Thisfigure is intended to convey that as studies of weightloss and mortality rate have become methodologi-cally more sound, what initially appeared to be aharmful effect has progressively
Trang 1Table 3.2 Two by two table weight loss by intention
No intention to lose weight Intention to lose weight
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 tional studies of so-called intentional weight loss,subjects were initially measured decades ago (82,87) By what methods did they achieve weight lossdecades ago? Among others, by drugs and surgicalprocedures that are far less safe than those currentlyavailable Even as late as 1997 some widely pre-scribed drugs were removed from the market be-cause of dangerous effects (89) Still today, methodsfor intentionally inducing weight loss include faddiets (90), herbal supplements of untested safety,bulimia and other methods of highly questionablesafety Hence, it appears ill advised to estimate theeffects weight loss achieved by medically recom-mended methods by studying weight loss that ismerely reported to be ‘intentional.’ What is needed
observa-is studies of weight loss that observa-is produced amongobese humans by modern methods that are accep-ted by mainstream medicine
Presently, a well-controlled non-randomized,
study of weight loss produced by surgery amongmorbidly obese adults is underway (91) Mortalityresults are not yet available A randomized clinicaltrial (RCT) testing whether producing weight lossthrough medically accepted methods among obesepeople can reduce mortality rate could settle theseissues (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 secondaryoutcome, it is not necessarily powered to detectdifferences in mortality rates
Our perspective of the admittedly incomplete 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 aconjecture of what the future might bring Thisfigure is intended to convey that as studies of weightloss and mortality rate have become methodologi-cally more sound, what initially appeared to be aharmful effect has progressively shifted to be neutral
evi-43 BODY WEIGHT, BODY COMPOSITION AND LONGEVITY
Trang 2’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 shouldonly be recommended under conditions where asufficient 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 reiteratingkey methodological conclusions We follow thiswith a discussion of what we believe the currentlyavailable data on relative body weight and mortal-ity show and what the currently available data onbody weight and mortality mean We can point outthat these are not necessarily the same thing.Based on the information reviewed above, wereach the following conclusions:
1 While controlling for smoking either by cation or statistical adjustment is a sound pro-cess and smoking is a plausible confounder of the
stratifi-BMI—mortality relationship, in actual data sets,
adjusting for smoking has very little impact onthe results of the analysis This does not implythat one should not control for smoking It onlyimplies that smoking appears to be an unlikelyexplanation for the U- and J-shaped relation-ships frequently observed between BMI andmortality
2 Excluding subjects who die during the first fewyears of follow-up is not a reliable way of con-trolling for confounding due to occult disease Inthe 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 Becausesuch exclusions can actually increase the biasunder some circumstances and result in an over-all reduction of sample size, we do not recom-mend that subjects dying during the first fewyears be excluded from the analyses
3 There is no a priori reason to assume, if a ratic model is fitted to describe the relationshipbetween BMI and mortality and the minimum ofthis quadratic equation solved for the resultingestimated BMI associated with minimum mor-tality, that the estimate will systematicallyoverestimate the true BMI associated with mini-mum mortality However, other methods for es-
Trang 3timating 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 futureresearch investigating the effect of variations in rela-tive body weight on longevity Collectively, theysuggest that measures of body composition should
be used over measures of body weight wheneverpossible, that subjects dying during the first severalyears not be excluded from the analysis, thatmeasures of either body composition or relativebody weight can be treated as continuous variables,that statistical methods can be used to estimate theBMI (or degree of adiposity) associated with mini-mum mortality, and, finally, that alternativemethods be pursued to reduce the possibility ofconfounding due to occult disease (e.g more carefulclinical evaluation at baseline)
However, what the data show and what the datamean are not necessarily the same thing Becausethe association between BMI and mortality is U-shaped does not mean that the causal relationshipbetween BMI and mortality is U-shaped As wehave shown, the fact that relationship persists evenafter eliminating subjects who die during the firstseveral 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 haveshown, the fact that the relationship between BMIand mortality may be U-shaped does not necessar-ily imply that the relationship between adiposityand mortality is U-shaped Thus, it is difficult toknow exactly what to conclude from the currentlyavailable data Certainly, the currently availabledata do demonstrate that unusually high levels ofBMI (e.g BMIs greater than the high 20s) are asso-ciated with increased mortality and this is entirelyconsistent with a great deal of clinical and basiclaboratory research However, over the range ofBMI from about 28 down, the picture is not clear.The human epidemiological data suggest that lowerBMIs are associated with increased mortality but,
as we have argued, there are limits to the strength ofthe 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 notconsistent with the clinical evidence that suggeststhat intentional weight loss is almost always asso-ciated with a reduction in morbidities even among
45 BODY WEIGHT, BODY COMPOSITION AND LONGEVITY
Trang 4those who are only mildly overweight (93).
