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Kim J, Suh SI, Park YJ, Kang M, Chung SJ, Lee ES, Jung HN, Eo JS, Koh SB, Oh K, Kang SH. Sarcopenia is a predictor for Alzheimer's continuum and related clinical outcomes. Sci Rep 2024; 14:21074. [PMID: 39256402 PMCID: PMC11387779 DOI: 10.1038/s41598-024-62918-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/22/2024] [Indexed: 09/12/2024] Open
Abstract
Low body mass index is closely related to a high risk of Alzheimer's disease (AD) and related biomarkers including amyloid-β (Aβ) deposition. However, the association between sarcopenia and Aβ-confirmed AD remains controversial. Therefore, we investigated the relationship between sarcopenia and the AD continuum. We explored sarcopenia's association with clinical implications of participants on the AD continuum. We prospectively enrolled 142 participants on the AD continuum (19 with preclinical AD, 96 with mild cognitive impairment due to AD, and 28 with AD dementia) and 58 Aβ-negative cognitively unimpaired participants. Sarcopenia, assessed using dual-energy X-ray absorptiometry and hand grip measurements, was considered a predictor. AD continuum, defined by Aβ deposition on positron emission tomography served as an outcome. Clinical severity in participants on the AD continuum assessed using hippocampal volume, Mini-Mental State Examination (MMSE), Seoul Verbal Learning Test (SVLT), and Clinical Dementia Rating Scale Sum of Boxes Scores (CDR-SOB) were also considered an outcome. Sarcopenia (odds ratio = 4.99, p = 0.004) was associated independently with the AD continuum after controlling for potential confounders. Moreover, sarcopenia was associated with poor downstream imaging markers (decreased hippocampal volume, β = - 0.206, p = 0.020) and clinical outcomes (low MMSE, β = - 1.364, p = 0.025; low SVLT, β = - 1.077, p = 0.025; and high CDR-SOB scores, β = 0.783, p = 0.022) in participants on the AD continuum. Sarcopenia was associated with the AD continuum and poor clinical outcome in individuals with AD continuum. Therefore, our results provide evidence for future studies to confirm whether proper management of sarcopenia can effective strategies are required for sarcopenia management to prevent the AD continuum and its clinical implications.
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Affiliation(s)
- Jeonghun Kim
- Korea Testing Laboratory, Bio and Medical Health Division, Seoul, Korea
| | - Sang-Il Suh
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
| | - Yu Jeong Park
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, 148 Gurodong-ro, Guro-gu, Seoul, 08308, Korea
| | - Minwoong Kang
- Department of Biomedical Research Center, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
| | - Su Jin Chung
- Department of Neurology, Inje University Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Korea
| | - Eun Seong Lee
- Department of Nuclear Medicine, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
| | - Hye Na Jung
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
| | - Jae Seon Eo
- Department of Nuclear Medicine, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
| | - Seong-Beom Koh
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, 148 Gurodong-ro, Guro-gu, Seoul, 08308, Korea
| | - Kyungmi Oh
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, 148 Gurodong-ro, Guro-gu, Seoul, 08308, Korea.
| | - Sung Hoon Kang
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, 148 Gurodong-ro, Guro-gu, Seoul, 08308, Korea.
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Lee EH, Yoo H, Kim YJ, Cheon BK, Ryu S, Chang Y, Yun J, Jang H, Kim JP, Kim HJ, Koh SB, Jeong JH, Na DL, Seo SW, Kang SH. Different associations between body mass index and Alzheimer's markers depending on metabolic health. Alzheimers Res Ther 2024; 16:194. [PMID: 39210402 PMCID: PMC11363444 DOI: 10.1186/s13195-024-01563-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2024] [Accepted: 08/21/2024] [Indexed: 09/04/2024]
Abstract
BACKGROUND Increasing evidence supports the association between body mass index (BMI), Alzheimer's disease, and vascular markers. Recently, metabolically unhealthy conditions have been reported to affect the expression of these markers. We aimed to investigate the effects of BMI status on Alzheimer's and vascular markers in relation to metabolic health status. METHODS We recruited 1,736 Asians without dementia (71.6 ± 8.0 years). Participants were categorized into underweight, normal weight, or obese groups based on their BMI. Each group was further divided into metabolically healthy (MH) and unhealthy (MU) groups based on the International Diabetes Foundation definition of metabolic syndrome. The main outcome was Aβ positivity, defined as a Centiloid value of 20.0 or above and the presence of vascular markers, defined as severe white matter hyperintensities (WMH). Logistic regression analyses were performed for Aβ positivity and severe WMH with BMI status or interaction terms between BMI and metabolic health status as predictors. Mediation analyses were performed with hippocampal volume (HV) and baseline Mini-Mental State Examination (MMSE) scores as the outcomes, and linear mixed models were performed for longitudinal change in MMSE scores. RESULTS Being underweight increased the risk of Aβ positivity (odds ratio [OR] = 2.37, 95% confidence interval [CI] 1.13-4.98), whereas obesity decreased Aβ positivity risk (OR = 0.63, 95% CI 0.50-0.80). Especially, obesity decreased the risk of Aβ positivity (OR = 0.38, 95% CI 0.26-0.56) in the MH group, but not in the MU group. Obesity increased the risk of severe WMH (OR = 1.69, 1.16-2.47). Decreased Aβ positivity mediate the relationship between obesity and higher HV and MMSE scores, particularly in the MH group. Obesity demonstrated a slower decline in MMSE (β = 1.423, p = 0.037) compared to being normal weight, especially in the MH group. CONCLUSIONS Our findings provide new evidence that metabolic health has a significant effect on the relationship between obesity and Alzheimer's markers, which, in turn, lead to better clinical outcomes.
