International Textbook of Obesity - part 2 pdf

Table 3.2 Two by two table weight loss by intention No intention to lose weight Intention to lose weight Did not lose weight 0.187 0.414 Lost weight 0.045 0.354 Based on Meltzer and Everhart (88). have been conducted to address this issue. William- son and colleagues observed that when analyses are restricted to weight loss among never-smoking, overweight individuals who reported that their weight loss was intentional, weight loss was asso- ciated with either a beneficial effect or no effect. For further discussion of these studies, see French et al. (85), Kuller (86), and Williamson et al. (87). However, it is important to realize that the so- called intentional weight loss studied by these inves- tigators may often by unintentional. Consider the following. Meltzer and Everhart (88) studied par- ticipant attributions of weight loss intention in a large population-based survey. Among women, they found the following: ∑ 76.8% of overweight women reported attempting to lose weight. ∑ Of those women attempting to lose weight, 46.1% did lose weight. ∑ The adjusted odds ratio for weight loss given that one intends to lose weight is reported to be 3.52. Similar results were obtained for men. Using these three numbers and some algebra, one can derive the 2 ; 2 table shown in Table 3.2 expressed in proportions. Applying the standard attributable risk approach, this implies that 46% of overweight women who intend to lose weight do lose weight, but that 19% would have lost weight even if they had not intended to do so. Therefore, the fraction of weight loss among overweight women who intend to lose weight that is due to factors other than their intention is about 41% (i.e. 19/46). These calcula- tions suggest that some large sub-portion of those who have been designated as intentional weight losers in past studies may have actually lost weight through some other mechanism such as occult ill- ness. If this were true, the currently observed equivocally beneficial effects of what we currently label intentional weight loss may markedly under- estimate true benefits due to residual confounding by occult disease. This points out the severe limita- tions of observational (non-experimental) studies in this area. Moreover, perhaps we are misguided by focusing on ‘intentionality’ at all. In many of the observa- tional studies of so-called intentional weight loss, subjects were initially measured decades ago (82, 87). By what methods did they achieve weight loss decades ago? Among others, by drugs and surgical procedures that are far less safe than those currently available. Even as late as 1997 some widely pre- scribed drugs were removed from the market be- cause of dangerous effects (89). Still today, methods for intentionally inducing weight loss include fad diets (90), herbal supplements of untested safety, bulimia and other methods of highly questionable safety. Hence, it appears ill advised to estimate the effects weight loss achieved by medically recom- mended methods by studying weight loss that is merely reported to be ‘intentional.’ What is needed is studies of weight loss that is produced among obese humans by modern methods that are accep- ted by mainstream medicine. Presently, a well-controlled non-randomized, study of weight loss produced by surgery among morbidly obese adults is underway (91). Mortality results are not yet available. A randomized clinical trial (RCT) testing whether producing weight loss through medically accepted methods among obese people can reduce mortality rate could settle these issues (92). Presently, the National Institute of Dia- betes, Digestive and Kidney Diseases is designing a large multi-center study termed SHOW. Although this RCT will examine mortality as a secondary outcome, it is not necessarily powered to detect differences in mortality rates. Our perspective of the admittedly incomplete evi- dence regarding the effect of weight loss on mortal- ity rate is portrayed in Figure 3.1—an iconic repre- sentation of the currently available literature and a conjecture of what the future might bring. This figure is intended to convey that as studies of weight loss and mortality rate have become methodologi- cally more sound, what initially appeared to be a harmful effect has progressively shifted to be neutral 43BODY WEIGHT, BODY COMPOSITION AND LONGEVITY Estimated effect of WL Bad Neutral Good ’Generic’ WL ’Generic’ WL among the obese Poorly measured ’intentional’ WL among obese True ’intentional’ WL among obese WL achieved by recommended method among obese ? ? Figure 3.1 Iconic presentation of the estimated effects of weight loss (WL) on mortality with varying study designs at worst and possibly even somewhat positive. When studies of weight loss that is intentionally induced among obese individuals through accepted medical interventions are included, it is plausible to conjecture that the effect may become strongly posi- tive. Still, there is a great gap between conjecture and demonstration and we must continue to look for stronger studies that can provide this demon- stration (or lack thereof). Change in Body Composition Finally, as discussed above, studies of body compo- sition at a single point in time, as opposed to just body