The Body Mass Index (BMI) is a weight-for-height index that
classifies underweight, overweight, and obesity in adults.
BMI is calculated by dividing weight in kilograms by the square of
height in meters (BMI values represent units of kg/m2, and
all BMI values throughout this article should be assumed to be
measured in this unit).
There are different BMI categories, explored in detail below.
Underweight BMI Range
BMI result: Below 18.5
Those who fall into this category are defined as underweight.
Being underweight could be a sign you're not eating enough or may
indicate an underlying medical condition. If you're underweight,
contact your doctor for further evaluation.
Normal Weight BMI Range
BMI result: Between 18.5 and 24.9
The medical community recommends keeping your weight within this range.
Overweight BMI Range
BMI result: between 25 and 29.9
People in this category may be at risk of developing obesity.
There may also be a risk of developing other health problems or
current health problems worsening over time. Based on European and
American clinical guidelines for obesity management in adults, the
following are the most likely recommendations based on BMI ranges.
BMI result between 25 and 26.9 People who do not have weight-related health problems (for
example, high blood pressure or high cholesterol) are encouraged to
eat healthy and increase their physical activity to prevent further
weight gain.
BMI result between 27 and 29.9 People in this range who also have weight-related health
problems will likely be recommended to lose weight by combining
lifestyle interventions and consider anti-obesity medications.
Committing to health interventions can lead to weight loss, improved
health, and experiencing a higher quality of life.
Classification of obesity
There are three classes of obesity based on BMI ranges:
Obesity classification
BMI
Class I
30.0–34.9
Class II
35.0–39.9
Class III
Above 40
BMI ranges are based on the effect excess body fat has on an
individual’s health, life expectancy, and the risk of developing
weight-related health complications.
BMI result: 30.0 or higher
People who have a BMI equal to or above 30 may suffer from obesity.
Health organizations now recognize obesity as a chronic, but
manageable disease that is best dealt with using a
multi-disciplinary weight loss treatment approach.
As BMI increases into the range to be considered living with
obesity, so does the risk factor of certain chronic diseases.
Higher BMIs (greater than or equal to 30) have statistically
significant associations with several medical conditions. These
conditions include but are not limited to - cardiovascular disease,
type 2 diabetes, osteoarthritis, and various cancers.
BMI is not a diagnosis of obesity but instead can be used to screen
for health risks.
People with a BMI equal to or above 30 are highly recommended to
consult a doctor trained in obesity management.
There are various scientifically proven treatment options for
obesity. Treatment options are dependent on the specific needs of the
individual, their current health status, and presence of
weight-related health complications.
Treatments may include a combination of the following options:
*Bariatric surgery is considered for adults with a BMI of 40 or
above or adults with a BMI of over 35 who also have weight-related comorbidities.
Disclaimer: This information is not a substitute for the advice of a
healthcare provider. If you have any questions regarding your health,
you should contact your general practitioner or another qualified
healthcare provider.
Let’s talk: 13 questions to ask your doctor about obesity
These thirteen questions can help to start a dialogue and take the first
steps towards understanding what treatment options for weight management
are available.
Obesity is a chronic disease which requires medical attention. For
most populations, living with overweight or obesity (BMI greater than
or equal to 25) is associated with increased risk of mortality and
increased risk of comorbidities.
Obesity screenings can take BMI threshold levels into account.
Obesity can also be an indicator of potential future health issues
that may require medical advice.
In general, the higher your BMI in the range to be considered living
with obesity, the greater the risk of developing other chronic
obesity-related diseases including:
Evidence suggests that fat distribution and body composition differ
across certain ethnic groups.
People with certain ethnic backgrounds may have higher or lower
health risks compared to those from another ethnic background with the
same BMI.
This means some groups may have a greater predisposition for
developing obesity-related diseases such as type 2 diabetes or heart
disease, for example. Therefore, based on your genetics, you may incur
greater health risks associated with excess weight.
This has led to proposals for specific BMI thresholds based on
ethnic backgrounds.
For instance, lower BMI cut-off values has been suggested for
certain Asian populations. For example, lowering the cut-off
values by 3 units for Indonesian, Singaporean, and Hong Kong Chinese populations.
This is because Asian populations tend to have a higher amount of
body fat at a lower BMI. As a result, there is an increased risk of
associated health complications, compared to other populations.
In the section below, research has pointed to links between BMI and
different ethnic backgrounds. These factors may be a consideration
when determining if taking action is right for you.
South Asian ethnic group and BMI (Indian, Bangladeshi,
Pakistani)
In a study comparing Singaporean Indian, Malay, Chinese, and
Caucasian populations, Indians had the highest body fat percentage at
equivalent BMIs. Higher body fat percentages have been linked with
greater cardiovascular risk factors.
Indian populations were shown to have same amount of body fat at a
BMI of 26, as Caucasians with a BMI of 30, the threshold to be
considered obese.
