Obesity in Europe and its treatment.
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Biomedical subjects
Publications and source records attributed to T B Van Itallie.
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We measured the effects of iopanoic acid on thyroid hormone metabolism in obese men during severe calorie restriction to study the nutrition regulation of thyroid hormone metabolism. Eight morbidly obese men received a weight-maintenance diet followed by 6 wk of 600 kcal/d. During underfeeding, patients received iopanoic acid or placebo for 2-wk periods in a double-blind crossover fashion. Underfeeding alone (UF) produced a 28.3% decline in the serum triiodothyronine (T3) concentration, and iopanoic acid plus underfeeding (IOP) produced a 49.5% decline in T3 concentration from baseline. Serum reverse T3 concentrations increased 289% during IOP compared with UF alone (p less than 0.001). Serum TSH concentration was unchanged by underfeeding but increased twofold during IOP treatment. Thyroid hormone kinetics demonstrated a decrease in T3 production during IOP compared with UF. These findings suggest that calorie restriction regulates T3 production by modulating only type I 5'-deiodinase activity.
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This study investigated differences between measured and predicted resting energy expenditure (REE) in 80 women who averaged 104.6 kg in weight and were 49 kg and 88% overweight. Body composition analysis showed that 68% of the excess weight was fat and 32% was fat-free mass (FFM). Normalized for height, total body potassium (TBK) was 113 +/- 15% and total body water (TBW) was 133 +/- 21% of values in nonobese controls. The health of the FFM, defined as the potassium content per kg of FFM, was 84 +/- 13% of normal. Measured and predicted REE were only modestly related (r = +.59), and only 59% of measured REEs were within 10% of predicted values. A stepwise multiple regression indicated that weight was the single best predictor of measured REE and that the size of the FFM made a significantly greater contribution to REE than did the size of the fat mass. Commonly used equations for the prediction of REE are not appropriate for moderately or severely obese patients. Caloric prescription for weight reduction must be tailored to individuals rather than recommending the same caloric intake to persons with varying metabolic rates.
This study validated further the bioelectrical impedance analysis (BIA) method for body composition estimation. At four laboratories densitometrically-determined lean body mass (LBMd) was compared with BIA in 1567 adults (1069 men, 498 women) aged 17-62 y and with 3-56% body fat. Equations for predicting LBMd from resistance measured by BIA, height, weight, and age were obtained for the men and women. Application of each equation to the data from the other labs yielded small reductions in R values and small increases in SEEs. Some regression coefficients differed among labs but these differences were eliminated after adjustment for differences among labs in the subjects' body fatness. All data were pooled to derive fatness-specific equations for predicting LBMd: the resulting R values ranged from 0.907 to 0.952 with SEEs of 1.97-3.03 kg. These results confirm the validity of BIA and indicate that the precision of predicting LBM from impedance can be enhanced by sex- and fatness-specific equations.
Fat patterning was studied in a sample of 76 obese women before and after weight reduction by principal components analysis. Circumferences were selected as variables rather than skinfolds because circumferences correlated more highly with body composition determined either by total body water or total body potassium. A separate sample of 136 males and females, measured once, was employed to validate the results. The validation sample yielded similar findings. Two significant components were found, the first contrasting upper and lower fatness and the second trunk and extremity fatness. Waist circumference was the best predictor of upper-body fat patterning. For the lower body, thigh circumference loaded more highly than did the iliac crest circumference. Arm circumference did not contribute significantly to upper-lower fat patterning.
This study examined changes in body fat distribution in 68 women who lost an average of 12.3 kg from an initial weight of 103.6 kg. Weight reduction was accompanied by a small but statistically significant reduction of 1.2% in the waist-to-hip ratio, suggestive of a reduction in upper-body obesity. Subjects with greater upper-body obesity tended to achieve greater reductions in the waist-to-hip ratio. Changes in five circumference measures were highly correlated with losses of fat and showed that subjects with lower-body obesity tended to lose large amounts of fat from both their upper and lower fat depots while subjects with upper-body obesity lost fat primarily from their upper depots. Women with lower-body obesity tended to lose more total body fat than did women with upper body obesity (r = -0.26, p less than 0.04).
