Large-scale proteomic analysis of human brain identifies proteins associated with cognitive trajectory in advanced age
Open accessPublished version of a preprint
Abstract
In advanced age, some individuals maintain a stable cognitive trajectory while others experience a rapid decline. Such variation in cognitive trajectory is only partially explained by traditional neurodegenerative pathologies. Hence, to identify new processes underlying variation in cognitive trajectory, we perform an unbiased proteome-wide association study of cognitive trajectory in a discovery ( n = 104) and replication cohort ( n = 39) of initially cognitively unimpaired, longitudinally assessed older-adult brain donors. We find 579 proteins associated with cognitive trajectory after meta-analysis. Notably, we present evidence for increased neuronal mitochondrial activities in cognitive stability regardless of the burden of traditional neuropathologies. Furthermore, we provide additional evidence for increased synaptic abundance and decreased inflammation and apoptosis in cognitive stability. Importantly, we nominate proteins associated with cognitive trajectory, particularly the 38 proteins that act independently of neuropathologies and are also hub proteins of protein co-expression networks, as promising targets for future mechanistic studies of cognitive trajectory.
AI snapshot
Written from the paper’s full text.
TL;DR
This study performed an unbiased proteome-wide association study of cognitive trajectory in discovery (n = 104) and replication (n = 39) cohorts of initially cognitively unimpaired older-adult brain donors. Meta-analysis identified 579 proteins associated with cognitive trajectory, with cognitive stability linked to increased neuronal mitochondrial and synaptic proteins and decreased inflammation/apoptosis proteins.
Key findings
- Meta-analysis found 579 proteins associated with cognitive trajectory at FDR < 0.05 among 2,752 protein isoforms detected in both cohorts; 350 had increased abundance and 229 had decreased abundance in cognitive stability.
- Higher-abundance proteins in cognitive stability were enriched for mitochondrial function and synaptic transmission, and for neurons; lower-abundance proteins were enriched for inflammatory response, apoptosis, endothelial function, and RNA processing, and for oligodendrocyte, astrocyte, and microglia.
- After adjusting for β-amyloid plaques and neurofibrillary tangles, 232 proteins remained associated with cognitive trajectory; higher-abundance proteins were still enriched for mitochondrial activities and synaptic transmission, while lower-abundance proteins changed to processes including glycolysis and neurofilament bundle assembly.
- 38 proteins were associated with cognitive trajectory independently of β-amyloid plaques and neurofibrillary tangles and were hub proteins in both cohorts; the top protein was DLG4/PSD95, and the first principal component of the PSD95 interactome was associated with cognitive trajectory (β = 0.016; p = 0.0001; N = 104).
Methods
- Discovery cohort: Banner Sun Health Research Institute participants (n = 104), non-demented at baseline, followed up to 14 years; replication cohort: Baltimore Longitudinal Study of Aging participants (n = 39), followed up to 20 years. Dorsolateral prefrontal cortex was analyzed by label-free proteomics; 3,710 proteins in Banner and 3,933 in BLSA were detected with 10% or fewer missing values, with 2,752 isoforms common to both.
- Person-specific cognitive trajectory was estimated using a linear mixed model of annual MMSE score with random intercept and random slope per subject. Proteome-wide association used linear regression per cohort followed by METAL meta-analysis, adjusting for sex, age at enrollment, education, age at death, PMI, and batch; a secondary analysis additionally adjusted for β-amyloid plaques and neurofibrillary tangles.
- Protein co-expression networks were constructed with WGCNA in Banner, and published BLSA networks were used; hub proteins were defined as those with intramodular kME in the top 90th percentile. GO and brain cell-type enrichment used Fisher’s exact test with FDR correction.
Limitations
- The paper states that this is an association study and precludes causal inference; label-free proteomics yielded approximately 3,900 proteins, which is not as comprehensive as ideal; MMSE is a coarse screening tool; and sample sizes were relatively small (n = 104 and n = 39) and exclusively Caucasian, limiting generalizability and power.
- The paper notes that BLSA protein co-expression modules were previously built using dorsolateral prefrontal cortex and precuneus, so module memberships might differ from those based on dorsolateral prefrontal cortex alone.
- The paper notes that in BLSA only one module remained significantly associated with cognitive trajectory after multiple testing adjustment, likely due to small sample size and lower power.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 71 cited sources checked · 0 supported · 0 flagged
- Findingown data
Mitochondrial proteins were featured prominently among cognitive trajectory-associated proteins.
“Interestingly, mitochondrial proteins were featured prominently among cognitive trajectory-associated proteins, and more findings about mitochondrial proteins are followed.”
Quote in the paperNot a substantial claim - Findingown data
The higher-abundance proteins in cognitive stability were enriched in modules involved in synaptic function (Banner M1, BLSA M1, and M4) and mitochondrial function (Banner M3 and BLSA M3) in both cohorts at adjusted p < 0.05 by Fisher's exact test.
“The higher-abundance proteins in cognitive stability were enriched in modules involved in synaptic function (Banner M1, BLSA M1, and M4) and mitochondrial function (Banner M3 and BLSA M3) in both cohorts at adjusted p < 0.05 by Fisher’s exact test (Fig. 5a).”
Quote in the paperSubstantial claim - Findingown data
Within each category of pathology or clinical diagnosis of cognitive status, different persons have different cognitive trajectories.
“These data reveal that (i) cognitive trajectory varied within a given clinical diagnosis or Braak stage;”
Quote in the paperSubstantial claim - Findingown data
Analyzed jointly, the Spearman's rank correlation between cognitive trajectory and Braak stage was −0.39 (95% CI −0.51 to −0.24, p = 1.5 × 10−6).
“and between cognitive trajectory and Braak stage was −0.39 (95% CI of −0.51 to −0.24, p-value of 1.5 × 10–6).”
Quote in the paperSubstantial claim - Findingown data
The higher-abundance proteins in cognitive stability were strongly enriched for neurons and mildly enriched for astrocytes at adjusted p-value < 0.05 by Fisher's exact test.
“The higher-abundance proteins were strongly enriched for neurons and mildly enriched for astrocytes at adjusted p-value < 0.05 by Fisher’s exact test (Table 2, Supplementary Data 4).”
Quote in the paperSubstantial claim
- Findingown data
The lower-abundance proteins in cognitive stability were significantly enriched for signatures of oligodendrocyte, astrocyte, and microglia after multiple testing correction.
“The lower-abundance proteins in cognitive stability were significantly enriched for signatures of oligodendrocyte, astrocyte, and microglia after multiple testing correction (Table 2, Supplementary Data 4).”
Quote in the paperSubstantial claim - Findingown data
The lower-abundance proteins in cognitive stability were enriched for inflammatory response, apoptosis, endothelial function, and RNA processing at adjusted p-value < 0.05 by Fisher's exact test.
“The lower-abundance proteins were enriched for inflammatory response, apoptosis, endothelial function, and RNA processing at adjusted p-value < 0.05 by Fisher’s exact test (Table 2, Supplementary Data 4).”
Quote in the paperSubstantial claim - Findingown data
MAPT protein had decreased abundance in cognitive stability.
“For instance, VGF protein had increased abundance and MAPT protein had decreased abundance in cognitive stability (Fig. 3b, c).”
Quote in the paperNot a substantial claim - Findingown data
The top five proteins in the Banner discovery cohort were VGF, SEPT5, DBI, MAPT, and KIAA1045 (aka PHF24).
“The top five proteins were VGF, SEPT5, DBI, MAPT, and KIAA1045 (aka PHF24).”
Quote in the paperNot a substantial claim - Findingown data
Higher-abundance proteins in cognitive stability were enriched for processes of mitochondrial activities and synaptic transmission, and for neurons.
“Higher-abundance proteins in cognitive stability were enriched for processes of mitochondrial activities and synaptic transmission, and for neurons (Table 3; Supplementary Data 6).”
Quote in the paperSubstantial claim - Findingown data
In the Banner discovery cohort, 354 proteins were associated with cognitive trajectory at FDR < 0.05 after adjusting for sex, age at enrollment, education, age at death, PMI, and batch (N = 104).
“In the Banner discovery cohort, 354 proteins were associated with cognitive trajectory at FDR < 0.05 after adjusting for sex, age at enrollment, education, age at death, PMI, and batch (N = 104, Supplementary Data 2; Supplementary Fig. 1A).”
Quote in the paperSubstantial claim - Findingown data
The meta-analysis revealed 232 proteins associated with cognitive trajectory at FDR < 0.05 in the same directions in both datasets after adjusting for neuropathology.
“The meta-analysis revealed 232 proteins associated with cognitive trajectory at FDR < 0.05 in the same directions in both datasets (Supplementary Data 5).”
Quote in the paperSubstantial claim - Findingown data
The five largest modules were enriched for synaptic function (M1, 359 proteins), catabolic process and apoptosis (M2, 296 proteins), mitochondrial function (M3, 276 proteins), myelination (M4, 141 proteins), and transcriptional activities (M5, 83 proteins) at adjusted p < 0.05 by Fisher's exact test.
“The five largest modules were enriched for synaptic function (M1, 359 proteins), catabolic process and apoptosis (M2, 296 proteins), mitochondrial function (M3, 276 proteins), myelination (M4, 141 proteins), and transcriptional activities (M5, 83 proteins) at adjusted p < 0.05 by Fisher’s exact test (Supplementary Data 7).”
Quote in the paperNot a substantial claim - Findingown data
Analyzed jointly, the Spearman's rank correlation between cognitive trajectory and clinical diagnosis of cognitive status was −0.49 (95% CI −0.60 to −0.35, p = 4.3 × 10−10).
“Analyzed jointly, the Spearman’s rank correlation between cognitive trajectory and clinical diagnosis of cognitive status was −0.49 (95% confidence interval [CI] of −0.60 to −0.35, p-value of 4.3 × 10–10)”
Quote in the paperSubstantial claim - Findingown data
Faster cognitive decline was not exclusively seen in AD cases or in brains with greater Braak stages.
“(ii) faster cognitive decline was not exclusively seen in AD cases or in brains with greater Braak stages;”
Quote in the paperSubstantial claim - Findingown data
VGF protein had increased abundance in cognitive stability.
“For instance, VGF protein had increased abundance and MAPT protein had decreased abundance in cognitive stability (Fig. 3b, c).”
Quote in the paperNot a substantial claim - Findingown data
Adjusting for traditional neuropathology influenced proteins with lower-abundance in cognitive stability to a much greater degree and changed biological processes represented, in contrast to higher-abundance proteins.
“In contrast to the proteins with higher-abundance in cognitive stability, adjusting for traditional neuropathology influenced those with lower-abundance in cognitive stability to a much greater degree and changed biological processes represented.”
Quote in the paperSubstantial claim - Findingown data
The top 5 proteins in the BLSA replication cohort were DLD, ABHD10, VDAC1, NDUFV1, and PDHB.
“The top 5 proteins were DLD, ABHD10, VDAC1, NDUFV1, and PDHB.”
Quote in the paperNot a substantial claim - Findingown data
At FDR < 0.05, 57 proteins in Banner and 12 proteins in BLSA were associated with cognitive trajectory after adjusting for sex, age at enrollment, education, age at death, PMI, β-amyloid plaques, and neurofibrillary tangles.
“At FDR < 0.05, 57 proteins in Banner and 12 proteins in BLSA were associated with cognitive trajectory after adjusting for sex, age at enrollment, education, age at death, PMI, β-amyloid plaques, and neurofibrillary tangles (Supplementary Data 5).”
Quote in the paperSubstantial claim - Findingown data
The higher-abundance proteins in cognitive stability were enriched for mitochondrial function and synaptic transmission at adjusted p-value < 0.05 by Fisher's exact test.
“The higher-abundance proteins were enriched for mitochondrial function and synaptic transmission at adjusted p-value < 0.05 by Fisher’s exact test (Table 2, Supplementary Data 4).”
Quote in the paperSubstantial claim - Findingown data
Focusing on PPIs originating from postsynaptic proteins, there were 353 proteins with interactions (nodes) and 864 PPIs (edges).
“Focusing on PPIs originating from postsynaptic proteins, we found 353 proteins with interactions (nodes) and 864 PPIs (edges) (Supplementary Fig. 5B).”
Quote in the paperNot a substantial claim - Findingown data
We identified 20 modules of strongly co-expressed proteins in the Banner cohort using WGCNA.
“We identified 20 modules of strongly co-expressed proteins in the Banner cohort using WGCNA and then gleaned the biological processes enriched in these modules with GO enrichment analysis (Fig. 4; Supplementary Data 7).”
Quote in the paperSubstantial claim - Findingown data
The lower-abundance proteins in cognitive stability were enriched in modules involved in apoptosis (Banner M2, BLSA M13), myelination (Banner M4, BLSA M2), and inflammatory response (BLSA M6 and indirectly Banner M2 by virtue of overlap) in both cohorts at adjusted p < 0.05 by Fisher's exact test.
“On the other hand, the lower-abundance proteins in cognitive stability were enriched in the modules involved in apoptosis (Banner M2, BLSA M13), myelination (Banner M4, BLSA M2), and inflammatory response (BLSA M6 and indirectly Banner M2 by virtue of overlap) in both cohorts at adjusted p < 0.05 by Fisher’s exact test (Fig. 5b).”
Quote in the paperSubstantial claim - Findingown data
In the BLSA replication cohort, 127 proteins were associated with cognitive trajectory at FDR < 0.05 after adjusting for sex, age at enrollment, education, age at death, and PMI (N = 39).
“In the BLSA replication cohort, 127 proteins were associated with cognitive trajectory at FDR < 0.05 after adjusting for sex, age at enrollment, education, age at death, and PMI (N = 39; Supplementary Data 2, Supplementary Fig. 1B).”
Quote in the paperSubstantial claim - Findingown data
The first principal component of these 109 proteins, which captures 99.3% of the variance of the PSD95 interactome, was significantly associated with cognitive trajectory by linear regression (β = 0.016; p = 0.0001; N = 104).
“The first principal component of these 109 proteins, which captures 99.3% of the variance of the PSD95 interactome, was significantly associated with cognitive trajectory by linear regression (β = 0.016; p = 0.0001; N = 104; Supplementary Fig. 6).”
- Also stated in: § Discussion · boosted
“Here we show that the aggregate of PSD-95 interactome abundance is also associated with cognitive trajectory.”
Quote in the paperSubstantial claim - Also stated in: § Discussion · boosted
- Findingown data
After adjusting for β-amyloid plaques and neurofibrillary tangles by linear regression, only two Banner modules M1/Synaptic function (β = 0.039, p = 6.74E−06, adjust p = 0.00013) and M2/Catabolic processes and apoptosis (β = −0.032, p = 3.02E−05, adjust p = 0.00030) remained significantly associated with cognitive trajectory after multiple testing adjustment at FDR < 0.05.
“After adjusting for β-amyloid plaques and neurofibrillary tangles by linear regression, only two of the Banner modules M1/Synaptic function (β = 0.039, p = 6.74E−06, adjust p = 0.00013) and M2/Catabolic processes and apoptosis (β = −0.032, p = 3.02E−05, adjust p = 0.00030) remained significantly associated with cognitive trajectory after multiple testing adjustment at FDR < 0.05.”
Quote in the paperSubstantial claim - Findingown data
Among the 123 hub proteins in Banner and 127 hub proteins in BLSA, 67 and 62 respectively were also associated with cognitive trajectory independently of β-amyloid plaques and neurofibrillary tangles.
“We found that among the 123 hub proteins in Banner and 127 hub proteins in BLSA, 67 and 62 respectively were also associated with cognitive trajectory independently of β-amyloid plaques and neurofibrillary tangles (Supplementary Data 8).”
Quote in the paperSubstantial claim - Findingown data
In common between the 67 Banner and 62 BLSA hub and cognitive trajectory-associated proteins were 38 proteins.
“In common between these 67 (Banner) and 62 (BLSA) hub and cognitive trajectory-associated proteins were 38 proteins (Table 5, Supplementary Data 8).”
Quote in the paperSubstantial claim - Findingown data
In BLSA, among the 131 hub proteins in the seven modules, 99 (76%) were associated with cognitive trajectory.
“In BLSA, among the 131 hub proteins in the seven modules, 99 (76%) were associated with cognitive trajectory (Supplementary Data 8).”