It appears that, to date, the approaches that
in-vestigators have taken for evaluating the
associ-ation between variassoci-ations 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)
ACKNOWLEDGEMENTS
This work was supported in part by National
Insti-tutes of Health grants R01DK51716, P30DK26687
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Trang 7Part II
Diagnosis
Trang 8Anthropometric Indices of Obesity and Regional Distribution of Fat
Depots
T.S Han and M.E.J 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
use-is for cross-sectional or longitudinal assessment Inresearch studies, physiological characterization ofindividuals is assessed by a range of anthropometricmeasurements One of the most fundamental issues
in employing anthropometric measurements to sess body fat is that the prediction equations must
as-be validated in a similar population to that to whichthe equations are being applied
METHODS COMMONLY USED TO MEASURE BODY FATNESS Laboratory Standard Methods
For small studies, total body fat is estimated bystandard methods (Table 4.1) such as underwater
International Textbook of Obesity Edited by Per Bjorntorp.
Copyright © 2001 John Wiley & Sons Ltd Print ISBNs: 0-471-988707 (Hardback); 0-470-846739 (Electronic)
Trang 9Table 4.1 Methods of measuring body fat and fat distribution
Sensitivity to
Fat distribution detection
Laboratory: ‘standard’ methods
Field: anthropometric methods
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 magneticresonance imaging (MRI) (Figure 4.2) All thesemethods make assumptions about composition of
‘average’ tissues, e.g density of fat, constantK of
Trang 10Figure 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 andbones Cadaver dissection, coupled with chemicalanalysis, should theoretically overcome this prob-lem, but there is a very real practical limitationthrough the time required for dissection and alter-ation in tissue hydration
Field Anthropometric Methods
Using an underwater weighing method to predictbody fat is impractical for large field studies, requir-ing facilities and the cooperation of subjects andexpertise of the investigators Proxy anthropomet-ric methods (Table 4.1) have been employed includ-ing skinfolds (2), body mass index (BMI) (3), andskinfolds combined with various body circumferen-
ces (4—6) to predict body fat estimated by
under-water weighing Body fat predicted from these tions shows high correlations with body fatmeasured by underwater weighing and relativelysmall errors of prediction However, there havebeen few major validations of these equations inindependent populations to test their generalizabil-ity or applicability in special population subgroups.The most widely used field method for total fat hasbeen the four-skinfold methods (Figure 4.3), derivedfrom underwater weighing (2) Recognizing possibleerrors of predicting body fat in subpopulations withaltered fat distribution, regression equations includ-ing waist circumference (Figure 4.4) appear to haveadvantages in predicting total body fat by takingsome account of this variation in fat distribution (6).Waist circumference, alone, predicts health (7) aswell as body composition and is recommended forpublic health promotion (8,9)
equa-Previously, little attention has been paid to oping an index of adiposity that could be used bylay people The BMI has been the traditional index
devel-of obesity, but its concept and calculations are notreadily understood by many Criteria for classifica-tion of overweight and obesity have been inconsist-ent Conventional classification of BMI, using thesame criteria for both men and women, is based onlife insurance and epidemiological data Waaler (10)has shown a U-shaped relationship between BMIand mortality rates, with exponential increases ofmortality in adult subjects with high BMI( 30 kg/m) or low BMI ( 20 kg/m) These cri-
53 ANTHROPOMETRIC INDICES OF OBESITY
Trang 11Figure 4.3 Measuring subcutaneous skinfold thicknesses at the sites of biceps, triceps, subscapular and suprailiac using skinfold
calipers
teria for interpreting BMI do not apply to children,
whose BMI is normally much lower than that of
adults The arbitrary cut-offs for overweight at BMI
25 and for obesity at BMI 30 kg/m have now been
adopted by the National Institutes of Health (8) in
America and World Health Organization (11)
These cut-offs have been used widely in Europe for
many years
MEASURING BODY COMPOSITION
IN SPECIAL GROUPS
There are major effects of age on body composition
which mean that anthropometric methods may not
always be valid A major pitfall is the unquestioning
use of equations to predict body fat derived from
one population group, without subsequent
valida-tion in another specific populavalida-tion group
Infants and Children
For infants, the ‘reference’ methods used to mine body composition in infants includeO iso-tope dilution (12) and DEXA (13) The ponderalindex (weight divided by height cubed) has beenused as an anthropometric method to assess bodyfatness Measuring children’s body composition isproblematic, as their tissue composition varies withgrowth, the rate and timing of growth vary widely,and physical activity influences the composition offat free mass (14) Several studies have used doublylabelled water to monitor children’s growth andestimate their total body water, and thus body com-position Reference values for body weight andtriceps skinfold thickness of British children have
