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To examine the association between dietary factors to daily activity energy expenditure (DAEE) and mortality among older adults.
A sub-study of Health, Aging, and Body Composition study.
298 older participants (aged 70–82 years) in the Health, Aging, and Body Composition Energy Expenditure sub-study.
Dietary factors, DAEE, and all-cause mortality were measured in 298 older participants. Dietary factors include dietary intake assessed by the Block Food Frequency Questionnaire (FFQ), Healthy Eating Index (HEI), and self-reported appetite and enjoyment of eating. DAEE was assessed using doubly labeled water. All-cause mortality was evaluated over a 9 year period.
Participants in the highest tertile of DAEE were more likely to be men and to report having a ‘good’ appetite; BMI among men, proportion married, IL-6 and CRP levels and energy intake were also higher. Fewer black participants were in the ‘good’ HEI category. Participants in the ‘good’ HEI category had higher cognitive scores and a higher education level. Participants who reported improvement in their appetite as well as participants who reported a ‘good’ appetite were at lower risk for mortality (HR (95% CI): 0.42 (0.24–0.74) and 0.50 (0.26–0.88), respectively) even after adjusting for DAEE, demographic, nutritional and health indices.
We showed an association between DAEE and appetite and mortality among well-functioning, community-dwelling older adults. These findings may have some practical use for the health providers. Inclusion of a question regarding appetite of an elderly patient may provide important information regarding risk for health deterioration and mortality.
Lifestyle factors, such as smoking, physical activity, and nutrition, play important roles in the onset and progression of chronic diseases. These factors may be especially important in older people, who are at an increased risk for morbidity and mortality (1). Dietary patterns, such as the Mediterranean diet, have been associated with longevity in older populations in several studies (2–4). Likewise the consumption of ‘healthy’ foods as opposed to ‘non-healthy’ foods have been shown to be associated with reduced cancer mortality among women in Sweden (5). Specific food groups including vegetables and fruits (6), legumes (7) and nuts (8) were also found to be related to longevity. Similarly, specific nutrients, namely omega 3 fatty acids, folate and vitamin B12, are associated with decreased morbidity and mortality (9, 10). The aging process is associated with a decline in food intake thought to be due to several physiological, psychological and emotional changes (11, 12). Previous studies have shown that dietary patterns and eating problems are both important predictors for the decrease in dietary intake (11–15). Other studies have indicated that health outcomes including weight loss, length of hospitalization, medical complication rate, and mortality are affected by nutritional problems and particularly by decreased appetite (16–18). Improved health outcomes are known to be linked to physical activity and energy expenditure that in turn tend to increase appetite and dietary intake (19). Furthermore, several studies indicate that health habits tend to cluster together (20, 21). For example, in a study conducted in Brazil (22), the highest Healthy Eating Index (HEI) scores, indicating healthier diet, were obtained by individuals who were nonsmokers, were physically active, and had adequate living conditions.
Doubly labeled water (DLW) can be used to measure daily activity energy expenditure (DAEE) and captures all forms of physical activity ranging from purposeful exercise to simple fidgeting. In contrast, physical activity questionnaires generally address basic volitional activities. DLW is considered the most accurate and precise method to determine free-living energy expenditure (23). In a recent study published by Manini et al. using data from the Health ABC study (24), higher DAEE was strongly associated with lower risk of mortality in healthy older adults. We seek to further investigate the relative contribution of additional modifiable dietary factors to mortality in this sample from the Health ABC study.
The Health, Aging, and Body Composition(Health ABC)study is a cohort study initiated by the Laboratory of Epidemiology, Demography and Biometry and carried out by investigators from the University of Pittsburgh(Pittsburgh, PA) and the University of Tennessee (Memphis). Three thousand and seventy five participants aged 70 to 79 years were recruited from a random sample of white Medicare beneficiaries and all age-eligible black community residents to participate in the Health ABC study in 1997–1998. Eligibility criteria included self-reporting no difficulty in walking a distance of ¼ mile (0.4 km) or climbing at least 10 stairs, independently performing activities of daily living, having plans to live in the area for the next 3 years, and having no evidence of life-threatening illnesses.
