Association between serum lactate dehydrogenase and frailty among individuals with metabolic syndrome

Abstract

While metabolic syndrome (MetS) is associated with frailty, the correlation of serum lactate dehydrogenase (sLDH) and frailty with MetS remain uncertain. To investigate the relationship between sLDH and frail components in the US with MetS. A total of 4,066 participants aged 40–90 years were assessed from the database of the third National Health and Nutrition Examination Survey, 1988–1994. The participants were classified into MetS and non-MetS groups. Multivariate logistic regression analysis with four models were performed to assess the odds ratio (OR) of the divided tertiles of sLDH levels with frailty, and frail components including slow walking (SW), weakness, exhaustion, low physical activity (LPA), and low body weight (LBW). Higher sLDH levels were positively associated with frailty in the MetS group (p = 0.024) but not in non-MetS group (p = 0.102). After covariate adjustments, the OR of frailty in the upper two tertiles compared to the lowest tertile and revealed statistical significance (p < 0.05). Frail components of SW, weakness, exhaustion, and LPA were associated with higher sLDH (p < 0.05) except for LBW in MetS and non-MetS groups. The results demonstrated the strong association of higher sLDH levels and frailty among US individuals with MetS.

About the Authors:

Li-Hsiang Chen

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Division of Family Medicine, Department of Family and Community Medicine, Kaohsiung Armed Forces General Hospital, Kaohsiung, Taiwan, Republic of China, Division of Family Medicine, Department of Family and Community Medicine, Tri-Service General Hospital; and School of Medicine, National Defense Medical Center, Taipei, Taiwan, Republic of China

Li-Wei Wu

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – review & editing

Affiliations Division of Family Medicine, Department of Family and Community Medicine, Tri-Service General Hospital; and School of Medicine, National Defense Medical Center, Taipei, Taiwan, Republic of China, Division of Geriatric Medicine, Department of Family and Community Medicine, Tri-Service General Hospital, Taipei, Taiwan, Republic of China, Health Management Center, Department of Family and Community Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China, Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan, Republic of China

ORCID logo https://orcid.org/0000-0003-4744-7646

Introduction

LDH is an indispensable enzyme for glycolysis in cytoplasmic anaerobic metabolism, which catalyzes the interconversion of the reduced form of nicotinamide adenine dinucleotide (NADH) to the oxidized form of NAD+, and pyruvate to lactate, producing the adenosine triphosphate (ATP) as the energy for human [1–4]. The five LDH isoforms consist of homomeric and dimeric tetramers of LDHA (= LDH-M, M) and LDHB (= LDH-H, H) subunits (i.e. LDH1 = 4H, LDH2 = 3H1M, LDH3 = 2H2M, LDH4 = 1H3M, LDH5 = 4M). In skeletal muscle LDH5 and in the heart LDH1 is predominant [1, 2, 5]. LDH exists in various tissues such as the brain, skeletal muscle, heart, lung, liver, pancreas, red blood cells, and kidney [5]. The enhanced sLDH signifies the pathological conditions of acute tissue or cellular damage [1, 5]. Previous studies demonstrated that elevated sLDH were associated with multiple health problems, including osteoporosis, congestive heart failure (CHF), chronic pulmonary obstructive disease (COPD), hypoxia, cirrhosis of the liver, inflammatory disease, human immunodeficiency disease, and malignancy [3, 4, 6–8]. Moreover, higher sLDH enhanced the cardiovascular mortality of individuals with chronic arsenic exposure in Taiwan and increased the risk of all-cause mortality in US populations with MetS [2, 3, 9].

According to the criteria of the Third Report of National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III), MetS is a constellation of various metabolic disorders, including obesity, dyslipidemia, hyperglycemia, and hypertension [10]. The clusters of metabolic abnormalities substantially increase the risk of insulin resistance (IR), type 2 diabetes mellitus (DM), CVD, inflammation, and oxidative stress (OS), which accelerates the aging process [10–12]. Individuals aged more than 40 years increase the risk of incident MetS in the NHANES cohort [11, 13, 14]. The average rate of MetS calculated from approximately 24.3% to 39.1% globally [11, 13, 14]. The Longitudinal Aging Study Amsterdam with 1,247 participants reported MetS was associated with a 19-year all-cause mortality [15].

