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10. Additional file 1 of APOE ε4 gene dose effect on imaging and blood biomarkers of neuroinflammation and beta-amyloid in cognitively unimpaired elderly

Author

Snellman, Anniina, Ekblad, Laura L., Tuisku, Jouni, Koivumäki, Mikko, Ashton, Nicholas J., Lantero-Rodriguez, Juan, Karikari, Thomas K., Helin, Semi, Bucci, Marco, Löyttyniemi, Eliisa, Parkkola, Riitta, Karrasch, Mira, Schöll, Michael, Zetterberg, Henrik, Blennow, Kaj, and Rinne, Juha O.

Abstract

Additional file 1: eTable 1. Data availability for all variables. eTable 2. Demographics stratified by Aβ positivity. eTable 3. Regional TSPO binding in Aβ+ and Aβ- participants. eFigure 1. Voxel-level differences in 11C-PiB binding between (A) APOE ε4 homozygotes and non-carriers, and (B) APOE ε4 homozygotes and APOE ε4 heterozygotes. eFigure 2. Interaction between Aβ status and APOE ε4 gene dose on regional 11C-PK11195 binding.

Published
2023
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13. APOE ε4 gene dose effect on imaging and blood biomarkers of glial reactivity and β-amyloid pathology

Author

Snellman, Anniina, primary, Ekblad, Laura L., additional, Tuisku, Jouni, additional, Koivumäki, Mikko, additional, Ashton, Nicholas J., additional, Lantero-Rodriguez, Juan, additional, Karikari, Thomas K., additional, Helin, Semi, additional, Bucci, Marco, additional, Löyttyniemi, Eliisa, additional, Parkkola, Riitta, additional, Karrasch, Mira, additional, Schöll, Michael, additional, Zetterberg, Henrik, additional, Blennow, Kaj, additional, and Rinne, Juha O., additional

Published
2022
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14. Prevalence Estimates of Amyloid Abnormality Across the Alzheimer Disease Clinical Spectrum

