Plasma pTau 217:β-amyloid 1-42 ratio for enhanced accuracy and reduced uncertainty in detecting amyloid pathology.

BACKGROUND: According to the latest revised National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (now known as the Alzheimer's Association) (NINCDS-ADRDA) diagnostic criteria for Alzheimer's disease dementia of the National Institute on Aging and Alzheimer Association, the confidence in diagnosing mild cognitive impairment (MCI) due to Alzheimer's disease dementia is raised with the application of biomarkers based on measures in the cerebrospinal fluid (CSF) or imaging. These tests, added to core clinical criteria, might increase the sensitivity or specificity of a testing strategy. However, the accuracy of biomarkers in the diagnosis of Alzheimer's disease dementia and other dementias has not yet been systematically evaluated. A formal systematic evaluation of sensitivity, specificity, and other properties of plasma and CSF amyloid beta (Aß) biomarkers was performed. OBJECTIVES: To determine the accuracy of plasma and CSF Aß levels for detecting those patients with MCI who would convert to Alzheimer's disease dementia or other forms of dementia over time. SEARCH METHODS: The most recent search for this review was performed on 3 December 2012. We searched MEDLINE (OvidSP), EMBASE (OvidSP), BIOSIS Previews (ISI Web of Knowledge), Web of Science and Conference Proceedings (ISI Web of Knowledge), PsycINFO (OvidSP), and LILACS (BIREME). We also requested a search of the Cochrane Register of Diagnostic Test Accuracy Studies (managed by the Cochrane Renal Group).No language or date restrictions were applied to the electronic searches and methodological filters were not used so as to maximise sensitivity. SELECTION CRITERIA: We selected those studies that had prospectively well defined cohorts with any accepted definition of cognitive decline, but no dementia, with baseline CSF or plasma Aß levels, or both, documented at or around the time the above diagnoses were made. We also included studies which looked at data from those cohorts retrospectively, and which contained sufficient data to construct two by two tables expressing plasma and CSF Aß biomarker results by disease status. Moreover, studies were only selected if they applied a reference standard for Alzheimer's dementia diagnosis, for example the NINCDS-ADRDA or Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria. DATA COLLECTION AND ANALYSIS: We screened all titles generated by the electronic database searches. Two review authors independently assessed the abstracts of all potentially relevant studies. We assessed the identified full papers for eligibility and extracted data to create standard two by two tables. Two independent assessors performed quality assessment using the QUADAS-2 tool. Where data allowed, we derived estimates of sensitivity at fixed values of specificity from the model we fitted to produce the summary receiver operating characteristic (ROC) curve. MAIN RESULTS: Alzheimer's disease dementia was evaluated in 14 studies using CSF Aß42. Of the 1349 participants included in the meta-analysis, 436 developed Alzheimer's dementia. Individual study estimates of sensitivity were between 36% and 100% while the specificities were between 29% and 91%. Because of the variation in assay thresholds, we did not estimate summary sensitivity and specificity. However, we derived estimates of sensitivity at fixed values of specificity from the model we fitted to produce the summary ROC curve. At the median specificity of 64%, the sensitivity was 81% (95% CI 72 to 87). This equated to a positive likelihood ratio (LR+) of 2.22 (95% CI 2.00 to 2.47) and a negative likelihood ratio (LR-) of 0.31 (95% CI 0.21 to 0.48).The accuracy of CSF Aß42 for all forms of dementia was evaluated in four studies. Of the 464 participants examined, 188 developed a form of dementia (Alzheimer's disease and other forms of dementia).The thresholds used were between 209 mg/ml and 512 ng/ml. The sensitivities were between 56% and 75% while the specificities were between 47% and 76%. At the median specificity of 75%, the sensitivity was estimated to be 63% (95% CI 22 to 91) from the meta-analytic model. This equated to a LR+ of 2.51 (95% CI 1.30 to 4.86) and a LR- of 0.50 (95% CI 0.16 to 1.51).The accuracy of CSF Aß42 for non-Alzheimer's disease dementia was evaluated in three studies. Of the 385 participants examined, 61 developed non-Alzheimer's disease dementia. Since there were very few studies and considerable variation between studies, the results were not meta-analysed. The sensitivities were between 8% and 63% while the specificities were between 35% and 67%.Only one study examined the accuracy of plasma Aß42 and the plasma Aß42/Aß40 ratio for Alzheimer's disease dementia. The sensitivity of 86% (95% CI 81 to 90) was the same for both tests while the specificities were 50% (95% CI 44 to 55) and 70% (95% CI 64 to 75) for plasma Aß42 and the plasma Aß42/Aß40 ratio respectively. Of the 565 participants examined, 245 developed Alzheimer's dementia and 87 non-Alzheimer's disease dementia.There was substantial heterogeneity between studies. The accuracy of Aß42 for the diagnosis of Alzheimer's disease dementia did not differ significantly (P = 0.8) between studies that pre-specified the threshold for determining test positivity (n = 6) and those that only determined the threshold at follow-up (n = 8). One study excluded a sample of MCI non-Alzheimer's disease dementia converters from their analysis. In sensitivity analyses, the exclusion of this study had no impact on our findings. The exclusion of eight studies (950 patients) that were considered at high (n = 3) or unclear (n = 5) risk of bias for the patient selection domain also made no difference to our findings. AUTHORS' CONCLUSIONS: The proposed diagnostic criteria for prodromal dementia and MCI due to Alzheimer's disease, although still being debated, would be fulfilled where there is both core clinical and cognitive criteria and a single biomarker abnormality. From our review, the measure of abnormally low CSF Aß levels has very little diagnostic benefit with likelihood ratios suggesting only marginal clinical utility. The quality of reports was also poor, and thresholds and length of follow-up were inconsistent. We conclude that when applied to a population of patients with MCI, CSF Aß levels cannot be recommended as an accurate test for Alzheimer's disease.