Frailty is a geriatric syndrome of decreased physical function. On the basis of the investigation with the Cardiovascular Health Study (CHS) by Fried et al., the original concepts of frailty included three or more of five phenotypes: shrinking (unintentional weight loss), weakness (declined grip strength), exhaustion, slowness, and low activity [16]. According to the theories of cycle of frailty, the mutual exacerbation of negative energy balance, sarcopenia, nutritional deficiency, inflammation, and neuroendocrine dysregulation increase vulnerability to environmental stressors in frail subjects; these issues in turn, lead to a risk of impaired physiological homeostasis, disability, falls, cognitive disorders, chronic diseases, hospitalization, and mortality [16–19]. Older age, malnutrition, smoking, alcohol consumption, CVD, DM, and multiple comorbidities are all more prevalent in frail people [16, 17, 20, 21]. Aging, lack of physical activity (PA), chronic diseases, adverse lifestyle, and nutrient imbalance make a close link between frailty and MetS in adults [11, 19, 22–25]. The prevalence of frail individuals 60 years or older ranged from 2% to 34%, compared to various population-based studies [16, 18, 22, 26, 27]. Kane et al. speculated that frailty in older adults with MetS had been estimated to be about 45.5% by the NHANES database in 2003~2006 [28]. Frailty and MetS correlate with comorbidities and adverse health outcomes for the elderly, whereas few literatures have discussed frailty and MetS in middle-aged populations. One research enrolled 10,020 participants aged 20 years and older addressed the association between frailty, MetS and mortality [28]. A cohort study of 493,737 participants aged 37–73 years exhibited the relationship between frailty, multimorbidity, specific long-term conditions, and mortality [29]. sLDH may act as a predictive biomarker reflecting the impacts of hemostatic dysregulation. Yuichi Nakazato et al. pointed out that frail patients who underwent maintenance hemodialysis significantly correlated with elevated sLDH [30]. However, the concepts of correlation between LDH levels, frailty, and MetS have not been elucidated. Therefore, we aim to explore the association between sLDH and frailty among middle-aged and older US populations with MetS by NHANES III datasets.

Materials and methods

Ethics statement

The execution of the NHANES III was approved in accordance with the protocol of the Institutional Review Board by National Center for Health Statistics of the Centers for Disease Control and Prevention (CDC). All eligible participants were carefully informed of the benefits and risks, and commenced the survey with informed consents. All procedures performed in this study were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments.

Study designs and subjects

The enrolled data was obtained from the NHANES III between 1988 and 1994, which constituted complete laboratory samples, physical examinations, questionnaires for multistaged and stratified investigations of the US population by home examinations, household interviews and mobile examination centers (MECs) [31]. The study was composed of 4,066 US participants from 40 to 90 years, classified in two groups of 2,268 individuals with MetS and 1,798 individuals with non-MetS. The demographic characteristics, laboratory tests, physical function, and mental conditions were analyzed thoroughly, and complied with the NHANES III protocol. Data with unavailable relevance for sLDH, frailty, and MetS were excluded from our study.

Definition of MetS

Participants diagnosed with MetS met at least three of the five identification markers, based on criteria of NCEP-ATP III [10]: (1) abdominal obesity with waist circumference > 102 cm in men and > 88 cm in women; (2) triglyceride (TG) ≥150 mg/dL; (3) high-density lipoprotein cholesterol (HDL-C) < 40 mg/dL in men and < 50 mg/dL in women; (4) impaired plasma fasting glucose ≥ 110 mg/dL (6.1 mmol/L) or current DM; (5) elevated systolic blood pressure (SBP) ≥ 130 mmHg or DBP ≥ 85 mmHg.

Construction of frailty

Frailty as syndrome has been widely clarified in a variety of theories, with clinical phenotypes by Fried or the FI by Rockwood et al. [16, 17]. In this article, frailty was defined as three or more of the following components based on modifications of phenotypic criteria constructed by Wilhelm-Leen et al. [27] in NHANES III cohorts: (1) SW, defined as the slowest quintile using the 8-foot gait speed test adjusted for gender; (2) Weakness, defined as the answer of “some difficulty,” “much difficulty,” or “unable to do” from participants with the question of how much difficulty they had while “lifting or carrying something as heavy as 10 pounds (like a sack of potatoes or rice).”; (3) Exhaustion, defined as the answer of “some difficulty,” “much difficulty,” or “unable to do” from participants with the question of how much difficulty while “walking from one room to the other on the same level.”; (4) LPA, defined as the answer of “less active” from participants with the issue being “compared with most (men/women) your age, would you say that you are more active, less active, or about the same?”; (5) LBW was, defined as BMI ≤ 18.5 kg/m2.

