Dietary patterns throughout adult life are associated with body mass index, waist circumference, blood pressure, and red cell folate

Dietary patterns are important in the prevention of chronic disease; however, there are few studies that include repeat measures of dietary patterns. The objective of this study was to assess the relations between dietary patterns during adult life (at ages 36, 43, and 53 y) and risk factors for chronic disease at age 53 y. Participants of a longitudinal study of health completed a 5-d food diary at 3 occasions during adult life (n = 1265). Factor analysis was used to identify dietary patterns and a pattern score was calculated from the consumption of the food items in each dietary pattern. Means and 95% CI for dietary pattern scores were calculated for each risk factor category using random effects models adjusted for socio-demographic and health-related behaviors. In women, the fruit, vegetables, and dairy pattern was inversely associated with BMI (P < 0.004), waist circumference (P = 0.0007), blood pressure (P = 0.02), and was positively associated with red cell folate (P < 0.03). The ethnic foods and alcohol pattern was also inversely associated with blood pressure (P = 0.008), whereas the meat, potatoes and sweet foods pattern was positively associated with glycated hemoglobin (P = 0.01). In men, a mixed pattern was inversely associated with waist circumference (P = 0.02) and blood pressure (P = 0.01), whereas there were no significant associations with the ethnic foods and alcohol pattern. Specific dietary patterns throughout adult life were associated with chronic disease risk factors.

Change in women's body mass index and waist circumference, 1997 to 2002: the Nepean study

Objective: To investigate character istics associated with body mass index (BMI) and waist circumference stability over a five-year period in women with school-age children.

Methods: Women with 7–8 year-old children from western Sydney, Australia, had anthropometric measures taken in 1996/97 (n====436) and five years later (n=327). Socio-demographic characteristics examined at baseline included age, socioeconomic status, smoking, and number of children.

Results: Over five years, less than half of the women maintained a stable BMI (38.8%) or waist circumference (31.5%), with the majority gaining in both indicators of adiposity. BMI and socio-demographic characteristics were not predictive of BMI or waist circumference stability or decrease.

Conclusions and Implications: Total and abdominal adiposity increased in these Australian women who have children. The results support the need to develop effective weight gain prevention initiatives.

Body fatness charts based on BMI and waist circumference

Objective To present percent body fat (PBF) charts based on body mass index (BMI) and waist circumference (WC) which can supplement current public health guidelines for obesity. Methods Based on data from the National Health and Nutrition Examination Survey (NHANES) III for 18- to 65-year-olds, a semi-parametric spline approach was utilized, in which no specific functional forms for BMI and WC are assumed, to depict graphically the relationship between BMI, WC, and PBF. Four distinct PBF charts were created, categorized by gender and ethnicity which are based on data from 2,170 white females, 1,902 African American females, 1,905 white males, and 1,635 African American males. Results PBF prediction based on the semi-parametric spline model outperformed competing linear models. For men, BMI is largely inconsequential, and WC plays a primary role in determining PBF levels. For women, the interaction between BMI and WC is more complex. To have low body fat, women would need to watch both their BMI and WC measurements carefully. Conclusions PBF charts, which incorporate information from three dimensions that are as simple to read as a BMI chart to help determine a person's level of fatness, were proposed.

Estimating the proportion of metabolic health outcomes attributable to obesity: a cross-sectional exploration of body mass index and waist circumference combinations

BACKGROUND: Recent evidence suggests that a substantial subgroup of the population who have a high-risk waist circumference (WC) do not have an obese body mass index (BMI). This study aimed to explore whether including those with a non-obese BMI but high risk WC as 'obese' improves prediction of adiposity-related metabolic outcomes.

METHODS: Eleven thousand, two hundred forty-seven participants were recruited. Height, weight and WC were measured. Ten thousand, six hundred fifty-nine participants with complete data were included. Adiposity categories were defined as: BMI(N)/WC(N), BMI(N)/WC(O), BMI(O)/WC(N), and BMI(O)/WC(O) (N = non-obese and O = obese). Population attributable fraction, area under the receiver operating characteristic curve (AUC), and odds ratios (OR) were calculated.

