Lifestyle and Genetic Predictors of Stiffness Index in Community-dwelling Elderly Korean Men and Women

Asian Nursing Research 9 (2015) 251e258

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Research Article

Lifestyle and Genetic Predictors of Stiffness Index in Communitydwelling Elderly Korean Men and Women Kyung-Ae Park, PhD, 1 Yeon-Hwan Park, PhD, RN, 2 Min-Hee Suh, PhD, RN, 3 Smi Choi-Kwon, PhD, RN 2, * 1 2 3

Department of Hotel Culinary Arts and Nutrition, Kaya University, Kyungnam, South Korea College of Nursing & The Research Institute of Nursing Science, Seoul National University, Seoul, South Korea Department of Nursing, Inha University, Incheon, South Korea

a r t i c l e i n f o

s u m m a r y

Article history: Received 27 July 2014 Received in revised form 29 April 2015 Accepted 26 May 2015

Purpose: Differing lifestyle, nutritional, and genetic factors may lead to a differing stiffness index (SI) determined by quantitative ultrasound in elderly men and women. The purpose of this study was to determine SI and the gender-specific factors associated with low SI in a Korean elderly cohort. Methods: This was a cross-sectional descriptive study identifying the gender-specific factors related to SI in 252 men and women aged 65 years and greater from local senior centers in Seoul, Korea between January and February 2009. Results: The mean SI of elderly men was significantly higher than that of the women's. A multiple regression analysis reveals that age, nutritional status, and physical activity were predictive factors of lower SI in men, whereas age, alcohol consumption, educational level, and genetic polymorphism were predictive factors for elderly women. Conclusions: Low SI was common in both elderly men and women. We found gender differences in factors linked to low SI. In multiple regression analysis, nutritional status and physical activity were more important factors in men, whereas alcohol consumption, educational level, and genetic polymorphism were significant factors predicting low SI in women. Gender-specific modifiable risk factors associated with low SI should be considered when developing osteoporosis prevention programs for the elderly. Copyright © 2015, Korean Society of Nursing Science. Published by Elsevier. All rights reserved.

Keywords: aged bone density gender genetic polymorphisms motor activity

Introduction Osteoporosis is a metabolic bone disease characterized by low bone mass and microarchitectural deterioration of bony tissue leading to enhanced bone fragility and a consequent increase in fracture risk [1]. Osteoporosis is diagnosed when a person's bone mineral density obtained from dual-energy X-ray absorptiometry (DXA) is at 2.5 standard deviations below the World Health Organization (WHO)edefined threshold value or more. This value, called the T score, is a measurement derived from healthy young adults' bone mineral density [2]. Osteoporotic fractures account for more disability-adjusted life years than many other diseases, including hypertension, stomach cancer, and breast cancer [3].

* Correspondence to: Smi Choi-Kwon, PhD, RN, College of Nursing, Seoul National University, 28 Yeongeon-Dong, Jongro-Gu, Seoul, 03080, South Korea. E-mail address: [email protected]

Osteoporosis-related fractures can also lead to a huge economic burden on society worldwide [4e6], with a trend towards further increases [4,7] because of the direct (medical care costs, medical services, and nonmedical costs) and indirect (work loss) costs of osteoporosis [7,8]. The Fourth Korean National Health and Nutrition Examination Survey revealed that the elderly population (aged
65 years) is rapidly increasing in Korea [9], just as it is in most Western countries. Although elderly females are known to suffer more from osteoporosis-related fractures than males do, the risks of osteoporosis development and related fractures are also common in elderly men [10]. The residual lifetime probability of osteoporosis-related fractures in Korean male adults was reported to be 23.8% [10]. Although bone mineral density obtained from DXA is a standard diagnostic technique for osteoporosis, it is difficult to apply in community-based studies because of a lack of portability, high cost, and exposure to ionizing radiation. Quantitative ultrasound (QUS, Achilles Express ultrasonometer (GE Lunar Healthcare Corporation,

http://dx.doi.org/10.1016/j.anr.2015.05.006 p1976-1317 e2093-7482/Copyright © 2015, Korean Society of Nursing Science. Published by Elsevier. All rights reserved.

