Nutritional status of healthy elderly persons living in Dordogne, France ...

European Journal of Clinical Nutrition (2002) 56, 305–312 ß 2002 Nature Publishing Group All rights reserved 0954–3007/02 $25.00 www.nature.com/ejcn

ORIGINAL COMMUNICATION Nutritional status of healthy elderly persons living in Dordogne, France, and relation with mortality and cognitive or functional decline V Deschamps1*, X Astier1, M Ferry2, M Rainfray3, JP Emeriau3 and P Barberger-Gateau1 1 3

INSERM U330, Universite´ Victor Se´galen Bordeaux 2, France; 2Service de Ge´riatrie, Centre Hospitalier de Valence, France; and Centre de Ge´riatrie Henri Choussat, Universite´ Victor Se´galen Bordeaux 2, France

OBJECTIVE: Description of the nutritional status of healthy elderly people and investigation of its longitudinal relationship with mortality and cognitive or functional decline. DESIGN: Longitudinal study. SETTING: In Dordogne, France. SUBJECTS: A total of 169 French elderly community dwellers aged 68 y and older from in the PAQUID (Personnes Ageés QUID) study were included. Dietary intake was assessed by a 3 day food record and a dietary history. Self-reported weight and height were used to calculate the body mass index (BMI, kg=m2). Mortality, activities of daily living (ADL), instrumental activities of daily living (IADL) and the Mini Mental State Examination (MMSE) were measured at 5 y follow-up. RESULTS: Nutritional intake and BMI vary according to age and sex. Men generally have a higher nutritional intake than women. Intake decreases with age especially in men. Among the 169 subjects, 22 died. When analyzed by logistic regression, there was no relation between markers of risk of poor nutrition and mortality but a BMI greater or equal 27 at baseline was associated with a increased risk of 5 y mortality (OR ¼ 6.27, 95% CI 1.29 – 30.37) adjusted for sex and age. With regard to cognitive decline, subjects with a BMI greater or equal than 23 kg=m2 had 3.6 times lower chance of presenting a decline in the subsequent 5 y adjusted by age and sex (OR ¼ 0.28, 95%, CI 0.09 – 0.90). BMI ranging between 23 and 27 was associated with a significantly decreased risk of IADL disability (OR ¼ 0.31, 95% CI 0.10 – 0.93) in multivariate analyses. CONCLUSION: In apparently healthy elderly people a BMI ranging between 23 and 27 is associated with lower risks of functional and cognitive declines in the subsequent 5 y. European Journal of Clinical Nutrition (2002) 56, 305 – 312. DOI: 10.1038=sj=ejcn=1601311 Keywords: elderly; nutrition; anthropometry; mortality; cognition; disability

Introduction The number of elderly persons is increasing in the developed countries and, moreover, improvements in health care have

*Correspondence: V Deschamps, INSERM U330, Universite´ Victor Se´galen Bordeaux 2, 146 rue Leó Saignat, 33076 Bordeaux cedex, France. E-mail: [email protected] Guarantor: V Deschamps. Contributors: PB-G was involved in the study conception and design, data analysis and interpretation. MF designed the study and participated in the protocol design and interpreted the results. XA contributed to bibliographical analysis and interpretation of results. PE and MR provided a clinical expertise. VD did the statistical analysis and wrote the paper. Received 18 December 2000; revised 18 July 2001; accepted 23 July 2001

led to an increase in life expectancy. As a consequence, cognitive impairment and age-related disability have become a huge and increasingly important medical and social problem (Schneider & Guralnik, 1990). Disability is defined in the International Classification of Impairments, Disabilities, and Handicaps (ICIDH) developed by the World Health Organization as ‘a restriction or lack of ability to perform an activity in normal manner’. In addition to physiological health, an important issue for older people is maintenance of cognitive functions. Moreover, cognitive decline is one of the major determinants of disability in elderly people (Barberger-Gateau & Fabrigoule, 1997). There is heightened interest today in achieving better acknowledgement of the factors associated with cognitive decline and functional disability in order to improve the quality of life of older persons.