RESULTS: Participants were on average 48 years old and 50 % were men. The proportions of BMI(N)/WC(N), BMI(N)/WC(O), BMI(O)/WC(N) and BMI(O)/WC(O) were 68, 12, 2 and 18 %, respectively. A lower proportion of diabetes was attributable to obesity defined using BMI alone compared to BMI and WC combined (32 % vs 47 %). AUC for diabetes was also lower when obesity was defined using BMI alone (0.62 vs 0.66). Similar results were observed for all outcomes. The odds for hypertension, dyslipidaemia, diabetes and CVD were increased for those with BMI(N)/WC(O) (OR range 1.8-2.7) and BMI(O)/WC(O) (OR 1.9-4.9) compared to those with BMI(N)/WC(N).

CONCLUSIONS: Current population monitoring, assessing obesity by BMI only, misses a proportion of the population who are at increased health risk through excess adiposity. Improved identification of those at increased health risk needs to be considered for better prioritisation of policy and resources.

Predictors of increased body weight and waist circumference for middle-aged adults

OBJECTIVE: To identify predictors of increased adiposity for different measures of adiposity. DESIGN: Prospective cohort study, the Melbourne Collaborative Cohort Study (MCCS), with data at baseline (1990-1994) and wave 2 (2003-2007). SETTING: Participants recruited from the community. SUBJECTS: Australian-born participants (n 5879) aged 40 to 69 years who were not current smokers and who were free from common chronic diseases at recruitment. At baseline and at wave 2, weight and waist circumference were measured; while demographic and lifestyle variables were obtained at baseline via structured interviews. RESULTS: Participants who reported any recreational physical activity at baseline had lower weight and smaller waist circumference at wave 2 than those who did not, particularly for younger participants and for vigorous physical activity. Walking for leisure was not associated, and greater physical activity at work was associated, with greater adiposity measures at wave 2. A diet low in carbohydrates and fibre, but high in fat and protein, predicted greater weight and waist circumference at wave 2. Participants were less likely to have elevated weight or waist circumference at wave 2 if they consumed low to moderate amounts of alcohol. CONCLUSIONS: Our findings indicate that promoting vigorous physical activity, encouraging a diet high in carbohydrate and fibre but low in fat and protein, and limiting alcohol intake could be promising approaches for preventing obesity in middle-aged adults. Similar interventions should successfully address the management of both weight and waist circumference, as they were predicted by similar factors.

Quantifying the proportion of deaths due to body mass index- and waist circumference-defined obesity

OBJECTIVE: To determine the risk of mortality associated with and quantify the deaths attributable to combinations of body mass index (BMI) and waist circumference (WC). METHODS: This study included 41,439 participants. For the hazard ratio (HR) calculation, adiposity categories were defined as: BMI(N) /WC(N) , BMI(N) /WC(O) , BMI(O) /WC(N) , and BMI(O) /WC(O) (N = non-obese, O = obese). For the population attributable fraction analysis, obesity was classified as: (i) obese by BMI and/or WC; (ii) obese by BMI; and (iii) obese by WC. Mortality data was complete to the end of 2012. RESULTS: The prevalence of BMI(N) /WC(N) , BMI(N) /WC(O) , BMI(O) /WC(N) , and BMI(O) /WC(O) was 73%, 6%, 6%, and 15%, respectively. There was an increased risk of all-cause and cardiovascular disease (CVD) mortality in those with BMI(N) /WC(O) (HR (95% CI) 1.2 (1.2, 1.3) and 1.3 (1.1, 1.6)) and BMI(O) /WC(O) (1.3 (1.3, 1.4) and 1.7 (1.5, 1.9)) compared to those with BMI(N) /WC(N) . The estimated proportion of all-cause and CVD mortality attributable to obesity defined using WC or using BMI and/or WC was higher compared to obesity defined using BMI. CONCLUSIONS: Current population obesity monitoring misses those with BMI(N) /WC(O) who are at increased risk of mortality. By targeting reductions in population WC, the potential exists to prevent more deaths in the population than if we continue to target reductions in BMI alone.

