Obesity is associated with cardiometabolic risk [
Dual-energy X-ray absorptiometry (DXA) devices estimate FM% with acceptable accuracy and have become the reference method for estimating body composition [
Equations that estimate FM% based on simple anthropometric measures such as weight, height, and waist circumference (FM% equations) overcome some of the shortcomings of DXA and BIA: they are simple and inexpensive and can be applied to existing epidemiological data. Most of the FM% equations have been validated against DXA measurements in different populations [
The hypothesis tested in this study is that this loss of accuracy in estimating FM% is irrelevant in the assessment of obesity-related cardiometabolic risk on a group level, and hence using weight and height-based FM% equations to categorize obesity yields a similar discriminative ability as DXA or BIA measurements. Further, we investigate whether any of the FM%-based measures of obesity improves on BMI. Thus, we intend to test the null-hypothesis that all these methods have similar predictive power for hypertension, impaired fasting glucose, dyslipidaemia, and the metabolic syndrome. If confirmed, FM% equations and/or BMI can be used to identify individuals with elevated cardiometabolic risk instead of DEXA and/or BIA. This offers considerable economic savings, both in clinical health care and research.
The study population consisted of 40–79-year-old male (
Blood samples were taken in the morning between 0730 and 0900 after the subjects had fasted for 12 hours. Blood pressure (BP) was measured by the manual oscillometric method after 5 min rest. If women were in the pre menopausal state, the blood sample was drawn on the 5th day from the start of menstruation. Serum was separated within 30 minutes and stored at −80°C until analysis. Serum glucose, total and high-density lipoprotein (HDL) cholesterol and triacylglycerol concentrations were measured by enzymatic photometry on a Kone Pro Clinical Chemistry Analyzer (Thermo Clinical Labsystems Oy, Vantaa, Finland) with commercial kits. Low-density lipoprotein (LDL) cholesterol was calculated using the Friedewald equation [
In grading hypertension, we followed the definitions of the European Society of Hypertension [
Dyslipidaemia is defined as either triacylglycerol ≥ 1.7 mmol/L or HDL cholesterol < 1.0 mmol/L men/1.30 mmol/L women. For Impaired fasting glucose, we examined both the stricter International Diabetes Federation (IDF) sponsored 5.6 mmol/L (100 mg/dL) and higher level of 6.1 mmol/L (110 mg/dL) recommended by the WHO [
For definition of metabolic syndrome, we adopted cutoffs values suggested by the common task force from the IDF and the American Heart Association/National Heart, Lung and Blood Institute (AHA/NHBLI) [
All measurements were performed after an overnight fast. Participants were weighed without shoes and with light clothes. Height was determined to the nearest 0.1 cm using a fixed wall-scale measuring device. Weight was determined within 0.1 kg for each subject using an electronic scale, calibrated before each measurement session. BMI was calculated as weight (kg) per height (m2). Waist circumference was measured with a measuring tape at the largest circumference location between landmarks of the most proximal iliac and the most distal rib bone as a mean value of two measurements.
From a literature search we found five different equations for FM% estimation in adult men and women (Table
Anthropometry- and bioimpedance-analysis- (BIA-) based estimates of fat mass percentage (FM%) and their respective bias versus DXA measurements.
