Abstract
Background: The identification of surrogate measures of central obesity is of clinical importance, and the waist-to-height ratio (WtHR) has recently attracted great interest as an alternative method. Objective: For this reason, we aimed to establish specific WtHR cut-off points for adiposity (i.e., central obesity) in four different ethnicity groups across both sexes based on data from the National Health and Nutrition Examination Survey (NHANES) population. Methods: Of the total 23,037 participants who completed four cycles of the survey between the years 2011 and 2018, anthropometric measures (i.e., body weight, waist circumference, and height) and dual X-ray absorptiometry-derived visceral adipose tissue (DXA-derived VAT) results were available for 3566 individuals who were assessed in this cross-sectional study. Participants with an overweight status defined according to the World Health Organization (WHO) body mass index (BMI) cut-off points (25–29.9 kg/m2) were included. The sample was then categorized by adiposity according to the DXA-derived VAT tertiles (highest), and based on the receiver operating characteristic (ROC) curve analysis, the best sensitivity and specificity were attained for predicting central obesity using the WtHR. Results: The following WtHR cut-offs were identified as having the best discriminating ability for central obesity: 0.57 for White males and 0.58 for White females; 0.55 for Black males and 0.57 for Black females; 0.56 for Asian males and 0.59 for Asian females; and 0.57 for Hispanic males and 0.59 for Hispanic females. Conclusions: These new WtHR cut-off points should be utilized in adults with overweight to screen for central adiposity based on their sex and ethnicity, and obesity guidelines therefore need to be revised accordingly.
Keywords: BMI, body composition, obesity, body fat, visceral adipose tissue
1. Introduction
Obesity is a growing health problem, and its prevalence is continually increasing worldwide [1]. It is associated with several medical [2] and psychosocial diseases [3,4] that increase the risks of morbidity and mortality [5]. Hence, the early identification and treatment of obesity is vital and has clinical importance [6]. In fact, the international guidelines on the topic recommend a wide range of weight loss interventions such as lifestyle modification programs (i.e., behavioral and cognitive behavioral ones) [7], as well as several anti-obesity drugs (i.e., glucagon-like peptide-1 [GLP-1] and gastric inhibitory polypeptide agonists) [8] and bariatric surgery (sleeve gastrectomy or mini gastric bypass) [9].
Although obesity is best defined as an excessive and abnormal fat deposition in the adipose tissue [10,11], the World Health Organization (WHO) still relies on body mass index (BMI) to classify individuals’ adiposity based on universal cut-off points for all adults, regardless of their ethnicity, age, or gender [12]. Specifically, the WHO BMI classification system considers a unique cut-off point of 30 kg/m2 to be indicative of obesity in White, Hispanic, and Black populations across all age groups (i.e., young, middle, and older adults) and both genders (i.e., males and females) [12]. However, this traditional classification system has always been subject to criticism [13] due to several limitations [14,15], such as that it is not suitable for all ethnicities (e.g., Asians) [16] and its inability to discriminate between the body compartments (e.g., bone, fat, and muscle) [17]. The identification of obesity based on body fat (BF) quantity and distribution therefore remains the most accurate method [18]. For instance, the visceral adipose tissue (VAT) is a component of total body fat, with high secreting activity by a large spectrum of adipokines related to inflammatory processes [19,20]. A significant deposition of VAT is known as visceral obesity [21] and is strongly associated with several obesity-related complications [22,23,24]. For this reason, VAT has attracted clinical and research interest and is considered a direct expression of central obesity, for which some papers have tried to suggest relative cut-off points regardless of body mass index (BMI) levels and total BF [25,26]. However, an accurate measurement of VAT requires sophisticated techniques such as a computed tomography (CT) scan or magnetic resonance imaging (MRI), which are not always available in all clinical settings, especially those related to nutrition [27].
As a result, some surrogate indices based on anthropometric measurements have been proposed, such as the waist circumference (WC) [28], waist-to-height ratio (WtHR) [29], and abdominal bioimpedance [27], as well as equations for VAT estimation [30]. For instance, the European Association for the Study of Obesity (EASO) has recently recommended a new framework for the diagnosis, staging, and management of obesity in adults, suggesting a WtHR ≥ 0.5 in people with a BMI between 25 kg/m2 and 29.9 kg/m2 as an expression of high adiposity for use in all ethnic groups across both sexes [31]. However, this cut-off point (i.e., WtHR ≥ 0.5) is not widely accepted, as even if it may be suitable for certain populations, its validity is not certain in others, and in some it may be inaccurate and generic [32]. Testing its accuracy, especially across different ethnic groups, is therefore strongly demanded.
Based on these considerations, the current study therefore aims to establish suitable WtHR cut-off points across different ethnic groups using data derived from the National Health and Nutrition Examination Survey (NHANES) of the non-institutionalized US population. We hypothesize that the WtHR cut-off point ≥ 0.5 is probably low for people with overweight (BMI ≥ 25 but <30 kg/m2), and higher cut-offs should be recommended.
2. Materials and Methods
2.1. Participants and the Design of the Study
The National Health and Nutrition Examination Survey (NHANES) is a large ongoing dietary survey of a nationally representative sample of the non-institutionalized US population [33]. It is conducted by the Centers for Disease Control and Prevention (CDC) and National Center for Health Statistics (NCHS) and has been monitoring the nation’s nutrition and health for more than five decades by collecting data through interviews, standard exams, and biospecimen collection [33]. Detailed descriptions of the survey design and the data collection procedures have been reported extensively elsewhere [33]. The variables of interest in our study are reported below.
