Animal Protein Intake and Functional Capacity in the Elderly
Animal Protein Intake and Functional Capacity in the Elderly
The present study was a part of the Ohasama Study, a longitudinal community-based observational study in Ohasama, Iwate Prefecture, Japan. The geographic and demographic characteristics of the study participants have been described previously. This study used a validated 141-item food frequency questionnaire to determine nutrient and food intakes at baseline and 7 years later. The intake results were examined to determine associations with higher-level functional decline. The institutional review board of the Tohoku University School of Medicine and the Department of Health of the Ohasama town government approved this study.
Figure 1 shows a flow diagram of the present study. There were 2,614 individuals aged 60 and older in Ohasama in 1998, 2,348 of whom participated in the study from February 1 to March 28, 1998. Overall, 1,082 participants were excluded from the follow-up measurements for the following reasons: incomplete answers to the questionnaire at baseline (n = 372), not fully independent in basic ADLs (<1 point for the 6-item physical function measurement of the Medical Outcomes Study Short-Form General Health Survey; n = 334), low level of higher-level functional capacity (<10 points for the 13-item Tokyo Metropolitan Institute of Gerontology Index of Competence (TMIG); n = 309), and extreme levels of energy intake (in the upper or lower 2.5% of the range; n = 67). Of the remaining 1,266 eligible individuals at the end of the 7-year study period, 259 were excluded because they had died (n = 120) or moved away (n = 23) or did not agree to continue participation (n = 116), which resulted in data being collected from 1,007 participants for analysis.
(Enlarge Image)
Figure 1.
Flowchart of study participants included in the present analysis of the Ohasama Study, Japan, 1998–2005. ADL = Activity of Daily Living; TMIG = Tokyo Metropolitan Institute of Gerontology Index of Competence; SDs = Standard Deviations.
The TMIG was used to measure higher-level functional capacity. This measurement comprises three subscales: social role, intellectual activity, and instrumental ADLs (IADLs). Social roles were defined according to participants' levels of adequacy as a spouse, parent, and informal social group member and in intellectual activity, which was used instead of Lawton's term "effectance." Effectance, or intellectual activity as stated here, represents the motivation behind human needs to create tension, explore, and vary the psychoenvironmental field, such as having an interest in health-related information through the mass media. Each item was scored as 1 for yes (able to do) or 0 for no (unable to do). The TMIG index is a scale developed in Japan consisting of three dimensions: IADLs (5 items: using public transportation, shopping, preparing meals, paying bills, managing deposits), intellectual activity (4 items: filling out forms, reading newspapers, reading books or magazines, being interested in stories or programs addressing health), and social role (4 items: visiting the homes of friends, being called on for advice, visiting sick friends, initiating conversations with young people). The total score represented the sum of scores for the 13 items; a higher score indicated a higher level of competence. The validity and reliability of the TMIG questionnaire have been established, and 1-year mortality was used as the criterion variable to confirm the high predictive validity. The TMIG has been widely accepted in Japan. The changes in higher-level functional capacity during the 7-year period were calculated by subtracting the sum of the TMIG scores in 1998 from that in 2005. Participants were classified into two groups: decline (TMIG change <−1) and no decline (stable or improved) (TMIG change ≥−1). This cutoff point was used because a previous study reported that variation of 1 point in total TMIG scores for intellectual activity and social role subscales was regarded as within the range of possible measurement error. Three subscales of competence were also calculated, and a score of 0 to 5 was applied for IADLs, a score of 0–4 for intellectual activity, and a score of 0–4 for social roles. Participants were divided into two groups according to each subscale in a manner similar to that stated above: decline (<−1) and no decline (≥−1) for intellectual activity and social role and decline (<0) and no decline (>0) for IADLs.
Standardized methodology was used to calculate protein intake from data obtained in a Japanese version of a food frequency questionnaire. The reproducibility and validity of this questionnaire have previously been reported in detail. The questionnaire asked about the average frequency of intake of each food during the previous year according to nine frequency categories ranging from no consumption to seven or more times per day. A standard portion size of one serving was specified for each food, and respondents were asked whether their usual portion was larger (>1.5 times), the same, or smaller (<0.5) than the standard. In this study, energy from food sources of alcohol were taken into account (e.g., seasonings that included alcohol), but alcohol derived from alcoholic drinks such as beer and wine was not considered in the total energy count because such alcohol intake was treated as a separate variable. Nutritional supplements were not taken into account because there were few supplement users. Many studies have demonstrated that the use of total nutrient intake, not adjusted for energy intake, could skew true dietary characteristics because of under- and overreporting of dietary intake, which would cause significant problems when investigating the association between diet and health outcomes and diseases. Therefore, the residual method was used to adjust all food and nutrient intakes for total energy intake, and separate regression models were analyzed to obtain the residuals for men and women. After this, subjects were divided into quartiles according to protein intake. The lowest quartiles were used as reference categories.