In 1998–1999, an energy expenditure (EE) sub-study was carried on a subset of323 Health ABC participants. The study procedure is described in detail elsewhere (25, 26). Three hundred twenty three individuals were randomly selected from the cohort, stratified by sex and race groups, and asked to participate in this EE sub-study.
Individuals who agreed were paid. Twenty-one individuals were excluded from this analysis because of failure to complete the protocol, lack of appropriate urine volume specimens, or lack of isotope or resting metabolic rate data to meet a priori criteria, leaving an analytic sample of 302 individuals (150 men and 152 women). DLW data was collected in 1998 (n = 179) and 1999 (n = 123). Each participant signed a written informed consent which was approved by the institutional review boards at the University of Pittsburgh and the University of Tennessee.
A modified Block Food Frequency Questionnaire (FFQ) (27) was administered by trained dietary interviewers at the year 2 follow-up visit of the main study. A second FFQ was administered for participants who attended the EE sub-study in year 3 (1999). The Healthy Eating Index (HEI) was calculated to measure compliance with U.S. dietary guidelines (28, 29). The HEI consists of 10 components: 5 measured conformity to the sex and age specific serving recommendations from the 1992 USDA Food Guide Pyramid for grains, fruit, vegetables, dairy and meat, and the other 5 assessed intakes of total fat consumption as a percentage of total energy intake, saturated fat consumption as a percentage of total energy intake, total cholesterol intake, total sodium intake and variety in the diet. Each component was scored from 1 to 10 with higher scores indicating better compliance with recommended intake range or amounts.
Eating problems and difficulties (chewing problems, difficulty shopping for food, eating alone, enjoyment of eating and having enough food to satisfy hunger) were assessed by interview during the baseline Health ABC clinic visit. A self evaluation of appetite (category score of 1–5 corresponding with categories of excellent to poor) was conducted. For the purpose of the current analyses, appetite was divided into 2 levels, moderate-poor-fair (0) and good-excellent (1). In addition, participants were asked to rate their current appetite or desire to eat compared with one year ago using 5 categories from much better to much worse, with answers recoded into better and somewhat better (1) and same, worse, and much worse (0). Section one of the 51-item Stunkard and Messick’s questionnaire was used to measure appetite and eating restraint (30). Possible scores ranged from 1 to 21 where high scores indicate higher eating restraint.
Total energy expenditure was measured using doubly labeled water. This procedure was previously described in detail (25). Measurements were obtained between 2 visits separated by 2 weeks. On the first visit, participants ingested an estimated 2-g/kg total body water dose of doubly labeled water. This dose was composed of an estimated 1.9-g/kg total body water of 10% H218O and an estimated 0.12-g/kg total body water of 99.9% 2H2O. After dosing,3 urine samples were obtained at approximately 2, 3, and 4 hours. Two consecutive urine voids were taken during a second visit to the laboratory 15 days after the first visit. Plasma from a 5-mL blood sample was obtained from everyone but only used for those who had evidence of delayed isotopic equilibration likely caused from urine retention in the bladder (n = 28) (25). Urine and plasma samples were stored at −20°C until analysis by isotope ratio mass spectrometry.
Dilution spaces for 2H and 18O were calculated according to the method by Coward (31). Total body water was calculated as the average of the dilution spaces of 2H and 18O after correction for isotopic exchange (1.041 for 2H and 1.007 for 18O). Carbon dioxide production was calculated using the 2-point doubly labeled water method outlined by Schoeller et al (32, 33) and total energy expenditure was derived using the equation by Weir (34) with a respiratory quotient of 0.86. All values of energy expenditure were converted to kilocalories per day and the thermic effect of meals was assumed to be 10% of total energy expenditure (35). The within-subject repeatability of total energy expenditure was based on blinded, repeated, urine isotopic analysis and was excellent (mean [SD], 1.2% [5.4%]; n = 16) and compared well with rates given in a review article (36).
Resting metabolic rate was measured via indirect calorimetry using a Deltatrac II respiratory gas analyzer (Datex Ohmeda Inc, Helsinki, Finland) and has been described in detail elsewhere (26). While in a fasting state and after 30 minutes of rest, a respiratory gas exchange hood was placed over the individual’s head and the resting metabolic rate was measured for 40 minutes. To avoid a gas exchange created by the initial placement of the hood, only the final 30 minutes were used in subsequent calculations. Movement or sleeping during the test was noted and those values were excluded from the resting metabolic rate calculation.
