Journal of Gerontology 1975, Vol. 30, No. 3, 349-356
Longitudinal Study of Age and Cohort Influences on Dietary Patterns1 Pilar A. Garcia, PhD,2 George E. Battese, PhD,3 and Wilma D. Brewer, PhD4
A major problem in analysis of data from •**• longitudinal studies is the isolation of effects of age from other factors that might influence indices of nutritional status. One important factor that has been identified is the "generation" or "cohort" effect (see statistical analysis subsection). A cohort represents a group of individuals born in the same year or time interval. All members of a given cohort presumably will have been exposed to similar environmental conditions. Changes in cohort characteristics may be brought about by the combined influences of environmental factors such as technological and medical advances, economic fluctuations, and educational opportunities. Dietary habits are sensitive to changes in these factors; thus, any effects of genera1 Journal Paper No. J-7945 of the Iowa Agriculture and Home Economics Experiment Station, Ames. Project No. 1965. The study was part of a regional subproject, "The Nutritional Status and Dietary Needs of Older People," of North Central Regional Cooperative Project NC-5, "Nutritional Status and Dietary Needs of Population Groups." The project was supported partly by a grant from the National Institute of Arthritis and Metabolic Diseases, DHEW. We thank Donald K. Hotchkiss, Wayne A. Fuller, Michael Hidiroglou, and Kathy Morrison for assistance in the analysis of the data. We are grateful to our subjects for their cooperation and to, Dr. Pearl P. Swanson and her staff for their contribution to this study. 2 Professor, Dept. of Food & Nutrition. Iowa State Univ., Ames 50010. 3 Assistant Professor, Dept. of Statistics, Iowa State Univ. Present address: Lecturer, Dept. of Economic Statistics, Univ. of New England, Armidale, NSW 2351, Australia. 4 Professor and Head, Dept. of Food & Nutrition, Iowa State Univ.
tional patterns of eating must be accounted for if valid inferences are to be made as to the effects of aging per se on dietary intakes. Baltes (1968) discussed the significance of generation effects in aging research and also considered the influence of testing effects and of selective sampling, survival, and dropout in longitudinal studies of aging. Testing effects may arise in longitudinal studies if the participation of individuals in the study influences the values of their subsequent measurements. Selective sampling biases may exist, for example, if subjects for longitudinal studies tend to be of higher average intelligence or of higher socioeconomic status than participants selected for a cross-sectional study. Selective survival and dropout effects may result when subjects living to an advanced age, or failing to continue in the study, have characteristics different from those of other individuals. In 1948, the research staff in human nutrition at the Iowa Agriculture and Home Economics Experiment Station carried out a study of the nutritional status of women at least 30 years of age. These women were selected randomly from an area sample of Ames, Iowa, and nearby communities. The area sample was drawn by the Statistical Laboratory of Iowa 349
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Dietary data from weighed food intake records were obtained from 35 women 29 years old and older during 4 studies from 1948 to 1966. Longitudinal data were analyzed by the use of a multiple linear-regression model with year of birth and age as independent variables. With increasing age, mean intakes declined significantly for fat, saturated fatty acids, and oleic acid, and increased significantly for calcium. After accounting for cohort effects, no significant changes occurred in mean intakes of food energy, carbohydrate, protein, phosphorus, iron, thiamin, riboflavin, preformed niacin, vitamin A, and ascorbic acid. For successive year of birth, mean intakes increased significantly for protein, calcium, phosphorus, riboflavin, preformed niacin, and linoleic acid. The regression model used explained approximately 20% of the variability in intakes of food energy and most nutrients examined.
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long-term study of nutritional status of women. This report presents the results of the analyses of the dietary patterns of these women. METHOD
Subjects.—The distribution of the 35 subjects by year of birth and their ages during periodic observations between 1948 and 1969 is given in Fig. 1. Not all of the 35 subjects participated in all the repeat studies nor necessarily in all phases of a study. The number of observations per subject varied from 2 to 6. Ages ranged from 29 to 88 years. Birth years covered a span from 1873 to 1931. Thirty-two of the 35 women had been married, and 28 had children of their own. Their educational background was: 8th grade or less for 4 women; 9th to 12th grade for 11 women, with 5 who completed 12t3x grade; college for 20 women, with 12 who earned college degrees and 5 who took graduate courses. All 35 participants were
D I S T R I B U T I O N BY YEAR OF BIRTH AND AGE OF 35 WOMEN OBSERVED LONGITUDINALLY BETWEEN 1948 and 1969 AGE, yr. 30 40 50 60 70
1930
80
90
• , O » 0 r x " individual subjects ® , 0 , o r f • two subjects of the same aqe at the same observation period
1920
1910
1900
1890
1880
1870
Fig. 1. Distribution by year of birth and age of 35 w o m e n observed longitudinally between 1948 and 1969.