When compared to Caucasian, Chinese, and Aboriginal ethnic groups, a
study found that South Asian populations had a higher body fat to lean
mass ratio. This indicates that South Asian populations have increased
risk of diabetes.
Populations of South Asian descent living in the UK experienced
higher incidences of coronary heart disease and stroke than
Caucasians. Both coronary heart disease and stroke are associated with
body fat and body fat distribution.
East Asian ethnic group and BMI (Chinese, Taiwanese,
Japanese, Korean)
A study of East Asian populations found that those with a BMI of 25
or above had a higher risk of mortality from cardiovascular disease,
coronary heart disease, and stroke when compared to the reference
range typically associated with a given BMI.
In the study of Taiwanese populations, significantly increased
mortality risks were experienced at BMI greater than or equal to 25.
These findings support the need to define obesity in East Asian
populations differently, lowering the BMI cut-off point by 5 units
from 30 to 25.
Similarly, data suggested that for Japanese populations obesity
should be categorized as a BMI greater than or equal to 25.
Studies have shown that Asian populations had a higher body fat
percentage compared to Caucasians for a given BMI. Asian populations
also have elevated risk of comorbidities such as type 2 diabetes,
hypertension, and hyperlipidemia.
Southeast Asian ethnic group and BMI (Vietnamese, Thai,
Filipino, Indonesian, Malaysian)
In a study of the Thai population, waist circumference was shown to
be a better predictor of diabetes than BMI. This may be because most
studies evaluating the association of BMI with mortality risks have
been conducted on populations of European descent (Caucasians).
According to a multi-ethnic study in Singapore, Malay populations
had a higher body fat percentage than Caucasians at an equivalent BMI.
Data suggested that average amount of body fat in Caucasians with
obesity (BMI 30), could be found in Malays with a BMI of 27.
Studies of Southeast Asian populations in California found that
Filipino- and Vietnamese-Americans were significantly more likely to
suffer from type 2 diabetes compared to Caucasians. Therefore, such
populations should take into account that being overweight/obese can
increase risk factors for type 2 diabetes.
Pacific Islander ethnic group and BMI
Certain Pacific Islander populations have lower body fat
percentages compared to Caucasians at the same BMI, though this is not
the case for all populations considered to be ‘Pacific Islander’.
A study showed that Pacific Islanders suffering from overweight or
obesity had a significantly higher likelihood of having hypertension,
compared to Caucasian populations.
Native Hawaiians have been shown to be 3 times more likely to have
diabetes compared to Caucasian populations, across all BMI categories.
Latin(o/a) & Hispanic ethnic group and BMI
A study found that for populations of the same BMI, Mexicans had a
greater total body fat and less skeletal muscle compared to Caucasians.
A separate study found that Mexican-Americans had a higher
prevalence of obesity compared to Caucasians, but a lower prevalence
of associated hypertension.
Similarly, research on Latino populations in the USA found them to
be at least twice as likely to suffer from diabetes when compared with
Caucasians, regardless of BMI category.
Various studies of Hispanic/Latino populations have suggested that
BMI and waist circumference threshold levels for predicting the
likelihood of chronic diseases should be unique to ethnic populations.
Black ethnic group and BMI
In the USA, differences in BMI between populations account for almost
30% of the difference in Black and White mortality rates amongst
females aged 40-79.
When comparing Caucasians with a BMI of 25, the equivalent type 2
diabetes risk in Black populations was seen at lower BMIs (between
21-23). For a BMI of 30 in Caucasian populations, the equivalent
diabetes risk in black populations is at BMI values 0.1–4 units lower (26–29.9).
Middle Eastern ethnic group and BMI
A study conducted in Saudi Arabia highlighted the limitation of using
the current BMI cut-off (≥30) in diagnosing obesity among the Saudi
and possibly the Arab population. The study suggested lowering the BMI
threshold for obesity to 27 in order to accurately reflect the
obesity-related health risks in these populations.
In a study conducted in Sweden, the diabetes risk of Caucasians
living with obesity (BMI of 30) was equal to that of Iraqi men with a
BMI of 28.5, and Iraqi women with BMI of 27.5.
This is one of the main determinants of type 2 diabetes, implying a
greater risk for Iraqi populations than Caucasians at the same level
of BMI.
When observing the population in Jeddah, Saudi Arabia, researchers
reported that women with obesity had a higher risk of prediabetes,
diabetes, and dyslipidemia. Men in the same population with
obesity had greater risk of having high blood pressure.
White/Caucasian ethnic group and BMI
Studied White/Caucasian populations were shown to have a greater risk
of mortality associated with a higher BMI, compared to Black
populations at equivalent BMI levels.
The researchers noted greater risk associated with obesity for
coronary heart disease, stroke and heart disease among White/Caucasian populations.
