Summary: Researchers made a significant discovery in understanding the mechanisms behind protein accumulation in neurodegenerative diseases like Alzheimer’s. By studying fruit flies, the team found that a reduction in mitochondria within neuron axons leads directly to this detrimental protein buildup.
They pinpointed a rise in the protein eIF2β as a critical factor; reducing its levels restored protein recycling and improved neuron function. This breakthrough suggests a new target for therapies aimed at treating conditions like Alzheimer’s and ALS, potentially improving outcomes for patients.
Key Facts:
The study revealed that depletion of mitochondria in neuron axons causes abnormal protein accumulation, a hallmark of diseases like Alzheimer’s.
Researchers identified an increase in the protein eIF2β as a key contributor to this process; adjusting its levels could reverse the effects.
The findings, derived from genetic studies in fruit flies, open the door to developing new treatments that could target mitochondrial health or regulate protein levels to combat neurodegenerative diseases.
Source: Tokyo Metropolitan University
Researchers from Tokyo Metropolitan University have identified how proteins collect abnormally in neurons, a feature of neurodegenerative diseases like Alzheimer’s. They used fruit flies to show that depletion of mitochondria in axons can directly lead to protein accumulation.
At the same time, significantly high amounts of a protein called eIF2β were found. Restoring the levels to normal led to a recovery in protein recycling. Such findings promise new treatments for neurodegenerative diseases.
It is known that the levels of mitochondria in axons can drop with age, and during the progress of neurodegenerative diseases.
Every cell in our bodies is a busy factory, where proteins are constantly being produced and disassembled. Any changes or lapses in either the production or recycling phases can lead to serious illnesses. Neurodegenerative diseases such as Alzheimer’s and Amyotrophic Lateral Sclerosis (ALS), for example, are known to be accompanied by an abnormal build-up of proteins in neurons. However, the trigger behind this accumulation remains unknown.
A team led by Associate Professor Kanae Ando of Tokyo Metropolitan University have been trying to determine the causes of abnormal protein build-up by studying Drosophila fruit flies, a commonly studied model organism that has many key similarities with human physiology.
They focused on the presence of mitochondria in axons, the long tendril-like appendages that stretch out of neurons and form the necessary connections that allow signals to be transmitted inside our brains. It is known that the levels of mitochondria in axons can drop with age, and during the progress of neurodegenerative diseases.
Now, the team have discovered that the depletion of mitochondria in axons has a direct bearing on protein build-up. They used genetic modification to suppress the production of milton, a key protein in the transport of mitochondria along axons.
It was found that this led to abnormal levels of protein building up in fruit fly neurons, a result of the breakdown of autophagy, the recycling of proteins in cells. Through proteomic analysis, they were able to identify a significant upregulation in eIF2β, a key subunit of the eIF2 protein complex responsible for the initiation of protein production (or translation).
The eIF2α subunit was also found to be chemically modified. Both of these issues hamper the healthy action of eIF2.
Importantly, by artificially suppressing levels of eIF2β, the team discovered that they could restore the autophagy that was lost and regain some of the neuron function that was impaired due to axonal mitochondria loss. This not only shows that depletion of mitochondria in axons can cause abnormal protein accumulation, but that this happens via upregulation of eIF2β.
As populations age and the prevalence of neurodegenerative conditions continues to increase, the team’s findings present a vital step in developing therapies to combat these serious illnesses.
Abstract
Axonal distribution of mitochondria maintains neuronal autophagy during aging via eIF2β
Neuronal aging and neurodegenerative diseases are accompanied by proteostasis collapse, while cellular factors that trigger it are not identified.
Impaired mitochondrial transport in the axon is another feature of aging and neurodegenerative diseases. Using Drosophila, we found that genetic depletion of axonal mitochondria causes dysregulation of translation and protein degradation.
Axons with mitochondrial depletion showed abnormal protein accumulation, and autophagic defects. Lowering neuronal ATP levels by blocking glycolysis did not reduce autophagy, suggesting that autophagic defects are associated with mitochondrial distribution.
We found eIF2β was upregulated by depletion of axonal mitochondria via proteome analysis. Phosphorylation of eIF2α, another subunit of eIF2, was lowered, and global translation was suppressed.
Neuronal overexpression of eIF2β phenocopied the autophagic defects and neuronal dysfunctions, and lowering eIF2β expression rescued those perturbations caused by depletion of axonal mitochondria.
These results indicate the mitochondria-eIF2β axis maintains proteostasis in the axon, of which disruption may underly the onset and progression of age-related neurodegenerative diseases.
Patients’ comorbidities can affect Alzheimer disease (AD) blood biomarker concentrations. Because a limited number of factors have been explored to date, our aim was to assess the proportion of the variance in fluid biomarker levels explained by the clinical features of AD and by a large number of non–AD-related factors.
Methods
MEMENTO enrolled 2,323 individuals with cognitive complaints or mild cognitive impairment in 26 French memory clinics. Baseline evaluation included clinical and neuropsychological assessments, brain MRI, amyloid-PET, CSF (optional), and blood sampling. Blood biomarker levels were determined using the Simoa-HDX analyzer. We performed linear regression analysis of the clinical features of AD (cognition, AD genetic risk score, and brain atrophy) to model biomarker concentrations. Next, we added covariates among routine biological tests, inflammatory markers, demographic and behavioral determinants, treatments, comorbidities, and preanalytical sample handling in final models using both stepwise selection processes and least absolute shrinkage and selection operator (LASSO).
Results
In total, 2,257 participants were included in the analysis (median age 71.7, 61.8% women, 55.2% with high educational levels). For blood biomarkers, the proportion of variance explained by clinical features of AD was 13.7% for neurofilaments (NfL), 11.4% for p181-tau, 3.0% for Aβ-42/40, and 1.4% for total-tau. In final models accounting for non–AD-related factors, the variance was mainly explained by age, routine biological tests, inflammatory markers, and preanalytical sample handling. In CSF, the proportion of variance explained by clinical features of AD was 24.8% for NfL, 22.3% for Aβ-42/40, 19.8% for total-tau, and 17.2% for p181-tau. In contrast to blood biomarkers, the largest proportion of variance was explained by cognition after adjustment for covariates. The covariates that explained the largest proportion of variance were also the most frequently selected with LASSO. The performance of blood biomarkers for predicting A+ and T+ status (PET or CSF) remained unchanged after controlling for drivers of variance.
Discussion
This comprehensive analysis demonstrated that the variance in AD blood biomarker concentrations was mainly explained by age, with minor contributions from cognition, brain atrophy, and genetics, conversely to CSF measures. These results challenge the use of blood biomarkers as isolated stand-alone biomarkers for AD.
Introduction
Over the past decade, biological markers of Alzheimer disease (AD) have increasingly been used to guide AD diagnosis. The National Institute on Aging and the Alzheimer’s Association introduced the AT(N) classification research framework, which defines AD on the basis of biomarkers without considering the clinical presentation.1 Recently, the US Food and Drug Administration granted accelerated approval for the use of aducanumab and lecanemab (then converted to a traditional approval few months later for lecanemab) based on their ability to significantly reduce amyloid plaques because this surrogate endpoint “reflects the effect of the drug on an important aspect of the disease.”2
Traditionally, the pathologic hallmarks of AD, such as β-amyloid (Aβ) peptides and phosphorylated tau levels, and markers of neurodegeneration, including neurofilament light chain (NfL) and total-tau, were primarily measured in the CSF, typically in specialized clinical centers. However, recent technical advancements using ultra-sensitive assays have enabled the measurement of these biomarkers in blood. Blood-based biomarker measurements offer advantages such as easier and more rapid collection, lower costs, and better patient acceptance compared with the corresponding CSF measurements. Multiple studies have consistently shown correlations of these markers with amyloid status,3–5 their ability to distinguish AD from other neurodegenerative diseases,6 as well as their potential in predicting cognitive decline7–9 or progression from mild cognitive impairment (MCI) to dementia.10–14
The use of blood biomarkers for clinical purposes in the evaluation of patients with cognitive impairment and dementia (population screening, diagnosis, and disease monitoring in clinical trials) has been discussed extensively13–15 and raises concerns regarding their widespread availability. Ideal biomarkers should exhibit abnormal levels exclusively in affected individuals and be unaffected by unrelated factors. However, recent studies have shown that the concentrations of these blood biomarkers are affected by various conditions, including chronic kidney disease, myocardial infarction, body mass index (BMI), diabetes, dyslipidemia, arterial hypertension, ethnicity, and preanalytical sample handling.16–20 However, these studies focused on few variables, and there may be other unidentified covariates. Furthermore, even when there is a statistical association, it remains unclear how much of the variability of the measure is explained by these covariates, particularly when compared with conventional clinical features of AD, such as cognition, imaging, and genetic risk score.
To address these questions, we used data from the MEMENTO cohort, a large multicentric French memory clinic cohort for which comprehensive data collection was implemented. Our primary objective was to estimate the variance explained in blood and CSF biomarker concentrations by clinical features of AD and other covariates. In addition, we evaluated the effect of these covariates on the performance of blood biomarkers for predicting amyloid (A+) or tau (T+) pathologic status, as assessed by CSF or PET measurements.
