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Relation between nutritional risk and metabolic syndrome in the elderly
Archives of Gerontology and Geriatrics 52 (2011) e19–e22
Contents lists available at ScienceDirect
Archives of Gerontology and Geriatrics journal homepage: www.elsevier.com/locate/archger
Relation between nutritional risk and metabolic syndrome in the elderly Hae Jin Kim a, Kang Soo Lee b, Jin-Sup Eom c, Ki-Young Lim d, Kwan Woo Lee a, Chang Hyung Hong d,e,* a
Department of Endocrinology and Metabolism, Ajou University School of Medicine, San 5, Woncheon-Dong, Yeongtong-Gu, Suwon 443-721, Republic of Korea Department of Psychiatry, Kwandong University College of Medicine, Hwajung-Dong, Dukyang-Gu, Goyang 412-270, Republic of Korea c Department of Psychology, Chungbuk National University, Gashin-Dong, Heungduk-Gu, Cheongju 361-711, Republic of Korea d Department of Psychiatry, Ajou University School of Medicine, San 5, Woncheon-Dong, Yeongtong-Gu, Suwon 443-721, Republic of Korea e Memory Impairment Center, Ajou University Hospital, San 5, Woncheon-Dong, Yeongtong-Gu, Suwon 443-721, Republic of Korea b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 12 November 2009 Received in revised form 1 April 2010 Accepted 2 April 2010 Available online 6 May 2010
Nutrition is regarded as a major factor in the development of metabolic syndrome (MS). Undernutrition or nutritional imbalance, rather than overnutrition, can be associated with MS. We evaluated the relationship between nutritional risk and MS in the elderly. We analyzed 2284 Koreans aged over 60 years (689 men and 1595 women) from baseline data of a large prospective study called the Gwangju Dementia and Mild Cognitive Impairment Study (GDEMCIS). MS was determined according to the National Cholesterol Education Program Adult Treatment Panel III, and nutritional risk was evaluated using the Nutrition Screening Initiative (NSI) checklist. Among 2284 subjects, 1219 (53.4%) had MS. NSI score was higher in subjects with MS than in those without MS (2.46 1.89 vs. 2.18 1.87, p < 0.001). The risks of abdominal obesity, elevated blood pressure, elevated glucose, and MS were higher in subjects with moderate or high nutritional risk compared to subjects in a good nutritional state. Nutritional risk was independently associated with MS for subjects in their 60s, but not in their 70s or 80s and above. In conclusion, high nutritional risk is associated with increased risk of MS in the elderly. Measurement of nutritional status in the elderly may serve as a marker for MS, especially for the younger elderly. ß 2010 Elsevier Ireland Ltd. All rights reserved.
Keywords: Nutritional risk Metabolic syndrome Elderly population
1. Introduction The metabolic syndrome (MS) describes a cluster of metabolic disturbances, including abdominal obesity, insulin resistance, dyslipidemia, and elevated blood pressure (Reaven, 1988). The pathogenesis of the syndrome is complex and, so far, not completely understood, but obesity, sedentary lifestyle, dietary factors, and genetic factors are known to contribute to its development (Liese et al., 1998). Nutrition is regarded as one of major factors in the MS (Roberts et al., 2001), and excessive intakes of energy have been considered dietary risk factors for MS (Giugliano et al., 2006). However, it has been suggested that not only overnutrition, but also undernutrition, may increase the risk of MS (Misra, 2002), and the Framingham Offspring–Spouse study reported that high nutritional risk predicted the development of MS (Millen et al., 2006). Nutrition may become compromised with aging due to various factors, such as medical, social, and psychological problems
* Corresponding author at: Department of Psychiatry, Ajou University School of Medicine, San 5, Woncheon-Dong, Yeongtong-Gu, Suwon 443-721, Republic of Korea. Tel.: +82 31 219 5180; fax: +82 31 219 5179. E-mail address: [email protected] (C.H. Hong). 0167-4943/$ – see front matter ß 2010 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.archger.2010.04.004
(Hickson, 2006). Thus, nutritional risk in the elderly may have a more significant relevance to MS. However, the relationship between nutritional risk and MS in the elderly has not been studied adequately. Therefore, the aim of this study was to evaluate the relationship between nutritional risk and the MS in the elderly, using baseline data from a large prospective study called the Gwangju Dementia and Mild Cognitive Impairment Study (GDEMCIS) (Lee et al., 2009). 2. Subjects and methods 2.1. Study population This study was based on baseline data from a large prospective study called the GDEMCIS, which involved a cohort comprising nonrandom convenience samples of elderly people, all of whom were ethnic Koreans aged over 60 (Lee et al., 2009). In brief, with the assistance of the regional public health center, we recruited elderly people over 60 years old and residing in community health centers or senior citizen centers in 10 towns in Gwangju city. The baseline study of the GDEMCIS was conducted between October 2005 and March 2007. Exclusion criteria included the following: inability to communicate with the interviewer; undergoing active treatment for cancer in the last 5 years; neurologic disorders including stroke, Parkinson’s disease, or epilepsy; intake of
