Review methods used to measure and classify obesity in individuals with spinal cord injuries (SCI). Outline the strengths and weaknesses of each method used to measure obesity in individuals with SCI.
PubMed was used to identify articles before 2016. Search terms (‘obesity’ or ‘weight status’ and ‘spinal cord injury’). Filters: adults, English and human. Studies were retained that (1) included participants, 18 years or older, with SCI; (2) took place in inpatient, outpatient or community-based settings and (3) measured obesity status. Unique methods for classifying individuals with SCI as obese were identified and examples are presented.
Methods identified for classifying obesity were as follows: World Health Organization body mass index (BMI) cutoff⩾30 kg m−2, BMI cutoff ⩾25-29 kg m−2, and SCI-specific BMI cutoff ⩾22 kg m−2, waist circumference cutoff (women >102 cm, men >88 cm), percent body fat cutoffs ⩾25% using bioelectrical impedance analysis and dual-energy X-ray absorptiometry, computerized tomography scan visceral fat area ⩾100 cm2 and percentage of ideal body weight.
BMI is the most widely used measure of obesity in the SCI literature. Although some studies identified alternative cutoffs or other metrics, there is no standardized obesity classification in SCI. However, research is needed to determine and validate obesity classification specific to SCI due to physiological changes that occur following injury. We recommend that researchers and clinicians proceed with caution and use methodology based on the purpose of measurement.
‘Obesity’ is a clinical term referring to excess adiposity.1 Many methods of assessing body composition to identify individuals with obesity are used in epidemiology, research and clinical settings. Body composition methods used to measure adiposity include underwater weighing, dilution methods, whole-body potassium counting, dual-energy X-ray absorptiometry (DXA), computerized tomography (CT), magnetic resonance imaging, bioelectrical impedance analysis (BIA) and anthropometric measures such as height and weight to calculate body mass index (BMI), waist circumference (WC) and hip circumference.2
The utility of obesity classification in the US general population arose in aiding life insurance companies in assessing the risk of mortality in overweight policyholders. Ideal body weight tables were developed using life insurance company and NHANES data.3 More recently, the Hamwi sex-specific formula has been used in developing body weight tables and is most commonly used by healthcare providers in the US.4 For men, it is calculated by summing 106 pounds for the first 5 feet, plus 6 pounds for each additional inch. For women, it is calculated by summing 100 pounds for the first 5 feet, plus 5 pounds for each additional inch. Formulas provide healthcare providers a streamlined method that requires no chart or burden on medical staff to memorize a table of numbers. However, the origins of this method are unknown, and there is very little published research on its validity.
Because of the uncertainty in using Hamwi’s table, other measures of obesity are being used in the general population to identify individuals with obesity. BMI is now the most common method used in the literature to classify obesity used in research and practice due to ease of measurement in the general population. It was derived by Adolphe Quetelet in the 1800 s as a metric for understanding human body growth and later applied by Keys as a useful alternative to body fat percentage in population-based studies.5 The World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) define obesity as having a BMI of 30 kg m−2 or more.6 The BMI cutoffs for classifying weight status (underweight ⩽18.5 kg m−2, normal =18.5–24.9 kg m−2, overweight =25–29.9 kg m−2 and obese ⩾30 kg m−2) are based on longitudinal studies of disease risk in thousands of individuals from the general population.6 Standard BMI cutoffs were established to reflect the points along the weight/height continuum, where marked increases occur in morbidity and mortality related to a number of health conditions,1 including hypertension, cardiovascular disease (CVD) and diabetes mellitus.7 However, these values may not be valid for certain segments of the population including other racial/ethnic groups (for example, Asians). Alternative obesity cutoff of BMI ⩾25 kg m−2 for Asians was developed during the WHO expert consultation in 2002.8 Other common anthropometric measures of obesity are waist and hip circumference. WC specifically is a valid measure of abdominal adiposity or visceral fat in the general population.9 The WHO assembly in 1995 released sex-specific WC cutoffs for obesity—women >102 cm and men >88 cm. Cutoffs for WC were established based on the correlation to BMI, total body fat and CVD risk factors using European population data.10
Anthropometric measures allow for the estimation of body fat, but more precise measures of fat mass can also be used. Densitometry or underwater weighing is the classic technique used for measuring fat mass. It is based on the fact that fat is less dense than water, and therefore individuals with more body fat will have a lower body density. This method is well established, but methodological issues including lung volume measurement can be a major source of error.2 Air displacement methods for measuring body volume and density including the BODPOD show high correlation with densitometry and are easier to administer.2 Whole-body potassium counting is another method used for estimating total body fat from estimates of potassium, but due to ethnic and gender differences in the rates of total body potassium, interpretation of results from this method is complicated.2 BIA is another common estimate of body composition and body fat, considered a gold standard in some fields because measures of opposition to the flow of an electric current through body tissues can then be used to calculate an estimate of total body water.11 Validation studies compare BIA to skinfold anthropometry, deuterium oxide dilution (D2O) and densitometry, but primarily in adult, healthy, European and North American non-Hispanic White participants.11