Definition of the LDH tertiles

Serum LDH levels were defined as reference values of 0–210 U/L and abnormalities > 210 U/L according to the laboratory tests results reporting criteria from NHANES datasets [31]. The evaluation of sLDH levels (65–668 U/L) in our study included normal and abnormal values. To verify the correlation between LDH and frailty with MetS, the examinations of LDH were plotted into three tertiles based on the research by Wu et al. using the LDH tertiles to explore the significant association between sLDH and all-cause mortality with MetS [3]. The reference group was regarded as the lowest tertile for all participants. The cut-off points for serum LDH tertiles were T1 (65–149), T2 (149–176) and T3 (176–668) U/L.

Covariates measurements

The demographic information was obtained from participants in NHANES III, including age, gender, race/ethnicity, smoking status, medical history (congestive heart failure (CHF), stroke, asthma, malignancy, and type 2 DM), and PA. BMI was measured by dividing a person’s weight in kilograms with the square of height in meters (kg/m2). The average values of SBP and DBP were calculated after three or four times records with a mercury sphygmomanometer in the MEC and home survey. Hypertension was defined by the average BP ≥ 140/90 mmHg of a self-reported physician’s diagnosis. DM was defined by either the examinations of serum fasting glucose level ≥ 126 mg/dl, random glucose level ≥ 200 mg/dl, current diabetic medication use, or a self-report of a medical diagnosis. The biochemical values used various analyzers and instrumentation at the Lipoprotein Analytical Laboratory of Johns Hopkins University, Baltimore, Maryland from 1988 to 1994. All calculated metrics were performed with standardized methods and were compatible with the accurate reference protocol from the CDC. The participants were asked about the frequency of PA, which they did in their leisure time for the most recent month, which included swimming, dancing, jogging, walking, running, riding a bike, callisthenic exercise, resistance training, gardening, or other sports; however, the duration of PA in the US population was not evaluated in NHANES III (1988–1994). On the basis of intensity thresholds of metabolic equivalent tasks (METs), accessing the metabolic ratio of energy expense during activity to resting status, people were defined by three types, which differed with the engaged levels of PA weekly: (1) Ideal type (physically active), defined as an individual’s PA reaching the METs between 3 and 6, performing 5 or more times, or the METs more than 6 with performing 3 or more times, or moderate intensity for more than 150 minutes, or vigorous intensity for more than 75 minutes, or moderate and vigorous intensity for more than 150 minutes; (2) Intermediate type, defined as an individual’s PA, was between active and none, or moderate intensity was between 1 and 149 minutes, or vigorous intensity was between 1 and 74 minutes, or moderate and vigorous intensity was between 1 and 149 minutes; (3) None type (physically inactive), was defined as an individual with poor PA.

Mortality and follow-up data

According to a probabilistic match between NHANES III participants older than 17 years and National Death Index (NDI) death certificate records, the mortality status, and follow-up data were obtained from the publicly available NHANES III Linked Mortality File by National Center for Health Statistics. Person-months of follow-up data on mortality of the NHANES III survey participants recorded from 1988 to December 31, 2006 [32].

Statistical analysis

All statistical parameters were analyzed by the Statistical Package for the Social Sciences (SPSS, Inc., Version 18 for Windows, Chicago, IL, USA). The mean and standard deviation (SD) were measued for quantitative analysis, whereas numbers and percentages were measured for qualitative analysis. The tests for continous and discrete variables included the Wilcoxon Rank sum test, the independent t-test, and the Chi-square test, respectively. Statistical significance was asserted as two-sided p-values of less than 0.05. The association between sLDH and frail components in MetS populations was performed by multivariate logistic regression. An extensive-model design was applied for covariate adjustments for potential confounding factors as follows: model 1 = unadjusted; model 2 = adjusted by model 1 + age, sex, race, BMI; model 3 = adjusted by model 2 + SBP, serum TGs, CRP, total bilirubin, and glucose levels; model 4 = adjusted by model 3 + PA, smoking, CHF, stroke, asthma, and malignancy.

Results

Preliminary study population characteristics

Among the 4,066 participants from 40 to 90 years in the present study, 2,268 participants had MetS, while 1,798 participants did not have MetS. The analytical parameters and demographic characteristics in the NHANES III datasets were available for this research. The mean age of MetS groups was 71.93 ± 7.99 years old, and 44.5% were male. The mean age of non-MetS groups were 71.00 ± 7.94 years old, and 56.5% were male. The comparisons of participants in MetS and non-MetS groups with frailty and without frailty are summarized in Table 1. Participants with MetS in the frailty group were prone to higher sLDH levels, older age, higher body mass index (BMI), lower diastolic pressure (DBP), higher serum TGs, lower serum total bilirubin, higher serum uric acid, higher serum glucose, and higher serum total protein (all, p <0.05). However, there were no significant correlations between higher sLDH levels and frailty in the non-MetS group (p = 0.102). In both MetS and non-MetS groups, those with frailty were inclined to CHF, stroke, type 2 DM, lack of PA, whereas malignancy displayed nonsignificant findings in MetS (p = 0.861) and non-MetS groups (p = 0.617).

