Pub Date : 2024-05-01DOI: 10.1016/S1474-4422(24)00083-8
Rebecca R Valentino, William J Scotton, Shanu F Roemer, Tammaryn Lashley, Michael G Heckman, Maryam Shoai, Alejandro Martinez-Carrasco, Nicole Tamvaka, Ronald L Walton, Matthew C Baker, Hannah L Macpherson, Raquel Real, Alexandra I Soto-Beasley, Kin Mok, Tamas Revesz, Elizabeth A Christopher, Michael DeTure, William W Seeley, Edward B Lee, Matthew P Frosch, Laura Molina-Porcel, Tamar Gefen, Javier Redding-Ochoa, Bernardino Ghetti, Andrew C Robinson, Christopher Kobylecki, James B Rowe, Thomas G Beach, Andrew F Teich, Julia L Keith, Istvan Bodi, Glenda M Halliday, Marla Gearing, Thomas Arzberger, Christopher M Morris, Charles L White, Naguib Mechawar, Susana Boluda, Ian R MacKenzie, Catriona McLean, Matthew D Cykowski, Shih-Hsiu J Wang, Caroline Graff, Rashed M Nagra, Gabor G Kovacs, Giorgio Giaccone, Manuela Neumann, Lee-Cyn Ang, Agostinho Carvalho, Huw R Morris, Rosa Rademakers, John A Hardy, Dennis W Dickson, Jonathan D Rohrer, Owen A Ross
<p><strong>Background: </strong>Pick's disease is a rare and predominantly sporadic form of frontotemporal dementia that is classified as a primary tauopathy. Pick's disease is pathologically defined by the presence in the frontal and temporal lobes of Pick bodies, composed of hyperphosphorylated, three-repeat tau protein, encoded by the MAPT gene. MAPT has two distinct haplotypes, H1 and H2; the MAPT H1 haplotype is the major genetic risk factor for four-repeat tauopathies (eg, progressive supranuclear palsy and corticobasal degeneration), and the MAPT H2 haplotype is protective for these disorders. The primary aim of this study was to evaluate the association of MAPT H2 with Pick's disease risk, age at onset, and disease duration.</p><p><strong>Methods: </strong>In this genetic association study, we used data from the Pick's disease International Consortium, which we established to enable collection of data from individuals with pathologically confirmed Pick's disease worldwide. For this analysis, we collected brain samples from individuals with pathologically confirmed Pick's disease from 35 sites (brainbanks and hospitals) in North America, Europe, and Australia between Jan 1, 2020, and Jan 31, 2023. Neurologically healthy controls were recruited from the Mayo Clinic (FL, USA, or MN, USA between March 1, 1998, and Sept 1, 2019). For the primary analysis, individuals were directly genotyped for the MAPT H1-H2 haplotype-defining variant rs8070723. In a secondary analysis, we genotyped and constructed the six-variant-defined (rs1467967-rs242557-rs3785883-rs2471738-rs8070723-rs7521) MAPT H1 subhaplotypes. Associations of MAPT variants and MAPT haplotypes with Pick's disease risk, age at onset, and disease duration were examined using logistic and linear regression models; odds ratios (ORs) and β coefficients were estimated and correspond to each additional minor allele or each additional copy of the given haplotype.</p><p><strong>Findings: </strong>We obtained brain samples from 338 people with pathologically confirmed Pick's disease (205 [61%] male and 133 [39%] female; 338 [100%] White) and 1312 neurologically healthy controls (611 [47%] male and 701 [53%] female; 1312 [100%] White). The MAPT H2 haplotype was associated with increased risk of Pick's disease compared with the H1 haplotype (OR 1·35 [95% CI 1·12 to 1·64], p=0·0021). MAPT H2 was not associated with age at onset (β -0·54 [95% CI -1·94 to 0·87], p=0·45) or disease duration (β 0·05 [-0·06 to 0·16], p=0·35). Although not significant after correcting for multiple testing, associations were observed at p less than 0·05: with risk of Pick's disease for the H1f subhaplotype (OR 0·11 [0·01 to 0·99], p=0·049); with age at onset for H1b (β 2·66 [0·63 to 4·70], p=0·011), H1i (β -3·66 [-6·83 to -0·48], p=0·025), and H1u (β -5·25 [-10·42 to -0·07], p=0·048); and with disease duration for H1x (β -0·57 [-1·07 to -0·07], p=0·026).</p><p><strong>Interpretation: </strong>The Pick's disease Internatio
