An Optical-Fiber-Based Smart Textile (Smart Socks) to Manage Biomechanical Risk Factors Associated With Diabetic Foot Amputation

Bijan Najafi, PhD, MSc1,2 , Hooman Mohseni, PhD3 , Gurtej S. Grewal, PhD2 , Talal K. Talal, DPM4 , Robert A. Menzies, MSc4 , and David G. Armstrong, DPM, MD, PhD2

Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Division of Vascular Surgery and Endovascular Therapy, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA

2 Southern Arizona Limb Salvage Alliance (SALSA), Department of Surgery,University of Arizona, Tucson, Arizona, USA

3 Electrical Engineering and Computer Sciences, McCormick School of Engineering; Physics and Astronomy, Weinberg College of Arts and Sciences; Northwestern University, Evanston, IL, USA

4 Diabetic foot and Wound Clinic, Department of Medicine, Hamad Medical Co, Doha, Qatar

Corresponding Author:

Bijan Najafi, PhD, MSc, Interdisciplinary Consortium on Advanced Motion Performance, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, One Baylor Plaza, MS:BCM390, Houston, TX 77030, USA. Email: 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。

Journal of Diabetes Science and Technology 2017, Vol. 11(4) 668–677

© 2017 Diabetes Technology Society Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1932296817709022  journals.sagepub.com/home/dst

Abstract

Objective: This study aimed to validate a smart-textile based on fiber-optics for simultaneous measurement of plantar temperature, pressure, and joint angles in patients with diabetic peripheral neuropathy (DPN).

Methods: After in-vitro validation in the laboratory, 33 eligible subjects with DPN were recruited (age: 58 ± 8 years, BMI: 31.5 ± 8 kg/m2 ) for assessing plantar pressure and temperature during habitual gait-speed in a clinical-setting. All participants were asked to walk at their habitual speed while wearing a pair of sensorized socks made from highly flexible fiber optics (SmartSox). An algorithm was designed to estimate temperature, pressure, and toe range of motion from optical wavelength generated from SmartSox. To validate the device, results from thermal stress response (TSR) using thermography and peak pressure measured by computerized pressure insoles (F-Scan) were used as gold standards.

Results: In laboratory and under controlled conditions, the agreements for parameters of interest were excellent (r > .98, P = .000), and no noticeable cross-talks between measurements of temperature, angle, and pressure were observed. During clinical data acquisition, a significant correlation was found for pressure profile under different anatomical regions of interest between SmartSox and F-Scan (r = .67, P < .050) as well as between thermography and SmartSox (r = .55, P < .050).

Conclusion: This study demonstrates the validity of an innovative smart textile for assessing simultaneously the key parameters associated with risk of foot ulcers in patients with DPN. It may empower clinicians to objectively stratify foot risk and provide timely care. Another study is warranted to validate its clinical application in preventing limb threating problems in patients with DPN.

Keywords

diabetic foot ulcer, SmartSox, wearable, fiber optics, plantar pressure, plantar temperature

Comprehensive cost-of-illness analysis of pressure ulcer treatment: A real-world study at a Czech university hospital

Lenka Krupova
1 | Andrea Pokorna
2,3 | Miroslav Krupa4 | Klara Bene
sov ˇ a
3

1 Department of Dermatology, University Hospital Ostrava, Ostrava, Czech Republic

2 Department of Health Sciences, Faculty of Medicine, Masaryk University, Brno, Czech Republic

3 Institute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Brno, Czech Republic

4 Faculty of Business Administration, Prague University of Economics and Business, Prague, Czech Republic

Correspondence

Andrea Pokorn
a, Department of Health Sciences, Faculty of Medicine, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic. Email: 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。

Funding information

Ministerstvo Zdravotnictví Ceské Republiky, Grant/Award Number: NU20-09-00094

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

© 2025 The Author(s). International Wound Journal published by Medicalhelplines.com Inc and John Wiley & Sons Ltd.

