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The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 62:93-99 (2007)
© 2007 The Gerontological Society of America

Smoking Is a Risk Factor for Decreased Physical Performance in Elderly Women

Prema B. Rapuri, J. Christopher Gallagher and Lynette M. Smith

1 Bone Metabolism Unit, Creighton University, School of Medicine, Omaha, Nebraska.
2 Department of Preventive and Societal Medicine, University of Nebraska Medical Center, Omaha.

Address correspondence to Prema B. Rapuri, PhD. E-mail: premarapuri{at}yahoo.com


   Abstract
Top
 Abstract
Materials and Methods
 Results
 Discussion
 References
 
Background. In 487 elderly women aged 65–77 years, we examined the association of smoking with physical performance measures of muscle function and whether the effect of smoking on physical performance measures is mediated through its effect on vitamin D or estrogen metabolism.

Methods. Timed rise, timed walk at normal and fast speed, grip strength, and serum biochemical measurements were compared between smokers, past smokers, and nonsmokers. Analysis of covariance was used to compare physical performance variables while adjusting for confounding variables.

Results. Compared to nonsmokers and past smokers, current smokers were significantly (p <.05) slower on timed rise and timed walk tests and had decreased grip strength. From multivariate analysis, smoking, age, total body fat, and serum 1,25(OH)2D examined as quartiles were predictors of physical performance measures. The effect of current smoking on physical performance was equivalent to a normal age-related decline in physical performance tests of 7–11 years depending on the test.

Conclusions. The results of this study suggest that current smoking is a risk factor for decreased muscle strength leading to decreased physical performance in elderly women. The effect of smoking on physical performance is in part mediated by its effect on 1,25(OH)2D metabolism. Smoking may also have an independent effect on physical performance possibly by a direct effect on muscle or through an effect on vascular function.

A decline in muscle mass and function is a well known consequence of aging. Between the ages of 20 and 80 years, humans lose approximately 20%–30% of their skeletal muscle mass. The effects of age-related changes in muscle are declining physical function and loss of muscle strength, leading to increased incidence of falls in elderly people (1). The loss of muscle mass and function with age is the result of complex multifactorial processes, and the risk factors are multiple. Evidence suggests that increased muscle weakness and decreased functional ability with aging can be attributed in part to decreased vitamin D levels, and it has been shown that vitamin D supplementation improves physical performance in older people (2–5). Increased muscle strength has also been suggested to be associated with higher estrogen levels or estrogen therapy in some studies (6,7) but not all (8,9).

The health consequences of smoking are well established. There is ample evidence indicating that cigarette smoking contributes to a number of diseases, including cancer, vascular diseases, respiratory diseases, and osteoporosis, among others (10). Smoking adversely affects the vitamin D-parathyroid hormone (PTH) system (11) and has anti-estrogenic effect in women (12). Smoking is associated with lower total body fat mass, which limits the production of estradiol in postmenopausal women (12). Increased metabolism of estradiol by smoking is another possible reason proposed for decreased bioavailability of estradiol (12). Limited cross-sectional and longitudinal studies suggest that smoking also contributes to physical function decline (13–15).

In a cross-sectional study of elderly postmenopausal women, we examined the association between current smoking and physical performance measures. In addition, we also explored whether the effect of smoking on physical performance measures in these women could be mediated through its effect on vitamin D and/or estrogen metabolism.


   MATERIALS AND METHODS
Top
 Abstract
 Materials and Methods
Results
 Discussion
 References
 
The study population comprised 489 women aged 65–77 years who entered a multicenter osteoporosis intervention trial (STOP IT: Sites Testing Osteoporosis Prevention/or Intervention). The baseline data of this study population were used for the analysis. The study participants and the study design have been described in detail in an earlier publication (16). In brief, participants between the ages of 65 and 77 years were recruited through advertisements in local newspapers or by mass mailing of letters inviting them to participate in a 3-year study. The letter was followed-up with a phone call. About 8005 women were contacted by mail, and 1905 women agreed to come for a preliminary screening. Exclusion criteria have been described in an earlier osteoporosis publication (16); in general, women with any disease known to affect calcium metabolism were excluded from the study. To complete eligibility, femoral neck density had to be within the normal range (± 2 standard deviations [SD]). All participants had normal liver and kidney function for entry into the study. Four hundred eighty-nine women satisfied the eligibility criteria and were enrolled into the study. Participants using estrogen were put on a 6-month washout period before the start of the study. The volunteers recruited for the study were free living, in good health, ambulatory, and willing to give written informed consent.

