bluuebunny/crossref_metadata_2025 huggingface dataset:
Chronobiology of nutrition and cognitive function
Authors: Scarmeas Nikolaos , Yannakoulia Mary
Published: December 2023

Current literature on the association between nutrition and cognitive function has focused mainly on total daily dietary intake of nutrients, foods or dietary patterns, while there has been much less focus on the timing of intake, i.e. the distribution of intake throughout the day and/or during specific eating occasions. Meal timing interferes with the circadian clocks and it has been previously linked with several health outcomes, including obesity and glycemic control. At the same time, there are well established circadian rhythms in cognitive performance in humans, with worse performance in the early morning and late evening and best performance somewhere in the middle of the daytime.

Literature of the chronobiology of nutrition (chrononutrition) in relation to cognitive function and decline is extremely limited and to a certain extend conflicting. For example, breakfast consumption has been associated with decreased odds of having Mild Cognitive Impairment (MCI) in some studies, while, according to other studies, individuals with Alzheimer’s Disease (AD) and MCI tend to prefer early (rather than late) day times for food intake. Overall, there is very little research on the effect on cognitive function of time of day or energy content or nutrient distribution of the consumed meals. Furthermore, even though certain biomarkers of neurodegeneration are known to exhibit circadian properties, their associations with aspects of chrononutrition have not been explored.

In conclusion, there is growing interest in the field of chrononutrition, an emerging area of investigation that is revealing important insights into how the timing of meals and overall eating patterns can impact cognitive function in humans. Part of the reason that this field is still in its infancy is that not all methods of dietary assessment are suitable for such analyses: only the laborious and time‐consuming methods of 24h dietary recalls or food records, that collect information on the timing of each eating occasion, allow for the evaluation of intake (nutrient of foods) distribution during the day.

Overall, much more investigational effort is needed to fully understand the mechanisms underlying potential associations of chrononutrition and cognition and to develop practical recommendations for maintaining cognitive health through dietary interventions.
The food metabolome for precision nutrition in dementia prevention
Authors: Samieri Cécilia
Published: December 2023

Understanding which aspects of diet and lifestyle may predispose to the development of Alzheimer’s disease and identifying populations vulnerable to prevention by lifestyle modification is a critical asset for precision prevention. However, diet is a complex exposure that is extremely challenging to evaluate using questionnaires in human populations, and inherent measurement error in self‐reported dietary intake data has certainly hampered our way to study properly the impact of nutrition on brain health in epidemiological studies.

Measuring markers of nutritional exposure in biological matrices represents a unique way to monitor nutrition and dietary intakes. Candidate biomarkers have been complemented recently by a comprehensive investigation in biological matrices of the chemicals derived from foods after digestion and metabolism, the so‐called food metabolome, that has started to reveal novel dietary biomarkers. High‐throughput metabolomics now enable the measurement of markers of food exposures in biological matrices with unprecedented depth, while capturing the heterogeneity in the response to dietary bioactives from diet across individuals and populations. Thus biomarkers are critical for precision nutrition, as they reflect nutritional status, a product of intakes and of endogenous metabolism, itself linked to many factors, including personal genetic background and microbiome.

This “Perspectives” presentation will overview a few recent exploratory studies on signatures of healthy diets and brain health in the food metabolome as examples to discuss challenges and opportunities for further developments. We will also address opportunities of development of multi‐biomarker panels combining self‐reported nutritional intake data with biomarker data from biofluids (blood and urine) to improve assessment of healthy dietary patterns as preventive approaches in dementia.
Personalized Nutrition for APOE4 carriers: mechanisms and biomarkers that can inform future trials
Authors: Yassine Hussein N.
Published: December 2023

Current studies suggest that the beneficial effects of increased polyunsaturated fat consumption (Mediterranean diet, fish consumption or long‐term DHA supplementation) on cognition are stronger in younger and middle aged APOE4 carriers. The efficacy of these nutritional interventions in APOE4 appears to wane with advancing age and cognitive disease. In addition, a recent systematic review revealed that ketogenic diets do not appear to have a beneficial effect on cognitive performance in APOE4 carriers compared to non‐carriers.

In this talk, we focus on leading mechanisms of cerebral bioenergetic breakdown in the aging APOE4 brain that implicate neuroinflammation in several brain cell types including those of the blood‐brain barrier (BBB). We then address consequences of neuroinflammation and disturbed bioenergetics at the BBB on the delivery and brain metabolism of nutrients such as omega‐3s, nutrients and ketones. We provide examples of novel lipidomic biomarkers that can capture neuroinflammation in the Religious Order Study, DHA Brain Pilot Trial, and the ADCS DHA trial. Advances in brain imaging of polyunsaturated fatty acid brain uptake by PET are provided to obtain insights into APOE4 brain fatty acid consumption changes during the lifespan. Finally, we highlight how the ongoing PreventE4 trial is integrating measures of the gut microbiome to understand the effects of omega‐3 supplementation on the brain in individuals carrying the APOE4 allele and at risk for dementia.
The cerebrospinal fluid soluble TREM‐1 concentration in Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI)
Authors: Doroszkiewicz Julia , Kulczynska‐Przybik Agnieszka , Dulewicz Maciej , Borawska Renata , Slowik Agnieszka , Mroczko Barbara
Published: December 2023

Background

The most prevalent form of dementia in the world is Alzheimer Disease (AD), a common neurodegenerative condition. Amyloid peptides, phosphorylated tau proteins, and neuroinflammation are the disease’s hallmarks. Finding new biomarkers that can assist in early AD detection is crucial due to the late appearance of the disease symptoms. Triggering receptor expressed on myeloid cell 1 (TREM‐1) is hypothesized that may contribute to the development of AD by causing neuroinflammation. Due of its significance in inflammatory responses, it has been regarded as a promising biomarker. Literature data indicate that TREM‐1 could be potential biomarker of AD severity. Therefore the purpose of our research was to measure the concentration of soluble TREM‐1 in cerebrospinal fluid of AD and MCI patients and non‐demented controls and compare it with classical AD biomarkers.