Decision letter: Stage-dependent differential influence of metabolic and structural networks on memory across Alzheimer’s disease continuum

CSF tau and the CSF tau/ABeta ratio for the diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI).

BackgroundThe recruitment of participants with a high risk of decline is crucial for the success of clinical trials in the earliest phases of Alzheimer´s Disease (AD), where treatment benefits could be the largest. Subjective cognitive decline (SCD) and minor neuropsychological deficits (MNPD) are associated with an increased risk of cognitive decline, making them promising predictors for this risk stratification. However, the prognostic value of their interplay is understudied.MethodWe pooled and analyzed data from cognitively unimpaired participants from the Alzheimer´s Disease Neuroimaging Initiative (N = 599), DZNE Longitudinal Cognitive Impairment and Dementia study (N = 618), and National Alzheimer's Coordinating Center (N = 11,975). SCD was measured using questionnaires or anamnestic data. MNPD was defined as a median neuropsychological test performance of z≤‐0.5. We assessed the association of MNPD and SCD with the conversion to mild cognitive impairment (MCI) and dementia (cox regression) and baseline amyloid and tau positivity – measured by PET/CSF (logistic regression). We adjusted these models for the study cohorts and demographic covariates. Using power analyses, we calculated the sample sizes necessary to detect a 30% reduction in the risk of progressing to MCI over 4.5 years in amyloid positive participants.ResultIn the overall sample (N = 13,192), the SCD‐/MNPD+ (+:present, ‐:absent; HR=3.13[2.68‐3.66]), SCD+/MNPD‐ (HR=1.97[1.76‐2.20]), and SCD+/MNPD+ (HR=6.23[5.23‐7.42]) groups had an increased risk of MCI compared to the SCD‐/MNPD‐ group. These groups also had an increased risk of dementia. In amyloid positive participants (n = 497), this pattern persisted for the progression to MCI, while only the SCD+/MNPD+ group had an increased risk for dementia. In participants with biomarker data (n = 2,616), the SCD+/MNPD‐ (OR=1.47[1.20‐1.81]) and SCD+/MNPD+ (OR=1.64[1.04‐2.59]) groups had an increased risk of amyloid positivity. The risk of tau positivity was increased in the SCD+/MNPD+ group (OR=2.10[1.13‐3.90]). In the power analyses, the required clinical trial size was reduced by approximately one third after excluding SCD‐/MNPD‐ individuals and approximately two thirds by focusing only on SCD+/MNPD+ individuals (Figure).ConclusionSCD and MNPD have a complementary prognostic value. SCD+/MNPD+ individuals are at particularly high risk of pathology and decline. These clinical symptoms should be taken into account in the recruitment for clinical trials in preclinical AD.