BMI in special populations
BMI can be misleading in certain cases. Research has shown that BMI
can less accurately predict the disease risks for some groups of people:
Elderly
Athletes
People of tall or
short stature
Body types with more muscle mass
For instance, in certain populations such as elite athletes or
bodybuilders an elevated BMI does not directly correlate with their
health status. Their increased muscle mass and therefore weight also
increases their BMI.
The table below shows how the average body fat percentages
differ according to specific groups and categories:
Description
Men
Women
Essential fat
2 - 5 %
10 - 13
%
Athletes
6 - 13 %
14 -
20 %
Fitness
14 - 17 %
21 -
24 %
Acceptable
18 - 24 %
25 -
31 %
Obesity
>25 %
>32
%
BMI & gender
At present, there is no individual BMI calculation for women and men.
However, whilst gender is not factored into BMI calculations, the
physiological differences between genders may imply a difference in
the degree of risk at a given BMI.
Men:
In terms of weight distribution, it has been reported that men tend
to accumulate body fat in the upper body, including the abdomen.
Abdominal obesity and higher concentrations of visceral fat in men
lead to a higher risk of heart disease and type 2 diabetes.
Women:
Whilst women tend to have a higher percentage of body fat than men,
fat deposits in females tends to be distributed in the hips and buttocks.
Due to differences in fat deposition, women may be at lower risk of
the comorbidities associated with obesity compared to men at the same
or similar BMI.
BMI & age
Adult BMI calculations do not take age into account. However,
research suggests that whilst obesity increases mortality risk at any
age, this correlation is much stronger in people below the age of 50.
Accelerated weight gain as a child has been shown to imply further
weight gain during adolescence and adulthood. Weight gain as a child
is therefore a strong indicator of obesity in adulthood. A study shows
that 40% of children with obesity will become obese adults.
Whilst BMI is interpreted differently for children and adolescents
compared to adults, growing evidence suggests that BMI guidelines
should be age-specific for adult populations as well.
Children & adolescents
BMI interpretation in children and adolescents is both age- and
gender-specific. This is because girls and boys develop at different
rates, with body fat varying during developmental periods such as puberty.
Obesity in childhood has been shown to be a strong predictor of
various obesity-related diseases such as type 2 diabetes,
dyslipidemia, and sleep apnea. Children with obesity are also more
likely to suffer from psychological distress. This can include low
self-esteem, anxiety disorders, and depressive symptoms.
If you are a parent concerned about the health of your child with
relation to their weight, consult your doctor for guidance on weight
management and possible treatment options.
Elderly adult
The composition of our bodies naturally changes with age. An increase
in body fat is statistically likely to occur over adulthood, whilst
total muscle mass also decreases with age.
Muscle mass and strength are considered important for the
maintenance of physical activity.
Studies have shown that when using standard BMI calculations, being
slightly overweight is associated with a reduced risk of mortality
compared to the ‘normal’ weight range in older populations.
The standard BMI calculation can also underestimate or overestimate
the amount of excess fat carried by elderly persons. Assessments such
as waist circumference have therefore been recommended as better
options when measuring body fat in the elderly.
Diagnosis of obesity
Diagnosing obesity should not be limited to measuring BMI alone.
However, BMI can help identify people who would experience health
improvements from obesity management.
Diagnostic testing is often ordered during initial obesity
assessments, with the aim of discovering metabolic problems and
personalizing treatment options. Screening will typically involve
various types of laboratory testing:
HbA1C
Electrolytes renal function tests (creatinine,
eGFR)
Total cholesterol, HDL- and LDL-cholesterol,
triglycerides
Alanine aminotransferase (ALT)
Age
appropriate cancer screening
In addition to these tests, healthcare providers may take a
comprehensive diagnostic approach to understand the underlying causes
of obesity. A comprehensive approach aims to discover
potential contributing factors to a person’s obesity, and therefore provide an individualized treatment program.
Consult your doctor if you would like to learn more about
comprehensive diagnoses for obesity.
Waist circumference vs BMI
To gain a better understanding of health, other diagnostics and
measurements may be taken alongside BMI (for example, waist circumference).
Waist circumference is an indirect measure of abdominal fat, whereas
BMI is a representation of total body fat. Waist circumference has
therefore been cited as a more accurate measure of obesity-related
health risk, such as comorbidity and mortality.
Researchers have recommended that waist circumference be used
together with BMI to more accurately evaluate an individual’s health
risk factors.
Regardless of BMI, you should consult your doctor if you have
concerns about your health.
Weight management programs may be relevant if your waist
measurements are or exceed:
Men: 94cm (37in) or more
Women: 80cm (31.5in) or more
Higher waist circumferences are associated with greater health risk.
You may want to consider obesity treatment programs if your waist
measurements are as follows:
Men: 102cm (40in) or more
Women: 88cm (34.5in) or more
Is it possible to have a higher BMI and be healthy?