Quote in the paperSubstantial claim - Findingown data
For BLSA, only one module, M3/mitochondrial function (β = −0.097, p = 0.0059, adjust p = 0.095), was associated with cognitive trajectory at adjusted p-value < 0.1 by linear regression after adjusting for β-amyloid plaques and neurofibrillary tangles.
“For BLSA, only one module, M3/mitochondrial function (β = −0.097, p = 0.0059, adjust p = 0.095), was associated with cognitive trajectory at adjusted p-value < 0.1 by linear regression after adjusting for β-amyloid plaques and neurofibrillary tangles.”
Quote in the paperSubstantial claim - Findingown data
Among the 232 proteins associated with cognitive trajectory independently of β-amyloid plaques and neurofibrillary tangles, higher-abundance proteins in cognitive stability were enriched for modules of synaptic function (Banner M1, BLSA M1, BLSA M4) and mitochondrial function (Banner M3, BLSA M3) in both cohorts at adjusted p < 0.05 by Fisher's exact test.
“Among the 232 proteins associated with cognitive trajectory independently of β-amyloid plaques and neurofibrillary tangles, we found that the higher-abundance proteins in cognitive stability were enriched for modules of synaptic function (Banner M1, BLSA M1, and BLSA M4), and mitochondrial function (Banner M3 and BLSA M3) in both cohorts at adjusted p < 0.05 by Fisher’s exact test (Fig. 5c).”
Quote in the paperSubstantial claim - Findingown data
The top six of these 10 Banner modules were the same modules found to be enriched among the proteins associated with cognitive trajectory.
“The top six of these 10 modules were the same modules found to be enriched among the proteins associated with cognitive trajectory (Table 4, Fig. 5a).”
Quote in the paperNot a substantial claim - Findingown data
BLSA module M3, enriched for mitochondrial function, was most significantly associated with cognitive trajectory both before and after adjusting for plaques and tangles.
“For instance, BLSA module M3, enriched for mitochondrial function, was most significantly associated with cognitive trajectory both before and after adjusting for plaques and tangles.”
Quote in the paperSubstantial claim - Findingown data
The top protein of the 38-protein list is DLG4, also known as PSD95.
“Remarkably, the top protein of this 38-protein list is DLG4, also known as PSD95”
Quote in the paperSubstantial claim - Findingown data
Among the 232 proteins associated with cognitive trajectory independently of β-amyloid plaques and neurofibrillary tangles, lower-abundance proteins in cognitive stability were enriched for modules involving apoptosis (Banner M2, BLSA M13), myelination (Banner M4, BLSA M2), and inflammatory response (BLSA M6 and Banner M2 by overlap) in both cohorts at adjusted p < 0.05 by Fisher's exact test.
“The lower-abundance proteins in cognitive stability were enriched for the modules involving apoptosis (Banner M2, BLSA M13), myelination (Banner M4, BLSA M2), and inflammatory response (BLSA M6 and Banner M2 by virtue of overlap) in both cohorts at adjusted p < 0.05 by Fisher’s exact test (Fig. 5d).”
Quote in the paperSubstantial claim - Findingown data
Among the 118 proteins known to interact with PSD95, 109 were detected in the Banner cohort.
“Among the 118 proteins known to interact with PSD95, 109 were detected in the Banner cohort.”
Quote in the paperSubstantial claim - Findingown data
In BLSA, four modules were associated with cognitive trajectory at linear regression p < 0.05.
“In BLSA, four modules were associated with cognitive trajectory at by linear regression p < 0.05”
Quote in the paperNot a substantial claim - Findingown data
In BLSA, only one module (M3/mitochondrial function) remained significantly associated with cognitive trajectory after adjusting for multiple testing (p = 0.0025, adjusted p = 0.039).
“but only one remained significantly associated with cognitive trajectory after adjusting for multiple testing (M3/mitochondrial function, p = 0.0025, adjusted p = 0.039)”
Quote in the paperSubstantial claim - Findingown data
In the Banner network, 10 of its 20 modules were associated with cognitive trajectory at FDR < 0.05.
“In the Banner network, 10 of its 20 modules were associated with cognitive trajectory at FDR < 0.05 (Table 4).”
Quote in the paperSubstantial claim - Findingown data
Through both proteome-wide association study of cognitive trajectory and protein co-expression networks, over-expressed proteins in cognitive stability were enriched for mitochondrial activities and synaptic function independently of β-amyloid plaques and neurofibrillary tangles.
“We note that through both approaches—proteome-wide association study of cognitive trajectory and protein co-expression networks—we found consistent results that the over-expressed proteins in cognitive stability were enriched for mitochondrial activities and synaptic function independently of β-amyloid plaques and neurofibrillary tangles.”
- Also stated in: § Discussion · plain
“Our most notable finding is that proteins involving mitochondrial activities or synaptic functions had increased abundance among individuals with cognitive stability regardless of the burden of β-amyloid plaques or neurofibrillary tangles.”
Quote in the paperSubstantial claim - Also stated in: § Discussion · plain
- Findingown data
Proteins involved in mitochondrial activities had lower-abundance in persons with faster cognitive decline but higher-abundance in persons with cognitive stability in an unbiased human proteome-wide study.
“Here, we show proteins involved in mitochondrial activities had lower-abundance in persons with faster cognitive decline but higher-abundance in persons with cognitive stability in an unbiased human proteome-wide study.”
Quote in the paperSubstantial claim - Findingnone given
Some proteins, including NEFM and MBP, participate in myelination on the list of 38 proteins.
“Some proteins, including NEFM and MBP, participate in myelination on this list of 38 proteins.”
Quote in the paperNot a substantial claim - Findingown data
In both Banner and BLSA, lower-abundance of SNAP25 was associated with faster cognitive decline.
“Consistently, in both Banner and BLSA, we found that lower-abundance of SNAP25 was associated with faster cognitive decline.”
Quote in the paperSubstantial claim - Findingown data
Overexpression of PSD95 protein was associated with cognitive stability.
“Their finding is consistent with ours in which overexpression of PSD95 protein was associated with cognitive stability.”
Quote in the paperSubstantial claim - Findingown data
The protein level of HOMER1 was increased in cognitive stability.
“Consistently, we found that the protein level of HOMER1 was increased in cognitive stability.”
Quote in the paperSubstantial claim - Findingown data
SLC30A3 protein level was increased in cognitive stability.
“Consistently, we found that SLC30A3 protein level was increased in cognitive stability.”
Quote in the paperSubstantial claim - Findingown data
Cognitive trajectory-associated proteins were enriched in astrocytes.
“We found cognitive trajectory-associated proteins to be enriched in astrocytes, which perform important roles for neural functioning.”
Quote in the paperSubstantial claim - Findingown data
Lower-abundance of NEFM was associated with cognitive resilience.
“These findings are consistent with ours, in which lower-abundance of NEFM was associated with cognitive resilience.”
Quote in the paperSubstantial claim - Findingown data
VGF is a hub protein for Banner M1 module, which involves synaptic function.
“VGF is also a hub protein for Banner M1 module, which involves synaptic function.”
Quote in the paperNot a substantial claim - Findingown data
Both higher- and lower-abundance proteins in cognitive stability were enriched in astrocytes.
“We found that both higher- and lower-abundance proteins in cognitive stability were enriched in astrocytes.”
Quote in the paperSubstantial claim - Findingown data
VGF is strongly associated with cognitive trajectory with or without adjusting for AD neuropathology.
“Furthermore, we note that VGF is strongly associated with cognitive trajectory with or without adjusting for AD neuropathology.”
Quote in the paperSubstantial claim - Findingown data
From the protein co-expression network analysis, we found 10 Banner protein modules and 1 BLSA protein module associated with cognitive trajectory.
“From the protein co-expression network analysis, we found 10 Banner protein modules and 1 BLSA protein module associated with cognitive trajectory.”
Quote in the paperSubstantial claim - Findingcitation
Decreased abundance of MBP was associated with cognitive stability.
“consistent with our finding of decreased abundance of MBP being associated with cognitive stability.”
Quote in the paperSubstantial claim - Findingown data
Fewer networks remained associated with cognitive trajectory after adjusting for β-amyloid plaques and neurofibrillary tangles.
“Fewer networks remained associated with cognitive trajectory after adjusting for β-amyloid plaques and neurofibrillary tangles”
Quote in the paperSubstantial claim - Findingown data
In each proteomic dataset, age, sex, neuropathology, post-mortem interval (PMI), and batch were identified as sources of variation in protein levels.
“This approach was used to quantify the main sources of variation in each proteomic dataset separately and identified the variance attributable to age, sex, neuropathology, post-mortem interval (PMI), and batch.”
Quote in the paperSubstantial claim - Findingown data
After Combat and bootstrap regression, batch, sex, age at death, and PMI explained only minimal amounts of variance in the proteomic profile.
“Supplementary Fig. 3 shows that after Combat and bootstrap regression, batch, sex, age at death, and PMI explained only minimal amounts of the variance of the proteomic profile.”
Quote in the paperSubstantial claim - Interpretationreasoning
Cognitive trajectory provides more comprehensive information than traditional case/control status based on clinical or pathological diagnosis.
“Thus, it provides more comprehensive information than the traditional case/control status based on clinical or pathological diagnosis.”
Quote in the paperSubstantial claim - Premisecitation
Cognitive trajectory likely captures co-occurring disease processes and co-occurring age-related pathologies, which are known to be prevalent in the brains of aged individuals.
“Second, it likely captures co-occurring disease processes and co-occurring age-related pathologies, which are known to be prevalent in the brains of aged individuals4,5.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [4] Schneider, J. A., Aggarwal, N. T., Barnes, L., Boyle, P. & Bennett, D. A. The neuropathology of older persons with and without dementia from community versus clinic cohorts. J. Alzheimers. Dis. 18, 691–701 (2009). source
Not checked in the free run.
- cites [5] Boyle, P. A. et al. Person-specific contribution of neuropathologies to cognitive loss in old age. Ann. Neurol. 83, 74–83 (2018). source
Not checked in the free run.
- Premisecitation
Studying individual cognitive trajectory captures all the factors affecting cognition, including diverse pathologies as well as biological mechanisms independent of pathologies.
“First, it captures all the factors affecting cognition, including diverse pathologies as well as biological mechanisms independent of pathologies4–6.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [4] Schneider, J. A., Aggarwal, N. T., Barnes, L., Boyle, P. & Bennett, D. A. The neuropathology of older persons with and without dementia from community versus clinic cohorts. J. Alzheimers. Dis. 18, 691–701 (2009). source
Not checked in the free run.
- cites [5] Boyle, P. A. et al. Person-specific contribution of neuropathologies to cognitive loss in old age. Ann. Neurol. 83, 74–83 (2018). source
Not checked in the free run.
- cites [6] Terry, R. D. et al. Physical basis of cognitive alterations in Alzheimer's disease: synapse loss is the major correlate of cognitive impairment. Ann. Neurol. 30, 572–580 (1991). source
Not checked in the free run.
- Interpretationown data
The 350 proteins with increased abundance in cognitive stability participate in mitochondrial activities and synaptic transmission in neurons.
“Together, our findings suggest that the 350 proteins with increased abundance in cognitive stability participate in mitochondrial activities and synaptic transmission in neurons.”
Quote in the paperSubstantial claim - Interpretationown data
Cognitive trajectory provides complementary information to clinical diagnosis and neuropathology findings.
“(iii) cognitive trajectory provides complementary information to the clinical diagnosis and neuropathology findings.”
Quote in the paperSubstantial claim - Interpretationreasoning
It is unclear what causes the large majority of the variance in cognitive trajectory.
“It is unclear what causes the large majority of the variance in cognitive trajectory.”
Quote in the paperSubstantial claim - Premisecitation
Pathologies such as β-amyloid plaques, neurofibrillary tangles, microinfarct, macroinfarct, and Lewy bodies together capture only about 41% of the variance in cognitive trajectory, leaving 59% unexplained.
“However, pathologies such as β-amyloid plaques, neurofibrillary tangles, microinfarct, macroinfarct, and Lewy bodies together capture only about 41% of the variance in cognitive trajectory, leaving 59% unexplained5,7.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [5] Boyle, P. A. et al. Person-specific contribution of neuropathologies to cognitive loss in old age. Ann. Neurol. 83, 74–83 (2018). source
Not checked in the free run.
- cites [7] Boyle, P. A. et al. Much of late life cognitive decline is not due to common neurodegenerative pathologies. Ann. Neurol. 74, 10.1002/ana.23964 (2013). source
Not checked in the free run.
- Interpretationown data
The proteins with increased abundance in cognitive stability were involved in mitochondrial activities and synaptic transmission in neurons independently of effects of β-amyloid plaques and neurofibrillary tangles.
“Notably, the proteome-wide association study reveals that the proteins with increased abundance in cognitive stability were involved in mitochondrial activities and synaptic transmission in neurons independently of effects of β-amyloid plaques and neurofibrillary tangles.”
- Also stated in: § Abstract · plain
“Notably, we present evidence for increased neuronal mitochondrial activities in cognitive stability regardless of the burden of traditional neuropathologies.”
Quote in the paperSubstantial claim - Also stated in: § Abstract · plain
- Premisecitation
Our group previously performed a detailed co-expression network analysis for the BLSA cohort and identified 16 networks of co-expressed proteins.
“Of note, our group previously performed a detailed co-expression network analysis for the BLSA cohort and identified 16 networks of co-expressed proteins23.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [23] Wingo, T. S. et al. Integrating next-generation genomic sequencing and mass spectrometry to estimate allele-specific protein abundance in human brain. J. Proteome. Res. 16, 3336–3347 (2017). source
Not checked in the free run.
- Premisecitation
BLSA module M3 was not significantly associated with clinical diagnosis or AD pathologies after multiple testing correction in our previous publication.
“This module was not significantly associated with clinical diagnosis or AD pathologies after multiple testing correction in our previous publication23”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [23] Wingo, T. S. et al. Integrating next-generation genomic sequencing and mass spectrometry to estimate allele-specific protein abundance in human brain. J. Proteome. Res. 16, 3336–3347 (2017). source
Not checked in the free run.
- Interpretationown data
Higher expression of the PSD95 interacting partners was associated with cognitive stability.
“This implies that higher expression of the PSD95 interacting partners was associated with cognitive stability (Supplementary Fig. 6).”
Quote in the paperSubstantial claim - Interpretationown data
Cognitive trajectory captures biological processes that are independent of traditional AD pathologies.
“suggesting that cognitive trajectory captures biological processes that are independent of traditional AD pathologies.”
Quote in the paperSubstantial claim - Interpretationown data
Mitochondrial activities are strikingly present in our analysis in a way not seen when considering the clinical diagnosis of cognitive status or AD pathologic outcomes.
“Most interestingly, mitochondrial activities are strikingly present in our analysis in a way not seen when considering the clinical diagnosis of cognitive status or AD pathologic outcomes.”
Quote in the paperSubstantial claim - Premisecitation
HOMER1 is an important protein for synaptic plasticity and memory consolidation because it drives homeostatic scaling-down of excitatory synapses during sleep to remodel synapses and consolidate contextual memory.
“Another protein on this list, HOMER1 (i.e., HOMER1a) is an important protein for synaptic plasticity and memory consolidation because it drives homeostatic scaling-down of excitatory synapses during sleep to remodel synapses and consolidate contextual memory40.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [40] Diering, G. H. et al. Homer1a drives homeostatic scaling-down of excitatory synapses during sleep. Science 355, 511–515 (2017). source
Not checked in the free run.
- Premisecitation
SYT1 was found to have reduced abundance in AD.
“The second protein on the list, SYT1, was found to have reduced abundance in AD39.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [39] Zoltowska, K. M. et al. Dynamic presenilin 1 and synaptotagmin 1 interaction modulates exocytosis and amyloid beta production. Mol. Neurodegener. 12, 15 (2017). source
Not checked in the free run.
- Premisecitation
Deletion of MBP gene led to a significant reduction in cerebral β-amyloid levels in Tg-3xFAD mice.
