Refereed)
    Abstract [en]

    Alzheimer’s disease (AD) is highly heritable and recent studies have identified over 20 disease-associated genomic loci. Yet these only explain a small proportion of the genetic variance, indicating that undiscovered loci remain. Here, we performed a large genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 cases, 383,378 controls). AD-by-proxy, based on parental diagnoses, showed strong genetic correlation with AD (rg = 0.81). Meta-analysis identified 29 risk loci, implicating 215 potential causative genes. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver, and microglia). Gene-set analyses indicate biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomization results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD. 

  • 10. Jiang, M.
    et al.
    Foebel, A. D.
    Kuja-Halkola, R.
    Karlsson, Ida K.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Pedersen, N. L.
    Hägg, S.
    Jylhävä, J.
    Frailty index as a predictor of all-cause and cause-specific mortality in a Swedish population-based cohort2017In: Aging, ISSN 1945-4589, E-ISSN 1945-4589, Vol. 9, no 12, p. 2629-2646Article in journal (Refereed)
    Abstract [en]

    Frailty is a complex manifestation of aging and associated with increased risk of mortality and poor health outcomes. However, younger individuals (under 65 years) are less-studied in this respect. Also, the relationship between frailty and cause-specific mortality in community settings is understudied. We used a 42-item Rockwood-based frailty index (FI) in the Swedish Adoption/Twin Study of Aging (n=1477; 623 men, 854 women; aged 29-95 years) and analyzed its association with all-cause and cause-specific mortality in up to 30-years of follow-up. Deaths due to cardiovascular disease (CVD), cancer, dementia and other causes were considered as competing risks. The FI was independently associated with increased risk for all-cause mortality in younger (< 65 years; HR per increase in one deficit 1.11, 95%CI 1.07-1.17) and older (≥65 years; HR 1.07, 95%CI 1.04-1.10) women and in younger men (HR 1.05, 95%CI 1.01-1.10). In cause-specific mortality analysis, the FI was strongly predictive of CVD mortality in women (HR per increase in one deficit 1.13, 95%CI 1.09-1.17), whereas in men the risk was restricted to deaths from other causes (HR 1.07, 95%CI 1.01-1.13). In conclusion, the FI is a strong mortality predictor especially among younger individuals and its associations with cause-specific mortality are sex-specific.

  • 11.
    Karlsson, Ida K.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Dementia and its comorbidities: genetic and epigenetic influences2017Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Dementia is a multifactorial disorder of late life, characterized by memory deficits, personality changes, and impaired reasoning abilities. There is considerable co-morbidity between dementia, cardiovascular disease (CVD), and late-life depression, but the nature of the associations remains elusive. We therefore seek to investigate how genetic and epigenetic factors act, independently and in concert, to contribute to dementia as well as to its association with CVD and depression. The first two studies focused on what role specific genes play in the association between dementia, depression, and CVD. In study I, we investigated how apolipoprotein E (APOE) genotype affects the association between depression and dementia, and whether the timing of depression onset is of importance. Utilizing a nested case-control design with 804 dementia cases and 1,600 matched controls, we found that depression within ten years of dementia onset was associated with disease regardless of APOE genotype, while depression more distal to dementia was a risk factor only in carriers of the ε4 risk allele. Study II focused on the shared genetic architecture between dementia and CVD, and entailed two parts. In the first part we used data from 13,231 Swedish twins, and found that genetically predisposed CVD was a stronger risk factor for dementia compared to CVD with a lower genetic risk. In the second part of the study we utilized summary statistics from previously published genome-wide association studies to investigate the genetic overlap between Alzheimer´s disease (AD), the most common form of dementia, and coronary artery disease. We found no evidence of genetic overlap between the disorders, but that both diseases have a significant number of genes in common with lipid levels. The last two studies focused on epigenetic factors and investigated how gene specific methylation is associated with dementia. Study III focused on the APOE gene, and how methylation levels in leukocytes relate to the risk of dementia, AD, and CVD. Using data from 447 Swedish twins, we demonstrated that hypermethylation in the promoter region of the gene was associated with dementia and AD, but not with CVD. Results were similar within discordant twin pairs, and did not differ as a function of APOE genotype. In study IV, we focused on five other AD related genes that are differentially methylated in post-mortem brain samples from AD patients compared to controls. The aim was to investigate whether these differences could also be detected in blood samples collected pre-mortem. There was a significant difference in methylation of SORL1 in leukocytes from dementia patients and of BIN1 in leukocytes from AD patients. Findings were stronger in discordant twin pairs, indicating that the association cannot be attributed to genetic factors. In conclusion, the studies included in this thesis highlight the complexity of late-life comorbidities, and the importance of taking both genetic factors and the timing of disease into account when studying these associations. Furthermore, methylation of genes related to AD is of importance for dementia, and has the potential to serve both as a biomarker and identify mechanisms of disease development.

  • 12.
    Karlsson, Ida K.
    et al.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping).
    Arpawong, T. E.
    Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States.
    Zhan, Y.
    School of Public Health, Sun Yat-Sen University, Guangzhou, China.
    Lehto, K.
    Institute of Genomics, University of Tartu, Tartu, Estonia.
    Editorial: Genetics of Age-Related Diseases and Their Risk and Protective Factors2021In: Frontiers in Genetics, E-ISSN 1664-8021, Vol. 12, article id 771109Article in journal (Other academic)
  • 13.
    Karlsson, Ida K.
    et al.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Bennet, A. M.
    Ploner, A.
    Andersson, T. M. -L
    Reynolds, C. A.
    Gatz, M.
    Pedersen, Nancy L.
    Apolipoprotein E ε4 genotype and the temporal relationship between depression and dementia2015In: Neurobiology of Aging, ISSN 0197-4580, E-ISSN 1558-1497, Vol. 36, no 4, p. 1751-1756Article in journal (Refereed)
    Abstract [en]

    To investigate how apolipoprotein E (. APOE) affects the temporal relationship between depression and dementia, we conducted a nested case-control study with longitudinal depression and dementia evaluations from several population studies by using 804 dementia cases and 1600 matched controls, totaling 1519 unique individuals. Depression within 10 years of onset of dementia was strongly associated with dementia diagnosis regardless of APOE status (incidence rate ratio [IRR] 5.25, 95% confidence interval [95% CI] 3.32-8.31 for ε4 carriers, IRR 4.40, 95%CI 3.23-5.99 for noncarriers). However, we found a significant interaction between depression more than 10 years before the onset of dementia and APOE (. p= 0.01), with depression more distal to dementia being a risk factor only in ε4 carriers (IRR 3.39, 95% CI 1.69-6.78 for carriers, IRR 1.01, 95% CI 0.60-1.70 for noncarriers). Thus, depression with onset close in time to dementia onset is associated with disease irrespective of APOE genotype, whereas depression more distal to dementia onset is a risk factor only in ε4-carriers. This is the first study to show the interaction between APOE and depression to be dependent on timing of depression onset.

  • 14.
    Karlsson, Ida K.
    et al.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Ericsson, M.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Wang, Y.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Jylhävä, J.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Hägg, S.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Dahl Aslan, Anna K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Reynolds, C. A.
    Department of Psychology, University of California, Riverside, United States.
    Pedersen, N. L.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Epigenome-wide association study of level and change in cognitive abilities from midlife through late life2021In: Clinical Epigenetics, ISSN 1868-7075, Vol. 13, no 1, article id 85Article in journal (Refereed)
    Abstract [en]

    Background: Epigenetic mechanisms are important in aging and may be involved in late-life changes in cognitive abilities. We conducted an epigenome-wide association study of leukocyte DNA methylation in relation to level and change in cognitive abilities, from midlife through late life in 535 Swedish twins. Results: Methylation levels were measured with the Infinium Human Methylation 450 K or Infinium MethylationEPIC array, and all sites passing quality control on both arrays were selected for analysis (n = 250,816). Empirical Bayes estimates of individual intercept (age 65), linear, and quadratic change were obtained from latent growth curve models of cognitive traits and used as outcomes in linear regression models. Significant sites (p &lt; 2.4 × 10–7) were followed up in between-within twin pair models adjusting for familial confounding and full-growth modeling. We identified six significant associations between DNA methylation and level of cognitive abilities at age 65: cg18064256 (PPP1R13L) with processing speed and spatial ability; cg04549090 (NRXN3) with spatial ability; cg09988380 (POGZ), cg25651129 (-), and cg08011941 (ENTPD8) with working memory. The genes are involved in neuroinflammation, neuropsychiatric disorders, and ATP metabolism. Within-pair associations were approximately half that of between-pair associations across all sites. In full-growth curve models, associations between DNA methylation and cognitive level at age 65 were of small effect sizes, and associations between DNA methylation and longitudinal change in cognitive abilities of very small effect sizes. Conclusions: Leukocyte DNA methylation was associated with level, but not change in cognitive abilities. The associations were substantially attenuated in within-pair analyses, indicating they are influenced in part by genetic factors.