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Madison, WI, USA), however, is inexpensive and easy to carry, and also estimates the bone density of the calcaneus. Many studies have suggested that stiffness index (SI) obtained from QUS was correlated strongly with DXA measurements [11,12]. SI is the sum of the scaled and normalized speed of sound (SOS) and broad ultrasound attenuation (BUA) values, which is a measure of bone strength (bone density and bone quality) and is sensitive to bone structure used to predict the risk of bone fracture due to osteoporosis in both men and women [13e15]. Low SI can indicate and screen individuals with low bone mass [16], assisting medical personnel in the screening of osteoporosis. SI was reported to decrease with age and be higher for men than for women [17]. Heritability estimates of QUS parameters have ranges between 53% and 74% at the calcaneus [18,19]. Different lifestyle, nutritional status, nutrient intakes, and genetic factors may lead to a different SI of each gender group, but gender-specific factors associated with low bone mass have yet to be identified. Data correlating lifestyle/nutritional status and low SI in each gender are often inconsistent or insufficient. The level of education was found to be correlated with low SI on QUS in elderly women [20], whereas physical activity and body mass index (BMI) have been reported to be related to SI as measured by QUS in elderly men [21]. Some have reported that malnutrition, which is frequent in elderly is a risk factor for osteoporotic fractures (repetitive) [22] and correlations between nutritional markers and QUS parameter in Australian elderly care residents were found [23]. However, another study reported no correlation between nutritional status and SI measured by QUS in elderly women [23]. Among a large number of osteoporosis risk candidate genes, the estrogen receptor (ER) gene (X03635, X99101) and vitamin D receptor (VDR) gene (J03258) have been most widely studied. ER and VDR genotype polymorphisms may act differently on elderly men and elderly women [24e26]. Moreover, the difference may present in different ethnic groups [27]. Many studies have reported a positive relationship between the ER gene/VDR gene and low bone mass based on QUS in postmenopausal women [28e30], whereas an association between genetic polymorphism and low SI in elderly males has been far less studied, except for one study. This study has suggested a relationship between ER and low bone mass in Korean men [14]. Therefore, no conclusion has yet been reached as to whether ER and VDR genotype polymorphisms are associated with SI in elderly men and women. In the present study, therefore, we assessed the calcaneal SI measured by QUS and investigated the relationship of the SI with a comprehensive set of factors including lifestyle factors, nutritional status, nutrient intakes, and genetic factors in a cohort of Korean elderly men and women.

participants (164 elderly men and 143 elderly women) were enrolled after seeing the advertisement posted at the centers. The inclusion criteria were age 65 years or more and the absence of dementia. The participants were excluded if they had dementia severe enough to preclude a reliable interview, which was determined by the modified Korean version of the Mini Mental State Examination (K-MMSE). The K-MMSE had been developed regarding age, educational attainment and gender [31,32] due to their generational characteristics, which is that age and gender differences exist in educational levels in Korean elderly [33]. We also excluded older adults with involvement in any osteoporosis treatments or drugs, and presence of diseases affecting bone metabolism in order to rule out any effects of those on SI. Of the initial 307 participants who wished to participate, 28 (22 men and 6 women) were excluded due to dementia, and 27 (15 men and 12 women) were excluded due to incomplete data, history of fracture, or taking osteoporosis medication. Thus, in the end, 252 participants were included in the study.

Methods

SI ¼ ð0:67  BUAÞ þ ð0:28  SOSÞ  420

Study design

Sociodemographic and lifestyle data

This study was a secondary data analysis identifying the genderspecific factors related to SI. Briefly, between 2009 and 2011, consecutive elderly from two senior centers were participated in our study. We first investigated the effects of lifestyle factors, nutritional status and nutrient intakes on SI. We also investigated the relationship of the SI with gene polymorphisms.

The participants were interviewed using a structured questionnaire to obtain sociodemographic data, including age, gender, years of formal education, income, and family structure (living alone/living with spouse/living with others). The lifestyle data were assessed. When the participants reported being current smokers/ alcohol drinkers, information on the amounts of alcohol/tobacco they consumed were also obtained. The level of physical activity was assessed using the International Physical Activity Questionnaire (IPAQ). IPAQ levels were grouped into three categories: > 2,500 MET/min/week, 500e2,500 MET/min/week, and < 500 MET/min/week and classified as “vigorous”, “moderate”, and “low” physical activity, respectively [37]. Reasonable reliability of Korean version of IPAQ was reported in Korean female adult [38]. Recently

Setting and sample The participants were recruited from two nearby local senior centers in Seoul, Korea who wished to participate in the study on a voluntary basis, between January and February in 2009. Out of the 381 participants who attended the center on a regular basis, 307