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antidepressants, sedatives, or other psychiatric drugs; and abuse of alcohol. Among the cohort, we analyzed 2284 subjects for whom data of nutritional risk and metabolic parameters were available. This study was approved by the Severance Mental Health Hospital Institutional Review Board, and informed consent was obtained from each subject. 2.2. Measurement and definition of metabolic variables All participants were interviewed to identify a past history of diabetes mellitus, hypertension, and drug history, as well as alcohol and smoking history. Height, weight, and waist circumferences were measured to the nearest half-centimeter or halfkilogram. Body mass index (BMI) was calculated as weight (kg) divided by the square of height (m2). Waist circumference was measured at the midpoint between the lower border of the rib cage and the iliac crest. Blood pressure was measured using a standard mercury sphygmomanometer after the subject had been seated for at least 10 min. Laboratory evaluation included fasting blood sugar, total cholesterol, high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG). Low-density lipoprotein cholesterol (LDL-C) was calculated using the Friedewald equation (Friedewald et al., 1972). Plasma glucose concentrations were determined by the glucose oxidase method. Serum cholesterol and TG concentrations were measured enzymatically. The MS was defined according to the Adult Treatment Panel III (ATP III) of the National Cholesterol Education Program’s (NCEP’s) guidelines (Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults, 2001). Participants fulfilling three or more of the following five criteria were defined as having MS: high blood pressure (130/85 mmHg), elevated fasting blood glucose ( 110 mg/dl), hypertriglyceridemia (150 mg/dl), low HDL-cholesterol (men: <40 mg/dl; women: <50 mg/dl), and abdominal obesity by waist circumference (WC) (men: >90 cm; women: >80 cm). Men and women with WC values of 90 and 80 cm, respectively, were considered to have abdominal obesity, according to the WHO Western Pacific Region on Asians (Steering Committee of the Western Pacific Region of the World Health Organization International Association for the Study of Obesity, and International Obesity Task Force, 2000). Participants receiving antihypertensive or antidiabetic medications were included in the high blood pressure group and the high fasting blood glucose group, respectively. 2.3. Assessment of nutritional risk We evaluated nutritional risk using the Nutrition Screening Initiative (NSI) checklist, designed by the American Dietetic Association, the American Academy of Family Physicians, and the National Council on the Aging to screen elderly people who are community dwellers (White et al., 1991; Posner et al., 1993). The NSI checklist (DETERMINE Your Nutritional Health Checklist) consists of 10 yes/no questions covering dietary, general and social assessments, each of which has a score assigned to it. The NSI checklist categorizes a cumulative score of 6 or higher as identifying persons at high nutritional risk, a cumulative score of 3–5 for persons at moderate nutritional risk, and a cumulative score of 0–2 for persons at good nutritional risk (White et al., 1991; Posner et al., 1993). 2.4. Statistical analysis We performed statistical analyses using the SPSS Version 13.0 software package (SPSS Inc, Chicago, USA). Differences between groups with and without MS were analyzed using unpaired t-tests.
We used a one-way analysis of variance (ANOVA) to assess NSI score according to number of metabolic components. When found to be significant, we used a post hoc (Scheffe method) multiple comparison to establish differences between groups. The odds ratio (OR) of metabolic disorders in the subjects at moderate or high nutritional risk compared to the good nutritional risk was calculated for 2 2 cross-tables and is expressed with the 95% confidence interval (CI). We used multiple logistic regression analysis to examine the factors associated with MS. Levels of p < 0.05 were considered statistically significant. 3. Results 3.1. Clinical characteristics of the subjects The characteristics of subjects are shown in Table 1. The mean age was 72.0 6.7 years, and the age range was 60–98 years, 32.7% of the subjects had diabetes, and 68.5% had hypertension. Among 2284 subjects, 1219 (53.4%) had MS. There were significant differences between the subjects with and without MS in the parameters, including BMI, body fat, waist circumference, blood pressure, fasting blood glucose, lipid profile, and NSI score (Table 1). 3.2. Association of nutritional risk with MS Fig. 1 shows significant differences in NSI score according to the number of components of MS. When we assessed risks for each component of MS in the subjects with moderate (NSI score: 3–5 points, n = 677) or high (NSI score: 6 points, n = 111) nutritional risk compared to the subjects in a good nutritional state (NSI score: 0–2 points, n = 1496), the risks of abdominal obesity, elevated blood pressure, elevated glucose, and MS were higher in subjects with moderate or high nutritional risk (Table 2). 3.3. Relationship between nutritional risk and MS using multiple logistic regression analysis according to age group We performed multiple logistic regression analysis with MS as a dependent variable and sex, age, NSI and the interaction of NSI with age as independent variables (Table 3). The interaction of NSI with age showed a significant relationship with MS. When we evaluated the relationship between NSI and MS according to age Table 1 Clinical and laboratory characteristics of the subjects, mean SD. Parameters
Total
MS( )
MS(+)
Number Sex, % male Male/female Age (years) BMI (kg/m2) WC (cm) SBP (mmHg) DBP (mmHg) FBS (mg/dl) TC (mg/dl) HDL-C (mg/dl) LDL-C (mg/dl) TG (mg/dl) Smoking habit (%) Non-smoker Ex-smoker Current smoker Alcohol drinker (%) NSI
2284 30.2 689/1595 72.0 6.7 24.6 3.4 86.4 9.0 132.1 18.5 81.3 10.6 117.4 31.7 217.2 40.9 54.9 16.4 130.4 38.6 162.1 89.6
1065 41.1 438/627 71.7 6.9 23.3 3.3 82.6 8.6 127.8 18.5 79.8 10.9 109.3 28.9 213.2 39.8 61.5 15.6 127.3 38.0 122.8 52.1
1219 20.6 251/968 72.3 6.5 25.7 3.0 89.8 7.8 135.6 17.8 82.5 10.1 124.3 32.4 220.7 41.6 49.1 14.7 133.2 38.9 196.4 100.8
75.8 14.3 9.9 19.9 2.33 1.88
68.8 18.7 12.5 24.9 2.18 1.87
81.9 10.5 7.6 15.7 2.46 1.89
pa
<0.001 0.03 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
<0.001 <0.001
Abbreviations. DBP: diastolic blood pressure; FBS: fasting blood sugar; SBP: systolic blood pressure; TC, total cholesterol. a p between subjects with and without MS using independent t-test (for continuous variables) or x2-test (for dichotomous variables).