A significant proportion of the interindividual variance in human fatness is attributable to genetic factors. This fact is indicated (inter alia) by studies of identical twins demonstrating that the degree of efficiency with which the body uses excess dietary energy for fat storage is, to a considerable extent, inherited. Genetic factors also appear to render particular individuals or groups especially vulnerable to pervasive obesity-promoting influences (excess food and too little exercise) in the environment. If, indeed, much human obesity results from an interaction between a genetically determined disposition to store fat efficiently and a food-laden, overmechanized environment, then preventive and therapeutic strategies should be directed toward (a) helping the susceptible individual protect himself from the "calorie pollution" that surrounds him, and (b) finding pharmacologic agents to modify the inherited metabolic factors that favor excessive fat storage.
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The accuracy of prediction equations for estimating lean body mass (LBM) from total-body electrical conductivity (TOBEC) was examined by cross-validation. Two samples of adults, aged 18-35 yr, were drawn from separate geographic locations. LBM was determined by densitometry and TOBEC was measured with TOBEC II instrument. LBM and TOBEC were highly correlated in both samples (r = 0.96 and 0.97). Cross-validation of LBM prediction equations was accomplished by exchanging equations and comparing predicted LBM values. There was a mean difference of 0.974 kg LBM between the two equations (p less than 0.0001). Thus, data from 157 subjects were pooled and one equation was developed that incorporated height (cm), sex (males = 0, females = 1), and the zero-, first-, and second-order Fourier coefficients (FC0, FC1, and FC2) of the TOBEC phase value: LBM, kg = -36.410 + (-1.324 X sex) + (0.01185 X (FC1(0.5)xht)) + (12.347 X FC2(0.5)) + (0.0627 X FC0)-(0.9232 X FC2) This equation, developed from 157 subjects, accounted for 96% of the variability in LBM and had a standard error of estimate of 2.17 kg LBM.
This comparative study, conducted on 28 boys and girls of widely varying fatness, was designed to validate a new whole-body composition method [total body electrical conductivity (TOBEC)], based on bioelectrical properties of the human body. A significant correlation [r = 0.911; standard error of the estimate (SEE) = 5.3 kg] was demonstrated between the transformed TOBEC scores (TOBEC0.5 X Ht) and lean body mass (LBM) determined by hydrodensitometry and corrected for individual variations in hydration (LBMd + W). TOBEC determinations also correlated well with 1) total body water determined by deuterium oxide dilution (r = 0.877; SEE = 4.5 liters), 2) total body potassium determined by means of a 4 pi whole-body counter (r = 0.860; SEE = 430.7 meq), 3) LBM derived from skinfold thicknesses (r = 0.850; SEE = 5.8 kg). The residuals of the regression between LBMd + W and TOBEC scores did not show any significant correlation with either the potassium or the water content of the LBM. The results indicate that TOBEC is a simple, rapid, reliable, and noninvasive technique for delineating changes in body composition that occur in children during growth.
The association of obesity and hyperlipidemia does not mean that fatness per se is the primary determinant of the lipid abnormality. To evaluate the contribution of fatness to fasting levels of serum triglycerides (TG), LDL cholesterol (LDL-C), and HDL cholesterol (HDL-C), we analyzed data on 368 caucasian adults (286 women, 82 men) consecutively entering a weight control program. Although most subjects were overweight, the population represented a wide spectrum of body weights and lipid levels. Study variables included body fat mass (by total body water), fat free mass (FFM), body build (chest to height ratio), fat cell size and number (from bilateral buttock biopsy specimens), upper-lower body fat pattern by arm to thigh circumference ratio, central-peripheral fat pattern by subcapsular to triceps skinfold ratio, waist to hip ratio, and the presence or absence of diabetes. Our results concurred with previously noted correlations of body weight with TG (r = 0.29, P less than 0.0001) and with HDL-C (r = -0.28, P less than 0.0001) at least in the larger sample of women, but there was no significant correlation with LDL-C (r = -0.06). In order to evaluate the relative contribution of the various components of body composition and fat distribution to lipid levels, stepwise regression analyses were performed on the subgroups of women and men. Among women: TG level was predicted best by FFM, upper body fat pattern, age, and diabetes (explaining 30 percent of TG variance); LDL-C level was predicted by age only (explaining 12 percent of variance); and HDL-C level was predicted by body build only (8 percent). Among men: TG level was predicted best by central and upper body fat patterns and diabetes (31 percent of variance); LDL-C and HDL-C levels were not significantly predicted by any of the 11 study variables. These results, obtained from cross-sectional analysis of a predominantly obese sample, suggest that lipid levels may be more directly related to body fat pattern, fat free mass and body build than to body fatness itself.