Imaging methods are becoming more common in practice and in research for estimating total body fat. DXA is a method for measuring bone mineral density; however, DXA scans also can be used for direct measurement of total body composition and fat content.2 DXA was first applied to body composition in 1981.12 It has since become recognized as the gold standard for measuring adiposity because it directly assesses body composition. CT and magnetic resonance imaging can also be used to measure fat mass in the whole body or in specific body segments. These methods are considered the most accurate methods for assessing body composition at the tissue and organ level. Validation studies of CT scan use for assessing subcutaneous and visceral fat began in the early 1980s.13 Asian-specific guidelines have been developed for abdominal obesity, determined by cross-sections of abdomen, equal to visceral fat area ⩾100 cm2.14
All the methods outlined can be used to assess obesity in the general populations and have strengths and weaknesses in ease of measurement, cost and risks; however, validity of each measure in diverse populations is understudied. The focus of this narrative review is to identify methods used to classify obesity in individuals with spinal cord injuries (SCI). It is well documented that BMI and other standard cutoffs may not be valid for individuals with SCI, given the substantial physiological changes that occur following injury. For example, even when classified as being within normal weight status, individuals with SCI generally have higher body fat percentage15 and lower percentage of muscle mass16 per unit of body mass compared with the nondisabled population. Although many studies of people with SCI support the relationship between obesity and CVD-risk factors, like hypertension, dyslipidemia and elevated high-density lipoprotein cholesterol levels,17, 18 other studies have found increased rates of these CVD risk factors irrespective of obesity status.19, 20 It is possible that conflicting information about risks associated with obesity among people with SCI is due to the inconsistent use of obesity measurement or classification methods not validated for this population.
The physiology of SCI and circumstances surrounding acute care and chronic management make measurement of adiposity problematic and complicate establishing parameters for obesity. Transfer from/to wheelchair requirements for height and weight measurement is a significant barrier that can impede individuals from performing measurements independently. Individuals with SCI require platform scales for weight measurement that are often not available in clinical settings let alone at home.16 Although estimating height based on arm span is commonly used to replace stadiometer measurements, it has been shown to be the least accurate measure compared to recumbent length, which requires transfers, knee height or self-report.21 Therefore, measurement issues, as well as changes in muscle mass and body composition following SCI, may lead to unreliable BMI measurement.16 Other measures of adiposity exist, but barriers to transferring and high cost limit access and feasibility of use in clinical or personal settings.
The current study is a narrative review of the literature that asks the question, ‘What measurement and classification methods have been used to determine obesity in individuals with SCI?’ This review provides clinicians and researchers with information to make informed decisions on measurements to use in studies and practice by outlining strengths and weaknesses of each method, and it also provides recommendations to guide future research in designing obesity studies involving people with SCI.
A literature search was performed in PubMed using the terms ‘obesity’ OR ‘weight status’ AND ‘spinal cord injury’, and the filters ‘adults,’ ‘English’ and ‘human’ were applied to the database. Studies that (1) included participants 18 years or older with SCI, (2) took place in inpatient, outpatient or community-based settings and (3) measured obesity status were retained for review. Only articles published before March 2016 were considered. Studies using methods for measuring adiposity that did not classify individuals into obesity categories were not considered. Studies that specifically classify subjects into obesity categories were included to provide examples for each method of measuring obesity.
The research team examined articles to identify each unique method used for classifying obesity in individuals with SCI. We provide examples of each method used in the literature, describe strengths and weaknesses of each method for obesity classification in terms of population validation, ease of measurement, cost of measurement, validation to disease risk factors and reliability, and provide recommendations for measurement studies and clinical practice.