背景：皮克病是一种罕见的、以散发性为主的额颞叶痴呆，被归类为原发性tau蛋白病。皮克病的病理特征是在额叶和颞叶出现皮克体，皮克体由高磷酸化、三重复的 tau 蛋白组成，由 MAPT 基因编码。MAPT 有两种不同的单倍型，即 H1 和 H2；MAPT H1 单倍型是四重复 tau 病（如进行性核上麻痹和皮质基底变性）的主要遗传风险因素，而 MAPT H2 单倍型对这些疾病具有保护作用。本研究的主要目的是评估 MAPT H2 与 Pick's 病风险、发病年龄和病程的相关性：在这项遗传关联研究中，我们使用了皮克病国际联合会（Pick's disease International Consortium）的数据。为了进行这项分析，我们在 2020 年 1 月 1 日至 2023 年 1 月 31 日期间从北美、欧洲和澳大利亚的 35 个地点（脑库和医院）收集了病理确诊的皮克病患者的脑样本。1998年3月1日至2019年9月1日期间，从梅奥诊所（美国佛罗里达州或美国明尼苏达州）招募神经健康的对照组。在主要分析中，我们直接对个体进行了 MAPT H1-H2 单倍型定义变体 rs8070723 的基因分型。在次要分析中，我们对六个变异定义（rs1467967-rs242557-rs3785883-rs2471738-rs8070723-rs7521）的 MAPT H1 亚单倍型进行了基因分型和构建。我们使用逻辑和线性回归模型研究了 MAPT 变体和 MAPT 单倍型与 Pick's 病风险、发病年龄和病程的关系；估算了几率比（ORs）和 β 系数，它们与每一个额外的小等位基因或给定单倍型的每一个额外拷贝相对应：我们从338名病理确诊的皮克病患者（男性205人[61%]，女性133人[39%]；白人338人[100%]）和1312名神经系统健康的对照组患者（男性611人[47%]，女性701人[53%]；白人1312人[100%]）中获得了脑样本。与 H1 单倍型相比，MAPT H2 单倍型与 Pick's 病风险的增加有关（OR 1-35 [95% CI 1-12 to 1-64]，p=0-0021）。MAPT H2与发病年龄（β -0-54 [95% CI -1-94 to 0-87]，p=0-45）或病程（β 0-05 [-0-06 to 0-16]，p=0-35）无关。虽然经多重检验校正后并不显著，但仍可观察到小于 0-05 的相关性：H1f亚单倍型与皮克病的发病风险有关（OR 0-11 [0-01 至 0-99]，P=0-049）；H1b（β 2-66 [0-63 to 4-70]，p=0-011）、H1i（β -3-66 [-6-83 to -0-48]，p=0-025）和H1u（β -5-25 [-10-42 to -0-07]，p=0-048）与发病年龄有关；H1x（β -0-57 [-1-07 to -0-07]，p=0-026）与病程有关。解释：皮克病国际联盟提供了一个进行大型研究的机会，以加深我们对皮克病病理生物学的了解。这项研究表明，与四重复tauopathies风险降低不同，MAPT H2单倍型与欧洲血统人群罹患Pick病的风险增加有关。这一发现可为开发治疗tau病的同工酶相关疗法提供依据：惠康基金会、罗塔-亚伯拉罕基金会、英国脑研究中心、杜比基金、痴呆症研究所（医学研究委员会）、美国国立卫生研究院和梅奥诊所基金会。
{"title":"MAPT H2 haplotype and risk of Pick's disease in the Pick's disease International Consortium: a genetic association study.","authors":"Rebecca R Valentino, William J Scotton, Shanu F Roemer, Tammaryn Lashley, Michael G Heckman, Maryam Shoai, Alejandro Martinez-Carrasco, Nicole Tamvaka, Ronald L Walton, Matthew C Baker, Hannah L Macpherson, Raquel Real, Alexandra I Soto-Beasley, Kin Mok, Tamas Revesz, Elizabeth A Christopher, Michael DeTure, William W Seeley, Edward B Lee, Matthew P Frosch, Laura Molina-Porcel, Tamar Gefen, Javier Redding-Ochoa, Bernardino Ghetti, Andrew C Robinson, Christopher Kobylecki, James B Rowe, Thomas G Beach, Andrew F Teich, Julia L Keith, Istvan Bodi, Glenda M Halliday, Marla Gearing, Thomas Arzberger, Christopher M Morris, Charles L White, Naguib Mechawar, Susana Boluda, Ian R MacKenzie, Catriona McLean, Matthew D Cykowski, Shih-Hsiu J Wang, Caroline Graff, Rashed M Nagra, Gabor G Kovacs, Giorgio Giaccone, Manuela Neumann, Lee-Cyn Ang, Agostinho Carvalho, Huw R Morris, Rosa Rademakers, John A Hardy, Dennis W Dickson, Jonathan D Rohrer, Owen A Ross","doi":"10.1016/S1474-4422(24)00083-8","DOIUrl":"10.1016/S1474-4422(24)00083-8","url":null,"abstract":"<p><strong>Background: </strong>Pick's disease is a rare and predominantly sporadic form of frontotemporal dementia that is classified as a primary tauopathy. Pick's disease is pathologically defined by the presence in the frontal and temporal lobes of Pick bodies, composed of hyperphosphorylated, three-repeat tau protein, encoded by the MAPT gene. MAPT has two distinct haplotypes, H1 and H2; the MAPT H1 haplotype is the major genetic risk factor for four-repeat tauopathies (eg, progressive supranuclear palsy and corticobasal degeneration), and the MAPT H2 haplotype is protective for these disorders. The primary aim of this study was to evaluate the association of MAPT H2 with Pick's disease risk, age at onset, and disease duration.