Typically, people suffering from obesity present a variety of health
conditions collectively known as metabolic syndrome.
Screening for metabolic syndrome is recommended for the majority of
people with higher BMIs.
This involves looking for the metabolic risk factors associated with
obesity, including the following:
Waist circumference
High triglyceride levels
Low HDL cholesterol levels
High LDL cholesterol
levels
High blood pressure
High blood sugar
At least three metabolic risk factors must be present to be
diagnosed with metabolic syndrome. As such, metabolic syndrome is
defined as a cluster of conditions, and can raise the risk of heart
disease, type 2 diabetes, and stroke.
Metabolic Healthy Obesity
The link between obesity and obesity-related complications is strong
but not absolute.
Some people with obesity do not present with metabolic syndrome and
are reported to have limited health risks at higher BMIs. This group
is defined as metabolically healthy obese individuals.
These individuals have lower risk of developing diabetes and heart
disease compared to other people with obesity who suffer from
metabolic syndrome.
However, clinical guidelines specify that people who are
metabolically-healthy and obese cannot be considered ‘medically
healthy’. They are at greater risk of mortality, as well as
other non-metabolic conditions such as depression, back pain, and
sleep apnea.
Despite the absence of metabolic risk factors, a study found
that people who are metabolically-healthy and obese were likely to
develop metabolic abnormalities within 10 years. This means they are
still at risk of progressing to an unhealthy metabolic state.
If you are living with obesity but do not present with chronic
disease symptoms associated with obesity, you may consider consulting
your doctor to evaluate your metabolic risk factors.
Adopting a healthier lifestyle can help this risk group to prevent
medical complications and avoid further weight gain.
History of the BMI
The BMI was conceived by the Belgian mathematician, Lambert Adolphe
Jacques Quetelet, in the mid-19th century.
Despite not being a doctor, Quetelet introduced the concept of
social averages. He noted the relationship between weight and height
in what was first known as the “Quetelet Index.”
Keys et al later popularised the measurement, describing it as
the Body Mass Index and using it as a classification in
population-based studies.
The BMI has been adopted into modern medical practices, especially
in Western societies with rising obesity rates.
Limitations of BMI
BMI is a simple and objective measurement, which can be easily
conducted by a doctor or anyone concerned about their health.
However, beyond the previously discussed limitations, you may also
consider that BMI measurements do not take account of the following:
Hereditary risk factors associated with obesity-related
diseases such as metabolic syndrome.
Environmental and
lifestyle factors other than obesity that can contribute to chronic
disease risk.
Individual body fat distribution.
It is also important to remember that obesity is not a definitive
indicator of ill-health, just as being at a ‘normal’ weight does not
mean you are healthy.
Your BMI does not define you, but knowing and understanding your BMI
can be a powerful tool for taking charge of your own health.
Consult with your doctor in order to discuss your weight and health
status and evaluate what actions may be needed.
Conclusion
There is an important relationship between the amount of body fat a
person has and the impacts on our health. Studies have demonstrated
health risks associated with both extremes of the BMI spectrum.
Various factors independent of weight can put you at risk of
developing chronic diseases (such as ethnicity and genetics). It is
important to be aware of these factors, and how they might contribute
to your risk if you do suffer from obesity.
BMI levels greater than or equal to 30 are associated with increased
mortality and risk of health complications. Obesity screenings should
take such BMI thresholds into account.
There are ‘metabolically healthy’ people that sustain limited health
issues at higher BMIs. However, obesity can still increase other
health risks for such individuals compared to those at lower BMIs.
For the majority of people with obesity, screening for metabolic
syndrome is recommended.
Understanding your BMI can help you find a healthy weight range and
identify the best way to reach or maintain it together with your
healthcare team. For most populations, having a BMI over 25 increases
health risk factors.
Your BMI should be used as a guideline and first step towards
understanding your weight and height. Adhering to a healthy diet and
lifestyle is recommended by healthcare professionals – regardless of
your current BMI.
For BMIs equal to or above 25, other actions might be needed in
addition to diet and physical exercise. Consulting your doctor is the
best way to define the right solutions for you.
Seek medical advice if you have any concerns regarding your weight.
Yumuk, V et al, “European Guidelines for Obesity
Management in Adults” Obes Facts. 2015 Dec; 8(6): 402–424.
Published online 2015 Dec 5. doi: 10.1159/000442721
Garvey,
W T et al, “American Association of Clinical Endocrinologists
and American College of Endocrinology comprehensive clinical
practice guidelines for medical care of patients with obesity.”
Endocrine Practice 2016;22:1–203.
DOI:https://doi.org/10.4158/EP161365.GL
Guh, D P et
al, “The incidence of co-morbidities related to obesity and
overweight: A systematic review and meta-analysis,” BMC Public
Health, vol. 9, no. 1, p. 88, 2009, doi:
10.1186/1471-2458-9-88.