Nutrition, mortality, cognitive and functional decline V Deschamps et al

306 Many environmental factors may influence the functional balance of the elderly. The role played by food, for example, in the development or worsening of some illness has been proven (American Dietetic Association, 2000). The role that an inadequate nutrition plays in inducing mortality and morbidity is well accepted, as seen in the effect from malnutrition or specific dietary deficiencies (Garry et al, 1982). A lot of studies have analyzed the role of malnutrition on mortality (Sullivan et al, 1999), morbidity (Sullivan, 1995b) and time of hospitalization (Ryan, 1990). Poor nutrition in elderly persons is associated with more doctors visits, more emergency room visits, more hospitalizations, more nursing home placement, more health costs, reduced survival rates, and reduced perception of health and well-being (Delhey et al, 1989). However, the impact of poor nutritional status on functional and cognitive declines is not well known in elderly people. Malnutrition is a common problem in older people which has been mostly studied on hospitalized subjects (Mowe & Bohmer, 1991). It can adversely affect prognosis, delay recovery from disease, reduce immune function, and increase the susceptibility to infections (Sullivan, 1995a). There is a lack of longitudinal studies conducted in freeliving elderly persons to analyze the association between their nutritional status and evolution in terms of mortality, cognitive and functional capacity. Such studies could enable the identification of subjects who are apparently healthy but at risk of adverse outcomes, based on simple indicators of nutritional status. Good candidates for such indicators are general markers of nutritional status such as anthropometric measurements and overall levels of protein and energy intakes (Reuben et al, 1995). In this paper, we examined the association of general indicators of nutritional status with the evolution of health status in terms of cognitive decline, disability and mortality in a population of healthy elderly persons living at home.

Methods Subiects The subjects took part in the PAQUID (Personnes Age´ es QUID) study. This research program is a prospective cohort study of mental and physical aging in a representative sample of elderly subjects living at home in south-western France (the Gironde and the Dordogne administrative regions). The general PAQUID methodology has been described elsewhere (Dartigues et al, 1992). At baseline, 3777 elderly community dwellers aged 65 y and over were included. In Dordogne, the study began in 1989 and 985 subjects were seen at the initial visit and followed up 3, 5 and 8 y after. The nutritional study was conducted on a subsample of 171 volunteers among the 689 subjects living in Dordogne who completed the 3 y examination and agreed to answer a detailed nutritional questionnaire. European Journal of Clinical Nutrition

Sample for this study Among the 171 persons who accepted the nutritional survey, two demented subjects were excluded, thus leaving 169 subjects. Five years later, 126 surviving subjects (74%) were re-examined. Twenty-two subjects (13%) died and 21 (13%) refused the follow-up. Vital status was known for all participants.

Health questionnaire At each follow-up, physical functional disabilities were measured by the activities of daily living scale (ADL; Katz et al, 1970). Subjects who needed help for at least one of the six activities assessed by this scale were classified as ‘dependent’. Functional assessment also included the eight items of the instrumental activities of daily living (IADL) scale of Lawton and Brody (1969). Five items were used for both sexes (ability to use telephone, shopping, mode of transportation, responsibility for own medication, ability to handle finances). Three household activities were added when assessing women: food preparation, housekeeping and doing laundry. Dependency was defined for at least one limitation in five in men and one in eight in women. The Mini Mental State Examination (MMSE) was used to assess cognitive functions. Results of this test are expressed as a score ranging from 0 to 30. Cognitive decline was defined as a drop of at least three points on the MMSE over a 5 y period. Mortality data were available yearly.

