We present the first optical spectroscopy of five confirmed (or strong candidate) redback millisecond pulsar binaries, obtaining complete radial velocity curves for each companion star. The ...properties of these millisecond pulsar binaries with low-mass, hydrogen-rich companions are discussed in the context of the 14 confirmed and 10 candidate field redbacks. We find that the neutron stars in redbacks have a median mass of 1.78 +/- 0.09 M_sun with a dispersion of sigma = 0.21 +/- 0.09. Neutron stars with masses in excess of 2 M_sun are consistent with, but not firmly demanded by, current observations. Redback companions have median masses of 0.36 +/- 0.04 M_sun with a scatter of sigma = 0.15 +/- 0.04, and a tail possibly extending up to 0.7-0.9 M_sun. Candidate redbacks tend to have higher companion masses than confirmed redbacks, suggesting a possible selection bias against the detection of radio pulsations in these more massive candidate systems. The distribution of companion masses between redbacks and the less massive black widows continues to be strongly bimodal, which is an important constraint on evolutionary models for these systems. Among redbacks, the median efficiency of converting the pulsar spindown energy to gamma-ray luminosity is ~10%.
Abstract Purpose We evaluated whether plasma Alzheimer disease (AD)–related biomarkers were associated with cancer-related cognitive decline among older breast cancer survivors. Methods We included ...survivors aged 60-90 years with primary stage 0-III breast cancers (n = 236) and frequency-matched noncancer control paricipant (n = 154) who passed a cognitive screen and had banked plasma specimens. Participants were assessed at baseline (presystemic therapy) and annually for up to 60 months. Cognition was measured using tests of attention, processing speed, and executive function and learning and memory; perceived cognition was measured by the Functional Assessment of Cancer Therapy-Cognitive Function v3 Perceived Cognitive Impairments. Baseline plasma neurofilament light, glial fibrillary acidic protein, β-amyloid 42 and 40 and phosphorylated tau 181 were assayed using single molecule arrays. Mixed models tested associations between cognition and baseline AD biomarkers, time, group (survivor vs control participant), and their 2- and 3-way interactions, controlling for age, race, Wide Range 4 Achievement Test Word Reading score, comorbidity, and body mass index; 2-sided P values of .05 were considered statistically significant. Results There were no group differences in baseline AD-related biomarkers except survivors had higher baseline neurofilament light levels than control participants (P = .013). Survivors had lower adjusted longitudinal attention, processing speed, and executive function than control participants starting from baseline and continuing over time (P ≤ .002). However, baseline AD-related biomarker levels were not independently associated with adjusted cognition over time, except control participants had lower attention, processing speed, and executive function scores with higher glial fibrillary acidic protein levels (P = .008). Conclusion The results do not support a relationship between baseline AD-related biomarkers and cancer-related cognitive decline. Further investigation is warranted to confirm the findings, test effects of longitudinal changes in AD-related biomarkers, and examine other mechanisms and factors affecting cognition presystemic therapy.
Abstract
Background
Variation in biospecimen collection, processing, and storage parameters can introduce pre‐analytical variables that may confound interpretation of biomarker results. Samples ...collected under different study protocols or over a long period of time may require different labware, such as storage tubes. Comparability of storage tubes is necessary to better interpretate data collected both longitudinally within studies and across studies that may utilize various labware. The focus of this experiment was to perform comparability studies between plasma biomarkers measured in two polypropylene tube types: Sarstedt tube (72.694.006) and an automation friendly Micronic tube (MP52755).
Methods
Blood was collected from healthy controls (N=23) and Alzheimer’s Disease (AD) subjects (N=16) by the Indiana Biobank. Participants’ brain amyloid status was unknown. Plasma from the same subjects was aliquoted (0.5 mL) into both Micronic and Sarstedt polypropylene tubes following standard NCRAD protocols and then frozen and stored at ‐80 °C for <7 months prior to analysis. Blinded samples were thawed once and analyzed for ApoE proteotype, Aβ40 and Aβ42 using liquid chromatography‐high resolution tandem mass spectrometry (LC‐MS/MS; C
2
N Diagnostics, St. Louis, MO). Data was analyzed in JMP 16.0.0 with the method comparison add‐in and GraphPad PRISM 9.3.1 to determine comparability of the measurements made in the two sample tube types.