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State University. Each subject was apparently healthy and active in the maintenance of her home. The information obtained from the women included dietary intake, anthropometric, biochemical, and physiological measurements. Complete physical examination records and medical histories also were obtained. In 1950, measurements of metabolic balance were made while the women maintained their usual patterns of living and consumed selfchosen diets. Data from these early studies provided the baseline for a longitudinal study of nutrition and aging. The first reappraisal of nutritional status was conducted in 19581960 and, the second, in 1963-1966. Some indices of nutritional status were reassessed in 1968-1969. In 1958, the age range included 1 subject 29 years old. A total of 35 women participated in the longitudinal study. The women, at the time of first contact, were given no indication that they were to participate in a
AGING AND DIETARY PATTERNS
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Caucasians. All except 2 of the women reXn denotes the difference between the year mained physically capable of maintaining their of birth for the i-th subject and the year households between periods of observation for 1900; the study. Thirty-two women were full-time X2ij denotes the difference between the age homemakers, but some had occasional partof the i-th subject when the j-th measuretime employment. Three of the remaining ment is taken and 50 years of age; women were employed full-time during 1 or /?os Pi, and /?2 denote unknown coefficients more of the study periods. Most of the women to be estimated; and were active members of their communities. Uij, the random error associated with the Procedure.—The dietary data included a j-th measurement on the i-th subject, is total of 96 observations from weighed food assumed to have zero mean. records obtained in the 4 study periods between Given this model, it follows that the coeffi1948 and 1966. Ten of the 35 women were cient, /?0> is the average dietary intake for observed four times; 13 women, three times; 5 50-year-old women born in the year 1900. The women, twice; and 7 women, once. Similar coefficient, /?i, measures the year-of-birth or procedures and methods were used in all the cohort effect on dietary intake; that is, fit repstudy periods. The women were instructed to resents the difference in average dietary intake, weigh their food and beverages and to keep at any given age, between women born in any either a 7-day or a 10-day record. In the early given year and women born 1 year earlier. study periods, a dietetic scale was used by each The coefficient, /? 2, measures the year-to-year subject, and a Mettler balance (800-gm capacity; Mettler Instrument Corporation, 20 Nassau change in dietary intake for women having the St., Princeton, NJ) was used in the later pe- same year of birth. Thus, we say that /?2 repreriods. Mean daily intakes of food energy and sents the "pure-age" effects. The relationship between dietary intake, 14 nutrients were estimated by computer year of birth, and age of subjects that is asprocessing with master food cards based on data in Watt & Merrill (1963). After each of sumed in equation (1) is obviously a simplified the first three studies, the subjects received model. For example, for some variables, a reports with dietary recommendations and in- model that contains a second-order age and formation on some physiological and biochem- year-of-birth effects, or an age-by-year-ofical indices. These individual reports kept birth interaction may be more appropriate. Preliminary analyses with our data, however, many subjects interested in the study. Statistical analysis.—For each dietary var- indicated that the regression model specified by iable, we consider the estimation of a multiple equation (1) adequately accounted for year-oflinear-regression model that has, as independ- birth and age effects for all dietary variables, ent variables, year of birth and age of the sub- with the possible exception of iron intake. In preliminary analyses, consideration was jects at the time of measurement. Because given to the possibility that multiple measureseveral measurements were obtained on 35 subments on subjects over time had a "learning" jects, we present the statistical model by denoteffect on the individuals in the study; i.e., the ing a given dietary variable by Y with two subjects' participation in the study may have subscripts. The first subscript distinguishes the subject in the study, and the second distin- increased their concern for better nutrition and guishes the particular measurement for that thus have had an effect upon subsequent meassubject. In the presentation of the model, the urements. A variable indicating the number of number of measurements on the i-th subject is previous measurements on the subjects was denoted by n i3 where the ni-values are 1, 2, 3, included in the model to account for "learnor 4. The statistical model that we estimate ing" effects, but its coefficient was not statistically significant. is expressed as The parameters in the statistical model (1) are not most efficiently estimated by ordinary UlJ, (1) least-squares regression because we believe that j = l , . . . , n , ; i = 1,...,35, the repeated measurements of dietary intakes where for the same subject are positively correlated. ij denotes the value of the j-th measure- We assume that the repeated measurements on subjects have the same positive correlation ment for the i-th subject;
GARCIA, BATTESE, AND BREWER
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year of birth = 1960 - Age (in 1960), or, in terms of the variables in equation (1), we obtain x
= io-x 2 .
(2)
Thus, from a cross-sectional study it is not possible to estimate the coefficients of model (1) because the variables, Xx (year of birth) and X2, (age of subject) are linearly related. If, however, cohort effects are present and a regression relationship between dietary intake and age of subjects is estimated from crosssectional data, pure-age effects are not estimated. Because equation (2) is obtained for a cross-sectional study in 1960, the statistical model (1) is expressed by
Yl=fa + fa(l0-Xtl)+faXtl + ul = (ft + lOfii) + (fa-fix) X2i + u,, (3) where only 1 subscript is needed to distinguish subjects in the cross-sectional study. In equation (3), the coefficient of the age variable, fa —Pi, does not represent pure-age effects unless the cohort effect, represented by Pi, is zero.
Revalues reported are slightly less than those obtained when ordinary least-squares regression is used to estimate the coefficients in the model (1). The estimated longitudinal trends in the energy value of the diets of women at different ages and born in given years are shown in Fig. 2. The estimated coefficient for the age variable indicates that the predicted mean daily energy values for women decrease by about 6.5 kcal per year. The estimated age coefficient was statistically significant at the 10% level. The positive year-of-birth coefficient indicates that the predicted mean daily energy intake of women at a given age increases by about 6.6 kcal for each year. The estimated year-ofbirth coefficient, however, was not statistically significant. The standard errors of the coefficients, bi and b 2 , for total food energy were Table 1. Estimated Regression Coefficients for Predicting Daily Dietary Intakes with Independent Variables, Year of Birth minus 1900 and Age minus 50. Estimated Coefficients*
Food Energy, kcal Protein, gm Carbohydrate, gm Fat, gm Saturated Fatty Acids, gm Oleic Acid, gm Linoleic Acid, gm Calcium, mg Phosphorus, mg Iron, mg Thiamin, mg
RESULTS
The estimates for the coefficients in the statistical model (1) and their estimated standard errors are presented in Table 1 for 15 dietary variables. The estimates for the coefficients in the model (1), /?0, Pi, and /?2, are denoted by b0> bi, and b 2 , respectively. The coefficients of determination (R 2 ) for the regressions used to estimate the coefficients in the model also are presented in Table 1. The
Riboflavin, mg Preformed Niacin, mg Vitamin A, I.U. Ascorbic Acid, mg
b0 1776.83 (47.3) 61.62 (1.5) 207.97 (5.9) 81.49 (2.8) 20.20 (1.0) 17.37 (0.9) 4.76 (0.4) 636.97 (37.2) 996.82 (34.5) 10.74 (0.3) 0.94 (0.03) 1.40 (0.07) 13.8 (0.49) 6963.5 (730.1) 75.47 (4.3)
bi
6.62 (4.94) 0.71** (0.18) 0.30 (0.68) 0.18 (0.29) -0.19 (0.12) -0.03 (0.10) 0.11* (0.05) 12.97** (3.72) 12.31** (3.78) 0.067* (0.03) 0.005 (0.003) 0.024** (0.008) 0.147** (0.055) 40.60 (84.66) 0.55 (0.48)
to -6.46 (3.77) 0.28 (0.15) -0.71 (0.55) -0.62** (0.22) -0.38** (0.09) -0.21** (0.08) 0.05 (0.04)
Coefficient of Determination Rs 0.17 0.19 0.08 0.21 0.21 0.16 0.07
5.58* (2.71) 3.31 (3.00) 0.012 (0.03)
0.12
-0.003 (0.003)
0.20
0.010 (0.006) 0.036 (0.044)
0.11
-11.70 (70.10) 0.37 (0.39)
0.15 0.08
0.11 0.01 0.01
"The numbers within parentheses estimate the standard errors of the estimated coefficients. A single asterisk with an estimated coefficient indicates significance at the 5% level, and two asterisks indicate significance at the 1% level.
