{"id":2595,"date":"2024-08-21T13:08:03","date_gmt":"2024-08-21T17:08:03","guid":{"rendered":"https:\/\/pressbooks.bccampus.ca\/humannutrition\/?post_type=chapter&p=2595"},"modified":"2025-01-25T09:43:40","modified_gmt":"2025-01-25T14:43:40","slug":"bmi","status":"publish","type":"chapter","link":"https:\/\/pressbooks.bccampus.ca\/humannutrition\/chapter\/bmi\/","title":{"raw":"Body Mass Index (BMI)","rendered":"Body Mass Index (BMI)"},"content":{"raw":"Body mass index (BMI) is calculated using height and weight measurements and is thought to be more predictive of body fatness than weight alone. BMI measurements are used to classify an individual as being underweight (with a BMI less than 18.5), overweight (with a BMI over 25), or having obesity (class 1: 30-34..9, class 2: 35-39.9, class 3 > 40). High BMI measurements may increase your chances of developing health problems such as cardiovascular disease, Type 2 diabetes, and other chronic diseases. But it's important to be aware that the BMI health risk relationship was originally developed using data from white, European men, therefore, associated BMI-health risks vary by race. Also, BMI is not good indication of overweight\/obesity in those over 65 or under 20 and has many other limitations outlined below.\r\n

Calculating BMI<\/h1>\r\nTo calculate your BMI, multiply your weight in kilograms by the square of your height in meters.\r\n\r\nBMI = [weight (kg)] \u00f7 height2<\/sup> (m)2<\/sup>\r\n\r\nTo see which weight category your BMI falls under, you may refer to Table 14.1.\r\n
\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
Table 14.1: BMI categories<\/caption>\r\n
Categories<\/td>\r\nBMI<\/td>\r\n<\/tr>\r\n
Underweight<\/td>\r\n< 18.5<\/td>\r\n<\/tr>\r\n
Normal weight<\/td>\r\n18.5\u201324.9<\/td>\r\n<\/tr>\r\n
Overweight<\/td>\r\n25\u201329.9<\/td>\r\n<\/tr>\r\n
Obese<\/td>\r\n<\/tr>\r\n
Obese class 1<\/td>\r\n30.0-34.9<\/td>\r\n<\/tr>\r\n
Obese class 2<\/td>\r\n35-39.9<\/td>\r\n<\/tr>\r\n
Obese class 3<\/td>\r\n> 40.0<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/div>\r\n

BMI Limitations<\/h1>\r\n

Fat Mass and Distribution<\/span><\/h2>\r\nBMI is a fairly simple measurement but it does not take into account fat mass or fat distribution in the body, which are both important predictors of disease risk. For example, in those with a BMI of 25kg\/m2\u00a0<\/sup>women's body fat percentage varied between 26% and 43%, and men's between 14% and 35%.<\/span>\u00a0[footnote]Nuttal, F. 2015. Body Mass Index Obesity, BMI, and Health: A Critical Review. Nutr Today. Apr 7;50(3):117\u2013128. doi:\u00a010.1097\/NT.0000000000000092<\/a>. https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC4890841\/<\/a>[\/footnote] If BMI is considered a surrogate measure of adiposity and this is then used to determine disease and mortality risk, you can see BMI may be a limited tool. In fact waist to hip ratio, which is another simple measure, is strongly and consistently associated with mortality, irrespective of BMI.[footnote]Khan, I. 2023. Surrogate Adiposity Markers and Mortality. JAMA Netw Open. 2023 Sep 20;6(9):e2334836. doi: 10.1001\/jamanetworkopen.2023.34836. https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC10512100<\/a>[\/footnote]\u00a0<\/span>\r\n\r\nBMI also does not indicate where body fat is stored, for example upper body stores of fat are more related to the risk of chronic disease vs. lower body stores.\r\n