One participant with suspected Paget's disease and one participant with undetermined smoking status were excluded from this analysis. The analysis was performed on the remaining 487 women. The study was approved by Creighton University Institutional Review Board. All participants signed an informed consent form.

Smoking and Alcohol and Dietary Intake
At baseline recruitment, participants were provided with a questionnaire to report their smoking and alcohol history. They were asked to furnish information on smoking status (current, past, and/or never), number of years smoked, and number of years stopped smoking. Using the above information, we categorized the women into current smokers, past smokers, or nonsmokers. Furthermore, the smokers were classified into light (<1 pack/day) or heavy (>1 pack/day) smokers. The number of pack-years smoked was also calculated for smokers and past smokers (packs/day x number of years smoked). Comparisons were made between nonsmokers, past smokers, and current smokers and between nonsmokers, past smokers, light smokers, and heavy smokers.

Dietary intake data were collected using 7-day food dairies. The average daily calcium, vitamin D, and caffeine intakes were calculated by a dietician, using the Food Processor II Plus nutrition and diet analysis system (version 5.1; ESHA Research, Salem, OR).

The information on alcohol consumption was also obtained by a self-administered questionnaire. Drinking and nondrinking status was noted. Reproductive history, medical history, and present use of medications and vitamin and mineral supplements were also assessed by a questionnaire.

Biochemical Analysis
Fasting blood was collected from each participant. Blood samples were allowed to clot and were then centrifuged at 4°C for 15 minutes at 2056 x g to separate serum. All samples were stored frozen at –70°C until analysis.

Serum 25-hydroxyvitamin D (calcidiol, 25OHD) was assayed by a competitive protein binding assay (17). The procedure involves initial chromatographic extraction and purification of serum on Sep-Pak cartridges (Waters Associates, Milford, MA) (18). The limit of detection was 12.5 mmol/L (5 ng/mL), and the interassay variation was 5%. Serum 1,25(OH)2 D was measured by a nonequilibrium radioreceptor assay (Incstar Corp., Stillwater, MN) using the calf thymus receptor, after extraction and purification of the serum on nonpolar C18OH octadecysilanol silica cartridge as described (11,16). The limit of detection for the assay was 12 pmol/L, and our interassay variation was 10%. Serum intact PTH was measured with the Allegro immunoradiometric assay (Nichols Institute, San Juan Capistrano, CA) (19). The limit of detection for the assay was 1 ng/L, and our interassay variation was 3.5%. Serum total estradiol, testosterone, and sex hormone binding globulin (SHBG) were measured in fasting baseline serum samples by radioimmunoassay kits obtained from Diagnostic Systems Laboratories (Webster, TX). Serum total estradiol was measured using an ultrasensitive assay. The minimum detection limit for these assays was 2.2 pg/mL for serum estradiol, 0.05 ng/mL for serum testosterone, and 3 nmol/L for serum SHBG. The intraassay coefficient was less than 5% for these assays. The interassay coefficient for the last 10 assays of serum total estradiol for the lowest standard used (5 pg/mL) was 8.4%, and the interassay coefficient for the in-house serum control (mean ± SD, 11.8 ± 1.35 pg/mL) used was 11.4%. The interassay coefficient for the low kit control (mean ± SD, 0.55 ± 0.03 ng/mL) and in-house control (mean ± SD, 0.33 ± 0.029 ng/mL) tested for serum total testosterone were 6% and 9%, respectively. The interassay coefficient for the lowest kit control tested for SHBG was 8.5%. The bioavailable (non-SHBG-bound) serum estradiol and serum testosterone were measured as described by Khosla and colleagues (20). Briefly, tracer amounts of 3H-estradiol or 3H-testosterone were added to aliquots of serum (200 µL), made to 500 µL with saline. To this, an equal volume of saturated solution of ammonium sulfate (final concentration, 50%) was added to precipitate the SHBG with its bound steroid. The SHBG-bound and -unbound steroid were separated by centrifugation at 1100 x g for 30 minutes at 4°C. The percentage of labeled estradiol or testosterone remaining in the supernatant (free and albumin-bound fractions) was then calculated. The bioavailable estradiol or testosterone concentration was obtained by multiplying the total estradiol or testosterone concentrations, as determined by radioimmunoassay, by the fraction that was non-SHBG-bound. All women except one had measurable levels of serum estradiol. Serum testosterone levels were undetectable in about 22 women.

