Few studies have comprehensively examined how health and disease risk influence Alzheimer's disease (AD) biomarkers. The present study examined the association of 14 protein-based health indicators with plasma and neuroimaging biomarkers of AD and neurodegeneration. In 706 cognitively normal adults, we examined whether 14 protein-based health indices (ie, SomaSignal® tests) were associated with concurrently measured plasma-based biomarkers of AD pathology (amyloid-β [Aβ]42/40 , tau phosphorylated at threonine-181 [pTau-181]), neuronal injury (neurofilament light chain [NfL]), and reactive astrogliosis (glial fibrillary acidic protein [GFAP]), brain volume, and cortical Aβ and tau. In a separate cohort (n = 11,285), we examined whether protein-based health indicators associated with neurodegeneration also predict 25-year dementia risk. Greater protein-based risk for cardiovascular disease, heart failure mortality, and kidney disease was associated with lower Aβ42/40 and higher pTau-181, NfL, and GFAP levels, even in individuals without cardiovascular or kidney disease. Proteomic indicators of body fat percentage, lean body mass, and visceral fat were associated with pTau-181, NfL, and GFAP, whereas resting energy rate was negatively associated with NfL and GFAP. Together, these health indicators predicted 12, 31, 50, and 33% of plasma Aβ42/40 , pTau-181, NfL, and GFAP levels, respectively. Only protein-based measures of cardiovascular risk were associated with reduced regional brain volumes; these measures predicted 25-year dementia risk, even among those without clinically defined cardiovascular disease. Subclinical peripheral health may influence AD and neurodegenerative disease processes and relevant biomarker levels, particularly NfL. Cardiovascular health, even in the absence of clinically defined disease, plays a central role in brain aging and dementia. ANN NEUROL 2024;95:260-273.

Microneedling is a cosmetic procedure that leverages the skin's natural ability to heal in order to promote collagen formation and skin rejuvenation. To provide improved results, the technique can be combined with topical formulations. A new formulation of multiple actives, including omega-3 (n-3)polyunsaturated fatty acids (PUFAs), was designed to accelerate the resolution of inflammation and wound healing following micro-injury treatments, while enhancing the visible appearance of procedure results, including erythema, luminosity and skin texture. In this randomised, controlled, split-face study, we examined 32 healthy female participants aged 30-70 years for 4weeks following microneedling treatment with a novel multiple-active-ingredient formulation or conventional microneedling protocol with a hyaluronic acid control serum. Changes in skin condition were assessed by blinded clinical photography and expert evaluation. Measurements were collected at baseline, 1h, 1day, 7days and 28days post treatment. Significantly greater improvements in expert-assessed erythema, luminosity and skin texture were reported following application of the novel multiple-active-ingredient formulation than the hyaluronic acid control serum. This was confirmed by representative VISIA®-CR imaging. These data provide new evidence for the role of a novel multiple-active-ingredient formulation for improving skin outcomes up to 28days following microneedling in adults with healthy skin when compared with a hyaluronic acid serum. The n-3 PUFA content of this formulation may drive accelerated inflammation resolution and wound healing alongside the complementary action of the other active ingredients, leading to the observed improvements in erythema, luminosity and skin texture.