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Affiliation(s)
- Eun Hye Lee
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Heejin Yoo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Young Ju Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Bo Kyoung Cheon
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Seungho Ryu
- Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Yoosoo Chang
- Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jihwan Yun
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Department of Neurology, Soonchunhyang University Bucheon Hospital, Gyeonggi-do, Republic of Korea
| | - Hyemin Jang
- Department of Neurology, Seoul National University Hospital, Seoul National University college of Medicine, Seoul, Republic of Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Seong-Beom Koh
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, 148 Gurodong-ro, Guro-gu, Seoul, 08308, Republic of Korea
| | - Jee Hyang Jeong
- Department of Neurology, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea.
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea.
| | - Sung Hoon Kang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, 148 Gurodong-ro, Guro-gu, Seoul, 08308, Republic of Korea.
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Wang AY, Hu HY, Huang LY, Xiao CY, Li QY, Tan L, Hu H. Risk factors for cognitive decline in non-demented elders with amyloid-beta positivity. Alzheimers Res Ther 2024; 16:189. [PMID: 39160609 PMCID: PMC11331665 DOI: 10.1186/s13195-024-01554-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 08/11/2024] [Indexed: 08/21/2024]
Abstract
BACKGROUND As a currently incurable but preventable disease, the prevention and early diagnosis of Alzheimer's disease (AD) has long been a research hotspot. Amyloid deposition has been shown to be a major pathological feature of AD. Notably, not all the people with amyloid-beta (Aβ) pathology will have significant cognitive declines and eventually develop AD. Therefore, the aim of this study was to explore the risk factors for cognitive decline in Aβ-positive participants. METHODS We included 650 non-demented participants who were Aβ-positive at baseline from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Mixed effects and COX regression models were applied to assess 37 potential risk factors. Mixed effects models were employed to assess the temporal associations between potential risk factors and four cognitive assessment scales. COX regression models were used to assess the impact of potential risk factors on cognitive diagnosis conversion. Univariate and multivariate analyses were applied to the above models. Additionally, we used the Cochran-Armitage trend test to examine whether the incidence of cognitive decline increased with the number concurrent of risk factors. RESULTS Six factors (low diastolic pressure, low body mass index, retired status, a history of drug abuse, Parkinsonism, and depression) were the identified risk factors and four factors (a history of urinary disease, musculoskeletal diseases, no major surgical history, and no prior dermatologic-connective tissue diseases) were found to be suggestive risk factors. The incidence of cognitive decline in the Aβ-positive participants gradually increased as the number of concurrent risk factors increased (p for trend = 0.0005). CONCLUSIONS Our study may facilitate the understanding of the potential pathological processes in AD and provide novel targets for the prevention of cognitive decline among participants with Aβ positivity.
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Affiliation(s)
- An-Yi Wang
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, No.5 Donghai Middle Road, Qingdao, China
| | - He-Ying Hu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, No.5 Donghai Middle Road, Qingdao, China
| | - Liang-Yu Huang
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, No.5 Donghai Middle Road, Qingdao, China
| | - Chu-Yun Xiao
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, No.5 Donghai Middle Road, Qingdao, China
| | - Qiong-Yao Li
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, No.5 Donghai Middle Road, Qingdao, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, No.5 Donghai Middle Road, Qingdao, China.
| | - Hao Hu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, No.5 Donghai Middle Road, Qingdao, China.
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Paprzycka O, Wieczorek J, Nowak I, Madej M, Strzalka-Mrozik B. Potential Application of MicroRNAs and Some Other Molecular Biomarkers in Alzheimer's Disease. Curr Issues Mol Biol 2024; 46:5066-5084. [PMID: 38920976 PMCID: PMC11202417 DOI: 10.3390/cimb46060304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 05/05/2024] [Accepted: 05/21/2024] [Indexed: 06/27/2024] Open
Abstract
Alzheimer's disease (AD) is the world's most common neurodegenerative disease, expected to affect up to one-third of the elderly population in the near future. Among the major challenges in combating AD are the inability to reverse the damage caused by the disease, expensive diagnostic tools, and the lack of specific markers for the early detection of AD. This paper highlights promising research directions for molecular markers in AD diagnosis, including the diagnostic potential of microRNAs. The latest molecular methods for diagnosing AD are discussed, with particular emphasis on diagnostic techniques prior to the appearance of full AD symptoms and markers detectable in human body fluids. A collection of recent studies demonstrates the promising potential of molecular methods in AD diagnosis, using miRNAs as biomarkers. Up- or downregulation in neurodegenerative diseases may not only provide a new diagnostic tool but also serve as a marker for differentiating neurodegenerative diseases. However, further research in this direction is needed.
