Hindawi Evidence-Based Complementary and Alternative Medicine Volume 2017, Article ID 9532073, 8 pages https://doi.org/10.1155/2017/9532073
Research Article Cold and Spleen-Qi Deficiency Patterns in Korean Medicine Are Associated with Low Resting Metabolic Rate Sujeong Mun, Sujung Kim, Kwang-Ho Bae, and Siwoo Lee Mibyeong Research Center, Korea Institute of Oriental Medicine, 1672 Yuseong-daero, Yuseong-gu, Daejeon 305-811, Republic of Korea Correspondence should be addressed to Siwoo Lee;
[email protected] Received 19 October 2016; Accepted 20 February 2017; Published 6 March 2017 Academic Editor: Vesna Sendula-Jengic Copyright © 2017 Sujeong Mun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. Korean medicine (KM) patterns such as cold, heat, deficiency, and excess patterns have been associated with alterations of resting metabolic rate (RMR). However, the association of KM patterns with accurately measured body metabolic rate has not been investigated. Methods. Data on cold (CP), heat (HP), spleen-qi deficiency (SQDP), and kidney deficiency (KDP) patterns were extracted by a factor analysis of symptoms experienced by 954 participants. A multiple regression analysis was conducted to determine the association between KM patterns and RMR measured by an indirect calorimeter. Results. The CP and SQDP scores were higher and the HP score was lower in women. The HP and SQDP scores decreased with age, while KDP scores increased with age. A multiple regression analysis revealed that CP and SQDP scores were negatively associated with RMR independently of gender and age, and the CP remained significantly and negatively associated with RMR even after adjustment for fat-free mass. Conclusions. The underlying pathology of CP and SQDP might be associated with the body’s metabolic rate. Further studies are needed to investigate the usefulness of RMR measurement in pattern identification and the association of CP and SQDP with metabolic disorders.
1. Introduction Resting metabolic rate (RMR) is the amount of energy needed by the body to maintain homeostatic functions during resting conditions. It constitutes the largest fraction of total energy expenditure, accounting for about 65–70% [1]. RMR can be determined using indirect calorimetry by measuring oxygen consumption and carbon dioxide production, which is considered to be the gold standard for assessing RMR [2]. It has been demonstrated that RMR is influenced by various factors, including gender, age, ethnicity, and body composition. Among those, fat-free mass (FFM) has been recognized to be the major determinant of RMR [3]. Alterations in RMR are associated with obesity, metabolic syndrome, diabetes mellitus, multimorbidity, and even mortality [4–8]. Pattern identification, also known as syndrome differentiation, is an essential part of diagnosis in Korean medicine (KM). During the process of pattern identification, all symptoms and signs are analyzed to determine the patient’s physical condition and the cause, nature, and location of a disease [9]. Pattern identification is principally used to guide medical
intervention, and some studies have claimed that it improves the successful treatment outcome rate when used in both Eastern and Western interventions [10, 11]. Various books and research papers on KM have described the underlying pathology of KM patterns in relation to decreased or increased metabolic rate. For example, a cold pattern and a deficiency pattern are often reported to be related to a lowered body metabolism, while a heat pattern and excess pattern are related to excessive hyperactivity of the body metabolism, suggesting that the symptoms of the KM patterns such as cold/hot sensation in the body, decreased/ increased sweating, and clear/red color of urine may be related to the altered body metabolic rate [12–15]. Few studies, however, have investigated the association of KM patterns with accurately measured body metabolic rate. To our knowledge, there was only one study conducted with twelve Japanese women that reported that RMR was lower in women with cold pattern than in women without cold pattern [16]. Thus, this study aims to further investigate the association of KM patterns with RMR and anthropometric and body composition measurements in a larger sample.