</p><p><strong>Methods: </strong>In this genetic association study, we used data from the Pick's disease International Consortium, which we established to enable collection of data from individuals with pathologically confirmed Pick's disease worldwide. For this analysis, we collected brain samples from individuals with pathologically confirmed Pick's disease from 35 sites (brainbanks and hospitals) in North America, Europe, and Australia between Jan 1, 2020, and Jan 31, 2023. Neurologically healthy controls were recruited from the Mayo Clinic (FL, USA, or MN, USA between March 1, 1998, and Sept 1, 2019). For the primary analysis, individuals were directly genotyped for the MAPT H1-H2 haplotype-defining variant rs8070723. In a secondary analysis, we genotyped and constructed the six-variant-defined (rs1467967-rs242557-rs3785883-rs2471738-rs8070723-rs7521) MAPT H1 subhaplotypes. Associations of MAPT variants and MAPT haplotypes with Pick's disease risk, age at onset, and disease duration were examined using logistic and linear regression models; odds ratios (ORs) and β coefficients were estimated and correspond to each additional minor allele or each additional copy of the given haplotype.</p><p><strong>Findings: </strong>We obtained brain samples from 338 people with pathologically confirmed Pick's disease (205 [61%] male and 133 [39%] female; 338 [100%] White) and 1312 neurologically healthy controls (611 [47%] male and 701 [53%] female; 1312 [100%] White). The MAPT H2 haplotype was associated with increased risk of Pick's disease compared with the H1 haplotype (OR 1·35 [95% CI 1·12 to 1·64], p=0·0021). MAPT H2 was not associated with age at onset (β -0·54 [95% CI -1·94 to 0·87], p=0·45) or disease duration (β 0·05 [-0·06 to 0·16], p=0·35). Although not significant after correcting for multiple testing, associations were observed at p less than 0·05: with risk of Pick's disease for the H1f subhaplotype (OR 0·11 [0·01 to 0·99], p=0·049); with age at onset for H1b (β 2·66 [0·63 to 4·70], p=0·011), H1i (β -3·66 [-6·83 to -0·48], p=0·025), and H1u (β -5·25 [-10·42 to -0·07], p=0·048); and with disease duration for H1x (β -0·57 [-1·07 to -0·07], p=0·026).</p><p><strong>Interpretation: </strong>The Pick's disease Internatio","PeriodicalId":17989,"journal":{"name":"Lancet Neurology","volume":"23 5","pages":"487-499"},"PeriodicalIF":48.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140851675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01DOI: 10.1016/S1474-4422(24)00132-7
Joep Killestein, Mike P Wattjes
{"title":"Surrogate endpoints for progressive multifocal leukoencephalopathy.","authors":"Joep Killestein, Mike P Wattjes","doi":"10.1016/S1474-4422(24)00132-7","DOIUrl":"https://doi.org/10.1016/S1474-4422(24)00132-7","url":null,"abstract":"","PeriodicalId":17989,"journal":{"name":"Lancet Neurology","volume":"23 5","pages":"455-456"},"PeriodicalIF":48.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140859607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01DOI: 10.1016/S1474-4422(24)00089-9
Thiviya Selvanathan, Steven P Miller
{"title":"Factors affecting brain maturation trajectories in early childhood.","authors":"Thiviya Selvanathan, Steven P Miller","doi":"10.1016/S1474-4422(24)00089-9","DOIUrl":"https://doi.org/10.1016/S1474-4422(24)00089-9","url":null,"abstract":"","PeriodicalId":17989,"journal":{"name":"Lancet Neurology","volume":"23 5","pages":"456-458"},"PeriodicalIF":48.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140856597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01DOI: 10.1016/S1474-4422(24)00079-6
Jacob Pellinen, Emma C Foster, Jo M Wilmshurst, Sameer M Zuberi, Jacqueline French
Epilepsy diagnosis is often delayed or inaccurate, exposing people to ongoing seizures and their substantial consequences until effective treatment is initiated. Important factors contributing to this problem include delayed recognition of seizure symptoms by patients and eyewitnesses; cultural, geographical, and financial barriers to seeking health care; and missed or delayed diagnosis by health-care providers. Epilepsy diagnosis involves several steps. The first step is recognition of epileptic seizures; next is classification of epilepsy type and whether an epilepsy syndrome is present; finally, the underlying epilepsy-associated comorbidities and potential causes must be identified, which differ across the lifespan. Clinical history, elicited from patients and eyewitnesses, is a fundamental component of the diagnostic pathway. Recent technological advances, including smartphone videography and genetic testing, are increasingly used in routine practice. Innovations in technology, such as artificial intelligence, could provide new possibilities for directly and indirectly detecting epilepsy and might make valuable contributions to diagnostic algorithms in the future.