The Student t-test was used for continuous variables and the chi-square test for categorical variables to evaluate differences in the mean values of nutrient intake together with subject characteristics between the higher-level functional decline and the no-decline groups. For nutrient and food intakes, one-way analysis of variance and the Bonferroni post hoc test were used for continuous variables by comparing quartile 4 (lowest) of each nutrient and food group.
Multiple logistic regression analyses were used, with adjustment for other putative confounding variables, to examine how protein intake was associated with future declines in higher-level functional capacity in older adults. A two-step process was used to determine the best model for the study. First, to examine how each variable independently affected the odds ratios, univariate logistic regression analysis was used to determine the associations between all variables added separately and TMIG change in the present study, and all variables with a univariate test P ≤ .10 were considered for inclusion for adjustment in the multiple logistic regression models. These putative confounding factors were sex, age, body mass index (BMI; <25.0, ≥25.0 kg/m), educational attainment (<10, ≥10 years), eating out (≥1, <1 time/wk), living alone, smoking status (never, past, current smoker), sleep duration (<7, 7–9, >9 h/d), self-rated health (excellent, good to fair, poor), history of hypercholesterolemia, and hearing impairment. Results are expressed as prospective odds ratios (ORs) with 95% confidence intervals (CIs) for the association between quartile of protein and future declines in higher-level functional capacity. Moreover, subgroup analyses were performed according to sex, age group, and BMI (25.0 kg/m as cutoff) to explore associations related to these factors. Interactions were tested by introducing a multiplicative term into the main effects model. The combined effect of animal protein and other nutrient intake was also examined for risk for decline. For all analyses, two-tailed P-Values <.05 were considered to indicate statistical significance. SPSS software version 19.0 for Windows (SPSS, Inc., Chicago, IL) was used to perform all analyses.
Methods
Study Design
The present study was a part of the Ohasama Study, a longitudinal community-based observational study in Ohasama, Iwate Prefecture, Japan. The geographic and demographic characteristics of the study participants have been described previously. This study used a validated 141-item food frequency questionnaire to determine nutrient and food intakes at baseline and 7 years later. The intake results were examined to determine associations with higher-level functional decline. The institutional review board of the Tohoku University School of Medicine and the Department of Health of the Ohasama town government approved this study.
Study Population
Figure 1 shows a flow diagram of the present study. There were 2,614 individuals aged 60 and older in Ohasama in 1998, 2,348 of whom participated in the study from February 1 to March 28, 1998. Overall, 1,082 participants were excluded from the follow-up measurements for the following reasons: incomplete answers to the questionnaire at baseline (n = 372), not fully independent in basic ADLs (<1 point for the 6-item physical function measurement of the Medical Outcomes Study Short-Form General Health Survey; n = 334), low level of higher-level functional capacity (<10 points for the 13-item Tokyo Metropolitan Institute of Gerontology Index of Competence (TMIG); n = 309), and extreme levels of energy intake (in the upper or lower 2.5% of the range; n = 67). Of the remaining 1,266 eligible individuals at the end of the 7-year study period, 259 were excluded because they had died (n = 120) or moved away (n = 23) or did not agree to continue participation (n = 116), which resulted in data being collected from 1,007 participants for analysis.
(Enlarge Image)
Figure 1.
Flowchart of study participants included in the present analysis of the Ohasama Study, Japan, 1998–2005. ADL = Activity of Daily Living; TMIG = Tokyo Metropolitan Institute of Gerontology Index of Competence; SDs = Standard Deviations.