Ethical consideration This study was a secondary data analysis. The institutional review board at Seoul National University approved the study (2011e50). Researchers explained the purpose and the procedures of the study and obtained written informed consent before the data collection procedure. Participants were informed that they were not obliged to participate in the study and incentives were provided after the study. Calcaneal QUS measurement We measured bone mass with QUS, which has been used as a screening tool for osteoporosis and low bone mass in elderly men and women [12,34]. QUS measurements were performed with an Achilles Express ultrasonometer (GE Lunar Healthcare Corporation, Madison, WI, USA) at the right calcaneus. Quality control was performed according to the manufacturer's instructions before the first measurement of each survey day. The measurement was taken with a single ultrasonometer by the same trained nurse. All measurements were performed at room temperature. This ultrasonometer provides parameters in less than 1 minute, providing real-time imaging of the calcaneus. Two main parameters, SOS and BUA can be measured by QUS devices, derived from the velocity or attenuation of the ultrasound waves through the bone tissue. SI, introduced by the manufacturer to measure bone fragility, was defined as a combination of normalized SOS and BUA [35]. SI was calculated using the following equation [36]:

K.-A. Park et al. / Asian Nursing Research 9 (2015) 251e258

validity and reliability of Korean version of IPAQ was also established in Korean older adults [39]. Nutritional status The anthropometric measurements took place in a physical examination room of each welfare center. The anthropometric measurements included height, weight, midarm circumference (MAC) and triceps skinfold thickness (TSF) measurements. MAC and TSF were measured midway between the tip of the acromion and olecranon process. MAC measurements were taken to the nearest millimeter using an insertion tape, with the right arm hanging relaxed at the subject's side. TSF was measured at the vertical fold midway between the tip of the acromion and olecranon process with the right arm held vertically. The skinfold thickness measurement was made at the same level as the MAC on the posterior aspect of the arm, over the long head of the triceps. Skinfolds were measured using a Skyndex skinfold caliper (Caldwell-Justiss & Co., Inc., Fayetteville, AR, USA). Midarm muscle circumference (MAMC) was calculated accordingly [40]. MAMC was calculated from the following equation:

MAMC ðcmÞ ¼ MAC ðcmÞ  ½0:341  TSF ðmmÞ Once the anthropometric measurements were done, blood samples were obtained in the same room from all of the participants after overnight fasting to obtain biochemical parameters (serum albumin, transferrin, total lymphocyte counts, and hemoglobin levels). To assess the nutritional status more comprehensively, we included anthropometric and biochemical measurements and determined the nutritional status accordingly [41]. Undernutrition was considered to be present when the subject had at least one anthropometric and one biochemical parameter that were indicative of undernutrition. Overnutrition was defined as having one or more anthropometric parameters indicative of overnutrition [41]. The following summarizes the undernutrition/overnutrition criterion for each parameter: BMI < 18.5 kg/m2 for undernutrition [42], > 25.0 kg/m2 for overnutrition [41]; TSF and MAMC < 80.0% for undernutrition, > 120.0% for overnutrition. Serum albumin levels, transferrin levels, and total lymphocyte counts of < 3.5 g/dL, < 212.0 mg/dL, and < 1,500/mm3, respectively were considered to denote undernutrition [40] and hemoglobin levels of < 13.0 g/dL in men and < 12.0 g/dL in women also indicated undernutrition. For statistical purpose, we further categorized the nutritional status into 2 groups (undernutrition vs. normal and overnutrition), since low bone mineral density has been reported to be associated with undernutrition [23,43]. Nutrient intake Nutritional intake was assessed with the Semi-Quantitative Food Frequency Questionnaire [44] and analyzed using the ComputerAided Nutritional Analysis Program (CAN-PRO; version 3.0; The Korean Nutrition Society, Seoul, South Korea). Korean version of this questionnaire was previously validated as a useful tool for estimating nutrient intakes [45]. The nutrients analyzed in our study were calories, protein, fiber, calcium, and phosphorus, all of which have been reported to be related to osteoporosis [46]. A deficient intake of each nutrient was defined when the participants took in < 75.0% of its corresponding Korean dietary reference intake [47].