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coefficient for all subjects. The coefficients of the model (1) are estimated by ordinary leastsquares regression after a statistical transformation is employed to obtain nearly uncorrelated observations. The transformation used is described by Fuller & Battese (1973). In this transformation the number of measurements for each individual does not have to be the same. In cross-sectional studies, individuals of different ages are observed at one point in time. For such studies, age and year of birth for all individuals add to the same number (namely, the year of the study), and, consequently, age and cohort effects cannot be distinguished. For example, if a study was conducted in 1960, then, for each individual in the study, we obtain that
AGING AND DIETARY PATTERNS
there is a trend toward increasing protein intakes over time. But, because the year-of-birth coefficient is estimated to be greater than the age coefficient, a cross-sectional study would indicate that there is a trend toward decreased protein intake with age (cf., equation ( 3 ) ) . The estimated regression functions for protein also are shown in Fig. 2. The negative estimated coefficient, b2, for carbohydrate intake indicates that there is a downward trend in mean daily carbohydrate intake with age. The estimated coefficients of the year-of-birth and age variables, however, were not significantly different from zero. It can be shown that the coefficient for age obGm./day Protei n
Food energy
90 1930
1930
80
1920
70 60
1700 -
1880
50
1600 1890
1500 -
1880
1400
60
40
30
70
Carbohydrate
1880 50
60 70 Age, y r.
80
40
50
60 70 Age, yr.
80
Fig. 2. Estimated intakes of food energy, protein, carbohydrate, and fat for women in successive decades born between 1880 and 1930. Trie broken lines estimate intake when year-ofbirth effects are ignored.
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large. The broken line on the graph indicates the predicted mean intakes of women of different ages in 1960. This line is obtained by substitution of the estimated coefficients for food energy in equation (3). It estimates the regression line that would have been obtained from a cross-sectional study in 1960. The decline in energy value of the daily diet with age is much greater than that predicted from equation (1). Both regression coefficients, bi and b2, were positive for predicting mean protein intakes. There was a significant positive influence of year of birth on protein intake. That is, after accounting for age effects, it is estimated that
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GARCIA, BATTESE, AND BREWER similar to that for protein. The estimated coefficients for both year of birth and age were positive. With the exception of calcium, however, the positive age effects were not significantly different from zero. The estimated coefficients for year-of-birth effects for these four nutrients were significant at the 1% level. These positive year-of-birth effects probably are due to the upward trend in the consumption of meat, poultry, and dairy products (excluding butter) since the turn of the century (Friend, 1967). Mean iron intakes showed significant cohort effects but nonsignificant age effects. Further analysis with iron intake indicated that by including a second-order year-of-birth variable, about 16% of the variability in iron intake could be explained by the model. By adding the second-order year-of-birth variable, the estimated regression obtained was ** =11.26 + 0.060X1-0.003X12 (0.3)
(0.03)
(0.001)
= 0.16. (0.03)
From this equation it is evident that the iron intake for the women born in about 1910 tended to be higher than those born in earlier or subsequent years. A larger sample size is required to satisfactorily test for interaction and second-order age and cohort effects. Estimated coefficients of age for predicted mean intakes of vitamin A and thiamin were negative, but not statistically significant. The positive coefficients for year-of-birth effects on intakes of these two nutrients also were not significant nor were the estimated positive coefficients of both age and year of birth for ascorbic acid. The coefficients of determination (R2) for the variables considered in this study do not exceed 0.21. This implies that approximately 80% of the variation in dietary intakes is not explained by the year-of-birth and age effects that are defined in our statistical model. Although our analyses have explained only a small proportion of the variation in the dietary intakes, the analyses have, for all variables except vitamin A and ascorbic acid, accounted for a statistically significant proportion of the total variation. For a multiple-regression model with 2 independent variables and 96 observations, the coefficient of determination is significant at the 5% level provided it exceeds 0.06, and it is significant at the 1%
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tained in a cross-sectional study, b2 —bl5 is significantly different from zero for carbohydrate. Such a result, however, does not imply that the carbohydrate intake significantly declines as given individuals in the population age. Fig. 2 shows the estimated regression functions for carbohydrate. For the regression involving fat, the coefficient for year of birth (bx = 0.18) was not statistically significant, but the coefficient for age (b2 = —0.62) was significant at the 1% level. The predicted mean intakes of fat per day are shown in Fig. 2 as a function of age of individuals born in successive decades. The decline in fat intake with age is attributed to a significant reduction in intake of saturated fatty acids and of oleic acid (Table 1). Mean intakes of linoleic acid tended to increase with age, but the estimated coefficient for age was not statistically significant. The cohort effect was positive and statistically significant at the 5% level. This observation may be associated with the increased use of vegetable fats from the early 1900s (Antar, Ohlson, & Hodges, 1964; Friend, 1967). In a cross-sectional study of 1,072 Iowa women, 30 years old or older, the nutritive value of diets was estimated to decrease, on the average, by about 85 kcal and 4 gm of protein for every increase of 10 years of age (Swanson, Willis, Jebe, Smith, Ohlson, Biester, & Burrill, 1959). Regressions of intake of food energy and protein on age were significant. Steinkamp, Cohen, and Walsh (1965) compared the energy value of the diets recorded in 1948 and again in 1962 by 58 women. Diets recorded in 1962 supplied 182, 459, and 329 fewer calories than the previously recorded diets for women 50 to 59, 60 to 69, and 70 years and older, respectively. These authors did not find a decrease in intake of animal protein with increasing age. If the data in our study had been analyzed without isolation of the cohort effect, the intakes of calories and protein would be estimated to decrease by approximately 130.8 kcal and 4.3 gm of protein in the diets with each successive decade of age. After isolation of the cohort effect, changes associated with age represented a decrease of 64.6 kcal and an increase of 2.8 gm of protein with each increase of 10 years of age. Longitudinal patterns for calcium, phosphorus, riboflavin, and preformed niacin were
AGING AND DIETARY PATTERNS
355
level provided it exceeds 0.10. These critical values for R2 are obtained from the statistic,
