FFM Index , FM Index and PBF in Subjects with Normal , Overweight , and Obese BMI in Saudi Arabia Female Population

Aims: To assess Fat Free Mass Index, Fat Mass Index and Percent Body Fat in subjects with normal, overweight, and obese BMI and to examine if FFMI and FMI as compared to BMI have higher predictability in identification of high risk groups as defined by metabolic measurements among female college students and employees in Hail, Northern part of Saudi Arabia. Methods: Sample of 514 female college students and employees were enrolled and body composition was measured by using bioelectrical impendence technique. FFMI and FMI are calculated using the standard formula. Blood pressure (BP) and pulse were measured using automatic BP reader in a resting sitting position. Random blood glucose was tested using strip method (One touch, Simple). Results: Around 11 percent of study subjects were underweight while 25 percent were overweight and another 22 percent were obese. Only 42 percent of study population had normal weight. Except for height there were significant differences for weight, BMI, FM, FFM and %BF across age groups. Weight, FM, FFM shows a linear trend till the age 40 yrs after which an inverse trend begins. BMI continues to show linear trend across all ages. Mean FFMI was around 14 kg/m2 (range 5th – 95th percentile: 12.5 – 17.8 kg/m2) and was modestly but significantly higher (P < 0.001) in the higher age group. Similarly, Mean FMI was 8.4 kg/m (range 5th – 95th percentile: 3.8 – 18.3 kg/m2) and significantly higher (P < 0.001) in the higher age group. In Regression models for SBP, BMI and %BF explain 18.7 % of variance; while for DBP, WC and %BF explain 11.2 % of variance. For blood glucose, it is FFMI, FMI and Visceral fat which explain maximum variance. Conclusion: BMI alone cannot provide information about the respective contribution of FFM or fat mass to body weight. This study presents FFMI and BFMI values that correspond to low, normal, overweight, and obese BMIs. FFMI and BFMI provide information about body compartments, regardless of height.


Introduction
Research also has indicated that body composition, more than BMI, is a primary determinant of health5 and a better predictor of mortality risk than BMI (Van Itallie et al., 2000).According to Ng and Zaghloul (2011), Musaiger (2012), KSA is witnessing rapid rise in obesity because of urbanisation and lifestyle changes.The available literature indicates that obesity is emerging as a major health problem with approximately three quarters of females and nearly two-thirds of males of adult population in the Kingdom being either overweight or obese (El-Hazmi, 2002).Most public health interventions are aimed primarily at prevention of obesity.Body mass index (BMI) is the most popular simple assessment tool for the degree of obesity in most epidemiological studies.
Obesity traditionally defined by Body mass index (BMI) may not accurately represent the complex scenario of obesity.The major limitation in using BMI as a measure for body fat is that BMI doesn't reflect actual composition of body weight.BMI cannot essentially differentiate between excess body weight coming from increased adipose tissue or lean muscle tissue which is certainly a limitation for the index.Heber D and Ingles S suggested in their researches that underweight as indicated by BMI could be a result of either fat-free mass (FFM) deficit (sarcopenia) or adipose tissue (fat mass-FM) deficit or both combined.
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Discussion
Malnick (2006) concluded in their study that obesity increases risk for many chronic diseases thereby increasing mortality rates across the world.According to Sun et al. (2009), females have higher risk associated with reduced health even with increased obesity in mid-life years.Obesity therefore has become primary address for prevention efforts at both public as well as individual level.Thus, clinical detection of obese individuals has become clinically very important.
BMI does not separate body compartments into FFM and BF.Because research has indicated that body composition is a primary determinant of health (Segal et al., 2002).FFM and BF compartments should be determined as part of a health assessment.FFM and BF change with height, weight, and age.It is therefore difficult to determine whether individual subjects have low or high FFM or BF.
BMI, or Body Mass Index, is a simple formula using a person's height and weight to calculate a value which is supposed to be representing body fat level.It has gained immense popularity in epidemiological studies owing to its simplicity in measurement and non invasive nature.However recent studies done by Romero-Corral et al. (2008), indicate that BMI may not be an accurate indicator of body fat especially in normal weight categories.
Average ranges for %BF in the present study were 36.5% -46.2%.Results of recent studies done in North American by Bartlett HL et al and on European populations by Baarends et al. (1997) indicated that significant weight gains are responsible for large numbers of subjects being above the suggested %BF ranges of 12 to 20 for men and 20 to 30 for women (Westerterp et al., 1997).Forty-five percent of all men and 38% of all women in a recent study conducted by Mostert et al. (2000), had values above these "desirable" levels.
The recent concepts of fat-free mass index and fat mass index, could provide an definitive alternative to BMI in the classification of overweight/over fat subjects or underweight/under lean subjects.There are no reference standards established till now for FFMI and FMI, at least in healthy people.Given that FFMI and FMI can explain better the complexities of body composition and their relationship with chronic diseases, developing population references for these indexes is need of the hour.It is proposed by researchers like (Engelen et al., 1999(Engelen et al., , 2000)), that the development of population wide reference values could be of great value to future epidemiological studies in both clinical setting as well as field surveys for comparative analysis of nutritional status among various BMI groups.
FFMI and BFMI eliminate differences in FFM and BF due to height and offer the advantage of having one set of recommended ranges, regardless of age and height.FFMI and BFMI have been reported in studies with small numbers of healthy subjects (Schutz et al., 2002) and patients (Flegal, 2003;Seidell, 1998;Abernathy, 2001).
Recently percentiles for FFMI and BFMI for healthy adults have been published by Kyle et al. (2003).However, these studies have not evaluated the FFMI and BFMI ranges for various BMI classifications.Our current study presents FFMI, BFMI, and %BF values for low, normal, overweight, and obese BMIs.
Large longitudinal studies will be necessary to determine whether an increase in weight or BMI is necessary to counteract the age-related decrease in FFMI.Schutz et al. (2002) found in their study that the effects of aging are noticeable only in adults older than 75 y and that the 25th and 75th percentiles of FFMI are lower in men older than 75 y than in men 18 to 34 y, whereas the same was not found in women.Because FFMI remained constant with aging, an adjustment in FFMI reference values does not appear to be necessary.
The present study established reference ranges for FFMI and FMI in apparently healthy female subjects but investigations in large groups of males and females across various age groups and in children is required for further understanding.Future investigations analysing the relationship between body composition measurements and chronic disease risk factors will help to understand better the contribution of FMI (respectively FFMI) to potential risk factors and subsequent mortality.

Conclusion
FMI vs FFMI can be useful tools for nutritional status assessment for over nutrition and under nutrition of healthy female subjects.Development of reference standards could help in prediction of risk factors.BMI alone cannot provide information about the respective contributions of FFM and FM to body weight.This study presented the FFMI, BFMI, and %BF values that correspond to low, normal, overweight, and obese BMIs.FFMI and BFMI can provide meaningful information about body composition, regardless of height.FFMI and BFMI could be more accurate indicators of nutrition status.

Table 2 .
Body composition analysis of the subjects according to age

Table 5 .
Regression model for SBP and anthropometric variables

Table 6 .
Regression model for DBP and anthropometric variables

Table 7 .
Regression model for blood glucose and anthropometric variables