Subjective cognitive decline (SCD) is recognized to be in the Alzheimer disease (AD) cognitive continuum. The SCD Initiative International Working Group recently proposed SCD-plus (SCD+) features that increase risk for future objective cognitive decline but that have not been assessed in a large community-based setting. To assess SCD risk for mild cognitive impairment (MCI), AD, and all-cause dementia, using SCD+ criteria among cognitively normal adults. The Framingham Heart Study, a community-based prospective cohort study, assessed SCD between 2005 and 2019, with up to 12 years of follow-up. Participants 60 years and older with normal cognition at analytic baseline were included. Cox proportional hazards (CPH) models were adjusted for baseline age, sex, education, APOE ε4 status, and tertiles of AD polygenic risk score (PRS), excluding the APOE region. Data were analyzed from May 2021 to November 2023. SCD was assessed longitudinally using a single question and considered present if endorsed at the last cognitively normal visit. It was treated as a time-varying variable, beginning at the first of consecutive, cognitively normal visits, including the last, at which it was endorsed. Consensus-diagnosed MCI, AD, and all-cause dementia. This study included 3585 participants (mean [SD] baseline age, 68.0 [7.7] years; 1975 female [55.1%]). A total of 1596 participants (44.5%) had SCD, and 770 (21.5%) were carriers of APOE ε4. APOE ε4 and tertiles of AD PRS status did not significantly differ between the SCD and non-SCD groups. MCI, AD, and all-cause dementia were diagnosed in 236 participants (6.6%), 73 participants (2.0%), and 89 participants (2.5%), respectively, during follow-up. On average, SCD preceded MCI by 4.4 years, AD by 6.8 years, and all-cause dementia by 6.9 years. SCD was significantly associated with survival time to MCI (hazard ratio [HR], 1.57; 95% CI, 1.22-2.03; P <.001), AD (HR, 2.98; 95% CI, 1.89-4.70; P <.001), and all-cause dementia (HR, 2.14; 95% CI, 1.44-3.18; P <.001). After adjustment for APOE and AD PRS, the hazards of SCD were largely unchanged. Results of this cohort study suggest that in a community setting, SCD reflecting SCD+ features was associated with an increased risk of future MCI, AD, and all-cause dementia with similar hazards estimated in clinic-based settings. SCD may be an independent risk factor for AD and other dementias beyond the risk incurred by APOE ε4 and AD PRS.

Plasma amyloid-β (Aβ) was associated with brain Aβ deposition and Alzheimer’s disease (AD) development. However, changes of plasma Aβ over the course of cognitive decline in the Alzheimer’s continuum remained uncertain. We recruited 449 participants to this study, including normal controls (NC), subjective cognitive decline (SCD), mild cognitive impairment (MCI), AD, and non-AD dementia. All the participants underwent plasma Aβ42, Aβ40, and t-tau measurements with single-molecule array (Simoa) immunoassay and PET scan with 18F-florbetapir amyloid tracer. In the subgroup of Aβ-PET positive, plasma Aβ42 and Aβ42/Aβ40 ratio was significantly lower in AD than NC, SCD and MCI, yet SCD had significantly higher levels of plasma Aβ42 than both NC and MCI. In the diagnostic groups of MCI and dementia, participants with Aβ-PET positive had lower plasma Aβ42 and Aβ42/40 ratio than participants with Aβ-PET negative, and the increasing levels of plasma Aβ42 and Aβ42/40 ratio indicated lower risks of Aβ-PET positive. However, in the participants with SCD, plasma Aβ42 and Aβ40 were higher in the subgroup of Aβ-PET positive than Aβ-PET negative, and the increasing levels of plasma Aβ42 and Aβ40 indicated higher risks of Aβ-PET positive. No significant association was observed between plasma Aβ and Aβ-PET status in normal controls. These findings showed that, in the continuum of AD, plasma Aβ42 had a significantly increasing trend from NC to SCD before decreasing in MCI and AD. Furthermore, the predictive values of plasma Aβ for brain amyloid deposition were inconsistent over the course of cognitive decline.