{"title":"Improving epilepsy diagnosis across the lifespan: approaches and innovations.","authors":"Jacob Pellinen, Emma C Foster, Jo M Wilmshurst, Sameer M Zuberi, Jacqueline French","doi":"10.1016/S1474-4422(24)00079-6","DOIUrl":"https://doi.org/10.1016/S1474-4422(24)00079-6","url":null,"abstract":"<p><p>Epilepsy diagnosis is often delayed or inaccurate, exposing people to ongoing seizures and their substantial consequences until effective treatment is initiated. Important factors contributing to this problem include delayed recognition of seizure symptoms by patients and eyewitnesses; cultural, geographical, and financial barriers to seeking health care; and missed or delayed diagnosis by health-care providers. Epilepsy diagnosis involves several steps. The first step is recognition of epileptic seizures; next is classification of epilepsy type and whether an epilepsy syndrome is present; finally, the underlying epilepsy-associated comorbidities and potential causes must be identified, which differ across the lifespan. Clinical history, elicited from patients and eyewitnesses, is a fundamental component of the diagnostic pathway. Recent technological advances, including smartphone videography and genetic testing, are increasingly used in routine practice. Innovations in technology, such as artificial intelligence, could provide new possibilities for directly and indirectly detecting epilepsy and might make valuable contributions to diagnostic algorithms in the future.</p>","PeriodicalId":17989,"journal":{"name":"Lancet Neurology","volume":"23 5","pages":"511-521"},"PeriodicalIF":48.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140859343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01DOI: 10.1016/S1474-4422(24)00084-X
Julie K Wisch, Nicole S McKay, Anna H Boerwinkle, James Kennedy, Shaney Flores, Benjamin L Handen, Bradley T Christian, Elizabeth Head, Mark Mapstone, Michael S Rafii, Sid E O'Bryant, Julie C Price, Charles M Laymon, Sharon J Krinsky-McHale, Florence Lai, H Diana Rosas, Sigan L Hartley, Shahid Zaman, Ira T Lott, Dana Tudorascu, Matthew Zammit, Adam M Brickman, Joseph H Lee, Thomas D Bird, Annie Cohen, Patricio Chrem, Alisha Daniels, Jasmeer P Chhatwal, Carlos Cruchaga, Laura Ibanez, Mathias Jucker, Celeste M Karch, Gregory S Day, Jae-Hong Lee, Johannes Levin, Jorge Llibre-Guerra, Yan Li, Francisco Lopera, Jee Hoon Roh, John M Ringman, Charlene Supnet-Bell, Christopher H van Dyck, Chengjie Xiong, Guoqiao Wang, John C Morris, Eric McDade, Randall J Bateman, Tammie L S Benzinger, Brian A Gordon, Beau M Ances
<p><strong>Background: </strong>In people with genetic forms of Alzheimer's disease, such as in Down syndrome and autosomal-dominant Alzheimer's disease, pathological changes specific to Alzheimer's disease (ie, accumulation of amyloid and tau) occur in the brain at a young age, when comorbidities related to ageing are not present. Studies including these cohorts could, therefore, improve our understanding of the early pathogenesis of Alzheimer's disease and be useful when designing preventive interventions targeted at disease pathology or when planning clinical trials. We compared the magnitude, spatial extent, and temporal ordering of tau spread in people with Down syndrome and autosomal-dominant Alzheimer's disease.