deter-been provided by Tanner and co-workers (15—17),
although the Tanner reference values for weight are
no longer appropriate in the UK In a validation
study, Reilly et al (18) have shown that the skinfold
method produces large errors in predicting body fat
Trang 12Figure 4.4 Measuring waist circumference midway between
iliac crest and lowest rib margin, and hip circumference at the
level of the greater trochanters
of 9-year-old children These workers used
under-water weighing as the reference method and found
it acceptable even to very young children As a
generalization, anthropometric methods to
esti-mate body fat are not reliable in children BMI can
be used, but with caution in its interpretation
be-cause of variable stages of development at the same
age There are standard BMI reference curves
(Fig-ure 4.5) developed by Cole et al (19) for the Child
Growth Foundation
Ageing and Elderly
Intra-abdominal fat increases with age and
immo-bility, and thereby tends to invalidate the
subcu-taneous skinfold methods In postmenopausal
women, body fat may accumulate
intra-abdomi-nally as a result of hormonal changes
Consequent-ly, subcutaneous skinfold methods may
underesti-mate their total body fat
Athletes
In athletes, BMI does not reflect body fat very well,particularly in power athletes who have largemuscle mass Inaccuracies in anthropometric pre-diction equations stem from the reference methods,such as underwater weighing, from which they arederived since the density of these subjects’ lean tis-sues is considerably higher than the 1.1 kg/L value(1) used for estimating body composition in the
‘normal’ population Muscle varies considerably as
a proportion of total lean body mass
Illness
Conventional anthropometric prediction equationsbreak down with altered relative body composition.For example, patients with advanced tuberculosisand cancer or with benign oesophageal stenosismay have similar BMIs as a result of weight loss,but muscle loss is likely to be greater in a cachecticinflammatory condition Errors will therefore resultfrom using the same body composition predictionequations Illnesses that result in considerable loss
of minerals or specific tissues, e.g muscle wasting inpatients with acquired immune deficiency syn-drome (AIDS), may result in an overestimation ofbody fat using conventional prediction equations
In contrast, in patients with non-insulin-dependentdiabetes mellitus (NIDDM) (Type 2 diabetes) whohave increased intra-abdominal fat, there is under-estimation of body fat using skinfold methods,which increases with the amount of central fat de-position (20) There is a problem in measuring bodycomposition of amputees whose substantial ab-sence of muscle mass gives unrealistic BMI values
In bed-or chair-bound patients, height ment is not available for calculation of BMI Alter-native methods including arm span and lower leglength can be used to predict height with an accu-racy within 4 cm (21)
measure-ANTHROPOMETRIC ASSESSMENT OF
OBESITY Predicting Total Body Fat from Skinfold
Thicknesses
Four skinfold thicknesses are conventionally
meas-55 ANTHROPOMETRIC INDICES OF OBESITY
Trang 13Figure 4.5 Standard body mass index curves from birth to 20 years for boys (a, above) and girls (b, opposite) Copyright © Child
Growth Foundation Reproduced by permission Copies of the CGF BMI charts are available from Harlow Printing, Maxwell Street, South Shileds NE33 4PU, UK
ured (Figure 4.3), using calipers at biceps, triceps,
subscapular and suprailiac, and the sum of all four
skinfolds (equation 1), or just the triceps skinfold
(equation 2), with subjects’ age, are used in linear
multiple regression to predict total body fat The
original equations for use in adults (2) have been
cross-validated in a separate sample and found to
be robust in adults aged 20—60 years (6), but tend to
underestimate substantially the total fat in the derly, particularly women (6,22) (Figure 4.6).Body fat % (men): [30.9 ; log skinfolds(mm)]; [0.271 ; Age (years)] 9 39.9 (1)Body fat % (women): [30.8 ; log skinfolds(mm)]; [0.274 ; Age (years)] 9 31.7
Trang 14Body fat % (men): (1.31 ; Triceps)
; (0.430 ; Age) 9 9.2 (2)
Body fat % (women): (0.944 ; Triceps)
; (0.279 ; Age) ; 4.6
Predicting Total Body Fat from Waist
Circumference and Triceps Skinfold
Han and Lean (20) have observed a systematic
underestimation of body fat by equations using
subcutaneous skinfold thicknesses (2) in subjectswith increased intra-abdominal fat mass, reflected
by a high waist circumference or waist-to-hip ratio,including the elderly and those with type 2 diabetes.Waist circumference (Figure 4.4) has been found tocorrelate highly with both intra-abdominal and to-tal fat masses (6,23), and was used on its own andwith skinfold thicknesses to develop new regressionequations to correct for the intra-abdominal fatmass (6) These equations were validated in a largeDutch sample from previous study of body fat dis-tribution (3)
57 ANTHROPOMETRIC INDICES OF OBESITY
Trang 15Figure 4.6 Plots of errors of predicting body fat by underwater
weighing from equations using waist circumference (— —,
Lean et al (6) and subcutaneous skinfolds
( -Womersley (2) against age in men (a) and women (b) aged 18 to
83 years
Equations using waist circumference alone,
ad-justed for age (equation 3), showed good prediction
of body fat in the independent Dutch sample
(r : 78%) with similar error of prediction as other