This was a cross-sectional study. Our final sample included 3566 participants from the National Health and Nutrition Examination Survey (NHANES, n = 23,037) who were non-institutionalized civilian adults below the age of 60 years residing in the United States across four different survey cycles between the years 2011 and 2018. The inclusion criteria were the following: individuals (i) with an age ≥ 20 years, (ii) who are within the WHO BMI classification for overweight (BMI ≥ 25 kg/m2), (iii) and have completed anthropometric measurements (i.e., body weight, waist circumference, and height) and (iv) body composition assessment by means of a dual X-ray absorptiometry (DXA) scan (i.e., DXA-derived VAT) (Figure 1). The exclusion criteria included having (i) an age < 20 years, (ii) an underweight or normal weight (<25 kg/m2) or obesity status (BMI ≥ 30 kg/m2) according to the WHO, and (iii) missing and unavailable WC, DXA-derived VAT, or data (Figure 1).
Figure 1.
Flowchart and shift work analysis. Abbreviations: NHANES, National Health and Nutrition Examination Survey; BMI = body mass index; VAT = visceral adipose tissue; WC = waist circumference.
2.2. Data Collection
2.2.1. Anthropometric and Demographic Data and Body Composition Assessment
Trained health technicians measured the participants’ waist circumference (WC), weight, and height. The WtHR was calculated as the WC in cm to height in cm and BMI as weight in kg to the square of height in m. The DXA-derived VAT area was expressed as cm2 between the fourth and fifth lumbar vertebrae, as defined by the Hologic APEX 4.0 software used in DXA scan analysis. Age was reported in years, sex as male or female, and ethnicity characteristics as White, Black, Hispanic, or Asian.
2.2.2. Statistical Analysis
The statistical analysis was performed based on the analytic guidelines of the Centers for Disease Control [34]. Descriptive statistics are presented as means and standard deviations for continuous variables and frequencies and proportions for categorical ones. The association between the WtHR and DXA-derived VAT area (cm2) was tested with Pearson’s correlation coefficient. A positive linearity association between the WtHR and DXA-derived VAT area was confirmed by the cumulative sum (CUSUM) linearity test with a p-value > 0.05 [35]. A classification analysis, using the receiver operating characteristic (ROC) curve, was performed to evaluate the diagnostic performance of the WtHR in detecting central obesity. The sensitivity, specificity, and area under the curve (AUC) were calculated for the ROC curve. Central obesity was defined as the DXA-derived VAT area falling within the age-, sex-, and ethnicity-specific third tertile of the DXA-derived VAT area utilized as a gold standard [29]. The criterion values of the WtHR with maximum sensitivity and specificity as well as the shortest distance from the corner of the ROC curve were selected for the sex- and ethnicity-specific WtHR cut-off points. An AUC > 0.8 indicated that the criterion value has an excellent discriminating ability, and an AUC between 0.7 and 0.8 denoted an acceptable one [36]. All the tests were considered significantly different at p < 0.05.
To compare the performance of the new defined cut-off points and the arbitrarily proposed cut-off in the literature (0.5), the difference between the true positive rates was determined between the two cut-off points. The Number Cruncher Statistical System (NCSS) 12.02 (NCSS, Kaysville, UT, USA) package was employed for the statistical analysis [37]. A post hoc power analysis for the sample size was conducted with PASS 11 software [38]. For each of the subsamples from different ethnicities and sexes, given the number falling within the highest DXA-derived VAT tertile (TP) versus those in lower ones (TN) with an alpha of 0.05 and the observed AUC, the power was 1.000.
3. Results
The study sample included 2077 male (58.2%) and 1489 female (41.8%) individuals with a mean age of 40.6 ± 11.1 years and 40.7 ± 11.2 years, respectively. The mean BMI was 27.4 ± 1.4 kg/m2 in males and females and did not vary across ethnicities (Table 1, Table 2 and Table 3).
Table 1.
Anthropometric characteristics and body composition of the total study sample by ethnicity (n = 3566).
| Ethnicity | |||||
|---|---|---|---|---|---|
| Total (n = 3566) |
White (n = 1285) |
Black (n = 722) |
Asian (n = 536) |
Hispanic (n = 1023) |
|
| Age | 40.6 (11.1) | 40.1 (11.2) | 40.5 (11.5) | 42.0 (10.6) | 40.1 (11.1) |
| Sex | |||||
| Males | 2077 (58.2) | 760 (59.1) | 410 (56.8) | 342 (63.8) | 565 (55.2) |
| Females | 1489 (41.8) | 525 (40.9) | 312 (43.2) | 194 (36.2) | 458 (44.8) |
| BMI (kg/m2) | 27.4 (1.4) | 27.4 (1.4) | 27.4 (1.4) | 27.2 (1.4) | 27.6 (1.4) |
| Weight (kg) | 78.2 (10.1) | 81.3 (9.8) | 80.2 (9.3) | 74.6 (9.3) | 74.9 (9.6) |
| Height (cm) | 166.7 (10.1 | 168.9 (9.9) | 168.9 (9.6) | 163.2 (9.4) | 163.1 (9.5) |
| WC (cm) | 94.9 (6.8) | 96.7 (6.9) | 93.3 (7.0) | 93.7 (6.2) | 94.4 (6.4) |
| WtHR | 0.56 (0.04) | 0.56 (0.04) | 0.55 (0.04) | 0.57 (0.04) | 0.58 (0.04) |
| <0.5 | 200 (5.6) | 61 (4.7) | 102 (14.1) | 14 (2.6) | 23 (2.2) |
| ≥0.5 | 3366 (94.4) | 1224 (95.3) | 620 (85.9) | 522 (97.4) | 1000 (97.8) |
| DXA-derived VAT area (cm2) | 100.5 (38.8) | 104.6 (41.9) | 79.4 (32.4) | 108.0 (34.9) | 106.2 (35.7) |
BMI = body mass index; WC = waist circumference; WtHR = waist-to-height ratio.
Table 2.