H.J. Kim et al. / Archives of Gerontology and Geriatrics 52 (2011) e19–e22
Fig. 1. NSI score according to the number of components of MS. p < 0.05 on ANOVA; a p < 0.05 vs. group with 0 component of MS; bp < 0.05 vs. group with 1 component of MS; cp < 0.05 vs. group with 2 components of MS, on post hoc multiple comparison. n = 89, 359, 617, 625, 414, and 180 in each group, respectively in order from 0 to 5, according to the number of components of MS.
Table 2 OR for MS components in subjects with moderate or high nutritional risk compared to subjects in a good nutritional state. OR (95% CI) Abdominal obesity Elevated blood pressure Elevated glucose Elevated TG Low HDL-C MS (3 of the above) a
1.252 1.421 1.248 1.044 1.109 1.271
(1.047–1.498)a (1.158–1.744)a (1.049–1.485)a (0.876–1.243) (0.924–1.331) (1.069–1.513)a
Significant differences.
Table 3 Multiple logistic regression analysis of the factors associated with MS. Independent variables
OR
95% CI
p
Sex Age NSI NSI age interaction
2.694 1.013 1.038 0.990
2.233–3.250 0.999–1.026 0.990–1.088 0.983–0.997
<0.001 0.06 0.13 0.006
Table 4 Multiple logistic regression analysis with MS as the dependent variable according to age group. Independent variables Age group 60–69 (n = 853) 70–79 (n = 1098) >80 (n = 333)
NSI NSI NSI
OR
95% CI
p
1.089 1.011 0.968
1.006–1.179 0.944–1.082 0.858–1.092
0.04 0.76 0.60
Adjusted for age and sex.
group after adjustment for age and sex, NSI increased the risk of MS for subjects in their 60s, but not in their 70s or 80s and above (Table 4). Probability curves for MS by NSI according to age group are shown in Fig. 2. 4. Discussion We demonstrated that high nutritional risk is related with an increased risk of MS in elderly people. NSI scores were higher in subjects with MS than in those without MS. The risks of abdominal obesity, elevated blood pressure, elevated glucose, and MS were higher in subjects with moderate or high
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Fig. 2. Probability curves for MS by NSI score according to age group.
nutritional risk compared to subjects in a good nutritional state. Nutritional risk was independently associated with MS for subjects in their 60s. However, the association disappeared in those of a more advanced age. Aging is associated with various changes in the endocrine, immune, and nervous systems, resulting in metabolic imbalance, a progressive catabolism, and changes in body composition although the exact etiology is unknown (Lytras and Tolis, 2007). Aging is characterized by progressively increased concentrations of glucocorticoids and catecholamines, and a decreasing production of growth hormone and sex hormones, which result in decreased lean body mass, decreased muscle strength, and increased visceral adiposity (Roubenoff, 2000; Hickson, 2006). Aging is also accompanied by increased inflammatory cytokines, including IL1, IL-6, tumor necrosis factor-a (TNF-a) and IL-1b, which are associated with muscle loss, the shift of muscle metabolism to a catabolic state, and the development of insulin resistance (Roubenoff, 2000; Abbatecola et al., 2004). A lack of physical activity in the elderly worsens the metabolic changes. Nutrition may also become compromised with aging. During aging, medical, social, and psychological factors contribute to increasing the risk of malnutrition. Poor appetite, poor dentition, endocrine disorders, physical disability, isolation, depression and other factors can increase the risk of malnutrition in elderly (Hickson, 2006). In general, excessive intakes of energy are considered to be dietary risk factors for MS (Giugliano et al., 2006). However, undernutrition or nutritional imbalance, opposed to overnutrition, can also be associated with MS. Malnutrition is accompanied by a loss of muscle mass and muscle function, and exaggerates the loss of fat-free mass observed in the elderly (He´buterne et al., 2001); it therefore decreases the metabolic reserve and insulin sensitivity (Hickson, 2006). Furthermore, an animal study showed that chronic malnutrition impaired insulin sensitivity through both receptor and postreceptor defects in mild diabetic rats (Rao and Menon, 1993). A previous study showed that higher nutritional risk predicted the development of abdominal obesity and MS during 12 years of follow up in healthy women aged 30–69 years (Millen et al., 2006). The authors reported that the women with the highest nutritional risk had lower intakes of energy, carbohydrates, fiber, and most micronutrients and consumed more dietary lipids and alcohol, the so called ‘‘Empty Calorie pattern’’, than did women with the lowest nutritional risk (Sonnenberg et al., 2005). Das (2002) reported that South Asians had a higher prevalence of MS with the presence of
H.J. Kim et al. / Archives of Gerontology and Geriatrics 52 (2011) e19–e22
