The validity of BMI as an acceptable measure of overweight or obesity, and as an accurate reflection of body fat (BF) content, has been repeatedly questioned and the need for population specific BMI cut‐off points has been highlighted.7,10,11,12,13
Ideally, individualised assessment of BF should be pursued in the clinical setting, as BF percentage is a more reliable measure of fatness than BMI, at least in the general population.31
Indeed, our data indicate that only 58% of the variance in BMI can be predicted, as opposed to 77% in BF. BF in vivo can be determined via a number of methods such as underwater weighing, dual energy x ray absorptiometry, total body water, total body nitrogen, 40
K whole body counting and urinary creatinine excretion.32,33,34
BF can also be estimated from the thickness of partial subcutaneous fat, near infrared rays and ultrasound.35
However, none of these methods can be practically used in the routine clinical setting as they require sophisticated apparatus and specialised personnel.33
In recent years, a bioelectrical impedance method for the estimation of BF in different populations has become popular and widely recommended, as it is reliable, objective, practical, relatively inexpensive and does not require highly trained personnel.32,33
The validity of this method has been confirmed in various studies.32,36,37,38,39
Devices with eight tactile electrodes using single frequency electrical current, similar to the one used in this study, generate highly reproducible measurements of total BF and segmental fat distribution.40
Their correlation with the “gold standards” of dual energy x ray absorptiometry and hydrostatic weighing is 0.90 and 0.80, respectively, with a standard error of around 3.0, producing a coefficient of variation of <10%.33
This suggests that bioelectrical impedance measurements (especially when using eight electrodes) are valid and suitable for body composition studies.32,39,40
Patients are usually happy to undergo such a measurement because of its simplicity and similarity to normal weighing.
In the absence of the necessary equipment or expertise, the predictive model presented here can be used to easily calculate BF of RA patients from BMI. The cross validation of this predictive model in patients with RA is reassuring. Even though there was a statistically significant difference between the measured and the predicted BF, closer examination of the means indicates that this difference is at a level of less than 0.5% of BF with a coefficient of variation of <10%. The statistical significance of such a small difference can be attributed to the very large number of the validation group and is clinically not significant. However, the parts of the equation referring to OA patients and healthy individuals need further prospective validation in sufficiently large samples of the relevant populations.
BMI remains the most commonly used indicator of body fatness in the clinical setting, and the cut‐off points of 25 kg/m2
and 30 kg/m2
(for overweight and obesity, respectively) used for the general population are also routinely applied in RA patients. This study shows that application of these BMI cut‐off points misclassified 9% of male and 15% of female RA patients in terms of actual body fatness. For a given BMI, RA patients exhibited an average 4.3% increase in BF compared to healthy controls. In contrast, for the same level of BF, RA patients had BMI values almost 2 kg/m2
lower than those of healthy controls. We propose that BMI cut‐off points in the RA population should be lowered to 23 kg/m2
(from 25 kg/m2
) for overweight, and 28 kg/m2
(from 30 kg/m2
) for obesity. The lowest limit for normal BMI (that is, 18.5 kg/m2
) should remain unaltered, as low BMI levels have been related to increased cardiovascular risk in patients with RA.41,42
We also provide a chart for the classification of RA patients in normal, overweight and obese categories according to these BMI cut‐offs, for use in the routine clinical setting (fig 2B).
The most likely explanation for the BMI and BF differences observed in RA is rheumatoid cachexia associated with the chronic inflammatory response, given that such differences were not as prominent in OA. RA patients experience accelerated involuntary loss of fat‐free mass, predominantly in the skeletal muscle, in excess of what is normally expected as a result of the ageing process.43
Although the underlying mechanisms for rheumatoid cachexia remain unknown, possible contributing factors include the overproduction of inflammatory cytokines such as tumour necrosis factor α and interleukin 1β.43,44
Our subanalyses within the RA population revealed that neither BMI nor BF were associated with current clinical or serological disease activity, seropositivity for rheumatoid factor (which tends to associate with more severe disease) or corticosteroid administration. This is not totally surprising as disease activity may vary within small periods of time, depending on medication and the disease itself, whereas changes in body composition are longer term processes. On the other hand, disease duration appeared to be of some importance. It is possible that most alterations in body composition of RA patients occur in the first few years of the disease, as it has previously been reported,21
irrespective of disease characteristics or medical treatment.
The results of the present study are reminiscent of the observations made for Asian populations, which have significantly higher CVD risk than white people: BF in Asians has been found to be 3–5% higher than that of white people with similar BMI, whereas BMI was 3–4 kg/m2
lower than that of white people with similar BF.32
Differences in body build (trunk to leg length ratio and slenderness) and in muscularity have been suggested as possible explanations for these discrepancies. As a result, new cut‐off points for Asian populations have been set at 23 kg/m2
and 27 kg/m2
for overweight and obesity, respectively,10
and have been shown to be more sensitive in identifying Asians at increased risk for CVD.45
In our participants, lowered BMI cut‐off points would reflect an average reduction of 5–6 kg, or 8%, in the ideal weight (the weight one should have in order to be below the BMI cut‐off for overweight). Such reductions in body weight are likely to lead to physiological benefits in the cardiovascular system: in the general population, even a 5% reduction of body weight is known to favourably affect most classic CVD risk factors.46,47
The reduced BMI cut‐off points for RA suggested here may be of significance both for the management of individual patients and for further research into the cardiovascular morbidity and mortality of RA. In the clinical arena, the reduction of these thresholds would identify an additional 10–15% of people with RA as overweight or obese, and may trigger closer scrutiny for other CVD risk factors and appropriate intervention, if necessary. Moreover, obesity, defined by the BMI, is one of the WHO criteria for the metabolic syndrome.46
Aggressive identification and reduction of classic CVD risk factors in patients with RA is an obvious strategy for reducing the increased cardiovascular mortality of this disease.19
From the research perspective, the new thresholds may trigger re‐analysis of previously published cohorts or further analysis of prospective cohorts as to the importance of body fat as a predictor of CVD in RA and its association with other individual risk factors.
We conclude that, in the clinical setting, body fatness of RA patients should be evaluated based on the BMI cut‐off points of 23 kg/m2 for overweight and 28 kg/m2 for obesity. In the absence of specialised equipment, if necessary, BF of patients with RA can be estimated from BMI using the equation provided.