“Indeed, deletion of MBP gene led to a significant reduction in cerebral β-amyloid levels in Tg-3xFAD mice49”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [49] Ou-Yang, M. H. & Van Nostrand, W. E. The absence of myelin basic protein promotes neuroinflammation and reduces amyloid β-protein accumulation in Tg-5xFAD mice. J. Neuroinflammation. 10, 134 (2013). source
Not checked in the free run.
- Premisecitation
The decline in MMSE generally parallels that of more detailed testing.
“Nevertheless, the decline in MMSE generally parallels that of more detailed testing55.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [55] Tombaugh, T. N. & McIntyre, N. J. The mini-mental state examination: a comprehensive review. J. Am. Geriatr. Soc. 40, 922–935 (1992). source
Not checked in the free run.
- Interpretationnone given
Proteins and protein co-expression networks that are consistently changed within the two regions likely reflect biologically meaningful differences.
“On the other hand, proteins and protein co-expression networks that are consistently changed within the two regions likely reflect biologically meaningful differences.”
Quote in the paperNot a substantial claim - Premisecitation
Myelin basic protein (MBP) can influence β-amyloid accumulation in the brain.
“Myelin basic protein, MBP, can influence β-amyloid accumulation in the brain.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [49] Ou-Yang, M. H. & Van Nostrand, W. E. The absence of myelin basic protein promotes neuroinflammation and reduces amyloid β-protein accumulation in Tg-5xFAD mice. J. Neuroinflammation. 10, 134 (2013). source
Not checked in the free run.
- Interpretationreasoning
VGF likely acts through mechanisms independent of β-amyloid plaques and neurofibrillary tangles in contributing to cognitive decline.
“Our findings suggest that VGF likely act through mechanisms independent of β-amyloid plaques and neurofibrillary tangles in contributing to cognitive decline.”
Quote in the paperSubstantial claim - Premisecitation
The same approach (Combat and bootstrap regression) successfully removed the effects of batch, sex, age at death, and PMI from the BLSA proteome.
“The same approach was used to successfully remove the effects of these same covariates from the BLSA proteome23.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [23] Wingo, T. S. et al. Integrating next-generation genomic sequencing and mass spectrometry to estimate allele-specific protein abundance in human brain. J. Proteome. Res. 16, 3336–3347 (2017). source
Not checked in the free run.
- Classified as probably not a substantial claim. 51
- May combine two claims. 51
Probably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataOf the 157 proteins designated as mitochondrial, 112 were presynaptic, 10 postsynaptic, and 35 were other.
“For instance, of the 157 proteins designated as mitochondrial, 112 were presynaptic, 10 postsynaptic, and 35 were other (Supplementary Data 3).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataFor the 569 unique proteins, 484 had at least one PPI, and there were a total of 2587 PPIs among these 484 proteins.
“For the 569 unique proteins, 484 had at least one PPI, and there were a total of 2587 PPIs among these 484 proteins (Supplementary Data 3; Supplementary Fig. 4).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataAmong the 579 proteins associated with cognitive trajectory, 350 proteins had increased abundance while 229 had decreased abundance in cognitive stability.
“Regarding the direction of association, 350 proteins had increased abundance while 229 had decreased abundance in cognitive stability.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
FindingcitationThe 569 unique proteins among the 579 proteins (10 had isoforms) were categorized into presynaptic (N = 159), postsynaptic (N = 150), both (N = 20), or other (N = 240).
“The 569 unique proteins among these 579 proteins (10 had isoforms) were categorized into presynaptic (N = 159), postsynaptic (N = 150), both (present in both presynaptic and postsynaptic density; N = 20), or other (N = 240) using Boyken et. al. presynaptic list16 and Bayes et. al. postsynaptic density list17 (Supplementary Data 3).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [16] Boyken, J. et al. Molecular profiling of synaptic vesicle docking sites reveals novel proteins but few differences between glutamatergic and GABAergic synapses. Neuron 78, 285–297 (2013). source
Not checked in the free run.
- cites [17] Bayes, A. et al. Characterization of the proteome, diseases and evolution of the human postsynaptic density. Nat. Neurosci. 14, 19–21 (2011). source
Not checked in the free run.
May combine two claims.
Findingown dataThe median rate of cognitive change was −0.3 unit of MMSE score per year for Banner participants and −0.1 unit of MMSE per year for BLSA participants.
“The median rate of cognitive change was −0.3 unit of MMSE score per year for Banner participants and −0.1 unit of MMSE per year for BLSA participants (Table 1).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe meta-analysis showed 579 proteins associated with cognitive trajectory in the same directions in both the discovery and replication cohorts and at FDR < 0.05 among the 2752 protein isoforms commonly detected in both cohorts (N = 143 individuals).
“The meta-analysis showed 579 proteins associated with cognitive trajectory in the same directions in both the discovery and replication cohorts and at FDR < 0.05 among the 2752 protein isoforms commonly detected in both cohorts (N = 143 individuals; Fig. 3a, Supplementary Data 2).”
- Also stated in: § Abstract · plain
“We find 579 proteins associated with cognitive trajectory after meta-analysis.”
Quote in the paperSubstantial claim- Also stated in: § Abstract · plain
May combine two claims.
Findingown dataFocusing on PPIs emanating from presynaptic proteins, there were 374 proteins with interactions (nodes) and 1745 PPIs (edges).
“Focusing on PPIs emanating from presynaptic proteins, we found 374 proteins with interactions (nodes) and 1745 PPIs (edges) (Supplementary Fig. 5A).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataLower-abundance proteins in cognitive stability were enriched for glycolysis, assembly of neurofilament bundle, cell-cell adhesion, and RNA binding, and for oligodendrocyte, astrocyte, and microglia.
“Lower-abundance proteins were enriched for the processes of glycolysis, assembly of neurofilament bundle, cell–cell adhesion, and RNA binding, and for oligodendrocyte, astrocyte, and microglia (Table 3; Supplementary Data 6).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataIn Banner, the seven modules that the 579 cognitive trajectory-associated proteins were enriched in had 123 hub proteins, of which 102 (83%) were associated with cognitive trajectory.
“In Banner, these seven modules had 123 hub proteins, of which 102 (83%) were associated with cognitive trajectory (Supplementary Data 8).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAfter adjusting for β-amyloid plaques and neurofibrillary tangles, there were 232 cognitive trajectory-associated proteins, divided into 81 lower-abundance and 151 higher-abundance proteins in cognitive stability.
“After adjusting for β-amyloid plaques and neurofibrillary tangles, there were 232 cognitive trajectory-associated proteins, and enrichment was performed separately for lower-abundance proteins in cognitive stability (81 proteins, colored in salmon) and higher-abundance proteins in cognitive stability (151 proteins, colored in turquoise), respectively.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThese 38 proteins were associated with cognitive trajectory independently of β-amyloid plaques and neurofibrillary tangles in both Banner and BLSA, and were also hub proteins in both Banner and BLSA.
“Notably, these 38 proteins were associated with cognitive trajectory independently of β-amyloid plaques and neurofibrillary tangles in both Banner and BLSA, and were also hub”
- Also stated in: § Discussion · plain
“The next notable finding is the 38 proteins that were associated with cognitive trajectory independently of typical AD neuropathologies and were also hub proteins in both Banner and BLSA protein networks.”
Quote in the paperSubstantial claim- Also stated in: § Discussion · plain
May combine two claims.
Findingown dataThere are 579 cognitive trajectory-associated proteins in Banner and BLSA cohorts, divided into 229 lower-abundance proteins in cognitive stability and 350 higher-abundance proteins in cognitive stability.
“There are 579 cognitive trajectory-associated proteins in Banner and BLSA cohorts, which were divided into lower-abundance proteins in cognitive stability (229 proteins, colored in salmon) and higher-abundance proteins in cognitive stability (350 proteins, colored in turquoise), respectively.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data38 proteins act independently of neuropathologies and are also hub proteins of protein co-expression networks.
“particularly the 38 proteins that act independently of neuropathologies and are also hub proteins of protein co-expression networks”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data157 proteins of the 579 cognitive trajectory-associated proteins are mitochondrial proteins, and of these, 122 are located in the mitochondria either in the pre- or postsynaptic density.
“This is consistent with our finding that 157 proteins of the 579 cognitive trajectory-associated proteins are mitochondrial proteins and of these, 122 are located in the mitochondria either in the pre- or postsynaptic density.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAmong the proteins SNAP47, SYBU, LRFN2, SV2C, and GRIA3 found by Bereczki et al. to be associated with cognitive impairment and faster cognitive decline, only GRIA3 was detected in our datasets and had lower-abundance in faster cognitive decline in both Banner and BLSA.
“Among these proteins, only GRIA3 was detected in our datasets and had lower-abundance in faster cognitive decline in both Banner and BLSA, consistent with Bereczki and colleagues’ finding.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataAmong the 38 proteins, those that are higher-abundance in cognitive stability are involved in either mitochondrial activities or synaptic function, and those with lower-abundance in cognitive stability are involved in myelination or apoptosis.
“Among these 38 proteins, those that are higher-abundance in cognitive stability are involved in either mitochondrial activities or synaptic function, and those with lower-abundance in cognitive stability are involved in myelination or apoptosis.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAfter label-free quantification, 3710 protein groups in Banner and 3933 protein groups in BLSA cohorts were detected, and 2752 protein isoforms were detected in both Banner and BLSA cohorts.
“After label-free quantification, 3710 protein groups in Banner and 3933 protein groups in BLSA cohorts were detected, and 2752 protein isoforms were detected in both Banner and BLSA cohorts.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataIn the Banner cohort, batch and sex explained substantial proportions of variance in the proteomic profile, while age at death and PMI explained some proportions.
“For instance, in the Banner cohort, batch and sex explained substantial proportions while age at death and PMI explained some proportions of variance of the proteomic profile (Supplementary Fig. 2).”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
PremisecitationA stable cognitive trajectory in advanced age may reflect cognitive resilience, while decline in cognitive performance may be the earliest manifestation of a serious neurodegenerative disease.
“A stable cognitive trajectory in advanced age is desirable and may reflect cognitive resilience, while decline in cognitive performance may be the earliest manifestation of a serious neurodegenerative disease1,3.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [1] Zaninotto, P., Batty, G. D., Allerhand, M. & Deary, I. J. Cognitive function trajectories and their determinants in older people: 8 years of follow-up in the English Longitudinal Study of Ageing. J. Epidemio.l Community Health 72, 685–694 (2018). source
Not checked in the free run.
- cites [3] Rajan, K. B., Wilson, R. S., Weuve, J., Barnes, L. L. & Evans, D. A. Cognitive impairment 18 years before clinical diagnosis of Alzheimer disease dementia. Neurology 85, 898–904 (2015). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationCognitive trajectory varies from person to person and is determined by a combination of genetic and environmental factors including premorbid cognitive abilities, educational attainment, lifestyle choices, and environmental exposures.
“Like all complex human traits, cognitive trajectory varies from person to person and is determined by a combination of genetic and environmental factors including premorbid cognitive abilities, educational attainment, lifestyle choices, and environmental exposures1,2.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [1] Zaninotto, P., Batty, G. D., Allerhand, M. & Deary, I. J. Cognitive function trajectories and their determinants in older people: 8 years of follow-up in the English Longitudinal Study of Ageing. J. Epidemio.l Community Health 72, 685–694 (2018). source
Not checked in the free run.
- cites [2] Plassman, B. L., Williams, J. W. Jr., Burke, J. R., Holsinger, T. & Benjamin, S. Systematic review: factors associated with risk for and possible prevention of cognitive decline in later life. Ann. Intern. Med. 153, 182–193 (2010). source
Not checked in the free run.
Classified as probably not a substantial claim.
InterpretationreasoningVariation in cognitive trajectory can influence onset age for MCI or dementia.
“Hence, variation in cognitive trajectory can influence onset age for MCI or dementia.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationAggregation of hyperphosphorylated tau proteins can lead to the formation of neurofibrillary tangles and Alzheimer's dementia.
“and aggregation of hyperphosphorylated tau proteins can lead to the formation of neurofibrillary tangles and Alzheimer’s dementia14.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [14] Ballatore, C., Lee, V. M. & Trojanowski, J. Q. Tau-mediated neurodegeneration in Alzheimer's disease and related disorders. Nat. Rev. Neurosci. 8, 663–672 (2007). source
Not checked in the free run.
May combine two claims.
Interpretationown dataThere is ample evidence that the proteins associated with cognitive trajectory interact with each other and reveal many PPIs within and between presynaptic and postsynaptic proteins associated with cognitive trajectory.
“Thus there is ample evidence that the proteins associated with cognitive trajectory interact with each other and reveal many PPIs within and between presynaptic and postsynaptic proteins associated with cognitive trajectory.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationMAPT (microtubule-associated protein tau) is involved in axonal transport, synaptic plasticity, and synaptic function.
“MAPT (microtubule-associated protein tau) is involved in axonal transport, synaptic plasticity, and synaptic function13,”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [13] Caillet-Boudin, M. L., Buee, L., Sergeant, N. & Lefebvre, B. Regulation of human MAPT gene expression. Mol. Neurodegener. 10, 28 (2015). source
Not checked in the free run.
May combine two claims.
Interpretationown dataThe proteins with decreased abundance in cognitive stability were involved in inflammatory response, apoptosis, and endothelial function in glial cells, likely acting via β-amyloid plaques and neurofibrillary tangles.
“In addition, the proteins with decreased abundance in cognitive stability were involved in inflammatory response, apoptosis, and endothelial function in glial cells, likely acting via β-amyloid plaques and neurofibrillary tangles.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationVGF is a neuropeptide that regulates synaptic function, synaptic plasticity, and hippocampal memory consolidation.
“VGF is a neuropeptide that regulates synaptic function10, synaptic plasticity11, and hippocampal memory consolidation12.”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [10] Alder, J. et al. Brain-derived neurotrophic factor-induced gene expression reveals novel actions of VGF in hippocampal synaptic plasticity. J. Neurosci. 23, 10800–10808 (2003). source
Not checked in the free run.
- cites [11] Bozdagi, O. et al. The neurotrophin-inducible gene Vgf regulates hippocampal function and behavior through a brain-derived neurotrophic factor-dependent mechanism. J. Neurosci. 28, 9857–9869 (2008). source
Not checked in the free run.
- cites [12] Lin, W. J. et al. VGF and Its C-terminal peptide TLQP-62 regulate memory formation in hippocampus via a BDNF-TrkB-dependent mechanism. J. Neurosci. 35, 10343–10356 (2015). source
Not checked in the free run.
May combine two claims.
Interpretationown dataIncreased synaptic abundance and decreased inflammation and apoptosis occur in cognitive stability.
“Furthermore, we provide additional evidence for increased synaptic abundance and decreased inflammation and apoptosis in cognitive stability.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationMitochondria in neurons are crucial for maintaining synaptic function, and most mitochondrial proteins are encoded in the nuclear genome.
“Mitochondria in neurons are crucial for maintaining synaptic function, and most of the mitochondrial proteins are encoded in the nuclear genome29.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [29] Devine, M. J. & Kittler, J. T. Mitochondria at the neuronal presynapse in health and disease. Nat. Rev. Neurosci. 19, 63–80 (2018). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationSynaptic mitochondria are vital for the maintenance of synaptic function and transmission.
“Synaptic mitochondria, therefore, are vital for the maintenance of synaptic function and transmission29–31.”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [29] Devine, M. J. & Kittler, J. T. Mitochondria at the neuronal presynapse in health and disease. Nat. Rev. Neurosci. 19, 63–80 (2018). source
Not checked in the free run.
- cites [30] Du, H. et al. Early deficits in synaptic mitochondria in an Alzheimer's disease mouse model. Proc. Natl Acad. Sci. USA 107, 18670–18675 (2010). source
Not checked in the free run.
- cites [31] Reddy, P. H. et al. Amyloid-beta and mitochondria in aging and Alzheimer's disease: implications for synaptic damage and cognitive decline. J. Alzheimers. Dis. 20(Suppl 2), S499–S512 (2010). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationIn the brain, mitochondria regulate synaptic transmission by generating ATP to power the process and by modulating presynaptic calcium level, which in turn determines the release of neurotransmitters.