equations These equations are particularly good
for estimating body fat in the elderly without the
systematic underestimation of body fat that occurs
in the subcutaneous skinfold method (Figure 4.6)
Body fat % (men): [0.567 ; Waist circumference
(cm)]; [0.101 ; Age (years)] 9 31.8 (3)
Body fat % (women): [0.439 ; Waist
circumfer-ence (cm)]; [0.221 ; Age (years)] 9 9.4
Equations combining waist circumference and
triceps skinfold, adjusted for age (equation 4), have
been shown to improve predictive power of body fat
estimation without systematic errors over
equa-tions employing subcutaneous skinfolds alone in
subjects with type 2 diabetes who had increased
intra-abdominal fat mass (20)
Body fat % (men): [0.353 ; Waist (cm)] ;
[0.756; Triceps (mm)] ; 0.235 ; Age (years)]
Body fat % (women): [0.232 ; Waist (cm)] ;
[0.657; Triceps (mm)] ; [0.215 ; Age (years)]
9 5.5
Calculations of Body Mass Index
(Quetelet Index), and its Use to Predict
Body Fat
Bigger people—both taller and fatter—are heavier
than small people Body weight includes fat, muscle
and all other organs For people of the same height,
most of the variation in weight is accounted for by
different amounts of body fat BMI aims to describe
weight for height in a way which will relate
maxi-mally to body weight (or body fat) with minimal
relation to height (24) BMI is calculated as the ratio
of weight in kilograms divided by height squared
(m) Since BMI uses height, the height
measure-ment needs to be very accurate Classification of
BMI (Table 4.2) uses the same criteria for both men
and women is now adopted by both the NIH (8) and
WHO (25) A BMI of 18.5 to 24.9 kg/m is
consider-ed as in the normal range, above 25 kg/m as
over-weight and above 30 kg/m as obese For some
purposes, the obese category is subclassified by the
WHO (25) as 30—34.9 (moderately obese), 35—39.9
(severely obese), and greater than 40 (very severelyobese) kg/m
BMI can be used to predict body fat from
under-water weight (r : 79%) with age and sex tions (3) Our derived equations using BMI to pre-dict body fat were validated in the independent
correc-sample provided by Deurenberg et al (3) and
showed similarly good prediction of body fat asother equations currently in use (equation 5).Body fat % (men): [1.33 ; BMI (kg/m)] ;[0.236; Age (years)] 9 20.2 (5)Body fat % (women): [1.21 ; BMI (kg/m)] ;[0.262; Age (years)] 9 6.7
ANTHROPOMETRIC ASSESSMENT OF BODY FAT DISTRIBUTION
People with central fat distribution in both sexes
Trang 16Table 4.2 Classification of body mass index for body fatness
adopted by the World Health Organization (25) and US National Institutes of Health (8)
BMI (kg/m ) Classification
18.5—24.9 Acceptable
25—29.9 Overweight—increased health risks P30 Obesity—high health risks
tend to have a distinct body shape, said to resemble
that of an apple (Figure 4.7), a physical
characteris-tic of men (termed ‘android’ by Vague) which tends
to be associated with metabolic abnormalities and
chronic diseases (26—31).
Body circumferences and their ratios are used to
indicate the distribution of body fat The most
im-portant variations, in terms of health associations,
are between the amounts of fat in internal, mainly
intra-abdominal sites, as distinct from
subcu-taneous sites (Figure 4.8) The ‘gold standard’ for
measuring fat depots in these sites is scanning by
MRI (Figure 4.2) CT gives almost equal
informa-tion but the small radiainforma-tion exposure limits its
ac-ceptability
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 shown to relate to
mor-tality from coronary heart disease and type 2
dia-betes independent of BMI (28,29) Most of the value
in indicating body fat is derived from waist
circum-ference, the hip circumference probably reflecting
several other body tissues such as bones and
muscles The waist-to-hip ratio may have some
par-ticular value in reflecting diseases which involve
muscle reduction as well as fat deposition, e.g type
2 diabetes (33)
Waist-to-thigh Ratio
In some studies waist-to-thigh ratio has been used
as an index for fat distribution to relate to metabolic
risk factors (34) This ratio is also influenced by
abdominal fat as well as fat mass, muscle mass and
bone structures of the thigh, which may be a strong
indicator of certain health conditions involving
both abdominal fat accumulation and skeletal
muscle wasting such as NIDDM
Conicity Index
The conicity index was formulated by Valdez (35) toestimate abdominal fat, based on the theory thatleaner subjects have a body shape similar to a cylin-der, but as fat is accumulated around the abdomen,the body shape changes towards that of a doublecone (two cones with a common base at the waist).With the assumption that the average human bodydensity is 1.05 kg/m, the equation was derived as:Conicity index: Waist
(0.109; (weight/height) (6)The conicity index is theorized to have a built-inadjustment for height and weight so that abdominaladiposity can be compared across different popula-tions of varying heights and weights (36) The index
is related to the ratio of intra-abdominal abdominal fat mass similarly to waist-to-hip ratio,and may be useful when hip measurement is not
fat/extra-available Valdez et al (36) found the conicity index
to be correlated to cardiovascular risk factors larly to that of waist-to-hip ratio in different coun-tries A drawback is that the index has not beencross-validated to ensure applicability
simi-Sagittal Abdominal Diameter