Prospective Studies Collaboration,
“Body-mass index and cause-specific mortality in 900000 adults:
collaborative analyses of 57 prospective studies,” Lancet,
vol. 373, no. 9669, pp. 1083–1096, Mar. 2009, doi:
10.1016/S0140-6736(09)60318-4.
Hussain, A et al, “Type 2 Diabetes and obesity: A review”
Journal of Diabetology, June 2010; 2:1
Katzmarzyk,
P T et al, “Body mass index and risk of cardiovascular
disease, cancer and all-cause mortality” Can. J. Public
Health, vol. 103, no. 2, pp. 147–151, 2012, doi:
10.1007/BF03404221.
Kurth, T et al, “Prospective Study
of Body Mass Index and Risk of Stroke in Apparently Healthy Women,”
Circulation, vol. 111, no. 15, pp. 1992–1998, Apr. 2005, doi:
10.1161/01.CIR.0000161822.83163.B6.
Landi, F et al,
“Body Mass Index is Strongly Associated with Hypertension: Results
from the Longevity Check-Up 7+ Study” Nutrients. 2018 Dec;
10(12): 1976. Published online 2018 Dec 13. doi:
10.3390/nu10121976
Dağ, Z Ö et al, “Impact of obesity
on infertility in women,” J. Turkish Ger. Gynecol. Assoc.,
vol. 16, no. 2, pp. 111–117, Jun. 2015, doi:
10.5152/jtgga.2015.15232.
Moussa, O M et al, “Effect
of body mass index on depression in a UK cohort of 363037 obese
patients: A longitudinal analysis of transition,” Clin.
Obes., vol. 9, no. 3, p. e12305, Jun. 2019, doi:
https://doi.org/10.1111/cob.12305.
Zhao, G et al,
“Depression and anxiety among US adults: associations with body mass
index,” Int. J. Obes., vol. 33, no. 2, pp. 257–266, 2009,
doi: 10.1038/ijo.2008.268.
Lamon-Fava, S et al,
“Impact of Body Mass Index on Coronary Heart Disease Risk Factors in
Men and Women,” Arterioscler. Thromb. Vasc. Biol., vol. 16,
no. 12, pp. 1509–1515, Dec. 1996, doi:
10.1161/01.ATV.16.12.1509.
Van Hemelrijck, M et al,
“Longitudinal study of body mass index, dyslipidemia, hyperglycemia,
and hypertension in 60,000 men and women in Sweden and Austria”
Published: June 13,
2018https://doi.org/10.1371/journal.pone.0197830
Loomis, A K
et al, “Body Mass Index and Risk of Nonalcoholic Fatty
Liver Disease: Two Electronic Health Record Prospective Studies,”
J. Clin. Endocrinol. Metab., vol. 101, no. 3, pp. 945–952,
Mar. 2016, doi: 10.1210/jc.2015-3444.
Zafar, S et
al, “Correlation of gastroesophageal reflux disease symptoms
with body mass index,” Saudi J. Gastroenterol., vol. 14, no.
2, pp. 53–57, Apr. 2008, doi: 10.4103/1319-3767.39618.
Han,
T S et al, “A clinical perspective of obesity, metabolic
syndrome and cardiovascular disease,” JRSM Cardiovasc. Dis.,
vol. 5, pp. 2048004016633371–2048004016633371, Feb. 2016, doi:
10.1177/2048004016633371.
Subak, L L et al, “Obesity
and Urinary Incontinence: Epidemiology and Clinical Research Update”
J Urol. 2009 Dec; 182(6 Suppl): S2–S7 doi:
10.1016/j.juro.2009.08.071
Romero-Corral, A et al,
“Interactions Between Obesity and Obstructive Sleep Apnea -
Implications for Treatment” Chest. 2010 Mar; 137(3): 711–719.
doi: 10.1378/chest.09-0360
Herrington, W G et al,
“Body-mass index and risk of advanced chronic kidney disease:
Prospective analyses from a primary care cohort of 1.4 million
adults in England,” PLoS One, vol. 12, no. 3, p. e0173515,
Mar. 2017, [Online]. Available:
https://doi.org/10.1371/journal.pone.0173515.
Bhaskaran, K
et al, “Body-mass index and risk of 22 specific cancers: a
population-based cohort study of 5.24 million UK adults,”
Lancet, vol. 384, no. 9945, pp. 755–765, Aug. 2014, doi:
10.1016/S0140-6736(14)60892-8.
Zheng, H et al, “Body
mass index and risk of knee osteoarthritis: Systematic review and
meta-analysis of prospective studies,” BMJ Open, vol. 5, no.
12, 2015, doi: 10.1136/bmjopen-2014-007568.