</p><p><strong>Methods: </strong>In this cross-sectional observational study, we included participants (aged ≥25 years) from two cohort studies. First, we collected data from the Dominantly Inherited Alzheimer's Network studies (DIAN-OBS and DIAN-TU), which include carriers of autosomal-dominant Alzheimer's disease genetic mutations and non-carrier familial controls recruited in Australia, Europe, and the USA between 2008 and 2022. Second, we collected data from the Alzheimer Biomarkers Consortium-Down Syndrome study, which includes people with Down syndrome and sibling controls recruited from the UK and USA between 2015 and 2021. Controls from the two studies were combined into a single group of familial controls. All participants had completed structural MRI and tau PET (<sup>18</sup>F-flortaucipir) imaging. We applied Gaussian mixture modelling to identify regions of high tau PET burden and regions with the earliest changes in tau binding for each cohort separately. We estimated regional tau PET burden as a function of cortical amyloid burden for both cohorts. Finally, we compared the temporal pattern of tau PET burden relative to that of amyloid.</p><p><strong>Findings: </strong>We included 137 people with Down syndrome (mean age 38·5 years [SD 8·2], 74 [54%] male, and 63 [46%] female), 49 individuals with autosomal-dominant Alzheimer's disease (mean age 43·9 years [11·2], 22 [45%] male, and 27 [55%] female), and 85 familial controls, pooled from across both studies (mean age 41·5 years [12·1], 28 [33%] male, and 57 [67%] female), who satisfied the PET quality-control procedure for tau-PET imaging processing. 134 (98%) people with Down syndrome, 44 (90%) with autosomal-dominant Alzheimer's disease, and 77 (91%) controls also completed an amyloid PET scan within 3 years of tau PET imaging. Spatially, tau PET burden was observed most frequently in subcortical and medial temporal regions in people with Down syndrome, and within the medial temporal lobe in people with autosomal-dominant Alzheimer's disease. Across the brain, people with Down syndrome had greater concentrations of tau for a given level of amyloid compared with people with autosomal-dominant Alzheimer's disease. Temporally, increases in tau were more strongly associat
{"title":"Comparison of tau spread in people with Down syndrome versus autosomal-dominant Alzheimer's disease: a cross-sectional study.","authors":"Julie K Wisch, Nicole S McKay, Anna H Boerwinkle, James Kennedy, Shaney Flores, Benjamin L Handen, Bradley T Christian, Elizabeth Head, Mark Mapstone, Michael S Rafii, Sid E O'Bryant, Julie C Price, Charles M Laymon, Sharon J Krinsky-McHale, Florence Lai, H Diana Rosas, Sigan L Hartley, Shahid Zaman, Ira T Lott, Dana Tudorascu, Matthew Zammit, Adam M Brickman, Joseph H Lee, Thomas D Bird, Annie Cohen, Patricio Chrem, Alisha Daniels, Jasmeer P Chhatwal, Carlos Cruchaga, Laura Ibanez, Mathias Jucker, Celeste M Karch, Gregory S Day, Jae-Hong Lee, Johannes Levin, Jorge Llibre-Guerra, Yan Li, Francisco Lopera, Jee Hoon Roh, John M Ringman, Charlene Supnet-Bell, Christopher H van Dyck, Chengjie Xiong, Guoqiao Wang, John C Morris, Eric McDade, Randall J Bateman, Tammie L S Benzinger, Brian A Gordon, Beau M Ances","doi":"10.1016/S1474-4422(24)00084-X","DOIUrl":"10.1016/S1474-4422(24)00084-X","url":null,"abstract":"<p><strong>Background: </strong>In people with genetic forms of Alzheimer's disease, such as in Down syndrome and autosomal-dominant Alzheimer's disease, pathological changes specific to Alzheimer's disease (ie, accumulation of amyloid and tau) occur in the brain at a young age, when comorbidities related to ageing are not present. Studies including these cohorts could, therefore, improve our understanding of the early pathogenesis of Alzheimer's disease and be useful when designing preventive interventions targeted at disease pathology or when planning clinical trials. We compared the magnitude, spatial extent, and temporal ordering of tau spread in people with Down syndrome and autosomal-dominant Alzheimer's disease.