Anthropometric characteristics and body composition among males by ethnicity (n = 2077).
| Ethnicity | |||||
|---|---|---|---|---|---|
| Total (n = 2077) |
White (n = 760) |
Black (n = 410) |
Asian (n = 342) |
Hispanic (n = 565) |
|
| Age | 40.6 (11.1) | 40.8 (11.1) | 40.2 (11.5) | 41.5 (10.6) | 40.2 (11.1) |
| BMI (kg/m2) | 27.4 (1.4) | 27.4 (1.4) | 27.3 (1.4) | 27.1 (1.3) | 27.7 (1.4) |
| Weight (kg) | 83.4 (8.4) | 86.5 (8.0) | 85.3 (7.7) | 79.2 (7.5) | 80.5 (7.9) |
| Height (cm) | 174.2 (7.7) | 177.4 (6.8) | 176.6 (6.8) | 170.7 (7.1) | 170.4 (7.3) |
| WC (cm) | 96.6 (6.6) | 98.7 (6.4) | 94.4 (7.5) | 95.2 (5.8) | 96.5 (6.0) |
| WtHR | 0.56 (0.04) | 0.56 (0.04) | 0.53 (0.04) | 0.56 (0.03) | 0.57 (0.03) |
| <0.5 | 165 (7.9) | 47 (6.2) | 88 (21.5) | 12 (3.5) | 18 (3.2) |
| ≥0.5 | 1912 (92.1) | 713 (93.8) | 322 (78.5) | 330 (96.5) | 547 (96.8) |
| DXA-derived VAT area (cm2) | 105.4 (39.3) | 111.2 (41.8) | 82.4 (33.2) | 111.3 (35.6) | 110.7 (36.1) |
BMI = body mass index; WC = waist circumference; WtHR = waist-to-height ratio.
Table 3.
Anthropometric characteristics and body composition among females by ethnicity (n = 1489).
| Females | Ethnicity | ||||
|---|---|---|---|---|---|
| Total (n = 1489) |
White (n = 525) |
Black (n = 312) |
Asian (n = 194) |
Hispanic (n = 458) |
|
| Age | 40.7 (11.2) | 40.4 (11.3) | 40.9 (11.6) | 42.9 (10.6) | 40.0 (11.0) |
| BMI (kg/m2) | 27.4 (1.4) | 27.3 (1.4) | 27.6 (1.4) | 27.2 (1.4) | 27.6 (1.4) |
| Weight (kg) | 71.0 (7.3) | 73.7 (6.9) | 73.6 (6.6) | 66.6 (6.1) | 68.0 (6.5) |
| Height (cm) | 160.7 (7.3) | 164.1 (6.4) | 163.3 (6.5) | 156.4 (5.9) | 157.0 (6.5) |
| WC (cm) | 92.5 (6.4) | 93.8 (6.7) | 91.9 (6.2) | 91.1 (6.1) | 91.9 (6.1) |
| WtHR | 0.58 (0.04) | 0.57 (0.04) | 0.56 (0.04) | 0.58 (0.04) | 0.59 (0.04) |
| <0.5 | 35 (2.4) | 14 (2.7) | 14 (4.5) | 2 (1.0) | 5 (1.1) |
| ≥0.5 | 1454 (97.6) | 511 (97.3) | 298 (95.5) | 192 (99.0) | 453 (98.9) |
| DXA-derived VAT area (cm2) | 93.6 (37.0) | 95.0 (40.1) | 75.5 (31.1) | 102.3 (32.8) | 100.8 (34.5) |
BMI = body mass index; WC = waist circumference; WtHR = waist-to-height ratio.
The mean DXA-derived VAT area was 105.4 ± 39.3 cm2 among males and 93.6 ± 37.0 cm2 among females. The WtHR was lower among males (0.56 ± 0.04) compared to females (0.58 ± 0.04). Over 90% of both males and females across almost all ethnicities, as well as over 75% of Black males, were classified as above the arbitrary WtHR (0.5) cut-off point proposed by the literature (Table 2 and Table 3).
The correlation analysis revealed a significant positive correlation between the WtHR and the DXA-derived VAT area among males and females from different ethnicities (Figure 2a–g). Furthermore, a significant positive linear association (Figure 2a–g) between the DXA-derived VAT area and WtHR in different ethnicities was confirmed by the CUSUM linearity test (p > 0.05).
Figure 2.
Association between DXA-derived VAT area (cm2) and WtHR in (a) White males; (b) White females; (c) Black males; (d) Black females; (e) Asian males; (f) Asian females; (g) Hispanic males; and (h) Hispanic females. WtHR = waist-to-height ratio; DXA-derived VAT area (cm2). ** p-value > 0.01.
The results of the ROC analysis for the diagnostic performance of the WtHR by sex and ethnicity are presented in Table 4 and Figure 3a–d. Among White males, the mean age was 40.8 ± 11.1 years, the mean DXA-derived VAT area was 111.2 ± 41.8 cm2, and the mean WtHR was 0.56 ± 0.04. The majority (93.8%) were classified as having central obesity based on the arbitrary cut-off point (≥0.5) (Table 2). The ROC analysis for White males showed that the most appropriate cut-off for identifying central obesity based on the DXA-derived VAT area tertiles was 0.57 (Table 4). This cut-off point achieved good sensitivity (71.8%) and specificity (74.6%). Comparatively, the proportion correctly diagnosed by the new cut-off point was improved by 24.4%, reaching almost 60% (59.7%) (Table 5 and Figure 4). The AUC (0.792) confirmed that the WtHR has an acceptable discriminating ability, at almost 80% (79.2%), for detecting central obesity (Table 4 and Figure 3a).
Table 4.