Author

Jansen, Willemijn J., Janssen, Olin, Tijms, Betty M., Vos, Stephanie J.B., Ossenkoppele, Rik, Visser, Pieter Jelle, Aarsland, Dag, Alcolea, Daniel, Altomare, Daniele, Von Arnim, Christine, Baiardi, Simone, Baldeiras, Ines, Barthel, Henryk, Bateman, Randall J., Van Berckel, Bart, Binette, Alexa Pichet, Blennow, Kaj, Boada, Merce, Boecker, Henning, Bottlaender, Michel, Den Braber, Anouk, Brooks, David J., Van Buchem, Mark A., Camus, Vincent, Carill, Jose Manuel, Cerman, Jiri, Chen, Kewei, Chételat, Gaël, Chipi, Elena, Cohen, Ann D., Daniels, Alisha, Delarue, Marion, Didic, Mira, Drzezga, Alexander, Dubois, Bruno, Eckerström, Marie, Ekblad, Laura L., Engelborghs, Sebastiaan, Epelbaum, Stéphane, Fagan, Anne M., Fan, Yong, Fladby, Tormod, Fleisher, Adam S., Van Der Flier, Wiesje M., Förster, Stefan, Fortea, Juan, Frederiksen, Kristian Steen, Freund-Levi, Yvonne, Frings, Lars, Frisoni, Giovanni B., Fröhlich, Lutz, Gabryelewicz, Tomasz, Gertz, Hermann Josef, Gill, Kiran Dip, Gkatzima, Olymbia, Gómez-Tortosa, Estrella, Grimmer, Timo, Guedj, Eric, Habeck, Christian G., Hampel, Harald, Handels, Ron, Hansson, Oskar, Hausner, Lucrezia, Hellwig, Sabine, Heneka, Michael T., Herukka, Sanna Kaisa, Hildebrandt, Helmut, Hodges, John, Hort, Jakub, Huang, Chin Chang, Iriondo, Ane Juaristi, Itoh, Yoshiaki, Ivanoiu, Adrian, Jagust, William J., Jessen, Frank, Johannsen, Peter, Johnson, Keith A., Kandimalla, Ramesh, Kapaki, Elisabeth N., Kern, Silke, Kilander, Lena, Klimkowicz-Mrowiec, Aleksandra, Klunk, William E., Koglin, Norman, Kornhuber, Johannes, Kramberger, Milica G., Kuo, Hung Chou, Van Laere, Koen, Landau, Susan M., Landeau, Brigitte, Lee, Dong Young, De Leon, Mony, Leyton, Cristian E., Lin, Kun Ju, Lleó, Alberto, Löwenmark, Malin, Madsen, Karine, Maier, Wolfgang, Marcusson, Jan, Marquié, Marta, Martinez-Lage, Pablo, Maserejian, Nancy, Mattsson, Niklas, De Mendonça, Alexandre, Meyer, Philipp T., Miller, Bruce L., Minatani, Shinobu, Mintun, Mark A., Mok, Vincent C.T., Molinuevo, Jose Luis, Morbelli, Silvia Daniela, Morris, John C., Mroczko, Barbara, Na, Duk L., Newberg, Andrew, Nobili, Flavio, Nordberg, Agneta, Olde Rikkert, Marcel G.M., De Oliveira, Catarina Resende, Olivieri, Pauline, Orellana, Adela, Paraskevas, George, Parchi, Piero, Pardini, Matteo, Parnetti, Lucilla, Peters, Oliver, Poirier, Judes, Popp, Julius, Prabhakar, Sudesh, Rabinovici, Gil D., Ramakers, Inez H., Rami, Lorena, Reiman, Eric M., Rinne, Juha O., Rodrigue, Karen M., Rodríguez-Rodriguez, Eloy, Roe, Catherine M., Rosa-Neto, Pedro, Rosen, Howard J., Rot, Uros, Rowe, Christopher C., Rüther, Eckart, Ruiz, Agustín, Sabri, Osama, Sakhardande, Jayant, Sánchez-Juan, Pascual, Sando, Sigrid Botne, Santana, Isabel, Sarazin, Marie, Scheltens, Philip, Schröder, Johannes, Selnes, Per, Seo, Sang Won, Silva, Dina, Skoog, Ingmar, Snyder, Peter J., Soininen, Hilkka, Sollberger, Marc, Sperling, Reisa A., Spiru, Luisa, Stern, Yaakov, Stomrud, Erik, Takeda, Akitoshi, Teichmann, Marc, Teunissen, Charlotte E., Thompson, Louisa I., Tomassen, Jori, Tsolaki, Magda, Vandenberghe, Rik, Verbeek, Marcel M., Verhey, Frans R.J., Villemagne, Victor, Villeneuve, Sylvia, Vogelgsang, Jonathan, Waldemar, Gunhild, Wallin, Anders, Wallin, Åsa K., Wiltfang, Jens, Wolk, David A., Yen, Tzu Chen, Zboch, Marzena, Zetterberg, Henrik, Jansen, Willemijn J., Janssen, Olin, Tijms, Betty M., Vos, Stephanie J.B., Ossenkoppele, Rik, Visser, Pieter Jelle, Aarsland, Dag, Alcolea, Daniel, Altomare, Daniele, Von Arnim, Christine, Baiardi, Simone, Baldeiras, Ines, Barthel, Henryk, Bateman, Randall J., Van Berckel, Bart, Binette, Alexa Pichet, Blennow, Kaj, Boada, Merce, Boecker, Henning, Bottlaender, Michel, Den Braber, Anouk, Brooks, David J., Van Buchem, Mark A., Camus, Vincent, Carill, Jose Manuel, Cerman, Jiri, Chen, Kewei, Chételat, Gaël, Chipi, Elena, Cohen, Ann D., Daniels, Alisha, Delarue, Marion, Didic, Mira, Drzezga, Alexander, Dubois, Bruno, Eckerström, Marie, Ekblad, Laura L., Engelborghs, Sebastiaan, Epelbaum, Stéphane, Fagan, Anne M., Fan, Yong, Fladby, Tormod, Fleisher, Adam S., Van Der Flier, Wiesje M., Förster, Stefan, Fortea, Juan, Frederiksen, Kristian Steen, Freund-Levi, Yvonne, Frings, Lars, Frisoni, Giovanni B., Fröhlich, Lutz, Gabryelewicz, Tomasz, Gertz, Hermann Josef, Gill, Kiran Dip, Gkatzima, Olymbia, Gómez-Tortosa, Estrella, Grimmer, Timo, Guedj, Eric, Habeck, Christian G., Hampel, Harald, Handels, Ron, Hansson, Oskar, Hausner, Lucrezia, Hellwig, Sabine, Heneka, Michael T., Herukka, Sanna Kaisa, Hildebrandt, Helmut, Hodges, John, Hort, Jakub, Huang, Chin Chang, Iriondo, Ane Juaristi, Itoh, Yoshiaki, Ivanoiu, Adrian, Jagust, William J., Jessen, Frank, Johannsen, Peter, Johnson, Keith A., Kandimalla, Ramesh, Kapaki, Elisabeth N., Kern, Silke, Kilander, Lena, Klimkowicz-Mrowiec, Aleksandra, Klunk, William E., Koglin, Norman, Kornhuber, Johannes, Kramberger, Milica G., Kuo, Hung Chou, Van Laere, Koen, Landau, Susan M., Landeau, Brigitte, Lee, Dong Young, De Leon, Mony, Leyton, Cristian E., Lin, Kun Ju, Lleó, Alberto, Löwenmark, Malin, Madsen, Karine, Maier, Wolfgang, Marcusson, Jan, Marquié, Marta, Martinez-Lage, Pablo, Maserejian, Nancy, Mattsson, Niklas, De Mendonça, Alexandre, Meyer, Philipp T., Miller, Bruce L., Minatani, Shinobu, Mintun, Mark A., Mok, Vincent C.T., Molinuevo, Jose Luis, Morbelli, Silvia Daniela, Morris, John C., Mroczko, Barbara, Na, Duk L., Newberg, Andrew, Nobili, Flavio, Nordberg, Agneta, Olde Rikkert, Marcel G.M., De Oliveira, Catarina Resende, Olivieri, Pauline, Orellana, Adela, Paraskevas, George, Parchi, Piero, Pardini, Matteo, Parnetti, Lucilla, Peters, Oliver, Poirier, Judes, Popp, Julius, Prabhakar, Sudesh, Rabinovici, Gil D., Ramakers, Inez H., Rami, Lorena, Reiman, Eric M., Rinne, Juha O., Rodrigue, Karen M., Rodríguez-Rodriguez, Eloy, Roe, Catherine M., Rosa-Neto, Pedro, Rosen, Howard J., Rot, Uros, Rowe, Christopher C., Rüther, Eckart, Ruiz, Agustín, Sabri, Osama, Sakhardande, Jayant, Sánchez-Juan, Pascual, Sando, Sigrid Botne, Santana, Isabel, Sarazin, Marie, Scheltens, Philip, Schröder, Johannes, Selnes, Per, Seo, Sang Won, Silva, Dina, Skoog, Ingmar, Snyder, Peter J., Soininen, Hilkka, Sollberger, Marc, Sperling, Reisa A., Spiru, Luisa, Stern, Yaakov, Stomrud, Erik, Takeda, Akitoshi, Teichmann, Marc, Teunissen, Charlotte E., Thompson, Louisa I., Tomassen, Jori, Tsolaki, Magda, Vandenberghe, Rik, Verbeek, Marcel M., Verhey, Frans R.J., Villemagne, Victor, Villeneuve, Sylvia, Vogelgsang, Jonathan, Waldemar, Gunhild, Wallin, Anders, Wallin, Åsa K., Wiltfang, Jens, Wolk, David A., Yen, Tzu Chen, Zboch, Marzena, and Zetterberg, Henrik