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undernutrition and deficiencies in vitamin C, vitamin E, bcarotene, and -3 fatty acids. Another study of elderly Korean people showed that undernutrition (especially a deficiency of antioxidant vitamins), not excess intake of energy, fat, or cholesterol, was associated with risk for MS (Kim et al., 2007). Our study also showed that high nutritional risk was associated with the increased risk of MS and its components such as central obesity, elevated blood pressure, and elevated glucose in the elderly. Although we did not concretely evaluate actual nutrients intake, insulin resistance index, and body composition, we speculate that micronutrients deficiencies and nutritional imbalance in subjects with high nutritional risk can aggravate insulin resistance and increase the risk of developing MS. When we evaluated the relationship between nutritional risk and MS according to age group after adjustment for age and sex, nutritional risk was independently associated with MS for subjects in their 60s, but not in those beyond this. Similarly, previous studies on MS and related disorders, such as the association between the MS and cognitive function showed a discrepancy between middle-aged/old and very old persons (Kalmijn et al., 2000; Whitmer et al., 2005; Van den Berg et al., 2007). Although the exact cause of this is unclear, it is possible that other factors such as endocrine and immunologic changes, rather than malnutrition, could affect the development of MS more in advanced aging. This suggests that careful nutritional assessment and the correction of malnutrition could decrease the risk of MS, especially for the younger elderly. Our data have several limitations. First, because we analyzed cross-sectional data, a causal relationship between nutritional risk and MS cannot be clearly ascertained. Second, the NSI checklist used in this study is not a diagnostic tool to measure the real nutrient intakes, but a screening tool to identify older persons with nutritional risk. Thus, being at risk of malnutrition can be different from actually being malnourished. However, the NSI checklist is known as an easy to use scoring instrument that can accurately identify older persons at risk for low nutrient intake (Posner et al., 1993). In conclusion, we found that high nutritional risk is associated with an increased risk of MS in elderly people. The measurement of nutritional status in the elderly may serve as a marker for MS, especially for the younger elderly. A longitudinal study to elucidate the causal relationship between nutritional risk and MS in the elderly is still needed. Conflict of interest statement None. Acknowledgements This study was supported by grants from the Korea Health 21 R&D Project, Ministry of Health & Welfare, Republic of Korea (A050079 and A050463).
References Abbatecola, A.M., Ferrucci, L., Grella, R., Bandinelli, S., Bonafe`, M., Barbieri, M., Corsi, A.M., Lauretani, F., Franceschi, C., Paolisso, G., 2004. Diverse effect of inflammatory markers on insulin resistance and insulin-resistance syndrome in the elderly. J. Am. Geriatr. Soc. 52, 399–404. Das, U.N., 2002. Nutritional deficiencies and the prevalence of syndrome X in South Asians. Nutrition 18, 282. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults, 2001. Executive summary of the third report of the National Cholesterol Education Program (NCEP). Expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). J. Am. Med. Assoc. 285, 2486–2497. Friedewald, W.T., Levy, R.I., Fredrickson, D.S., 1972. Estimation of the concentration of low density lipoprotein cholesterol in plasma without use of preparative ultracentrifugation. Clin. Chem. 18, 499–502. Giugliano, D., Ceriello, A., Esposito, K., 2006. The effects of diet on inflammation: emphasis on the metabolic syndrome. J. Am. Coll. Cardiol. 48, 677–685. He´buterne, X., Bermon, S., Schneider, S.M., 2001. Ageing and muscle: the effects of malnutrition, re-nutrition, and physical exercise. Curr. Opin. Clin. Nutr. Metab. Care 4, 295–300. Hickson, M., 2006. Malnutrition and ageing. Postgrad. Med. J. 82, 2–8. Kalmijn, S., Foley, D., White, L., Burchfiel, C.M., Curb, J.D., Petrovitch, H., Ross, G.W., Havlik, R.J., Launer, L.J., 2000. Metabolic cardiovascular syndrome and risk of dementia in Japanese-American elderly men: the Honolulu-Asia aging study. Arterioscler. Thromb. Vasc. Biol. 20, 2255–2260. Kim, M.H., Lee, H.S., Park, H.J., Kim, W.Y., 2007. Risk factors associated with metabolic syndrome in Korean elderly. Ann. Nutr. Metab. 51, 533–540. Lee, K.S., Cheong, H.K., Kim, E.A., Kim, K.R., Oh, B.H., Hong, C.H., 2009. Nutritional risk and cognitive impairment in the elderly. Arch. Gerontol. Geriatr. 