weight at a single point in time, may tell different stories. The same may hold true for studies of change in body composition versus change in weight. To examine this possibility, Allison et al. (32) analyzed mortality rate in two epidemiologic studies, the Tecumseh Community Health Study and the Framingham Heart Study. In both, change in weight and fat (via skinfolds) across two points in time were available. In both studies, weight loss and fat loss were, respectively, associated with an elev- ated and reduced mortality rate. Each standard deviation (SD) of weight loss (approximately 5.5 kg across both studies) was estimated to increase the hazard of mortality by about 35%. In contrast, each SD of fat loss (10.0 mm in Tecumseh and 4.8 mm in Framingham) reduced the hazard of mortality by about 16%. Thus, among individuals that are not severely obese, weight loss (conditional upon fat loss) is associated with increased mortality rate and fat loss (conditional upon weight loss) with de- creased mortality rate. These results, if confirmed in future studies, have important implications for cli- nical and public health recommendations regarding weight loss. They suggest that weight loss should only be recommended under conditions where a sufficient proportion of the weight lost can be ex- pected to be fat. Unfortunately, what those condi- tions are and what the minimum proportion is re- mains unknown at his time. DISCUSSION In this discussion section, we begin by reiterating key methodological conclusions. We follow this with a discussion of what we believe the currently available data on relative body weight and mortal- ity show and what the currently available data on body weight and mortality mean. We can point out that these are not necessarily the same thing. Based on the information reviewed above, we reach the following conclusions: 1. While controlling for smoking either by stratifi- cation or statistical adjustment is a sound pro- cess and smoking is a plausible confounder of the BMI—mortality relationship, in actual data sets, adjusting for smoking has very little impact on the results of the analysis. This does not imply that one should not control for smoking. It only implies that smoking appears to be an unlikely explanation for the U- and J-shaped relation- ships frequently observed between BMI and mortality. 2. Excluding subjects who die during the first few years of follow-up is not a reliable way of con- trolling for confounding due to occult disease. In the presence of confounding due to occult dis- ease such exclusions can either increase or de- crease the bias, although in practice such exclu- sions appear to make little difference. Because such exclusions can actually increase the bias under some circumstances and result in an over- all reduction of sample size, we do not recom- mend that subjects dying during the first few years be excluded from the analyses. 3. There is no a priori reason to assume, if a quad- ratic model is fitted to describe the relationship between BMI and mortality and the minimum of this quadratic equation solved for the resulting estimated BMI associated with minimum mor- tality, that the estimate will systematically overestimate the true BMI associated with mini- mum mortality. However, other methods for es- 44 INTERNATIONAL TEXTBOOK OF OBESITY timating the BMI associated with minimum mortality are available and may be superior. These methods do not require that BMI be cat- egorized into quantiles but can be applied to BMI treated as a continuous variable. 4. BMI is a continuous variable and, as with other continuous variables, there is little advantage to categorizing BMI in the final analysis. It is cer- tainly useful to treat BMI categorically in an exploratory manner. However, it is possible to treat BMI continuously in the final analysis and there are a number of advantages to doing so. 5. Though highly correlated with body fatness, BMI is not a true measure of body fatness and it cannot be assumed that BMI will have the same relationship with mortality in either direction or form as will a valid measure of body composi- tion. Therefore, it is strongly suggested that fu- ture research consider including measures of body composition rather than just BMI. 6. There is substantial variation in results from study to study, some of which is probably due solely to random sampling variations. Because of this, selective review of the data can be used to support virtually any conclusion. Therefore, it is essential that reviews of the literature, if they are intended to be objective, evaluate the entire body of the literature to the greatest extent possible. This approach is exemplified in the recent papers by Allison et al. (32), Troiano et al. (9), and The BMI in Diverse Populations Collaborative Group (47). 