Lynch, W. | bioRxiv Science

bioRxiv2024

Exercise-stimulated Resolvin Biosynthesis in Adipose Tissue is Abrogated by High Fat Diet-induced Adrenergic Deficiency

ObjectiveDiet-induced white adipose tissue inflammation is associated with insulin resistance and metabolic perturbations. Conversely, exercise (Exe) protects against the development of chronic inflammation and insulin resistance independent of changes in weight; however, the mechanisms remain largely unknown. We have recently shown that, through adrenergic stimulation of macrophages, exercise promotes resolution of acute peritoneal inflammation by enhancing the biosynthesis of specialized pro-resolving lipid mediators (SPMs). In this study, we sought to determine if exercise stimulates pro-resolving pathways in adipose tissue and whether this response is modified by diet. Specifically, we hypothesized that high fat diet feeding disrupts exercise-stimulated resolution by inhibiting adrenergic signaling, priming the development of chronic inflammation in adipose tissue (AT). Approach and ResultsTo explore the dietary dependence of the pro-resolving effects of Exe, mice were fed either a control or high-fat diet (HFD) for 2 weeks prior to, and throughout, a 4 wk period of daily treadmill running. Glucose handling, body weight and composition, and exercise performance were evaluated at the end of the feeding and exercise interventions. Likewise, catecholamines and their biosynthetic enzymes were measured along with AT SPM biosynthesis and macrophage phenotype and abundance. When compared with sedentary controls (Sed), macrophages isolated from mice exposed to 4 wk of exercise display elevated expression of the SPM biosynthetic enzyme Alox15, while whole AT SPM levels and anti-inflammatory CD301+ M2 macrophages increased. These changes were dependent upon diet as 6 wk of feeding with HFD abrogated the pro-resolving effect of exercise when compared with control diet-fed animals. Interestingly, exercise-induced epinephrine production was inhibited by HFD, which diminished expression of the epinephrine biosynthetic enzyme phenylethanolamine N-methyltransferase (PNMT) in adrenal glands. ConclusionTaken together, these results suggest that a diet high in fat diminishes the pro-resolving effects of exercise in adipose tissue via decreasing the biosynthesis of catecholamines. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=192 SRC="FIGDIR/small/608014v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@1b508e7org.highwire.dtl.DTLVardef@5b9d8corg.highwire.dtl.DTLVardef@c46707org.highwire.dtl.DTLVardef@48083e_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2019

A machine learning approach to identifying objectivebiomarkers of anxiety and stress

Anxiety and stress-related disorders are highly prevalent and debilitating conditions that impose an enormous burden on society. Sensitive measurements that can enable early diagnosis could mitigate suffering and potentially prevent onset of these conditions. Self-reports, however, are intrusive and vulnerable to biases that can conceal the true internal state. Physiological responses, on the other hand, manifest spontaneously and can be monitored continuously, providing potential objective biomarkers for anxiety and stress. Recent studies have shown that algorithms trained on physiological measurements can predict stress states with high accuracy. Whether these predictive algorithms generalize to untested situations and participants, however, remains unclear. Further, whether biomarkers of momentary stress indicate trait anxiety - a vulnerability foreshadowing development of anxiety and mood disorders - remains unknown. To address these gaps, we monitored skin conductance, heart rate, heart rate variability and EEG in 39 participants experiencing physical and social stress and compared these measures to non-stressful periods of talking, rest, and playing a simple video game. Self-report measures were obtained periodically throughout the experiment. A support vector machine trained on physiological measurements identified stress conditions with ~96% accuracy. A decision tree that optimally combined physiological and self-report measures identified individuals with high trait anxiety with ~84% accuracy. Individuals with high trait anxiety also displayed high baseline state anxiety but a muted physiological response to acute stressors. Overall, these results demonstrate the potential for using machine learning tools to identify objective biomarkers useful for diagnosing and monitoring mental health conditions like anxiety and depression.

neuroscience↗