253

the QuickGene DNA whole blood kit S and QuickGene-810 (Fujifilm, Corporation Tokyo, Japan). One single-nucleotide polymorphism (SNP) of the VDR promyelocytic leukemia gene (Bsm I) and two SNPs of the ER a gene (Xba I and Pvu II) were chosen for their ability to tag all common haplotypes at a given locus. All SNPs were genotyped using TaqMan SNP allelic discrimination (ABI 7900HT System, Applied Biosystems, Foster City, CA, USA). Allelic discrimination involves end-point plate reading. SDS version 2.3 software (Applied Biosystems, Foster City, CA, USA) was used to calculate the fluorescence measurements made during the plate reading, plotting Rn values (the fluorescence of the reporter dye divided by the fluorescence of a passive reference dye) based on the signals from each well. The analyzed plates were then subjected to automatic or manual allele calls [28e30]. Data collection and procedure Data collection was performed in two separate rooms, a nursing education and counseling room and a physical examination room, in each welfare center by skilled nurses who had experience from previous related studies [40,41]. To secure inter-observer consistency, a training session consisting of a 1-hour lecture and 1-hour practice run was held prior to the participant interviews by one of the authors. Subsequently, each rater's initial interview sessions were supervised at the data collection site by the same author, and any disagreements were resolved by discussion. In order to receive signed consent and provide a question and answer session for each participant, the interviews were conducted prior to any other measurements. Each participant was, first, interviewed in the nursing education and counseling room to gather sociodemographic and lifestyle data and nutrient intake data. Once the interview was completed, the participant was relocated to the physical examination room for QUS, anthropometric measurements, and blood sampling. The whole data collection procedure for each participant was conducted in a single visit. Data analysis Statistical analyses were performed using the SPSS software package (version 21.0 for Windows, SPSS Inc., Chicago, IL, USA). To investigate the relationship of the SI with a comprehensive set of factors including lifestyle factors, nutritional status, nutrient intakes, and genetic factors. First, continuous data sets were summarized as means and standard deviations, and compared using analysis of variance and Duncan's multiple range test or t test. Categorical data sets were characterized in N and percentage values to be compared using the c2 test. Multiple linear regression analysis was used to determine the association between gender specific factors and SI. We included all the variables that were significant on univariate analysis. To test for multicollinearity, the variance inflation factor was assessed. When it was smaller than 10, multicollinearity was not considered to €t Düsseldorf, exist [48]. G*Power 3.1.2 (Heinrich-Heine-Universita Düsseldorf, North Rhine-Westphalia, Germany) program was used to calculate the power of the study. With 125 patients in each gender group, the power of this study was 0.91 based on a medium effect size of 0.15 and a type 1 error of 0.05 [49]. The level of statistical significance was set at p < .05. Results Gender-specific characteristics

Gene polymorphisms From blood samples collected from each participant, DNA was extracted from the buffy coat fraction of centrifuged blood using

A total of 252 Korean elderly men and elderly women aged 65 years and greater participated in this study (127 men and 125 women). The mean SI, sociodemographic and lifestyle factors of the

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participants are given in Table 1. The mean SI was significantly higher for the men than for the women (p < .001). There was no gender difference in the age of participants. Educational attainment was higher for the men than for the women (p < .001). The men more often lived with a spouse (p ¼ .003). The men more often smoked (p < .001), drank (p < .001), and maintained higher physical activity levels (p ¼ .026) than the women did. Nutritional status, nutrient intakes, and gene polymorphisms of the participants are given in Table 2. The nutritional status did not differ between the genders. BMI was significantly higher for the women than for the men (p < .001). However, albumin was not significantly different between the groups. While the proportions of deficiencies in caloric and fiber intake were higher in the men than in the women (p < .001, respectively), no differences were found in other nutrient intake levels between the groups. There were significant differences in genotypes of ER a gene Pvu II (p ¼ .010) and Xba I (p ¼ .018) between the two groups. Elderly women more often had the pp genotype of ER a gene Pvu II and the xx genotype of ER a gene Xba I than did the men, although no gender difference in VDR gene Bsm I genotype distributions was found. Factors associated with low SI Since the elderly men and women had differences in sociodemographic and lifestyle factors, nutrient intake, and genetic polymorphisms, and these factors might have different effects on the SI of each gender group, we analyzed the males and females separately. In elderly men, older age (p ¼ .001) and lower physical activity level (p ¼ .006) were significantly associated with lower SI, while in elderly women, older age (p < .001), lower educational level (p ¼ .035) and not drinking alcohol (p ¼ .020) were significantly associated with lower SI (Table 3). In elderly men, poorer nutritional status (p ¼ .014) and deficiency of calcium intake (p ¼ .041) were significantly associated with lower SI, and carrying bb genotype of VDR BsmI showed a marginal positive effect on lower SI (p ¼ .087) (Table 4). In elderly women, poorer nutritional status (p ¼ .018) and carrying the pp genotype of the ER a gene Pvu II (p ¼ .009) and the xx genotype of the ER a gene Xba I (p ¼ .037) were significantly associated with lower SI (Table 4). We further analyzed the correlations between anthropometric/biochemical data and SI, and again found a positive

Table 1 Stiffness Index and Sociodemographic and Lifestyle Factors of Elderly Participants. Characteristics