Abstract

Pressure ulcers (PUs) impose a significant economic burden on healthcare sys tems, affecting patient quality of life and leading to substantial treatment costs. This study presents a cost-of-illness analysis of PU treatment in hospitalized patients in the Czech Republic, based on real-world clinical data. The analysis was conducted using a comprehensive methodology at a Czech university hos pital, involving 304 hospitalizations. The study included all hospitalized patients with PUs. Data were collected employing a bottom-up, person-based approach, which refers to the collection and analysis of cost data at the individ ual patient level. This method captures detailed resource utilization for each patient. The methodology accounted for both systemic and local costs, includ ing materials, medications, caregiver time, and procedures. The study involved 304 hospitalizations, with a mean length of stay of 13 days. The total cost of PU treatment, excluding pharmacotherapy, had a median of €678, while including pharmacotherapy, the median cost rose to €929. Younger patients incurred higher treatment costs. Significant cost variations were observed among different departments. We developed and applied a novel cost model to quantify the expenses associated with PUs, which accurately highlighted the financial burden in the hospital care setting. We present a rigorous methodol ogy for PU cost-of-illness analysis, providing a valuable tool for future research and clinical practice. This comprehensive approach supports the development of targeted interventions to reduce the incidence and severity of PUs, ulti mately improving patient care and reducing healthcare costs.

KEYWORDS

cost analysis, healthcare costs, patients, pressure ulcers, treatment costs

Key Messages

Accurate cost-of-illness analysis of pressure ulcer treatment is crucial for improving patient outcomes and reducing healthcare costs.

This study developed a methodology to calculate PU treatment costs, using data from 304 hospitalizations at a Czech university hospital.

The median PU treatment cost was €678 (excluding pharmacotherapy) and €929 (including pharmacotherapy), with significant cost variations across

The cost model provides a tool for targeting interventions in pressure ulcers' management to reduce financial burden and enhance patient care.

Evidence-based interventions for identifying candidate quality indicators to assess quality of care in diabetic foot clinics: a scoping review

Flora Mbela Lusendi1,2* , An‑Sofie Vanherwegen1 , Kris Doggen1 , Frank Nobels3 and Giovanni Arnoldo Matricali2,4 *Correspondence: Flora Mbela Lusendi 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。

1 Health Services Research, Sciensano, Rue Juliette Wytsmanstraat 14, Brussels 1050, Belgium

2 Department of Development and Regeneration, KU Leuven, Leuven, Belgium

3 Multidisciplinary Diabetic Foot Clinic, Onze-Lieve-Vrouwziekenhuis, Aalst, Belgium

4 Multidisciplinary Diabetic Foot Clinic, University Hospital Leuven, Leuven, Belgium

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the  original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecom‑ mons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Abstract

Background Foot ulcers in people with diabetes are a serious complication requiring a complex management and have a high societal impact. Quality monitoring systems to optimize diabetic foot care exist, but a formal and more evidence-based approach to develop quality indicators (QIs) is lacking. We aimed to identify a set of candi‑ date indicators for diabetic foot care by adopting an evidence-based methodology.

Methods A systematic search was conducted across four academic databases: PubMed, Embase CINAHL and Cochrane Library. Studies that reported evidence-based interventions related to organization or delivery of dia‑ betic foot care were searched. Data from the eligible studies were summarized and used to formulate process and structure indicators. The evidence for each candidate QI was described in a methodical and transparent manner. The review process was reported according to the “Preferred Reported Items for Systematic reviews and Meta-Analy‑ sis” (PRISMA) statements and its extension for scoping reviews.

Results In total, 981 full-text articles were screened, and 322 clinical studies were used to formulate 42 candidate QIs.

Conclusions An evidence-based approach could be used to select candidate indicators for diabetic foot ulcer care, relating to the following domains: wound healing interventions, peripheral artery disease, offloading, secondary pre‑ vention, and interventions related to organization of care. In a further step, the feasibility of the identified set of indica‑ tors will be assessed by a multidisciplinary panel of diabetic foot care stakeholders.