[Figure omitted. See PDF.]

Table 1. Characteristics of the study participants with MetS and non-MetS with and without frailty.

https://doi.org/10.1371/journal.pone.0256315.t001

Association between Frailty and LDH

The association between frailty and tertiles of sLDH levels in adults with and without MetS are shown in Table 2. The utilization of multivariate logistic regression to approach the comparisons in four models demonstrated that participants with frailty had significantly higher sLDH levels in the MetS group (all, p < 0.05) after we adjusted for potential confounding variables in model 2, model 3, and model 4. The OR of the highest tertile in the adjusted models 2, 3 and 4 compared to the lowest tertile in the unadjusted model 1 were 2.08 (95% CI 1.12 to 3.84; p = 0.02), 2.21 (95% CI 1.19 to 4.11; p = 0.012), 2.45 (95% CI 1.32 to 4.56; p = 0.005), respectively. There was no significant association between increased LDH tertiles and frailty in the non-MetS group. Table 3 exhibited the relationship between frail components and LDH tertiles in four models of the MetS and non-MetS groups. Among the five components of frailty, SW, weakness, exhaustion, and LPA, we found positive associations with MetS in the highest LDH tertiles after adjustments for all covariates in all models (p <0.05). Moreover, weakness, exhaustion, and LPA showed statistical significance in the non-MetS group (p < 0.05) and in the MetS group. The OR of the comparison between the highest and lowest tertile of LBW revealed statistical significance only in model 1, at 2.70 (95% CI 1.17 to 6.25; p = 0.020), in the MetS group and 1.82 (95% CI 1.29 to 2.57; p = 0.001), and in the non-MetS group, respectively. After adjusting for all covariates, there was no statistical significance in models 2, 3, and 4 in both MetS and non-MetS subjects.

[Figure omitted. See PDF.]

Table 2. Association between frailty and serum LDH level in study participants with MetS and non-MetS.

https://doi.org/10.1371/journal.pone.0256315.t002

[Figure omitted. See PDF.]

Table 3. Association between frail components and serum LDH level in study participants with MetS and non-MetS.

https://doi.org/10.1371/journal.pone.0256315.t003

Discussion

The remarkable findings of the present study indicate a significant association between higher sLDH levels and frailty in MetS. Moreover, sLDH was positively associated with frail components: SW, weakness, exhaustion, and LPA with MetS. The characteristics of frail individuals were more likely middle-age and the elderly, the female gender, obese, smokers, and those with a medical history of CHF, stroke, and type 2 DM. To the best of our knowledge, this is the first study to demonstrate a positive association between sLDH and frail components in US populations with MetS.

Prior articles have shown that most people with MetS and frailty were older, had multiple comorbidities, and LPA [15, 28, 33]. Aging plays a crucial role in enhancing the risk of frailty, MetS, and the clear disadvantages of health problems. The predisposing factors of aging include obesity, OS, IR, inflammation, and malnutrition [11, 12, 21]. Many detrimental effects of aging lead to mitochondrial dysfunction, malnutrition, sedentary behavior (SB), muscle weakness, diminished bone mass, metabolic abnormalities, and inflammation [12, 24, 33–36]. Multifactorial circumstances might interconnect aging, MetS, and frailty. As such, the OS caused by reactive oxygen species (ROS) influenced human physiological function and pathophysiology. The production of free radicals from aging, mitochondrial dysfunction, and MetS increase OS, which in turn accelerates the aging process and overwhelms the cellular homeostasis, leading to mitochondrial DNA mutation, tissue injury, inflammation, CVD, MetS, and neurodegenerative disorders [12, 37, 38]. Aging, OS, mitochondrial dysregulation, and MetS might inflict elevated sLDH and impaired ATP production for an individual’s energy, leading to a decline of muscular strength and mass, physical inactivity, generalized weakness, SW, and gradual development of frailty [12, 35, 37–39]. Under the conditions of age-linked MetS and OS, sLDH might have a close relationship with frailty. Our study elucidated increased OR of frailty as being associated with higher sLDH in MetS individuals after various covariate adjustments.

