Association between Dietary Fiber Intake and Physical Performance in Older Adults: A Nationwide Study in Taiwan
et al. (2013) Association between Dietary Fiber Intake and Physical Performance in Older
Adults: A Nationwide Study in Taiwan. PLoS ONE 8(11): e80209. doi:10.1371/journal.pone.0080209
Association between Dietary Fiber Intake and Physical Performance in Older Adults: A Nationwide Study in Taiwan
I-Chien Wu 0
Hsing-Yi Chang 0
Chih-Cheng Hsu 0
Yen-Feng Chiu 0
Shu-Han Yu 0
Yi-Fen Tsai 0
Ken N. Kuo 0
Ching-Yu Chen 0
Kiang Liu 0
Marion M. Lee 0
Chao A. Hsiung 0
Alejandro Lucia, Universidad Europea de Madrid, Spain
0 1 Institute of Population Health Sciences, National Health Research Institutes , Miaoli, County, Taiwan, 2 Program for Aging , College of Medicine, China Medical University , Taichung, Taiwan , 3 Chung Shan Medical University Hospital , Taichung, Taiwan , 4 Mennonite Christian Hospital , Hualien, Taiwan , 5 Center for Evidence-Based Medicine, Taipei Medical University , Taipei, Taiwan , 6 Department of Family Medicine, College of Medicine and Hospital, National Taiwan University , Taipei, Taiwan , 7 Department of Preventive Medicine, Northwestern University Feinberg School of Medicine , Chicago , Illinois, United States of America, 8 Department of Epidemiology and Biostatistics, University of California San Francisco , San Francisco, California , United States of America
Background: Physical performance is a major determinant of health in older adults, and is related to lifestyle factors. Dietary fiber has multiple health benefits. It remains unclear whether fiber intake is independently linked to superior physical performance. We aimed to assess the association between dietary fiber and physical performance in older adults. Methods: This was a cross-sectional study conducted with community-dwelling adults aged 55 years and older (n=2680) from the ongoing Healthy Aging Longitudinal Study (HALST) in Taiwan 2008-2010. Daily dietary fiber intake was assessed using a validated food frequency questionnaire. Physical performance was determined objectively by measuring gait speed, 6-minute walk distance, timed up and go (TUG), summary performance score, hand grip strength. Results: Adjusting for all potential confounders, participants with higher fiber intake had significantly faster gait speed, longer 6-minute walk distance, faster TUG, higher summary performance score, and higher hand grip strength (all P <.05). Comparing with the highest quartile of fiber intake, the lowest quartile of fiber intake was significantly associated with the lowest sex-specific quartile of gait speed (adjusted OR, 2.18 in men [95% CI, 1.33-3.55] and 3.65 in women [95% CI, 2.20-6.05]), 6-minute walk distance (OR, 2.40 in men [95% CI, 1.38-4.17] and 4.32 in women [95% CI, 2.37-7.89]), TUG (OR, 2.42 in men [95% CI, 1.43-4.12] and 3.27 in women [95% CI, 1.94-5.52]), summary performance score (OR, 2.12 in men [95% CI, 1.19-3.78] and 5.47 in women [95% CI, 3.20-9.35]), and hand grip strength (OR, 2.64 in men [95% CI, 1.61-4.32] and 4.43 in women [95% CI, 2.62-7.50]). Conclusions: Dietary fiber intake was independently associated with better physical performance.
Funding: This study was supported by the National Health Research Institutes in Taiwan (Project no. BS-097-SP-04, PH-098-SP-02, PH-099-SP-01). The
sponsor had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or
approval of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
These authors contributed equally to this work.
Physical performance is a major determinant of health in
older adults. Good physical performance is critical if older
adults wish to remain independent. Moreover, physical
performance at midlife and late life have recently been
demonstrated to be novel and strong predictors of numerous
major health outcomes in older adults. The decline in
objective measures of physical performance is believed to
represent the early stage of a disablement process that leads
to adverse health outcomes in older age. For instance, a low
summary performance score, short 6-minute walk distance,
slow timed up and go (TUG), and low hand grip strength, are
all independently associated with future disability,
institutionalization and death[2,5-10]. One recent study showed
that slow gait speed alone was an independent predictor of
mortality in older people.