Predictor | Equation for estimating FM% | Men | Women | Combined | |||||
---|---|---|---|---|---|---|---|---|---|
|
Mean biasa | SD |
|
Mean biasa | SD |
|
Mean biasa | ||
( |
Women: FM% = (−24.18 + 1.181 * weight/height)/weight |
205 | −1.3 | 4.7 | 388 | 0.1 | 4.1 | 593 | −0.6 |
( |
FM% = 64.5 – 848 * (1/BMI) + 0.079 * age −16.4 * sexb − 0.05 * sexb* age + 39.0 * sexb* (1/BMI) | 205 | −2.4 | 4.6 | 388 | −1.0 | 4.1 | 593 | −1.7 |
( |
FM% = 1.2*BMI + 0.23*age −10.8*sexb − 5.4 | 205 | 1.7 | 4.9 | 388 | 2.3 | 4.8 | 593 | 2.0 |
( |
bioimpedance-based proprietary algorithm | 82 | −4.8 | 3.9 | 58 | −3.6 | 3.2 | 140 | −4.2 |
( |
bioimpedance-based proprietary algorithm | 181 | −4.6 | 3.4 | 273 | −4.7 | 3.0 | 454 | −4.6 |
( |
Women: FM% = (−2.28 + 1.268 (weight/height) |
205 | −11.7 | 5.3 | 388 | −1.6 | 4.2 | 593 | −6.7 |
( |
FM% = −11.4 * sexb + 0.2 * age + 1.294 * BMI − 8 | 205 | 5.0 | 6.4 | 388 | 16.5 | 8.9 | 593 | 10.8 |
( |
Arithmetic mean of equations ( |
205 | −0.7 | 4.6 | 388 | 0.5 | 4.2 | 593 | −0.1 |
For the first equation see [
For the second equation see [
For the third equation see [
Prodigy with software version 9.3 GE Lunar, Madison, WI, USA was used to estimate FM and FM%. Precision of the repeated measurements expressed as the coefficient of variation was 2.2% for FM.
InBody (720) (Biospace, Seoul, Korea) is a multifrequency impedance body composition analyzer. Total body water (TBW) was estimated with the manufacturer-provided device specific software from area, volume, length, impedance, and a constant proportion (specific resistivity). Fat free mass (FFM) was estimated by dividing TBW by 0.73. Readings of FFM and FM% are reported in this paper. Precision of the repeated measurements expressed as coefficient of variation was, on average, 0.6% for FM%.
The concepts of reclassification index and integrated discrimination improvement were introduced by Pencina et al. [
The integrated discrimination improvement (IDI) describes the mean difference in predicted probabilities between case patients and noncase participants for two models. It is calculated from individual predicted probabilities for each participant in the respective models:
Categories of obesity according to each specific obesity-estimation method were formed based on age and gender. Each of the 4 subgroups (men and women aged above and below 60 years, resp.) was sorted by degree of obesity, separately for each definition (i.e., BMI and various FM% measurements and estimates). Percentiles at BMI 25 and 30 were used to obtain corresponding cutoffs for obesity categories according to each of the FM%-measurement and estimation methods. Thus we obtained categories of obesity with identical numbers of subjects but partly different individuals sorted by degree of obesity according to the respective method. Due to small numbers of underweight and severely obese subjects we settled on the following categories:
Anthropometric and metabolic characteristics of the study population.
Men | Women | |||||
---|---|---|---|---|---|---|
|
Mean | 95% CI |
|
Mean | 95% CI | |
Age (years) | 205 | 57 | (55–58) | 388 | 56 | (55–57) |
Height (cm) | 205 | 176 | (175–177) | 388 | 163 | (162–164) |
Weight (kg) | 205 | 82.5 | (81.0–83.9) | 388 | 70.6 | (69.3–71.9) |
BMI (kg/m2) | 205 | 26.6 | (26.2–27.1) | 388 | 26.6 | (26.1–27.1) |
Fat mass (kg) | 205 | 22.7 | (22–23.6) | 388 | 26.9 | (25.9–27.9) |
Fat mass (%) | 205 | 27.1 | (26–27.9) | 388 | 37.1 | (36.4–37.9) |
Waist circumference (cm) | 200 | 95 | (93–96) | 376 | 86 | (85–87) |
Chest circumference (cm) | 166 | 102 | (101–103) | 269 | 98 | (96–99) |
Systolic blood pressure (mmHg) | 201 | 146 | (144–149) | 377 | 142 | (140–145) |
Diastolic blood pressure (mmHg) | 201 | 85 | (84–87) | 377 | 83 | (82–84) |
Fasting glucose (mmol/L) | 166 | 5.8 | (5.6–5.9) | 301 | 5.6 | (5.5–5.7) |
Fasting insulin ( |
166 | 10.1 | (6.5–14) | 302 | 8.2 | (7.5–9.0) |
Total cholesterol (mmol/L) | 167 | 5.3 | (5.1–5.4) | 302 | 5.5 | (5.4–5.6) |
HDL (mmol/L) | 167 | 1.5 | (1.4–1.6) | 302 | 1.8 | (1.7–1.9) |
LDL (mmol/L) | 167 | 3.1 | (3–3.3) | 302 | 3.1 | (3.0–3.2) |
Triglycerides (mmol/L) | 167 | 1.5 | (1.3–1.6) | 302 | 1.2 | (1.2-1.3) |
Free fatty acids (mmol/L) | 167 | 496 | (450–542) | 302 | 541 | (502–580) |
Bland-Altman plots were used to compare the mean difference of the various FM% equations to DXA (Figures
Bland-Altman plots for estimates of fat mass percent (FM%): DXA versus FM%-prediction equations and bioimpedance in men.
