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Xu Y, Sun Z, Jonaitis E, Deming Y, Lu Q, Johnson SC, Engelman CD. Mid-to-Late Life Healthy Lifestyle Modifies Genetic Risk for Longitudinal Cognitive Aging among Asymptomatic Individuals. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.26.24307953. [PMID: 38853902 PMCID: PMC11160812 DOI: 10.1101/2024.05.26.24307953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
IMPORTANCE Genetic and lifestyle factors contribute to an individual's risk of developing Alzheimer's disease. However, it is unknown whether and how adherence to healthy lifestyles can mitigate the genetic risk of Alzheimer's. OBJECTIVE The aim of this study is to investigate whether adherence to healthy lifestyles can modify the impact of genetic predisposition to Alzheimer's disease on later-life cognitive decline. DESIGN SETTING AND PARTICIPANTS This prospective cohort study included 891 adults of European ancestry, aged 40 to 65, who were without dementia and had complete healthy-lifestyle and cognition data during the follow-up. Participants joined the Wisconsin Registry for Alzheimer's Prevention (WRAP) beginning in 2001. We conducted replication analyses using a subsample with similar baseline age range from the Health and Retirement Study (HRS). EXPOSURES We assessed participants' exposures using a continuous non-APOE polygenic risk score for Alzheimer's, a binary indicator for APOE-ε4 carrier status, and a weighted healthy-lifestyle score, including factors such as no current smoking, regular physical activity, healthy diet, light to moderate alcohol consumption, and frequent cognitive activities. MAIN OUTCOMES AND MEASURES We z-standardized cognitive scores for global (Preclinical Alzheimer's Cognitive Composite score 3 - PACC3) and domain-specific assessments (delayed recall and immediate learning). RESULTS We followed 891 individuals for up to 10 years (mean [SD] baseline age, 58 [6] years, 31% male, 38% APOE-ε4 carriers). After false discovery rate (FDR) correction, we found statistically significant PRS × lifestyle × age interactions on preclinical cognitive decline but the evidence is stronger among APOE-ε4 carriers. Among APOE-ε4 carriers, PRS-related differences in overall and memory-related domains between people scoring 0-1 and 4-5 regarding healthy lifestyles became evident around age 67 after FDR correction. These findings were robust across several sensitivity analyses and were replicated in the population-based HRS. CONCLUSION A favorable lifestyle can mitigate the genetic risk associated with current known non-APOE genetic variants for longitudinal cognitive decline, and these protective effects are particularly pronounced among APOE-ε4 carriers.
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Affiliation(s)
- Yuexuan Xu
- G.H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University
| | - Zhongxuan Sun
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison
| | - Erin Jonaitis
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison
| | - Yuetiva Deming
- G.H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison
| | - Sterling C. Johnson
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison
| | - Corinne D. Engelman
- G.H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University
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Forte A, Lara S, Peña-Bautista C, Baquero M, Cháfer-Pericás C. New approach for early and specific Alzheimer disease diagnosis from different plasma biomarkers. Clin Chim Acta 2024; 556:117842. [PMID: 38417780 DOI: 10.1016/j.cca.2024.117842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 01/25/2024] [Accepted: 02/19/2024] [Indexed: 03/01/2024]
Abstract
BACKGROUND Alzheimer Disease (AD) is a complex pathology, in which several biochemical pathways could be involved. Therefore, the development of clinical studies combining different nature biomarkers in an AD diagnosis approach is required. Specifically, the present study evaluated blood biomarkers from different molecular pathways (epigenomics, lipid metabolism, lipid peroxidation), to obtain an early and specific AD diagnosis approach. METHODS The participants were classified into early AD (n = 53), and non-AD (healthy controls, other dementias) (n = 83). Blood samples were collected and biochemical determinations (microRNAs, lipids, lipid peroxidation compounds) were carried out by quantitative PCR and liquid chromatography coupled to mass spectrometry, respectively. Then, a logistic regression model with a Bayesian variable selection procedure was developed. RESULTS The Bayesian variable selection procedure for microRNAs did not show any relevant variable. Therefore, microRNA biomarkers were excluded. So, the developed model considered only lipids and lipid peroxidation compounds. The corresponding selected variables were age, 18:0 LPC, PGE2, isoprostanes and, isofurans. The validated model (by leave-one-out cross-validation) provided satisfactory diagnosis indexes (AUC 0.83, Sensitivity 87 %, Specificity 79 %). CONCLUSION The developed model included biomarkers from different pathways (lipid metabolism, oxidative stress), achieving a promising approach to early, specific and, minimally invasive AD diagnosis. Nevertheless, further work to validate clinically these preliminary results with an external cohort is required. Also, the integration of different compounds coming from several biochemical pathways could constitute a relevant research field for the development of AD therapeutic targets.
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Affiliation(s)
- Anabel Forte
- Faculty of Mathematical Sciences, University of Valencia, 46100 Burjassot, Valencia, Spain
| | - Sergio Lara
- Faculty of Mathematical Sciences, University of Valencia, 46100 Burjassot, Valencia, Spain
| | - Carmen Peña-Bautista
- Alzheimer's Disease Research Group, Health Research Institute La Fe, 46026 Valencia, Spain
| | - Miguel Baquero
- Alzheimer's Disease Research Group, Health Research Institute La Fe, 46026 Valencia, Spain; Division of Neurology, University and Polytechnic Hospital La Fe, 46026 Valencia, Spain
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Xu Y, Sun Z, Jonaitis E, Deming Y, Lu Q, Johnson SC, Engelman CD. Apolipoprotein E moderates the association between non-APOE polygenic risk score for Alzheimer's disease and aging on preclinical cognitive function. Alzheimers Dement 2024; 20:1063-1075. [PMID: 37858606 PMCID: PMC10916952 DOI: 10.1002/alz.13515] [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: 06/09/2023] [Revised: 09/01/2023] [Accepted: 09/25/2023] [Indexed: 10/21/2023]
Abstract
INTRODUCTION Variation in preclinical cognitive decline suggests additional genetic factors related to Alzheimer's disease (eg, a non-APOE polygenic risk score [PRS]) may interact with the APOE ε4 allele to influence cognitive decline. METHODS We tested the PRS × APOE ε4 × age interaction on preclinical cognition using longitudinal data from the Wisconsin Registry for Alzheimer's Prevention. All analyses were fitted using a linear mixed-effects model and adjusted for within individual/family correlation among 1190 individuals. RESULTS We found statistically significant PRS × APOE ε4 × age interactions on immediate learning (P = 0.038), delayed recall (P < 0.001), and Preclinical Alzheimer's Cognitive Composite 3 score (P = 0.026). PRS-related differences in overall and memory-related cognitive domains between people with and without APOE ε4 emerge around age 70, with a much stronger adverse PRS effect among APOE ε4 carriers. The findings were replicated in a population-based cohort. DISCUSSIONS APOE ε4 can modify the association between PRS and cognition decline. HIGHLIGHTS APOE ε4 can modify the association between polygenic risk scores (PRSs) and longitudinal cognition decline, with the modifying effects more pronounced when the PRS is constructed using a conservative P threshold (eg, P < 5e-8 ). The adverse genetic effect caused by the combined effect of the currently known genetic variants is more detrimental among APOE ε4 carriers around age 70. Individuals who are APOE ε4 carriers with high PRSs are the most vulnerable to the harmful effects caused by genetic burden.
