Combining Visual Rating Scales for Medial Temporal Lobe Atrophy and Posterior Atrophy to Identify Amnestic Mild Cognitive Impairment from Cognitively Normal Older Adults: Evidence Based on Two Cohorts

Relationship between topological efficiency of white matter structural connectome and plasma biomarkers across the Alzheimer's disease continuum

Both plasma biomarkers and brain network topology have shown great potential in the early diagnosis of Alzheimer's disease (AD). However, the specific associations between plasma AD biomarkers, structural network topology, and cognition across the AD continuum have yet to be fully elucidated. This retrospective study evaluated participants from the Sino Longitudinal Study of Cognitive Decline cohort between September 2009 and October 2022 with available blood samples or 3.0-T MRI brain scans. Plasma biomarker levels were measured using the Single Molecule Array platform, including β-amyloid (Aβ), phosphorylated tau181 (p-tau181), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). The topological structure of brain white matter was assessed using network efficiency. Trend analyses were carried out to evaluate the alterations of the plasma markers and network efficiency with AD progression. Correlation and mediation analyses were conducted to further explore the relationships among plasma markers, network efficiency, and cognitive performance across the AD continuum. Among the plasma markers, GFAP emerged as the most sensitive marker (linear trend: t = 11.164, p = 3.59 × 10-24 ; quadratic trend: t = 7.708, p = 2.25 × 10-13 ; adjusted R2  = 0.475), followed by NfL (linear trend: t = 6.542, p = 2.9 × 10-10 ; quadratic trend: t = 3.896, p = 1.22 × 10-4 ; adjusted R2  = 0.330), p-tau181 (linear trend: t = 8.452, p = 1.61 × 10-15 ; quadratic trend: t = 6.316, p = 1.05 × 10-9 ; adjusted R2  = 0.346) and Aβ42/Aβ40 (linear trend: t = -3.257, p = 1.27 × 10-3 ; quadratic trend: t = -1.662, p = 9.76 × 10-2 ; adjusted R2  = 0.101). Local efficiency decreased in brain regions across the frontal and temporal cortex and striatum. The principal component of local efficiency within these regions was correlated with GFAP (Pearson's R = -0.61, p = 6.3 × 10-7 ), NfL (R = -0.57, p = 6.4 × 10-6 ), and p-tau181 (R = -0.48, p = 2.0 × 10-4 ). Moreover, network efficiency mediated the relationship between general cognition and GFAP (ab = -0.224, 95% confidence interval [CI] = [-0.417 to -0.029], p = .0196 for MMSE; ab = -0.198, 95% CI = [-0.42 to -0.003], p = .0438 for MOCA) or NfL (ab = -0.224, 95% CI = [-0.417 to -0.029], p = .0196 for MMSE; ab = -0.198, 95% CI = [-0.42 to -0.003], p = .0438 for MOCA). Our findings suggest that network efficiency mediates the association between plasma biomarkers, specifically GFAP and NfL, and cognitive performance in the context of AD progression, thus highlighting the potential utility of network-plasma approaches for early detection, monitoring, and intervention strategies in the management of AD.

Visual atrophy rating scales and amyloid PET status in an Alzheimer's disease clinical cohort

OBJECTIVES

Visual rating scales (VRS) are the quantification method closest to the approach used in routine clinical practice to assess brain atrophy. Previous studies have suggested that the medial temporal atrophy (MTA) rating scale is a reliable diagnostic marker for AD, equivalent to volumetric quantification, while others propose a higher diagnostic utility for the Posterior Atrophy (PA) scale in early-onset AD.

METHODS

Here, we reviewed 14 studies that assessed the diagnostic accuracy of PA and MTA, we explored the issue of cut-off heterogeneity, and assessed 9 rating scales in a group of patients with biomarker-confirmed diagnosis. A neuroradiologist blinded to all clinical information rated the MR images of 39 amyloid-positive and 38 amyloid-negative patients using 9 validated VRS assessing multiple brain regions. Automated volumetric analyses were performed on a subset of patients (n = 48) and on a group of cognitively normal individuals (n = 28).

RESULTS

No single VRS could differentiate amyloid-positive from amyloid-negative patients with other neurodegenerative conditions. 44% of amyloid-positive patients were deemed to have age-appropriate levels of MTA. In the amyloid-positive group, 18% had no abnormal MTA or PA scores. These findings were substantially affected by cut-off selection. Amyloid-positive and amyloid-negative patients had comparable hippocampal and parietal volumes, and MTA but not PA scores correlated with the respective volumetric measures.

