The prevalence of abdominal obesity is increasing in Western populations, possibly due to a combination of low physical activity and high-calorie diets, and also in developing countries, where it is associated with the urbanization of populations.

Other environmental factors, such as maternal smoking , estrogenic compounds in the diet, and endocrine -disrupting chemicals may be important also. Hypercortisolism , such as in Cushing's syndrome , also leads to central obesity.

Many prescription drugs , such as dexamethasone and other steroids , can also have side effects resulting in central obesity, [38] especially in the presence of elevated insulin levels. In those with a body mass index BMI under 35, intra-abdominal body fat is related to negative health outcomes independent of total body fat.

BMI and waist measurements are well recognized ways to characterize obesity. However, waist measurements are not as accurate as BMI measurements. Waist measurement e. BMI will illustrate the best estimate of one's total body fatness, while waist measurement gives an estimate of visceral fat and risk of obesity-related disease.

While central obesity can be obvious just by looking at the naked body see the picture , the severity of central obesity is determined by taking waist and hip measurements. A differential diagnosis includes distinguishing central obesity from ascites and intestinal bloating.

In the cohort of 15, people participating in the National Health and Nutrition Examination Survey NHANES III , waist circumference explained obesity-related health risk better than BMI when metabolic syndrome was taken as an outcome measure and this difference was statistically significant.

In other words, excessive waist circumference appears to be more of a risk factor for metabolic syndrome than BMI. a waist circumference at least half of the individual's height is predictive of increased risk. The increased amount of fat in this region relates to the higher levels of plasma lipid and lipoproteins as per studies mentioned by Eric Poehlman review.

Index of Central Obesity ICO is the ratio of waist circumference and height first proposed by a Parikh et al. in [85] as a better substitute to the widely used waist circumference in defining metabolic syndrome. suggested that these cutoffs are not applicable among Indians and the cutoffs be lowered to 90 cm 35 in and 80 cm 31 in for males and females.

Parikh et al. looked at the average heights of various races and suggested that by using ICO various race- and gender-specific cutoffs of waist circumference can be discarded. further tested a modified definition of metabolic syndrome in which waist circumference was replaced with ICO in the National Health and Nutrition Examination Survey NHANES database and found the modified definition to be more specific and sensitive.

This parameter has been used in the study of metabolic syndrome [92] [93] and cardiovascular disease. Central obesity in individuals with normal BMI is referred to as normal weight obesity.

Males are more susceptible to upper-body fat accumulation, most likely in the belly, due to sex hormone differences. In women, estrogen is believed to cause fat to be stored in the buttocks , thighs , and hips.

Central obesity is positively associated with coronary heart disease risk in women and men. It has been hypothesized that the sex differences in fat distribution may explain the sex difference in coronary heart disease risk.

A permanent routine of exercise, eating healthily, and, during periods of being overweight, consuming the same number or fewer calories than used will prevent and help fight obesity.

Adjunctive therapies which may be prescribed by a physician are orlistat or sibutramine , although the latter has been associated with increased cardiovascular events and strokes and has been withdrawn from the market in the US , [] the UK , [] the EU , [] Australia , [] Canada , [] Hong Kong , [] and Thailand.

A study published in the International Journal of Sport Nutrition and Exercise Metabolism , [] suggests that combining cardiovascular aerobic exercise with resistance training is more effective than cardiovascular training alone in getting rid of abdominal fat.

An additional benefit to exercising is that it reduces stress and insulin levels, which reduce the presence of cortisol , a hormone that leads to more belly fat deposits and leptin resistance. Self-motivation by understanding the risks associated with abdominal obesity is widely regarded as being far more important than worries about cosmetics.

In addition, understanding the health issues linked with abdominal obesity can help in the self-motivation process of losing the abdominal fat. As mentioned above, abdominal fat is linked with cardiovascular disease, diabetes, and cancer. Specifically it is the deepest layer of belly fat the fat that cannot be seen or grabbed that poses health risks, as these "visceral" fat cells produce hormones that can affect health e.

The risk increases considering the fact that they are located in the proximity or in between organs in the abdominal cavity. For example, fat next to the liver drains into it, causing a fatty liver , which is a risk factor for insulin resistance, setting the stage for type 2 diabetes.

However, visceral fat is more responsive to the circulation of catecholamines. In the presence of type 2 diabetes , the physician might instead prescribe metformin and thiazolidinediones rosiglitazone or pioglitazone as antidiabetic drugs rather than sulfonylurea derivatives.

Thiazolidinediones may cause slight weight gain but decrease "pathologic" abdominal fat visceral fat , and therefore may be prescribed for diabetics with central obesity. Low-fat diets may not be an effective long-term intervention for obesity: as Bacon and Aphramor wrote, "The majority of individuals regain virtually all of the weight that was lost during treatment.

The conclusion was that mean weight decreased significantly in the intervention group from baseline to year 1 by 2. This difference from baseline between control and intervention groups diminished over time, but a significant difference in weight was maintained through year 9, the end of the study.

There is a common misconception that spot exercise that is, exercising a specific muscle or location of the body most effectively burns fat at the desired location, but this is not the case. Spot exercise is beneficial for building specific muscles, but it has little effect, if any, on fat in that area of the body, or on the body's distribution of body fat.

The same logic applies to sit-ups and belly fat. Sit-ups , crunches and other abdominal exercises are useful in building the abdominal muscles , but they have little effect, if any, on the adipose tissue located there. A large central adiposity deposit has been assigned many common use names, including "spare tire", "love handle", "paunch", and "potbelly".

However, there is little scientific evidence that beer drinkers are more prone to central obesity, despite its being known colloquially as "beer belly", "beer gut", or "beer pot". One of the few studies conducted on the subject did not find that beer drinkers are more prone to central obesity than nondrinkers or drinkers of wine or spirits.

These symptoms can suggest the appearance of central obesity. Deposits of excess fat at the sides of one's waistline or obliques are commonly referred to as "love handles".

Researchers in Copenhagen examined the relationship between waist circumferences and costs among 31, subjects aged 50—64 years of age with different waist circumferences.

Their study showed that an increase in just an additional centimetre above normal waistline caused a 1. To put this in perspective, a woman with a waistline of 95 cm approx Contents move to sidebar hide. Article Talk. Read Edit View history. Tools Tools.