The measurements of sLDH have been clinically utilized as indicators of tissue damage, muscle fatigue, inflammatory disease, and multiple organ disease worldwide. The chronic inflammatory process may be a crucial factor for chronic disease and cancer. The common etiologies of chronic inflammation and disease due to adverse lifestyles are compromised by smoking, alcohol abuse, unhealthy diet (red meat, high fat, glucose, and calories), nutrition deficiencies, stress, and LPA [36, 40, 41]. MetS is considered a chronic inflammatory state [9, 11]. Emerging evidence has demonstrated that obesity is associated with IR, type 2 DM, and chronic inflammation, especially in skeletal muscle inflammation [11, 42, 43]. Previous researches have exhibited that miscellaneous connections between obesity-derived IR and skeletal muscle enhanced the inflammatory process and impairment of myocytes and adipocytes [9, 11, 39, 42]. The dysregulation of adipose tissue, owing to IR and chronic inflammation, results in decreased glucose uptake, endothelial dysfunction, and metabolic disorder, thus facilitating the reduction of age-associated skeletal muscle mass (MM) [9, 11, 39, 42]. The loss of skeletal MM and declining MS increase the risks of frailty and mortality in the elderly [33, 35]. A cross-sectional study of 512 subjects aged 60 years or older using datasets of the NAMGARAM-2 cohort by Yoo et al. [43] showed female population over 65 years old with low handgrip strength showed a higher prevalence of osteoporosis and lower gait speed than normal-hand grip strength. Among frail components, the most common presentation of this symptom initially was weakness and could become a predictor for the onset of frailty [18]. Moreover, MetS was also an independent variable with weakness and viewed as a predictor of changes in frailty status of declining-hand grip strength and gait speed with longitudinal follow-up [21].

Inflammation plays a primary factor for frailty. Elevated plasma inflammatory markers inclusive of IL-6, white blood cells (WBC), TNF-α, CRP, and fibrinogen have been noted in the relationship with frailty [44, 45]. LDH was regarded as a biomarker for inflammation [4, 46], and consequences of MetS-related inflammatory processes cause a number of biological disorders, contributing to the elevated sLDH and increased risk of frailty. Inflammation could be an additional explanation for the association of higher sLDH with frailty in people with MetS.

The state of exhaustion is related to the interaction of physical and mental fatigue. Frail individuals are inclined to have depressive symptoms versus those without frailty [26, 44]. The presence of exhaustion, accompanied with MetS-related IR, may cause the presence of SB and LPA in middle-aged or older frail populations. Prior studies have shown that a sedentary lifestyle with LPA increased the risk of chronic disease, MetS, mortality from cardiometabolic, and all-cause diseases [25, 34, 47, 48]. Meanwhile, there were higher serum hydroxyproline, LDH, creatine kinase, troponin I, lower calcium, and 25(OH)D levels among the elderly with LPA of SB, which accelerated deconditioning, muscle fatigability, declined MM, MS, and antioxidant capacity [49]. This supported the result of our study regarding the relationship between higher sLDH and frailty with MetS.

Unhealthy dietary lifestyle and malnutrition in older adults increase the risks for a cluster of adverse health problems such as inflammation, declining antioxidant function, sarcopenia, obesity, or lower BMI, MetS with IR, cancer, mortality, and frailty [20, 21]. Frail subjects with MetS are prone to have nutritional deficiency, and disturb homeostasis and physical function. Therefore, malnutrition is potentially positively associated with elevated sLDH level among frail populations with MetS. Despite LBW showing a nonsignificant correlation with elevated sLDH in all adjusted models, the higher sLDH showed increasing trends to the other four frail components, thus fulfilling the criteria of frailty and could address the relationships. People with underweight or obesity significantly increased the risk of frailty than those with normal weight [22, 26]. Among frail individuals with MetS, higher fat mass reduced the metabolic rate and physical performance, resulting in weight gain; as such, the obvious decrease in weight might not occur rapidly but turned into LBW until assessing long-term follow-up. We concluded that higher sLDH levels can be regarded as independent predictors of frailty in those with clinical MetS.

There were several limitations in this article. First, the secondary analysis of the NHANES III versus persistent long-term follow-up restricted the causal inference. Second, the bias of results might have been contributed from the measurement of sLDH only once, without repeatedly collected data during periods of follow-up. The correlation of serum LDH levels collected only at a single point is a drawback for the study. Third, phenotypes of frailty relied on self-report and individual examinations. Inappropriate manifestations and reported bias might occur if participants with acute illness at that time. The direct assessments of the handgrip test, sit and walk, and other validated tests did not include in the article. Fourth, the comparisons between LDH and frail components were assessed by frail phenotypes, which limited the outcomes in consideration of other factors among frailty index (FI) [17] or various measurements, as well as the different identification of participants with MetS by the criteria of NCEP-ATP III or International Diabetes Federation and cut-off points of LDH levels that might cause unpredictable findings. Fifth, the trend of LBW among frail patients with MetS (under the influence of LDH) needs more evidence to demonstrate results. Sixth, the under measurement of residual confounders should be assessed, as they might have potential effects on the association between LDH and frailty, despite adjusting for confounding factors. Activity of specific types of the LDH isoform, linked with frailty in those with MetS, needs rigorous investigation and research. Finally, the sLDH level impacting frailty only in people with MetS but not in the non-MetS group requires substantial evidence to prove the argument.

