Data processing and analysis were carried out using R version 4.1.2 (R Core Team, n.d. ).1 All data were examined for normality, skew and restriction of range. All quantitative variables were normally distributed. Frequency analysis and measures of central tendency and dispersion were used to describe the demographic (age, sex, education) and clinical (hypertension, diabetes mellitus, dyslipidemia, heart disease, chronic obstructive pulmonary disease (COPD) and smoking) variables among the three AT(N) groups (Normal, Alzheimer, and SNAP). To summarize the distribution of these demographic and clinical variables among the three AT(N) groups, bivariate Analysis of Variance (ANOVA) and Pearson’s chi-squared tests were executed.
For the final multivariate model, we included all clinical variables (hypertension, diabetes mellitus, dyslipidemia, heart disease, COPD, and smoking) as adjusting factors. To identify which demographic variables should be additionally included as adjusting factors in the final model, a multinomial regression analysis was executed to determine their differential distribution among the three AT(N) groups. An analysis was performed for the demographic variables (age, sex, education, and APOE ε4 status) including all the clinical variables (hypertension, diabetes mellitus, dyslipidemia, heart disease, COPD and smoking habit) as adjusting factors. The Normal AT(N) group was considered the reference category. For these analyses, alpha level was set at p < 0.05.
The main analyses consisted of four multivariate regression analyses, one for every macular VD measure (nasal, superior, temporal, and inferior quadrants), including the three AT(N) groups (Normal, Alzheimer and SNAP) as discriminant factors and adjusting their effect by all six clinical variables and those demographic factors that showed any significant effect in the former multinomial regression analysis. The Normal AT(N) group was considered the reference category. Regression coefficients (the mean change in the outcome variable for one unit of change in the predictor variable while holding other predictors in the model constant), betas (the degree of change in the outcome variable for every one unit of change in the predictor variable) and t (assessing whether the beta coefficient is significantly different from zero) are reported.
The former four multivariate regression analysis were rerun without including the A+T-N- participants within the AT(N) Alzheimer group (amyloidosis alone without tauopathy or neurodegeneration, n = 9).
Additionally, the former four multivariate regression analyses were repeated including a group of participants with subjective cognitive decline (SCD) and absent brain amyloid uptake in a FBB-PET scan (SCD Aβ-) from the Fundació ACE Healthy Brain Initiative (FACEHBI) cohort (Rodriguez-Gomez et al., 2017 (link)) as the reference category (n = 83).
For the former multivariate regression analyses, alpha level was set up at p < 0.004 (0.05/12) after Bonferroni’s correction for multiple comparisons.
The association between individual CSF biomarkers (Aβ1-42, p181-tau and t-tau) and each of the four macular VD measurements (nasal, superior, temporal and inferior quadrants) was explored using separate partial correlations, including the same covariates. These analyses were performed separately for the ELISA and CLEIA groups and also for the whole cohort after a log-transformation of each CSF biomarker value. For these correlation analyses, alpha level was set at p < 0.004 (0.05/12), after Bonferroni’s correction for multiple comparisons.
To investigate whether a differential effect could be detected when considering sex, the previous four multivariate regression analyses were executed again, including now the interaction between AT(N) group and sex as the main factor of interest and the same covariates. For these analyses, alpha level was set at p < 0.0125 (0.05/4), after Bonferroni’s correction for multiple comparisons.
For the final multivariate model, we included all clinical variables (hypertension, diabetes mellitus, dyslipidemia, heart disease, COPD, and smoking) as adjusting factors. To identify which demographic variables should be additionally included as adjusting factors in the final model, a multinomial regression analysis was executed to determine their differential distribution among the three AT(N) groups. An analysis was performed for the demographic variables (age, sex, education, and APOE ε4 status) including all the clinical variables (hypertension, diabetes mellitus, dyslipidemia, heart disease, COPD and smoking habit) as adjusting factors. The Normal AT(N) group was considered the reference category. For these analyses, alpha level was set at p < 0.05.
The main analyses consisted of four multivariate regression analyses, one for every macular VD measure (nasal, superior, temporal, and inferior quadrants), including the three AT(N) groups (Normal, Alzheimer and SNAP) as discriminant factors and adjusting their effect by all six clinical variables and those demographic factors that showed any significant effect in the former multinomial regression analysis. The Normal AT(N) group was considered the reference category. Regression coefficients (the mean change in the outcome variable for one unit of change in the predictor variable while holding other predictors in the model constant), betas (the degree of change in the outcome variable for every one unit of change in the predictor variable) and t (assessing whether the beta coefficient is significantly different from zero) are reported.
The former four multivariate regression analysis were rerun without including the A+T-N- participants within the AT(N) Alzheimer group (amyloidosis alone without tauopathy or neurodegeneration, n = 9).
Additionally, the former four multivariate regression analyses were repeated including a group of participants with subjective cognitive decline (SCD) and absent brain amyloid uptake in a FBB-PET scan (SCD Aβ-) from the Fundació ACE Healthy Brain Initiative (FACEHBI) cohort (Rodriguez-Gomez et al., 2017 (link)) as the reference category (n = 83).
For the former multivariate regression analyses, alpha level was set up at p < 0.004 (0.05/12) after Bonferroni’s correction for multiple comparisons.
The association between individual CSF biomarkers (Aβ1-42, p181-tau and t-tau) and each of the four macular VD measurements (nasal, superior, temporal and inferior quadrants) was explored using separate partial correlations, including the same covariates. These analyses were performed separately for the ELISA and CLEIA groups and also for the whole cohort after a log-transformation of each CSF biomarker value. For these correlation analyses, alpha level was set at p < 0.004 (0.05/12), after Bonferroni’s correction for multiple comparisons.
To investigate whether a differential effect could be detected when considering sex, the previous four multivariate regression analyses were executed again, including now the interaction between AT(N) group and sex as the main factor of interest and the same covariates. For these analyses, alpha level was set at p < 0.0125 (0.05/4), after Bonferroni’s correction for multiple comparisons.
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