The use of sagittal diameter of the waist has beenproposed as an index of abdominal fatness based on
a theory that fat deposition in the anteroposterioraxis is more ‘dangerous’ than lateral fat deposition.This index has not been validated in an independentpopulation Sagittal diameter can be measured us-ing a pelviometer in the standing position, or amore sophisticated instrument that is modifiedfrom a sliding stadiometer in the supine position(37) Gadgets are on sale with a back plate which is
59 ANTHROPOMETRIC INDICES OF OBESITY
Trang 17Figure 4.7 Silhouette photographs showing variation in
hu-man body fat distribution
flexible, thereby introducing enormous errors The
measurement of sagittal diameter of the waist has
not been used very widely This method has recently
been validated by CT scanning and found to have
high reproducible results
Abdominal Cross-sectional Area
Abdominal cross-sectional area (CSA ) has also
been proposed by van der Kooy et al (38) as an
index of abdominal fat and is calculated from waist
sagittal (WSD) and waist transverse diameters
(WTD) as: CSA :(4 ;WSD; WTD)/,but this
more complicated method is not likely to be muchdifferent from a circumference or a single measure-ment of waist diameter
Waist Circumference
Recent proposals for the use of waist circumference
as a single measurement of body fat and fat tion have now been adopted by several major pub-lic health promotion agencies and organizations(8,9,21)
distribu-Waist circumference has been suggested as asimple measurement to identify individuals withhigh BMI or high waist-to-hip ratio Waist circum-ference correlates significantly with BMI (both men
and women: r : 0.89; P 0.001) Lean et al (39)
have derived the ‘action levels’ for weight ment based on the waist circumference of over 2000men and women (Table 4.3) Action level 1: Waistcircumference of P 94 cm in men or P 80 cm inwomen identifies as overweight with increasedhealth risks, those with BMI P 25 kg/m and highwaist-to-hip ratio (P 0.95 for men; P 0.80 forwomen) These subjects are advised not to gainfurther body weight and to increase physical activ-ity Action level 2: Waist circumference of P 102 cm
manage-in men or P 80 cm in women identifies as weight with high health risks, those with BMI
over-P 30 kg/m and high waist-to-hip ratio ( over-P 0.95 inmen and P 0.80 in women) Weight loss and con-sultation of health professionals are recommendedfor these individuals These action levels for weightmanagement have a sensitivity (correctly identifiesindividuals who need weight management by waistcircumference above action levels) and a specificity(correctly identifies individuals who do not needweight management by waist circumference belowaction levels) of more than 96% for identifyingoverweight and obese subjects with high waist-to-hip ratio Waist circumference is not importantlyinfluenced by height (40) (Figure 4.9), thus it is notnecessary to divide waist by height when usingwaist circumference as an index of adiposity Toavoid problems with over-tightening during waistmeasurement, a specially designed ‘Waist Watcher’spring-loaded tape measure has been producedwith three colour bands based on cut-offs of thewaist circumference action levels (Figure 4.10)
Trang 18Table 4.3 Action levels to identify overweight and obese men and women with increased abdominal fat
Waist circumference Approximate equivalents
Action level cm
Body mass index
Waist-to-hip ratio Classification of health risks Weight management Men
Action level 1 P94 P25 P0.95 Increased health risks Prevent further weight gain, try to get
down to below action level 1 (94 cm) Action level 2 P102 P30 P0.95 High health risks Seek advice to lose weight, aim for 5—10%
weight loss permanently Women
Action level 1 P80 P25 P0.80 Increased health risks Prevent further weight gain, try to get
down to below action level 1 (80 cm) Action level 2 P88 P30 P0.80 High health risks Seek advice to lose weight, aim for 5—10%
weight loss permanently
METHODS FOR ANTHROPOMETRIC
MEASUREMENTS
Body Weight
Weight is measured by digital scales or beam
bal-ance to the nearest 100 g For those unable to stand,
electronic chair scales (Weighcare C, Marsden Ltd,
London) are available For field work, portable
scales are used Equipment is calibrated regularly
by standard weights (4; 10 kg and 8 ; 10 kg), and
the results of test weighing recorded in a book
Subjects are weighed in light clothing, fasting and
with an empty bladder, preferably at the same time
of day
Height
Height is measured by stadiometer to the nearest
millimetre, which is calibrated by meter rule before
use When possible, a wall mounted stadiometer is
preferred For field work, a portable stadiometer
(Leicester Height Measure, Child Growth
Founda-tion, London, UK; Holtain, Crymych, UK) is
avail-able Subjects stand in bare feet which are kept
together and pointing forward The head is level
with horizontal Frankfurt plane (line from lower
border of the eye orbit to the auditory meatus)
Subjects are encouraged to stretch upwards by
ap-plying gentle pressure at the mastoid processes and
height is recorded with subjects taking in a deep
breath for maximum measurement
Limb Lengths
