Evaluation of serum fructosamine concentration as an index of blood glucose control in cats with diabetes mellitus.

Fructosamine, a glycated serum protein, was evaluated as an index of glycemic control in normal and diabetic cats. Fructosamine was determined manually by use of a modification of an automated method. The within-run precision was 2.4 to 3.2%, and the day-to-day precision was 2.7 to 3.1%. Fructosamine was found to be stable in serum samples stored for 1 week at 4 C and for 2 weeks at -20 C. The reference range for serum fructosamine concentration in 31 clinically normal colony cats was 2.19 to 3.47 mmol/L (mean, 2.83 +/- 0.32 mmol/L). In 27 samples from 16 cats with poorly controlled diabetes mellitus, the range for fructosamine concentration was 3.04 to 8.83 mmol/L (mean, 5.93 +/- 1.35 mmol/L). Fructosamine concentration was directly and highly correlated to blood glucose concentration. Fructosamine concentration also remained high in consort with increased blood glucose concentration in cats with poorly controlled diabetes mellitus over extended periods. It is concluded that measurement of serum fructosamine concentration can be a valuable adjunct to blood glucose monitoring to evaluate glycemic control in diabetic cats. The question of whether fructosamine can replace glucose for monitoring control of diabetes mellitus requires further study.

Relation of fructosamine to serum protein, albumin, and glucose concentrations in healthy and diabetic dogs.

The relation of the glycated serum protein, fructosamine, to serum protein, albumin, and glucose concentrations was examined in healthy dogs, dogs with hypo- or hyperproteinemia, and diabetic dogs. Fructosamine was determined by use of an adaptation of an automated kit method. The reference range for fructosamine in a composite group of control dogs was found to be 1.7 to 3.38 mmol/L (mean +/- SD, 2.54 +/- 0.42 mmol/L). Fructosamine was not correlated to serum total protein, but was highly correlated to albumin in dogs with hypoalbuminemia. To normalize the data with respect to albumin, it is suggested that the lower limit of the reference range for albumin concentration (2.5 g/dl) be used for adjustment of fructosamine concentration and only in hypoalbuminemic dogs. In 6 hyperglycemic diabetic dogs, fructosamine concentration was well above the reference range. It is concluded that although fructosamine may be a potentially useful guide to assess the average blood glucose concentration over the preceding few days in dogs, further study is required to establish its value as a guide to glucose control in diabetic dogs.

Body mass, maintenance and basal metabolism in dogs.

Basal metabolism and body mass are related by the metabolic power function: P = aMb, where P = basal metabolism in Watts, a = mass coefficient, M = body mass in kg, and b = mass exponent. The mass exponent of 117 dogs from the literature b dog = 0.885 +/- 0.024 (r = 0.960; F = 1387; df = 1,115). This mass exponent is significantly greater than the commonly accepted value of 0.75 for mammals. The dog's 95% confidence ellipse is compared with that of mammals with body mass (M) less than 3.2 kg (the lower limit of the mass range in dogs) and greater than 3.2 kg. When M greater than 3.2 kg the interspecific metabolic mass exponent (bi) in mammals is also significantly greater than 0.75 and not different from b dog (bi = 0.869 +/- 0.034; r = 0.919; F = 648; df = 1,120). In mammals M less than 3.2 kg bi is significantly smaller than 0.75 (bi = 0.634 +/- 0.010; r = 0.941; F = 4319; df = 1,561). These data show that in mammals the relationship between the logarithms of basal metabolism and body mass is not accurately described by a single regression line. They also indicate that the commonly accepted 0.75 mass exponent is not applicable to the prediction of basal metabolism in dogs and mammals. The relationship between body mass and maintenance energy metabolism (MEM) in 332 dogs shows that the prediction interval is too wide to reasonably predict MEM in individual dogs. However, the minimum maintenance energy metabolism (MMEM in Watts) can be accurately predicted by a simple algorithm: MMEM = 10.3 + 1.41 x M. The theoretical meaning of the basal metabolic power function is discussed.

Size and power in mammals.

The relationship between basal metabolism P and body mass M of 391 mammalian species has been analysed by least-squares regression, robust regression and covariance analyses. This relationship is a power function: P = aMb, where the mass exponent b is 0.678 +/- 0.007 (mean +/- S.D.) and the mass coefficient a takes different values. Theory of measurement revealed that the 2/3 mass exponent is due to an underlying dimensional relationship between the primary quantity of mass and the secondary quantity of power. This paper shows that the 2/3 mass exponent is not the physiological problem of interest. It is not the slope of the metabolic regression line, but its location in the mass/power plane, that must be explained. This location is given by the value of the mass coefficient, the explanation of which is, and remains, the central question in comparative physiology.

Log-normal variation belts for growth curves.

