Inflammation Mediates Body Weight and Ageing Effects on Psychomotor Slowing

Optimizing human performance in extreme environments through precision medicine: From spaceflight to high-performance operations on Earth

Humans operating in extreme environments often conduct their operations at the edges of the limits of human performance. Sometimes, they are required to push these limits to previously unattained levels. As a result, their margins for error in execution are much smaller than that found in the general public. These same small margins for error that impact execution may also impact risk, safety, health, and even survival. Thus, humans operating in extreme environments have a need for greater refinement in their preparation, training, fitness, and medical care. Precision medicine (PM) is uniquely suited to address the needs of those engaged in these extreme operations because of its depth of molecular analysis, derived precision countermeasures, and ability to match each individual (and his or her specific molecular phenotype) with any given operating context (environment). Herein, we present an overview of a systems approach to PM in extreme environments, which affords clinicians one method to contextualize the inputs, processes, and outputs that can form the basis of a formal practice. For the sake of brevity, this overview is focused on molecular dynamics, while providing only a brief introduction to the also important physiologic and behavioral phenotypes in PM. Moreover, rather than a full review, it highlights important concepts, while using only selected citations to illustrate those concepts. It further explores, by demonstration, the basic principles of using functionally characterized molecular networks to guide the practical application of PM in extreme environments. At its core, PM in extreme environments is about attention to incremental gains and losses in molecular network efficiency that can scale to produce notable changes in health and performance. The aim of this overview is to provide a conceptual overview of one approach to PM in extreme environments, coupled with a selected suite of practical considerations for molecular profiling and countermeasures.

Longitudinal Body Weight Change, Visit-To-Visit Body Weight Fluctuation, and Cognitive Decline Among Older Adults

BACKGROUND

The evidence regarding dementia and late-life weight change is inconsistent, and data on body weight fluctuation and dementia are limited.

OBJECTIVE

To test the hypothesis that weight loss and substantial weight fluctuation predict cognitive decline independent of body weight and traditional risk factors of dementia.

METHODS

This study utilized longitudinal data from the National Alzheimer's Coordinating Center for 10,639 stroke- and dementia-free older adults (60.9%female, mean age 71.6 years, median follow-up 5.5 years). Trends in weight change and weight fluctuation were estimated for each individual by regressing repeated body weight measurements on time. Cognitive decline was examined as diagnostic progression from normal to mild cognitive impairment (MCI) or dementia and from MCI to dementia.

RESULTS

Compared to participants with stable weight, those with weight loss had increased odds of diagnostic progression (adjusted OR = 1.35, 95%CI [1.21, 1.51]). Also, large weight fluctuation was associated with increased odds of diagnostic progression (OR comparing the extreme quartiles = 1.20, 95%CI [1.04, 1.39]) after adjusting for traditional risk factors for dementia and body weight change. The magnitude of the association appeared larger among those older than 80 and those with 3 or more cardiometabolic risk factors at baseline (both p for interaction < 0.05).

CONCLUSION

Weight loss and substantial weight fluctuation during late-life were associated with increased odds of cognitive decline independent of body weight and traditional risk factors of dementia. Our results suggested the linkage between late-life body weight instability and cognitive decline especially among those with greater age or higher cardiometabolic risk.

Proteomic Profiling of Extracellular Vesicles Separated from Plasma of Former National Football League Players at Risk for Chronic Traumatic Encephalopathy

Chronic Traumatic Encephalopathy (CTE) is a tauopathy that affects individuals with a history of exposure to repetitive head impacts, including National Football League (NFL) players. Extracellular vesicles (EVs) are known to carry tau in Alzheimer's disease and other tauopathies. We examined protein profiles of EVs separated from the plasma of former NFL players at risk for CTE. EVs were separated from the plasma from former NFL players and age-matched controls using size-exclusion chromatography. Label-free quantitative proteomic analysis identified 675 proteins in plasma EVs, and 17 proteins were significantly differentially expressed between former NFL players and controls. Total tau (t-tau) and tau phosphorylated at threonie181 (p-tau181) in plasma-derived EVs were measured by ultrasensitive immunoassay. Level of t-tau and p-tau181 in EVs were significantly different, and the area under the receiver operating characteristic curve (AUC) of t-tau and p-tau181 showed 0.736 and 0.715, respectively. Machine learning analysis indicated that a combination of collagen type VI alpha 3 and 1 chain (COL6A3 and COL6A1) and reelin (RELN) can distinguish former NFL players from controls with 85% accuracy (AUC = 0.85). Based on the plasma EV proteomics, these data provide protein profiling of plasma EVs for CTE, and indicate combination of COL6A3, RELN and COL6A1 in plasma EVs may serve as the potential diagnostic biomarkers for CTE.