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Affiliation(s)
- Olga Paprzycka
- Department of Molecular Biology, Faculty of Pharmaceutical Sciences in Sosnowiec, Medical University of Silesia, 40-055 Katowice, Poland; (O.P.); (J.W.); (M.M.)
| | - Jan Wieczorek
- Department of Molecular Biology, Faculty of Pharmaceutical Sciences in Sosnowiec, Medical University of Silesia, 40-055 Katowice, Poland; (O.P.); (J.W.); (M.M.)
| | - Ilona Nowak
- Silesia LabMed, Centre for Research and Implementation, Medical University of Silesia, 40-752 Katowice, Poland;
| | - Marcel Madej
- Department of Molecular Biology, Faculty of Pharmaceutical Sciences in Sosnowiec, Medical University of Silesia, 40-055 Katowice, Poland; (O.P.); (J.W.); (M.M.)
- Silesia LabMed, Centre for Research and Implementation, Medical University of Silesia, 40-752 Katowice, Poland;
| | - Barbara Strzalka-Mrozik
- Department of Molecular Biology, Faculty of Pharmaceutical Sciences in Sosnowiec, Medical University of Silesia, 40-055 Katowice, Poland; (O.P.); (J.W.); (M.M.)
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Enduru N, Fernandes BS, Zhao Z. Dissecting the shared genetic architecture between Alzheimer's disease and frailty: a cross-trait meta-analyses of genome-wide association studies. Front Genet 2024; 15:1376050. [PMID: 38706793 PMCID: PMC11069310 DOI: 10.3389/fgene.2024.1376050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 04/04/2024] [Indexed: 05/07/2024] Open
Abstract
Introduction: Frailty is the most common medical condition affecting the aging population, and its prevalence increases in the population aged 65 or more. Frailty is commonly diagnosed using the frailty index (FI) or frailty phenotype (FP) assessments. Observational studies have indicated the association of frailty with Alzheimer's disease (AD). However, the shared genetic and biological mechanism of these comorbidity has not been studied. Methods: To assess the genetic relationship between AD and frailty, we examined it at single nucleotide polymorphism (SNP), gene, and pathway levels. Results: Overall, 16 genome-wide significant loci (15 unique loci) (p meta-analysis < 5 × 10-8) and 22 genes (21 unique genes) were identified between AD and frailty using cross-trait meta-analysis. The 8 shared loci implicated 11 genes: CLRN1-AS1, CRHR1, FERMT2, GRK4, LINC01929, LRFN2, MADD, RP11-368P15.1, RP11-166N6.2, RNA5SP459, and ZNF652 between AD and FI, and 8 shared loci between AD and FFS implicated 11 genes: AFF3, C1QTNF4, CLEC16A, FAM180B, FBXL19, GRK4, LINC01104, MAD1L1, RGS12, ZDHHC5, and ZNF521. The loci 4p16.3 (GRK4) was identified in both meta-analyses. The colocalization analysis supported the results of our meta-analysis in these loci. The gene-based analysis revealed 80 genes between AD and frailty, and 4 genes were initially identified in our meta-analyses: C1QTNF4, CRHR1, MAD1L1, and RGS12. The pathway analysis showed enrichment for lipoprotein particle plasma, amyloid fibril formation, protein kinase regulator, and tau protein binding. Conclusion: Overall, our results provide new insights into the genetics of AD and frailty, suggesting the existence of non-causal shared genetic mechanisms between these conditions.
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Affiliation(s)
- Nitesh Enduru
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Brisa S. Fernandes
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
- Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, United States
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Buchholz E, Gillespie NA, Hunt JF, Reynolds CA, Rissman RA, Schroeder A, Cortes I, Bell T, Lyons MJ, Kremen WS, Franz CE. Midlife cumulative deficit frailty predicts Alzheimer's disease-related plasma biomarkers in older adults. Age Ageing 2024; 53:afae028. [PMID: 38454901 PMCID: PMC10921085 DOI: 10.1093/ageing/afae028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Indexed: 03/09/2024] Open
Abstract
BACKGROUND The study explores whether frailty at midlife predicts mortality and levels of biomarkers associated with Alzheimer's disease and related dementias (ADRD) and neurodegeneration by early old age. We also examine the heritability of frailty across this age period. METHODS Participants were 1,286 community-dwelling men from the Vietnam Era Twin Study of Aging at average ages 56, 62 and 68, all without ADRD at baseline. The cumulative deficit frailty index (FI) comprised 37 items assessing multiple physiological systems. Plasma biomarkers at age 68 included beta-amyloid (Aβ40, Aβ42), total tau (t-tau) and neurofilament light chain (NfL). RESULTS Being frail doubled the risk of all-cause mortality by age 68 (OR = 2.44). Age 56 FI significantly predicted age 68 NfL (P = 0.014), Aβ40 (P = 0.001) and Aβ42 (P = 0.023), but not t-tau. Age 62 FI predicted all biomarkers at age 68: NfL (P = 0.023), Aβ40 (P = 0.002), Aβ42 (P = 0.001) and t-tau (P = 0.001). Age 68 FI scores were associated with age 68 levels of NfL (P = 0.027), Aβ40 (P < 0.001), Aβ42 (P = 0.001) and t-tau (P = 0.003). Genetic influences accounted for 45-48% of the variance in frailty and significantly contributed to its stability across 11 years. CONCLUSIONS Frailty during one's 50s doubled the risk of mortality by age 68. A mechanism linking frailty and ADRD may be through its associations with biomarkers related to neurodegeneration. Cumulative deficit frailty increases with age but remains moderately heritable across the age range studied. With environmental factors accounting for about half of its variance, early interventions aimed at reducing frailty may help to reduce risk for ADRD.