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reduced intake of fat with advancing years may have resulted from (a) a decrease in fat tolerance due to physiological changes asso:——, which has F-distribu- ciated with aging such as a diminished diges-p.—P2N . / 2 (1-R ) / ( n - k - 1 ) tive capacity (Werner & Hambraeus, 1972), tion with k and n — k — 1 degrees of freedom, (b) efforts to control body weight, (c) a given that the coefficients of the k independent change in food pattern brought about by the variables in the regression model are zero and increased publicity on dietary fat intake and that the errors in the model are normally distributed. For our data k=2 and n=96. The incidence of coronary heart disease, or (d) low R2 for vitamin A and ascorbic acid intakes combined influences of these and other unidenmay be attributed partly to the uneven distri- tified factors. Schlenker, Feurig, Stone, Ohlson, bution of these vitamins in foods; hence, a wide and Mickelsen (1973) have reported an inverse variation in individual intakes. Factors that relationship between the level of dietary fat influence dietary intake and that have not been intake and longevity from a study of previous isolated in our model include family income, records of 52 deceased women in Michigan. number in household, education (especially These women were first observed from 1948 to with respect to good nutrition), incidence of 1955. In our study, the reduction in the mean fat disease requiring dietary modification, and intake accompanied by some decrease in carbosuch factors as faulty dentition, sore gums, "acid stomach," "too much gas." Other real hydrate intake was not sufficient to signifor imagined factors also' may influence the icantly reduce the energy value of the diets. Forty-seven percent of our subjects sustained acceptance of food by elderly persons. body weights that exceeded suggested standards DISCUSSION (see Hathaway & Foard, 1960) by 15% or Longitudinal studies with humans have cer- more. Because of the persistent problems of tain inherent difficulties. With successive ob- overweight, recommendations for dietary servations over time, the reduction in sample change must continue to stress judicious selecsize (from loss of interest, death, illness or tion of foods to provide a high nutrient density mobility of subjects) may alter the composition when the food energy is reduced. For individof the original group. Further, the possibility uals like our subjects, food energy from carboexists that experimental conditioning of sub- hydrates may be decreased significantly by jects may result from repeated observations or limiting the use of refined and concentrated that reports and feedback information may food sources of simple carbohydrates such as initiate changes. Preliminary analyses of our sugars and syrups. In the average American data, however, indicated that a learning effect diet, the proportion of the total carbohydrates from repeated participation was not statistically derived from sugars has increased steadily significant. The lack of learning effect prob- since the turn of the century (Antar et al., ably was associated with the length of time 1964; Friend, 1967). between observations. Our longitudinal data also indicate that food It is recognized that various intrinsic and patterns do not change significantly from early extrinsic factors (health status, income, house- middle age (30 years) to old age. Because of hold size, education) influence the dietary the persistence of eating habits, older people patterns of aging individuals. Our study in- may tend to respond less readily to dietary cluded a relatively small sample of women. modifications. The habitual eating patterns of Estimation of these sources of variation might individuals, therefore, must be considered in be possible with a much, larger sample, but was the nutritional care and group feeding of the elderly. Our observations reemphasize the not attempted in our study. Estimation of the coefficients of a regression need for education in nutrition at an earlier model for dietary intake was possible with data age and reinforced throughout the life-span. from our longitudinal study of 35 women. When the year-of-birth or cohort effect is SUMMARY accounted for, it seems that these adult women Dietary data from weighed food intake recmaintained their dietary habits during aging, ords were obtained from 35 women 29 years except for a decrease in fat consumption. The old and older. These women were observed
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GARCIA, BATTESE, AND BREWER American Journal of Clinical Nutrition, 1964, 14, 169-178. Baltes, P. B. Longitudinal and cross-sectional sequences in the study of age and generation effects. Human Development, 1968, 11, 145-171. Friend, B. Nutrients in United States food supply: A review of trends, 1909-191S to 1965. American Journal of Clinical Nutrition, 1967, 20, 907-914. Fuller, W. A., & Battese, G. E. Transformations for estimation of linear models with nested-error structure. Journal of the American Statistical Association, 1973, 68, 626-632. Hathaway, M. L., & Foard, E. D. Heights and weights of adults in the United States. US Dept. of Agriculture, Home Economics Research Report 10, 1960. Schlenker, E. D., Feurig, J. S., Stone, L. H., Ohlson, M. A., & Mickelsen, O. Nutrition and health of older people. American Journal of Clinical Nutrition, 1973, 26, 1111-1119. Steinkamp, R. C , Cohen, N. L.s & Walsh, H. E. Resurvey of an aging population: fourteen-year follow-up. The San Mateo nutrition study. Journal of the American Dietetic Association, 1965, 46, 103-110. Swanson, P., Willis, E., Jebe, E., Smith, J. M., Ohlson, M. A., Biester, A. & Burrill, L. M. Food intakes of 2,189 women in five North Central States. Iowa Agricultural and Home Economics Experiment Station Research Bulletin 468, 1959. Watt, B. K., & Merrill, A. L. Composition of foods:
REFERENCES
culture Handbook 8, 1963. Werner, I., & Hambraeus, L. The digestive capacity of elderly people. In L. A. Carlson (Ed.), Nutrition in old age. Symposia of the Swedish Nutrition Foundation X. Almquist and Wiksell, Uppsala, Sweden, 1972.
Raw, processed, prepared (Rev.). US Dept. of Agri-
Antar, M. A., Ohlson, M. A., & Hodges, R. E. Changes in retail market food supplies in the United States in the last seventy years in relation to the incidence of coronary heart disease, with special reference to dietary carbohydrates and essential fatty acids.
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during four studies over an 18-year period. Longitudinal data were analyzed by use of a multiple linear-regression model with two independent variables, year of birth and age of the subjects at the time of observation. The estimated age coefficients were negative and highly significant for fat, saturated fatty acids, and oleic acid. A positive age coefficient was significant with increasing age for calcium. Mean dietary intakes did not change significantly for food energy, protein, carbohydrate, phosphorus, iron, thiamin, riboflavin, preformed niacin, vitamin A, and ascorbic acid. After adjusting for age, the estimated positive coefficients for year of birth were significant for protein, calcium, phosphorus, riboflavin, preformed niacin, and linoleic acid. Year-of-birth (cohort) and age effects defined in the regression model explained approximately 20% of the variation in dietary intakes except for vitamin A and ascorbic acid. The statistical model used did not isolate other variables that affect dietary intakes of aging women.