Methods
Study Population
The MEMENTO cohort recruited a total of 2,323 nondemented participants from 26 French memory clinics between 2011 and 2014. Participants were referred to the clinics by their general practitioners (49%) or other physicians (17%) or sought consultations independently (34%). Participants were consecutively recruited if they presented a cognitive complaint with a Clinical Dementia Rating (CDR) score at 0 (normal) or 0.5 (a proxy of mild cognitive impairment). Level of cognitive impairment was evaluated using a comprehensive battery of neuropsychological tests, and the intensity of cognitive complaints was assessed using visual analog scales. Details of the cohort have been published previously.21 Main exclusion criteria were a history of head trauma with persistent neurologic deficits, stroke in the past 3 months or with persistent neurologic deficits, brain tumor, epilepsy, schizophrenia, known alteration in familial AD genes, and illiteracy. Written informed consent was obtained from all participants, and the study protocol received approval from the ethics committee “CPP Sud-Ouest et Outre-Mer III.” The study was registered at ClinicalTrials.gov (NCT01926249).
Data Collection
At the time of enrolment, participants underwent comprehensive clinical and neuropsychological assessment. The CDR scale was administered and a neuropsychological test battery to evaluate memory, executive functions, language, and attention domains.21 To ensure standardized test scoring, investigators and neuropsychologists received training. Medications and comorbidities were obtained from medical records or reported by the participants. In addition, participants were requested to provide a blood sample at their local biochemistry department for routine biological measurements, including glucose, lipids, creatinine, blood counts, hemoglobin, liver enzymes, and electrolytes.
Neuropsychological Evaluation
Verbal episodic memory was assessed using the Free and Cued Selective Reminding Test (FCSRT), following the procedure proposed by Grober and Buschke22 and adapted to the French population.23 Episodic memory performance was determined by summing the 3 total (free and cued) recalls. Executive functions were assessed using the Trail Making Test part B (TMT-B).24 In this article, we considered the time required for a good move (i.e., the total time divided by the total number of correct moves until test completion). The CDR scale is widely used to assess cognition and function for staging dementia.25 It includes 6 domains (memory, orientation, judgment, community affairs, home hobbies, and personal care) scored from 0 (no impairment) to 3 (severe impairment) by a trained physician based on interviews with both participants and their informants. For the analysis, we considered the sum of 6 domains (CDR-SoB, 0–18).
MRI and Amyloid PET Imaging
At the time of inclusion in the cohort, participants underwent brain MRI, and 1-mm isotropic 3-dimensional T1-weighted images and 2D T2-weighted fluid-attenuated inversion recovery images were acquired. The Centre pour l’Acquisition et le Traitement des Images in Paris, France,26 conducted preacquisition standardization and centralized quality checks on image acquisition. They applied postprocessing pipelines for standardized measurement of intracranial volumes using SPM12, hippocampal volumes using SACHA,27 and cortical thicknesses using FreeSurfer 5.3.
To evaluate the amyloid load by amyloid PET, participants had the opportunity to participate in 2 ancillary studies: Insight-PreAD28 at baseline and AMYGING (NCT02164643) during follow-up (2 years on average after inclusion in MEMENTO). Depending on the clinical site, the radiotracer administered was 18F-florbetapir (Amyvid) or 18F-flutemetamol (Vizamyl). Each tracer has a specific threshold for amyloid positivity (florbetapir >0.88, flutemetamol >1.063).29
CSF Biomarkers
Lumbar puncture was an optional procedure. For participants who accepted, their CSF was collected into polypropylene tubes following standardized protocols. The CSF samples were transferred to the local CSF bank within 4 hours after collection and centrifuged at 1,000g and 4°C for 10 minutes. After centrifugation, the CSF samples were aliquoted into polypropylene tubes and stored at −80°C. Aβ 42 peptide (Aβ-42), Aβ-40, total-tau, and 181-phosphorylated tau (p181-tau) levels were determined in a central location using INNOTEST kits (Fujirebio, Ghent, Belgium). NfL levels were assessed using the Single Molecular Array Ultra-sensitive Immunoassay (Simoa) with commercial kits on a Quanterix HD-X analyzer (Quanterix, Billerica, MA). The assay had a sensitivity cutoff of 17.4 pg/mL. Only CSF collected within 90 days after blood sampling was included in the analysis.
Blood Biomarkers
At the time of inclusion, blood samples for biobanking were collected and stored at −80°C in a central biobank, the Genomic Analysis Laboratory-Biological Resource Centre (LAG-CRB) at the Pasteur Institute, Lille (BB-0033-00071). The baseline samples were used for whole-genome sequencing and analysis of inflammatory markers and AD blood biomarkers.
Genomic DNA from peripheral blood samples was extracted at LAG-CRB using Gentra Puregene blood kits (Qiagen, Hilden, Germany). The genotypes of 35 single-nucleotide polymorphisms associated with AD dementia risk, including APOE genotype, were determined. A genetic risk score was computed as the sum of the risk alleles for AD weighted by their estimated effect sizes.30 The genetic risk score was computed without the APOE genotype to exclude its potentially large effect due to its high frequency and large effect size.
The levels of serum inflammatory markers (referred to as “inflammatory markers” in the subsequent text), including interleukin (IL)-6, IL-10, IL-12p70, IL-18, tumor necrosis factor-α, RANTES (CCL5), and IP-10 (CXCL10), were measured using the commercial Bio-Plex Pro Human kit (Bio-Rad, Hercules, CA).
Baseline AD blood biomarkers were measured using commercial Simoa immunoassay kits on a Quanterix HD-X analyzer. Plasma Aβ42, Aβ40, and total-tau were measured using the Neurology 3-Plex A Advantage kit, serum p181-tau using the p181-tau Advantage V2 kit, and serum NfL using the NF-light Advantage kit. The lower limits of detection for the kits were (in pg/mL) 0.69 for NfL, 0.33 for p181-tau, 0.25 for total-tau, 2.7 for Aβ40, and 0.56 for Aβ42.31 The measurements were conducted at the Bordeaux University Hospital research platform (PARS). The internal coefficient of variation was 8.0% for Aβ-40, 9.7% for Aβ-42, 10.9% for total-tau, 11.1% for p181-tau, and 12.7% for NfL.
The evaluation of CSF markers, genetic single-nucleotide polymorphisms, and blood markers was performed by investigators who were blinded to the clinical data.
Statistical Analysis
Variables are presented as median and interquartile, or frequency and percentage as appropriate. Correlations between CSF and blood biomarker levels were evaluated using the nonparametric Spearman rank correlation test. To evaluate the proportion of biomarker variance explained, we used R2. R2 values range from 0 to 1, with a value of 1 signifying a perfect model that explains all of the variance in the data. For clearer interpretation, we present R2 values as a percentage (0%–100%), representing the proportion of variance explained by each variable in the overall model. The linear regression assumptions of normality and homoscedasticity of the residuals were assessed graphically, and collinearity was evaluated using the variance inflation factor.
Identification of Characteristics Associated With Biomarker Concentrations
In the first step, linear regression models were used to estimate the associations between the biomarkers of interest and clinical features of AD as explanatory variables. These features of AD, which have previously been shown to be associated with incident AD,10 were selected based on their expected ability to explain a substantial proportion of the biomarkers’ variance.
The clinical features of AD included cognitive performance (CDR-SoB, FCSRT, and TMT-B, as described above), morphological MRI features (normalized hippocampal volume [hippocampal volume/intracranial volume] and mean cortical thickness in the entorhinal, inferior temporal, middle temporal, and inferior parietal cortices; fusiform gyrus; and precuneus according to the Desikan atlas, corresponding to the Dickerson signature of AD32), and AD genetic risk factors (APOE ε4 status and a genetic risk score excluding APOE). These variables were included in a multivariable linear regression model without selection. The AD biomarkers (i.e., Aβ-42, Aβ-40, tau, p181-tau, and NfL) and Aβ-42/Aβ-40 and p181-tau/tau ratio data were log transformed to achieve a normal distribution and then standardized. Subgroup analyses were performed by stratifying according to the baseline global CDR (0 vs 0.5), amyloid status on amyloid-PET scan or abnormal CSF Aβ-42 level (whichever was available first), and educational level (the French baccalaureate corresponded to a high educational level).
Next, we explored non–AD-related factors associated with blood and CSF biomarker concentrations. A wide range of candidate variables were tested, including age, sex, routine biological measurements, blood inflammatory markers, behavioral and cardiovascular risk factors (systolic and diastolic blood pressure, heart rate, BMI, alcohol and tobacco consumption, and physical activity), treatments (if ≥50 individuals were exposed), comorbidities (if present in ≥50 individuals), and preanalytical sample handling (delay in hours from collection to freezing, duration of storage at −80°C, time of day for collection, and fasting status). eTable 1 presents a comprehensive list of the candidate variables.
Using individual biomarkers as outcomes, we performed univariate linear regression analyses with each candidate covariate included separately (i.e., 1 model per biomarker and covariate). To account for type I error inflation in the context of multiple testing, a false discovery rate correction was applied, and a statistical significance level was set at 0.05. Then, we computed multivariate linear regression including significantly associated clinical features of AD and non–AD-related factors, and a stepwise selection (based on p-values <0.05) was performed to determine the final models. In parallel, a least absolute shrinkage and selection operator (LASSO) approach was implemented to select among all clinical features of AD and non–AD-related factor variables associated with biomarker concentrations. Owing to the instability of LASSO in variable selection, 100 bootstrap samples were generated from the original data set. We selected variables independently for each bootstrap sample, determining the optimal tuning parameter (λ) through a fivefold cross-validation process. The results of this selection process are the selection frequency (%) for each variable (i.e., the number of times the variable was finally selected). The final model for each biomarker included covariates statistically significant after stepwise selection and the corresponding LASSO selection. Overall and partial R2 values were derived from the final models to determine the proportion of variance that the models explained.