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Affiliation(s)
- Yuexuan Xu
- Department of Population Health ScienceSchool of Medicine and Public HealthUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Zhongxuan Sun
- Department of Biostatistics and Medical InformaticsSchool of Medicine and Public HealthUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Erin Jonaitis
- Wisconsin Alzheimer's InstituteUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Wisconsin Alzheimer's Disease Research CenterUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Yuetiva Deming
- Department of Population Health ScienceSchool of Medicine and Public HealthUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of MedicineSchool of Medicine and Public HealthUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Qiongshi Lu
- Department of Biostatistics and Medical InformaticsSchool of Medicine and Public HealthUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Sterling C. Johnson
- Wisconsin Alzheimer's InstituteUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of MedicineSchool of Medicine and Public HealthUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Corinne D. Engelman
- Department of Population Health ScienceSchool of Medicine and Public HealthUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
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Yang J, Liu X, Oveisgharan S, Zammit AR, Nag S, Bennett DA, Buchman AS. Inferring Alzheimer's Disease Pathologic Traits from Clinical Measures in Living Adults. J Alzheimers Dis 2024; 98:95-107. [PMID: 38427476 PMCID: PMC11034758 DOI: 10.3233/jad-230639] [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] [Indexed: 03/03/2024]
Abstract
Background Alzheimer's disease neuropathologic changes (AD-NC) are important to identify people with high risk for AD dementia (ADD) and subtyping ADD. Objective Develop imputation models based on clinical measures to infer AD-NC. Methods We used penalized generalized linear regression to train imputation models for four AD-NC traits (amyloid-β, tangles, global AD pathology, and pathologic AD) in Rush Memory and Aging Project decedents, using clinical measures at the last visit prior to death as predictors. We validated these models by inferring AD-NC traits with clinical measures at the last visit prior to death for independent Religious Orders Study (ROS) decedents. We inferred baseline AD-NC traits for all ROS participants at study entry, and then tested if inferred AD-NC traits at study entry predicted incident ADD and postmortem pathologic AD. Results Inferred AD-NC traits at the last visit prior to death were related to postmortem measures with R2 = (0.188,0.316,0.262) respectively for amyloid-β, tangles, and global AD pathology, and prediction Area Under the receiver operating characteristic Curve (AUC) 0.765 for pathologic AD. Inferred baseline levels of all four AD-NC traits predicted ADD. The strongest prediction was obtained by the inferred baseline probabilities of pathologic AD with AUC = (0.919,0.896) for predicting the development of ADD in 3 and 5 years from baseline. The inferred baseline levels of all four AD-NC traits significantly discriminated pathologic AD profiled eight years later with p-values < 1.4×10-10. Conclusions Inferred AD-NC traits based on clinical measures may provide effective AD biomarkers that can estimate the burden of AD-NC traits in aging adults.
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Affiliation(s)
- Jingjing Yang
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, 615 Michael St, Atlanta, GA, 30322, USA
| | - Xizhu Liu
- Department of Biostatistics, Yale University School of Public Health, 60 College St, New Haven, CT, 06510, USA
| | - Shahram Oveisgharan
- Rush Alzheimer’s Disease Center, Rush University Medicine Center, 1620 W Harrison St, Chicago, IL, 60612, USA
| | - Andrea R. Zammit
- Rush Alzheimer’s Disease Center, Rush University Medicine Center, 1620 W Harrison St, Chicago, IL, 60612, USA
| | - Sukriti Nag
- Rush Alzheimer’s Disease Center, Rush University Medicine Center, 1620 W Harrison St, Chicago, IL, 60612, USA
| | - David A Bennett
- Rush Alzheimer’s Disease Center, Rush University Medicine Center, 1620 W Harrison St, Chicago, IL, 60612, USA
| | - Aron S Buchman
- Rush Alzheimer’s Disease Center, Rush University Medicine Center, 1620 W Harrison St, Chicago, IL, 60612, USA
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Panyard DJ, McKetney J, Deming YK, Morrow AR, Ennis GE, Jonaitis EM, Van Hulle CA, Yang C, Sung YJ, Ali M, Kollmorgen G, Suridjan I, Bayfield A, Bendlin BB, Zetterberg H, Blennow K, Cruchaga C, Carlsson CM, Johnson SC, Asthana S, Coon JJ, Engelman CD. Large-scale proteome and metabolome analysis of CSF implicates altered glucose and carbon metabolism and succinylcarnitine in Alzheimer's disease. Alzheimers Dement 2023; 19:5447-5470. [PMID: 37218097 PMCID: PMC10663389 DOI: 10.1002/alz.13130] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 02/23/2023] [Accepted: 04/04/2023] [Indexed: 05/24/2023]
Abstract
INTRODUCTION A hallmark of Alzheimer's disease (AD) is the aggregation of proteins (amyloid beta [A] and hyperphosphorylated tau [T]) in the brain, making cerebrospinal fluid (CSF) proteins of particular interest. METHODS We conducted a CSF proteome-wide analysis among participants of varying AT pathology (n = 137 participants; 915 proteins) with nine CSF biomarkers of neurodegeneration and neuroinflammation. RESULTS We identified 61 proteins significantly associated with the AT category (P < 5.46 × 10-5 ) and 636 significant protein-biomarker associations (P < 6.07 × 10-6 ). Proteins from glucose and carbon metabolism pathways were enriched among amyloid- and tau-associated proteins, including malate dehydrogenase and aldolase A, whose associations with tau were replicated in an independent cohort (n = 717). CSF metabolomics identified and replicated an association of succinylcarnitine with phosphorylated tau and other biomarkers. DISCUSSION These results implicate glucose and carbon metabolic dysregulation and increased CSF succinylcarnitine levels with amyloid and tau pathology in AD. HIGHLIGHTS Cerebrospinal fluid (CSF) proteome enriched for extracellular, neuronal, immune, and protein processing. Glucose/carbon metabolic pathways enriched among amyloid/tau-associated proteins. Key glucose/carbon metabolism protein associations independently replicated. CSF proteome outperformed other omics data in predicting amyloid/tau positivity. CSF metabolomics identified and replicated a succinylcarnitine-phosphorylated tau association.
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Affiliation(s)
- Daniel J. Panyard
- Department of Population Health Sciences, University of Wisconsin-Madison; 610 Walnut Street, 707 WARF Building, Madison, WI 53726, United States of America
| | - Justin McKetney
- National Center for Quantitative Biology of Complex Systems, University of Wisconsin-Madison; Madison, WI 53706, United States of America
- Department of Biomolecular Chemistry, University of Wisconsin-Madison; Madison, WI 53506, United States of America
| | - Yuetiva K. Deming
- Department of Population Health Sciences, University of Wisconsin-Madison; 610 Walnut Street, 707 WARF Building, Madison, WI 53726, United States of America
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison; 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison; 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
| | - Autumn R. Morrow
- Department of Population Health Sciences, University of Wisconsin-Madison; 610 Walnut Street, 707 WARF Building, Madison, WI 53726, United States of America
| | - Gilda E. Ennis
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison; 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
| | - Erin M. Jonaitis
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison; 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison; 610 Walnut Street, 9 Floor, Madison, WI 53726, United States of America
| | - Carol A. Van Hulle
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison; 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison; 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
| | - Chengran Yang
- Department of Psychiatry, Washington University School of Medicine; St Louis, MO 63110, United States of America
- NeuroGenomics and Informatics Center, Washington University School of Medicine; St Louis, MO 63110, United States of America
- Hope Center for Neurological Disorders, Washington University School of Medicine; St Louis, MO 63110, United States of America
| | - Yun Ju Sung
- Department of Psychiatry, Washington University School of Medicine; St Louis, MO 63110, United States of America
- NeuroGenomics and Informatics Center, Washington University School of Medicine; St Louis, MO 63110, United States of America
- Hope Center for Neurological Disorders, Washington University School of Medicine; St Louis, MO 63110, United States of America
| | - Muhammad Ali
- Department of Psychiatry, Washington University School of Medicine; St Louis, MO 63110, United States of America
- NeuroGenomics and Informatics Center, Washington University School of Medicine; St Louis, MO 63110, United States of America
- Hope Center for Neurological Disorders, Washington University School of Medicine; St Louis, MO 63110, United States of America
| | | | | | | | - Barbara B. Bendlin
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison; 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison; 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison; 610 Walnut Street, 9 Floor, Madison, WI 53726, United States of America
- William S. Middleton Memorial Veterans Hospital; 2500 Overlook Terrace, Madison, WI 53705, United States of America