48, 95–99. Liese, A.D., Mayer-Davis, E.J., Haffner, S.M., 1998. Development of the multiple metabolic syndrome: an epidemiologic perspective. Epidemiol. Rev. 20, 157– 172. Lytras, A., Tolis, G., 2007. Assessment of endocrine and nutritional status in agerelated catabolic states of muscle and bone. Curr. Opin. Clin. Nutr. Metab. Care 10, 604–610. Millen, B.E., Pencina, M.J., Kimokoti, R.W., Zhu, L., Meigs, J.B., Ordovas, J.M., D’Agostino, R.B., 2006. Nutritional risk and the metabolic syndrome in women: opportunities for preventive intervention from the Framingham Nutrition Study. Am. J. Clin. Nutr. 84, 434–441. Misra, A., 2002. Overnutrition and nutritional deficiency contribute to metabolic syndrome and atherosclerosis in Asian Indians. Nutrition 18, 702–703. Posner, B.M., Jette, A.M., Smith, K.W., Miller, D.R., 1993. Nutrition and health risks in the elderly: the nutrition screening initiative. Am. J. Public Health 83, 972–978. Rao, R.H., Menon, R.K., 1993. Chronic malnutrition impairs insulin sensitivity through both receptor and postreceptor defects in rats with mild streptozocin diabetes. Metabolism 42, 772–779. Reaven, G.M., 1988. Role of insulin resistance in human disease. Diabetes 37, 1595– 1607. Roberts, C.K., Vaziri, N.D., Liang, K.H., Barnard, R.J., 2001. Reversibility of chronic experimental syndrome X by diet modification. Hypertension 37, 1323–1328. Roubenoff, R., 2000. Sarcopenia and its implications for the elderly. Eur. J. Clin. Nutr. 54, S40–S47. Sonnenberg, L., Pencina, M., Kimokoti, R., Quatromoni, P., Nam, B.H., D’Agostino, R., Meigs, J.B., Ordovas, J., Cobain, M., Millen, B., 2005. Dietary patterns and the metabolic syndrome in obese and non-obese Framingham women. Obes. Res. 13, 153–162. Steering Committee of the Western Pacific Region of the World Health Organization International Association for the Study of Obesity, and International Obesity Task Force, 2000. The Asia-Pacific Perspective: Redefining Obesity and Its Treatment. Health Communications Australia Pty Limited Publication, Melbourne, pp. 15–21. Van den Berg, E., Biessels, G.J., de Craen, A.J., Gussekloo, J., Westendorp, R.G., 2007. The metabolic syndrome is associated with decelerated cognitive decline in the oldest old. Neurology 69, 979–985. White, J.V., Ham, R.J., Lipschitz, D.A., Dwyer, J.T., Wellman, N.S., 1991. Consensus of the Nutrition Screening Initiative: risk factors and indicators of poor nutritional status in older Americans. J. Am. Diet. Assoc. 91, 783–787. Whitmer, R.A., Sidney, S., Selby, J., Johnston, S.C., Yaffe, K., 2005. Midlife cardiovascular risk factors and risk of dementia in late life. Neurology 64, 277–281.
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Archives of Gerontology and Geriatrics journal homepage: www.elsevier.com/locate/archger
Relation between nutritional risk and metabolic syndrome in the elderly Hae Jin Kim a, Kang Soo Lee b, Jin-Sup Eom c, Ki-Young Lim d, Kwan Woo Lee a, Chang Hyung Hong d,e,* a
Department of Endocrinology and Metabolism, Ajou University School of Medicine, San 5, Woncheon-Dong, Yeongtong-Gu, Suwon 443-721, Republic of Korea Department of Psychiatry, Kwandong University College of Medicine, Hwajung-Dong, Dukyang-Gu, Goyang 412-270, Republic of Korea c Department of Psychology, Chungbuk National University, Gashin-Dong, Heungduk-Gu, Cheongju 361-711, Republic of Korea d Department of Psychiatry, Ajou University School of Medicine, San 5, Woncheon-Dong, Yeongtong-Gu, Suwon 443-721, Republic of Korea e Memory Impairment Center, Ajou University Hospital, San 5, Woncheon-Dong, Yeongtong-Gu, Suwon 443-721, Republic of Korea b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 12 November 2009 Received in revised form 1 April 2010 Accepted 2 April 2010 Available online 6 May 2010
Nutrition is regarded as a major factor in the development of metabolic syndrome (MS). Undernutrition or nutritional imbalance, rather than overnutrition, can be associated with MS. We evaluated the relationship between nutritional risk and MS in the elderly. We analyzed 2284 Koreans aged over 60 years (689 men and 1595 women) from baseline data of a large prospective study called the Gwangju Dementia and Mild Cognitive Impairment Study (GDEMCIS). MS was determined according to the National Cholesterol Education Program Adult Treatment Panel III, and nutritional risk was evaluated using the Nutrition Screening Initiative (NSI) checklist. Among 2284 subjects, 1219 (53.4%) had MS. NSI score was higher in subjects with MS than in those without MS (2.46 1.89 vs. 2.18 1.87, p < 0.001). The risks of abdominal obesity, elevated blood pressure, elevated glucose, and MS were higher in subjects with moderate or high nutritional risk compared to subjects in a good nutritional state. Nutritional risk was independently associated with MS for subjects in their 60s, but not in their 70s or 80s and above. In conclusion, high nutritional risk is associated with increased risk of MS in the elderly. Measurement of nutritional status in the elderly may serve as a marker for MS, especially for the younger elderly. ß 2010 Elsevier Ireland Ltd. All rights reserved.