When height measurement is not available in and chair-bound patients Height can be predictedfrom arm span or lower leg length (21) Arm span ismeasured between finger tips with subjects standingagainst the wall, and both arms fully stretch hori-zontally Demi-arm span is measured as the hori-zontal distance from the web space between middleand fourth fingers to the midpoint of the sternalnotch to the nearest millimetre, in the sitting posi-tion Lower leg length is measured with subjectssitting in a chair adjusted to about their knee height,and the lower legs and bare feet flexed at 90° The
bed-lower legs, 25—30 cm apart, are adjusted to vertical
position both side and front views A ruler standing
on its edge is placed on top of the patellae Lowerleg length is taken to the nearest millimetre from themidpoint of the ruler to the floor with a woodenmetre rule
Waist Circumference
Waist circumference is measured midway betweenthe lower rib margin and iliac crest, with a horizon-tal tape at the end of gentle expiration (Figure 4.4),
with feet kept 20—30 cm apart Subjects should be
asked not to hold in their stomach, and a constanttension spring-loaded tape device reduces errorsfrom over-enthusiastic tightening during measure-ment Waist circumference measurement reflectsbody fat and does not include most of the bone
61 ANTHROPOMETRIC INDICES OF OBESITY
Trang 19Figure 4.8 Subcutaneous and intra-abdominal fat images obtained from magnetic resonance imaging (a, above) Male; (b, opposite)
female Light areas indicate fat
structure (only the spine) or large muscle masses,
whose variations between subjects might otherwise
introduce errors
Hip Circumference
Maximum hip circumference is measured with a
horizontal steel tape at the widest part of the
tro-chanters at horizontal position (Figure 4.4) with feet
kept 20—30 cm apart It is related more closely to
subcutaneous fat than to intra-abdominal fat mass
Hip circumference has limited value on its own in
body composition estimation The circumference of
the hip is influenced by gluteal muscle mass and
pelvic size, which vary between subjects, as well as
by fat
Thigh Circumference
Thigh circumference is measured at the level ofgluteal fold with the leg being measured relaxed byplacing it forward and slightly bent, with bodyweight transferred to the other leg It estimates fat
on the thigh but will also be altered by muscle mass
Waist Diameter
Abdominal fat deposition is further classified intomedial (fat is accumulated at the middle of theabdomen) and lateral (fat is accumulated at thesides of the abdomen) Waist diameters are meas-ured using a pelviometer or a more expensive devicethat measures the supine sagittal abdominal diam-eter (37) The pelviometer is a cheaper instrument
Trang 20that looks like a pair of large calipers and measures
the waist diameter at the level between the lower rib
margin and iliac crest Waist sagittal diameter is
taken as the distance from the back to the front of
the abdomen measured with the subject standing
Waist transverse diameter is taken as the distance
from the sides of the abdomen
Skinfold Thickness
Skinfold thicknesses are measured on the left side of
the body with calipers (Holtain Ltd, Crymych, UK)
in triplicate, to the nearest 0.2 mm All the sites
intended for measurements should be marked
clear-ly on the skin after making measurements from
bony landmarks (Figure 4.3) When the subjects
relax their mucles, the subcutaneous fat layer
(commonly referred to as skinfold thickness)
cover-ing the muscles is relatively loose and can usually be
pinched easily by two fingers (thumb and indexfinger) which hold the skinfold firmly throughoutthe measurement (11) The pinch is made at about 1
to 2 cm above the ink mark so that the jaw of thecalipers can be applied at the mark The thickness ofthe skinfold is read about 2 seconds after closing thejaw of the calipers
Biceps and triceps skinfold thicknesses are made
at the midpoint of the upper arm, between the romion process and the tip of the bent elbow Sub-scapular skinfold thickness is picked up at the natu-
ac-ral fold about 2—3 cm below the shoulder blade in
an oblique angle Suprailiac skinfold is pinched at
about 2—3 cm above the iliac crest, in either a
verti-cal or oblique angle on the lateral side and axillary line The upper limit of skinfold calipers is
mid-50 mm, which is exceeded for the subscapular sitewhen BMI is greater than 40 kg/m Thus for veryoverweight people, other methods are required
63 ANTHROPOMETRIC INDICES OF OBESITY
Trang 21Figure 4.9 The relationship between waist circumference and
height in 2183 men (a) and 2698 women (b) showing regression
line (solid) and the line of zero correlation (dashed)
Figure 4.10 ‘‘Waist Watcher’’ tape measures with three colour
bands (green, orange and red) based on cut-off waist ence action levels (BGA, The Spire, Egypt Road, Nottingham NG7 7GD, UK)
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65 ANTHROPOMETRIC INDICES OF OBESITY
Trang 23Screening the Population
Bernt Lindahl
Umea University, Umea, Sweden
An epidemic of obesity and type 2
(non-insulin-dependent) diabetes is in progress across the world
The global burden of diabetes has been projected to
rise, with an increase in the total number of people
with type 2 diabetes from about 120 million in 1997
to about 215 million in 2010 (1) The obvious
rem-edy for this development is to combat overweight
and obesity, and to counteract the sedentary
life-style of modern society The basis for these actions
must be a population strategy of prevention
How-ever, an equally important mission, not least from
an ethical standpoint, is to find and engage those
individuals that are in most need of help (high-risk