</p><p><strong>Methods: </strong>In this cross-sectional observational study, we included participants (aged ≥25 years) from two cohort studies. First, we collected data from the Dominantly Inherited Alzheimer's Network studies (DIAN-OBS and DIAN-TU), which include carriers of autosomal-dominant Alzheimer's disease genetic mutations and non-carrier familial controls recruited in Australia, Europe, and the USA between 2008 and 2022. Second, we collected data from the Alzheimer Biomarkers Consortium-Down Syndrome study, which includes people with Down syndrome and sibling controls recruited from the UK and USA between 2015 and 2021. Controls from the two studies were combined into a single group of familial controls. All participants had completed structural MRI and tau PET (<sup>18</sup>F-flortaucipir) imaging. We applied Gaussian mixture modelling to identify regions of high tau PET burden and regions with the earliest changes in tau binding for each cohort separately. We estimated regional tau PET burden as a function of cortical amyloid burden for both cohorts. Finally, we compared the temporal pattern of tau PET burden relative to that of amyloid.</p><p><strong>Findings: </strong>We included 137 people with Down syndrome (mean age 38·5 years [SD 8·2], 74 [54%] male, and 63 [46%] female), 49 individuals with autosomal-dominant Alzheimer's disease (mean age 43·9 years [11·2], 22 [45%] male, and 27 [55%] female), and 85 familial controls, pooled from across both studies (mean age 41·5 years [12·1], 28 [33%] male, and 57 [67%] female), who satisfied the PET quality-control procedure for tau-PET imaging processing. 134 (98%) people with Down syndrome, 44 (90%) with autosomal-dominant Alzheimer's disease, and 77 (91%) controls also completed an amyloid PET scan within 3 years of tau PET imaging. Spatially, tau PET burden was observed most frequently in subcortical and medial temporal regions in people with Down syndrome, and within the medial temporal lobe in people with autosomal-dominant Alzheimer's disease. Across the brain, people with Down syndrome had greater concentrations of tau for a given level of amyloid compared with people with autosomal-dominant Alzheimer's disease. Temporally, increases in tau were more strongly associat","PeriodicalId":17989,"journal":{"name":"Lancet Neurology","volume":"23 5","pages":"500-510"},"PeriodicalIF":46.5,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11209765/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140863317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01Epub Date: 2024-02-13DOI: 10.1016/S1474-4422(24)00025-5
Cristina Tassorelli, Krisztián Nagy, Patricia Pozo-Rosich, Michel Lanteri-Minet, Sara Sacco, Tomáš Nežádal, Hua Guo, Rosa De Abreu Ferreira, Giovanna Forero, Joel M Trugman
<p><strong>Background: </strong>Atogepant, an oral calcitonin gene-related peptide receptor antagonist, has been approved for the preventive treatment of migraine, but its efficacy and safety in people who have been failed by conventional oral preventive migraine treatments has not yet been evaluated in a dedicated clinical trial. The ELEVATE trial evaluated the safety, tolerability, and efficacy of atogepant for the preventive treatment of episodic migraine in participants for whom two to four classes of conventional oral preventive treatments have failed.</p><p><strong>Methods: </strong>ELEVATE was a randomised, double-blind, placebo-controlled, parallel-group, phase 3b trial done at 73 sites in Canada, the Czech Republic, Denmark, France, Germany, Hungary, Italy, the Netherlands, Poland, Russia, Spain, the UK, and the USA. Adults (18-80 years) with episodic migraine who had previously been failed by two to four classes of conventional oral treatments for migraine prevention were randomly assigned (1:1) using interactive web response technology to oral atogepant 60 mg once a day or placebo, stratified by baseline monthly migraine days, number of treatment classes participants have been failed by, and region. The primary endpoint was change from baseline in mean monthly migraine days across