7. The relationship between BMI and mortality appears to vary substantially by age, sex, and race. Other variables yet to be fully explored may also moderate this relationship. Therefore, it is ill advised to generalize from studies in the one population (e.g. white middle-aged females) to other populations (e.g. young black males or elderly Asian females). Moreover, investigators who wish to make broad statements about the overall ‘average’ relationship between BMI and mortality for the US population will need to rely on samples that are representative of the US population. Finally, this implies that it is wise for investigators to attempt to stratify by or fit inter- action terms with their demographic variables and other possible moderators when analyzing the relationship between BMI and mortality. What the Available Data Show The above conclusions can be used to guide future research investigating the effect of variations in rela- tive body weight on longevity. Collectively, they suggest that measures of body composition should be used over measures of body weight whenever possible, that subjects dying during the first several years not be excluded from the analysis, that measures of either body composition or relative body weight can be treated as continuous variables, that statistical methods can be used to estimate the BMI (or degree of adiposity) associated with mini- mum mortality, and, finally, that alternative methods be pursued to reduce the possibility of confounding due to occult disease (e.g. more careful clinical evaluation at baseline). However, what the data show and what the data mean are not necessarily the same thing. Because the association between BMI and mortality is U- shaped does not mean that the causal relationship between BMI and mortality is U-shaped. As we have shown, the fact that relationship persists even after eliminating subjects who die during the first several years of follow-up cannot be taken as evi- dence that this relationship is not due to confound- ing for pre-existing disease. Moreover, as we have shown, the fact that the relationship between BMI and mortality may be U-shaped does not necessar- ily imply that the relationship between adiposity and mortality is U-shaped. Thus, it is difficult to know exactly what to conclude from the currently available data. Certainly, the currently available data do demonstrate that unusually high levels of BMI (e.g. BMIs greater than the high 20s) are asso- ciated with increased mortality and this is entirely consistent with a great deal of clinical and basic laboratory research. However, over the range of BMI from about 28 down, the picture is not clear. The human epidemiological data suggest that lower BMIs are associated with increased mortality but, as we have argued, there are limits to the strength of the conclusions one can draw from here. Moreover, these data are not easily reconcilable with the re- sults of animal work which show that caloric re- striction is capable of producing substantial in- creases in longevity (81). Finally, such work is not consistent with the clinical evidence that suggests that intentional weight loss is almost always asso- ciated with a reduction in morbidities even among 45BODY WEIGHT, BODY COMPOSITION AND LONGEVITY those who are only mildly overweight (93). It appears that, to date, the approaches that in- vestigators have taken for evaluating the associ- ation between variations in relative body adiposity and mortality have been to rely on weak epi- demiologic data. By ‘weak’ we mean data in which the measured independent variable (e.g. BMI) is only a proxy for the conceptual independent vari- able (i.e. adiposity) and the most plausible con- founding factor (i.e. occult disease) is not measured but only inferred. In the face of such weak data, the approach that some authors seem to believe will yield valid conclusions is a strong statistical analy- sis. In our opinion, this is an example of what has been called ‘under-design and over-analysis’. Though we are as appreciative of the power and beauty of good statistical models as anyone, we believe that no amount of statistical analysis will make weak data strong. If stronger conclusions are to be drawn from future studies we believe that stronger measurements and designs will have to be employed. Such designs should clearly include measures of adiposity, detailed and thorough clini- cal evaluations of health status at study onset, and possibly even the use of large-scale randomized trials (92). 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Lean Wolfson College, Cambridge and Glasgow Royal Infirmary, Glasgow INTRODUCTION AND BACKGROUND Body fatness and body shapes have been topics of interest to people over the ages because of health considerations, but scientific assessment and pres- entation have been complicated by changing fashions and a range of myths. Many methods of measuring body fatness have been developed for epidemiological field studies or clinical use, based on laboratory methods such as underwater weigh- ing as a conventional ‘gold standard’. This two- compartment model estimates body composition with the assumption that the densities of lean (1.1 kg/L) and adipose (0.9 kg/L) tissues are con- stant (1). Indices of obesity have been derived to assess body composition and health at the present, and to predict future health. Rarely a method has been developed specifically for self-monitoring by lay people. One of the tantalizing features of re- search in body composition is the lack of any true gold standard from which to calibrate other methods. Direct measurement by chemical analysis, either by macroscopic dissection or by lipid extrac- tion, is of limited value as