Results
Comparability analysis found a high correlation of measured Aβ40, Aβ42 and the Aβ42/Aβ40 ratio in plasma stored in Micronic and Sarstedt tubes. Correlation analysis revealed slopes and intercepts within 95% confidence limits of 1 or 0 respectively. Bland Altman analysis showed low measurement bias (4‐7%) between the two tube types. ApoE isoform specific proteotypes were identical between tube types for all subjects.
Conclusion
Results of the tube type study were within the validated performance range of the measurements. Plasma stored in Micronic and Sarstedt tubes can be used interchangeably when detecting ApoE proteotype and quantifying Aβ40 and Aβ42 concentrations using C
2
N’s LC‐MS/MS analytical platform. These results support the efforts of large biorepositories, like NCRAD to transition the storage of biospecimens from the larger, less automation friendly Sarstedt tube to the more automation friendly, freezer‐space saving Micronic tube. Supported by U24 AG021886 and UL1TR002529.
Background
Variation in biospecimen collection, processing, and storage parameters can introduce pre‐analytical variables that may confound interpretation of biomarker results. Samples collected ...under different study protocols or over a long period of time may require different labware, such as storage tubes. Comparability of storage tubes is necessary to better interpretate data collected both longitudinally within studies and across studies that may utilize various labware. The focus of this experiment was to perform comparability studies between plasma biomarkers measured in two polypropylene tube types: Sarstedt tube (72.694.006) and an automation friendly Micronic tube (MP52755).
Methods
Blood was collected from healthy controls (N=23) and Alzheimer’s Disease (AD) subjects (N=16) by the Indiana Biobank. Participants’ brain amyloid status was unknown. Plasma from the same subjects was aliquoted (0.5 mL) into both Micronic and Sarstedt polypropylene tubes following standard NCRAD protocols and then frozen and stored at ‐80 °C for <7 months prior to analysis. Blinded samples were thawed once and analyzed for ApoE proteotype, Aβ40 and Aβ42 using liquid chromatography‐high resolution tandem mass spectrometry (LC‐MS/MS; C2N Diagnostics, St. Louis, MO). Data was analyzed in JMP 16.0.0 with the method comparison add‐in and GraphPad PRISM 9.3.1 to determine comparability of the measurements made in the two sample tube types.
Results
Comparability analysis found a high correlation of measured Aβ40, Aβ42 and the Aβ42/Aβ40 ratio in plasma stored in Micronic and Sarstedt tubes. Correlation analysis revealed slopes and intercepts within 95% confidence limits of 1 or 0 respectively. Bland Altman analysis showed low measurement bias (4‐7%) between the two tube types. ApoE isoform specific proteotypes were identical between tube types for all subjects.
Conclusion
Results of the tube type study were within the validated performance range of the measurements. Plasma stored in Micronic and Sarstedt tubes can be used interchangeably when detecting ApoE proteotype and quantifying Aβ40 and Aβ42 concentrations using C2N’s LC‐MS/MS analytical platform. These results support the efforts of large biorepositories, like NCRAD to transition the storage of biospecimens from the larger, less automation friendly Sarstedt tube to the more automation friendly, freezer‐space saving Micronic tube. Supported by U24 AG021886 and UL1TR002529.
Background
Knowledge regarding associations between plasma and neuroimaging biomarkers indexing neurodegeneration and neuropathology observed in dementia is limited. Further, it is uncertain how ...comorbid health complications (e.g., kidney function) may alter plasma levels and impact associations with neuroimaging biomarkers.
Method
We examined associations between plasma and neuroimaging biomarkers in cognitively normal participants (NC; N = 300) and individuals with consensus diagnosis (Dx) of mild cognitive impairment (MCI; N = 192) or dementia (DEM; N = 64) enrolled in the Wake Forest ADRC (Table 1). We examined plasma biomarkers (Quanterix SIMOA HD‐X: Aβ42/40, GFAP, NfL, p‐tau181) and neuroimaging measures of amyloid deposition (global PiB PET SUVr; Aβ‐PET), total brain volume (BVOL), global white matter hyperintensity volume (WMH), diffusion‐weighted fractional anisotropy (FA) and NODDI freewater (FW; white matter). Linear models adjusted for APOE‐ε4 carrier status, demographics (age, sex, race, education), and cardiometabolic factors (estimated glomerular filtration rate (eGFR); BMI).
