Revising BMI Cut-Off Points for Obesity in a Weight

International Journal of Environmental Research and Public Health

Brief Report Revising BMI Cut-Oﬀ Points for Obesity in a Weight Management Setting in Lebanon

Leila Itani, Dima Kreidieh, Dana El Masri, Hana Tannir, Leila Chehade and Marwan El Ghoch * Department of Nutrition and Dietetics, Faculty of Health Sciences, Beirut Arab University, P.O. Box 11-5020 Riad El Solh, Beirut 11072809, Lebanon; [email protected] (L.I.); [email protected] (D.K.); [email protected] (D.E.M.); [email protected] (H.T.); [email protected] (L.C.) * Correspondence: [email protected]

Received: 22 April 2020; Accepted: 27 May 2020; Published: 28 May 2020

Abstract: Obesity is defined by the World Health Organization (WHO) as a body mass index (BMI) 30 Kg/m2. This study aimed to test the validity of this BMI cut-off point for adiposity in a ≥ weight management clinical setting in Lebanon. This cross-sectional study of 442 adults of mixed gender, categorized by the WHO BMI classification, included: 66 individuals of normal weight, 110 who were overweight and 266 with obesity. The clinical sample was referred to the Outpatient Clinic in the Department of Nutrition and Dietetics at Beirut Arab University (BAU) in Lebanon. All participants underwent anthropometric evaluation. The gold standard for defining obesity was based on the National Institutes of Health (NIH)/WHO guidelines for total body fat percentage (BF%). The best sensitivity and specificity were attained to predict obesity, according to the receiver operating characteristic curve (ROC) analysis. The BMI cut-off point for predicting obesity in the clinical sample was nearly 31.5 Kg/m2, and more than 90% of individuals with obesity and cardiometabolic disease were above this cut-off point. In conclusion, this new BMI cut-off point, an obesity definition higher than suggested in Western populations, was demonstrated to have clinical usefulness. Obesity guidelines in Lebanon, therefore, need revising.

Keywords: adiposity; body composition; body fat; overweight

1. Introduction Obesity is a growing health problem, with prevalence increasing continually worldwide [1]. It is considered a major risk factor for several medical indications (e.g., diabetes, cardiovascular diseases, and cancer) [2] and for psychosocial diseases (e.g., depression, eating disorders and health-related quality-of-life impairment) [3,4], which are all conditions that unavoidably increase rates of morbidity and mortality [5]. Accordingly, international guidelines advocated for a broad spectrum of weight loss interventions for clinical practice, including lifestyle modification programs [6], Pharmacologic treatment [7], and bariatric surgery [8]. The World Health Organization (WHO) defines obesity as an excessive fat deposition in the adipose tissue [9,10]; therefore, the classification of adiposity based on body fat (BF) quantification and assessment seems to be the most accurate [11]. To classify obesity in adults, however, the WHO mainly relies on a body mass index (BMI) cut-off point that has been derived mainly from Western populations (i.e., Europeans); a BMI 30 Kg/m2 indicates obesity [9]. Currently, this cut-off point is ≥ widely used in clinical and research settings to define obesity, since it is considered a simple and cheap tool for assessment [12], especially because it is also highly correlated with BF [12]. However, BMI classification has its limitations [13] for at least two main reasons. Firstly, despite the strong association between BMI and BF, the former is not able to discriminate the latter from lean body mass. In fact, in a group of individuals with the same BMI, different values of BF% may be recognized. Secondly,

Int. J. Environ. Res. Public Health 2020, 17, 3832; doi:10.3390/ijerph17113832 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2020, 17, 3832 2 of 8 this cut-off point has been determined in certain populations (i.e., Western populations) and its validity is not a certainty in others, where available literature reports inconsistent evidence and raises the need to test the accuracy of evidence in several ethnic groups [14], foremost in those from North Africa and Middle East countries. The current study, therefore, aims to investigate the extent to which the WHO BMI cut-off point for obesity classification, based on Western populations, is universal and valid in other populations, namely in a clinical setting of Lebanese patients with obesity, who are seeking treatment. Within this scope, the hypothesis is that the cut-off point in our population will vary from that proposed by the WHO.

