Amyloidosis

In this study, male WT C57BL/6 J mice (n = 52) and male transgenic APP_KM670/671NL/PS1_L166P mice were used (n = 67, referred to as APP/PS1 mice) [11 (link)]. Mice were housed in the animal facility of the University of Antwerp during the whole experiment. During the study, mice were kept on a normal 12-h/12-h day-night cycle with ad libitum access to food and water. Additional file 1 shows the weight evolution of the mice in the longitudinal cohort.
APP/PS1 mice start developing Aβ plaques from the age of 6 to 8 weeks and show aggressive amyloidosis in subsequent months [11 (link)]. As such, we acquired the first dataset when mice were 2 months old, when only low Aβ plaque deposition is present (n = 20 WT mice and n = 19 APP/PS1 mice). We then longitudinally followed these mice by acquiring a DKI datasets when they were 4 and 6 months of age (intermediate Aβ plaque load) and finally at 8 months of age, which corresponds to an extensive Aβ plaque load. After acquisition of this last DKI dataset at 8 months of age, mice were then sacrificed for histological analysis. In addition, three further cohorts of WT and APP/PS1 mice were scanned once each at 2 months (n = 11 WT mice and n = 16 APP/PS1 mice), 4 months (n = 10 WT mice and n = 16 APP/PS1 mice) and 6 months (n = 11 WT mice and n = 16 APP/PS1 mice) of age and killed thereafter for histological analysis. The complete experimental design is shown in Fig. 1.Fig. 1

Experimental setup of the study. Diffusion kurtosis imaging (DKI) was performed in three cohorts of male wild-type (WT) and APP/PS1 mice at 2, 4 and 6 months of age, and these mice were sacrificed for histological analysis thereafter. In a fourth, longitudinal cohort of male WT and APP/PS1 mice, we performed DKI at 2, 4, 6 and 8 months of age and thereafter killed these mice for histological analysis

Consecutive patients followed at the Cardiac Amyloidosis Clinic at our institution with a diagnosis of ATTR-CM from November 2016 to January 2021 were included in this retrospective cohort analysis. Patients with both ATTR subtypes were included if they met the following criteria; (1) exclusion of light-chain (AL) amyloidosis through the absence of serum and urine monoclonal protein, (2) evidence of cardiac amyloidosis by either myocardial biopsy or positive technetium-99 m-pyrophosphate nuclear scintigraphy defined by grade 2–3 myocardial uptake or heart-to-contralateral lung ratio > 1.5, as previously described [10 (link)], and (3) either hATTR or wtATTR based upon results of genetic testing or proteomic analysis by mass spectrometry performed on biopsy tissue samples. Patients with non-ATTR subtypes of amyloidosis or those with < 12 months clinical follow-up were excluded. Clinical, medication, biochemical and cardiac imaging data were collected at the time of ATTR-CM diagnosis. This study was approved by the University of Calgary Research Ethics Board, and the requirement for informed written patient consent was waived.

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.

The subjects in this study were patients with T2DM that underwent dual-energy X-ray absorptiometry (DXA) and NCS. The diagnosis of T2DM was according to the World Health Organization 1999 criteria (23 ). Differential diagnosis of DPN included assessing the etiology of neuropathies that mimic clinical presentation of DPN, so the patients were excluded from this study if they had the following: alcohol abuse, vitamin B₁₂ deficiency, neoplasia, HIV treatment, chemotherapy, amyloidosis, and genetic neuropathies (24 (link)).