Abstract:Background: Considering the close link between metabolic syndrome (MetSyn) and cardiovascular diseases, considerable attention has been devoted to the identification of their shared underlying pathological mechanisms in recent decades. Objectives: This study aimed to investigate the association between pro-inflammatory factors and newly-diagnosed MetSyn. Methods: This case-control study recruited obese and nonobese individuals who were newly diagnosed with MetSyn (cases, n = 84) and healthy individuals (contro… Show more
“…Different studies have concluded that low values in VLF are related to worse clinical outcomes in different pathological conditions, providing even greater prognostic value than LF or HF [13,[42][43][44]. Likewise, an inverse relationship between VLF and proinflammatory states has been reported in several studies [45][46][47], which could be related with MS, since it is considered a condition which coexists with a low-grade chronic inflammatory processes [47].…”
Background: Our aim was to determine the impact that metabolic syndrome (MS) produces in long-term heart rate variability (HRV), quantitatively synthesizing the results of published studies to characterize the cardiac autonomic dysfunction in MS. Methods: We searched electronic databases for original research works with long-term HRV recordings (24 h) that compared people with MS (MS+) versus healthy people as a control group (MS−). This systematic review and meta-analysis (MA) was performed according to PRISMA guidelines and registered at PROSPERO (CRD42022358975). Results: A total of 13 articles were included in the qualitative synthesis, and 7 of them met the required criteria to be included in the MA. SDNN (−0.33 [−0.57, 0.09], p = 0.008), LF (−0.32 [−0.41, −0.23], p < 0.00001), VLF (−0.21 [−0.31, −0.10], p = 0.0001) and TP (−0.20 [−0.33, −0.07], p = 0.002) decreased in patients with MS. The rMSSD (p = 0.41), HF (p = 0.06) and LF/HF ratio (p = 0.64) were not modified. Conclusions: In long-term recordings (24 h), SDNN, LF, VLF and TP were consistently decreased in patients with MS. Other parameters that could be included in the quantitative analysis were not modified in MS+ patients (rMSSD, HF, ratio LF/HF). Regarding non-linear analyses, the results are not conclusive due to the low number of datasets found, which prevented us from conducting an MA.
“…Different studies have concluded that low values in VLF are related to worse clinical outcomes in different pathological conditions, providing even greater prognostic value than LF or HF [13,[42][43][44]. Likewise, an inverse relationship between VLF and proinflammatory states has been reported in several studies [45][46][47], which could be related with MS, since it is considered a condition which coexists with a low-grade chronic inflammatory processes [47].…”
Background: Our aim was to determine the impact that metabolic syndrome (MS) produces in long-term heart rate variability (HRV), quantitatively synthesizing the results of published studies to characterize the cardiac autonomic dysfunction in MS. Methods: We searched electronic databases for original research works with long-term HRV recordings (24 h) that compared people with MS (MS+) versus healthy people as a control group (MS−). This systematic review and meta-analysis (MA) was performed according to PRISMA guidelines and registered at PROSPERO (CRD42022358975). Results: A total of 13 articles were included in the qualitative synthesis, and 7 of them met the required criteria to be included in the MA. SDNN (−0.33 [−0.57, 0.09], p = 0.008), LF (−0.32 [−0.41, −0.23], p < 0.00001), VLF (−0.21 [−0.31, −0.10], p = 0.0001) and TP (−0.20 [−0.33, −0.07], p = 0.002) decreased in patients with MS. The rMSSD (p = 0.41), HF (p = 0.06) and LF/HF ratio (p = 0.64) were not modified. Conclusions: In long-term recordings (24 h), SDNN, LF, VLF and TP were consistently decreased in patients with MS. Other parameters that could be included in the quantitative analysis were not modified in MS+ patients (rMSSD, HF, ratio LF/HF). Regarding non-linear analyses, the results are not conclusive due to the low number of datasets found, which prevented us from conducting an MA.