What links here Related changes Upload file Special pages Permanent link Page information Cite this page Get shortened URL Download QR code Wikidata item. Download as PDF Printable version. In other projects. Wikimedia Commons. Excess fat around the stomach and abdomen. Medical condition.

See also: Diet and obesity. This section needs expansion. You can help by adding to it. January doi : PMID S2CID Dementia and Geriatric Cognitive Disorders.

Diabetes Care. Coronary Heart Disease. Journal of Diabetes and Its Complications. S2CID — via PubMed. Clinical Cornerstone. Bibcode : Natur. Retrieved European Journal of Epidemiology. PMC Somers, V. Adapted from 1. Like the waist circumference, the waist-to-hip ratio WHR is also used to measure abdominal obesity.

Equations are used to predict body fat percentage based on these measurements. BIA equipment sends a small, imperceptible, safe electric current through the body, measuring the resistance.

The current faces more resistance passing through body fat than it does passing through lean body mass and water. Equations are used to estimate body fat percentage and fat-free mass. Individuals are weighed in air and while submerged in a tank.

Fat is more buoyant less dense than water, so someone with high body fat will have a lower body density than someone with low body fat. This method is typically only used in a research setting.

This method uses a similar principle to underwater weighing but can be done in the air instead of in water. Individuals drink isotope-labeled water and give body fluid samples.

This was by far the most physically and mentally challenging periods in my life; my body, which was far from strong, had to undergo major surgery and endure a hard road to recovery. However, without a doubt Steve played an instrumental part in my full recovery and our work together achieved my first natural period in over 10 years.

It felt like a miracle and was a huge achievement for me. Since then, my periods have been regular and the side affects of having PCOS stopped — all because of Steve and his tremendous knowledge and work.

Steve is truly remarkable. His understanding of the human body and how it correlates with and responds to what we consume is outstanding. I owe a great deal to him for his dedication and commitment in seeing me achieve optimum health.

You really can turn your health around, with the right information and the right mind-set. Thank you Steve. Working with Steve was an essential part of my preparations for filming Firebird, and as my part of my ongoing health and well-being as an actor.

His knowledge and recommendations are invaluable! I have learnt so much of how to master my food choices and required nutrients from Steve. I can whole heartedly recommend his expertise! I then had the good sense to ask him to He is able to provide the most personal and bespoke care wherever I travel.

I have referred several colleagues as well as my own daughter. I will certainly to continue this as I am in awe of his knowledge, ability and manner. Steve has truly been a life changer for me and I am forever grateful. After eating certain types of foods I would become very lethargic, I knew that my gut health and diet needed some attention.

I went to see Steve after a conversation with a friend who went to see him also. I discovered that I had some of the same symptoms as I discovered that I had some of the same symptoms as her and therefore should invest some time into exploring my gut health.

Steve was very professional and succinct. I found that I had hypoglycaemia tendencies, which resulted in a total change into the way I ate and thought about food. He helped me plan meals, gave me ideas for meals and educated me into what types of foods would maintain my blood sugar levels and would be complimentary to my condition.

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Look no Further. Join our newsletter list for free weekly content, discounts and your FREE seasonal recipe eBook. Elevated waist circumference and visceral fat, what are the implications to health.

Why is body mass index BMI an insufficient indicator of health status? Why waist circumference WC measurements? What waist circumference is considered borderline? Inflammation A chronic state of low-grade inflammation is associated with numerous chronic diseases — from type 2 diabetes to heart disease.

Insulin resistance Insulin is an important chemical messenger that plays a number of roles throughout the body. Type 2 diabetes T2DM Research indicates a strong association between increased WC and risk of developing T2DM Borel et al.

High blood pressure Research indicates that individuals with an increased WC are at risk of experiencing high blood pressure Jalal et al. High cholesterol levels Research indicates that a larger WC is significantly associated with lower levels of beneficial cholesterol high density lipoprotein and higher levels of unhealthy cholesterol levels low density lipoprotein Seidell et al.

Non-alcoholic fatty liver disease NAFLD NAFLD occurs when fat accumulates in the liver, causing inflammation, progressively leading to scarring and potentially impairing liver function. Cardiovascular CVD risk Numerous studies show a strong association between increased WC and increased CVD risk Hong et al.

Join pain and lower back pain An increased WC may be associated with increased vulnerability to lower back pain Hussain et al. Can I improve this? Key takeaways: Weight and BMI may be insufficient indicators of health status : since they do not take into account body size, body composition and body fat distribution.

References 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. Zhu et al.

However, BMI and waist circumference are not diagnostic tools for disease risks. A trained healthcare provider should perform other health assessments to evaluate disease risk and diagnose disease status. A high BMI can indicate high body fatness, and a low BMI can indicate too low body fatness.

To calculate your BMI, see the BMI Calculator. Or determine your BMI by finding your height and weight in this BMI Index Chart. Weight that is higher than what is considered as a healthy weight for a given height is described as overweight or obese.

Weight that is lower than what is considered as healthy for a given height is described as underweight. At an individual level, BMI can be used as a screening tool but is not diagnostic of the body fatness or health of an individual.

Height and weight must be measured to calculate BMI. It is most accurate to measure height in meters and weight in kilograms. However, the BMI formula has been adapted for height measured in inches and weight measured in pounds. For more, see About Adult BMI. Another way to estimate your potential disease risk is to measure your waist circumference.

Excessive abdominal fat may be serious because it places you at greater risk for developing obesity-related conditions, such as Type 2 Diabetes, high blood pressure, and coronary artery disease. Your waistline may be telling you that you have a higher risk of developing obesity-related conditions if you are 1 :.

Waist circumference can be used as a screening tool but is not diagnostic of the body fatness or health of an individual. Note : The information on these pages is intended for adult men and non-pregnant women only.

WC showed 0. In conclusion, WC and BMI not only the simplest to obtain, but are also the most accurate surrogate markers of visceral adiposity in young adults, and are good indicators of insulin resistance and powerful predictors of the presence of hepatic steatosis.