The review showed that six distinct methods of classifying obesity status have been used in individuals with SCI. Three methods estimate body composition through anthropometric measure calculations (BMI, WC and IBW), and three methods estimate body fat mass (DXA, CT scan and BIA). Estimates of obesity rates varied widely across the studies, from 5.6 to 70.4% of participants.22, 23 All methods provided a continuous variable to which cutoff values were imposed to constitute an ‘obesity’ classification.
Methods for classifying obesity
BMI is a widely used equation of body mass and the most common measure of obesity used in individuals with SCI. Some studies use obesity classification by BMI cutoff to screen participants for inclusion, others use BMI cutoff as an independent or dependent variable and others test the validity of BMI among people with SCI against a gold standard measure of body fat in the general population. For example, Chen et al.24 used WHO BMI classification in examining longitudinal SCI Model Systems data and classified 1381 individuals with SCI as underweight, overweight or obese. Findings from this study show that over 50% of this sample was overweight or obese, and BMI classification groups were compared on physical and psychosocial health measures. WHO BMI guidelines were validated on white adults of European descent.25 Asian-specific BMI cutoffs to classify obesity have been used in populations with SCI.26 Additionally, others cite population-based studies that documented associations among CVD, metabolic syndrome and BMI in a range from 27 to 29.9 kg m−2.27 These alternative cutoffs are not widely used or validated for individuals with SCI.
Further, standard BMI cutoffs have been shown to be a poor predictor of CVD in SCI.28 Given concerns that standard BMI cutoffs may not be valid for classifying obesity in people with SCI, there have been attempts to use alternative BMI values for this population. Ayas et al.29 used the median of the BMI values for a sample of 197 individuals with SCI, 25.3 kg m−2, as the threshold for classifying participants as obese. More recently, SCI-adjusted BMI was established by Laughton et al. using C-reactive protein cutoff scores as indicators of CVD risk compared to BMI and percent fat mass (%FM) measured by BIA. This study validated a new SCI-specific BMI cutoff equal to >22 kg m−2 by comparing it to a gold standard measure of CVD risk and %FM as an indication of obesity status in SCI.30 The SCI-specific BMI method is not widely used in the literature and has not been validated in other large population-based studies.
WC is another widely used measure of obesity that is cited in the literature as a good indicator of abdominal obesity. Studies have investigated the relationship between WC and CVD in populations with SCI and generally recommend this method due to high correlation to CVD indicators and ease of measurement.18, 31 However, Han et al.32 found that WC underestimated visceral adipose tissue measured by DXA and it is not widely used in the literature. Lee et al.33 used another method, percentage of IBW, to define obesity in individuals with SCI. IBW tables and Hawmi’s formula have not been validated in populations with SCI.
In populations with SCI, imaging-based measures of fat mass have also been used. DXA has been used in obesity measurement validation studies, but SCI-specific cutoffs have not been established.31, 32, 34 Similarly, CT has been used to measure adiposity in populations with SCI, but SCI-specific cutoffs for obesity using body fat determined by CT scans are not available.26 BIA has also been used as the gold standard in obesity measurement validation studies in people with SCI.16 However, cutoffs for people with SCI have not been established. Therefore, direct measurement of fat mass in individuals with SCI is possible, but validation studies of unique %FM cutoffs do not currently exist.
Papers comparing methods of obesity classification
Some studies in the literature compare methods of classifying obesity for validation purposes. Examples of studies that clearly classify individuals into obesity categories using multiple methods are noted to provide context for recommendations.
Buchholz et al.35 used both BMI- and sex-specific %FM cutoffs for total body water using BIA to measure obesity in individuals with SCI. They cite methodological issues surrounding total body water estimate validation in the general population, but posit that there is reason to believe that hydration values in individuals with paraplegia and tetraplegia do not vary greatly from the Pace and Rathbun lean tissue hydration constant value of 73.2%.35 Further, they found that BMI showed specificity for identifying individuals who are not obese, but, using BIA cutoffs, only 20% of obese subjects with paraplegia were correctly identified with WHO BMI ⩾30 kg m−2 cutoff. Therefore, BMI is great at identifying nonobese individuals, but individuals with a BMI lower than 30 are often considered obese using BIA, indicating low sensitivity of BMI cutoff values for individuals with paraplegia. This study used a very small sample (n=31 individuals with paraplegia and 62 control subjects), but it highlights a major concern of not identifying individuals with SCI who are obese using WHO BMI cutoffs.