Diagnostic performance of the new WtHR cut-off points for central obesity across sex and ethnicity groups (n = 3566).
| n | AUC | 95%CI | p Value | Cut-Off | Sensitivity | Specificity | |
|---|---|---|---|---|---|---|---|
| Males | |||||||
| White | 760 | 0.7920 | 0.7563–0.8230 | <0.0001 | 0.57 | 0.7183 | 0.7461 |
| Black | 410 | 0.8550 | 0.8145–0.8872 | <0.0001 | 0.55 | 0.7353 | 0.7774 |
| Asian | 342 | 0.7695 | 0.7125–0.8165 | <0.0001 | 0.56 | 0.7257 | 0.6550 |
| Hispanic | 565 | 0.7785 | 0.7356–0.8151 | <0.0001 | 0.57 | 0.7302 | 0.7048 |
| Females | |||||||
| White | 525 | 0.8241 | 0.7850–0.8566 | <0.0001 | 0.58 | 0.7257 | 0.7514 |
| Black | 312 | 0.7632 | 0.7046–0.8114 | <0.0001 | 0.57 | 0.7184 | 0.6890 |
| Asian | 194 | 0.8342 | 0.7660–0.8839 | <0.0001 | 0.59 | 0.7385 | 0.7364 |
| Hispanic | 458 | 0.7710 | 0.7228–0.8118 | <0.0001 | 0.59 | 0.7434 | 0.702 |
Figure 3.
Receiver operator characteristics curve by sex and ethnicity for WtHR to detect central obesity (DXA-derived VAT area): (a) White; (b) Black; (c) Asian; and (d) Hispanic.
Table 5.
Proportion correctly diagnosed by ≥0.5 cut-off point and new cut-off points (n = 3566).
| Total Sample | WtHR ≥ 0.5 | New Cut-Off Point ≥ C§ | Δ % Detected | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| DXA-Derived VAT (cm2) | Total Classified ≥ 0.5 | Proportion Correctly Diagnosed (TP) | Proportion Incorrectly Diagnosed (FP) | Total Classified ≥ C§ | Proportion Correctly Diagnosed (TP) | Proportion Incorrectly Diagnosed (FP) | ||||
| 3rd Tertile | 1st and 2nd Tertiles | |||||||||
| n (%) | n (%) | n (%) | ||||||||
| Males | ||||||||||
| White | 760 (100) | 252 (33.2) | 508 (66.8) | 713 (100) | 252 (35.3) | 461 (64.7) | 273 (100) | 163 (59.7) | 110 (40.3) | +24.4 |
| Black | 410 (100) | 136 (33.2) | 274 (66.8) | 322 (100) | 186 (57.8) | 136 (42.2) | 183 (100) | 109 (59.6) | 74 (27.0) | +17.4 |
| Asian | 342 (100) | 113 (33.0) | 229 (66.9) | 330 (100) | 113 (34.2) | 217 (65.8) | 155 (100) | 79 (51.0) | 76 (49.0) | +16.8 |
| Hispanic | 565 (100) | 189 (33.5) | 376 (66.5) | 547 (100) | 189 (34.6) | 358 (65.4) | 254 (100) | 138 (54.3) | 116 (45.7) | +19.7 |
| Females | ||||||||||
| White | 525 (100) | 175 (33.3) | 350 (66.7) | 511 (100) | 175 (34.2) | 336 (65.8) | 215 (100) | 127 (59.1) | 88 (40.9) | +18.2 |
| Black | 312 (100) | 103 (33.0) | 195 (62.5) | 298 (100) | 103 (34.6) | 195 (65.5) | 132 (100) | 70 (53.0) | 62 (47.0) | +18.4 |
| Asian | 194 (100) | 65 (33.5) | 129 (66.5) | 192 (100) | 65 (33.9) | 127 (66.1) | 79 (100) | 48 (60.8) | 31 (39.2) | +26.9 |
| Hispanic | 458 (100) | 152 (33.2) | 306 (66.8) | 453 (100) | 152 (33.6) | 301 (98.4) | 212 (100) | 115 (54.2) | 97 (45.8) | +20.6 |
C§ New sex and ethnicity specific WtHR cut-off point; TP = true positive; FP = false positive.
Figure 4.
Distribution of correctly diagnosed (true positive) cases by the new ethnicity and sex specific cut-off point by (a) males and (b) females. WtHR = waist-to-height ratio; C§ = new sex- and ethnicity-specific WtHR cut-off point.
Among White females, the mean age was 40.4 ± 11.3 years, the mean DXA-derived VAT area was 95.0 ± 40.1 cm2, and the mean WtHR was 0.57 ± 0.04. The majority (97.3%) were classified as having central obesity based on the arbitrary cut-off point (≥0.5) (Table 3). The ROC analysis for White females revealed that the most appropriate cut-off for detecting central obesity based on the DXA-derived VAT area tertiles was 0.58 (Table 4). This cut-off point achieved good sensitivity (72.6%) and specificity (75.1%). In relative terms, the proportion correctly diagnosed by the new cut-off point increased by 18.2%, attaining almost 60% (59.1%) (Table 5 and Figure 4). The AUC (0.824) showed that the WtHR has an excellent discriminating ability, at over 80% (82.4%), for identifying central obesity (Table 4 and Figure 3a).
For Black males, the mean age was 40.2 ± 11.5 years, the mean DXA-derived VAT area was 82.4 ± 33.2 cm2, and the mean WtHR was 0.53 ± 0.04. More than three quarters (78.5%) were categorized as having central obesity based on the arbitrary cut-off point (≥0.5) (Table 2). The ROC analysis for Black males showed that the most appropriate cut-off for identifying central obesity based on the DXA-derived VAT area tertiles was 0.55 (Table 4). This cut-off point achieved good sensitivity (73.5%) and specificity (77.7%). In comparative terms, the proportion correctly diagnosed by the new cut-off point improved by 17.4%, reaching almost 60% (59.6%) (Table 5 and Figure 4). The AUC (0.855) demonstrated that the WtHR has an excellent discriminating ability, at almost 86% (85.5%), for detecting central obesity (Table 4 and Figure 3b).