Abstract Disclosure: S. Yoshiji: None. T. Lu: Employee; Self; 5 Prime Sciences. G. Butler-Laporte: None. J. Carrasco-Zanini-Sanchez: None. Y. Chen: None. K. Liang: None. J.D. Willett: None. C. Su: None. S. Wang: Employee; Self; SomaLogic. D. Adra: None. Y. Ilbudo: None. S. Takayoshi: None. V. Forgetta: None. Y. Farjoun: Employee; Self; Fulcrum Genomics. H. Zeberg: None. S. Zhou: None. M. Machiela: None. M. Hultstrom: None. N. Wareham: None. N.J. Timpson: None. V. Mooser: None. C. Langenberg: None. B. Richards: Advisory Board Member; Self; GlaxoSmithKline. Owner/Co-Owner; Self; 5 Prime Sciences. Background: Obesity strongly increases the risk of cardiometabolic diseases; however, the underlying mediators of this relationship are not fully understood. As obesity strongly influences the plasma proteome, one strategy to disentangle this relationship is to identify plasma proteins mediating this relationship in humans. Since plasma proteins can be measured and in some cases modulated, they may offer attractive therapeutic targets. Aims: To identify plasma proteins mediating the relationship between obesity and coronary artery disease, stroke, and type 2 diabetes using an integrative analysis of proteome-wide Mendelian randomization (MR), statistical colocalization, mediation analyses, and single-cell RNA sequencing. Results: We screened 4,907 plasma proteins to identify proteins influenced by body mass index (BMI) with MR, wherein we used genome-wide association studies in up to one million individuals to make causal inference. This identified 2,714 BMI-influenced proteins (false discovery rate <0.5%), whose effects on coronary artery disease, stroke, and type 2 diabetes were assessed, again using MR. Moreover, we performed statistical colocalization and mediation analyses to increase the robustness of the findings. The integrative analysis identified seven plasma protein mediators, including collagen type VI alpha-3 (COL6A3). COL6A3 was strongly increased by BMI (β = 0.32, 95% CI: 0.26-0.38, P = 3.7 × 10-8) and increased the risk of coronary artery disease (odds ratio = 1.47, 95% CI:1.26-1.70, P =4.5 × 10-7) per s.d. increase in COL6A3 level. Further analyses found that a C-terminal fragment of COL6A3 known as “endotrophin” mediated the effect. In single-cell RNA sequencing of adipose tissues and coronary arteries, COL6A3 was highly expressed in cell types involved in metabolic dysfunction and fibrosis. Finally, we found that body fat reduction can lower plasma levels of COL6A3-derived endotrophin and other protein mediators and reduce cardiometabolic risk, highlighting clinical translation of these findings. Conclusions: We provide actionable insights into how circulating proteins mediate the effect of obesity on cardiometabolic diseases using the integrative proteogenomic approach. Our study highlights the importance of body fat reduction to reduce the risk of cardiometabolic diseases and offers potential therapeutic targets, including COL6A3-derived endotrophin, which may be prioritized for drug development. Presentation: Thursday, June 15, 2023

Risk assessment tools are needed for timely identification of patients with heart failure (HF) with reduced ejection fraction (HFrEF) who are at high risk of adverse events. In this study, we aim to derive a small set out of 4210 repeatedly measured proteins, which, along with clinical characteristics and established biomarkers, carry optimal prognostic capacity for adverse events, in patients with HFrEF. In 382 patients, we performed repeated blood sampling (median follow-up: 2.1 years) and applied an aptamer-based multiplex proteomic approach. We used machine learning to select the optimal set of predictors for the primary endpoint (PEP: composite of cardiovascular death, heart transplantation, left ventricular assist device implantation, and HF hospitalization). The association between repeated measures of selected proteins and PEP was investigated by multivariable joint models. Internal validation (cross-validated c-index) and external validation (Henry Ford HF PharmacoGenomic Registry cohort) were performed. Nine proteins were selected in addition to the MAGGIC risk score, N-terminal pro-hormone B-type natriuretic peptide, and troponin T: suppression of tumourigenicity 2, tryptophanyl-tRNA synthetase cytoplasmic, histone H2A Type 3, angiotensinogen, deltex-1, thrombospondin-4, ADAMTS-like protein 2, anthrax toxin receptor 1, and cathepsin D. N-terminal pro-hormone B-type natriuretic peptide and angiotensinogen showed the strongest associations [hazard ratio (95% confidence interval): 1.96 (1.17-3.40) and 0.66 (0.49-0.88), respectively]. The multivariable model yielded a c-index of 0.85 upon internal validation and c-indices up to 0.80 upon external validation. The c-index was higher than that of a model containing established risk factors (P = 0.021). Nine serially measured proteins captured the most essential prognostic information for the occurrence of adverse events in patients with HFrEF, and provided incremental value for HF prognostication beyond established risk factors. These proteins could be used for dynamic, individual risk assessment in a prospective setting. These findings also illustrate the potential value of relatively 'novel' biomarkers for prognostication. https://clinicaltrials.gov/ct2/show/NCT01851538?term=nCT01851538&draw=2&rank=1 24.