Recommendations for obesity cutoffs for Asian populations have already been addressed in the general population, but the question of modified cutoffs in individuals with SCI is just beginning. Inayama et al.26 studied Asian-specific cutoffs for BMI, WC and visceral fat measured by CT scan. They identified 43% of individuals having visceral fat area ⩾100 cm2, which is the Asian-specific guideline for obese status. Linear regression analysis showed significant associations between visceral fat area, Asian-specific BMI and WC cutoffs, with WC coefficients indicating more accuracy than BMI in this sample of Japanese individuals with SCI. Using their equations, they estimated that visceral fat areas ⩾100 cm2 would be equivalent to 81.3 cm WC and 22.5 kg m−2 BMI. The identified BMI is very close to the SCI-specific BMI cutoff suggested by Laughton et al. of 22 kg m−2 showing promise of that lowered cutoff, and use of WC and CT scan measured visceral fat area in this population as indicators of abdominal obesity.
Jones et al.34 used a DXA fat mass cutoff of 25% and compared groups to WHO BMI cutoff. This study found that at almost every BMI value, individuals with SCI have a greater amount of fat mass measured by DXA than the able-bodied controls. These results are consistent for different body regions as well. For example, in men with SCI, total kg trunk fat was on average 11.0 (s.d. 6.0), whereas in able-bodied matched controls the average kg trunk fat was 7.3 (s.d. 3.5) P<0.5. Other areas showed significant results, but trunk fat is highlighted due to metabolic risks and high incidence of abdominal adiposity in men in general.
These studies help to highlight the issues in the literature. BMI is the most widely used measure of obesity in individuals with SCI, but studies show that it is not a valid measure. Therefore, other methods must be explored and changes in BMI cutoffs are needed, along with recommendations for acquiring required measures of height and weight.
Strengths and weaknesses of methods for classifying obese status
Strengths and weaknesses of each method for measuring obesity found in this review are outlined in Table 1. Examples of strengths identified by the research team include ease of measurement and the validity of fat measurement in tools such as CT and DXA. However, the major concern of this review is the lack of validated and reliable methods of obesity classification in individuals with SCI who are at risk for CVD and other health complications following injury. In addition, measurement issues are magnified in individuals with SCI due to difficulty in accurate height and weight measurement. A major weakness in using any identified method is the lack of validation in individuals with SCI. Therefore, validation studies of numerous methods are needed in large, diverse populations with SCI to provide accurate indicators of risk as individuals are living to older ages. Current recommendations for obesity classification in populations with SCI are outlined in the discussion.
Results of this review indicate that there is currently no generally accepted or consensus definition of obesity for people with SCI. One study sought to validate a SCI-adjusted BMI cutoff, but this classification is not being widely used, cited or validated in larger, diverse samples. Researchers and clinicians are using a variety of methods of obesity classification, limiting the potential for comparison of results and meta-analyses. The lack of a validated, widely used, SCI-specific classification of obesity is detrimental to the advancement of the field and our understanding of factors associated with mortality and health in individuals with SCI.
The unreasonably large range of obesity rates across these studies indicates several weaknesses in the state of the science in both obesity and SCI, as well as disability in general. Specifically, rates of obesity reported among participants ranged from 5.6 to 70.4% of subjects. Gorgey and Gater36 reviewed the literature on obesity following SCI and reported obesity rates of 20–30% in a large cross-sectional research on individuals with SCI. The obesity range in this review reflects diverse studies, not all of which focused on population-level analysis of obesity prevalence.