Correction of Blood Biomarker Concentrations to Predict Pathologic Status
To test whether the prediction accuracy of disease status could be improved, a correction was applied to the blood biomarker concentrations. This correction accounted for the effects of medication intake, comorbidities, laboratory results, and inflammatory markers. The coefficients derived from the final multiple linear regression models served as the basis for these adjustments. For instance, individuals exposed to certain treatments exhibited an X-point increase in p181-tau levels compared with untreated individuals. To determine the corrected level for individuals under this treatment, we predicted the level that would be observed in the absence of this treatment by subtracting X from the actual level. The value corrected on the basis of the laboratory results was computed using the sample mean. The aim of this adjustment was to evaluate the extent to which accounting for covariates improved the correlations of blood biomarkers with CSF markers and had better discriminatory performance in predicting amyloid positivity on PET or pathologic levels of CSF Aβ-42 (<750 pg/mL) or p181-tau (>60 pg/mL). The change in discriminative performance was assessed by calculating the difference in the area under the curve (AUC) between the corrected and uncorrected blood biomarkers. Confidence intervals for the differences in AUC values were obtained through bootstrap resampling.
Statistical analyses were performed using SAS 9.4 software (SAS Institute Inc., Cary, NC).
Results
Population Characteristics
Blood biomarkers of 2,257 participants were assessed at the time of inclusion in MEMENTO (Table 1). The median age of the participants was 72 years, with 61.8% being women and 55.2% having a high educational level. Table 1 presents the baseline characteristics of the participants, 305 (13.5%) of whom underwent lumbar puncture within 90 days (median 7 days) after inclusion.Table 1 Baseline Sample Characteristics: Whole Cohort (n = 2,257) and CSF Subsample (n = 305): The MEMENTO Cohort
The proportion of variance of blood biomarkers explained by clinical features of AD (i.e., cognitive performance, AD genetic risk score, normalized hippocampal volume, and cortical signature on brain MRI) is presented in Figure 1. The amount of variance explained by the clinical features was as follows (in decreasing order): 13.7% for NfL, 11.4% for p181-tau, 5.9% for the p181-tau/tau ratio, 3.0% for the Aβ-42/Aβ-40 ratio, 1.9% for Aβ-40, 1.4% for total-tau, and 1.3% for Aβ-42. Similar results were obtained for blood biomarkers in the CSF subsample, although a slightly higher proportion of the variance was explained. Regarding CSF biomarkers, the clinical features of AD explained a larger proportion of the variance, particularly for Aβ-42 (26.8%), NfL (24.8%), Aβ-42/Aβ-40 ratio (22.3%), total-tau (19.8%), and p181-tau (17.2%).
Figure 1 Proportions of Variance Explained by Clinical Features of AD for Blood and CSF Biomarker Concentrations: The MEMENTO Cohort
To account for the potential of amyloid positivity, baseline cognitive status, or educational level to modulate levels of fluid biomarkers and their association with clinical features of AD, we conducted stratified analyses (Figure 2 and eFigure 1). The proportion of variance in blood biomarkers explained by the clinical features of AD was substantially higher in the amyloid-positive subgroup compared with the amyloid-negative subgroup. The proportion of variance explained was roughly comparable according to CDR or educational level. Regarding CSF biomarkers, the effect of CDR, amyloid positivity, and educational level on the proportions of variance explained by clinical AD correlates was minor.
Figure 2 Proportions of the Variance Explained by Clinical Features of AD for Blood and CSF Biomarker Concentrations According to Amyloid Status: The MEMENTO Cohort
Variance Explained by Age and Other Associated Covariates
Beyond clinical features of AD, a comprehensive analysis was conducted to identify other factors associated with biomarker levels, including demographics, routine biological measurements, inflammatory markers, behavioral and cardiovascular risk factors, alcohol consumption, physical activity, treatments, comorbidities, and preanalytical sample handling. Table 2 summarizes the overall variance explained by the final models and the contribution of each category of covariates. Data on the assumptions of homoscedasticity and normality of residuals are presented in the supplementary materials. We found no evidence of heteroscedasticity of residuals (eFigure 2), and their distributions were close to normal (eFigure 3). We found no substantial collinearity across covariates (all variance inflation factors <5; eTable 2).Table 2 Variance Explained by the Factors Associated With the Measure of Blood and CSF Biomarker Concentration: The MEMENTO Cohort
N
Proportion of variance explained (100 × R2) in the final models
Overall
Clinical features of AD
Age
Sex
Routine biological measurements
Blood inflammatory markers
Behavorial risk factors
Treatments
Comorbidities
Preanalytical sample handling
Blood biomarkers
Aβ-42
2,215
8.3
1.3
4.0
1.6
0.6
0.4
0.5
Aβ-40
2,213
11.5
0.3
6.1
2.5
1.0
0.8
0.2
0.6
Aβ-42/Aβ-40
2,210
8.7
1.5
4.2
0.3
0.2
2.4
p181-tau
2,057
20.5
6.7
9.2
2.5
0.7
0.6
0.8
Total-tau
2,224
12.3
0.6
0.7
2.7
2.0
0.8
1.1
4.5
p181-tau/tau
2,028
14.3
2.2
7.2
1.9
0.6
0.6
0.3
0.6
0.9
NfL
2,255
46.0
0.3
35.9
5.0
0.4
3.5
0.2
0.2
0.5
CSF biomarkers
Aβ-42
303
29.7
21.6
8.1
NA
Aβ-40
297
2.2
1.2
1.1
NA
Aβ-42/Aβ-40
297
28.7
18.1
10.5
NA
p181-tau
303
21.5
13.8
3.1
4.6
NA
Total-tau
302
29.1
15.2
6.1
5.6
2.3
NA
p181-tau/tau
302
21.4
10.7
7.4
3.4
NA
NfL
286
26.0
11.9
14.1
NA
EXPAND TABLE
Abbreviations: Aβ = β-amyloid; AD = Alzheimer disease; NfL = neurofilament light chain; NA = not applicable.
Only variables significantly associated (p < 0.05) with biomarker concentrations contribute to R2 calculation. A comprehensive list of the variables is provided in Table 3 and eTable 2.
In the final models, a larger proportion of the variance in blood biomarkers was explained by age (Aβ-40: 6.1%, Aβ-42/Aβ-40: 4.2%, p181-tau: 9.2%, p181-tau/tau: 7.2%, and NfL: 35.9%) or non–AD-related factors (Aβ-42: 7.1%, Aβ-40: 5.1%, Aβ-42/Aβ-40: 2.9%, p181-tau: 4.6%, p181-tau/tau: 4.9%, and NfL: 9.8%). Conversely, clinical features of AD accounted for a smaller fraction, except for p181-tau (6.7%, including 3.1% for genetic score, 3.1% for cognitive function, and 0.5% for AD signature on brain MRI; Table 3). Overall, sex, inflammatory markers, behavioral and cardiovascular risk factors, treatments, and comorbidities made a limited contribution to the explained variance. The standard biological measurements explained a larger fraction of variance compared with the clinical features of AD for Aβ-42 (4.0% for creatinine [2.85%], alanine aminotransferase [ALT; 0.61%], and triglycerides [0.50%]), Aβ-40 (2.5% for creatinine [1.68%], alkaline phosphatase [0.45%], platelets [0.28%], and ALT [0.14%]), total-tau (2.7% for ALT [0.66%], high-density lipoprotein [HDL] cholesterol [0.55%], chloride [0.52%], hemoglobin [0.49%], and total bilirubin [0.48%]), and NfL (5% for creatinine [3.93%], hemoglobin [1.02%], and HDL cholesterol [0.05%]). Table 3 and eTable 2 present detailed descriptions of the associated factors. Overall, the covariates that explained the largest proportion of the variance were the most frequently selected with LASSO (e.g., 100% for age and p181-tau, 96% for age Aβ-42/Aβ-40, and 94% for creatinine and NfL), whereas those with a small R2 value were infrequently selected (eTable 2).Table 3 AD and Non–AD-Related Factors Explaining Blood Biomarker Variance: The MEMENTO Cohort
β (95% CI)
Proportion of variance explained (%)
LASSO selection (%)
Aβ-42/Aβ-40
Clinical features of AD
APOE ε4
−0.229 (−0.319 to −0.139)
1.25
38
ROI cortical thickness
−0.511 (−0.851 to −0.170)
0.29
0
Non–AD-associated covariates
Age
−0.026 (−0.031 to −0.021)
4.20
96
Fasting status
−0.153 (−0.295 to −0.010)
1.68
67
Duration of storage at −80°C
−0.101 (−0.152 to −0.049)
0.74
14
IP-10
−0.000 (−0.000 to −0.000)
0.33
18
α-Adrenoreceptor antagonists
−0.139 (−0.272 to −0.006)
0.18
1
P181-tau
Clinical features of AD
APOE ε4
0.274 (0.188 to 0.360)
2.65
48
FCSRT
−0.016 (−0.024 to −0.009)
2.38
81
TMT-B
0.022 (0.009 to 0.035)
0.65
38
ROI cortical thickness
−0.463 (−0.812 to −0.114)
0.54
68
Genetic risk score
0.191 (0.081 to 0.301)
0.49
3
Non–AD-associated covariates
Age
0.020 (0.015 to 0.026)
9.23
100
Creatinine
0.006 (0.004 to 0.009)
1.09
18
Vitamin K antagonists
0.307 (0.155 to 0.460)
0.80
13
IL-12p70
0.008 (0.004 to 0.011)
0.66
5
Total cholesterol
−0.042 (−0.077 to −0.006)
0.59
0
BMI
−0.017 (−0.026 to −0.007)
0.50
3
Hemoglobin
−0.055 (−0.091 to −0.018)
0.44
3
Alkaline phosphatase
−0.002 (−0.003 to −0.001)
0.39
1
Tobacco consumption
0.077 (0.012 to 0.141)
0.12
1
NfL
Clinical features of AD
CDR SoB
0.072 (0.023 to 0.121)
0.27
6
Non–AD-associated covariates
Age
0.053 (0.048 to 0.057)
35.94
100
Creatinine
0.013 (0.011 to 0.015)
3.93
94
BMI
−0.038 (−0.046 to −0.030)
2.90
94
Hemoglobin
−0.077 (−0.106 to −0.048)
1.02
65
Time of day for collection
0.204 (0.100 to 0.307)
0.43
0
TNFα
0.001 (0.001 to 0.002)
0.42
9
Diastolic blood pressure
−0.007 (−0.011 to −0.003)
0.29
25
Tobacco consumption
0.076 (0.026 to 0.126)
0.26
1
Breast cancer
0.196 (0.059 to 0.334)
0.19
0
Vitamin K antagonists
0.150 (0.026 to 0.273)
0.14
0
Vitamin D and analogs
0.077 (0.007 to 0.148)
0.09
0
HDL cholesterol
0.068 (0.005 to 0.132)
0.05
5
Hypertension
0.091 (0.015 to 0.168)
0.03
0
Fasting status
0.191 (0.095 to 0.288)
0.03
0
EXPAND TABLE
Abbreviations: AD = Alzheimer disease; BMI = body mass index; CDR SoB = Clinical Dementia Rating-Sum of Boxes; FCSRT = Free and Cued Selective Reminding Test; HDL = high-density lipoprotein; IL = interleukin; IP = inducible protein; LASSO = least absolute shrinkage and selection operator; ROI = region of interest; TMT = Trail Making Test; TNF = tumor necrosis factor.