Male (n ¼ 127) Female (n ¼ 125) t or c2 n (%) or M ± SD

Stiffness index 86.02 ± 16.18 Sociodemographic factors Age 75.67 ± 4.88 Educational attainment (yr) 8.15 ± 4.44 Family structure Living alone 26 (20.5) Living with a spouse 55 (43.3) Living with others 46 (36.2) Lifestyle factors Smoking Yes 18 (14.2) No 109 (85.8) Drinking Yes 69 (54.3) No 58 (45.7) Physical activity Low 16 (12.6) Moderate 78 (61.4) Vigorous 33 (26.0) a

p by t test or c2 test.

pa

Table 2 Nutritional Status, Nutrient Intake and Gene Polymorphism of Elderly Participants. Variables

Male (n ¼ 127) Female (n ¼ 125) t or c2 n (%) or M ± SD

Nutritional status Undernutrition 11 (8.7) Normal 56 (44.1) Overnutrition 60 (47.2) 2 23.21 ± 2.97 BMI (kg/m ) Albumin (g/dL) 4.13 ± 0.19 Nutrient intakes Caloric intake Deficient 64 (50.4) Normal 63 (49.6) Protein intake Deficient 19 (15.0) Normal 108 (85.0) Fiber intake Deficient 70 (55.1) Normal 57 (44.9) Calcium intake Deficient 76 (59.8) Normal 51 (40.2) Phosphorus intake Deficient 19 (15.0) Normal 108 (85.0) Gene polymorphism Estrogen receptor Pvu II pp 36 (28.3) Pp, PP 91 (71.7) Estrogen receptor Xba I xx 69 (54.3) Xx, XX 58 (45.7) Vitamin D receptor Bsm I bb 111 (87.4) Bb 16 (12.6)

pa

n (%) or M ± SD 1.04 9 (7.2) 49 (39.2) 67 (53.6) 25.07 ± 3.42 4.13 ± 0.36

.596

4.61 < .001 0.02 .988 13.25 < .001

35 (28.0) 90 (72.0) 0.78

.376

14 (11.2) 111 (88.8) 16.79 < .001 37 (29.6) 88 (70.4) 0.02

.877

0.25

.620

6.69

.010

5.57

.018

0.57

.449

76 (60.8) 49 (39.2) 16 (12.8) 109 (87.2)

55 (44.0) 70 (56.0) 86 (68.8) 39 (31.2) 113 (90.4) 12 (9.6)

Note. BMI ¼ body mass index. a p by t test or c2 test.

correlation between SI and albumin (r ¼ .33, p < .001)/BMI (r ¼ .26, p < .003) only in elderly men. Multiple regression analysis revealed that older age (b ¼ 0.29, p ¼ .001), not having vigorous physical activity (b ¼ 0.23, p ¼ .006), and poorer nutritional status (b ¼ 0.19, p ¼ .019) were statistically significant predictors of lower SI in the elderly men, explaining 20.9% of the variance. In the elderly women, older age (b ¼ 0.26, p ¼ .002), not drinking alcohol (b ¼ 0.24, p ¼ .003), lower education level (b ¼ 0.22, p ¼ .007) and a pp genotype of the ER a gene Pvu II (b ¼ 0.21, p ¼ .011) were statistically significant predictors of lower SI, explaining 24.1% of the variance (Table 5).

n (%) or M ± SD 75.37 ± 12.99

5.76 < .001

74.83 ± 5.37 5.30 ± 4.07

1.30 .196 5.30 < .001 11.63 .003

46 (36.8) 32 (25.6) 47 (37.6) 16.16 < .001 1 (0.8) 124 (99.2) 27.13 < .001 28 (22.4) 97 (77.6) 7.29 32 (25.6) 69 (55.2) 24 (19.2)

.026

Discussion A longer lifespan has made osteoporosis a major health risk among both male and female elders in Korea. This study is the first to evaluate the differences in SI measured by QUS between men and women among Korean elderly individuals, and the genderspecific risk factors of low SI with regard to lifestyle factors, nutritional status, nutrient intakes, and gene polymorphisms. The mean SI for Korean men and women was 86.02 and 75.37, respectively. Our SI values for men were comparable to those reported in previous studies in Korea (85.80) [12], Japan (76.70) [35], and China (82.8) [50] for elderly men. Our SI values for women were higher than those in studies of Koreans (69.40) [12] and Japanese (64.80) [35], but were lower than those in Greek (84.40) [51] and Swedish studies (78.00) [52] of elderly women. This difference between the populations from the Eastern and Western countries may be attributed to several factors including genetic differences. Previous studies found