Method

The concentrations of TREM‐1 were measured in cerebrospinal fluid (CSF) of 20 AD patients and 15 MCI patients as well as 11 non‐demented controls using multiplexing method. Classical biomarkers, such as Aβ‐42, Aβ‐42/Aβ‐40, tau and pTau181 were assessed by immunoenzyme assays.

Result

TREM‐1 concentrations were significantly higher in AD and MCI patients in comparison to non‐demented controls. Moreover, in MCI patients, the levels of this protein were significantly higher than in AD patients. Additionally, increased CSF levels of TREM‐1 correlated positively with Tau, pTau181 in the whole study group.

Conclusion

Findings of our research suggest that TREM‐1 could be early indicator of AD pathology. However, follow‐up studies on larger study group are needed.
Artificial Intelligence and Machine Learning (AIML) approaches to empower genomics, drug, and biomarker discovery in ADRD
Authors: Thompson Paul M , Raj Towfique
Published: December 2023

Background

In 2021, the NIA launched the Artificial Intelligence and Machine Learning (AIML) Consortium, to develop novel AI and machine learning techniques to tackle pressing questions in Alzheimer’s disease research.

Method

Drawing on vast repositories of data from the Alzheimer’s Disease Sequencing Project (ADSP) and other large public biobanks, the 8 awardees funded under this program meet monthly to present their innovations in data analytics in 4 main areas: (1) genome‐guided drug discovery and drug repositioning for AD; (2) discovery of novel genetic and molecular targets for AD therapeutics using whole genome and omics data; and (3) understanding the regulatory code of the genome using deep learning and bioinformatics, and (4) use of electronic health records (EHR) to study treatment outcomes in ADRD. A second branch of AIML activity draws on repositories of neuroimaging data (MRI/FLAIR, DTI, and amyloid‐ and tau‐sensitive PET) to (1) identify ADRD subtypes based on neuroimaging, clinical and neuropsychological data, that differ in terms of their genomic drivers and expected treatment response (for precision medicine and drug trial stratification), (2) perform genetic analysis of endophenotypes derived from brain images (such as longitudinal hippocampal volume changes, atrophy subtype scores, and PET or WMH signatures) for biomarker‐guided gene discovery, and (3) predict neuropathology (TDP‐43, CAA, LBD) from multimodal imaging using AI. The AIML initiative benefits from and helps the ADSP’s Phenotype Harmonization Consortium, as AI methods can also be used to adjust neuroimaging data for site and protocol effects (harmonization).

Result

In this overview of the AIML Consortium, we highlight some recent successes using (1) convolutional neural networks (CNNs), GANs and deep learning for ADRD subtyping and staging; (2) discovery of AD‐relevant motifs and networks in whole genome sequences and functional genomics data; (3) prognostic models of clinical outcomes based on WGS and biomarkers; and (4) novel methods from statistical physics, based on evolutionary action, which model the fitness effect of coding mutations analytically.

Conclusion

AIML investigators are hosting regular workshops at AAIC and upcoming genomics and clinical neurology conferences to disseminate and teach these approaches to the AD and broader scientific community.
QyPredict prognostic model enriches selection for faster decliners in mild cognitive impairment
Authors: Gordon Elizabeth , Samper‐González Jorge , Villa Luca , Jubault Thomas , Guizard Nicolas
Published: December 2023

Background

The suboptimal selection of patients is a key challenge for disease‐modifying trials in mild cognitive impairment (MCI) and Alzheimer’s disease. Improved selection strategies are urgently needed to better power trials. Recent advances in AI predictive modeling, such as the QyPredict® algorithm, are promising tools to improve the selection of patients likely to clinically progress during the clinical trial timeframe.

Method

QyPredict® was applied to 519 MCI patients from ADNI: age: (71.8(±7.1), 303 Male, MMSE range 24‐30, CDR = 0.5, with available amyloid and APOE‐ε4 + status. QyPredict®, a tunable machine learning model, incorporated baseline QyScore® volumetric MRI results, demographic (age and sex) and clinical (MMSE and CDR) inputs. A QyPredict® score (0‐1) was produced for each individual, representing the probability they will demonstrate a modelled outcome (here an increase in CDR‐SOB of 0.5+ over 24 months). A score close to zero indicating very low probability of change.

Predictive performance was evaluated using positive predictive value (PPV), sensitivity and specificity. Mean and standard deviation for change in CDR‐SOB over 24 months were calculated and compared using Mann‐Whitney test at increasing QyPredict® cut‐offs (0.1, 0.2, 0.3, 0.4 and 0.5). Actual change versus predicted change in CDR‐SB scores was further investigates for ‘Stable’ (QyPredict®<0.5) versus ‘Decliners’ (QyPredict®>0.5) (Figure 1). Sample sizes to detect a 30% treatment effect (reduction in change in CDR‐SOB) were calculated for the full sample and a cohort enriched with only ‘Decliners’.

