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Body Mass Index

  • VA Health Care System, United States, Minneapolis, MN

Abstract and Figures

The body mass index (BMI) is the metric currently in use for defining anthropometric height/weight characteristics in adults and for classifying (categorizing) them into groups. The common interpretation is that it represents an index of an individual's fatness. It also is widely used as a risk factor for the development of or the prevalence of several health issues. In addition, it is widely used in determining public health policies.The BMI has been useful in population-based studies by virtue of its wide acceptance in defining specific categories of body mass as a health issue. However, it is increasingly clear that BMI is a rather poor indicator of percent of body fat. Importantly, the BMI also does not capture information on the mass of fat in different body sites. The latter is related not only to untoward health issues but to social issues as well. Lastly, current evidence indicates there is a wide range of BMIs over which mortality risk is modest, and this is age related. All of these issues are discussed in this brief review. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License, where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially. Copyright
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Body Mass Index
Obesity, BMI, and Health: A Critical Review
Frank Q. Nuttall, MD, PhD
The body mass index (BMI) is the metriccurrently inuse for
defining anthropometric height/weight characteristics in
adults and for classifying (categorizing) them into groups.
The common interpretation is that it represents an index of
an individual’s fatness. It also is widely used as a risk factor
for the development of or the prevalence of several health
issues. In addition, it is widely used in determining public
health policies.The BMI has been useful in population-
based studies by virtue of its wide acceptance in defining
specific categories of body mass as a health issue. However, it
is increasingly clear that BMI is a rather poor indicator of
percent of body fat. Importantly, the BMI also does not
capture information on the mass of fat in different body sites.
The latter is related not only to untoward health issues but to
social issues as well. Lastly, current evidence indicates there is
a wide range of BMIs over which mortality risk is modest, and
this is age related. All of these issues are discussed in this brief
review. Nutr Today. 2015;50(3):117Y128
Body fatness has been an important psychosocial
issue among humans for millennia. It is clearly
manifested by paleolithic statuettes of exceedingly
plump women. This suggests being ‘‘full figured’’ was highly
desirable at least for women. In contrast, images of obese
people, males or females, are never exhibited in ancient
Egyptian funerary wall paintings, stellae, or statues suggest-
ing that fatness was not considered to be a desirable trait
there. This also is the case in artifacts from other cultures in
the Middle East in that era. Why the degree of fatness has
varied in different cultures is not clear. However, it may
have depended on the availability of a reliable food supply
and the effort required in obtaining it.
More recently, the degree of rotundity considered ideal
also has varied considerably in the general population, but
particularly for young women. Before the 1920s, ‘‘full figured’’
women were considered to be desirable as long as the dis-
tribution was hourglass in type. However, the 1920s Flapper
era introduced abbreviated and revealing dresses. The result
was that thinness was not only desirable but also required.
This concept has moderated but still influences women’s
views of beauty and eating habits at present.
Fatness as a Personal or Society Issue
Traditionally, a person’s fatness has been defined at a per-
sonal level as well as at a societal level. However, this is
difficult to quantify. That is, each individual has his/her own
perception of how fat he/she should be. As indicated above,
this often depends on a general concept of societal norms
or is due to peer pressure. For example, currently in Western
societies, young women are often concerned about their body
image, and most consider themselves to be too fat, even though
they are well within population-based references. This is
not only due to societal concepts of an ideal degree of fat-
ness, but also due to thinness being a goal promulgated by the
fashion industry and reinforced by commercial advertising.
At a societal level, although poorly described or quantified,
there also is a degree of fatness beyond which a person
generally is considered to be unacceptably fat; that is,
there is an ill-defined threshold at which a person is la-
beled as being‘‘fat’’ or‘‘obese.’’ However, it is based on the
‘‘I can’t define it but I know it when I see it’’ concept. In
addition, implicit in this context is that the location of the
excess fat plays a role, as does a person’s age. It is much
more acceptable to be ‘‘overweight’’ when one is old than
when one is young. Also particularly in women, the ac-
cumulation of fat in certain areas of the body is considered
to be much more acceptable than in other areas. For example,
truncal (belly fat) accumulation would be considered to be
less acceptable than the accumulation of fat in the peripelvic
and thigh areas as well as in the breast area
; that is, one may
be statistically ‘‘fat’’ but with an appropriate figure be merely
referred to ‘‘as amply endowed’’ or ‘‘pleasingly plump.’’
The social consequences of being ‘‘too fat’’ are severe.
Discrimination begins in childhood and results in serious
emotional scars. Societal discrimination limits career choices,
and indeed many career paths are closed to those consid-
ered to be too fat. Also, societal stigmatization often im-
pairs a person’s ability to express his/her intellectual and other
talents; that is, they become underachievers. In addition, the
Nutrition Research
Volume 50, Number 3, May/June 2015 Nutrition Today
Frank Q. Nuttall, MD, PhD, is a full professor at the University of
Minnesota, Minneapolis, and chief of the Endocrine, Metabolic and Nu-
trition Section at the Minneapolis VA Medical Center, Minnesota. His PhD
degree is in biochemistry. He has more than 250 scientific publications in
peer-reviewed journals, and he is the winner of numerous prestigious aca-
demic and scientific awards, including the 2014 Physician/Clinician Award of
the American Diabetes Association.
The author has no conflicts of interest to disclose.
Correspondence: Frank Q. Nuttall, MD, PhD, Minneapolis VA Medical Center,
One Veterans Dr 111G, Minneapolis, MN 55417 (
This is an open-access article distributed under the terms of the Creative
Commons Attribution-NonCommercial-NoDerivatives 3.0 License, where it
is permissible to download and share the work provided it is properly cited.
The work cannot be changed in any way or used commercially.
DOI: 10.1097/NT.0000000000000092
Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.
potential pool of mates is limited because of their per-
ceived unattractiveness. Thus, obese people tend to marry
other obese people and, parenthetically, to produce obese
Fatness as a Medical Issue
Not only the societal but also the functional and indirectly
the medical consequences of an excessive accumulation
of fat also have been recognized for millennia. Nevertheless,
the concept that ‘‘body build’’ (fatness) is a major population-
based medical issue gained popularity in this country only
shortly before 1900. Life insurance data accumulated at
that time
and subsequently
indicated that body weight,
adjusted for height (Wt/Ht), was an independent deter-
minant of life expectancy, and in 1910, the effects of being
overweight were noted to be greater for younger people
than for the elderly.
Subsequently, the Metropolitan Life Insurance Company
in 1959 published tables of average body weights for heights
(Wt/Ht) by gender and at different ages.
This was based
on data from 1935 to 1953 from more than 4 million adults,
mostly men, insured by 26 different insurance companies.
The risk for development of certain diseases as well as mor-
tality data related to Wt/Ht differences also were analyzed
and reported in the 1960 Statistical Bulletin of the Metro-
politan Life Insurance Co.
The Wt/Ht tables were used for many years as a reference
for population-based studies. If a person’s Wt/Ht was 20%
above or below the mean for that height category, he/she
was considered to be overweight or underweight, re-
spectively. The insurance data also indicated the ratios of
weights for heights (the term used was ‘‘body build’’) at
which mortality was lowest in adults. The latter was re-
ferred to as the ‘‘ideal’’ or later the ‘‘desirable’’ weight. All
of these data were periodically updated.
from 1959 to 1983, the desirable weight, that is, the weight/
height representing the lowest mortality had increased.
However, a ‘‘desirable body’’ weight for height was in-
variably lower than the average weight for height in the
insured population.
Problems With the Wt/Ht (Body Build) Index
Early on it was recognized that tall people had a lower
death rate than did short people
with the same Wt/Ht
ratio. It also was recognized that a person’s height in general
and leg length in particular could affect the calculated
body mass adjusted for height. A person’s bony frame, that
is, bone mass, also could affect the interpretation of this
ratio. In general, it reflected whether one was narrowly or
broadly built. Thus, efforts were made to eliminate lower
limb length and frame size as variables.
The strategy
was to develop representations of body build, that is, charts
of weight/height that were independent of these variables.
The overall goal was to have the same distribution of
Wt/Ht at each level of height.
Although not stated, the implicit goal in developing these
tables was to define a person’s fat mass as a proportion of
their total mass, irrespective of their heightor frame size.
Efforts were made to adjust for frame size (nonfat mass)
by categorizing people as those with a small, medium, or
large frame. Estimation of frame size was attempted using
a number of measurements including shoulder width, elbow
width, knee width, ankle width, and so on.
None of these
were widely adopted. Nevertheless, frame size based on
elbow width was included in the MetropolitanLife weight/
height tables,
even though it was never validated.
Mathematical Adjustment of Body Build
Mathematically, the issue of adjusting body build for dif-
ferences in height was approached with the concept that
the body, particularly the trunk, could be considered as
being a 3-dimensional volume or mass. Thus if a tall per-
son were simply a scaled-up version of a short person,
weight would increase approximately with the cube of
Indeed, several equations were developed and
tested based on this concept; that is, the cube root of weight
divided by height (
¾Wt/Ht) or weight/height,
and so on,
but none were ideal.
This is because tall people are not
just scaled-up versions of short people. As indicated pre-
viously, they tend to be more narrowly built resulting in a
greater lean/fat proportion of body mass.
Later, it was shown that the body mass for height actually
scaled best with weight for height when the height was
raised to the 1.6 to 1.7 exponent (Wt/Ht,
with an increase in Ht, the effect of Ht on the ratio is expo-
nential, whereas the change in Wt is linear. This has the effect
of Ht on the ratio to be magnified as Ht increases. Overall,
it results in a lower ratio in tall people than will be obtained
with just a Wt/Ht ratio. Thus, it potentially compensates for
a narrower build in tall compared with short people.
This exponent is not convenient for use in population-
based studies, and it was determined that Wt/Ht
was satisfactory.
The latter represents the Quetelet
Index. It was developed by Dr Quetelet in the 1800s.
Lambert Adolph Jacque Quetelet
I would like to briefly mention who Dr Quetelet was and
how the ‘‘Quetelet Index’’ was derived.
Lambert Adolphe
Jacque Quetelet (1796Y1874) was a Flemish astronomer
and statistician. Indeed, he is considered to be the patriarch
of statisticians. He introduced the concept of ‘‘social aver-
ages.’’ In developing the ‘‘social average’’ concept, his goal
was to determine the characteristics of an ‘‘average man’’
and the distribution of various human characteristics around
the ‘‘average man.’’ Overall, it was his desire to obtain a dis-
tribution such that it formed a bell-shaped curve, that is, a
Gaussian or normal distribution. He referred to his studies
as ‘‘social physics.’’ Thus, this represents the first appli-
cation of distribution mathematics to human characteris-
tics. In 1835, Quetelet noted the body mass relationship
118 Nutrition Today
Volume 50, Number 3, May/June 2015
Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.
to height in normal young adults was least affected by
height when the ratio of weight to height squared was
used rather than merely using the ratio of the weight to
height or weight to height raised to the third power.