INTERPRETATION

Consensus guidelines are needed before VRS can be recommended for use in the diagnostic workup of AD. Our data are suggestive of high intragroup variability and non-superiority of volumetric quantification of atrophy over visual assessment.

Generative adversarial network constrained multiple loss autoencoder: A deep learning-based individual atrophy detection for Alzheimer's disease and mild cognitive impairment

Exploring individual brain atrophy patterns is of great value in precision medicine for Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, the current individual brain atrophy detection models are deficient. Here, we proposed a framework called generative adversarial network constrained multiple loss autoencoder (GANCMLAE) for precisely depicting individual atrophy patterns. The GANCMLAE model was trained using normal controls (NCs) from the Alzheimer's Disease Neuroimaging Initiative cohort, and the Xuanwu cohort was employed to validate the robustness of the model. The potential of the model for identifying different atrophy patterns of MCI subtypes was also assessed. Furthermore, the clinical application potential of the GANCMLAE model was investigated. The results showed that the model can achieve good image reconstruction performance on the structural similarity index measure (0.929 ± 0.003), peak signal-to-noise ratio (31.04 ± 0.09), and mean squared error (0.0014 ± 0.0001) with less latent loss in the Xuanwu cohort. The individual atrophy patterns extracted from this model are more precise in reflecting the clinical symptoms of MCI subtypes. The individual atrophy patterns exhibit a better discriminative power in identifying patients with AD and MCI from NCs than those of the t-test model, with areas under the receiver operating characteristic curve of 0.867 (95%: 0.837-0.897) and 0.752 (95%: 0.71-0.790), respectively. Similar findings are also reported in the AD and MCI subgroups. In conclusion, the GANCMLAE model can serve as an effective tool for individualised atrophy detection.

Combining visual rating scales to identify prodromal Alzheimer's disease and Alzheimer's disease dementia in a population from a low and middle-income country

Background

Many low- and middle-income countries, including Latin America, lack access to biomarkers for the diagnosis of prodromal Alzheimer's Disease (AD; mild cognitive impairment due to AD) and AD dementia. MRI visual rating scales may serve as an ancillary diagnostic tool for identifying prodromal AD or AD in Latin America. We investigated the ability of brain MRI visual rating scales to distinguish between cognitively healthy controls, prodromal AD and AD.

Methods

A cross-sectional study was conducted from a multidisciplinary neurology clinic in Lima, Peru using neuropsychological assessments, brain MRI and cerebrospinal fluid amyloid and tau levels. Medial temporal lobe atrophy (MTA), posterior atrophy (PA), white matter hyperintensity (WMH), and MTA+PA composite MRI scores were compared. Sensitivity, specificity, and area under the curve (AUC) were determined.

Results

Fifty-three patients with prodromal AD, 69 with AD, and 63 cognitively healthy elderly individuals were enrolled. The median age was 75 (8) and 42.7% were men. Neither sex, mean age, nor years of education were significantly different between groups. The MTA was higher in patients with AD (p < 0.0001) compared with prodromal AD and controls, and MTA scores adjusted by age range (p < 0.0001) and PA scores (p < 0.0001) were each significantly associated with AD diagnosis (p < 0.0001) but not the WMH score (p=0.426). The MTA had better performance among ages <75 years (AUC 0.90 [0.85-0.95]), while adjusted MTA+PA scores performed better among ages>75 years (AUC 0.85 [0.79-0.92]). For AD diagnosis, MTA+PA had the best performance (AUC 1.00) for all age groups.

Conclusions

Combining MTA and PA scores demonstrates greater discriminative ability to differentiate controls from prodromal AD and AD, highlighting the diagnostic value of visual rating scales in daily clinical practice, particularly in Latin America where access to advanced neuroimaging and CSF biomarkers is limited in the clinical setting.

Associations of multiple visual rating scales based on structural magnetic resonance imaging with disease severity and cerebrospinal fluid biomarkers in patients with Alzheimer’s disease