Result

For the full sample without QyPredict® enrichment, change in CDR‐SOB at 24 months was 2.3 (±2.11), which significantly increase (p<0.001) from a QyPredict®>0.2, reaching a change of 3.4 (±2.30) points for MCI patients with QyPredict®>0.5. PPV, sensitivity and specificity were: 0.76, 0.70 and 0.75 (the full cohort); 0.83, 0.77 and 0.70 (Amyloid positive patients) and 0.82, 0.75 and 0.74 (APOE‐ε4 + patients). Sample sizes for a putative trial where 304 (full sample) compared with 110 (enriched cohort) per treatment arm.

Conclusion

Using baseline QyScore® metrics, basic demographic, and typical clinical data available at screening visits, QyPredict® accurately modelled the likelihood of future clinical decline, resulting in substantially reduced patient cohorts to detect treatment effects when enriched with ‘decliners’.
Use of a diagnostic decision support tool in memory clinic daily practice – The DAILY feasibility study
Authors: van Gils Aniek M. , Meester Hanneke F.M. Rhodius‐ , Handgraaf Dédé , Hendriksen Heleen M.A. , van Strien Astrid M , Schoonenboom Niki S.M. , Schipper Annemieke C , Kleijer Mariska , Griffioen Annemiek , Muller Majon , Lötjönen Jyrki , Tolonen Antti , van der Flier Wiesje M. , Visser Leonie N.C.
Published: December 2023

Background

To optimally serve increasing numbers of memory clinic patients through a timely and adequate diagnosis, digital health technologies offer a unique potential. Diagnostic decision support systems (DDSS) can aid clinicians in diagnostics and patient communication, but such tools are not widely used in daily practice. We assessed the feasibility of a DDSS prototype consisting of four modules (Figure 1): (1) web‐based cognitive test tool (cCOG); (2) automated MRI‐quantification (cMRI); (3) AI‐based classifier (cDSI); (4) diagnostic report page, providing an easy‐to‐understand overview of all information for clinicians and patients.

Method

We conducted the DAILY feasibility study in five Dutch memory clinics, including 11 clinicians (internal medicine/geriatricians(n = 5), neurologists(n = 2), residents(n = 3), nurse practitioner(n = 1), mean age 38±7y, 11±6y experience), and 43 patients(7 subjective complaints, 19 mild cognitive impairment, 14 dementia, 3 postponed diagnosis, mean age 73±11y), and their care partners(n = 28, 65±11y). Clinicians received instructions to integrate the tool into routine care. Feasibility, usability, and satisfaction were assessed using questionnaires (clinicians, patients, care partners) and interviews (clinicians).

Result

Clinicians reported using the tool in 34/43(80%) patients. Their overall satisfaction with the tool was acceptable (median 70(IQR 50‐80), scale 0‐100). Out of the four modules, the diagnostic report was most often used (in 70% of patients) and rated as useful by 89% of clinicians, with 70% indicating it contributed to more efficient patient communication. Patients and care partners evaluated the diagnostic report as understandable and helpful. In the interviews, clinicians were predominantly positive regarding the diagnostic report, indicating that it helped create an overview for themselves and their patients. Widely varying opinions indicated that (time‐efficient) use of the complete tool demands more education and practice to obtain the proficiency level needed to adequately integrate the tool into routine practice.

Conclusion

The lasting adoption of digital technologies in healthcare is challenging, and efficient and user‐friendly tools are needed. In this study, clinicians, patients, and care partners valued an automatically‐generated, comprehensible report summarizing individual test results. Implementing such a digital report page could contribute to improved patient communication and, ultimately, better care management. Furthermore, our results identified an educational need regarding the use of digital tools.
Separating Alzheimer’s disease from Dementia with Lewy bodies using a volumetric MRI classifier
Authors: van Gils Aniek M. , Tolonen Antti , Meester Hanneke F.M. Rhodius‐ , Mecocci Patrizia , Vanninen Ritva , Frederiksen Kristian Steen , Barkhof Frederik , van der Flier Wiesje M. , Lemstra Afina
Published: December 2023

Background

Separating Alzheimer’s dementia (AD) from dementia with Lewy bodies (DLB) can be challenging, especially in DLB patients with concomitant AD pathology. It is imperative to distinguish the diseases for optimal patient management. To date, no single MRI feature can accurately separate the two diseases. This study examines whether a linear combination of regional atrophy measures can differentiate cognitively normal (CN), AD, and DLB patients.

Method

We computed volumes of 70 brain regions using an automatic segmentation tool. For each region, we derived age, sex, and head size‐dependent z‐scores. A linear classifier with all brain regions was used to predict if a patient belonged to CN, AD, or DLB. Diagnoses were based on international consensus criteria and CSF‐biomarkers. The classifier was tested in 327 CN (mean age 67±10y, 48%F), 501 AD (70±9y, 49%F), and 99 DLB (69±7y, 15%F) patients from three cohorts (ADNI, PredictND, ADC). The ADC cohort was assessed separately, as almost all DLB patients were from the ADC. In addition, we assessed classification performance in a subgroup of DLB patients with AD‐like CSF (DLB+, n = 57). Performance was evaluated using 10‐fold cross‐validation.

