Correlation between glycemic signature classes and measures of glucose homeostasis.

<p>(A) Forest plots for each of the glucotypes. A Pearson’s correlation test was used to determine the correlation between the clinical metabolic tests—listed 1 per line—and the fraction of time spent in each glucotype class (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005143#pbio.2005143.s014" target="_blank">S5 Data</a>). The center dot line is the resulting correlation coefficient, with the line representing the corresponding 95% confidence interval. (B) Forest plot with the lines representing age and BMI. (C) OGTT 2hr is plotted against the fraction of time in the severe glucotype for each individual. The line of best fit is shown in blue with the 95% confidence interval shaded in gray. The correlation coefficient, r, was derived from a Pearson’s correlation test. OGTT 2hr, blood glucose concentration 2 hours after the start of oral glucose tolerance test; SSPG, steady-state plasma glucose.</p

Summary of methods.

<p>We enrolled 57 participants with different diagnoses for diabetes and continuously monitored their interstitial glucose levels for 2–4 weeks. Spectral clustering was used to classify different patterns of glycemic responses based on their variability. We then compared different classes of glucose variability with common clinical parameters and in relation to the effect of meal with standardized nutrient content. Finally, we analyzed insulin metabolism to elucidate potential physiological mechanisms underlying glycemic dysregulation detected through our classification. CGM, continuous glucose monitoring; IR, insulin resistance; IS, insulin sensitivity.</p

Glucotypes and insulin metabolism.

<p>(A) Comparison of glycemic response, insulin secretion, and insulin sensitivity across different glucotypes (low, moderate, severe) (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005143#pbio.2005143.s014" target="_blank">S5 Data</a>). Each column is a participant. The glycemic response is shown in the horizontal panels as fasting blood sugar concentration and as blood glucose concentration 2 hours after oral glucose load in an OGTT. Dashed lines for fasting blood sugar and 2-hour glucose in OGTT correspond to the ADA thresholds for prediabetes and diabetes. Insulin secretion rate was calculated using deconvolution of C-peptide concentrations at 0, 30, and 120 minutes during OGTT (ISEC software [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005143#pbio.2005143.ref025" target="_blank">25</a>], see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005143#sec011" target="_blank">Methods</a>). The average derivative of the insulin secretion curve is shown as aggregate measure of insulin secretion rate over the whole OGTT time course. Insulin sensitivity is shown as glucose concentration from SSPG test, with higher values reflecting greater insulin resistance. (B, C, D, E, F) Insulin secretion rate (black solid line) and blood glucose concentration (dots and purple solid line) during OGTT for 5 individuals (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005143#pbio.2005143.s017" target="_blank">S8 Data</a>). Shaded areas in panels B-F aggregate data of distribution of insulin secretion rate for participants diagnosed with diabetes (pink), prediabetes (green), and nondiabetic (blue). Each panel represents an individual with a different physiology. (B) Insulin-sensitive individual with normal insulin secretion and a low blood glucose 2 hours postprandially; (C) Individual diagnosed with diabetes demonstrating high glycemic concentrations despite high insulin secretion; (D) Nondiabetic individual with normal fasting blood glucose but high 2-hour OGTT value in setting of insulin resistance and absolute but relative deficiency of insulin secretion; (E) Nondiabetic individual with insulin resistance with low insulin secretion, characterized by early glucose rise after load; (F) Nondiabetic individual with insulin resistance and high compensatory insulin secretion with relatively normal postprandial glucose following oral glucose load. ADA, American Diabetes Association; ISEC, Insulin SECretion; OGTT, oral glucose tolerance test; SSPG, steady-state plasma glucose.</p

Classification of CGM with classes of glycemic signatures.

<p>(A-C) Segregation of the 2.5-hour windows into the three classes of glycemic signatures derived from spectral clustering. The lines in each panel show an example of the glycemic signatures in each class. This separation of windows explains approximately 73% of the variance. (D) One day of CGM data for 3 separate individuals. Color indicates classification of glycemic signatures. Note that since overlapping windows were used for clustering and classification, some periods of the day have multiple classifications. (E) Heat map showing the fraction of time individuals spent in each of the glycemic classes. Rows represent unique individuals in the cohort, while columns represent each of the glycemic signature classes shown in A-C. Color of the tiles corresponds to the fraction of time spent in each class, with 1 being 100% of the time. There were 238 windows per participant (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005143#pbio.2005143.s012" target="_blank">S3 Data</a>). Rows of individuals are arranged according to hierarchical clustering. CGM, continuous glucose monitoring.</p

Correlation between carbohydrate content and frequency of severe glycemic signature responses.

<p>(A) Heat map of glycemic signature class responses to standardized meals. Rows represent individuals, and columns represent meals. Color indicates classification of response, and intensity indicates fraction of total responses (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005143#pbio.2005143.s016" target="_blank">S7 Data</a>). The number of total responses per individual corresponds to the number of standard meals each participant ate. Individuals are sorted by hierarchical clustering (left) based on the fraction of responses in a given class, regardless of the type of meal that triggered the response. (B) Contingency table reporting the number of responses to standardized meals assigned to a given glycemic signature class. Meals are sorted based on their net carbohydrate content (total carbohydrates–fiber). The size of the dots is proportional to the number of windows, and the intensity is the chi-squared ratio between observed and expected counts. There is a significant association between net carbohydrate content and severity of the response (<i>p</i>-value: 0.06, chi-squared test). “PB”: bread and peanut butter; “Bar”: PROBAR protein bar; “CF”: cornflakes and milk. For both panels A and B, the classification of glycemic responses to meals is based on the entire CGM profiles rather than on the initial set of clustered windows (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005143#sec011" target="_blank">Methods</a>). CGM, continuous glucose monitoring.</p