  • 15.
    Karlsson, Ida K.
    et al.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping).
    Ericsson, M.
    Wang, Y.
    Jylhävä, J.
    Hägg, S.
    Pedersen, N. L.
    Reynolds, C. A.
    Dahl Aslan, Anna K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping).
    DNA methylation and body mass index during late-life - a longitudinal twin study2019Conference paper (Refereed)
  • 16.
    Karlsson, Ida K.
    et al.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Ericsson, M.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Wang, Y.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Jylhävä, J.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Hägg, S.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Pedersen, N. L.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Department of Psychology, University of Southern California, Los Angeles, CA, USA.
    Reynolds, C. A.
    Department of Psychology, University of California, Riverside, CA, United States.
    Dahl Aslan, Anna K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Replicating associations between DNA methylation and body mass index in a longitudinal sample of older twins2020In: International Journal of Obesity, ISSN 0307-0565, E-ISSN 1476-5497, Vol. 44, no 6, p. 1397-1405Article in journal (Refereed)
    Abstract [en]

    Background:

    There is an important interplay between epigenetic factors and body weight, and previous work has identified ten sites where DNA methylation is robustly associated with body mass index (BMI) cross-sectionally. However, interpretation of the associations is complicated by the substantial changes in BMI often occurring in late-life, and the fact that methylation is often driven by genetic variation. This study therefore investigated the longitudinal association between these ten sites and BMI from midlife to late-life, and whether associations persist after controlling for genetic factors.

    Methods:

    We used data from 535 individuals (mean age 68) in the Swedish Adoption/Twin Study of Aging (SATSA) with longitudinal measures of both DNA methylation from blood samples and BMI, spanning up to 20 years. Methylation levels were measured with the Infinium Human Methylation 450K or Infinium MethylationEpic array, with seven of the ten sites passing quality control. Latent growth curve models were applied to investigate longitudinal associations between methylation and BMI, and between–within models to study associations within twin pairs, thus adjusting for genetic factors.

    Results:

    Baseline DNA methylation levels at five of the seven sites were associated with BMI level at age 65 (cg00574958 [CPT1A]; cg11024682 [SREBF1]), and/or change (cg06192883 [MYO5C]; cg06946797 [RMI2]; cg08857797 [VPS25]). For four of the five sites, the associations remained comparable within twin pairs. However, the effects of cg06192883 were substantially attenuated within pairs. No change in DNA methylation was detected for any of the seven evaluated sites.

    Conclusion:

    Five of the seven sites investigated were associated with late-life level and/or change in BMI. The effects for four of the sites remained similar when examined within twin pairs, indicating that the associations are mainly environmentally driven. However, the substantial attenuation in the association between cg06192883 and late-life BMI within pairs points to the importance of genetic factors in this association.

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  • 17.
    Karlsson, Ida K.
    et al.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping).
    Ericsson, Malin
    Karolinska Institute, Department of Medical Epidemiology & Biostatistics, Stockholm, Sweden.
    Wang, Yunzhang
    Karolinska Institute, Department of Medical Epidemiology & Biostatistics, Stockholm, Sweden.
    Jylhava, Juulia
    Karolinska Institute, Department of Medical Epidemiology & Biostatistics, Stockholm, Sweden.
    Hagg, Sara
    Karolinska Institute, Department of Medical Epidemiology & Biostatistics, Stockholm, Sweden.
    Dahl Aslan, Anna K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping).
    Reynolds, Chandra
    Univ Calif Riverside, Dept Psychol, Riverside, CA 92521 USA..
    Pedersen, Nancy L.
    Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden..
    DNA methylation and change in late-life cognitive abilities2019In: Behavior Genetics, ISSN 0001-8244, E-ISSN 1573-3297, Vol. 49, no 6, p. 503-503Article in journal (Refereed)
  • 18.
    Karlsson, Ida K.
    et al.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden.;Jonkoping Univ, Sch Hlth & Welf 6, Aging Res Network Jonkoping ARNJ, Jonkoping, Sweden..
    Escott-Price, Valentina
    Cardiff Univ, Inst Psychol Med & Clin Neurosci, UK Dementia Res Inst Cardiff, Cardiff, Wales..
    Gatz, Margaret
    Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden.;Univ Southern Calif, Ctr Econ & Social Res, Los Angeles, CA 90007 USA..
    Hardy, John
    UCL Queen Sq Inst Neurol, Dept Neurodegenerat Dis, Queen Sq, London WC1N 3BG, England.;UCL, UCL Inst Neurol, UK Dementia Res Inst UCL, London, England.;UCL, UCL Inst Neurol, Dept Neurodegenerat Dis, London, England.;UCL Queen Sq Inst Neurol, Reta Lila Weston Inst, 1 Wakefield St, London WC1N 1PJ, England.;UCL, UCL Movement Disorders Ctr, London, England.;Hong Kong Univ Sci & Technol, Inst Adv Study, Hong Kong, Peoples R China..
    Pedersen, Nancy L.
    Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden.;Univ Southern Calif, Dept Psychol, Los Angeles, CA 90007 USA..
    Shoai, Maryam
    UCL Queen Sq Inst Neurol, Dept Neurodegenerat Dis, Queen Sq, London WC1N 3BG, England.;UCL, UCL Inst Neurol, UK Dementia Res Inst UCL, London, England.;UCL, UCL Inst Neurol, Dept Neurodegenerat Dis, London, England..
    Reynolds, Chandra A.
    Univ Calif Riverside, Dept Psychol, 900 Univ Ave,1111 Psychol Bldg, Riverside, CA 92521 USA..
    Measuring heritable contributions to Alzheimer's disease: polygenic risk score analysis with twins2022In: Brain Communications, E-ISSN 2632-1297, Vol. 4, no 1, article id fcab308Article in journal (Refereed)
    Abstract [en]

    The heritability of Alzheimer's disease estimated from twin studies is greater than the heritability derived from genome-based studies, for reasons that remain unclear. We apply both approaches to the same twin sample, considering both Alzheimer's disease polygenic risk scores and heritability from twin models, to provide insight into the role of measured genetic variants and to quantify uncaptured genetic risk. A population-based heritability and polygenic association study of Alzheimer's disease was conducted between 1986 and 2016 and is the first study to incorporate polygenic risk scores into biometrical twin models of Alzheimer's disease. The sample included 1586 twins drawn from the Swedish Twin Registry which were nested within 1137 twin pairs (449 complete pairs and 688 incomplete pairs) with clinically based diagnoses and registry follow-up (M-age = 85.28, SD = 7.02; 44% male; 431 cases and 1155 controls). We report contributions of polygenic risk scores at P < 1 x 10(-5), considering a full polygenic risk score (PRS), PRS without the APOE region (PRS.no.APOE) and PRS.no.APOE plus directly measured APOE alleles. Biometric twin models estimated the contribution of environmental influences and measured (PRS) and unmeasured genes to Alzheimer's disease risk. The full PRS and PRS.no.APOE contributed 10.1 and 2.4% to Alzheimer's disease risk, respectively. When APOE e4 alleles were added to the model with the PRS.no.APOE, the total contribution was 11.4% to Alzheimer's disease risk, where APOE e4 explained 9.3% and PRS.no.APOE dropped from 2.4 to 2.1%. The total genetic contribution to Alzheimer's disease risk, measured and unmeasured, was 71% while environmental influences unique to each twin accounted for 29% of the risk. The APOE region accounts for much of the measurable genetic contribution to Alzheimer's disease, with a smaller contribution from other measured polygenic influences. Importantly, substantial background genetic influences remain to be understood. Karlsson et al. report that measured polygenic scores from genome-based studies, including an outsized role for APOE, explain only a fraction of the heritability indicated by twin models of Alzheimer's disease, leaving most genetic risk for Alzheimer's disease unexplained. Sensitive designs are needed to capture all the genetic influences.

  • 19.
    Karlsson, Ida K.
    et al.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping).
    Gatz, M.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Arpawong, T. E.
    Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States.
    Dahl Aslan, Anna K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping).
    Reynolds, C. A.
    Department of Psychology, University of California, Riverside, United States.
    The dynamic association between body mass index and cognition from midlife through late-life, and the effect of sex and genetic influences2021In: Scientific Reports, E-ISSN 2045-2322, Vol. 11, no 1, article id 7206Article in journal (Refereed)
    Abstract [en]

    Body mass index (BMI) is associated with cognitive abilities, but the nature of the relationship remains largely unexplored. We aimed to investigate the bidirectional relationship from midlife through late-life, while considering sex differences and genetic predisposition to higher BMI. We used data from 23,892 individuals of European ancestry from the Health and Retirement Study, with longitudinal data on BMI and three established cognitive indices: mental status, episodic memory, and their sum, called total cognition. To investigate the dynamic relationship between BMI and cognitive abilities, we applied dual change score models of change from age 50 through 89, with a breakpoint at age 65 or 70. Models were further stratified by sex and genetic predisposition to higher BMI using tertiles of a polygenic score for BMI (PGSBMI). We demonstrated bidirectional effects between BMI and all three cognitive indices, with higher BMI contributing to steeper decline in cognitive abilities in both midlife and late-life, and higher cognitive abilities contributing to less decline in BMI in late-life. The effects of BMI on change in cognitive abilities were more evident in men compared to women, and among those in the lowest tertile of the PGSBMI compared to those in the highest tertile, while the effects of cognition on BMI were similar across groups. In conclusion, these findings highlight a reciprocal relationship between BMI and cognitive abilities, indicating that the negative effects of a higher BMI persist from midlife through late-life, and that weight-loss in late-life may be driven by cognitive decline.