Result

Performance measures for the classifier are summarized in Table 1. The classifier accuracy was assessed in the three‐class classification task and in two‐class classifications where one of the groups was excluded from the analysis. The balanced accuracy in the three‐class classification was 67% for the DLB group and the DLB+ subgroup. For AD vs. DLB, the AUC was 0.77(95% CI:0.70‐0.82), for AD vs. DLB+ AUC was 0.72(0.64‐0.80). The classifier correctly classified CN and AD patients more often than DLB patients (Table 2). Most DLB+ patients were misclassified as AD. The hippocampus was the best single region for distinguishing AD vs. DLB, with an AUC of 0.70(0.64‐0.76).

Conclusion

The joint classifier achieved high accuracy in separating AD vs. DLB patients. Although the accuracy was lower, the classifier could distinguish DLB patients with concomitant AD pathology from AD. Next, we will combine different brain regions to discern disease‐specific patterns and add clinical data to the classifier to improve differential diagnostic accuracy for DLB and DLB with concomitant AD pathology.
Translating AD Clinical Trial Results to Meaningful Benefits: Expectations, Definitions, Challenges and Opportunities.
Authors: Atri Alireza
Published: December 2023

Background

Clinical trials in Alzheimer’s disease (AD) are designed and powered to detect treatment impact on validated research outcome measures necessary for regulatory approval. However, what constitutes a clinically meaningful benefit, how to define, measure and translate it from the results of clinical trial results to multiple stakeholders, including clinicians and patients requires better clarification.

Methods

Review and synthesis of recent publications (Assuncao, Sperling, et al. Alz Res Therapy 2022; Petersen, Aisen Andrews, Atri, et al. Alzheimers Dementia. 2023 in press) and AD therapeutics developments to provide overview and frameworks for translating AD clinical trial results to meaningful benefits with respect to expectations, definitions, challenges and opportunities that may resonate with multiple stakeholders, including patients, patient care partners and clinicians.

Results

Rational for realistic expectations of putative AD disease‐modifying treatments (DMTs) are reviewed including expectations of not improving symptoms but of slowing disease progression and modestly moderating between‐group (treatment versus placebo) clinical decline. Definitions postulated to represent clinically meaningful benefits are reviewed. Confusion, misunderstanding or misinterpretations in regards to representation of thresholds for meaningful within‐patient progression are discussed, including recent misinterpretation of necessary thresholds for determining meaningful group‐level differences having created misguided expectations for clinical meaningfulness in DMT clinical trial results. Discuss confounding minimal detectable change (MCD) and utilizing between‐group differences (to assess outcome efficacy) with and to define minimal clinically important differences (MCID) (an individual, within‐patient change in outcome assessment). Within‐patient change thresholds are not intended to assess meaningfulness of differences between group‐level changes over time, but may represent meaningful within‐patient trajectories via complementary (e.g. responder/progressor) analyses. Multiple frameworks show the challenges and opportunities to multidimensionally better represent and translate results to face‐valid and patient‐centered representations to resonate with AD stakeholders. These include expanded outcomes (e.g. neuropsychiatric symptoms, socioeconomic burden); patient‐ and caregiver‐reported outcomes; complementary analyses (standardized effect‐sizes, RR/OR, NNT, Time‐to‐Event, Time‐Equivalent‐Gained); and concepts such as predictive benefit (via a disease biomarker/core domain) and cumulative benefits (accrual and potentially increasing over time).

Conclusion

Expanded data, analyses and frameworks for defining and translating clinically meaningful patient‐centered benefit to multiple stakeholders, particularly patients, care‐partners and clinicians, are proposed.
Establishing glial responses to anti‐Aβ immunotherapy through single‐cell RNAseq to gain insights into mechanisms of ARIA
Authors: Foley Kate E
Published: December 2023

Background

While anti‐Aβ immunotherapies effectively clear Aβ plaques, mouse and human data show consistent and significant cerebrovascular adverse events of this treatment approach. Anti‐Aβ immunotherapies have been plagued in clinical trials by the occurrence of amyloid‐related imaging abnormalities (ARIA), signifying cerebrovascular disruptions of the edema (ARIA‐E) or hemorrhagic (ARIA‐H) type in the brain. Despite these cerebrovascular adverse effects, the FDA granted accelerated approval to aducanumab and lecanemab. However, our mechanistic understanding underlying ARIA remains minimal. Elucidating the mechanism(s) by which anti‐Aβ antibodies cause ARIA is critical towards improving safety of these therapies for AD.

Method

In the current study, we aimed to determine the glial responses to anti‐Ab antibodies with the hypothesis that astrocytes and microglia have temporal cellular shifts that contribute to cerebrovascular dysfunction after acute anti‐Aβ antibody administration. To identify changes in glial gene expression in response to acute anti‐Aβ antibody we performed a single cell sequencing and digital spatial profiling (DSP) on 14mo Tg2576 APP mice (Fig. 1). We observed differences in size of clusters between anti‐Aβ (6E10) and IgG control (IgG1) intracranial injections after 24hr and 3days, suggesting some clusters of cells respond to anti‐Aβ in a time‐dependent or antibody manner (Fig. 2).

Result

Preliminary data identified some of the clusters as microglia (Cx3cr1) and astrocytes (Aldoc), as well as oligodendrocytes (Plp). Apoe was increased in multiple cell types. DSP on these mice is ongoing, however we aim to further characterize the transcriptional profile of glia at the vasculature, and in the vicinity of Aβ plaques and cerebral amyloid angiopathy (CAA). Contralateral hemispheres are utilized for downstream validation studies.

