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The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 61:608-615 (2006)
© 2006 The Gerontological Society of America

Nutrition and Aging: RESEARCH ARTICLE

Eating Behavior and Weight Change in Healthy Postmenopausal Women: Results of a 4-Year Longitudinal Study

Nicholas P. Hays, Gaston P. Bathalon, Ronenn Roubenoff, Megan A. McCrory and Susan B. Roberts

1 Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, Boston, Massachusetts.
2 Donald W. Reynolds Department of Geriatrics, University of Arkansas for Medical Sciences, Little Rock.
3 U.S. Army Research Institute of Environmental Medicine, Natick, Massachusetts.

Address correspondence and reprint requests to Susan B. Roberts, PhD, Energy Metabolism Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, 711 Washington St., Boston, MA 02111. E-mail: susan.roberts{at}tufts.edu

Abstract

Background. The association of psychological eating behavior constructs with overweight and obesity during early adult life and middle age has been documented in several studies. However, the association of eating behavior with unexplained weight change in old age is relatively unexplored.

Methods. Body weight, eating behavior (dietary restraint, disinhibition, and hunger as assessed by the Eating Inventory), reported dietary intake, and physical activity level were assessed at baseline in 36 nonobese postmenopausal women aged 61.3 ± 3.1 years (mean ± standard deviation). Measurements were repeated 4.4 ± 0.9 years later, and changes in body weight were examined in relation to baseline and follow-up eating behavior scores, reported dietary intake, and physical activity level.

Results. Participants had no major changes in health or lifestyle characteristics over the study interval. Weight change ranged from –7.5 to +5.8 kg (mean –0.3 ± 3.5 kg), and there were no significant changes in reported dietary intake. Mean hunger score (calculated as the mean of baseline and follow-up hunger score) predicted weight change per year over the study period (bivariate r = 0.386, p =.020), even in statistical models adjusted for mean dietary intake variables (partial r = 0.658, p =.003). Restraint, disinhibition, and physical activity level did not predict weight change.

Conclusions. Reported hunger assessed by the Eating Inventory was associated with unintentional weight change in healthy postmenopausal women. The Eating Inventory questionnaire may provide a clinically useful tool for identifying older individuals at risk of undesirable weight change, and particularly unintentional weight loss, a factor strongly associated with increased morbidity and premature death in this population.

THE maintenance of a stable, nonobese body weight has been suggested to be an important factor in the preservation of health in older individuals (1–4). Unintentional weight loss in particular has been shown to be increasingly common after the age of 65 years (5,6), and a decrease in body weight from middle to older age has been associated with functional disability, morbidity, and mortality among elderly individuals (7–10). Several studies (11–14) have suggested that impaired regulation of food intake in old age underlies the problem of unintentional weight loss. Currently, however, there are no simple methods available for identifying apparently healthy older individuals who may be at particular risk of weight loss.

Eating behavior is a potentially important determinant of body weight (15–23) and has the potential to predict weight change stemming from impaired mechanisms regulating food intake. Three psychological constructs of eating behavior—termed dietary restraint, disinhibition, and hunger—are assessed by the Eating Inventory (EI) of Stunkard and Messick (24). Dietary restraint is defined as a tendency to consciously restrict food intake either to prevent weight gain or to promote weight loss (25), disinhibition is defined as a tendency to overeat in the presence of palatable foods or other stimuli such as boredom or sadness (26), and hunger is the susceptibility to perceived body symptoms that signal the need for food (26).

We previously reported that a high disinhibition score is strongly associated with increased weight gain from the age interval 30–39 to 55–60 years and high body mass index (BMI, kg/m2) at 55–65 years in a large sample of healthy women (15). In the same study, a higher restraint score attenuated the effect of disinhibition on both weight gain and BMI, such that women who were highly disinhibited were less overweight if they were also highly restrained (15). A similar interaction between disinhibition and restraint scores in association with BMI has been reported by other investigators (16,17), suggesting that disinhibition and restraint may be important determinants of body weight during early and middle adult life. In a recent 6-year prospective examination, Drapeau and colleagues (27) reported that an increase in dietary restraint score was associated with body weight loss in men and women aged approximately 41 years at baseline. However, to our knowledge there is no information on whether eating behavior may predict unintentional weight change and, in particular, unintentional weight loss in older adults.