  • 20.
    Karlsson, Ida K.
    et al.
    Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Stockholm, Sweden.
    Haegg, Sara
    Karolinska Institutet, Stockholm, Sweden.
    Ploner, Alexander
    Karolinska Institutet, Stockholm, Sweden.
    Song, Ci
    Karolinska Institutet, Stockholm, Sweden.
    Gatz, Margaret
    University of Southern California, Los Angeles, CA, USA.
    Pedersen, Nancy L.
    Karolinska Institutet, Stockholm, Sweden.
    Genetic susceptibility to cardiovascular disease and risk of dementia2015In: Behavior Genetics, ISSN 0001-8244, E-ISSN 1573-3297, Vol. 45, no 6, p. 664-664Article in journal (Refereed)
  • 21.
    Karlsson, Ida K.
    et al.
    Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Stockholm, Sweden.
    Hagg, Sara
    Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Stockholm, Sweden.
    Pedersen, Nancy L.
    Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Stockholm, Sweden.
    Apolipoprotein E DNA Methylation in Dementia and Cardiovascular Disease2015In: The Gerontologist, ISSN 0016-9013, E-ISSN 1758-5341, Vol. 55Article in journal (Refereed)
  • 22.
    Karlsson, Ida K.
    et al.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping).
    Hallgren, Jenny
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping).
    Pedersen, Nancy L.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Reynolds, C. A.
    Department of Psychology, University of California, Riverside, USA.
    Dahl Aslan, Anna K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping).
    Genetic influences on body mass index across Adulthood and late-life2018In: Innovation in Aging, E-ISSN 2399-5300, Vol. 2, no suppl_1, p. 620-620Article in journal (Refereed)
    Abstract [en]

    Although genetic factors significantly contribute to variation in body-mass index (BMI), the effects appear to differ with age. To investigate this, we applied polygenic methods to longitudinal data from the Swedish Twin Registry where BMI was available for age categories ranging from 20–35 to above 80. Using GCTA, a polygenic method to estimate heritability, we showed that heritability explains around 20% of the variability in BMI across age categories. However, a polygenic risk score based on the largest GWAS of BMI (PRSBMI) explained 4–6% of the variation in BMI before 65, but less than 0.5% after age 65. This indicates that while there is substantial heritability of BMI across adulthood and late-life, the genetic variants identified in GWAS mainly predict BMI before age 65. Hence, more work is warranted studying the genetics of BMI in late-life, to better understand its biology and what distinguishes it from BMI earlier in adulthood.

  • 23.
    Karlsson, Ida K.
    et al.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping).
    Lehto, Kelli
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Gatz, Margaret
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Reynolds, Chandra A.
    Department of Psychology, University of California, Riverside, United States.
    Dahl Aslan, Anna K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping).
    Age-dependent effects of body mass index across the adult life span on the risk of dementia: A cohort study with a genetic approach2020In: BMC Medicine, E-ISSN 1741-7015, Vol. 18, no 1, article id 131Article in journal (Refereed)
    Abstract [en]

    Background: While a high body mass index (BMI) in midlife is associated with higher risk of dementia, high BMI in late-life may be associated with lower risk. This study combined genetic designs with longitudinal data to achieve a better understanding of this paradox. Methods: We used longitudinal data from 22,156 individuals in the Swedish Twin Registry (STR) and 25,698 from the Health and Retirement Study (HRS). The STR sample had information about BMI from early adulthood through late-life, and the HRS sample from age 50 through late-life. Survival analysis was applied to investigate age-specific associations between BMI and dementia risk. To examine if the associations are influenced by genetic susceptibility to higher BMI, an interaction between BMI and a polygenic score for BMI (PGSBMI) was included in the models and results stratified into those with genetic predisposition to low, medium, and higher BMI. In the STR, co-twin control models were applied to adjust for familial factors beyond those captured by the PGSBMI. Results: At age 35-49, 5 units higher BMI was associated with 15% (95% CI 7-24%) higher risk of dementia in the STR. There was a significant interaction (p = 0.04) between BMI and the PGSBMI, and the association present only among those with genetic predisposition to low BMI (HR 1.38, 95% CI 1.08-1.78). Co-twin control analyses indicated genetic influences. After age 80, 5 units higher BMI was associated with 10-11% lower risk of dementia in both samples. There was a significant interaction between late-life BMI and the PGSBMI in the STR (p = 0.01), but not the HRS, with the inverse association present only among those with a high PGSBMI (HR 0.70, 95% CI 0.52-0.94). No genetic influences were evident from co-twin control models of late-life BMI. Conclusions: Not only does the association between BMI and dementia differ depending on age at BMI measurement, but also the effect of genetic influences. In STR, the associations were only present among those with a BMI in opposite direction of their genetic predisposition, indicating that the association between BMI and dementia across the life course might be driven by environmental factors and hence likely modifiable. © 2020 The Author(s).

  • 24.
    Karlsson, Ida K.
    et al.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping).
    Lehto, Kelli
    Karolinska Inst, Stockholm, Sweden.
    Reynolds, Chandra A.
    Univ Calif Riverside, Riverside, USA.
    Dahl Aslan, Anna K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping).
    Age-Dependent Effects Of Body Mass Index On The Risk Of Dementia2019In: European Neuropsychopharmacology, ISSN 0924-977X, E-ISSN 1873-7862, Vol. 29, no Suppl. 5, p. S180-S181, article id M26Article in journal (Refereed)
  • 25.
    Karlsson, Ida K.
    et al.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Ploner, A.
    Song, C.
    Gatz, M.
    Pedersen, Nancy L.
    Hägg, S.
    Genetic susceptibility to cardiovascular disease and risk of dementia2017In: Translational Psychiatry, E-ISSN 2158-3188, Vol. 7, no 5Article in journal (Refereed)
    Abstract [en]

    Several studies have shown cardiovascular disease (CVD) to be associated with dementia, but it is not clear whether CVD per se increases the risk of dementia or whether the association is due to shared risk factors. We tested how a genetic risk score (GRS) for coronary artery disease (CAD) affects dementia risk after CVD in 13 231 Swedish twins. We also utilized summarized genome-wide association data to study genetic overlap between CAD and Alzheimer´s disease (AD), and additionally between shared risk factors and each disease. There was no direct effect of a CAD GRS on dementia (hazard ratio 0.99, 95% confidence interval (CI): 0.98-1.01). However, the GRS for CAD modified the association between CVD and dementia within 3 years of CVD diagnosis, ranging from a hazard ratio of 1.59 (95% CI: 1.05-2.41) in the first GRS quartile to 1.91 (95% CI: 1.28-2.86) in the fourth GRS quartile. Using summary statistics, we found no genetic overlap between CAD and AD. We did, however, find that both AD and CAD share a significant genetic overlap with lipids, but that the overlap arose from clearly distinct gene clusters. In conclusion, genetic susceptibility to CAD was found to modify the association between CVD and dementia, most likely through associations with shared risk factors.

  • 26.
    Karlsson, Ida K.
    et al.
    Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Stockholm, Sweden.
    Ploner, Alexander
    Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Stockholm, Sweden.
    Wang, Yunzhang
    Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Stockholm, Sweden.
    Gatz, Margaret
    Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Stockholm, Sweden.
    Pedersen, Nancy L.
    Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Stockholm, Sweden.
    Hagg, Sara
    Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Stockholm, Sweden.
    Apolipoprotein E DNA methylation and late-life disease2018In: International Journal of Epidemiology, ISSN 0300-5771, E-ISSN 1464-3685, Vol. 47, no 3, p. 899-907Article in journal (Refereed)
    Abstract [en]

    Background: This study aims to investigate if DNA methylation of the apolipoprotein E (APOE) locus affects the risks of dementia, Alzheimers disease (AD) or cardiovascular disease (CVD).

    Methods: DNA methylation across the APOE gene has previously been categorized into three distinct regions: a hypermethylated region in the promoter, a hypomethylated region in the first two introns and exons and a hypermethylated region in the 3'exon that also harbours the APOE epsilon 2 and epsilon 4 alleles. DNA methylation levels in leukocytes were measured using the Illumina 450K array in 447 Swedish twins (mean age 78.1 years). We used logistic regression to investigate whether methylation levels in those regions affect the odds of disease.