Longitudinal Study of Age and Cohort Influences on Dietary Patterns1 Pilar A. Garcia, PhD,2 George E. Battese, PhD,3 and Wilma D. Brewer, PhD4
A major problem in analysis of data from •**• longitudinal studies is the isolation of effects of age from other factors that might influence indices of nutritional status. One important factor that has been identified is the "generation" or "cohort" effect (see statistical analysis subsection). A cohort represents a group of individuals born in the same year or time interval. All members of a given cohort presumably will have been exposed to similar environmental conditions. Changes in cohort characteristics may be brought about by the combined influences of environmental factors such as technological and medical advances, economic fluctuations, and educational opportunities. Dietary habits are sensitive to changes in these factors; thus, any effects of genera1 Journal Paper No. J-7945 of the Iowa Agriculture and Home Economics Experiment Station, Ames. Project No. 1965. The study was part of a regional subproject, "The Nutritional Status and Dietary Needs of Older People," of North Central Regional Cooperative Project NC-5, "Nutritional Status and Dietary Needs of Population Groups." The project was supported partly by a grant from the National Institute of Arthritis and Metabolic Diseases, DHEW. We thank Donald K. Hotchkiss, Wayne A. Fuller, Michael Hidiroglou, and Kathy Morrison for assistance in the analysis of the data. We are grateful to our subjects for their cooperation and to, Dr. Pearl P. Swanson and her staff for their contribution to this study. 2 Professor, Dept. of Food & Nutrition. Iowa State Univ., Ames 50010. 3 Assistant Professor, Dept. of Statistics, Iowa State Univ. Present address: Lecturer, Dept. of Economic Statistics, Univ. of New England, Armidale, NSW 2351, Australia. 4 Professor and Head, Dept. of Food & Nutrition, Iowa State Univ.
tional patterns of eating must be accounted for if valid inferences are to be made as to the effects of aging per se on dietary intakes. Baltes (1968) discussed the significance of generation effects in aging research and also considered the influence of testing effects and of selective sampling, survival, and dropout in longitudinal studies of aging. Testing effects may arise in longitudinal studies if the participation of individuals in the study influences the values of their subsequent measurements. Selective sampling biases may exist, for example, if subjects for longitudinal studies tend to be of higher average intelligence or of higher socioeconomic status than participants selected for a cross-sectional study. Selective survival and dropout effects may result when subjects living to an advanced age, or failing to continue in the study, have characteristics different from those of other individuals. In 1948, the research staff in human nutrition at the Iowa Agriculture and Home Economics Experiment Station carried out a study of the nutritional status of women at least 30 years of age. These women were selected randomly from an area sample of Ames, Iowa, and nearby communities. The area sample was drawn by the Statistical Laboratory of Iowa 349
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Dietary data from weighed food intake records were obtained from 35 women 29 years old and older during 4 studies from 1948 to 1966. Longitudinal data were analyzed by the use of a multiple linear-regression model with year of birth and age as independent variables. With increasing age, mean intakes declined significantly for fat, saturated fatty acids, and oleic acid, and increased significantly for calcium. After accounting for cohort effects, no significant changes occurred in mean intakes of food energy, carbohydrate, protein, phosphorus, iron, thiamin, riboflavin, preformed niacin, vitamin A, and ascorbic acid. For successive year of birth, mean intakes increased significantly for protein, calcium, phosphorus, riboflavin, preformed niacin, and linoleic acid. The regression model used explained approximately 20% of the variability in intakes of food energy and most nutrients examined.
GARCIA, BATTESE, AND BREWER
350
long-term study of nutritional status of women. This report presents the results of the analyses of the dietary patterns of these women. METHOD
Subjects.—The distribution of the 35 subjects by year of birth and their ages during periodic observations between 1948 and 1969 is given in Fig. 1. Not all of the 35 subjects participated in all the repeat studies nor necessarily in all phases of a study. The number of observations per subject varied from 2 to 6. Ages ranged from 29 to 88 years. Birth years covered a span from 1873 to 1931. Thirty-two of the 35 women had been married, and 28 had children of their own. Their educational background was: 8th grade or less for 4 women; 9th to 12th grade for 11 women, with 5 who completed 12t3x grade; college for 20 women, with 12 who earned college degrees and 5 who took graduate courses. All 35 participants were
D I S T R I B U T I O N BY YEAR OF BIRTH AND AGE OF 35 WOMEN OBSERVED LONGITUDINALLY BETWEEN 1948 and 1969 AGE, yr. 30 40 50 60 70
1930
80
90
• , O » 0 r x " individual subjects ® , 0 , o r f • two subjects of the same aqe at the same observation period
1920
1910
1900
1890
1880
1870
Fig. 1. Distribution by year of birth and age of 35 w o m e n observed longitudinally between 1948 and 1969.