Bland-Altman plots for estimates of fat mass percent (FM%): DXA versus FM%-prediction equations and bioimpedance in women.
For models using BMI and FM% as a continuous variables, nonnormally distributed variables were power-transformed. Distributions of the resulting variables fulfilled criteria for goodness of fit with normal distribution (Kolmogorov-Smirnov
Receiver operating characteristics were calculated from sex-specific logistic regressions with method specific obesity-categories and 10-year age categories as independent variables. The SAS-procedure: PROC LOGISTIC/roc-contrast was used to estimate differences in area under curve (AUC).
The Statistical Analysis System (SAS for Windows, version 9.2, SAS Institute, Carry, NC, USA) was used for all statistical evaluations. For calculation of net reclassification index and integrated discrimination improvement we adapted SAS-macros provided by Cook and Ridker as a supplement [
Anthropometric and metabolic characteristics of the study population are given in Table
Values in fat mass percentage (FM%) for the method-specific percentile corresponding to BMI percentiles at BMI 25 and 30, respectively.
FM% cutoffs corresponding to BMI 25 | FM% cut-offs corresponding to BMI 30 | |||||||
---|---|---|---|---|---|---|---|---|
Men | Women | Men | Women | |||||
Methoda/age-group | <60 | ≥60 y | <60 | ≥60 y | <60 | ≥60 y | <60 | ≥60 y |
DXA | 24.0 | 26.1 | 36.7 | 34.7 | 32.3 | 37.5 | 44.0 | 43.8 |
BIA InBodyb | 19.3 | 23.0 | 31.5 | 31.9 | 28.7 | 34.0 | 38.7 | 40.8 |
FM%-equationc | 24.0 | 26.3 | 35.3 | 37.4 | 30.1 | 31.7 | 41.6 | 43.8 |
Comparisons of predictive powers of the various obesity measures for hypertension grades 1 and 2 [
Prediction/discrimination of hypertension with degree of obesity as defined by dual-energy X-ray absorptiometry (DXA) bioimpedance analysis (BIA), an anthropometry-based estimate of fat mass percentage (FM% equation) and BMI.