In conclusion, the present study highlighted higher serum LDH level had a significant association with frailty in middle-aged and older US populations with MetS. LDH was positively associated with frail phenotypes particularly in slow walking, weakness, exhaustion, and low physical activity. LDH might act as a predictive biomarker for frail people with MetS, whereas the correlation between elevated LDH and LBW or other causes of frailty beyond our discussion in patients with MetS remained controversial. The issues focused on the precise biological mechanisms underlying the molecular pathway and the correlation of higher serum LDH and frailty; we look forward to assessing this in further studies.

Citation: Chen L-H, Wu L-W (2021) Association between serum lactate dehydrogenase and frailty among individuals with metabolic syndrome. PLoS ONE 16(9): e0256315. https://doi.org/10.1371/journal.pone.0256315

References

1. Valvona CJ, Fillmore HL, Nunn PB, Pilkington GJJBP. The regulation and function of lactate dehydrogenase a: therapeutic potential in brain tumor. Brain Pathol. 2016;26(1):3–17. Epub 2015/9/17. pmid:26269128.

2. Liao Y-T, Chen C-J, Li W-F, Hsu L-I, Tsai L-Y, Huang Y-L, et al. Elevated lactate dehydrogenase activity and increased cardiovascular mortality in the arsenic-endemic areas of southwestern Taiwan. Toxicol Appl Pharmacol. 2012;262(3):232–7. Epub 2012/5/1. pmid:22569360.

3. Wu L-W, Kao T-W, Lin C-M, Yang H-F, Sun Y-S, Liaw F-Y, et al. Examining the association between serum lactic dehydrogenase and all-cause mortality in patients with metabolic syndrome: a retrospective observational study. BMJ Open. 2016;6(5):e011186. pmid:27217285.

4. Faruqi S, Wilmot R, Wright C, Morice AHJTcrj. Serum LDH in chronic cough: a potential marker of airway inflammation. Clin Respir J. 2012;6(2):81–7. Epub 2011/7/18. pmid:21651741.

5. Kopperschläger G, Kirchberger JJJoCBBS, Applications. Methods for the separation of lactate dehydrogenases and clinical significance of the enzyme. J Chromatogr B Biomed Appl. 1996;684(1–2):25–49. pmid:8906464.

6. Click JHJAjocp. Serum lactate dehydrogenase isoenzyme and total lactate dehydrogenase values in health and disease, and clinical evaluation of these tests by means of discriminant analysis. Am J Clin Pathol. 1969;52(3):320–8. pmid:5805997.

7. Hu EC, He JG, Liu ZH, Ni XH, Zheng YG, Gu Q, et al. High levels of serum lactate dehydrogenase correlate with the severity and mortality of idiopathic pulmonary arterial hypertension. Exp Ther Med. 2015;9(6):2109–13. Epub 2015/3/20. pmid:26136943.

8. Quist J, Hill ARJC. Serum lactate dehydrogenase (LDH) in Pneumocystis carinii pneumonia, tuberculosis, and bacterial pneumonia. Chest. 1995;108(2):415–8. pmid:7634877.

9. Kaur JJCr, practice. A comprehensive review on metabolic syndrome. Dis Model Mech. 2014;2014:943162. Epub 2014/3/11. pmid:7634877.

10. Expert Panel on Detection EJJ. 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). JAMA. 2001;285(19):2486. pmid:11368702.

11. Kassi E, Pervanidou P, Kaltsas G, Chrousos GJBm. Metabolic syndrome: definitions and controversies. BMC Med. 2011;9(1):48. pmid:21542944

12. Bonomini F, Rodella LF, Rezzani RJA, disease. Metabolic syndrome, aging and involvement of oxidative stress. Aging Dis. 2015;6(2):109. eCollection 2015/3. pmid:25821639.

13. Moore JX, Chaudhary N, Akinyemiju T. Metabolic Syndrome Prevalence by Race/Ethnicity and Sex in the United States, National Health and Nutrition Examination Survey, 1988–2012. Prev Chronic Dis. 2017;14:E24. Epub 2017/03/17. pmid:28301314.