Race\/ Ethnicity<\/h2>\r\nBMI also has limitations for different race\/ethnicities. For example, for the same BMI, Chinese and south Asian adults have a higher body fat percentage than White adults. Additionally, for the same BMI some ethnicities have a higher risk of health problems (e.g., for the same BMI Asian, Hispanic and African American women have a higher risk of diabetes than White women). Other research also suggests that BMI is associated with fat around the heart in New Zealand patients but not in Maori\/Pacific patients [footnote]Moharram, MA. Et al., 2020 Correlation between epicardial adipose tissue and BMI in New Zealand ethnic populations. NZMJ. 2020;133(1516):22-32.[\/footnote].\r\n\r\nAs previously mentioned BMI \/ BMI cut offs are based on research in European white men but the optimal BMI may be difference amongst races. For example the greatest longevity occurs in White people with a BMI of 23-25, compared to 23-30 in Black people [footnote]Fonatine, KR. Et al., 2003 Years of Life Lost Due to ObesityJAMA. 2003;289(2):187-193. doi:10.1001\/jama.289.2.187.[\/footnote].\r\n

Muscle Mass<\/h2>\r\nBMI is not a good indication of health risk in those with high muscle mass.\u00a0 For instance, a muscular athlete will have more muscle mass (which is heavier than fat mass) than a sedentary individual of the same height. Based on their BMIs, the muscular athlete may be categorized as more overweight or having obesity than the sedentary individual. Additionally, an older person with osteoporosis (decreased bone mass) will have a lower BMI than an older person of the same height without osteoporosis, even though the person with osteoporosis may have more fat mass. BMI is a useful inexpensive tool to categorize people and is highly correlative with disease risk, but other measurements are needed to diagnose obesity and more accurately assess disease risk.\r\n

BMI does not always indicate health status or mortality<\/h2>\r\nIn addition to the limitations above, it's important to understand that BMI does not always indicate health status. For example. on average 35% of people classified as having obesity were metabolically healthy, but this percentage varied between sexes, as well as country of origin ( 38% in women vs. 31% in men, 86% in Africa, 71% in South America, 43% in North America, 26% in Europe). Additionally, over 30% of \u201cnormal\u201d weight individuals were metabolically unhealthy [footnote]Lin et al., 2017. The prevalence, metabolic risk and effects of lifestyle intervention for metabolically healthy obesity: a systematic review and meta-analysis: A PRISMA-compliant article. Medicine. 2017 Nov;96(47):e8838. doi: 10.1097\/MD.0000000000008838 Tomiyama AJ. et al., 2016 Misclassification of cardiometabolic health when using body mass index categories in NHANES 2005-2012. Int J Obes (Lond) 2016 May;40(5):883-6. doi: 10.1038\/ijo.2016.17. Epub 2016 Feb 4.[\/footnote].\r\n\r\nOther factors such as cardiometabolic risk factors, cardiorespiratory fitness, muscular fitness, body dissatisfaction and weight stigma may be better predictors or health and mortality compared to BMI.\r\n

BMI, weight loss and health?<\/h2>\r\nThose categorized as being overweight or having obesity are often encouraged to lose weight before any additional health analysis is performed, so people may be encouraged to lose weight when they are metabolically healthy and may not need to. Furthermore, weight loss does not universally improve health. Specifically, small improvements in risk factors following weight loss are not correlated to amount of weight lost[footnote]Tomiyama AJ, Ahlstrom B, Mann T. Long-term effects of dieting: Is weight loss related to health? Social and Personality Psychology Compass 2013; 7(12): 861-877.[\/footnote], intentional weight loss does not consistently reduce mortality [footnote]Gasser et al., iScience . 2021 Sep 20;24(10):102995. doi: 10.1016\/j.isci.2021.102995. eCollection 2021 Oct 22.[\/footnote] and weight loss due to liposuction has minimal effect on cardiometabolic risk factors [footnote]Gasser et al., iScience . 2021 Sep 20;24(10):102995. doi: 10.1016\/j.isci.2021.102995. eCollection 2021 Oct 22[\/footnote].","rendered":"

Body mass index (BMI) is calculated using height and weight measurements and is thought to be more predictive of body fatness than weight alone. BMI measurements are used to classify an individual as being underweight (with a BMI less than 18.5), overweight (with a BMI over 25), or having obesity (class 1: 30-34..9, class 2: 35-39.9, class 3 > 40). High BMI measurements may increase your chances of developing health problems such as cardiovascular disease, Type 2 diabetes, and other chronic diseases. But it’s important to be aware that the BMI health risk relationship was originally developed using data from white, European men, therefore, associated BMI-health risks vary by race. Also, BMI is not good indication of overweight\/obesity in those over 65 or under 20 and has many other limitations outlined below.<\/p>\n