ROC analysesn | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Reference method/modela | New method/modelb |
|
Reclassification index, %f | IDI, %k | Men | Women | |||||||
Casesd | Non-casese | Netg |
|
Casesi | Non-casesj |
|
|
|
|
|
| ||
Hypertensionq, grade 1 (≥140/90 mmHg) | |||||||||||||
DXA | BIA InBodyr | 269 | 185 | 5% | 0.214 | −1% | 6% | 1.7% | 0.017 | 0.03 | 0.127 | 0.06 | 0.000 |
BMI | 335 | 258 | 6% | 0.220 | 2% | 3% | 1.9% | 0.006 | 0.00 | 0.977 | 0.04 | 0.073 | |
Estimates | 335 | 258 | 6% | 0.208 | 2% | 3% | 1.5% | 0.019 | 0.03 | 0.383 | 0.07 | 0.000 | |
BIA InBody | BMI | 269 | 185 | 4% | 0.360 | 1% | 3% | 0.5% | 0.534 | −0.03 | 0.330 | −0.03 | 0.147 |
Estimate | 269 | 185 | 3% | 0.502 | 0% | 3% | 0.1% | 0.885 | 0.00 | 0.979 | 0.01 | 0.606 | |
BMI | Estimate | 335 | 258 | 0% | 0.803 | 0% | 0% | −0.4% | 0.144 | 0.03 | 0.021 | 0.04 | 0.000 |
| |||||||||||||
Hypertension, grade 2 (≥160/100 mmHg) | |||||||||||||
DXA | BIA InBody | 93 | 361 | −1% | 0.848 | −4% | 3% | 1.4% | 0.063 | 0.02 | 0.396 | 0.05 | 0.000 |
BMI | 117 | 476 | −9% | 0.128 | −8% | −1% | −1.2% | 0.049 | −0.09 | 0.064 | −0.03 | 0.255 | |
Estimate | 117 | 476 | −8% | 0.154 | −7% | −1% | −1.2% | 0.036 | −0.01 | 0.746 | 0.01 | 0.626 | |
BIA InBody | BMI | 93 | 361 | −9% | 0.161 | −8% | −1% | −2.5% | 0.003 | −0.11 | 0.006 | −0.08 | 0.000 |
Estimate | 93 | 361 | −10% | 0.096 | −9% | −1% | −2.8% | 0.001 | −0.04 | 0.309 | −0.04 | 0.044 | |
BMI | Estimate | 117 | 476 | 1% | 0.682 | 1% | 0% | 0.0% | 0.870 | 0.07 | 0.001 | 0.04 | 0.000 |
| |||||||||||||
Dyslipidaemiat | |||||||||||||
DXA | BIA InBody | 111 | 304 | −2% | 0.616 | −5% | 3% | −0.1% | 0.928 | −0.03 | 0.161 | −0.01 | 0.510 |
BMI | 124 | 345 | 6% | 0.320 | 2% | 4% | 3.5% | 0.015 | −0.01 | 0.816 | 0.02 | 0.378 | |
Estimate | 124 | 345 | 4% | 0.496 | 0% | 4% | 2.7% | 0.040 | −0.02 | 0.640 | 0.01 | 0.766 | |
BIA InBody | BMI | 111 | 304 | 8% | 0.149 | 6% | 2% | 3.1% | 0.022 | 0.02 | 0.568 | 0.03 | 0.162 |
Estimate | 111 | 304 | 6% | 0.240 | 5% | 2% | 2.5% | 0.044 | 0.01 | 0.734 | 0.02 | 0.390 | |
BMI | Estimate | 124 | 345 | −2% | 0.237 | −2% | −1% | −0.8% | 0.111 | −0.01 | 0.598 | −0.01 | 0.148 |
bDifferent method of estimating obesity, the predictive power of which is compared to reference model/reference method.
cNumber of participants.
dNumber of participants that are positive with regard to respective outcome.
eNumber of participants that are negative with regard to respective outcome.
fPercentage improvement (+) or deterioration (−) in predictive power of new model compared to reference model. Categories of obesity/FM% as independent variable.
gNet reclassification of cases + net reclassification of noncases. A positive number denotes increased predictive power for the new model.
hLikelihood of net reclassification index to be 0, that is, the new model showing no improvement/deterioration over reference model.
iNet reclassification of cases = percentage of cases reclassified by the new model into a higher risk category − percentage of cases reclassified by the new model into a lower risk category
jNet reclassification of non-cases = percentage of non-cases reclassified by the new model into a lower risk category − percentage of non-cases reclassified by the new model into a higher risk category.
kIntegrated discrimination improvement (+) or deterioration (−) of new model compared to reference model. Categories of obesity/FM% as independent variable in an age-adjusted model.
lMean difference in predicted individual probabilities between cases and non-cases for two models. A positive number denotes increased predictive power for the new model.
mLikelihood of net reclassification index to be 0, that is, the new model showing no improvement/deterioration over reference model.