Adoption of the BMI as an Index of Obesity
In 1972, Keys et al
severely criticized the validity of
Metropolitan Life Insurance published data per se, and the
then-published tables of desirable weight for height, as
weight or overweight.
(The pejorative term ‘‘obese’’ was
rarely used in that era.) Instead, Keys et al, using better
documented weight for height data, popularized the Quetelet
Index in population-based studies. They referred to it as
the body mass index (BMI). Thus, Quetelet Index = body
weight (kilograms) divided by height squared (meters) = BMI.
As indicated above, by squaring the height, it reduces the
contribution of leg length in the equation and tends to
normalize the body mass distribution at each level of height;
that is, it reduces the effect of a variance in height in the
relationship of weight to height. This was considered to be
important because most of body fat is in the trunk. Nev-
ertheless, as also pointed out by Keys et al,
even the BMI
rather poorly represents a person’s percent of body fat.
Despite all the criticisms, the Metropolitan Life Tables cri-
teria for defining obesity were widely used in the United
States until the early 1990s.
At about that time, the
World Health Organization (WHO) classification of body
weight for height, based on the BMI, was published,
later it was widely adopted.
BMI Distribution in a Normal Population
Although a BMI determination reduces the effect of lower-
extremity length on the Wt/Ht ratio, whether one uses the
BMI or merely the ratio of weight to height, the population
distribution is still not Gaussian. That is, it is not symmet-
rical but is always skewed to the right, that is, toward a
higher ratio of weight (body mass) to height. For example,
the distribution of BMIs in adult American men and women
median BMI was 24, but the mean BMI was 25. The dis-
tribution curve clearly indicated a skewing toward an in-
crease in BMI, and this trend has continued.
This skewing is not surprising because a markedly reduced
BMI, theoretically and actually, would be incompatible
with life because of an excessive reduction in lean as well
as fat mass as a result of under nutrition
or disease. In
contrast, excessive accumulation of body fat with main-
tenance or usually an increase in lean mass
is at least
compatible with life, even though it may eventually affect
long-term survival.
WHO and the Categorization of BMIs Into Quartiles
In 1993, the WHO assembled an Expert Consultation Group
with a charge of developing uniform categories of the BMI.
The results were published as a technical report in 1995.
Four categories were established: underweight, normal,
overweight, and obese. An individual would be considered
to be underweight if his/her BMI was in the range of 15 to
19.9, normal weight if the BMI was 20 to 24.9, overweight if
the BMI was 25 to 29.9, and obese if it was 30 to 35 or greater.
Using linear regression, a BMI of 16.9 in men and 13.7 in
women represents a complete absence of body fat stores.
The above 4 categories are similar to those suggested by John S.
Garrow in 1981,
but the terminology was changed. The
terminology he used was ‘‘desirable’’ for a BMI up to 25, ‘‘grade
I obesity’’ between 25 and 29.9, ‘‘grade II obesity,’’ between
30 and 40, and ‘‘grade III obesity’’ for BMI greater than 40.
The latter classification was based on Rosenbaum and col-
own data obtained in a survey of an adult popula-
tion, aged 16 to 64 years, in Great Britain and published in 1985.
The population-based data indicated the majority of people
were in the ‘‘desirable’’ range of the BMI distribution as in-
dicated in Table 1. Unfortunately, this distribution is not
and has not been similar to those found in other surveys.
The BMIs have been higher.
At the time that the WHO classification was published, the
National Institutes of Health (NIH) in the United States
classified people with a BMI of 27.8 (men) and 27.3 (women)
FIGURE. Distribution of BMI in Adult American Men and Women
(Carnegie Institute of Washington, Publ No 329. 1923). Adapted
from Rony.
27, p192
TABLE 1 Garrow Classification
Desirable Grade I Grade II Grade III
BMI (Up to 25) (25Y29.9) (30Y40) (Q40)
Women 67.6 24 8 0.4
Men 58.2 34 6 0.2
Abbreviation: BMI, body mass index
Volume 50, Number 3, May/June 2015 Nutrition Today
Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.
or greater as being overweight. If they were below this
BMI, they were considered to be ‘‘normal.’’ This was based
on an 85% cutoff point of people examined in the National
Health and Nutrition Examination Study (NHANES) II.
Subsequently, in 1998, the cutoff point between normal and
overweight was reduced to a BMI of 25 to bring it into line with
the 4 categories in the WHO guidelines.
this instantaneously converted millions of Americans from
being ‘‘normal weight’’ to being ‘‘overweight.’’
In 1997, the International Obesity Task Force expanded
the number of BMI categories to include different degrees
of obesity and changed the terminology modestly.
BMI of 25 to 29.9 is referred as ‘‘preobesity,’’ a BMI of 30 to
34.9 is class I obesity, 34.9 to 39.9 is class II obesity, and a
BMI of 40 or greater is class III obesity.
The new terminology appears to be a bit presumptuous
and careless because the BMI is not a direct measure of
percent of fat mass, and the dynamic concept that those in
the former ‘‘overweight’’ category are now in the ‘‘preobesity’’
category invariably going on to ‘‘obesity’’ is not the case.
Also those with a lower BMI initially, but with a dynamic
weight gain over time, would have to transition through
this category in order to become classified as ‘‘obese’’ regard-
less of the terminology. By analogy, should those classified
as ‘‘underweight’’ now be referred to as being ‘‘prenormal’’?
Distribution of BMI in the General Population
It should be understood that in Western population-based
studies, generally the mean or median BMI is about 24 to
Thus, the consequence of adopting the WHO
classification is that ~50% or more of the general adult
population will always be in the overweight (now pre-
obese) and obese categories. Indeed, the term ‘‘overweight’’
or particularly ‘‘preobesity’’ is prejudicial since people in
this category are a major part of the expected normal dis-
tribution of BMI in the general population, and this has
been the case for decades. Unfortunately, in discussing the
so-called ‘‘obesity epidemic,’’ the number of people in the
overweight (preobese) category generally is lumped to-
gether with those in the obese category in order to advertise
and dramatize the perceived seriousness of this issue.
Regardless of the terminology and population reference
issues, at present the BMI is the currency by which we
define the obesity issue throughout the Western world. It
was developed for the convenience of the epidemiolo-
gists, and indeed it did provide a uniform codification of
body weight for height reporting. The BMI categories are
shown in (Table 2).
BMI as a Determinant of Body Fat Mass
A particularproblem with BMI as an index of obesity isthat
it does not differentiate between body lean mass and body
fat mass; that is, a person can have a high BMI but still have
a very low fat mass and vice versa.
From an anatomical and metabolic perspective, the term
obesity should refer to an excessive accumulation of body
fat (triacylglycerols), and upon these grounds, the accuracy
of the BMI as a determinant of body fat mass has been
repeatedly questioned,
because it clearly has
limitations in this regard. Gender, age, ethnic group, and
leg length are important variables.
It should be noted
that in population-based studies women generally have a
BMI that is lower than that in men, even though their fat
mass relative to their body build or BMI is considerably
greater (~20% to 45%+).
The relatively poor correlation between percent of body
fat mass and BMI in males has been known for many
and was clearly shown in a study in which percent
of body fat was determined by a densitometric method.
For men with a BMI of 27 in that study, the 95% confi-
dence intervals for percent of body fat were 10% to 32%;
that is, in this group, the percent of body fat varied from
very little to that considered to be in the obesity range.
(NIH-suggested criterion for obesity based on percent of
body fat for men is Q25%, and that for women is Q35%.
The relatively poor correlation between percent of body
fat mass and BMI also clearly has been shown more re-
cently in the NHANES III database in which bioelectrical
impedance was used to estimate the fat component of
body composition.
In subjects with a BMI of 25 kg/m
the percent of body fat in men varied between 14% and
35%, and in women it varied between 26% and 43%.
Thus, using the NIH-suggested criterion based on per-
cent of body fat to define obesity, subjects with a BMI of
25, a group that would be considered to be essentially
normal, were associated with a body fat mass that varied
again between low normal to obese. Also it is of interest
that in the entire NHANES cohort, the BMI correlated better
with lean body mass than with fat mass in men.
recent NHANES data also indicate a poor correlation of BMI
with percent of body fat, particularly in men.
In addition, in a recent study in individuals with or without
diabetes in which the loss of lean body mass with aging
TABLE 2 Categories of BMI
Underweight 15Y19.9
Normal weight 20Y24.9
Overweight 25Y29.9
Class I obesity 30Y34.9
Class II obesity 35Y39.9
Class III obesity Q40
Abbreviation: BMI, body mass index
120 Nutrition Today
Volume 50, Number 3, May/June 2015
Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.
was reported, the mean BMI in those without diabetes was
26.8. In those with diabetes, the BMI was 29.1; that is, it
was higher as generally expected. However, the percent of
lean body mass was the same; that is, the increased BMI in
those with diabetes was not due only to an excessive ac-
cumulation of fat.
Trends in Body Weight and Height
Over the past several decades, there has been an increase
in BMI in the general population. This has resulted in
predictions of a public health disaster. It should be recog-
nized that in the United States during the period from 1960
to 2002 not only has the mean weight increased by 24 lb
among men aged 20 to 74 years, but also the height has
increased by about 1 in. We can then calculate that the
weight increase per year has only been 0.57 lb, and as in-
dicated above, this could be due to an increase in lean mass
rather than fatmass, or it may bea combination of the two.
In women, there was a similar increase in weight and height.
In an earlier report, life-insured men up to age 40 years were
reported to be 0.5 to 1.5 inches taller and 2 to 9 lb heavier for
the same height in 1959 than those studied 50 to 60 years
prior to 1959. Also, in the earlier study, the mortality rate
was lowest in those with higher weight-to-height ratios.
This was attributed to the presence in the population of
wasting diseases such as tuberculosis that resulted in an
increased death rate.
Previously, a secular upward trend
in height in adults in the United Kingdom also was re-
In addition, in a twin study in the United Kingdom,
children in 2005 were not only heavier but also taller than
1990 norms, whereas their BMIs were essentially the same.
Overall, the history of changes in height and weight in
Western European men and probably women has been
that of an increase in both weight and height. In the 17th
century, the average height of men in Northern Europe
was ~5 ft 3 in. It now approaches 6 ft.
These data suggest
that the BMI categories should be adjusted upward peri-
odically to accommodate population-based changes. Im-
provements in mortality rates also suggest an adjustment
would be useful.
Body Fat Location
An additional limitation of the BMI is it does not capture
body fat location information. This is an important variable
in assessing the metabolic as well as mortality consequences
of excessive fat accumulation. It was first recognized in
France by Dr Jon Vague
in the 1940-1950s. He noted that
accumulation of fat in the upper part of the body versus
the lower part of the body was associated with an increased
risk for coronary heart disease, diabetes, and also gallstones
and gout. That is, individuals who accumulated excessive
fat in the lower body segment were relatively spared from
these complications. The body fat distribution was referred
to as being ‘‘android’’ if it occurred in the upper body and
‘‘gynecoid’’ when it occurred in the lower segment of the
body. This is because men tend to accumulate fat in the
abdominal (upper body) area, whereas women tend to
accumulate it in the peripelvic (gluteal) area and the thighs.