Detailed mechanisms underlying the prognostic significance
of physical performance remain unclear. However, evidence
suggests that poor physical performance is closely related to
the aging process and subclinical conditions, and represents a
state of inflammation and metabolic derangements[12-15]. An
individuals nutrition status is thought to play an important role
in the pathogenesis of declining physical performance
[4,16-19]; fortunately, nutrition is a modifiable lifestyle factor.
Dietary fiber comprises indigestible plant carbohydrates, and
its intake is associated with multiple health effects[20,21].
Extensive research has demonstrated that fiber intake protects
against common chronic diseases in older adults, including
diabetes, obesity, and cardiovascular disease[21-23]. One
recent study showed that dietary fiber can promote
longevity. Moreover, dietary fiber intake has been
associated with a strong anti-inflammatory effect and beneficial
metabolic effects, including a lowering of blood glucose and
lipids. Thus, it is likely that dietary fiber has a protective
effect against physical performance impairment by influencing
the pathways of aging, diseases processes, and metabolic or
This study investigated the relationship between dietary fiber
intake and objective measures of physical performance in an
Asian population. We hypothesized that low dietary fiber intake
is independently associated with poor physical performance.
To test this hypothesis, we analyzed the nutritional status in
relation to their physical performance from a cross-sectional
data of a nationwide observational study of community-dwelling
older adults in Taiwan.
This study was approved by the institutional review boards
(IRB) of National Health Research Institutes and Changhua
Christian Hospital. The three local hospitals Yee Zen General
Hospital, Hope Doctors Hospital and Potz Hospital recognized
the IRB approval of National Health Research Institutes, since
they do not have their own IRB. All participants signed
informed consent forms at study entry.
The Healthy Aging Longitudinal Study in Taiwan (HALST) is
an ongoing population-based longitudinal study of adults aged
55 years and older. Data collection began in October, 2008.
Being one of the few population-based long-term observational
studies of aging in Taiwan, the study is designed to thoroughly
examine the determinants of late-life health in an Asian
population. A sample of community-dwelling older adults with
diverse socio-demographic backgrounds was recruited from
multiple areas across Taiwan, including 2 areas in the north
region, 2 in central region, 2 in the south region, and one in the
In brief, a hospital was selected for each catchment area,
and eligible residents living within about a 2-km radius of each
hospital were ascertained. In order to recruit a sample
consisting of elderly with different socio-demographic
backgrounds, eligible residents in each geographic area were
stratified according to age (55 to 64 y, and 65 y or older), sex
and education levels. Subjects were then selected from each
stratum by using the systemic random sampling method. To be
eligible, a person was required to be 55 years or older and free
of the following conditions: highly contagious infectious
disease; severe illness (including malignancy undergoing
active treatment); and severe hearing, speech, mental, or
cognitive impairments. Adults who were bedbound or too frail
to stand and ambulate, and adults who were institutionalized or
hospitalized, were excluded from the study. At study entry, all
participants received standardized physical performance
assessments and laboratory examinations. They also
completed interviewer-administered questionnaires to obtain
information on sociodemographic status, health status, and
lifestyle factors. During the laboratory examination, a blood
sample was collected (after 8 hours or more of fasting) and
was promptly centrifuged and stored at -80 C. All blood
sample analyses were performed in two central laboratories.
Interviewers and laboratory personnel were all blinded to
physical performance status of the participants.
We analyzed data from the baseline examinations conducted
between October 2008 and October 2010, during which a
cohort of 7060 adults was randomly sampled. A total of 6450
participants met our selection criteria, of whom 3121 agreed to
participate. We further excluded 441 participants, yielding data
for 2680 participants for analysis. Excluded were individuals
too ill to undergo physical performance assessments (n= 216);
and individuals who refused assessments (n= 225). Among
these excluded participants, 38 individuals reported an
unreliable or implausible dietary intake.