Nutritional questionnaire Dietary intake was assessed by a 3 day food record and a dietary history. An interviewer (a psychologist) gave the form of the 3 day record to the subjects. Participants recorded all their food consumption over 3 days. Three days later, a dietician visited them at home and conducted an historical inquiry about food consumption to assess frequencies, and then completed the quantitative questionnaire. For analyses, food consumption data were converted into energy and nutrients using the food consumption tables of the EURONUT=SENECA study (Van’t Hof et al, 1991). Anthropometric data as body weight and height were selfreported at baseline. The body mass index (BMI) was calculated by the ratio: weight (kg), height (m2).

Statistical analysis Subjects were classified as being at risk of poor nutritional status based on abnormal anthropometric measurement (BMI) or lower level of protein or energy intake. A risk of poor nutritional status was appreciated with three indicators: BMI lower than 23 kg=m2 or protein intake lower than 1 g per kg of body weight and per day or energy intake lower or equal than 30 kcal per kg of body weight and per day. The cut-off point of a BMI lower than 23 was one of the cut-off points used in the Mini Nutritional Assessment


307 (Guigoz et al, 1996). This questionnaire is validated in France to evaluate the nutritional status of elderly persons. This cutoff point is the first degree used to detect a risk of poor nutrition in this questionnaire. When the number of subjects permitted it and to exhibit a potential U-shape or linear effect of BMI on health evolution at 5 y, we categorized this variable into three categories. The cut-off point of a BMI greater than 27 identified light overweight. This cut-off point was reported on a recent review to be a significant prognostic factor for all-cause and cardiovascular mortality among 65 – 74 y old persons (Heiat et al, 2001). For energy and protein intake we considered subjects at risk of poor nutrition if their intake was lower than French recommended dietary allowances for elderly persons (Martin, 2001), that is lower than 1 g per kg of body weight per day for protein intake and intake lower or equal than 30 kcal per kg of body weight per day for energy intake. We used logistic regressions with mortality, dependency for ADL and IADL (subjects always independent 5 y later for each domain as reference), cognitive decline (decline in MMSE score less than three points over the 5 y period as reference), as dependent variables. The independent variables were successively the three indicators of nutritional status. The evolution of the different tests described above was calculated over a 5 y period (between the 3 y and the 8 y visits). The following variables, already found to be associated with mortality and cognitive decline and which could be associated with particular nutritional habits, were considered as potential confounders: age, gender, level of education (no schooling at all or only primary schooling without any diploma vs at least graduate from primary school; Letenneur et al, 1999). All variables significantly associated with the different endpoints in univariate analyses (P < 0.05) were retained as explanatory variables in the multivariate analysis. A stepwise logistic regression model was used to estimate the odds ratios of association between nutritional status and indicators of health status as dependent variables, adjusting for the potential confounders cited above. Odds ratios are presented with a 95% confidence interval (95% CI). We used SAS software version 6.12 with the logistic procedure in the analyses.

Results The study sample included 80 (47%) men and 89 women (53%). Median age was 75.4 y (ranging from 69 to 89). Fortyone (24%) had no diploma. Median energy intake was 35 kcal=kg and median protein intake was 1.33 g protein= day. Thirty-two percent had an energy intake lower or equal than 30 kcal=kg=day whereas 17% had a protein intake lower than 1 g protein=kg=day which defined poor nutritional status. Median BMI was 24 kg=m2 and 36% had a BMI lower than 23 kg=m2. Figure 1 shows the distribution of BMI among men and women. The prevalence of risk of