“In the brain, mitochondria regulate synaptic transmission by generating ATP to power the process and by modulating presynaptic calcium level, which in turn determines the release of neurotransmitters29.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [29] Devine, M. J. & Kittler, J. T. Mitochondria at the neuronal presynapse in health and disease. Nat. Rev. Neurosci. 19, 63–80 (2018). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningThe limited number of significant BLSA modules after multiple testing is likely due to small sample size and thus lower power.
“likely due to small sample size and thus lower power”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationMitochondrial dysfunction has been suggested to occur early in the progression to AD and continues into later stages of AD.
“Mitochondrial dysfunction has been suggested to occur early in the progression to AD and continues into later stages of AD31–34.”
Quote in the paperNot a substantial claim0 of 4 sources checked- cites [31] Reddy, P. H. et al. Amyloid-beta and mitochondria in aging and Alzheimer's disease: implications for synaptic damage and cognitive decline. J. Alzheimers. Dis. 20(Suppl 2), S499–S512 (2010). source
Not checked in the free run.
- cites [32] Valla, J. et al. Reduced posterior cingulate mitochondrial activity in expired young adult carriers of the APOE epsilon4 allele, the major late-onset Alzheimer's susceptibility gene. J. Alzheimers. Dis. 22, 307–313 (2010). source
Not checked in the free run.
- cites [33] Liang, W. S. et al. Alzheimer's disease is associated with reduced expression of energy metabolism genes in posterior cingulate neurons. Proc. Natl Acad. Sci. USA 105, 4441–4446 (2008). source
Not checked in the free run.
- cites [34] Mastroeni, D. et al. Nuclear but not mitochondrial-encoded oxidative phosphorylation genes are altered in aging, mild cognitive impairment, and Alzheimer's disease. Alzheimers Dement. 13, 510–519 (2017). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationDLG4/PSD95 is one of the most abundant scaffold proteins at excitatory brain synapses and is known to affect brain development, neuronal plasticity, memory and learning, and overall cognitive function.
“which is one of the most abundant scaffold proteins at the excitatory brain synapses and known to affect brain development, neuronal plasticity, memory and learning, and overall cognitive function24–28.”
Quote in the paperNot a substantial claim0 of 5 sources checked- cites [24] Fernandez, E. et al. Targeted tandem affinity purification of PSD-95 recovers core postsynaptic complexes and schizophrenia susceptibility proteins. Mol. Syst. Biol. 5, 269 (2009). source
Not checked in the free run.
- cites [25] Fernandez, E. et al. Arc Requires PSD95 for Assembly into Postsynaptic Complexes Involved with Neural Dysfunction and Intelligence. Cell Rep 21, 679–691 (2017). source
Not checked in the free run.
- cites [26] Krishnan, M. L. et al. Integrative genomics of microglia implicates DLG4 (PSD95) in the white matter development of preterm infants. Nat. Commun. 8, 428 (2017). source
Not checked in the free run.
- cites [27] Migaud, M. et al. Enhanced long-term potentiation and impaired learning in mice with mutant postsynaptic density-95 protein. Nature 396, 433–439 (1998). source
Not checked in the free run.
- cites [28] Nithianantharajah, J. et al. Synaptic scaffold evolution generated components of vertebrate cognitive complexity. Nat. Neurosci. 16, 16–24 (2013). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationPSD-95 is a major scaffold protein of the dendritic spines and its expression level has been shown to be altered in aging, AD, and several psychiatric disorders.
“PSD-95 is a major scaffold protein of the dendritic spines and its expression level has been shown to be altered in aging, AD, and several psychiatric disorders37.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [37] Savioz, A., Leuba, G. & Vallet, P. G. A framework to understand the variations of PSD-95 expression in brain aging and in Alzheimer's disease. Ageing Res. Rev. 18, 86–94 (2014). source
Not checked in the free run.
May combine two claims.
Interpretationown dataA notable theme among the proteins participating in synaptic function on the list of 38 proteins is that they are important for neuronal plasticity and memory consolidation and their overexpression is associated with cognitive stability.
“A notable theme among the proteins participating in synaptic function on this list of 38 proteins is that they are important for neuronal plasticity and memory consolidation and their overexpression is associated with cognitive”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationA transgenic mouse model showed that amyloid pathology, even at a low level, can induce deficits in the functioning of synaptic mitochondria leading to synaptic degeneration.
“Furthermore, a transgenic mouse model showed that amyloid pathology, even at a low level, can induce deficits in the functioning of synaptic mitochondria leading to synaptic degeneration30.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [30] Du, H. et al. Early deficits in synaptic mitochondria in an Alzheimer's disease mouse model. Proc. Natl Acad. Sci. USA 107, 18670–18675 (2010). source
Not checked in the free run.
May combine two claims.
PremisecitationIn a study of expired young-adult APOE4 carriers vs. non-APOE4 carriers, young-adult participants in both groups did not have amyloid or tangle deposition in the posterior cingulate cortex (PCC); yet APOE4 carriers showed reduced mitochondrial activity in PCC neurons, as measured by cytochrome oxidase levels.
“In that study, young-adult participants in both groups did not have amyloid or tangle deposition in the posterior cingulate cortex (PCC); yet, APOE4 carriers, i.e., those at greater risk for developing AD, showed reduced mitochondrial activity in PCC neurons, as measured by cytochrome oxidase levels”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [32] Valla, J. et al. Reduced posterior cingulate mitochondrial activity in expired young adult carriers of the APOE epsilon4 allele, the major late-onset Alzheimer's susceptibility gene. J. Alzheimers. Dis. 22, 307–313 (2010). source
Not checked in the free run.
May combine two claims.
PremisecitationDecreased abundance of SLC17A7 can lead to susceptibility to neuroinflammation and disruption of synaptic plasticity.
“Decreased abundance of this protein can lead to susceptibility to neuroinflammation and disruption of synaptic plasticity42.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [42] Rodriguez-Perdigon, M. et al. Down-regulation of glutamatergic terminals (VGLUT1) driven by Abeta in Alzheimer's disease. Hippocampus 26, 1303–1312 (2016). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationPSD-95 is important for neuronal plasticity and memory consolidation via its regulation of synaptic glutamate receptors, signaling proteins, adhesion molecules, and cytoskeletal proteins.
“PSD-95 is important for neuronal plasticity and memory consolidation via its regulation of synaptic glutamate receptors, signaling proteins, adhesion molecules, and cytoskeletal proteins37,38.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [37] Savioz, A., Leuba, G. & Vallet, P. G. A framework to understand the variations of PSD-95 expression in brain aging and in Alzheimer's disease. Ageing Res. Rev. 18, 86–94 (2014). source
Not checked in the free run.
- cites [38] Bustos, F. J. et al. Epigenetic editing of the Dlg4/PSD95 gene improves cognition in aged and Alzheimer's disease mice. Brain 140, 3252–3268 (2017). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationA transcriptomic study of post-mortem brain of AD cases and controls in the PCC and other brain regions found that nuclear genes encoding the mitochondrial electron transport chain subunits had decreased expression in AD cases compared to controls.
“This study found that the nuclear genes encoding the mitochondrial electron transport chain subunits had decreased expression in AD cases compared to controls in PCC and other brain regions33.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [33] Liang, W. S. et al. Alzheimer's disease is associated with reduced expression of energy metabolism genes in posterior cingulate neurons. Proc. Natl Acad. Sci. USA 105, 4441–4446 (2008). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationSLC30A3 (ZNT3) is a synapse-specific vesicular zinc transporter that is important for zinc homeostasis and required for hippocampus-dependent memory.
“SLC30A3, aka ZNT3, is a synapse-specific vesicular zinc transporter that is important for zinc homeostasis and required for hippocampus-dependent memory41.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [41] Sindreu, C., Palmiter, R. D. & Storm, D. R. Zinc transporter ZnT-3 regulates presynaptic Erk1/2 signaling and hippocampus-dependent memory. Proc. Natl.Acad. Sci. USA 108, 3366–3370 (2011). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationSYNPO has been found to be important for synaptic plasticity.
“Along the same theme, SYNPO has been found to be important for synaptic plasticity43,44.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [43] Deller, T. et al. Synaptopodin-deficient mice lack a spine apparatus and show deficits in synaptic plasticity. Proc. Natl Acad. Sci. USA 100, 10494–10499 (2003). source
Not checked in the free run.
- cites [44] Vlachos, A. et al. Synaptopodin regulates denervation-induced homeostatic synaptic plasticity. Proc. Natl Acad. Sci. USA 110, 8242–8247 (2013). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationSynaptic degeneration has been suggested to be one of the earliest cellular events in AD pathogenesis, and synaptic loss to be the best correlate of cognitive impairment in AD.
“Synaptic degeneration has been suggested to be one of the earliest cellular event in AD pathogenesis, and synaptic loss to be the best correlate of cognitive impairment in AD (see review by Reddy et al.31).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [31] Reddy, P. H. et al. Amyloid-beta and mitochondria in aging and Alzheimer's disease: implications for synaptic damage and cognitive decline. J. Alzheimers. Dis. 20(Suppl 2), S499–S512 (2010). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationOverexpression of Syt1 in mouse hippocampi was found to promote protective presenilin-1 conformation.
“Overexpression of Syt1 in mouse hippocampi was found to promote protective presenilin-1 conformation39”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [39] Zoltowska, K. M. et al. Dynamic presenilin 1 and synaptotagmin 1 interaction modulates exocytosis and amyloid beta production. Mol. Neurodegener. 12, 15 (2017). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationSLC17A7 (VGLUT1) is a sodium-dependent phosphate transporter that is specifically expressed in the neuron-rich regions of the brain and transports glutamate.
“SLC17A7, aka VGLUT1, is a sodium-dependent phosphate transporter that is specifically expressed in the neuron-rich regions of the brain and transports glutamate.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [42] Rodriguez-Perdigon, M. et al. Down-regulation of glutamatergic terminals (VGLUT1) driven by Abeta in Alzheimer's disease. Hippocampus 26, 1303–1312 (2016). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationVGF is induced by BDNF and serotonin and is upregulated by antidepressants, exercise, and is reduced in animal models of depression.
“VGF is induced by BDNF and serotonin and is upregulated by antidepressants, exercise, and is reduced in animal models of depression51.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [51] Thakker-Varia, S. & Alder, J. Neuropeptides in depression: role of VGF. Behav. Brain. Res. 197, 262–278 (2009). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenThe label-free proteomic method yielded approximately 3900 proteins in the proteome, which is not as comprehensive of a proteomic profile as would be ideal.
“Second, our label-free proteomic method yielded approximately 3900 proteins in the proteome, which is not as comprehensive of a proteomic profile as would be ideal”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningThis is an association study and thus it precludes causal inference.
“First, this is an association study and thus it precludes causal inference.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationMyelination refers to the formation of a membranous sheath surrounding axons to increase signal transmitting speed between neurons and is not limited to early development but occurs throughout adulthood.
“Myelination refers to the formation of a membranous sheath surrounding axons to increase signal transmitting speed between neurons and is not limited to early development but occurs throughout adulthood45.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [45] Snaidero, N. & Simons, M. Myelination at a glance. J. Cell Sci. 127, 2999 (2014). source
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationThe AD pathology measures for the Banner cohort were derived as described by Beach et al.
“Beach et al. described in detail how these measures were derived for the Banner cohort56.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [56] Beach, T. G. et al. Arizona Study of Aging and Neurodegenerative Disorders and Brain and Body Donation Program. Neuropathology 35, 354–389 (2015). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationcitationOur findings may reflect the two types of astrocytes as described in the literature-reactive astrocytes that lose the ability to promote neuronal survival, outgrowth, and synaptogenesis, and induce death of neurons and oligodendrocytes, and homeostatic astrocytes that promote neural functioning.
“Our findings may reflect the two types of astrocytes as described in the literature—reactive astrocytes that lose the ability to promote neuronal survival, outgrowth, and synaptogenesis, and induce death of neurons and oligodendrocytes, and homeostatic astrocytes that promote neural functioning54.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [54] Liddelow, S. A. et al. Neurotoxic reactive astrocytes are induced by activated microglia. Nature 541, 481–487 (2017). source
Not checked in the free run.
May combine two claims.
Interpretationown dataSome networks act through pathology while others act independently of pathology to contribute to variation in cognitive trajectory.
“suggesting that some networks act through pathology while others act independently of pathology to contribute to variation in cognitive trajectory.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationProteomic quantification for both cohorts used the approach described by Seyfried et al.
“Proteomic quantification for both cohorts used the approach described in detail in Seyfried et al.23 and enumerated below.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [23] Wingo, T. S. et al. Integrating next-generation genomic sequencing and mass spectrometry to estimate allele-specific protein abundance in human brain. J. Proteome. Res. 16, 3336–3347 (2017). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe AD pathology measures for the BLSA cohort were derived as described by O'Brien et al.
“In the BLSA cohort, O'Brien et al. described how estimates of β-amyloid positive plaques and neurofibrillary tangles were derived62.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [62] O'Brien, R. J. et al. Neuropathologic studies of the Baltimore Longitudinal Study of Aging (BLSA). J Alzheimers Dis 18, 665–675 (2009). source
Not checked in the free run.
May combine two claims.
PremisecitationFrom a genome-wide screen, a prominent observation after genetic ablation of IGF1 receptor in AD mice is the reduction in expression level of NEFM, down to control level, suggesting that lower-abundance of NEFM is protective.
“From a genome-wide screen, a prominent observation after genetic ablation of IGF1 receptor in AD mice is the reduction in expression level of NEFM, down to control level, suggesting that lower-abundance of NEFM is protective46.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [46] George, C. et al. The Alzheimer's disease transcriptome mimics the neuroprotective signature of IGF-1 receptor-deficient neurons. Brain 140, 2012–2027 (2017). source
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationNEFM was shown to accumulate in the hippocampus in the presence of amyloid pathology.
“NEFM was shown to accumulate in the hippocampus in the presence of amyloid pathology46.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [46] George, C. et al. The Alzheimer's disease transcriptome mimics the neuroprotective signature of IGF-1 receptor-deficient neurons. Brain 140, 2012–2027 (2017). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataThe published BLSA protein co-expression modules used here were constructed using protein expression levels from both the dorsolateral prefrontal cortex (dPFC) and precuneus, so the module memberships might be slightly different than those from the dPFC alone.
“We also note that the published BLSA protein co-expression modules used here were constructed using the protein expression levels from both the dorsolateral prefrontal cortex (dPFC) and precuneus so the module memberships might be slightly different than those from the dPFC alone.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenSubjects were followed for a total of 887 person-years over 676 visits.
“These subjects were followed for a total of 887 person-years over 676 visits.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductnone givenWe employed a discovery and replication cohort design followed by meta-analysis to reduce false positive findings.
“We also employed a discovery and replication cohort design followed by meta-analysis to reduce false positive findings.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationAstrocytes provide neurotrophic support to promote neuronal survival, are important in the formation and maturation of synapses, help control neurotransmitter concentrations, and play a role in maintaining the blood-brain barrier.
“Astrocytes provide neurotrophic support to promote neuronal survival, are important in the formation and maturation of synapses, help control neurotransmitter concentrations, and play a role in maintaining the blood-brain barrier53.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [53] Liddelow, S. & Barres, B. SnapShot: astrocytes in health and disease. Cell 162, 1170–1170 (2015). e1171. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationBLSA is a prospective cohort study of aging in community-dwelling individuals.
“BLSA is a prospective cohort study of aging in community-dwelling individuals59,60.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [59] Seddighi, S. et al. SPARCL1 Accelerates symptom onset in Alzheimer's disease and influences brain structure and function during aging. J. Alzheimers. Dis. 61, 401–414 (2018). source
Not checked in the free run.
- cites [60] Simpson, B. N. et al. Blood metabolite markers of cognitive performance and brain function in aging. J. Cereb. Blood Flow Metab. 36, 1212–1223 (2016). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationVGF is a neuropeptide that promotes hippocampal neurogenesis, dendritic maturation, and synaptic activity.
“VGF is a neuropeptide that promotes hippocampal neurogenesis, dendritic maturation, and synaptic activity50.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [50] Behnke, J. et al. Neuropeptide VGF promotes maturation of hippocampal dendrites that is reduced by single nucleotide polymorphisms. Int. J. Mol. Sci. 18, pii: E612 (2017). source
Not checked in the free run.