Results
Plasma biomarkers were moderately correlated with each other (absolute r = .22‐.64; all p<.001) and significantly elevated (Aβ42/40 lower) in DEM and MCI (Figure 1) versus NC, and Aβ‐PET‐positive (SUVr >1.21) versus negative individuals (all p<.001). Plasma and neuroimaging markers were significantly associated in both unadjusted models and models including eGFR and BMI (all p<.05; attenuation effect <10% with and without Dx; Figure 2a & 2b). In fully adjusted models (Figure 2b: Dx and all covariates), age, sex, and race differentially impacted associations of Aβ42/40, p‐tau181, and NfL with neuroimaging biomarkers (coefficients p <.05). APOE‐ε4 status exclusively impacted associations with Aβ‐PET SUVr/status. GFAP remained significantly associated with all neuroimaging biomarkers after covariate adjustment; no Aβ42/40 associations survived adjustment. P‐tau181 remained significantly associated with Aβ‐PET and BVOL, while associations with NfL were reduced in models stratified by Dx.
Conclusion
Among aging community‐dwelling participants, plasma biomarkers significantly differed between diagnostic groups (DEM>MCI>NC), were elevated in Aβ‐PET positive individuals, and associated with poorer brain health. Except for GFAP, and to an extent p‐tau181, associations between plasma and neuroimaging biomarkers were differentially impacted by inclusion of comorbidities and covariates when stratified by diagnosis. Future work will examine high‐dimensional interactions among comorbidities, demographic information, and plasma and neuroimaging biomarkers in individuals with or at‐risk of dementia.
Background
AD Imaging biomarkers (IBM) include measures of amyloid (A) on PET, and neurodegeneration (N) on MRI, with thresholds for classification of imaging A/N positivity commonly used at research ...centers. Novel AD plasma biomarkers (PBM) can potentially enhance neuropathological characterization; however, knowledge regarding which thresholds to use to characterize PBM abnormalities is limited.
Method
We evaluated a range of PBM thresholds based on (1) plasma data alone (gaussian mixture models GMM), and (2) when classifying A/N IBM using both existing and data‐driven cutoffs (ROC analyses). Baseline plasma and imaging data were obtained in a community‐dwelling cohort enrolled in the Wake Forest ADRC, including cognitively normal participants (N = 300) and individuals with consensus diagnosis of mild cognitive impairment (N = 192) or dementia (N = 64; Table 1). We examined PBMs (Quanterix SIMOA HD‐X from NCRAD: Aβ42/40, GFAP, NfL, p‐tau181) and IBM measures of A (global PiB PET; A+>1.21 SUVR) and N (FreeSurfer temporal cortical thickness; MAYOTHCK), N+≤2.68mm; FreeSurfer hippocampal volume, N+≤0.454% head size; HPC‐VOL).
Results
Optimal/low/high GMM‐derived thresholds based on PBM distributions (Figure 1a) are provided in Figure 1b. ROC analysis using existing IBM cutoffs are in Figure 1c, with details (sensitivity/specificity/AUC) in Figure 1d. GMM‐ and ROC‐derived p‐tau181 thresholds were highly aligned (range 3.698‐4.071 pg/ml). For NfL and GFAP, imaging ROC‐derived thresholds were lower than GMM‐derived thresholds. AUCs were highest when PBM classification was used to predict A‐PET using existing PET‐positivity cutoffs, with PBM performing more poorly for MRI‐based cutoffs. A/N IBM histograms and current/optimal/low/high GMM‐derived thresholds for each IBM are in Figure 2a. Figure 2b represents how different data‐driven IBM cutoffs impact PBM thresholds in ROC analyses, with AUC statistics for these ROC analyses in Figures 2c‐e. Notably, GMM‐optimal IBM cutoffs performed similarly to current cutoffs for defining PBM thresholds, especially when using A‐PET (AUCs for current and GMM‐optimal >0.747).
Conclusion
While use of existing and data‐driven IBM cutoffs to define PBM thresholds in a community‐dwelling cohort is feasible, some plasma assays (e.g., p‐tau181) are more consistent than others, and caution is warranted against selecting a single threshold. To facilitate clinical use, future work will focus on establishing confidence intervals for PBM abnormalities.
Background
AD Imaging biomarkers (IBM) include measures of amyloid (A) on PET, and neurodegeneration (N) on MRI, with thresholds for classification of imaging A/N positivity commonly used at research ...centers. Novel AD plasma biomarkers (PBM) can potentially enhance neuropathological characterization; however, knowledge regarding which thresholds to use to characterize PBM abnormalities is limited.