We therefore conducted a study to examine the association of eating behavior constructs with longitudinal changes in body weight in older postmenopausal women, to test the hypotheses that unintentional weight loss is associated with higher restraint, lower disinhibition, and lower hunger as assessed by the EI.

METHODS

Participants
Participants identified for this study were 67 healthy postmenopausal women aged 55–65 years at baseline, who were recruited for a study designed to examine the association of eating behavior with metabolism and physiology. Following a screening physical examination, nonsmoking, nonobese women who were free from disorders and medications which might influence energy metabolism and who scored high (≥13) or low (≤5) on the restraint subscale of the EI (24) were invited to participate in an 18-day outpatient study involving several measurements including reported dietary intake and physical activity as described previously (28,29). Baseline data were collected during this measurement period.

Individuals were contacted for participation in a subsequent study 4.4 ± 0.9 years (range: 3.0–5.9 years) following baseline data collection. Of the 56 individuals who had completed all necessary measurements at baseline at least 3 years previously, 6 declined to participate, 4 had medical or personal issues that prevented participation, and 10 were lost to follow-up due to lack of forwarding address or erroneous contact information; 36 participants were available for follow-up measures. Ethical approval was given by the Tufts/New England Medical Center Institutional Review Board, and participants provided written informed consent prior to study participation.

Procedures
All study procedures were completed at baseline and repeated during the follow-up testing period. Eating behavior variables were calculated from the EI questionnaire according to published guidelines (24). In this questionnaire, three separate groups of questions are used to calculate the three constructs' (restraint, disinhibition, and hunger) scores. Higher scores reflect a proportionately greater tendency to exhibit that particular eating behavior characteristic.

Body weight was measured to ±0.1 kg using a calibrated electronic scale (model 15S; Ohaus; Pine Brook, NJ) with participants wearing a preweighed gown. Height was measured to ±0.1 cm using a wall-mounted stadiometer.

Participants also completed a self-administered food frequency questionnaire (Fred Hutchinson Cancer Research Center/Block Food Frequency Questionnaire, version 06.10.88, 1988; Cancer Prevention Research Program, Fred Hutchinson Cancer Research Center, Seattle, WA) which assessed dietary intake over the previous 6-month period. Daily macronutrient intakes were used in data analysis. Only those participants (n = 21) deemed to have accurately reported energy intake at baseline were used in analyses involving dietary intake. The assessment of energy-reporting accuracy was as reported elsewhere (30), with accuracy defined as within ±30% of total energy expenditure.

Physical activity level (PAL) was calculated as the ratio of resting energy expenditure (REE) to total energy expenditure (TEE). REE was determined using indirect calorimetry in fasting participants according to our usual protocol (29), with the mean of two measurements used in analyses. TEE was measured using the doubly labeled water procedure over a 14-day period (29). Although restrained eating has been associated with underreporting of food intake (30), the impact of inaccurate food records on the accuracy of TEE calculations is very small (31).

In addition, participants were asked to recall episodes of illness, injury, or medication use which may have influenced dietary intake, physical activity patterns, or body weight both prior to the baseline measurement period (with unsuitable individuals excluded from baseline participation) and between the baseline and follow-up measurement periods. Participants were also asked to report the number of times they altered their dietary intake or physical activity patterns to promote weight loss or gain over the study interval, and whether they experienced periods of depression or anxiety which may have resulted in weight loss or gain. A physical examination by a physician was completed at follow-up as well, to provide an objective assessment of relevant medical issues that may have occurred over the study interval. These questions and examination were designed to identify and exclude participants whose weight may have changed between baseline and follow-up measurement periods either because of unexpected medical issues or because of voluntary changes in lifestyle. However, reported medical and lifestyle changes were minimal, and no participants were excluded based on their responses to these inquiries. Specifically, 58% of the sample reported never dieting between baseline and follow-up measurement periods, with an additional 28% reporting dieting only 1–3 times, with a mean maximum weight loss of 4.7 ± 4.0 kg occurring on average 1.8 ± 0.9 years prior to follow-up. The majority of participants (89%) also reported no change in weight or appetite due to depression during the study interval, and either no change in physical activity (53%) or small changes not reflected by increased or decreased structured physical activity (33%). Approximately 33% of the participants reported medical issues occurring during the study period, but these were judged to be minor (e.g., bone fractures, minor surgery).