A surrogate for this information has been to determine the
abdominal circumference or an abdominal/hip circum-
ference ratio.Subsequent data indicatethat indeed the risk
for development of diabetes and the so-called ‘‘metabolic
syndrome,’’ as well as coronary heart disease, is more strongly
related to the accumulation of upper body fat than lower
body fat in both sexes.
That is, an android (male) dis-
tribution more closely predicts the development of the
chronic diseases of aging than does the gynecoid (female)
More specifically, both visceral fat accumulation
an expanded girth have been associated with development
of insulin resistance, diabetes, and risk for coronary heart
disease and hypertension.
Accumulation of fat in
the abdominal area appears to correlate best with triacylgly-
cerols accumulating in the liver and skeletal muscle. These
may actually represent the pathogeneticially important me-
tabolic consequences that result in insulin resistance and
more directly correlate with development of the above
adverse medical conditions.
Incidentally, the rela-
tively small accumulation of fat in these organs would not
be detectible by BMI determinations, and they do not
correlate simply with total body fat mass.
The Life Cycle and Location of Accumulated Fat
Prior to puberty, boys and girls tend to be lean and not
much different in this regard. Girls tend to accumulate
relatively large amounts of fat during and after puberty,
particularly in the peripelvic and thigh region; boys do not.
During and after puberty, boys accumulate a relatively
large amount of lean mass (bone and muscle) but not fat
mass. In both sexes, these changes are reflected in an in-
creased BMI. With aging, both sexes tend to develop fat in
the upper part of the body (circumferentially), that is, the
middle-age spread.
The reason for these changes in
amount and distribution is not completely understood.
Generally, it is considered to be due to hormonal changes.
It is of some interest that accumulation of fat in the lower
body at puberty in females is unique to humans, is not
present in any of the great apes, and most likely is es-
trogen mediated.
In a teleological sense, why this occurs, if due to estrogen,
is uncertain. It could represent a means of maintaining
body fat during pregnancy without an undue expansion in
abdominal girth. It also may act as a counterbalance when
women carry a child either during pregnancy or afterward.
It also may be a space-filling fat site due to the relatively
larger pelvis in postpubertal females.
Overall, it may re-
present an adaptation to the human upright bipedal pos-
ture. In any event, it results in a lower center of gravity among
Volume 50, Number 3, May/June 2015 Nutrition Today
Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.
women compared with men. Indeed, the lower body seg-
ment in females becomes ~40% greater than in males (quoted
in Singh, 1993),
and it has strong sex-related overtones.
Not only is thigh fat greater in women than in men, but also
women generally have a preponderance of slow-twitch
fibers, whereas men have a preponderance of fast-twitch
fibers in their quadriceps muscles, as do upper-body-obese
suggesting either genetic or earlier developmental
differentiation events. Could this be an adaptation for load-
bearing versus speed as a group survival adaptation?
As indicated above, the accumulation of fat with aging in
both sexes tends to occur in the truncal area and is asso-
ciated with an increase in visceral fat. In women, this could
be explained by a decrease in circulating estradiol, that is,
a decreased estrogen/testosterone ratio associated with
the menopause. (Again of some interest, it is only humans
who have a defined menopause).
In men, with aging, there is a decrease in testosterone and
a relative increase in estrogen, resulting in a decrease in
the testosterone/estrogen ratio.
Thus, in men, a change
in sex hormone concentrations could possibly explain the
increased accumulation of fat in general. However, why
there is a preferential accumulation in the truncal location,
that is, why they too develop an increase in visceral fat, is
unclear. Clearly, location of fat in this area would help to
maintain mobility. The latter could be of great importance
in hunter-gatherer societies and in defense of the tribe.
Perhaps the distribution is programmed by gender earlier
in life.
In this regard, it should be recognized that the accumu-
lation of fat in certain body areas as well as the total amount
of fat accumulated has a strong genetic or at least a familial
component that diminishes with age.
Methods of Estimating Body Fat Mass and
Location of the Fat
At present, simple, accurate methods for measuring per-
cent of body fat and, in particular, body fat in different fat
depots are not available. The indirect methods currently in
use for estimating total percent of body fat include un-
derwater weighing, an air displacement and density de-
termination using a Bod Pod, a bioelectrical impedance
analyzer, and a determination of the isotopically labeled
water mass. In the past, determination of the total body
radioactive potassium and thus metabolizing tissue mass
have been used to estimate lean body mass, and by dif-
ference, the fat mass.
Anthropometric determination of fat mass directly has been
done using skin-fold thickness measured at various sites.
A dual-energy x-ray absorptiometry (DEXA) scan, which
provides a 3-dimensional picture of body organ densities,
can be used for estimating total body fat. Its location also
can be determined. Single computed tomography (CT)
slices of the abdomen and thigh can be used to obtain 2
dimensions of those fat depots from which a 3-dimensional
fat area can be reconstructed. This also can be done using
magnetic resonance imaging, but magnetic resonance im-
aging is very expensive. One cannot do serial sections of
the body using CT to determine fat mass because of the
excess radiation associated with this procedure.
Because of their convenience, bioelectric impedance methods
or DEXA scans are the most commonly used to estimate
the amount and, with DEXA scans, the location of body fat
depots. Estimates of abdominal and thigh fat depots also
can be estimated using CT slices.
All of the previously mentioned methods use certain as-
sumptions in the calculation of body fat mass, and all are
subject to potential error. Nevertheless, there are more
specific methods of determining body fat mass than is the
BMI. Important information regarding the location of the
stored fat also can be determined with some methods.
It now is generally accepted that a relationship between
BMI and mortality risk should be applied only to large
populations. It should not be applied to an individual in an
unqualified fashion. As indicated previously, there is the
issue of being ‘‘overweight’’ versus ‘‘over fat.’’ In addition,
a segment of the population is now considered to be
‘‘fat’’ by any criteria but ‘‘fit’’ and not at risk for early
BMI and Morbidity and Mortality
The BMI classification system currently is being widely
used in population-based studies to assess the risk for
mortality in the different categories of BMI. It also is being
used in regard to specific etiologies for mortality risk.
However, as with its use to estimate percent of body fat, it
is a rather crude approach. Even when some comorbidities
are considered, the correlation of mortality rates with BMI
often does not take into consideration such factors as family
history of diabetes, hypertension, coronary heart disease,
metabolic syndrome, dyslipidemias, familial longevity or the
family prevalence of carcinomas, and so on. Recently it has
been reported that more than 50% of susceptibility to cor-
onary artery disease is accounted for by genetic variants.
Frequently, when correlations are made they also do not
take into consideration a past as well as a current history of
smoking, alcohol abuse, serious mental disorders or the
duration of obesity, when in the life cycle it appeared, and
whether the body weight is relatively stable or rapidly
progressive, that is, type 1 or type 2 obesity.
In most
population-based studies, only the initial weight and/or
BMI are given, even though weight as well as fat stores are
known to increase and height to decrease with aging. In ad-
dition, the rate of weight gain varies among individuals,
as does the loss of muscle mass.
Muscle mass has been
correlated negatively with insulin resistance and predia-
Lastly, population-based studies do not take into con-
sideration the present and past history of a person’s occ upation,
122 Nutrition Today
Volume 50, Number 3, May/June 2015
Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.
medication-induced obesity, and how comorbidities are
being treated. All of the above are significant issues.
More Explicit Problems in Relating the BMI to
Medical Issues
Based on data in the literature, there are several additional
problems in determining associations between BMI and
overall death rate or, more specifically, cardiovascular
events or death rates. Many obese people do not have car-
diovascular risk factors, and in those who do, BMI no longer
correlates with cardiovascular events
when the un-
toward effects of these other factors are factored out. An-
other issue is that the treatment status of the previously
mentioned cardiovascular risk factors often is unknown or
not stated; that is, the efficacy of treatment is rarely con-
sidered. This also is the case for diabetes. For example, the
prevalence of diabetes has been increasing but not the
disease-specific death rate.
Also, in people with dia-
betes, the death rate from cardiovascular disease has de-
creased dramatically.
The ‘‘Obesity Epidemic’’
Recently, there has been concern that an epidemic of
obesity is occurring, not only in the United States, but also
worldwide based on BMI data. In the NHANES data, there
has been a change in the mean but to a greater extent in
the distribution of BMI for adults aged 20 to 74 years in the
United States.
That is, the mean BMI has increased, but
there has been a greater increase in skewing toward the
right and very large BMI. This results in more individuals
being categorized as ‘‘obese.’’ The reason for the recent
increase in mean BMI, but particularly in those in the
obese category, is unknown, although there are many
speculations. The dramatic decrease in smoking is likely
to have been a contributor.
Smoking contributes
to population-based BMI by at least 2 mechanisms. Smoking
impairs appetite per se. It also is pathogenetically impor-
tant in the development of chronic obstructive pulmonary
disease, which itself results in a lower body mass. Of some
interest, NHANES data also indicate that the trend of an
increase in BMI has not continued since 1999 in women
Smoking rates also have
stabilized at a low level.
Is Being ‘‘Overweight’’ by BMI Criteria a
Medical Issue?
Regardless of an observed increased skewing in the BMI
distribution, it is important to note that several recent
studies indicate that for most of us being a bit overweight
(preobese?) as determined by BMI may not be so bad.
The EPIC observational study is a population-based study
that includes 359 387 individuals aged 25 to 70 years living
in Europe.
The mean age of this group at the initiation
of the study was 51.5 years, and the mean follow-up has
been 9.7 T2 years. In this study, both the crude and ad-
justed relative risk of death among men was actually the
lowest in those with a BMI of 26.5 to 28, that is, those in
the overweight (preobese) category. Also, a significant
increase in risk of death was present only among those
with a BMI of less than 21 or greater than 30. That is, there
is a wide range of BMIs in the central part of this popu-
lation in which there was relatively little impact of BMI on
risk of death over a 9.7-year period.
Similar data were obtained in the NIHYAmerican Associ-
ation of Retired Persons study of 527 265 men and women
between the ages of 50 and 71 years in the United States
and followed for up to 10 years.
The lowest death rate
in the entire cohort was among those in the ‘‘overweight’’
category, and this was particularly true among the men.
There also was a broad range of BMIs over which there
was little difference in mortality (BMI of 23.5 to 30).
The NHANES data going back to 1971 and up to 1994 also
indicate that the relative mortality risk is lowest in men
with a BMI of 25 to 30 in all age groups, that is, from the age
of 25 years up to the age of 70 years.
In addition, the risk
of mortality was little affected by a BMI from 18.5 up to a
BMI of 30 in all age categories. Indeed, in those older than
70 years, there was little impact on the death rate even if
they were in the obese category. Similar results have been
reported for women in the NHANES reports.
The lowest
mortality occurred with a BMI of 27.
In a Canadian study, the age-adjusted mortality rate over
13 years in men was essentially unchanged in those with a
BMI of 18.5 up to 35, that is, from the Normal Weight
category through the obesity class I category. In women,
there was only a modest increase over the same range.