nMeasures of obesity (BMI/FM%) as continuous variable in a logistic regression model predicting respective outcomes.
oDifference in area under curve of receiver operating characteristic compared to reference method.
pProbability of 0-hypothesis (no difference).
qDefinitions of hypertension according to European Societies for Hypertension and Cardiology {Mancia, 2007 #2897}.
rEstimation of FM% with bioimpedance device InBody (720) (Biospace, Korea).
sAnthropometry-based estimate; arithmetic mean of FM% estimations according to prediction methods Deurenberg et al. [
tTriacylglycerols ≥ 1.7 mmol/L or HDL cholesterol ≤ 1.29 mmol/L in men or HDL ≤ 1.03 mmol/L in women.
A comparison of the various obesity-measures ability to indicate different levels of impaired fasting glucose and metabolic syndrome is given in Table
Prediction/discrimination of impaired fasting glucose and the metabolic syndrome with degree of obesity as defined by dual-energy X-ray absorptiometry (DXA) bioimpedance analysis (BIA), an anthropometry-based estimate of fat mass percentage (FM%-equation) and BMI.
ROC analysesn | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Reference method/modela | New method/modelb |
|
Reclassification index, %f | IDI, %k | Men | Women | |||||||
Casesd | Non-casese | Netg |
|
Casesi | Non-casesj |
|
|
|
|
|
| ||
Impaired fasting glucose (≥5.6 mmol/L = 100 mg/dL) |
|||||||||||||
DXA | BIA InBodyq | 164 | 249 | −6% | 0.181 | −7% | 1% | −0.5% | 0.506 | −0.03 | 0.102 | −0.01 | 0.394 |
BMI | 191 | 276 | −2% | 0.727 | −4% | 2% | 0.3% | 0.723 | −0.04 | 0.286 | −0.02 | 0.394 | |
Estimater | 191 | 276 | −1% | 0.771 | −4% | 2% | 0.2% | 0.809 | −0.03 | 0.404 | −0.01 | 0.597 | |
BIA InBody | BMI | 164 | 249 | 2% | 0.752 | 1% | 0% | 0.2% | 0.796 | −0.01 | 0.888 | −0.01 | 0.744 |
Estimate | 164 | 249 | 1% | 0.769 | 1% | 1% | −0.1% | 0.882 | 0.00 | 0.890 | 0.00 | 0.981 | |
BMI | Estimate | 191 | 276 | 0% | 1.000 | 0% | 0% | −0.1% | 0.733 | 0.01 | 0.514 | 0.01 | 0.386 |
| |||||||||||||
Impaired fasting glucose (≥6.1 mmol/L = 110 mg/dL) | |||||||||||||
DXA | BIA InBody | 70 | 343 | −1% | 0.901 | −4% | 3% | 3.5% | 0.009 | −0.01 | 0.584 | 0.01 | 0.462 |
BMI | 80 | 387 | 6% | 0.394 | 3% | 4% | 3.2% | 0.009 | 0.00 | 0.900 | 0.00 | 0.918 | |
Estimate | 80 | 387 | 3% | 0.616 | 0% | 3% | 2.6% | 0.023 | 0.02 | 0.438 | 0.01 | 0.796 | |
BIA InBody | BMI | 70 | 343 | 7% | 0.341 | 6% | 1% | −0.7% | 0.609 | 0.02 | 0.648 | −0.02 | 0.504 |
Estimate | 70 | 343 | 2% | 0.754 | 1% | 1% | −1.5% | 0.205 | 0.04 | 0.253 | −0.01 | 0.799 | |
BMI | Estimate | 80 | 387 | −3% | 0.251 | −3% | -1% | −0.6% | 0.176 | 0.02 | 0.315 | 0.01 | 0.248 |
| |||||||||||||
Metabolic syndrome (AHA/NHBLI)s | |||||||||||||
DXA | BIA InBody | 144 | 268 | −4% | 0.400 | −6% | 2% | −0.7% | 0.691 | −0.03 | 0.120 | 0.01 | 0.625 |
BMI | 165 | 301 | 4% | 0.461 | 0% | 4% | 2.5% | 0.257 | −0.02 | 0.610 | 0.02 | 0.309 | |
Estimate | 165 | 301 | 3% | 0.519 | −1% | 4% | 1.7% | 0.407 | −0.02 | 0.466 | 0.02 | 0.329 | |
BIA InBody | BMI | 144 | 268 | 3% | 0.595 | 2% | 1% | 0.9% | 0.662 | 0.01 | 0.697 | 0.01 | 0.429 |
Estimate | 144 | 268 | 4% | 0.480 | 2% | 1% | 0.8% | 0.681 | 0.01 | 0.812 | 0.01 | 0.409 | |
BMI | Estimate | 165 | 301 | −1% | 0.577 | −1% | 0% | −0.7% | 0.252 | −0.01 | 0.622 | 0.00 | 0.958 |
bDifferent method of estimating obesity, the predictive power of which is compared to reference model/reference method.
