In the past decades, the increasing prevalence of obesity is becoming a greater public health and health economic issue . Obesity is a recognized risk factor for various cardiometabolic diseases and several indices are used clinically to assess the overall cardiometabolic risk . BMI is traditionally the most widely used measure of obesity. Abnormal BMI is an independent predictor of mortality, and BMI is also used by some as part of the criteria for metabolic syndrome [3,4]. The main advantage of BMI is its simplicity to use both by clinicians and patients. It can also effectively rule out metabolic syndrome in certain populations ; however, the BMI is unable to differentiate between lean mass and fat mass, nor to consider body fat distribution.
Other measures of adiposity consider body fat distribution, like waist circumference, waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR), and more recently developed body adiposity index (BAI), none proved overall superiority [6–9].
In the light of the conflicting data, the best adiposity measure to help predict cardiovascular risk factors has remained controversial. Relative fat mass (RFM) had been recently developed as a new estimator of whole body fat percentages among American adult individuals of Mexican, European or African ethnicity. RFM is calculated using the equation: 64 − (20 × height/waist circumference) + (12 × sex); sex = 0 for men and 1 for women. Abnormal RFM (above 33.9 for women and 22.8 for men) showed promising results in its validation study. Compared with BMI, RFM better predicted abdominal obesity among men using dual energy x-ray absorptiometry as the gold standard; RFM was also found to be superior to BMI as a predictor of diabetes . Only a few later studies were conducted using RFM as an obesity indicator. In a small trial, RFM was validated to estimate fat percentage among men and women with and without Down’s syndrome . One study found RFM to be a better predictor of severe liver disease and mortality than BMI . Our earlier reported results suggest RFM may be used instead of waist circumference to define metabolic syndrome . Up to date, to the best of our knowledge, no further studies were published and the question whether RFM correlates better than BMI to metabolic disturbances is yet to be determined.
The aim of this study, therefore, was to add to the body of evidence regarding the clinical applicability of RFM and to examine whether RFM is superior to BMI and correlates better with various cardiometabolic risk factors.
A retrospective, observational, cohort-based, cross-sectional study performed at the Rambam Health Care Campus (RHCC) Periodic Examinations Institute between the years 2008 and 2016. RHCC is a primary and tertiary care university-affiliated hospital in northern Israel and operates an outpatients’ periodic examinations institute. The study was approved by the hospital’s ethics committee, with a waiver of consent.
The Rambam Periodic Examinations Institute (RPEI) is an independent institute which operates within RHCC. It provides service of comprehensive medical examination for patients, including physical examination, blood tests, exercise test (when indicated), etc.
We included any adult patient who underwent medical testing at the RPEI. Patients are instructed to fast prior to their visit to the periodic examinations institute; hence, all blood tests done during the visit are fasting blood tests. Patients were identified using RHCC electronic patient data file. RHCC operates a full electronic patient register that includes clinical, laboratory and radiological data. It also has access to medical summary reports and diagnoses from other healthcare providers. Patients who did not have the required measurement for BMI or RFM calculation were excluded from the study.
Abnormal RFM was defined as above 33.9 for women and 22.8 for men, as defined previously , abnormal BMI was defined as above 25, as commonly accepted.
Metabolic syndrome was defined by the ATP III criteria – at least three of the five conditions – central obesity (as defined by men waist circumference >40 inch or women waist circumference >35 inch), hypertriglyceridemia (above 150 mg/dl or on treatment), reduce HDL (below 40 mg/dl for men or 50 mg/dl for women), hypertension (above 130/85 mmHg or on treatment) and abnormal fasting plasma glucose (above 110 mg/dl or on treatment) .
Hypertriglyceridemia, reduced HDL and impaired fasting glucose were assessed by the blood test results during the visit, or the presence of appropriate diagnosis or treatment in the patient history.
Hypertension was assessed by the blood pressure measurement during the examination at RPEI, a confirmed diagnosis of hypertension or chronic use of antihypertensive medications.
Descriptive statistics in terms of mean, SD and ranges were presented to the all parameters in the study. We compared the associations of BMI and RFM to various categorical variables (presence of cardiometabolic risk factors) using the Fisher exact test and continuous variables were compared using the t-test.
Diagnostic parameters such as sensitivity, specificity, positive predicted variable and negative predicted variable were calculated related to occurrence of metabolic syndrome.
A receiver operating characteristic (ROC) curve with area under the curve and 95% confidence interval (CI) was demonstrated to describe the relationship between the sensitivity and the false positive rate for different values of BMI in identification of patients at risk for metabolic Syndrome. The Youden index was used for describing the best cutoff for identification.