    Results: We found that methylation levels in the promoter region were associated with dementia and AD after adjusting for sex, age at blood draw, education, smoking and relatedness among twins [odds ratio (OR) 1.32 per standard deviation increase in methylation levels, 95% confidence interval (CI) 1.08-1.62 for dementia; OR 1.38, 95% CI 1.07-1.78 for AD). We did not detect any difference in methylation levels between CVD cases and controls. Results were similar when comparing within discordant twin pairs, and did not differ as a function of APOE genotype.

    Conclusions: We found that higher DNA methylation levels in the promoter region of APOE increase the odds of dementia and AD, but not CVD. The effect was independent of APOE genotype, indicating that allelic variation and methylation variation in APOE may act independently to increase the risk of dementia.

  • 27.
    Karlsson, Ida K.
    et al.
    Karolinska Institutet, Stockholm, Sweden.
    Ploner, Alexander
    Karolinska Institutet, Stockholm, Sweden.
    Wang, Yunzhang
    Karolinska Institutet, Stockholm, Sweden.
    Pedersen, Nancy L.
    Karolinska Institutet, Stockholm, Sweden.
    Hagg, Sara
    Karolinska Institutet, Stockholm, Sweden.
    DNA methylation in Alzheimer's disease associated genes2017In: Behavior Genetics, ISSN 0001-8244, E-ISSN 1573-3297, Vol. 47, no 6, p. 709-709Article in journal (Refereed)
  • 28.
    Karlsson, Ida K.
    et al.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping).
    Reynolds, Chandra A.
    University of California-Riverside.
    Escott-Price, Valentina
    MRC Centre for Neuropsychiatric Genetics & Genomics, Cardiff University.
    Hardy, John
    Institute of Neurology UCL.
    Gatz, Margaret
    University of Southern California.
    Pedersen, Nancy L.
    Karolinska Institutet.
    Heritability of Alzheimer's disease – Comparison of twin and polygenic methods2019In: European Neuropsychopharmacology, ISSN 0924-977X, E-ISSN 1873-7862, Vol. 29, no Suppl. 4, p. S1208-S1209Article in journal (Refereed)
  • 29.
    Karlsson, Ida K.
    et al.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Karolinska Inst, Dept Med Epidemiol & Biostat, SE-1177 Stockholm, Sweden.;Jonkoping Univ, Sch Hlth & Welfare, Inst Gerontol & Aging Res Network Jonkoping ARN J, Jonkoping, Sweden..
    Zhan, Yiqiang
    Karolinska Inst, Inst Environm Sci, Stockholm, Sweden..
    Gatz, Margaret
    Karolinska Inst, Dept Med Epidemiol & Biostat, SE-1177 Stockholm, Sweden.;Univ Southern Calif, Ctr Econ & Social Res, Los Angeles, CA 90007 USA..
    Reynolds, Chandra A.
    Univ Calif Riverside, Dept Psychol, Riverside, CA 92521 USA..
    Dahl Aslan, Anna K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Karolinska Inst, Dept Med Epidemiol & Biostat, SE-1177 Stockholm, Sweden.;Jonkoping Univ, Sch Hlth & Welfare, Inst Gerontol & Aging Res Network Jonkoping ARN J, Jonkoping, Sweden.;Univ Skovde, Sch Hlth Sci, Skovde, Sweden..
    Change in cognition and body mass index in relation to preclinical dementia2021In: Alzheimer’s & Dementia: Translational Research & Clinical Interventions, E-ISSN 2352-8737, Vol. 7, no 1, article id e12176Article in journal (Refereed)
    Abstract [en]

    Introduction To study if declining cognition drives weight loss in preclinical dementia, we examined the longitudinal association between body mass index (BMI) and cognitive abilities in individuals who did or did not later develop dementia. Methods Using data from individuals spanning age 50 to 89, we applied dual change score models separately in individuals who remained cognitively intact (n = 1498) and those who were diagnosed with dementia within 5 years of last assessment (n = 459). Results Among the cognitively intact, there was a bidirectional association: Stable BMI predicted stable cognition and vice versa. Among individuals who were subsequently diagnosed with dementia, the association was unidirectional: Higher BMI predicted declining cognition but cognition did not predict change in BMI. Discussion Although BMI and cognition stabilized each other when cognitive functioning was intact, this buffering effect was missing in the preclinical dementia phase. This finding indicates that weight loss in preclinical dementia is not driven by declining cognition.

  • 30.
    Karlsson, Ida K.
    et al.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Zhan, Yiqiang
    School of Public Health, Sun Yat-Sen University, Shenzhen, China.
    Wang, Yunzhang
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Li, Xia
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Jylhava, Juulia
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Hägg, Sara
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Dahl Aslan, Anna K.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; School of Health Sciences, University of Skövde, Skövde, Sweden.
    Gatz, Margaret
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Center for Economic and Social Research, University of Southern California, Los Angeles, United States.
    Pedersen, Nancy L.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Department of Psychology, University of Southern California, Los Angeles, United States.
    Reynolds, Chandra A.
    Department of Psychology, University of California, Riverside, United States.
    Adiposity and the risk of dementia: mediating effects from inflammation and lipid levels2022In: European Journal of Epidemiology, ISSN 0393-2990, E-ISSN 1573-7284, Vol. 37, p. 1261-1271Article in journal (Refereed)
    Abstract [en]

    While midlife adiposity is a risk factor for dementia, adiposity in late-life appears to be associated with lower risk. What drives the associations is poorly understood, especially the inverse association in late-life. Using results from genome-wide association studies, we identified inflammation and lipid metabolism as biological pathways involved in both adiposity and dementia. To test if these factors mediate the effect of midlife and/or late-life adiposity on dementia, we then used cohort data from the Swedish Twin Registry, with measures of adiposity and potential mediators taken in midlife (age 40-64, n = 5999) or late-life (age 65-90, n = 7257). Associations between body-mass index (BMI), waist-hip ratio (WHR), C-reactive protein (CRP), lipid levels, and dementia were tested in survival and mediation analyses. Age was used as the underlying time scale, and sex and education included as covariates in all models. Fasting status was included as a covariate in models of lipids. One standard deviation (SD) higher WHR in midlife was associated with 25% (95% CI 2-52%) higher dementia risk, with slight attenuation when adjusting for BMI. No evidence of mediation through CRP or lipid levels was present. After age 65, one SD higher BMI, but not WHR, was associated with 8% (95% CI 1-14%) lower dementia risk. The association was partly mediated by higher CRP, and suppressed when high-density lipoprotein levels were low. In conclusion, the negative effects of midlife adiposity on dementia risk were driven directly by factors associated with body fat distribution, with no evidence of mediation through inflammation or lipid levels. There was an inverse association between late-life adiposity and dementia risk, especially where the body's inflammatory response and lipid homeostasis is intact.

  • 31.
    Kodesh, Arad
    et al.
    Department of Community Mental Health, University of Haifa, Haifa, Israel.
    Sandin, Sven
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Reichenberg, Abraham
    The Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY.
    Rotstein, Anat
    Department of Community Mental Health, University of Haifa, Haifa, Israel.
    Pedersen, Nancy L.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Ericsson, Malin
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Karlsson, Ida K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Davidson, Michael
    Sackler Medical School, Tel Aviv University, Tel Aviv, Israel.
    Levine, Stephen Z.
    Department of Community Mental Health, University of Haifa, Haifa, Israel.
    Antidepressants and the risk of dementia: Appropriate consideration of confounding by indication2020In: The American journal of geriatric psychiatry, ISSN 1064-7481, E-ISSN 1545-7214, Vol. 28, no 4, p. 499-500Article in journal (Other academic)
  • 32.
    Kodesh, Arad
    et al.
    Department of Community Mental Health, University of Haifa, Haifa, Israel.
    Sandin, Sven
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Reichenberg, Abraham
    The Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
    Rotstein, Anat
    Department of Community Mental Health, University of Haifa, Haifa, Israel.
    Pedersen, Nancy L.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Ericsson, Malin
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Karlsson, Ida K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Davidson, Michael
    Sackler Medical School, Tel Aviv University, Tel Aviv, Israel.
    Levine, Stephen Z.
    Department of Community Mental Health, University of Haifa, Haifa, Israel.
    Exposure to antidepressant medication and the risk of incident dementia2019In: The American journal of geriatric psychiatry, ISSN 1064-7481, E-ISSN 1545-7214, Vol. 27, no 11, p. 1177-1188Article in journal (Refereed)
    Abstract [en]

    Objective: To test competing hypotheses that monotherapeutic antidepressant exposure is associated with an increased versus a decreased risk of dementia.

