Masked hypertension and its associations with glycemic variability metrics in children and adolescents with type 1 diabetes

Masked Hypertension in Healthy Children and Adolescents: Who Should Be Screened?

PURPOSE OF REVIEW

The goal is to review masked hypertension (MH) as a relatively new phenomenon when patients have normal office BP but elevated out-of-office BP. Firstly, it was described in children in 2004. It has received increased attention in the past decade.

RECENT FINDINGS

The prevalence of MH in different pediatric populations differs widely between 0 and 60% based on the population studied, definition of MH, or method of out-of-office BP measurement. The highest prevalence of MH has been demonstrated in children with chronic kidney disease (CKD), obesity, diabetes, and after heart transplantation. In healthy children but with risk factors for hypertension such as prematurity, overweight/obesity, diabetes, chronic kidney disease, or positive family history of hypertension, the prevalence of MH is 9%. In healthy children without risk factors for hypertension, the prevalence of MH is very low ranging 0-3%. In healthy children, only patients with the following clinical conditions should be screened for MH: high-normal/elevated office BP, positive family history of hypertension, and those referred for suspected hypertension who have normal office BP in the secondary/tertiary center.

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.

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.

The many faces of hypertension in individuals with type 1 diabetes

Several disturbed blood pressure (BP) patterns, including disparities between office and out-of-office BP measurements (such as white-coat and masked hypertension), disturbed circadian BP variability (such as abnormal dipping patterns and nocturnal hypertension) and treatment-resistant hypertension, are common in individuals with type 1 diabetes. Consequently, office or home BP measurements alone may not reflect real BP variation and may lead to inadequate diagnosis and treatment of hypertension. The early detection of these disturbed BP patterns is especially crucial in individuals with type 1 diabetes, as these patterns may indicate future development of adverse cardiovascular and renal outcomes. In this review we will describe these disturbed BP patterns and discuss recent findings on their prevalence and outcomes. We will also address critical areas for future research to determine the true prevalence and prognosis of disturbed BP patterns, and to optimize and improve the knowledge and management of high-risk individuals with type 1 diabetes and disturbed BP patterns.

Technological Ecological Momentary Assessment Tools to Study Type 1 Diabetes in Youth: Viewpoint of Methodologies

Type 1 diabetes (T1D) is one of the most common chronic childhood diseases, and its prevalence is rapidly increasing. The management of glucose in T1D is challenging, as youth must consider a myriad of factors when making diabetes care decisions. This task often leads to significant hyperglycemia, hypoglycemia, and glucose variability throughout the day, which have been associated with short- and long-term medical complications. At present, most of what is known about each of these complications and the health behaviors that may lead to them have been uncovered in the clinical setting or in laboratory-based research. However, the tools often used in these settings are limited in their ability to capture the dynamic behaviors, feelings, and physiological changes associated with T1D that fluctuate from moment to moment throughout the day. A better understanding of T1D in daily life could potentially aid in the development of interventions to improve diabetes care and mitigate the negative medical consequences associated with it. Therefore, there is a need to measure repeated, real-time, and real-world features of this disease in youth. This approach is known as ecological momentary assessment (EMA), and it has considerable advantages to in-lab research. Thus, this viewpoint aims to describe EMA tools that have been used to collect data in the daily lives of youth with T1D and discuss studies that explored the nuances of T1D in daily life using these methods. This viewpoint focuses on the following EMA methods: continuous glucose monitoring, actigraphy, ambulatory blood pressure monitoring, personal digital assistants, smartphones, and phone-based systems. The viewpoint also discusses the benefits of using EMA methods to collect important data that might not otherwise be collected in the laboratory and the limitations of each tool, future directions of the field, and possible clinical implications for their use.