the 12-week treatment period in the off-treatment hypothetical estimand (OTHE) population, which included participants in the safety population (all participants who received ≥1 dose of study intervention) who had evaluable data available for the baseline period and for one or more of the 4-week post-baseline periods (whether on treatment or off treatment). The primary endpoint was analysed using a mixed model for repeated measures and a fixed-sequence procedure was used to control for multiple comparisons. The trial is registered with ClinicalTrials.gov (NCT04740827) and EudraCT (2019-003448-58), and is completed.</p><p><strong>Findings: </strong>Between March 5, 2021, and Aug 4, 2022, 540 participants were screened, 315 were randomly assigned, and 313 participants (280 [89%] female, 33 [11%] male, and 300 [96%] White) received at least one dose of study intervention. In the OTHE population, which comprised 309 participants (155 assigned to placebo and 154 to atogepant), least squares mean changes from baseline in monthly migraine days across 12 weeks were -1·9 (SE 0·4) with placebo and -4·2 (0·4) with atogepant (least squares mean difference -2·4, 95% CI -3·2 to -1·5; adjusted p<0·0001). The most common treatment-emergent adverse event with atogepant was constipation in 16 (10%) of 156 participants (vs four [3%] of 157 for placebo). Serious adverse events occurred in four [3%] of 156 participants in the atogepant group vs none in the placebo group, and treatment-emergent adverse events resulting in treatment discontinuation occurred in three [2%] in the atogepant group vs two [1%] in the placebo group.</p><p><strong>Interpretation: </strong>Atogepant 60 mg
{"title":"Safety and efficacy of atogepant for the preventive treatment of episodic migraine in adults for whom conventional oral preventive treatments have failed (ELEVATE): a randomised, placebo-controlled, phase 3b trial.","authors":"Cristina Tassorelli, Krisztián Nagy, Patricia Pozo-Rosich, Michel Lanteri-Minet, Sara Sacco, Tomáš Nežádal, Hua Guo, Rosa De Abreu Ferreira, Giovanna Forero, Joel M Trugman","doi":"10.1016/S1474-4422(24)00025-5","DOIUrl":"10.1016/S1474-4422(24)00025-5","url":null,"abstract":"<p><strong>Background: </strong>Atogepant, an oral calcitonin gene-related peptide receptor antagonist, has been approved for the preventive treatment of migraine, but its efficacy and safety in people who have been failed by conventional oral preventive migraine treatments has not yet been evaluated in a dedicated clinical trial. The ELEVATE trial evaluated the safety, tolerability, and efficacy of atogepant for the preventive treatment of episodic migraine in participants for whom two to four classes of conventional oral preventive treatments have failed.</p><p><strong>Methods: </strong>ELEVATE was a randomised, double-blind, placebo-controlled, parallel-group, phase 3b trial done at 73 sites in Canada, the Czech Republic, Denmark, France, Germany, Hungary, Italy, the Netherlands, Poland, Russia, Spain, the UK, and the USA. Adults (18-80 years) with episodic migraine who had previously been failed by two to four classes of conventional oral treatments for migraine prevention were randomly assigned (1:1) using interactive web response technology to oral atogepant 60 mg once a day or placebo, stratified by baseline monthly migraine days, number of treatment classes participants have been failed by, and region. The primary endpoint was change from baseline in mean monthly migraine days across the 12-week treatment period in the off-treatment hypothetical estimand (OTHE) population, which included participants in the safety population (all participants who received ≥1 dose of study intervention) who had evaluable data available for the baseline period and for one or more of the 4-week post-baseline periods (whether on treatment or off treatment). The primary endpoint was analysed using a mixed model for repeated measures and a fixed-sequence procedure was used to control for multiple comparisons. The trial is registered with ClinicalTrials.gov (NCT04740827) and EudraCT (2019-003448-58), and is completed.