Physical Performance Measurements
Physical performance measures of muscle function were evaluated by tests of muscle strength (grip strength, right hand), agility and coordination (timed rising), and gait and balance (timed walk, normal and fast). These tests measure physical tasks important for performing the common activities of daily living, and are easily performed in a clinical setting with simple equipment.

Grip strength was measured in the right hand by a handheld Jamar dynamometer (Jackson, MI) as described, with some modification (21) and used by others (22). The participant was asked to stand with the right arm flexed at 90° (anterior–posterior plane) and parallel to the floor. The elbow was fully extended, and the wrist and forearm were in neutral anatomical position. In this position, the participants were instructed to squeeze the dynamometer (setting 2) as hard as possible. The average of three trials was recorded (measured in kilograms). Timed walk at normal and brisk pace was tested as described, with some modifications (23,24). Walking speed was assessed as the time taken to walk a 15-foot walkway at the participant's normal pace and then as quickly as possible. Timed rise test involved the time taken by the participant to rise from a chair 3 times as quickly as possible, following the protocol described, with some changes (24,25). The participants sat on a chair with no arm rests. They were asked to stand up and sit down 3 times and were timed with a stopwatch. In addition to these measurements, Physical Activity Scale for the Elderly (PASE) score was calculated based on questionnaire items assessing various domains of physical activity, including walking, sitting activities, light sport and recreational activities, moderate sport and recreational activities, strenuous sport and recreational activities, exercise, and light housework (26,27).

Total Body Fat and Lean Muscle Mass
Total body fat and lean muscle mass (defined as the weight of the muscle and organs and excluding total body fat and bone weight) were obtained from total body dual-energy absorptiometry scans of the study participants.

Statistical Analysis
All analyses were performed by the SAS statistical package (version 9.1; SAS Inc., Chicago, IL). Baseline patient characteristics (age; height; weight; dietary calcium, caffeine, and vitamin D intake; alcohol use; total body fat; lean muscle mass; medication use; and comorbid conditions) were compared by smoking status (nonsmokers vs past smokers vs current smokers) with one-way ANOVA for continuous variables and chi-square tests for categorical variables. The biochemical variables in Table 1 were compared by smoking status with ANCOVA while adjusting for significant baseline characteristics. If the overall F tests from the ANOVA or ANCOVA models were significant at a.05 level, then pair-wise comparisons were conducted and Tukey's method was used to adjust p values for multiple comparisons. Univariate comparisons of the physical performance variables were conducted with ANOVA. Multivariate analyses of the physical performance variables were conducted by ANCOVA, examining the effect of smoking status on timed rise, timed walk (normal), timed walk (fast), and grip strength, while adjusting for significant confounding variables including age, body mass index, calcium intake, caffeine intake, and total body fat. Backward selection was used to select significant confounders to adjust for while retaining smoking status in the model. An alpha level of 0.05 was used for model selection. The biochemical variables [serum 25OHD, 1,25(OH)2D, total estradiol, bioavailable estradiol, testosterone, and SHBG] were examined in the ANCOVA models to see if the effect of smoking is mediated through these biochemical variables. The biochemical variable was considered a mediator when it was a significant predictor of the physical performance tests and affected the association of smoking and physical performance measures (ß coefficient and significance of smoking). The interaction of smoking status with serum 25OHD, 1,25(OH)2D, total estradiol, bioavailable estradiol, testosterone, and SHBG was also examined.


