Glycated Hemoglobin, Plasma Glucose, and Erythrocyte Aging.

BACKGROUND: The relationship between HbA1c and blood glucose averages has been characterized many times, yet, a unifying, mechanistic description is still lacking. METHODS: We calculated the level of HbA1c from plasma glucose averages based solely on the in vivo rate of hemoglobin glycation, and the different turnover rates for erythrocytes of different ages. These calculations were then compared to the measured change of HbA1c due to changes in mean blood glucose (MBG), to complex models in the literature, and our own experiments. RESULTS: Analysis of data on erythrocyte ageing patterns revealed that 2 separate RBC turnover mechanisms seem to be present. We calculated the mean red blood cell (RBC) life span within individuals to lie between 60 and 95 days. Comparison of expected HbA1c levels to data taken from continuous glucose monitors and finger-stick MBG yielded good agreement (r = .87, P < .0001). Experiments on the change with time of HbA1c induced by a change of MBG were in excellent agreement with our calculations (r = .98, P < .0001). CONCLUSIONS: RBC turnover seems to be dominated by a constant rate of cell loss, and a mechanism that targets cells of a specific age. Average RBC life span is 80 ± 10.9 days. Of HbA1c change toward treatment goal value, 50% is reached in about 30 days. Many factors contribute to the ratio of glycated hemoglobin, yet we can make accurate estimations considering only the in vivo glycation constant, MBG, and the age distribution of erythrocytes.

Continuous glucose monitoring analysis as predictor of islet yield and insulin requirements in autologous islet transplantation after complete pancreatectomy.

We analyzed the pretransplant continuous glucose monitoring (CGM) data of 45 patients that underwent total pancreatectomy followed by autologous islet transplantation (AIT) at the University of Arizona Medical Center. Traditional and novel metrics of CGM time series were correlated to the total islet count (TIC), islet equivalents (IEQs), and weight-normalized IEQs (IEQ/kg). In a subset cohort (n = 26) we analyzed the relationship among the infused number of islets, the CGM indicators, and the first recorded insulin requirement after the procedure. We conclude that receiving a high islet yield is sufficient yet not necessary to achieve low or null insulin requirements within the first 50 days after surgery. Furthermore, CGM inertia and CGM length of curve (2 novel CGM indicators) are shown to be correlated to islet yield, and the CGMs normalized area (Ao) and time ratio above hyperglycemic level (To) are strongly correlated to insulin requirement. A screening test based on To is shown to have 100% sensitivity and 88% specificity discriminating insulin independence upon discharge.

Universality of rank-ordering distributions in the arts and sciences.

Searching for generic behaviors has been one of the driving forces leading to a deep understanding and classification of diverse phenomena. Usually a starting point is the development of a phenomenology based on observations. Such is the case for power law distributions encountered in a wealth of situations coming from physics, geophysics, biology, lexicography as well as social and financial networks. This finding is however restricted to a range of values outside of which finite size corrections are often invoked. Here we uncover a universal behavior of the way in which elements of a system are distributed according to their rank with respect to a given property, valid for the full range of values, regardless of whether or not a power law has previously been suggested. We propose a two parameter functional form for these rank-ordered distributions that gives excellent fits to an impressive amount of very diverse phenomena, coming from the arts, social and natural sciences. It is a discrete version of a generalized beta distribution, given by f(r) = A(N+1-r)(b)/r(a), where r is the rank, N its maximum value, A the normalization constant and (a, b) two fitting exponents. Prompted by our genetic sequence observations we present a growth probabilistic model incorporating mutation-duplication features that generates data complying with this distribution. The competition between permanence and change appears to be a relevant, though not necessary feature. Additionally, our observations mainly of social phenomena suggest that a multifactorial quality resulting from the convergence of several heterogeneous underlying processes is an important feature. We also explore the significance of the distribution parameters and their classifying potential. The ubiquity of our findings suggests that there must be a fundamental underlying explanation, most probably of a statistical nature, such as an appropriate central limit theorem formulation.