“…Meanwhile, modi ed LDLs had the ability to activate the toll-like receptors, thereby priming the Nod-like receptor protein 3 in ammasomes and ultimately lead to the activation of interleukin-1β and secondary in ammatory responses [19]. In newly diagnosed patients with metabolic syndrome, there were associations between TNF-α and fasting blood glucose (r = 0.179, P = 0.021), LDL-C (r = 0.199, P = 0.01), atherogenic index (r = 0.219, P = 0.004), TG (r = 0.351, P < 0.001), and HDL-C (r = -0.244, P = 0.001) [20]. Among individuals without severe cardiovascular risks, there was a positive association between serum TG and high-sensitivity C-reactive protein (CRP) (r = 0.298, P < 0.001) [21].…”
Object: To investigate the possible association between pan-immune-inflammation value (PIV) and hyperlipidemia.
Methods: The authors selected the relevant data from National Health and Nutrition Examination Survey (NHANES) for a detailed cross-sectional study. The independent variable used the logarithmic form of PIV-log10 (PIV). The definition of dependent variable-hyperlipidemiawas based on the National Cholesterol Education Program standards. Both variables were calculated from measured laboratory data. Weighted multivariate logistic regression analyses and restricted cubic splines (RCS) were conducted to analyze the association between PIV and hyperlipidemia. Stratified analyses were used to identify potential associations between PIV and hyperlipidemia with other covariates. The study also constructed the receiver operating characteristic (ROC) curve to assess the predictive value for hyperlipidemia of PIV compared to systemic immune-inflammation index (SII).
Results: In the study, 7,715 participants from NHANES were included. After adjusting for all confounders, PIV and hyperglycemia had an significantly positive association (OR (95%CI): 1.55 (1.17-2.06); P = 0.002). Compared to participants with lowest quartile (Q1) of PIV, participants with the highest quartile (Q4) had a significantly higher risk of hyperlipidemia (OR (95%CI): 1.47 (1.21-1.79); P < 0.001). The RCS curve showed a linear relationship between PIV and hyperlipidemia (P-nonlinear = 0.0633, P-overall < 0.001). The ROC curve found that compared with SII, PIV had a slightly higher predictive value (0.547 vs 0.542, P = 0.267).
Conclusion: This national cross-sectional study discovered that PIV had a significantly positive relationship with hyperlipidemia, particularly in young overweight individuals. More prospective studies are needed to verify whether the PIV is a more reliable and effective index for assessing the risk of hyperlipidemia.
“…More recent reports indicate that tau protein/β-amyloid ratio increases in the cerebro spinal fluid after a surgical procedure, independent of the class of anesthetic used, additionally raising doubt about the foretelling quality of the biomarkers [1,14]. Chronic inflammatory disorders such as diabetes mellitus, metabolic syndrome or atherosclerosis have been pinpointed as favouring factors for postoperative cognitive decline, as well as anesthetic drugs, duration of the surgical procedure and pain [15,16]. No significant difference was found between emergency or elective surgery settings in regards to the incidence of early postoperative cognitive decline [17].…”
Postoperative neurocognitive impairments following surgery are a growing concern, especially in the elderly population, since it is associated with a significantly increased risk of morbi-mortality in the postoperative period. Among them, delirium or the early postoperative cognitive decline is associated with a further risk of prolonged cognitive dysfunction and it may quicken long-term cognitive impairment or postoperative cognitive dysfunction (POCD). The current knowledge regarding preventive strategies for delirium is not focused anymore only on pharmacological and behavioral management strategies in the postoperative period, but also supports the preoperative cognitive training programs. Since preoperative cognitive evaluation and proactive interventions to optimize surgical patient outcomes are rather impossible in the emergency setting, what are the appropriate preventive strategies that can be implemented in day-to-day practice? In this review, we try to highlight the most recent experimental and clinical strategies, and outline the most relevant recommendations for clinicial practicioners based on the available data.
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