Among Black females, the mean age was 40.9 ± 11.6 years, the mean DXA-derived VAT area was 75.5 ± 31.1 cm2, and the mean WtHR was 0.56 ± 0.04. The majority (95.5%) were classified as having central obesity based on the arbitrary cut-off point (≥0.5) (Table 3). The ROC analysis for Black females confirmed that the most appropriate cut-off for detecting central obesity based on the DXA-derived VAT area tertiles was 0.57 (Table 4). This cut-off point achieved good sensitivity (71.8%) and specificity (68.9%). The proportion correctly diagnosed by the new cut-off point rose by 18.4%, attaining 53% (Table 5 and Figure 4). The AUC (0.763) confirmed that the WtHR has an acceptable discriminating ability, at almost 80% (76.3%), for identifying central obesity (Table 4 and Figure 3b).
For Asian males, the mean age was 41.5 ± 10.6 years, the mean DXA-derived VAT area was 111.3 ± 35.6 cm2, and the mean WtHR was 0.56 ± 0.03. The majority (96.5%) were categorized as having central obesity based on the arbitrary cut-off point (≥0.5) (Table 2). The ROC analysis for Asian males showed that the most appropriate cut-off for determining central obesity based on the DXA-derived VAT area tertiles was 0.56 (Table 4). This cut-off point achieved good sensitivity (72.6%) and specificity (65.5%). In comparative terms, the proportion correctly diagnosed by the new cut-off point increased by 16.8%, reaching 50% (51.0%) (Table 5 and Figure 4). The AUC (0.769) indicated that the WtHR has an acceptable discriminating ability, at over 75% (76.9%), for detecting central obesity (Table 4 and Figure 3c).
Among Asian females, the mean age was 42.9 ± 10.6 years, the mean DXA-derived VAT was 102.3 ± 32.8 cm2, and the mean WtHR was 0.58 ± 0.04. The majority (99.0%) were classified as having central obesity based on the arbitrary cut-off point (≥0.5) (Table 3). The ROC analysis for Asian females confirmed that the most appropriate cut-off for identifying central obesity based on DXA-derived VAT area tertiles was 0.59 (Table 4). This cut-off point achieved good sensitivity (73.9%) and specificity (73.6%). Comparatively, the proportion correctly diagnosed by the new cut-off point improved by 26.9%, attaining 61% (60.8%) (Table 5 and Figure 4). The AUC (0.834) demonstrated that the WtHR has an excellent discriminating ability, at over 80% (83.4%), for determining central obesity (Table 4 and Figure 3c).
For Hispanic males, the mean age was 40.2 ± 11.1 years, the mean DXA-derived VAT area was 110.7 ± 36.1 cm2, and the mean WtHR was 0.57 ± 0.03. The majority (96.8%) were categorized as having central obesity based on the arbitrary cut-off point (≥0.5) (Table 2). The ROC analysis for Hispanic males showed that the most appropriate cut-off for identifying central obesity based on the DXA-derived VAT area tertiles was 0.57 (Table 4). This cut-off point achieved good sensitivity (73.0%) and specificity (70.5%). In comparative terms, the proportion correctly diagnosed by the new cut-off point was improved by 19.7%, reaching almost 55% (54.3%) (Table 5 and Figure 4). The AUC (0.779) confirmed that the WtHR has an acceptable discriminating ability, at almost 80% (77.9%), for detecting central obesity (Table 5 and Figure 3d).
Among Hispanic females, the mean age was 40.0 ± 11.0 years, the mean DXA-derived VAT area was 100.8± 34.5 cm2, and the mean WtHR was 0.59 ± 0.04. The majority (98.9%) were classified as having central obesity based on the arbitrary cut-off point (≥0.5) (Table 3). The ROC analysis for Hispanic females showed that the most appropriate cut-off for determining central obesity based on the DXA-derived VAT area tertiles was 0.59 (Table 4). This cut-off point achieved good sensitivity (74.3%) and specificity (70.3%). Comparatively, the proportion correctly diagnosed by the new cut-off point was improved by 20.6%, attaining almost 55% (54.2%) (Table 5). The AUC (0.771) indicated that the waist-to-height ratio has an acceptable discriminating ability, at almost 80% (77.1%), for detecting central obesity (Table 5 and Figure 3d).
4. Discussion
The current study aimed to determine WtHR cut-off points that better discriminate central obesity in adults with overweight according to the WHO BMI classification of both sexes and different ethnicities, using data from the NHANES population in the US.
4.1. Findings and Comparison with the Previous Literature
In the current population-based study, our main finding was the identification of new WtHR cut-off points that better discriminate central adiposity in participants with overweight according to the WHO BMI classification (BMI = 25.0–29.9 kg/m2), based on data covering four different ethnicities from the NHANES in the US. These new WtHR cut-off points identified in the NHANES population across all different ethnicities and sexes were ≥0.55, slightly higher than the one (i.e., WtHR ≥ 0.5) recently suggested by some scientific organizations (i.e., the EASO framework) [31]. Notably, all the new cut-offs proposed for each ethnicity and sex, when compared to ≥ 0.5, appear to improve the performance of the WtHR in determining central adiposity. In other words, the new cut-offs in all ethnicities and sexes (≥0.55) generally increase the correct identification rate for cases and reduce the false positives [39].
Although several works are available on the identification of WtHR cut-offs for screening cardiometabolic diseases in adults [40,41,42], to date, few analyses have tried to establish WtHR cut-offs as indicators of adiposity, especially central obesity [29]. Remarkably, the cross-sectional investigation conducted by Roriz et al. among a sample of 191 adults reported WtHR cut-off points ranging from 0.54 to 0.59 for predicting high visceral adiposity (a VAT area of ≥130 cm2 determined by CT) in men and women aged 20–59 years and ≥60 years, respectively [43]. These cut-offs are very similar to those reported in our paper, which, despite the small sample size, relied on a gold standard assessment of VAT (i.e., the CT scan), as well as defined cut-offs of the latter that indicated central obesity [43].