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg; Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital; Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology; London, UK
- UK Dementia Research Institute at UCL; London, UK
- Hong Kong Center for Neurodegenerative Diseases; Hong Kong, China
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg; Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital; Mölndal, Sweden
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine; St Louis, MO 63110, United States of America
- NeuroGenomics and Informatics Center, Washington University School of Medicine; St Louis, MO 63110, United States of America
- Hope Center for Neurological Disorders, Washington University School of Medicine; St Louis, MO 63110, United States of America
| | - Cynthia M. Carlsson
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison; 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison; 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison; 610 Walnut Street, 9 Floor, Madison, WI 53726, United States of America
- William S. Middleton Memorial Veterans Hospital; 2500 Overlook Terrace, Madison, WI 53705, United States of America
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison; 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison; 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison; 610 Walnut Street, 9 Floor, Madison, WI 53726, United States of America
- William S. Middleton Memorial Veterans Hospital; 2500 Overlook Terrace, Madison, WI 53705, United States of America
| | - Sanjay Asthana
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison; 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison; 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
- William S. Middleton Memorial Veterans Hospital; 2500 Overlook Terrace, Madison, WI 53705, United States of America
| | - Joshua J. Coon
- National Center for Quantitative Biology of Complex Systems, University of Wisconsin-Madison; Madison, WI 53706, United States of America
- Department of Biomolecular Chemistry, University of Wisconsin-Madison; Madison, WI 53506, United States of America
- Morgridge Institute for Research; Madison, WI 53706, United States of America
- Department of Chemistry, University of Wisconsin-Madison; Madison, WI 53506, United States of America
| | - Corinne D. Engelman
- Department of Population Health Sciences, University of Wisconsin-Madison; 610 Walnut Street, 707 WARF Building, Madison, WI 53726, United States of America
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Choi JJ, Koscik RL, Jonaitis EM, Panyard DJ, Morrow AR, Johnson SC, Engelman CD, Schmitz LL. Assessing the Biological Mechanisms Linking Smoking Behavior and Cognitive Function: A Mediation Analysis of Untargeted Metabolomics. Metabolites 2023; 13:1154. [PMID: 37999250 PMCID: PMC10673384 DOI: 10.3390/metabo13111154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 11/10/2023] [Accepted: 11/11/2023] [Indexed: 11/25/2023] Open
Abstract
(1) Smoking is the most significant preventable health hazard in the modern world. It increases the risk of vascular problems, which are also risk factors for dementia. In addition, toxins in cigarettes increase oxidative stress and inflammation, which have both been linked to the development of Alzheimer's disease and related dementias (ADRD). This study identified potential mechanisms of the smoking-cognitive function relationship using metabolomics data from the longitudinal Wisconsin Registry for Alzheimer's Prevention (WRAP). (2) 1266 WRAP participants were included to assess the association between smoking status and four cognitive composite scores. Next, untargeted metabolomic data were used to assess the relationships between smoking and metabolites. Metabolites significantly associated with smoking were then tested for association with cognitive composite scores. Total effect models and mediation models were used to explore the role of metabolites in smoking-cognitive function pathways. (3) Plasma N-acetylneuraminate was associated with smoking status Preclinical Alzheimer Cognitive Composite 3 (PACC3) and Immediate Learning (IMM). N-acetylneuraminate mediated 12% of the smoking-PACC3 relationship and 13% of the smoking-IMM relationship. (4) These findings provide links between previous studies that can enhance our understanding of potential biological pathways between smoking and cognitive function.
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Affiliation(s)
- Jerome J. Choi
- Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53726, USA; (J.J.C.); (A.R.M.)
| | - Rebecca L. Koscik
- Wisconsin Alzheimer’s Institute, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53726, USA; (R.L.K.); (E.M.J.)
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, Madison, WI 53792, USA
| | - Erin M. Jonaitis
- Wisconsin Alzheimer’s Institute, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53726, USA; (R.L.K.); (E.M.J.)
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, Madison, WI 53792, USA
| | - Daniel J. Panyard
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA 94305, USA;
| | - Autumn R. Morrow
- Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53726, USA; (J.J.C.); (A.R.M.)
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Institute, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53726, USA; (R.L.K.); (E.M.J.)
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, Madison, WI 53792, USA
- William S. Middleton Memorial Veterans Hospital, Middleton, WI 53705, USA
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53792, USA
| | - Corinne D. Engelman
- Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53726, USA; (J.J.C.); (A.R.M.)
| | - Lauren L. Schmitz
- La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI 53706, USA;
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Fuller H, Zhu Y, Nicholas J, Chatelaine HA, Drzymalla EM, Sarvestani AK, Julián-Serrano S, Tahir UA, Sinnott-Armstrong N, Raffield LM, Rahnavard A, Hua X, Shutta KH, Darst BF. Metabolomic epidemiology offers insights into disease aetiology. Nat Metab 2023; 5:1656-1672. [PMID: 37872285 PMCID: PMC11164316 DOI: 10.1038/s42255-023-00903-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 09/06/2023] [Indexed: 10/25/2023]
Abstract
Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.
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Affiliation(s)
- Harriett Fuller
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Yiwen Zhu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jayna Nicholas
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Haley A Chatelaine
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Emily M Drzymalla
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Afrand K Sarvestani
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | | | - Usman A Tahir
- Department of Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | | | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Xinwei Hua
- Department of Cardiology, Peking University Third Hospital, Beijing, China
| | - Katherine H Shutta
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Burcu F Darst
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
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Dong R, Lu Q, Kang H, Suridjan I, Kollmorgen G, Wild N, Deming Y, Van Hulle CA, Anderson RM, Zetterberg H, Blennow K, Carlsson CM, Asthana S, Johnson SC, Engelman CD. CSF metabolites associated with biomarkers of Alzheimer's disease pathology. Front Aging Neurosci 2023; 15:1214932. [PMID: 37719875 PMCID: PMC10499619 DOI: 10.3389/fnagi.2023.1214932] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 07/17/2023] [Indexed: 09/19/2023] Open
Abstract
Introduction Metabolomics technology facilitates studying associations between small molecules and disease processes. Correlating metabolites in cerebrospinal fluid (CSF) with Alzheimer's disease (AD) CSF biomarkers may elucidate additional changes that are associated with early AD pathology and enhance our knowledge of the disease. Methods The relative abundance of untargeted metabolites was assessed in 161 individuals from the Wisconsin Registry for Alzheimer's Prevention. A metabolome-wide association study (MWAS) was conducted between 269 CSF metabolites and protein biomarkers reflecting brain amyloidosis, tau pathology, neuronal and synaptic degeneration, and astrocyte or microglial activation and neuroinflammation. Linear mixed-effects regression analyses were performed with random intercepts for sample relatedness and repeated measurements and fixed effects for age, sex, and years of education. The metabolome-wide significance was determined by a false discovery rate threshold of 0.05. The significant metabolites were replicated in 154 independent individuals from then Wisconsin Alzheimer's Disease Research Center. Mendelian randomization was performed using genome-wide significant single nucleotide polymorphisms from a CSF metabolites genome-wide association study. Results Metabolome-wide association study results showed several significantly associated metabolites for all the biomarkers except Aβ42/40 and IL-6. Genetic variants associated with metabolites and Mendelian randomization analysis provided evidence for a causal association of metabolites for soluble triggering receptor expressed on myeloid cells 2 (sTREM2), amyloid β (Aβ40), α-synuclein, total tau, phosphorylated tau, and neurogranin, for example, palmitoyl sphingomyelin (d18:1/16:0) for sTREM2, and erythritol for Aβ40 and α-synuclein. Discussion This study provides evidence that CSF metabolites are associated with AD-related pathology, and many of these associations may be causal.