BMI is one of the most commonly employed measures of adiposity in nondisabled individuals. BMI calculation requires accurate and reliable height and weight measurement. As previously mentioned, Froehlich-Grobe et al.21 found that self-reported height and weight in populations with SCI are prone to error, possibly to a greater extent than in the general population. Accurate measurement of weight in SCI requires expensive scales, assistance with transferring and extra calculations that lead to barriers in obtaining accurate data. Another alternative for height estimation in individuals with SCI who cannot stand unassisted is the use of ulnar measurement. This measurement does not require transfer assistance and has been validated in diverse populations, including adults with contractures.37 However, we caution that it has a positive bias that may be further exaggerated in individuals with pediatric onset of SCI.38 The ruler function on the DXA can be used to measure height in this population, which is currently not widely used in the field, but shows promise as a feasible option for individuals who are able to transfer to a DXA table. Further, SCI-adjusted cutoffs for BMI-based determinations of obesity have been proposed and used in some studies,30, 39 but barriers in measurement need to be addressed if these determinations are to be valid. In addition, many studies determine obesity using fat mass cutoffs of 25% for men and 35% for women, citing a WHO 1995 assembly report.25 However, this is not accurate and has been improperly cited.40 Healthy body fat percentage guidelines in 18 categories that are age- and sex-specific, based on BMI classification, exist, providing a more comprehensive and validated guideline.41
Very few validation studies for each obesity measure in people with SCI exist. SCI-adjusted BMI classification of obesity at ⩾22 kg m−2 is suggested based on connection with C-reactive protein cutoff scores indicating CVD risk. SCI-adjusted BMI cutoff values have not been widely tested and validated in diverse populations with SCI and further research is needed. Several studies suggest that WC is a good measure for clinical practice with people who have SCI due to its correlation with CVD risk, specifically abdominal adiposity, ease of measurement and low cost.18 However, cutoff values for WC specific to people with SCI do not exist. BIA is emerging as a more valid measure of obesity in SCI than BMI. BIA provides an accurate value indicating changes in fat mass following injury. It is common for individual’s weight to not change substantially, but physiological changes in total fat mass and areas where fat accumulation occurs are well documented in individuals with SCI in the years following injury. CT and DXA provide gold standard measurements of body composition and %FM in segments of the body or total body, but these measurements are expensive, not widely available and cutoff scores specific to people with SCI do not exist. In addition, current clinical guidelines for %FM in the general population have not been validated, and an incorrect citation of WHO 1995 has been widely used in the literature; therefore, researchers must use %FM cutoffs with caution. Furthermore, IBW is not a good measure of body composition in research or clinical practice and is not used in any of the validation studies for obesity in this population. Empirical testing of these different methods for classifying obesity in individuals with SCI in relation to morbidity and mortality is necessary to provide recommendations for research and practice.
On the basis of our findings and the strengths and weaknesses of each method, we recommend that clinicians and researchers proceed with caution. Clinicians should use their best judgment in considering the strengths and weaknesses outlined and choose methods on an individual basis. For example, the use of multiple methods such as WC and SCI-specific BMI when assessing disease risk in patients may be appropriate for a clinician seeking a quick and simple indicator of abdominal adiposity that is also able to give an accurate measure of an individual's height and weight. However, in a research setting more rigor and research is needed to develop a validated measurement of obesity in individuals with SCI. Further, we posit that we currently do not have enough evidence to provide public health recommendations for obesity cutoffs for individuals with SCI, leaving a large gap in knowledge that must be filled.
It is important to highlight again that the physiological changes common following SCI predispose individuals to excess adiposity and related conditions, making work in this area vital.31 Regardless of level or completeness of injury, people with SCI experience skeletal muscle atrophy, increased regional and total body fat mass and increased intramuscular fat mass, which are strongly associated with insulin resistance and other CVD risk factors.31 Despite being classified as within normal weight status based on WHO BMI cutoffs, evidence indicates that people with SCI have a higher body fat percentage per unit of body mass compared with the nondisabled population.15, 42 For instance, compared to nondisabled controls matched by weight status using mainstream measures, people with SCI have significantly greater amounts of visceral adipose tissue, a risk factor for coronary heart disease.42 Because risk factors occur at a lower weight status in people with SCI compared to the nondisabled population, people with SCI who are classified as normal weight by mainstream measures may not be aware of their risk. Discrepancy in identifying at-risk individuals creates universal challenges in defining obesity in this population. Physiological differences following SCI limit the ability of researchers and clinicians to draw conclusions about their health risks based on current standards. It is reasonable to suspect that current obesity measures similarly overlook key differences between the population with SCI and normative samples for which they were developed.