Calculating BMI<\/h1>\n

To calculate your BMI, multiply your weight in kilograms by the square of your height in meters.<\/p>\n

BMI = [weight (kg)] \u00f7 height2<\/sup> (m)2<\/sup><\/p>\n

To see which weight category your BMI falls under, you may refer to Table 14.1.<\/p>\n

\n\n\n\n\n\n\n\n\n\n\n
Table 14.1: BMI categories<\/caption>\n
Categories<\/td>\nBMI<\/td>\n<\/tr>\n
Underweight<\/td>\n< 18.5<\/td>\n<\/tr>\n
Normal weight<\/td>\n18.5\u201324.9<\/td>\n<\/tr>\n
Overweight<\/td>\n25\u201329.9<\/td>\n<\/tr>\n
Obese<\/td>\n<\/tr>\n
Obese class 1<\/td>\n30.0-34.9<\/td>\n<\/tr>\n
Obese class 2<\/td>\n35-39.9<\/td>\n<\/tr>\n
Obese class 3<\/td>\n> 40.0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

BMI Limitations<\/h1>\n

Fat Mass and Distribution<\/span><\/h2>\n

BMI is a fairly simple measurement but it does not take into account fat mass or fat distribution in the body, which are both important predictors of disease risk. For example, in those with a BMI of 25kg\/m2\u00a0<\/sup>women’s body fat percentage varied between 26% and 43%, and men’s between 14% and 35%.<\/span>\u00a0[1]<\/sup><\/a> If BMI is considered a surrogate measure of adiposity and this is then used to determine disease and mortality risk, you can see BMI may be a limited tool. In fact waist to hip ratio, which is another simple measure, is strongly and consistently associated with mortality, irrespective of BMI.[2]<\/sup><\/a>\u00a0<\/span><\/p>\n

BMI also does not indicate where body fat is stored, for example upper body stores of fat are more related to the risk of chronic disease vs. lower body stores.<\/p>\n

Race\/ Ethnicity<\/h2>\n

BMI also has limitations for different race\/ethnicities. For example, for the same BMI, Chinese and south Asian adults have a higher body fat percentage than White adults. Additionally, for the same BMI some ethnicities have a higher risk of health problems (e.g., for the same BMI Asian, Hispanic and African American women have a higher risk of diabetes than White women). Other research also suggests that BMI is associated with fat around the heart in New Zealand patients but not in Maori\/Pacific patients [3]<\/sup><\/a>.<\/p>\n

As previously mentioned BMI \/ BMI cut offs are based on research in European white men but the optimal BMI may be difference amongst races. For example the greatest longevity occurs in White people with a BMI of 23-25, compared to 23-30 in Black people [4]<\/sup><\/a>.<\/p>\n

Muscle Mass<\/h2>\n

BMI is not a good indication of health risk in those with high muscle mass.\u00a0 For instance, a muscular athlete will have more muscle mass (which is heavier than fat mass) than a sedentary individual of the same height. Based on their BMIs, the muscular athlete may be categorized as more overweight or having obesity than the sedentary individual. Additionally, an older person with osteoporosis (decreased bone mass) will have a lower BMI than an older person of the same height without osteoporosis, even though the person with osteoporosis may have more fat mass. BMI is a useful inexpensive tool to categorize people and is highly correlative with disease risk, but other measurements are needed to diagnose obesity and more accurately assess disease risk.<\/p>\n

BMI does not always indicate health status or mortality<\/h2>\n

In addition to the limitations above, it’s important to understand that BMI does not always indicate health status. For example. on average 35% of people classified as having obesity were metabolically healthy, but this percentage varied between sexes, as well as country of origin ( 38% in women vs. 31% in men, 86% in Africa, 71% in South America, 43% in North America, 26% in Europe). Additionally, over 30% of \u201cnormal\u201d weight individuals were metabolically unhealthy [5]<\/sup><\/a>.<\/p>\n

Other factors such as cardiometabolic risk factors, cardiorespiratory fitness, muscular fitness, body dissatisfaction and weight stigma may be better predictors or health and mortality compared to BMI.<\/p>\n