Abstract

Importance: One characteristic histopathological event in Alzheimer disease (AD) is cerebral amyloid aggregation, which can be detected by biomarkers in cerebrospinal fluid (CSF) and on positron emission tomography (PET) scans. Prevalence estimates of amyloid pathology are important for health care planning and clinical trial design. Objective: To estimate the prevalence of amyloid abnormality in persons with normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia and to examine the potential implications of cutoff methods, biomarker modality (CSF or PET), age, sex, APOE genotype, educational level, geographical region, and dementia severity for these estimates. Design, Setting, and Participants: This cross-sectional, individual-participant pooled study included participants from 85 Amyloid Biomarker Study cohorts. Data collection was performed from January 1, 2013, to December 31, 2020. Participants had normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia. Normal cognition and subjective cognitive decline were defined by normal scores on cognitive tests, with the presence of cognitive complaints defining subjective cognitive decline. Mild cognitive impairment and clinical AD dementia were diagnosed according to published criteria. Exposures: Alzheimer disease biomarkers detected on PET or in CSF. Main Outcomes and Measures: Amyloid measurements were dichotomized as normal or abnormal using cohort-provided cutoffs for CSF or PET or by visual reading for PET. Adjusted data-driven cutoffs for abnormal amyloid were calculated using gaussian mixture modeling. Prevalence of amyloid abnormality was estimated according to age, sex, cognitive status, biomarker modality, APOE carrier status, educational level, geographical location, and dementia severity using generalized estimating equations. Results: Among the 19097 participants (mean [SD] age, 69.1 [9.8] years; 10148 women [53.1%])