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  		Table 1. Characteristics and Biochemical Variables According to Smoking Status.

 
An age related decline was calculated for each of the physical performance variables, using the slope of the regression coefficient for age (ß) in Table 2, a 5-year increase in age corresponds to a 5 x ß change in physical performance. This age-related decline was then compared to the decline in physical performance seen in smokers (as determined by the regression coefficient for smoking status) as a percentage change, using the change in physical performance tests of nonsmokers as the reference value.


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  		Table 2. Multivariate ANCOVA Models With Physical Performance Measure Outcomes, Looking at Possible Mediator Variables.

 

   RESULTS
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 Abstract
 Materials and Methods
 Results
Discussion
 References
 
Characteristics of Study Population
A comparison of the characteristics among nonsmokers, past smokers, and current smokers is given in Table 1. Twelve percent of the study population were current smokers, 33% were past smokers, and 55% constituted the nonsmokers. The median pack-years for smokers was 25.9 years (0.15–174 years), and for past smokers was 12 years (0.02–84.0 years). The median time that past smokers stopped smoking was 16 years (0.5–55 years). The median time that current smokers smoked was 50 years (1–64 years), and the median time since past smokers smoked was 20 years (0.6–55 years). On average, smokers weighed 8 kg less than nonsmokers (p <.001) and had lower dietary calcium intake compared to both nonsmokers and past smokers. Smokers had significantly (p <.001) lower total body fat compared to nonsmokers and past smokers. Amount of lean muscle mass was not significantly different between the groups. Past and current smokers had significantly higher caffeine intakes (p <.001) compared to nonsmokers. The percentage of alcohol consumers was slightly higher in smokers compared to nonsmokers (Table 1). Smokers were similar to nonsmokers with respect to age, height, and dietary vitamin D intake. The occurrence of comorbidities classified as 0–2, 3–4, 5–6, and ≥7 was not significantly different between the nonsmokers, past smokers, and current smokers. Medication use in the study population classified as 0, 1, and > 1 was not different between nonsmokers, past smokers, and current smokers (Table 1).

Smoking and Biochemical Variables
As seen in Table 1, serum 25OHD, 1,25(OH)2D, PTH, total estradiol, and bioavailable estradiol were lower in current smokers than in nonsmokers or past smokers, although the differences were not statistically significant. When the data were examined according to quartiles of 1,25(OH)2D, the percentage of smokers was marginally higher in the lowest quartile (Q1 = 16%) compared to higher quartiles (Q2 = 7%, Q3 = 12%, and Q4 = 13%). In univariate analyses, serum bioavailable estradiol was significantly lower (p =.034) in smokers than in nonsmokers and past smokers; adjustment for total body fat made the differences nonsignificant. Serum total testosterone levels were significantly higher in current smokers than in nonsmokers and past smokers. Serum bioavailable testosterone levels were lower in smokers than in nonsmokers and past smokers. Serum SHBG levels were significantly higher in current smokers than in nonsmokers or past smokers.

Smoking and Physical Performance Measures
As shown in Table 1 and Figure 1, on average, current smokers were slower on timed rise and timed walk tests and had decreased grip strength compared to past and nonsmokers after adjusting for significant confounders: age, body mass index, calcium intake, caffeine intake, and total body fat. Mean PASE scores (± standard error [SE]) were not different between the current (116.3 ± 7.4), past (110.9 ± 4.1), and nonsmokers (117.7 ± 3.5). From multivariate analysis models, the effect of current smoking on physical performance measures was much higher than that observed with a 1-year age-related decline in physical function in nonsmokers (Table 2). The effect of smoking (smokers vs nonsmokers) compared to a 5-year increase in age was higher by 39% on the timed rise test, 136% on the timed walk (normal) test, 95% on the timed walk (fast) test, and 90% on the grip strength test. Past smokers had physical performance measures similar to those of nonsmokers (Table 1 and Figure 1), except in those participants with a long pack-year history who were slower in the timed walk test (data not given). In contrast, no such effect was seen when the physical performance measures were compared according to pack-years of smoking in current smokers (data not given). There was no significant difference in physical performance measures between the light (<1 pack/day) and heavy (>1 pack/day) smokers (data not given).