May combine two claims.
Study design and conductown dataOur sample sizes are relatively small (n = 104 and n = 39) and exclusively Caucasians, which may limit the generalizability of our findings and power.
“Fourth, our sample sizes are relatively small (n = 104 and n = 39) and exclusively Caucasians, which may limit the generalizability of our findings and power.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe available cognitive data was from the MMSE, a widely used screening tool that provides a coarse gauge of cognitive performance.
“Third, the available cognitive data was from the MMSE, a widely used screening tool that provides a coarse gauge of cognitive performance.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Study design and conductnone givenOurs is the largest brain proteomic study of cognitive trajectory to our knowledge.
“Despite this, ours is the largest brain proteomic study of cognitive trajectory to our knowledge.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable). May combine two claims.
Interpretationnone givenOur findings establish a framework for understanding mechanisms underlying cognitive trajectory and nominate a list of highly promising targets for future mechanistic studies of cognitive stability.
“Finally, our findings establish a framework for understanding mechanisms underlying cognitive trajectory and nominate a list of highly promising targets for future mechanistic studies of cognitive stability.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductnone givenMeasures of AD pathology (β-amyloid plaques and neurofibrillary tangles) were used as covariates to adjust for the degree of AD pathology in each sample.
“Measures of AD pathology, i.e., β-amyloid plaques and neurofibrillary tangles, were used as covariates to adjust for the degree of AD pathology in each sample.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenThe false discovery rate (FDR) for peptide spectral matches, proteins, and site decoy fraction were all set to 1 percent.
“The false discovery rate (FDR) for peptide spectral matches, proteins, and site decoy fraction were all set to 1 percent.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataBatch, PMI, sex, and age at death may confound association analyses between cognitive trajectory and protein levels.
“We removed these technical (batch and PMI) and biological effects (sex and age at death) because they may confound our association analyses between cognitive trajectory and protein levels.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenThe BLSA study was approved by the Institutional Review Board and the National Institute on Aging.
“The BLSA study was approved by the Institutional Review Board and the National Institute on Aging.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductcitationFor enrichment analysis, a modified version of the GeneOverlap function and Fisher's exact test were used so that all pair-wise tests were adjusted for multiple testing using the BH method to control the FDR.
“For enrichment analysis, we used a modified version of the GeneOverlap function (from package of the same name) in R and Fisher’s exact test so that all pair-wise tests were adjusted for multiple testing using BH method to control the FDR67.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [67] Benjamini, Y. & Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Ann. Statist. 29, 1165–1188 (2001). source
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
Study design and conductcitationFor all analyses, the Benjamini-Hochberg (BH) method was used to control the false discovery rate (FDR), and significantly associated proteins were declared as those with BH FDR p < 0.05.
“For all analyses, we used Benjamini–Hochberg (BH) method to control the false discovery rate (FDR)67, and declared significantly associated proteins as those with BH FDR p < 0.05.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [67] Benjamini, Y. & Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Ann. Statist. 29, 1165–1188 (2001). source
Not checked in the free run.
Classified as probably not a substantial claim.
Study design and conductnone givenOnly proteins quantified in at least 90% of the samples were included in the analysis.
“Only proteins quantified in at least 90% of the samples were included in the analysis.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductcitationAll BLSA participants provided written informed consent at each visit.
“All BLSA participants provided written informed consent at each visit61.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [61] Ferrucci, L. The Baltimore Longitudinal Study of Aging (BLSA): a 50-year-long journey and plans for the future. J Gerontol A Biol Sci Med Sci 63, 1416–1419 (2008). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe BLSA protein co-expression networks used in this study were previously built by Seyfried et al. using proteins measured from the precuneus and prefrontal cortex in the same individual.
“For BLSA, we used the BLSA networks from Seyfried et al.23, which were previously built using proteins measured from the precuneus and prefrontal cortex in the same individual.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [23] Wingo, T. S. et al. Integrating next-generation genomic sequencing and mass spectrometry to estimate allele-specific protein abundance in human brain. J. Proteome. Res. 16, 3336–3347 (2017). source
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
Study design and conductreasoningWithin each cohort, the effect of batch was removed using Combat, and the effects of sex, age at death, and PMI were removed using bootstrap regression.
“Therefore, within each cohort, effect of batch was removed using Combat64, and effects of sex, age at death, and PMI were removed using bootstrap regression.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [64] Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007). source
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
Study design and conductown dataIn the Banner network analysis, batch effects were removed using Combat, and age at death, sex, and PMI were regressed from the proteomic profiles using bootstrap regression.
“Then, batch effects were removed using Combat64, and age at death, sex, and PMI were regressed from the proteomic profiles using Bootstrap regression.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [64] Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007). source
Not checked in the free run.
The paper relies on this without support.
No proteome-wide study of cognitive trajectory has been performed to date.
“Yet, no proteome-wide study of cognitive trajectory has been performed to date.”
The paper relies on this without support.
Removing batch, sex, age at death, and PMI effects from the proteomic profiles does not remove variance related to cognitive trajectory.
“We removed these technical (batch and PMI) and biological effects (sex and age at death) because they may confound our association analyses between cognitive trajectory and protein levels.”
The paper relies on this without support.
The BLSA protein co-expression networks from Seyfried et al. are applicable to the current BLSA proteomic data despite being built from different brain regions (precuneus and prefrontal cortex).
“For BLSA, we used the BLSA networks from Seyfried et al.23, which were previously built using proteins measured from the precuneus and prefrontal cortex in the same individual.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Elevated levels of myelin inhibitor OMG are positively correlated with cerebral atherosclerosis and cognitive decline.
“On the other hand, elevated levels of the myelin inhibitor OMG are positively correlated with cerebral atherosclerosis and cognitive decline [78, 79].”
From CI-994 is a dual modulator of class I HDACs and Wnt/β-catenin signaling for the treatment of Alzheimer's disease (2026) · cites this paper as [79]
The Discussion discusses myelination proteins NEFM and MBP, but does not address OMG. Searches for OMG and cerebral atherosclerosis found no passage linking OMG to either atherosclerosis or cognitive decline in our copy. (LLM, conf=0.73)
Experimental — accuracy not yet measuredNearly a dozen network-based analyses of the AD proteome have been performed in the human brain, including the dorsolateral prefrontal cortex, temporal cortex, and other cortical regions heavily affected in AD.
“Nearly a dozen informative network-based analyses of the AD proteome have been performed in the human brain [62–64, 74, 78, 96, 104–108], including the dorsolateral prefrontal cortex (DLPFC), temporal cortex, and other cortical regions heavily affected in AD.”
From Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau (2021) · cites this paper as [106]
Wingo et al. demonstrated increased abundance of synaptic proteins in postmortem DLPFC of individuals with antemortem stable cognitive trajectories, regardless of Aβ plaque or tau tangle burden.
“Wingo et al. used co-expression network analysis to demonstrate the increased abundance of synaptic proteins in the postmortem brain tissues (DLPFC) of individuals with antemortem stable cognitive trajectories, regardless of the burden of Aβ plaques or tau tangles [106].”
From Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau (2021) · cites this paper as [106]
Wingo et al. found heightened levels of mitochondrial proteins among cases with preserved cognition.
“this study also found heightened levels of mitochondrial proteins among cases with preserved cognition”
From Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau (2021) · cites this paper as [106]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Table 5 describes an effect size greater than one as a Pearson correlation
MinorThe note says the table reports Pearson correlations, but DLG4's listed effect size is 1.6554. A Pearson correlation cannot exceed one, so the effect-size column cannot contain the correlations described in the note.
“| DLG4\|P78352-3 | Synaptic function | 1.6554 | 0.00016497 | ++ |”
“Note: This table summarizes 38 hub proteins that were associated with cognitive trajectory after adjusting for β-amyloid plaques and neurofibrillary tangles and their Pearson correlation with cognitive trajectory.”
Cites
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) · Journal of Psychiatric Research · doi:10.1016/0022-3956(75)90026-6 · Cited by 88,282 · see citing works
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252 · see citing works
Hirono, N., Mori, E., Ikejiri, Y., Imamura, T., Shimomura, T., Hashimoto, M., Yamashita, H., & Ikeda, M. (1997) · PubMed · Cited by 21,010 · see citing works
Kok, J. S., Berg, I. J., & Scherder, E. J. A. (2013) · Dementia and Geriatric Cognitive Disorders Extra · doi:10.1159/000353441 · Cited by 20,589 · see citing works
Older Adults' Reasons for Using Technology while Aging in Place
Peek, S. T. M., Luijkx, K. G., Rijnaard, M. D., Nieboer, M. E., van der Voort, C. S., Aarts, S., van Hoof, J., Vrijhoef, H. J., & Wouters, E. (2015) · Gerontology · doi:10.1159/000430949 · Cited by 19,803 · see citing works
Sensitivity and False Alarm Rate of a Fall Sensor in Long-Term Fall Detection in the Elderly
Kangas, M., Korpelainen, R., Vikman, I., Nyberg, L., & Jämsä, T. J. (2014) · Gerontology · doi:10.1159/000362720 · Cited by 19,003 · see citing works
The control of the false discovery rate in multiple testing under dependency
Benjamini, Y., & Yekutieli, D. (2001) · The Annals of Statistics · doi:10.1214/aos/1013699998 · Cited by 10,490 · see citing works
UniProt: the Universal Protein knowledgebase
Apweiler, R., Bairoch, A., Wu, C. H., Barker, W. C., Boeckmann, B., Ferro, S., Gasteiger, E., Huang, H.-Z., Lopez, R., Magrane, M., et al. (2004) · Nucleic Acids Res. · doi:10.1093/nar/gkh131 · Cited by 8,795 · see citing works
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658 · see citing works
Neurotoxic reactive astrocytes are induced by activated microglia
Liddelow, S. A., Guttenplan, K. A., Clarke, L. E., Bennett, F. C., Bohlen, C. J., Schirmer, L., Bennett, M. L., Münch, A. E., Chung, W.-S., Peterson, T. C., et al. (2017) · Nature · doi:10.1038/nature21029 · Cited by 7,971 · see citing works
METAL: fast and efficient meta-analysis of genomewide association scans
Willer, C. J., Li, Y., & Abecasis, G. R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq340 · Cited by 5,205 · see citing works
Cox, J., Hein, M. Y., Luber, C. A., Paron, I., Nagaraj, N., & Mann, M. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.031591 · Cited by 5,180 · see citing works
The Mini‐Mental State Examination: A Comprehensive Review
Tombaugh, T. N., & McIntyre, N. J. (1992) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.1992.tb01992.x · Cited by 5,152 · see citing works
UniProt: the universal protein knowledgebase
The UniProt Consortium (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1099 · Cited by 4,436 · see citing works
Terry, R. D., Masliah, E., Salmon, D. P., Butters, N., DeTeresa, R., Hill, R., Hansen, L. A., & Katzman, R. (1991) · Annals of Neurology · doi:10.1002/ana.410300410 · Cited by 4,397 · see citing works
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124 · see citing works
A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules
Stuart, J. M., Segal, E., Koller, D., & Kim, S. K. (2003) · Science · doi:10.1126/science.1087447 · Cited by 2,235 · see citing works
Tau-mediated neurodegeneration in Alzheimer’s disease and related disorders
Ballatore, C., Lee, V. M.-Y., & Trojanowski, J. Q. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2194 · Cited by 2,174 · see citing works
A survey of human brain transcriptome diversity at the single cell level
Darmanis, S., Sloan, S. A., Zhang, Y., Enge, M., Caneda, C., Shuer, L. M., Hayden Gephart, M. G., Barres, B. A., & Quake, S. R. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1507125112 · Cited by 1,401 · see citing works
Migaud, M., Charlesworth, P., Dempster, M., Webster, L. C., Watabe, A. M., Makhinson, M., He, Y., Ramsay, M. F., Morris, R. G. M., Morrison, J. H., et al. (1998) · Nature · doi:10.1038/24790 · Cited by 1,160 · see citing works
The BioGRID interaction database: 2017 update
Chatr‐aryamontri, A., Oughtred, R., Boucher, L., Rust, J., Chang, C., Kolas, N. K., O’Donnell, L., Oster, S. K., Theesfeld, C. L., Sellam, A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1102 · Cited by 960 · see citing works
Cell type- and brain region-resolved mouse brain proteome
Sharma, K., Schmitt, S., Bergner, C. G., Tyanova, S., Kannaiyan, N., Manrique-Hoyos, N., Kongi, K., Cantuti, L., Hanisch, U.-K., Philips, M.-A., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.4160 · Cited by 871 · see citing works
Creavin, S., Wisniewski, S., Noel-Storr, A. H., Trevelyan, C. M., Hampton, T., Rayment, D., Thom, V. M., Nash, K. J. E., Elhamoui, H., Milligan, R., et al. (2016) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd011145.pub2 · Cited by 842 · see citing works
variancePartition: interpreting drivers of variation in complex gene expression studies
Hoffman, G. E., & Schadt, E. E. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1323-z · Cited by 831 · see citing works
Multi-ethnic genome-wide association study for atrial fibrillation
Roselli, C., Chaffin, M. D., Weng, L.-C. C., Aeschbacher, S., Ahlberg, G., Albert, C. M., Almgren, P., Alonso, Á., Anderson, C. D., Aragam, K. G., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0133-9 · Cited by 754 · see citing works
Tau and Aβ imaging, CSF measures, and cognition in Alzheimer’s disease
Brier, M. R., Gordon, B., Friedrichsen, K., McCarthy, J., Stern, A., Christensen, J., Owen, C., Aldea, P., Su, Y., Hassenstab, J., et al. (2016) · Science Translational Medicine · doi:10.1126/scitranslmed.aaf2362 · Cited by 734 · see citing works
Early deficits in synaptic mitochondria in an Alzheimer's disease mouse model
Du, H., Guo, L., Yan, S., Sosunov, A. A., McKhann, G. M., & ShiDu Yan, S. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1006586107 · Cited by 704 · see citing works
Meta‐Analysis in Medical Research
Haidich, A. (2005) · Wiley eBooks · doi:10.1002/9780470994894 · Cited by 691 · see citing works
Plassman, B. L., Williams, J. W., Burke, J. R., Holsinger, T., & Benjamin, S. (2010) · Annals of Internal Medicine · doi:10.7326/0003-4819-153-3-201008030-00258 · Cited by 681 · see citing works