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Affiliation(s)
- Erik Buchholz
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA 92093, USA
- Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR 72204 USA
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, VA 23298, USA
| | - Jack F Hunt
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA 92093, USA
| | - Chandra A Reynolds
- Department of Psychology, University of California Riverside, Riverside, CA 92521, USA
| | - Robert A Rissman
- Department of Neurosciences, University of California, San Diego and VA San Diego Healthcare System, La Jolla, CA 92093, USA
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA 92121, USA
| | - Angelica Schroeder
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA 92093, USA
| | - Isaac Cortes
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA 92093, USA
| | - Tyler Bell
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA 92093, USA
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215, USA
| | - William S Kremen
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA 92093, USA
| | - Carol E Franz
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA 92093, USA
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Faisal M, Aid J, Nodirov B, Lee B, Hickey MA. Preclinical trials in Alzheimer's disease: Sample size and effect size for behavioural and neuropathological outcomes in 5xFAD mice. PLoS One 2023; 18:e0281003. [PMID: 37036878 PMCID: PMC10085059 DOI: 10.1371/journal.pone.0281003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 01/13/2023] [Indexed: 04/11/2023] Open
Abstract
5xFAD transgenic (TG) mice are used widely in AD preclinical trials; however, data on sample sizes are largely unaddressed. We therefore performed estimates of sample sizes and effect sizes for typical behavioural and neuropathological outcome measures in TG 5xFAD mice, based upon data from single-sex (female) groups. Group-size estimates to detect normalisation of TG body weight to WT littermate levels at 5.5m of age were N = 9-15 depending upon algorithm. However, by 1 year of age, group sizes were small (N = 1 -<6), likely reflecting the large difference between genotypes at this age. To detect normalisation of TG open-field hyperactivity to WT levels at 13-14m, group sizes were also small (N = 6-8). Cued learning in the Morris water maze (MWM) was normal in Young TG mice (5m of age). Mild deficits were noted during MWM spatial learning and memory. MWM reversal learning and memory revealed greater impairment, and groups of up to 22 TG mice were estimated to detect normalisation to WT performance. In contrast, Aged TG mice (tested between 13 and 14m) failed to complete the visual learning (non-spatial) phase of MWM learning, likely due to a failure to recognise the platform as an escape. Estimates of group size to detect normalisation of this severe impairment were small (N = 6-9, depending upon algorithm). Other cognitive tests including spontaneous and forced alternation and novel-object recognition either failed to reveal deficits in TG mice or deficits were negligible. For neuropathological outcomes, plaque load, astrocytosis and microgliosis in frontal cortex and hippocampus were quantified in TG mice aged 2m, 4m and 6m. Sample-size estimates were ≤9 to detect the equivalent of a reduction in plaque load to the level of 2m-old TG mice or the equivalent of normalisation of neuroinflammation outcomes. However, for a smaller effect size of 30%, larger groups of up to 21 mice were estimated. In light of published guidelines on preclinical trial design, these data may be used to provide provisional sample sizes and optimise preclinical trials in 5xFAD TG mice.
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Affiliation(s)
- Mahvish Faisal
- Department of Pharmacology, Institute of Biomedicine and
Translational Medicine, University of Tartu, Tartu, Estonia
| | - Jana Aid
- Department of Pharmacology, Institute of Biomedicine and
Translational Medicine, University of Tartu, Tartu, Estonia
| | - Bekzod Nodirov
- Department of Pharmacology, Institute of Biomedicine and
Translational Medicine, University of Tartu, Tartu, Estonia
| | - Benjamin Lee
- Department of Pharmacology, Institute of Biomedicine and
Translational Medicine, University of Tartu, Tartu, Estonia
| | - Miriam A. Hickey
- Department of Pharmacology, Institute of Biomedicine and
Translational Medicine, University of Tartu, Tartu, Estonia
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Cross-sectional and prospective associations between homocysteine and a frailty index: A post-hoc analysis of the multidomain Alzheimer's prevention trial (MAPT). Exp Gerontol 2023; 172:112066. [PMID: 36549548 DOI: 10.1016/j.exger.2022.112066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 12/12/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND Homocysteine (Hcy) has been associated with several health problems, including reduced physical capacity. No study appears to have looked at the role of Hcy values longitudinally on physical capacity deterioration in older adults. The objective is to examine cross-sectional and prospective associations between Hcy values and frailty in the elderly and investigate Hcy potential association with the onset of frailty. METHODS 769 community-dwelling older adults from the MAPT study were recruited for this study. Total Hcy was measured at baseline. Frailty was evaluated at 5 different collection timepoints: baseline, 6-month, 1-, 2-, and 3-year using a frailty index (FI) composed of 19 items. Linear regressions adjusted for all the confounders (age, gender, educational level, MAPT group allocation and Omega-3) were performed to examine the cross-sectional associations of homocysteine values with the FI. A cox model was used to test the association of Hcy with the onset of frailty. RESULTS Mean Hcy values (15.9 ± 5.6 μmol\L) were obtained from 769 community-dwelling adults (75.7 ± 4.6 years old). After adjustments, a significant (β = 0.002, (00002-0.003)) and positive association between baseline Hcy values and FI was found (ß = 0.002). Additionally, higher values of Hcy were associated with a worsening of FI after 3 years (ß = 0.002, p = 0.003). A significant association between baseline Hcy values and the likelihood of developing frailty was discovered by incident event analysis (HR: 1.04 (1.01-1.06), p = 0.004). CONCLUSION High levels of Hcy are associated with the fragility process in community-dwelling older adults.
