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      Associations of circulating saturated long-chain fatty acids with risk of mild cognitive impairment and Alzheimer’s disease in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort

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          Summary

          Background

          No study has examined the associations between peripheral saturated long-chain fatty acids (LCFAs) and conversion from mild cognitive impairment (MCI) to Alzheimer’s disease (AD). This study aimed to examine whether circulating saturated LCFAs are associated with both risks of incident MCI from cognitively normal (CN) participants and incident AD progressed from MCI in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort.

          Methods

          We conducted analysis of data from older adults aged 55–90 years who were recruited at 63 sites across the USA and Canada. We examined associations between circulating saturated LCFAs (i.e., C14:0, C16:0, C18:0, C20:0) and risk for incident MCI in CN participants, and incident AD progressed from MCI.

          Findings

          829 participants who were enrolled in ADNI-1 had data on plasma saturated LCFAs, of which 618 AD-free participants were included in our analysis (226 with normal cognition and 392 with MCI; 60.2% were men). Cox proportional-hazards models were used to account for time-to-event/censor with a 48-month follow-up period for the primary analysis. Other than C20:0, saturated LCFAs were associated with an increased risk for AD among participants with MCI at baseline (Hazard ratios (HRs) = 1.3 to 2.2, P = 0.0005 to 0.003 in fully-adjusted models). No association of C20:0 with risk of AD among participants with MCI was observed. No associations were observed between saturated LCFAs and risk for MCI among participants with normal cognition.

          Interpretation

          Saturated LCFAs are associated with increased risk of progressing from MCI to AD. This finding holds the potential to facilitate precision prevention of AD among patients with MCI.

          Funding

          doi 10.13039/100000002, National Institutes of Health; .

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          Most cited references37

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          The Role of Short-Chain Fatty Acids From Gut Microbiota in Gut-Brain Communication

          Ygor Parladore Silva, Andressa Bernardi, Rudimar Luiz Frozza (2020)
          A substantial body of evidence supports that the gut microbiota plays a pivotal role in the regulation of metabolic, endocrine and immune functions. In recent years, there has been growing recognition of the involvement of the gut microbiota in the modulation of multiple neurochemical pathways through the highly interconnected gut-brain axis. Although amazing scientific breakthroughs over the last few years have expanded our knowledge on the communication between microbes and their hosts, the underpinnings of microbiota-gut-brain crosstalk remain to be determined. Short-chain fatty acids (SCFAs), the main metabolites produced in the colon by bacterial fermentation of dietary fibers and resistant starch, are speculated to play a key role in neuro-immunoendocrine regulation. However, the underlying mechanisms through which SCFAs might influence brain physiology and behavior have not been fully elucidated. In this review, we outline the current knowledge about the involvement of SCFAs in microbiota-gut-brain interactions. We also highlight how the development of future treatments for central nervous system (CNS) disorders can take advantage of the intimate and mutual interactions of the gut microbiota with the brain by exploring the role of SCFAs in the regulation of neuro-immunoendocrine function.
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            Robust causal inference using directed acyclic graphs: the R package ‘dagitty’

            George Ellison, Maciej Liskiewicz, Johannes Textor … (2017)
            Directed acyclic graphs (DAGs), which offer systematic representations of causal relationships, have become an established framework for the analysis of causal inference in epidemiology, often being used to determine covariate adjustment sets for minimizing confounding bias. DAGitty is a popular web application for drawing and analysing DAGs. Here we introduce the R package 'dagitty', which provides access to all of the capabilities of the DAGitty web application within the R platform for statistical computing, and also offers several new functions. We describe how the R package 'dagitty' can be used to: evaluate whether a DAG is consistent with the dataset it is intended to represent; enumerate 'statistically equivalent' but causally different DAGs; and identify exposure-outcome adjustment sets that are valid for causally different but statistically equivalent DAGs. This functionality enables epidemiologists to detect causal misspecifications in DAGs and make robust inferences that remain valid for a range of different DAGs. The R package 'dagitty' is available through the comprehensive R archive network (CRAN) at [https://cran.r-project.org/web/packages/dagitty/]. The source code is available on github at [https://github.com/jtextor/dagitty]. The web application 'DAGitty' is free software, licensed under the GNU general public licence (GPL) version 2 and is available at [http://dagitty.net/].
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              Neuropsychological criteria for mild cognitive impairment improves diagnostic precision, biomarker associations, and progression rates.