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Affiliation(s)
- Ruocheng Dong
- Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Hyunseung Kang
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | | | | | | | - Yuetiva Deming
- Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Wisconsin Alzheimer’s Disease Research Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Carol A. Van Hulle
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Wisconsin Alzheimer’s Disease Research Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Rozalyn M. Anderson
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Geriatrics Research Education and Clinical Center, Middleton VA Hospital, Madison, WI, United States
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- UK Dementia Research Institute at UCL, London, United Kingdom
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, Hong Kong SAR, China
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Cynthia M. Carlsson
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Wisconsin Alzheimer’s Disease Research Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Geriatrics Research Education and Clinical Center, Middleton VA Hospital, Madison, WI, United States
| | - Sanjay Asthana
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Wisconsin Alzheimer’s Disease Research Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Geriatrics Research Education and Clinical Center, Middleton VA Hospital, Madison, WI, United States
| | - Sterling C. Johnson
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Wisconsin Alzheimer’s Disease Research Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Geriatrics Research Education and Clinical Center, Middleton VA Hospital, Madison, WI, United States
- Wisconsin Alzheimer’s Institute, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Corinne D. Engelman
- Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Wisconsin Alzheimer’s Disease Research Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Wisconsin Alzheimer’s Institute, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
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Vasiljevic E, Koscik RL, Jonaitis E, Betthauser T, Johnson SC, Engelman CD. Cognitive trajectories diverge by genetic risk in a preclinical longitudinal cohort. Alzheimers Dement 2023; 19:3108-3118. [PMID: 36723444 PMCID: PMC10390653 DOI: 10.1002/alz.12920] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 12/06/2022] [Accepted: 12/12/2022] [Indexed: 02/02/2023]
Abstract
INTRODUCTION We sought to characterize the timing of changes in cognitive trajectories related to genetic risk using the apolipoprotein E (APOE) score, a continuous measure of Alzheimer's disease (AD) risk. We also aimed to determine whether that timing was different when genetic risk was measured using an AD polygenic risk score (PRS) that contains APOE. METHODS We analyzed trajectories (N ≈1135) for four neuropsychological composite scores using mixed effects regression for longitudinal change across APOE scores and PRS of participants in the Wisconsin Registry for Alzheimer's Prevention, a longitudinal study of adults aged 40 to 70 at baseline, with a median participant follow-up time of 7.8 years. RESULTS We found a significant non-linear age-by-APOE score interaction in predicting cognitive decline. Cognitive trajectories diverged by APOE score at approximately 65 years of age. A 0.5 standard deviation difference in cognition between extreme percentiles of the PRS was predicted to occur 1 to 2 years before that of the APOE score. DISCUSSION Cognitive decline differs across time and APOE score. Estimates did not substantially shift with the AD PRS. HIGHLIGHTS The apolipoprotein E (APOE) score, a continuous measure, accounts for non-linear genetic risk of Alzheimer's disease. Non-linear age interacts with the APOE score to affect cognition. Cognitive decline starts to differ by APOE score levels at approximately age 65. Cognitive decline timing by polygenic risk (including APOE) is similar to APOE alone.
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Affiliation(s)
- Eva Vasiljevic
- Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, 610 Walnut Dr., Madison, WI 53726, USA
- Center for Demography of Health and Aging, University of Wisconsin-Madison, 1180 Observatory Drive Madison, WI 53706, USA
| | - Rebecca Langhough Koscik
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, 610 Walnut Street, 9th Floor, Madison, WI 53726, USA
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, MC 2420, Madison, Wisconsin 53792, USA
| | - Erin Jonaitis
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, 610 Walnut Street, 9th Floor, Madison, WI 53726, USA
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, MC 2420, Madison, Wisconsin 53792, USA
| | - Tobey Betthauser
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, MC 2420, Madison, Wisconsin 53792, USA
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, USA
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, 610 Walnut Street, 9th Floor, Madison, WI 53726, USA
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, MC 2420, Madison, Wisconsin 53792, USA
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI 53705, USA
| | - Corinne D. Engelman
- Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, 610 Walnut Dr., Madison, WI 53726, USA
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, 610 Walnut Street, 9th Floor, Madison, WI 53726, USA
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, MC 2420, Madison, Wisconsin 53792, USA
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10
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Strefeler A, Jan M, Quadroni M, Teav T, Rosenberg N, Chatton JY, Guex N, Gallart-Ayala H, Ivanisevic J. Molecular insights into sex-specific metabolic alterations in Alzheimer's mouse brain using multi-omics approach. Alzheimers Res Ther 2023; 15:8. [PMID: 36624525 PMCID: PMC9827669 DOI: 10.1186/s13195-023-01162-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 01/02/2023] [Indexed: 01/11/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is a progressive neurodegenerative disorder that is characterized by altered cellular metabolism in the brain. Several of these alterations have been found to be exacerbated in females, known to be disproportionately affected by AD. We aimed to unravel metabolic alterations in AD at the metabolic pathway level and evaluate whether they are sex-specific through integrative metabolomic, lipidomic, and proteomic analysis of mouse brain tissue. METHODS We analyzed male and female triple-transgenic mouse whole brain tissue by untargeted mass spectrometry-based methods to obtain a molecular signature consisting of polar metabolite, complex lipid, and protein data. These data were analyzed using multi-omics factor analysis. Pathway-level alterations were identified through joint pathway enrichment analysis or by separately evaluating lipid ontology and known proteins related to lipid metabolism. RESULTS Our analysis revealed significant AD-associated and in part sex-specific alterations across the molecular signature. Sex-dependent alterations were identified in GABA synthesis, arginine biosynthesis, and in alanine, aspartate, and glutamate metabolism. AD-associated alterations involving lipids were also found in the fatty acid elongation pathway and lysophospholipid metabolism, with a significant sex-specific effect for the latter. CONCLUSIONS Through multi-omics analysis, we report AD-associated and sex-specific metabolic alterations in the AD brain involving lysophospholipid and amino acid metabolism. These findings contribute to the characterization of the AD phenotype at the molecular level while considering the effect of sex, an overlooked yet determinant metabolic variable.
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Affiliation(s)
- Abigail Strefeler
- grid.9851.50000 0001 2165 4204Metabolomics Unit, Faculty of Biology and Medicine, University de Lausanne, Lausanne, Switzerland
| | - Maxime Jan
- grid.9851.50000 0001 2165 4204Bioinformatics Competence Center, Faculty of Biology and Medicine, University de Lausanne, Lausanne, Switzerland
| | - Manfredo Quadroni
- grid.9851.50000 0001 2165 4204Protein Analysis Facility, Faculty of Biology and Medicine, University de Lausanne, Lausanne, Switzerland
| | - Tony Teav
- grid.9851.50000 0001 2165 4204Metabolomics Unit, Faculty of Biology and Medicine, University de Lausanne, Lausanne, Switzerland
| | - Nadia Rosenberg
- grid.9851.50000 0001 2165 4204Department of Fundamental Neurosciences, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Jean-Yves Chatton
- grid.9851.50000 0001 2165 4204Department of Fundamental Neurosciences, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Nicolas Guex
- grid.9851.50000 0001 2165 4204Bioinformatics Competence Center, Faculty of Biology and Medicine, University de Lausanne, Lausanne, Switzerland
| | - Hector Gallart-Ayala
- grid.9851.50000 0001 2165 4204Metabolomics Unit, Faculty of Biology and Medicine, University de Lausanne, Lausanne, Switzerland
| | - Julijana Ivanisevic
- grid.9851.50000 0001 2165 4204Metabolomics Unit, Faculty of Biology and Medicine, University de Lausanne, Lausanne, Switzerland
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11
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Panyard DJ, Yu B, Snyder MP. The metabolomics of human aging: Advances, challenges, and opportunities. SCIENCE ADVANCES 2022; 8:eadd6155. [PMID: 36260671 PMCID: PMC9581477 DOI: 10.1126/sciadv.add6155] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
As the global population becomes older, understanding the impact of aging on health and disease becomes paramount. Recent advancements in multiomic technology have allowed for the high-throughput molecular characterization of aging at the population level. Metabolomics studies that analyze the small molecules in the body can provide biological information across a diversity of aging processes. Here, we review the growing body of population-scale metabolomics research on aging in humans, identifying the major trends in the field, implicated biological pathways, and how these pathways relate to health and aging. We conclude by assessing the main challenges in the research to date, opportunities for advancing the field, and the outlook for precision health applications.