In summary, there is a large range of BMIs over which
there is little association with the death rate. Generally,
the range is from a BMI of 21 up to and often including
30. It is centered in the 24-to-28 BMI range. This infor-
mation is not entirely new. Andres
in 1980 summarized
16 different population-based studies in which anthro-
pometrically determined obesity was not associated with
increased mortality rate. A detailed analysis in 1960 of the
Metropolitan Life Insurance data also suggested little in-
crease in mortality rates in people with a degree of over-
weight less than 20% or more above the average for a
given height and age (quoted in Keys et al
Interestingly, in the EPIC observational Study,
when the
waist circumferenceYtoYBMI ratio was calculated, that is,
adjusting the waist circumference for BMI, it tended to
linearize the association of BMI with risk for death, and
the ratio was greatest for those with a low BMI. Thus, even
if an individual had a low BMI but a relatively increased
waist circumference, the risk was increased. Indeed, for
any given BMI, a 5-cm increase in circumference increased
the risk of death by a factor of 1.17 among men and 1.13 for
women. Also in this study, the overall greatest mortality
risk was in those individuals with the lowest BMI and not
Volume 50, Number 3, May/June 2015 Nutrition Today
Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.
those with the highest BMI. Nevertheless, even in the
category with the lowest BMI, adjusting for waist circum-
ference affected the mortality rate negatively. This again
indicates the importance of the location of body fat in
addition to the total amount of fat accumulated.
A recent analysis of 50 prospective observational studies
indicated the lowest mortality at a BMI of 23 to 25. How-
ever, these data were obtained in the 1970s and 1980s in an
aggregate population with a mean BMI of 24.8, that is,
lower than at present. The increased mortality at higher
BMI’s was modest up to a BMI of 27.5, and the authors could
account for the excess mortality largely on the risk factors
known to be associated with obesity. The latter are cur-
rently being much better treated than in that era.
Issues to be Resolved When Relating BMI With
Health Determinants
Overall, a major unresolved issue is which factor of the
following is more important in the prediction of comor-
bidities such as cardiovascular disease, diabetes, hyper-
tension, malignancies, or overall death rates. Is it BMI, total
body fat mass, or the distribution of body fat, that is, vis-
ceral versus subcutaneous, or upper body fat accumula-
tion (as determined by abdominal circumference, or a
waist/hip ratio, or some combination of these, and so on)?
data suggest that where fat is accumulated is
much more important than merely the BMI, with the ex-
ception of those with an exceeding large total fat mass.
It is time to move beyond the BMI as a surrogate for de-
termining body fat mass. Alternatively, if BMI continues to
be used, the categories and definitions should be changed
to reflect the current distribution of BMIs in the general
A better means than the BMI for estimating percent of
body fat and its relationship to mortality and various mor-
bidities clearly would be desirable.
The BMI was not originally developed for use specifically
as an index of fatness in population-based studies. How-
ever, it has been coopted for this use because it is a readily
obtained metric. It should be understood that the BMI has
serious limitations when used as an indicator of percent of
body fat mass. Indeed, it may be misleading in this regard,
particularly in men. The terminology currently used also is
prejudicial. By definition, one-half or more adults in the
recent past and currently are overweight (preobese) or
obese in Western, industrialized nations.
The current BMI classification system also is misleading in
regard to effects of body fat mass on mortality rates. The
role of fat distribution in the prediction of medically sig-
nificant morbidities as well as for mortality risk is not
captured by use of the BMI. Also, numerous comorbidities,
lifestyle issues, gender, ethnicities, medically significant
familial-determined mortality effectors, duration of time
one spends in certain BMI categories, and the expected
accumulation of fat with aging are likely to significantly
affect interpretation of BMI data, particularly in regard to
morbidity and mortality rates. Such confounders as well as
the known clustering of obesity in families, the strong role
of genetic factors in the development of obesity, the lo-
cation in which excessive fat accumulates, its role in the
development of type 2 diabetes and hypertension, and so
health policies that are designed to apply to the general
population and are based on BMI data alone.
Clearly, obesity, as determined by BMI, is not a monotypic,
age-invariant condition requiring a general public health
‘‘preventative’’ approach. A BMI-determined categoriza-
tion of an individual should not be used exclusively in
counseling or in the design of a treatment regimen. In
addition, when considering weight loss regimens, varia-
tions in body weight attributed to weight loss and dietary
cycling may be hazardous.
They have been associ-
ated with an increased mortality rate.
concept of starvation-associated obesity
also needs
to be considered.
Prevention and/or Treatment of
BMI-Determined Overweight or Obesity
Clearly episodic starvation or semistarvation regimens are
not the answer,
nor are population-based efforts to
increase fresh fruits and vegetables and tax soda pop, and
so on. In my opinion, the major focus on prevention and
treatment should be on those unfortunate individuals who
are grossly obese, mechanically compromised, and who
are at very high risk for death.
Surgical gastrointestinal
intervention has proven to be at least partially successful
in improving fuel regulation and storage.
medications will be developed that will reinstitute a
metabolic fuel regulatory system that prevents the re-
lentless accumulation of body fat, which is characteristic
of those who are grossly obese. For others, an improve-
ment in physical fitness may be salutary.
A Personal Perspective Regarding the
Obesity Epidemic
Currently there are 4 truths regarding historical changes in
body weights and the prevalence of obesity. People of
Western European extraction are on average (1) heavier,
(2) taller, and (3) more likely to be ‘‘overweight’’ or ‘‘obese’’
as defined by current BMI standards than those in other
parts of the world. However, (4) it also should be pointed
out they are healthier and are living longer than in any
previous period in history.
Beginning in the 17th century,
the general underlying
theme in all the studies done on weight gain in populations
is an increase in height as well as weight. These changes
are likely to be due to an increase in high-quality dietary
124 Nutrition Today
Volume 50, Number 3, May/June 2015
Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.
protein (animal products), as well as an increased avail-
ability of total food energy in the diet. That is, there was not
only an increase in food availability and variety, but also an
increase in food quality.
The near elimination of chronic
and serious acute infectious diseases alsomay have played
a role, as has the dramatic decrease in cigarette smoking
and its serious medical consequences.
The net effect of the above is that the chronic diseases of
aging have become more of a public health problem, but
better treatments are widely available. The prevalence of
type 2 diabetes has increased, but overall the cardiovascular
death rate has decreased dramatically. The death rate from
malignancies is decreasing, and there has been a remark-
able improvement in longevity, which is continuing.
The latter also is likely to continue into the future.
Some view the secular trend in the US population over the
past 40 years as being one in which the population in
general is ‘‘more obese, more diabetic, more arthritic, more
disabled, and more medicated’’ but living longer.
sanguine view is indicated by others.
Many consider the
overabundance of ‘‘calorie dense, processed foods,’’ the
availability of soda pop,
and presence of fast-food res-
taurants and large food portion sizes to be strong, patho-
genetic, obesity-inducing factors,
or more broadly, they
consider obesity to be due to a ‘‘toxic’’ or ‘‘poisonous’’ food
Some also are concerned that the increase in
obesity (defined by BMI) will overwhelm any gains in
health and life expectancy noted over the past several de-
cades, that is, an Apocalypse awaits us.
I and others
do not share this pessimism.
Finally, I would like the political activists and doomsday
prophets whose professional careers appear to depend on
frightening the public and inducing politicians to pass
restrictive laws without proven value, to be introduced to
the prescient comments made by A. E. Harper
33 years
ago. It is clear that currently we have a case of ‘‘de´ja`vuall
over again.’’
In regard to predicting the future, a wise person whose
name I cannot recall stated presciently ‘‘Predicting the
future is a fool’s playground’’; the physicist Neils Bohr said,
‘‘Prediction is very difficult, especially about the future,’’ or
as stated by that sage of the baseball world, Yogi Berra,
‘‘The future ain’t what it used to be.’’ Bertrand Russell said,
‘‘Fools and fanatics are always so sure of themselves, but
wiser people are so full of doubt.’’ The true scientist should
always be a skeptic.
The author thanks Rachel Anderson for help in preparing the
manuscript for submission and Dr Mary C. Gannon for reading the
manuscript and making numerous helpful comments.
1. Singh D. Body shape and women’s attractiveness: the critical
role of waist-to-hip ratio. Human Nature. 1993;4(3):297Y321.
2. Katzmarzyk PT, Perusse L, Rao DC, Bouchard C. Familial risk of
overweight and obesity in the Canadian population using the
WHO/NIH criteria. Obes Res. 2000;8(2):194Y197.
3. Wardle J, Carnell S, Haworth CM, Plomin R. Evidence for a
strong genetic influence on childhood adiposity despite the
force of the obesogenic environment. Am J Clin Nutr. 2008;
4. Katzmarzyk PT, Hebebrand J, Bouchard C. Spousal resem-
blance in the Canadian population: implications forthe obesity
epidemic. Int J Obes Relat Metab Disord. 2002;26(2):241Y246.
5. Rogers O. Build as a Factor Influencing Longevity. In Proceed-
ings of the Association of Life Insurance Medical Directors of
America from Organization to and including the 10th annual
meeting: 12th annual meeting held at the Hunt Memorial Build-
ing of the Hartford Medical Society; May 29, 1901; Hartford, CT.
New York: Knickerbocker Press; 1901:280Y288.
6. The Association of Life Insurance Medical Directors and the
Actuarial Society of America. Medico-Actuarial Mortality In-
vestigation. Vol 1Y3. New York, NY: 1912Y1913.
7. Metropolitan Life Insurance Company. New weight standards
for men and women. Stat Bull. 1959;40:1Y4.
8. Metropolitan Life Insurance Company. Mortality record for
1959. Stat Bull. 1960;41(February):1Y11.
9. Metropolitan Life Insurance Company. Mortality among over-
weight women. Stat Bull. 1960;41(March):1Y11.
10. Metropolitan Life Insurance Company. 1983 Metropolitan height
and weight tables for men and women, according to frame, ages
25Y29. Stat Bull. 1983;64(JanYJune):2Y9.
11. Burton BT, Foster WR, Hirsch J, Van Itallie TB. Health impli-
cations of obesity: an NIH Consensus Development Confer-
ence. Int J Obes. 1985;9(3):155Y170.
12. Health implications of obesity. National Institutes of Health
Consensus Development Conference; February 11Y13, 1985.
Ann Intern Med. 1985;103(6 (pt 2)):977Y1077.
13. Blackburn H, Parlin RW. Antecedents of disease. Insurance
mortality experience. Ann N Y Acad Sci. 1966;134:965Y1017.
14. Khosla T, Lowe CR. Indices of obesity derived from body
weight and height. Br J Prev Soc Med. 1967;21(3):122Y128.
15. Himes JH, Bouchard C. Do the new Metropolitan Life Insurance
weight-height tables correctly assess body frame and body fat
relationships? Am J Public Health. 1985;75(9):1076Y1079.