Su, Y P et
al, “Strong association between metabolically-abnormal obesity
and gallstone disease in adults under 50 years” BMC
Gastroenterol 19, 117 (2019).
https://doi.org/10.1186/s12876-019-1032-y
Yang, G et
al, “The effects of obesity on venous thromboembolism: A review”
Open J Prev Med. 2012 Nov; 2(4): 499–509. doi:
10.4236/ojpm.2012.24069
Bai, L et al, “Incident gout
and weight change patterns: a retrospective cohort study of US
adults” Arthritis Res Ther. 2021; 23: 69. Published online
2021 Mar 2. doi: 10.1186/s13075-021-02461-7
Klatsky, A L
et al, “Body Mass Index and Mortality in a Very Large
Cohort: Is It Really Healthier to Be Overweight?,” Perm. J.,
vol. 21, pp. 16–142, 2017, doi: 10.7812/TPP/16-142.
Heymsfield, S B et al, “Why are there race/ethnic
differences in adult body mass index-adiposity relationships? A
quantitative critical review,” Obes. Rev., vol. 17, no. 3,
pp. 262–275, Mar. 2016, doi: 10.1111/obr.12358.
Cossrow, N et al, “Race/Ethnic Issues in Obesity and
Obesity-Related Comorbidities,” J. Clin. Endocrinol. Metab.,
vol. 89, no. 6, pp. 2590–2594, Jun. 2004, doi:
10.1210/jc.2004-0339.
Deurenberg-Yap, M et al, “The paradox of low body mass
index and high body fat percentage among Chinese, Malays and Indians
in Singapore.” Int J Obes Relat Metab Disord. 2000
Aug;24(8):1011-7. doi: 10.1038/sj.ijo.0801353. PMID: 10951540.
Valentino, G et al, “Body fat and its relationship with
clustering of cardiovascular risk factors” Nutr Hosp.
2015;31(5):2253-2260 ISSN 0212-1611 • CODEN NUHOEQ S.V.R. 318
Lear, S A et al, “Ethnic Variation in Fat and Lean Body
Mass and the Association with INS Resistance” The Journal of
Clinical Endocrinology & Metabolism, Volume 94, Issue 12,
1 December 2009, Pages 4696–4702, https://doi.org/10.1210/jc.2009-1030
Tillin, T et al, “The relationship between metabolic risk
factors and incident cardiovascular disease in Europeans, South
Asians, and African Caribbeans: SABRE (Southall and Brent Revisited)
-- a prospective population-based study,” J. Am. Coll.
Cardiol., vol. 61, no. 17, pp. 1777–1786, Apr. 2013, doi:
10.1016/j.jacc.2012.12.046.
Tanne, D et al, “Body Fat
Distribution and Long-Term Risk of Stroke Mortality” Stroke.
Originally published 31 Mar 2005
https://doi.org/10.1161/01.STR.0000162584.39366.1c
Chen, Y
et al, “Association between body mass index and
cardiovascular disease mortality in east Asians and south Asians:
pooled analysis of prospective data from the Asia Cohort
Consortium,” BMJ Br. Med. J., vol. 347, p. f5446, Oct. 2013,
doi: 10.1136/bmj.f5446.
Wen, C P et al, “Are Asians
at greater mortality risks for being overweight than Caucasians?
Redefining obesity for Asians” Public Health Nutrition,
12(4), 497-506. doi:10.1017/S1368980008002802
J. S. for the
S. of O. The Examination Committee of Criteria for `Obesity Disease’
in Japan, “New Criteria for `Obesity Disease’ in Japan,” Circ.
J., vol. 66, no. 11, pp. 987–992, 2002, doi:
10.1253/circj.66.987.
Zheng, W et al, “Association
between Body-Mass Index and Risk of Death in More Than 1 Million
Asians,” N. Engl. J. Med., vol. 364, no. 8, pp. 719–729, Feb.
2011, doi: 10.1056/NEJMoa1010679.
Aekplakorn, W et
al, “Obesity indices and cardiovascular risk factors in Thai
adults.” Int J Obes 30, 1782–1790 (2006).
https://doi.org/10.1038/sj.ijo.0803346
Choi, S E et
al, “Do Risk Factors Explain the Increased Prevalence of Type 2
Diabetes Among California Asian Adults?” J Immigr Minor
Health. 2011; 13(5): 803–808. Published online 2010 Oct 9. doi:
10.1007/s10903-010-9397-6
Swinburn, B et al, “Body
size and composition in Polynesians” Int J Obes 23, 1178–1183
(1999). https://doi.org/10.1038/sj.ijo.0801053
Young, D R
et al, “Associations of overweight/obesity and
socioeconomic status with hypertension prevalence across racial and
ethnic groups,” J. Clin. Hypertens., vol. 20, no. 3, pp.
532–540, Mar. 2018, doi: https://doi.org/10.1111/jch.13217.