Statistics
Statistical analyses were performed using SPSS 12.0.0 for Windows (SPSS, Chicago, IL) and SYSTAT 10.2 (Systat Software, Richmond, CA). Changes in body weight, eating behavior, and other variables were calculated as follow-up minus baseline value. Examination of each variable using normal probability plots failed to reveal any serious departures from normality. Restraint, disinhibition, and hunger scores were log-transformed prior to analysis, to improve the linearity of relationships between these variables and weight change (overall results were very similar when raw scores were used). For descriptive analyses, a paired t test was used to examine differences between variables at baseline and follow-up. For analyses predicting change in body mass (defined as annual change [kg/y] to account for the varying length of follow-up among study participants, although the results of analyses using total change [kg] were very similar), both Pearson bivariate correlations (r) and multiple linear regression techniques were used. A subset of participants (n = 21) classified as accurate dietary reporters (as described above) were used in analyses incorporating reported dietary intake variables; the entire sample (n = 36) was used in the remaining analyses. The {alpha} level for all tests was set at 0.05. Two- and three-way interactions for eating behavior variables were considered in multiple regression models along with main effects; however, no examined interaction term was significant.

RESULTS

Participant characteristics at both baseline and follow-up are shown in Table 1. Participants were generally Caucasian, had a modal reported household income of $50,000–$74,999 (with a modal household size of two individuals), and a modal reported highest educational level of college graduate. Mean reported leisure physical activity at baseline was 300 ± 250 kcal/d, with a mean baseline PAL of 1.8 ± 0.2. There was substantial weight gain or weight loss in individual participants (–7.5 to +5.8 kg), but mean body weight and BMI at follow-up did not significantly differ from baseline values. The small observed mean change in weight is consistent with previous data reported from women of the same approximate age range (32). Individuals who participated in the follow-up study had a significantly lower BMI at baseline compared to individuals who did not participate (23.5 ± 3.1 kg/m2 for participants and 25.1 ± 3.1 kg/m2 for nonparticipants, p =.043 by independent t test). Although it is possible that selection bias introduced error into our results, the small overall difference in BMI between participants and nonparticipants, combined with the lack of significant differences in baseline EI scores, reported dietary intake, or physical activity (data not shown), suggests that the bias, if any, was minor.


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  		Table 1. Participant Characteristics at Baseline and Follow-Up (N = 36).

 
EI scores and reported dietary intake at baseline and follow-up for the study sample are shown in Table 2. Whereas restraint and disinhibition scores decreased slightly but significantly over the approximate 4-year study period, hunger scores at follow-up were not significantly different from scores at baseline. In general, baseline and follow-up EI scores were moderately correlated, as illustrated in Figure 1. There were also no significant differences in reported dietary intake between the two measurement periods in the accurate subset (there were also no significant differences when analyzing the entire sample; data not shown). Correlations between baseline and follow-up dietary intake variables were all significant (all p <.01). Given the importance of dietary fat and fiber in predicting body weight changes in general, correlations of these variables are shown in Figure 2.


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  		Table 2. Eating Inventory Scores and Reported Dietary Intake at Baseline and Follow-Up.