Citation: Borruel S, Moltó JF, Alpañés M, Fernández-Durán E, Álvarez-Blasco F, Luque-Ramírez M, et al. PLoS ONE 9 12 : e Received: June 18, ; Accepted: November 3, ; Published: December 5, Copyright: © Borruel et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper. The sponsors had no role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the article for publication.

Competing interests: The authors have declared that no competing interests exist. Visceral adiposity is associated with cardiovascular and metabolic disorders [1] including insulin resistance [2] , low-grade chronic inflammation [3] , type 2 diabetes [4] , dyslipidemia [5] , polycystic ovary syndrome PCOS [6] , male hypogonadism [7] , nonalcoholic fatty liver disease [8] , hypertension [9] and several cancers [10].

Amelioration of visceral adiposity is of importance for the prevention and management of most of these disorders [11].

Assessment of visceral adiposity is advised in many situations and is becoming routine clinical practice nowadays. However, the most accurate measurements of visceral adiposity — magnetic resonance imaging, computed tomography and ultrasound — are seldom available for most clinicians, since the imaging techniques involved are expensive, may involve exposure to radiation, or require intensive training [12].

Hence, imaging techniques are frequently substituted by surrogate indexes of visceral adiposity. Waist circumference WC and waist-hip ratio WHR are the most commonly used surrogate indexes of visceral adiposity. In fact, increased WC is a requisite for the diagnosis of the metabolic syndrome according to the International Diabetes Federation [13].

Visceral adiposity index VAI has been proposed as and indicator of visceral adipose function and insulin sensitivity that may reflect cardiometabolic risk [14]. VAI included a sex-specific model of adipose distribution MOAD based on the linear relationship between WC and body mass index BMI in each sex that is corrected for fat function introducing triglycerides and high-density lipoprotein HDL -cholesterol concentrations into the equation [14].

MOAD showed statistically significant correlations with the area and volume of visceral adipose tissue assessed by magnetic resonance imaging is a subset of 26 metabolically healthy individuals [14]. In a subsequent series of 1, primary-care patients, the number of patients with metabolic syndrome, diabetes, high blood pressure, dyslipidemia, coronary heart disease and cerebrovascular disease increased significantly across VAI quintiles in parallel with the increase in age [14].

Moreover, in a mixed series of 74 patients with type 1 diabetes, type 2 diabetes, nonalcoholic fatty liver disease and PCOS, VAI correlated negatively with glucose disposal rate M-values during a euglycemic hyperinsulinemic clamp, whereas WC and BMI did not [14]. However, the application of the VAI in different populations and in clinical series of patients with metabolic disorders such as nonalcoholic fatty liver disease, obstructive sleep apnea and PCOS yielded conflicting results regarding its role as marker of abdominal adiposity, insulin resistance and risk of disease [15] — [22].

We aimed to evaluate WC, WHR, MOAD and VAI as surrogate indexes of visceral adiposity, objectively assessed by ultrasound examination in young adults including healthy women, women with PCOS, and healthy men, presenting with or without obesity.

This study derived from a previous work aiming to assess global and visceral adiposity in women with PCOS [23]. Ninety-nine young Caucasian adults, including 20 women without androgen excess, 53 women with PCOS, and 26 men were included in the present analysis of the data.

We included patients with PCOS in addition to non-obese and obese healthy women and men to include a subset of patients with global and abdominal adiposity and insulin resistance, because PCOS associates these disorders irrespective of obesity [23].

All the patients suffered the classic hyperandrogenic PCOS phenotype and, even when ovarian morphology was not analyzed, by having hyperandrogenism and oligoovulation all patients also fulfilled all the current definitions of PCOS [25] — [27].

On the contrary, we considered as controls, women presenting without menstrual and ovulatory dysfunction and who had no evidence of androgen excess. The methods and assays used to diagnose in the patients and to exclude hyperandrogenic disorders in healthy women have been described in detail elsewhere [23] , [28] , [29] and, besides extensive hormonal testing, included a standard 2-h 75 g oral glucose tolerance test that permitted the calculation of the composite insulin sensitivity index from glucose and insulin concentrations [30].

Total body fat mass was estimated using a body fat monitor Omron BF , Omron Corp. None of the subjects had received treatment with any drug known to interfere with sex hormone secretion and metabolism such as oral contraceptives, antiandrogens or insulin sensitizers for the previous 6 months.

Written informed consent was obtained from all the adult participants. Minors gave verbal consent that was confirmed in writing and in their behalf by their legal guardians.

The study and the informed consent procedures were approved by the Ethics Committee of Hospital Universitario Ramón y Cajal. We used a non-stretchable measuring tape to measure waist and hip circumferences.

The smallest abdominal circumference between the lowest rib and the iliac crest was used as WC. WHR was calculated by dividing WC by the hip circumference at the level of greater trochanters. In experienced hands, ultrasonography is a precise and reliable method for evaluation of visceral fat, showing an excellent correlation and concordance with CT scan [31] — [33].

Adipose tissue depots were estimated using a Toshiba Nemio XG SSAA Diagnostic Ultrasound System Toshiba Medical Systems S. Subjects were examined in the fasting state and in the supine position, and were asked to hold their breath during the examination while the frozen images were taken, to avoid the influence of the respiratory status or abdominal wall tension.

Special care was taken to keep the probe just touching the skin to prevent compression of the fat layers. Minimum and maximum subcutaneous, preperitoneal, intraperitoneal, mesenteric, epicardial and perirenal adipose tissue thicknesses were measured as described [23].

Three measures of intraperitoneal fat thickness were obtained: distance from the fascia of rectus abdominis muscle to vertebral column, distance from peritoneum to vertebral column, and distance from linea alba to vertebral column.

Mesenteric fat thickness was measured as described by Liu et al. Finally, epicardial fat thickness was measured in the free wall of the right ventricle from still images obtained by two-dimensional transthoracic echocardiography using a 3 MHz transducer, as described by Ahn et al.

Mediastinal fat presenting as an echo-lucent area above the parietal pericardium was excluded from the measurement. Values obtained in long-axis and short axis view were similar and, hence, only values obtained from long-axis views were submitted to statistical analysis.

For each ultrasound measurement of fat thickness, intra-operator coefficients of variation CVs were calculated by repeating 20 measurements in a single individual. Four cycles of five consecutive measurements, separated at 10 minutes intervals, were used to calculate CVs.