Maskarinec, G et al, “Diabetes Prevalence and Body Mass
Index Differ by Ethnicity: The Multiethnic Cohort” Ethn Dis.
2009; 19(1): 49–55. PMCID: PMC2702477
Aleman-Mateo, H et
al, “Elderly Mexicans have less muscle and greater total and
truncel fat compared to African-Americans and Caucasians with the
same BMI” J Nutr Health Aging. 2009 Dec; 13(10): 919. doi:
10.1007/s12603-009-0252-1
Cossrow, N et al,
“Race/Ethnic Issues in Obesity and Obesity-Related Comorbidities”
The Journal of Clinical Endocrinology & Metabolism,
Volume 89, Issue 6, 1 June 2004, Pages 2590–2594,
https://doi.org/10.1210/jc.2004-0339
Berber, A et al,
“Anthropometric indexes in the prediction of type 2 diabetes
mellitus, hypertension and dyslipidaemia in a Mexican population,”
Int. J. Obes., vol. 25, no. 12, pp. 1794–1799, 2001, doi:
10.1038/sj.ijo.0801827.
Chirinos, D A et al, “Defining
Abdominal Obesity as a Risk Factor for Coronary Heart Disease in the
U.S.: Results From the Hispanic Community Health Study/Study of
Latinos (HCHS/SOL)” Diabetes Care Aug 2020, 43 (8) 1774-1780;
DOI: 10.2337/dc19-1855
Elo, I T et al, “The
Contribution of Weight Status to Black-White Differences in
Mortality” Biodemography Soc Biol. 2017; 63(3): 206–220. doi:
10.1080/19485565.2017.1300519
Alammar, M et al, “Diagnostic Accuracy of Body Mass Index
(BMI) When Diagnosing Obesity in a Saudi Adult Population in a
Primary Care Setting, Cross Sectional, Retrospective Study”
Diabetes Metab Syndr Obes. 2020; 13: 2515–2520. Published
online 2020 Jul 14. doi: 10.2147/DMSO.S263063
Bennet, L
et al, “BMI and waist circumference cut-offs for
corresponding levels of INS sensitivity in a Middle Eastern
immigrant versus a native Swedish population – the MEDIM population
based study” BMC Public Health. 2016; 16: 1242. Published
online 2016 Dec 9. doi: 10.1186/s12889-016-3892-1
Al-Raddadi, R et al, “The prevalence of obesity and
overweight, associated demographic and lifestyle factors, and health
status in the adult population of Jeddah, Saudi Arabia,” Ther.
Adv. Chronic Dis., vol. 10, p. 2040622319878997, Jan. 2019,
doi: 10.1177/2040622319878997.
Abell, J E et al,
“Differences in Cardiovascular Disease Mortality Associated With
Body Mass Between Black and White Persons,” Am. J. Public
Health, vol. 98, no. 1, pp. 63–66, Jan. 2008, doi:
10.2105/AJPH.2006.093781.
Batsis, J A et al,
“Diagnostic Accuracy of Body Mass Index to Identify Obesity in Older
Adults: NHANES 1999–2004” Int J Obes (Lond). 2016 May; 40(5):
761–767. Published online 2015 Dec 1. doi: 10.1038/ijo.2015.243
Sperrin, M et al, “Body mass index relates weight to
height differently in women and older adults: serial cross-sectional
surveys in England (1992-2011)” J. Public Health (Oxf)., vol.
38, no. 3, pp. 607–613, Sep. 2016, doi: 10.1093/pubmed/fdv067.
Weir CB, et al, “BMI Classification Percentile And Cut
Off Points.” In: StatPearls [Internet]. Treasure Island (FL):
StatPearls Publishing; 2021 Jan
Nuttall, F Q. “Body Mass Index: Obesity, BMI, and Health: A
Critical Review” Nutr Today. 2015 May; 50(3): 117–128.
Published online 2015 Apr 7. doi: 10.1097/NT.0000000000000092
Karastergiou, K et al, “Sex differences in human adipose
tissues – the biology of pear shape,” Biol. Sex Differ., vol.
3, no. 1, p. 13, 2012, doi: 10.1186/2042-6410-3-13.
Nauli,
A M et al, “Why Do Men Accumulate Abdominal Visceral Fat?”
Front. Physiol., vol. 10, p. 1486, Dec. 2019, doi:
10.3389/fphys.2019.01486.
Thorpe Jr., R J et al,
“Aging, Obesity, and Mortality: Misplaced Concern About Obese Older
People?” Res Aging. 2004 Jan 1; 26(1): 108–129. doi:
10.1177/0164027503258738
Yanovski, J A “Pediatric obesity. An
introduction” Appetite. 2015 Oct 1; 93: 3–12. Published
online 2015 Mar 30. doi: 10.1016/j.appet.2015.03.028
Chung, S “Growth and Puberty in Obese Children and Implications
of Body Composition” J Obes Metab Syndr. 2017 Dec; 26(4):
243–250. Published online 2017 Dec 30. doi:
10.7570/jomes.2017.26.4.243
St-Onge, M P et al, “Body
composition changes with aging: The cause or the result of
alterations in metabolic rate and macronutrient oxidation?”