Free-living activity energy expenditure was calculated as [(total energy expenditure × 0.90) – resting metabolic rate]; removing energy expenditure from the thermic effect of meals and subtracting energy devoted to basal metabolism (37). Activity energy expenditure was defined as the amount of kilocalories an individual expends in any activity per day. The total daily activity energy expenditure (DAEE) was divided into tertiles.
Vital status was ascertained by telephone contact every 6 months over a 9-year period (1998–2007). Date of death was verified with death certificates and survival time was defined as the time of the second energy expenditure visit to the date of death or date of last contact.
Sociodemographics measurements included age, sex, race (black or white), study site (Memphis or Pittsburgh), education (<high school, high school graduate, and >high school), and marital status (married, not currently married). Family income included wages, salaries, social security or retirement benefits, help from relatives, and rent from property and was categorized as lower than $50,000 annually and greater than or equal to $50,000. Thirty-nine participants (13.1%) had missing family income values. Smoking behavior (never, former, or current) was assessed at the baseline Health ABC clinic visit. Self-reported health status was divided into fair or poor and good, very good or excellent. Body fat, body weight, and height were measured at the first energy expenditure visit. Body fat was assessed using dual-energy x-ray absorptiometry (QDR-4500, version 8.21, Hologic Inc, Bedford, Mass) and calculated as the ratio of body fat mass to total mass (percentage of body fat). Body weight was measured on a calibrated balance beam scale with the individual wearing a hospital gown and with shoes removed. Height was measured with a stadiometer. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared.
Depressed mood was assessed using the Center for Epidemiologic Studies Depression (CES-D-10) short scale (38) and cognitive status was assessed using the Modified Mini-Mental State Examination (3MS) score (39) administered at the year 3 clinic visit. Participants were instructed to bring all prescription and over-the-counter medications used in the past 2 weeks with them to the clinic visit and the total number of medications was calculated.
Measures of interleukin-6 (IL-6) and C-reactive protein (CRP) were obtained from frozen stored plasma or serum using the year 2 Health ABC clinic visit. Specimens were obtained by venipuncture in the morning after an overnight fast, processed, aliquoted into cryovials, frozen at −70°C, and subsequently shipped to the Health ABC Core Laboratory at the University of Vermont. Cytokine levels were measured in duplicate using ELISA kits (R&D Systems, Minneapolis, MN, USA). The HS600 Quantikine kit was used to measure IL-6 with a detectable limit of 0.10 pg/ml. Serum levels of high-sensitivity CRP were measured in duplicate by ELISA based on purified protein and polyclonal anti-CRP antibodies (Calbiochem, San Diego, CA, USA). The CRP assay was standardized according to the World Health Organization’s First International Reference Standard, with a sensitivity of 0.08 μg/ml. Reliability, as determined by blind duplicate analyses of IL-6 and CRP, revealed average interassay coefficients of variation of 10.3% and 8.0%, respectively. In older adults, levels of IL-6 and CRP measured at one point in time have been shown to be reliable, reproducible, and representative over extended periods of time (40, 41).
Both dietary data and DAEE were available for 298 people. The HEI score was divided into 3 categories: good (81+), needs improvement (51–80), and poor (<51) (28). DAEE was divided into tertiles (<521, 521–756 and >761). Participant characteristics were assessed using analysis of variance for continuous variables and the χ2 statistic for categorical variables. For non-normally distributed variables, Kruskal-Wallis tests were performed to compare median values. Variables were compared across tertiles of DAEE and categories of HEI. Dietary intake and food groups were compared across tertiles of DAEE using General Linear Models (GLM), adjusted for demographic characteristics. Nutrient intake was also adjusted for energy intake. Eating habits and difficulties were compared across tertiles of DAEE and categories of HEI using χ2 statistic for categorical variables. Cox proportional hazard models were used to test the association between HEI, eating habits and difficulties, and DAEE and all-cause mortality. Model 1 was estimated after adjusting for demographic characteristics. Model 2 was adjusted for the factors in model 1 plus weight and energy intake. Model 3 was adjusted for the factors in model 3 plus self-reported health and cognitive function. The proportional hazard assumption was confirmed in all independent variables using log minus log (LML) survival plots and interactions with time (log transformed). The associations between the nutritional variables were determined before constructing the regression models in order to prevent the inclusion of highly correlated variables. Results were considered statistically significant at P<0.05.