14. Scuteri A, Laurent S, Cucca F, Cockcroft J, Cunha PG, Mañas LR, et al. Metabolic syndrome across Europe: different clusters of risk factors. Eur J Prev Cardiol. 2015;22(4):486–91. pmid:24647805.

15. Hoogendijk EO, Huisman M, van Ballegooijen AJJEg. The role of frailty in explaining the association between the metabolic syndrome and mortality in older adults. Exp Gerontol. 2017;91:5–8. Epub 2017/2/14. pmid:28229926.

16. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146–M57. pmid:11253156

17. Blodgett J, Theou O, Kirkland S, Andreou P, Rockwood KJAog geriatrics. Frailty in NHANES: comparing the frailty index and phenotype. Arch Gerontol Geriatr. 2015;60(3):464–70. Epub 2015/2/3. pmid:25697060.

18. Xue Q-LJCigm. The frailty syndrome: definition and natural history. Clin Geriatr ed. 2011;27(1):1–15. pmid:21093718.

19. Clegg A, Hassan-Smith Z. Frailty and the endocrine system. Lancet Diabetes Endocrinol. 2018;6(9):743–52. Epub 2018/7/17. pmid:30017798.

20. Lorenzo-López L, Maseda A, de Labra C, Regueiro-Folgueira L, Rodríguez-Villamil JL, Millán-Calenti JCJBg. Nutritional determinants of frailty in older adults: A systematic review. BMC Geriatr. 2017;17(1):108. pmid:28506216.

21. Chang SFJJoNS. Frailty is a major related factor for at risk of malnutrition in community‐dwelling older adults. J Nurs Scholarsh. 2017;49(1):63–72. pmid:27779822.

22. Fugate Woods N, LaCroix AZ, Gray SL, Aragaki A, Cochrane BB, Brunner RL, et al. Frailty: emergence and consequences in women aged 65 and older in the Women’s Health Initiative Observational Study. J Am Geriatr Soc. 2005;53(8):1321–30. pmid:16078957.

23. Esmaillzadeh A, Kimiagar M, Mehrabi Y, Azadbakht L, Hu FB, Willett WCJTAjocn. Dietary patterns, insulin resistance, and prevalence of the metabolic syndrome in women. Am J Clin Nutr. 2007;85(3):910–8. pmid:17344515.

24. Ahmed T, Haboubi NJCiia. Assessment and management of nutrition in older people and its importance to health. Clin Interv Aging. 2010;5:207. pmid:20711440.

25. Katzmarzyk PTJD. Physical activity, sedentary behavior, and health: paradigm paralysis or paradigm shift? Diabetes. 2010;59(11):2717–25. pmid:20980470.

26. Lee L, Patel T, Hillier LM, Maulkhan N, Slonim K, Costa AJG, et al. Identifying frailty in primary care: A systematic review. Geriatr Gerontol Int. 2017; 17(10):1358–77. pmid:28401737.

27. Wilhelm-Leen ER, Hall YN, Tamura MK, Chertow GMJTAjom. Frailty and chronic kidney disease: the third national health and nutrition evaluation survey. Am J Med. 2009;122(7): 664–71.e2. pmid:19559169.

28. Kane AE, Gregson E, Theou O, Rockwood K, Howlett SEJG. The association between frailty, the metabolic syndrome, and mortality over the lifespan. Geroscience. 2017;39(2):221–9. Epub 2017/3/9. pmid:28281219.

29. Hanlon P, Nicholl BI, Jani BD, Lee D, McQueenie R, Mair FSJTLPH. Frailty and pre-frailty in middle-aged and older adults and its association with multimorbidity and mortality: a prospective analysis of 493 737 UK Biobank participants. Lancet Public Health. 2018 Jul;3(7):e323–e332. Epub 2018/6/14. pmid:29908859.

30. Nakazato Y, Sugiyama T, Ohno R, Shimoyama H, Leung DL, Cohen AA, et al. Estimation of homeostatic dysregulation and frailty using biomarker variability: a principal component analysis of hemodialysis patients. Scientific Reports. 2020;10(1):1–12. pmid:31913322.

31. Plan and operation of the Third National Health and Nutrition Examination Survey, 1988–94. Series 1: programs and collection procedures. Vital and health statistics Ser 1, Programs and collection procedures. Vital Health Stat 1. 1994;(32):1–407. Epub 1994/07/01. pmid:7975354.