Assessment of Physical Performance
Physical performance was determined by measuring gait
speed, 6-minute walk distance, TUG, summary performance
score and hand grip strength. Gait speed was determined by
observing participants walk 4 meters (m) at their usual pace,
and timing the task according to a standardized protocol[2,11].
Time was measured by a trained examiner using a handheld
stopwatch that measured to the nearest hundredth of a second
(s). Participants were permitted to use a walking device such
as a walker or cane. Gait speed was calculated as distance
walked (m) divided by time (s).
The 6-minute walk test was performed according to a
standardized protocol[6,25]. Briefly, participants were
encouraged to walk as much distance as possible in 6 minutes.
Although resting was allowed during the test, participants were
instructed to resume walking as soon as possible. Total
distance walked (m) was measured and recorded by a trained
For the TUG test, participants were observed standing up
from a chair, walking 3 meters at their usual pace, turning
around, and walking back to sit in the chair. Time was
measured by a trained examiner using a handheld stopwatch
that recorded the nearest hundredth of a second.
Global lower extremity performance was assessed as a
summary performance score using the Short Physical
Performance Battery (SPPB)[2,27]. The SPPB includes the
measurement of gait speed, standing balance, and time taken
to rise 5 times from a chair. A score of 1 to 4 was assigned to
each task with a higher score representing a better
performance. The summary performance score was calculated
by adding the scores for the 3 tasks.
Hand grip strength was measured using a North Coast hand
dynamometer (North Coast Medical Inc, Gilroy, CA, USA).
Participants were seated in a chair with shoulder adducted and
neutrally rotated and elbow flexed at 90, and instructed to hold
the dynamometer in the hand, and then to squeeze the
dynamometer as hard as they could. Three trials were allowed
for each hand in turn. The best performance, recorded in kg,
was used for the analysis.
Assessment of Diet
Diet was assessed using a validated semiquantitative food
frequency questionnaire, as described previously[28,29]. In
brief, during face-to-face interviews, participants were asked of
their usual intake frequency and portion size of 80 food and
food items over the previous12 months. The responses for
intake frequency were predefined and included never or less
than once per month, once per month, 2-3 times per month,
1-2 times per week, 3-5 times per week, daily, and 2 or
more per day. Appropriate food models were used to estimate
the portion size of each food and food item. The intake of each
nutrient per day (including fiber, calorie, protein, vitamin A,
vitamin E, vitamin C, vitamin B6, and vitamin B12) was then
computed from the estimated content of the relevant nutrient in
specific foodstuffs, as reported by Taiwans Department of
Health Nutrient Database of Taiwanese Foods. An example
of dietary fiber intake calculation is shown in Table S1.
Covariates included the participants age; sex; marital status
(unmarried, divorced, widowed, married); education level (
< high school, high school, > high school); smoking status
(current, former, never); alcohol intake (drinker, non-drinker);
physical activity (every day, less than every day, rarely or
never); body mass index; status for metabolic syndrome;
comorbidities; and the serum levels of high-sensitivity
Creactive protein (hs-CRP), a validated marker of chronic
inflammation. Participants who reported having smoked at
least 100 cigarettes in their lifetime and were smokers at the
time of the study were defined as current smokers, and
participants who had smoked at least 100 cigarettes in their
lifetime but no longer smoked cigarettes were classified as
former smokers. Alcohol use classification was based on the
National Institute on Alcohol Abuse and Alcoholism's guidelines
for screening older adults for heavy drinking. A non-drinker
was defined as someone who reportedly had not consumed
any alcoholic beverages in the past 12 months. Body mass
index (BMI) was calculated as body weight (kg) divided by the
square of the height (m2) and was categorized according to the
WHO criteria recommended for Asians (overweight: 23 BMI <
27.5; obesity: BMI 27.5). Body weight and height were
directly measured. A participant was considered to have