Waist-to-Hip Ratio and Unhealthy Body Change in Women

The role of waist-to-hip ratio (WHR) in unhealthy body change was examined in a sample of 143 women university students. They completed the Eating Disorder Examination-Questionnaire (EDE-Q), reported their level of concern with weight and with WHR, and used unmarked measuring tapes to record their subjective (self-perceived), ideal, and objective (measured) waist and hip circumference. Although body shape was reported as important, and concern with WHR correlated significantly with symptoms of disordered eating, the relationships involving WHR were not independent of those involving body weight. Thus, there appears to be little behavioural and/or clinical significance in the aesthetic evaluations made by women of their WHR. Reasons for this are considered in light of evidence that women regard WHR as more difficult to control and less amenable to change than their overall body weight.

Changes in weight and waist circumference over 9 years in a Scottish population

Objective: To document patterns of measured weight and waist circumference (WC) change and the increase in overweight and obesity over a 9-year period. Subjects/Methods: A total of 1044 subjects from two age-defined cohorts aged 39 and 59 in 1991. Height, weight and WC were measured in 1991, 1995 and 2000 and body mass index (BMI) was calculated. Pattern of weight and WC change was studied over approximately 9 years. Results: The prevalence of overweight and obesity increased markedly and the younger cohort showed greater increases in weight and WC than the older cohort. There was no significant difference in mean BMI and/or mean 9-year weight change between men and women in either age cohort, and mean weight gain was similar for all occupational groups. Only 20% of subjects maintained a stable weight (plusminus2 kg), while 42.2 and 17.6% gained greater than 5 and 10 kg over the 9-year period, respectively. The rate of weight gain appeared to be relatively steady over the 9 years among younger subjects but declined in the older subjects in the second half of the observation period. Conclusions: Health promotion strategies to prevent weight gain need to be population-based, targeting all social and age groups, but particularly those in their early middle-age.

Waist circumference, waist-hip ratio and BMI and their correlation with cardiovascular risk factors in Australian adults

Objectives. To compare body mass index (BMI), waist circumference and waist–hip ratio (WHR) as indices of obesity and assess the respective associations with type 2 diabetes, hypertension and dyslipidaemia.

Design and setting. A national sample of 11 247 Australians aged ≥25 years was examined in 2000 in a cross-sectional survey.

Main outcome measures. The examination included a fasting blood sample, standard 2-h 75-g oral glucose tolerance test, blood pressure measurements and questionnaires to assess treatment for dyslipidaemia and hypertension. BMI, waist circumference and WHR were measured to assess overweight and obesity.

Results. The prevalence of obesity amongst Australian adults defined by BMI, waist circumference and WHR was 20.8, 30.5 and 15.8% respectively. The unadjusted odds ratio for the fourth vs. first quartile of each obesity measurement showed that WHR had the strongest relationship with type 2 diabetes, dyslipidaemia (women only) and hypertension. Following adjustment for age, however, there was little difference between the three measures of obesity, with the possible exceptions of hypertension in women, where BMI had a stronger association, and dyslipidaemia in women and type 2 diabetes in men, where WHR was marginally superior.

Conclusions. Waist circumference, BMI and WHR identified different proportions of the population, as measured by both prevalence of obesity and cardiovascular disease (CVD) risk factors. Whilst WHR had the strongest correlations with CVD risk factors before adjustment for age, the three obesity measures performed similarly after adjustment for age. Given the difficulty of using age-adjusted associations in the clinical setting, these results suggest that given appropriate cut-off points, WHR is the most useful measure of obesity to use to identify individuals with CVD risk factors.

Lifetime psychiatric disorders and body composition : a population-based study

Background This study aimed to investigate the relationship between depressive and anxiety disorders and indices of adiposity, including body fat mass and percent body fat, as measured by dual energy X-ray absorptiometry.

Methods In this observational study of 979 randomly-selected women aged 20–93 years, psychiatric history was ascertained using a structured clinical interview (SCID-I/NP). Total body fat was assessed using dual-energy X-ray absorptiometry and weight, height and waist circumference were measured. Medication use and lifestyle factors were self-reported.

Results Those with a lifetime history of depression had increased fat mass (+ 7.4%) and percent body fat (+ 4.3%), as well as greater mean weight (+ 3.3%), waist circumference (+ 2.9%) and BMI (+ 3.5%) after adjustment for age, anxiety, alcohol consumption, physical activity and past smoking. Furthermore, those meeting criteria for a lifetime history of depression had a 1.7-fold increased odds of being overweight or obese (BMI ≥ 25), a 2.0-fold increased odds of being obese (BMI ≥ 30) and a 1.8-fold increased odds of having a waist circumference ≥ 80 cm. These patterns persisted after further adjustment for psychotropic medication use, smoking status and energy intake. No differences in any measures of adiposity were observed among those with anxiety disorders compared to controls.