Table 1 presents the characteristics of the participants of this study by DAEE tertiles and HEI categories. Participants in the highest tertile of DAEE were slightly younger than those in the lower tertiles (P=0.07), where as participants in the good HEI category tended to be older (P=0.08). Participants in the highest tertile of DAEE were more likely to be men; BMI among men and proportion married were also higher. In addition, more of these participants were from Pittsburgh. Fewer black participants were in the good HEI category. Participants in the good HEI category had higher cognitive scores, a higher education level, and were less likely to be a current or past smoker. Median IL-6 and CRP levels were significantly lower among participants in the highest DAEE tertile (p=0.02 for both). There were no differences in IL-6 or CRP across HEI categories.
Figure 1 presents the distribution of tertiles of DAEE by category of HEI. Among those with a poor HEI score, 46% were in the highest tertile of DAEE, where as 53% were in the highest DAEE tertile among those with a good HEI score (χ2=21.6, p<0.001).
Table 2 presents the dietary intake of macronutrients and micronutrients by DAEE tertiles adjusted for gender, site, weight and marital status. Energy intake and dietary intake of fiber from grains were significantly higher for participants in the highest tertile of DAEE. No difference was detected in macronutrients intake between DAEE tertiles. Adjusting for energy intake in addition to the other covariates revealed significantly lower intakes of iron, niacin, folate, vitamins B1 and B6, and retinol in the highest tertile of DAEE. No differences were detected in other micronutrients including vitamin B12 after energy adjustment (data not shown). Consumption of food groups and HEI by tertiles of DAEE, adjusted for gender, site, weight, marital status and energy intake was determined as well. No significant differences were detected in intake of grains, meat, fruits, dairy, fats and oils and daily servings of vegetables or HEI score across DAEE tertiles (data not shown).
Table 3 presents differences in eating habits and problems by tertiles of DAEE and categories of HEI. The prevalence of good appetite was significantly higher and fewer participants reported that they did not find their meals enjoyable among those in the highest DAEE tertile. A good HEI score was associated with a lower number of days of feeling like not eating at all. A trend for better appetite in people with a good HEI score was also found (P=0.07). In the highest DAEE tertile, fewer participants reported to “not have enough money to buy food”. The opposite was observed for HEI.
Mortality data was available for 285 people (data for 13 participants (4%) was missing). Over a period of 9 years (starting at the EE visit) 39 men and 32 women died. Table 4 presents the results of the Cox regression models predicting mortality. For each group of nutritional variables, we evaluated correlations in order to decide which variables may be collinear. The decision on which variables to include was made based on findings from previous studies (13–19). For example, enjoyment of eating was strongly associated with appetite and, therefore, only the appetite variables were included in the model. “Appetite and restraint eating” score was strongly associated with HEI; therefore, only HEI was included in the model. Participants in the highest tertile of DAEE had significantly lower mortality risk in all the models. However, HEI score was not related to mortality. We evaluated the interaction terms between HEI and DAEE as well as health status and appetite. None of these interactions were significant and none improved the goodness of fit of the model. Good appetite and better appetite compared with one year ago were both inversely associated with all cause mortality even after controlling for DAEE, demographic and health variables.
Our study is one of the first to look at the impact of diet quality jointly with measured energy expenditure using doubly labeled water on mortality among the elderly. The caloric intake of participants in the highest tertile of daily activity energy expenditure (DAEE) was significantly higher, they reported having a better appetite and finding their meals more enjoyable. Participants who reported improvement in their appetite as well as participants who reported a good appetite were at lower risk for mortality even after adjusting for DAEE, demographic, nutritional and health indices.
It is interesting to note that an objective measure of energy expenditure using doubly labeled water remained significant even after adjusting for diet quality. In a previous analysis of the Health ABC cohort (24), DAEE was a strong predictor for mortality after adjusting for several health conditions.