2. Materials and Methods In this cross-sectional diagnostic study that adhered to the statement, Strengthening The Reporting of OBservational Studies in Epidemiology (STROBE) (Table S1), a total of 442 individuals were recruited including: (i) a clinical sample of 376 individuals with overweight or obesity (BMI 25.0 kg/m2) who ≥ sought weight loss treatment and referred by general practitioners to the Nutritional and Weight Management Outpatient Clinic in the Department of Nutrition and Dietetics (BAU) in Lebanon and (ii) a sample of 66 normal weight (18.5 BMI < 25 Kg/m2) individuals from the general community. ≥ Recruitment took place during the period between January 2015 and November 2019. Classification into normal, overweight, or obese was based on the National Institutes for Health (NIH)/WHO guidelines for BMI classification [9,10]. Participants were included in the clinical sample if they were aged 18 years, have a BMI 25.0 kg/m2 and had at least one weight loss responsive comorbidity ≥ ≥ (i.e., type 2 diabetes, cardiovascular disease, sleep apnoea, severe joint disease, or two or more risk factors) as defined by the Adult Treatment Panel III [15]. Excluded patients were either pregnant or lactating, taking medication that affects body weight, or presented with weight loss related to medical comorbidities, or severe psychiatric disorders. The 66 normal weight individuals were recruited from the general population through a randomized community-based email survey. The study was approved by the Institutional Review Board of the BAU (No. 2017H-0034-HS-R-0241), and all participants were included after giving written informed consent. A questionnaire that retrieved information on social and demographic data (e.g., age, marital status, employment, level of education, etc.) was administered to all participants. An electronic weighing scale (SECA 2730-ASTRA, Germany) and a stadiometer were used to measure body weight and height, respectively. BMI was calculated using the standard formula dividing body weight measured in kilograms by the square of the height in meters. A segmental body composition analyzer (MC-780MA, Tanita Corp., Tokyo, Japan) [16] was used to measure body composition using a standardized technique. Participant age, gender, and height information were entered into the device. Then the participant was instructed to stand in a stable position barefooted. Separate reading for different body segments compositions was obtained. These readings are based on an algorithm incorporating impedance, age, and height to estimate the total and regional BF and fat-free mass (FFM) [16]. Age and gender-specific obesity cut-off points for BF% were suggested by Gallagher and colleagues [17]:

Females:

20–39 years: BF% 39% • ≥ 40–59 years: BF% 40% • ≥ 60–79 years: BF% 42% • ≥ Males:

20–39 years: BF% 25% • ≥ 40–59 years: BF% 28% • ≥ 60–79 years: BF% 30% • ≥ Int. J. Environ. Res. Public Health 2020, 17, 3832 3 of 8

Cardiometabolic disease in this study is deﬁned as the presence of any diseases, such as type 2 diabetes, cardiovascular diseases (coronary heart disease, stroke, transient ischaemic attack, and peripheral arterial disease) and dyslipidemia (lowered level of high-density lipoprotein cholesterol, and increased level of low-density lipoprotein cholesterol and triglycerides). These were self-reported to be experienced either simultaneously or separately.