16. Keys A, Fidanza F, Karvonen MJ, Kimura N, Taylor HL. Indices
of relative weight and obesity. J Chron Dis. 1972;25(6):329Y343.
17. Watson PE, Watson ID, Batt RD. Obesity indices. Am J Clin
Nutr. 1979;32(4):736Y737.
18. Benn RT. Some mathematical properties of weight-for-height
indices used as measures of adiposity. Br J Prev Soc Med.
19. Eknoyan G. Adolphe Quetelet (1796-1874)Vthe average man
and indices of obesity. NephrolDialTransplant. 2008;23(1):47Y51.
20. Quetelet LAJ. Physique Sociale. Vol 2. Brussels, Belgium: C.
Muquardt; 1869:92.
21. Jelliffe DB, Jelliffe EF. Underappreciated pioneers. Quetelet:
man and index. Am J Clin Nutr. 1979;32(12):2519Y2521.
22. Kuczmarski RJ, Flegal KM, Campbell SM, Johnson CL. Increas-
ing prevalence of overweight among US adults. The National
Health and Nutrition Examination Surveys, 1960 to 1991. JAMA.
23. Manson JE, Stampfer MJ, Hennekens CH, Willett WC. Body
weight and longevity. A reassessment. JAMA. 1987;257(3):353Y358.
24. Must A, Dallal GE, Dietz WH. Reference data for obesity: 85th
and 95th percentiles of body mass index (wt/ht2) and triceps
skinfold thickness. Am J Clin Nutr. 1991;53(4):839Y846.
25. WHO. Physical Status: The Use and Interpretation of Anthro-
pometry: Report of a World Health Organization (WHO) Expert
Committee. Geneva, Switzerland: World Health Organization; 1995.
26. Flegal KM, Carroll MD, Kuczmarski RJ, Johnson CL. Over-
weight and obesity in the United States: prevalence and trends,
1960Y1994. Int J Obes Relat Metab Disord. 1998;22(1):39Y47.
Volume 50, Number 3, May/June 2015 Nutrition Today
Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.
27. Rony H. The homeostatic body weight regulation. Obesity
and Leanness. Philadelphia, PA: Lea & Febiger; 1940:192Y209.
28. Leiter LA, Marliss EB. Survival during fasting may depend on
fat as well as protein stores. JAMA. 1982;248(18):2306Y2307.
29. Salans LB, Horton ES, Sims EA. Experimental obesity in man: cellular
character of the adipose tissue. JClinInvest. 1971;50(5):1005Y1011.
30. Forbes GB, Welle SL. Lean body mass in obesity. Int J Obes.
31. Garrow JS, Webster J. Quetelet’s index (W/H2) as a measure
of fatness. Int J Obes. 1985;9(2):147Y153.
32. Garrow JS. Treat Obesity Seriously: A Clinical Manual. Edinburgh,
Scotland: Churchill Livingstone; 1981.
33. Rosenbaum S, Skinner RK, Knight IB, Garrow JS. A survey of
heights and weights of adults in Great Britain, 1980. Ann Hum
Biol. 1985;12(2):115Y127.
34. Najjar MF, Rowland M. Anthropometric reference data and pre-
valence of overweight, United States, 1976-80. Vital Health Stat 11.
35. National Institutes of Health, National Heart, Lung, and Blood
Institute. Clinical Guidelines on the Identification, Evaluation,
and Treatment of Overweight and Obesity in Adults: The Evidence
Report. Bethesda, MD: National Institutes of Health; 1998.
36. WHO. Managing the global epidemic of obesity. Report of the
World Health Organization (WHO) consultation on obesity.
Paper presented at the International Obesity Task Force;
June 3Y5, 1997; Geneva, Switzerland.
37. International Obesity Task Force. Managing the Global Epi-
demic of Obesity. Report of the World Health Organization (WHO)
Consultation on Obesity; June 5Y7, 1997; Geneva, Switzerland.
38. WHO. Obesity: Preventing and Managing the Global Epidemic.
Geneva, Switzerland: WHO; 2000.
39. Wellens RI, Roche AF, Khamis HJ, Jackson AS, Pollock ML,
Siervogel RM. Relationships between the body mass index and
body composition. Obes Res. 1996;4(1):35Y44.
40. Ogden CL, Fryar CD, Carroll MD, Flegal KM. Mean body
weight, height, and body mass index, United States 1960Y2002.
Adv Data. 2004;(347):1Y17.
41. Strain GW, Zumoff B. The relationship of weight-height indices
of obesity to body fat content. JAmCollNutr. 1992;11(6):715Y718.
42. Segal KR, Dunaif A, Gutin B, Albu J, Nyman A, Pi-Sunyer FX.
Body composition, not body weight, is related to cardiovascu-
lar disease risk factors and sex hormone levels in men. J Clin
Invest. 1987;80(4):1050Y1055.
43. Romero-Corral A, Somers VK, Sierra-Johnson J, et al. Normal
weight obesity: a risk factor for cardiometabolic dysregulation
and cardiovascular mortality. Eur Heart J. 2010;31(6):737Y746.
44. Garn SM, LaVelle M, Rosenberg KR, Hawthorne VM. Matura-
tional timing as a factor in female fatness and obesity. Am J Clin
Nutr. 1986;43(6):879Y883.
45. Norgan NG. Relative sitting height and the interpretation of
the body mass index. Ann Hum Biol. 1994;21(1):79Y82.
46. Flegal KM, Shepherd JA, Looker AC, et al. Comparisons of per-
centage body fat, body mass index, waist circumference, and
waist-stature ratio in adults. Am J Clin Nutr. 2009;89(2):500Y508.
47. Garn SM, Leonard WR, Hawthorne VM. Three limitations of
the body mass index. Am J Clin Nutr. 1986;44(6):996Y997.
48. Heitmann BL, Erikson H, Ellsinger BM, Mikkelsen KL, Larsson B.
Mortality associated with body fat, fat-free mass and body mass
index among 60-year-old swedish men-a 22-year follow-up.
The study of men born in 1913. Int J Obes Relat Metab Disord.
49. Borkan GA, Hults DE, Gerzof SG, Robbins AH, Silbert CK. Age
changes in body composition revealed by computed tomog-
raphy. J Gerontol. 1983;38(6):673Y677.
50. Kuczmarski RJ. Prevalence of overweight and weight gain in
the United States. Am J Clin Nutr. 1992;55(Suppl 2):495SY502S.
51. Romero-Corral A, Somers VK, Sierra-Johnson J, et al. Accuracy
of body mass index in diagnosing obesity in the adult general
population. Int J Obes (Lond). 2008;32(6):959Y966.
52. Rice CL, Cunningham DA, Paterson DH, Lefcoe MS. Arm and leg
composition determined by computed tomography in young
and elderly men. Clin Physiol. 1989;9(3):207Y220.
53. Appropriate body-mass index for Asian populations and its
implications for policy and intervention strategies. Lancet.
54. Deurenberg P, Yap M, van Staveren WA. Body mass index
and percent body fat: a meta analysis among different ethnic
groups. Int J Obes Relat Metab Disord. 1998;22(12):1164Y1171.
55. Wang J, Thornton JC, Russell M, Burastero S, Heymsfield S,
Pierson RN Jr. Asians have lower body mass index (BMI) but
higher percent body fat than do whites: comparisons of anthro-
pometric measurements. Am J Clin Nutr. 1994;60(1):23Y28.
56. Smalley KJ, Knerr AN, Kendrick ZV, Colliver JA, Owen OE.
Reassessment of body mass indices. Am J Clin Nutr. 1990;
57. National Institutes of Health. Understanding Adult Obesity.
Bethesda, MD: National Institutes of Health; 2008.
58. Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and
trends in obesity among US adults, 1999-2008. JAMA20;303(3):
59. Park SW, Goodpaster BH, Lee JS, et al. Excessive loss of
skeletal muscle mass in older adults with type 2 diabetes.
Diabetes Care. 2009;32(11):1993Y1997.
60. Morant GM. A discussion on the measurement of growth and
form; secular changes in the heights of British people. Proc R
Soc Lond B Biol Sci. 1950;137(889):443Y452.
61. Fogel RW. The Escape From Hunger and Premature Death,
1700-2100. Cambridge, UK: The Press Syndicate of the Uni-
versity of Cambridge; 2004.
62. Vague J. The degree of masculine differentiation of obesities: a
factor determining predisposition to diabetes, atherosclerosis,
gout, and uric calculous disease. Am J Clin Nutr. 1956;4(1):20Y34.
63. Kissebah AH, Freedman DS, Peiris AN. Health risks of obesity.
Med Clin North Am. 1989;73(1):111Y138.
64. Kissebah AH, Vydelingum N, Murray R, et al. Relation of body
fat distribution to metabolic complications of obesity. J Clin
Endocrinol Metab. 1982;54(2):254Y260.
65. Ohlson LO, Larsson B, Svardsudd K, et al. The influence of
body fat distribution on the incidence of diabetes mellitus.
13.5 years of follow-up of the participants in the study of men
born in 1913. Diabetes. 1985;34(10):1055Y1058.
66. Lapidus L, Bengtsson C, Larsson B, Pennert K, Rybo E, Sjostrom L.
Distribution of adipose tissue and risk of cardiovascular disease
and death: a 12 year follow up of participants in the population
study of women in Gothenburg, Sweden. Br Med J (Clin Res
Ed). 1984;289(6454):1257Y1261.
67. Fox KA, Despres JP, Richard AJ, Brette S, Deanfield JE. Does
abdominal obesity have a similar impact on cardiovascular
disease and diabetes? A study of 91,246 ambulant patients in
27 European countries. Eur Heart J. 2009;30(24):3055Y3063.
68. Wajchenberg BL, Giannella-Neto D, da Silva ME, Santos RF.
Depot-specific hormonal characteristics of subcutaneous and
visceral adipose tissue and their relation to the metabolic
syndrome. Horm Metab Res. 2002;34(11Y12):616Y621.
69. Nguyen-Duy TB, Nichaman MZ, Church TS, Blair SN, Ross R.
Visceral fat and liver fat are independent predictors of
metabolic risk factors in men. Am J Physiol Endocrinol Metab.
70. Despres JP, Moorjani S, Lupien PJ, Tremblay A, Nadeau A,
Bouchard C. Regional distribution of body fat, plasma lipopro-
teins, and cardiovascular disease. Arteriosclerosis. 1990;10(4):
71. Pouliot MC, Despres JP, Lemieux S, et al. Waist circumference
and abdominal sagittal diameter: best simple anthropometric
indexes of abdominal visceral adipose tissue accumulation and
related cardiovascular risk in men and women. Am J Cardiol.
126 Nutrition Today
Volume 50, Number 3, May/June 2015
Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.
72. Pouliot MC, Despres JP, Nadeau A, et al. Associations between
regional body fat distribution, fasting plasma free fatty acid
levels and glucose tolerance in premenopausal women. Int J
Obes. 1990;14(4):293Y302.