K.-A. Park et al. / Asian Nursing Research 9 (2015) 251e258

255

Table 3 Stiffness Index According to Sociodemographic and Lifestyle Factors of Elderly Participants. Male (n ¼ 127)

Variables

Sociodemographic factors Age (yr) < 75
75 Educational attainment (yr) <6
6 Family structure Living alone Living with a spouse Living with others Lifestyle factors Smoking Yes No Drinking Yes No Physical activity Low Moderate Vigorous

n

SI (M ± SD)

54 73

91.44 ± 17.06 82.00 ± 14.35

19 108

87.63 ± 13.94 85.73 ± 16.59

26 55 46

87.12 ± 14.49 86.40 ± 14.88 84.93 ± 18.68

18 109

82.94 ± 13.66 86.52 ± 16.56

69 58

85.71 ± 16.71 86.38 ± 15.68

16 78 33

81.88 ± 19.06b 83.64 ± 15.74b 93.64 ± 13.53c

Female (n ¼ 125) t or F

pa

3.30

.001

0.47

0.18

0.87

0.23

5.37

n

SI (M ± SD)

57 68

79.77 ± 13.23 71.68 ± 11.65

48 77

72.27 ± 10.89 77.30 ± 13.87

46 32 47

72.89 ± 13.02 77.00 ± 10.92 76.68 ± 14.01

1 124

77.00 ± 0.00 75.35 ± 13.04

28 97

80.36 ± 14.01 73.93 ± 12.39

32 69 24

72.09 ± 12.03 77.01 ± 13.59 75.37 ± 12.99

.639

t or F 3.64

< .001

2.13

.035

1.34

.267

0.13

.900

2.35

.020

1.60

.207

.839

.387

.818

pa

.006

Note. SI ¼ stiffness index. a p by t test or analysis of variance. b,cMeans with same letters in the same column do not differ significantly from one another at p < .05 by analysis of variance and Duncan's multiple range test.

that 60.0e85.0% of bone mass variations are determined by genetic factors [53,54]. BMI can be another contributing factor since a high BMI is reported to have a protective effect against osteoporosis [55,56]. Our participants had a lower BMI than Western participants [51,52,55], therefore, this may have resulted in a lower SI than that of their Western counterparts. We found that age was a significant predictor of low SI in elderly men and elderly women. This is consistent with many previous

cross-sectional studies that have shown a significant decrease in SI with age in both men [35,50] and women [35]. The association between age and SI may be explained by the decline of hormones (estrogen in women, and testosterone, androgens, growth hormone, and insulin-like growth factor in men), which causes bone loss [57,58]. Although age was not a gender-specific factor related to SI in our elderly cohort, it was interesting to find that educational

Table 4 Stiffness Index According to Nutritional Status, Nutrient Intake and Gene Polymorphism of Elderly Participants. Male (n ¼ 127)

Variables

Nutritional status Undernutrition Normal, overnutrition Nutrient intakes Caloric intake Deficient Normal Protein intake Deficient Normal Fiber intake Deficient Normal Calcium intake Deficient Normal Phosphorus intake Deficient Normal Gene polymorphism Estrogen receptor Pvu II Pp Pp, PP Estrogen receptor Xba I xx Xx, XX Vitamin D receptor Bsm I bb Bb Note. SI ¼ stiffness index. a p by t test.

n

SI (M ± SD)

11 116

74.64 ± 17.23 87.09 ± 15.74

64 63

85.81 ± 15.82 86.22 ± 16.67

19 108

81.58 ± 12.01 86.80 ± 16.74

74 53

84.78 ± 14.21 87.74 ± 18.60

76 51

83.62 ± 16.05 89.59 ± 15.87

19 108

84.00 ± 14.22 86.37 ± 16.54

36 91

86.19 ± 18.07 85.95 ± 15.48

69 58

87.03 ± 16.99 84.81 ± 15.23

111 16

85.08 ± 15.54 92.50 ± 19.46

Female (n ¼ 125) t

pa

2.49

.014

0.14

1.64

0.97

2.06

0.59

0.08

0.77

1.73

n

SI (M ± SD)

9 116

65.56 ± 9.11 76.13 ± 12.97

35 90

76.83 ± 13.37 74.80 ± 12.87

14 111

73.07 ± 13.22 75.66 ± 13.00

37 88

76.57 ± 11.70 74.86 ± 13.53

76 49

75.34 ± 13.20 75.41 ± 12.79

16 109

75.56 ± 12.52 75.34 ± 13.11

55 70

71.96 ± 11.00 78.04 ± 13.86

86 39

73.74 ± 13.01 78.95 ± 12.38

113 12

75.35 ± 13.16 75.58 ± 11.75

.887

.112

.334

.041

.558

.938

.444

.087

t

pa

2.40

.018

0.78

.435

0.77

.485

0.67

.505

0.03

.978

0.06

.949

2.66

.009

2.10

.037

0.06

.952

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K.-A. Park et al. / Asian Nursing Research 9 (2015) 251e258