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State University. Each subject was apparently healthy and active in the maintenance of her home. The information obtained from the women included dietary intake, anthropometric, biochemical, and physiological measurements. Complete physical examination records and medical histories also were obtained. In 1950, measurements of metabolic balance were made while the women maintained their usual patterns of living and consumed selfchosen diets. Data from these early studies provided the baseline for a longitudinal study of nutrition and aging. The first reappraisal of nutritional status was conducted in 19581960 and, the second, in 1963-1966. Some indices of nutritional status were reassessed in 1968-1969. In 1958, the age range included 1 subject 29 years old. A total of 35 women participated in the longitudinal study. The women, at the time of first contact, were given no indication that they were to participate in a
AGING AND DIETARY PATTERNS
351
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Caucasians. All except 2 of the women reXn denotes the difference between the year mained physically capable of maintaining their of birth for the i-th subject and the year households between periods of observation for 1900; the study. Thirty-two women were full-time X2ij denotes the difference between the age homemakers, but some had occasional partof the i-th subject when the j-th measuretime employment. Three of the remaining ment is taken and 50 years of age; women were employed full-time during 1 or /?os Pi, and /?2 denote unknown coefficients more of the study periods. Most of the women to be estimated; and were active members of their communities. Uij, the random error associated with the Procedure.—The dietary data included a j-th measurement on the i-th subject, is total of 96 observations from weighed food assumed to have zero mean. records obtained in the 4 study periods between Given this model, it follows that the coeffi1948 and 1966. Ten of the 35 women were cient, /?0> is the average dietary intake for observed four times; 13 women, three times; 5 50-year-old women born in the year 1900. The women, twice; and 7 women, once. Similar coefficient, /?i, measures the year-of-birth or procedures and methods were used in all the cohort effect on dietary intake; that is, fit repstudy periods. The women were instructed to resents the difference in average dietary intake, weigh their food and beverages and to keep at any given age, between women born in any either a 7-day or a 10-day record. In the early given year and women born 1 year earlier. study periods, a dietetic scale was used by each The coefficient, /? 2, measures the year-to-year subject, and a Mettler balance (800-gm capacity; Mettler Instrument Corporation, 20 Nassau change in dietary intake for women having the St., Princeton, NJ) was used in the later pe- same year of birth. Thus, we say that /?2 repreriods. Mean daily intakes of food energy and sents the "pure-age" effects. The relationship between dietary intake, 14 nutrients were estimated by computer year of birth, and age of subjects that is asprocessing with master food cards based on data in Watt & Merrill (1963). After each of sumed in equation (1) is obviously a simplified the first three studies, the subjects received model. For example, for some variables, a reports with dietary recommendations and in- model that contains a second-order age and formation on some physiological and biochem- year-of-birth effects, or an age-by-year-ofical indices. These individual reports kept birth interaction may be more appropriate. Preliminary analyses with our data, however, many subjects interested in the study. Statistical analysis.—For each dietary var- indicated that the regression model specified by iable, we consider the estimation of a multiple equation (1) adequately accounted for year-oflinear-regression model that has, as independ- birth and age effects for all dietary variables, ent variables, year of birth and age of the sub- with the possible exception of iron intake. In preliminary analyses, consideration was jects at the time of measurement. Because given to the possibility that multiple measureseveral measurements were obtained on 35 subments on subjects over time had a "learning" jects, we present the statistical model by denoteffect on the individuals in the study; i.e., the ing a given dietary variable by Y with two subjects' participation in the study may have subscripts. The first subscript distinguishes the subject in the study, and the second distin- increased their concern for better nutrition and guishes the particular measurement for that thus have had an effect upon subsequent meassubject. In the presentation of the model, the urements. A variable indicating the number of number of measurements on the i-th subject is previous measurements on the subjects was denoted by n i3 where the ni-values are 1, 2, 3, included in the model to account for "learnor 4. The statistical model that we estimate ing" effects, but its coefficient was not statistically significant. is expressed as The parameters in the statistical model (1) are not most efficiently estimated by ordinary UlJ, (1) least-squares regression because we believe that j = l , . . . , n , ; i = 1,...,35, the repeated measurements of dietary intakes where for the same subject are positively correlated. ij denotes the value of the j-th measure- We assume that the repeated measurements on subjects have the same positive correlation ment for the i-th subject;
GARCIA, BATTESE, AND BREWER
352
year of birth = 1960 - Age (in 1960), or, in terms of the variables in equation (1), we obtain x
= io-x 2 .
(2)
Thus, from a cross-sectional study it is not possible to estimate the coefficients of model (1) because the variables, Xx (year of birth) and X2, (age of subject) are linearly related. If, however, cohort effects are present and a regression relationship between dietary intake and age of subjects is estimated from crosssectional data, pure-age effects are not estimated. Because equation (2) is obtained for a cross-sectional study in 1960, the statistical model (1) is expressed by
Yl=fa + fa(l0-Xtl)+faXtl + ul = (ft + lOfii) + (fa-fix) X2i + u,, (3) where only 1 subscript is needed to distinguish subjects in the cross-sectional study. In equation (3), the coefficient of the age variable, fa —Pi, does not represent pure-age effects unless the cohort effect, represented by Pi, is zero.
Revalues reported are slightly less than those obtained when ordinary least-squares regression is used to estimate the coefficients in the model (1). The estimated longitudinal trends in the energy value of the diets of women at different ages and born in given years are shown in Fig. 2. The estimated coefficient for the age variable indicates that the predicted mean daily energy values for women decrease by about 6.5 kcal per year. The estimated age coefficient was statistically significant at the 10% level. The positive year-of-birth coefficient indicates that the predicted mean daily energy intake of women at a given age increases by about 6.6 kcal for each year. The estimated year-ofbirth coefficient, however, was not statistically significant. The standard errors of the coefficients, bi and b 2 , for total food energy were Table 1. Estimated Regression Coefficients for Predicting Daily Dietary Intakes with Independent Variables, Year of Birth minus 1900 and Age minus 50. Estimated Coefficients*
Food Energy, kcal Protein, gm Carbohydrate, gm Fat, gm Saturated Fatty Acids, gm Oleic Acid, gm Linoleic Acid, gm Calcium, mg Phosphorus, mg Iron, mg Thiamin, mg
RESULTS
The estimates for the coefficients in the statistical model (1) and their estimated standard errors are presented in Table 1 for 15 dietary variables. The estimates for the coefficients in the model (1), /?0, Pi, and /?2, are denoted by b0> bi, and b 2 , respectively. The coefficients of determination (R 2 ) for the regressions used to estimate the coefficients in the model also are presented in Table 1. The
Riboflavin, mg Preformed Niacin, mg Vitamin A, I.U. Ascorbic Acid, mg
b0 1776.83 (47.3) 61.62 (1.5) 207.97 (5.9) 81.49 (2.8) 20.20 (1.0) 17.37 (0.9) 4.76 (0.4) 636.97 (37.2) 996.82 (34.5) 10.74 (0.3) 0.94 (0.03) 1.40 (0.07) 13.8 (0.49) 6963.5 (730.1) 75.47 (4.3)
bi
6.62 (4.94) 0.71** (0.18) 0.30 (0.68) 0.18 (0.29) -0.19 (0.12) -0.03 (0.10) 0.11* (0.05) 12.97** (3.72) 12.31** (3.78) 0.067* (0.03) 0.005 (0.003) 0.024** (0.008) 0.147** (0.055) 40.60 (84.66) 0.55 (0.48)
to -6.46 (3.77) 0.28 (0.15) -0.71 (0.55) -0.62** (0.22) -0.38** (0.09) -0.21** (0.08) 0.05 (0.04)
Coefficient of Determination Rs 0.17 0.19 0.08 0.21 0.21 0.16 0.07
5.58* (2.71) 3.31 (3.00) 0.012 (0.03)
0.12
-0.003 (0.003)
0.20
0.010 (0.006) 0.036 (0.044)
0.11
-11.70 (70.10) 0.37 (0.39)
0.15 0.08
0.11 0.01 0.01
"The numbers within parentheses estimate the standard errors of the estimated coefficients. A single asterisk with an estimated coefficient indicates significance at the 5% level, and two asterisks indicate significance at the 1% level.
