Arterial stiffness and mental decline
It is known that aortic stiffness plays a role in cardiovascular disease. Recent studies suggest it is also a risk factor in cognitive decline and dementia, but the changes in the brain underlying this risk are unclear. To better understand the mechanisms at play, researchers at University of Oxford’s Warneford Hospital and University College London examined results from the Whitehall II Imaging Sub-study to see if aortic stiffening during a four-year follow-up in mid to late life was associated with brain structure and mental acuity. Specifically, the team looked at 542 individuals who received measurements of aortic stiffness at 64 years old and again at about 68 years. About a year later, they were put through a series of cognitive tests and MRI scans to evaluate the blood supply, its size and connections, in different regions of the brain. “We observed that a faster rate of aortic stiffening during the four years in mid-to-late life was linked to lower blood flow and poorer markers of brain connectivity across several brain regions,” the researchers wrote. “We also found that lower memory performance in older age was most closely linked to the first measures of aortic stiffness, taken at baseline, rather than the later measures of stiffness.” The findings suggest that diffusion and perfusion MRI “may be sensitive markers of recent damage caused by aortic stiffening and could therefore be potentially relevant outcome measures in intervention studies.” The retrospective, cohort study was published Dec. 29, 2020, in PLoS Medicine.
Wearables can help to predict COVID-19
San Francisco-based Fitbit Research showed a correlation between changes in certain health metrics measured on wearable devices and the presence of diseases assessed via laboratory tests and other traditional means. The team analyzed data on 2,745 people diagnosed with COVID-19 using an active infection polymerase chain reaction (PCR) swab tests between Feb. 16, 2020, and Sept. 9, 2020. All subjects wore Fitbit devices and resided in the U.S. or Canada. Among male and female participants, 11.9% and 11.2%, respectively, were asymptomatic, 48.3% and 47.8% recovered at home on their own, 29.7% and 33.7% recovered at home with the aid of another person, 9.3% and 6.6% required hospitalization without ventilation and 0.5% and 0.4% required ventilation. A total of 21 symptoms were reported, the most common being fatigue. Fever was present in 59.4% of males and 52% of females. Importantly, the team showed that respiration rate, heart rate and heart rate variation (HRV) were strong indicators of illness onset, with the first type typically elevated and HRV decreased. “We trained a convolutional neural network to predict illness on any specific day given health metrics for that day and the preceding four days,” they wrote. Based on self-reported symptoms alone, they obtained an area under the curve (AUC) of 0.82 ± 0.017 for predicting the need for hospitalization. When physiological signs were used, the AUC was 0.77 ± 0.018 for predicting illness on a given day. “Measuring these metrics, taken in conjunction with molecular-based diagnostics, may lead to better early detection and monitoring of COVID-19,” the team said. Their work was published online Nov. 30, 2020, in npj digital medicine.
Putting patients in control of type 2 diabetes with smart choices, CGMs
A researcher at the University of Virginia School of Medicine is exploring a new approach to managing type 2 diabetes that combines continuous glucose monitoring with smart eating choices and well-timed exercise to help reduce blood sugar levels. Rather than focus on weight loss, the conventional approach, Daniel Cox, professor of psychiatry and internal medicine, said helping people understand how their food choices affect their blood sugar can be as effective as reducing their weight – a goal many type 2 diabetes struggle to achieve. By wearing a continuous glucose monitor, patients can get real-time information on how a sugary treat or a seemingly healthy snack affects their blood and make choices about their food intake accordingly. When they do opt for a sugar-boosting food, the program advises light exercise, such as walking, to help bring their glucose level back to a healthy range. Cox is testing his approach in small clinical trials at UVA, West Virginia University and the University of Colorado. Four patients at each site with newly diagnosed type 2 diabetes who have not begun taking medication will receive treatment manuals, continuous glucose monitors (CGMs) and sleep/activity trackers. Investigators will check in virtually over several weeks to see how well they are keeping their blood sugar in check. In a previous study, 52% of patients continued to respond to the treatment program at 12 months.