The CVs were 4. Hepatic steatosis was estimated using a 3. Nominal and ordinal variables were analyzed by Pearson's χ 2 test. Continuous variables are reported as means ± SD range in the text or as means ± SEM in the figures.

The Kolmogorov-Smirnov statistic was applied to continuous variables. We applied logarithmic or square-root transformations as needed to ensure normal distribution of the variables.

Relationships between the surrogate indexes of visceral adiposity with the thickness of the different adipose tissue depots and other clinical and biochemical variables were analyzed by Pearson's correlation analysis. Differences in phenotypic variables among men, women and patients with PCOS were analyzed by univariate one-way general linear models GLMs.

Surrogate markers of visceral adiposity were tested by univariate two-way GLMs in which group of subjects and presence or absence of obesity were introduced as independent variables. A two-way GLM is a statistical model where changes in a continuous and normally distributed dependent variable are explained by a linear combination of functions of several independent explanatory variables group of subjects and obesity in our study.

In essence, a two-way GLM is similar to two-way analysis of variance, although the computational background of both tests is entirely different. Given that obesity was included as one of the dependent variables, and that the groups of subjects were not different in terms of BMI, the impact of both obesity and BMI was automatically considered by the GLM when analyzing differences between men, control women and patients with PCOS.

Hence, BMI was not introduced as dependent variable in these comparisons. Also, in these GLMs age was introduced as a covariate to control for a difference in age among patients with PCOS and the other groups.

One-way GLMs were used to explore differences in surrogate indexes of visceral adiposity and BMI in subjects depending on the grade of hepatic steatosis. Only when univariate GLMs showed differences among the group of subjects, pairwise comparisons among the three groups of individuals were analyzed by the least significant difference test for post-hoc comparisons.

Correlation and multiple linear regression analyses were conducted as described below. Receiver operating characteristic curve ROC analysis was used to assess the accuracy of surrogate indexes of visceral adiposity as diagnostic predictors of the presence or absence of hepatic steatosis.

We used SPSS Statistics Therefore, age was introduced as a covariate in the comparisons of all other variables. The clinical, metabolic and hormonal characteristics of the three groups of subjects compared in the study are summarized in Table 1. When expressed as percentage of total body mass, the higher fat mass was observed in women with PCOS followed by control women and finally men.

Systolic blood pressure was higher in men compared with both groups of women, whereas diastolic blood pressure was increased in men only when compared with control women. The hirsutism score was increased in patients with PCOS compared with control women.

Total and free testosterone concentrations were higher in men compared with control women with patients with PCOS presenting with intermediate values that were increased compared with control women. Opposite changes were found for serum estradiol concentrations.

Sex hormone-binding globulin concentrations were reduced in men compared with both groups of women. Dehydroepiandrosterone-sulfate concentrations were similar in all the groups but androstenedione levels were increased in patients with PCOS compared with both control women and men.

Regarding metabolic and inflammatory variables, fasting glucose concentrations were similar in the three groups of subjects, whereas fasting insulin levels were increased, and the composite insulin sensitivity index derived from an oral glucose tolerance test was decreased, in both men and patients with PCOS compared with control women.

In patients with PCOS and men, HDL-cholesterol concentrations were lower and serum triglycerides were higher than those of control women. Men presented increased transaminases compared with both groups of women, yet no differences were observed for γ-glutamyltransferase and alkaline phosphatase.

The differences in surrogate indexes of visceral adiposity between healthy women, patients with PCOS and men, and between non-obese and obese subjects, were larger for waist circumference, followed by WHR, VAI and MOAD Figure 1.

WC was the index showing the smallest variation in all the groups of subjects studied here Figure 1. S2CID — via PubMed. Clinical Cornerstone. Bibcode : Natur. Retrieved European Journal of Epidemiology. PMC Somers, V. Lopez-Jimenez, F.

Korenfeld, Y. Palin, S. Boelaert, K. Boarin, S. Sierra-Johnson, J. Rahim, A. World Journal of Diabetes. August Diabetes Butr Metab. October November The Journal of Clinical Endocrinology and Metabolism.

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Canadian Journal of Public Health. Ultra-processed food and drink products in Latin America: Sales, sources, nutrient profiles, and policy implications. ISBN Retrieved 15 March PLOS ONE. Bibcode : PLoSO.. PLOS Medicine.

Global Food Security. Mediterranean Journal of Nutrition and Metabolism. March FASEB Journal. Abnormal obesity and your health. European Journal of Nutrition. A systematic review and meta-analysis". Nutrition Reviews. Abdominal Obesity. Current Opinion in Lipidology.

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Error: This is required. Error: Not a valid value. Most fat is stored underneath the skin and is known as subcutaneous fat. This is the fat that is visible and that you can feel.

The rest of the fat in the body is stored around your internal organs, including your heart, liver and intestines.

This is visceral fat. Visceral fat produces more of these toxic substances than subcutaneous fat, so it can be more harmful to your health. Because of this, visceral fat carries a range of health risks for everyone.

Having visceral fat in the belly is a sign of metabolic syndrome , a collection of disorders that include high blood pressure , obesity , high cholesterol and insulin resistance.

Together, these increase the risk of stroke , heart disease and type 2 diabetes. ARE YOU AT RISK? Use the Risk Checker to find out. The best way to tell if you have visceral fat is to measure your waist. Your waist circumference is a good indicator of how much fat is deep inside your belly, around your organs.

If you think your waist measurement may be too large, talk to your doctor. ASK YOUR DOCTOR — Preparing for an appointment? Use the Question Builder for general tips on what to ask your GP or specialist. Measuring your Body Mass Index BMI may also tell help you tell whether you are in a healthy weight range for your height.

NEED TO LOSE WEIGHT? The best way to reduce visceral fat is through losing weight if you are above a healthy weight range and maintaining a healthy diet. Regular exercise is especially effective in reducing visceral fat and preventing it from coming back.

Even though you cannot change your genetics, hormones or your age, you can reduce your risk of disease by:. Learn more here about the development and quality assurance of healthdirect content.