4.2. Clinical Implications
This finding has several implications. Firstly, while the assessment of VAT is usually costly and requires specialized equipment and, in some cases, specific skills, we strongly encourage the use of the WtHR, which would allow for easy-to-obtain adiposity measures. Secondly, policy-makers, at least those in the US, are invited to take these results as preliminary evidence of new cut-off points for identifying central adiposity in this specific population (i.e., the overweight NHANES population). Finally, awareness should be raised among all healthcare professionals dealing with obesity in regard to recognizing these new cut-off points when screening for central adiposity and sharing or discussing this new information with their patients.
4.3. Strengths and Limitations
This study has certain strengths. Primarily, it considered a large sample of participants with an overweight status according to WHO BMI categorization, including four different ethnicities and both sexes, and relied on a reliable and validated parameter to determine central adiposity, in the form of DXA-derived VAT. It is one of the very few works to derive tentative cut-off points for the WtHR for the prediction of high DXA-derived VAT. However, at the same time, our paper also has certain limitations. Firstly, as mentioned above, there is no consensus regarding the definition of central obesity based on DXA-derived VAT. Defining the latter as the highest VAT tertile within each ethnicity may therefore be criticized. Secondly, the fact that we studied a specific population (i.e., young and middle-aged adults, people living in the US, etc.) means that our findings cannot be generalized to others (i.e., older adults, people outside the US, etc.), and our investigation thus lacks external validity in different settings [44]. Thirdly, DXA software (Hologic APEX 4.0) was employed to estimate the VAT area, and although it is an acceptable and validated technique [45], it is not considered a gold standard measure for this purpose when compared with other methods [46]. Finally, this investigation adopted a cross-sectional design [47] and was therefore unable to detect DXA-derived VAT trends or changes [48], which usually requires longitudinal assessment [49].
4.4. New Directions for Future Research
Some new directions for future research derived from our findings still need to be explored. Firstly, additional investigations should replicate our findings to confirm these WtHR cut-off points in the US, perhaps in a purely real-world clinical setting (i.e., primary care, clinical specialized units, etc.). Moreover, subsequent studies are also needed to determine the WtHR cut-off points in individuals with a normal weight (BMI 18.5–24.9 kg/m2) or obesity (BMI ≥ 30 kg/m2) based on WHO BMI categorization. Finally, other works should extend our analysis to different populations worldwide.
5. Conclusions
Central visceral obesity is clinically associated with severe comorbidities, and its early identification, especially by means of surrogate measures such as the WtHR, is thus crucial for managing the progression of the latter. In this study, we provide evidence that the optimal WtHR cut-off point (i.e., in all cases ≥ 0.55) corresponds to central obesity in adults of a both-sex population composed of different ethnicities. We therefore recommend the use of these new WtHR cut-off points when screening individuals for central obesity while taking into account their ethnicity and sex.
Author Contributions
All authors claim authorship and have approved and made substantial contributions to the conception, drafting, and final version of the paper. The study was designed by M.E.G., while L.I. conducted the statistical analysis. M.E.G. and L.I. co-wrote the manuscript. All authors have read and agreed to the published version of the manuscript.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The original data presented in the study are openly available on the following site: https://wwwn.cdc.gov/nchs/nhanes/Default.aspx (accessed on 10 September 2024).
Conflicts of Interest
The authors declare no conflicts of interest.
Funding Statement
This research received no external funding.
Footnotes
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References
- 1.Reilly J.J., El-Hamdouchi A., Diouf A., Monyeki A., Somda S.A. Determining the worldwide prevalence of obesity. Lancet. 2018;391:1773–1774. doi: 10.1016/S0140-6736(18)30794-3. [DOI] [PubMed] [Google Scholar]
- 2.Pi-Sunyer X. The medical risks of obesity. Postgrad. Med. 2009;121:21–33. doi: 10.3810/pgm.2009.11.2074. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Sarwer D.B., Polonsky H.M. The Psychosocial Burden of Obesity. Endocrinol. Metab. Clin. 2016;45:677–688. doi: 10.1016/j.ecl.2016.04.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.El Ghoch M., Calugi S., Dalle Grave R. The Effects of Low-Carbohydrate Diets on Psychosocial Outcomes in Obesity/Overweight: A Systematic Review of Randomized, Controlled Studies. Nutrients. 2016;8:402. doi: 10.3390/nu8070402. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Abdelaal M., le Roux C.W., Docherty N.G. Morbidity and mortality associated with obesity. Ann. Transl. Med. 2017;5:161. doi: 10.21037/atm.2017.03.107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Lin X., Li H. Obesity: Epidemiology, Pathophysiology, and Therapeutics. Front. Endocrinol. 2021;12:706978. doi: 10.3389/fendo.2021.706978. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Wadden T.A., Tronieri J.S., Butryn M.L. Lifestyle modification approaches for the treatment of obesity in adults. Am. Psychol. 2020;75:235–251. doi: 10.1037/amp0000517. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Toplak H., Woodward E., Yumuk V., Oppert J.M., Halford J.C., Fruhbeck G. 2014 EASO Position Statement on the Use of Anti-Obesity Drugs. Obes. Facts. 