Table 5 Multiple Regression Analysis in Elderly Participants. Dependent variable Stiffness index (Male, n ¼ 127)

Stiffness index (Female, n ¼ 125)

Independent variable

B

b

t

p

R2

F

Age Low physical activity (Reference) Moderate physical activity Vigorous physical activity Absence of undernutrition Normal calcium intake Age Drinking alcohol Educational attainment Estrogen receptor Pvu II (Pp or PP) Absence of undernutrition Estrogen receptor Xba I (Xx or XX)

0.97

0.29

3.56

.001

.21

10.80

0.01 0.23 0.19 0.12 0.26 0.24 0.22 0.21 0.15 0.10

0.09 2.79 2.38 1.54 3.16 2.99 2.72 2.59 1.87 0.97

.929 .006 .019 .127 .002 .003 .007 .011 .064 .336

.24

9.55

8.37 10.97 0.63 7.41 0.71 5.40

attainment was found to be positively correlated with SI in elderly women but not in elderly men, consistent with results from previous studies [20,59]. The reason for this finding is not clear, but it is likely that the education level was associated with economic status, thus resulting in a good bone mass [60]. Our results partly support this hypothesis, since the education level was highest for participants with high income. It is therefore reasonable to assume that having a higher education level increases the tendency to perform health-promoting behaviors, and also increases preventive behaviors against osteoporosis. Surprisingly, alcohol drinking was another significant predictor of higher SI in elderly women but not elderly men, which is consistent with results from previous studies [56,61,62]. The protective effect may be due to the effects of alcohol on estrogen concentrations [62], which may be stronger among postmenopausal women because their low estrogen concentrations are boosted by alcohol [62,63]. However, we need to be cautious about the beneficial effects of alcohol consumption on higher SI [61], since others have reported either negative or no effects of alcohol drinking on either bone mass or SI in postmenopausal women [15,34,64]. In addition, the daily alcohol intake was relatively low (5.7 g) among the female alcohol drinkers of our study (16.1%). Finally, a high alcohol intake may increase the risk of falls (fracture risk), cirrhosis, and breast cancer in elderly women [61]. Therefore, a carefully designed cohort study regarding the effect of alcohol on bone mass must be conducted before promoting our findings regarding the possible protective effect of alcohol on bone mass. On the other hand, a male specific factor related to low SI was low physical activity, but not in women. This is in agreement with results from previous studies suggesting that bone mass tends to be strongly associated with the level of physical activity in middleaged men [21,65], but not in elderly women [34]. Again, we are puzzled by the gender specificity in the relationship between physical activity and SI since about 90.0% of our female participants also reported that they were engaged in physical activity. It is likely that the degree and duration of the physical activities in female elderly may not have been sufficient enough to increase their bone mass. It is also likely that the lifetime intensity of physical activity was low among the elderly women. Traditionally in Korea, elderly women have not engaged in sports or related physical activities [66]. It is also possible that bone mass in women is more strongly determined by factors other than physical activity level and nutritional status, such as genetic factors. The percentage of normal nutritional status of our subjects was higher than that of Korean low-income urban elders [67], probably due to differences in economic status and physical activity in two populations. Nutritional status was related to low SI but was not gender specific in univariate analysis. Our result was not consistent with previous study that reported no correlation between nutritional status using the mini nutritional assessment and SI in elderly women [22]. The reasons for this discrepancy are possibly due to the