Fat is stored throughout the body and that it produces chemicals and hormones which can be toxic to the body. Heart J. Vazquez G, Duval S, Jacobs DR, Jr. Qiao Q, Nyamdorj R. Is the association of type II diabetes with waist circumference or waist-to-hip ratio stronger than that with body mass index?

Eur J Clin Nutr. Grundy SM, Cleeman JI, Daniels SR, et al. International Diabetes Federation. The IDF consensus worldwide definition of metabolic syndrome.

World Health Organization. Definition, Diagnosis, and Classification of Diabetes Mellitus and its Complications: Report of a WHO Consultation.

Part I: Diagnosis and Classification of Diabetes Mellitus. Geneva: World Health Organization. Assessed on January 26, Skip to content Obesity Prevention Source. Obesity Prevention Source Menu. Search for:. Home Obesity Definition Why Use BMI? Notably, for a given BMI, Canadians had a larger waist circumference in compared with Specifically, the researchers observed a waist circumference that was greater by 1.

Similarly, Sandra Albrecht and colleagues examined the secular changes in waist circumference in the USA — , England — , China — and Mexico — 36 and reported statistically significantly increased waist circumference values relative to BMI in all countries studied and in most subpopulations.

These observations are consistent with those of Tommy Visscher and colleagues, who performed an extensive review and concluded that the majority of the evidence suggests a trend in which the relative increases in waist circumference were larger than the relative increases in BMI This observation is seemingly independent of age, sex and ethnicity, as few groups failed to demonstrate the general trend of secular waist circumference increasing beyond that expected by BMI Fig.

The failure of BMI to detect such an increase in abdominal obesity confirms the limitations of BMI alone to identify the phenotype of obesity that conveys the greatest health risk. Changes in the prevalence of abdominal obesity measured using waist circumference and general obesity measured using BMI measured in different studies during the time period indicated on the x axis.

However, Xi et al. In addition, Barzin et al. Years given for example, — indicate the years in which data were collected. F, female; M, male. Data are from refs 37 , , , , , , , , , Although the prevalence of obesity measured by BMI might have plateaued in some countries, the prevalence of abdominal obesity as measured by waist circumference is generally increasing.

The lack of inclusion of waist circumference in global obesity surveillance might inadequately characterize the health risk associated with the global obesity prevalence, as it seems that the prevalence of abdominal obesity is increasing.

Current obesity prevalence trends based on BMI alone should be interpreted with caution. We recommend that serious consideration should be given to the inclusion of waist circumference in obesity surveillance studies. It is not surprising that waist circumference and BMI alone are positively associated with morbidity 15 and mortality 13 independent of age, sex and ethnicity, given the strong association between these anthropometric variables across cohorts.

However, it is also well established that, for any given BMI, the variation in waist circumference is considerable, and, in any given BMI category, adults with higher waist circumference values are at increased adverse health risk compared with those with a lower waist circumference 38 , 39 , This observation is well illustrated by James Cerhan and colleagues, who pooled data from 11 prospective cohort studies with , white adults from the USA, Australia and Sweden aged 20—83 years This finding is consistent with that of Ellen de Hollander and colleagues, who performed a meta-analysis involving over 58, predominantly white older adults from around the world and reported that the age-adjusted and smoking-adjusted mortality was substantially greater for those with an elevated waist circumference within normal weight, overweight and obese categories as defined by BMI The ability of waist circumference to add to the adverse health risk observed within a given BMI category provides the basis for the current classification system used to characterize obesity-related health risk 8 , Despite the observation that the association between waist circumference and adverse health risk varies across BMI categories 11 , current obesity-risk classification systems recommend using the same waist circumference threshold values for all BMI categories We propose that important information about BMI and waist circumference is lost when they are converted from continuous to broad categorical variables and that this loss of information affects the manner in which BMI and waist circumference predict morbidity and mortality.

Specifically, when BMI and waist circumference are considered as categorical variables in the same risk prediction model, they are both positively related to morbidity and mortality However, when BMI and waist circumference are considered as continuous variables in the same risk prediction model, risk prediction by waist circumference improves, whereas the association between BMI and adverse health risk is weakened 10 , Evidence in support of adjusting waist circumference for BMI comes from Janne Bigaard and colleagues who report that a strong association exists between waist circumference and all-cause mortality after adjustment for BMI Consistent with observations based on asymptomatic adults, Thais Coutinho and colleagues report similar observations for a cohort of 14, adults with CVD who were followed up for 2.

The cohort was divided into tertiles for both waist circumference and BMI. In comparison with the lowest waist circumference tertile, a significant association with risk of death was observed for the highest tertile for waist circumference after adjustment for age, sex, smoking, diabetes mellitus, hypertension and BMI HR 1.

By contrast, after adjustment for age, sex, smoking, diabetes mellitus, hypertension and waist circumference, increasing tertiles of BMI were inversely associated with risk of death HR 0.

The findings from this systematic review 44 are partially confirmed by Diewertje Sluik and colleagues, who examined the relationships between waist circumference, BMI and survival in 5, individuals with T2DM over 4.

In this prospective cohort study, the cohort was divided into quintiles for both BMI and waist circumference. After adjustment for T2DM duration, insulin treatment, prevalent myocardial infarction, stroke, cancer, smoking status, smoking duration, educational level, physical activity, alcohol consumption and BMI, the HR for risk of death associated with the highest tertile was 2.

By contrast, in comparison with the lowest quintile for BMI adjusted for the same variables, with waist circumference replacing BMI , the HR for risk of death for the highest BMI quintile was 0.

In summary, when associations between waist circumference and BMI with morbidity and mortality are considered in continuous models, for a given waist circumference, the higher the BMI the lower the adverse health risk. Why the association between waist circumference and adverse health risk is increased following adjustment for BMI is not established.

It is possible that the health protective effect of a larger BMI for a given waist circumference is explained by an increased accumulation of subcutaneous adipose tissue in the lower body This observation was confirmed by Sophie Eastwood and colleagues, who reported that in South Asian adults the protective effects of total subcutaneous adipose tissue for T2DM and HbA 1c levels emerge only after accounting for visceral adipose tissue VAT accumulation A causal mechanism has not been established that explains the attenuation in morbidity and mortality associated with increased lower body adiposity for a given level of abdominal obesity.