metabolic syndrome if 3 or more of the following 5 criteria were
present: elevated waist circumference (men 90cm; women
80cm); elevated triglycerides (150 mg/dl; or treatment for
elevated triglycerides); reduced HDL cholesterol (men <40
mg/dl; women <50 mg/dl; or treatment for reduced HDL
cholesterol); elevated blood pressure (systolic blood pressure
130 mm Hg; diastolic blood pressure 85 mm Hg; or treated
hypertension); or elevated fasting glucose (glucose 100 mg/dl
or treated diabetes). Blood glucose, triglyceride, and HDL
cholesterol levels were determined enzymatically using the
ADVIA 1800 Chemistry System (Siemens AG, Munich,
Germany). The presence of hypertension was based on
drug treatment, or an average blood pressure of 140 /90
mmHg or greater. Diabetes mellitus was defined by
selfreport, medication use, fasting plasma glucose of 126 mg/dl or
greater, or hemoglobin A1c of 6.5% or greater. Hemoglobin
A1c was measured by an ion exchange high-performance
liquid chromatography using a HLC-723G8 system (Tosoh
Corporation, Tokyo, Japan). The Modification of Diet in Renal
Disease (MDRD) equation proposed by Levey et al was used
to estimate the glomerular filtration rate (GFR). Chronic
kidney disease was defined as a GFR less than 60 mlmin-1
(1.73 m2)-1. Depressed mood was defined by a Center for
Epidemiological Studies Depression Scale (CES-D) score of 20
or greater. Other comorbidities were assessed by
selfreported doctor's diagnosis, and included stroke,
cardiovascular disease, arthritis, cancer, and lung disease. The
serum high-sensitivity C-reactive protein (hs-CRP) was
measured using latex-enhanced immunoturbidimetric assay
using the ADVIA 1800 Chemistry System. The assay is
capable of detecting CRP concentration as low as 0.012 mg/dl.
The coefficient of variation was 3.78%.
Descriptive statistics were calculated to characterize the
population. The results for all continuous variables are
presented as mean SD. Median and interquartile range were
used to present data that were skewed. Dietary factors were
adjusted for total energy intake by the residual method.
Dietary fiber intake was further categorized into sex-specific
quartiles. All analyses were stratified by sex. Differences in
continuous variables among groups were analyzed with
oneway analysis of variance (ANOVA), and differences in
categorical variables (proportions) were analyzed with
Linear regression analysis was performed to examine the
associations between dietary fiber intake and physical
performance and to estimate the adjusted physical
performance. Tests for linear trend across quartiles of fiber
intake were based on regression analyses, in which physical
performance measures were regressed on the median values
in each quartile of fiber intake. No major nonlinear associations
were noted. Covariates were selected a priori based on
documented associations with physical performance reported
in the literature as well as biological plausibility. Covariates
were organized into 5 related groups: age, risk factors for poor
health (marital status, education level, smoking status, alcohol
intake, physical activity, obesity, and metabolic syndrome),
comorbidities (hypertension, diabetes mellitus, stroke,
cardiovascular disease, arthritis, chronic kidney disease,
cancer, lung disease, and depressed mood), nutrients (daily
intake of energy, protein, vitamin A, vitamin E, vitamin C,
vitamin B6, and vitamin B12), and inflammation (blood levels of
hs-CRP). Using a forward stepwise method, adjustments were
made for 5 models, with each successive model repeating the
adjustments of the previous model, as follows: model 1
(adjustment for age), model 2 (plus adjustment for risk factors
of poor health), model 3 (plus adjustment for comorbidities),
model 4 (plus adjustment for nutrient intake), and model 5 (plus
adjustment for inflammation).
To further clarify how dietary fiber intake was related to
physical performance, the associations were evaluated using
forward stepwise multinomial logistic regression analysis with
adjustment for covariates in model 4. In this analysis, physical
performance was coded into categories based on the quartile
values within the sex-specific study cohort. For all analyses,
differences were considered significant if P < .05. We
calculated 95% confidence intervals (CI) and reported the CI
for each parameter estimate. All the analyses were performed
using SPSS version 16.0 (SPSS Inc., Chicago, IL, USA).