    Methods: A prospective national matched cohort study from Israel (N = 71,515) without dementia (2002–2012) aged 60 and over were followed up for incident dementia from May 2013 to October 2017. Exposure to antidepressant monotherapy was classified with Anatomical Therapeutic Chemical Codes (N06A) from January 1, 2013 to December 31, 2016. The association between antidepressant monotherapy and the risk of incident dementia was quantified with hazard ratios (HR) and their 95% confidence intervals (CI) obtained from Cox regression models unadjusted and adjusted for 42 covariates. The robustness of the results was tested with 24 sensitivity analyses: 19 analyses restricted to subsamples with plausible differential dementia risks (e.g., anxiety and depression), and 5 analyses across and within antidepressant drug classes.

    Results: In the primary analysis, the risk of incident dementia for the group exposed to antidepressant monotherapy compared to the group unexposed to antidepressants was estimated with an unadjusted HR = 4.09 (df = 1, 95% Wald CI = 3.64, 4.60) and an adjusted HR = 3.43 (df = 1, 95% Wald CI = 3.04, 3.88). Across the 24 sensitivity analyses the estimated adjusted HR values ranged from 1.99 to 5.47.

    Conclusion: In this study, monotherapeutic antidepressant exposure in old age was associated with increased incident dementia. Clinicians, caregivers, and patients may wish to consider this potentially negative consequence of antidepressant exposure and aim to balance the costs and benefits of treatment. 

  • 33.
    Konki, Mikko
    et al.
    Turku Bioscience Centre, University of Turku, Åbo Akademi University, Turku, Finland.
    Malonzo, Maia
    Department of Computer Science, Aalto University School of Science, Helsinki, Finland.
    Karlsson, Ida K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Lindgren, Noora
    Drug Research Doctoral Program, University of Turku, Turku, Finland.
    Ghimire, Bishwa
    Turku Bioscience Centre, University of Turku, Åbo Akademi University, Turku, Finland.
    Smolander, Johannes
    Turku Bioscience Centre, University of Turku, Åbo Akademi University, Turku, Finland.
    Scheinin, Noora M.
    Turku PET Centre, University of Turku, Turku, Finland.
    Ollikainen, Miina
    Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.
    Laiho, Asta
    Turku Bioscience Centre, University of Turku, Åbo Akademi University, Turku, Finland.
    Elo, Laura L.
    Turku Bioscience Centre, University of Turku, Åbo Akademi University, Turku, Finland.
    Lönnberg, Tapio
    Turku Bioscience Centre, University of Turku, Åbo Akademi University, Turku, Finland.
    Röyttä, Matias
    Department of Pathology/Neuropathology, Turku University Hospital, University of Turku, Turku, Finland.
    Pedersen, Nancy L.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Kaprio, Jaakko
    Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.
    Lähdesmäki, Harri
    Department of Computer Science, Aalto University School of Science, Helsinki, Finland.
    Rinne, Juha O.
    Division of Clinical Neurosciences, Turku University Hospital, Turku, 4, Finland.
    Lund, Riikka J.
    Turku Bioscience Centre, University of Turku, Åbo Akademi University, Turku, Finland.
    Peripheral blood DNA methylation differences in twin pairs discordant for Alzheimer's disease.2019In: Clinical Epigenetics, E-ISSN 1868-7083, Vol. 11, no 1, article id 130Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Alzheimer's disease results from a neurodegenerative process that starts well before the diagnosis can be made. New prognostic or diagnostic markers enabling early intervention into the disease process would be highly valuable. Environmental and lifestyle factors largely modulate the disease risk and may influence the pathogenesis through epigenetic mechanisms, such as DNA methylation. As environmental and lifestyle factors may affect multiple tissues of the body, we hypothesized that the disease-associated DNA methylation signatures are detectable in the peripheral blood of discordant twin pairs.

    RESULTS: Comparison of 23 disease discordant Finnish twin pairs with reduced representation bisulfite sequencing revealed peripheral blood DNA methylation differences in 11 genomic regions with at least 15.0% median methylation difference and FDR adjusted p value ≤ 0.05. Several of the affected genes are primarily associated with neuronal functions and pathologies and do not display disease-associated differences in gene expression in blood. The DNA methylation mark in ADARB2 gene was found to be differentially methylated also in the anterior hippocampus, including entorhinal cortex, of non-twin cases and controls. Targeted bisulfite pyrosequencing of the DNA methylation mark in ADARB2 gene in 62 Finnish and Swedish twin pairs revealed that, in addition to the disease status, DNA methylation of this region is influenced by gender, age, zygosity, APOE genotype, and smoking. Further analysis of 120 Swedish twin pairs indicated that this specific DNA methylation mark is not predictive for Alzheimer's disease and becomes differentially methylated after disease onset.

    CONCLUSIONS: DNA methylation differences can be detected in the peripheral blood of twin pairs discordant for Alzheimer's disease. These DNA methylation signatures may have value as disease markers and provide insights into the molecular mechanisms of pathogenesis. We found no evidence that the DNA methylation marks would be associated with gene expression in blood. Further studies are needed to elucidate the potential importance of the associated genes in neuronal functions and to validate the prognostic or diagnostic value of the individual marks or marker panels.

  • 34.
    Lehto, Kelli
    et al.
    Karolinska Inst, Stockholm, Sweden.
    Karlsson, Ida K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping).
    Gatz, Margaret
    University of Southern California, Los Angeles, CA, USA.
    Pedersen, Nancy L.
    Karolinska Institute, Stockholm, Sweden.
    Does depression in old age reflect prodromal dementia? A polygenic risk score approach2019In: Behavior Genetics, ISSN 0001-8244, E-ISSN 1573-3297, Vol. 49, no 6, p. 512-512Article in journal (Other academic)
  • 35.
    Lehto, Kelli
    et al.
    Karolinska Inst, Stockholm, Sweden.
    Karlsson, Ida K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping).
    Gatz, Margaret
    Univ Southern Calif, Los Angeles, CA, USA.
    Pedersen, Nancy L.
    Karolinska Inst, Stockholm, Sweden.
    Does depression in old age reflect prodromal dementia? A polygenic risk score approach in two longitudinal studies2019In: European Neuropsychopharmacology, ISSN 0924-977X, E-ISSN 1873-7862, Vol. 29, no 5 Suppl., p. S181-S182Article in journal (Refereed)
  • 36.
    Lehto, Kelli
    et al.
    Karolinska Institutet, Stockholm, Sweden.
    Karlsson, Ida K.
    Karolinska Institutet, Stockholm, Sweden.
    Lundholm, Cecilia
    Karolinska Institutet, Stockholm, Sweden.
    Pedersen, Nancy L.
    Karolinska Institutet, Stockholm, Sweden.
    Early life stress and genetic risk for neuroticism predicting health outcomes in older Swedish twins2017In: Behavior Genetics, ISSN 0001-8244, E-ISSN 1573-3297, Vol. 47, no 6, p. 669-669Article in journal (Refereed)
  • 37.
    Ler, Peggy
    et al.
    Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Jönköping University, School of Health and Welfare, The Jönköping Academy for Improvement of Health and Welfare.
    Li, X.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Hassing, L. B.
    Department of Psychology and Centre for Ageing and Health, University of Gothenburg, Gothenburg, Sweden.
    Reynolds, C. A.
    Department of Psychology, University of California - Riverside, Riverside, CA, USA.
    Finkel, Deborah
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Department of Psychology, Indiana University Southeast, New Albany, IN, United States.
    Karlsson, Ida K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Dahl Aslan, Anna K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). School of Health Sciences, University of Skövde, Sweden.
    Independent and joint effects of body mass index and metabolic health in mid- and late-life on all-cause mortality: a cohort study from the Swedish Twin Registry with a mean follow-up of 13 Years2022In: BMC Public Health, E-ISSN 1471-2458, Vol. 22, no 1, article id 718Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: There is robust evidence that in midlife, higher body mass index (BMI) and metabolic syndrome (MetS), which often co-exist, are associated with increased mortality risk. However, late-life findings are inconclusive, and few studies have examined how metabolic health status (MHS) affects the BMI-mortality association in different age categories. We, therefore, aimed to investigate how mid- and late-life BMI and MHS interact to affect the risk of mortality. METHODS: This cohort study included 12,467 participants from the Swedish Twin Registry, with height, weight, and MHS measures from 1958-2008 and mortality data linked through 2020. We applied Cox proportional hazard regression with age as a timescale to examine how BMI categories (normal weight, overweight, obesity) and MHS (identification of MetS determined by presence/absence of hypertension, hyperglycemia, low HDL, hypertriglyceridemia), independently and in interaction, are associated with the risk of all-cause mortality. Models were adjusted for sex, education, smoking, and cardiovascular disease. RESULTS: The midlife group included 6,252 participants with a mean age of 59.6 years (range = 44.9-65.0) and 44.1% women. The late-life group included 6,215 participants with mean age 73.1 years (65.1-95.3) and 46.6% women. In independent effect models, metabolically unhealthy status in midlife increased mortality risks by 31% [hazard ratio 1.31; 95% confidence interval 1.12-1.53] and in late-life, by 18% (1.18;1.10-1.26) relative to metabolically healthy individuals. Midlife obesity increased the mortality risks by 30% (1.30;1.06-1.60) and late-life obesity by 15% (1.15; 1.04-1.27) relative to normal weight. In joint models, the BMI estimates were attenuated while those of MHS were less affected. Models including BMI-MHS categories revealed that, compared to metabolically healthy normal weight, the metabolically unhealthy obesity group had increased mortality risks by 53% (1.53;1.19-1.96) in midlife, and across all BMI categories in late-life (normal weight 1.12; 1.01-1.25, overweight 1.10;1.01-1.21, obesity 1.31;1.15-1.49). Mortality risk was decreased by 9% (0.91; 0.83-0.99) among those with metabolically healthy overweight in late-life. CONCLUSIONS: MHS strongly influenced the BMI-mortality association, such that individuals who were metabolically healthy with overweight or obesity in mid- or late-life did not carry excess risks of mortality. Being metabolically unhealthy had a higher risk of mortality independent of their BMI. 