2015;8:166–174. doi: 10.1159/000430801. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Fried M., Yumuk V., Oppert J.M., Scopinaro N., Torres A.J., Weiner R., Yashkov Y., Fruhbeck G. European Association for the Study of O, International Federation for the Surgery of Obesity-European C. Interdisciplinary European Guidelines on metabolic and bariatric surgery. Obes. Facts. 2013;6:449–468. doi: 10.1159/000355480. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Di Renzo L., Itani L., Gualtieri P., Pellegrini M., El Ghoch M., De Lorenzo A. New BMI Cut-Off Points for Obesity in Middle-Aged and Older Adults in Clinical Nutrition Settings in Italy: A Cross-Sectional Study. Nutrients. 2022;14:4848. doi: 10.3390/nu14224848. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Weir C.B., Jan A. StatPearls. StatPearls; Treasure Island, FL, USA: 2020. BMI Classification Percentile and Cut off Points. [PubMed] [Google Scholar]
- 12.World Health Organization . Obesity: Preventing and Managing the Global Epidemic. World Health Organization; Geneva, Switzerland: 1998. [PubMed] [Google Scholar]
- 13.Pray R., Riskin S. The History and Faults of the Body Mass Index and Where to Look Next: A Literature Review. Cureus. 2023;15:e48230. doi: 10.7759/cureus.48230. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Garn S.M., Leonard W.R., Hawthorne V.M. Three limitations of the body mass index. Am. J. Clin. Nutr. 1986;44:996–997. doi: 10.1093/ajcn/44.6.996. [DOI] [PubMed] [Google Scholar]
- 15.Nuttall F. Body Mass Index: Obesity, BMI, and Health: A Critical Review. Nutr. Today. 2015;50:117–128. doi: 10.1097/NT.0000000000000092. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.WHO Expert Consultation Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363:157–163. doi: 10.1016/S0140-6736(03)15268-3. [DOI] [PubMed] [Google Scholar]
- 17.Wu Y., Li D., Vermund S.H. Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity. Int. J. Environ. Res. Public. Health. 2024;21:757. doi: 10.3390/ijerph21060757. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Pasco J.A., Holloway K.L., Dobbins A.G., Kotowicz M.A., Williams L.J., Brennan S.L. Body mass index and measures of body fat for defining obesity and underweight: A cross-sectional, population-based study. BMC Obes. 2014;1:9. doi: 10.1186/2052-9538-1-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Shuster A., Patlas M., Pinthus J.H., Mourtzakis M. The clinical importance of visceral adiposity: A critical review of methods for visceral adipose tissue analysis. Br. J. Radiol. 2012;85:1–10. doi: 10.1259/bjr/38447238. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Sam S. Differential Effect of Subcutaneous Abdominal and Visceral Adipose Tissue on Cardiometabolic Risk. Horm. Mol. Biol. Clin. Investig. 2018;33:438–448. doi: 10.1515/hmbci-2018-0014. [DOI] [PubMed] [Google Scholar]
- 21.Neeland I.J., Ayers C.R., Rohatgi A.K., Turer A.T., Berry J.D., Das S.R., Vega G.L., Khera A., McGuire D.K., Grundy S.M., et al. Associations of visceral and abdominal subcutaneous adipose tissue with markers of cardiac and metabolic risk in obese adults. Obesity. 2013;21:E439–E447. doi: 10.1002/oby.20135. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Fox C.S., Massaro J.M., Hoffmann U., Pou K.M., Maurovich-Horvat P., Liu C.Y., Vasan R.S., Murabito J.M., Meigs J.B., Cupples L.A., et al. Abdominal visceral and subcutaneous adipose tissue compartments: Association with metabolic risk factors in the Framingham Heart Study. Circulation. 2007;116:39–48. doi: 10.1161/CIRCULATIONAHA.106.675355. [DOI] [PubMed] [Google Scholar]
- 23.Nicklas B.J., Penninx B.W., Ryan A.S., Berman D.M., Lynch N.A., Dennis K.E. Visceral adipose tissue cutoffs associated with metabolic risk factors for coronary heart disease in women. Diabetes Care. 2003;26:1413–1420. doi: 10.2337/diacare.26.5.1413. [DOI] [PubMed] [Google Scholar]
- 24.Poirier P., Despres J.P. Waist circumference, visceral obesity, and cardiovascular risk. J. Cardiopulm. Rehabil. 2003;23:161–169. doi: 10.1097/00008483-200305000-00001. [DOI] [PubMed] [Google Scholar]
- 25.Da Rosa S.E., Costa A.C., Fortes M.S.R., Marson R.A., Neves E.B., Rodrigues L.C., Ferreira P.F., Filho J.F. Cut-Off Points of Visceral Adipose Tissue Associated with Metabolic Syndrome in Military Men. Healthcare. 2021;9:886. doi: 10.3390/healthcare9070886. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Ofenheimer A., Breyer-Kohansal R., Hartl S., Burghuber O.C., Krach F., Schrott A., Wouters E.F.M., Franssen F.M.E., Breyer M.K. Reference values of body composition parameters and visceral adipose tissue (VAT) by DXA in adults aged 18–81 years-results from the LEAD cohort. Eur. J. Clin. Nutr. 2020;74:1181–1191. doi: 10.1038/s41430-020-0596-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Gómez-Ambrosi j González-Crespo I., Catalán V., Rodríguez A., Moncada R., Valentí V., Romero S., Beatriz Ramírez B., Silva C., Gil M.J., Salvador J., et al. Clinical usefulness of abdominal bioimpedance (ViScan) in the determination of visceral fat and its application in the diagnosis and management of obesity and its comorbidities. Clin. Nutr. 2018;37:580–589. doi: 10.1016/j.clnu.2017.01.010. [DOI] [PubMed] [Google Scholar]