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coefficient for all subjects. The coefficients of the model (1) are estimated by ordinary leastsquares regression after a statistical transformation is employed to obtain nearly uncorrelated observations. The transformation used is described by Fuller & Battese (1973). In this transformation the number of measurements for each individual does not have to be the same. In cross-sectional studies, individuals of different ages are observed at one point in time. For such studies, age and year of birth for all individuals add to the same number (namely, the year of the study), and, consequently, age and cohort effects cannot be distinguished. For example, if a study was conducted in 1960, then, for each individual in the study, we obtain that
AGING AND DIETARY PATTERNS
there is a trend toward increasing protein intakes over time. But, because the year-of-birth coefficient is estimated to be greater than the age coefficient, a cross-sectional study would indicate that there is a trend toward decreased protein intake with age (cf., equation ( 3 ) ) . The estimated regression functions for protein also are shown in Fig. 2. The negative estimated coefficient, b2, for carbohydrate intake indicates that there is a downward trend in mean daily carbohydrate intake with age. The estimated coefficients of the year-of-birth and age variables, however, were not significantly different from zero. It can be shown that the coefficient for age obGm./day Protei n
Food energy
90 1930
1930
80
1920
70 60
1700 -
1880
50
1600 1890
1500 -
1880
1400
60
40
30
70
Carbohydrate
1880 50
60 70 Age, y r.
80
40
50
60 70 Age, yr.
80
Fig. 2. Estimated intakes of food energy, protein, carbohydrate, and fat for women in successive decades born between 1880 and 1930. Trie broken lines estimate intake when year-ofbirth effects are ignored.
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large. The broken line on the graph indicates the predicted mean intakes of women of different ages in 1960. This line is obtained by substitution of the estimated coefficients for food energy in equation (3). It estimates the regression line that would have been obtained from a cross-sectional study in 1960. The decline in energy value of the daily diet with age is much greater than that predicted from equation (1). Both regression coefficients, bi and b2, were positive for predicting mean protein intakes. There was a significant positive influence of year of birth on protein intake. That is, after accounting for age effects, it is estimated that
353
354
GARCIA, BATTESE, AND BREWER similar to that for protein. The estimated coefficients for both year of birth and age were positive. With the exception of calcium, however, the positive age effects were not significantly different from zero. The estimated coefficients for year-of-birth effects for these four nutrients were significant at the 1% level. These positive year-of-birth effects probably are due to the upward trend in the consumption of meat, poultry, and dairy products (excluding butter) since the turn of the century (Friend, 1967). Mean iron intakes showed significant cohort effects but nonsignificant age effects. Further analysis with iron intake indicated that by including a second-order year-of-birth variable, about 16% of the variability in iron intake could be explained by the model. By adding the second-order year-of-birth variable, the estimated regression obtained was ** =11.26 + 0.060X1-0.003X12 (0.3)
(0.03)
(0.001)
= 0.16. (0.03)
From this equation it is evident that the iron intake for the women born in about 1910 tended to be higher than those born in earlier or subsequent years. A larger sample size is required to satisfactorily test for interaction and second-order age and cohort effects. Estimated coefficients of age for predicted mean intakes of vitamin A and thiamin were negative, but not statistically significant. The positive coefficients for year-of-birth effects on intakes of these two nutrients also were not significant nor were the estimated positive coefficients of both age and year of birth for ascorbic acid. The coefficients of determination (R2) for the variables considered in this study do not exceed 0.21. This implies that approximately 80% of the variation in dietary intakes is not explained by the year-of-birth and age effects that are defined in our statistical model. Although our analyses have explained only a small proportion of the variation in the dietary intakes, the analyses have, for all variables except vitamin A and ascorbic acid, accounted for a statistically significant proportion of the total variation. For a multiple-regression model with 2 independent variables and 96 observations, the coefficient of determination is significant at the 5% level provided it exceeds 0.06, and it is significant at the 1%
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tained in a cross-sectional study, b2 —bl5 is significantly different from zero for carbohydrate. Such a result, however, does not imply that the carbohydrate intake significantly declines as given individuals in the population age. Fig. 2 shows the estimated regression functions for carbohydrate. For the regression involving fat, the coefficient for year of birth (bx = 0.18) was not statistically significant, but the coefficient for age (b2 = —0.62) was significant at the 1% level. The predicted mean intakes of fat per day are shown in Fig. 2 as a function of age of individuals born in successive decades. The decline in fat intake with age is attributed to a significant reduction in intake of saturated fatty acids and of oleic acid (Table 1). Mean intakes of linoleic acid tended to increase with age, but the estimated coefficient for age was not statistically significant. The cohort effect was positive and statistically significant at the 5% level. This observation may be associated with the increased use of vegetable fats from the early 1900s (Antar, Ohlson, & Hodges, 1964; Friend, 1967). In a cross-sectional study of 1,072 Iowa women, 30 years old or older, the nutritive value of diets was estimated to decrease, on the average, by about 85 kcal and 4 gm of protein for every increase of 10 years of age (Swanson, Willis, Jebe, Smith, Ohlson, Biester, & Burrill, 1959). Regressions of intake of food energy and protein on age were significant. Steinkamp, Cohen, and Walsh (1965) compared the energy value of the diets recorded in 1948 and again in 1962 by 58 women. Diets recorded in 1962 supplied 182, 459, and 329 fewer calories than the previously recorded diets for women 50 to 59, 60 to 69, and 70 years and older, respectively. These authors did not find a decrease in intake of animal protein with increasing age. If the data in our study had been analyzed without isolation of the cohort effect, the intakes of calories and protein would be estimated to decrease by approximately 130.8 kcal and 4.3 gm of protein in the diets with each successive decade of age. After isolation of the cohort effect, changes associated with age represented a decrease of 64.6 kcal and an increase of 2.8 gm of protein with each increase of 10 years of age. Longitudinal patterns for calcium, phosphorus, riboflavin, and preformed niacin were
AGING AND DIETARY PATTERNS
355
level provided it exceeds 0.10. These critical values for R2 are obtained from the statistic,