Glucotypes reveal new patterns of glucose dysregulation

<div><p>Diabetes is an increasing problem worldwide; almost 30 million people, nearly 10% of the population, in the United States are diagnosed with diabetes. Another 84 million are prediabetic, and without intervention, up to 70% of these individuals may progress to type 2 diabetes. Current methods for quantifying blood glucose dysregulation in diabetes and prediabetes are limited by reliance on single-time-point measurements or on average measures of overall glycemia and neglect glucose dynamics. We have used continuous glucose monitoring (CGM) to evaluate the frequency with which individuals demonstrate elevations in postprandial glucose, the types of patterns, and how patterns vary between individuals given an identical nutrient challenge. Measurement of insulin resistance and secretion highlights the fact that the physiology underlying dysglycemia is highly variable between individuals. We developed an analytical framework that can group individuals according to specific patterns of glycemic responses called “glucotypes” that reveal heterogeneity, or subphenotypes, within traditional diagnostic categories of glucose regulation. Importantly, we found that even individuals considered normoglycemic by standard measures exhibit high glucose variability using CGM, with glucose levels reaching prediabetic and diabetic ranges 15% and 2% of the time, respectively. We thus show that glucose dysregulation, as characterized by CGM, is more prevalent and heterogeneous than previously thought and can affect individuals considered normoglycemic by standard measures, and specific patterns of glycemic responses reflect variable underlying physiology. The interindividual variability in glycemic responses to standardized meals also highlights the personal nature of glucose regulation. Through extensive phenotyping, we developed a model for identifying potential mechanisms of personal glucose dysregulation and built a webtool for visualizing a user-uploaded CGM profile and classifying individualized glucose patterns into glucotypes.</p></div

Diabetes classification and prevalence of severe variability.

<p>(A) Principal component (labeled “PC”) analysis of common measures used to describe glucose control and evaluate CGM data (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005143#pbio.2005143.s014" target="_blank">S5 Data</a>). Individual participants are colored based on their glucotype, the glycemic signature class in which they spent the majority of their time. The dot size is proportional to the fraction of time spent with severe variability. (B) Same principle component analysis, but participants are colored based on diabetes diagnosis. This diagnosis was based on the ADA Guidelines of HbA1c, fasting blood glucose, and blood glucose concentration at 2 hours after the start of an OGTT. (C) Box and whisker plot of fraction of time spent with severe variability for nondiabetic, prediabetic, and diabetic individuals. The classification is based on all time windows, not restricting to the ones after standardized meals. (D) Proportion of CGM data in prediabetic and diabetic glycemic ranges defined in the ADA Guidelines. Participants are grouped by their diabetes diagnosis and colored by their glucotype. Some normoglycemic participants who demonstrated the severe glucotype reached prediabetic glycemic levels up to 15% of the time and diabetic glycemic levels 2% of the time. ADA, American Diabetes Association; CGM, continuous glucose monitoring; OGTT, oral glucose tolerance test.</p

Digital Health: Tracking Physiomes and Activity Using Wearable Biosensors Reveals Useful Health-Related Information

<div><p>A new wave of portable biosensors allows frequent measurement of health-related physiology. We investigated the use of these devices to monitor human physiological changes during various activities and their role in managing health and diagnosing and analyzing disease. By recording over 250,000 daily measurements for up to 43 individuals, we found personalized circadian differences in physiological parameters, replicating previous physiological findings. Interestingly, we found striking changes in particular environments, such as airline flights (decreased peripheral capillary oxygen saturation [SpO₂] and increased radiation exposure). These events are associated with physiological macro-phenotypes such as fatigue, providing a strong association between reduced pressure/oxygen and fatigue on high-altitude flights. Importantly, we combined biosensor information with frequent medical measurements and made two important observations: First, wearable devices were useful in identification of early signs of Lyme disease and inflammatory responses; we used this information to develop a personalized, activity-based normalization framework to identify abnormal physiological signals from longitudinal data for facile disease detection. Second, wearables distinguish physiological differences between insulin-sensitive and -resistant individuals. Overall, these results indicate that portable biosensors provide useful information for monitoring personal activities and physiology and are likely to play an important role in managing health and enabling affordable health care access to groups traditionally limited by socioeconomic class or remote geography.</p></div

Circadian and diurnal patterns in physiological parameters.

<p>Participant #1 hourly summaries in (A) sleep, (B) HRs, (C) skin temperature, and (D) steps as measured using the Basis Peak device over 71 nontravel d. (E) Summaries of 43-person cohort for daily HR and skin temperature from all data and (F) differences in resting (fewer than five steps) nighttime and daytime HRs (Note: one person did not have nighttime measurements and is not included) and skin temperature. (G) Daily activity plots for 43 individuals. Based on number of peaks in the curves, four general patterns of activity behavior are evident. The plots in Fig 2G were aligned according to the first increase in activity.</p

Physiological and activity profiles for 43 individuals.

<p>(A) The relationship between the average number of steps per day and resting HR (<i>n</i> = 43) and (B) average steps per minute and change in body mass index (BMI; <i>n</i> = 20) over the course of approximately 1 y was analyzed. Average resting HRs (C) were calculated by gender (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001402#sec014" target="_blank">Material and Methods</a>; <i>n</i> = 38).</p