Keywords: Nutritional risk Metabolic syndrome Elderly population
1. Introduction The metabolic syndrome (MS) describes a cluster of metabolic disturbances, including abdominal obesity, insulin resistance, dyslipidemia, and elevated blood pressure (Reaven, 1988). The pathogenesis of the syndrome is complex and, so far, not completely understood, but obesity, sedentary lifestyle, dietary factors, and genetic factors are known to contribute to its development (Liese et al., 1998). Nutrition is regarded as one of major factors in the MS (Roberts et al., 2001), and excessive intakes of energy have been considered dietary risk factors for MS (Giugliano et al., 2006). However, it has been suggested that not only overnutrition, but also undernutrition, may increase the risk of MS (Misra, 2002), and the Framingham Offspring–Spouse study reported that high nutritional risk predicted the development of MS (Millen et al., 2006). Nutrition may become compromised with aging due to various factors, such as medical, social, and psychological problems
* Corresponding author at: Department of Psychiatry, Ajou University School of Medicine, San 5, Woncheon-Dong, Yeongtong-Gu, Suwon 443-721, Republic of Korea. Tel.: +82 31 219 5180; fax: +82 31 219 5179. E-mail address: [email protected] (C.H. Hong). 0167-4943/$ – see front matter ß 2010 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.archger.2010.04.004
(Hickson, 2006). Thus, nutritional risk in the elderly may have a more significant relevance to MS. However, the relationship between nutritional risk and MS in the elderly has not been studied adequately. Therefore, the aim of this study was to evaluate the relationship between nutritional risk and the MS in the elderly, using baseline data from a large prospective study called the Gwangju Dementia and Mild Cognitive Impairment Study (GDEMCIS) (Lee et al., 2009). 2. Subjects and methods 2.1. Study population This study was based on baseline data from a large prospective study called the GDEMCIS, which involved a cohort comprising nonrandom convenience samples of elderly people, all of whom were ethnic Koreans aged over 60 (Lee et al., 2009). In brief, with the assistance of the regional public health center, we recruited elderly people over 60 years old and residing in community health centers or senior citizen centers in 10 towns in Gwangju city. The baseline study of the GDEMCIS was conducted between October 2005 and March 2007. Exclusion criteria included the following: inability to communicate with the interviewer; undergoing active treatment for cancer in the last 5 years; neurologic disorders including stroke, Parkinson’s disease, or epilepsy; intake of
e20
H.J. Kim et al. / Archives of Gerontology and Geriatrics 52 (2011) e19–e22
antidepressants, sedatives, or other psychiatric drugs; and abuse of alcohol. Among the cohort, we analyzed 2284 subjects for whom data of nutritional risk and metabolic parameters were available. This study was approved by the Severance Mental Health Hospital Institutional Review Board, and informed consent was obtained from each subject. 2.2. Measurement and definition of metabolic variables All participants were interviewed to identify a past history of diabetes mellitus, hypertension, and drug history, as well as alcohol and smoking history. Height, weight, and waist circumferences were measured to the nearest half-centimeter or halfkilogram. Body mass index (BMI) was calculated as weight (kg) divided by the square of height (m2). Waist circumference was measured at the midpoint between the lower border of the rib cage and the iliac crest. Blood pressure was measured using a standard mercury sphygmomanometer after the subject had been seated for at least 10 min. Laboratory evaluation included fasting blood sugar, total cholesterol, high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG). Low-density lipoprotein cholesterol (LDL-C) was calculated using the Friedewald equation (Friedewald et al., 1972). Plasma glucose concentrations were determined by the glucose oxidase method. Serum cholesterol and TG concentrations were measured enzymatically. The MS was defined according to the Adult Treatment Panel III (ATP III) of the National Cholesterol Education Program’s (NCEP’s) guidelines (Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults, 2001). Participants fulfilling three or more of the following five criteria were defined as having MS: high blood pressure (130/85 mmHg), elevated fasting blood glucose ( 110 mg/dl), hypertriglyceridemia (150 mg/dl), low HDL-cholesterol (men: <40 mg/dl; women: <50 mg/dl), and abdominal obesity by waist circumference (WC) (men: >90 cm; women: >80 cm). Men and women with WC values of 90 and 80 cm, respectively, were considered to have abdominal obesity, according to the WHO Western Pacific Region on Asians (Steering Committee of the Western Pacific Region of the World Health Organization International Association for the Study of Obesity, and International Obesity Task Force, 2000). Participants receiving antihypertensive or antidiabetic medications were included in the high blood pressure group and the high fasting blood glucose group, respectively. 2.3. Assessment of nutritional risk We evaluated nutritional risk using the Nutrition Screening Initiative (NSI) checklist, designed by the American Dietetic Association, the American Academy of Family Physicians, and the National Council on the Aging to screen elderly people who are community dwellers (White et al., 1991; Posner et al., 1993). The NSI checklist (DETERMINE Your Nutritional Health Checklist) consists of 10 yes/no questions covering dietary, general and social assessments, each of which has a score assigned to it. The NSI checklist categorizes a cumulative score of 6 or higher as identifying persons at high nutritional risk, a cumulative score of 3–5 for persons at moderate nutritional risk, and a cumulative score of 0–2 for persons at good nutritional risk (White et al., 1991; Posner et al., 1993). 2.4. Statistical analysis We performed statistical analyses using the SPSS Version 13.0 software package (SPSS Inc, Chicago, USA). Differences between groups with and without MS were analyzed using unpaired t-tests.