P <0.05 was consider as significant. SPSS version 25 was used for the statistical analysis.
During the study period, 20 167 patients underwent medical examination at the RPEI and were included in the study. The mean age of participants was 52.3 years. Patients with abnormal RFM were older had higher BMI and more comorbidities (see Table 1).
RFM and BMI showed high level of correlation both among men (R2 = 0.858, P < 0.001) and women (R2 = 0.825, P < 0.001) (Fig. 1).
We tried to identify the correlation between BMI and RFM to cardiometabolic risk factors. When compared to BMI, RFM showed significantly better predictability [odds ratio (OR), 95% CI] to low HDL [2.944 (2.569–3.373, P < 0.001) vs. 2.177 (2–2.369, P < 0.001) in men, 2.947 (2.519–3.448, P < 0.001) vs. 1.9 (1.658–2.176, P < 0.001) in women] and high triglycerides [4.019 (3.332–4.847, P < 0.001) vs. 1.994 (1.823–2.181, P < 0.001) in men, 3.93 (2.943–5.247, P < 0.001) vs. 2.24 (1.887–2.62, P < 0.001) in women] among both men and women, with no overlap in CIs of ORs. RFM correlated with high LDL, among men and women, while BMI did not show significant correlation to high LDL among women and showed reverse ratio correlation among men [1.618 (1.441–1.816, P < 0.001) vs. 0.732 (0.67–0.8, P < 0.001) in men; 1.572 (1.377–1.794, P < 0.001) vs. 0.938 (0.849–1.163, P = 0.94) in women]. Both BMI and RFM showed statistically significant correlation with hypertension and diabetes mellitus/impaired fasting glucose. Among men, BMI performed slightly better, while among women, RFM showed superiority, but with overlap in the CIs (Table 2).
When diagnostic accuracy of BMI and RFM for metabolic syndrome is examined, the negative and positive predictive values of RFM among women (99.4 and 18.2%, respectively) were higher than among men (98.9 and 15.9%, respectively). Compared to BMI (96 and 18% among women; 98 and 24% among men), RFM showed better negative predictive value in both genders and slightly lower positive predictive value among men. Bivariant analysis for metabolic syndrome showed higher OR (95% CI, P value) of RFM compared to BMI for women (16.247 [8.348–31.619, P < 0.001 vs. 5.995 [5.099–7.048, P < 0.001]), and for men (7.479, [4.876–11.47, P < 0.001] vs. 3.263 [2.944–3.616, P < 0.001]); again, there was no overlap in the ORs’ CIs.
Using ROC curves, we described the relationship between the sensitivity and the false positive rate for different values of RFM in identification of patients at risk for metabolic syndrome. Using the Youden index, RFM of 39.8 was found to be the ideal value for identification of metabolic syndrome in women with area under the curve (AUC) of 0.864 (95% CI 0.852–0.857, P < 0.001), not significantly different from the AUC of the ideal BMI that was found, 27.4 (AUC: 0.845, 95% CI: 0.833–0.857, P < 0.001). For men, the ideal RFM was found to be 27.72 with AUC of 0.739 (95% CI: 0.728–0.75, P < 0.001) similar to that of ideal BMI that was found 27.4, same as for women (AUC: 0.727, 95% CI: 0.716–0.738, P < 0.001) (Fig. 2).
The question of which obesity marker should be used by the primary physician is still open. Nowadays, BMI is the most commonly and widely used obesity marker. Other markers such as waist circumference, WHtR, WHR and BAI are also used by physicians [2,6–9]. Recently, RFM was proposed and showed promising results in its validation trail and few later trials [5,10–12]. We aimed to examine whether RFM provides better predictability than BMI to various cardiometabolic endpoints.
In our cohort, when compared to BMI, RFM was a significantly better predictor of high LDL, low HDL, high triglyceride and metabolic syndrome, both in men and women. RFM also showed better negative predictive value to rule out metabolic syndrome in both genders. BMI provided slightly better predictability for hypertension and diabetes mellitus in women (RFM among men), but these findings were not statistically significant. In our cohort, high BMI in men was associated to normal LDL, suggesting a protective effect. This finding requires further research.
The cutoff values of abnormal RFM were to be 33.9 for women and 22.8 for men . In our trial, using ROC curve, we tried to identify the ideal RFM for identification of metabolic syndrome, as a signal of higher cardiometabolic risk. RFM of 39.8 was found to be the ideal value for identification of metabolic syndrome in women with AUC of 0.864 (95% CI: 0.852–0.857, P < 0.001), while for men, the ideal RFM was found to be 27.72 with AUC of 0.739 (95% CI: 0.728–0.75, P < 0.001). These values are higher than the suggested cutoff values previously used , and further studies should be performed in order to determine the ideal cutoff values.