Nutrition. 2010 Feb; 26(2): 152–155. Published online 2009
Dec 8. doi: 10.1016/j.nut.2009.07.004
Studenski, S A et
al, “The FNIH sarcopenia project: rationale, study description,
conference recommendations, and final estimates,” J. Gerontol. A.
Biol. Sci. Med. Sci., vol. 69, no. 5, pp. 547–558, May 2014,
doi: 10.1093/gerona/glu010.
McKee, A et al, “Obesity
in the Elderly” In: Endotext [Internet]. South Dartmouth (MA):
MDText.com, Inc.; 2000.
Van Der Valk, E S et al, “A
comprehensive diagnostic approach to detect underlying causes of
obesity in adults” Obesity Reviews First published: 01 March
2019 https://doi.org/10.1111/obr.12836
Hetherington-Rauth, M et al, “Comparison of direct
measures of adiposity with indirect measures for assessing
cardiometabolic risk factors in preadolescent girls” Nutr J.
2017; 16: 15. Published online 2017 Feb 23. doi:
10.1186/s12937-017-0236-7
Janssen, I et al, “Waist
circumference and not body mass index explains obesity-related
health risk” The American Journal of Clinical Nutrition,
Volume 79, Issue 3, March 2004, Pages 379–384,
https://doi.org/10.1093/ajcn/79.3.379
Robert Ross et
al, “Waist circumference as a vital sign in clinical practice: a
Consensus Statement from the IAS and ICCR Working Group on Visceral
Obesity” Nat Rev Endocrinol. 2020; 16(3): 177–189. Published
online 2020 Feb 4. doi: 10.1038/s41574-019-0310-7
Yanai, H et al, “The underlying mechanisms for
development of hypertension in the metabolic syndrome” Nutr
J. 2008; 7: 10. Published online 2008 Apr 17. doi:
10.1186/1475-2891-7-10
Arenillas, J F et al, “The Metabolic Syndrome and Stroke
- Potential Treatment Approaches” Stroke Originally published
31 May 2007 https://doi.org/10.1161/STROKEAHA.106.480004
Lin, H et al, “The prevalence, metabolic risk and effects
of lifestyle intervention for metabolically healthy obesity: a
systematic review and meta-analysis: A PRISMA-compliant article”
Medicine (Baltimore). 2017 Nov; 96(47): e8838. Published
online 2017 Nov 27. doi: 10.1097/MD.0000000000008838
Hinnouho, G M et al, “Metabolically healthy obesity and
risk of mortality: does the definition of metabolic health matter?,”
Diabetes Care, vol. 36, no. 8, pp. 2294–2300, Aug. 2013,
doi: 10.2337/dc12-1654.
Eknoyan, G. “Adolphe Quetelet
(1796–1874) — the average man and indices of obesity” Nephrology
Dialysis Transplantation, Volume 23, Issue 1, January 2008,
Pages 47–51, https://doi.org/10.1093/ndt/gfm517
Abou Ziki, M D et al, “Metabolic Syndrome: Genetic
Insights into Disease Pathogenesis” Curr Opin Lipidol. 2016
Apr; 27(2): 162–171. doi: 10.1097/MOL.0000000000000276
Kolb, H et al, “Environmental/lifestyle factors in the
pathogenesis and prevention of type 2 diabetes.” BMC Med 15,
131 (2017). https://doi.org/10.1186/s12916-017-0901-x
Jensen, M D “Role of Body Fat Distribution and the Metabolic
Complications of Obesity” J Clin Endocrinol Metab. 2008 Nov;
93(11 Suppl 1): S57–S63. doi: 10.1210/jc.2008-1585
Wildman,
R P et al, “The Obese Without Cardiometabolic Risk Factor
Clustering and the Normal Weight With Cardiometabolic Risk Factor
Clustering: Prevalence and Correlates of 2 Phenotypes Among the US
Population (NHANES 1999-2004),” Arch. Intern. Med., vol. 168,
no. 15, pp. 1617–1624, Aug. 2008, doi:
10.1001/archinte.168.15.1617.
Bhaskaran, K et al,
“Association of BMI with overall and cause-specific mortality: a
population-based cohort study of 3.6 million adults in the UK”
Lancet Diabetes Endocrinol. 2018 Dec; 6(12): 944–953. doi:
10.1016/S2213-8587(18)30288-2
Let’s talk: 13 questions to ask your doctor about obesity
These thirteen questions can help to start a dialogue and take the first
steps towards understanding what treatment options for weight management
are available.