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Kang SH, Kim JH, Chang Y, Cheon BK, Choe YS, Jang H, Kim HJ, Koh SB, Na DL, Kim K, Seo SW. Independent effect of body mass index variation on amyloid-β positivity. Front Aging Neurosci 2022; 14:924550. [PMID: 35936766 PMCID: PMC9354132 DOI: 10.3389/fnagi.2022.924550] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 07/04/2022] [Indexed: 11/16/2022] Open
Abstract
Objectives The relationship of body mass index (BMI) changes and variability with amyloid-β (Aβ) deposition remained unclear, although there were growing evidence that BMI is associated with the risk of developing cognitive impairment or AD dementia. To determine whether BMI changes and BMI variability affected Aβ positivity, we investigated the association of BMI changes and BMI variability with Aβ positivity, as assessed by PET in a non-demented population. Methods We retrospectively recruited 1,035 non-demented participants ≥50 years of age who underwent Aβ PET and had at least three BMI measurements in the memory clinic at Samsung Medical Center. To investigate the association between BMI change and variability with Aβ deposition, we performed multivariable logistic regression. Further distinctive underlying features of BMI subgroups were examined by employing a cluster analysis model. Results Decreased (odds ratio [OR] = 1.68, 95% confidence interval [CI] 1.16–2.42) or increased BMI (OR = 1.60, 95% CI 1.11–2.32) was associated with a greater risk of Aβ positivity after controlling for age, sex, APOE e4 genotype, years of education, hypertension, diabetes, baseline BMI, and BMI variability. A greater BMI variability (OR = 1.73, 95% CI 1.07–2.80) was associated with a greater risk of Aβ positivity after controlling for age, sex, APOE e4 genotype, years of education, hypertension, diabetes, baseline BMI, and BMI change. We also identified BMI subgroups showing a greater risk of Aβ positivity. Conclusion Our findings suggest that participants with BMI change, especially those with greater BMI variability, are more vulnerable to Aβ deposition regardless of baseline BMI. Furthermore, our results may contribute to the design of strategies to prevent Aβ deposition with respect to weight control.
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Affiliation(s)
- Sung Hoon Kang
- Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Jong Hyuk Kim
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, South Korea
| | - Yoosoo Chang
- Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, School of Medicine, Sungkyunkwan University, Seoul, South Korea
| | - Bo Kyoung Cheon
- Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, South Korea
| | - Yeong Sim Choe
- Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
- Department of Health Sciences & Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Seong-Beom Koh
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Duk L. Na
- Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Kyunga Kim
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, South Korea
- Biomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, South Korea
- Department of Data Convergence and Future Medicine, School of Medicine, Sungkyunkwan University, Seoul, South Korea
- *Correspondence: Kyunga Kim,
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
- Department of Health Sciences & Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea
- Samsung Alzheimer Research Center, Center for Clinical Epidemiology Medical Center, Seoul, South Korea
- Department of Intelligent Precision Healthcare Convergence, SAIHST, Sungkyunkwan University, Seoul, South Korea
- Sang Won Seo,
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10
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Wang S, Tang YJ. Sulforaphane ameliorates amyloid-β-induced inflammatory injury by suppressing the PARP1/SIRT1 pathway in retinal pigment epithelial cells. Bioengineered 2021; 12:7079-7089. [PMID: 34982643 PMCID: PMC8973853 DOI: 10.1080/21655979.2021.1976503] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Age-associated macular degeneration (AMD) is a progressive eye disorder that leads to irreversible impairment of central vision, and effective therapies are lacking. Here, we explore how oligomeric amyloid-β1-42 can trigger inflammatory injury in retinal pigment epithelial cells and how sulforaphane can mitigate such injury. ARPE-19 retinal pigment epithelial cells expressing low, endogenous, or high levels of poly(ADP-ribose) polymerase (PARP1) were treated with oligomeric amyloid-β1-42 in the presence or absence of various signaling inhibitors or sulforaphane. Cell viability, apoptosis, inflammatory responses, and activity of the PARP1/Sirtuin (SIRT1) axis were assayed. Treating ARPE-19 cells with oligomeric amyloid-β1-42 promoted the production of IL-1β, IL-6, IL-8, and TNF-ɑ, which was partially reversed by inhibiting PARP1 and activating SIRT1. PARP1 was found to act upstream of SIRT1, and expression of the two proteins correlated negatively with each other. Sulforaphane also mitigated the injury due to oligomeric amyloid-β1-42 through a mechanism involving inactivation of the PARP1/SIRT1 pathway. Oligomeric amyloid-β1-42 can trigger AMD-like injury in retinal pigment epithelium by activating PARP1 and repressing SIRT1. Moreover, sulforaphane can induce cell viability and SIRT1 expression, but reduce cell apoptosis, the activity of caspase-3 or -9, and PARP1 expression in oAβ1-42-treated cells. However, PARP1 inactivation or SIRT1 activation weaken these effects. In summary, sulforaphane reduces the inflammatory injury induced by oAβ1-42 in ARPE-19 cell by inactivating the PARP1/SIRT1 pathway. Thus, the compound may be an effective therapy against AMD.