Details of AD and non–AD-related factors for Aβ-42, Aβ-40, total-tau, and p181-Tau/Tau are available in eTable 2.
The clinical features of AD explained more of the variance in the levels of all CSF biomarkers except NfL. We found significant associations between activated partial thromboplastin time and p181-tau (4.6% of variance explained) and total-tau (5.6%), as well as between migraine and total-tau (2.3%) and p181-tau/tau ratio (3.4%). No other associations were observed among inflammatory markers, behavioral risk factors, and treatments.
Corrected Blood Biomarker Concentrations: Correlations With CSF Measure and Prediction of Pathologic Amyloid (A+) and Tau (T+) Status
Based on final models presented in Tables 2 and 3 and eTable 2, we incorporated the effects of standard biological measurements, inflammatory marker concentrations, treatments, and comorbidities to estimate corrected blood biomarker concentrations. Correlations between uncorrected and corrected blood and CSF levels are presented in eFigure 4. No major differences in correlations were found between the corrected and uncorrected levels, with the largest increases being observed for Aβ-42 (from r = 0.27 to r = 0.33 after correction) and blood p181-tau (from r = 0.30 to r = 0.33).
Regarding the discriminative performances of uncorrected and corrected blood biomarker levels for the prediction of amyloid and tau pathologic status, a slight improvement was observed in the performance of blood Aβ42 for predicting positive CSF Aβ-42 status (delta AUC: +0.026, 95% CI 0.024–0.028) and amyloid PET status (delta AUC: +0.013, 95% CI 0.012–0.014). However, the corrected values for other biomarkers showed small differences (<0.01) or lower AUC values (Table 4).Table 4 Discriminative Performances of Uncorrected and Corrected Blood Biomarker Concentrations to Predict Pathologic Amyloid (A+) or Tau (T+) Status: The MEMENTO Cohort
In this study, we investigated the factors associated with fluid biomarker concentrations in a large clinical cohort of individuals with subjective cognitive complaints or MCI. Several clinical features of AD, including episodic memory, executive function, normalized hippocampal volume, the cortical signature of AD, APOE ε4 status, and AD genetic risk score, accounted for a small proportion of the variance in blood biomarker levels. Moreover, both age and other covariates, such as routine biological tests, inflammatory markers, behavioral risk factors, treatments, comorbidities, and preanalytical sample handling, explained a greater proportion of variance in blood biomarker levels than the clinical features of AD, except for p181-tau (where age explained 9.2% of the variance, clinical features of AD 6.7%, and other covariates 4.6%). Conversely, clinical features of AD accounted for a large fraction of the variance in CSF biomarker levels, surpassing the effects of age by a factor of 10, except for NfL and Aβ-40.
Although numerous variables were considered, a large proportion of the variance in blood biomarker levels remains unexplained (54%–92%). Several hypotheses can account for this observation. First, these peptides are also produced by peripheral organs, such as kidneys, skeletal muscles, and breasts,33 and the production source cannot be differentiated. However, there is potential for brain-derived tau to offer promising insight in the future.34 Second, these markers exhibit intrinsic variability, as indicated by the manufacturer, with internal coefficients of variation ranging from 8.0% for Aβ-40 to 12.7% for NfL. Repeated measurements of these markers over time would be valuable to better disentangle the effect of the disease progression to the intraindividual heterogeneity.
Our analysis confirmed the significant associations between creatinine levels and p181-tau, Aβ-42, Aβ-40, NfL, and the p181-tau/tau ratio. However, no significant associations were observed with total-tau or the Aβ-42/Aβ-40 ratio.16–18,35 In addition, we found associations among BMI, p181-tau, and NfL concentrations.18 Furthermore, we identified numerous novel associations with medical treatments, inflammatory markers, routine biological measurements, and preanalytical sample handling (Table 3 and eTable 2). The LASSO approach confirmed that the variables explaining the most variance had higher proportion of selection, while covariates with minimal contributions were poorly selected. The specific role of these characteristics is somewhat unclear, and our findings require confirmation in an independent population because we cannot exclude the possibility of false-positive results.
We report contrasting results regarding CSF biomarkers. Age explained a smaller proportion of the variance in CSF biomarker concentrations compared with blood biomarkers, whereas clinical features of AD explained a larger proportion. These differences between CSF and blood biomarkers can be attributed to the blood-brain barrier, which may protect the CSF from the effects of medications, peripheral inflammation, kidney disease, and other factors. As a result, CSF biomarkers are more likely to accurately reflect AD pathology and are less susceptible to the effects of confounding factors compared with their corresponding blood biomarkers. These findings are in line with those of a recent study that demonstrated a substantial effect of blood-brain barrier integrity on the diagnostic performance of AD blood biomarkers for brain pathology.36
We also investigated the effect of non–AD-related factors on predictions of amyloid (A+) and tau (T+) status based on AD blood biomarkers. After adjusting for routine laboratory measurements, treatments, and comorbidities, the correlations between blood and CSF biomarkers, as well as the performance of blood biomarkers in predicting A+ or T+ status (based on PET or CSF analysis), remained unchanged. Despite statistically significant results, routine laboratory tests, treatments, and comorbidities had a small effect on biomarker levels, as reported previously.18 Furthermore, considering these confounders when interpreting biomarker levels in clinical practice could lead to significant complexities and only marginal improvement in predictions of pathologic levels of Aβ-42 or p181-tau.
Our findings raise concerns regarding the clinical usefulness of AD blood biomarkers. Given their limited ability to reflect the clinical features of AD, they may not provide reliable information for patient management in the absence of confirmatory examinations, such as CSF analysis or PET.15 Moreover, their added value in predicting dementia compared with conventional clinical information is questionable.10 Therefore, 1 potential use for these biomarkers could be as a screening tool to identify patients who would benefit from further investigations and those for whom additional action is unnecessary.15 In this context, our results emphasize the significance of considering age as a key factor when establishing norms or clinical cutoffs.
This study had several strengths. This is a comprehensive analysis of the factors associated with AD blood biomarker levels that explores simultaneously neuropsychological tests, imaging and genetic data, biological and inflammatory markers, comorbidities, treatments, behavioral and cardiovascular risk factors, and preanalytical sample handling. Second, we examined multiple blood biomarkers and used a similar methodological approach for analyzing CSF markers. Finally, the proportion of variance offers easily interpretable findings and depicts the extent to which biomarker levels are explained by various factors beyond statistical significance.