Method
We evaluated a range of PBM thresholds based on (1) plasma data alone (gaussian mixture models GMM), and (2) when classifying A/N IBM using both existing and data‐driven cutoffs (ROC analyses). Baseline plasma and imaging data were obtained in a community‐dwelling cohort enrolled in the Wake Forest ADRC, including cognitively normal participants (N = 300) and individuals with consensus diagnosis of mild cognitive impairment (N = 192) or dementia (N = 64; Table 1). We examined PBMs (Quanterix SIMOA HD‐X from NCRAD: Aß42/40, GFAP, NfL, p‐tau181) and IBM measures of A (global PiB PET; A+>1.21 SUVR) and N (FreeSurfer temporal cortical thickness; MAYOTHCK), N+ = 2.68mm; FreeSurfer hippocampal volume, N+ = 0.454% head size; HPC‐VOL).
Result
Optimal/low/high GMM‐derived thresholds based on PBM distributions (Figure 1a) are provided in Figure 1b. ROC analysis using existing IBM cutoffs are in Figure 1c, with details (sensitivity/specificity/AUC) in Figure 1d. GMM‐ and ROC‐derived p‐tau181 thresholds were highly aligned (range 3.698‐4.071 pg/ml). For NfL and GFAP, imaging ROC‐derived thresholds were lower than GMM‐derived thresholds. AUCs were highest when PBM classification was used to predict A‐PET using existing PET‐positivity cutoffs, with PBM performing more poorly for MRI‐based cutoffs. A/N IBM histograms and current/optimal/low/high GMM‐derived thresholds for each IBM are in Figure 2a. Figure 2b represents how different data‐driven IBM cutoffs impact PBM thresholds in ROC analyses, with AUC statistics for these ROC analyses in Figures 2c‐e. Notably, GMM‐optimal IBM cutoffs performed similarly to current cutoffs for defining PBM thresholds, especially when using A‐PET (AUCs for current and GMM‐optimal >0.747).
Conclusion
While use of existing and data‐driven IBM cutoffs to define PBM thresholds in a community‐dwelling cohort is feasible, some plasma assays (e.g., p‐tau181) are more consistent than others, and caution is warranted against selecting a single threshold. To facilitate clinical use, future work will focus on establishing confidence intervals for PBM abnormalities.
Background
As blood‐based biomarkers of Alzheimer’s disease enter clinical diagnostic use, key considerations remain including the concordance with CSF or amyloid PET and the comparability of results ...between different assay platforms.
Method
Participants (n = 42) from the Clinical Core of the Wake Forest ADRC, were selected using CSF Aβ42/Aβ40 values which clearly indicated amyloid pathology status (‐ or +). A subset of 29 participants had amyloid (PiB) PET measures within one year of CSF. Participants were considered amyloid positive when CSF Aβ42/40 ≤0.058 and/or amyloid PET was adjudicated as “positive”. CSF and plasma Aβ42, Aβ40, and pTau181 were assessed using the Lumipulse platform at the Wake Forest ADRC; plasma Aβ42, Aβ40, and pTau181 were also assessed using the Quanterix SIMOA platform at the National Centralized Repository for Alzheimer’s disease. CSF and plasma from each participant were collected on the same day.
Result
Amyloid positive and negative groups differed significantly in age and APOE‐ε4 status (Table 1). Although raw values between plasma platforms differed significantly, biomarkers were highly correlated (Table 2). The correlation between CSF and plasma was: Lumipulse for Aβ42/Aβ40 (ρS = 0.76, p<0.0001) and pTau181 (ρS = 0.64, p<0.0001) and SIMOA Aβ42/Aβ40 (ρS = 0.63, p<0.0001) and pTau181 (ρS = 0.50, p = 0.0009). When comparing groups positive or negative for either CSF or PET, the ROC AUC for plasma Aβ42/Aβ40 was 0.94 for Lumipulse and 0.92 for SIMOA. Ptau181 AUC was 0.80 for Lumipulse and 0.82 for SIMOA. Figure 1 shows a clear relationship between decreasing Aβ42/Aβ40 and increasing pTau181 in the CSF which is present but diminished in Lumipulse plasma and largely absent in SIMOA plasma data.
Conclusion
Raw values from both platforms differed significantly which may limit generalizability and translatability of plasma data across platforms, but results were correlated. CSF values correlated marginally with both Lumipulse and SIMOA plasma. Plasma indices performed better than previously reported in other AD cohorts, likely due to our stringent CSF‐based selection criteria, and AUC did not differ between Lumipulse and SIMOA for either Aβ42/Aβ40 or pTau181. Further work is underway to expand the dataset and define population‐based cutoffs which also consider comorbidities, such as chronic kidney disease.