Conclusion

Our work will elucidate the mechanisms of anti‐Aβ antibody‐induced glial response and establish the role of astrocytes and microglia in cerebrovascular dysfunction and ARIA. This work is critical to improve safety and increase generalizability of these therapies.
Testing the Feasibility of Tracking Behavior and Physiology Patterns using Wearable Technology to Detect Potential Cognitive Risk among Rural, Multicultural Older Adults
Authors: Cook Diane J , Wiese Lisa Ann Kirk , Luna Catherine , Schmitter‐Edgecombe Maureen
Published: December 2023

Background

Rural residents often experience multiple factors that increase their risk of cognitive impairment, e.g. less access to formal education and specialized healthcare (Livingston et al., 2020). Early, accurate diagnosis in the mild cognitive impairment stage could save up to 7.9 trillion dollars in health and long‐term care costs by 2050 (Alzheimer’s Association, 2021). First year results from this federally‐funded new investigator mentoring program between computer science researchers and a nurse investigator are reported. The purpose was to test the feasibility of a novel approach to detecting early cognitive decline using smartwatch technology among a 92% racially and ethnically diverse rural community challenged by low digital access (51%) and digital literacy (48%).

Method

We tested smartwatches programmed with the novel “n‐back” shape test for cognitive assessment, ecological momentary assessment, and movement tracking. Paper‐and pencil measures to assess cognitive risk were the PROMIS ® Applied Cognition, Mini‐Cog (Borson, 2003), and Mini‐MoCA (Nasreddine, 2019). Another novel aspect of the study was hiring the local high school students to mentor older adults in use of smartwatches during home visits. Student engagement with their mentees was monitored by their high school advisor, with oversight by well‐known faith‐based research assistants. Six additional pre‐post intervention surveys included “Technology Activities of Daily Living (T‐ADL) (Muñoz‐Neira, 2012) and “Relating to Older People” (Cherry and Palmore, 2008).

Result

Mean years lived rural of the n = 28 enrolled was 60.3 (SD = 18.2), with an average age of 71.6 (SD = 8.1). Results regarding cognitive risk using the n‐back Shape test included correlation with Ecological Momentary Assessment Question 7 (“Right now, my environment is distracting”; r = ‐.35, p = .046) and approached significance with Question 1 (“In the past 2 hours, do you think your mind has been as sharp as usual?”; r = .33, p = .058). Pre‐post correlations in T‐ADL (r = .87) and Applied Cognition (r = .38) were significant (p < .001, p < .04).

Conclusion

Testing this novel two‐pronged approach of detecting cognitive risk through smartwatch technology, and hiring high school students to mentor older adults in the home setting yielded implications for ADRD‐related research among larger samples of underrepresented groups.
Ameliorating amyloid beta oligomer toxicity with NAC
Authors: Keske Ezgi , TUNALI Nagehan ERSOY
Published: December 2023

Background

Alzheimer’s Disease (AD) has a complex neuropathology, involving diverse cellular pathways. Molecular mechanisms induced by beta‐amyloid oligomer (Aβo) toxicity are considered to be the major initiators of neuropathology. Therefore, Aβos are commonly used to mimic toxicity in AD. The primary effect of Abeta toxicity is increase in cellular oxidative stress. Likewise, recent AD drug research and preclinical trials has been focused on reducing ROS and/or increasing antioxidant capacity. N‐acetyl cysteine (NAC) is a well‐known mucolytic agent and a powerful antioxidant via increasing GSH levels. In this respect, we aimed to investigate the effect of NAC in reducing oxidative stress in Aβo‐induced cellular toxicity.

Method

SH‐SY5Y cells were cultured in DMEM medium containing 10% FBS and 1% pen/strep. In order to establish Aβo toxicity, Aβ(1‐42) peptide was dissolved in NH4OH at various concentrations (5‐100 μM) and incubated at 4°C for 24h. Oligomer formation was verified with Bis‐ANS test and Aβo toxicity was analyzed using WST‐1. Changes in the oxidative stress levels of the cells due to Aβo toxicity and NAC was quantified using Total Oxidant/Antioxidant Capacity kits based on spectrophotometric absorbance.

Result

Bis‐ANS test showed a linear correlation between oligomer formation and Aβo concentration. According to the cell viability assay results, 10 μM Aβ42o for 24 hrs was used to establish Aβo toxicity in vitro. At 1 and 5mM concentrations, NAC was shown to increase the viability, GSH levels and antioxidant capacity of the SH‐SY5Y cells significantly. Likewise, 1,5, and 10mM NAC was shown to reduce Aβo‐induced toxicity significantly. Furthermore, although 20 and 40mM NAC decreases viability of healthy cells, they increase viability in Aβo‐treated cells.