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KDC KM symptom questionnaire, RMR measurement data requests, and approvals n = 954
KM symptom questionnaire (symptoms regarding cold/heat, perspiration, defecation, urination, digestion, drinking, sleep, and fatigue experienced within the past 6 months)
Indirect calorimetry
Factor analysis
KM pattern scores (CP, HP, SQDP, and KYDP)
RMR
Analysis of the association of KM pattern scores and RMR
Figure 1: Flow chart of the study procedure. KDC, Korean Medicine Data Center; CP, cold pattern; SQDP, spleen-qi deficiency pattern; HP, heat pattern; KDP, kidney deficiency pattern.
2. Materials and Methods 2.1. Participants. This cross-sectional study was conducted between 2009 and 2015. The KM symptom questionnaire, RMR, and body composition measurement data were derived from the Korean Medicine Data Center (KDC) of the Korea Institute of Oriental Medicine [17] (Figure 1). A total of 954 healthy volunteers between 20 and 70 years of age without any chronic disease and history of hospitalization in the previous 5 years were included in the study. The study was approved by the Korea Institute of Oriental Medicine (Number I1004/001-003) and informed consent was obtained from all participants prior to inclusion in the study. 2.2. Data Collection 2.2.1. KM Patterns. Participants were asked to complete the KM symptom questionnaire that consisted of questions about symptoms experienced by the individual within the past 6 months. The symptoms referred to certain conditions, that is, cold/heat, perspiration, defecation, urination, digestion, drinking, sleep, and fatigue, which are deemed important during the basic examination in KM [12]. A factor analysis was conducted to identify symptom patterns. Four patterns, namely, cold pattern (CP), heat pattern (HP), spleen-qi deficiency pattern (SQDP), and kidney deficiency pattern (KDP), were extracted along with their respective scores (Supplementary Table S1 in Supplementary Material available online at https://doi.org/10.1155/2017/9532073). CP was characterized by cold sensation in the feet, cold sensation in the hands,
no reddish urine, cold sensation in the abdomen, preference for drinking warm water, and frequent urination; HP was characterized by no aversion to cold, increased sweating, and no preference for drinking warm water; SQDP was characterized by irregular defecation, less frequent bowel movements, feeling of incomplete defecation, indigestion, and fatigue; and KDP was characterized by urination at night, awakening during the night, poor quality of sleep, frequent urination, and loose/watery stool (Supplementary Table S2). 2.2.2. RMR Measurements. Participants were asked to fast and refrain from stimulants (smoking, alcohol, and coffee) and heavy exercise for at least 12 h prior to visiting the laboratory. RMR was measured with a breath-by-breath gas exchange analysis using an indirect calorimeter (Vmax ENCORE 29c; Sensormedic, Viasys HealthCare, Yorba Linda, CA, USA). The gas flow calibration as well as oxygen and carbon dioxide analyzer calibration was performed according to the manufacturer’s instructions. RMR was measured for 20 min while participants were awake and lying in a supine position. Oxygen uptake and carbon dioxide production were measured and RMR was calculated using the Weir equation [18]. 2.2.3. Body Composition Measurements. Body height was measured with a digital scale (GL-150; G Tech International Co., Uijeongbu, Korea), and weight, fat-free mass (FFM), and body fat mass (BFM) were measured with a bioelectrical impedance analyzer (Inbody 720, InBody, Seoul, South
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Table 1: Characteristics of the study participants.
Height (cm) Weight (kg) BMI (kg/m2 ) FFM (kg) BFM (kg) RMR (kcal/day)
All (𝑛 = 954) 166.0 ± 8.6 64.0 ± 11.5 23.1 ± 3.0 47.8 ± 9.9 16.2 ± 5.7 1378.9 ± 313.5
Men (𝑛 = 454) 173.0 ± 5.8 71.9 ± 10.0 24.0 ± 2.9 56.7 ± 6.2 15.2 ± 5.9 1579.5 ± 293.1
Women (𝑛 = 500) 159.7 ± 5.3 56.9 ± 7.4 22.3 ± 2.9 39.7 ± 3.9 17.2 ± 5.3 1197.2 ± 200.2
𝑝 value