</p><p><strong>Findings: </strong>Between March 5, 2021, and Aug 4, 2022, 540 participants were screened, 315 were randomly assigned, and 313 participants (280 [89%] female, 33 [11%] male, and 300 [96%] White) received at least one dose of study intervention. In the OTHE population, which comprised 309 participants (155 assigned to placebo and 154 to atogepant), least squares mean changes from baseline in monthly migraine days across 12 weeks were -1·9 (SE 0·4) with placebo and -4·2 (0·4) with atogepant (least squares mean difference -2·4, 95% CI -3·2 to -1·5; adjusted p<0·0001). The most common treatment-emergent adverse event with atogepant was constipation in 16 (10%) of 156 participants (vs four [3%] of 157 for placebo). Serious adverse events occurred in four [3%] of 156 participants in the atogepant group vs none in the placebo group, and treatment-emergent adverse events resulting in treatment discontinuation occurred in three [2%] in the atogepant group vs two [1%] in the placebo group.</p><p><strong>Interpretation: </strong>Atogepant 60 mg ","PeriodicalId":17989,"journal":{"name":"Lancet Neurology","volume":" ","pages":"382-392"},"PeriodicalIF":48.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139746848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01Epub Date: 2024-03-14DOI: 10.1016/S1474-4422(24)00038-3
<p><strong>Background: </strong>Disorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.</p><p><strong>Methods: </strong>We estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.</p><p><strong>Findings: </strong>Globally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378-521), affecting 3·40 billion (3·20-3·62) individuals (43·1%, 40·5-45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7-26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6-38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5-32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7-2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm bir
{"title":"Global, regional, and national burden of disorders affecting the nervous system, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021.","authors":"","doi":"10.1016/S1474-4422(24)00038-3","DOIUrl":"10.1016/S1474-4422(24)00038-3","url":null,"abstract":"<p><strong>Background: </strong>Disorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.</p><p><strong>Methods: </strong>We estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.</p><p><strong>Findings: </strong>Globally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378-521), affecting 3·40 billion (3·20-3·62) individuals (43·1%, 40·5-45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7-26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6-38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5-32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7-2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm bir","PeriodicalId":17989,"journal":{"name":"Lancet Neurology","volume":" ","pages":"344-381"},"PeriodicalIF":48.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10949203/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140143805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01Epub Date: 2024-03-14DOI: 10.1016/S1474-4422(24)00085-1
Amy Tausch, Kiran T Thakur, Renato Oliveira E Souza
{"title":"Addressing neurological burden in the Americas.","authors":"Amy Tausch, Kiran T Thakur, Renato Oliveira E Souza","doi":"10.1016/S1474-4422(24)00085-1","DOIUrl":"10.1016/S1474-4422(24)00085-1","url":null,"abstract":"","PeriodicalId":17989,"journal":{"name":"Lancet Neurology","volume":" ","pages":"327-328"},"PeriodicalIF":48.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140143804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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