Limitations There is potential for unrecognised confounding, interpretations are limited to women and a temporal relationship could not be inferred.

Conclusions Depression was associated with greater adiposity. The difference in body fat mass was numerically greater than differences in indirect measures of adiposity, suggesting that the latter may underestimate the extent of adiposity in this population.

Bipolar disorder and adiposity : a study using whole body dual energy X-ray absorptiometry scans

Objective: Previous research has demonstrated a relationship between adiposity and bipolar disorder, although data are derived predominantly from patient samples and use indirect methods of assessing adiposity. This study investigated the association between bipolar disorder and several indices of adiposity, including body fat mass as measured by dual energy X-ray absorptiometry (DXA), in a community-based sample.

Methods: In this study, 21 women with bipolar disorder and 523 healthy controls were drawn from an age-stratified, random, community-based sample of women (20–93 years) participating in the Geelong Osteoporosis Study. Bipolar disorder was diagnosed utilising a semi-structured clinical interview. Anthropometric measurements (weight, height, waist and hip circumference) were taken and fat mass was determined from whole body DXA scans (Lunar DPX-L).

Results: Those with bipolar disorder tended to have greater adiposity. Age-adjusted mean (95% CI) values for bipolar versus controls according to adiposity indices were weight 75.6 (68.9–82.3) versus 72.6 (71.3–74.0) kg, waist circumference 89.8 (84.1–95.6) versus 87.3 (86.1–88.5) cm, waist:hip ratio 0.85 (0.82–0.87) versus 0.84 (0.83–0.84), body mass index 27.6 (25.1–30.1) versus 27.5 (27.0–28.0) kg/m2, fat mass 31.4 (26.5–36.3) versus 28.6 (27.5–29.5) kg and %body fat 40.4 (36.9–43.9) versus 38.0 (37.3–38.7)%; all p > 0.05. Further adjustment for height, smoking, alcohol, psychotropic medication, energy intake or physical activity did not influence these patterns.

Conclusion: Although a pattern suggestive of greater adiposity among those with bipolar disorder was observed, no significant differences were detected. We cannot exclude the possibility of a type II error. Further research with a larger sample may produce more conclusive results.

Involvement of young Australian adults in meal preparation : cross-sectional associations with abdominal obesity and body mass index

Previous research has shown that involvement in meal preparation is positively associated with better diet quality. However, it is unclear whether there is an association between involvement in meal preparation and being overweight or obese. This study investigated whether the level of involvement in meal preparation was associated with objectively measured weight status in young adults. During 2004-2006, a national sample of 1,996 Australian adults aged 26 to 36 years completed a self-administered questionnaire including questions on sociodemographic characteristics, diet, and physical activity. Participants were asked to report who usually prepared the main meal on working days and responses were categorized as “myself,” “shared,” or “someone else.” Waist circumference, weight, and height were measured by trained staff. Moderate abdominal obesity was defined as waist circumference >94 cm for men and >80 cm for women. Overweight was defined as body mass index (calculated as kg/m2) >25. Prevalence ratios were calculated using log binomial regression. After adjusting for age, education, and leisure time physical activity, men who shared the meal preparation had a slightly lower prevalence of moderate abdominal obesity (prevalence ratio=0.92; 95% confidence interval [CI]: 0.86 to 0.99) than those whose meals were usually prepared by someone else. There was no association with having sole responsibility (prevalence ratio=0.99; 95% CI: 0.92 to 1.06). There were no associations between level of involvement in meal preparation and being overweight (shared responsibility prevalence ratio=0.99; 95% CI: 0.92 to 1.07; sole responsibility prevalence ratio=0.98; 95% CI: 0.91 to 1.05). For women, level of involvement was not associated with moderate abdominal obesity (shared responsibility prevalence ratio=0.93; 95% CI: 0.84 to 1.03; sole responsibility prevalence ratio=0.94; 95% CI: 0.86 to 1.03) or being overweight (shared responsibility prevalence ratio=0.93; 95% CI: 0.84 to 1.02; sole responsibility prevalence ratio=0.93; 95% CI: 0.85 to 1.02). In this sample of young adults, level of involvement in meal preparation was not strongly related to weight status.