The relationships between multiple visual rating scales based on structural magnetic resonance imaging (sMRI) with disease severity and cerebrospinal fluid (CSF) biomarkers in patients with Alzheimer’s disease (AD) were ambiguous. In this study, a total of 438 patients with clinically diagnosed AD were recruited. All participants underwent brain sMRI scan, and medial temporal lobe atrophy (MTA), posterior atrophy (PA), global cerebral atrophy-frontal sub-scale (GCA-F), and Fazekas rating scores were visually evaluated. Meanwhile, disease severity was assessed by neuropsychological tests such as the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Clinical Dementia Rating (CDR). Among them, 95 patients were tested for CSF core biomarkers, including Aβ_1–42, Aβ_1–40, Aβ_1–42/Aβ_1–40, p-tau, and t-tau. As a result, the GCA-F and Fazekas scales showed positively significant correlations with onset age (r = 0.181, p < 0.001; r = 0.411, p < 0.001, respectively). Patients with late-onset AD (LOAD) showed higher GCA-F and Fazekas scores (p < 0.001, p < 0.001). With regard to the disease duration, the MTA and GCA-F were positively correlated (r = 0.137, p < 0.05; r = 0.106, p < 0.05, respectively). In terms of disease severity, a positively significant association emerged between disease severity and the MTA, PA GCA-F, and Fazekas scores (p < 0.001, p < 0.001, p < 0.001, p < 0.05, respectively). Moreover, after adjusting for age, gender, and APOE alleles, the MTA scale contributed to moderate to severe AD in statistical significance independently by multivariate logistic regression analysis (p < 0.05). The model combining visual rating scales, age, gender, and APOE alleles showed the best performance for the prediction of moderate to severe AD significantly (AUC = 0.712, sensitivity = 51.5%, specificity = 84.6%). In addition, we observed that the MTA and Fazekas scores were associated with a lower concentration of Aβ_1–42 (p < 0.031, p < 0.022, respectively). In summary, we systematically analyzed the benefits of multiple visual rating scales in predicting the clinical status of AD. The visual rating scales combined with age, gender, and APOE alleles showed best performance in predicting the severity of AD. MRI biomarkers in combination with CSF biomarkers can be used in clinical practice.

Combination of gut microbiota and plasma amyloid-β as a potential index for identifying preclinical Alzheimer's disease: a cross-sectional analysis from the SILCODE study

Background

Plasma amyloid-β (Aβ) may facilitate identification of individuals with brain amyloidosis. Gut microbial dysbiosis in Alzheimer’s disease (AD) is increasingly being recognized. However, knowledge about alterations of gut microbiota in preclinical AD, as well as whether the combination of plasma Aβ and gut microbiota could identify preclinical AD, remains largely unknown.

Methods

This study recruited 34 Aβ-negative cognitively normal (CN−) participants, 32 Aβ-positive cognitively normal (CN+) participants, and 22 patients with cognitive impairment (CI), including 11 patients with mild cognitive impairment (MCI) and 11 AD dementia patients. All participants underwent neuropsychological assessments and fecal microbiota analysis through 16S ribosomal RNA (rRNA) Illumina Miseq sequencing technique. Meso Scale Discovery (MSD) kits were used to quantify the plasma Aβ₄₀, Aβ₄₂, and Aβ₄₂/Aβ₄₀ in CN− and CN+ participants. Using Spearman’s correlation analysis, the associations of global standard uptake value rate (SUVR) with altered gut microbiota and plasma Aβ markers were separately evaluated. Furthermore, the discriminative power of the combination of gut microbiota and plasma Aβ markers for identifying CN+ individuals was investigated.

Results

Compared with the CN− group, the CN+ group showed significantly reduced plasma Aβ₄₂ (p = 0.011) and Aβ₄₂/Aβ₄₀ (p = 0.003). The relative abundance of phylum Bacteroidetes was significantly enriched, whereas phylum Firmicutes and class Deltaproteobacteria were significantly decreased in CN+ individuals in comparison with that in CN− individuals. Particularly, the relative abundance of phylum Firmicutes and its corresponding SCFA-producing bacteria exhibited a progressive decline tendency from CN− to CN+ and CI. Besides, the global brain Aβ burden was negatively associated with the plasma Aβ₄₂/Aβ₄₀ (r = −0.298, p = 0.015), family Desulfovibrionaceae (r = −0.331, p = 0.007), genus Bilophila (r = −0.247, p = 0.046), and genus Faecalibacterium (r = −0.291, p = 0.018) for all CN participants. Finally, the combination of plasma Aβ markers, altered gut microbiota, and cognitive performance reached a relatively good discriminative power in identifying individuals with CN+ from CN− (AUC = 0.869, 95% CI 0.782 ~ 0.955).

Conclusions

This study provided the evidence that the gut microbial composition was altered in preclinical AD. The combination of plasma Aβ and gut microbiota may serve as a non-invasive, cost-effective diagnostic tool for early AD screening. Targeting the gut microbiota may be a novel therapeutic strategy for AD.

Trial registration

This study has been registered in ClinicalTrials.gov (NCT03370744, https://www.clinicaltrials.gov) in November 15, 2017.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-022-00977-x.