BackgroundWe have updated our previous amyloid positivity prevalence estimates (Jansen, 2015; Ossenkoppele, 2015; Table 1) with data from 19,097 individuals from 85 cohorts. We tested if prevalence estimates differed for positron emission tomography (PET) or cerebrospinal fluid (CSF) amyloid. We calculated data‐driven CSF cut‐points as previous studies suggested a drift in Innotest‐assay performance.MethodWe included 9,908 participants with normal cognition (CN); 1,542 with subjective cognitive decline (SCD); 5,405 with mild cognitive impairment (MCI); and 2,260 with clinical Alzheimer’s disease (AD) dementia. 10,139 participants had an amyloid‐PET measure and 8,958 participants had an amyloid‐CSF measure. Amyloid‐ß aggregation was originally dichotomized as normal or abnormal according to study‐specific cut‐points, and we defined data‐driven CSF cut‐points using Gaussian mixture modelling. Generalized‐estimating‐equations adjusting for within‐study clustering of individuals were used to estimate amyloid positivity prevalence according to age, cognitive status, and biomarker modality and compared original with updated cut‐points.ResultAmyloid positivity prevalence was higher with older age and advancing disease severity. Using original cut‐points as provided by the cohort, the prevalence of amyloid positivity at the median age of 70 was similar for PET and CSF estimates for CN (24%), SCD (26%), and MCI (51%), and was higher in PET (87%) than CSF (79%) for clinical AD dementia. Using the data‐driven CSF cut‐points, the prevalence of amyloid positivity was higher for CSF‐based estimates than PET‐based estimates in CN (CSF 33%, PET 24%), SCD (CSF 36%, PET 27%), and MCI (CSF 60%, PET 49%), but comparable in CSF and PET in clinically diagnosed AD dementia (CSF 83%, PET 87) (Table 1, Figure 1).ConclusionWe provide updated amyloid positivity estimates from the Amyloid Biomarker Study. CSF‐based estimates using a data‐driven approach resulted in higher estimates (up to 11% higher) in people without clinical AD dementia than PET‐based estimates. Whether CSF‐based estimates are more sensitive than PET‐based estimates for amyloid pathology among people without dementia needs to be explored in cohorts that use both modalities. These updated estimates may be useful to understand potential eligible patient population sizes for anti‐amyloid therapies and to inform recruitment strategies for clinical trials investigating anti‐amyloid therapies.

Introduction: Amyloid beta 1-43 (Aβ43), with its additional C-terminal threonine residue, is hypothesized to play a role in early Alzheimer’s disease pathology possibly different from that of amyloid beta 1-42 (Aβ42). Cerebrospinal fluid (CSF) Aβ43 has been suggested as a potential novel biomarker for predicting conversion from mild cognitive impairment (MCI) to dementia in Alzheimer’s disease. However, the relationship between CSF Aβ43 and established imaging biomarkers of Alzheimer’s disease has never been assessed.Materials and Methods: In this observational study, CSF Aβ43 was measured with ELISA in 89 subjects; 34 with subjective cognitive decline (SCD), 51 with MCI, and four with resolution of previous cognitive complaints. All subjects underwent structural MRI; 40 subjects on a 3T and 50 on a 1.5T scanner. Forty subjects, including 24 with SCD and 12 with MCI, underwent 18F-Flutemetamol PET. Seventy-eight subjects were assessed with 18F-fluorodeoxyglucose PET (21 SCD/7 MCI and 11 SCD/39 MCI on two different scanners). Ten subjects with SCD and 39 with MCI also underwent diffusion tensor imaging.Results: Cerebrospinal fluid Aβ43 was both alone and together with p-tau a significant predictor of the distinction between SCD and MCI. There was a marked difference in CSF Aβ43 between subjects with 18F-Flutemetamol PET scans visually interpreted as negative (37 pg/ml, n = 27) and positive (15 pg/ml, n = 9), p < 0.001. Both CSF Aβ43 and Aβ42 were negatively correlated with standardized uptake value ratios for all analyzed regions; CSF Aβ43 average rho -0.73, Aβ42 -0.74. Both CSF Aβ peptides correlated significantly with hippocampal volume, inferior parietal and frontal cortical thickness and axial diffusivity in the corticospinal tract. There was a trend toward CSF Aβ42 being better correlated with cortical glucose metabolism. None of the studied correlations between CSF Aβ43/42 and imaging biomarkers were significantly different for the two Aβ peptides when controlling for multiple testing.Conclusion: Cerebrospinal fluid Aβ43 appears to be strongly correlated with cerebral amyloid deposits in the same way as Aβ42, even in non-demented patients with only subjective cognitive complaints. Regarding imaging biomarkers, there is no evidence from the present study that CSF Aβ43 performs better than the classical CSF biomarker Aβ42 for distinguishing SCD and MCI.