Prevalence of obesity and the relationship between the body mass index and body fat : cross-sectional, population-based data

Background: Anthropometric measures such as the body mass index (BMI) and waist circumference are widely used as convenient indices of adiposity, yet there are limitations in their estimates of body fat. We aimed to determine the prevalence of obesity using criteria based on the BMI and waist circumference, and to examine the relationship between the BMI and body fat.

Methodology/Principal Findings: This population-based, cross-sectional study was conducted as part of the Geelong Osteoporosis Study. A random sample of 1,467 men and 1,076 women aged 20–96 years was assessed 2001–2008. Overweight and obesity were identified according to BMI (overweight 25.0–29.9 kg/m2; obesity $30.0 kg/m2) and waist circumference (overweight men 94.0–101.9 cm; women 80.0–87.9 cm; obesity men $102.0 cm, women $88.0 cm); body fat mass was assessed using dual energy X-ray absorptiometry; height and weight were measured and lifestyle factors documented by self-report. According to the BMI, 45.1% (95%CI 42.4–47.9) of men and 30.2% (95%CI 27.4–33.0) of women were overweight and a further 20.2% (95%CI 18.0–22.4) of men and 28.6% (95%CI 25.8–31.3) of women were obese. Using waist circumference, 27.5% (95%CI 25.1–30.0) of men and 23.3% (95%CI 20.8–25.9) of women were overweight, and 29.3% (95%CI 26.9–31.7) of men and 44.1% (95%CI 41.2–47.1) of women, obese. Both criteria indicate that approximately 60% of the population exceeded recommended thresholds for healthy body habitus. There was no consistent pattern apparent between BMI and energy intake. Compared with women, BMI overestimated adiposity in men, whose excess weight was largely attributable to muscular body builds and greater bone mass. BMI also underestimated adiposity in the elderly. Regression models including gender, age and BMI explained 0.825 of the variance in percent body fat.

Conclusions/Significance: As the BMI does not account for differences in body composition, we suggest that gender- and age-specific thresholds should be considered when the BMI is used to indicate adiposity.

The influence of body build on estimates of body composition from anthropometric measurements in premenopausal women

Objective: To assess the influence of body build on the bias and limits of agreement for estimates of body fat obtained from anthropometric prediction equations when compared with the same data obtained by dual energy X-ray absorptiometry (DEXA).

Survey design and subjects: Ninety-one premenopausal women, aged between 20 and 54 years, were chosen to represent a range of skeletal body build (relative silting height 0.50-0.56) and body fatness [body mass index (BMI) 18-34 kg/m²]. Measurements of weight, sitting height, stature, skinfold thickness, waist, umbilical and hip circumference and total body resistance and reactance were made on all subjects by standard techniques after an overnight fast. A DEXA measurement of total body fat, fat-free soft tissue and total body bone mineral mass was also obtained within 2 weeks of the anthropometric assessment.

Results: At the group level the mean difference (bias) between DEXA and the anthropometric estimates of body fat was similar for all three anthropometric estimates ranging from 2.7 kg with impedance to 1.8 kg with skinfold thickness. The 95% limits of agreement were also similar, ranging from ±5.3 kg with body mass index to ±4.1 kg with impedance. Umbilical circumference, BMI and the amount of bone mineral expressed as a proportion of the fat-free soft-tissue mass were all significantly (P < 0.01) correlated with the level of bias between DEXA and the anthropometric estimates of body fat. This was not the case for relative sitting height or measures of body fat distribution. Regression equations which included BMI or umbilical circumference in combination with the predicted estimates of body fat essentially eliminated the association between the level of bias in predicted body fat and the level of body fatness. They also reduced the 95% limits of agreement between DEXA and the anthropometric estimates of body fat.

Conclusions: Using DEXA estimates of body fat as the standard of reference our results suggest that the comparability and precision of body fat estimates derived from age- and/or sex-specific anthropometric prediction equations based on skinfolds and BMI, but not impedance, can be improved by adjusting for differences in BMI and umbilical circumference respectively.