We acknowledge some limitations. First, the CSF subsample was relatively small (n = 305). Although R2 of AD blood biomarker were higher in this subsample, the overall results remained consistent with the entire cohort. Second, calculating the proportion of variance explained (R2) through linear models carries inherent limitations. To address these limitations, the biomarker concentrations were log transformed and standardized to enable comparison across analyses. Moreover, we did not use R2 for variable selection and abstained from drawing conclusions regarding the goodness-of-fit or predictive performance of the models. Second, compared with the MCI individuals of The French Alzheimer Databank, a French registry of all persons consulting French Memory Clinics,37 our sample has a comparable proportion of female (62% vs 59%), is younger (72 years vs 76 years), and has a higher “high educational level attainment” (55% vs 15%) and a higher Mini-Mental State Examination score (28 vs 25). These differences might have influenced our results, although our conclusions remained consistent after controlling for baseline cognition and educational level. Furthermore, although we studied a clinical population, some of our findings are aligned with those published on the population-based Mayo Clinic Study of Aging,16,38 regarding APOE, BMI, and renal function. Still, our findings should be extrapolated cautiously to the general population. Finally, p217-tau, p231-tau, and glial fibrillary acidic protein were not included in our analysis because they are not currently available in the MEMENTO database. Compared with the other assays for blood biomarker quantification, the Quanterix commercial kits used in this study have demonstrated only moderate ability to detect abnormal amyloid load and also have moderate correlations with CSF biomarkers.39
In conclusion, the concentrations of AD blood biomarkers were predominantly explained by age and various non–AD-related factors, in contrast to CSF markers. Only blood p181-tau exhibited a modest level of variance explained by clinical features of AD in the final models that considered all associated variables including cognition, genetics, and brain MRI. We identified several non–AD-related factors associated with blood biomarker levels. However, even after accounting for these covariates, the correlations between CSF and blood biomarkers, as well as their predictions of A+ and T+ status, did not significantly improve. Our findings emphasize the need for further methodological strategies to identify the factors that contribute to the variability of AD blood biomarker concentrations. Such efforts are essential to help physicians interpret results more effectively in the future.
Glossary
Aββ-amyloidADAlzheimer diseaseALTalanine aminotransferaseAUCarea under the curveBMIbody mass indexCDRClinical Dementia RatingCDR SoBCDR-Sum of BoxesFCSRTFree and Cued Selective Reminding TestHDLhigh-density lipoproteinILinterleukinLAG-CRBGenomic Analysis Laboratory-Biological Resource CentreLASSOleast absolute shrinkage and selection operatorMCImild cognitive impairmentNfLneurofilament light chainp181-tau181-phosphorylated tauTMT-BTrail Making Test part B
A promising clinical study in the treatment of Alzheimer disease leveraged focused ultrasound (FUS) technology to open the blood-brain barrier with delivery of a systemic monoclonal antibody infusion, enhancing the removal of cerebral beta-amyloid, a hallmark of Alzheimer disease. This innovative treatment approach shows promise in enhancing targeted drug delivery in Alzheimer disease and other neurological conditions.
The study, conducted with WVU Rockefeller Neuroscience Institute (RNI) and reported in the latest issue of The New England Journal of Medicine, presents encouraging benefits for Alzheimer disease patients. Researchers at RNI collaborated with Insightec, a manufacturer of FUS technology.
“Focused ultrasound provides a new opportunity for Insightec collaborations with drug companies to improve drug delivery to the brain,” says Maurice R. Ferré, MD, Insightec’s CEO and chairman of the board. “Only 1-2% of drugs can cross the blood-brain barrier, making progress difficult and patient safety challenging when using large systemic drug concentrations. The ability to disrupt the blood-brain barrier to effectively deliver treatment demonstrates the power and potential of using focused ultrasound technology when addressing complex neurological conditions.”
“The results from this proof-of-concept study are encouraging and chart a path forward to improve drug delivery in combating Alzheimer disease,” said Ali Rezai, MD, executive chair of RNI. “Our long-time close collaboration with Insightec has helped accelerate advances in treatment of patients with Alzheimer disease and other neurological diseases.”
Alzheimer disease is a condition that affects 6.7 million Americans today –and promising new therapies are emerging to address this unmet medical need. This latest breakthrough discovery opens up new avenues for research and provides renewed hope for patients and their loved ones. As research progresses, it is anticipated that this treatment approach will continue to evolve and refine, paving the way for even more effective treatments.
Darmiyan, Inc. announced the FDA’s approval of its first-in-class (De Novo) clinical test, BrainSee, the first clinical application of Darmiyan’s patented core proprietary technology powered by advanced whole-brain image analysis and medical AI.
BrainSee is a highly-scalable and fully-automated software platform that combines standard clinical brain MRI and cognitive assessments – part of the routine, non-invasive workup of patients concerned with memory loss – and generates an objective score that predicts the likelihood of progression from aMCI to Alzheimer dementia within 5 years. BrainSee addresses a critical unmet need for over 10 million Americans and over 100 million patients worldwide grappling with aMCI. With an aging global population, the socio-economic impact of BrainSee is expected to grow rapidly and exponentially.
“Our vision is to redefine brain health screening and monitoring standards and impact the lives of patients and their family members in a meaningful way. BrainSee is the first product of this vision, backed by our solid technological infrastructure that is capable of driving further transformations and scalable innovations in the brain health landscape,” stated Dr Padideh Kamali-Zare, Founder and CEO of Darmiyan.
Early screening and risk stratification by BrainSee enables timely and personalized treatments for those aMCI patients at high risk of progression to Alzheimer dementia, aiming to delay dementia onset, while reassuring those at lower risk of progression, hence reducing the need for costly and invasive tests and the heavy burdens of financial and emotional abuse. This shifts the patient experience from prolonged anxiety to proactive management, which is crucial in an era of emerging Alzheimer treatments where accurate prognosis can help determine suitable treatment candidates. The economic impact of BrainSee will be significant for all stakeholders in healthcare, promising to reduce the billions of dollars annually spent on Alzheimer care, through more effective management and treatment.
BrainSee was previously granted FDA breakthrough designation in 2021. It stands out for its prognostic accuracy, patient convenience, same-day test results and seamless integration into the clinical workflow. Global availability of MRI significantly enhances BrainSee’s clinical utility. Most notably, BrainSee shifts the paradigm in aMCI workup from biomarker-based methods that have limited real-world capabilities due to their invasiveness, non-specificity, cost, and inaccessibility, to non-invasive and actionable forecasts of future improvement or progression.
Question Does donanemab, a monoclonal antibody designed to clear brain amyloid plaque, provide clinical benefit in early symptomatic Alzheimer disease?
Findings In this randomized clinical trial that included 1736 participants with early symptomatic Alzheimer disease and amyloid and tau pathology, the least-squares mean change in the integrated Alzheimer Disease Rating Scale score (range, 0-144; lower score indicates greater impairment) at 76 weeks was −6.02 in the donanemab group and −9.27 in the placebo group for the low/medium tau population and −10.19 in the donanemab group and −13.11 in the placebo group in the combined study population, both of which were significant differences.
Meaning Among participants with early symptomatic Alzheimer disease and amyloid and tau pathology, donanemab treatment significantly slowed clinical progression at 76 weeks.
Abstract
Importance There are limited efficacious treatments for Alzheimer disease.
Objective To assess efficacy and adverse events of donanemab, an antibody designed to clear brain amyloid plaque.
Design, Setting, and Participants Multicenter (277 medical research centers/hospitals in 8 countries), randomized, double-blind, placebo-controlled, 18-month phase 3 trial that enrolled 1736 participants with early symptomatic Alzheimer disease (mild cognitive impairment/mild dementia) with amyloid and low/medium or high tau pathology based on positron emission tomography imaging from June 2020 to November 2021 (last patient visit for primary outcome in April 2023).
Interventions Participants were randomized in a 1:1 ratio to receive donanemab (n = 860) or placebo (n = 876) intravenously every 4 weeks for 72 weeks. Participants in the donanemab group were switched to receive placebo in a blinded manner if dose completion criteria were met.
Main Outcomes and Measures The primary outcome was change in integrated Alzheimer Disease Rating Scale (iADRS) score from baseline to 76 weeks (range, 0-144; lower scores indicate greater impairment). There were 24 gated outcomes (primary, secondary, and exploratory), including the secondary outcome of change in the sum of boxes of the Clinical Dementia Rating Scale (CDR-SB) score (range, 0-18; higher scores indicate greater impairment). Statistical testing allocated α of .04 to testing low/medium tau population outcomes, with the remainder (.01) for combined population outcomes.
Results Among 1736 randomized participants (mean age, 73.0 years; 996 [57.4%] women; 1182 [68.1%] with low/medium tau pathology and 552 [31.8%] with high tau pathology), 1320 (76%) completed the trial. Of the 24 gated outcomes, 23 were statistically significant. The least-squares mean (LSM) change in iADRS score at 76 weeks was −6.02 (95% CI, −7.01 to −5.03) in the donanemab group and −9.27 (95% CI, −10.23 to −8.31) in the placebo group (difference, 3.25 [95% CI, 1.88-4.62]; P < .001) in the low/medium tau population and −10.2 (95% CI, −11.22 to −9.16) with donanemab and −13.1 (95% CI, −14.10 to −12.13) with placebo (difference, 2.92 [95% CI, 1.51-4.33]; P < .001) in the combined population. LSM change in CDR-SB score at 76 weeks was 1.20 (95% CI, 1.00-1.41) with donanemab and 1.88 (95% CI, 1.68-2.08) with placebo (difference, −0.67 [95% CI, −0.95 to −0.40]; P < .001) in the low/medium tau population and 1.72 (95% CI, 1.53-1.91) with donanemab and 2.42 (95% CI, 2.24-2.60) with placebo (difference, −0.7 [95% CI, −0.95 to −0.45]; P < .001) in the combined population. Amyloid-related imaging abnormalities of edema or effusion occurred in 205 participants (24.0%; 52 symptomatic) in the donanemab group and 18 (2.1%; 0 symptomatic during study) in the placebo group and infusion-related reactions occurred in 74 participants (8.7%) with donanemab and 4 (0.5%) with placebo. Three deaths in the donanemab group and 1 in the placebo group were considered treatment related.
Conclusions and Relevance Among participants with early symptomatic Alzheimer disease and amyloid and tau pathology, donanemab significantly slowed clinical progression at 76 weeks in those with low/medium tau and in the combined low/medium and high tau pathology population.