strategy) The screening procedure is used to
ident-ify these high-risk individuals Screening in this
chapter refers to prescriptive screening, whose aim
is to use early detection and early treatment to
control the outcome of the disease In
epidemiologi-cal surveys, the principal aim of the screening is to
explore the prevalence and natural history of the
variable in question and not to bring patients to
treatment
PRINCIPLES OF SCREENING
Screening may be defined as the examination of
apparently well or asymptomatic people in order to
find out if they are likely or unlikely to suffer from
disease They can then, if they are likely to have the
disease, be placed under treatment early in the
natu-ral course of the disease The goal of screening is to
detect and treat the disease as early as possible and
thereby reverse or retard the disease process times the object of the screening procedure is to findpeople at high risk of getting a disease By identi-fying precursors of disease and correcting these, thedisease may be postponed or at best prevented.There is no sharp line between a risk factor and adisease (2)
Some-The screening procedure must always be lowed by a treatment programme offering treat-ment to those found to have a disease or an in-creased risk of getting the disease A screeningprogramme can thus be divided into a diagnosticand a therapeutic component In 1968, an increasedinterest in screening inspired the WHO to publish aPublic Health Paper with the title ‘Principles andpractice of screening for disease’ (3) This presentedbasic principles of screening together with practicaland ethical considerations (Table 5.1) Launching ascreening programme is a complex task, which ifnot done appropriately may lead to serious conse-quences Several questions of an ethical and practi-cal nature must be considered By using certainscreening principles, the chance of success will in-crease and the risk of serious adverse consequenceswill diminish
fol-The Importance of the Problem
The importance of the health problem needs to beregarded from the point of view of the individual aswell as of the community An uncommon conditionwith serious consequences for the individual, such
International Textbook of Obesity Edited by Per Bjorntorp.
Copyright © 2001John Wiley & Sons Ltd Print ISBNs: 0-471-988707 (Hardback); 0-470-846739 (Electronic)
Trang 24Table 5.1 Principles of screening
1 The condition sought should be an important health
problem
2 There should be an accepted treatment for patients with
recognized disease (condition)
3 Facilities for diagnosis and treatment should be available
4 There should be a recognizable latent or early symptomatic
stage
5 There should be a suitable test
6 The natural history of the condition, including development
from latent to manifest disease, should be adequately
understood
Modified from Wilson and Jungner (3).
as phenylketonuria, may warrant screening
measures just as much as a more common but
individually less serious condition
The Necessity of an Accepted
Treatment and Resources for its
Implementation
Perhaps the most important criterion for a
screen-ing programme is to have an accepted treatment for
those screened positive It must be admitted that
there are many difficulties in evaluating the
out-come of a screening programme Often, months or
years must pass before the gains may be
measur-able It is vitally important to determine whether
earlier treatment really does give a better prognosis
If this is not the case, then there is no advantage in
alerting the patient of the risk condition by early
detection No screening programme should be
im-plemented without first having ensured that there
are adequate resources in personnel and equipment
for all individuals in need of treatment to get it The
responsibility is clearly on the health care
organiz-ation that has initiated the screening examinorganiz-ation
and urged the individual to respond
A Latent or Early Symptomatic Stage of
the Disease
The disease must have a recognizable latent or early
stage that can be detected by the screening test The
interval from this detection to the time when
diag-nosis ordinarily would have been made due to
symptoms, which would make the person seek
medical attention, is known as the lead time Inother words, lead time is the amount of time bywhich treatment may be begun earlier due to thescreening programme To summarize, if early treat-ment is not especially helpful, then there is no point
in early detection
Characteristics of the Screening Test
The validity of a screening test is measured by itssensitivity and specificity ‘Sensitivity’ is the ability
of the test to classify people as positive when theyhave the disease or the risk factor under study
‘Specificity’ is defined as the ability of the test toclassify people as negative when they do not havethe disease or the risk factor under study Having ornot having the disease or risk factor in questionshould ideally be determined by a test that is a part
of the diagnostic procedure A diagnostic test maytake more time and be more expensive to perform,and may have a lower degree of safety, but it isessential that it has the highest degree of accuracy.Consequently, an estimate of sensitivity should beregarded as the sensitivity of one test (the screeningtest) relative to another (the diagnostic test) Thesame kind of argument may be applied to the use ofspecificity In reality, it is often impossible to use thediagnostic test on all screenees to verify the result ofthe screening test For instance, the diagnostic test