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reduced intake of fat with advancing years may have resulted from (a) a decrease in fat tolerance due to physiological changes asso:——, which has F-distribu- ciated with aging such as a diminished diges-p.—P2N . / 2 (1-R ) / ( n - k - 1 ) tive capacity (Werner & Hambraeus, 1972), tion with k and n — k — 1 degrees of freedom, (b) efforts to control body weight, (c) a given that the coefficients of the k independent change in food pattern brought about by the variables in the regression model are zero and increased publicity on dietary fat intake and that the errors in the model are normally distributed. For our data k=2 and n=96. The incidence of coronary heart disease, or (d) low R2 for vitamin A and ascorbic acid intakes combined influences of these and other unidenmay be attributed partly to the uneven distri- tified factors. Schlenker, Feurig, Stone, Ohlson, bution of these vitamins in foods; hence, a wide and Mickelsen (1973) have reported an inverse variation in individual intakes. Factors that relationship between the level of dietary fat influence dietary intake and that have not been intake and longevity from a study of previous isolated in our model include family income, records of 52 deceased women in Michigan. number in household, education (especially These women were first observed from 1948 to with respect to good nutrition), incidence of 1955. In our study, the reduction in the mean fat disease requiring dietary modification, and intake accompanied by some decrease in carbosuch factors as faulty dentition, sore gums, "acid stomach," "too much gas." Other real hydrate intake was not sufficient to signifor imagined factors also' may influence the icantly reduce the energy value of the diets. Forty-seven percent of our subjects sustained acceptance of food by elderly persons. body weights that exceeded suggested standards DISCUSSION (see Hathaway & Foard, 1960) by 15% or Longitudinal studies with humans have cer- more. Because of the persistent problems of tain inherent difficulties. With successive ob- overweight, recommendations for dietary servations over time, the reduction in sample change must continue to stress judicious selecsize (from loss of interest, death, illness or tion of foods to provide a high nutrient density mobility of subjects) may alter the composition when the food energy is reduced. For individof the original group. Further, the possibility uals like our subjects, food energy from carboexists that experimental conditioning of sub- hydrates may be decreased significantly by jects may result from repeated observations or limiting the use of refined and concentrated that reports and feedback information may food sources of simple carbohydrates such as initiate changes. Preliminary analyses of our sugars and syrups. In the average American data, however, indicated that a learning effect diet, the proportion of the total carbohydrates from repeated participation was not statistically derived from sugars has increased steadily significant. The lack of learning effect prob- since the turn of the century (Antar et al., ably was associated with the length of time 1964; Friend, 1967). between observations. Our longitudinal data also indicate that food It is recognized that various intrinsic and patterns do not change significantly from early extrinsic factors (health status, income, house- middle age (30 years) to old age. Because of hold size, education) influence the dietary the persistence of eating habits, older people patterns of aging individuals. Our study in- may tend to respond less readily to dietary cluded a relatively small sample of women. modifications. The habitual eating patterns of Estimation of these sources of variation might individuals, therefore, must be considered in be possible with a much, larger sample, but was the nutritional care and group feeding of the elderly. Our observations reemphasize the not attempted in our study. Estimation of the coefficients of a regression need for education in nutrition at an earlier model for dietary intake was possible with data age and reinforced throughout the life-span. from our longitudinal study of 35 women. When the year-of-birth or cohort effect is SUMMARY accounted for, it seems that these adult women Dietary data from weighed food intake recmaintained their dietary habits during aging, ords were obtained from 35 women 29 years except for a decrease in fat consumption. The old and older. These women were observed
356
GARCIA, BATTESE, AND BREWER American Journal of Clinical Nutrition, 1964, 14, 169-178. Baltes, P. B. Longitudinal and cross-sectional sequences in the study of age and generation effects. Human Development, 1968, 11, 145-171. Friend, B. Nutrients in United States food supply: A review of trends, 1909-191S to 1965. American Journal of Clinical Nutrition, 1967, 20, 907-914. Fuller, W. A., & Battese, G. E. Transformations for estimation of linear models with nested-error structure. Journal of the American Statistical Association, 1973, 68, 626-632. Hathaway, M. L., & Foard, E. D. Heights and weights of adults in the United States. US Dept. of Agriculture, Home Economics Research Report 10, 1960. Schlenker, E. D., Feurig, J. S., Stone, L. H., Ohlson, M. A., & Mickelsen, O. Nutrition and health of older people. American Journal of Clinical Nutrition, 1973, 26, 1111-1119. Steinkamp, R. C , Cohen, N. L.s & Walsh, H. E. Resurvey of an aging population: fourteen-year follow-up. The San Mateo nutrition study. Journal of the American Dietetic Association, 1965, 46, 103-110. Swanson, P., Willis, E., Jebe, E., Smith, J. M., Ohlson, M. A., Biester, A. & Burrill, L. M. Food intakes of 2,189 women in five North Central States. Iowa Agricultural and Home Economics Experiment Station Research Bulletin 468, 1959. Watt, B. K., & Merrill, A. L. Composition of foods:
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culture Handbook 8, 1963. Werner, I., & Hambraeus, L. The digestive capacity of elderly people. In L. A. Carlson (Ed.), Nutrition in old age. Symposia of the Swedish Nutrition Foundation X. Almquist and Wiksell, Uppsala, Sweden, 1972.
Raw, processed, prepared (Rev.). US Dept. of Agri-
Antar, M. A., Ohlson, M. A., & Hodges, R. E. Changes in retail market food supplies in the United States in the last seventy years in relation to the incidence of coronary heart disease, with special reference to dietary carbohydrates and essential fatty acids.
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during four studies over an 18-year period. Longitudinal data were analyzed by use of a multiple linear-regression model with two independent variables, year of birth and age of the subjects at the time of observation. The estimated age coefficients were negative and highly significant for fat, saturated fatty acids, and oleic acid. A positive age coefficient was significant with increasing age for calcium. Mean dietary intakes did not change significantly for food energy, protein, carbohydrate, phosphorus, iron, thiamin, riboflavin, preformed niacin, vitamin A, and ascorbic acid. After adjusting for age, the estimated positive coefficients for year of birth were significant for protein, calcium, phosphorus, riboflavin, preformed niacin, and linoleic acid. Year-of-birth (cohort) and age effects defined in the regression model explained approximately 20% of the variation in dietary intakes except for vitamin A and ascorbic acid. The statistical model used did not isolate other variables that affect dietary intakes of aging women.