Keywords Diabetic foot ulcer, Quality of healthcare, Quality indicators, Evidence-based medicine, Health service research

Sex and APOE ε4 allele differences in longitudinal white matter microstructure in multiple cohorts of aging and Alzheimer’s disease

Amalia Peterson1,2 | Aditi Sathe1 | Dimitrios Zaras1 | Yisu Yang1 | Alaina Durant1 | Kacie D. Deters3 | Niranjana Shashikumar1 | Kimberly R. Pechman1 | Michael E. Kim4 | Chenyu Gao5 | Nazirah Mohd Khairi5 | Zhiyuan Li5 | Tianyuan Yao4 | Yuankai Huo4,5 | Logan Dumitrescu1,2,6,7 | Katherine A. Gifford1,2 | Jo Ellen Wilson1,8,9 | Francis E. Cambronero1 | Shannon L. Risacher10,11 | Lori L. Beason-Held12 | Yang An12 | Konstantinos Arfanakis13,14,15 | Guray Erus16 | Christos Davatzikos16 | Duygu Tosun17 | Arthur W. Toga18 | Paul M. Thompson19 | Elizabeth C. Mormino20 | Mohamad Habes21 | Di Wang21 | Panpan Zhang1,22 | Kurt Schilling23,24 | Alzheimer’s Disease Neuroimaging Initiative (ADNI) | The BIOCARD | Study Team | The Alzheimer’s Disease Sequencing Project (ADSP) | Marilyn Albert25 | Walter Kukull26 | Sarah A. Biber26 | Bennett A. Landman2,4,5,7,23,24,27 | Sterling C. Johnson28,29 | Julie Schneider14 | Lisa L. Barnes14 | David A. Bennett14 | Angela L. Jefferson1,2,4 | Susan M. Resnick12 | Andrew J. Saykin10,11 | Timothy J. Hohman1,2,6,7 | Derek B. Archer1,2,6,7

Correspondence

Derek B. Archer, Vanderbilt Memory and Alzheimer’s Center, 3319 West End Ave., Nashville, TN 37203, USA. Email: 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。

Funding information

BAL, Grant/Award Number: R01-EB017230; DBA, Grant/Award Number: K01-AG073584; TJH, Grant/Award Number: U24-AG074855; Vanderbilt Clinical Translational Science Award, Grant/Award Numbers: UL1-TR000445, UL1-TR002243; Vanderbilt’s High-Performance Computer Cluster for Biomedical Research, Grant/Award Numbers: R01-AG080821, S10-OD023680; Alzheimer’s Association, Grant/Award Number: IIRG-08-88733(ALJ); NIH, Grant/Award Numbers: K01-EB032898 (KGS), R01-EB017230 (BAL), K01-AG073584 (DBA), U24-AG074855 (TJH), R01-AG059716 (TJH), UL1-TR000445 (Vanderbilt Clinical Translational Science Award), UL1-TR002243 (Vanderbilt Clinical Translational Science Award), S10-OD02380 (Vanderbilt’s High-Performance Computer Cluster for Biomedical Research), R01-AG080821 (MH), R01-AG034962 (ALJ), R01-AG056534 (ALJ), R01-AG062826 (KAG), U19-AG03655 (MA); Intramural NIH, Grant/Award Number: 75N95D22P00141

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2024 The Author(s). Alzheimer’s & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer’s Association.

Abstract

INTRODUCTION: The effects of sex and apolipoprotein E (APOE)—Alzheimer’s disease (AD) risk factors—on white matter microstructure are not well characterized.

METHODS: Diffusion magnetic resonance imaging data from nine well-established longitudinal cohorts of aging were free water (FW)–corrected and harmonized. This dataset included 4741 participants (age = 73.06 ± 9.75) with 9671 imaging sessions over time. FW and FW-corrected fractional anisotropy (FAFWcorr) were used to assess differences in white matter microstructure by sex and APOE ε4 carrier status.

RESULTS: Sex differences in FAFWcorr in projection tracts and APOE ε4 differences in FW limbic and occipital transcallosal tracts were most pronounced.

DISCUSSION: There are prominent differences in white matter microstructure by sex and APOE ε4 carrier status. This work adds to our understanding of disparities in AD. Additional work to understand the etiology of these differences is warranted.