  • 38.
    Li, Xia
    et al.
    The Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Ploner, Alexander
    The Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Karlsson, Ida K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). The Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Liu, Xingrong
    The Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Magnusson, Patrik K E
    The Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Pedersen, Nancy L
    The Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Hägg, Sara
    The Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Jylhävä, Juulia
    The Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    The frailty index is a predictor of cause-specific mortality independent of familial effects from midlife onwards: a large cohort study2019In: BMC Medicine, E-ISSN 1741-7015, Vol. 17, no 1, article id 94Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Frailty index (FI) is a well-established predictor of all-cause mortality, but less is known for cause-specific mortality and whether familial effects influence the associations. Middle-aged individuals are also understudied for the association between FI and mortality. Furthermore, the population mortality impact of frailty remains understudied.

    METHODS: We estimated the predictive value of FI for all-cause and cause-specific mortality, taking into account familial factors, and tested whether the associations are time-dependent. We also assessed the proportion of all-cause and cause-specific deaths that are attributable to increased levels of frailty. We analyzed 42,953 participants from the Screening Across the Lifespan Twin Study (aged 41-95 years at baseline) with up to 20 years' mortality follow-up. The FI was constructed using 44 health-related items. Deaths due to cardiovascular disease (CVD), respiratory-related causes, and cancer were considered in the cause-specific analysis. Generalized survival models were used in the analysis.

    RESULTS: Increased FI was associated with higher risks of all-cause, CVD, and respiratory-related mortality, with the corresponding hazard ratios of 1.28 (1.24, 1.32), 1.31 (1.23, 1.40), and 1.23 (1.11, 1.38) associated with a 10% increase in FI in male single responders, and 1.21 (1.18, 1.25), 1.27 (1.15, 1.34), and 1.26 (1.15, 1.39) in female single responders. No significant associations were observed for cancer mortality. No attenuation of the mortality associations in unrelated individuals was observed when adjusting for familial effects in twin pairs. The associations were time-dependent with relatively greater effects observed in younger ages. Before the age of 80, the proportions of deaths attributable to FI levels > 0.21 were 18.4% of all-cause deaths, 25.4% of CVD deaths, and 20.4% of respiratory-related deaths in men and 19.2% of all-cause deaths, 27.8% of CVD deaths, and 28.5% of respiratory-related deaths in women. After the age of 80, the attributable proportions decreased, most notably for all-cause and CVD mortality.

    CONCLUSIONS: Increased FI predicts higher risks of all-cause, CVD, and respiratory-related mortality independent of familial effects. Increased FI presents a relatively greater risk factor at midlife than in old age. Increased FI has a significant population mortality impact that is greatest through midlife until the age of 80.

  • 39.
    Luo, Jing
    et al.
    Univ Southern Calif, Dept Psychol, 3620 South McClintock Ave, Los Angeles, CA 90089 USA..
    Beam, Christopher R.
    Univ Southern Calif, Dept Psychol, 3620 South McClintock Ave, Los Angeles, CA 90089 USA.;Univ Southern Calif, Leonard Davis Sch Gerontol, Los Angeles, CA 90089 USA..
    Karlsson, Ida K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden..
    Pike, Christian J.
    Univ Southern Calif, Leonard Davis Sch Gerontol, Los Angeles, CA 90089 USA..
    Reynolds, Chandra A.
    Univ Calif Riverside, Dept Psychol, Riverside, CA 92521 USA..
    Gatz, Margaret
    Univ Southern Calif, Dept Psychol, 3620 South McClintock Ave, Los Angeles, CA 90089 USA.;Univ Southern Calif, Leonard Davis Sch Gerontol, Los Angeles, CA 90089 USA.;Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden.;Univ Southern Calif, Ctr Econ & Social Res, Los Angeles, CA 90089 USA..
    Dementia risk in women higher in same-sex than opposite-sex twins2020In: Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, ISSN 2352-8729, Vol. 12, no 1, article id e12049Article in journal (Refereed)
    Abstract [en]

    Introduction

    Hormones may be one possible mechanism underlying sex differences in dementia incidence. We examined whether presumed differential prenatal hormone milieu is related to dementia risk by comparing dementia rates in same- and opposite-sex dizygotic twin pairs in male and female twins.

    Methods

    The sample comprised 43,254 individuals from dizygotic twin pairs aged 60 and older from the Swedish Twin Registry. Survival analyses were conducted separately for females and males.

    Results

    Female twins from opposite-sex pairs had significantly lower dementia risk than female twins from same-sex pairs, but the differences emerged only after age 70 (hazard ratio = 0.64, P = 0.004). Results were not explained by postnatal risk factors for dementia, and no interaction between twin type and apolipoprotein E (APOE) epsilon 4 was found. Male twins from same-sex versus opposite-sex pairs did not differ significantly.

    Discussion

    The results suggest that relatively masculine prenatal hormone milieus correlate with lower dementia risk in females.

  • 40.
    Ojalehto, E.
    et al.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Finkel, Deborah
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping).
    Russ, T. C.
    Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, United Kingdom.
    Karlsson, Ida K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Ericsson, M.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Influences of genetically predicted and attained education on geographic mobility and their association with mortality2023In: Social Science and Medicine, ISSN 0277-9536, E-ISSN 1873-5347, Vol. 324, article id 115882Article in journal (Refereed)
    Abstract [en]

    Introduction: Both educational attainment and genetic propensity to education (PGSEdu) have been associated with geographic mobility. Socioeconomic conditions are, in turn, associated with individuals’ health. Geographic mobility could therefore lead to better health for some since it could provide better opportunities, like education. Our aim was to study how attained education and genetic predisposition for higher education are related to geographic mobility, and how they affect the association between geographic mobility and mortality. Methods: We used data from the Swedish Twin Registry (twins born 1926–1955; n = 14,211) in logistic regression models to test if attained education and PGSEdu predicted geographic mobility. Cox regression models were then performed to test if geographic mobility, attained education, and PGSEdu were associated with mortality. Results: The results show that both attained education and PGSEdu predicted geographic mobility, in both independent and joint effect models, with higher education associated with higher mobility. Geographic mobility was associated with lower mortality in the independent effect model, but joint effect models showed that this association was completely explained by attained education. Conclusions: To conclude, both attained education and PGSEdu were associated with geographic mobility. Moreover, attained education explained the relationship between geographic mobility and mortality.

  • 41.
    Qin, Xueying
    et al.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
    Karlsson, Ida K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Wang, Yunzhang
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Li, Xia
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Pedersen, Nancy
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Department of Psychology, University of Southern California, Los Angeles, CA, United States.
    Reynolds, Chandra A.
    Department of Psychology, University of California, Riverside, United States.
    Hägg, Sara
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    The epigenetic etiology of cardiovascular disease in a longitudinal Swedish twin study2021In: Clinical Epigenetics, E-ISSN 1868-7083, Vol. 13, no 1, article id 129Article in journal (Refereed)
    Abstract [en]

    Background: Studies on DNA methylation have the potential to discover mechanisms of cardiovascular disease (CVD) risk. However, the role of DNA methylation in CVD etiology remains unclear.

    Results: We performed an epigenome-wide association study (EWAS) on CVD in a longitudinal sample of Swedish twins (535 individuals). We selected CpGs reaching the Bonferroni-corrected significance level (2 × 10–7) or the top-ranked 20 CpGs with the lowest P values if they did not reach this significance level in EWAS analysis associated with non-stroke CVD, overall stroke, and ischemic stroke, respectively. We further applied a bivariate autoregressive latent trajectory model with structured residuals (ALT-SR) to evaluate the cross-lagged effect between DNA methylation of these CpGs and cardiometabolic traits (blood lipids, blood pressure, and body mass index). Furthermore, mediation analysis was performed to evaluate whether the cross-lagged effects had causal impacts on CVD. In the EWAS models, none of the CpGs we selected reached the Bonferroni-corrected significance level. The ALT-SR model showed that DNA methylation levels were more likely to predict the subsequent level of cardiometabolic traits rather than the other way around (numbers of significant cross-lagged paths of methylation → trait/trait → methylation were 84/4, 45/6, 66/1 for the identified three CpG sets, respectively). Finally, we demonstrated significant indirect effects from DNA methylation on CVD mediated by cardiometabolic traits.