poor nutritional status in terms of BMI lower than 23 kg=m2 is significantly greater in women (n ¼ 41, 45%) than in men (n ¼ 20, 25%; P < 0.001). In women, the prevalence of poor nutrition in terms of energy intake was 37% (n ¼ 33) and 26% for men (n ¼ 21). The prevalence of poor nutrition in terms of protein intake defined by an intake lower than 1 g=kg=day is lower than with the two other indicators (12.5% for men and 23% for women). Among the 169 subjects included, 22 (13%) died in the 5 y. The 5 y evolution in MMSE score was studied in 125 subjects who took part in both examinations. Sixteen (13%) presented a cognitive decline. For the ADL scale, 20 (18%) subjects became dependent at 5 y among the 110 who were initially independent. Of the 106 participants independent for the IADL scale, 27 (25%) became dependent. Table 1 presents the characteristics of the subjects according to health status 5 y afterwards. As expected, all health status variables (except MMSE decline) were significantly associated with age and sex (P < 0.05 at least). For this reason, all the following analyses were adjusted for age and sex. Indicators of poor nutritional status based on protein intake were not significantly associated with health status at 5 y. Only ADL dependency at 5 y was significantly associated with energy intake. Subjects with an intake > 30 kcal=kg=day had about three times less chance of being ADL dependent at 5 y, but this association was no longer significant when adjusting for age and sex. Some analyses stratified on sex were made but the low number of subjects in each stratum did not make it possible to conclude. BMI was significantly associated with mortality, MMSE decline and IADL dependency in univariate analyses. A BMI lower than 23 was associated with higher MMSE decline, IADL dependency and lower mortality. To exhibit a potential U-shaped or linear effect of BMI on health evolution at 5 y, we categorized this variable into three categories. Thus, the first category represented subjects at risk of poor nutritional status (BMI < 23 kg=m2), the second represented the middle category (BMI ranging between 23 and 27 kg=m2) and the last contained overweight subjects (BMI 27 kg=m2). Unfortunately, this classification could not be applied to MMSE decline because no subject with a BMI greater than 27 kg=m2 declined. So only the relation of BMI in three categories with mortality and IADL dependency at 5 y adjusted for sex and age was investigated. With this classification, the subjects with a BMI 27 had six times more risk of dying than subjects with BMI < 23 adjusted on age and sex (OR ¼ 6.27, 95% CI 1.29 – 30.37). These higher risks of mortality of subjects with BMI higher than 27 explains the lower mortality reported in Table 1 for persons with BMI lower than 23. Subjects with a BMI ranging between 23 and 27 kg=m2 had about three times lower chance of being IADL dependent at 5 y adjusted for age and sex (OR ¼ 0.31, 95% CI 0.10 – 0.93). With a backward stepwise regression model for each dependant variable, we investigated the role of potential confounders (age, gender and level of education). Only age and sex were retained in European Journal of Clinical Nutrition


308

Figure 1

Distribution of BMI in the PAQUID Study according to sex.

the final models (Table 2). A BMI greater than 27 kg=m2 was predictor of 5 y mortality with six times more risk than subjects with BMI < 23. In this model, women had six European Journal of Clinical Nutrition

times lower risk of dying than men. Risk increased with age: subjects over 75 y of age had four times more risk of dying than subjects of less than 75 y. BMI was a predictor of


309 Table 1 Characteristics of subjects according to health status at 5 y in the PAQUID study Mortality (n ¼ 169) n

MMSE decline (n ¼ 125)

Death (%)

ADL dependency (n ¼ 110)

IADL dependency (n ¼ 106)

n

Decline (%)

n

Dependency (%)

n

Dependency (%)

BMI < 23 kg=m2 23 kg=m2

60 109

5%* 18%

49 76

22%** 7%

40 70

25% 16%

38 68

40%** 20%

Protein < 1 g=kg=day 1 g=kg=day

29 140

10% 14%

21 104

10% 13%

18 92

17% 20%

20 86

20% 27%

Energy 30 kcal=kg=day > 30 kcal=kg=day

54 115

9% 15%

39 86

8% 15%

34 76

29% 15%

32 74

22% 27%

Sex Men Women

80 89

22%*** 4%

52 73

10% 15%

49 61

4%* 31%

41 65

15%* 32%

Age < 75 y 75 y

82 87

6%* 20%

67 58

6%* 21%

60 50

10%* 30%

62 44

18%* 23%

41 128

10% 14%

28 97

21% 10%

24 86

17% 20%

21 85

38% 22%

Level of education No primary diploma At least graduated

Significance: *P < 0.05; **P < 0.01; ***P < 0.001.