We suggest that the increased capacity to store excess energy consumption in the gluteal—femoral subcutaneous adipocytes might protect against excess lipid deposition in VAT and ectopic depots such as the liver, the heart and the skeletal muscle Fig.

Thus, for a given waist circumference, a larger BMI might represent a phenotype with elevations in lower body subcutaneous adipose tissue. Alternatively, adults with elevations in BMI for a given waist circumference could have decreased amounts of VAT. Excess lipid accumulation in VAT and ectopic depots is associated with increased cardiometabolic risk 47 , 48 , Moreover, VAT is an established marker of morbidity 50 , 51 and mortality 24 , These findings provide a plausible mechanism by which lower values for BMI or hip circumference for a given waist circumference would increase adverse health risk.

When this process becomes saturated or in situations where adipose tissue has a limited ability to expand, there is a spillover of the excess energy, which must be stored in visceral adipose tissue as well as in normally lean organs such as the skeletal muscle, the liver, the pancreas and the heart, a process described as ectopic fat deposition.

Visceral adiposity is associated with a hyperlipolytic state resistant to the effect of insulin along with an altered secretion of adipokines including inflammatory cytokines whereas a set of metabolic dysfunctions are specifically associated with increased skeletal muscle, liver, pancreas, and epicardial, pericardial and intra-myocardial fat.

FFA, free fatty acid. This notion is reinforced by Jennifer Kuk and colleagues who reported that BMI is an independent and positive correlate of VAT in adults before adjustment for waist circumference; however, BMI is negatively associated with VAT mass after adjustment for waist circumference This study also reported that, after adjustment for waist circumference, BMI was positively associated with lower body subcutaneous adipose tissue mass and skeletal muscle mass.

These observations support the putative mechanism described above and, consequently, that the negative association commonly observed between BMI and morbidity and mortality after adjustment for waist circumference might be explained by a decreased deposition of lower body subcutaneous adipose tissue and muscle mass, an increased accumulation of visceral adiposity, or both.

In summary, the combination of BMI and waist circumference can identify the highest-risk phenotype of obesity far better than either measure alone. Although guidelines for the management of obesity from several professional societies recognize the importance of measuring waist circumference, in the context of risk stratification for future cardiometabolic morbidity and mortality, these guidelines limit the recommendation to measure waist circumference to adults defined by BMI to have overweight or obesity.

On the basis of the observations described in this section, waist circumference could be just as important, if not more informative, in persons with lower BMI, where an elevated waist circumference is more likely to signify visceral adiposity and increased cardiometabolic risk.

This observation is particularly true for older adults In categorical analyses, waist circumference is associated with health outcomes within all BMI categories independent of sex and age. When BMI and waist circumference are considered as continuous variables in the same risk prediction model, waist circumference remains a positive predictor of risk of death, but BMI is unrelated or negatively related to this risk.

The improved ability of waist circumference to predict health outcomes over BMI might be at least partially explained by the ability of waist circumference to identify adults with increased VAT mass. For practitioners, the decision to include a novel measure in clinical practice is driven in large part by two important, yet very different questions.

The first centres on whether the measure or biomarker improves risk prediction in a specific population for a specific disease. For example, does the addition of a new risk factor improve the prognostic performance of an established risk prediction algorithm, such as the Pooled Cohort Equations PCE or Framingham Risk Score FRS in adults at risk of CVD?

The second question is concerned with whether improvement in the new risk marker would lead to a corresponding reduction in risk of, for example, cardiovascular events. In many situations, even if a biomarker does not add to risk prediction, it can still serve as an excellent target for risk reduction.

Here we consider the importance of waist circumference in clinical settings by addressing these two questions. The evaluation of the utility of any biomarker, such as waist circumference, for risk prediction requires a thorough understanding of the epidemiological context in which the risk assessment is evaluated.

In addition, several statistical benchmarks need to be met in order for the biomarker to improve risk prediction beyond traditional measures. These criteria are especially important for waist circumference, as established sex-specific and ethnicity-specific differences exist in waist circumference threshold levels 55 , In , the American Heart Association published a scientific statement on the required criteria for the evaluation of novel risk markers of CVD 57 , followed by recommendations for assessment of cardiovascular risk in asymptomatic adults in ref.

Novel biomarkers must at the very least have an independent statistical association with health risk, after accounting for established risk markers in the context of a multivariable epidemiological model. This characteristic alone is insufficient, however, as many novel biomarkers meet this minimum standard yet do not meaningfully improve risk prediction beyond traditional markers.

More stringent benchmarks have therefore been developed to assess biomarker utility, which include calibration , discrimination 58 and net reclassification improvement Therefore, to critically evaluate waist circumference as a novel biomarker for use in risk prediction algorithms, these stringent criteria need to be applied.

Numerous studies demonstrate a statistical association between waist circumference and mortality and morbidity in epidemiological cohorts.

Notably, increased waist circumference above these thresholds was associated with increased relative risk of all-cause death, even among those with normal BMI In the USA, prospective follow-up over 9 years of 14, black, white and mixed ethnicity participants in the Atherosclerosis Risk in Communities study showed that waist circumference was associated with increased risk of coronary heart disease events; RR 1.

Despite the existence of a robust statistical association with all-cause death independent of BMI, there is no solid evidence that addition of waist circumference to standard cardiovascular risk models such as FRS 62 or PCE 63 improves risk prediction using more stringent statistical benchmarks.

For example, a study evaluating the utility of the PCE across WHO-defined classes of obesity 42 in five large epidemiological cohorts comprised of ~25, individuals assessed whether risk discrimination of the PCE would be improved by including the obesity-specific measures BMI and waist circumference The researchers found that although each measure was individually associated BMI: HR 1.

On the basis of these observations alone, one might conclude that the measure of waist circumference in clinical settings is not supported as risk prediction is not improved. However, Nancy Cook and others have demonstrated how difficult it is for the addition of any biomarker to substantially improve prognostic performance 59 , 66 , 67 , Furthermore, any additive value of waist circumference to risk prediction algorithms could be overwhelmed by more proximate, downstream causative risk factors such as elevated blood pressure and abnormal plasma concentrations of glucose.

In other words, waist circumference might not improve prognostic performance as, independent of BMI, waist circumference is a principal driver of alterations in downstream cardiometabolic risk factors.