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Affiliation(s)
- Daniel J. Panyard
- Department of Genetics, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA
- Corresponding author. (D.J.P.); (M.P.S.)
| | - Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Michael P. Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA
- Corresponding author. (D.J.P.); (M.P.S.)
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12
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Dong R, Denier-Fields DN, Lu Q, Suridjan I, Kollmorgen G, Wild N, Betthauser TJ, Carlsson CM, Asthana S, Johnson SC, Zetterberg H, Blennow K, Engelman CD. Principal components from untargeted cerebrospinal fluid metabolomics associated with Alzheimer's disease biomarkers. Neurobiol Aging 2022; 117:12-23. [PMID: 35640460 PMCID: PMC9737218 DOI: 10.1016/j.neurobiolaging.2022.04.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 01/20/2022] [Accepted: 04/12/2022] [Indexed: 01/13/2023]
Abstract
Studying the correlation between cerebrospinal fluid (CSF) metabolites and the Alzheimer's Disease (AD) biomarkers may offer a window to the alterations of the brain metabolome and unveil potential biological mechanisms underlying AD. In this analysis, 308 CSF metabolites from 338 individuals of Wisconsin Registry for Alzheimer's Prevention and Wisconsin Alzheimer's Disease Research Center were included in a principal component analysis (PCA). The resulted principal components (PCs) were tested for association with CSF total tau (t-tau), phosphorylated tau (p-tau), amyloid β 42 (Aβ42), and Aβ42/40 ratio using linear regression models. Significant PCs were further tested with other CSF NeuroToolKit (NTK) and imaging biomarkers. Using a Bonferroni corrected p < 0.05, 5 PCs were significantly associated with CSF p-tau and t-tau and 3 PCs were significantly associated with CSF Aβ42. Pathway analysis suggested that these PCS were enriched in 6 pathways, including metabolism of caffeine and nicotinate and nicotinamide. This study provides evidence that CSF metabolites are associated with AD pathology through core AD biomarkers and other NTK markers and suggests potential pathways to follow up in future studies.
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Affiliation(s)
- Ruocheng Dong
- Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Diandra N Denier-Fields
- Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA; Department Nutrition Science, University of Wisconsin-Madison, Madison, WI, USA
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | | | | | | | - Tobey James Betthauser
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA; Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, School of Medicine and Public Health, Madison, WI, USA
| | - Cynthia M Carlsson
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA; Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, School of Medicine and Public Health, Madison, WI, USA; Geriatric Research Education and Clinical Center, Wm. S. Middleton Memorial VA Hospital, Madison, WI, USA
| | - Sanjay Asthana
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA; Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, School of Medicine and Public Health, Madison, WI, USA; Geriatric Research Education and Clinical Center, Wm. S. Middleton Memorial VA Hospital, Madison, WI, USA
| | - Sterling C Johnson
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA; Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, School of Medicine and Public Health, Madison, WI, USA; Geriatric Research Education and Clinical Center, Wm. S. Middleton Memorial VA Hospital, Madison, WI, USA; Wisconsin Alzheimer's Institute, University of Wisconsin-Madison, School of Medicine and Public Health, Madison, WI, USA
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; UK Dementia Research Institute at UCL, London, UK; Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Corinne D Engelman
- Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA; Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, School of Medicine and Public Health, Madison, WI, USA; Wisconsin Alzheimer's Institute, University of Wisconsin-Madison, School of Medicine and Public Health, Madison, WI, USA.
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Weng J, Muti IH, Zhong AB, Kivisäkk P, Hyman BT, Arnold SE, Cheng LL. A Nuclear Magnetic Resonance Spectroscopy Method in Characterization of Blood Metabolomics for Alzheimer's Disease. Metabolites 2022; 12:metabo12020181. [PMID: 35208255 PMCID: PMC8878886 DOI: 10.3390/metabo12020181] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 02/08/2022] [Accepted: 02/10/2022] [Indexed: 02/06/2023] Open
Abstract
There is currently a crucial need for improved diagnostic techniques and targeted treatment methods for Alzheimer's disease (AD), a disease which impacts millions of elderly individuals each year. Metabolomic analysis has been proposed as a potential methodology to better investigate and understand the progression of this disease. In this report, we present our AD metabolomics results measured with high resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) on human blood plasma samples obtained from AD and non-AD subjects. Our study centers on developments of AD and non-AD metabolomics differentiating models with procedures of quality assurance (QA) and quality control (QC) through pooled samples. Our findings suggest that analysis of blood plasma samples using HRMAS NMR has the potential to differentiate between diseased and healthy subjects, which has important clinical implications for future improvements in AD diagnosis methodologies.
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Affiliation(s)
- JianXiang Weng
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA; (J.W.); (I.H.M.); (A.B.Z.)
| | - Isabella H. Muti
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA; (J.W.); (I.H.M.); (A.B.Z.)
| | - Anya B. Zhong
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA; (J.W.); (I.H.M.); (A.B.Z.)
| | - Pia Kivisäkk
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA; (P.K.); (B.T.H.); (S.E.A.)
| | - Bradley T. Hyman
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA; (P.K.); (B.T.H.); (S.E.A.)
| | - Steven E. Arnold
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA; (P.K.); (B.T.H.); (S.E.A.)
| | - Leo L. Cheng
- Departments of Radiology and Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
- Correspondence: ; Tel.: +1-617-724-6593
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14
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Jaskiw GE, Xu D, Obrenovich ME, Donskey CJ. Small phenolic and indolic gut-dependent molecules in the primate central nervous system: levels vs. bioactivity. Metabolomics 2022; 18:8. [PMID: 34989922 DOI: 10.1007/s11306-021-01866-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 12/12/2021] [Indexed: 10/19/2022]
Abstract
INTRODUCTION A rapidly growing body of data documents associations between disease of the brain and small molecules generated by gut-microbiota (GMB). While such metabolites can affect brain function through a variety of mechanisms, the most direct action would be on the central nervous system (CNS) itself. OBJECTIVE Identify indolic and phenolic GMB-dependent small molecules that reach bioactive concentrations in primate CNS. METHODS We conducted a PubMed search for metabolomic studies of the primate CNS [brain tissue or cerebrospinal fluid (CSF)] and then selected for phenolic or indolic metabolites that (i) had been quantified, (ii) were GMB-dependent. For each chemical we then conducted a search for studies of bioactivity conducted in vitro in human cells of any kind or in CNS cells from the mouse or rat. RESULTS 36 metabolites of interests were identified in primate CNS through targeted metabolomics. Quantification was available for 31/36 and in vitro bioactivity for 23/36. The reported CNS range for 8 metabolites 2-(3-hydroxyphenyl)acetic acid, 2-(4-hydroxyphenyl)acetic acid, 3-(3-hydroxyphenyl)propanoic acid, (E)-3-(3,4-dihydroxyphenyl)prop-2-enoic acid [caffeic acid], 3-hydroxybenzoic acid, 4-hydroxybenzoic acid, 2-acetamido-3-(1H-indol-3-yl)propanoic acid [N-acetyltryptophan], 1H-indol-3-yl hydrogen sulfate [indoxyl-3-sulfate] overlapped with a bioactive concentration. However, the number and quality of relevant studies of CNS neurochemistry as well as of bioactivity were highly limited. Structural isomers, multiple metabolites and potential confounders were inadequately considered. CONCLUSION The potential direct bioactivity of GMB-derived indolic and phenolic molecules on primate CNS remains largely unknown. The field requires additional strategies to identify and prioritize screening of the most promising small molecules that enter the CNS.
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Affiliation(s)
- George E Jaskiw
- Psychiatry Service 116(A), Veterans Affairs Northeast Ohio Healthcare System (VANEOHS), 10701 East Blvd., Cleveland, OH, 44106, USA.