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Affiliation(s)
- Song Wang
- Department of Pharmacy, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Yu-Jie Tang
- Department of Pharmacy, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
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11
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Nilsson MH, Tangen GG, Palmqvist S, van Westen D, Mattsson-Carlgren N, Stomrud E, Hansson O. The Effects of Tau, Amyloid, and White Matter Lesions on Mobility, Dual Tasking, and Balance in Older People. J Gerontol A Biol Sci Med Sci 2021; 76:683-691. [PMID: 32506119 PMCID: PMC8011701 DOI: 10.1093/gerona/glaa143] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2019] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND This study aimed to investigate whether white matter lesions (WML), β-amyloid-, and tau pathologies are independently associated with mobility, dual tasking, and dynamic balance performance in older nondemented individuals. METHODS We included 299 older people (mean, SD, age: 71.8, 5.6 years) from the Swedish BioFINDER study, whereof 175 were cognitively unimpaired and 124 had mild cognitive impairment (MCI). In multivariable regression analyses, dependent variables included mobility (Timed Up & Go [TUG]), dual tasking (TUG with a simultaneous subtraction task, that is, TUG-Cog, as well as dual task cost), and balance (Figure-of-eight). The analyses were controlled for age, sex, education, diagnosis (ie, MCI), and comorbidity (stroke, diabetes, and ischemic heart disease). Independent variables included WML volume, and measures of β-amyloid (abnormal cerebrospinal fluid [CSF] Aβ42/40 ratio) and tau pathology (CSF phosphorylated tau [p-tau]). RESULTS Multivariable regression analyses showed that an increased WML volume was independently associated with decreased mobility, that is, TUG (standardized β = 0.247; p < .001). Tau pathology was independently associated with dual tasking both when using the raw data of TUG-Cog (β = 0.224; p = .003) and the dual-task cost (β= -0.246; p = .001). Amyloid pathology was associated with decreased balance, that is, Figure-of-eight (β = 0.172; p = .028). The independent effects of WML and tau pathology were mainly observed in those with MCI, which was not the case for the effects of amyloid pathology on balance. CONCLUSIONS Common brain pathologies have different effects where WML are independently associated with mobility, tau pathology has the strongest effect on dual tasking, and amyloid pathology seems to be independently associated with balance. Although these novel findings need to be confirmed in longitudinal studies, they suggest that different brain pathologies have different effects on mobility, balance, and dual-tasking in older nondemented individuals.
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Affiliation(s)
- Maria H Nilsson
- Department of Health Sciences, Faculty of Medicine, Lund University, Sweden
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Sweden
| | - Gro Gujord Tangen
- Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tonsberg, Norway
- Department of Geriatric Medicine, Oslo University Hospital, Norway
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Danielle van Westen
- Diagnostic Radiology, Department of Clinical Sciences, Lund University, Sweden
- Image and Function, Skane University Hospital, Lund, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
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12
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Biomarkers and phenotypic expression in Alzheimer's disease: exploring the contribution of frailty in the Alzheimer's Disease Neuroimaging Initiative. GeroScience 2020; 43:1039-1051. [PMID: 33210215 PMCID: PMC8110661 DOI: 10.1007/s11357-020-00293-y] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 10/23/2020] [Indexed: 12/15/2022] Open
Abstract
The present study aimed at investigating if the main biomarkers of Alzheimer’s disease (AD) neuropathology and their association with cognitive disturbances and dementia are modified by the individual’s frailty status. We performed a cross-sectional analysis of data from participants with normal cognition, mild cognitive impairment (MCI), and AD dementia enrolled in the Alzheimer’s Disease Neuroimaging Initiative 2 (ADNI2) study. Frailty was operationalized by computing a 40-item Frailty Index (FI). The following AD biomarkers were considered and analyzed according to the participants’ frailty status: CSF Aβ1-42, 181P-tau, and T-tau; MRI-based hippocampus volume; cortical glucose metabolism at the FDG PET imaging; amyloid deposition at the 18F-AV-45 PET imaging. Logistic regression models, adjusted for age, sex, and education, were performed to explore the association of biomarkers with cognitive status at different FI levels. Subjects with higher FI scores had lower CSF levels of Aβ1-42, hippocampus volumes at the MRI, and glucose metabolism at the FDG PET imaging, and a higher amyloid deposition at the 18F-AV-45 PET. No significant differences were observed among the two frailty groups concerning ApoE genotype, CSF T-tau, and P-tau. Increasing frailty levels were associated with a weakened relationship between dementia and 18F-AV-45 uptake and hippocampus volume and with a stronger relationship of dementia with FDG PET. Frailty contributes to the discrepancies between AD pathology and clinical manifestations and influences the association of AD pathological modifications with cognitive changes. AD and dementia should increasingly be conceived as “complex diseases of aging,” determined by multiple, simultaneous, and interacting pathophysiological processes.