Statistical Analysis Descriptive statistics are presented, including means and standard deviations or frequencies and proportions for continuous and categorical variables, respectively. The Student t-test was used to compare means, and the Chi squared test of independence was used for proportions. To test the diagnostic performance of BMI in detecting obesity status, Gallagher and colleagues’ definition for obesity based on age- and gender-specific BF% was used as a gold standard [17]. A classification analysis was done by calculating sensitivity and specificity and area under the curve (AUC) of the receiver operating characteristic curve (ROC). The criterion value of BMI with maximum sensitivity and specificity was selected for the BMI cut-off points. An AUC > 0.8 indicates an excellent discriminating ability [18]. The cut-off scores achieving 90% sensitivity, and their corresponding specificities were also calculated. All valuesInt. J. Environ. were Res. considered Public Health significant 2020, 17, x FOR at p PEER< 0.05. REVIEW NCSS 12.0.2 (NCSS, NCSS, LLC. Kaysville, UT, 4 of 8 USA) was used for the statistical analysis. A power analysis for the sample size was determined using Normal weight 66 (14.9) 36 (26.9) 30 (9.7) PASS software (PASS 11. NCSS, LLC. Kaysville, UT, USA). For the sample of 202 patients classified With overweight 110 (24.9) 21 (15.7) 89 (28.9) with obesity versus 240 with an alpha of 0.05 and AUC of 0.965, the power is 1.000. With Obesity 266 (60.2) 77 (57.5) 189 (61.4)

3. Results BF (Kg) 32.3 (12.04) 28.4 (15.3) 34.0 (9.9) p = 0.00014 Table1 showsBF% the demographic¥ characteristics37.3 (9.5) of the28.1 study (9.7) participants.41.3 (6.0) The sample includedp < 0.0001 134 (30.3%) males and 308 (69.7%) females with a mean age of 34.7 14.7 years and a meanX2 = 1.432 BMI; p = 0.231 ± of 31.8 6.2 KgWithout/m2. Almost obesity two thirds240 (64.5%) (54.3) of the participants67 (50.0) were young173 (56.2) (20–39 years) with a ± With obesity 202 (45.7) 67 (50.0) 135 (43.8) similar age distribution for males (68.7%) and females (62.7%). Participants were mostly unmarried Cardiometabolic disease X2 = 0.294; p = 0.588 (53.0%) and unemployed (59.8%), with females being more likely to be married (63.2% vs. 48.5%) No 319(73.2) 95 (71.4) 224 (73.9) and unemployed (70.3% vs. 35.8%) compared to males. Based on the WHO BMI classification [10], Yes 117(26.8) 38 (28.6) 79 (26.1) almost two thirds (60.2%) of the sample were classified with obesity (BMI 30 Kg/m2), with a similar Diabetes ≥ X2 = 0.002; p = 0.967 distribution for malesNo (57.5%) and females376 (61.4%). (90.4) In contrast,114 (90.5) only 45.7% of262 the (90.3) sample were classified with obesity based onYes Gallagher and colleagues’40 (9.6) age- and gender-specific12 (9.5) BF%28 classification(9.7) [17], with a similar distributionDyslipidemia for males (50.0%) and females (43.8%) (Table 1). X2 = 0.032; p = 0.858 Figure1 showsNo a strong association343 between (82.3) BMI and103 (81.8) BF% in males240 ( ρ =(82.5)0.895; p < 0.0001) and females (ρ = 0.669; pYes< 0.0001); however,74 for (17.8) a determined 23 BMI, (18.3) a wide range51 of (17.5) BF% values are evident in both genders.Hypertension X2 = 0.001; p = 0.980 No 380 (87.2) 116 (87.2) 264 (87.1) Table 1. Sociodemographic,Yes anthropometric,56 (12.8) and cardiometabolic17 (12.8) characteristics39 (12.9) of the study sample (n = 442) Ŧ .Values are Means (SD) or Medians (IQR) for continuous variables and n (%) for categorical variables. £ WHO classification for BMI. ¥ Gallagher et al., 2000 [17]. Demographics Total N = 442 Males N = 134 Females N = 308 Significance Age (years) 34.7 (14.7) 33.4(14.0) 35.3(15.0) 0.196 X2 = 1.77; p = 0.412 20–39 285 (64.5) 92 (68.7) 193 (62.7) 40–59 128 (29.0) 33 (24.6) 95 (30.8) 60–80 29 (6.56) 9 (6.7) 20 (6.5) Marital status X2 = 7.961; p = 0.0005 Unmarried 232 (53.0) 84 (63.2) 148 (48.5) Married 206 (47.0) 49 (36.8) 157 (51.5) Employment X2 = 45.971; p < 0.0001 Unemployed 263 (59.8) 48 (35.8) 215 (70.3) Employed 177 (40.2) 86 (64.2) 91 (29.7)

Figure 1. Association between BMI and BF% of the study sample (n = 442).