Higher-level Functional Capacity
The TMIG was used to measure higher-level functional capacity. This measurement comprises three subscales: social role, intellectual activity, and instrumental ADLs (IADLs). Social roles were defined according to participants' levels of adequacy as a spouse, parent, and informal social group member and in intellectual activity, which was used instead of Lawton's term "effectance." Effectance, or intellectual activity as stated here, represents the motivation behind human needs to create tension, explore, and vary the psychoenvironmental field, such as having an interest in health-related information through the mass media. Each item was scored as 1 for yes (able to do) or 0 for no (unable to do). The TMIG index is a scale developed in Japan consisting of three dimensions: IADLs (5 items: using public transportation, shopping, preparing meals, paying bills, managing deposits), intellectual activity (4 items: filling out forms, reading newspapers, reading books or magazines, being interested in stories or programs addressing health), and social role (4 items: visiting the homes of friends, being called on for advice, visiting sick friends, initiating conversations with young people). The total score represented the sum of scores for the 13 items; a higher score indicated a higher level of competence. The validity and reliability of the TMIG questionnaire have been established, and 1-year mortality was used as the criterion variable to confirm the high predictive validity. The TMIG has been widely accepted in Japan. The changes in higher-level functional capacity during the 7-year period were calculated by subtracting the sum of the TMIG scores in 1998 from that in 2005. Participants were classified into two groups: decline (TMIG change <−1) and no decline (stable or improved) (TMIG change ≥−1). This cutoff point was used because a previous study reported that variation of 1 point in total TMIG scores for intellectual activity and social role subscales was regarded as within the range of possible measurement error. Three subscales of competence were also calculated, and a score of 0 to 5 was applied for IADLs, a score of 0–4 for intellectual activity, and a score of 0–4 for social roles. Participants were divided into two groups according to each subscale in a manner similar to that stated above: decline (<−1) and no decline (≥−1) for intellectual activity and social role and decline (<0) and no decline (>0) for IADLs.
Dietary Protein Intakes
Standardized methodology was used to calculate protein intake from data obtained in a Japanese version of a food frequency questionnaire. The reproducibility and validity of this questionnaire have previously been reported in detail. The questionnaire asked about the average frequency of intake of each food during the previous year according to nine frequency categories ranging from no consumption to seven or more times per day. A standard portion size of one serving was specified for each food, and respondents were asked whether their usual portion was larger (>1.5 times), the same, or smaller (<0.5) than the standard. In this study, energy from food sources of alcohol were taken into account (e.g., seasonings that included alcohol), but alcohol derived from alcoholic drinks such as beer and wine was not considered in the total energy count because such alcohol intake was treated as a separate variable. Nutritional supplements were not taken into account because there were few supplement users. Many studies have demonstrated that the use of total nutrient intake, not adjusted for energy intake, could skew true dietary characteristics because of under- and overreporting of dietary intake, which would cause significant problems when investigating the association between diet and health outcomes and diseases. Therefore, the residual method was used to adjust all food and nutrient intakes for total energy intake, and separate regression models were analyzed to obtain the residuals for men and women. After this, subjects were divided into quartiles according to protein intake. The lowest quartiles were used as reference categories.
Statistical Analysis
The Student t-test was used for continuous variables and the chi-square test for categorical variables to evaluate differences in the mean values of nutrient intake together with subject characteristics between the higher-level functional decline and the no-decline groups. For nutrient and food intakes, one-way analysis of variance and the Bonferroni post hoc test were used for continuous variables by comparing quartile 4 (lowest) of each nutrient and food group.
Multiple logistic regression analyses were used, with adjustment for other putative confounding variables, to examine how protein intake was associated with future declines in higher-level functional capacity in older adults. A two-step process was used to determine the best model for the study. First, to examine how each variable independently affected the odds ratios, univariate logistic regression analysis was used to determine the associations between all variables added separately and TMIG change in the present study, and all variables with a univariate test P ≤ .10 were considered for inclusion for adjustment in the multiple logistic regression models. These putative confounding factors were sex, age, body mass index (BMI; <25.0, ≥25.0 kg/m), educational attainment (<10, ≥10 years), eating out (≥1, <1 time/wk), living alone, smoking status (never, past, current smoker), sleep duration (<7, 7–9, >9 h/d), self-rated health (excellent, good to fair, poor), history of hypercholesterolemia, and hearing impairment. Results are expressed as prospective odds ratios (ORs) with 95% confidence intervals (CIs) for the association between quartile of protein and future declines in higher-level functional capacity. Moreover, subgroup analyses were performed according to sex, age group, and BMI (25.0 kg/m as cutoff) to explore associations related to these factors. Interactions were tested by introducing a multiplicative term into the main effects model. The combined effect of animal protein and other nutrient intake was also examined for risk for decline. For all analyses, two-tailed P-Values <.05 were considered to indicate statistical significance. SPSS software version 19.0 for Windows (SPSS, Inc., Chicago, IL) was used to perform all analyses.
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