ABSTRACTIntroductionThe relationship between neuropsychiatric symptoms (NPSs) and Alzheimer's disease (AD) pathophysiology, considering cerebrospinal fluid (CSF) biomarkers, is still not clarified and the available results in the literature are contradictory especially when it comes to the prodromic stages of AD. Clarifying the role of the AD biomarkers in the development of NPS in subjective cognitive decline (SCD) and mild cognitive impairment (MCI) is relevant and can be useful for planning longitudinal interventions in the AD continuum. The present work studies the correlation between NPSs and AD CSF biomarkers in SCD and MCI Brazilian subjects.MethodsThis is a transversal study designed to evaluate the relationship between NPS and CSF AD biomarkers in SCD and MCI individuals. Voluntary participants were recruited through a virtually announced formulary. The inclusion criteria were age 55 years or older, of both sexes, without distinction of ethnicity or socioeconomic status, the presence of cognitive complaints, and that were willing to participate in all research procedures. Exclusion criteria for all subjects included clinical dementia, other neurological or psychiatric diseases, traumatic brain injury that resulted in a loss of consciousness, drug or alcohol addiction, prior chronic exposure to neurotoxic substances, Fazekas scale ≥ 2, and a score on the Montreal Cognitive Assessment (MoCA) less than 17 points. All participants had Pfeffer's Functional Activities Questionnaire < 5 and underwent medical and neuropsychological evaluation.ResultsSeventy‐one patients were included between 2019 and 2022, 54 with MCI and 17 with SCD. A significant correlation (p: 0.006) was observed between MBI‐C C domain and the concentrations of CSF amyloid‐beta peptide.ConclusionThe study identified a significant correlation between domain C of the MBI‐C and CSF beta‐amyloid peptide analysing Brazilian patients with SCD and MCI. The results reinforce the hypothesis of the relationship between impulsivity symptoms and amyloid pathology. Further studies involving the correlations between MBI and neuropsychiatric symptoms with AD CSF biomarkers are necessary.

BackgroundNon‐specific Alzheimer's Disease (AD) biomarkers like glial fibrillary acidic protein (GFAP) and neurofilament light chain (Nfl) are now included in the diagnosis and staging of AD, but more work is needed to fully understand their utility in early detection of AD. The present study examined plasma GFAP, Nfl, amyloid beta (Aβ) 40, and Aβ42 in individuals with AD, mild cognitive impairment (MCI) and subjective cognitive decline (SCD) who were either amyloid positive (A+) or negative (A‐) according to florbetapir positron emission tomography scan neuroradiological read. The goal was to determine whether individuals with preclinical AD (SCD/A+) show biomarker profiles similar to those expected in AD and MCI (i.e., higher GFAP and Nfl and lower Aβ40 and Aβ42) compared to SCD individuals at lower risk (SCD/A‐).MethodIndividuals with AD (24 A+, 5 A‐), MCI (21 A+, 17 A‐) and SCD (5 A+, 11 A‐), as determined by clinician referral, completed a blood draw and neuropsychological testing. Blood samples were collected, processed, and stored per previously published guidelines. Plasma samples were assayed using the Neurology 4‐Plex E+ assay on the HD‐X analyzer (Quanterix, MA). Coefficients of variation for all assays were ≤5%. Generalized linear models (GLMs) with false discovery rate correction examined the effects of diagnosis severity (AD, MCI, SCD) and amyloid status (A+, A‐) on plasma levels for each biomarker and the Aβ42/Aβ40 ratio, with covariates of age and sex. Associations between biomarkers and global cognitive functioning, as measured by the Montreal Cognitive Assessment (MoCA), were also examined.ResultThe Severity x Amyloid status interaction indicated that GFAP was higher (χ2(2)=6.9, p = .032) and Aβ42/Aβ40 was lower (χ2(2)=13.1, p = .001) in AD/A+ versus AD/A‐ and in SCD/A+ versus SCD/A‐, but amyloid status did not moderate GFAP or Aβ42/Aβ40 in MCI. GLMs for Nfl, Aβ 40, and Aβ 42 did not yield significant effects. GFAP was negatively correlated with MoCA for MCI (ρ=‐.61, p <.001) and SCD (rρ=‐.59, p = .016) groups but not for AD (ρ =‐.15, p = .45).ConclusionIndividuals with preclinical AD (SCD/A+) showed biomarker profiles consistent with AD (higher GFAP; lower Aβ42/Aβ40) and GFAP was associated with poorer global cognition in MCI and SCD.