cNumber of participants.
dNumber of participants that are positive with regard to respective outcome.
eNumber of participants that are negative with regard to respective outcome.
fPercentage improvement (+) or deterioration (−) in predictive power of new model compared to reference model. Categories of obesity/FM% as independent variable.
gNet reclassification of cases + net reclassification of non-cases. A positive number denotes increased predictive power for the new model.
hLikelihood of net reclassification index to be 0, that is, the new model showing no improvement/deterioration over reference model.
iNet reclassification of cases = percentage of cases reclassified by the new model into a higher risk category − percentage of cases reclassified by the new model into a lower risk category.
jNet reclassification of non-cases = percentage of non-cases reclassified by the new model into a lower risk category − percentage of non-cases reclassified by the new model into a higher risk category.
kIntegrated discrimination improvement (+) or deterioration (−) of new model compared to reference model. Categories of obesity/FM% as independent variable in an age-adjusted model.
lMean difference in predicted individual probabilities between cases and non-cases for two models. A positive number denotes increased predictive power for the new model.
mLikelihood of net reclassification index to be 0, that is, the new model showing no improvement/deterioration over reference model.
nMeasures of obesity (BMI/FM%) as continuous variable in a logistic regression model predicting respective outcomes.
oDifference in area under curve of receiver operating characteristic compared to reference method.
pProbability of 0-hypothesis (no difference).
qEstimation of FM% with bioimpedance device InBody (720) (Biospace, Korea).
rAnthropometry-based estimate; arithmetic mean of FM% estimations according to prediction methods Deurenberg et al. [
sDefinition of metabolic syndrome suggested by the common task force from the IDF and the American Heart Association/National Heart, Lung and Blood Institute (AHA/NHBLI) [
The receiver operated characteristics of the different obesity measures as predictors of cardiometabolic risk factors are shown in Figures
Receiver operating characteristic of DXA, BIA, BMI, and anthropometry-based estimate of fat mass percent (FM%-equation) as predictors of hypertension and dyslipidaemia. (a) In direct comparisons, the area under curve (AUC) for the anthropometry-based estimate of fat mass percentage (FM%-equation) is larger than AUC for BMI (
Receiver operating characteristic of DXA, BIA, BMI, and anthropometry-based estimate of fat mass percent (FM%-equation) as predictors of elevated fasting glucose and the metabolic syndrome. (a)–(f) There are no significant differences in direct comparisons of areas under curve (AUC) for the different methods.
A comparison of 95% confidence intervals for differences in integrated discrimination and AUC between DXA and the anthropometry-based estimate is given in Figure
Comparison of DXA and anthropometry-based estimate of fat mass percent (FM% equation) as predictors of cardiometabolic risk factors. (a) Comparison of the integrated discrimination (= mean individual prediction of cases−mean individual prediction of referents) between categories of obesity based on DXA measurements and categories-based on anthropometry-based estimate of fat mass percentage (FM% equation) basis: whole study population, both men and women. (b) Difference in area under curve between fat mass % as a continuous variable measured by DXA and estimated by FM%-equation.