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
| | - Dongyan Xu
- Psychiatry Service 116(A), Veterans Affairs Northeast Ohio Healthcare System (VANEOHS), 10701 East Blvd., Cleveland, OH, 44106, USA
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Mark E Obrenovich
- Pathology and Laboratory Medicine Service, VANEOHS, Cleveland, OH, USA
- Research Service, VANEOHS, Cleveland, OH, USA
- Department of Chemistry, Case Western Reserve University, Cleveland, OH, USA
| | - Curtis J Donskey
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Geriatric Research, Education and Clinical Center (GRECC), VANEOHS, Cleveland, OH, USA
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Chacko S, Haseeb YB, Haseeb S. Metabolomics Work Flow and Analytics in Systems Biology. Curr Mol Med 2021; 22:870-881. [PMID: 34923941 DOI: 10.2174/1566524022666211217102105] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 08/26/2021] [Accepted: 09/24/2021] [Indexed: 11/22/2022]
Abstract
Metabolomics is an omics approach of systems biology that involves the development and assessment of large-scale, comprehensive biochemical analysis tools for metabolites in biological systems. This review describes the metabolomics workflow and provides an overview of current analytic tools used for the quantification of metabolic profiles. We explain analytic tools such as mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy, ionization techniques, and approaches for data extraction and analysis.
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Affiliation(s)
- Sanoj Chacko
- Division of Cardiology, Queen's University, Kingston, Ontario, Canada
| | - Yumna B Haseeb
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Ontario, Canada
| | - Sohaib Haseeb
- Division of Cardiology, Queen's University, Kingston, Ontario, Canada
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Peña-Bautista C, Álvarez-Sánchez L, Cañada-Martínez AJ, Baquero M, Cháfer-Pericás C. Epigenomics and Lipidomics Integration in Alzheimer Disease: Pathways Involved in Early Stages. Biomedicines 2021; 9:1812. [PMID: 34944628 PMCID: PMC8698767 DOI: 10.3390/biomedicines9121812] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 11/23/2021] [Accepted: 11/29/2021] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Alzheimer Disease (AD) is the most prevalent dementia. However, the physiopathological mechanisms involved in its development are unclear. In this sense, a multi-omics approach could provide some progress. METHODS Epigenomic and lipidomic analysis were carried out in plasma samples from patients with mild cognitive impairment (MCI) due to AD (n = 22), and healthy controls (n = 5). Then, omics integration between microRNAs (miRNAs) and lipids was performed by Sparse Partial Least Squares (s-PLS) regression and target genes for the selected miRNAs were identified. RESULTS 25 miRNAs and 25 lipids with higher loadings in the sPLS regression were selected. Lipids from phosphatidylethanolamines (PE), lysophosphatidylcholines (LPC), ceramides, phosphatidylcholines (PC), triglycerides (TG) and several long chain fatty acids families were identified as differentially expressed in AD. Among them, several fatty acids showed strong positive correlations with miRNAs studied. In fact, these miRNAs regulated genes implied in fatty acids metabolism, as elongation of very long-chain fatty acids (ELOVL), and fatty acid desaturases (FADs). CONCLUSIONS The lipidomic-epigenomic integration showed that several lipids and miRNAs were differentially expressed in AD, being the fatty acids mechanisms potentially involved in the disease development. However, further work about targeted analysis should be carried out in a larger cohort, in order to validate these preliminary results and study the proposed pathways in detail.
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Affiliation(s)
- Carmen Peña-Bautista
- Alzheimer’s Disease Research Group, Health Research Institute La Fe, 46026 Valencia, Spain; (C.P.-B.); (L.Á.-S.); (M.B.)
| | - Lourdes Álvarez-Sánchez
- Alzheimer’s Disease Research Group, Health Research Institute La Fe, 46026 Valencia, Spain; (C.P.-B.); (L.Á.-S.); (M.B.)
- Division of Neurology, University and Polytechnic Hospital La Fe, 46026 Valencia, Spain
| | | | - Miguel Baquero
- Alzheimer’s Disease Research Group, Health Research Institute La Fe, 46026 Valencia, Spain; (C.P.-B.); (L.Á.-S.); (M.B.)
- Division of Neurology, University and Polytechnic Hospital La Fe, 46026 Valencia, Spain
| | - Consuelo Cháfer-Pericás
- Alzheimer’s Disease Research Group, Health Research Institute La Fe, 46026 Valencia, Spain; (C.P.-B.); (L.Á.-S.); (M.B.)
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York A, Everhart A, Vitek MP, Gottschalk KW, Colton CA. Metabolism-Based Gene Differences in Neurons Expressing Hyperphosphorylated AT8- Positive (AT8+) Tau in Alzheimer's Disease. ASN Neuro 2021; 13:17590914211019443. [PMID: 34121475 PMCID: PMC8207264 DOI: 10.1177/17590914211019443] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Metabolic adaptations in the brain are critical to the establishment and maintenance of normal cellular functions and to the pathological responses to disease processes. Here, we have focused on specific metabolic pathways that are involved in immune-mediated neuronal processes in brain using isolated neurons derived from human autopsy brain sections of normal individuals and individuals diagnosed as Alzheimer's disease (AD). Laser capture microscopy was used to select specific cell types in immune-stained thin brain sections followed by NanoString technology to identify and quantify differences in mRNA levels between age-matched control and AD neuronal samples. Comparisons were also made between neurons isolated from AD brain sections expressing pathogenic hyperphosphorylated AT8- positive (AT8+) tau and non-AT8+ AD neurons using double labeling techniques. The mRNA expression data showed unique patterns of metabolic pathway expression between the subtypes of captured neurons that involved membrane based solute transporters, redox factors, and arginine and methionine metabolic pathways. We also identified the expression levels of a novel metabolic gene, Radical-S-Adenosyl Domain1 (RSAD1) and its corresponding protein, Rsad1, that impact methionine usage and radical based reactions. Immunohistochemistry was used to identify specific protein expression levels and their cellular location in NeuN+ and AT8+ neurons. APOE4 vs APOE3 genotype-specific and sex-specific gene expression differences in these metabolic pathways were also observed when comparing neurons from individuals with AD to age-matched individuals.
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Affiliation(s)
- Audra York
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, North Carolina, United States
| | - Angela Everhart
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, North Carolina, United States
| | - Michael P Vitek
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, North Carolina, United States
| | - Kirby W Gottschalk
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, North Carolina, United States
| | - Carol A Colton
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, North Carolina, United States
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Dong R, Darst BF, Deming Y, Ma Y, Lu Q, Zetterberg H, Blennow K, Carlsson CM, Johnson SC, Asthana S, Engelman CD. CSF metabolites associate with CSF tau and improve prediction of Alzheimer's disease status. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12167. [PMID: 33969169 PMCID: PMC8087982 DOI: 10.1002/dad2.12167] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 02/15/2021] [Indexed: 01/14/2023]
Abstract
INTRODUCTION Cerebrospinal fluid (CSF) total tau (t-tau) and phosphorylated tau (p-tau) are biomarkers of Alzheimer's disease (AD), yet much is unknown about AD-associated changes in tau metabolism and tau tangle etiology. METHODS We assessed the variation of t-tau and p-tau explained by 38 previously identified CSF metabolites using linear regression models in middle-age controls from the Wisconsin Alzheimer's Disease Research Center, and predicted AD/mild cognitive impairment (MCI) versus an independent set of older controls using metabolites selected by the least absolute shrinkage and selection operator (LASSO). RESULTS The 38 CSF metabolites explained 70.3% and 75.7% of the variance in t-tau and p-tau, respectively. Of these, seven LASSO-selected metabolites improved the prediction ability of AD/MCI versus older controls (area under the curve score increased from 0.92 to 0.97 and 0.78 to 0.93) compared to the base model. DISCUSSION These tau-correlated CSF metabolites increase AD/MCI prediction accuracy and may provide insight into tau tangle etiology.