Introduction
Deposition of β-amyloid in the brain is an early event in Alzheimer disease that leads to neurofibrillary tangles composed of tau protein and other characteristic brain changes referred to as the amyloid cascade.1,2 Abnormal β-amyloid is a key pathological hallmark of Alzheimer disease defined by the 2018 National Institute on Aging and the Alzheimer’s Association Research Framework3 and is one of the major targets in Alzheimer disease research and drug development.
Over the past decade, considerable advances occurred in testing the amyloid cascade hypothesis in Alzheimer disease clinical trials. Numerous amyloid-targeting therapy trials failed to show appreciable slowing of clinical disease progression4–7; however, aducanumab, lecanemab, and donanemab recently showed promising amyloid plaque clearance, potentially benefitting patients.8–10
Donanemab is an immunoglobulin G1 monoclonal antibody directed against insoluble, modified, N-terminal truncated form of β-amyloid present only in brain amyloid plaques. Donanemab binds to N-terminal truncated form of β-amyloid and aids plaque removal through microglial-mediated phagocytosis.11 In the phase 2 TRAILBLAZER-ALZ trial of donanemab vs placebo, the primary outcome was met, as measured by the integrated Alzheimer Disease Rating Scale (iADRS), an integrated assessment of cognition and daily function.9 Adverse events of interest included amyloid-related imaging abnormalities and infusion-related reactions.9 To confirm and expand results from TRAILBLAZER-ALZ, we report results from TRAILBLAZER-ALZ 2, a global phase 3 randomized clinical trial that assessed donanemab efficacy and adverse events in a larger group of participants with low/medium tau pathology (the population studied in the phase 2 trial) and in a combined population including those with high tau pathology, a population hypothesized to be more difficult to treat due to more advanced disease.
Discussion
In this phase 3 trial, donanemab significantly slowed Alzheimer disease progression, based on the iADRS score, compared with placebo in the low/medium tau and combined tau populations and across secondary clinical outcomes of CDR-SB, ADAS-Cog13, and ADCS-iADL scores.
Donanemab treatment resulted in clinically meaningful benefit (considered to be >20% slowing of clinical progression39–41) on the iADRS and CDR-SB scales for both the low/medium tau and combined populations, regardless of statistical model. Additional support for clinical relevance is the 38.6% risk reduction of disease progression as measured on the CDR-G score and the 4.4 to 7.5 months saved over the 18-month study (low/medium tau population). Furthermore, an estimated 47% of participants receiving donanemab had no change in the CDR-SB at 1 year (no disease progression), compared with 29% of participants receiving placebo.
This trial used a definition of a MWPC28 based on any incremental change on the CDR-G scale (Alzheimer disease with MCI to mild Alzheimer disease or mild Alzheimer disease to moderate Alzheimer disease) or point changes of −5 on the iADRS and 1 on the CDR-SB for those with Alzheimer disease with MCI or −9 on the iADRS and 2 on the CDR-SB for those with Alzheimer disease with mild dementia at consecutive visits from baseline. In analyses assessing whether individual participants reached thresholds of clinically important progression over the course of the trial, donanemab resulted in significantly lower risk of meaningful change on the CDR-G as well as the prespecified nongated analyses of the iADRS and CDR-SB outcomes.
These clinical outcomes were achieved in 52% of low/medium tau participants completing donanemab treatment by 1 year, based on when a participant met amyloid clearance criteria. Limited-duration dosing was a distinct trial design feature reflecting donanemab binding specificity for amyloid plaque and implemented to decrease burden, cost, and potentially unnecessary treatments.11 Early significant changes on both brain amyloid PET scans and P-tau217 blood test results suggest opportunities for clinical monitoring of therapy. Donanemab treatment resulted in significantly reduced brain amyloid plaque in participants at all time points assessed, with 80% (low/medium tau population) and 76% (combined population) of participants achieving amyloid clearance at 76 weeks. Clearance beyond 76 weeks, and associated Alzheimer disease biomarkers levels, are currently being studied in the ongoing extension phase. The lack of response in frontal tau-PET is inconsistent with the TRAILBLAZER-ALZ phase 2 results.9,38 Additional regions have yet to be analyzed and reported. Factors resulting in this inconsistency will be examined. Changes in vMRI (including a greater decrease in whole brain volume in the donanemab group) were consistent with previous reports9,42 and would benefit from further exploration.
The general belief is that treating Alzheimer disease at the earliest disease stage is likely to result in more clinically meaningful effects.43,44 Post hoc evaluation in only high tau participants demonstrated no differences (P< .05) on the primary outcome or on most secondary clinical outcomes in donanemab-treated compared with placebo-treated participants within the 18-month trial, with the exception of CDR-SB. Compared with significant differences in the low/medium tau population, this supports the hypothesis that a greater benefit from amyloid-lowering therapies may occur when initiated at an earlier disease stage.
Similar to other amyloid-lowering drugs, and the phase 2 TRAILBLAZER-ALZ trial, amyloid-related imaging abnormalities are an associated adverse event. When amyloid-related imaging abnormalities occur, they are mostly asymptomatic and resolve in approximately 10 weeks. When symptoms occur, they are usually mild, consisting of a headache or increase in confusion, but can have more severe symptoms such as seizures. In some instances, these events can be life-threatening and result in, or lead to, death. For 1.6% of participants in the donanemab treatment group, amyloid-related imaging abnormalities led to serious outcomes, such as hospitalization, and required supportive care and/or corticosteroid use. It is also important to note that 3 deaths in TRAILBLAZER-ALZ 2 occurred after serious amyloid-related imaging abnormalities. Further evaluation of the risks associated with serious and life-threatening amyloid-related imaging abnormalities will be important to identify the best approaches for managing risks and maximizing benefit, in addition to earlier treatment of the disease when less amyloid pathology is present and, theoretically, when amyloid-related imaging abnormalities risk is lower.
Limitations
This study has several limitations. First, an inherent limitation to limited-duration dosing was variability in total donanemab doses received and/or duration of donanemab dosing. Second, data collection was for 76 weeks, limiting long-term understanding of donanemab; however, a study extension is ongoing. Third, the studied populations were primarily White (91.5%), which may limit generalizability to other populations due to a lack of racial and ethnic diversity. Fourth, although no related protocol amendments were necessary, this trial was conducted during the COVID-19 pandemic, and COVID-19 was the most commonly reported adverse event across treatment groups (see eMethods in Supplement 3). Fifth, direct comparison of results to other amyloid-targeting trials is not possible due to trial design differences such as stratification by baseline tau PET category. Sixth, amyloid-related imaging abnormality and infusion-related reaction occurrences may have caused participants and investigators to infer treatment assignment; attempts to minimize bias included blinding CDR raters to adverse event information and, based on sensitivity analyses, censoring change scores after the first observation of amyloid-related imaging abnormalities of edema/effusion and/or infusion-related reactions did not impact the results.
Conclusions
Among participants with early symptomatic Alzheimer disease and amyloid and tau pathology, donanemab significantly slowed clinical progression at 76 weeks in those with low/medium tau and in the combined low/medium and high tau pathology population.
Jennifer Rose V. Molano, MD, reviewing van Dyck CH et al. N Engl J Med 2023 Jan 5 Reish NJ et al. N Engl J Med 2023 Jan 4 Sabbagh M and van Dyck CH. N Engl J Med 2023 Jan 4
Patients with early Alzheimer disease taking an anti-amyloid monoclonal antibody showed improvements in cognitive, functional, and biomarker outcomes compared with placebo; monitoring for potential adverse effects is needed.
In this phase 3, industry-sponsored, multicenter, double-blind trial, researchers studied if lecanemab, a humanized immunoglobulin G1 monoclonal antibody targeting soluble amyloid-beta protofibrils, was safe and effective in treating early Alzheimer disease (AD). Participants met criteria for mild cognitive impairment (MCI) or mild dementia due to AD and had evidence of amyloid deposition on cerebrospinal fluid (CSF) or amyloid positron emission tomography (PET) imaging.
The 1795 participants (mean age, 71; about half female; about 75% white) were randomized 1:1 to 10 mg/kg of lecanemab or placebo every 2 weeks for 18 months. Approximately 80% of participants completed the study. The primary outcome was a change in the Clinical Dementia Rating-Sum of Boxes (CDR-SB) score, which assesses cognition and function as observed by patients and caregivers. Secondary outcomes included change in amyloid burden on PET imaging and additional cognitive and functional assessments.
The mean CDR-SB score at baseline was 3.2. Significantly less cognitive and functional decline was seen with lecanemab versus placebo (adjusted mean difference, –0.45; 95% confidence interval, –0.67 to –0.23; P<0.001). The amyloid burden was significantly lower with the lecanemab group than with placebo. Other secondary outcomes were also significantly better with lecanemab. Adverse effects included infusion-related reactions, amyloid-related imaging abnormalities (ARIA), headache, and falls. Serious adverse effects were seen in 14% of lecanemab recipients versus 11% of placebo recipients. Most cases of ARIA were asymptomatic (78%), occurred during the first 3 months of treatment, and resolved within 4 months. Death occurred in 0.7% of the lecanemab group (vs. 0.8% of the placebo group), which was not attributed to treatment.