in cancer screening might be extensive surgery.False positives are people who tested positive but
do not have the disease or risk factor under studyand false negatives are people who tested negativebut do show the disease or risk factor under study.The ‘positive predictive value’ is the ability of thescreening test to predict that there is a state of earlydisease or a high risk The positive predictive valuewill depend in part on the screening test characteris-tics (sensitivity and specificity) and in part on theprevalence of the disease or risk situation The relia-bility of the test is its capacity to give the same result
on repeated measurements Ideally, a screening testshould be valid, safe, simple to perform, acceptable
to the subject and inexpensive The sensitivity of thetest may be increased or decreased by changing thelevel at which the test is considered positive Anincrease in the sensitivity will decrease the specific-ity and vice versa (Figure 5.1)
Trang 25Figure 5.1 Schematic illustration of the relationship between
different cut-off points and sensitivity and specificity of defining
high-risk individuals and normals Sensitivity is represented by
the solid line and specificity by the dotted line
Adequate Understanding of the Natural
History of the Target Disease
Whether a screening programme will be useful to a
given population or not depends to a large extent
on the natural history of the target disease A main
question is: Does early detection and treatment
af-fect the course and prognosis of the disease? In
order to answer that question well-planned and
preferably randomized experimental studies must
be conducted In many cases, these clinical trials
must be started as soon as possible If not carried
out speedily enough, the time trend may change the
practice of treatment and treating the latent stage of
the disease may be regarded as normal practice In
such a situation, it would no longer be considered
ethical to perform a randomized clinical trial As a
result of this, scientific evidence, showing that the
early detection and treatment really changes the
natural course of the disease and improves its
prog-nosis, may be lacking
SCREENING FOR A RISK FACTOR
INSTEAD OF A DISEASE
When it comes to the use of screening for a risk
factor instead of a disease, two additional points
may be formulated (4) The first of these defines the
purpose of the screening The intention must be to
find reversible risk—not risk factors The object of
the screening procedure must be to find those
per-sons who will benefit from an ensuing intervention
It is the effect of the intervention that may increasethe individual’s health and not the risk classification
per se The second point states that selected
screen-ing is more cost effective than mass screenscreen-ing.
Simple and easily attainable information, such asage and sex, may help to select a segment of thepopulation with a heightened risk The screeningprocedure may then be done within that segment
In a situation where there is a lack of resources foradvice and long-term support, it is better to concen-trate the resources on those who need it most.Obesity is a predisposing factor for type 2 dia-betes and cardiovascular disease (CVD) In ascreening programme focused on obesity with theaim of preventing or postponing diabetes and CVD,obesity may be regarded as a detectable but asymp-tomatic phase of these outcomes, i.e a preclinicalphase of CVD or type 2 diabetes The distinctionbetween screening for a risk factor and screening for
a disease is that when a disease is the object of thescreening, as in the case of breast cancer, then even-tually most screen-detected cases will developsymptomatic disease When a risk factor is the ob-ject of the screening, often only a minority willdevelop symptomatic disease This has been thecase in screening for hypertension, where only aminority of those screened as mild hypertensiveswill develop stroke or myocardial infarction
MASS SCREENING, OPPORTUNISTIC SCREENING OR SELF-REFERRAL
A screening programme uses a screening test todetect early disease or a risk factor for future dis-ease This detection part of a screening programmeneed not to be that costly At least, this is what is to
be expected in screening for obesity irrespectivewhether body mass index or other anthropometricmeasures are used as screening tools However, totreat those screened to be high-risk individuals, in along-term weight management programme, willneed considerable resources
The decision to launch a mass screening gramme (the whole population) for overweight andobesity must incorporate a commitment to allocat-ing enough resources to give all high-risk individ-uals the opportunity to participate in a treatmentprogramme Otherwise such a decision would beunethical
pro-69 SCREENING THE POPULATION
... (Figure 4 .2) CT gives almost equalinforma-tion but the small radiainforma-tion exposure limits its
ac-ceptability
Waist-to-hip Ratio
The ratio of waist-to-hip circumferences... amount of intra-abdominal
fat mass ( 32) , and has been shown to relate to
mor-tality from coronary heart disease and type
dia-betes independent of BMI (28 ,29 ) Most of the... Distribution of adipose tissue and risk of
car-diovascular disease and death 12 year follow-up of
partici-pants in the population study of women in