Table 1 displays the characteristics of the participants by
sex. For age, the mean SD was 69 8 years; and 53% of
participants were women. For fiber intake (energy-adjusted),
the mean SD was 29 13 g/d, ranging from 13 g/d (10th
percentile) to 47 g/d (90th percentile) in men, and from 17 to 45
g/d in women. Table 2 shows the characteristics of men and
women stratified by quartiles of dietary fiber intake. Participants
with a low fiber intake tended to be older, less educated, and
physically inactive, and were more likely to have lower intakes
of protein, vitamin A, vitamin E, vitamin C, and vitamin B6; they
were also more likely to exhibit a higher level of hs-CRP.
We found that participants with a lower fiber intake had
significantly slower gait speed, shorter six-minute walk
distance, slower TUG, lower summary performance score, and
weaker hand grip strength (Table 2). Linear regression
analyses revealed a strong relationship between dietary fiber
intake and all physical performance measures, for both men
and women (all P < .001). For both men and women, dietary
fiber intake remained significantly associated with gait speed,
6-minute walk distance, TUG, summary performance score,
and hand grip strength after adjustments for the following
variables: age (model 1;all P < .005), risk factors for poor
health (model 2; all P < .01), and comorbidities (model 3; all P
< .05). Additional adjustment for nutrient intake did not change
the associations between fiber intake and physical
performance (model 4; all P <.05). Participants with higher fiber
intake had significantly faster gait speed, longer 6-min walk
distance, faster TUG, higher summary performance score, and
higher hand grip strength (Table 3). Compared with men in the
highest quartile for dietary fiber intake (>35.12 g/d), men in the
lowest quartile for fiber intake (<19.18 g/d) had a mean 0.09
m/s slower gait speed (P < .001); a mean 23.8 m shorter 6-min
walk distance (P < .001); a mean 1.2 s longer TUG (P < .001);
a mean 0.6 lower summary performance score (P < .001); and
a mean 3.7 kg weaker hand grip strength (P < .001). Similarly,
compared with women in the highest quartile for dietary fiber
intake (>35.35 g/d), women in the lowest quartile for fiber
intake (<21.42 g/d) had a mean 0.08 m/s slower gait speed (P
< .001); a mean 32.8 m shorter 6-min walk distance (P < .001);
a mean 1.5 s longer TUG (P < .001); a mean 1.0 lower
summary performance score (P < .001); and a mean 1.9 kg
weaker hand grip strength (P < .001).
Multinomial logistic regression analysis confirmed the
independent association between dietary fiber intake and
physical performance, after adjustment for all covariates, for
both men (Table 4) and in women (Table 5). For men, those
with a fiber intake less than 19.18 g/d had at least a 2-fold
increase for the following odds: gait speed less than 0.75 m/s;
6-minute walk distance less than 349 m; TUG longer than 13.3
s; summary performance score less than 9; and hand grip
strength less than 33 kg, compared with men whose dietary
fiber intake was more than 35.12 g/d (Table 4). For women,
those with a fiber intake less than 21.42 g/d had at least a
3fold increase for the following odds: gait speed less than 0.63
m/s; 6-minute walk distance less than 315 m; TUG longer than
14.6 s; summary performance score less than 8; and hand grip
strength less than 20 kg, compared with women whose dietary
fiber intake was more than 35.35 g/d (Table 5). Among women,
different fiber intake was not associated with different
prevalence of metabolic syndrome, obesity, hypertension and
chronic kidney disease. Analysis was repeated in women
without adjustment for metabolic syndrome, BMI, hypertension
and chronic kidney disease. Since there were no changes in
risk estimates, we present results after adjustment for all
To determine whether any relationships existed between
dietary fiber intake and physical performance independently of
inflammation, we performed linear regression analysis with
adjustment for inflammation marker levels (model 5). For both
men and women, fiber intake continued to show significant
associations to gait speed (all P <.05), six-minute walk distance
(all P <.05), timed up and go (all P <.05), summary
performance score (all P <.05) and hand grip strength (all P <.
