The relationship between circulating irisin, retinol binding protein-4, adiponectin and inflammatory mediators in patients with metabolic syndrome

Objective: We wanted to investigate whether there is a relationship between circulating irisin, retinol binding protein-4 (RBP-4), adiponectin and proinflammatory mediators implicated in the development of insulin resistance (IR) in metabolic syndrome (MetS). Subjects and methods: In 180 individuals, including controls and patients with MetS, we measured fasting plasma insulin, high sensitivity C-reactive protein (hsCRP), pentraxin-3 (PTX-3), interleukin-33 (IL-33), irisin, RBP-4, and adiponectin using ELISA kits. Results: While fasting plasma hsCRP, PTX-3, IL-33, irisin, RBP-4 concentrations were higher, adiponectin levels were lower in patients with MetS than in controls. A correlation analysis revealed that plasma irisin levels were positively associated with MetS components such as waist circumference and waist-hip ratio, low density lipoprotein (LDL) and markers of systemic inflammation such as PTX-3, hsCRP, uric acid, and RBP-4. Adiponectin levels were negatively associated with waist circumference, waist-hip ratio, PTX-3 and LDL. Conclusions: Although the precise mechanisms are still unclear, irisin, RBP-4, adiponectin and PTX-3 are hallmarks of the MetS, which is related to low-grade inflammation. It is conceivable that irisin and adiponectin might contribute to the development of MetS and may also represent novel MetS components. Future clinical studies are needed to confirm and extend these data

Optimization of Neurite Tracing and Further Characterization of Human Monocyte-Derived-Neuronal-like Cells

Deficits in neuronal structure are consistently associated with neurodevelopmental illnesses such as autism and schizophrenia. Nonetheless, the inability to access neurons from clinical patients has limited the study of early neurostructural changes directly in patients’ cells. This obstacle has been circumvented by differentiating stem cells into neurons, although the most used methodologies are time consuming. Therefore, we recently developed a relatively rapid (~20 days) protocol for transdifferentiating human circulating monocytes into neuronal-like cells. These monocyte-derived-neuronal-like cells (MDNCs) express several genes and proteins considered neuronal markers, such as MAP-2 and PSD-95. In addition, these cells conduct electrical activity. We have also previously shown that the structure of MDNCs is comparable with that of human developing neurons (HDNs) after 5 days in culture. Moreover, the neurostructure of MDNCs responds similarly to that of HDNs when exposed to colchicine and dopamine. In this manuscript, we expanded our characterization of MDNCs to include the expression of 12 neuronal genes, including tau. Following, we compared three different tracing approaches (two semi-automated and one automated) that enable tracing using photographs of live cells. This comparison is imperative for determining which neurite tracing method is more efficient in extracting neurostructural data from MDNCs and thus allowing researchers to take advantage of the faster yield provided by these neuronal-like cells. Surprisingly, it was one of the semi-automated methods that was the fastest, consisting of tracing only the longest primary and the longest secondary neurite. This tracing technique also detected more structural deficits. The only automated method tested, Volocity, detected MDNCs but failed to trace the entire neuritic length. Other advantages and disadvantages of the three tracing approaches are also presented and discussed.</jats:p

178 Sleep Disparities in Adolescent Women: Role of Pubertal Development, Menstrual Cycle and Premenstrual Symptoms

Abstract Introduction About 20-30% of children experience sleep difficulties and about 50-60% of those persist into adolescence. Sex differences in sleep become more apparent after the onset of puberty, suggesting a role for maturational changes in the sleep of males and females. In addition, adolescent females experience greater sleep difficulties with the advancement of pubertal stages and around the time of menstruation. Although adult studies have shown sex differences in sleep continuity and architecture, there is a large gap of knowledge in adolescents. Methods We analyzed data from the Penn State Child Cohort, a random, population-based sample of 421 adolescents (16.5±2.3y, 53.9% male) who underwent one-night in-lab polysomnography (PSG) and seven-night at-home actigraphy (ACT) as well as a thorough physical exam and clinical history, including self-reports of Tanner staging, menstrual cycle and use of oral contraceptives (OC). Results Upon PSG, females had a longer sleep latency (p&amp;lt;0.05), while males a greater number of awakenings (p&amp;lt;0.05), longer wake after sleep onset (p&amp;lt;0.01) and greater stage N1 (p&amp;lt;0.05). Per ACT, females had longer total sleep time and greater sleep efficiency (p&amp;lt;0.01). Sex differences in PSG and ACT parameters were more prominent among adolescents reporting Tanner stages 4-5, including females having greater stage N3 than males (p&amp;lt;0.01) and females reporting premenstrual symptoms (PMS) having a longer sleep latency than males or than those not reporting PMS (P&amp;lt;0.05). Among females, those who had their last period 8-14 days prior to the PSG had a shorter sleep latency than those who had their period within the previous 7 days (p&amp;lt;0.01) or 15-25 days before (p&amp;lt;0.05). Females using OC (n=38) did not show significantly different PSG or ACT parameters than those not using OC (n=156). Conclusion Our study provides evidence for sex-related health disparities in objective sleep arising in adolescence. Females sleep objectively better than males from a sleep continuity and sleep architecture perspective, particularly when examined at the same pubertal stage. Additionally, sleep onset is significantly impacted by the menstrual cycle and associated symptoms but females preserve greater levels of deep sleep, a sign of sleep resilience. Support (if any) NIH Awards Number R01MH118308, R01HL136587, R01HL97165, R01HL63772, UL1TR000127 </jats:sec

Optimization of neurite tracing and further characterization of human monocyte-derived-neuronal-like cells

Deficits in neuronal structure are consistently associated with neurodevelopmental illnesses such as autism and schizophrenia. Nonetheless, the inability to access neurons from clinical patients has limited the study of early neurostructural changes directly in patients’ cells. This obstacle has been circumvented by differentiating stem cells into neurons, although the most used methodologies are time consuming. Therefore, we recently developed a relatively rapid (~20 days) protocol for transdifferentiating human circulating monocytes into neuronal-like cells. These monocyte-derived-neuronal-like cells (MDNCs) express several genes and proteins considered neuronal markers, such as MAP-2 and PSD-95. In addition, these cells conduct electrical activity. We have also previously shown that the structure of MDNCs is comparable with that of human developing neurons (HDNs) after 5 days in culture. Moreover, the neurostructure of MDNCs responds similarly to that of HDNs when exposed to colchicine and dopamine. In this manuscript, we expanded our characterization of MDNCs to include the expression of 12 neuronal genes, including tau. Following, we compared three different tracing approaches (two semi-automated and one automated) that enable tracing using photographs of live cells. This comparison is imperative for determining which neurite tracing method is more efficient in extracting neurostructural data from MDNCs and thus allowing researchers to take advantage of the faster yield provided by these neuronal-like cells. Surprisingly, it was one of the semi-automated methods that was the fastest, consisting of tracing only the longest primary and the longest secondary neurite. This tracing technique also detected more structural deficits. The only automated method tested, Volocity, detected MDNCs but failed to trace the entire neuritic length. Other advantages and disadvantages of the three tracing approaches are also presented and discussed