To determine the extent to which changes in plasma proteins, previously predictive of cardiometabolic outcomes, predict changes in two diabetes remission trials. We applied SomaSignal predictive tests (each derived from ∼5,000 plasma protein measurements using aptamer-based proteomics assay) to baseline and 1-year samples of trial intervention (Diabetes Remission Clinical Trial [DiRECT], n = 118, and Diabetes Intervention Accentuating Diet and Enhancing Metabolism [DIADEM-I], n = 66) and control (DiRECT, n = 144, DIADEM-I, n = 76) group participants. Mean (SD) weight loss in DiRECT (U.K.) and DIADEM-I (Qatar) was 10.2 (7.4) kg and 12.1 (9.5) kg, respectively, vs. 1.0 (3.7) kg and 4.0 (5.4) kg in control groups. Cardiometabolic SomaSignal test results showed significant improvement (Bonferroni-adjusted P < 0.05) in DiRECT and DIADEM-I (expressed as relative difference, intervention minus control) as follows, respectively: liver fat (-26.4%, -37.3%), glucose tolerance (-36.6%, -37.4%), body fat percentage (-8.6%, -8.7%), resting energy rate (-8.0%, -5.1%), visceral fat (-34.3%, -26.1%), and cardiorespiratory fitness (9.5%, 10.3%). Cardiovascular risk (measured with SomaSignal tests) also improved in intervention groups relative to control, but this was significant only in DiRECT (DiRECT, -44.2%, and DIADEM-I, -9.2%). However, weight loss >10 kg predicted significant reductions in cardiovascular risk, -19.1% (95% CI -33.4 to -4.91) in DiRECT and -33.4% (95% CI -57.3, -9.6) in DIADEM-I. DIADEM-I also demonstrated rapid emergence of metabolic improvements at 3 months. Intentional weight loss in recent-onset type 2 diabetes rapidly induces changes in protein-based risk models consistent with widespread cardiometabolic improvements, including cardiorespiratory fitness. Protein changes with greater (>10 kg) weight loss also predicted lower cardiovascular risk, providing a positive outlook for relevant ongoing trials.

Detergent-free immunolabeling has been proven feasible for correlated light and electron microscopy, but its application is restricted by the availability of suitable affinity reagents. Here we introduce CAptVE, a method using slow off-rate modified aptamers for cell fluorescence labeling on ultrastructurally reconstructable electron micrographs. CAptVE provides labeling for a wide range of biomarkers, offering a pathway to integrate molecular analysis into recent approaches to delineate neural circuits via connectomics.

Persistent symptoms of COVID-19 survivors constitute long COVID syndrome, also called post-acute sequelae of SARS-CoV-2 infection (PASC). Neurologic manifestations of PASC (Neuro-PASC) are particularly debilitating, long lasting, and poorly understood. To gain insight into the pathogenesis of PASC, we leveraged a well-characterized group of Neuro-PASC (NP) patients seen at our Neuro-COVID-19 clinic who had mild acute COVID-19 and never required hospitalization to investigate their plasma proteome. Using the SomaLogic platform, SomaScan, the plasma concentration of >7000 proteins was measured from 92 unvaccinated individuals, including 48 NP patients, 20 COVID-19 convalescents (CC) without lingering symptoms, and 24 unexposed healthy controls (HC) to interrogate underlying pathobiology and potential biomarkers of PASC. We analyzed the plasma proteome based on post-COVID-19 status, neurologic and non-neurologic symptoms, as well as subjective and objective standardized tests for changes in quality-of-life (QoL) and cognition associated with Neuro-PASC. The plasma proteome of NP patients differed from CC and HC subjects more substantially than post-COVID-19 groups (NP and CC combined) differed from HC. Proteomic differences in NP patients 3–9 months following acute COVID-19 showed alterations in inflammatory proteins and pathways relative to CC and HC subjects. Proteomic associations with Neuro-PASC symptoms of brain fog and fatigue included changes in markers of DNA repair, oxidative stress, and neutrophil degranulation. Furthermore, we discovered a correlation between NP patients lower subjective impression of recovery to pre-COVID-19 baseline with an increase in the concentration of the oxidative phosphorylation protein COX7A1, which was also associated with neurologic symptoms and fatigue, as well as impairment in QoL and cognitive dysfunction. Finally, we identified other oxidative phosphorylation-associated proteins correlating with central nervous system symptoms. Our results suggest ongoing inflammatory changes and mitochondrial involvement in Neuro-PASC and pave the way for biomarker validation for use in monitoring and development of therapeutic intervention for this debilitating condition.