Discrimination analysis: 95% CI
Receiver operating characteristic: 95% CI
In this population of healthy, middle aged, and elderly Finns we found that anthropometry-based FM%-predictions and BMI had similar predictive power for obesity-associated cardiometabolic risk markers as FM% derived from DXA- or BIA measurements. Some of the studied obesity measures have small advantages in discriminating one single cardiometabolic risk factor. With regard to metabolic syndrome—which combines all of the studied risk factors—discriminative ability of all obesity measures is similar.
Our results are consistent with findings from other healthy populations that used DXA as reference method. In a multi ethnic survey of adults [
Conversely, a study combining weight-loss outpatients and hospital staff [
Ethnicity influenced the methodology of our study. The relationship between anthropometric measures and FM% is different, not only among the main racial categories [
Absence of data regarding prescribed medication is a further limitation of our study. Both antihypertensive and cholesterol-lowering agents weaken the association between obesity and hypertension/dyslipidaemia, as they attenuate the outcome (hypertension, dyslipidaemia) without changing the exposure we study (obesity). Neither DXA nor BIA measurements are part of routine health care in Finland. However, anthropometric measures are taken frequently in primary care and—if indicating obesity—often lead to further laboratory testing which may ultimately result in the prescription of lipid-lowering or antihypertensive drugs. Thus, in our comparison with DXA and BIA disregarding medication is likely to have resulted in underestimation of BMI and FM% equations as risk indicator.
Both reclassification index and integrated discrimination improvement are comparatively new statistical tools, which may complicate the interpretation of results. The reclassification index is exclusively based on participants that change risk categories between models. It provides no confidence intervals and, as in our study, it is difficult to judge if nonsignificant results imply small differences or lack of power. The integrated discrimination improvement takes into account all changes in individual probabilities between models and is, thus, a more sensitive measure. Receiver operated characteristics show a similar picture of only small differences in predictive power between FM%-measurements and anthropometry-based estimates of FM%. Thus, all three different statistical methods yielded similar results.
Measurement and estimation methods that are feasible in an epidemiological context may still have too wide a range of error to be used to predict individual risk levels in a clinical setting to predict individual risk levels. Although the mean bias of some of the weight and height based FM% estimates was smaller than that of bioimpedance, the standard deviations were consistently larger. Whether that lack of precision in estimating individual FM% translates into unacceptably imprecise estimates of individual cardiometabolic risks cannot be answered in the current study. Both reclassification index and integrated discrimination are used on the group level only and provide no measures of individual variability.
Our results suggest that for predicting cardiometabolic risk in healthy middle aged and elderly Finns at population level, anthropometry-based categories of obesity are equivalent to obesity categories derived from DXA and BIA measurements. Further, our results indicate that discriminative ability of anthropometry-based FM% equations and BMI are similar.
Low-cost measures of obesity can be utilized in screening for obesity related risk.
The authors declared no conflict of interests.
Sulin Cheng is the principal investigator of the study and revised the paper. Sulin Cheng has full access to all of the data in the study and takes full responsibility for the integrity of the data and for the accuracy of the data analysis. Benno Krachler performed data analysis and drafted the paper. Kai Savonen participated in data analysis and revision of the paper. Eszter Völgyi, Frances A. Tylavsky, and Markku Alén participated in data collection and assembly as well as revision of the manuscript.
The authors thank the entire research staff and, especially, Ms. Shu Mei Cheng, Mrs. Heli Vertamo, Ms. Sirpa Mäkinen, and Mr. Erkki Helkala for their valuable work and technical assistance in this project. The study was supported by grants from the Academy of Finland, Finnish Ministry of Education, University of Jyväskylä, Centre for International Mobility, and Juho Vainion Säätiö Foundation. Benno Krachler was supported by grants from Bruno Krachler and the Swedish Council for Working Life and Social Research. Kai Savonen was supported by a grant from the Finnish Medical Foundation. The funding sources had no role in the collection, analysis, and interpretation of the data or in the decision to submit the paper for publication.