The results of the ROC analysis on the diagnostic performance are shown in Table 2 and Figure 2a,b. The AUCs for males (0.965, p < 0.001) and females (0.789, p < 0.001) indicate excellent and acceptable discriminating ability of BMI with a 97% and 79% chance respectively to detect [18,19] BF% defined obesity (Table 2). The optimal BMI cut-off points for discriminating males and females

Int. J. Environ. Res. Public Health 2020, 17, 3832 4 of 8

Table 1. Cont.

Demographics Total N = 442 Males N = 134 Females N = 308 Signiﬁcance Anthropometrics Weight (Kg) 85.0 (18.7) 95.0 (23.2) 80.9 (14.1) p < 0.0001 Height (cm) 163.7 (9.5) 174.3 (6.8) 159.1 (6.2) p < 0.0001 Int. J. Environ.BMI Res. Kg Public/m2 £ Health 2020, 131.87, x FOR (6.2) PEER REVIEW 31.3 (7.3) 32.0 (5.6) p = 0.298 4 of 8 X2 = 26.676; p < 0.0001 NormalNormal weight weight 66 66 (14.9) 3636(26.9) (26.9) 3030 (9.7) (9.7) WithWith overweight overweight 110 110 (24.9) 2121(15.7) (15.7) 8989 (28.9) (28.9) WithWith Obesity Obesity 266 266 (60.2) 7777(57.5) (57.5) 189189 (61.4) (61.4)