Figure 01
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  		Figure 1. Mean baseline physical performance measures according to smoking status. The values are unadjusted mean ± standard error of the mean. Means were compared by analysis of covariance adjusting for significant confounding variables that were significant in the models and included the covariates given in Table 2. Significance was derived from adjusted data using Tukey's post hoc multiple comparison test. *p <.05 compared to nonsmokers; {dagger}p <.05 compared to past smokers

 
The interactions of smoking status with serum 25OHD, serum 1,25(OH)2D, serum SHBG, serum total estradiol, and serum bioavailable estradiol on timed rising, timed walk (normal), timed walk (fast), and grip strength were not statistically significant (all p >.05). Serum 25OHD, 1, 25(OH)2D, total estradiol, bioavailable estradiol, testosterone, and SHBG were examined in the ANCOVA models with smoking status while adjusting for age and total body fat (for serum total and bioavailable estradiol, the models were examined without total body fat) to see if the effect of smoking is mediated through these biochemical variables. When serum 1,25(OH)2D was categorized into quartiles, it was a significant predictor of grip strength (p =.026) and a marginally significant predictor of timed rise (p =.06) and timed walk normal (p =.07) (Table 2). The effect of smoking was still significant in models with serum 1,25(OH)2D, though slightly lower than that seen in models without 1,25(OH)2D. Serum 25OHD, total estradiol, bioavailable estradiol (Table 2), and SHBG were not significant predictors of physical performance measures. However, serum total testosterone was found to be a significant predictor of grip strength.


   DISCUSSION
Top
 Abstract
 Materials and Methods
 Results
 Discussion
References
 
The results presented in this cross-sectional study demonstrate that smoking reduces physical performance in elderly postmenopausal women. Current smokers were slower on timed rise and timed walk tests, and had decreased grip strength. The effect of smoking is equivalent to the normal age-related decline in physical performance tests of 7–11 years. The grip strength measurement in nonsmokers (65–77 years) in the present study (25.2 ± 4.41 kg) is close to the normative data reported in the literature for women between the ages of 60–79 years (24.5 ± 5.0 kg) (28). In the present study, past smokers had physical performance measures similar to those of nonsmokers, apart from the timed walk test, which was slower in those women with more pack-years of smoking. There was no significant association between the quantity of smoking and physical performance measures.

There are few studies in the literature that report that smoking is associated with a decrease in physical performance. The results of the present study are in agreement with those of Nelson and colleagues (13), who demonstrated that current smoking is associated with impaired neuromuscular performance. Also, Guralnik and Kaplan (14) reported a higher physical function in nonsmokers than in smokers after 19 years of follow-up. In the elderly and disabled Medicare population, Arday and colleagues (29) reported that current smokers have worse physical and mental functional status than do never smokers. Stovring and colleagues (30) reported that the cumulative effect of smoking from age 50–70 years adversely affects the functional ability at age 75 years. None of these studies suggested the mechanism of how smoking affects physical function.

Changes in vitamin D metabolism may play a role in decreased physical performance. 1,25 dihydroxyvitamin D receptor (VDR) is expressed in skeletal muscle tissue, and evidence indicates that 1,25(OH)2D plays a key role in muscle contractility, cell calcium and phosphate transport, and protein and phospholipid synthesis in muscle cells (31). VDR in muscle tissue has been reported to decrease with age (32). Several cross-sectional studies have shown that low serum 1,25(OH)2D and low serum 25OHD levels are related to lower muscle strength and falls in older men and women (2,3). In healthy elderly women, we have shown decreased physical performance in the lower quartiles of serum 1,25(OH)2D and 25OHD compared to the corresponding higher quartiles (33). Vitamin D and calcium supplementation has been demonstrated to improve musculoskeletal function and decrease fall incidence in the elderly population (5,34). In the present study, smokers have marginally lower serum 25OHD and serum 1,25(OH)2D levels. In multivariate analysis, serum 1,25(OH)2D examined as quartiles was a predictor of physical performance measures and lessened the effect of smoking, indicating that some of the effect of smoking is mediated through changes in serum 1,25(OH)2D. Although serum 25OHD levels were lower in smokers, adjustment for serum 25OHD did not affect the association of smoking and physical performance measures.