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Affiliation(s)
- Ruocheng Dong
- Department of Population Health SciencesUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Burcu F. Darst
- Center for Genetic EpidemiologyKeck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Yuetiva Deming
- Department of Population Health SciencesUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Yue Ma
- Alzheimer's Disease Research CenterUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Qiongshi Lu
- Department of Biostatistics and Medical InformaticsUniversity of WisconsinMadisonWisconsinUSA
| | - Henrik Zetterberg
- Institute of Neuroscience and PhysiologyThe Sahlgrenska Academy at University of GothenburgMölndalSweden
- Clinical Neurochemistry LaboratorySahlgrenska University HospitalMölndalSweden
- UK Dementia Research Institute at UCLLondonUK
- Department of Neurodegenerative DiseaseUCL Institute of NeurologyLondonUK
| | - Kaj Blennow
- Institute of Neuroscience and PhysiologyThe Sahlgrenska Academy at University of GothenburgMölndalSweden
- Clinical Neurochemistry LaboratorySahlgrenska University HospitalMölndalSweden
| | - Cynthia M. Carlsson
- Alzheimer's Disease Research CenterUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- Wisconsin Alzheimer's InstituteUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- Geriatric Research Education and Clinical CenterWm. S. Middleton Memorial VA HospitalMadisonWisconsinUSA
| | - Sterling C. Johnson
- Alzheimer's Disease Research CenterUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- Wisconsin Alzheimer's InstituteUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Sanjay Asthana
- Alzheimer's Disease Research CenterUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- Geriatric Research Education and Clinical CenterWm. S. Middleton Memorial VA HospitalMadisonWisconsinUSA
| | - Corinne D. Engelman
- Department of Population Health SciencesUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- Alzheimer's Disease Research CenterUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- Wisconsin Alzheimer's InstituteUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
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19
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Panyard DJ, Kim KM, Darst BF, Deming YK, Zhong X, Wu Y, Kang H, Carlsson CM, Johnson SC, Asthana S, Engelman CD, Lu Q. Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations. Commun Biol 2021; 4:63. [PMID: 33437055 PMCID: PMC7803963 DOI: 10.1038/s42003-020-01583-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 12/09/2020] [Indexed: 02/07/2023] Open
Abstract
The study of metabolomics and disease has enabled the discovery of new risk factors, diagnostic markers, and drug targets. For neurological and psychiatric phenotypes, the cerebrospinal fluid (CSF) is of particular importance. However, the CSF metabolome is difficult to study on a large scale due to the relative complexity of the procedure needed to collect the fluid. Here, we present a metabolome-wide association study (MWAS), which uses genetic and metabolomic data to impute metabolites into large samples with genome-wide association summary statistics. We conduct a metabolome-wide, genome-wide association analysis with 338 CSF metabolites, identifying 16 genotype-metabolite associations (metabolite quantitative trait loci, or mQTLs). We then build prediction models for all available CSF metabolites and test for associations with 27 neurological and psychiatric phenotypes, identifying 19 significant CSF metabolite-phenotype associations. Our results demonstrate the feasibility of MWAS to study omic data in scarce sample types.
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Grants
- R01 AG037639 NIA NIH HHS
- UL1 TR000427 NCATS NIH HHS
- T15 LM007359 NLM NIH HHS
- T32 LM012413 NLM NIH HHS
- RF1 AG027161 NIA NIH HHS
- T32 AG000213 NIA NIH HHS
- P2C HD047873 NICHD NIH HHS
- UL1 TR002373 NCATS NIH HHS
- P30 AG062715 NIA NIH HHS
- P50 AG033514 NIA NIH HHS
- R01 AG027161 NIA NIH HHS
- R01 AG054047 NIA NIH HHS
- P30 AG017266 NIA NIH HHS
- R21 AG067092 NIA NIH HHS
- U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
- U.S. Department of Health & Human Services | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
- U.S. Department of Health & Human Services | NIH | U.S. National Library of Medicine (NLM)
- NSF | Directorate for Mathematical & Physical Sciences | Division of Mathematical Sciences (DMS)
- U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (NCATS)
- This research is supported by National Institutes of Health (NIH) grants R01AG27161 (Wisconsin Registry for Alzheimer Prevention: Biomarkers of Preclinical AD), R01AG054047 (Genomic and Metabolomic Data Integration in a Longitudinal Cohort at Risk for Alzheimer’s Disease), R21AG067092 (Identifying Metabolomic Risk Factors in Plasma and Cerebrospinal Fluid for Alzheimer’s Disease), R01AG037639 (White Matter Degeneration: Biomarkers in Preclinical Alzheimer’s Disease), P30AG017266 (Center for Demography of Health and Aging), and P50AG033514 and P30AG062715 (Wisconsin Alzheimer’s Disease Research Center Grant), the Helen Bader Foundation, Northwestern Mutual Foundation, Extendicare Foundation, State of Wisconsin, the Clinical and Translational Science Award (CTSA) program through the NIH National Center for Advancing Translational Sciences (NCATS) grant UL1TR000427, and the University of Wisconsin-Madison Office of the Vice Chancellor for Research and Graduate Education with funding from the Wisconsin Alumni Research Foundation. This research was supported in part by the Intramural Research Program of the National Institute on Aging. Computational resources were supported by a core grant to the Center for Demography and Ecology at the University of Wisconsin-Madison (P2CHD047873). Author DJP was supported by an NLM training grant to the Bio-Data Science Training Program (T32LM012413). Author BFD was supported by an NLM training grant to the Computation and Informatics in Biology and Medicine Training Program (NLM 5T15LM007359). Author YKD was supported by a training grant from the National Institute on Aging (T32AG000213). Author HK was supported by National Science Foundation (NSF) grant DMS-1811414 (Theory and Methods for Inferring Causal Effects with Mendelian Randomization).
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Affiliation(s)
- Daniel J Panyard
- Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 707 WARF Building, Madison, WI, 53726, USA
| | - Kyeong Mo Kim
- Department of Biotechnology, Yonsei University, 50 Yonsei-ro Seodaemun-gu, Seoul, 03722, Republic of Korea
| | - Burcu F Darst
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, 1450 Biggy Street, Los Angeles, CA, 90033, USA
| | - Yuetiva K Deming
- Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 707 WARF Building, Madison, WI, 53726, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI, 53792, USA
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI, 53705, USA
| | - Xiaoyuan Zhong
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WARF Room 201, 610 Walnut Street, Madison, WI, 53726, USA
| | - Yuchang Wu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WARF Room 201, 610 Walnut Street, Madison, WI, 53726, USA
| | - Hyunseung Kang
- Department of Statistics, University of Wisconsin-Madison, 1300 University Avenue, Madison, WI, 53706, USA
| | - Cynthia M Carlsson
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI, 53792, USA
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI, 53705, USA
- William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI, 53705, USA
| | - Sterling C Johnson
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI, 53792, USA
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI, 53705, USA
- William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI, 53705, USA
| | - Sanjay Asthana
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI, 53792, USA
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI, 53705, USA
- William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI, 53705, USA
| | - Corinne D Engelman
- Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 707 WARF Building, Madison, WI, 53726, USA
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WARF Room 201, 610 Walnut Street, Madison, WI, 53726, USA.
- Department of Statistics, University of Wisconsin-Madison, 1300 University Avenue, Madison, WI, 53706, USA.