Conclusion

According to our results, NAC can be regarded as a potent molecule in ameliorating Aβo‐induced toxicity.
Examining a telecognitive assessment‐derived Preclinical Alzheimer’s Cognitive Composite, the tPACC, for cognitive migration over one year in community‐dwelling older adults
Authors: Duran Tugce , Bateman James R. , Gaussoin Sarah A. , Rundle Melissa M. , Okonmah‐Obazee Stephanie E. , Espeland Mark A. , Williams Benjamin J. , Hughes Tim M. , Craft Suzanne , Lockhart Samuel N. , Sachs Bonnie C.
Published: December 2023

Background

ADRD clinical trials often target preclinical disease, where hope of averting the pathologic cascade and eventual cognitive decline remains highest. The preclinical Alzheimer’s cognitive composite (PACC5) was developed for in‐person administration to capture subtle cognitive decline. Early cognitive fluctuations may inform multidirectional disease progression, defined as cognitive migration. It is desirable to have a harmonized composite measurement derived from in‐person and remote assessments for detecting cognitive migration.

Method

We examined in‐person data from 417 adults (70.6±8.0y) from the Wake Forest ADRC Clinical Core, who received MRI, clinical evaluation and cognitive testing. The primary outcome was cognitive migration status determined by global CDR (0 and 0.5) increase/decrease between baseline (BL) and follow‐up (mean difference = 13.9 months): CDR‐0 Stable (51% maintained CDR‐0), CDR‐0.5 Stable (27% maintained CDR‐0.5), Reverter+ (13% migrated from CDR‐0.5 to CDR‐0) and Migrant− (9% migrated from CDR‐0 to CDR‐0.5). In‐person PACC5 was calculated (RAVLT Delayed Recall, Digit Symbol Coding [DSC], semantic fluency, Craft Story Delayed Verbatim, total MMSE) using BL participants with global CDR = 0 as reference. tPACC used measurements available at both in‐person and remote visits with minor modifications from PACC5: Montreal Cognitive Assessment (MoCA) replaced MMSE and DSC not included. We performed intraclass correlation coefficient (ICC) analysis and generated Bland‐Altman plots for BL tPACC and PACC5. We also evaluated associations between tPACC and neuroimaging.

Result

Participant characteristics and group differences in MRI markers are in Table 1. Bland‐Altman plots show differences and mean of tPACC and PACC5 (dashed line: ±1.96 SD, solid line: mean difference/bias). 92% of values lie within limit of agreement, indicating good agreement (overall ICC = .960, 95%CI = [.952, .967]), between tPACC and PACC5 in each group (Figure 1). At BL, there was a significant absolute agreement between in‐person tPACC and PACC5 in all groups (Figure 1). tPACC showed significant associations (Table 2) with Temporal Meta ROI thickness, hippocampal volume as % head size (ICV) and white matter hyperintensities (logWMH controlled for ICV) overall and in some cognitive migration groups.

Conclusion

There is generally good agreement between BL tPACC and PACC5 across cognitive migration groups. BL tPACC may predict BL neuroimaging based on cognitive migration status. Longitudinal tPACC examination is warranted.
Feasibility of a digital cognitive screening test to detect cognitive impairment
Authors: Sullivan Sydney C. , Mankodiya Kunal , Constant Nicholas , Denby Charles , Ott Brian R. , Korthauer Laura E. , De Vito Alyssa N. , Maynard Taylor R. , Zammitti Melissa G. , Tremont Geoffrey
Published: December 2023

Background

Assessment of cognitive functioning is important for early detection of Alzheimer’s disease and related dementias. Current cognitive screening tests are not often administered due to time limitations and the availability of trained staff. We developed the Rhode Island Cognitive Assessment Tool (RIMCAT), a self administered tablet‐based test with special attention given to creating an age‐friendly interface. The objective of this project was to collect feasibility data on the instrument.

Method

50 participants (Healthy Controls [HC] n = 33; Cognitively Impaired [CI]; diagnosed with mild cognitive impairment or dementia n = 17) were administered the RIMCAT with a proctor who documented any difficulty with a test component and provided necessary verbal prompts. Participants also completed a survey about their experience. The RIMCAT includes measures of orientation, attention, language, verbal and visual memory, computation, and executive functioning. Data were analyzed using frequencies, descriptive statistics, and independent sample t‐tests.

Result

Participants were 98% white, 76% female, had a mean age of 71.28 (SD = 8.63), and were well‐educated (M = 15.88; SD = 2.34). They were regular users of smartphones (83%), computers (85%), and tablets (61%). The HC group (M = 2.06; SD = 1.90) required significantly fewer prompts during the RIMCAT administration compared to the CI group (M = 4.82, SD = 3.57), p < .001. Two CI individuals were unable to complete the measure despite verbal prompting. The verbal memory task was the most technologically challenging, with comparable rates of prompting in both groups. Common problems included difficulty with input methods (keyboard and microphone entry), button use, and comprehension of instructions. Survey responses however, revealed that 80% or more participants rated instruction and image clarity, video and voice quality, and button usage as very good or excellent.

Conclusion

Results demonstrated initial feasibility of the RIMCAT for individuals with and without cognitive impairment. Design features were rated highly and most individuals completed the measure with minimal prompting, although CI individuals needed significantly more prompts. Therefore, future research will continue to refine the RIMCAT using participatory design and feedback to increase the likelihood of successful self‐administration for older adults with varying levels of cognition.
Uncovering new tau aggregation and spreading mechanism through AD tau‐seed interactome analysis
Authors: Martinez Pablo , You Yanwen , Patel Henika , Garfe Nur Jury , Redding Javier , Troncoso Juan C , Reeves Cristian A Lasagna
Published: December 2023

Background

Pathological tau aggregates are key features of Alzheimer’s disease and other tauopathies. The current research aims to understand how tau aggregates spread in AD patient brains, but the mechanisms remain unclear. Our recent‐published study found that the protein Bassoon plays a key role in tau spreading and pathology in a mouse model for tauopathy using mass spectrometry to identify proteins interacting with tau seeds. Considering our finding enhancing the relevance of tau‐seed interactor on disease pathology, we aim to characterize the interactome of the tau‐seed isolated from AD brains using a similar approach.