Prediction (confidence) or tolerance belts compound the uncertainty of sample estimates with the estimated extent of individual variation. The latter is therefore better described by variation belts, in which sample estimates are simply substituted for population parameters. Variation belts can provide valuable graphical indications concerning the goodness of fit of postulated error models. While multiplicative least-squares (MLS) methods appear appropriate in principle for biological growth, they are unsatisfactory in practice when logarithmically transformed data are heteroscedastic. Heteroscedastic multiplicative error models can be fitted by iteratively reweighted multiplicative least squares (IRMLS), but unacceptable negative or infinite residual variance estimates and unreasonably wide variation belts are occasionally obtained. These difficulties can be prevented by constrained iteratively reweighted multiplicative least squares (CIRMLS). Examples are presented concerning the metabolic allometry of white rats, the somatic growth of male elephant seals, and the growth of an experimental population of Paramecium caudatum.

Biological similitude: statistical and functional relationships in comparative physiology.

A concept of biological similitude based on the distinction between extensive and intensive properties of animals is discussed. This concept provides a theoretical basis for using the power function as a mathematical-physical model whose numerical and dimensional constraints give some insight into the nature of the relationship between body mass and energy metabolism. In particular a theoretically mass-independent expression for energy metabolism is derived that can be used for qualitative intra- and interspecific comparisons of energy metabolism.

Mathematical expression of the effects of changes in body size on pulmonary function and structure.

The biological meaning of the mass coefficient "a" and mass exponent "b" of the power function relating body mass "M" and a morphometric or physiologic variable "y" is discussed (y = aMb). The mass coefficient represents the effect of intensive or qualitative factors on the considered function or structure. The theoretical mass exponent is a criterion for the constancy of the mass coefficient and the qualitative sameness of the compared animals. The Theory of Homomorphism shows that the power function is a mathematical-physical model for analyzing structure and function in animals of different size.

Body size, energy metabolism, and the lungs.

The theoretical basis for applying the power function y = aMb to the study of structure-function relationships is discussed. Dimensional analysis and the distinction between intensive and extensive properties of animals show that the mass coefficient a represents the effect of intensive or qualitative factors on the considered function or structure. The theoretical mass exponent is a criterion for the constancy of a and the qualitative sameness of the compared structure or function. Examples from respiratory and metabolic physiology are given.

Volumetric measure and lick count of liquid food intake of rats.

A simple and relatively inexpensive lick counter-volumeter is described which automatically records liquid intake and drinking behavior of rats. It counts lick contacts while accurately measuring the rate of volume intake in constant unit volumes. The unit volumes are adjustable over a wide range. For a unit volume on the order of 60 microliter, the precision was 1.2% over a range of withdrawal rates from 1 to 10 ml/min. The average lick volume of rats consuming a liquid food was systematically lower by 18% during the second meal following a 15 hr fast when compared to that of the first. This change in average lick volume appears to reflect a change in the rat's motivation to drink the liquid food.

Coulometric microrespirometry.

Capacitive coulometry is based on the automatic quantitative replacement of O2 consumed by an animal in a closed system with electrolytic O2, produced by discharging a capacitor through a CuSO4 solution. The sensitivity of the device is better than 0.1 nl. The unit volume of O2 produced (1 nl) is both accurate and precise within 1% in a temperature range from 2.8 to 40 degrees C. The upper limit of recordable O2 consumption is 1 ml/h. The microrespirometer can be autoclaved, making the technique ideally suited for metabolic studies in microbiology, cell and organ cultures, and comparative physiology.

Energy metabolism and body size. I. Is the 0.75 mass exponent of Kleiber's equation a statistical artifact?

The statistical derivation of Kleiber's 0.75 interspecific mass exponent 'b' is based on an assumption that the mass coefficient 'a' is constant irrespective of a mammal's size and/or species. Analysis of covariance, a statistical technique not based on this assumption, reveals that the mass coefficient is not constant in a series of 7 species (Peromyscus m., mice, rats, cats, dogs, sheep, and cattle) but increases threefold with the size of the animal. THe mass coefficient is a power x mass-2/3, the power being expressed in watts and the mass in kg. (Peromyscus m.: a = 1.91 +/- 0.09; cattle: a = 6.06 +/- 0.14). The intragroup mass exponent is equal to 0.67 +/- 0.03 and is significantly different from 0.75. This study shows that the 0.75 interspecific mass exponent in Kleiber's equation is a statistical artifact and suggests that the data from literature are consistent with the theory of biological similitude of Lambert and Teissier.

Energy metabolism and body size. II. Dimensional analysis and energetic non-similarity.

The allometric equation P = aMb (P: standard metabolism, M: body mass, a: mass coefficient, and b: mass exponent) can be theoretically derived from the following relations: l/L = t/T = lambda, m/M = lambda 3 where 1 and L are homologous lengths, t and T homologous times and lambda is the coefficient of similitude of two animals. Animals are homomorphic when b = 2/3, a = constant, and when their density is the same. These conditions appear to be realized in mature mammals of the same species, but mammals of different species are not homomorphic. Homomorphism means that the physiological time-scale is not the same in small and large animals, but that the energy spent per unit mass and unit of physiological time remain the same in homomorphic animals [mass-specific physiological power, phi]. The mass coefficient 'a' is equal to phi, therefore 'a' is physiologically the most significant parameter in the allometric equation. The physiological implications of phi are discussed.