Physical activity measured by DLW was shown to be associated with better appetite among our group and with lower CRP and IL-6 levels. Lower IL-6 levels were shown among Health ABC participants with better appetite in a previous analysis performed for the entire group (42). A fall in the level of cytokines may be one of the mechanisms through which physical activity enhances appetite in elderly persons. In a small study among nursing home patients, inactive patients were at increased risk for malnutrition and loss of appetite (19). Fiatarone (43) and Gray-Donald (44) have both suggested that physical exercise increases energy expenditure and, thereby, appetite, total energy intake and diet quality, and, thus, slows the process of frailty. Likewise, high energy expenditure is a characteristic of higher activity level and a higher lean body mass, both more typical of the fit, rather than the frail elderly (18, 19).
The association between poor appetite and mortality has not been deeply investigated. It has often been thought that decreased appetite may be an indicator or a result of other health problems, and that malnutrition, rather than low appetite, was associated with mortality. Interestingly, in this Health ABC sub-study, poor appetite remained an independent predictor for mortality even after adjusting for several health and functional measurements. To the best of our knowledge, this is the first time that this direct association has been shown among a relatively independent group of elderly people.
The Healthy Eating Index that was used in our study to capture diet quality did not differ by DAEE tertiles and did not predict mortality in this Health ABC sub-study. Previous studies in different population groups showed a decreased risk for mortality among people consuming a Mediterranean diet (2). Other dietary patterns have also shown a positive impact on mortality (1). The HEI is a measure of compliance with the general dietary guidelines for the public (30, 31). It was shown to be negatively associated with obesity (45) and major chronic diseases (46) as well as with mobility limitation (47); however, we did not find any studies that suggested an association with mortality.
The version of the HEI that we used did not take into account several health-related food groups and food items such as trans fatty acids, mono and omega 3 fatty acids and nuts. In addition, whole grains are included as part of the grain food group and legumes are included as part of the meat or vegetable food groups, but neither is considered as a separate food group. These changes are taken into account in the HEI-2005 (48) and in the Mediterranean diet score. Since our questionnaire was administered in 1998–1999, it was analyzed according to the “older” version of the HEI. Application of the newer HEI-2005 version using the Health ABC cohort was not technically feasible at this time. Moreover a recent study that was conducted as part of the EPIC study among elderly people in Italy identified four major dietary patterns by using an exploratory factor analysis. ‘Olive Oil & Salad’ pattern, characterized by a high consumption of olive oil, raw vegetables, soups and poultry, emerged as being inversely associated with overall mortality in both crude and adjusted models (49). These findings may suggest the need to develop more specific and objective measures of dietary exposure for the elderly population.
In conclusion we showed an interesting association between DAEE and appetite and mortality rates among a group of 298 elderly people participating in the Health ABC study. These findings may have some practical use for the health providers. Inclusion of a question regarding appetite as part of the medical assessment of an elderly patient may provide important information regarding risk for health deterioration and mortality.
This study was supported by National Institute on Aging contracts N01-AG-6-2101, N01-AG-6-2103, and N01-AG-6-2106 with additional support from the National Institute of Diabetes and Digestive and Kidney Diseases. This research was supported in part by the Intramural Research Program of the NIH, National Institute on Aging.
Author Contributions: D.R. Shahar conceptualized the idea, analyzed the data, and wrote the first draft of the manuscript. T.B. Harris (project officer at NIA) contributed to the conceptualization of the idea, interpreted the data, and contributed to drafts of the manuscript. D.K. Huston, J.S. Lee interpreted the statistical analyses and reviewed drafts of the paper. F. A. Tylavsky and S. B. Kritchevsky are co-investigators of the study, contributed to the data collection, interpreted the results, and reviewed drafts of the paper. S.M. Rubin and D.E. Sellmeyer contributed to the data management and data analyses, interpreted the data, and contributed to drafts of the manuscript. B. Yu was responsible for the statistical analyses and interpretation and reviewed drafts of the manuscript. All authors approved the last version of the manuscript.
Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the author and has determined that none of the authors have any financial or any other kind of personal conflicts with this article.
Sponsor’s Role: None.