Mitochondria at the neuronal presynapse in health and disease
Devine, M. J., & Kittler, J. T. (2018) · Nature reviews. Neuroscience · doi:10.1038/nrn.2017.170 · Cited by 615 · see citing works
Liang, W. S., Reiman, E. M., Valla, J., Dunckley, T., Beach, T. G., Grover, A., Niedzielko, T. L., Schneider, L. E., Mastroeni, D., Caselli, R., et al. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0709259105 · Cited by 585 · see citing works
Self-citationHomer1a drives homeostatic scaling-down of excitatory synapses during sleep
Diering, G. H., Nirujogi, R. S., Roth, R. H., Worley, P. F., Pandey, A., & Huganir, R. L. (2017) · Science · doi:10.1126/science.aai8355 · Cited by 516 · see citing works
Person-specific contribution of neuropathologies to cognitive loss in old age
Boyle, P. A., Yu, L., Wilson, R. S., Leurgans, S. E., Schneider, J. A., & Bennett, D. A. (2018) · Annals of Neurology · doi:10.1002/ana.25123 · Cited by 508 · see citing works
Characterization of the proteome, diseases and evolution of the human postsynaptic density
Bayés, À., van de Lagemaat, L. N., Collins, M. O., Croning, M. D. R., Whittle, I. R., Choudhary, J. S., & Grant, S. G. N. (2011) · Nature Neuroscience · doi:10.1038/nn.2719 · Cited by 499 · see citing works
Reduced IGF-1 signaling delays age-associated proteotoxicity in mice
Cohen, E., Paulsson, J. F., Blinder, P., Burstyn-Cohen, T., Du, D., Estepa, G., Adame, A., Pham, H. M., Holzenberger, M., Kelly, J. W., et al. (2009) · Cell · doi:10.1016/j.cell.2009.11.014 · Cited by 488 · see citing works
Seyfried, N. T., Dammer, E. B., Swarup, V., Nandakumar, D., Duong, D. M., Yin, L., Deng, Q., Nguyen, T., Hales, C. M., Wingo, T., et al. (2017) · Cell Systems · doi:10.1016/j.cels.2016.11.006 · Cited by 477 · see citing works
Self-citationBeach, T. G., Adler, C. H., Sue, L. I., Serrano, G., Shill, H. A., Walker, D. G., Lue, L., Roher, A. E., Dugger, B. N., Maarouf, C., et al. (2015) · Neuropathology · doi:10.1111/neup.12189 · Cited by 460 · see citing works
Self-citationThe Neuropathology of Older Persons with and Without Dementia from Community versus Clinic Cohorts
Schneider, J. A., Aggarwal, N. T., Barnes, L., Boyle, P., & Bennett, D. A. (2009) · Journal of Alzheimer's Disease · doi:10.3233/jad-2009-1227 · Cited by 372 · see citing works
Much of late life cognitive decline is not due to common neurodegenerative pathologies
Boyle, P. A., Wilson, R. S., Yu, L., Barr, A. M., Honer, W. G., Schneider, J. A., & Bennett, D. A. (2013) · Annals of Neurology · doi:10.1002/ana.23964 · Cited by 323 · see citing works
Zaninotto, P., Batty, G. D., Allerhand, M., & Deary, I. J. (2018) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2017-210116 · Cited by 316 · see citing works
Alder, J., Thakker‐Varia, S., Bangasser, D. A., Kuroiwa, M., Plummer, M. R., Shors, T. J., & Black, I. B. (2003) · Journal of Neuroscience · doi:10.1523/jneurosci.23-34-10800.2003 · Cited by 303 · see citing works
The Baltimore Longitudinal Study of Aging (BLSA): A 50-Year-Long Journey and Plans for the Future
Ferrucci, L. (2008) · The Journals of Gerontology Series A · doi:10.1093/gerona/63.12.1416 · Cited by 293 · see citing works
Synaptopodin-deficient mice lack a spine apparatus and show deficits in synaptic plasticity
Deller, T., Körte, M., Chabanis, S., Drakew, A., Schwegler, H., Stefani, G. G., Zúñiga, A., Schwarz, K., Bonhoeffer, T., Zeller, R., et al. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1832384100 · Cited by 291 · see citing works
Fernández, E., Collins, M. O., Uren, R. T., Kopanitsa, M. V., Komiyama, N. H., Croning, M. D. R., Zografos, L., Armstrong, J. D., Choudhary, J. S., & Grant, S. G. N. (2009) · Molecular Systems Biology · doi:10.1038/msb.2009.27 · Cited by 275 · see citing works
GO-Elite: a flexible solution for pathway and ontology over-representation
Zambon, A. C., Gaj, S., Ho, I., Hanspers, K., VRANIZAN, K. M., Evelo, C. T., Conklin, B. R., Pico, A. R., & Salomonis, N. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts366 · Cited by 263 · see citing works
Cognitive impairment 18 years before clinical diagnosis of Alzheimer disease dementia
Rajan, K. B., Wilson, R. S., Weuve, J., Barnes, L. L., & Evans, D. A. (2015) · Neurology · doi:10.1212/wnl.0000000000001774 · Cited by 260 · see citing works
Synaptic scaffold evolution generated components of vertebrate cognitive complexity
Nithianantharajah, J., Komiyama, N. H., McKechanie, A., Johnstone, M., Blackwood, D. H., Clair, D. S., Emes, R. D., van de Lagemaat, L. N., Saksida, L. M., Bussey, T. J., et al. (2013) · Nature Neuroscience · doi:10.1038/nn.3276 · Cited by 251 · see citing works
Reddy, P. H., Mańczak, M., Mao, P., Calkins, M. J., Reddy, A. P., & Shirendeb, U. P. (2010) · Journal of Alzheimer s Disease · doi:10.3233/jad-2010-100504 · Cited by 240 · see citing works
Synaptic markers of cognitive decline in neurodegenerative diseases: a proteomic approach
Bereczki, E., Branca, R. M., Francis, P. T., Pereira, J. B., Baek, J.-H., Hortobágyi, T., Winblad, B., Ballard, C., Lehtiö, J., & Aarsland, D. (2018) · Brain · doi:10.1093/brain/awx352 · Cited by 220 · see citing works
Epigenetic editing of the Dlg4/PSD95 gene improves cognition in aged and Alzheimer’s disease mice
Bustos, F. J., Ampuero, E., Jury, N., Aguilar, R., Falahi, F., Toledo, J. A., Ahumada, J., Lata, J., Cubillos, P., Henríquez, B., et al. (2017) · Brain · doi:10.1093/brain/awx272 · Cited by 205 · see citing works
Regulation of human MAPT gene expression
Caillet‐Boudin, M., Buée, L., Sergeant, N., & Lefebvre, B. (2015) · Molecular Neurodegeneration · doi:10.1186/s13024-015-0025-8 · Cited by 193 · see citing works
Zinc transporter ZnT-3 regulates presynaptic Erk1/2 signaling and hippocampus-dependent memory
Sindreu, C. B., Palmiter, R. D., & Storm, D. R. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1019166108 · Cited by 176 · see citing works
Multiscale causal networks identify VGF as a key regulator of Alzheimer’s disease
Beckmann, N. D., Lin, W.-J., Wang, M., Cohain, A. T., Charney, A. W., Wang, P., Ma, W., Wang, Y.-C., Jiang, C., Audrain, M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17405-z · Cited by 172 · see citing works
Self-citationSnaidero, N., & Simons, M. (2014) · Journal of Cell Science · doi:10.1242/jcs.151043 · Cited by 169 · see citing works
Synaptic proteins predict cognitive decline in Alzheimer's disease and Lewy body dementia
Bereczki, E., Francis, P. T., Howlett, D., Pereira, J. B., Höglund, K., Bogstedt, A., Cedazo‐Minguez, A., Baek, J., Hortobágyi, T., Attems, J., et al. (2016) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2016.04.005 · Cited by 167 · see citing works
Boyken, J., Grønborg, M., Riedel, D., Urlaub, H., Jahn, R., & Chua, J. J. E. (2013) · Neuron · doi:10.1016/j.neuron.2013.02.027 · Cited by 152 · see citing works
Valla, J., Yaari, R., Wolf, A. B., Kusne, Y., Beach, T. G., Roher, A. E., Corneveaux, J. J., Huentelman, M. J., Caselli, R. J., & Reiman, E. M. (2010) · Journal of Alzheimer s Disease · doi:10.3233/jad-2010-100129 · Cited by 150 · see citing works
Self-citationSnapShot: Astrocytes in Health and Disease
Liddelow, S. A., & Barres, B. A. (2015) · Cell · doi:10.1016/j.cell.2015.08.029 · Cited by 148 · see citing works
Bozdagi, O., Rich, E. L., Tronel, S., Sadahiro, M., Patterson, K., Shapiro, M. L., Alberini, C. M., Huntley, G. W., & Salton, S. R. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.3145-08.2008 · Cited by 145 · see citing works
Gontier, G., George, C., Chaker, Z., Holzenberger, M., & Aïd, S. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.0343-15.2015 · Cited by 142 · see citing works
Neuropathologic Studies of the Baltimore Longitudinal Study of Aging (BLSA)
O'Brien, R. J., Resnick, S. M., Zonderman, A. B., Ferrucci, L., Crain, B. J., Pletnikova, O., Rudow, G., Iacono, D., Riudavets, M. A., Driscoll, I., et al. (2009) · Journal of Alzheimer's Disease · doi:10.3233/jad-2009-1179 · Cited by 137 · see citing works
Mastroeni, D., Khdour, O. M., Delvaux, E., Nolz, J., Olsen, G., Berchtold, N. C., Cotman, C. W., Hecht, S. M., & Coleman, P. D. (2016) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2016.09.003 · Cited by 131 · see citing works
Savioz, A., Leuba, G., & Vallet, P. G. (2014) · Ageing Research Reviews · doi:10.1016/j.arr.2014.09.004 · Cited by 127 · see citing works
Lin, W., Jiang, C. S., Sadahiro, M., Bozdagi, O., Vulchanova, L., Alberini, C. M., & Salton, S. R. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.0584-15.2015 · Cited by 118 · see citing works
Synaptopodin regulates denervation-induced homeostatic synaptic plasticity
Vlachos, A., Ikenberg, B., Lenz, M., Becker, D., Reifenberg, K., Bas-Orth, C., & Deller, T. (2013) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.1213677110 · Cited by 105 · see citing works
Krishnan, M. L., Van Steenwinckel, J., Schang, A.-L., Yan, J., Arnadottir, J., Le Charpentier, T., Csaba, Z., Dournaud, P., Cipriani, S., Auvynet, C., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-00422-w · Cited by 105 · see citing works
Fernández, E., Collins, M. O., Frank, R., Zhu, F., Kopanitsa, M. V., Nithianantharajah, J., Lemprière, S. A., Fricker, D. G., Elsegood, K. A., McLaughlin, C. L., et al. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.09.045 · Cited by 88 · see citing works
Neuropeptides in depression: Role of VGF
Thakker‐Varia, S., & Alder, J. (2008) · Behavioural Brain Research · doi:10.1016/j.bbr.2008.10.006 · Cited by 86 · see citing works
George, C., Gontier, G., Lacube, P., François, J., Holzenberger, M., & Aïd, S. (2017) · Brain · doi:10.1093/brain/awx132 · Cited by 82 · see citing works
Blood metabolite markers of cognitive performance and brain function in aging
Simpson, B. N., Kim, M. G., Chuang, Y., Beason‐Held, L. L., Kitner‐Triolo, M. H., Kraut, M. A., Lirette, S. T., Windham, B. G., Griswold, M. E., Legido‐Quigley, C., et al. (2015) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x15611678 · Cited by 65 · see citing works
Self-citationVojinovic, D., Adams, H. H., Jian, X., Yang, Q., Smith, A. V., Bis, J. C., Teumer, A., Scholz, M., Armstrong, N. J., Hofer, E., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06234-w · Cited by 52 · see citing works
Wingo, T. S., Duong, D. M., Zhou, M., Dammer, E. B., Wu, H., Cutler, D. J., Lah, J. J., Levey, A. I., & Seyfried, N. T. (2017) · Journal of Proteome Research · doi:10.1021/acs.jproteome.7b00324 · Cited by 51 · see citing works
Self-citationDown‐regulation of glutamatergic terminals (VGLUT1) driven by Aβ in Alzheimer's disease
Rodriguez‐Perdigon, M., Tordera, R. M., Gil-Bea, F. J., Gereñu, G., Ramírez, M. J., & Solas, M. (2016) · Hippocampus · doi:10.1002/hipo.22607 · Cited by 47 · see citing works
Seddighi, S., Varma, V. R., An, Y., Varma, S., Beason‐Held, L. L., Tanaka, T., Kitner‐Triolo, M. H., Kraut, M. A., Davatzikos, C. A., & Thambisetty, M. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-170557 · Cited by 41 · see citing works
Self-citationDynamic presenilin 1 and synaptotagmin 1 interaction modulates exocytosis and amyloid β production
Zoltowska, K. M., Maesako, M., Lushnikova, I., Takeda, S., Keller, L. J., Skibo, G., Hyman, B. T., & Berezovska, O. (2017) · Molecular Neurodegeneration · doi:10.1186/s13024-017-0159-y · Cited by 36 · see citing works
Ou-Yang, M.-H., & Van Nostrand, W. E. (2013) · Journal of Neuroinflammation · doi:10.1186/1742-2094-10-134 · Cited by 30 · see citing works
Behnke, J. A., Cheedalla, A., Bhatt, V., Bhat, M., Teng, S. L., Palmieri, A. J., Windon, C., Thakker‐Varia, S., & Alder, J. (2017) · International Journal of Molecular Sciences · doi:10.3390/ijms18030612 · Cited by 29 · see citing works
Multiscale causal network models of Alzheimer’s disease identify VGF as a key regulator of diseasePreprint
Beckmann, N. D., Lin, W., Wang, M., Cohain, A. T., Wang, P., Ma, W., Wang, Y., Jiang, C. S., Audrain, M., Comella, P., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/458430 · Cited by 12 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Brain energy rescue: an emerging therapeutic concept for neurodegenerative disorders of ageing
Cunnane, S. C., Trushina, E., Morland, C., Prigione, A., Casadesus, G., Andrews, Z. B., Beal, M. F., Bergersen, L. H., Brinton, R. D., de la Monte, S., et al. (2020) · Nature Reviews Drug Discovery · doi:10.1038/s41573-020-0072-x · Cited by 1,003 · see citing works
Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression
Bai, B., Wang, X., Li, Y., Chen, P.-C., Yu, K., Dey, K. K., Yarbro, J. M., Han, X., Lutz, B. M., Rao, S., et al. (2020) · Neuron · doi:10.1016/j.neuron.2019.12.015 · Cited by 632 · see citing works
“Analysis of proteome in AD and during aging has been attempted (Seyfried et al., 2017; Wingo et al., 2019).”
BackgroundSelf-citationJohnson, E. C. B., Carter, E. K., Dammer, E. B., Duong, D. M., Gerasimov, E. S., Liu, Y., Liu, J., Betarbet, R., Ping, L., Yin, L., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-021-00999-y · Cited by 561 · see citing works
Self-citationCai, Y., Song, W., Li, J., Jing, Y., Liang, C., Zhang, L., Zhang, X., Zhang, W., Liu, B., An, Y., et al. (2022) · Science China Life Sciences · doi:10.1007/s11427-022-2161-3 · Cited by 436 · see citing works
Higginbotham, L., Ping, L., Dammer, E. B., Duong, D. M., Zhou, M., Gearing, M., Hurst, C., Glass, J. D., Factor, S. A., Johnson, E. C. B., et al. (2020) · Science Advances · doi:10.1126/sciadv.aaz9360 · Cited by 367 · see citing works
Self-citationBao, H., Cao, J., Chen, M., Min, C., Chen, W., Chen, X., Chen, Y., Chen, Y., Chen, Y., Chen, Z., et al. (2023) · Science China Life Sciences · doi:10.1007/s11427-023-2305-0 · Cited by 389 · see citing works
The essential elements of Alzheimer’s disease
Lei, P., Ayton, S., & Bush, A. I. (2020) · Journal of Biological Chemistry · doi:10.1074/jbc.rev120.008207 · Cited by 336 · see citing works
Brain proteome-wide association study implicates novel proteins in depression pathogenesis
Wingo, T. S., Liu, Y., Gerasimov, E. S., Gockley, J., Logsdon, B. A., Duong, D. M., Dammer, E. B., Lori, A., Kim, P. J., Ressler, K. J., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00832-6 · Cited by 159 · see citing works
Self-citationShared proteomic effects of cerebral atherosclerosis and Alzheimer’s disease on the human brain
Wingo, A. P., Fan, W., Duong, D. M., Gerasimov, E. S., Dammer, E. B., Liu, Y., Harerimana, N. V., White, B., Thambisetty, M., Troncoso, J. C., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0635-5 · Cited by 161 · see citing works
Self-citationFang, J., Zhang, P., Wang, Q., Chiang, C.-W., Zhou, Y., Hou, Y., Xu, J., Chen, R., Zhang, B., Lewis, S. J., et al. (2022) · Alzheimer's Research & Therapy · doi:10.1186/s13195-021-00951-z · Cited by 140 · see citing works
Cortical Proteins Associated With Cognitive Resilience in Community-Dwelling Older Persons
Yu, L., Tasaki, S., Schneider, J. A., Arfanakis, K., Duong, D. M., Wingo, A. P., Wingo, T. S., Kearns, N., Thatcher, G. R. J., Seyfried, N. T., et al. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.1807 · Cited by 127 · see citing works
Self-citationVGF as a biomarker and therapeutic target in neurodegenerative and psychiatric diseases
Quinn, J. P., Kandigian, S. E., Trombetta, B. A., Arnold, S. E., & Carlyle, B. C. (2021) · Brain Communications · doi:10.1093/braincomms/fcab261 · Cited by 125 · see citing works
Global quantitative analysis of the human brain proteome and phosphoproteome in Alzheimer’s disease
Ping, L., Kundinger, S. R., Duong, D. M., Yin, L., Gearing, M., Lah, J. J., Levey, A. I., & Seyfried, N. T. (2020) · Scientific Data · doi:10.1038/s41597-020-00650-8 · Cited by 127 · see citing works
Self-citationSystems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau
Rayaprolu, S., Higginbotham, L., Bagchi, P., Watson, C. M., Zhang, T., Levey, A. I., Rangaraju, S., & Seyfried, N. T. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-020-00840-3 · Cited by 118 · see citing works
Self-citationAutophagy–lysosomal-associated neuronal death in neurodegenerative disease
Nixon, R. A. (2024) · Acta Neuropathologica · doi:10.1007/s00401-024-02799-7 · Cited by 117 · see citing works
“TRPML1-and TPC2-mediated calcium release is linked to mitochondrial dysfunction, and mTORC1 activation [106, 129], and Banner [239] AD cohorts indicate early and sustained inhibition of mTOR [129].”
