Abstract
Cardiovascular disease risk assessment relies on single time-point measurement of risk factors. Although significant daily rhythmicity of some risk factors (e.g., blood pressure and blood glucose) suggests that carefully timed samples or biomarker timeseries could improve risk assessment, such rhythmicity in lipid risk factors is not well understood in free-living humans. As recent advances in at-home blood testing permit lipid data to be frequently and reliably self-collected during daily life, we hypothesized that total cholesterol, HDL-cholesterol or triglycerides would show significant time-of-day variability under everyday conditions. To address this hypothesis, we worked with data collected by 20 self-trackers during personal projects. The dataset consisted of 1,319 samples of total cholesterol, HDL-cholesterol and triglycerides, and comprised timeseries illustrating intra and inter-day variability. All individuals crossed at least one risk category in at least one output within a single day. 90% of fasted individuals (n = 12) crossed at least one risk category in one output during the morning hours alone (06:00–08:00) across days. Both individuals and the aggregated group show significant, rhythmic change by time of day in total cholesterol and triglycerides, but not HDL-cholesterol. Two individuals collected additional data sufficient to illustrate ultradian (hourly) fluctuation in triglycerides, and total cholesterol fluctuation across the menstrual cycle. Short-term variability of sufficient amplitude to affect diagnosis appears common. We conclude that cardiovascular risk assessment may be augmented via further research into the temporal dynamics of lipids. Some variability can be accounted for by a daily rhythm, but ultradian and menstrual rhythms likely contribute additional variance.
Highlights
Cardiovascular Disease Risk Assessment: The Utility of High Temporal Resolution Data Cardiovascular disease (CVD) is the most common cause of death globally, making optimal CVD risk assessment a major public health priority [1,2,3,4]
CVD risk can be assessed by blood factors including low-density lipoprotein- cholesterol (LDL-c), blood pressure, glucose, total cholesterol (TC), triglycerides and high-density lipoprotein-cholesterol (HDL-c) [1, 5,6,7]
The average total error (TE) was 11% +/– 5%, very close to the current gold standard. (The average TE manufacturer has reported across several large validations is 18% for TC, 8% for HDL-c and 13% triglycerides)
Summary
Cardiovascular Disease Risk Assessment: The Utility of High Temporal Resolution Data Cardiovascular disease (CVD) is the most common cause of death globally, making optimal CVD risk assessment a major public health priority [1,2,3,4]. CVD risk can be assessed by blood factors including low-density lipoprotein- cholesterol (LDL-c), blood pressure, glucose, total cholesterol (TC), triglycerides and high-density lipoprotein-cholesterol (HDL-c) [1, 5,6,7]. These risk factors are most commonly assessed via measurement at a single time point, with the result guiding clinical action (e.g., prescription for a cholesterol-lowering statin drug) [1, 8, 9]. Like blood glucose, vary significantly by time of day (e.g., Dawn phenomenon, ultradian glucose pulsatility), and current research aims to use knowledge of this variability to personalize clinical recommendations [19,20,21,22,23,24,25]
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