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20
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Darst BF, Huo Z, Jonaitis EM, Koscik RL, Clark LR, Lu Q, Kremen WS, Franz CE, Rana B, Lyons MJ, Hogan KJ, Zhao J, Johnson SC, Engelman CD. Metabolites Associated with Early Cognitive Changes Implicated in Alzheimer's Disease. J Alzheimers Dis 2021; 79:1041-1054. [PMID: 33427733 PMCID: PMC8054536 DOI: 10.3233/jad-200176] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
BACKGROUND Understanding metabolic mechanisms associated with cognitive changes preceding an Alzheimer's disease (AD) diagnosis could advance our understanding of AD progression and inform preventive methods. OBJECTIVE We investigated the metabolomics of the early changes in executive function and delayed recall, the earliest aspects of cognitive function to change in the course of AD development, in order to better understand mechanisms that could contribute to early stages and progression of this disease. METHODS This investigation used longitudinal plasma samples from the Wisconsin Registry for Alzheimer's Prevention (WRAP), a cohort of participants who were dementia free at enrollment and enriched with a parental history of AD. Metabolomic profiles were quantified for 2,324 fasting plasma samples among 1,200 participants, each with up to three study visits, which occurred every two years. Metabolites were individually tested for association with executive function and delayed recall trajectories across age. RESULTS Of 1,097 metabolites tested, levels of seven were associated with executive function trajectories, including an amino acid cysteine S-sulfate and three fatty acids, including erucate (22 : 1n9), while none were associated with delayed recall trajectories. Replication was attempted for four of these metabolites that were present in the Vietnam Era Twin Study of Aging (VETSA). Although none reached statistical significance, three of these associations showed consistent effectdirections. CONCLUSION Our results suggest potential metabolomic mechanisms that could contribute to the earliest signs of cognitive decline. In particular, fatty acids may be associated with cognition in a manner that is more complex than previously suspected.
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Affiliation(s)
- Burcu F. Darst
- Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Zhiguang Huo
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
| | - Erin M. Jonaitis
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Rebecca L. Koscik
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Lindsay R. Clark
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Geriatric Research Education and Clinical Center, Wm. S. Middleton Memorial VA Hospital, Madison, WI, USA
- Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Qiongshi Lu
- Department of Biostatistics & Medical Informatics, University of Wisconsin, Madison, WI, USA
| | | | | | - Brinda Rana
- University of California, San Diego, La Jolla, CA, USA
| | - Michael J. Lyons
- Department of Psychological & Brain Sciences, Boston University, Boston, MA, USA
| | - Kirk J. Hogan
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Department of Anesthesiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Jinying Zhao
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Geriatric Research Education and Clinical Center, Wm. S. Middleton Memorial VA Hospital, Madison, WI, USA
- Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Corinne D. Engelman
- Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
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21
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Huo Z, Rana BK, Elman JA, Dong R, Engelman CD, Johnson SC, Lyons MJ, Franz CE, Kremen WS, Zhao J. Metabolic Profiling of Cognitive Aging in Midlife. Front Aging Neurosci 2020; 12:555850. [PMID: 33250761 PMCID: PMC7674168 DOI: 10.3389/fnagi.2020.555850] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Accepted: 10/16/2020] [Indexed: 01/08/2023] Open
Abstract
Alzheimer's dementia (AD) begins many years before its clinical symptoms. Metabolic dysfunction represents a core feature of AD and cognitive impairment, but few metabolomic studies have focused on cognitive aging in midlife. Using an untargeted metabolomics approach, we identified metabolic predictors of cognitive aging in midlife using fasting plasma sample from 30 middle-aged (mean age 57.2), male-male twin pairs enrolled in the Vietnam Era Twin Study of Aging (VETSA). For all twin pairs, one twin developed incident MCI, whereas his co-twin brother remained to be cognitively normal during an average 5.5-year follow-up. Linear mixed model was used to identify metabolites predictive of MCI conversion or cognitive change over time, adjusting for traditional risk factors. Results from twins were replicated in an independent cohort of middle-aged adults (mean age 59.1) in the Wisconsin Registry for Alzheimer's Prevention (WRAP). Results in twins showed that higher baseline levels of four plasma metabolites, including sphingomyelin (d18:1/20:1 and d18:2/20:0), sphingomyelin (d18:1/22:1, d18:2/22:0, and d16:1/24:1), DAG (18:2/20:4), and hydroxy-CMPF, were significantly associated with a slower decrease in one or more domains of cognitive function. The association of sphingomyelin (d18:1/20:1 and d18:2/20:0) was replicated in WRAP. Our results support that metabolic perturbation occurs many years before cognitive impairment and plasma metabolites may serve as early biomarkers for prediction or monitoring of cognitive aging and AD in midlife.
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Affiliation(s)
- Zhiguang Huo
- Department of Biostatistics, University of Florida, Gainesville, FL, United States
| | - Brinda K. Rana
- Department of Psychiatry, University of California, San Diego, San Diego, CA, United States
| | - Jeremy A. Elman
- Department of Psychiatry, University of California, San Diego, San Diego, CA, United States
| | - Ruocheng Dong
- Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Corinne D. Engelman
- Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
- Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
- Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Michael J. Lyons
- Department of Psychological & Brain Sciences, Boston University, Boston, MA, United States
| | - Carol E. Franz
- Department of Psychiatry, University of California, San Diego, San Diego, CA, United States
| | - William S. Kremen
- Department of Psychiatry, University of California, San Diego, San Diego, CA, United States
- Center of Excellence for Stress and Mental Health, Veterans Affairs San Diego Healthcare System, San Diego, CA, United States
| | - Jinying Zhao
- Department of Epidemiology, University of Florida, Gainesville, FL, United States
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22
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Tolstikov V, Moser AJ, Sarangarajan R, Narain NR, Kiebish MA. Current Status of Metabolomic Biomarker Discovery: Impact of Study Design and Demographic Characteristics. Metabolites 2020; 10:metabo10060224. [PMID: 32485899 PMCID: PMC7345110 DOI: 10.3390/metabo10060224] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 05/21/2020] [Accepted: 05/27/2020] [Indexed: 12/16/2022] Open
Abstract
Widespread application of omic technologies is evolving our understanding of population health and holds promise in providing precise guidance for selection of therapeutic interventions based on patient biology. The opportunity to use hundreds of analytes for diagnostic assessment of human health compared to the current use of 10–20 analytes will provide greater accuracy in deconstructing the complexity of human biology in disease states. Conventional biochemical measurements like cholesterol, creatinine, and urea nitrogen are currently used to assess health status; however, metabolomics captures a comprehensive set of analytes characterizing the human phenotype and its complex metabolic processes in real-time. Unlike conventional clinical analytes, metabolomic profiles are dramatically influenced by demographic and environmental factors that affect the range of normal values and increase the risk of false biomarker discovery. This review addresses the challenges and opportunities created by the evolving field of clinical metabolomics and highlights features of study design and bioinformatics necessary to maximize the utility of metabolomics data across demographic groups.
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Affiliation(s)
- Vladimir Tolstikov
- BERG, Precision Medicine Division, Framingham, MA 01701, USA; (V.T.); (R.S.); (N.R.N.)
| | - A. James Moser
- Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215, USA;
| | | | - Niven R. Narain
- BERG, Precision Medicine Division, Framingham, MA 01701, USA; (V.T.); (R.S.); (N.R.N.)
| | - Michael A. Kiebish
- BERG, Precision Medicine Division, Framingham, MA 01701, USA; (V.T.); (R.S.); (N.R.N.)
- Correspondence: ; Tel.: +1-617-588-2245
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23
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Long NP, Nghi TD, Kang YP, Anh NH, Kim HM, Park SK, Kwon SW. Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine. Metabolites 2020; 10:E51. [PMID: 32013105 PMCID: PMC7074059 DOI: 10.3390/metabo10020051] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 01/17/2020] [Accepted: 01/21/2020] [Indexed: 12/18/2022] Open
Abstract
Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.
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Affiliation(s)
- Nguyen Phuoc Long
- College of Pharmacy, Seoul National University, Seoul 08826, Korea; (N.P.L.); (N.H.A.); (H.M.K.)
| | - Tran Diem Nghi
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea; (T.D.N.); (S.K.P.)
| | - Yun Pyo Kang
- Department of Cancer Physiology, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA;
| | - Nguyen Hoang Anh
- College of Pharmacy, Seoul National University, Seoul 08826, Korea; (N.P.L.); (N.H.A.); (H.M.K.)
| | - Hyung Min Kim
- College of Pharmacy, Seoul National University, Seoul 08826, Korea; (N.P.L.); (N.H.A.); (H.M.K.)
| | - Sang Ki Park
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea; (T.D.N.); (S.K.P.)
| | - Sung Won Kwon
- College of Pharmacy, Seoul National University, Seoul 08826, Korea; (N.P.L.); (N.H.A.); (H.M.K.)
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