Published
2022

16. Prevalence Estimates of Amyloid Abnormality Across the Alzheimer Disease Clinical Spectrum

Author

Jansen, Willemijn J, Janssen, Olin, von Arnim, Christine, Marquié, Marta, Martinez-Lage, Pablo, Maserejian, Nancy, Mattsson, Niklas, de Mendonça, Alexandre, Meyer, Philipp T, Miller, Bruce L, Minatani, Shinobu, Mintun, Mark A, Mok, Vincent C T, Baiardi, Simone, Molinuevo, Jose Luis, Morbelli, Silvia Daniela, Morris, John C, Mroczko, Barbara, Na, Duk L, Newberg, Andrew, Nobili, Flavio, Nordberg, Agneta, Olde Rikkert, Marcel G M, de Oliveira, Catarina Resende, Baldeiras, Ines, Olivieri, Pauline, Orellana, Adela, Paraskevas, George, Parchi, Piero, Pardini, Matteo, Parnetti, Lucilla, Peters, Oliver, Poirier, Judes, Popp, Julius, Prabhakar, Sudesh, Barthel, Henryk, Rabinovici, Gil D, Ramakers, Inez H, Rami, Lorena, Reiman, Eric M, Rinne, Juha O, Rodrigue, Karen M, Rodríguez-Rodriguez, Eloy, Roe, Catherine M, Rosa-Neto, Pedro, Rosen, Howard J, Bateman, Randall J, Rot, Uros, Rowe, Christopher C, Rüther, Eckart, Ruiz, Agustín, Sabri, Osama, Sakhardande, Jayant, Sánchez-Juan, Pascual, Sando, Sigrid Botne, Santana, Isabel, Sarazin, Marie, Van Berckel, Bart, Scheltens, Philip, Schröder, Johannes, Selnes, Per, Seo, Sang Won, Silva, Dina, Skoog, Ingmar, Snyder, Peter J, Soininen, Hilkka, Sollberger, Marc, Sperling, Reisa A, Binette, Alexa Pichet, Spiru, Luisa, Stern, Yaakov, Stomrud, Erik, Takeda, Akitoshi, Teichmann, Marc, Teunissen, Charlotte E, Thompson, Louisa I, Tomassen, Jori, Tsolaki, Magda, Vandenberghe, Rik, Blennow, Kaj, Verbeek, Marcel M, Verhey, Frans R J, Villemagne, Victor, Villeneuve, Sylvia, Vogelgsang, Jonathan, Waldemar, Gunhild, Wallin, Anders, Wallin, Åsa K, Wiltfang, Jens, Wolk, David A, Boada, Merce, Yen, Tzu-Chen, Zboch, Marzena, Zetterberg, Henrik, Boecker, Henning, Tijms, Betty M, Bottlaender, Michel, den Braber, Anouk, Brooks, David J, Van Buchem, Mark A, Camus, Vincent, Carill, Jose Manuel, Cerman, Jiri, Chen, Kewei, Chételat, Gaël, Chipi, Elena, Vos, Stephanie J B, Cohen, Ann D, Daniels, Alisha, Delarue, Marion, Didic, Mira, Drzezga, Alexander, Dubois, Bruno, Eckerström, Marie, Ekblad, Laura L, Engelborghs, Sebastiaan, Epelbaum, Stéphane, Ossenkoppele, Rik, Fagan, Anne M, Fan, Yong, Fladby, Tormod, Fleisher, Adam S, Van der Flier, Wiesje M, Förster, Stefan, Fortea, Juan, Frederiksen, Kristian Steen, Freund-Levi, Yvonne, Frings, Lars, Visser, Pieter Jelle, Frisoni, Giovanni B, Fröhlich, Lutz, Gabryelewicz, Tomasz, Gertz, Hermann-Josef, Gill, Kiran Dip, Gkatzima, Olymbia, Gómez-Tortosa, Estrella, Grimmer, Timo, Guedj, Eric, Habeck, Christian G, Group, Amyloid Biomarker Study, Hampel, Harald, Handels, Ron, Hansson, Oskar, Hausner, Lucrezia, Hellwig, Sabine, Heneka, Michael, Herukka, Sanna-Kaisa, Hildebrandt, Helmut, Hodges, John, Hort, Jakub, Aarsland, Dag, Huang, Chin-Chang, Iriondo, Ane Juaristi, Itoh, Yoshiaki, Ivanoiu, Adrian, Jagust, William J, Jessen, Frank, Johannsen, Peter, Johnson, Keith A, Kandimalla, Ramesh, Kapaki, Elisabeth N, Alcolea, Daniel, Kern, Silke, Kilander, Lena, Klimkowicz-Mrowiec, Aleksandra, Klunk, William E, Koglin, Norman, Kornhuber, Johannes, Kramberger, Milica G, Kuo, Hung-Chou, Van Laere, Koen, Landau, Susan M, Altomare, Daniele, Landeau, Brigitte, Lee, Dong Young, de Leon, Mony, Leyton, Cristian E, Lin, Kun-Ju, Lleó, Alberto, Löwenmark, Malin, Madsen, Karine, Maier, Wolfgang, Marcusson, Jan, Clinical sciences, Neuroprotection & Neuromodulation, Neurology, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Neurodegeneration, Radiology and nuclear medicine, Laboratory Medicine, Amsterdam Neuroscience - Neuroinfection & -inflammation, APH - Personalized Medicine, APH - Methodology, RS: MHeNs - R1 - Cognitive Neuropsychiatry and Clinical Neuroscience, and Psychiatrie & Neuropsychologie

Subjects
Male, MILD COGNITIVE IMPAIRMENT, epidemiology [Cognitive Dysfunction], positron emission tomography, Alzheimer`s disease Donders Center for Medical Neuroscience [Radboudumc 1], epidemiology [Alzheimer Disease], Neuroscience(all), diagnostic imaging [Cognitive Dysfunction], Amyloidogenic Proteins, tau Proteins, cerebrospinal fluid [Amyloid beta-Peptides], DIAGNOSIS, cerebrospinal fluid, Apolipoproteins E, Alzheimer Disease, Prevalence, Humans, Amyloid, Alzheimer, PET, Cognitive Dysfunction, ddc:610, cerebrospinal fluid [Peptide Fragments], Aged, Amyloid beta-Peptides, neurology, DEMENTIA, Correction, ASSOCIATION, Amyloidosis, Middle Aged, Disorders of movement Donders Center for Medical Neuroscience [Radboudumc 3], health care planning, clinical trial design, Peptide Fragments, cerebrospinal fluid [Alzheimer Disease], PET, DRIFT, Cross-Sectional Studies, cerebrospinal fluid [Biomarkers], cerebrospinal fluid [tau Proteins], Radiology Nuclear Medicine and imaging, Positron-Emission Tomography, genetics [Apolipoproteins E], Female, Neurology (clinical), diagnostic imaging [Alzheimer Disease], cerebral amyloid aggregation, Biomarkers
Abstract