We aimed to present the study design of an independent validation cohort from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s disease (AD) (KBASE-V) and to investigate the baseline characteristics of the participants according to the AD clinical spectrum. We recruited 71 cognitively normal (CN) participants, 96 with subjective cognitive decline (SCD), 72 with mild cognitive impairment (MCI), and 56 with AD dementia (ADD). The participants are followed for three years. The Consortium to Establish a Registry for AD scores was significantly different between all of the groups. The logical memory delayed recall scores were significantly different between all groups, except between the MCI and ADD groups. The Mini-Mental State Examination score, hippocampal volume, and cerebrospinal fluid (CSF) amyloid-β42 level were significant difference among the SCD, MCI, and ADD groups. The frequencies of participants with amyloid pathology according to PET or CSF studies were 8.9%, 25.6%, 48.3%, and 90.0% in the CN, SCD, MCI, and ADD groups, respectively. According to ATN classification, A+/T+/N+ or A+/T+/N− was observed in 0%, 15.5%, 31.0%, and 78.3% in the CN, SCD, MCI, and ADD groups, respectively. The KBASE-V showed a clear difference according to the AD clinical spectrum in neuropsychological tests and AD biomarkers.

IntroductionSeveral studies have reported alterations in gut microbiota composition of Alzheimer’s disease (AD) patients. However, the observed differences are not consistent across studies. We aimed to investigate associations between gut microbiota composition and AD biomarkers using machine learning models in patients with AD dementia, mild cognitive impairment (MCI) and subjective cognitive decline (SCD).Materials and MethodsWe included 170 patients from the Amsterdam Dementia Cohort, comprising 33 with AD dementia (66 ± 8 years, 46%F, mini-mental state examination (MMSE) 21[19-24]), 21 with MCI (64 ± 8 years, 43%F, MMSE 27[25-29]) and 116 with SCD (62 ± 8 years, 44%F, MMSE 29[28-30]). Fecal samples were collected and gut microbiome composition was determined using 16S rRNA sequencing. Biomarkers of AD included cerebrospinal fluid (CSF) amyloid-beta 1-42 (amyloid) and phosphorylated tau (p-tau), and MRI visual scores (medial temporal atrophy, global cortical atrophy, white matter hyperintensities). Associations between gut microbiota composition and dichotomized AD biomarkers were assessed with machine learning classification models. The two models with the highest area under the curve (AUC) were selected for logistic regression, to assess associations between the 20 best predicting microbes and the outcome measures from these machine learning models while adjusting for age, sex, BMI, diabetes, medication use, and MMSE.ResultsThe machine learning prediction for amyloid and p-tau from microbiota composition performed best with AUCs of 0.64 and 0.63. Highest ranked microbes included several short chain fatty acid (SCFA)-producing species. Higher abundance of [Clostridium] leptum and lower abundance of [Eubacterium] ventriosum group spp., Lachnospiraceae spp., Marvinbryantia spp., Monoglobus spp., [Ruminococcus] torques group spp., Roseburia hominis, and Christensenellaceae R-7 spp., was associated with higher odds of amyloid positivity. We found associations between lower abundance of Lachnospiraceae spp., Lachnoclostridium spp., Roseburia hominis and Bilophila wadsworthia and higher odds of positive p-tau status.ConclusionsGut microbiota composition was associated with amyloid and p-tau status. We extend on recent studies that observed associations between SCFA levels and AD CSF biomarkers by showing that lower abundances of SCFA-producing microbes were associated with higher odds of positive amyloid and p-tau status.