BMI, weight loss and health?<\/h2>\n

Those categorized as being overweight or having obesity are often encouraged to lose weight before any additional health analysis is performed, so people may be encouraged to lose weight when they are metabolically healthy and may not need to. Furthermore, weight loss does not universally improve health. Specifically, small improvements in risk factors following weight loss are not correlated to amount of weight lost[6]<\/sup><\/a>, intentional weight loss does not consistently reduce mortality [7]<\/sup><\/a> and weight loss due to liposuction has minimal effect on cardiometabolic risk factors [8]<\/sup><\/a>.<\/p>\n

  1. Nuttal, F. 2015. Body Mass Index Obesity, BMI, and Health: A Critical Review. Nutr Today. Apr 7;50(3):117\u2013128. doi:\u00a010.1097\/NT.0000000000000092<\/a>. https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC4890841\/<\/a> ↵<\/a><\/li>
  2. Khan, I. 2023. Surrogate Adiposity Markers and Mortality. JAMA Netw Open. 2023 Sep 20;6(9):e2334836. doi: 10.1001\/jamanetworkopen.2023.34836. https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC10512100<\/a> ↵<\/a><\/li>
  3. Moharram, MA. Et al., 2020 Correlation between epicardial adipose tissue and BMI in New Zealand ethnic populations. NZMJ. 2020;133(1516):22-32. ↵<\/a><\/li>
  4. Fonatine, KR. Et al., 2003 Years of Life Lost Due to ObesityJAMA. 2003;289(2):187-193. doi:10.1001\/jama.289.2.187. ↵<\/a><\/li>
  5. Lin et al., 2017. The prevalence, metabolic risk and effects of lifestyle intervention for metabolically healthy obesity: a systematic review and meta-analysis: A PRISMA-compliant article. Medicine. 2017 Nov;96(47):e8838. doi: 10.1097\/MD.0000000000008838 Tomiyama AJ. et al., 2016 Misclassification of cardiometabolic health when using body mass index categories in NHANES 2005-2012. Int J Obes (Lond) 2016 May;40(5):883-6. doi: 10.1038\/ijo.2016.17. Epub 2016 Feb 4. ↵<\/a><\/li>
  6. Tomiyama AJ, Ahlstrom B, Mann T. Long-term effects of dieting: Is weight loss related to health? Social and Personality Psychology Compass 2013; 7(12): 861-877. ↵<\/a><\/li>
  7. Gasser et al., iScience . 2021 Sep 20;24(10):102995. doi: 10.1016\/j.isci.2021.102995. eCollection 2021 Oct 22. ↵<\/a><\/li>
  8. Gasser et al., iScience . 2021 Sep 20;24(10):102995. doi: 10.1016\/j.isci.2021.102995. eCollection 2021 Oct 22 ↵<\/a><\/li><\/ol><\/div>","protected":false},"author":1806,"menu_order":2,"template":"","meta":{"pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"class_list":["post-2595","chapter","type-chapter","status-publish","hentry"],"part":2124,"_links":{"self":[{"href":"https:\/\/pressbooks.bccampus.ca\/humannutrition\/wp-json\/pressbooks\/v2\/chapters\/2595","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pressbooks.bccampus.ca\/humannutrition\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/pressbooks.bccampus.ca\/humannutrition\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/pressbooks.bccampus.ca\/humannutrition\/wp-json\/wp\/v2\/users\/1806"}],"version-history":[{"count":18,"href":"https:\/\/pressbooks.bccampus.ca\/humannutrition\/wp-json\/pressbooks\/v2\/chapters\/2595\/revisions"}],"predecessor-version":[{"id":2815,"href":"https:\/\/pressbooks.bccampus.ca\/humannutrition\/wp-json\/pressbooks\/v2\/chapters\/2595\/revisions\/2815"}],"part":[{"href":"https:\/\/pressbooks.bccampus.ca\/humannutrition\/wp-json\/pressbooks\/v2\/parts\/2124"}],"metadata":[{"href":"https:\/\/pressbooks.bccampus.ca\/humannutrition\/wp-json\/pressbooks\/v2\/chapters\/2595\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/pressbooks.bccampus.ca\/humannutrition\/wp-json\/wp\/v2\/media?parent=2595"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/pressbooks.bccampus.ca\/humannutrition\/wp-json\/pressbooks\/v2\/chapter-type?post=2595"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/pressbooks.bccampus.ca\/humannutrition\/wp-json\/wp\/v2\/contributor?post=2595"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/pressbooks.bccampus.ca\/humannutrition\/wp-json\/wp\/v2\/license?post=2595"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}