Method

In this study, we used Size Exclusion Chromatography (SEC) to separate TBS‐soluble AD and healthy brain extracts into fractions and measured tau seeding activity using a flow cytometry‐based biosensor cell line (n = 3). Then, the fractions with higher seeding activity were subjected to immunoprecipitation to isolate the tau seed and performed a TMT‐tags‐based unbiased quantitative mass spectrometry to uncover the specific tau species involved in tau propagation and identify new tau‐seed interactors.

Result

The strongest seeding activity in AD brains was found in fraction 9, which contains high molecular weight (HMW) proteins (>2,000 kDa), including only 5% of total tau, as assayed by ELISA for tau. Levels of tau were similar in fraction 9 in age‐matched controls but lacked seeding activity. Our bioinformatic analysis revealed that a total of 504 proteins interact with HWM tau without seeding activity isolated from controls and 1192 proteins interact with HWM tau‐seed isolated from AD cases. In addition, a total of 385 interacting proteins were found in both HWM tau from control and tau‐seed from AD cases. Interestingly, DAVID analysis as well as networks and interactions analysis (STRING analysis), revealed enrichment in proteins related to both synaptic and mitochondrial pathways.

Conclusion

Our findings significantly contribute to our understanding of the mechanisms involved in tau propagation and offer new insights into the potential for targeting tau‐seed interactions as a therapeutic strategy for tauopathies. Previously, we have identified BSN as a key interactor of tau seeds, and our data suggest that inhibiting these interactions could represent a promising new approach to treating these debilitating conditions.
Neuroprotective Potential of Limonene in LPS Induced PD in Rats
Authors: Kapoor Satyan , Deshmukh Rahul Renukadas
Published: December 2023

Background

Parkinson’s disease is 2nd most disabling neurodegenerative disorder characterized by progressive loss of dopaminergic neurons in basal ganglia. There are different pathologies that count for PD like neuro‐inflammation. This study highlights the role of oxidative stress and inflammation in progression of PD where limonene shows antioxidant and anti‐inflammatory response.

Method

Wistar rats (200‐250gm) of either sex were taken and divided into 6 groups. Animals were treated with three different doses of limonene i.e. (25, 50, 100 mg/kg p.o.) for 21 day, starting 1 day prior to LPS 5μl(1mg/ml PBS) infused stereotaxically into the SN of rats.

Result

The catalepsy technique was used to analyze the clinical signs of Parkinson’s disease in rats. LPS significantly increased oxidative stress indicators in the animals and decreased systemic antioxidant synthesis. Biochemically limonene significantly reduced the oxidative–nitrative stress, as evidenced by decrease in malondialdehyde and nitrite levels, and restored the reduced catalase, glutathione and Superoxide dismutase levels. The observed beneficial effects of limonene in motor defects may be due to its ability reduce oxidative burden.

Conclusion

LPS is potent inflammatory biomarker but this also has major role in development of oxidative stress. Interaction between reactive oxygen species and dopamine leads to its depletion & considered as one of the major mechanism for dopaminergic degeneration in PD. LPS caused significant elevation in malondialdehyde, nitrite and decline in SOD, catalase & glutathione, indicating development of oxidative‐nitrosative stress. Limonene treatment significantly reduced striatal MDA and nitrite concentration & restored antioxidant enzyme activities.
The Therapeutic Landscape of Tauopathies: Evaluating the Tau Pipeline
Authors: Harris Glenn A. , Cummings Jeffrey L. , Gonzalez Isabel , Pritchard Martyn , Toledo‐Sherman Leticia , May Patrick C.
Published: December 2023

Background

Many disease modifying therapeutics for neurodegenerative diseases (NDs) have focused on the reduction of amyloid, neuroinflammation and synaptic plasticity. Many NDs include tau pathology either with amyloid deposits or without. These NDs can collectively classified as tauopathies. We have provided an overview of the therapeutic landscape of programs targeting tauopathies covering drug candidates in the clinical development as well as programs still in discovery and the preclinical stages.

Method

Search results from targeted queries and key word searches for MAPT and tau were carried out on GlobalData and SciFinder databases as the principal sources of information. Company websites, PubMed and conference proceedings were also researched for additional information. All data presented here is current to Aug. 31, 2022. All therapeutics in clinical phases I, II and III, as well as in preclinical development and discovery phase were included.

Result

The majority of therapeutic approaches focused on tau by directly targeting Microtubule Associated Protein Tau (MAPT) although indirect mechanisms also afford strategies that may ultimately affect tau. Our search through the end of August 2022 showed that the tau landscape currently contains 160 compounds, of which 38 are in clinical development. Of those, a total of 135 drugs directly target MAPT, with 33 of them combining MAPT with other mechanisms, mostly alpha synuclein (α‐syn) or amyloid protein targets. There is a growing variety of modalities that are being explored in targeting tau, with small molecules remaining dominant accounting for approximately 44% of all molecules in development. There were 18 therapies in both phase I and phase II clinical trials, and only one currently in phase three. There were also two therapies currently with an IND/CTA filing in process.