Table 2 Final models of stepwise logistic regressions on mortality, cognitive decline and IADL dependency 5 y afterwards, adjusted on sex, age and level of education, the PAQUID study Mortality (n ¼ 169) OR

95% CI

IADL dependency (n ¼ 106) OR

95% CI

BMI < 23 kg=m2 23 kg=m2

Cognitive decline (n ¼ 125) OR

95% CI

1 0.28*

0.09 – 0.90

BMI 2 < 23 kg=m 23 – 27 kg=m2 27 kg=m2

1 3.13 6.27*

0.79 – 12.30 1.29 – 30.37

0.31* 0.46

0.10 – 0.93 0.12 – 1.72

Protein < 1 g=kg=day 1 g=kg=day

1 1.11

0.28 – 4.35

1 1.47

0.45 – 4.84

1 1.92

0.38 – 9.62

Energy 30 kcal=kg=day > 30 kcal=kg=day

1 1.61

0.52 – 4.91

1 1.43

0.52 – 3.96

1 2.61

0.67 – 10.16

Significance: *P < 0.05; **P < 0.01, ***P < 0.001.

IADL dependency at 5 y. Subjects with a BMI ranging between 23 and 27 kg=m2 had about three times less chance of being IADL dependent at 5 y. Subjects over 75 y of age had about three times higher risk of becoming IADL dependent than subjects of less than 75 y. With regard to cognitive decline, subjects with a BMI greater or equal than 23 kg=m2 had 3.6 times lower chance of presenting a decline in the 5 y subsequent adjusted for age and sex (OR ¼ 0.28,

95% CI 0.09 – 0.90). This risk of cognitive decline was four times more important for subjects over 75 y than for subjects less than 75 y.

Discussion These results show that, in non-demented elderly community dwellers, BMI is a strong predictor of the evolution of European Journal of Clinical Nutrition


310 health status in the 5 y subsequent. There was no significant relation between mortality and risk of poor nutrition in terms of BMI lower than 23. A BMI greater than 27 kg=m2 was associated with mortality. On the other hand, a BMI ranging between 23 and 27 kg=m2 was inversely associated with IADL dependency after 5 y. A BMJ greater or equal to 23 kg=m2 was inversely associated with cognitive decline. Effects of age, sex or level of education could not explain these results. There was no relation between the different end-points and risk of poor nutrition in terms of protein or energy intake. In terms of BMI, there were few mal-nourished persons in our population: only 23 persons (20 women and three men) presented a BMI lower than 21. Thus, this category could not be studied apart. So we used a cut-off point of BMI lower than 23 which is currently used in a validated questionnaire (Guigoz et al, 1996). The upper cut-off point of a BMI greater or equal to 27 was used to identified subjects who presented slight overweight. The cut-off point of a BMI greater than 30 could not be used in our population to identify obese persons because only nine subjects presented this BMI. So the cut-off point of a BMI equal or greater than 27 figure a sufficient number of subjects to analyze the effect of slight overweight on different end-points. These different cut-off points are a compromise between statistical analyses and clinical interest adapted to our healthy population. This can probably explain the lack of relationship between BMI and mortality. Relations between BMI, IADL dependency and cognitive decline are similar, probably because of the important cognitive component of IADL items (Barberger-Gateau et al, 1999). For IADL, the relative risk of 0.46 for BMI > 27 is not significant, probably because slight overweight can lead to physical limitation (Lichtenstein et al, 2000). The cut-off point of protein intake lower than 1 g per kg of bodyweight per day and energy intake lower than or equal to 30 kcal per kg of body weight per day is currently used in France to identify risk of poor nutrition (Martin, 2001). A lower cut-off point at 0.8 g per kg of body weight could not be used because only nine persons presented this profile. Only 23 subjects had an energy intake lower than or equal to 25 kcal per kg of body weight per day. Subjects included in this study were in good health. The 169 elderly persons had a better functional and cognitive functions than the subjects who lived in Dordogne at the 3 y follow-up. Only 7.7% of the 169 subjects had a MMS score less than 24 vs 17.4% of the full Dordogne sample. The prevalence of IADL or ADL dependency was lower than in the Dordogne sample at the 3 y follow-up (respectively 18.9% vs 32.7% for ADL; 12.4% vs 13.7%). So study participants are healthy elderly community dwellers who cannot be considered as representative of the whole elderly population. Only six persons in PAQUID presented an MMS score less than 24 and demented subjects were excluded to achieve a better reliability of information about BMI and nutritional intake. European Journal of Clinical Nutrition