Background and purposeWe aimed to evaluate the accuracy of plasma neurofilament light chain (NfL) in predicting Alzheimer's disease (AD) and the progression of cognitive decline in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI).MethodsThis longitudinal cohort study involved 140 patients (45 with SCD, 73 with MCI, and 22 with AD dementia [AD‐D]) who underwent plasma NfL and AD biomarker assessments (cerebrospinal fluid, amyloid positron emission tomography [PET], and 18F‐fluorodeoxyglucose‐PET) at baseline. The patients were rated according to the amyloid/tau/neurodegeneration (A/T/N) system and followed up for a mean time of 2.72 ± 0.95 years to detect progression from SCD to MCI and from MCI to AD. Forty‐eight patients (19 SCD, 29 MCI) also underwent plasma NfL measurements 2 years after baseline.ResultsAt baseline, plasma NfL detected patients with biomarker profiles consistent with AD (A+/T+/N+ or A+/T+/N−) with high accuracy (area under the curve [AUC] 0.82). We identified cut‐off values of 19.45 pg/mL for SCD and 20.45 pg/mL for MCI. During follow‐up, nine SCD patients progressed to MCI (progressive SCD [p‐SCD]), and 14 MCI patients developed AD dementia (progressive MCI [p‐MCI]). The previously identified cut‐off values provided good accuracy in identifying p‐SCD (80% [95% confidence interval 65.69: 94.31]). The rate of NfL change was higher in p‐MCI (3.52 ± 4.06 pg/mL) compared to non‐progressive SCD (0.81 ± 1.25 pg/mL) and non‐progressive MCI (−0.13 ± 3.24 pg/mL) patients. A rate of change lower than 1.64 pg/mL per year accurately excluded progression from MCI to AD (AUC 0.954).ConclusionPlasma NfL concentration and change over time may be a reliable, non‐invasive tool to detect AD and the progression of cognitive decline at the earliest stages of the disease.

Individuals experiencing subjective cognitive decline (SCD) are at an increased risk of developing mild cognitive impairment and dementia. Early identification of SCD and neurodegenerative diseases using biomarkers may help clinical decision-making and improve prognosis. However, few cross-sectional and longitudinal studies have explored plasma biomarkers in individuals with SCD using immunomagnetic reduction. To identify plasma biomarkers for SCD. Fifty-two participants [38 with SCD, 14 healthy controls (HCs)] underwent baseline assessments, including measurements of plasma Aβ42, Aβ40, t-tau, p-tau, and α-synuclein using immunomagnetic reduction (IMR) assays, cognitive tests and the Mini-Mental State Examination (MMSE). Following initial cross-sectional analysis, 39 individuals (29 with SCD, 10 HCs) entered a longitudinal phase for reassessment of these biomarkers and the MMSE. Biomarker outcomes across different individual categories were primarily assessed using the area under the receiver operating characteristic (ROC) curve. The SCD subgroup with an MMSE decline over one point was compared to those without such a decline. Higher baseline plasma Aβ1-42 levels significantly discriminated participants with SCD from HCs, with an acceptable area under the ROC curve (AUC) of 67.5% [95% confidence interval (CI), 52.7-80.0%]. However, follow-up and changes in MMSE and IMR data did not significantly differ between the SCD and HC groups (p > 0.05). Furthermore, lower baseline plasma Aβ1-42 levels were able to discriminate SCD subgroups with and without cognitive decline with a satisfied performance (AUC, 75.0%; 95% CI, 55.6-89.1%). At last, the changes in t-tau and Aβ42 × t-tau could differentiate between the two SCD subgroups (p < 0.05). Baseline plasma Aβ42 may help identify people with SCD and predict SCD progression. The role of plasma Aβ42 levels as well as their upward trends from baseline in cases of SCD that progress to mild cognitive impairment and Alzheimer's disease require further investigation.

Diffusion changes as determined by diffusion tensor imaging are potential indicators of microstructural lesions in people with mild cognitive impairment (MCI), prodromal Alzheimer’s disease (AD), and AD dementia. Here we extended the scope of analysis toward subjective cognitive complaints as a pre-MCI at risk stage of AD. In a cohort of 271 participants of the prospective DELCODE study, including 93 healthy controls and 98 subjective cognitive decline (SCD), 45 MCI, and 35 AD dementia cases, we found reductions of fiber tract integrity in limbic and association fiber tracts in MCI and AD dementia compared with controls in a tract-based analysis (p < 0.05, family wise error corrected). In contrast, people with SCD showed spatially restricted white matter alterations only for the mode of anisotropy and only at an uncorrected level of significance. DTI parameters yielded a high cross-validated diagnostic accuracy of almost 80% for the clinical diagnosis of MCI and the discrimination of Aβ positive MCI cases from Aβ negative controls. In contrast, DTI parameters reached only random level accuracy for the discrimination between Aβ positive SCD and control cases from Aβ negative controls. These findings suggest that in prodromal stages of AD, such as in Aβ positive MCI, multicenter DTI with prospectively harmonized acquisition parameters yields diagnostic accuracy meeting the criteria for a useful biomarker. In contrast, automated tract-based analysis of DTI parameters is not useful for the identification of preclinical AD, including Aβ positive SCD and control cases.