A subsequent correspondence presented a patient who had received at least three doses of lecanemab in the trial, developed multiple cerebral hemorrhages after receiving intravenous tissue plasminogen activator for acute ischemic stroke, and subsequently died, with autopsy results showing multifocal intracerebral hemorrhages, cerebral amyloid angiopathy, AD changes, and vasculitis. In response, study authors noted that the patient was homozygous for apolipoprotein-E ℇ4, which, in addition to systolic blood pressure >200 mm Hg, may have contributed to the patient’s clinical progression. They also noted that vasculitis has not been reported previously with lecanemab.
Comment
A clear benefit from targeting amyloid to treat AD has been elusive. The results of this study showed promising results on outcomes in early AD, although consideration of safety issues in those homozygous for apolipoprotein-E ℇ4 is needed. Close monitoring for potential adverse effects is needed. The FDA recently approved lecanemab for mild cognitive impairment or mild dementia due to AD. An ongoing open-label study will provide additional data on whether lecanemab is a viable treatment option for AD.
Telomeres are regions at the end of chromosomes that protect against DNA damage.
Telomeres get shorter with every division that a cell undergoes, and the shortening of telomeres is associated with biological aging and age-related conditions such as Alzheimer’s disease.
A new large study found that shorter telomere length in white blood cells was associated with a greater increase in markers of brain degeneration measured using MRI.
Shorter telomere length was also associated with a higher risk of dementia, suggesting that longer telomere length could protect against dementia.
Previous studies suggest that shorter telomere length is associated with biological aging of the brain and a greater risk of neurodegenerative conditions. A recent study published in PLOS ONETrusted Sourcesuggests that long telomere length of chromosomes in white blood cells was associated with fewer brain markers of neurodegeneration and a lower risk of dementia.
Changes in these markers of brain structure and function precede clinical symptoms of dementia, and these findings suggest that the association between telomere length and dementia risk may be mediated through these changes in brain structure.
“This is the largest and most in-depth study of telomere length (a marker of biological aging) and brain structure/function. We found links between longer telomeres and larger volumes of the brain (such as the hippocampus) that are affected by dementia. These may explain why/how longer telomeres are protective against dementia.” — Dr. Anya Topiwala, of Oxford Population Health, part of the University of Oxford, UK, and lead study author
Each of the chromosomes present in the cell nucleus consists of a double-stranded molecule of DNA. The end of each chromosome consists of telomeres, a region made up of repeats of a short DNA sequence (TTAGGG).
These short repeated DNA sequences in the telomere are covered by shelterin proteins. The telomer-shelterin complexes help protect the ends of the chromosome from being damaged and avert the fusion of one chromosome with another.
Moreover, the ends of the chromosome resemble breaks in the DNA and can be recognized as broken by DNA repair enzymes. This can, in turn, activate pathways involved in senescence, which is the irreversible arrest of cell division. Senescence is one of the mechanisms that contribute to biological aging.
The telomere-shelterin complex prevents the recognition of the chromosome ends as being broken.
Each of the two DNA strands of the chromosome is duplicated during cell division, and this process is carried out by an enzyme called DNA polymerase. The DNA polymerase binds to a short fragment of RNA called primer to initiate DNA synthesis.
The process of DNA replication occurs at multiple sites along the same strand, and the gap left behind between newly created DNA fragments after the removal of the RNA primer is filled in by DNA polymerase.
However, DNA polymerase cannot fill the gap left at the end of the chromosome after the removal of the RNA primer. Thus, the telomeres start to get increasingly shorter with each cell division and, consequently, with aging.
“Dementia impacts more than 45 million people worldwide. Because shorter chromosomal telomeres are a sign of aging and related to both neurodegeneration and incidence of dementia, it is plausible that interventions targeting telomere length preservation could one day aid in preventing or delaying Alzheimer’s disease and related dementias.” — Dr. Jennifer Bramen, senior research scientist at the Pacific Neuroscience Institute at Providence Saint John’s Health Center in Santa Monica, California, speaking to Medical News Today
The shortening of telomeres can result in the cells undergoing senescence and, thus, biological aging. In addition, studies suggest that the decrease in telomere length is also associated with an increased risk of neurodegenerative conditions such as Alzheimer’s disease (AD).
Individuals with Alzheimer’s disease show changes in brain structure before the manifestation of clinical symptoms. Specifically, individuals with Alzheimer’s disease show changes in both the grayTrusted Source and whiteTrusted Source matter in the brain.
The gray matter is composed of neuronal cells and is involved in the processing of information. Alzheimer’s disease is characterized by a decline in gray matter volume throughout the brain, including in the hippocampus, a brain region involved in memory. Disruption to neuronal networks within the gray matter predicts brain atrophyTrusted Source and is evident before the emergence of clinical symptoms.
In contrast, white matter consists of neuronal processes involved in the transmission of information. Studies have shown that individuals who show reduced integrity of white matter tracts are at an increased riskTrusted Source of AD.
However, the association between telomere length and these markers of brain degeneration has not been extensively examined.
In the present study, the researchers used MRI scans to examine the association between telomere length and changes in brain structure. The study consisted of MRI data from 31,661 individuals participating in the UK Biobank, a database that contains biomedical data from over a half million U.K. residents.
The researchers used blood samples obtained at baseline from these participants to isolate DNA from leukocytes, also known as white blood cells, and assess telomere length. The cognitive function of the individuals was assessed at baseline and then about 5.8 years later via an online survey.
The researchers found that longer leukocyte telomere length was associated with a larger volume of gray matter across the entire brain. Longer telomere length was also associated with greater volume in several brain regions, such as the hippocampus, associated with executive functions.
In addition, individuals with longer leukocyte telomeres length were also more likely to show greater integrity of white matter fiber tracts and fewer signs of lesions. Specifically, longer telomere length in leukocytes was associated with greater integrity of the corpus callosum, the white matter tract that conducts information between the two brain hemispheres, and major association fiber tracts that conduct impulses within the same hemisphere.
MRI measurements showing the contrast between the signal intensity of gray matter and white matter can be used as a marker of neurodegeneration associated with Alzheimer’s disease. A decrease in the contrast has been linked to increasing disease severity.
Consistent with this, the present study found that longer leukocyte telomere length was associated with lower contrast between grey and white matter in brain regions involved in processing sensory information.
Lastly, there was a negative correlation between longer leukocyte telomere length and the risk of developing dementia. Such an association was not observed between leukocyte telomere length and the risk of stroke or Parkinson’s disease.
Although these results suggest that longer telomere length may be associated with a lower risk of dementia and reduced brain degeneration, the researchers cautioned that these results are based on analysis of telomere length in white blood cells rather than brain tissue.
“For this study, there are some limitations to keep in mind. Telomere length was measured in blood, not the brain (not possible in living humans!), and it is not yet clear how closely the two align. Also, the UK Biobank sample is healthier than the general population,” noted Dr. Topiwala.
Scientists are trying to see if supplements can have an impact on brain aging. Extreme Media/Getty Images
Aging can cause cognitive decline due to changes that happen in our brain cells; however, it is not clear how much of this is intrinsic or due to diseases such as Alzheimer’s.
In order to improve energy metabolism in the brain, a group of scientists looked at the effect of supplementing a group of adults with a form of vitamin B3.
The researchers found that the supplement nicotinamide riboside was converted into a molecule involved in energy metabolism in neurons.
They also observed a small but significant decrease in the levels of amyloid beta protein in neurons, following supplementation.
Age isn’t just a number, and aging mechanisms affect us at a cellular level. The reason why some people age faster than others has been the focus of much recent research.
One condition for which age is a risk factor is dementia. About one-third of people who are over the age of 85 have some form of dementiaTrusted Source.
As humans are living longer, the number of people with dementia in the population is also growing, and the World Health OrganisationTrusted Source reports there are currently more than 55 million people living with dementia worldwide, and nearly 10 million new cases every year.
Despite this high prevalence, the mechanisms and risk factors underlying dementia are poorly understood.
The prevailing understanding is that Alzheimer’s disease is thought to be underpinned by the presence of clumps of certain forms of a protein called beta-amyloid between neurons, or nerve cells, in the brain. This is thought to affect their ability to signal, causing the cognitive decline seen in individuals with the condition.
However, it is important to note there is still significant debate over this mechanism, and how much of an impact it has on the development of Alzheimer’s disease, as well as its suitability as a potential target for treatment.
One theory is that the decline in cognition observed in people with Alzheimer’s disease is due to the disruption of typical energy production and metabolism in the brain.
A recent paper published in Aging Cellon the subject explores whether vitamin B could help offset this disruption.
The brain is hugely energy dependent, and uses up to 20% of oxygen and therefore calories, of those used by the whole body, despite making up just 2% of its mass. This energy metabolism is understood to be disrupted in the brains of people with Alzheimer’s disease.
One way this can be disrupted is when nerve cells in the brain become insulin resistant. Insulin resistance means that the cells do not take up glucose for energy as they should. When this occurs in the brain energy metabolism, signalling and immune response functions are all affected negatively.
This can occur in individuals who have type 2 diabetesTrusted Source, which is characterised by insulin resistance, and there is a correlation between the condition and Alzheimer’s disease, though it is unclear why.
Dr. Kellie Middleton, orthopaedic surgeon at Northside Hospital, Atlanta, Georgia, who was not involved in the study, explained to Medical News Today:
“Neurodegeneration is a term used to describe the progressive loss of nerve cells in the brain and spinal cord, leading to problems with memory, cognition, movement, and other neurological functions. It can be caused by genetic or [underlying medical conditions including] aging, diabetes, stroke, Parkinson’s disease, Alzheimer’s disease, or traumatic brain injury.”