73. Lemieux S, Prud’homme D, Tremblay A, Bouchard C, Despres JP.
Anthropometric correlates to changes in visceral adipose tissue
over 7 years in women. Int J Obes Relat Metab Disord. 1996;
74. Wildman RP, Muntner P, Reynolds K, et al. The obese without
cardiometabolic risk factor clustering and the normal weight
with cardiometabolic risk factor clustering: prevalence and cor-
relates of 2 phenotypes among the US population (NHANES
1999Y2004). Arch Intern Med. 2008;168(15):1617Y1624.
75. Stefan N, Kantartzis K, Machann J, et al. Identification and
characterization of metabolically benign obesity in humans.
Arch Intern Med. 2008;168(15):1609Y1616.
76. Savage DB, Petersen KF, Shulman GI. Disordered lipid meta-
bolism and the pathogenesis of insulin resistance. Physiol Rev.
77. Lemieux S, Prud’homme D, Bouchard C, Tremblay A, Despres JP.
A single threshold value of waist girth identifies normal-weight
and overweight subjects with excessvisceral adipose tissue. Am
J Clin Nutr. 1996;64(5):685Y693.
78. LemieuxS,PrudhommeD,NadeauA,TremblayA,BouchardC,
Despres JP. Seven-year changes in body fat and visceral adipose
tissue in women. Association with indexes of plasma glucose-
insulin homeostasis. Diabetes Care. 1996;19(9):983Y991.
79. Toth MJ, Tchernof A, Sites CK, Poehlman ET. Effect of meno-
pausal status on body composition and abdominal fat distri-
bution. Int J Obes Relat Metab Disord. 2000;24(2):226Y231.
80. Skerlj B. Age changes in fat distribution in the female body.
Acta Anat (Basel). 1959;38:56Y63.
81. Tanner JM. Growth at Adolescence. Oxford: Blackwell Scientific
Publications; 1955.
82. Bjorntorp P. The android womanVa risky condition. J Intern
Med. 1996;239(2):105Y110.
83. Davidson JM, Chen JJ, Crapo L, Gray GD, Greenleaf WJ,
Catania JA. Hormonal changes and sexual function in aging men.
JClinEndocrinolMetab. 1983;57(1):71Y77.
84. Malis C, Rasmussen EL, Poulsen P, et al. Total and regional fat
distribution is strongly influenced by genetic factors in young
and elderly twins. Obes Res. 2005;13(12):2139Y2145.
85. Bouchard C, Despres JP, Mauriege P. Genetic and nongenetic
determinants of regional fat distribution. Endocr Rev. 1993;
86. Larsson I, Lindroos AK, Peltonen M, Sjostrom L. Potassium per
kilogram fat-free mass and total body potassium: predictions
from sex, age, and anthropometry. Am J Physiol Endocrinol
Metab. 2003;284(2):E416YE423.
87. Garn SM. Anthropometry in clinical appraisal of nutritional status.
Am J Clin Nutr. 1962;11:418Y432.
88. Borkan GA, Hults DE, Gerzof SG, Robbins AH. Comparison of
body composition in middle-aged and elderly males using com-
puted tomography. Am J Phys Anthropol. 1985;66(3):289Y295.
89. Sims EA. Are there persons who are obese, but metabolically
healthy? Metabolism. 2001;50(12):1499Y1504.
90. Ruderman NB, Schneider SH, Berchtold P. The ‘‘metabolically-
obese,’’ normal-weight individual. Am J Clin Nutr. 1981;34(8):
91. Pisinger C, Jorgensen T. Waist circumference and weight follow-
ing smoking cessation in a general population: the Inter99 study.
Prev Med. 2007;44(4):290Y295.
92. McPherson R. Chromosome 9p21 and coronary artery disease.
N Engl J Med. 2010;362(18):1736Y1737.
93. Nuttall FQ. Diet and the diabetic patient. Diab Care. 1983;
94. Chaudhry ZW, Gannon MC, Nuttall FQ. Stability of body
weight in type 2 diabetes. Diabetes Care. 2006;29(3):493Y497.
95. Forbes GB, Reina JC. Adult lean body mass declines with age:
some longitudinal observations. Metabolism. 1970;19:653Y663.
96. Srikanthan P, Karlamangla AS. Relative muscle mass is in-
versely associated with insulin resistance and prediabetes.
Findings from the third National Health and Nutrition Exa-
mination Survey. J Clin Endocrinol Metab. 2011;96(9):2898Y2903.
97. Keys A, Aravanis C, Blackburn H, et al. Coronary heart disease:
overweight and obesity as risk factors. Ann Intern Med. 1972;
98. Chapman JM, Massey FJ Jr. The interrelationship of serum
cholesterol, hypertension, body weight, and risk of coronary
disease. results of the first ten years’ follow-up in the Los
Angeles Heart Study. J Chron Dis. 1964;17:933Y949.
99. Kip KE, Marroquin OC, Kelley DE, et al. Clinical importance of
obesity versus the metabolic syndrome in cardiovascular risk in
women: a report from the Women’s Ischemia Syndrome Eval-
uation (WISE) study. Circulation. 2004;109(6):706Y713.
100. St-Pierre AC, Cantin B, Mauriege P, et al. Insulin resistance syn-
drome, body mass index and the risk of ischemic heart disease.
CMAJ. 2005;172(10):1301Y1305.
101. Keys A. Overweight and the risk of sudden heart attack and
sudden death NIH; 1973:215Y223.
102. Preis SR, Pencina MJ, Hwang SJ, et al. Trends in cardiovascular
disease risk factors in individuals with and without diabetes
mellitus in the Framingham Heart Study. Circulation. 2009;120(3):
103. Filozof C, Fernandez Pinilla MC, Fernandez-Cruz A. Smoking
cessation and weight gain. Obes Rev. 2004;5(2):95Y103.
104. Flegal KM. The effects of changes in smoking prevalence on
obesity prevalence in the United States. Am J Public Health.
105. Yeh HC, Duncan BB, Schmidt MI, Wang NY, Brancati FL. Smoking,
smoking cessation, and risk for type 2 diabetes mellitus: a
cohort study. Ann Intern Med5;152(1):10Y17.
106. O’Hara P, Connett JE, Lee WW, Nides M, Murray R, Wise R.
Early and late weight gain following smoking cessation in the
Lung Health Study. Am J Epidemiol. 1998;148(9):821Y830.
107. Flegal KM, Graubard BI, Williamson DF, Gail MH. Excess
deaths associated with underweight, overweight, and obesity.
JAMA. 2005;293(15):1861Y1867.
108. Romero-Corral A, Montori VM, Somers VK, et al. Association of
bodyweight with total mortality and with cardiovascular events
in coronary artery disease: a systematic review of cohort studies.
Lancet. 2006;368(9536):666Y678.
109. Pischon T, Boeing H, Hoffmann K, et al. General and abdo-
minal adiposity and risk of death in Europe. N Engl J Med.
110. Adams KF, Schatzkin A, Harris TB, et al. Overweight, obesity,
and mortality in a large prospective cohort of persons 50 to
71 years old. N Engl J Med. 2006;355(8):763Y778.
111. Troiano RP, Frongillo EA Jr, Sobal J, Levitsky DA. The rela-
tionship between body weight and mortality: a quantitative
analysis of combined information from existing studies. Int J
Obes Relat Metab Disord. 1996;20(1):63Y75.
112. Zhu S, Heo M, Plankey M, Faith MS, Allison DB. Associations of
body mass index and anthropometric indicators of fat mass and
fat free mass with all-cause mortality among women in the first
and second National Health and Nutrition Examination Surveys
follow-up studies. Ann Epidemiol. 2003;13(4):286Y293.
113. Katzmarzyk PT, Craig CL, Bouchard C. Original article under-
weight, overweight and obesity: relationships with mortality in
the 13-year follow-up of the Canada Fitness Survey. JClin
Epidemiol. 2001;54(9):916Y920.
114. Andres R. Effect of obesity on total mortality. Int J Obes. 1980;
115. Whitlock G, Lewington S, Sherliker P, et al. Body-mass index
and cause-specific mortality in 900 000 adults: collaborative anal-
yses of 57 prospective studies. Lancet. 2009;373(9669):1083Y1096.
Volume 50, Number 3, May/June 2015 Nutrition Today
Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.
116. National Task Force on the Prevention and Treatment of Obe-
sity. Weight cycling. JAMA. 1994;272(15):1196Y1202.
117. Gaesser GA. Thinness and weight loss: beneficial or detrimen-
tal to longevity? Med Sci Sports Exerc. 1999;31(8):1118Y1128.
118. National Task Force on the Prevention and Treatment of
Obesity. Weight cycling. JAMA. 1994;272(15):1196Y1201.
119. Danforth E Jr, Sims EA. Obesity and efforts to lose weight.
N Engl J Med. 1992;327(27):1947Y1948.
120. Hamm P, Shekelle RB, Stamler J. Large fluctuations in body
weight during young adulthood and twenty-five-year risk of
coronary death in men. Am J Epidemiol. 1989;129(2):312Y318.
121. Lissner L, Odell PM, D’Agostino RB, et al. Variability of body
weight and health outcomes in the Framingham population.
N Engl J Med. 1991;324(26):1839Y1844.
122. Gregg EW, Gerzoff RB, Thompson TJ, Williamson DF. Trying
to lose weight, losing weight, and 9-year mortality in over-
weight U.S. adults with diabetes. Diabetes Care. 2004;27(3):
123. Sorensen TI, Rissanen A, Korkeila M, Kaprio J. Intention to
lose weight, weight changes, and 18-y mortality in overweight
individuals without co-morbidities. PLoS Med. 2005;2(6):e171.
124. Blair SN, Shaten J, Brownell K, Collins G, Lissner L. Body
weight change, all-cause mortality, and cause-specific mortality
in the Multiple Risk Factor Intervention Trial. Ann Intern Med.
1993;119(7 Pt 2):749Y757.
125. Dulloo AG. Human pattern of food intake and fuel-partitioning
during weight recovery after starvation: a theory of auto-
regulation of body composition. Proc Nutr Soc. 1997;56(1A):
126. Keys A, Brozek J, Henshel O, Mickleson O, Taylor HL. The
Biology of Human Starvation. Minneapolis, MN: University of
Minnesota Press; 1950.
127. Mann T, Tomiyama AJ, Westling E, Lew AM, Samuels B,
Chatman J. Medicare’s search for effective obesity treatments:
diets are not the answer. Am Psychol. 2007;62(3):220Y233.
128. Drenick EJ, Bale GS, Seltzer F, Johnson DG. Excessive mortality
and causes ofdeath in morbidly obesemen. JAMA. 1980;243(5):
129. Sjostrom L, Narbro K, Sjostrom CD, et al. Effects of bariatric
surgery on mortality in Swedish obese subjects. N Engl J Med.
130. Shah M, Simha V, Garg A. Review: long-term impact of
bariatric surgery on body weight, comorbidities, and nutri-
tional status. J Clin Endocrinol Metab. 2006;91(11):4223Y4231.
131. National Center forHealthStatistics. Health, United States, 2009: In
Brief. Hyattsville, MD; 2010.
hus09_InBrief.pdf. Accessed April 28, 2015.