 

Figure 01
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  		Figure 1. Correlations of baseline and follow-up Eating Inventory variables (24); restraint score (A), disinhibition score (B), and hunger score (C)

 

Figure 02
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  		Figure 2. Correlations of baseline and follow-up reported dietary fiber (A) and fat (B) intakes as assessed by a food frequency questionnaire

 
Correlations of log EI scores, dietary intake variables, and PAL with weight change are shown in Table 3. Baseline, follow-up, and the mean of baseline and follow-up independent variables were examined in association with weight change. No correlation was significant when baseline EI scores or dietary intake variables were examined, although the hunger score correlation closely approached significance (p =.051). When follow-up variables were correlated with weight change, the only significant correlation was with hunger score. When mean variables were considered, the correlation of hunger score with weight change remained significant, whereas no other correlation was significant. These correlations indicate that low hunger score predicts weight loss in this sample population, whereas restraint and disinhibition are not predictive of weight change. Associations of mean, follow-up, and baseline log hunger scores with weight change are illustrated in Figure 3.


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  		Table 3. Rate of Weight Change ({Delta} weight; kg/y)* Correlated With Independent Variables Measured at Baseline, Follow-Up, and the Mean of Baseline and Follow-Up Values.

 

Figure 03
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  		Figure 3. Correlations of log hunger score with rate of weight change (kg/y, calculated as follow-up minus baseline body weight, divided by years of follow-up). Scatterplots of rate of weight change against mean log hunger score (A), log follow-up score (B), and log baseline score (C)

 
The association of multiple variables with weight change was also examined by using EI scores, dietary intake, and physical activity variables in multiple regression analyses. Initial models included EI and dietary variables separately, and a final model was attained using a backward stepping procedure as shown in Table 4. In the final model, log hunger score and reported fiber intake were significantly positively associated with weight change, whereas reported carbohydrate and protein intakes were significantly inversely associated with weight change. Reported dietary fat intake was not significant and thus not included in the final model. Hunger score, along with reported carbohydrate, protein, and fiber intakes, accounted for approximately 38% of the variability in weight change in this population. Hormone replacement therapy (coded no/yes) and initial BMI were both examined as possible confounders in this model, but were not included, as they were not significant and their presence in the model resulted in only very small changes in the adjusted R2, beta coefficients, and p values associated with the other variables. The association between log hunger and rate of weight change, adjusting for the other variables in the model, is shown in Figure 4. The top panel in Figure 4 depicts the adjusted association of hunger with weight change per year, and the bottom panel shows the adjusted relationship with weight change over the entire study interval.


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  		Table 4. Multiple Linear Regression Model Predicting Rate of Weight Change ({Delta} Weight; kg/y)*.

 

Figure 04
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  		Figure 4. Relationship between mean log hunger score and weight change (calculated as follow-up minus baseline body weight), adjusted for mean reported carbohydrate, protein, and fiber intakes as assessed by a food frequency questionnaire. Adjusted relationships with rate of weight change (kg/y; A) and total weight change (kg; B)

 
DISCUSSION

Both weight gain and loss have been associated with increased morbidity, functional impairment, and mortality in older adults (7–10,33). Unintentional weight loss is particularly common among older individuals, and has been strongly linked as both a marker of and as a contributor to severe morbidity and premature death (34). Multiple causes for unintentional weight loss have been suggested to include a physiological anorexia of aging, perhaps mediated by age-associated declines in taste and smell (35) or altered regulation of satiety and hunger signals (36–38). The results of this study suggest that the impaired regulation of food intake in old age is associated with weight loss in free-living, healthy older women, and can be detected as reduced hunger scores using the EI. Previous studies (39,40) have observed reduced hunger after an overnight fast and reduced frequency of hunger during weight loss in elderly individuals compared to young adults. However, to our knowledge, this study is the first to identify a direct association between reduced hunger and unintentional weight loss within an apparently healthy older population.

Previous studies using the EI in younger individuals have provided conflicting results. Cross-sectionally, hunger appears to be, at most, marginally associated with BMI (41,42). Instead, high disinhibition strongly predicts high BMI and weight gain, whereas restraint attenuates the effects of high disinhibition in some but not all studies (15–18). In weight loss intervention programs, some (43–45) but not other (23,46–49) studies have reported that low hunger predicts reduced energy intake and/or greater weight loss. In the latter studies, successful weight loss and maintenance of weight loss was alternatively predicted by higher restraint and/or lower disinhibition scores.