In addition, when validating measures of obesity in SCI we must use caution; clinically significant indicator levels of CVD risk factors are based on general population data. Other health risk measures developed in nondisabled samples may be inaccurate when applied to samples with SCI due to physiological changes. For instance, following SCI, high-density lipoprotein cholesterol levels are markedly depressed, and the risk of coronary heart disease is proportionately higher compared to the general population.43 Other CVD risk factors, such as metabolic abnormalities and elevated high-sensitivity C-reactive protein levels, typically differ between the population with SCI and the general population. As a result, the Framingham risk scoring system may underestimate CVD risk in people with SCI.43 Similarly, sympathetic nervous system dysfunction after SCI results in orthostatic hypotension (that is, low blood pressure due to pooling) and autonomic dysreflexia (that is, intermittent spikes in blood pressure). Both conditions present challenges in assessing chronic hypotension or hypertension in people with SCI using standard blood pressure cutoff points.17
Obesity measurements and classifications found in this review are used in diverse ways in research methodology. Some studies use obesity status as part of eligibility criteria for participation, which can be a major threat to external validity of results. Conclusions drawn from studies using an invalid measure of obesity are potentially making nonvalid generalizations based on their study population. In addition, screening for obesity using a nonvalid cutoff or measurement technique may exclude individuals who could benefit from interventions and hinder our understanding of health risks associated with obesity following SCI. The majority of studies use nonvalidated obesity classifications as their outcome of interest or dependent variable, independent variable or covariate. This can have an impact on results driving future research and clinical practice. For example, Liang et al.44 used WC cutoff to assess risk factors for metabolic syndrome in men with SCI compared to men without disabilities. Different WC cutoffs may be needed for populations with SCI; therefore, comparisons are inappropriate, and conclusion that men with SCI do not have increased risk of metabolic syndrome is suspected. Validation studies are primarily testing the legitimacy of obesity classification measures used in nondisabled populations and general clinical practice on samples with SCI. Validation studies that classify individuals in obesity categories are not as great of a concern because they are aiming to address a gap in the literature. More validation of obesity measures in individuals with SCI is needed to provide clinical standards for use in studies of rehabilitation outcomes, quality of life and validity of research methodology and findings.
Limitations in this review include search of only one database, PubMed, but initial investigation of other databases showed similar results. This review focuses on SCI, identifying the barriers and necessary modifications to obesity determination, so that the results may not be applicable to other populations with disabilities.
The findings of this narrative review provide guidance for the field. While it is encouraging to see the issue of obesity in individuals with SCI gain more and more attention in the literature, this topic will advance considerably more quickly with higher quality studies and greater potential for the application of advanced research methodologies, if rehabilitation researchers can reach consensus on the most appropriate technique and cutoff scores for defining obesity in this population.
Obesity cutoffs based on sex, age and race exist but not based on disabling condition, specifically for people with SCI. People with SCI may require even more specific guidelines based on the level of injury (paraplegia vs tetraplegia), or functional status such as ASIA scores. Research is needed that compares methods for classifying obesity with indicators of mortality and morbidity as is done in research on the general population. The same rigorous and stringent methodologies are needed for populations with SCI and other disabilities that are used in global and national population health research. In addition, studies should continue comparing currently validated classifications for general population with potentially valid SCI-specific classifications to further our understanding of how different classifications impact conclusions and recommendations in research and clinical settings. Longitudinal and cross-sectional research in this area will have the potential to improve the health and well-being of individuals living with SCI.
Dulloo AG, Jacquet J, Solinas G, Montani JP, Schutz Y . Body composition phenotypes in pathways to obesity and the metabolic syndrome. Int J Obes (Lond) 2010; 34(Suppl 2): S4–S17.
Hu F . Obesity Epidemiology. Oxford University Press: England. 2008.
Shah B, Sucher K, Hollenbeck CB . Comparison of ideal body weight equations and published height-weight tables with body mass index tables for healthy adults in the United States. Nutr Clin Pract 2006; 21: 312–319.
Hamwi G . Changing dietary concept. Diabetes Mellitus: Diagnosis and Treatment, American Diabetes Association, New York, 1964; 1.
Keys A, Fidanza F, Karvonen MJ, Kimura N, Taylor HL . Indices of relative weight and obesity. J Chronic Dis 1972; 25: 329–343.
World Health Organization. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser 2000; 894: i–xii, 1–253.
Flegal KM, Carroll MD, Ogden CL, Johnson CL . Prevalence and trends in obesity among US adults, 1999–2000. JAMA 2002; 288: 1723–1727.
Barba C, Cavalli-Sforza T, Cutter J, Darnton-Hill I . Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004; 363: 157.
Klein S, Allison DB, Heymsfield SB, Kelley DE, Leibel RL, Nonas C et al. Waist circumference and cardiometabolic risk: a consensus statement from shaping America's health: association for weight management and obesity prevention; NAASO, the Obesity Society; the American Society for Nutrition; and the American Diabetes Association. Obesity 2007; 15: 1061–1067.