Genetic control of the human brain proteome
Robins, C., Wingo, A. P., Fan, W., Duong, D. M., Meigs, J., Gerasimov, E. S., Dammer, E. B., Cutler, D. J., Jager, P. L. D., Bennett, D. A., et al. (2019) · doi:10.1101/816652 · Cited by 104 · see citing works
“In addition to these hallmark proteins, the abundance of hundreds of other proteins have also been shown to correlate with neurodegenerative disease phenotypes such as rate of decline of cognition and diagnosis of AD [1, 2].”
BackgroundSelf-citationLiu, J., Ottaviani, D., Sefta, M., Desbrousses, C., Chapeaublanc, E., Aschero, R., Sirab, N., Lubieniecki, F. J., Lamas, G., Tonon, L., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25792-0 · Cited by 103 · see citing works
Hidden proteome of synaptic vesicles in the mammalian brain
Taoufiq, Z., Ninov, M., Villar‐Briones, A., Wang, H.-Y., Sasaki, T., Roy, M. C., Beauchain, F., Mori, Y., Yoshida, T., Takamori, S., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2011870117 · Cited by 105 · see citing works
Alzheimer’s disease: A clinical perspective and future nonhuman primate research opportunities
Haque, R. U., & Levey, A. I. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912954116 · Cited by 97 · see citing works
Self-citationOu, Y., Yang, Y., Deng, Y., Zhang, C. M., Hu, H., Wu, B., Liu, Y., Wang, Y., Zhu, Y., Suckling, J., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01251-6 · Cited by 98 · see citing works
“The Benjamini–Hochberg method was used to control the false discovery rate [19], which was estimated by the p.”
MethodDohm-Hansen, S., English, J. A., Lavelle, A., Fitzsimons, C. P., Lucassen, P. J., & Nolan, Y. M. (2024) · Trends in Neurosciences · doi:10.1016/j.tins.2024.02.001 · Cited by 101 · see citing works
Unfolded protein response IRE1/XBP1 signaling is required for healthy mammalian brain aging
Cabral‐Miranda, F., Tamburini, G., Martínez, G., Ardiles, Á. O., Medinas, D. B., Gerakis, Y., Hung, M.-L. D., Vidal, R. L., Fuentealba, M., Miedema, T., et al. (2022) · The EMBO Journal · doi:10.15252/embj.2022111952 · Cited by 95 · see citing works
“…altered expression of extracellular matrix component laminin-A and glial fibrillary-associated protein (GFAP), two proteomic hits highlighted in our analysis, have also been linked to resilience from cognitive impairment in studies on two human cohorts (Wingo et al , 2019; Tables 3 – 4; Fig EV4D).”
Zhou, M., Haque, R. U., Dammer, E. B., Duong, D. M., Ping, L., Johnson, E. C. B., Lah, J. J., Levey, A. I., & Seyfried, N. T. (2020) · Clinical Proteomics · doi:10.1186/s12014-020-09285-8 · Cited by 92 · see citing works
Self-citationAging Biomarkers: From Functional Tests to Multi‐Omics Approaches
Kudryashova, K. S., Burka, K., Kulaga, A. Y., Vorobyeva, N. S., & Kennedy, B. K. (2020) · PROTEOMICS · doi:10.1002/pmic.201900408 · Cited by 86 · see citing works
Aging biomarkers and the brain
Higgins‐Chen, A., Thrush, K., & Levine, M. E. (2021) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2021.01.003 · Cited by 86 · see citing works
Porcher, L., Bruckmeier, S., Burbano, S. D., Finnell, J. E., Gorny, N., Klett, J., Wood, S. K., & Kelly, M. P. (2021) · Journal of Neuroinflammation · doi:10.1186/s12974-021-02252-6 · Cited by 78 · see citing works
Latest advances in cerebrospinal fluid and blood biomarkers of Alzheimer’s disease
Milà‐Alomà, M., Suárez‐Calvet, M., & Molinuevo, J. L. (2019) · Therapeutic Advances in Neurological Disorders · doi:10.1177/1756286419888819 · Cited by 78 · see citing works
Plasma proteomics identify biomarkers and undulating changes of brain aging
Liu, W., You, J., Chen, S.-D., Zhang, Y., Feng, J., Xu, Y., Yu, J., & Cheng, W. (2024) · Nature Aging · doi:10.1038/s43587-024-00753-6 · Cited by 84 · see citing works
“Full details are given in Supplementary Tables 25–28.”
InfluentialMitochondrial dysfunction is a key pathological driver of early stage Parkinson’s
Toomey, C. E., Heywood, W., Evans, J. R., Lachica, J., Pressey, S. N., Foti, S. C., Shahrani, M. M. A., D’Sa, K., Hargreaves, I. P., Heales, S., et al. (2022) · Acta Neuropathologica Communications · doi:10.1186/s40478-022-01424-6 · Cited by 75 · see citing works
“Enrichment analysis was performed using Bioconductor R package GeneOverlap [38] that returns the Fisher’s exact test p-value and odds ratio as previously performed in Wingo et al, [39].”
MethodWilliams, J. B., Cao, Q., & Yan, Z. (2021) · Brain Communications · doi:10.1093/braincomms/fcab123 · Cited by 72 · see citing works
“Interestingly, we found that many of the lost genes in Alzheimer’s disease encode hub proteins whose increased abundance is required for CS in normal ageing.(15) These genes provide Graphical Abstract 2 | BRAIN COMMUNICATIONS 2021: Page 2 of 15 J.”
BackgroundMethodInfluentialAll-trans retinoic acid induces synaptic plasticity in human cortical neurons
Lenz, M., Kruse, P., Eichler, A., Muellerleile, J., Straehle, J., Jedlička, P., Beck, J., Deller, T., & Vlachos, A. (2021) · eLife · doi:10.7554/elife.63026 · Cited by 67 · see citing works
“, 2013) and has recently been linked to the cognitive trajectory in human aging (Wingo et al., 2019).”
BackgroundRistori, M. V., Mortera, S. L., Marzano, V., Guerrera, S., Vernocchi, P., Ianiro, G., Gardini, S., Torre, G., Valeri, G., Vicari, S., et al. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21176274 · Cited by 67 · see citing works
“Moreover, they found 38 proteins related to cognitive trajectory independently of β-amyloid plaques and neurofibrillary tangles [52].”
BackgroundCarlyle, B. C., Kandigian, S. E., Kreuzer, J., Das, S., Trombetta, B. A., Kuo, Y., Bennett, D. A., Schneider, J. A., Petyuk, V. A., Kitchen, R. R., et al. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2021.04.012 · Cited by 65 · see citing works
“…of the synaptic proteome General synapse loss has long been suggested as a strong predictor of cognitive decline in AD (DeKosky and Scheff, 1990; Koffie et al., 2011; Terry et al., 1991), as reflected in whole-tissue studies of AD (Johnson et al., 2020, 2018; Ping et al., 2018; Wingo et al., 2019).”
InfluentialEnvironmental enrichment preserves a young DNA methylation landscape in the aged mouse hippocampus
Zocher, S., Overall, R. W., Lesche, M., Dahl, A., & Kempermann, G. (2021) · Nature Communications · doi:10.1038/s41467-021-23993-1 · Cited by 65 · see citing works
Modeste, E. S., Ping, L., Watson, C. M., Duong, D. M., Dammer, E. B., Johnson, E. C. B., Roberts, B. R., Lah, J. J., Levey, A. I., & Seyfried, N. T. (2023) · Molecular Neurodegeneration · doi:10.1186/s13024-023-00638-z · Cited by 62 · see citing works
“M1 members VGF and NPTX2, strongly correlate to antemortem cognitive measures (49-51) and VGF and NPTX2 has been nominated as biomarkers of neurodegeneration (N) as CSF levels enhance prediction of MCI to AD (51-53).”
BackgroundSelf-citationHaque, R., Watson, C. M., Liu, J., Carter, E. K., Duong, D. M., Lah, J. J., Wingo, A. P., Roberts, B. R., Johnson, E. C. B., Saykin, A. J., et al. (2023) · Science Translational Medicine · doi:10.1126/scitranslmed.adg4122 · Cited by 64 · see citing works
Self-citationPanitch, R., Hu, J.-M., Chung, J., Zhu, C.-C., Meng, G., Xia, W., Bennett, D. A., Lunetta, K., Ikezu, T., Au, R., et al. (2020) · Molecular Psychiatry · doi:10.1038/s41380-021-01266-z · Cited by 50 · see citing works
“In addition, heat shock-related 70-kDa protein 2 (encoded by HSPA2) has been linked to myelination function and fast cognitive decline [57].”
Watson, C. M., Dammer, E. B., Ping, L., Duong, D. M., Modeste, E. S., Carter, E. K., Johnson, E. C. B., Levey, A. I., Lah, J. J., Roberts, B. R., et al. (2023) · Scientific Data · doi:10.1038/s41597-023-02158-3 · Cited by 58 · see citing works
Self-citationQuantitative phosphoproteomics uncovers dysregulated kinase networks in Alzheimer’s disease
Morshed, N., Lee, M. J., Rodriguez, F. H., Lauffenburger, D. A., Mastroeni, D., & White, F. M. (2021) · Nature Aging · doi:10.1038/s43587-021-00071-1 · Cited by 58 · see citing works
Zhang, C., Qin, F., Li, X., Du, X., & Li, T. (2022) · BMC Medicine · doi:10.1186/s12916-022-02408-y · Cited by 55 · see citing works
“The current breakthrough in high-throughput proteome sequencing of complex tissues [15, 16] represents a significant step forward in the large-scale quantification of the human brain proteome.”
BackgroundPsychiatric symptoms and cognitive impairment in “Long COVID ”: the relevance of immunopsychiatry
Penninx, B. W. J. H. (2021) · World Psychiatry · doi:10.1002/wps.20913 · Cited by 54 · see citing works
Hurst, C. D., Pugh, D. A., Abreha, M. H., Duong, D. M., Dammer, E. B., Bennett, D. A., Herskowitz, J. H., & Seyfried, N. T. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100542 · Cited by 55 · see citing works
Self-citation(2023) · doi:10.1016/j.celrep.2023.112593 · Cited by 53 · see citing works
Hsp90 co-chaperones, FKBP52 and Aha1, promote tau pathogenesis in aged wild-type mice
Criado‐Marrero, M., Gebru, N. T., Blazier, D. M., Gould, L. A., Baker, J. D., Beaulieu‐Abdelahad, D., & Blair, L. J. (2021) · Acta Neuropathologica Communications · doi:10.1186/s40478-021-01159-w · Cited by 51 · see citing works
“Aha1 and FKBP52 were associated with reduced cognitive trajectory, in a recent study investigating the association of proteins with the stability of cognition through aging [102].”
BackgroundSherva, R., Gross, A., Mukherjee, S., Koesterer, R., Amouyel, P., Bellenguez, C., Dufouil, C., Bennett, D. A., Chibnik, L., Cruchaga, C., et al. (2020) · Alzheimer's & Dementia · doi:10.1002/alz.12106 · Cited by 54 · see citing works
Large‐scale deep proteomic analysis in Alzheimer's disease brain regions across race and ethnicity
Seifar, F., Fox, E. J., Shantaraman, A., Liu, Y., Dammer, E. B., Modeste, E., Duong, D. M., Yin, L., Trautwig, A. N., Guo, Q., et al. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.14360 · Cited by 52 · see citing works
Self-citationDiverse proteins aggregate in mild cognitive impairment and Alzheimer’s disease brain
Kepchia, D., Huang, L., Dargusch, R., Rissman, R. A., Shokhirev, M. N., Fischer, W. H., & Schubert, D. A. (2020) · Alzheimer s Research & Therapy · doi:10.1186/s13195-020-00641-2 · Cited by 48 · see citing works
Glycoproteomics Landscape of Asymptomatic and Symptomatic Human Alzheimer’s Disease Brain
Suttapitugsakul, S., Stavenhagen, K., Donskaya, S., Bennett, D. A., Mealer, R., Seyfried, N., & Cummings, R. (2022) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2022.100433 · Cited by 40 · see citing works
Self-citationLee, J. Y., Harney, D., Teo, J. D., Kwok, J. B., Sutherland, G. T., Larance, M., & Don, A. S. (2023) · Molecular Neurodegeneration · doi:10.1186/s13024-023-00650-3 · Cited by 45 · see citing works
“Another study established that mitochondrial, synaptic, and inflammatory protein networks are associated with cognitive trajectory [19], but did not investigate the effects of ageing on the brain proteome.”
BackgroundCary, G. A., Wiley, J. C., Gockley, J., Keegan, S., Amirtha Ganesh, S. S., Heath, L., Butler, R. R., Mangravite, L. M., Logsdon, B. A., Longo, F. M., et al. (2024) · Alzheimer's & Dementia: Translational Research & Clinical Interventions · doi:10.1002/trc2.12461 · Cited by 49 · see citing works
Self-citationKumar, P., Goettemoeller, A. M., Espinosa‐García, C., Tobin, B. R., Tfaily, A., Nelson, R. S., Natu, A., Dammer, E. B., Santiago, J. V., Malepati, S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47028-7 · Cited by 48 · see citing works
“We interrogated published human post-mortem bulk brain proteomic studies 58 – 67 which included >500 post-mortemdorsolateralpre-frontalcortex samples (non-disease controls, Asymptomatic AD, and AD with dementia cases) from the ROSMAP and Banner Sun Health cohorts 66,67 .”
Self-citationThe actin-modulating protein synaptopodin mediates long-term survival of dendritic spines
Yap, K. A. F., Drakew, A., Smilović, D., Rietsche, M., Paul, M. H., Vukšić, M., Turco, D. D., & Deller, T. (2020) · eLife · doi:10.7554/elife.62944 · Cited by 44 · see citing works
Podvin, S., Jones, A. C., Liu, Q., Aulston, B. D., Ransom, L., Ames, J., Shen, G., Lietz, C. B., Jiang, Z., O’Donoghue, A. J., et al. (2020) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra120.002079 · Cited by 47 · see citing works
“Human brain proteomics studies have identified proteins associated with cognitive stability in aged patients (70–90 years old) (126), and several of these proteins are present in”
Higginbotham, L. A., Carter, E. K., Dammer, E. B., Haque, R. U., Johnson, E. C. B., Duong, D. M., Yin, L., Jager, P. L. D., Bennett, D. A., Felsky, D., et al. (2023) · Neurobiology of Disease · doi:10.1016/j.nbd.2023.106286 · Cited by 44 · see citing works
Self-citationTripathi, K. K., & Ben‐Shachar, D. (2024) · Cells · doi:10.3390/cells13050410 · Cited by 45 · see citing works
“A large-scale proteomic analysis of the human brain identified mitochondrial proteins that were associated with cognitive stability in aging [79].”
BackgroundPsychosocial experiences are associated with human brain mitochondrial biology
Trumpff, C., Monzel, A. S., Sandi, C., Menon, V., Klein, H., Fujita, M., Lee, A., Petyuk, V., Hurst, C. D., Duong, D. M., et al. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2317673121 · Cited by 45 · see citing works
“We used data from ROSMAP (N=400) (65, 66, 97) and BLSA (N=47) (98-100), (99-101).”