KEYWORDS

aging, Alzheimer’s disease, sex differences, white matter disease

Highlights

∙ Sex and apolipoprotein E (APOE) ε4 carrier status relate to white matter microstruc tural integrity.

∙ Females generally have lower free water–corrected fractional anisotropy compared to males.

∙ APOE ε4 carriers tended to have higher free water than non-carriers.

Overview of Alzheimer’s Disease Neuroimaging Initiative and future clinical trials

Michael W. Weiner1,2,3,4,5,6  | Shaveta Kanoria1,6 | Melanie J. Miller1,6 | Paul S. Aisen7 | Laurel A. Beckett8 | Catherine Conti1,6 | Adam Diaz1,6 | Derek Flenniken6 | Robert C. Green9 | Danielle J. Harvey8 | Clifford R. Jack Jr.10 | William Jagust11 | Edward B. Lee12 | John C. Morris13,14,15 | Kwangsik Nho16,17 | Rachel Nosheny1,4 | Ozioma C. Okonkwo18 | Richard J. Perrin13,14,15 | Ronald C. Petersen19 | Monica Rivera-Mindt20,21 | Andrew J. Saykin16,22 | Leslie M. Shaw23 | Arthur W. Toga24 | Duygu Tosun1,2 | Dallas P. Veitch1,6 for the Alzheimer’s Disease Neuroimaging Initiative

1 Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, California, USA

2 Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA

3 Department of Medicine, University of California San Francisco, San Francisco, California, USA

4 Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, California, USA

5 Department of Neurology, University of California San Francisco, San Francisco, California, USA

6 Northern California Institute for Research and Education (NCIRE), San Francisco, California, USA

7 Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, USA

8 Division of Biostatistics, Department of Public Health Sciences, University of California, Medical Sciences 1C, Davis, California, USA

9 Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Broad Institute Ariadne Labs and Harvard Medical School, Boston, Massachusetts, USA

10 Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA

11 Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California, USA

12 Translational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of

Pennsylvania, Philadelphia, Pennsylvania, USA

13 Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, Saint Louis, Missouri, USA

14 Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, USA

15 Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA

16 Department of Radiology and Imaging Sciences and the Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA

17 Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, USA

18 Wisconsin Alzheimer’s Disease Research Center and Department of Medicine, University of Wisconsin School of Medicine and Public Health, Clinical Science Center, Madison, Wisconsin, USA

19 Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA

20 Department of Psychology, Latin American and Latino Studies Institute, African and African American Studies, Fordham University, Bronx, New York, USA

21 Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA

22 Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA

23 Department of Pathology and Laboratory Medicine and the PENN Alzheimer’s Disease Research Center, Center for Neurodegenerative Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA

24 Laboratory of Neuro Imaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of the University of Southern California, San Diego, California, USA

Correspondence

Michael W. Weiner, Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, 4150 Clement St, San Francisco, CA 94121, USA. Email: 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 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. Some ADNI investigators participated 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.pd

Funding information

NIH, Grant/Award Number: U19 -AG 024904; National Institute on Aging, Grant/Award Number: U19AG024904

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

© 2024 The Author(s). Alzheimer’s & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer’s Association.

Abstract

The overall goal of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) is to opti mize and validate biomarkers for clinical trials while sharing all data and biofluid samples with the global scientific community. ADNI has been instrumental in stan dardizing and validating amyloid beta (Aβ) and tau positron emission tomography (PET) imaging. ADNI data were used for the US Food and Drug Administration (FDA) approval of the Fujirebio and Roche Elecsys cerebrospinal fluid diagnostic tests. Additionally, ADNI provided data for the trials of the FDA-approved treatments aducanumab, lecanemab, and donanemab. More than 6000 scientific papers have been published using ADNI data, reflecting ADNI’s promotion of open science and data sharing. Despite its enormous success, ADNI has some limitations, particularly in generalizing its data and findings to the entire US/Canadian population. This introduction provides a historical overview of ADNI and highlights its significant accomplishments and future vision to pioneer “the clinical trial of the future” focusing on demographic inclusivity.