We used a one-way analysis of variance (ANOVA) to assess NSI score according to number of metabolic components. When found to be significant, we used a post hoc (Scheffe method) multiple comparison to establish differences between groups. The odds ratio (OR) of metabolic disorders in the subjects at moderate or high nutritional risk compared to the good nutritional risk was calculated for 2 2 cross-tables and is expressed with the 95% confidence interval (CI). We used multiple logistic regression analysis to examine the factors associated with MS. Levels of p < 0.05 were considered statistically significant. 3. Results 3.1. Clinical characteristics of the subjects The characteristics of subjects are shown in Table 1. The mean age was 72.0 6.7 years, and the age range was 60–98 years, 32.7% of the subjects had diabetes, and 68.5% had hypertension. Among 2284 subjects, 1219 (53.4%) had MS. There were significant differences between the subjects with and without MS in the parameters, including BMI, body fat, waist circumference, blood pressure, fasting blood glucose, lipid profile, and NSI score (Table 1). 3.2. Association of nutritional risk with MS Fig. 1 shows significant differences in NSI score according to the number of components of MS. When we assessed risks for each component of MS in the subjects with moderate (NSI score: 3–5 points, n = 677) or high (NSI score: 6 points, n = 111) nutritional risk compared to the subjects in a good nutritional state (NSI score: 0–2 points, n = 1496), the risks of abdominal obesity, elevated blood pressure, elevated glucose, and MS were higher in subjects with moderate or high nutritional risk (Table 2). 3.3. Relationship between nutritional risk and MS using multiple logistic regression analysis according to age group We performed multiple logistic regression analysis with MS as a dependent variable and sex, age, NSI and the interaction of NSI with age as independent variables (Table 3). The interaction of NSI with age showed a significant relationship with MS. When we evaluated the relationship between NSI and MS according to age Table 1 Clinical and laboratory characteristics of the subjects, mean SD. Parameters
Total
MS( )
MS(+)
Number Sex, % male Male/female Age (years) BMI (kg/m2) WC (cm) SBP (mmHg) DBP (mmHg) FBS (mg/dl) TC (mg/dl) HDL-C (mg/dl) LDL-C (mg/dl) TG (mg/dl) Smoking habit (%) Non-smoker Ex-smoker Current smoker Alcohol drinker (%) NSI
2284 30.2 689/1595 72.0 6.7 24.6 3.4 86.4 9.0 132.1 18.5 81.3 10.6 117.4 31.7 217.2 40.9 54.9 16.4 130.4 38.6 162.1 89.6
1065 41.1 438/627 71.7 6.9 23.3 3.3 82.6 8.6 127.8 18.5 79.8 10.9 109.3 28.9 213.2 39.8 61.5 15.6 127.3 38.0 122.8 52.1
1219 20.6 251/968 72.3 6.5 25.7 3.0 89.8 7.8 135.6 17.8 82.5 10.1 124.3 32.4 220.7 41.6 49.1 14.7 133.2 38.9 196.4 100.8
75.8 14.3 9.9 19.9 2.33 1.88
68.8 18.7 12.5 24.9 2.18 1.87
81.9 10.5 7.6 15.7 2.46 1.89
pa
<0.001 0.03 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
<0.001 <0.001
Abbreviations. DBP: diastolic blood pressure; FBS: fasting blood sugar; SBP: systolic blood pressure; TC, total cholesterol. a p between subjects with and without MS using independent t-test (for continuous variables) or x2-test (for dichotomous variables).
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Fig. 1. NSI score according to the number of components of MS. p < 0.05 on ANOVA; a p < 0.05 vs. group with 0 component of MS; bp < 0.05 vs. group with 1 component of MS; cp < 0.05 vs. group with 2 components of MS, on post hoc multiple comparison. n = 89, 359, 617, 625, 414, and 180 in each group, respectively in order from 0 to 5, according to the number of components of MS.
Table 2 OR for MS components in subjects with moderate or high nutritional risk compared to subjects in a good nutritional state. OR (95% CI) Abdominal obesity Elevated blood pressure Elevated glucose Elevated TG Low HDL-C MS (3 of the above) a
1.252 1.421 1.248 1.044 1.109 1.271
(1.047–1.498)a (1.158–1.744)a (1.049–1.485)a (0.876–1.243) (0.924–1.331) (1.069–1.513)a
Significant differences.