              Mark W. Bondi, Emily C Edmonds, Amy Jak … (2014)
              We compared two methods of diagnosing mild cognitive impairment (MCI): conventional Petersen/Winblad criteria as operationalized by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and an actuarial neuropsychological method put forward by Jak and Bondi designed to balance sensitivity and reliability. 1,150 ADNI participants were diagnosed at baseline as cognitively normal (CN) or MCI via ADNI criteria (MCI: n = 846; CN: n = 304) or Jak/Bondi criteria (MCI: n = 401; CN: n = 749), and the two MCI samples were submitted to cluster and discriminant function analyses. Resulting cluster groups were then compared and further examined for APOE allelic frequencies, cerebrospinal fluid (CSF) Alzheimer's disease (AD) biomarker levels, and clinical outcomes. Results revealed that both criteria produced a mildly impaired Amnestic subtype and a more severely impaired Dysexecutive/Mixed subtype. The neuropsychological Jak/Bondi criteria uniquely yielded a third Impaired Language subtype, whereas conventional Petersen/Winblad ADNI criteria produced a third subtype comprising nearly one-third of the sample that performed within normal limits across the cognitive measures, suggesting this method's susceptibility to false positive diagnoses. MCI participants diagnosed via neuropsychological criteria yielded dissociable cognitive phenotypes, significant CSF AD biomarker associations, more stable diagnoses, and identified greater percentages of participants who progressed to dementia than conventional MCI diagnostic criteria. Importantly, the actuarial neuropsychological method did not produce a subtype that performed within normal limits on the cognitive testing, unlike the conventional diagnostic method. Findings support the need for refinement of MCI diagnoses to incorporate more comprehensive neuropsychological methods, with resulting gains in empirical characterization of specific cognitive phenotypes, biomarker associations, stability of diagnoses, and prediction of progression. Refinement of MCI diagnostic methods may also yield gains in biomarker and clinical trial study findings because of improvements in sample compositions of 'true positive' cases and removal of 'false positive' cases.
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                Author and article information

                Contributors
                Qi Dai
                Journal
                Journal ID (nlm-ta): eBioMedicine
                Journal ID (iso-abbrev): EBioMedicine
                Title: eBioMedicine
                Publisher: Elsevier
                ISSN (Electronic): 2352-3964
                Publication date PMC-release: 02 October 2023
                Publication date Collection: November 2023
                Publication date (Electronic): 02 October 2023
                Volume: 97
                Electronic Location Identifier: 104818
                Affiliations
                [a ]Department of Medicine, Division of Epidemiology, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
                [b ]Division of Epidemiology, Herbert Wertheim School of Public Health and Human Longevity Science, University of California-San Diego, La Jolla, CA 92093, USA
                [c ]Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA
                [d ]Department of Radiology and Imaging Sciences, Center for Computational Biology and Bioinformatics, and the Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA
                [e ]College of Public Health, University of South Florida, Tampa, FL 33620, USA
                Author notes
                [∗ ]Corresponding author. Department of Medicine, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 800, Nashville, TN 37203-1738, USA. qi.dai@ 123456vanderbilt.edu
                [f]

                Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database ( adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

                Article
                Publisher Item ID: S2352-3964(23)00384-5 Publisher ID: 104818
                DOI: 10.1016/j.ebiom.2023.104818
                PMC ID: 10562835
                PubMed ID: 37793213
                SO-VID: 66e15b8f-f0ed-465d-943c-8a328b67b5ca
                Copyright © © 2023 The Authors
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Date received : 7 May 2023
                Date revision received : 15 September 2023
                Date accepted : 17 September 2023
                Categories
                Subject: Articles

                Keywords: mild cognitive impairment,alzheimer’s disease,saturated long-chain fatty acids,the alzheimer’s disease neuroimaging initiative cohort
                Data availability:
                Keywords: mild cognitive impairment, alzheimer’s disease, saturated long-chain fatty acids, the alzheimer’s disease neuroimaging initiative cohort

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