Our study, however, has some limitations; first, it was done on a specific group, Israeli adults who visited the RPEI during study years. Patients who attend periodic medical examination institute are assumingly more aware to preventive health issues than the general population. On the other hand, the large number of patients allowed us to achieve statistical significance to most of our findings. The characteristics of our cohort may also cause bias, as most of the cohort had obesity and abnormal RFM (vast majority among men). Second, the character of our study did not enable us to use endpoints as mortality, myocardial infarction or stroke. Longer follow-up or prospective trials may be done to examine whether RFM is a better predictor for these endpoints as well.
In conclusion, understanding of the advantages of different adiposity measures as predictor of cardiometabolic risk is important in maintaining and promoting health. The newly suggested obesity marker, RFM carries diagnostic benefits. When compared to BMI, RFM provides better predictability of various dyslipidemias (high LDL, low HDL and high triglycerides) and metabolic syndrome, which is an independent risk factor for coronary heart disease, peripheral artery disease, stroke and total mortality. A higher than previously suggested cutoff values of RFM may be used in men and women to predict metabolic syndrome. Further prospective studies are required in order to fully reveal the long-term clinical significance of RFM.
Conflicts of interest
There are no conflicts of interest.
1. Seidell JC, Halberstadt J. The global burden of obesity
and the challenges of prevention. Ann Nutr Metab. 2015; 66Suppl 27–12
2. Langenberg C, Sharp SJ, Schulze MB, Rolandsson O, Overved K, Forouhi NG, et al. Long-term risk of incident type 2 diabetes and measures of overall and regional obesity
: the EPIC-InterAct case-cohort study. PLoS Med. 2012; 9:e1001230
3. World Health OrganizationDefinition, Diagnosis and Classification of Diabetes Mellitus and Its Complications: Report of a WHO Consultation. Part 1: Diagnosis and Classification of Diabetes Mellitus. 1999. Geneva, Switzerland: World Health Organization, http://whqlibdoc.who.int/hq/1999/WHO_NCD_NCS_99.2.pdf
. [Accessed 12 December 2003].
4. Einhorn D, Reaven GM, Cobin RH, Ford E, Ganda OP, Handelsman Y, et al. American college of endocrinology position statement on the insulin resistance syndrome. Endocr Pract. 2003; 9:237–252
5. Kobo O, Leiba R, Avizohar O, Karban A. Relative fat mass
(RFM) as abdominal obesity
criterion for metabolic syndrome. Eur J Intern Med. 2019; 63:e9–e11
6. Savva SC, Lamnisos D, Kafatos AG. Predicting cardiometabolic risk: waist-to-height ratio or BMI. A meta-analysis. Diabetes Metab Syndr Obes. 2013; 6:403–419
7. Brenner DR, Tepylo K, Eny KM, Cahill LE, El-Sohemy A. Comparison of body mass index
and waist circumference as predictors of cardiometabolic health in a population of young canadian adults. Diabetol Metab Syndr. 2010; 2:28
8. Bergman RN, Stefanovski D, Buchanan TA, Sumner AE, Reynolds JC, Sebring NG, et al. A better index of body adiposity. Obesity
(Silver Spring). 2011; 19:1083–1089
9. Lam BC, Koh GC, Chen C, Wong MT, Fallows SJ. Comparison of body mass index
(BMI), body adiposity index (BAI), waist circumference (WC), waist-to-hip ratio (WHR) and waist-to-height ratio (whtr) as predictors of cardiovascular disease risk factors in an adult population in Singapore. Plos One. 2015; 10:e0122985
10. Woolcott OO, Bergman RN. Relative fat mass
(RFM) as a new estimator of whole-body fat percentage — A cross-sectional study in american adult individuals. Sci Rep. 2018; 8:10980
11. Fedewa MV, Russell AR, Nickerson BS, Fedewa MP, Myrick JW, Esco MR. Relative accuracy of body adiposity index and relative fat mass
in participants with and without Down syndrome. Eur J Clin Nutr. 2018. [Epub ahead of print]
12. Andreasson A, Carlsson AC, Önnerhag K, Hagström H. Predictive capacity for mortality and severe liver disease of the relative fat mass
algorithm. Clin Gastroenterol Hepatol. 2018. Nov 22. pii: S1542-3565(18)31269-2.
13. National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). Final report. Circulation. 2002; 106:3143–3421
Keywords:© 2019Wolters Kluwer Health Lippincott Williams Wilkins
body mass index; obesity; relative fat mass