The perils of using predicted values in place of observed covariates: an example of predicted values of body composition and mortality risk

ABSTRACT Background Several studies have assessed the relation of body composition to health outcomes by using values of fat and lean mass that were not measured but instead were predicted from anthropometric variables such as weight and height. Little research has been done on how substituting predicted values for measured covariates might affect analytic results. Objectives We aimed to explore statistical issues causing bias in analytical studies that use predicted rather than measured values of body composition. Methods We used data from 8014 adults ≥40 y old included in the 1999–2006 US NHANES. We evaluated the relations of predicted total body fat (TF) and predicted total body lean mass (TLM) with all-cause mortality. We then repeated the evaluation using measured body composition variables from DXA. Quintiles and restricted cubic splines allowed flexible modeling of the HRs in unadjusted and multivariable-adjusted Cox regression models. Results The patterns of associations between body composition and all-cause mortality depended on whether body composition was defined using predicted values or DXA measurements. The largest differences were observed in multivariable-adjusted models which mutually adjusted for both TF and TLM. For instance, compared with analyses based on DXA measurements, analyses using predicted values for males overestimated the HRs for TF in splines and in quintiles [HRs (95% CIs) for fourth and fifth quintiles compared with first quintile, DXA: 1.22 (0.88, 1.70) and 1.46 (0.99, 2.14); predicted: 1.86 (1.29, 2.67) and 3.24 (2.02, 5.21)]. Conclusions It is important for researchers to be aware of the potential pitfalls and limitations inherent in the substitution of predicted values for measured covariates in order to draw proper conclusions from such studies.

SARCOPENIA ACCORDING TO BRAZILIAN AND EWGSOP 2 CUTOFFS: DATA FROM SARCOS

Cutoff values for lean mass and muscle strength are still controversial in the diagnosis of sarcopenia. The use of European, American and Asian consensus outside these regions may lead to important diagnostic errors. We hypothesized that there are significant differences between the cutoff points from Brazil and Europe in older people. This is a cross-sectional analyses of 502 older adults from SARCOS study, conducted at São Paulo - Brazil. All subjects underwent DXA analyses of total body. Lean mass was obtained from appendicular lean mass by height2 and muscle strength by dynamometer of dominant hand. The Brazilian cutoff points were based on 25th percentile by gender. The European ones were from EWGSOP 2. Agreement was assessed by the Kappa coefficient. The mean age was 78.39 ± 7.08 years old and 277 (55.18%) individuals were women. Among the ethnic groups, 339 (67.53%) were caucasian, 145 (28.88%) afrodescendants and 18 (3.59%) asians. The Brazilian cutoffs for muscle strength were 26 kg for men and 16 kg for women (equivalent to EWGSOP2); while those for lean mass were significantly lower, 6.56 kg/m2 vs. 5.56 kg/m2, respectively. The prevalence by EWGSOP 2 was higher than that obtained by the Brazilian cutoff points (20.32% vs 14.14%, p <0.001), even though these criteria presented Kappa = 0.792; p <0.001. Considering these disparities, 6 out of 100 subjects are considered sarcopenic by European criteria and not by the Brazilian cutoffs. There are significant differences in sarcopenia cutoffs between Brazil and Europe, and this cause important diagnostic variations.