Conclusion

There has been a significant increase in the number of researchers focusing on tau as a main therapeutic approach. The landscape of anti‐tau therapeutic agents is growing but special consideration should be given to primary tauopathies which do not share the amyloid burden of other NDs like Alzheimer’s disease, and do not share in the recent success of approved anti‐amyloid therapeutics.
Functional Brain Activation in Mild Cognitive Impairment With Defined Small Vessel Disease Burden
Authors: Marcolini Sofia , Mondragon Jaime , Ramakers Inez H.G.B. , Biessels Geert Jan , Claassen Jurgen A.H.R. , Bron Esther E , Papma Janne M. , van der Flier Wiesje M. , van der Lugt Aad , Maurits Natasha M. , De Deyn Peter Paul
Published: December 2023

Background

Vascular dysregulations and changes in functional brain network integrity play a fundamental role in the pathogenesis of dementia. While often identified in individuals with dementia, the role of small vessel disease (SVD) in the development of dementia is not completely understood yet. Previously, structural and functional brain connectivity was shown to be different between individuals with and without cerebral SVD, but a comprehensive measure of SVD has not been used consistently. We aim to analyze functional brain activation differences in mild cognitive impairment (MCI) individuals with defined SVD burden.

Method

Functional brain activation differences were analyzed in MCI individuals with absent or low (n = 34) or high (n = 34) SVD burden using data from the Parelsnoer Institute, a multicenter study involving eight Dutch medical centers. SVD burden was characterized using an ordinal scale ranging from 0 to 4 (Klarenbeek et al., 2013) considering the following markers: lacunes; microbleeds; perivascular spaces in the basal ganglia; white matter hyperintensities based on the Fazekas score. Two groups were identified: the absent or low SVD burden group with a score of 0 or 1 and the high SVD group with a score between 2 and 4. Activation differences were calculated using resting state fMRI data acquired using 3Tesla scanners and analyzed with group‐independent component analysis using the CONN toolbox.

Result

Activation of two clusters in the high SVD burden group was lower than in the absent or low SVD group: the cerebellum (p‐FDR >. 001, F = 1.49) and the brainstem (p‐FDR >. 001, F = 2.79). The cerebellar cluster (4541 voxels, +20 ‐74 ‐38) consisted of the left cerebellum crus II, right cerebellum crus II, and left cerebellum lobule VIII. The brainstem cluster (218 voxels, +22 ‐28 +20) involved the right thalamus and the right caudate nucleus.

Conclusion

Brain activation differences between groups of individuals with MCI and absent or low or high SVD burden were found in the cerebellum and brainstem. These brain areas are mainly responsible for motor, attentional and executive functions, domains which were found in previous studies to be mostly associated with SVD markers.
Cognitive and Functional Connectivity Outcomes from a Phase I Trial of Accelerated High‐Dose Repetitive Transcranial Magnetic Stimulation in Amnestic MCI
Authors: Fountain‐Zaragoza Stephanie , McTeague Lisa M , Benitez Andreana
Published: December 2023

Background

Repetitive transcranial magnetic stimulation (rTMS), most widely used to treat depression, may have secondary benefits for cognition. Accelerated high‐dose protocols such as intermittent theta burst (iTBS)‐rTMS shorten the treatment course by >50%, potentially improving the feasibility of use in patients with cognitive impairments. Here, we present the results of a phase I trial of iTBS‐rTMS for improving cognition in individuals with amnestic mild cognitive impairment (aMCI) and we explore whether functional brain changes relate to improved cognition.

Method

Twenty‐two patients with clinically diagnosed aMCI received 24 sessions of open‐label iTBS‐rTMS to left dorsolateral prefrontal cortex (l‐dlPFC) over 3 days. Nineteen had complete brain MRI and cognitive testing pre‐ and immediately post‐treatment. The primary cognitive outcome was the norm‐adjusted fluid cognition composite T‐score from the NIH Toolbox Cognition Battery. Resting‐state functional connectivity was computed from subject‐specific parcellations mapped to each individual’s functional topology. We examined changes in connectivity within and between the FPN (of with the l‐dlPFC is a hub) and networks implicated in cognition (FPN, dorsal and ventral attention, limbic, default mode). We assessed whether connectivity changes (post minus pre) related to change in fluid cognition.

Result

We achieved a high retention rate of 95%. Treatment with iTBS‐rTMS was safe, with no adverse neuroradiological, neuropsychiatric, or neurocognitive effects. Side effects were minimal and acceptability ratings were high. Treatment produced significant, large effect size (d = 0.98) improvements in fluid cognition (Fig. 1A), and we observed preliminary evidence of related changes in functional connectivity. Specifically, there was a numerical increase in within‐FPN connectivity and a marginally significant increase in connectivity between cognitive networks (Fig. 1B). Further, there was a medium‐strength and marginally significant association between increased within‐FPN connectivity and improved fluid cognition (Fig. 1C).

Conclusion

This phase I trial demonstrated that iTBS‐rTMS is safe, feasible, and acceptable in aMCI. Although not dosed for efficacy, we observed large improvements in fluid cognition and preliminary evidence of increased functional connectivity in expected brain networks that related to improved cognition. These promising results support iTBS‐rTMS as potentially efficacious in improving cognition in aMCI, directly informing larger, planned follow‐up randomized controlled phase II trials.