132. Rodu B, Cole P. We’re living longer healthier lives. St Paul
Pioneer Press. January 30, 2007.
133. Harper AE. Dietary goals - a skeptical view. Am J Clin Nutr.
134. Gregg EW, Cheng YJ, Cadwell BL, et al. Secular trends in
cardiovascular disease risk factors according to body mass
index in US adults. JAMA. 2005;293(15):1868Y1874.
135. Kim S, Popkin BM. Commentary: understanding the epidemi-
ology of overweight and obesityVa real global public health
concern. Int J Epidemiol. 2006;35(1):60Y67 discussion 62Y81.
136. Brownell KD, Farley T, Willett WC, et al. The public health
and economic benefits of taxing sugar-sweetened beverages.
N Engl J Med. 2009;361(16):1599Y1605.
137. Nestle M, Jacobson MF. Halting the obesity epidemic: a public
health policy approach. Public Health Rep. 2000;115(1):12Y24.
138. Battle EK, Brownell KD. Confronting a rising tide of eating
disorders and obesity: treatment vs. prevention and policy.
Addict Behav. 1996;21(6):755Y765.
139. Olshansky SJ, Passaro DJ, Hershow RC, et al. A potential
decline in life expectancy in the United States in the 21st
century. N Engl J Med. 2005;352(11):1138Y1145.
140. Preston SH. Deadweight? The influence of obesity on
longevity. N Engl J Med. 2005;352(11):1135Y1137.
141. Couzin-Frankel J. A pitched battle over life span. Science.29;
Evidence-Based Approach to Fiber Supplements and Clinically Meaningful Health
Benefits, Parts 1 and 2: What to Look for and How to Recommend an Effective
Fiber Therapy: Erratum
The articles cited above, published in the March/April 2015 issue of Nutrition Today, were designated
for open access but were not identified as such in the print issue. Open access labels have been
applied and the articles are freely available on the journal’s Web site:
1. McRorie JW. Evidence-based approach to fiber supplements and clinically meaningful health benefits,
part 1: what to look for and how to recommend an effective fiber therapy. Nutr Today. 2015;50(2):
2. McRorie JW. Evidence-based approach to fiber supplements and clinically meaningful health
benefits, part 2: what to look for and how to recommend an effective fiber therapy. Nutr Today.
DOI: 10.1097/NT.0000000000000100
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... It is necessary to remember that BMI does not provide information on body composition and therefore does not distinguish between fat mass and lean mass. 16 Furthermore, in patients with gastric cancer, malnutrition is often associated with obesity and consequentially with a high BMI. It is therefore important to overcome the false belief that malnutrition only affects patients who are underweight and should be sought and treated in the presence of a low BMI. ...
Gastric cancer is the sixth most common malignancy in the world. However, its mortality has been decreasing in the last years thanks to improvement in diagnostics and therapeutics. Nevertheless, the high rate of malnutrition in patients with gastric cancer still has a major impact on the overall survival and quality of life of patients. The narrative review presents the most recent data on nutritional support in the resectable stages of gastric cancer, with a particular focus on perioperative strategies, and discusses malnutrition in gastric cancer, nutritional support before and after surgery, and the relationship between nutritional support and chemotherapy. Despite the predominantly methodological limitations related to the difficulty of performing randomized controlled trials on nutritional support in cancer patients, this review highlights important points. Nutritional counselling is essential starting from diagnosis. In limited or locally advanced forms (about 40% of cases), the therapeutic cornerstone is represented by gastric surgery. In most of these cases, perioperative chemotherapy is also indicated. Of note, nutritional support varies before and after surgery. In the preoperative period, the goal is to prepare the body for surgery, with available evidence recommending the prescription of immunonutrition (both oral and artificial, as appropriate). In the postoperative period, on the other hand, the objective is to facilitate recovery and adaptation to the new anatomy; an early and combined strategy (oral and enteral) seems to be the most suitable to pursue this. Unfortunately, rigorous data on the relationship between nutritional support and chemotherapy treatments used in resectable gastric cancer are not available. In the absence of strong scientific evidence, it may be useful to adopt a personalized multidisciplinary strategy for each patient wherein the chemotherapy programme is modulated based on nutritional status.
... Body composition was assessed using the Inbody body composition analyzer 720 (InBody Co., Ltd., Seoul, Korea) which uses a segmental multifrequency bioelectrical impedance analysis technique to analyze the percent body fat (%BF) that produces small individual error and can be used as a substitute when dual-energy X-ray absorptiometry is not available [19,20]. Cutoffs of ≥25% and ≥35% were used to define elevated %BF among men and women, respectively [21]. ...
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Peptide-hormones, including pancreatic peptide-YY(PYY), glucagon-like peptide-1 (GLP-1), cholecystokinin (CCK), insulin, and leptin function as satiety signals, while ghrelin promotes hunger. These hormones are also involved in glucose homeostasis and body-weight regulation. The aim of this cross-sectional analysis was to examine the association of these peptide-hormones with obesity-markers, insulin-resistance, and dyslipidemia (total-cholesterol (TC), low-density-lipoprotein-cholesterol (LDL-C), high-density-lipoprotein-cholesterol (HDL-C), triglyceride (TG)). Sixteen-obese (OB) adults and 21 normal-weight (NW) age-and gender-matched counterparts were recruited. OB-participants showed significantly higher levels of leptin, insulin, Homeostatic-Model Assessment of Insulin Resistance (HOMA-IR), and TG. NW participants had significantly higher levels of ghrelin. GLP-1 was positively correlated with insulin, HOMA-IR, and obesity-markers except percent body fat. Leptin was positively correlated with all markers (except glucose and dyslipidemia). PYY was positively correlated with BMI, insulin and HOMA-IR. Ghrelin was inversely correlated with all of the markers except glucose, TC, and LDL-C. In the regression analysis model, leptin was positively associated with obesity markers and insulin resistance. Our results indicate a significant difference in peptide hormones among OB and NW Lebanese individuals. Since there is controversial evidence regarding body-weight and peptide-hormones in the literature, this study highlights a step forward towards finding ethnic based strategies to treat obesity and its consequences.
Latent class analysis was used to explore intersections of material circumstances and health care access among 308 adults, and associations between classes with health outcomes. Good fit was found for a four-class model: Resource Stable (Class 1, 62.43%), Unbalanced Meals with Health Care (Class 2, 16.91%), Resource Insecurity with Delayed Health Care (Class 3, 14.75%), and Resource Stability without Access to Health Care (Class 4, 5.91%). Class 1 reported greater well-being and self-rated health than Class 2 and 3. Class 1 reported lower BMI than Class 2. Findings document intersections among economic marginalization indicators with varying health outcomes among classes.
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Background Overweight and obesity are defined by an anomalous or excessive fat accumulation that may compromise health. To find single-nucleotide polymorphisms (SNPs) influencing metabolic phenotypes associated with the obesity state, we analyze multiple anthropometric and clinical parameters in a cohort of 790 healthy volunteers and study potential associations with 48 manually curated SNPs, in metabolic genes functionally associated with the mechanistic target of rapamycin (mTOR) pathway. Results We identify and validate rs2291007 within a conserved region in the 3′UTR of folliculin-interacting protein FNIP2 that correlates with multiple leanness parameters. The T-to-C variant represents the major allele in Europeans and disrupts an ancestral target sequence of the miRNA miR-181b-5p, thus resulting in increased FNIP2 mRNA levels in cancer cell lines and in peripheral blood from carriers of the C allele. Because the miRNA binding site is conserved across vertebrates, we engineered the T-to-C substitution in the endogenous Fnip2 allele in mice. Primary cells derived from Fnip2 C/C mice show increased mRNA stability, and more importantly, Fnip2 C/C mice replicate the decreased adiposity and increased leanness observed in human volunteers. Finally, expression levels of FNIP2 in both human samples and mice negatively associate with leanness parameters, and moreover, are the most important contributor in a multifactorial model of body mass index prediction. Conclusions We propose that rs2291007 influences human leanness through an evolutionarily conserved modulation of FNIP2 mRNA levels. Graphical Abstract
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Background: Whether metformin is related to nonalcoholic fatty liver disease (NAFLD) is controversial. Our aim was to investigate the relationship between metformin and NAFLD that may predict the metformin potential of these lesions and new prevention strategies in NAFLD patients. Methods: The meta-analysis was analyzed by Revman 5.3 softwares systematically searched for works published through July 29, 2022. Network pharmacology research based on databases, Cytoscape 3.7.1 software and R software respectively. Results: The following variables were associated with metformin in NAFLD patients: decreased of alanine aminotransferase (ALT) level (mean difference [MD] = -10.84, 95% confidence interval [CI] = -21.85 to 0.16, P = .05); decreased of aspartate amino transferase (AST) level (MD = -4.82, 95% CI = -9.33 to -0.30, P = .04); decreased of triglyceride (TG) level (MD = -0.17, 95% CI = -0.26 to -0.08, P = .0002); decreased of total cholesterol (TC) level (MD = -0.29, 95% CI = -0.47 to -0.10, P = .003); decreased of insulin resistance (IR) level (MD = -0.42, 95% CI = -0.82 to -0.02, P = .04). In addition, body mass index (BMI) (MD = -0.65, 95% CI = -1.46 to 0.16, P = .12) had no association with metformin in NAFLD patients. 181 metformin targets and 868 NAFLD disease targets were interaction analyzed, 15 core targets of metformin for the treatment of NAFLD were obtained. The effect of metformin on NAFLD mainly related to cytoplasm and protein binding, NAFLD, hepatitis B, pathway in cancer, toll like receptor signaling pathway and type 2 diabetes mellitus (T2DM). The proteins of hypoxia inducible factor-1 (HIF1A), nuclear factor erythroid 2-related factor (NFE2L2), nitric oxide synthase 3 (NOS3), nuclear receptor subfamily 3 group C member 1 (NR3C1), PI3K catalytic subunit alpha (PIK3CA), and silencing information regulator 2 related enzyme 1 (SIRT1) may the core targets of metformin for the treatment of NAFLD. Conclusion: Metformin might be a candidate drug for the treatment of NAFLD which exhibits therapeutic effect on NAFLD patients associated with ALT, AST, TG, TC and IR while was not correlated with BMI. HIF1A, NFE2L2, NOS3, NR3C1, PIK3CA, and SIRT1 might be core targets of metformin for the treatment of NAFLD.