    Conclusions: We present evidence for a directional association from DNA methylation on cardiometabolic traits and CVD, rather than the opposite, highlighting the role of epigenetics in CVD development. 

  • 42.
    Rizzuto, Debora
    et al.
    Department of Neurobiology, Care Sciences and Society, Aging Research Center (ARC), Karolinska Institutet and Stockholm University, Stockholm, Sweden.
    Feldman, Adina L.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Karlsson, Ida K.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Dahl Aslan, Anna K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Gatz, Margaret
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, Department of Psychology, University of Southern California, Los Angeles, California.
    Pedersen, Nancy L.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, Department of Psychology, University of Southern California, Los Angeles, California.
    Detection of dementia cases in two Swedish health registers: A validation study2018In: Journal of Alzheimer's Disease, ISSN 1387-2877, E-ISSN 1875-8908, Vol. 61, no 4, p. 1301-1310Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Population-based health registers are potential assets in epidemiological research; however, the quality of case ascertainment is crucial.

    OBJECTIVE: To compare the case ascertainment of dementia, from the National Patient Register (NPR) and the Cause of Death Register (CDR) with dementia diagnoses from six Swedish population based studies.

    METHODS: Sensitivity, specificity, and positive predictive value (PPV) of dementia identification in NPR and CDR were estimated by individual record linkage with six Swedish population based studies (n = 19,035). Time to detection in NPR was estimated using data on dementia incidence from longitudinal studies with more than two decades of follow-up.

    RESULTS: Barely half of the dementia cases were ever detected by NPR or CDR. Using data from longitudinal studies we estimated that a record with a dementia diagnosis appears in the NPR on average 5.5 years after first diagnosis. Although the ability of the registers to detect dementia cases was moderate, the ability to detect non-dementia cases was almost perfect (99%). When registers indicate that there is a dementia diagnosis, there are very few instances in which the clinicians determined the person was not demented. Indeed, PPVs were close to 90%. However, misclassification between dementia subtype diagnoses is quite common, especially in NPR.

    CONCLUSIONS: Although the overall sensitivity is low, the specificity and the positive predictive value are very high. This suggests that hospital and death registers can be used to identify dementia cases in the community, but at the cost of missing a large proportion of the cases.

  • 43. Savage, J. E.
    et al.
    Jansen, P. R.
    Stringer, S.
    Watanabe, K.
    Bryois, J.
    De Leeuw, C. A.
    Nagel, M.
    Awasthi, S.
    Barr, P. B.
    Coleman, J. R. I.
    Grasby, K. L.
    Hammerschlag, A. R.
    Kaminski, J. A.
    Karlsson, R.
    Krapohl, E.
    Lam, M.
    Nygaard, M.
    Reynolds, C. A.
    Trampush, J. W.
    Young, H.
    Zabaneh, D.
    Hägg, S.
    Hansell, N. K.
    Karlsson, Ida K.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Linnarsson, S.
    Montgomery, G. W.
    Muñoz-Manchado, A. B.
    Quinlan, E. B.
    Schumann, G.
    Skene, N. G.
    Webb, B. T.
    White, T.
    Arking, D. E.
    Avramopoulos, D.
    Bilder, R. M.
    Bitsios, P.
    Burdick, K. E.
    Cannon, T. D.
    Chiba-Falek, O.
    Christoforou, A.
    Cirulli, E. T.
    Congdon, E.
    Corvin, A.
    Davies, G.
    Deary, I. J.
    Derosse, P.
    Dickinson, D.
    Djurovic, S.
    Donohoe, G.
    Conley, E. D.
    Eriksson, J. G.
    Espeseth, T.
    Freimer, N. A.
    Giakoumaki, S.
    Giegling, I.
    Gill, M.
    Glahn, D. C.
    Hariri, A. R.
    Hatzimanolis, A.
    Keller, M. C.
    Knowles, E.
    Koltai, D.
    Konte, B.
    Lahti, J.
    Le Hellard, S.
    Lencz, T.
    Liewald, D. C.
    London, E.
    Lundervold, A. J.
    Malhotra, A. K.
    Melle, I.
    Morris, D.
    Need, A. C.
    Ollier, W.
    Palotie, A.
    Payton, A.
    Pendleton, N.
    Poldrack, R. A.
    Räikkönen, K.
    Reinvang, I.
    Roussos, P.
    Rujescu, D.
    Sabb, F. W.
    Scult, M. A.
    Smeland, O. B.
    Smyrnis, N.
    Starr, J. M.
    Steen, V. M.
    Stefanis, N. C.
    Straub, R. E.
    Sundet, K.
    Tiemeier, H.
    Voineskos, A. N.
    Weinberger, D. R.
    Widen, E.
    Yu, J.
    Abecasis, G.
    Andreassen, O. A.
    Breen, G.
    Christiansen, L.
    Debrabant, B.
    Dick, D. M.
    Heinz, A.
    Hjerling-Leffler, J.
    Ikram, M. A.
    Kendler, K. S.
    Martin, N. G.
    Medland, S. E.
    Pedersen, N. L.
    Plomin, R.
    Polderman, T. J. C.
    Ripke, S.
    Van Der Sluis, S.
    Sullivan, P. F.
    Vrieze, S. I.
    Wright, M. J.
    Posthuma, D.
    Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence2018In: Nature Genetics, ISSN 1061-4036, E-ISSN 1546-1718, Vol. 50, no 7, p. 912-919Article in journal (Refereed)
    Abstract [en]

    Intelligence is highly heritable 1 and a major determinant of human health and well-being 2 . Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence 3-7, but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer's disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.

  • 44.
    Tomata, Y.
    et al.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Li, X.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Karlsson, Ida K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Mosing, M. A.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Pedersen, N. L.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Hägg, S.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Joint impact of common risk factors on incident dementia: A cohort study of the Swedish Twin Registry2020In: Journal of Internal Medicine, ISSN 0954-6820, E-ISSN 1365-2796, Vol. 288, no 2, p. 234-247Article in journal (Refereed)
    Abstract [en]

    Background: As common risk factors of dementia, nine factors (low education, hearing loss, obesity, hypertension, smoking, depression, physical inactivity, diabetes and social isolation) were proposed. However, the joint impact of these factors on incident dementia is still uncertain; hence, we aimed to examine this impact.

    Methods: We conducted a cohort study of 9017 cognitively intact individuals aged ≥ 65 years in the Swedish Twin Registry. The main exposure was the total number of reported risk factors (ranging from 0 to 9). Data on dementia diagnoses were based on clinical workup and national health registers. After estimating the adjusted hazard ratios of incident dementia, the population attributable fraction (PAF) was calculated. We then conducted additional analyses, including APOE ε4 status in a genotyped subsample (n = 2810) to check the relative impact of the main exposure and discordant twin pair (n = 1158) analysis to consider confounding by familial effects (shared genetic or familial environmental factors).

    Results: The number of dementia cases was 1950 (21.6%). A dose-response relationship between the number of risk factors and incident dementia was observed; hazard ratio (95% confidence interval) per one-unit increment in number of risk factors was 1.07 (1.03 to 1.11). The PAF for the combination of the nine risk factors was 10.4%. The PAF of all nine risk factors was smaller than that of APOE ε4 genotype (20.8%) in the subsample. Discordant pair analysis suggested that the observed association was not likely explained by familial effects.

    Conclusion: The nine risk factors may have considerable impact as modifiable factors on incident dementia. 

  • 45.
    Wang, Yunzhang
    et al.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Karlsson, Robert
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Jylhävä, Juulia
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Hedman, Åsa K.
    Rheumatology Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.
    Almqvist, Catarina
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Karlsson, Ida K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping). Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Pedersen, Nancy L.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Almgren, Malin
    Department of Clinical Neuroscience, Centrum for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.
    Hägg, Sara
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Comprehensive longitudinal study of epigenetic mutations in aging2019In: Clinical Epigenetics, E-ISSN 1868-7083, Vol. 11, article id 187Article in journal (Refereed)
    Abstract [en]

    Background: The role of DNA methylation in aging has been widely studied. However, epigenetic mutations, here defined as aberrant methylation levels compared to the distribution in a population, are less understood. Hence, we investigated longitudinal accumulation of epigenetic mutations, using 994 blood samples collected at up to five time points from 375 individuals in old ages.