Importance: One characteristic histopathological event in Alzheimer disease (AD) is cerebral amyloid aggregation, which can be detected by biomarkers in cerebrospinal fluid (CSF) and on positron emission tomography (PET) scans. Prevalence estimates of amyloid pathology are important for health care planning and clinical trial design.Objective: To estimate the prevalence of amyloid abnormality in persons with normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia and to examine the potential implications of cutoff methods, biomarker modality (CSF or PET), age, sex, APOE genotype, educational level, geographical region, and dementia severity for these estimates.Design, Setting, and Participants: This cross-sectional, individual-participant pooled study included participants from 85 Amyloid Biomarker Study cohorts. Data collection was performed from January 1, 2013, to December 31, 2020. Participants had normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia. Normal cognition and subjective cognitive decline were defined by normal scores on cognitive tests, with the presence of cognitive complaints defining subjective cognitive decline. Mild cognitive impairment and clinical AD dementia were diagnosed according to published criteria.Exposures: Alzheimer disease biomarkers detected on PET or in CSF.Main Outcomes and Measures: Amyloid measurements were dichotomized as normal or abnormal using cohort-provided cutoffs for CSF or PET or by visual reading for PET. Adjusted data-driven cutoffs for abnormal amyloid were calculated using gaussian mixture modeling. Prevalence of amyloid abnormality was estimated according to age, sex, cognitive status, biomarker modality, APOE carrier status, educational level, geographical location, and dementia severity using generalized estimating equations.Results: Among the 19 097 participants (mean [SD] age, 69.1 [9.8] years; 10 148 women [53.1%]) included, 10 139 (53.1%) underwent an amyloid PET scan and 8958 (46.9%) had an amyloid CSF measurement. Using cohort-provided cutoffs, amyloid abnormality prevalences were similar to 2015 estimates for individuals without dementia and were similar across PET- and CSF-based estimates (24%; 95% CI, 21%-28%) in participants with normal cognition, 27% (95% CI, 21%-33%) in participants with subjective cognitive decline, and 51% (95% CI, 46%-56%) in participants with mild cognitive impairment, whereas for clinical AD dementia the estimates were higher for PET than CSF (87% vs 79%; mean difference, 8%; 95% CI, 0%-16%; P = .04). Gaussian mixture modeling-based cutoffs for amyloid measures on PET scans were similar to cohort-provided cutoffs and were not adjusted. Adjusted CSF cutoffs resulted in a 10% higher amyloid abnormality prevalence than PET-based estimates in persons with normal cognition (mean difference, 9%; 95% CI, 3%-15%; P = .004), subjective cognitive decline (9%; 95% CI, 3%-15%; P = .005), and mild cognitive impairment (10%; 95% CI, 3%-17%; P = .004), whereas the estimates were comparable in persons with clinical AD dementia (mean difference, 4%; 95% CI, -2% to 9%; P = .18).Conclusions and Relevance: This study found that CSF-based estimates using adjusted data-driven cutoffs were up to 10% higher than PET-based estimates in people without dementia, whereas the results were similar among people with dementia. This finding suggests that preclinical and prodromal AD may be more prevalent than previously estimated, which has important implications for clinical trial recruitment strategies and health care planning policies.

Published
2022
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17. ASIC-E4: Interplay of Beta-Amyloid, Synaptic Density and Neuroinflammation in Cognitively Normal Volunteers With Three Levels of Genetic Risk for Late-Onset Alzheimer's Disease – Study Protocol and Baseline Characteristics

Author

Snellman, Anniina, primary, Ekblad, Laura L., additional, Koivumäki, Mikko, additional, Lindgrén, Noora, additional, Tuisku, Jouni, additional, Perälä, Merja, additional, Kallio, Lila, additional, Lehtonen, Riina, additional, Saunavaara, Virva, additional, Saunavaara, Jani, additional, Oikonen, Vesa, additional, Aarnio, Richard, additional, Löyttyniemi, Eliisa, additional, Parkkola, Riitta, additional, Karrasch, Mira, additional, Zetterberg, Henrik, additional, Blennow, Kaj, additional, and Rinne, Juha O., additional

Published
2022
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21. Association of Early β-Amyloid Accumulation and Neuroinflammation Measured With [11C]PBR28 in Elderly Individuals Without Dementia

Author

Toppala, Sini, Ekblad, Laura L., Tuisku, Jouni, Helin, Semi, Johansson, Jarkko, Laine, Hanna, Löyttyniemi, Eliisa, Marjamäki, Päivi, Blennow, Kaj, Zetterberg, Henrik, Jula, Antti, Viitanen, Matti, Rinne, Juha O., Toppala, Sini, Ekblad, Laura L., Tuisku, Jouni, Helin, Semi, Johansson, Jarkko, Laine, Hanna, Löyttyniemi, Eliisa, Marjamäki, Päivi, Blennow, Kaj, Zetterberg, Henrik, Jula, Antti, Viitanen, Matti, and Rinne, Juha O.