Prevalence of risk of poor nutrition depends on the nutritional indicator. Prevalence in both sexes is about 33% in terms of BMI and energy intake and 17% in terms of protein intake. These estimations are greater than in other studies on free-living elderly people in France (de Groot et al, 1996, 1991a). Yet comparison is difficult because thresholds to define poor nutrition and age groups are often different. However, the significantly lower prevalence of risk of poor nutritional status in terms of BMI in men (25%) than in women (45%) has been noted in previous studies (de Groot et al, 1991b). There was no relation between risk of poor nutrition in terms of protein or energy intake and mortality, cognitive function and ADL or IADL dependency. The distribution of energy and protein intake probably can explain this lack of association. In fact, our population is well nourished. Median of protein intake was 1.33 g=kg of body weight and 25% of the population had an intake greater than 1.54 g=kg of body weight. For energy intake, median was 35 g=kg of bodyweight and 25% of the population had an intake greater than 41 g=kg of body weight. The main data provided by the present study concern the strong association between BMI, cognitive decline, mortality and IADL dependency. It would be interesting to have several measurements of weight and height at different times. Evolution of BMI has been reported as a predictor of decline in previous studies. Weight loss among older people, often due to conditions such as cancer and heart disease, has been associated with increased risks of disability and mortality (Galanos et al, 1994). Weight loss precedes mild and moderate dementia (Barrett-Connor et al, 1996). However, in our longitudinal study height and weight were measured at baseline, before the decline. Such data are more valid than a retrospective declaration. The lack of validity of the measurement of BMI may be a limit of our study. Weight and height are self-reported by subjects, but a study made it possible to validate the reported weight. Indeed, weight has been measured on a subsample of 585 subjects who took part in the PAQUID study. The concordance between self-reported weight and the true measure, assessed on this sub-sample, was good by the method of Bland and Altman (1986; Figure 2). On the other hand, the height was not measured so it is possible that elderly persons over-reported their height. They may be reporting their adult height, which is probably higher than current height (Gunnell et al, 2000). In this case, our study under-estimates BMI. Consequently, it is possible that thresholds of BMI used in this study may be greater in reality. Moreover, another study conducted among 465 elderly people included in the longitudinal follow-up phase of the Canadian Study of Health and Aging analyzed the validity of self-reported height and weigh data (Payette et al 2001). The authors concluded that self-reported height and weight data can be obtained in normal and cognitively impaired elderly persons as well as in mild or moderate cases of dementia and can be used as a valid tool to screen for risk of undernutrition.


311

Figure 2 Difference between self-reported and measured weight in 585 patients of the PAQUID study. A ¼ measured weight; B ¼ self-reported weight Bland and Altman’s method.

In conclusion, we feel confident that BMI is useful for detecting elderly patients at risk of decline of health status. Our study shows that a BMI between 23 and 27 kg=m2 represents the optimal range associated with a lower risk functional and cognitive declines, in apparently healthy elderly community dwellers in France.

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