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Waist Circumference, Mortality Risk, and BMI in the Elderly

Waist Circumference, Mortality Risk, and BMI in the Elderly

Methods

Data Sources and Searches


Studies were identified by a PubMed search from 1984 until 1 November 2010, by examining the reference lists of identified reviews, and by suggestions from colleagues. The following search strategy was used: waist, or WC, or abdominal adiposity in the abstract, title or in the Medical Subject Heading (MeSH), and mortality in the abstract, title or mortality in MeSH, plus either prospective or cohort. This search resulted in 202 abstracts. Additionally, all investigators from a previous collaboration were contacted, and we searched on the website of the United States National Institute of Aging for eligible studies.

Study Selection


Eligible studies were prospective cohort studies conducted in predominantly Caucasian populations. The studies had to include at least 400 people in the age range of 65–74 years at baseline, this ensured smaller studies were also included. WC, BMI and all-cause mortality had to be available. Additionally, it had to be possible to calculate hazard ratios [relative risks (RRs)] for a follow-up period of 5–8 years (preferably closest to 5 years). This follow-up range was chosen to ensure most subjects were still alive during follow-up, since life expectancy is about 80 years, and also to reduce heterogeneity between studies. Also, baseline conditions tend to change considerably over a longer follow-up period.

In Appendix 1 (available as Supplementary Data at IJE online), a flowchart of the identified studies is presented. We identified 100 studies as possibly eligible for inclusion in our meta-analysis. The investigators of these studies received an e-mail with an explanation of the purpose of the study, an invitation for participation and a request to ensure their study would meet the inclusion criteria. No financial support was offered to participate in this meta-analysis.

We could not find valid e-mail addresses for four investigators, thus 96 investigators were contacted by e-mail of whom 60 responded. Eighteen of these declined because the data did not fully meet the inclusion criteria. Fourteen investigators declined for financial reasons, due to lack of time or interest, or lost contact after initial response. Finally, 28 investigators responded from whom 29 cohort studies were included in the meta-analysis.

Data Extraction


The investigators who agreed to participate were requested to perform Cox regression analyses to calculate RRs of mortality for WC as a categorical and continuous variable following a protocol with instruction. All analyses were stratified by sex.

For the combined WC–BMI categories, WC categories defined by Lean et al. and used in practice (i.e. <94, 94–101, ≥102 cm in men; <80, 80–87, ≥88 cm in women) and BMI categories underweight (<20 kg/m), 'healthy' weight (20–24.9 kg/m), overweight (25–29.9 kg/m) and obese (≥30 kg/m) were used. The investigators used a model to assess mortality risks for the 11 combined WC–BMI categories compared with the reference category ('healthy weight' and small waist) (Table 1). This model was adjusted for age and smoking status [current, former and never smokers (reference)].

Since previous studies have shown a U-shaped relation between WC and mortality, the investigators used a model with WC as a continuous variable, including the linear and quadratic term of WC (WC and WC). The models were first only adjusted for age and smoking status, and subsequently for BMI as well. All analyses were performed over a follow-up period of ~5 years for all-cause mortality and, if available, for mortality from cardiovascular disease (CVD), cancer and respiratory disease (see Table 2 for definitions).

Additional analyses were performed for the models with WC as a categorical variable and WC as a continuous variable (with adjustment for BMI) for the following subgroups: subjects aged 65–69 years and 70–74 years; subjects aged 65–74 years; excluding mortality during the first 2 years of follow-up; excluding those with major chronic diseases (i.e. CVD, cancer and respiratory disease) at baseline; and only including never smokers.

The investigators were not asked to test the proportional hazard assumption for each requested analysis because it was considered too onerous. Nevertheless, the proportional hazard assumption was tested for each analysis in eight cohort studies and no violations were found [(global) test of Schoenfeld P > 0.05].

Descriptive statistics for each cohort (e.g. mean age, BMI and WC, number of subjects, total deaths, deaths from CVD, cancer and respiratory disease and percentage never smokers) were provided by the investigators.

Data Synthesis and Analysis


First, heterogeneity of the pooled RRs for the combined WC–BMI categories (received from the investigators) was tested by calculating the Cochran's chi-square, its P-value and the I (percentage of variation across studies). Heterogeneity in the continuous analyses was tested by a chi-squared test from the random effects model. To account for any heterogeneity, a random-effects model was used for all models to pool the log RRs.

For the combined WC–BMI categories, the log RR for each WC–BMI category was pooled by a univariate meta-analysis.

For the continuous analyses, we used a bivariate meta-analysis to pool the log RRs with the variance of each term and the covariance between terms. To assess the association between continuous WC and mortality, we tested if the regression coefficients for both terms were equal to 0. To plot a parabolic function between WC and mortality, the lowest risk was calculated by −EstimateWC/(2*EstimateWC) which was the reference point (RR = 1.0) for the function. The RRs associated with the commonly used cut-points of 102 cm in men and 88 cm in women were reported. Also, the values of WC associated with a RR of 2.0 which we consider a clinically relevant increased mortality risk as supported by the National Cancer Institute.

For the continuous analyses without and with adjustment for BMI, we tested the effect of BMI by means of a meta-regression analysis.

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