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13
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Tian Q, Williams OA, Landman BA, Resnick SM, Ferrucci L. Microstructural Neuroimaging of Frailty in Cognitively Normal Older Adults. Front Med (Lausanne) 2020; 7:546344. [PMID: 33195297 PMCID: PMC7645067 DOI: 10.3389/fmed.2020.546344] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Accepted: 09/21/2020] [Indexed: 11/17/2022] Open
Abstract
Physical frailty is an age-related clinical syndrome that is associated with multiple adverse health outcomes, including cognitive impairment and dementia. Recent studies have shown that frailty is associated with specific volumetric neuroimaging characteristics. Whether brain microstructural characteristics, particularly gray matter, associated with frailty exist and what their spatial distribution is have not been explored. We identified 670 participants of the Baltimore Longitudinal Study of Aging who were aged 60 and older and cognitively normal and who had concurrent data on frailty and regional microstructural neuroimaging markers by diffusion tensor imaging (DTI), including mean diffusivity (MD) of gray matter and fractional anisotropy (FA) of white matter. We identified neuroimaging markers that were associated with frailty status (non-frail, pre-frail, frail) and further examined differences between three groups using multivariate linear regression (non-frail = reference). Models were adjusted for age, sex, race, years of education, body mass index, scanner type, and Apolipoprotein E e4 carrier status. Compared to the non-frail participants, those who were frail had higher MD in the medial frontal cortex, several subcortical regions (putamen, caudate, thalamus), anterior cingulate cortex, and a trend of lower FA in the body of the corpus callosum. Those who were pre-frail also had higher MD in the putamen and a trend of lower FA in the body of the corpus callosum. Our study demonstrates for the first time that the microstructure of both gray and white matter differs by frailty status in cognitively normal older adults. Brain areas were not widespread but mostly localized in frontal and subcortical motor areas and the body of the corpus callosum. Whether changes in brain microstructure precede future frailty development warrants further investigation.
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Affiliation(s)
- Qu Tian
- Longitudinal Studies Section, National Institute on Aging, Baltimore, MD, United States
| | - Owen A Williams
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, United States.,Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Bennett A Landman
- School of Engineering, Vanderbilt University, Nashville, TN, United States
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, United States
| | - Luigi Ferrucci
- Longitudinal Studies Section, National Institute on Aging, Baltimore, MD, United States
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14
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Maltais M, de Souto Barreto P, Perus L, Mangin JF, Grigis A, Chupin M, Bouyahia A, Gabelle A, Delrieux J, Rolland Y, Vellas B. Prospective Associations Between Diffusion Tensor Imaging Parameters and Frailty in Older Adults. J Am Geriatr Soc 2020; 68:1050-1055. [PMID: 31981370 DOI: 10.1111/jgs.16343] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 12/18/2019] [Accepted: 12/18/2019] [Indexed: 12/21/2022]
Abstract
BACKGROUND Cross-sectional associations have been found between frail individuals and worse white matter (WM) integrity. However, the prospective association between WM integrity and frailty is still unclear. Our objectives were to measure associations between WM integrity using diffusion tensor imaging (DTI) and the 5-year worsening of frailty in community-dwelling older adults. DESIGN Secondary analysis of the randomized controlled Multidomain Alzheimer Preventive Trial (MAPT). SETTING Thirteen memory centers in France and Monaco between 2008 and 2011. PARTICIPANTS Participants (mean age = 74.7 ± 3.9 years) with no dementia at baseline who had functional magnetic resonance imaging performed as part of the MAPT study (n = 227). MEASUREMENTS Fractional anisotropy and mean diffusivity (MD), axial diffusivity (AxD), and radial diffusivity (RD) were acquired for 10 different brain regions. Frailty was assessed by the Fried frailty phenotype (score from 0 to 5, higher is worse) at up to seven time points for 5 years. Mixed effect ordinal logistic regression model was used to assess the prospective association between DTI parameters (independent variables) and frailty (dependent variable). All the analyses were adjusted for age, sex, baseline total intracranial volume, and the presence of one of the following cardiovascular risk factors (hypertension, diabetes, and/or hypercholesterolemia). RESULTS A statistically significant association was found between the RD, AxD, and MD for different brain regions (anterior limb of internal capsule, external capsule, posterior corona radiata, posterior thalamic radiation, superior corona radiata, superior frontal occipital fasciculus, and superior longitudinal fasciculus) and worsening of frailty over 5 years after adjusting for multiple comparisons. CONCLUSIONS This is the first study to show that WM integrity is associated with frailty in older adults. The mechanisms related to these results require further investigation. J Am Geriatr Soc 68:1050-1055, 2020.