A detailed discussion of the merits of different approaches for example, c-statistic, net reclassification index and discrimination index to determine the utility of novel biomarkers to improve risk prediction is beyond the scope of this article and the reader is encouraged to review recent critiques to gain insight on this important issue 66 , Whether the addition of waist circumference improves the prognostic performance of established risk algorithms is a clinically relevant question that remains to be answered; however, the effect of targeting waist circumference on morbidity and mortality is an entirely different issue of equal or greater clinical relevance.

Several examples exist in the literature where a risk marker might improve risk prediction but modifying the marker clinically does not impact risk reduction. For example, a low level of HDL cholesterol is a central risk factor associated with the risk of coronary artery disease in multiple risk prediction algorithms, yet raising plasma levels of HDL cholesterol pharmacologically has not improved CVD outcomes Conversely, a risk factor might not meaningfully improve statistical risk prediction but can be an important modifiable target for risk reduction.

Indeed, we argue that, at any BMI value, waist circumference is a major driver of the deterioration in cardiometabolic risk markers or factors and, consequently, that reducing waist circumference is a critical step towards reducing cardiometabolic disease risk.

As we described earlier, waist circumference is well established as an independent predictor of morbidity and mortality, and the full strength of waist circumference is realized after controlling for BMI.

We suggest that the association between waist circumference and hard clinical end points is explained in large measure by the association between changes in waist circumference and corresponding cardiometabolic risk factors.

For example, evidence from randomized controlled trials RCTs has consistently revealed that, independent of sex and age, lifestyle-induced reductions in waist circumference are associated with improvements in cardiometabolic risk factors with or without corresponding weight loss 71 , 72 , 73 , 74 , 75 , These observations remain consistent regardless of whether the reduction in waist circumference is induced by energy restriction that is, caloric restriction 73 , 75 , 77 or an increase in energy expenditure that is, exercise 71 , 73 , 74 , We have previously argued that the conduit between change in waist circumference and cardiometabolic risk is visceral adiposity, which is a strong marker of cardiometabolic risk Taken together, these observations highlight the critical role of waist circumference reduction through lifestyle behaviours in downstream reduction in morbidity and mortality Fig.

An illustration of the important role that decreases in waist circumference have for linking improvements in lifestyle behaviours with downstream reductions in the risk of morbidity and mortality.

The benefits associated with reductions in waist circumference might be observed with or without a change in BMI. In summary, whether waist circumference adds to the prognostic performance of cardiovascular risk models awaits definitive evidence.

However, waist circumference is now clearly established as a key driver of altered levels of cardiometabolic risk factors and markers. Consequently, reducing waist circumference is a critical step in cardiometabolic risk reduction, as it offers a pragmatic and simple target for managing patient risk.

The combination of BMI and waist circumference identifies a high-risk obesity phenotype better than either measure alone. We recommend that waist circumference should be measured in clinical practice as it is a key driver of risk; for example, many patients have altered CVD risk factors because they have abdominal obesity.

Waist circumference is a critical factor that can be used to measure the reduction in CVD risk after the adoption of healthy behaviours. Evidence from several reviews and meta-analyses confirm that, regardless of age and sex, a decrease in energy intake through diet or an increase in energy expenditure through exercise is associated with a substantial reduction in waist circumference 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , For studies wherein the negative energy balance is induced by diet alone, evidence from RCTs suggest that waist circumference is reduced independent of diet composition and duration of treatment Whether a dose—response relationship exists between a negative energy balance induced by diet and waist circumference is unclear.

Although it is intuitive to suggest that increased amounts of exercise would be positively associated with corresponding reductions in waist circumference, to date this notion is not supported by evidence from RCTs 71 , 74 , 89 , 90 , A doubling of the energy expenditure induced by exercise did not result in a difference in waist circumference reduction between the exercise groups.

A significant reduction was observed in waist circumference across all exercise groups compared with the no-exercise controls, with no difference between the different prescribed levels Few RCTs have examined the effects of exercise intensity on waist circumference 74 , 90 , 91 , However, no significant differences were observed in VAT reduction by single slice CT between high-intensity and low-intensity groups.

However, the researchers did not fix the level of exercise between the intensity groups, which might explain their observations. Their observations are consistent with those of Slentz and colleagues, whereby differences in exercise intensity did not affect waist circumference reductions.

These findings are consistent with a meta-analysis carried out in wherein no difference in waist circumference reduction was observed between high-intensity interval training and moderate-intensity exercise In summary, current evidence suggests that increasing the intensity of exercise interventions is not associated with a further decrease in waist circumference.

VAT mass is not routinely measured in clinical settings, so it is of interest whether reductions in waist circumference are associated with corresponding reductions in VAT.

Of note, to our knowledge every study that has reported a reduction in waist circumference has also reported a corresponding reduction in VAT. Thus, although it is reasonable to suggest that a reduction in waist circumference is associated with a reduction in VAT mass, a precise estimation of individual VAT reduction from waist circumference is not possible.

Nonetheless, the corresponding reduction of VAT with waist circumference in a dose-dependent manner highlights the importance of routine measurement of waist circumference in clinical practice. Of particular interest to practitioners, several reviews have observed significant VAT reduction in response to exercise in the absence of weight loss 80 , Available evidence from RCTs suggests that exercise is associated with substantial reductions in waist circumference, independent of the quantity or intensity of exercise.

Exercise-induced or diet-induced reductions in waist circumference are observed with or without weight loss. We recommend that practitioners routinely measure waist circumference as it provides them with a simple anthropometric measure to determine the efficacy of lifestyle-based strategies designed to reduce abdominal obesity.

The emergence of waist circumference as a strong independent marker of morbidity and mortality is striking given that there is no consensus regarding the optimal protocol for measurement of waist circumference.

Moreover, the waist circumference protocols recommended by leading health authorities have no scientific rationale. In , a panel of experts performed a systematic review of studies to determine whether measurement protocol influenced the relationship between waist circumference, morbidity and mortality, and observed similar patterns of association between the outcomes and all waist circumference protocols across sample size, sex, age and ethnicity Upon careful review of the various protocols described within the literature, the panel recommended that the waist circumference protocol described by the WHO guidelines 98 the midpoint between the lower border of the rib cage and the iliac crest and the NIH guidelines 99 the superior border of the iliac crest are probably more reliable and feasible measures for both the practitioner and the general public.