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Compression of the animal stomach gives rise to vagal discharges that could signal satiety. To test a noninvasive method of inducing compression of the human stomach, we first employed extra-abdominal pressure levels of 0, 10, and 20 Torr, counterbalanced for sequence, in six lean and obese subjects. A large blood pressure cuff was wrapped around the abdomen of the subject with the air bladder over the epigastrium. The bladder was inflated before ingestion of a liquid meal by the subject until satiated. Relative to 0 Torr, pressures of 10 and 20 Torr significantly reduced spontaneous food intake (P less than 0.03) without producing discomfort. In a second study, extra-abdominal pressure of 20 Torr also raised intragastric pressure by a mean of 5.5 Torr (P less than 0.03). In a third study, extra-abdominal pressure of 20 Torr did not alter gastric emptying rate during meal ingestion but ultimately enhanced emptying rate when this pressure was maintained for greater than 100 min after meal completion (P less than 0.01).
Serum insulin has been implicated as a determinant of blood pressure (BP) level in obese hypertensive individuals. We examined the relationships among fasting insulin, mean arterial pressure (MAP), body composition, and fat distribution in 204 subjects unselected for glucose tolerance. All met the criteria of taking no oral hypoglycemic agents, insulin or antihypertensive medications. 95 percent were considered obese and 78 percent were female. Mean age was 39 years (range 15-71), percent ideal body weight (IBW) 171 percent (109-332), BP 125/81 mmHg (80-181/50-110) and fasting insulin 44 microU/ml (4-196). Lean body mass (LBM) and body fat were measured by tritium dilution. The correlation coefficient between insulin and MAP was 0.32 (P less than 0.0001). Both insulin and MAP correlated highly with percentage IBW, percentage body fat, fat mass, LBM, body build and upper fat pattern. Controlling for these variables simultaneously eliminated the association between insulin and BP. By contrast, the correlations between MAP and measures of body composition and fat distribution retained significance after controlling for insulin level. These findings suggest that measures of body composition and fat distribution have a relationship with BP that is not entirely accounted for by serum insulin level, and the relationship between serum insulin and BP is indirect and largely accounted for by their mutual association with measures of body composition and fat distribution.
An inventory control model is used to describe the regulation of depot fat and contribution made to the satiation process by putative feedback signals. In the two bin inventory control system, adipose tissue is viewed as a large storage reservoir which is periodically refilled by means of adjustments made in meal consumption. The gastrointestinal tract is viewed as a smaller bin which triggers renewed food ingestion when it becomes partially emptied. However, reduction in the contents of the adipose tissue bin below a critical level (reorder point quantity) generates a signal to the brain that appropriately modulates meal size and intermeal interval. Because this model evolved to cope with worst case situations, a strong bias for storage in time of plenty is inherent in the system. This fact helps to account for the high prevalence of obesity in energy-rich societies, where, in contrast to the situation that obtains in primitive societies, the energy cost of obtaining food is minimal.
This study 1) further validated the relationship between total body electrical conductivity (TOBEC) and densitometrically determined lean body mass (LBMd) and 2) compared with existing body composition techniques (densitometry, total body water, total body potassium, and anthropometry) two new electrical methods for the estimation of LBM: TOBEC, a uniform current induction method, and bioelectrical impedance analysis (BIA), a localized current injection method. In a sample of 75 male and female subjects ranging from 4.9 to 54.9% body fat the correlation between LBMd and LBM predicted from TOBEC by use of a previously developed regression equation was extremely strong (r = 0.962), thus confirming the validity of the TOBEC method. LBM predicted from BIA by use of prediction equations provided with the instrument also correlated with LBMd (r = 0.912) but overestimated LBM compared with LBMd in obese subjects. However, no such systematic error was apparent when new prediction equations derived from this heterogeneous sample of subjects were applied. Thus the TOBEC and BIA methods, which are based on the differing electrical properties of lean tissue and fat and which are convenient, rapid, and safe, correlate well with more cumbersome human body composition techniques.