- 28.Seimon R.V., Wild-Taylor A.L., Gibson A.A., Harper C., McClintock S., Fernando H.A., Hsu M.S.H., Luz F.Q.D., Keating S.E., Johnson N.A., et al. Less Waste on Waist Measurements: Determination of Optimal Waist Circumference Measurement Site to Predict Visceral Adipose Tissue in Postmenopausal Women with Obesity. Nutrients. 2018;10:239. doi: 10.3390/nu10020239. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Swainson M.G., Batterham A.M., Tsakirides C., Rutherford Z.H., Hind K. Prediction of whole-body fat percentage and visceral adipose tissue mass from five anthropometric variables. PLoS ONE. 2017;12:e0177175. doi: 10.1371/journal.pone.0177175. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.So R., Matsuo T., Saotome K., Tanaka K. Equation to estimate visceral adipose tissue volume based on anthropometry for workplace health checkup in Japanese abdominally obese men. Ind. Health. 2017;55:416–422. doi: 10.2486/indhealth.2017-0060. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Busetto L., Dicker D., Frühbeck G., Halford J.C.G., Sbraccia P., Yumuk V., Goossens G.H. A new framework for the diagnosis, staging and management of obesity in adults. Nat. Med. 2024;30:2395–2399. doi: 10.1038/s41591-024-03095-3. [DOI] [PubMed] [Google Scholar]
- 32.Nevill A.M., Leahy G.D., Mayhew J., Sandercock G.R.H., Myers T., Duncan M.J. At risk’ waist-to-height ratio cut-off points recently adopted by NICE and US Department of Defense will unfairly penalize shorter adults. What is the solution? Obes. Res. Clin. Pract. 2023;17:1–8. doi: 10.1016/j.orcp.2023.01.002. [DOI] [PubMed] [Google Scholar]
- 33.Ahluwalia N., Dwyer J., Terry A., Moshfegh A., Johnson C. Update on NHANES Dietary Data: Focus on Collection, Release, Analytical Considerations, and Uses to Inform Public Policy. Adv. Nutr. 2016;7:121–134. doi: 10.3945/an.115.009258. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Johnson C.L., Paulose-Ram R., Ogden C.L., Carroll M.D., Kruszon-Moran D., Sylvia M., Dohrmann S.M., Curtin L.R. National Health and Nutrition Examination Survey: Analytic Guidelines, 1999–2010. Volume 161. National Center for Health Statistics; Hyattsville, MD, USA: 2013. pp. 1–24. Vital Health Stat. [PubMed] [Google Scholar]
- 35.Bilić-Zulle L. Comparison of methods: Passing and Bablok regression. Biochem. Med. 2011;21:49–52. doi: 10.11613/BM.2011.010. [DOI] [PubMed] [Google Scholar]
- 36.Mandrekar J.N. Receiver operating characteristic curve in diagnostic test assessment. J. Thorac. Oncol. 2010;5:1315–1316. doi: 10.1097/JTO.0b013e3181ec173d. [DOI] [PubMed] [Google Scholar]
- 37.NCSS 12 Statistical Software; NCSS, LLC: Kaysville, UT, USA. 2018. [(accessed on 25 September 2024)]. Available online: https://www.ncss.com/software/ncss/
- 38.Hintze J. PASS 11. NCSS, LLC; Kaysville, UT, USA: 2011. [(accessed on 25 September 2024)]. Available online: www.ncss.com. [Google Scholar]
- 39.Van Ravenzwaaij D., Ioannidis J.P.A. True and false positive rates for different criteria of evaluating statistical evidence from clinical trials. BMC Med. Res. Methodol. 2019;19:218. doi: 10.1186/s12874-019-0865-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Gibson S., Ashwell M. A simple cut-off for waist-to-height ratio (0·5) can act as an indicator for cardiometabolic risk: Recent data from adults in the Health Survey for England. Br. J. Nutr. 2020;123:681–690. doi: 10.1017/S0007114519003301. [DOI] [PubMed] [Google Scholar]
- 41.ELMabchour A., Delisle H., Vilgrain C., Larco P., Sodjinou R., Batal M. Specific cut-off points for waist circumference and waist-to-height ratio as predictors of cardiometabolic risk in Black subjects: A cross-sectional study in Benin and Haiti. Diabetes Metab. Syndr. Obes. 2015;8:513–523. doi: 10.2147/DMSO.S88893. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Savva S.C., Lamnisos D., Kafatos A.G. Predicting cardiometabolic risk: Waist-to-height ratio or BMI. A meta-analysis. Diabetes Metab. Syndr. Obes. 2013;6:403–419. doi: 10.2147/DMSO.S34220. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Roriz A.K., Passos L.C., de Oliveira C.C., Eickemberg M., Moreira P.A. Evaluation of the accuracy of anthropometric clinical indicators of visceral fat in adults and elderly. PLoS ONE. 2014;9:e103499. doi: 10.1371/journal.pone.0103499. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Bleeker S.E., Moll H.A., Steyerberg E.W., Donders A.R.T., Derksen-Lubsen G., Grobbee D.E., Moons K.G.M. External validation is necessary in prediction research: A clinical example. J. Clin. Epidemiol. 2003;56:826–832. doi: 10.1016/S0895-4356(03)00207-5. [DOI] [PubMed] [Google Scholar]
- 45.Ashby-Thompson M., Heshka S., Rizkalla B., Zurlo R., Lemos T., Janumala I., Goodpaster B., DeLany J., Courcoulas A., Strain G., et al. Validity of dual-energy X-ray absorptiometry for estimation of visceral adipose tissue and visceral adipose tissue change after surgery-induced weight loss in women with severe obesity. Obesity. 2022;30:1057–1065. doi: 10.1002/oby.23415. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Kali A., Gusmanov A., Aripov M., Chan M.Y. Proposing new body mass index and waist circumference cut-offs based oncardiometabolic risks for a Central Asia population: A feasibility study. Front. Endocrinol. 2022;13:963352. doi: 10.3389/fendo.2022.963352. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Wang X., Cheng Z. Cross-Sectional Studies: Strengths, Weaknesses, and Recommendations. Chest. 2020;158:65–71. doi: 10.1016/j.chest.2020.03.012. [DOI] [PubMed] [Google Scholar]
- 48.Abildgaard J., Ploug T., Al-Saoudi E., Wagner T., Thomsen C., Ewertsen C., Bzorek M.B., Pedersen B.K., Pedersen A.T., Lindegaard B. Changes in abdominal subcutaneous adipose tissue phenotype following menopause is associated with increased visceral fat mass. Sci. Rep. 2021;11:14750. doi: 10.1038/s41598-021-94189-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Caruana E.J., Roman M., Hernández-Sánchez J., Solli P. Longitudinal studies. J. Thorac. Dis. 2015;7:537–540. doi: 10.3978/j.issn.2072-1439.2015.10.63. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The original data presented in the study are openly available on the following site: https://wwwn.cdc.gov/nchs/nhanes/Default.aspx (accessed on 10 September 2024).