KEYWORDS

Alzheimer’s disease, Alzheimer’s disease biomarkers, Alzheimer’s disease clinical trials, Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s disease progression, amyloid, Lab oratory of Neuro Imaging, magnetic resonance imaging, neurodegeneration, positron emission tomography, post-traumatic stress disorder, tau, underrepresented populations

Highlights

∙ The Alzheimer’s Disease Neuroimaging Initiative (ADNI) introduced a novel model for public-private partnerships and data sharing.

∙ It successfully validated amyloid and Tau PET imaging, as well as CSF and plasma biomarkers, for diagnosing Alzheimer’s disease.

∙ ADNI generated and disseminated vital data for designing AD clinical trials.

APOE ε4–associated heterogeneity of neuroimaging biomarkers across the Alzheimer’s disease continuum

Jason Mares1,2 Gautam Kumar1,3,4 Anurag Sharma1,3 Sheina Emrani5 Laura Beth McIntire6 Jia Guo7,8 Vilas Menon1,2 Tal Nuriel1,3 for the Alzheimer’s Disease Neuroimaging Initiative

1 Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, New York, USA

2 Department of Neurology, Columbia University, New York, New York, USA

3 Department of Pathology and Cell Biology, Columbia University, New York, New York, USA

4 Department of Neurobiology, University of Maryland, Baltimore, Maryland, USA

5 Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA

6 Lipidomics and Biomarker Discovery Lab, Brain Health Imaging Institute, Department of Radiology, Weill Cornell Medicine, New York, New York, USA

7 Department of Psychiatry, Columbia University, New York, New York, USA

8 Zuckerman Institute, Columbia University, New York, New York, USA

Correspondence

Tal Nuriel, Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, 630 W. 168th St., P&S 12-420E, New York, NY 10032, USA. Email: 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 The Alzheimer’s Disease Neuroimaging Initiative: 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.

Funding information

NIA, Grant/Award Numbers: K01 AG061264, R01 AG070202, R01 AG078800, R01 AG066831, U19 AG024904 This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

© 2025 The Author(s). Alzheimer’s & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer’s Association

Abstract

INTRODUCTION: While the role of apolipoprotein E (APOE) ε4 in Alzheimer’s dis ease (AD) susceptibility has been studied extensively, much less is known about the differences in disease presentation in APOE ε4 carriers versus non-carriers.

METHODS: To help elucidate these differences, we performed a broad analysis com paring the regional levels of six different neuroimaging biomarkers in the brains of APOE ε4 carriers versus non-carriers who participated in the Alzheimer’s Disease Neuroimaging Initiative (ADNI).

RESULTS:We observed significant APOE ε4–associated heterogeneity in regional amy loid beta deposition, tau accumulation, glucose uptake, brain volume, cerebral blood flow, and white matter hyperintensities within each AD diagnostic group. We also observed important APOE ε4–associated differences in cognitively unimpaired indi viduals who converted to mild cognitive impairment/AD versus those who did not

DISCUSSION: This observed heterogeneity in neuroimaging biomarkers between APOE ε4 carriers versus non-carriers may have important implications regarding the prevention, diagnosis, and treatment of AD in different subpopulations.

KEYWORDS

Alzheimer’s disease, Alzheimer’s Disease Neuroimaging Initiative, apolipoprotein E, biomarkers, heterogeneity, neuroimaging

Highlights

∙ An extensive study was performed on the apolipoprotein E (APOE) ε4–associated heterogeneity in neuroimaging biomarkers from the Alzheimer’s Disease Neu roimaging Initiative.

∙ Robust APOE ε4–associated increases in amyloid beta (Aβ) deposition throughout the brain, in every diagnostic group, were observed.

∙ APOE ε4–associated increases in tau pathology, decreases in glucose uptake, and increases in brain atrophy, which expand in regional scope and magnitude with disease progression, were observed.

∙ Significant sex- and age-related differences in APOE ε4–associated neuroimaging biomarker heterogeneity, with overall increases in pathological presentation in female APOE ε4 carriers, were observed.

∙ Regional differences in Aβ deposition, tau accumulation, glucose uptake, ventricle size, and white matter hyperintensities were observed in cognitively normal partic ipants who converted to mild cognitive impairment/Alzheimer’s disease, which may hold potential predictive value.