Progression of Myopic Maculopathy Based on the ATN Classification System.

INTRODUCTION: Myopic maculopathy is a sight-threatening disease, which causes irreversible vision faults and central vision loss. The purpose of this study is evaluating the risk factors of the myopic maculopathy progression according to the ATN classification system. METHODS: Clinic data of 69 high myopia patients aged older than 40 years with a follow-up time of more than 2 years, who underwent fundus photography and OCT examination were retrospectively collected. Fundus changes were evaluated with ATN classification at the first and last follow-up times. The related factors affecting progress including axial length (AL), spherical equivalence (SE), subfoveal choroidal thickness (SFCT), disc-foveal distance (DFD), optic disc tilt, and parapapillary atrophy (PPA) were analyzed. RESULTS: This study included 69 high-myopia patients with mean age 54.29 ± 10.41 years. The progression rate of myopic maculopathy (MM) was approximately 25.56%. Elongated DFD (5.37 ± 0.11 mm vs. 4.86 ± 0.37 mm; p < 0.001) and thinner SFCT (138.52 ± 29.38 µm vs. 184.87 ± 48.72 µm; p = 0.008) at baseline were linked with MM progression. In multiple logistic regression analysis, DFD was a substantial hazard risk factor (adjusted OR = 1.672, 95% CI: 1.135-2.498, p < 0.05) after adjusting for age, AL and SFCT. Receiver operating characteristic curve showed that DFD might serve as a predictor to discriminate the MM progression with a cut-off value of 5.15 mm and a substantial receiver operating characteristic curve area (AUC: 0.794). Compared with the non-progression group, the progression group had older age (p < 0.001), longer AL (p = 0.001), higher optic disc tilt rate (p < 0.001), and higher proportion of pre-existing PPA (p = 0.038) at baseline, the differences were statistically significant. CONCLUSION: Based on the ATN classification system, we found that the progression of MM was related to older age, longer AL, high disc tilt, pre-existing PPA, thinner SFCT, and longer DFD. The parameter of DFD was an important factor affecting the progression of MM, which is considered to have a higher probability of progression when the length is beyond 5.15 mm.

Revision of Alzheimer's diagnostic criteria or relocation of the Potemkin village.

The recently announced revision of the Alzheimer's disease (AD) diagnostic ATN classification adds to an already existing disregard for clinical assessment the rejection of image-based in vivo assessment of the brain's condition. The revision suggests that the diagnosis of AD should be based solely on the presence of cerebral amyloid-beta and tau, indicated by the "A" and "T". The "N", which stands for neurodegeneration - detected by imaging - should no longer be given importance, except that A+ ± T + = AD with amyloid PET being the main method for demonstrating A+ . We believe this is an artificial and misleading suggestion. It is artificial because it relies on biomarkers whose significance remains obscure and where the detection of "A" is based on a never-validated PET method using a tracer that marks much more than amyloid-beta. It is misleading because many patients without dementia will be falsely classified as having AD, but nonetheless candidates for passive immunotherapy, which may be more harmful than beneficial, and sometimes fatal.

ATN Classification and Clinical Progression of the Amyloid-Negative Group in Alzheimer's Disease Neuroimaging Initiative Participants.

Alzheimer's disease has recently been classified using three biological markers (amyloid [A], tau [T], and neurodegeneration [N]) to help elucidate its progression. We aimed to investigate whether there were differences between cognitive function and the clinical dementia symptoms over time relative to the ATN classification in the amyloid-negative group. In the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, 310 participants who underwent all the tests required for ATN classification were enrolled. The cognitive function score differences (Alzheimer's Disease Assessment Scale-Cognitive Subscale 13 [ADAS-Cog 13], Clinical Dementia Rating Sum of Boxes [CDR-SOB], and Mini-Mental State Examination [MMSE]) between the groups were analyzed using the analysis of covariance and score changes over time with a linear mixed-effects model. In the cross-sectional analysis, ADAS-Cog 13 scores were higher for A-T-N+ and A-T+N+ than for A-T-N- (p<0.001) and A-T+N- (p<0.001). In the longitudinal analysis, CDR-SOB scores for A-T+N+ deteriorated faster than A-T-N- (p<0.001), A-T+N- (p<0.001) and A-T-N+ (p<0.001). Hippocampal atrophy progressed faster in A-T-N+ (p<0.001) and A-T+N+ (p=0.02) than in A-T-N-. Through this study, we discovered that even in individuals classified as amyloid negative, neurodegeneration with tau deposition exacerbates cognitive decline and worsens clinical symptoms, underscoring the need for continuous monitoring and observation.

Where Should I Draw the Line: PET-Driven, Data-Driven, or Manufacturer Cut-Off?

Background: The optimal cut-off for Alzheimer's disease (AD) CSF biomarkers remains controversial. Objective: To analyze the performance of cut-off points standardized by three methods: one that optimized the agreement between 11C-Pittsburgh compound B PET (a-PET) and CSF biomarkers (Aß1-42, pTau, tTau, and Aß1-42/Aß1-40 ratio) in our population, called PET-driven; an unbiased cut-off using data from a healthy research cohort, called data-driven, and that provided by the manufacturer. We also compare changes in ATN classification. Methods: CSF biomarkers measured by the LUMIPULSE G600II platform and qualitative visualization of amyloid positron emission tomography (a-PET) were performed in all the patients. We established a cut-off for each single biomarker and Aß1-42/Aß1-40 ratio that optimized their agreement with a-PET using ROC curves. Sensitivity, Specificity, and Overall Percent of Agreement are assessed using a-PET or clinical diagnosis as gold standard for every cut-off. Also, we established a data-driven cut-off from our cognitively unimpaired cohort. We then analyzed changes in ATN classification. Results: One hundred and ten patients were recruited. Sixty-six (60%) were a-PET positive. PET-driven cut-offs were: pTau > 57, tTau > 362.62, Aß1-42/Aß1-40 < 0.069. For a single biomarker, pTau showed the highest accuracy (AUC 0.926). New PET-driven cut-offs classified patients similarly to manufacturer cut-offs (only two patients changed). However, 20 patients (18%) changed when data-driven cut-offs were used. Conclusions: We established our sample's best CSF biomarkers cut-offs using a-PET as the gold standard. These cut-offs categorize better symptomatic subjects than data-driven in ATN classification, but they are very similar to the manufacturer's.