In the present study, smokers had lower serum total and bioavailable estradiol, lower serum bioavailable testosterone, and higher serum SHBG levels. In univariate analyses, serum bioavailable estradiol was significantly lower in smokers compared to nonsmokers and past smokers. It is known that adipose tissue produces estrogen by aromatase conversion of androstenedione in postmenopausal women (12), so the lower body weight in smokers is a probable cause of lower circulating serum estradiol; smoking has also been suggested to increase the metabolism of estrogen (12). However, after adjustment for total body fat, the significance of estradiol was lost. Furthermore, in multivariate analyses, total estradiol and bioavailable estradiol (in absence of total body fat in the models) were not significant predictors of physical performance tests, thus the effect of smoking may not be mediated through changes in estrogen metabolism. Total body fat was a significant independent predictor of physical performance tests other than grip strength; heavier participants were slower on timed walking and chair rise tests. There was no difference in lean muscle mass in smokers compared to nonsmokers, so the loss of muscle mass is not a factor in decreased physical performance. The reason for an effect of smoking on total and bioavailable testosterone is not very clear, but it has been suggested that a decrease in adrenal production of androgens may be responsible (12). Serum total testosterone was a significant predictor of grip strength in the present study, but the mechanism of how it affects physical performance is not very clear. Decreased testosterone levels in men were reported to be associated with lower appendicular skeletal muscle mass and increased risk of falls, impairment of balance, and impaired ability to perform the tandem walk (35). Testosterone supplementation in elderly men has been reported to improve muscle mass and strength (35). Testosterone administration in men was associated with a dose-dependent increase in leg-press strength and leg power, which correlated with testosterone dose and circulating testosterone concentrations (36). However, Snyder and colleagues (37) reported no effect of testosterone on muscle strength in older men. In one controlled study of low weight HIV-infected young women, testosterone supplementation was associated with increase in shoulder flexion, elbow flexion, knee extension, and knee flexion, but not in grip strength (38); however, no corresponding studies have been reported in elderly women.

In the present study, the interactions of smoking with serum 25OHD, 1,25(OH)2D, total estradiol, bioavailable estradiol, testosterone, and SHBG on physical performance measures were not significant. This could be because our study was not adequately powered to assess these interactions. The present study has some limitations. The study is cross-sectional in nature. The women enrolled into the study were healthy volunteers, and our findings may not apply to the general population. The smoking status of women in the present study is based on self-report and possibly underestimates the true use.

Overall, smoking affects physical performance in part through its effect on 1,25(OH)2D status. However, an independent effect of smoking on physical performance by affecting vascular function (39) or by a direct effect on muscle (40,41) is a possibility. In summary, our results suggest that current smoking is a risk factor for decreased muscle strength leading to decreased physical performance in elderly women.


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  		Table 3. Co-morbidities in the Study Population.

 

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  		Table 4. Medication use in Study Population.

 

   Acknowledgments
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 Materials and Methods
 Results
 Discussion
 References
 
This work was supported by National Institutes of Health Research Grants UO1-AG10373 and RO1-AG10358.

We thank Karen A. Rafferty for her help in food diary data collection and analysis. We also thank Kurt E. Balhorn for the laboratory analysis.

This work was presented as an abstract at 27th Annual Meeting of the American Society for Bone and Mineral Research, Nashville, Tennessee, September 23–27, 2005.


   Footnotes
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 Materials and Methods
 Results
 Discussion
 References
 
Decision Editor: Luigi Ferrucci, MD, PhD

Received August 18, 2005

Accepted May 1, 2006


   References
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 Materials and Methods
 Results
 Discussion
 References
 

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