Proteomic Indicators of Health Predict Alzheimer's Disease Biomarker Levels and Dementia Risk.

OBJECTIVE: 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. METHODS: 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-ß [Aß]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 Aß 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. RESULTS: Greater protein-based risk for cardiovascular disease, heart failure mortality, and kidney disease was associated with lower Aß42/40 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 Aß42/40 , 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. INTERPRETATION: 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.

Proteomic signatures for identification of impaired glucose tolerance.

The implementation of recommendations for type 2 diabetes (T2D) screening and diagnosis focuses on the measurement of glycated hemoglobin (HbA1c) and fasting glucose. This approach leaves a large number of individuals with isolated impaired glucose tolerance (iIGT), who are only detectable through oral glucose tolerance tests (OGTTs), at risk of diabetes and its severe complications. We applied machine learning to the proteomic profiles of a single fasted sample from 11,546 participants of the Fenland study to test discrimination of iIGT defined using the gold-standard OGTTs. We observed significantly improved discriminative performance by adding only three proteins (RTN4R, CBPM and GHR) to the best clinical model (AUROC = 0.80 (95% confidence interval: 0.79-0.86), P = 0.004), which we validated in an external cohort. Increased plasma levels of these candidate proteins were associated with an increased risk for future T2D in an independent cohort and were also increased in individuals genetically susceptible to impaired glucose homeostasis and T2D. Assessment of a limited number of proteins can identify individuals likely to be missed by current diagnostic strategies and at high risk of T2D and its complications.

A proteomic surrogate for cardiovascular outcomes that is sensitive to multiple mechanisms of change in risk.

A reliable, individualized, and dynamic surrogate of cardiovascular risk, synoptic for key biologic mechanisms, could shorten the path for drug development, enhance drug cost-effectiveness and improve patient outcomes. We used highly multiplexed proteomics to address these objectives, measuring about 5000 proteins in each of 32,130 archived plasma samples from 22,849 participants in nine clinical studies. We used machine learning to derive a 27-protein model predicting 4-year likelihood of myocardial infarction, stroke, heart failure, or death. The 27 proteins encompassed 10 biologic systems, and 12 were associated with relevant causal genetic traits. We independently validated results in 11,609 participants. Compared to a clinical model, the ratio of observed events in quintile 5 to quintile 1 was 6.7 for proteins versus 2.9 for the clinical model, AUCs (95% CI) were 0.73 (0.72 to 0.74) versus 0.64 (0.62 to 0.65), c-statistics were 0.71 (0.69 to 0.72) versus 0.62 (0.60 to 0.63), and the net reclassification index was +0.43. Adding the clinical model to the proteins only improved discrimination metrics by 0.01 to 0.02. Event rates in four predefined protein risk categories were 5.6, 11.2, 20.0, and 43.4% within 4 years; median time to event was 1.71 years. Protein predictions were directionally concordant with changed outcomes. Adverse risks were predicted for aging, approaching an event, anthracycline chemotherapy, diabetes, smoking, rheumatoid arthritis, cancer history, cardiovascular disease, high systolic blood pressure, and lipids. Reduced risks were predicted for weight loss and exenatide. The 27-protein model has potential as a "universal" surrogate end point for cardiovascular risk.

The Golden Retriever Lifetime Study: Assessing factors associated with owner compliance after the first year of enrollment.

BACKGROUND: The Golden Retriever Lifetime Study (GRLS) is one of the largest canine cohort studies undertaken in the United States to date. This study design allows for evaluation of multiple exposures and outcomes throughout the lifetime of each dog, but relies on participants to comply with study requirements over a long period of time. Failure to do so can lead to biased reporting of results. OBJECTIVES: To examine factors associated with dog owner compliance for GRLS. ANIMALS: Golden Retrievers (n = 3044) whose owners elected to participate in GRLS. METHODS: Prospective, cohort study. A logistic regression model was constructed to examine associations between data collected at the time of initial enrollment in GRLS and the outcome of failure to fulfill all study obligations at the end of the first year after enrollment in GRLS. RESULTS: There were 192 (6.3%) owners who did not comply with study requirements 1 year after enrollment. Owners of dogs without a record of vaccination had nearly 4 times higher odds (adjusted OR: 3.7, 95% CI: 1.5, 9.2) of being noncompliant than owners of vaccinated dogs and owners of dogs that slept in the garage had nearly 6 times higher odds (adjusted OR: 5.7, 95% CI: 1.9, 17.0) of being noncompliant than owners of dogs that slept in their bedroom. CONCLUSIONS AND CLINICAL IMPORTANCE: Survey questions about a dog's sleeping location at night and vaccination status are important indicators of an owner's odds of compliance in a prospective study. Use of similar questions during enrollment in cohort studies might help to predict owner compliance that can aid in subject selection.

Inbreeding depression causes reduced fecundity in Golden Retrievers.

Inbreeding depression has been demonstrated to impact vital rates, productivity, and performance in human populations, wild and endangered species, and in recent years, the domestic species. In all cases, standardized, high-quality phenotype data on all individuals are invaluable for longitudinal analyses such as those required to evaluate vital rates of a study cohort. Further, many investigators agree upon the preference for and utility of genomic measures of inbreeding in lieu of pedigree-based estimates of inbreeding. We evaluated the association of measures of reproductive fitness in 93 Golden Retrievers enrolled in the Golden Retriever Lifetime Study with a genomic measurement of inbreeding, FROH. We demonstrate a statistically significant negative correlation between fecundity and FROH. This work sets the stage for larger scale analyses to investigate genomic regions associated with fecundity and other measures of fitness.