The relationship between glycemic variability and blood pressure variability in normoglycemic normotensive individuals

Glycemic variability assessed using continuous glucose monitoring in individuals without diabetes and associations with cardiometabolic risk markers: A systematic review and meta-analysis

BACKGROUND & AIMS

Continuous glucose monitoring (CGM) provides data on short-term glycemic variability (GV). GV is associated with adverse outcomes in individuals with diabetes. Whether GV is associated with cardiometabolic risk in individuals without diabetes is unclear. We systematically reviewed the literature to assess whether GV is associated with cardiometabolic risk markers or outcomes in individuals without diabetes.

METHODS

Searches were performed in PubMed/Medline, Embase and Cochrane from inception through April 2022. Two researchers were involved in study selection, data extraction and quality assessment. Studies evaluating GV using CGM for ≥24 h were included. Studies in populations with acute and/or critical illness were excluded. Both narrative synthesis and meta-analyzes were performed, depending on outcome.

RESULTS

Seventy-one studies were included; the majority were cross-sectional. Multiple measures of GV are higher in individuals with compared to without prediabetes and GV appears to be inversely associated with beta cell function. In contrast, GV is not clearly associated with insulin sensitivity, fatty liver disease, adiposity, blood lipids, blood pressure or oxidative stress. However, GV may be positively associated with the degree of atherosclerosis and cardiovascular events in individuals with coronary disease.

CONCLUSION

GV is elevated in prediabetes, potentially related to beta cell dysfunction, but less clearly associated with obesity or traditional risk factors. GV is associated with coronary atherosclerosis development and may predict cardiovascular events and type 2 diabetes. Prospective studies are warranted, investigating the predictive power of GV in relation to incident disease. GV may be an important risk measure also in individuals without diabetes.

Trends in Passive IoT Biomarker Monitoring and Machine Learning for Cardiovascular Disease Management in the U.S. Elderly Population

It is predicted that the growth in the U.S. elderly population alongside continued growth in chronic disease prevalence will further strain an already overburdened healthcare system and could compromise the delivery of equitable care. Current trends in technology are demonstrating successful application of artificial intelligence (AI) and machine learning (ML) to biomarkers of cardiovascular disease (CVD) using longitudinal data collected passively from internet-of-things (IoT) platforms deployed among the elderly population. These systems are growing in sophistication and deployed across evermore use-cases, presenting new opportunities and challenges for innovators and caregivers alike. IoT sensor development that incorporates greater levels of passivity will increase the likelihood of continued growth in device adoption among the geriatric population for longitudinal health data collection which will benefit a variety of CVD applications. This growth in IoT sensor development and longitudinal data acquisition is paralleled by the growth in ML approaches that continue to provide promising avenues for better geriatric care through higher personalization, more real-time feedback, and prognostic insights that may help prevent downstream complications and relieve strain on the healthcare system overall. However, findings that identify differences in longitudinal biomarker interpretations between elderly populations and relatively younger populations highlights the necessity that ML approaches that use data from newly developed passive IoT systems should collect more data on this target population and more clinical trials will help elucidate the extent of benefits and risks from these data driven approaches to remote care.

Oscillating Glucose Induces the Increase in Inflammatory Stress through Ninjurin-1 Up-Regulation and Stimulation of Transport Proteins in Human Endothelial Cells

Clinical data implicate fluctuations of high levels of plasma glucose in cardiovascular diseases. Endothelial cells (EC) are the first cells of the vessel wall exposed to them. Our aim was to evaluate the effects of oscillating glucose (OG) on EC function and to decipher new molecular mechanisms involved. Cultured human ECs (EA.hy926 line and primary cells) were exposed to OG (5/25 mM alternatively at 3 h), constant HG (25 mM) or physiological concentration (5 mM, NG) for 72 h. Markers of inflammation (Ninj-1, MCP-1, RAGE, TNFR1, NF-kB, and p38 MAPK), oxidative stress (ROS, VPO1, and HO-1), and transendothelial transport proteins (SR-BI, caveolin-1, and VAMP-3) were assessed. Inhibitors of ROS (NAC), NF-kB (Bay 11-7085), and Ninj-1 silencing were used to identify the mechanisms of OG-induced EC dysfunction. The results revealed that OG determined an increased expression of Ninj-1, MCP-1, RAGE, TNFR1, SR-B1, and VAMP-3 andstimulated monocyte adhesion. All of these effects were induced bymechanisms involving ROS production or NF-kB activation. NINJ-1 silencing inhibited the upregulation of caveolin-1 and VAMP-3 induced by OG in EC. In conclusion, OG induces increased inflammatory stress, ROS production, and NF-kB activation and stimulates transendothelial transport. To this end, we propose a novel mechanism linking Ninj-1 up-regulation to increased expression of transendothelial transport proteins.

Relationship between Short- and Mid-Term Glucose Variability and Blood Pressure Profile Parameters: A Scoping Review

Background. Increased variability of glucose (GV) and blood pressure (BPV) is linked to a higher risk of macro- and microvascular complications and other hard endpoints. This scoping review aims to summarize the existing evidence regarding the association between the parameters of the blood pressure (BP) profile, especially BPV, with indices of short- and mid-term GV. Methods. A literature search was conducted in the MEDLINE/PubMed, Cochrane, Embase, Web of Science, and Wiley Online Library databases. Results. The main findings of this review are as follows: (i) 13 studies were included, mainly with small sample sizes; (ii) there was a considerable degree of heterogeneity in the characteristics of the study participants (age range, individuals with normoglycemia, type 1 or 2 diabetes, normal BP, or hypertension), as well as in the methodologies (mainly in terms of the duration of the data collection period) and variability indices examined (mean amplitude of glycemic excursions and coefficient of glucose variation most frequently reported); and (iii) the results were heterogeneous regarding the association between GV and the parameters of the BP profile. Conclusions. There is a significant lack of evidence on the association between GV and BPV. Future research implementing a standardized methodology should focus on the determinants, association, and clinical relevance of GV and BPV.