Int.BF J. Environ. (Kg) Res. Public Health32.3 2020,(12.04) 17, x FOR PEER REVIEW 28.4 (15.3) 34.0 (9.9) p = 0.00014 4 of 8 BF (Kg) 32.3 (12.04) 28.4 (15.3) 34.0 (9.9) p = 0.00014 BF% ¥ 37.3 (9.5) 28.1 (9.7) 41.3 (6.0) p < 0.0001 Normal¥ weight 66 (14.9) 36 (26.9) 30 (9.7) BF% 37.3 (9.5) 28.1 (9.7) 41.3 (6.0) X2 = 1.432;p < p0.0001= 0.231 With overweight 110 (24.9) 21 (15.7) 89 (28.9) Without obesity 240 (54.3) 67 (50.0) 173 (56.2) X2 = 1.432; p = 0.231 With Obesity 266 (60.2) 77 (57.5) 189 (61.4) WithoutWith obesity 240 202 (54.3) (45.7) 6767(50.0) (50.0) 135173 (43.8) (56.2) CardiometabolicWith obesityBF disease (Kg) 202 32.3(45.7) (12.04) 28.467 (50.0) (15.3) 34.0135 (9.9) (43.8) p X= 20.00014= 0.294; p = 0.588 Cardiometabolic diseaseBF% ¥ 37.3 (9.5) 28.1 (9.7) 41.3 (6.0) p < 0.0001X2 = 0.294 ; p = 0.588 No 319(73.2) 95 (71.4) 224 (73.9) 2 No 319(73.2) 95 (71.4) 224 (73.9) X= 1.432; p = 0.231 YesWithout obesity 117(26.8) 240 (54.3) 38 67 (28.6) (50.0) 173 79(56.2) (26.1) Yes 117(26.8) 38 (28.6) 79 (26.1) DiabetesWith obesity 202 (45.7) 67 (50.0) 135 (43.8) X2 = 0.002; p = 0.967 DiabetesCardiometabolic disease X2 = 0.294;X p2 = 0.5880.002 ; p = 0.967 NoNo No 376 376 (90.4) 319(73.2) 114114 95 (90.5)(71.4) (90.5) 224 262 (73.9)262 (90.3) (90.3) Yes 40 (9.6) 12 (9.5) 28 (9.7) Yes Yes 40 (9.6)117(26.8) 3812 (28.6) (9.5) 79 (26.1)28 (9.7) Diabetes X2 = 0.002;2 p = 0.967 Dyslipidemia X =20.032; p = 0.858 DyslipidemiaNo 376 (90.4) 114 (90.5) 262 (90.3) X = 0.032; p = 0.858 NoNo Yes 343 343 (82.3) 40 (9.6) 103103 12 (81.8) (9.5)(81.8) 28 240 (9.7)240 (82.5) (82.5) YesYes Dyslipidemia 74 74 (17.8) 2323(18.3) (18.3) 51 51 (17.5) (17.5) X2 = 0.032; p = 0.858 HypertensionHypertensionNo 343 (82.3) 103 (81.8) 240 (82.5) X2 X= 20.001; = 0.001p ;= p0.980 = 0.980 Yes 74 (17.8) 23 (18.3) 51 (17.5) NoNo Hypertension 380 380 (87.2) 116116 (87.2) (87.2) 264264 (87.1) (87.1) X2 = 0.001; p = 0.980 YesYes No 56 56 (12.8) 380 (87.2) 1717 116(12.8) (12.8)(87.2) 26439 (87.1)39 (12.9) (12.9) Ŧ Values are are Means MeansYes (SD) (SD) or Medians or Medians56 (IQR) (12.8) for (IQR continuous) for 17 (12.8) continuous variables and 39variables n(12.9) (%) for and categorical n (%) variables. for categorical£ WHO Ŧ Values are¥ Means (SD) or Medians (IQR) for continuous variables and n (%) for categorical classiﬁcationvariables. £ WHO for BMI. classificationGallagher et for al., BMI. 2000 ¥ [ 17Gallagher]. et al., 2000 [17]. variables. £ WHO classification for BMI. ¥ Gallagher et al., 2000 [17].

Figure 1.FigureAssociation 1. Association between between BMI BMI andand BF% BF% of ofthe the study study sample sample (n = 442). (n = 442).

