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A Bayesian Sensitivity Analysis to Partition Body Mass Index Into Components of Body Composition: An Application to Head and Neck Cancer Survival.

ABSTRACT: Previous studies have suggested a "J-shaped" relationship between body mass index (BMI, calculated as weight (kg)/height (m)2) and survival among head and neck cancer (HNC) patients. However, BMI is a vague measure of body composition. To provide greater resolution, we used Bayesian sensitivity analysis, informed by external data, to model the relationship between predicted fat mass index (FMI, adipose tissue (kg)/height (m)2), lean mass index (LMI, lean tissue (kg)/height (m)2), and survival. We estimated posterior median hazard ratios and 95% credible intervals for the BMI-mortality relationship in a Bayesian framework using data from 1,180 adults in North Carolina with HNC diagnosed between 2002 and 2006. Risk factors were assessed by interview shortly after diagnosis and vital status through 2013 via the National Death Index. The relationship between BMI and all-cause mortality was convex, with a nadir at 28.6, with greater risk observed throughout the normal weight range. The sensitivity analysis indicated that this was consistent with opposing increases in risk with FMI (per unit increase, hazard ratio = 1.04 (1.00, 1.08)) and decreases with LMI (per unit increase, hazard ratio = 0.90 (0.85, 0.95)). Patterns were similar for HNC-specific mortality but associations were stronger. Measures of body composition, rather than BMI, should be considered in relation to mortality risk.

SUBMITTER: Bradshaw PT

PROVIDER: S-EPMC6825827 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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A Bayesian Sensitivity Analysis to Partition Body Mass Index Into Components of Body Composition: An Application to Head and Neck Cancer Survival.

Bradshaw Patrick T PT Zevallos Jose P JP Wisniewski Kathy K Olshan Andrew F AF

American journal of epidemiology 20191101 11

Previous studies have suggested a "J-shaped" relationship between body mass index (BMI, calculated as weight (kg)/height (m)2) and survival among head and neck cancer (HNC) patients. However, BMI is a vague measure of body composition. To provide greater resolution, we used Bayesian sensitivity analysis, informed by external data, to model the relationship between predicted fat mass index (FMI, adipose tissue (kg)/height (m)2), lean mass index (LMI, lean tissue (kg)/height (m)2), and survival. W ...[more]

PMID: 31504108

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Project description:ObjectivesAging, body composition, and body mass index (BMI) are important factors in bone mineral density (BMD). Although several studies have investigated the various parameters and factors that differentially influence BMD, the results have been inconsistent. Thus, the primary goal of the present study was to further characterize the relationships of aging, body composition parameters, and BMI with BMD in Chinese Han males older than 50 years.MethodsThe present study was a retrospective analysis of the body composition, BMI, and BMD of 358 Chinese male outpatients between 50 and 89 years of age that were recruited from our hospital between 2009 and 2011. Qualified subjects were stratified according to age and BMI as follows: 50-59 (n = 35), 60-69 (n = 123), 70-79 (n = 93), and 80-89 (n = 107) years of age and low weight (BMI: < 20 kg/m2; n = 21), medium weight (20 ? BMI < 24 kg/m2; n = 118), overweight (24 ? BMI < 28 kg/m2; n = 178), and obese (BMI ? 28 kg/m2; n = 41). Dual-energy X-ray absorptiometry (DEXA) was used to assess bone mineral content (BMC), lean mass (LM), fat mass (FM), fat-free mass (FFM), lumbar spine (L1-L4) BMD, femoral neck BMD, and total hip BMD. Additionally, the FM index (FMI; FM/height2), LM index (LMI; LM/height2), FFM index (FFMI; [BMC+LM]/height2), percentage of BMC (%BMC; BMC/[BMC+FM+LM] × 100%), percentage of FM (%FM; FM/[BMC+FM+LM] × 100%), and percentage of LM (%LM; LM/(BMC+FM+LM) × 100%) were calculated. Osteopenia or osteoporosis was identified using the criteria and T-score of the World Health Organization.ResultsAlthough there were no significant differences in BMI among the age groups, there was a significant decline in height and weight according to age (p < 0.0001 and p = 0.0002, respectively). The LMI and FFMI also declined with age (both p < 0.0001) whereas the FMI exhibited a significant increase that peaked in the 80-89-years group (p = 0.0145). Although the absolute values of BMC and LM declined with age (p = 0.0031 and p < 0.0001, respectively), there was no significant difference in FM. In terms of body composition, there were no significant differences in %BMC but there was an increase in %FM (p < 0.0001) and a decrease in %LM (p < 0.0001) with age. The femoral neck and total hip BMD significantly declined with age (p < 0.0001 and p = 0.0027, respectively) but there were no differences in L1-L4. BMD increased at all sites (all p < 0.01) as BMI increased but there were declines in the detection rates of osteoporosis and osteopenia (both p < 0.001). A logistic regression revealed that when the medium weight group was given a BMI value of 1, a decline in BMI was an independent risk factor of osteoporosis or osteopenia, while an increase in BMI was a protective factor for BMD. At the same time, BMD in L1-L4 exhibited a significant positive association with FMI (p = 0.0003) and the femoral neck and total hip BMDs had significant positive associations with FFMI and LMI, respectively (both p < 0.0001).ConclusionsThese data indicate that LMI and FFMI exhibited significant negative associations with aging in Chinese Han males older than 50 years, whereas FMI had a positive association. BMD in the femoral neck and total hip declined with age but an increased BMI was protective for BMD. LMI and FFMI were protective for BMD in the femoral neck and total hip.

Dataset Information

A Bayesian Sensitivity Analysis to Partition Body Mass Index Into Components of Body Composition: An Application to Head and Neck Cancer Survival.

Publications

A Bayesian Sensitivity Analysis to Partition Body Mass Index Into Components of Body Composition: An Application to Head and Neck Cancer Survival.

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