In this large observational study of older adults in Taiwan, we
found that low dietary fiber intake was associated with poor
physical performance, regardless of which objective measures
were used to assess physical performance. The associations
were independent of any known risk factors for poor physical
performance, including sociodemographic factors,
lifestylerelated factors, comorbidities, and intake of other nutrients.
Our study demonstrated that lower dietary fiber intake was
associated with slower gait speed, shorter 6-minute walk
distance, slower timed up and go, lower summary
performance score, and weaker hand grip strength. These
findings were congruent with the results of other recent
Table 1. Population Characteristics of the Study Participants*.
Significant difference between men and women (P <.05). Continuous variables were analyzed with t tests, while categorical variables (proportions) were analyzed with
Median (interquartile range).
Except for total energy intake, all nutrients were adjusted for total energy intake. To convert kilocalories to kilojoules, multiply by 4.186. RE indicates retinol equivalents;
and -TE, -tocopherol equivalents.
Table 2. Characteristics of Study Participants by Dietary Fiber Intake*.
Dietary Fiber Intake, Quartiles
Table 2 (continued).
Dietary Fiber Intake, Quartiles
studies[41,42]. Tomey et al prospectively examined the
relationship between dietary intake at midlife and reported
disability 4 years later; low fiber intake at baseline was
found to be associated with future functional limitations. An
ealier prospective study had shown that low intake of fruit and
vegetable at midlife was a risk factor for disability. These
studies had indicated that dietary fiber plays an important but
unknown role in the development of disability in the second half
of a persons lifetime. Our findings suggested that inadequte
dietary fiber intake might contribute to the disablement process
through an impairment of physical performance. Individuals
with low fiber intake (less than 19 g/day in men and less than
21g/day in women) were more likely to have gait speed less
than 0.6 m/sec, timed up and go more than 15 sec, summary
performance score less than 8 and hand grip strength less than
33 kg in men and 20 kg in women. Adults with these poor
physical performances are known to be at high risk of
disability[2,7,10,43-47]. In addition, the differences in physical
performance measures between participants in the highest and
lowest fiber intake categories were consistent with clinically
meaningful differences, further highlighting the clinical
significance of our results[48-50].
The independent nature of the relationship between dietary
fiber intake and physical performance is biologically
understandable. The amount of fiber present in a persons diet
is strongly and inversely related to the degree of systemic
inflammation. Human intervention trials have shown that solely
by increasing the daily fiber intake, systemic inflammation can
be reduced. The current study confirmed that participants
with lower fiber intake had higher levels of serum hs-CRP.
Lower fiber intake may thus lead to a greater degree of
systemic inflammation, which in turn contributes to poorer
physical performance[14,15]. In addition, dietary fiber exerts
numerous beneficial metabolic effects. Improved glucose
and lipid metabolism, together with a reduction in
hyperglycemia and hypercholesterolemia, as well as improved
insulin sensitivity, have been observed in people following a
high fiber intake[52,53]. Thus, inadequate fiber intake
exacerbates poor glucose and lipid metabolism and results in a
decline of physical performance[15,54]. However, we observed
that the association between low fiber intake and poor physical
performance remained significant after adjustment for
inflammation and metabolic markers. This finding indicates that
additional dietary fiber-related pathways play a role in
maintaining physical performance. Dietary fiber exerts a major
effect on the microbes living in the human gastrointestinal tract
(gut microbiota) [55-57], which increasing evidence has
identified as an important determinant of human health[57,58].
The strengths of the current study were as follows. To gain
an in-depth understanding of the relationship between diet and
physical performance, we examined more than one objective
measure of physical performance. To clarify the independent
role of fiber intake in physical performance, we carefully
controlled for conditions that might affect physical performance.