Background
Knowledge regarding associations between plasma and neuroimaging biomarkers indexing neurodegeneration and neuropathology observed in dementia is limited. Further, it is uncertain how ...comorbid health complications (e.g., kidney function) may alter plasma levels and impact associations with neuroimaging biomarkers.
Method
We examined associations between plasma and neuroimaging biomarkers in cognitively normal participants (NC; N = 300) and individuals with consensus diagnosis (Dx) of mild cognitive impairment (MCI; N = 192) or dementia (DEM; N = 64) enrolled in the Wake Forest ADRC (Table 1). We examined plasma biomarkers (Quanterix SIMOA HD‐X: Aß42/40, GFAP, NfL, p‐tau181) and neuroimaging measures of amyloid deposition (global PiB PET SUVr; Aß‐PET), total brain volume (BVOL), global white matter hyperintensity volume (WMH), diffusion‐weighted fractional anisotropy (FA) and NODDI freewater (FW; white matter). Linear models adjusted for APOE‐e4 carrier status, demographics (age, sex, race, education), and cardiometabolic factors (estimated glomerular filtration rate (eGFR); BMI).
Result
Plasma biomarkers were moderately correlated with each other (absolute r = .22‐.64; all p<.001) and significantly elevated (Aß42/40 lower) in DEM and MCI (Figure 1) versus NC, and Aß‐PET‐positive (SUVr >1.21) versus negative individuals (all p<.001). Plasma and neuroimaging markers were significantly associated in both unadjusted models and models including eGFR and BMI (all p<.05; attenuation effect <10% with and without Dx; Figure 2a & 2b). In fully adjusted models (Figure 2b: Dx and all covariates), age, sex, and race differentially impacted associations of Aß42/40, p‐tau181, and NfL with neuroimaging biomarkers (coefficients p <.05). APOE‐e4 status exclusively impacted associations with Aß‐PET SUVr/status. GFAP remained significantly associated with all neuroimaging biomarkers after covariate adjustment; no Aß42/40 associations survived adjustment. P‐tau181 remained significantly associated with Aß‐PET and BVOL, while associations with NfL were reduced in models stratified by Dx.
Conclusion
Among aging community‐dwelling participants, plasma biomarkers significantly differed between diagnostic groups (DEM>MCI>NC), were elevated in Aß‐PET positive individuals, and associated with poorer brain health. Except for GFAP, and to an extent p‐tau181, associations between plasma and neuroimaging biomarkers were differentially impacted by inclusion of comorbidities and covariates when stratified by diagnosis. Future work will examine high‐dimensional interactions among comorbidities, demographic information, and plasma and neuroimaging biomarkers in individuals with or at‐risk of dementia.
We evaluated whether plasma Alzheimer's Disease (AD)-related biomarkers were associated with cancer-related cognitive decline (CRCD) among older breast cancer survivors.
We included survivors 60-90 ...years with primary stage 0-III breast cancers (n = 236) and frequency-matched non-cancer controls (n = 154) who passed a cognitive screen and had banked plasma specimens. Participants were assessed at baseline (pre-systemic therapy) and annually for up to 60-months. Cognition was measured using tests of attention, processing speed and executive function (APE) and learning and memory (LM); perceived cognition was measured by the FACT-Cog PCI. Baseline plasma neurofilament light (NfL), glial fibrillary acidic protein (GFAP), beta-amyloid 42/40 (Aβ42/40) and phosphorylated tau (p-tau181) were assayed using single molecule arrays. Mixed models tested associations between cognition and baseline AD-biomarkers, time, group (survivor vs control) and their two- and three-way interactions, controlling for age, race, WRAT4 Word Reading score, comorbidity and BMI; two-sided 0.05 p-values were considered statistically significant.
There were no group differences in baseline AD-related biomarkers except survivors had higher baseline NfL levels than controls (p = .013). Survivors had lower adjusted longitudinal APE than controls starting from baseline and continuing over time (p = <0.002). However, baseline AD-related biomarker levels were not independently associated with adjusted cognition over time, except controls had lower APE scores with higher GFAP levels (p = .008).
The results do not support a relationship between baseline AD-related biomarkers and CRCD. Further investigation is warranted to confirm the findings, test effects of longitudinal changes in AD-related biomarkers and examine other mechanisms and factors affecting cognition pre-systemic therapy.