Abstract

OBJECTIVE: To examine whether early β-amyloid (Aβ) accumulation and metabolic risk factors are associated with neuroinflammation in elderly individuals without dementia. METHODS: We examined 54 volunteers (mean age 70.0 years, 56% women, 51% APOE ɛ4 carriers) with the translocator protein (TSPO) tracer [11C]PBR28 to assess neuroinflammation and with [11C] Pittsburgh compound B (PiB) to assess cerebral Aβ accumulation. [11C]PBR28 and [11C]PiB standardized uptake value ratios (SUVRs) were quantified in 6 regions of interests by using the cerebellar cortex as a pseudo-reference and reference region, respectively. Fasting venous glucose, insulin, and high-sensitivity C-reactive protein (hs-CRP) values were determined. Homeostatic model assessment of insulin resistance (HOMA-IR) was calculated. A subset of individuals (n = 11) underwent CSF sampling, and Aβ40, Aβ42, total tau, phospho-tau, soluble TREM2, and YKL-40 levels were measured. RESULTS: Among the whole study group, no significant association was found between [11C]PiB and [11C]PBR28 SUVR composite scores (slope 0.02, p = 0.30). However, higher [11C]PiB binding was associated with higher [11C]PBR28 binding among amyloid-negative ([11C]PiB composite score ≤1.5) (TSPO genotype-, age- and sex-adjusted slope 0.26, p = 0.008) but not among amyloid-positive (slope -0.004, p = 0.88) participants. Higher CSF soluble TREM2 (rs = 0.72, p = 0.01) and YKL-40 (rs = 0.63, p = 0.04) concentrations were associated with a higher [11C]PBR28 composite score. Higher body mass index, HOMA-IR, and hs-CRP were associated with higher [11C]PBR28 binding in brain regions where Aβ accumulation is first detected in Alzheimer disease. CONCLUSIONS: While there was no association between amyloid and neuroinflammation in the overall study group, neuroinflammation was associated with amyloid among the subgroup at early stages of amyloid pathology.

Published
2021
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23. Midlife insulin resistance, APOE genotype, and change in late‐life brain amyloid accumulation – a 5‐year follow‐up 11C‐PIB‐PET study.

Author

Pietilä, Elina M., Ekblad, Laura L., Snellman, Anniina, Toppala, Sini, Tuisku, Jouni, Helin, Semi, Viitanen, Matti, Jula, Antti, and Rinne, Juha O.

Abstract

Background: Midlife insulin resistance has been associated with an increased risk for cognitive decline (1) and amyloid accumulation (2). We hypothesized that midlife insulin resistance would predict amyloid accumulation 20 years later, and the change in brain amyloid accumulation during the 5‐year imaging follow‐up. Methods: We studied 60 cognitively normal participants from the Finnish population‐based Health2000 study in 2014‐2016 with 11C‐PIB‐PET (2). They were recruited according to their homeostatic model assessment of insulin resistance (HOMA‐IR) values measured 15 years earlier in 2000, and their APOEε4 genotype. The insulin resistance group (IR+, HOMA‐IR <1.25) and the control group (IR−, HOMA‐IR > 2.17) both included 30 participants and 50% APOEε4 carriers. In 2019‐2021 43 participants took part in the 5‐year 11C‐PIB‐PET‐imaging follow‐up. Here, we report the change in amyloid accumulation in the IR+ group (n = 19, 57% APOE ε4 carriers) and the IR‐ group (n = 24, 46% APOEε4 carriers) during this 5‐year interval. 11C‐PIB‐PET images were analyzed with an in‐house pipeline and standard uptake value ratio (SUVR) composite scores were calculated. Kruskal‐Wallis test and the Steel‐Dwass method was used to analyze the difference in amyloid accumulation rates between groups, and Spearman's correlation was utilized to assess the association between HOMA‐IR and amyloid accumulation. Result: At the 20‐year follow‐up, amyloid accumulation was significantly higher in the IR+ group than the IR‐ group (composite SUVR 2.43 vs. 1.96, p = 0.03) (Fig. 1). The change in amyloid accumulation during 5 years was significantly higher in the IR+/APOEε4+ group than in either IR−/APOEε4− (mean change in composite SUVR 0.79 vs. 0.32, p = 0.02) or IR+/APOEε4− groups (mean change in composite SUVR 0.79 vs. 0.26, p = 0.046,) (Fig. 2). HOMA‐IR in 2000 (rS = 0.39, p = 0.01) and 2014‐2016 (rS = 0.34, p = 0.03), but not in 2019‐2021 (rS = 0.24, p = 0.13), correlated with 11C‐PIB composite SUVR at the 20‐year follow‐up. Conclusion: These results suggest that IR could be an additive risk factor for late‐life amyloid accumulation in APOEε4 carriers, and that midlife, but not late‐life IR associates with amyloid accumulation. 1) Ekblad et al., Diabetes Care 2017 2) Ekblad et al., Neurology 2018 [ABSTRACT FROM AUTHOR]