    Results: We verified earlier cross-sectional evidence on the increase of epigenetic mutations with age, and identified important contributing factors including sex, CD19+ B cells, genetic background, cancer diagnosis, and technical artifacts. We further classified epigenetic mutations into High/Low Methylation Outliers (HMO/LMO) according to their changes in methylation, and specifically studied methylation sites (CpGs) that were prone to mutate (frequently mutated CpGs). We validated four epigenetically mutated CpGs using pyrosequencing in 93 samples. Furthermore, by using twins, we concluded that the age-related accumulation of epigenetic mutations was not related to genetic factors, hence driven by stochastic or environmental effects.

    Conclusions: Here we conducted a comprehensive study of epigenetic mutation and highlighted its important role in aging process and cancer development. 

  • 46. Wightman, D. P.
    et al.
    Jansen, I. E.
    Savage, J. E.
    Shadrin, A. A.
    Bahrami, S.
    Holland, D.
    Rongve, A.
    Børte, S.
    Winsvold, B. S.
    Drange, O. K.
    Martinsen, A. E.
    Skogholt, A. H.
    Willer, C.
    Bråthen, G.
    Bosnes, I.
    Nielsen, J. B.
    Fritsche, L. G.
    Thomas, L. F.
    Pedersen, L. M.
    Gabrielsen, M. E.
    Johnsen, M. B.
    Meisingset, T. W.
    Zhou, W.
    Proitsi, P.
    Hodges, A.
    Dobson, R.
    Velayudhan, L.
    Agee, M.
    Aslibekyan, S.
    Babalola, E.
    Bell, R. K.
    Bielenberg, J.
    Bryc, K.
    Bullis, E.
    Cameron, B.
    Coker, D.
    Partida, G. C.
    Dhamija, D.
    Das, S.
    Elson, S. L.
    Filshtein, T.
    Fletez-Brant, K.
    Fontanillas, P.
    Freyman, W.
    Gandhi, P. M.
    Hicks, B.
    Hinds, D. A.
    Huber, K. E.
    Jewett, E. M.
    Jiang, Y.
    Kleinman, A.
    Kukar, K.
    Lane, V.
    Lin, K. -H
    Lowe, M.
    Luff, M. K.
    McCreight, J. C.
    McIntyre, M. H.
    McManus, K. F.
    Micheletti, S. J.
    Moreno, M. E.
    Mountain, J. L.
    Mozaffari, S. V.
    Nandakumar, P.
    Noblin, E. S.
    O’Connell, J.
    Petrakovitz, A. A.
    Poznik, G. D.
    Schumacher, M.
    Shastri, A. J.
    Shelton, J. F.
    Shi, J.
    Shringarpure, S.
    Tian, C.
    Tran, V.
    Tung, J. Y.
    Wang, X.
    Wang, W.
    Weldon, C. H.
    Wilton, P.
    Sealock, J. M.
    Davis, L. K.
    Pedersen, N. L.
    Reynolds, C. A.
    Karlsson, Ida K.
    Jönköping University, School of Health and Welfare, HHJ, Institute of Gerontology. Jönköping University, School of Health and Welfare, HHJ. ARN-J (Aging Research Network - Jönköping).
    Magnusson, S.
    Stefansson, H.
    Thordardottir, S.
    Jonsson, P. V.
    Snaedal, J.
    Zettergren, A.
    Skoog, I.
    Kern, S.
    Waern, M.
    Zetterberg, H.
    Blennow, K.
    Stordal, E.
    Hveem, K.
    Zwart, J. -A
    Athanasiu, L.
    Selnes, P.
    Saltvedt, I.
    Sando, S. B.
    Ulstein, I.
    Djurovic, S.
    Fladby, T.
    Aarsland, D.
    Selbæk, G.
    Ripke, S.
    Stefansson, K.
    Andreassen, O. A.
    Posthuma, D.
    Team, 23andMe Research
    A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease2021In: Nature Genetics, ISSN 1061-4036, E-ISSN 1546-1718, Vol. 53, no 9, p. 1276-1282Article in journal (Refereed)
    Abstract [en]

    Late-onset Alzheimer’s disease is a prevalent age-related polygenic disease that accounts for 50–70% of dementia cases. Currently, only a fraction of the genetic variants underlying Alzheimer’s disease have been identified. Here we show that increased sample sizes allowed identification of seven previously unidentified genetic loci contributing to Alzheimer’s disease. This study highlights microglia, immune cells and protein catabolism as relevant to late-onset Alzheimer’s disease, while identifying and prioritizing previously unidentified genes of potential interest. We anticipate that these results can be included in larger meta-analyses of Alzheimer’s disease to identify further genetic variants that contribute to Alzheimer’s pathology.

  • 47.
    Williams, Dylan M.
    et al.
    Karolinska Institutet, Department of Medical Epidemiology & Biostatistics, Stockholm, Sweden.
    Karlsson, Ida K.
    Karolinska Institutet, Department of Medical Epidemiology & Biostatistics, Stockholm, Sweden.
    Pedersen, Nancy L.
    Karolinska Institutet, Department of Medical Epidemiology & Biostatistics, Stockholm, Sweden.
    Hägg, Sara
    Karolinska Institutet, Department of Medical Epidemiology & Biostatistics, Stockholm, Sweden.
    Circulating insulin-like growth factors and Alzheimer disease: A mendelian randomization study2018In: Neurology, ISSN 0028-3878, E-ISSN 1526-632X, Vol. 90, no 4, p. e291-e297Article in journal (Refereed)
    Abstract [en]

    OBJECTIVE: To examine whether genetically predicted variation in circulating insulin-like growth factor 1 (IGF1) or its binding protein, IGFBP3, are associated with risk of Alzheimer disease (AD), using a mendelian randomization study design.

    METHODS: We first examined disease risk by genotypes of 9 insulin-like growth factor (IGF)-related single nucleotide polymorphisms (SNPs) using published summary genome-wide association statistics from the International Genomics of Alzheimer's Project (IGAP; n = 17,008 cases; 37,154 controls). We then assessed whether any SNP-disease results replicated in an independent sample derived from the Swedish Twin Registry (n = 984 cases; 10,304 controls).

    RESULTS: Meta-analyses of SNP-AD results did not suggest that variation in IGF1, IGFBP3, or the molar ratio of these affect AD risk. Only one SNP appeared to affect AD risk in IGAP data. This variant is located in the gene FOXO3, implicated in human longevity. In a meta-analysis of both IGAP and secondary data, the odds ratio of AD per FOXO3 risk allele was 1.04 (95% confidence interval 1.01-1.08; p = 0.008).

    CONCLUSIONS: These findings suggest that circulating IGF1 and IGFBP3 are not important determinants of AD risk. FOXO3 function may influence AD development via pathways that are independent of IGF signaling (i.e., pleiotropic actions).

  • 48. Zhan, Y.
    et al.
    Karlsson, Ida K.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Karlsson, R.
    Tillander, A.
    Reynolds, C. A.
    Pedersen, Nancy L.
    Hägg, S.
    Exploring the Causal Pathway from Telomere Length to Coronary Heart Disease: A Network Mendelian Randomization Study2017In: Circulation Research, ISSN 0009-7330, E-ISSN 1524-4571, Vol. 121, no 3, p. 214-219Article in journal (Refereed)
    Abstract [en]

    Rationale: Observational studies have found shorter leukocyte telomere length (TL) to be a risk factor for coronary heart disease (CHD), and recently the association was suggested to be causal. However, the relationship between TL and common metabolic risk factors for CHD is not well understood. Whether these risk factors could explain pathways from TL to CHD warrants further attention.

    Objective: To examine whether metabolic risk factors for CHD mediate the causal pathway from short TL to increased risk of CHD using a network Mendelian randomization design.

    Methods and Results: Summary statistics from several genome-wide association studies were used in a 2-sample Mendelian randomization study design. Network Mendelian randomization analysis - an approach using genetic variants as the instrumental variables for both the exposure and mediator to infer causality - was performed to examine the causal association between telomeres and CHD and metabolic risk factors. Summary statistics from the ENGAGE Telomere Consortium were used (n=37 684) as a TL genetic instrument, CARDIoGRAMplusC4D Consortium data were used (case=22 233 and control=64 762) for CHD, and other consortia data were used for metabolic traits (fasting insulin, triglyceride, total cholesterol, low-density lipoprotein cholesterol, fasting glucose, diabetes mellitus, glycohemoglobin, body mass index, waist circumference, and waist:hip ratio). One-unit increase of genetically determined TL was associated with -0.07 (95% confidence interval, -0.01 to -0.12; P=0.01) lower log-transformed fasting insulin (pmol/L) and 21% lower odds (95% confidence interval, 3-35; P=0.02) of CHD. Higher genetically determined log-transformed fasting insulin level was associated with higher CHD risk (odds ratio, 1.86; 95% confidence interval, 1.01-3.41; P=0.04).

    Conclusions: Overall, our findings support a role of insulin as a mediator on the causal pathway from shorter telomeres to CHD pathogenesis.

1 - 48 of 48
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