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Affiliation(s)
- Mathieu Maltais
- Gérontopôle de Toulouse, Institut du Vieillissement, Centre Hospitalo-Universitaire de Toulouse, Toulouse, France
| | - Philipe de Souto Barreto
- Gérontopôle de Toulouse, Institut du Vieillissement, Centre Hospitalo-Universitaire de Toulouse, Toulouse, France.,France Faculté de Médecine, Unités mixtes de recherche (UMR) Institut national de la santé et de la recherche médicale (INSERM) 1027, University of Toulouse III, Toulouse, France
| | - Lisa Perus
- Memory Resources and Research Center, Montpellier University Hospital, INSERM U1061, University of Montpellier i-Site Montpellier Université d'Excellence (MUSE), Montpellier, France
| | - Jean-François Mangin
- CATI Multicenter Neuroimaging Platform, Neurospin, Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Paris Saclay University, Gif sur Yvette, France
| | - Antoine Grigis
- CATI Multicenter Neuroimaging Platform, Neurospin, Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Paris Saclay University, Gif sur Yvette, France
| | - Marie Chupin
- CATI Multicenter Neuroimaging Platform, Neurospin, Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Paris Saclay University, Gif sur Yvette, France
| | - Ali Bouyahia
- CATI Multicenter Neuroimaging Platform, Neurospin, Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Paris Saclay University, Gif sur Yvette, France
| | - Audrey Gabelle
- Memory Resources and Research Center, Montpellier University Hospital, INSERM U1061, University of Montpellier i-Site Montpellier Université d'Excellence (MUSE), Montpellier, France
| | - Julien Delrieux
- Gérontopôle de Toulouse, Institut du Vieillissement, Centre Hospitalo-Universitaire de Toulouse, Toulouse, France
| | - Yves Rolland
- Gérontopôle de Toulouse, Institut du Vieillissement, Centre Hospitalo-Universitaire de Toulouse, Toulouse, France.,France Faculté de Médecine, Unités mixtes de recherche (UMR) Institut national de la santé et de la recherche médicale (INSERM) 1027, University of Toulouse III, Toulouse, France
| | - Bruno Vellas
- Gérontopôle de Toulouse, Institut du Vieillissement, Centre Hospitalo-Universitaire de Toulouse, Toulouse, France.,France Faculté de Médecine, Unités mixtes de recherche (UMR) Institut national de la santé et de la recherche médicale (INSERM) 1027, University of Toulouse III, Toulouse, France
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15
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Duggan EC, Piccinin AM, Clouston S, Koval AV, Robitaille A, Zammit AR, Wu C, Brown CL, Lee LO, Finkel D, Beasley WH, Kaye J, Terrera GM, Katz M, Lipton RB, Deeg D, Bennett DA, Praetorius Björk M, Johansson B, Spiro A, Weuve J, Hofer SM. A Multi-study Coordinated Meta-analysis of Pulmonary Function and Cognition in Aging. J Gerontol A Biol Sci Med Sci 2019; 74:1793-1804. [PMID: 30825374 PMCID: PMC6777093 DOI: 10.1093/gerona/glz057] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2018] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Substantial research is dedicated to understanding the aging-related dynamics among individual differences in level, change, and variation across physical and cognitive abilities. Evaluating replicability and synthesizing these findings has been limited by differences in measurements and samples, and by study design and statistical analyses confounding between-person differences with within-person changes. In this article, we conducted a coordinated analysis and summary meta-analysis of new results on the aging-related dynamics linking pulmonary function and cognitive performance. METHODS We performed coordinated analysis of bivariate growth models in data from 20,586 participants across eight longitudinal studies to examine individual differences in baseline level, rate of change, and occasion-specific variability in pulmonary and cognitive functioning. Results were summarized using meta-analysis. RESULTS We found consistent but weak baseline and longitudinal associations in levels of pulmonary and cognitive functioning, but no associations in occasion-specific variability. CONCLUSIONS Results provide limited evidence for a consistent link between simultaneous changes in pulmonary and cognitive function in a normal aging population. Further research is required to understand patterns of onset of decline and differences in rates of change within and across physical and cognitive functioning domains, both within-individuals and across countries and birth cohorts. Coordinated analysis provides an efficient and rigorous approach for replicating and comparing results across independent longitudinal studies.
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Affiliation(s)
- Emily C Duggan
- Department of Psychology, University of Victoria, Victoria, BC, Canada
| | - Andrea M Piccinin
- Department of Psychology, University of Victoria, Victoria, BC, Canada
| | - Sean Clouston
- Department of Family, Population and Preventive Medicine, Stony Brook University, New York
| | - Andriy V Koval
- Department of Psychology, University of Victoria, Victoria, BC, Canada
| | - Annie Robitaille
- Department of Psychology, University of Victoria, Victoria, BC, Canada
| | - Andrea R Zammit
- Saul B. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, New York
| | - Chenkai Wu
- Global Health Research Center, Duke Kunshan University, China
| | - Cassandra L Brown
- Department of Psychology, University of Victoria, Victoria, BC, Canada
| | - Lewina O Lee
- Department of Psychiatry, Boston University School of Medicine, Massachusetts
| | - Deborah Finkel
- Department of Psychology, Indiana University Southeast, New Albany
| | - William H Beasley
- Department of Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma City
| | - Jeffrey Kaye
- Department of Neurology, Oregon Health & Science University, Portland
| | | | - Mindy Katz
- Saul B. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, New York
| | - Richard B Lipton
- Saul B. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, New York
| | - Dorly Deeg
- Department of Epidemiology and Biostatistics, VU University Medical Center and Amsterdam Public Health Research Institute, the Netherlands
| | - David A Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois
| | - Marcus Praetorius Björk
- Department of Psychology and Centre for Ageing and Health, AgeCap, University of Gothenburg, Sweden
| | - Boo Johansson
- Department of Psychology and Centre for Ageing and Health, AgeCap, University of Gothenburg, Sweden
| | - Avron Spiro
- Department of Psychiatry, Boston University School of Medicine, Massachusetts
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Department of Veterans Affairs Boston Healthcare System
- Department of Epidemiology, Boston University School of Public Health, Massachusetts
| | - Jennifer Weuve
- Department of Epidemiology, Boston University School of Public Health, Massachusetts
| | - Scott M Hofer
- Department of Psychology, University of Victoria, Victoria, BC, Canada
- Department of Neurology, Oregon Health & Science University, Portland
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