Published
2023
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24. Association of Early β-Amyloid Accumulation and Neuroinflammation Measured With [11C]PBR28 in Elderly Individuals Without Dementia

Author

Toppala, Sini, primary, Ekblad, Laura L., additional, Tuisku, Jouni, additional, Helin, Semi, additional, Johansson, Jarkko J., additional, Laine, Hanna, additional, Löyttyniemi, Eliisa, additional, Marjamäki, Päivi, additional, Blennow, Kaj, additional, Zetterberg, Henrik, additional, Jula, Antti, additional, Viitanen, Matti, additional, and Rinne, Juha O., additional

Published
2021
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28. Midlife Insulin Resistance as a Predictor for Late-Life Cognitive Function and Cerebrovascular Lesions

Author

Toppala, Sini, Ekblad, Laura L., Lötjönen, Jyrki, Helin, Semi, Hurme, Saija, Johansson, Jarkko, Jula, Antti, Karrasch, Mira, Koikkalainen, Juha, Laine, Hanna, Parkkola, Riitta, Viitanen, Matti, Rinne, Juha O., Toppala, Sini, Ekblad, Laura L., Lötjönen, Jyrki, Helin, Semi, Hurme, Saija, Johansson, Jarkko, Jula, Antti, Karrasch, Mira, Koikkalainen, Juha, Laine, Hanna, Parkkola, Riitta, Viitanen, Matti, and Rinne, Juha O.

Abstract

Background: Type 2 diabetes (T2DM) increases the risk for Alzheimer’s disease (AD) but not for AD neuropathology. The association between T2DM and AD is assumed to be mediated through vascular mechanisms. However, insulin resistance (IR), the hallmark of T2DM, has been shown to associate with AD neuropathology and cognitive decline. Objective: To evaluate if midlife IR predicts late-life cognitive performance and cerebrovascular lesions (white matter hyperintensities and total vascular burden), and whether cerebrovascular lesions and brain amyloid load are associated with cognitive functioning. Methods: This exposure-to-control follow-up study examined 60 volunteers without dementia (mean age 70.9 years) with neurocognitive testing, brain 3T-MRI and amyloid-PET imaging. The volunteers were recruited from the Finnish Health 2000 survey (n = 6062) to attend follow-up examinations in 2014–2016 according to their insulin sensitivity in 2000 and their APOE genotype. The exposure group (n = 30) had IR in 2000 and the 30 controls had normal insulin sensitivity. There were 15 APOE ɛ4 carriers per group. Statistical analyses were performed with multivariable linear models. Results: At follow-up the IR+group performed worse on executive functions (p = 0.02) and processing speed (p = 0.007) than the IR- group. The groups did not differ in cerebrovascular lesions. No associations were found between cerebrovascular lesions and neurocognitive test scores. Brain amyloid deposition associated with slower processing speed. Conclusion: Midlife IR predicted poorer executive functions and slower processing speed, but not cerebrovascular lesions. Brain amyloid deposition was associated with slower processing speed. The association between midlife IR and late-life cognition might not be mediated through cerebrovascular lesions measured here.

Published
2019
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29. Midlife Insulin Resistance as a Predictor for Late-Life Cognitive Function and Cerebrovascular Lesions

Author

Toppala, Sini, primary, Ekblad, Laura L., additional, Lötjönen, Jyrki, additional, Helin, Semi, additional, Hurme, Saija, additional, Johansson, Jarkko, additional, Jula, Antti, additional, Karrasch, Mira, additional, Koikkalainen, Juha, additional, Laine, Hanna, additional, Parkkola, Riitta, additional, Viitanen, Matti, additional, and Rinne, Juha O., additional

Published
2019
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33. Oral Glucose Tolerance Test Predicts Episodic Memory Decline: A 10-Year Population-Based Follow-up Study.

Author

Toppala, Sini, Ekblad, Laura L., Viitanen, Matti, Rinne, Juha O., and Jula, Antti

Abstract

Objective: To examine if the 2-h value of an oral glucose tolerance test (OGTT) can predict cognitive decline.Research Design and Methods: This study is based on a subpopulation of the Finnish population-based Health 2000 Survey and its follow-up, the Health 2011 study. Altogether, 961 individuals aged 45-74 (mean 55.6 years; 55.8% women) underwent OGTT in 2001-2002. Categorical verbal fluency, word-list learning, and word-list delayed recall were tested at baseline and at follow-up in 2011. Statistical analyses were performed with multivariable linear models adjusted for previously reported risk factors for cognitive decline.Results: A higher 2-h glucose value in the OGTT at baseline predicted worse performance (slope: -0.08; P = 0.01) and greater decline (slope: -0.07; P = 0.007) in the word-list delayed recall test after 10 years.Conclusions: Our results indicate that higher 2-h glucose values in the OGTT predict a decline in episodic memory after 10 years. [ABSTRACT FROM AUTHOR]

Published
2021
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41. SV2A PET shows hippocampal synaptic loss in cognitively unimpaired APOE ε4/ε4 homozygotes.