ABSTRACTObjectivesTo develop and validate a pipeline for quality controlled (QC) protein data for largescale analysis of synovial fluid (SF), using SomaLogic technology.DesignKnee SF and associated clinical data were from partner cohorts. SF samples were centrifuged, supernatants stored at −80 °C, then analysed by SomaScan Discovery Plex V4.1 (&gt;7000 SOMAmers/proteins).SettingAn international consortium of 9 academic and 8 commercial partners (STEpUP OA).Participants1746 SF samples from 1650 individuals comprising OA, joint injury, healthy controls and inflammatory arthritis controls, divided into discovery (n=1045) and replication (n=701) datasets.Primary and secondary outcome measuresAn optimised approach to standardisation was developed iteratively, monitoring reliability and precision (comparing coefficient of variation [%CV] of ‘pooled’ SF samples between plates and correlation with prior immunoassay for 9 analytes). Pre-defined technical confounders were adjusted for (by Limma) and batch correction was by ComBat. Poorly performing SOMAmers and samples were filtered. Variance in the data was determined by principal component (PC) analysis. Data were visualised by Uniform Manifold Approximation and Projection (UMAP).ResultsOptimal SF standardisation aligned with that used for plasma, but without median normalisation. There was good reliability (&lt;20 %CV for &gt;80% of SOMAmers in pooled samples) and overall good correlation with immunoassay. PC1 accounted for 48% of variance and strongly correlated with individual SOMAmer signal intensities (median correlation coefficient 0.70). These could be adjusted using an ‘intracellular protein score’. PC2 (7% variance) was attributable to processing batch and was batch-corrected by ComBat. Lesser effects were attributed to other technical confounders. Data visualisation by UMAP revealed clustering of injury and OA cases in overlapping but distinguishable areas of high-dimensional proteomic space.ConclusionsWe define a standardised approach for SF analysis using the SOMAscan platform and identify likely ‘intracellular’ protein as being a major driver of variance in the data.Strengths and limitationsThis is the largest number of individual synovial fluid samples analysed by a high content proteomic platform (SomaLogic technology)SomaScan offers reliable, precise relative SF data following standardisation for over 6000 proteinsSignificant variance in the data was driven by a protein signal which is likely intracellular in origin: it is not yet clear whether this is due to technical considerations, normal cell turnover or relevant pathological processesAdjusting for confounding factors might conceal the true structure of the data and reduce the ability to detect ‘molecular endotypes’ within disease groups

Early detection of cancer is vital for the best chance of successful treatment, but half of all cancers are diagnosed at an advanced stage. A simple and reliable blood screening test applied routinely would therefore address a major unmet medical need. To gain insight into the value of protein biomarkers in early detection and stratification of cancer we determined the time course of changes in the plasma proteome of mice carrying transplanted human lung, breast, colon, or ovarian tumors. For protein measurements we used an aptamer-based assay which simultaneously measures ~ 5000 proteins. Along with tumor lineage-specific biomarkers, we also found 15 markers shared among all cancer types that included the energy metabolism enzymes glyceraldehyde-3-phosphate dehydrogenase, glucose-6-phophate isomerase and dihydrolipoyl dehydrogenase as well as several important biomarkers for maintaining protein, lipid, nucleotide, or carbohydrate balance such as tryptophanyl t-RNA synthetase and nucleoside diphosphate kinase. Using significantly altered proteins in the tumor bearing mice, we developed models to stratify tumor types and to estimate the minimum detectable tumor volume. Finally, we identified significantly enriched common and unique biological pathways among the eight tumor cell lines tested.

BackgroundThe risk for and treatment of cardiovascular disease (CVD) in type 2 diabetes (T2DM) is often incorrect and delayed. We wished to determine if a novel test improved physicians’ ability to risk stratify, diagnose, and treat patients with T2DM.MethodsIn a 2-phase randomized controlled trial comparing the clinical workup, diagnosis, and management of online, simulated patients with T2DM in a nationwide sample of cardiologists and primary care physicians, participants were randomly assigned to control or one of two intervention groups. Intervention participants had access to standard of care diagnostic tools plus a novel diagnostic CVD risk stratification test.ResultsIn control, there was no change in CV risk stratification of simulated patients between baseline and round 2 (37.1 to 38.3%, p = 0.778). Pre-post analysis showed significant improvements in risk stratification in both Intervention 1 (38.7 to 65.3%) and Intervention 2 (41.9 to 65.8%) (p < 0.01) compared to controls. Both intervention groups significantly increased prescribing SGLT2 inhibitors/GLP1 receptor agonists versus control, + 18.9% for Intervention 1 (p = 0.020) and 1 + 9.4% for Intervention 2 (p = 0.014). Non-pharmacologic treatment improved significantly compared to control (+ 30.0% in Intervention 1 (p < 0.001) and + 22.8% in Intervention 2 (p = 0.001). Finally, monitoring HgbA1C, blood pressure, and follow-up visit frequency improved by + 20.3% (p = 0.004) in Intervention 1 and + 29.8% (p < 0.001) in Intervention 2 compared with control.ConclusionUse of the novel test significantly improved CV risk stratification among T2DM patients. Statistically significant increases treatments were demonstrated, specifically SGLT2 inhibitors and GLP1 receptor antagonists and recommendations of evidence-based non-pharmacologic treatments.Trial registration ClinicalTrials.gov, NCT05237271