“Biochemical pathways are known to be associated with various forms of neurodegeneration, and research into these pathways is ongoing. For example, studies have highlighted connections between imbalances in energy metabolism, oxidative stress, inflammation, and mitochondrial dysfunction with the development of neurological diseases,” she continued.
If the brain cells can’t produce the energy they need to be able to function, then they can’t signal, and if nerve cells in the brain can’t signal effectively then cognition will be affected. Whether this is a cause of the disease or a symptom is unclear, said Dr. Christopher Martens, director of the Delaware Center for Cognitive Aging Research, and lead author of the current study.
“One of the main challenges with Alzheimer’s disease is the disruption of energy metabolism in the brain, which may actually contribute to the development of the disease.” — Dr. Christopher Martens
Dr. Martens and his team looked at the role of a particular molecule involved in energy metabolism, called nicotinamide adenine dinucleotide, or NAD+.
“NAD+ is essential for cells to create energy and there is strong evidence from animal studies that aging and metabolic dysfunction results in a depletion of NAD+ within cells. Therefore, there is strong rationale that replenishing the NAD+ within the brain could have a positive effect on brain function,” he explained to MNT.
In order to do this a cohort of 10 adults were given a form of vitamin B3 called nicotinamide riboside as a supplement, as this molecule is a precursor for NAD+. This means that the body converts it into NAD+.
A group of 12 other adults received a placebo. Neither group knew whether they were receiving the supplement or a placebo.
In order to measure whether or not taking 500mg of the supplement twice a day for six weeks actually increased NAD+ in neurons, researchers measured the NAD+ in extracellular vesicles that are present in the neurons and end up in the blood. They extracted these from blood samples and found a small, but significant difference.
In addition to this finding, the team have now published data showing that changes in levels of NAD+ and its precursors were correlated with changes in the presence of insulin-signalling proteins and molecules involved in inflammation, also thought to play a role in the development of Alzheimer’s and dementia.
While decreases in the tau and amyloid proteins, thought to be involved in the development of Alzheimer’s disease, were not significant when comparing all supplemented participants to placebo, a small but significant change was observed in the levels of these marker proteins in the extracellular vesicles of a sub-set of the supplemented participants who responded.
Still, it is unclear if the supplement had crossed the blood-brain barrier and that these changes took place in brain cells.
“We don’t have definitive proof that the supplement itself crosses the blood-brain barrier, especially not from our data. What we do know is that taking the supplement results in an increase in NAD+ within tiny vesicles that likely originated in the brain and other neural tissue,” Dr. Martens told MNT.
“This is one of the big challenges in the field [d]etermining whether the compound can reach its intended target. [A]lthough we do not have direct evidence, the results of our study suggest that it is having an effect on the brain and also changing the metabolism of molecular pathways known to be involved in Alzheimer’s disease,” he added.
This was the next step for the team said Dr. Martens.
“This is something we are actively testing now in my laboratory in a follow-up trial in older adults with mild cognitive impairment, but first we wanted to understand whether we could detect an increase in NAD+ in brain tissue after taking the supplement,” he said.
“We did this using small vesicles found in the blood that we are quite confident originated in neurons. What’s really interesting is that we also found changes in more established markers of Alzheimer’s disease (e.g., amyloid beta) after taking the supplement,” he added.
“While there are some promising therapeutic strategies being explored for neurodegenerative diseases, more research is needed to fully understand their potential benefit.” — Dr. Kellie Middleton
The cells which clear Alzheimer’s plaques from the brain follow a 24-hour circadian rhythm.
A good night’s sleep has always been linked to better mood, and better health. Now, scientists have even more evidence of just how much sleep – and more specifically our circadian rhythm, which regulates our sleep cycle – is linked to certain diseases, such as Alzheimer’s disease. A team of researchers from the United States have found further evidence that the cells which help keep the brain healthy and prevent Alzheimer’s disease also follow a circadian rhythm.
Our circadian rhythm is a natural, internal process that follows a 24-hour cycle. It controls everything from sleep, digestion, appetite and even immunity. Things like outside light, when we eat our meals and physical activity all work to keep our circadian rhythm in sync. But even small things like staying up a bit later than normal, or even eating at a different time than we’re used to can knock this internal “clock” out of whack.
It’s important for our circadian rhythm to work properly, as disruption to this cycle is linked to a number of health problems, including mental health disorders, cancer, and Alzheimer’s.
Research shows that for patients with Alzheimer’s disease, circadian rhythm disruptions are usually seen as changes in a patient’s sleep habits that happen long before the disorder fully manifests. This is something that gets worse in the later stages of the disease. However it’s not yet fully understood whether poor sleep causes Alzheimer’s, or if it happens as a result of the disease.
Brain Plaques
One thing researchers consistently find in the brains of people with Alzheimer’s disease is an accumulation of a protein called beta-amyloid. These proteins tend to clump together in the brain and form “plaques”. These plaques disrupt the function of the brain’s cells, which may in turn lead to cognitive issues, such as memory loss. In normal brains, the protein is cleaned up before it has the chance to cause issues.
This latest study has now shown that the cells responsible for clearing up beta-amyloid plaques – and keeping the brain healthy – also follow a 24-hour circadian rhythm. This could mean that if the circadian rhythm is disrupted it could make it more difficult for these cells to remove the harmful plaques that are linked to Alzheimer’s.
The circadian rhythm is a natural, 24-hour cycle that controls many of the body’s processes.
To conduct their research, the team looked specifically at macrophages. These are immune cells that exist throughout the body, including in the brain. Macrophages essentially eat up anything (such bacteria, or even proteins that haven’t formed correctly) that might be considered a threat to the body.
To understand whether these immune cells follow a circadian rhythm, the researchers used macrophages from mice and grew them in the lab. When they fed the cells with beta-amyloid, they found that the ability of the macrophages to eliminate beta-amyloid changed throughout a 24-hour period.
They also found that specific proteins on the surface of the macrophages – called proteoglycans – have a similar circadian rhythm throughout the day. In fact, they found that when the amount of proteoglycans were at their lowest levels, beta-amyloid clearance was at its highest. So when the macrophages have a lot of these proteins, they don’t clear beta-amyloid as well. They also found that when the cells lost their natural circadian rhythm, they didn’t clear beta-amyloid as normal.
But in studies that have looked at full organisms (such as mice) instead of only cells, the results about the relationship between Alzheimer’s and circadian rhythm are more conflicting – they often fail to portray all the issues found in humans with Alzheimer’s disease, as they only study specific systems or proteins that might be affected by Alzheimer’s disease. This means they aren’t a fully accurate representation of how Alzheimer’s occurs in humans.
In studies that looked at people with Alzheimer’s, researchers have found that circadian rhythm dysfunction has worsened as the disease progressed. Other research also showed that this circadian rhythm disruption was linked with sleep problems and Alzheimer’s disease, alongside the brain being less able to clear brain “garbage” (including beta-amyloid) – which may further contribute to memory problems. But it’s difficult to say whether circadian rhythm disruption (and the problems it causes) happened as a result of Alzheimer’s disease, or if they were part of the cause.
Should the findings of this study be replicated in humans, this could bring us one step closer to understanding one of the ways in which our circadian rhythm is linked to Alzheimer’s disease. Nevertheless, it’s widely agreed that sleep is important for many aspects of our health. So protecting your circadian rhythm is not just good for your brain – but for your overall health.
In this file image, a picture of a human brain taken by a positron emission tomography scanner, also called PET scan, is seen on a screen at the Regional and University Hospital Center of Brest in western France on Jan. 9, 2019.
Researchers say that they may have discovered the molecular-level cause of Alzheimer’s disease.
Scientists at the University of California–Riverside said in recent findings that the key to understanding Alzheimer’s may have to do with “tau” proteins that likely cause neurofibrillary tangles—which are found in the brains of Alzheimer’s patients. Previously, researchers suggested that amyloid plaques, which are a buildup of amyloid peptides, may be the cause.
Both amyloid plaques and neurofibrillary tangles are critical indicators that doctors look for when trying to diagnose Alzheimer’s.
“Roughly 20 percent of people have the plaques, but no signs of dementia,” said UCR chemistry professor Ryan Julian in a statement. “This makes it seem as though the plaques themselves are not the cause.”
Researchers focused on the different structures a single molecule can manufacture, known as isomers.
“An isomer is the same molecule with a different three-dimensional orientation than the original. A common example would be hands. Hands are isomers of each other, mirror images but not exact copies. Isomers can actually have a handedness,” Julian said.
The team scanned proteins in brain samples that were donated to their lab, and in brains where there was an accumulation of the tau protein but no Alzheimer’s diagnoses, they found that the “normal” tau had a different-handed form than in individuals who had plaques or tangles, and who were diagnosed with Alzheimer’s. The proteins also survived longer than is considered normal, the researchers said.
If a protein stays too long—generally more than 48 hours—some amino acids in the proteins convert into the “other-handed” isomer, they noted.
“If you try to put a right-handed glove on your left hand, it doesn’t work too well,” Julian said. “It’s a similar problem in biology; molecules don’t work the way they’re supposed to after a while because a left-handed glove can actually convert into a right-handed glove that doesn’t fit.”
The human body has a process called autophagy, they noted, which clears used or defective proteins from cells. When people age, autophagy can slow down, although it isn’t clear why, Julian said, which is what his team is attempting to figure out.
“If a slowdown in autophagy is the underlying cause, things that increase it should have the beneficial, opposite effect,” he said.
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