We further controlled for physiological variables, including
inflammation and metabolic markers, to investigate how dietary
fiber is related to physical performance. Finally, the relationship
between nutritional status and physical function has been
examined mainly among Western populations. This might be
the first study to explore the relationship between dietary fiber
and physical performance using a large representative sample
of an Asian population; dietary habits differ between Asians
The study was subject to certain limitations. Its
crosssectional design meant that we were unable to establish
temporal relationships and infer causal links between dietary
fiber intake and physical performance. It is reasonable to
believe the lifelong dietary habit precedes performance
changes at old age, but it is also possible that older adults with
poor physical performance have difficulties in maintaining a
healthy diet. In addition, confounding variables might have
affected the outcome measures. Although we attempted to
include all possible confounders in the analysis, residual
confounding by unmeasured variables (e.g., detailed
healthrelated behaviors, other micronutrients and dietary factors) is
likely. In particular, low intake of dietary antioxidants is a known
risk factor for decline in physical function[18,19], and may
confound the observed link between dietary fiber intake and
physical performance. In assessing the participants disease
status, we relied mainly on self-reports of the presence of
disease. Subclinical conditions might thus have been missed.
Also of concern was potential information bias, which might
occur if frail older participants recalled their food consumption,
disease history, or health-related lifestyle status less accurately
than healthier participants. The HALST cohort consists mainly
of community-dwelling older adults, who might be healthier
than the age-matched general population. Thus, the findings of
this study might not be generalizable to the entire older
population. Lastly, the levels of dietary intake in our study
should be interpreted with caution. HALST participants might
have over-reported their food intakes on the semiquantitative
food frequency questionnaire. Although a semiquantitative
food frequency questionnaire may yield different estimates of
the actual daily foods intake as compared with other methods,
it has reasonable levels of reproducibility and validity, and
performs well in ranking individuals by intake levels, making it
useful in large scale epidemiologic studies examining the
relationships between diet and disease risk[28,29,60-62].
Further prospective observational studies are needed to
confirm the relationships we observed. Intervention trials might
be warranted to determine whether increasing peoples fiber
intake reduces their functional decline. In addition, more
detailed research is required to address the mechanisms
linking dietary fiber intake to physical performance. These
efforts will contribute to the development of novel preventive
and therapeutic strategies for frail older adults.
In conclusion, dietary fiber intake is independently and
positively associated with physical performance in older adults.
Dietary fiber may offer protection against disability associated
with old age.
We are grateful to the HALST studys advisory committee (Drs.
Luigi Ferrucci, Jack M. Guralnik, Dilip V. Jeste, and Kung-Yee
Liang) for the precious suggestions. We wish to thank men and
women who participated in the study, and all the members of
the HALST study group. The members of the HALST study
group are as follows: Drs. Chao-Agnes Hsiung, Chih-Cheng
Hsu, I-Chien Wu, Hsing-Yi Chang, Chu-Chih Chen, Yen-Feng
Chiu, Hui-Ju Tsai, and Ken N. Kuo, of National Heath
Research Institutes; Dr. Ching-Yu Chen of National Taiwan
University; Dr. Kiang Liu of Northwestern University Medical
School; Dr. Marion Lee of University of California at San
Francisco; Dr. Ida Chen of Cedars-Sinai Medical Center; Dr.
Shu-Han Yu of Chung Shan Medical University; Dr. Kai-Ting
Ko of Mackay Memorial Hospital; Dr. Tzuo-Yun Lan of National
Yang-Ming University; Dr. Hou-Chang Chiu of Shin Kong Wu
Ho-Su Memorial Hospital; Dr. Wen-Jin Liaw of Yee Zen
General Hospital; Dr. Yo-Hann Liu of Hope Doctors Hospital;
Dr. I-Ching Lin of Changhua Christian Hospital; Dr. Ping-An Wu
of Potz General Hospital; Dr. Chon-Chou Juan of Yuan's
General Hospital; and Dr. Shi-Chen Shen of Mennonite
Conceived and designed the experiments: I-CW H-YC MML
CAH. Performed the experiments: I-CW H-YC C-CH Y-FC
SHY S-CS C-YC KL MML CAH. Analyzed the data: I-CW Y-FT
MML CAH. Wrote the manuscript: I-CW MML CAH. Reviewed
the manuscript and gave input to the final version: H-YC C-CH
Y-FC S-HY S-CS KNK C-YC KL MML CAH. Supervised study:
KL MML CAH.
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