it cannot be related to measurements in vivo. PURPOSES AND APPLICATIONS OF ANTHROPOMETRY Simple and cheap anthropometric methods are use- ful for epidemiological surveys of large numbers of subjects across the population. For clinical use, an- thropometric methods are useful tools for diagnosis and monitoring patients. The most appropriate methods may vary depending on whether the need is for cross-sectional or longitudinal assessment. In research studies, physiological characterization of individuals is assessed by a range of anthropometric measurements. One of the most fundamental issues in employing anthropometric measurements to as- sess body fat is that the prediction equations must be validated in a similar population to that to which the equations are being applied. METHODS COMMONLY USED TO MEASURE BODY FATNESS Laboratory Standard Methods For small studies, total body fat is estimated by standard methods (Table 4.1) such as underwater International Textbook of Obesity. Edited by Per Bjo¨ rntorp. © 2001 John Wiley & Sons, Ltd. International Textbook of Obesity. Edited by Per Bjorntorp. Copyright © 2001 John Wiley & Sons Ltd Print ISBNs: 0-471-988707 (Hardback); 0-470-846739 (Electronic) Table 4.1 Methods of measuring body fat and fat distribution Methods Accuracy Practicality Sensitivity to change Cheapness Fat distribution detection Laboratory: ‘standard’ methods Underwater weighing ;;;; ;; ;;; ;;; 9 Potassium-40 counting ;;; ;;; ; ;;; 9 Dual-energy X-ray absorptiometry ;;; ;; ;; ;; ;; Computerized tomography ;;;;; ;;; ;;; ; ;;;;; Magnetic resonance imaging ;;;;; ;;;; ;;; ; ;;;;; Multi-compartment models ;;; ; ; ; 9 Air displacement (BOD POD) ? ;;;; ? ;; 9 Field: anthropometric methods Skinfold thickness ;;; ;;;; ;;; ;;;;; 9 Circumference ;;; ;;;; ;;; ;;;;; ;;;;; Body mass index ;;; ;;;; ;;;;; ;;;;; 9 Figure 4.1 Measuring total body fat by underwater weighing weighing (Figure 4.1), potassium-40 (K) counting, or more recently by imaging techniques such as dual-energy X-ray absorptiometry (DEXA) (which was itself calibrated against underwater weighing), computerized tomography (CT) scan and magnetic resonance imaging (MRI) (Figure 4.2). All these methods make assumptions about composition of ‘average’ tissues, e.g. density of fat, constant Kof 52 INTERNATIONAL TEXTBOOK OF OBESITY Figure 4.2 Magnetic resonance imaging scanner used to image body tissues muscles, or attenuation to X-rays from fat and lean tissues, and thus all the methods have inherent er- ror. Overhydration and dehydration also affect the estimation of body composition. In attempts to overcome these problems, three- and four-compart- ment models to predict body composition have been developed, which employ separate measure- ments of body fat, muscle mass, body water and bones. These multi-compartment methods may im- prove accuracy of measurement but patients are subjected to many tests. Time and costs will limit the number of subjects that can be studied, and accumulated errors from these methods also make this model unattractive. A recently invented tech- nique based on air displacement (BOD POD; Life Measurements Instruments, Concord, CA) has been introduced. This method measures rapidly and is less intimidating; thus it is useful for measur- ing body composition of children and the elderly. It is important to recognize that there is in fact no true gold standard or reference method for body composition analysis. Thus scanning methods de- pend on the resolution of imaging, and they also fail to detect fat within organs such as liver, muscles and bones. Cadaver dissection, coupled with chemical analysis, should theoretically overcome this prob- lem, but there is a very real practical limitation through the time required for dissection and alter- ation in tissue hydration. Field Anthropometric Methods Using an underwater weighing method to predict body fat is impractical for large field studies, requir- ing facilities and the cooperation of subjects and expertise of the investigators. Proxy anthropomet- ric methods (Table 4.1) have been employed includ- ing skinfolds (2), body mass index (BMI) (3), and skinfolds combined with various body circumferen- ces (4—6) to predict body fat estimated by under- water weighing. Body fat predicted from these equa- tions shows high correlations with body fat measured by underwater weighing and relatively small errors of prediction. However, there have been few major validations of these equations in independent populations to test their generalizabil- ity or applicability in special population subgroups. The most widely used field method for total fat has been the four-skinfold methods (Figure 4.3), derived from underwater weighing (2). Recognizing possible errors of predicting body fat in subpopulations with altered fat distribution, regression equations includ- ing waist circumference (Figure 4.4) appear to have advantages in predicting total body fat by taking some account of this variation in fat distribution (6). Waist circumference, alone, predicts health (7) as well as body composition and is recommended for public health promotion (8,9). Previously, little attention has been paid to devel- oping an index of adiposity that could be used by lay people. The BMI has been the traditional index of obesity, but its concept and calculations are not readily understood by many. Criteria for classifica- tion of overweight and obesity have been inconsist- ent. Conventional classification of BMI, using the same criteria for both men and women, is based on life insurance and epidemiological data. Waaler (10) has shown a U-shaped relationship between BMI and mortality rates, with exponential increases of mortality in adult subjects with high BMI ( 9 30 kg/m) or low BMI ( : 20 kg/m). These cri- 53ANTHROPOMETRIC INDICES OF OBESITY [...]