Although our current results differ from some of the previous investigations with regard to restraint and disinhibition, it is likely that this may be due to our unique use of an older, nonoverweight population in this study, and perhaps also our ability to identify and exclude individuals who inaccurately reported dietary intake from analyses involving dietary intake variables. Several research groups have documented impaired regulation of food intake in elderly persons (11–13,36,40), and hunger has been suspected as a probable contributing cause not only because of the reports of reduced hunger in response to fasting and weight loss as documented above (39,40), but also because of an apparently impaired hunger attenuation in response to duodenal infusions of nutrients (50). Our report, however, provides the first evidence of impaired hunger linked to functional weight loss that is detectable by means of a simple questionnaire.

In addition, it is possible that a low hunger score on the EI may be secondary to dietary or environmental factors that impact hunger. The question of whether low hunger is traceable to a loss of biological signals or quantifiable other physiological factors cannot be fully determined in this study. However, concerning potential dietary correlates of hunger, in this population we found that low hunger was not itself associated with dietary alterations that predict reduced hunger and body weight loss, such as higher fiber intake (51), higher consumption of vegetables relative to other foods (52), or lower fat or energy density (53). We also considered the possibility that low hunger could be due to underlying disease or to use of medications that caused anorexia. However, no correlation between hunger and reported disease or medication use was observed in this population. These negative findings lend support to the suggestion that the weight loss observed in our population was unexplained by conventional factors, and that the underlying reason for the association of hunger as assessed by the EI and weight loss is a diminished perception of sensations that signal a need for food and a reduced susceptibility to the mechanisms by which such sensations elicit eating, both associated with biological aging.

Although hunger score was the only eating behavior variable significantly correlated with weight loss in our study, a linear regression model combining low hunger score and dietary variables was the best predictor of weight loss, as indicated in Table 4. Of particular note, a higher dietary protein intake predicted weight loss in our regression model; this finding is consistent with those of several recent studies suggesting effects of higher protein on both satiety (54,55) and weight loss (56–58) in younger adults. In addition, the observed relationship of a higher dietary carbohydrate intake and weight loss in this model is supported by studies demonstrating weight loss following an ad libitum high-carbohydrate/low-fat diet in older adults (59). Thus, our findings are consistent with the suggestion that very high-protein or low-fat diets may be detrimental in older adults for whom weight loss is undesirable, because when combined with the apparently reduced ability to sense hunger observed in this study, these diets may contribute to the unexplained weight loss of old age.

One practical implication of our results is that the EI may have clinical utility in predicting weight loss in apparently healthy older individuals, a finding consistent with recent work in younger patients entering a drug treatment program for obesity (60). Baseline hunger scores were only marginally related to weight loss (p =.051), and the correlation was strongest when using either the follow-up hunger score or the mean of baseline and follow-up values. However, the interval between baseline and follow-up measures in this study was relatively long (>4 years on average); a shorter study interval might have increased the ability of baseline measurements to predict changes over time. Further studies are needed to examine the relationship between EI assessments of hunger and weight change with annual or biannual measurements.

Conclusion
Our results suggest an association between hunger and weight change within an apparently healthy population of older women. Further studies are needed to confirm these results and to examine the clinical utility of the EI in the identification of older individuals at risk of weight loss.

Acknowledgments

This research was supported in part by National Institutes of Health grants T32AG00209 and F32AG21374 (to N.P.H.), DK09747 (to M.A.M.), and AG12829 and DK46124 (to S.B.R.), and the U.S. Department of Agriculture, Agricultural Research Service under cooperative agreement 58-1950-9-001.

We thank Ruth Lipman, PhD, for assistance with study design and data interpretation.

Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture or Department of Defense.

Footnotes

Decision Editor: John E. Morley, MB, BCh

Received May 24, 2004

Accepted July 15, 2004

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