Age, BMI, and inflammation: Associations with emotion recognition

Experimental studies show that inflammation impairs the ability to interpret the mental state of another person, denoted theory of mind (ToM). The current study attempted a conceptual replication in states associated with elevated low-grade inflammation, i.e., high body weight and advanced age. Ninety young (M = 26.3 years, SD = 4.1) or older (M = 70.7 years, SD = 4.0) participants with either a normal body mass index (BMI) (M = 22.4, SD = 2.2) or high BMI (M = 33.1, SD = 3.8) completed the Reading the Mind in the Eyes Test (RMET) to assess emotion recognition. Plasma interleukin-6 (IL-6) level was measured to index low-grade inflammation. As anticipated, elevated IL-6 levels were found with higher BMI, although not with increased age. IL-6 was associated with poorer task performance, independent of potential demographic and health confounders (e.g., sex, education, smoking status, alcohol intake, presence of medical conditions, and medication intake). Analyses also revealed an interaction whereby young individuals with a high BMI showed worse RMET performance compared to their normal BMI counterparts, whereas the opposite pattern was found in older individuals. The present observational study replicated experimental results showing that elevated low-grade inflammation is correlated with a lower ability to infer the mental states of others. These findings suggest that also naturalistic conditions of (protracted) low-grade inflammation may alter emotion recognition.

Skeletal Muscle Density and Cognitive Function: A Cross-Sectional Study in Men

We aimed to investigate cross-sectional associations between skeletal muscle density, a proxy measure for fatty infiltration into muscle, and cognition. Contributions from body fat mass, systemic inflammation and lifestyle were explored, as these factors have been identified in both muscle and cognitive deterioration. For 281 men (60-95 year) from the Geelong Osteoporosis Study, radial and tibial muscle density were measured using peripheral quantitative computed tomography. Body fat and appendicular lean mass were measured using dual-energy X-ray absorptiometry. Cognitive function was assessed for psychomotor function (DET), visual identification/attention (IDN), visual learning (OCL) and working memory (OBK) (CogState Brief Battery). Composite scores signified overall cognitive function (OCF). Higher scores represent poorer performance except for OCL and OCF. Regression analyses examined associations between muscle density and cognition; potential confounders included age, muscle cross-sectional area (CSA), body composition, lifestyle and serum markers of inflammation. Negative associations with age were evident for muscle density, all cognitive domains and OCF. Muscle density at both sites was positively associated with DET, OCL and OCF. After adjustment for age, the association persisted for DET (radius: B =  - 0.006, p = 0.02; tibia: B =  - 0.003, p = 0.04) and OCL (radius B =  + 0.004, p = 0.02; tibia: B =  + 0.005, p < 0.001). At the radius, further adjustment for serum TNF-α explained the association between muscle density (B =  - 0.002, p = 0.66) and DET. Education and physical activity contributed to the model for radial muscle density and DET. There were no contributions from muscle CSA, appendicular lean mass, body fat mass, other markers of inflammation or other potential confounders. At the tibia, the association between muscle density and DET (B =  - 0.003, p = 0.04) was independent of TNF-α. There was an age-adjusted association between muscle density and OCL at both sites (radius: B =  + 0.004, p = 0.02; tibia: B =  + 0.005, p < 0.001). None of the potential confounders contributed to the models. Muscle density was associated with cognitive function in the DET and OCL domains. However, there was little evidence that this was explained by inflammation or body fat mass. No associations were identified between muscle density and IDN or OBK.