... Table 5 .2 Comparing the distribution of new diabetes cases at three different levels of body mass index in two populations Reference Colditz (33)? Chan (18)@ Body mass index Diabetes cases (% of total) Person-years of follow-up (% of total) P27 P31 P35 P27 P31 P35 76 49 26 61 27 9 22 .1 8.3 3.1 23 .5 4 .2 0.7 ?The Nurses’ Health Study @The Health Professionals’ Follow-up Study Figure 5 .2 (a) The age-adjusted... cases 29 .0—30.9 31.0— 32. 9 33.0—34.9 P35 329 26 3 22 4 579 84 880 47 119 29 885 46 636 Hypothetical Age-standardized number of incidence rate? diabetic cases@ 354.5 521 .2 703.6 1190.5 Body mass index Person-years of follow-up 329 167 106 165 ?Age-standardized rate per 100 000 persons @Hypothetical number of cases has been calculated by using the incidence rate among individuals in the BMI range of 29 .0—30.9... 1995; 73: 25 29 20 Han TS, Lean MEJ Body composition in patients with non-insulin-dependent diabetes and central fat distribution Diabet Med 1994; 11(Suppl 1): S39 21 Han TS, Lean MEJ Lower leg length as an index of stature in adults Int J Obes 1996; 20 : 21 27 22 Reilly JJ, Murray LA, Durnin JVGA Measuring the body composition of elderly subjects: a comparison of methods Br J Nutr 1994; 72: 33—44 23 Han... Clin Nutr 1956; 4: 20 —34 27 Kissebah AH, Vydeligum N, Murray R, Eveans DJ, Hartz J, Kalkhoff RK, Adams PW Relation of body fat distribution to metabolic complications of obesity J Clin Endo-crinol Metab 19 82; 54: 25 4 26 0 28 Lapidus L, Bengtsson C, Larsson B, Pennert K, Rybo E, Sjostrom L Distribution of adipose tissue and risk of car¨ ¨ diovascular disease and death 12 year follow-up of participants in... adults Int J Obes 1997; 21 : 83—89 International Textbook of Obesity Edited by Per Bjorntorp Copyright © 20 01 John Wiley & Sons Ltd Print ISBNs: 0-4 7 1-9 88707 (Hardback); 0-4 7 0-8 46739 (Electronic) 5 Screening the Population Bernt Lindahl Umea University, Umea, Sweden An epidemic of obesity and type 2 (non-insulindependent) diabetes is in progress across the world The global burden of diabetes has been... acceptability Waist-to-hip Ratio The ratio of waist-to-hip circumferences (Figure 4.4) was the first anthropometric method developed from epidemiological research as an indicator of fat distribution in relation to metabolic diseases Waist-to-hip ratio is related more closely to the ratio of intra-abdominal fat/extra-abdominal fat mass than the absolute amount of intra-abdominal fat mass ( 32) , and has been... anthropometric measures: the influence of stature Int J Obes 1997; 21 : 587—593 24 Khosla T, Lowe CR Indices of obesity derived from body weight and height Br J Prev Med 1967; 1: 122 — 128 25 World Health Organization Obesity: Preventing and Managing the Global Epidemic Geneva: World Health Organization, WHO/NUT/NCD/98.1, 1998 26 Vague J The degree of masculine differentiation of obesity factors determining predisposition... development of type 2 diabetes in the population In the Nurses’ Health Study more than 100 000 nurses participated and were followed with respect to diabetes incidence over 14 years More than 20 00 cases of diabetes were diagnosed during 1.49 million person-years of follow-up (33) In the Health Professionals’ Follow-up Study, more than 50 000 male health professionals participated During 5 years of observation,... prevention of type 2 diabetes might combine one screening test for obesity and one test for glucose tolerance In that way, only participants having the combination of obesity and impaired glucose tolerance are classified as high-risk individuals A further refinement of the programme, at least if the object is to reduce numbers-needed-to-screen to detect a high-risk individual, would be to choose a high-risk... L et al The influence of body fat distribution on the incidence of diabetes mellitus—13.5 years of followup of the participants in the study of men born in 1913 Diabetes 1985; 34: 1055—1058 31 Bjorntorp P Metabolic implications of body fat distribu¨ tion Diabetes Care 1991; 12: 11 32 1143 32 Ashwell M, Cole TJ, Dixon AK Obesity: new insight into the anthropometric classification of fat distribution shown . 23 years. Arch Intern Med 1971; 128 : 20 1 21 4. 21 . Menotti A, Descovich GC, Lanti M. Indexes of obesity and all-causes mortality in Italian epidemiological data. Prev Med 1993; 22 : 29 3—303. 22 effects of modest weight loss. Int J Obes Metab Disord 19 92; 16: 397—415. 48 INTERNATIONAL TEXTBOOK OF OBESITY MMMM Part II Diagnosis 4 Anthropometric Indices of Obesity and Regional Distribution of. underwater International Textbook of Obesity. Edited by Per Bjo¨ rntorp. © 20 01 John Wiley & Sons, Ltd. International Textbook of Obesity. Edited by Per Bjorntorp. Copyright © 20 01 John Wiley