32. National Center for Health Statistics. Office of Analysis and Epidemiology, The Third National Health and Nutrition Examination Survey (NHANES III) Linked Mortality File, Mortality follow-up through 2006: Matching Methodology May 2009. Hyattsville, Maryland: available from (http://www.cdc.gov/nchs/data/datalinkage/matching_methodology_nhanes3_final.pdf).

33. Buch A, Carmeli E, Boker LK, Marcus Y, Shefer G, Kis O, et al. Muscle function and fat content in relation to sarcopenia, obesity and frailty of old age—an overview. Exp Gerontol. 2016;76:25–32. Epub 2016/1/16. pmid:26785313

34. Harvey JA, Chastin SF, Skelton DAJIjoer, health p. Prevalence of sedentary behavior in older adults: a systematic review. Int J Environ Res Public Health. 2013;10(12):6645–61. pmid:24317382

35. Tieland M, Trouwborst I, Clark B. Skeletal muscle performance and ageing. J Cachexia Sarcopenia Muscle. 2018; 9 (1): 3–19. Epub 2017/11/19. pmid:29151281.

36. Prasad S, Sung B, Aggarwal BBJPM. Age-associated chronic diseases require age-old medicine: role of chronic inflammation. Prev Med. 2012;54:S29–S37. Epub 2011/12/9. pmid:22178471.

37. Valko M, Leibfritz D, Moncol J, Cronin MT, Mazur M, Telser JJTijob, et al. Free radicals and antioxidants in normal physiological functions and human disease. Int J Biochem Cell Biol. 2007;39(1):44–84. Epub 2006/8/4. pmid:16978905.

38. Forrester SJ, Kikuchi DS, Hernandes MS, Xu Q, Griendling KKJCR. Reactive oxygen species in metabolic and inflammatory signaling. Circ Res. 2018; 122(6):877–902. pmid:29700084.

39. Wu H, Ballantyne CM. Skeletal muscle inflammation and insulin resistance in obesity. J Clin Invest. 2017;127(1):43–54. Epub 2017/1/3. pmid:28045398.

40. Kunnumakkara AB, Sailo BL, Banik K, Harsha C, Prasad S, Gupta SC, et al. Chronic diseases, inflammation, and spices: how are they linked? J Transl Med. 2018;16(1):14. pmid:29370858.

41. Omodei D, Fontana LJFl. Calorie restriction and prevention of age‐associated chronic disease. FEBS Lett. 2011;585(11):1537–42. Epub 2011/3/12. pmid:21402069.

42. Kahn SE, Hull RL, Utzschneider KMJN. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature. 2006;444(7121):840. pmid:17167471.

43. Yoo JI, Kim MJ, Na JB, Chun YH, Park YJ, Park Y, et al. Relationship between endothelial function and skeletal muscle strength in community dwelling elderly women. J Cachexia Sarcopenia Muscle. 2018;9(6):1034–41. Epub 2018/8/6. pmid:30084166.

44. Ng TP, Feng L, Nyunt MS, Larbi A, Yap KB. Frailty in older persons: multisystem risk factors and the Frailty Risk Index (FRI). J Am Med Dir Assoc. 2014;15(9):635–42. Epub 2014/4/17. pmid:24746590.

45. Soysal P, Stubbs B, Lucato P, Luchini C, Solmi M, Peluso R, et al. Inflammation and frailty in the elderly: A systematic review and meta-analysis. Ageing Res Rev. 2016;31:1–8. Epub 2016/8/31. pmid:27592340.

46. Wulaningsih W, Holmberg L, Garmo H, Malmstrom H, Lambe M, Hammar N, et al. Serum lactate dehydrogenase and survival following cancer diagnosis. Br J Cancer. 2015;113(9):1389. Epub 2015/10/15. pmid:26469834.

47. Greer AE, Sui X, Maslow AL, Greer BK, Blair SNJJoPA, Health. The effects of sedentary behavior on metabolic syndrome independent of physical activity and cardiorespiratory fitness. J Phys Act Health. 2015;12(1):68–73. pmid:25760431.

48. Chau JY, Grunseit A, Midthjell K, Holmen J, Holmen TL, Bauman AE, et al. Sedentary behaviour and risk of mortality from all-causes and cardiometabolic diseases in adults: evidence from the HUNT3 population cohort. Br J Sports Med. 2015;49(11):737–42. Epub 2013/5/10. pmid:23666019.

49. Al-Eisa ES, Alghadir AH, Gabr SAJCiia. Correlation between vitamin D levels and muscle fatigue risk factors based on physical activity in healthy older adults. Clin Interv Aging. 2016;11:513. eCollection 2016. pmid:27217733.