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2 2 with Theobesity results were of the 31.53 ROC Kg/m analysisand on 31.44 the diagnostic Kg/m , respectively. performance areThe shown BMI incut-off Table 2point and Figure for males2a,b. Theachieved AUCs high for males sensitivity (0.965, p(85.1%)< 0.001) and and specificit females (0.789,y (85.1%),p < 0.001)indicating indicate a excellentlower chance and acceptable for false negativesdiscriminating and abilityfalse positives. of BMI with For a females, 97% and 79%the sensitivity chance respectively (71.9%) and to detect specificity [18,19 ](62.4%) BF% defined were 2 relativelyobesity (Table lower.2). The optimalcut-off point BMI cut-othat achievedff points 90% for discriminating sensitivity for malesmales andwas females30.48 Kg/m with, obesitywith a wererelatively 31.53 high Kg /specificitym2 and 31.44 of 83.6%, Kg/m which2, respectively. is close to Thethe WHO BMI cut-o cut-offff point point. for However, males achieved for females, high it 2 wassensitivity 28.85 Kg/m (85.1%) with and a specificity relatively (85.1%), low specificity indicating of a46.8%, lower indicating chance for a false higher negatives chance andfor false positives.positives (Table For females, 2). According the sensitivity to the (71.9%)determined and specificitycut-off points, (62.4%) obesity were affected relatively 50.0% lower. of Themales cut-o andff point52.6% thatof females, achieved which 90% sensitivityis closer to for Gallagher’s males was and 30.48 colleagues’ Kg/m2, with definition a relatively [17] highthan specificityto the WHO of definition83.6%, which [10]. is close to the WHO cut-off point. However, for females, it was 28.85 Kg/m2 with a 2 relativelyFinally, low among specificity individuals of 46.8%, with indicating a BMI a≥ higher30 Kg/m chance, 101 for had false cardiometabolic positives (Table disease2). According (34 males, to 2 67the females). determined Of those, cut-off amongpoints, males, obesity 31(91.2%) affected 50.0%had a ofBMI males ≥ 31.53 and Kg/m 52.6%, ofand females, only 3 (8.8%) which ishad closer a BMI to 2 2 betweenGallagher’s 30 and and 31.53 colleagues’ Kg/m ; definition among females, [17] than 60 to(89.6%) the WHO had definitiona BMI ≥ 31.44 [10 ].Kg/m , and only 7 (10.4%) had a BMI between 30 and 31.44 Kg/m2. Table 2. Diagnostic performance of the BMI cut off points to detect obesity by sex groups. Table 2. Diagnostic performance of the BMI cut off points to detect obesity by sex groups. Specificity Cut-off at Specificity at Cut-off at Sex N AUC 95%CI PP Value Sensitivity SpecificityCut Cut Off at 90% 90% Sex N AUC 95%CI Sensitivity Specificity 90% 90% Value Off Sensitivity Sensitivity Sensitivity Sensitivity Males 134 0.965 0.930–0.983 <0.0001 0.851 0.851 31.53 0.836 30.48 Males 134 0.965 0.930–0.983 <0.0001 0.851 0.851 31.53 0.836 30.48 FemalesFemales 308308 0.789 0.789 0.733–0.833 0.733–0.833 <0.0001<0.0001 0.719 0.719 0.624 0.624 31.44 31.44 0.468 0.468 28.85 28.85 True Positive Rate (Sensitivity)True True Positive Rate (Sensitivity) Positive True

(a) (b)

Figure 2. (a) ROC for the BMI cut-off point to detect obesity based on BF% as the gold standard in Figure 2. (a) ROC for the BMI cut-off point to detect obesity based on BF% as the gold standard in males; (b) ROC for the BMI cut-off point to detect obesity based on BF% as the gold standard in females. males; (b) ROC for the BMI cut-off point to detect obesity based on BF% as the gold standard in females.Finally, among individuals with a BMI 30 Kg/m2, 101 had cardiometabolic disease (34 males, ≥ 67 females). Of those, among males, 31(91.2%) had a BMI 31.53 Kg/m2, and only 3 (8.8%) had a BMI 4. Discussion ≥ between 30 and 31.53 Kg/m2; among females, 60 (89.6%) had a BMI 31.44 Kg/m2, and only 7 (10.4%) ≥ had aThe BMI current between study 30 and aimed 31.44 to Kgprovide/m2. benchmark data on the validity of the WHO BMI cut-off point by determining adiposity status in a clinical setting in Lebanon for weight management treatment4. Discussion of adults of both genders with obesity. The currentmain finding study aimedwas the to provideidentification benchmark of a datanew onBMI the cut-off validity point of the for WHO obesity BMI in cut-o a clinicalff point sampleby determining of participants adiposity undergoing status in a weight clinical loss setting treatment. in Lebanon This for new weight cut-off management point (i.e., treatmentBMI nearly of 31.5adults Kg/m of both2) is genderssignificantly with obesity.different from the WHO BMI cut-off point [10]. Several studies with a similarThe objective main finding have wasbeen the carried identification out on other of a newpopulations BMI cut-o [20–24].ff point Among for obesity these, in some a clinical confirmed sample theof participants suitability of undergoing the BMI cut-off weight point loss (i.e., treatment. ≥30 Kg/m This2) newsuggested cut-off bypoint the WHO (i.e., BMI to define nearly obesity 31.5 Kg [22],/m2) whileis significantly others did different not from[20,21,23,24]. the WHO Some BMI cut-o studiesff point found [10]. Severallower cut-off studies withpoints a similar[20,21,23] objective and another—inhave been carried line with out the on othercurrent populations findings—found [20–24 ].higher Among BMI these, cut-off some points confirmed [24]. the suitability of Moreover, in this study, almost 91% with both obesity and cardiometabolic disease scored above the newly established cut-off point (i.e., BMI ≥ 31.5 Kg/m2), which highlights the clinical