Body mass index (BMI) is widely used as a first-line screening biomarker for nutritional status assessment. The advantages of BMI are its simplicity, low cost, and non-invasiveness. However, this biomarker has a number of limitations, which lead to low sensitivity in the diagnosis of both malnutrition and obesity; for example, more than half of the people with a high percentage of body fat (e.g., >30%) are diagnosed as being in the BMI range for a normal weight. The shortcomings of BMI as a biomarker of malnutrition depend on: (a) the slow effect of decreased food intake on its value and (b) its weak correlation with biochemical and immunological parameters of malnutrition. Whereas, the limitations of BMI as a biomarker of obesity are related to: (a) an inability to distinguish between fat and fat-free (lean) body mass; (b) a failure to determine fat distribution; (c) a dependence on the accuracy of reported or measured height; and (d) the influence of age, gender, and comorbidities on the accuracy of the cut-offs used in the diagnosis of obesity. Nevertheless, BMI correlates with: (a) central body fat distribution; (b) laboratory biomarkers of metabolic (e.g., blood glucose, lipids, uric acid), inflammatory (e.g., C-reactive protein, interleukin-6, and tumor necrosis factor alpha), and endothelial (e.g., VEGF and ICAM) abnormalities. BMI is also useful as: (c) a risk factor (biomarker) in the development of a number of health conditions, such as diabetes mellitus, hypertension, infectious disease, and psoriasis; (d) as a prognostic factor for all-cause and cardiovascular mortality, in-hospital all-cause mortality, surgery complications and outcomes, hospital-acquired (nosocomial) infections, length of in-hospital stay, and risk of readmission; as well as (e) a biomarker for monitoring the clinical and metabolic effects of interventions on weight reduction, including bariatric surgery. This chapter presents an overview of scientific works related to the use of BMI as a biomarker for various clinical disorders and their clinical course.
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The increasing prevalence of obesity has become a demanding issue in both high-income and low-income countries. Treating obesity is challenging as the treatment options have many limitations. Recently, diet modification has been commonly applied to control or prevent obesity and its risks. In this study, we investigated novel therapeutic approaches using a combination of a potential probiotic source with prebiotics. Forty-eight adult male Sprague–Dawley rats were selected and divided into seven groups (eight rats per group). The first group was fed a high-fat diet, while the second group was a negative control. The other five groups were orally administered with a probiotic, Lactiplantibacillus plantarum (L. plantarum), and potential prebiotics sources (chia seeds, green tea, and chitosan) either individually or in combination for 45 days. We collected blood samples to analyze the biochemical parameters and dissected organs, including the liver, kidney, and pancreas, to evaluate obesity-related injuries. We observed a more significant decrease in the total body weight by combining these approaches than with individual agents. Moreover, treating the obese rats with this combination decreased serum catalase, superoxide dismutase, and liver malondialdehyde levels. A histopathological examination revealed a reduction in obesity-related injuries in the liver, kidney, and pancreas. Further docking studies indicated the potential role of chia seeds and green tea components in modulating obesity and its related problems. Therefore, we suggest that the daily administration of a pre- and probiotic combination may reduce obesity and its related problems.
Full-text available
The increasing prevalence of obesity has become a demanding issue in both high-income and low-income countries. Treating obesity is challenging as the treatment options have many limitations. Recently, diet modification has been commonly applied to control or prevent obesity and its risks. In this study, we investigated novel therapeutic approaches using a combination of a potential probiotic source with prebiotics. Forty-eight adult male Sprague–Dawley rats were selected and divided into seven groups (eight rats per group). The first group was fed a high-fat diet, while the second group was a negative control. The other five groups were orally administered with a probiotic, Lactiplantibacillus plantarum (L. plantarum), and potential prebiotics sources (chia seeds, green tea, and chitosan) either individually or in combination for 45 days. We collected blood samples to analyze the biochemical parameters and dissected organs, including the liver, kidney, and pancreas, to evaluate obesity-related injuries. We observed a more significant decrease in the total body weight by combining these approaches than with individual agents. Moreover, treating the obese rats with this combination decreased serum catalase, superoxide dismutase, and liver malondialdehyde levels. A histopathological examination revealed a reduction in obesity-related injuries in the liver, kidney, and pancreas. Further docking studies indicated the potential role of chia seeds and green tea components in modulating obesity and its related problems. Therefore, we suggest that the daily administration of a pre- and probiotic combination may reduce obesity and its related problems.
A WHO expert consultation addressed the debate about interpretation of recommended body-mass index (BMI) cut-off points for determining overweight and obesity in Asian populations, and considered whether population-specific cut-off points for BMI are necessary. They reviewed scientific evidence that suggests that Asian populations have different associations between BMI, percentage of body fat, and health risks than do European populations. The consultation concluded that the proportion of Asian people with a high risk of type 2 diabetes and cardiovascular disease is substantial at BMIs lower than the existing WHO cut-off point for overweight (greater than or equal to25 kg/m(2)). However, available data do not necessarily indicate a clear BMI cut-off point for all Asians for overweight or obesity. The cut-off point for observed risk varies from 22 kg/m(2) to 25 kg/m(2) in different Asian populations; for high risk it varies from 26 kg/m(2) to 31 kg/m(2). No attempt was made, therefore, to redefine cut-off points for each population separately. The consultation also agreed that the WHO BMI cut-off points should be retained as international classifications. The consultation identified further potential public health action, points (23.0, 27.5, 32.5, and 37.5 kg/m(2)) along the continuum of BMI, and proposed methods by which countries could make decisions about the definitions of increased risk for their population.
Dietary fiber that is intrinsic and intact in fiber-rich foods (eg, fruits, vegetables, legumes, whole grains) is widely recognized to have beneficial effects on health when consumed at recommended levels (25 g/d for adult women, 38 g/d for adult men). Most (90%) of the US population does not consume this level of dietary fiber, averaging only 15 g/d. In an attempt to bridge this "fiber gap," many consumers are turning to fiber supplements, which are typically isolated from a single source. Fiber supplements cannot be presumed to provide the health benefits that are associated with dietary fiber from whole foods. Of the fiber supplements on the market today, only a minority possess the physical characteristics that underlie the mechanisms driving clinically meaningful health benefits. The first part (current issue) of this 2-part series will focus on the 4 main characteristics of fiber supplements that drive clinical efficacy (solubility, degree/rate of fermentation, viscosity, and gel formation), the 4 clinically meaningful designations that identify which health benefits are associated with specific fibers, and the gel-dependent mechanisms in the small bowel that drive specific health benefits (eg, cholesterol lowering, improved glycemic control). The second part (next issue) of this 2-part series will focus on the effects of fiber supplements in the large bowel, including the 2 mechanisms by which fiber prevents/relieves constipation (insoluble mechanical irritant and soluble gel-dependent water-holding capacity), the gel-dependent mechanism for attenuating diarrhea and normalizing stool form in irritable bowel syndrome, and the combined large bowel/small bowel fiber effects for weight loss/maintenance. The second part will also discuss how processing for marketed products can attenuate efficacy, why fiber supplements can cause gastrointestinal symptoms, and how to avoid symptoms for better long-term compliance.
Background: Cigarette smoking is an established predictor of incident type 2 diabetes mellitus, but the effects of smoking cessation on diabetes risk are unknown. Objective: To test the hypothesis that smoking cessation increases diabetes risk in the short term, possibly owing to cessation-related weight gain. Design: Prospective cohort study. Setting: The ARIC (Atherosclerosis Risk in Communities) Study. Patients: 10,892 middle-aged adults who initially did not have diabetes in 1987 to 1989. Measurements: Smoking was assessed by interview at baseline and at subsequent follow-up. Incident diabetes was ascertained by fasting glucose assays through 1998 and self-report of physician diagnosis or use of diabetes medications through 2004. Results: During 9 years of follow-up, 1254 adults developed type 2 diabetes. Compared with adults who never smoked, the adjusted hazard ratio of incident diabetes in the highest tertile of pack-years was 1.42 (95% CI, 1.20 to 1.67). In the first 3 years of follow-up, 380 adults quit smoking. After adjustment for age, race, sex, education, adiposity, physical activity, lipid levels, blood pressure, and ARIC Study center, compared with adults who never smoked, the hazard ratios of diabetes among former smokers, new quitters, and continuing smokers were 1.22 (CI, 0.99 to 1.50), 1.73 (CI, 1.19 to 2.53), and 1.31 (CI, 1.04 to 1.65), respectively. Further adjustment for weight change and leukocyte count attenuated these risks substantially. In an analysis of long-term risk after quitting, the highest risk occurred in the first 3 years (hazard ratio, 1.91 [CI, 1.19 to 3.05]), then gradually decreased to 0 at 12 years. Limitation: Residual confounding is possible even with meticulous adjustment for established diabetes risk factors. Conclusion: Cigarette smoking predicts incident type 2 diabetes, but smoking cessation leads to higher short-term risk. For smokers at risk for diabetes, smoking cessation should be coupled with strategies for diabetes prevention and early detection.
The determinants of length of survival during total fasting are unknown. Media reports of hunger strikers in Northern Ireland have provided some basis for evaluating this question. Such "data" combined with standard concepts of body composition, fuel homeostasis, and responses to therapeutic fasts suggest that death occurred when fat stores were approaching exhaustion. Thus, fat stores may play a central role. (JAMA 1982;248:2306-2307)
A group of 200 morbidly obese men (average weight, 143.5 kg; age, 23 to 70 years) were admitted to a weight control program between 1960 and 1977 and were followed up for a mean period of 7 1/2 years. There was complete follow-up until the termination of the study or until death for 185 men. Fifteen men were followed up for fractional periods. Fifty of the 200 died during the course of the study. Life-table techniques, comparing the mortality among the obese with that among men in the general population, demonstrated a 12-fold excess mortality in the obese in the age group 25 to 34 years and a sixfold excess in the age group 35 to 44 years. This ratio diminished with advancing age. Cardiovascular disease was reported as the cause of death more frequently and malignancies less frequently than they were for men in the US general population.(JAMA 243:443-445, 1980)
Objective: To evaluate the relation between weight variability and death in high;risk, middle-aged men participating in the Multiple Risk Factor Intervention Trial (MRFIT). Design: Cohort study with 3.8 years of follow-up. Setting: Multicenter, collaborative, primary prevention trial conducted at 22 clinical centers in the United States. Participants: Men (n=10 529) who were 35 to 57 years old at baseline and who were in the upper 10% to 15% of risk for coronary heart disease because of smoking, high blood pressure, and elevated cholesterol level. Participants were seen at least annually for 6 to 7 years for medical evaluations in study clinical centers
Normal women produce small amounts of active androgens. When androgen levels are elevated, such as for example in the polycystic ovary syndrome, this is followed by the development of male physical characteristics of muscle mass, structure and function as well as android adipose tissue distribution and function. Psychological features and stress reactions also seem similar to those of men. Such women have an increased risk of developing hypertension, non-insulin-dependent diabetes mellitus and cardiovascular disease. Recent data have shown that these physical, and psychological characteristics, as well as risk of ill health, are also found in the population of women selected at random. Women in the lowest quintiles of levels of sex-hormone-binding globulin - an indicator inversely related to active androgens - are at risk of developing hypertension, non-insulin-dependent diabetes mellitus and cardiovascular mortality. The mechanism probably includes muscular insulin resistance, following a relative androgen excess.It is thus apparent that androgens, even within the highest levels of the nonselected population of women, are powerful predictors of serious disease development. The population at risk might be as large as about 20% of middle-aged women. This is an area of female disease risk which requires more attention in screening and intervention procedures.