This conclusion was made as both waist circumference measurement protocols use bony landmarks to identify the proper waist circumference measurement location. The expert panel recognized that differences might exist in absolute waist circumference measures due to the difference in protocols between the WHO and NIH methods.

However, few studies have compared measures at the sites recommended by the WHO and NIH. Jack Wang and colleagues reported no difference between the iliac crest and midpoint protocols for men and an absolute difference of 1.

Consequently, although adopting a standard approach to waist circumference measurement would add to the utility of waist circumference measures for obesity-related risk stratification, the prevalence estimates of abdominal obesity in predominantly white populations using the iliac crest or midpoint protocols do not seem to be materially different.

However, the waist circumference measurements assessed at the two sites had a similar ability to screen for the metabolic syndrome, as defined by National Cholesterol Education Program, in a cohort of 1, Japanese adults Several investigations have evaluated the relationship between self-measured and technician-measured waist circumference , , , , Instructions for self-measurement of waist circumference are often provided in point form through simple surveys Good agreement between self-measured and technician-measured waist circumference is observed, with strong correlation coefficients ranging between 0.

Moreover, high BMI and large baseline waist circumference are associated with a larger degree of under-reporting , Overall these observations are encouraging and suggest that self-measures of waist circumference can be obtained in a straightforward manner and are in good agreement with technician-measured values.

Currently, no consensus exists on the optimal protocol for measurement of waist circumference and little scientific rationale is provided for any of the waist circumference protocols recommended by leading health authorities. The waist circumference measurement protocol has no substantial influence on the association between waist circumference, all-cause mortality and CVD-related mortality, CVD and T2DM.

Absolute differences in waist circumference obtained by the two most often used protocols, iliac crest NIH and midpoint between the last rib and iliac crest WHO , are generally small for adult men but are much larger for women.

The classification of abdominal obesity might differ depending on the waist circumference protocol. We recommend that waist circumference measurements are obtained at the level of the iliac crest or the midpoint between the last rib and iliac crest.

The protocol selected to measure waist circumference should be used consistently. Self-measures of waist circumference can be obtained in a straightforward manner and are in good agreement with technician-measured values.

Current guidelines for identifying obesity indicate that adverse health risk increases when moving from normal weight to obese BMI categories. Moreover, within each BMI category, individuals with high waist circumference values are at increased risk of adverse health outcomes compared with those with normal waist circumference values Thus, these waist circumference threshold values were designed to be used in place of BMI as an alternative way to identify obesity and consequently were not developed based on the relationship between waist circumference and adverse health risk.

In order to address this limitation, Christopher Ardern and colleagues developed and cross-validated waist circumference thresholds within BMI categories in relation to estimated risk of future CVD using FRS The results of their study revealed that the current recommendations that use a single waist circumference threshold across all BMI categories are insufficient to identify those at increased health risk.

In both sexes, the use of BMI category-specific waist circumference thresholds improved the identification of individuals at a high risk of future coronary events, leading the authors to propose BMI-specific waist circumference values Table 1. For both men and women, the Ardern waist circumference values substantially improved predictions of mortality compared with the traditional values.

These observations are promising and support, at least for white adults, the clinical utility of the BMI category-specific waist circumference thresholds given in Table 1. Of note, BMI-specific waist circumference thresholds have been developed in African American and white men and women Similar to previous research, the optimal waist circumference thresholds increased across BMI categories in both ethnic groups and were higher in men than in women.

However, no evidence of differences in waist circumference occurred between ethnicities within each sex Pischon and colleagues investigated the associations between BMI, waist circumference and risk of death among , adults from nine countries in the European Prospective Investigation into Cancer and Nutrition cohort Although the waist circumference values that optimized prediction of the risk of death for any given BMI value were not reported, the findings reinforce the notion that waist circumference thresholds increase across BMI categories and that the combination of waist circumference and BMI provide improved predictions of health risk than either anthropometric measure alone.

Ethnicity-specific values for waist circumference that have been optimized for the identification of adults with elevated CVD risk have been developed Table 2. With few exceptions, the values presented in Table 2 were derived using cross-sectional data and were not considered in association with BMI.

Prospective studies using representative populations are required to firmly establish ethnicity-specific and BMI category-specific waist circumference threshold values that distinguish adults at increased health risk.

As noted above, the ethnicity-specific waist circumference values in Table 2 were optimized for the identification of adults with elevated CVD risk. The rationale for using VAT as the outcome was that cardiometabolic risk was found to increase substantially at this VAT level for adult Japanese men and women We recommend that prospective studies using representative populations are carried out to address the need for BMI category-specific waist circumference thresholds across different ethnicities such as those proposed in Table 1 for white adults.

This recommendation does not, however, diminish the importance of measuring waist circumference to follow changes over time and, hence, the utility of strategies designed to reduce abdominal obesity and associated health risk. The main recommendation of this Consensus Statement is that waist circumference should be routinely measured in clinical practice, as it can provide additional information for guiding patient management.

Indeed, decades of research have produced unequivocal evidence that waist circumference provides both independent and additive information to BMI for morbidity and mortality prediction.

On the basis of these observations, not including waist circumference measurement in routine clinical practice fails to provide an optimal approach for stratifying patients according to risk. The measurement of waist circumference in clinical settings is both important and feasible.

Self-measurement of waist circumference is easily obtained and in good agreement with technician-measured waist circumference. Gaps in our knowledge still remain, and refinement of waist circumference threshold values for a given BMI category across different ages, by sex and by ethnicity will require further investigation.

To address this need, we recommend that prospective studies be carried out in the relevant populations. Despite these gaps in our knowledge, overwhelming evidence presented here suggests that the measurement of waist circumference improves patient management and that its omission from routine clinical practice for the majority of patients is no longer acceptable.

Accordingly, the inclusion of waist circumference measurement in routine practice affords practitioners with an important opportunity to improve the care and health of patients. Health professionals should be trained to properly perform this simple measurement and should consider it as an important vital sign to assess and identify, as an important treatment target in clinical practice.

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