Int. J. Environ. Res. Public Health 2020, 17, 3832 6 of 8 the BMI cut-off point (i.e., 30 Kg/m2) suggested by the WHO to define obesity [22], while others did ≥ not [20,21,23,24]. Some studies found lower cut-off points [20,21,23] and another—in line with the current findings—found higher BMI cut-off points [24]. Moreover, in this study, almost 91% with both obesity and cardiometabolic disease scored above the newly established cut-off point (i.e., BMI 31.5 Kg/m2), which highlights the clinical implications ≥ of these findings: there is a need to adopt this cut-off point to define obesity in Lebanon. Moreover, a difference in BF% at different BMI values is reported for different populations [25]. As the population in the current is not European and might differ in BMI corresponding to different BF%, a BMI approaching 31.5 Kg/m2 is plausible to detect the need for clinical or public health action in the Lebanese population [25]. The current study has several strengths and limitations. The strengths reside in that it is one of the few studies to assess the validity of the WHO BMI cut-off point by determining the adiposity status in a relatively large group of treatment-seeking patients with obesity, in a ‘real-world’ clinical setting in an Arab-speaking country. However, several limitations are to be noted. First, the self-reported cardiometabolic disease information is not an objective clinical assessment. Second, the impedance analyzer is used for measuring body composition while it is still not accepted as a gold standard technique for patients with obesity; despite its validation versus dual-energy X-ray absorptiometry (DXA) scan [16]. Moreover, the clinical sample was based only on treatment-seeking outpatients at baseline of a weight loss treatment program. This bias prevents the findings from being representative of the population affected by obesity in all clinical settings. The findings in this study may not be extrapolated to patients with obesity seeking other treatment modalities (i.e., bariatric surgery, pharmacological interventions, etc.). For this reason, three new directions for future research are needed. Firstly, research needs to confirm this cut-off point (i.e., 31.5 Kg/m2) in other groups of patients with obesity in Lebanon who are seeking other weight management treatments (i.e., bariatric surgery, pharmacological interventions, etc.). Secondly, on a national level, large sample studies are required to assess the validity of normal ( 18.5 Kg/m2) and overweight ( 25 Kg/m2) cut-off points and, if necessary, to determine new ones. ≥ ≥ Finally, similar studies to this one are needed in countries in the Middle East and North Africa region, which is known to have a high prevalence of obesity.

5. Conclusions This study provides evidence that the optimal BMI cut-off point (i.e., 31.5 Kg/m2) corresponding ≥ to obesity in the population of the current study varies from that widely used (i.e., 30 Kg/m2). ≥ Therefore, it is recommended that this new cut-off point should be applied in clinical and public health settings for screening individuals for obesity in Lebanon. On a general scale, the ethnicity factor should be taken into account, where popular BMI cut-off point for classifying obesity determined in certain populations (i.e., Western populations) cannot be applied by default in others, and need to be tested initially for its validity and clinical usefulness.

Supplementary Materials: The following are available online at http://www.mdpi.com/1660-4601/17/11/3832/s1, Table S1. STROBE Statement Checklist of items. 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. D.K., D.E.M., H.T. and L.C. collected data. M.E.G. and L.I. co-wrote the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Conflicts of Interest: The authors declare no conflict of interest. Int. J. Environ. Res. Public Health 2020, 17, 3832 7 of 8

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