difference in the method of measurement of nutritional status (anthropometry and biochemical data vs. anthropometry) and participant age (74.7 vs. 85e89). In multivariate analysis, however, poor nutritional status was a predictive factor for low SI only in the male elders. In further analysis, we found a positive correlation between SI and albumin/BMI only in elderly men, which is consistent with findings for middle-aged Korean and other Asian male participants. Although this is consistent with the well-known association between BMI and bone mass [21,68], we are unable to provide a clear explanation as to why the association is present only in elderly men. It may have been due to the greater strength of biochemical forces [69] and a greater aromatization of androgens in the subcutaneous adipose tissue in men [70]. Among the nutrients we studied, we are surprised to find that deficiencies in caloric and protein intake were not related to low SI in elderly men and women. We found that calcium was the only nutrient that was correlated with low SI, and only in elderly men but not in elderly women in univariate analysis. A nutritional education program to prevent low SI should be considered, including strategies for elderly men to improve dietary calcium intakes from meals and snacks. Indeed, we found that gene polymorphism was another independent factor predicting poor bone mass in elderly women but not in elderly men. Interestingly, we found that the VDR gene itself was not related to SI in either gender, but that the Pvu II polymorphism was associated with SI in elderly women. Our results are similar to those found for postmenopausal Korean women [29] and other Asian populations [28,71]. However, they disagree with results from other studies that found no association between the Pvu II polymorphism and bone mass [72,73]. This discrepancy may be due to differences in the ages of the study populations [72,73], our participants being older and genetic factors are more important than other factors. Another possible explanation of this discrepancy is ethnic differences among the study populations [25,72]. Finally, ER polymorphism may be modulated by interactions between ER and VDR [15], and a low level of exercise [74]. There were some limitations in our study. We measured the bone status of male and female elderly Koreans using QUS methods instead of DXA measurements, which have been known to be more accurate. However, DXA measurements are very difficult to apply in community-based studies because of a lack of portability, cost, and exposure to ionizing radiation. QUS offers the additional advantages of rapid assessment, T scores that are aligned with WHO guidelines for spine and femur measurements, and a real-time image of heel for confident positioning. Many studies have suggested that SI is correlated more strongly with DXA measurements than are BUA or SOS measurements [11,12]. Moreover, the purpose of this study was to determine the gender-specific factors related to low SI, rather than the exact bone mass in our population. Secondly, our results cannot be generalized for all elderly Koreans. Although the study participants were recruited on a voluntary basis, they could be healthier than the general elderly population since they

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were able to visit the senior centers on a regular basis. Third, some data obtained from the participants, such as lifestyle and nutrient intake, were self-reported, while others, such as QUS measurement, anthropometry, and biochemical and genetic data were objectively measured during the same day. Although self-reported answers may be inaccurate and exaggerated since respondents tend to answer in a socially acceptable manner, we conducted one-on-one interviews with the participants by researchers to improve the reliability and clarity of self-reported answers. However, further study is warranted investigating the beneficial effect of alcohol consumption on SI in elderly women with a larger participant sample. The strength of this study, however, is that this is the first study that evaluated the differences in SI measured by QUS between men and women among Korean elderly individuals and the genderspecific risk factors of low SI. We also comprehensively determined the nutritional status of the participant by considering anthropometry and biochemical parameters. Most of all, not only the lifestyle and nutritional status, but the genetic predisposition of the participants was also included as one of the predictive factors for low SI. Although performing gene studies are currently not cost effective, genetic evidence obtained in this study could be very useful in osteoporosis assessment in Korean population in the future as the cost of genetic testing has been decreasing rapidly. Conclusion In conclusion, using simple and noninvasive QUS methods, this study showed that low SI is common among male and female elderly Koreans. The results of our study highlight the fact that there are gender differences in factors associated with low SI, with nutritional status and physical activity being more significant in elderly men but gene polymorphisms, educational level, and drinking alcohol being more significant in elderly women. Therefore, our results will help in optimizing geriatric nursing care to reduce osteoporotic fracture by providing gender-specific interventions for older Korean adults. Geriatric nurses should focus on the maintenance of nutritional status and physical activity of community dwelling older Korean males whereas special attention should be paid to supporting less educated older females in enhancing bone health. Future research on lifestyle and dietary interventions for Korean elders to maintain bone health, should take into account the gender specificity of each participant's risk factors. Conflicts of interest The authors have no conflicts of interest to disclose. Acknowledgment This research was supported by the National Research Foundation of Korea, South Korea (2012) (810-20120015). References 1. Khajuria DK, Razdan R, Mahapatra DR. Drugs for the management of osteoporosis: a review. Rev Bras Reumatol. 2011;51(4):365e82. 2. Czerwinski E, Badurski JE, Marcinowska-Suchowierska E, Osieleniec J. Current understanding of osteoporosis according to the position of the World Health Organization (WHO) and International Osteoporosis Foundation. Ortopedia Traumatologia Rehabilitacja. 2007;9(4):337e56. 3. World Health Organization (WHO). Assessment of osteoporosis at primary health care level. Summary report of a WHO scientific group. Geneva: WHO; 2007. 4. Burge R, Dawson-Hughes B, Solomon DH, Wong JB, King A, Tosteson A. Incidence and economic burden of osteoporosis-related fractures in the United States, 2005e2025. J Bone Miner Res. 2007;22(3):465e75. http://dx.doi.org/10.1359/JBMR.061113

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