Self-citationThe microglial lysosomal system in Alzheimer’s disease: Guardian against proteinopathy
Acker, Z. P. V., Perdok, A., Bretou, M., & Annaert, W. G. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101444 · Cited by 45 · see citing works
Insights into the changes in the proteome of Alzheimer disease elucidated by a meta-analysis
Haytural, H., Benfeitas, R., Schedin‐Weiss, S., Bereczki, E., Rezeli, M., Unwin, R. D., Wang, X., Dammer, E. B., Johnson, E. C. B., Seyfried, N. T., et al. (2021) · Scientific Data · doi:10.1038/s41597-021-01090-8 · Cited by 35 · see citing works
Self-citationLong, Noncoding RNA Dysregulation in Glioblastoma
DeSouza, P., Qu, X., Chen, H., Patel, B., Maher, C. A., & Kim, A. H. (2021) · Cancers · doi:10.3390/cancers13071604 · Cited by 36 · see citing works
“Although the protein and miRNA expression profiles of the central nervous system are more diverse than other organs, only a subset of these are specific to the nervous system [35,36].”
BackgroundAPOE expression and secretion are modulated by mitochondrial dysfunction
Wynne, M. E., Ogunbona, O., Lane, A. R., Gokhale, A., Zlatic, S. A., Xu, C., Wen, Z., Duong, D. M., Rayaprolu, S., Ivanova, A., et al. (2023) · eLife · doi:10.7554/elife.85779 · Cited by 35 · see citing works
“No covariate was applied because sex, age at enrollment, and education have been regressed out of cognitive trajectory ( Wingo et al., 2019 ).”
BackgroundMethodInfluentialSelf-citationNetwork Analysis of a Membrane-Enriched Brain Proteome across Stages of Alzheimer’s Disease
Higginbotham, L., Dammer, E., Duong, D., Modeste, E., Montine, T., Lah, J., Levey, A., & Seyfried, N. (2019) · Proteomes · doi:10.3390/proteomes7030030 · Cited by 32 · see citing works
“In addition, we have found that variation in synaptic protein abundance may contribute to cognitive resilience during the aging process [11].”
BackgroundSelf-citationPatel, P. J., Ren, Y., & Yan, Z. (2023) · Neurobiology of Disease · doi:10.1016/j.nbd.2023.106192 · Cited by 32 · see citing works
Is artificial consciousness achievable? Lessons from the human brain
Farisco, M., Evers, K., & Changeux, J. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106714 · Cited by 36 · see citing works
SMOC1 colocalizes with Alzheimer’s disease neuropathology and delays Aβ aggregation
Balcomb, K., Johnston, C., Kavanagh, T., Leitner, D., Schneider, J. A., Halliday, G. M., Wısnıewskı, T., Sunde, M., & Drummond, E. (2024) · Acta Neuropathologica · doi:10.1007/s00401-024-02819-6 · Cited by 32 · see citing works
TUFM is involved in Alzheimer’s disease-like pathologies that are associated with ROS
Zhong, B., Zhou, G., Song, L., Wen, Q., Deng, X., Ma, Y., Hu, L., & Chen, G. (2021) · The FASEB Journal · doi:10.1096/fj.202002461r · Cited by 28 · see citing works
Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq
Stokes, T., Cen, H. H., Kapranov, P., Gallagher, I. J., Pitsillides, A. A., Volmar, C., Kraus, W. E., Johnson, J. D., Phillips, S. M., Wahlestedt, C., et al. (2023) · Advanced Genetics · doi:10.1002/ggn2.202200024 · Cited by 26 · see citing works
Zhang, Y., Gao, H., Zheng, W., & Xu, H. J. (2022) · Neurobiology of Disease · doi:10.1016/j.nbd.2022.105824 · Cited by 30 · see citing works
Reddy, J. S., Heath, L., Linden, A. V., Allen, M., Lopes, K. D. P., Seifar, F., Wang, E., Ma, Y., Poehlman, W. L., Quicksall, Z. S., et al. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.14208 · Cited by 29 · see citing works
Self-citationGuo, X., Park, J. E., Gallart‐Palau, X., & Sze, S. K. (2020) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00861 · Cited by 28 · see citing works
Single-synapse analyses of Alzheimer’s disease implicate pathologic tau, DJ1, CD47, and ApoE
Phongpreecha, T., Gajera, C. R., Liu, C. C., Vijayaragavan, K., Chang, A. L., Becker, M., Fallahzadeh, R., Fernandez, R., Postupna, N. O., Sherfield, E., et al. (2021) · Science Advances · doi:10.1126/sciadv.abk0473 · Cited by 27 · see citing works
Brain microRNAs are associated with variation in cognitive trajectory in advanced age
Wingo, A. P., Wang, M., Liu, J., Breen, M. S., Yang, H., Tang, B., Schneider, J. A., Seyfried, N. T., Lah, J. J., Levey, A. I., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-01806-3 · Cited by 28 · see citing works
Self-citationZhang, H., Zhang, C., Xu, D., Wang, Q., & Xu, D. (2023) · Environmental Pollution · doi:10.1016/j.envpol.2023.121650 · Cited by 29 · see citing works
Boeddrich, A., Haenig, C., Neuendorf, N., Blanc, E., Ivanov, A., Kirchner, M., Schleumann, P., Bayraktaroğlu, I., Richter, M., Molenda, C., et al. (2023) · Genome Medicine · doi:10.1186/s13073-023-01206-2 · Cited by 27 · see citing works
“This view is supported by a recent large-scale proteome study, 1280 indicating that a stable cognitive trajectory in advanced age is associated with higher 1281 levels of mitochondrial proteins and increased neuronal mitochondrial activity [82].”
ResultTemporal Control of Axonal Transport: The Extreme Case of Organismal Ageing
Mattedi, F., & Vagnoni, A. (2019) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2019.00393 · Cited by 24 · see citing works
Big data-driven precision medicine: Starting the custom-made era of iatrology
Song, C. M., Kong, Y., Huang, L., Luo, H., & Zhu, X. M. (2020) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2020.110445 · Cited by 26 · see citing works
Datki, Z., Galik-Olah, Z., Mózes, E., Szegedi, V., Kálmán, J., Hunya, Á. G., Fülöp, L., Tamano, H., Takeda, A., Adlard, P. A., et al. (2020) · Molecular Psychiatry · doi:10.1038/s41380-020-0800-y · Cited by 25 · see citing works
Knowledge gaps in Alzheimer's disease immune biomarker research
Morgan, D., & Mielke, M. M. (2021) · Alzheimer s & Dementia · doi:10.1002/alz.12342 · Cited by 25 · see citing works
A metaproteomic-based gut microbiota profiling in children affected by autism spectrum disorders
Mortera, S. L., Vernocchi, P., Basadonne, I., Zandonà, A., Chierici, M., Durighello, M., Marzano, V., Gardini, S., Gasbarrini, A., Urbani, A., et al. (2021) · Journal of Proteomics · doi:10.1016/j.jprot.2021.104407 · Cited by 25 · see citing works
Site-specific mitochondrial dysfunction in neurodegeneration
Vodičková, A., Koren, S. A., & Wojtovich, A. P. (2022) · Mitochondrion · doi:10.1016/j.mito.2022.02.004 · Cited by 22 · see citing works
Zhang, Y., Guo, Y. X., He, Y., You, J., Zhang, Y., Wang, L., Chen, S., He, X., Yang, L., Huang, Y., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02840-x · Cited by 22 · see citing works
Gao, H., Jensen, K. N., Nesbitt, J. J., Ostroot, M., Cary, G. A., Wiley, J. C., Trushin, S., Watzlawik, J. O., Springer, W., Galkin, A. S., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70519 · Cited by 22 · see citing works
Expanding beyond ICP-MS to better understand selenium biochemistry
Cardoso, B. R., Ganio, K. E., & Roberts, B. R. (2019) · Metallomics · doi:10.1039/c9mt00201d · Cited by 22 · see citing works
Pifferi, F., Epelbaum, J., & Aujard, F. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.01291 · Cited by 18 · see citing works
Future horizons in Alzheimer's disease research
Wısnıewskı, T., & Drummond, E. (2019) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2019.08.001 · Cited by 20 · see citing works
Azzam, M., Xu, Z.-Y., Liu, R.-B., Li, L., Soh, K. M., Challagundla, K. B., Wan, S., & Wang, J.-Q. (2024) · Briefings in Functional Genomics · doi:10.1093/bfgp/elae042 · Cited by 16 · see citing works
HALL: a comprehensive database for human aging and longevity studies
Li, H., Wu, S., Li, J., Xiong, Z., Yang, K., Ye, W., Ren, J., Wang, Q., Xiong, M., Zheng, Z., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad880 · Cited by 18 · see citing works
Mei, Z. L., Liu, J., Schroeder, J. P., Weinshenker, D., Duong, D. M., Seyfried, N. T., Li, Y., Jin, P., Wingo, A. P., & Wingo, T. S. (2023) · Molecular Neurobiology · doi:10.1007/s12035-023-03791-0 · Cited by 18 · see citing works
“We have also previously reported that the decreased abundance of myelination-related proteins such as MBP was associated with cognitive stability in a proteome-wide association study [40].”
BackgroundSelf-citationSaloner, R., Staffaroni, A. M., Dammer, E. B., Johnson, E. C. B., Paolillo, E. W., Wise, A. B., Heuer, H. W., Forsberg, L. K., Lario‐Lago, A., Webb, J. D., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00878-2 · Cited by 18 · see citing works
Self-citationAll-trans retinoic acid induces synaptopodin-dependent metaplasticity in mouse dentate granule cells
Lenz, M., Eichler, A., Kruse, P., Muellerleile, J., Deller, T., Jedlička, P., & Vlachos, A. (2021) · eLife · doi:10.7554/elife.71983 · Cited by 18 · see citing works
Associations of VGF with Neuropathologies and Cognitive Health in Older Adults
Yu, L., Petyuk, V., Lopes, K. D. P., Tasaki, S., Menon, V., Wang, Y., Schneider, J. A., Jager, P. L. D., & Bennett, D. A. (2023) · Annals of Neurology · doi:10.1002/ana.26676 · Cited by 17 · see citing works
“with or without adjusting for AD pathology.(13) Together,”
MethodModulation of Mitochondrial Function as a Therapeutic Strategy for Neurodegenerative Diseases
Trushina, E. V., Nguyen, T. K. O., & Trushin, S. (2023) · The Journal of Prevention of Alzheimer s Disease · doi:10.14283/jpad.2023.108 · Cited by 17 · see citing works
Metabolic correlates of late midlife cognitive outcomes: findings from the 1946 British Birth Cohort
Green, R., Lord, J., Xu, J., Maddock, J., Kim, M. G., Dobson, R., Legido‐Quigley, C., Wong, A., Richards, M., & Proitsi, P. (2021) · Brain Communications · doi:10.1093/braincomms/fcab291 · Cited by 15 · see citing works
Similarity and Diversity of Presynaptic Molecules at Neuromuscular Junctions and Central Synapses
Takikawa, K., & Nishimune, H. (2022) · Biomolecules · doi:10.3390/biom12020179 · Cited by 18 · see citing works
“In addition, a proteomic analysis of the postmortem dorsolateral prefrontal cortices of aged humans showed decreased expression of proteins such as syntaxin 1A, synaptotagmin 1, Bassoon, and vGlut1 in elderly individuals with fast cognitive decline compared to elderly individuals with stable cognitive function through…”
BackgroundProteomic signatures of Alzheimer's disease and Lewy body dementias: A comparative analysis
Canal-García, A., Branca, R. M., Francis, P. T., Ballard, C. G., Winblad, B. G., Lehtiö, J., Nilsson, P., Aarsland, D., Pereira, J. B., & Bereczki, E. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14375 · Cited by 17 · see citing works
Global quantitative analysis of the human brain proteome and phosphoproteome in Alzheimer’s diseasePreprint
Ping, L., Kundinger, S. R., Duong, D. M., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.19.105197 · Cited by 15 · see citing works
Self-citationTsumagari, K., Sato, Y., Shimozawa, A., Aoyagi, H., Okano, H., & Kuromitsu, J. (2022) · iScience · doi:10.1016/j.isci.2022.104832 · Cited by 15 · see citing works
“Furthermore, some of the OXPHOS proteins including NDUFA10, one of the hub proteins, were shown to be associated with cognitive stability in a proteomic study of human brain (Wingo et al., 2019).”
BackgroundGudenschwager, C., Chavez, I., Cárdenas, C., & González-Billault, C. (2021) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2021/5586052 · Cited by 11 · see citing works
“mitochondrial proteins are diminished in the brains of older people experiencing accelerated cognitive decline [125].”
BackgroundGenetically engineered mouse models of FK506‐binding protein 5
Gebru, N. T., Hill, S. E., & Blair, L. J. (2023) · Journal of Cellular Biochemistry · doi:10.1002/jcb.30374 · Cited by 15 · see citing works
Morgan, G. R., & Carlyle, B. C. (2024) · Scientific Reports · doi:10.1038/s41598-024-57104-z · Cited by 17 · see citing works
Rosoff, D. B., Wagner, J., Bell, A. S., Mavromatis, L. A., Jung, J., & Lohoff, F. W. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02040-1 · Cited by 15 · see citing works
- Authors
- Wingo, A. P., Dammer, E. B., Breen, M. S., Logsdon, B. A., Duong, D. M., Troncosco, J. C., Thambisetty, M., Beach, T. G., Serrano, G. E., Reiman, E. M., Caselli, R. J., Lah, J. J., Seyfried, N. T., Levey, A. I., & Wingo, T. S.
- Affiliations
- Emory University (US)
- Icahn School of Medicine at Mount Sinai (US)
- Sage Bionetworks (US)
- Johns Hopkins University (US)
- National Institutes of Health (US)
- Banner Sun Health Research Institute (US)
- University of Arizona (US)
- Mayo Clinic (US)
- Corresponding author
- Seyfried, Nicholas T.
- ORCID
- Venue
- Nature Communications
- Published
- 2019-04-08
- Volume
- 10
- Issue
- 1
- Pages
- 1619-1619
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC6453881
- OpenAlex
- W2937979781
- Semantic Scholar
- 51944265c33e2326e17cfb3aa6324c6364c80f42
- Cited by
- 211
- References
- 78
- Citations by year
- 2026: 19 · 2025: 19 · 2024: 38 · 2023: 37 · 2022: 29 · 2021: 33 · 2020: 28 · 2019: 8
- FWCI
- 4.12
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UA Campus Repository (The University of Arizona) (acceptedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- U.S. Department of Veterans Affairs (I01BX003853, IK2 BX001820)
- Michael J. Fox Foundation for Parkinson's Research (U24 NS072026, 05-901, 211002, 11060, P30 AG19610)
- Alzheimer's Association (P50 AG025688, 11060)
- Emory University (P50AG025688, AG025688)
- Arizona Department of Health Services (P30 AG19610, P50 AG025688, U24 NS072026, 05-901, 211002, Contract 211002)
- Weston Brain Institute (11060)
- Arizona Biomedical Research Commission (P30 AG19610, 05–901, Contracts 4001, 0011, 05‐901 and 1001, U24 NS072026, 4001, 0011, 05-901, and 1001, 211002)
- Alzheimer's Disease Research Center, Emory University (P50 AG025688, AG025688)
- National Institute on Aging (211002, P30-AG-19610, R01AG056533, AG 025688, AG-19610, R01AG057911, AG057911, U01AG061357, I01 BX003853, P30AG019610, U01AG046161, 05-901, AG056533, AG061800, P50AG025688, R01AG053960, R01AG061800)
- National Institute of Mental Health (U01MH115484)
- National Institute of Neurological Disorders and Stroke (NS055077, NS072026, 211002, P30NS055077, U24NS072026, 05-901, U24 NS072026 National Brain, P30 AG19610)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Keywords
- cognitive trajectories, aging, human brain proteome, proteome-wide association study, cognitive stability, cognitive decline, mitochondrial activity, synaptic proteins, neuroinflammation, apoptosis, neuropathology, protein co-expression networks, hub proteins, older adults
- MeSH terms
- Biological Variation, Population, Aged, Aged, 80 and over, Aging, Alzheimer Disease, Brain, Cognition, Humans, Middle Aged, Parkinson Disease, Proteome, Proteomics, Healthy Volunteers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-10.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review