Author

Snellman A, Tuisku J, Koivumäki M, Wahlroos S, Aarnio R, Rajander J, Karrasch M, Ekblad LL, and Rinne JO

Abstract

Introduction: We investigated hippocampal synaptic density using synaptic vesicle 2A positron emission tomography (PET), and its association with amyloid beta (Aβ) and cognitive performance in healthy apolipoprotein E (APOE) ε4 carriers., Methods: Synaptic density was assessed in 46 individuals (APOE ε4/ε4 n = 14; APOE ε3/ε4 n = 16; APOE ε3/ε3 n = 16) with [ 11 C]UCB-J-PET standardized uptake value ratios (SUVRs), by using the centrum semiovale as a reference region. Differences in hippocampal [ 11 C]UCB-J SUVRs were analyzed with analysis of variance (ANOVA) and linear models. Associations among [ 11 C]UCB-J SUVR, Aβ, hippocampal volume, and cognitive variables were analyzed with Spearman correlation., Results: Hippocampal synaptic density was different among the APOE groups (P ANOVA  = 0.016): APOE ε4/ε4 carriers had lower [ 11 C]UCB-J SUVRs compared to APOE ε3/ε3 (p = 0.013). Hippocampal synaptic density did not correlate with Consortium to Establish a Registry for Alzheimer's Disease (CERAD) total score (rho = -0.052, p = 0.74), Alzheimer's Prevention Initiative Preclinical Cognitive Composite (APCC) score (rho = 0.17, p = 0.28), or [ 11 C]PiB uptake (rho = -0.10, p = 0.50)., Discussion: Hippocampal synaptic loss emerges early in the AD continuum and is measurable in vivo in cognitively unimpaired high-risk individuals., Highlights: Synaptic density was studied in vivo in healthy older adults using [ 11 C]UCB-J positron emission tomography. Apolipoprotein E (APOE) ε4/ε4 carriers had lower hippocampal synaptic density compared to APOE ε3/ε3. Synaptic density was not associated with cognitive performance in this population. Hippocampal synaptic alterations occur before clinical symptoms in APOE ε4/ε4 carriers., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Published
2024
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42. Association of Early β-Amyloid Accumulation and Neuroinflammation Measured With [ 11 C]PBR28 in Elderly Individuals Without Dementia.

Author

Toppala S, Ekblad LL, Tuisku J, Helin S, Johansson JJ, Laine H, Löyttyniemi E, Marjamäki P, Blennow K, Zetterberg H, Jula A, Viitanen M, and Rinne JO

Subjects
Aged, Aged, 80 and over, Amyloid beta-Peptides analysis, Aniline Compounds, Brain metabolism, Brain pathology, Female, Finland, Humans, Inflammation diagnostic imaging, Inflammation metabolism, Inflammation pathology, Magnetic Resonance Imaging, Male, Pyrimidines, Radiopharmaceuticals, Receptors, GABA analysis, Receptors, GABA metabolism, Thiazoles, Amyloid beta-Peptides metabolism, Brain diagnostic imaging, Neuroimaging methods, Positron-Emission Tomography methods
Abstract

Objective: To examine whether early β-amyloid (Aβ) accumulation and metabolic risk factors are associated with neuroinflammation in elderly individuals without dementia., Methods: We examined 54 volunteers (mean age 70.0 years, 56% women, 51% APOE ɛ4 carriers) with the translocator protein (TSPO) tracer [ 11 C]PBR28 to assess neuroinflammation and with [ 11 C] Pittsburgh compound B (PiB) to assess cerebral Aβ accumulation. [ 11 C]PBR28 and [ 11 C]PiB standardized uptake value ratios (SUVRs) were quantified in 6 regions of interests by using the cerebellar cortex as a pseudo-reference and reference region, respectively. Fasting venous glucose, insulin, and high-sensitivity C-reactive protein (hs-CRP) values were determined. Homeostatic model assessment of insulin resistance (HOMA-IR) was calculated. A subset of individuals (n = 11) underwent CSF sampling, and Aβ 40 , Aβ 42 , total tau, phospho-tau, soluble TREM2, and YKL-40 levels were measured., Results: Among the whole study group, no significant association was found between [ 11 C]PiB and [ 11 C]PBR28 SUVR composite scores (slope 0.02, p = 0.30). However, higher [ 11 C]PiB binding was associated with higher [ 11 C]PBR28 binding among amyloid-negative ([ 11 C]PiB composite score ≤1.5) (TSPO genotype-, age- and sex-adjusted slope 0.26, p = 0.008) but not among amyloid-positive (slope -0.004, p = 0.88) participants. Higher CSF soluble TREM2 ( r s = 0.72, p = 0.01) and YKL-40 ( r s = 0.63, p = 0.04) concentrations were associated with a higher [ 11 C]PBR28 composite score. Higher body mass index, HOMA-IR, and hs-CRP were associated with higher [ 11 C]PBR28 binding in brain regions where Aβ accumulation is first detected in Alzheimer disease., Conclusions: While there was no association between amyloid and neuroinflammation in the overall study group, neuroinflammation was associated with amyloid among the subgroup at early stages of amyloid pathology., (Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.)

Published
2021
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