Table 3 Multiple logistic regression analysis of the factors associated with MS. Independent variables
OR
95% CI
p
Sex Age NSI NSI age interaction
2.694 1.013 1.038 0.990
2.233–3.250 0.999–1.026 0.990–1.088 0.983–0.997
<0.001 0.06 0.13 0.006
Table 4 Multiple logistic regression analysis with MS as the dependent variable according to age group. Independent variables Age group 60–69 (n = 853) 70–79 (n = 1098) >80 (n = 333)
NSI NSI NSI
OR
95% CI
p
1.089 1.011 0.968
1.006–1.179 0.944–1.082 0.858–1.092
0.04 0.76 0.60
Adjusted for age and sex.
group after adjustment for age and sex, NSI increased the risk of MS for subjects in their 60s, but not in their 70s or 80s and above (Table 4). Probability curves for MS by NSI according to age group are shown in Fig. 2. 4. Discussion We demonstrated that high nutritional risk is related with an increased risk of MS in elderly people. NSI scores were higher in subjects with MS than in those without MS. The risks of abdominal obesity, elevated blood pressure, elevated glucose, and MS were higher in subjects with moderate or high
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Fig. 2. Probability curves for MS by NSI score according to age group.
nutritional risk compared to subjects in a good nutritional state. Nutritional risk was independently associated with MS for subjects in their 60s. However, the association disappeared in those of a more advanced age. Aging is associated with various changes in the endocrine, immune, and nervous systems, resulting in metabolic imbalance, a progressive catabolism, and changes in body composition although the exact etiology is unknown (Lytras and Tolis, 2007). Aging is characterized by progressively increased concentrations of glucocorticoids and catecholamines, and a decreasing production of growth hormone and sex hormones, which result in decreased lean body mass, decreased muscle strength, and increased visceral adiposity (Roubenoff, 2000; Hickson, 2006). Aging is also accompanied by increased inflammatory cytokines, including IL1, IL-6, tumor necrosis factor-a (TNF-a) and IL-1b, which are associated with muscle loss, the shift of muscle metabolism to a catabolic state, and the development of insulin resistance (Roubenoff, 2000; Abbatecola et al., 2004). A lack of physical activity in the elderly worsens the metabolic changes. Nutrition may also become compromised with aging. During aging, medical, social, and psychological factors contribute to increasing the risk of malnutrition. Poor appetite, poor dentition, endocrine disorders, physical disability, isolation, depression and other factors can increase the risk of malnutrition in elderly (Hickson, 2006). In general, excessive intakes of energy are considered to be dietary risk factors for MS (Giugliano et al., 2006). However, undernutrition or nutritional imbalance, opposed to overnutrition, can also be associated with MS. Malnutrition is accompanied by a loss of muscle mass and muscle function, and exaggerates the loss of fat-free mass observed in the elderly (He´buterne et al., 2001); it therefore decreases the metabolic reserve and insulin sensitivity (Hickson, 2006). Furthermore, an animal study showed that chronic malnutrition impaired insulin sensitivity through both receptor and postreceptor defects in mild diabetic rats (Rao and Menon, 1993). A previous study showed that higher nutritional risk predicted the development of abdominal obesity and MS during 12 years of follow up in healthy women aged 30–69 years (Millen et al., 2006). The authors reported that the women with the highest nutritional risk had lower intakes of energy, carbohydrates, fiber, and most micronutrients and consumed more dietary lipids and alcohol, the so called ‘‘Empty Calorie pattern’’, than did women with the lowest nutritional risk (Sonnenberg et al., 2005). Das (2002) reported that South Asians had a higher prevalence of MS with the presence of
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undernutrition and deficiencies in vitamin C, vitamin E, bcarotene, and -3 fatty acids. Another study of elderly Korean people showed that undernutrition (especially a deficiency of antioxidant vitamins), not excess intake of energy, fat, or cholesterol, was associated with risk for MS (Kim et al., 2007). Our study also showed that high nutritional risk was associated with the increased risk of MS and its components such as central obesity, elevated blood pressure, and elevated glucose in the elderly. Although we did not concretely evaluate actual nutrients intake, insulin resistance index, and body composition, we speculate that micronutrients deficiencies and nutritional imbalance in subjects with high nutritional risk can aggravate insulin resistance and increase the risk of developing MS. When we evaluated the relationship between nutritional risk and MS according to age group after adjustment for age and sex, nutritional risk was independently associated with MS for subjects in their 60s, but not in those beyond this. Similarly, previous studies on MS and related disorders, such as the association between the MS and cognitive function showed a discrepancy between middle-aged/old and very old persons (Kalmijn et al., 2000; Whitmer et al., 2005; Van den Berg et al., 2007). Although the exact cause of this is unclear, it is possible that other factors such as endocrine and immunologic changes, rather than malnutrition, could affect the development of MS more in advanced aging. This suggests that careful nutritional assessment and the correction of malnutrition could decrease the risk of MS, especially for the younger elderly. Our data have several limitations. First, because we analyzed cross-sectional data, a causal relationship between nutritional risk and MS cannot be clearly ascertained. Second, the NSI checklist used in this study is not a diagnostic tool to measure the real nutrient intakes, but a screening tool to identify older persons with nutritional risk. Thus, being at risk of malnutrition can be different from actually being malnourished. However, the NSI checklist is known as an easy to use scoring instrument that can accurately identify older persons at risk for low nutrient intake (Posner et al., 1993). In conclusion, we found that high nutritional risk is associated with an increased risk of MS in elderly people. The measurement of nutritional status in the elderly may serve as a marker for MS, especially for the younger elderly. A longitudinal study to elucidate the causal relationship between nutritional risk and MS in the elderly is still needed. Conflict of interest statement None. Acknowledgements This study was supported by grants from the Korea Health 21 R&D Project, Ministry of Health & Welfare, Republic of Korea (A050079 and A050463).
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