Han T, Van Leer E, Seidell J, Lean M . Waist circumference action levels in the identification of cardiovascular risk factors: prevalence study in a random sample. BMJ 1995; 311: 1401–1405.
National Institutes of Health, Bioelectrical Impedance Analysis in Body Composition Measurement Technology Assessment Conference, 1994 Am J Clin Nutr. 1996; 64(3 Suppl): 524S–532S.
Peppler WW, Mazess RB . Total body bone mineral and lean body mass by dual-photon absorptiometry. I. Theory and measurement procedure. Calcif Tissue Int 1981; 33: 353–359.
Tokunaga K, Matsuzawa Y, Ishikawa K, Tarui S . A novel technique for the determination of body fat by computed tomography. Int J Obes 1983; 7: 437–445.
Examination Committee of Criteria for 'Obesity Disease' in Japan. New criteria for 'obesity disease' in Japan. Circ J 2002; 66: 987–992.
Spungen AM, Adkins RH, Stewart CA, Wang J, Pierson RN Jr, Waters RL et al. Factors influencing body composition in persons with spinal cord injury: a cross-sectional study. J Appl Physiol 2003; 95: 2398–2407.
Eriks-Hoogland I, Hilfiker R, Baumberger M, Balk S, Stucki G, Perret C . Clinical assessment of obesity in persons with spinal cord injury: validity of waist circumference, body mass index, and anthropometric index. J Spinal Cord Med 2011; 34: 416–422.
Myers J, Lee M, Kiratli J . Cardiovascular disease in spinal cord injury: an overview of prevalence, risk, evaluation, and management. Am J Phys Med Rehabil 2007; 86: 142–152.
Ravensbergen HR, Lear SA, Claydon VE . Waist circumference is the best index for obesity-related cardiovascular disease risk in individuals with spinal cord injury. J Neurotrauma 2014; 31: 292–300.
Buchholz AC, Bugaresti JM . A review of body mass index and waist circumference as markers of obesity and coronary heart disease risk in persons with chronic spinal cord injury. Spinal Cord 2005; 43: 513–518.
Flank P, Wahman K, Levi R, Fahlstrom M . Prevalence of risk factors for cardiovascular disease stratified by body mass index categories in patients with wheelchair-dependent paraplegia after spinal cord injury. J Rehabil Med 2012; 44: 440–443.
Froehlich-Grobe K, Nary DE, Van Sciver A, Lee J, Little TD . Measuring height without a stadiometer: empirical investigation of four height estimates among wheelchair users. Am J Phys Med Rehabil 2011; 90: 658–666.
Buchholz AC, McGillivray CF, Pencharz PB . Physical activity levels are low in free-living adults with chronic paraplegia. Obes Res 2003; 11: 563–570.
Sabour H, Javidan AN, Vafa MR, Shidfar F, Nazari M, Saberi H et al. Obesity predictors in people with chronic spinal cord injury: an analysis by injury related variables. J Res Med Sci 2011; 16: 335–339.
Chen Y, Cao Y, Allen V, Richards JS . Weight matters: physical and psychosocial well being of persons with spinal cord injury in relation to body mass index. Arch Phys Med Rehabil 2011; 92: 391–398.
World Health OrganizationPhysical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. World Health Organization: Geneva. 1995.
Inayama T, Higuchi Y, Tsunoda N, Uchiyama H, Sakuma H . Associations between abdominal visceral fat and surrogate measures of obesity in Japanese men with spinal cord injury. Spinal Cord 2014; 52: 836–841.
Rajan S, McNeely MJ, Hammond M, Goldstein B, Weaver F . Association between obesity and diabetes mellitus in veterans with spinal cord injuries and disorders. Am J Phys Med Rehabil 2010; 89: 353–361.
Troiano RP, Frongillo EA Jr, Sobal J, Levitsky DA . The relationship between body weight and mortality: a quantitative analysis of combined information from existing studies. Int J Obes Relat Metab Disord 1996; 20: 63–75.
Ayas NT, Epstein LJ, Lieberman SL, Tun CG, Larkin EK, Brown R et al. Predictors of loud snoring in persons with spinal cord injury. J Spinal Cord Med 2001; 24: 30–34.
