Relationship Among Body Fat Percentage, Body Mass Index, and All Cause Mortality
Context A lower mortality rate among overweight and mildly to moderately obese adults (the obesity paradox) is unexplained. Studies documenting this paradox are limited because body mass index (BMI) is an imperfect and indirect measure of adiposity. Contribution In this population based cohort study of middle aged and older adults referred for bone mineral density testing, low BMI and high body fat percentage were independently associated with increased all cause mortality among men and women. Caution Body fat percentage and BMI were measured only once at baseline. Implication The independent relationship between increased body fat percentage and mortality may help explain the obesity paradox. Many general population cohort studies over the past century have reported that excess adiposity (usually estimated with body mass index [BMI] is associated with increased risk for all cause death (13) However, not all of the published evidence agrees; a recent meta analysis found that being overweight (BMI of 25.0 to 29.9 kg/m2) was associated with a decreased mortality risk and that mild obesity (BMI of 30.0 to 34.5 kg/m2) had a mortality risk similar to that of normal weight (BMI of 18.5 to 24.9 kg/m2) (4) Population based cohort studies performed in middle aged and older adults and more focused studies in persons with chronic diseases (such as heart failure and chronic kidney disease) have reported lower mortality rates in overweight and mildly or moderately obese persons than in those with normal weight (511) This unexpected finding has been termed the obesity paradox. These findings have generated considerable debate and discussion within and beyond the medical literature because they have obvious implications in terms of whether overweight and obese patients who are older or have chronic diseases should be counseled to lose weight to prolong their life (5, 9, 1215) Many explanations for the obesity paradox have been proposed. One commonly held view is that BMI, when used as a surrogate measure of body fat, confounds the relationship between excess adiposity and mortality risk (6, 16) Because it does not directly measure body fat, BMI is an imperfect measure of adiposity (6, 17, 18) Therefore, more sophisticated body fat measurements must be used in studies examining the relationship between adiposity and mortality so that this relationship can be more confidently and accurately assessed. The purpose of this study was to assess the relationship of BMI and body fat percentage (separately and together) with all cause mortality in a general population cohort of middle aged and older adults. We hypothesized that greater body fat percentage (rather than greater BMI) would be independently associated with increased mortality in sex stratified models containing both parameters and adjusted for other potential confounders. If confirmed, this hypothesis would support the need to use direct fat measures instead of BMI when examining the relationship between adiposity and mortality risk. Methods Study Design We performed a population based cohort study of all eligible residents of Manitoba, Canada, who were aged 40 years or older and who had initial dual energy x ray absorptiometry (DXA) of the spine and hip for bone mineral density (BMD) testing between 1999 and 2013. Participants were followed from the date of initial BMD testing until death or the end of the study. Only the initial BMD test was examined in each participant. Manitoba, which has a population of 1.25 million, provides government funded universal health care to all residents, regardless of age or income. The Health Research Ethics Board of the University of Manitoba approved the study, and the Manitoba Health Information Privacy Committee approved data access. Data Sources Population Health Research Data Repository This registry contains multiple population based, linked administrative health data sets provided by the provincial government and maintained by the Manitoba Centre for Health Policy of the University of Manitoba (1921) Data include sociodemographic variables, physician claims and hospital separations (including primary diagnosis and up to 25 secondary diagnoses) prescription drugs (drug name and date, dose, and quantity dispensed) and vital statistics (including mortality) The data sets are linked through a deidentified personal health information number that facilitates linkage to external data sources, including the provincial clinical BMD registry. These data are well validated and have been used extensively in previous research (2023) Provincial Bone Mineral Density Registry This validated population based registry contains data on all BMD tests performed for clinical purposes in Manitoba (2022) Uniform data collection standards are maintained, and data collection includes osteoporosis risk factors (including components of the World Health Organization [WHO] Fracture Risk Assessment [FRAX] risk calculator [24] height and weight, testing indications, and reporting. The primary indication for testing is to assess bone density and fracture risk. All BMD test results have been captured since the program was established, with greater than 99% completeness and accuracy of registry data (2022) Lumbar spine and hip DXA are performed and scans analyzed in accordance with manufacturer recommendations using one of the Manitoba Bone Density Program's cross calibrated DXA instruments (Prodigy in 98% and iDXA in 2% [GE Healthcare Lunar] All densitometers and personnel are subject to province wide quality assurance programs under the direction of a medical physicist, including daily evaluation of densitometer stability using anthropometric spine phantoms. Major Independent Variables: BMI and Body Fat Percentage Height was assessed with a wall mounted stadiometer, and weight (without shoes) was assessed using a standard floor scale. Body mass index was calculated as weight (in kilograms) divided by height (in meters) squared. Persons with BMI at either extreme <10 or >60 kg/m2) were excluded. Body fat percentages for the soft tissue regions included in the spine and hip scans were averaged to provide an index of total body adiposity. A high correlation between body fat percentages from the regional DXA scan and the total body DXA scan (the gold standard for total body fat measurement) was seen in a subgroup of participants with both measurements (r= 0.86 for 255 women and 0.87 for 46 men) Short term precision errorcalculated as the root mean square for measurements of body fat percentage in 30 participants having repeated scans on the same day, with repositioning between the scanswas 1.8% for the spine scans and 1.7% for the hip scans (25) Body mass index and body fat percentage were divided into quintiles. In women, BMI quintiles were defined as follows: 22.52 kg/m2 or lower (quintile 1) 22.53 to 24.99 kg/m2 (quintile 2) 25.00 to 27.55 kg/m2 (quintile 3) 27.56 to 31.12 kg/m2 (quintile 4) and greater than 31.12 kg/m2 (quintile 5) To facilitate clinical interpretability, in a sensitivity analysis we also characterized BMI according to the WHO classification scheme as follows: less than 18.5 kg/m2 (underweight) 18.5 to 24.9 kg/m2 (normal weight) 25.0 to 29.9 kg/m2 (overweight) 30.0 to 34.9 kg/m2 (class I obesity) and 35.0 kg/m2 or greater (class II or III obesity) Body fat percentage quintiles in women were 25.57% or lower (quintile 1) 25.58% to 30.40% (quintile 2) 30.41% to 34.41% (quintile 3) 34.42% to 38.68% (quintile 4) and greater than 38.68% (quintile 5) In men, BMI quintiles were 23.85 kg/m2 or lower (quintile 1) 23.86 to 26.03 kg/m2 (quintile 2) 26.04 to 28.13 kg/m2 (quintile 3) 28.14 to 30.84 kg/m2 (quintile 4) and greater than 30.84 kg/m2 (quintile 5) the WHO BMI categories were the same as for women. Body fat percentage quintiles in men were 23.14% or lower (quintile 1) 23.15% to 27.98% (quintile 2) 27.99% to 31.72% (quintile 3) 31.73% to 36.14% (quintile 4) and greater than 36.14% (quintile 5) Outcomes The primary dependent variable of interest was all cause mortality, ascertained from vital statistics data within the Population Health Research Data Repository. Potential Confounders Separate analyses were done a priori in men and women, with sequential adjustments. Potential confounders included age; Aggregated Diagnosis Groups (ADG) score; ethnicity (white vs. nonwhite) income quintile (lowest 2 vs. highest 3) residency (urban vs. nonurban) high alcohol use (yes vs. no) glucocorticoid use (yes vs. no) prior fracture; and presence of chronic obstructive pulmonary disease (COPD) diabetes, acute coronary syndrome, chronic kidney disease, or heart failure. The ADG score is an individualized comorbidity based score consisting of 32 diagnostic clusters based on the Johns Hopkins Adjusted Clinical Groups Case Mix System, version 10 (26) This score has been shown to accurately predict mortality in general ambulatory cohorts in Canada (27) Income data were at the neighborhood level and were sourced from the 2006 census. No data were missing for any of the covariates, but we lacked data on smoking status. However, we have previously shown that a COPD diagnosis in our population represents a valid measure of current smoking and can be substituted for smoking in risk stratification tools, such as FRAX (28, 29) Thirteen percent of Canadian women who are similar in age to those in our sample are smokers, and COPD was present in 8% of the women in our cohort; among men, both proportions were 18% (28, 29) Statistical Analysis Descriptive analyses were conducted according to BMI quintile using parametric or nonparametric tests as appropriate to test between group differences. Spearman correlations between BMI and mean body fat percentage were calculated separately in men and women. Multivariable Cox proportional hazards models were then constructed sequentially as described earlier. Proportional hazards assump
Annals of Internal Medicine