Laughton GE, Buchholz AC, Martin Ginis KA, Goy RE . Lowering body mass index cutoffs better identifies obese persons with spinal cord injury. Spinal Cord 2009; 47: 757–762.
Maruyama Y, Mizuguchi M, Yaginuma T, Kusaka M, Yoshida H, Yokoyama K et al. Serum leptin, abdominal obesity and the metabolic syndrome in individuals with chronic spinal cord injury. Spinal Cord 2008; 46: 494–499.
Han T, Seidell J, Currall J, Morrison C, Deurenberg P, Lean M . The influences of height and age on waist circumference as an index of adiposity in adults. Int J Obes 1997; 21: 83–90.
Lee BY, Agarwal N, Corcoran L, Thoden WR, Del Guercio LR . Assessment of nutritional and metabolic status of paraplegics. J Rehabil Res Dev 1985; 22: 11–17.
Jones LM, Legge M, Goulding A . Factor analysis of the metabolic syndrome in spinal cord-injured men. Metabolism 2004; 53: 1372–1377.
Buchholz AC, McGillivray CF, Pencharz PB . The use of bioelectric impedance analysis to measure fluid compartments in subjects with chronic paraplegia. Arch Phys Med Rehabil 2003; 84: 854–861.
Gorgey A, Gater D . Prevalence of obesity after spinal cord injury. Top Spinal Cord Injury Rehabil 2007; 12: 1–7.
Finch H, Arumugam V . Assessing the accuracy and reliability of direct height measurement for use in adult neurological patients with contractures: a comparison with height from ulna length. J Hum Nutr Diet 2014; 27: 48–56.
Haapala H, Peterson MD, Daunter A, Hurvitz EA . Agreement between actual height and estimated height using segmental limb lengths for individuals with cerebral palsy. Am J Phys Med Rehabil 2015; 94: 539–546.
de Groot S, Post MW, Hoekstra T, Valent LJ, Faber WX, van der Woude LH . Trajectories in the course of body mass index after spinal cord injury. Arch Phys Med Rehabil 2014; 95: 1083–1092.
Snitker S . Use of body fatness cutoff points. Mayo Clin Proc 2010; 85: 1057–1058.
Gallagher D, Heymsfield SB, Heo M, Jebb SA, Murgatroyd PR, Sakamoto Y . Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr 2000; 72: 694–701.
Edwards LA, Bugaresti JM, Buchholz AC . Visceral adipose tissue and the ratio of visceral to subcutaneous adipose tissue are greater in adults with than in those without spinal cord injury, despite matching waist circumferences. Am J Clin Nutr 2008; 87: 600–607.
Groah SL, Nash MS, Ward EA, Libin A, Mendez AJ, Burns P et al. Cardiometabolic risk in community-dwelling persons with chronic spinal cord injury. J Cardiopulm Rehabil Prev 2011; 31: 73–80.
Liang H, Chen D, Wang Y, Rimmer JH, Braunschweig CL . Different risk factor patterns for metabolic syndrome in men with spinal cord injury compared with able-bodied men despite similar prevalence rates. Arch Phys Med Rehabil 2007; 88: 1198–1204.
This study was funded by a field-initiated development grant from the National Institute on Disability, Independent Living and Rehabilitation Research in the Administration for Community Living, US Department of Health and Human Services (#90IF0036).
The authors declare no conflict of interest.
Rights and permissions
About this article
Cite this article
Silveira, S., Ledoux, T., Robinson-Whelen, S. et al. Methods for classifying obesity in spinal cord injury: a review. Spinal Cord 55, 812–817 (2017). https://doi.org/10.1038/sc.2017.79
This article is cited by
Level of injury is an independent determining factor of gut dysbiosis in people with chronic spinal cord injury: A cross-sectional study
Spinal Cord (2022)
Accuracy of bioelectrical impedance analysis and skinfold thickness in the assessment of body composition in people with chronic spinal cord injury
Spinal Cord (2022)
An observational study on body mass index during rehabilitation and follow-up in people with spinal cord injury in Denmark
Spinal Cord (2022)
Correlations between percent body fat measured by dual-energy X-ray absorptiometry and anthropometric measurements in Thai persons with chronic traumatic spinal cord injury
Spinal Cord (2022)
Prevalence of an insufficient vitamin D status at the onset of a spinal cord injury – a cross-sectional study
Spinal Cord (2022)