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Fatty liver index as an indicator of metabolic syndrome
Clinical Biochemistry 45 (2012) 68–71
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Clinical Biochemistry journal homepage: www.elsevier.com/locate/clinbiochem
Fatty liver index as an indicator of metabolic syndrome Dinko Rogulj a, Paško Konjevoda b, Mirta Milić c, Marin Mladinić c, Ana-Marija Domijan d,⁎ a
Polyclinic “Sunce”, Zagreb, Croatia NMR Center, Institute Ruđer Bošković, Zagreb, Croatia c Mutagenesis Unit, Institute for Medical Research and Occupational Health, Zagreb, Croatia d Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia b
a r t i c l e
i n f o
Article history: Received 13 July 2011 Received in revised form 29 September 2011 Accepted 18 October 2011 Available online 26 October 2011 Keywords: Fatty liver index Metabolic syndrome Oxidative stress REPTree and SimpleCART algorithms ROC curve analysis
a b s t r a c t Objective: The aim of this study was to find an early indicator of metabolic syndrome (MetS). Design and methods: We measured several anthropometric, biochemical, haematological, and oxidative damage parameters in 128 middle-aged Caucasian men divided into two groups: patients with MetS (n = 69) and healthy controls (n = 59), and used Weka REPTree and SimpleCART algorithms to identify the most reliable predictor of MetS. Results: Oxidative damage parameters did not differ between the groups, suggesting that oxidative damage is less prominent at the early stage of MetS. The algorithms singled out fatty liver index (FLI) as the best variable for discriminating between healthy and MetS subjects. This finding was confirmed by the receiver–operating characteristic (ROC) curve analysis, which set FLI 68.53 as the threshold value for MetS diagnosis. Conclusions: FLI is the most reliable tool for diagnosing MetS. The absence of oxidative damage does not rule out oxidative stress but may indicate that MetS is at an early stage. © 2011 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.
Introduction The metabolic syndrome (MetS) is a modern disease, characterised by excess weight, high blood sugar, blood pressure, and triglycerides, and low HDL cholesterol [1]. The incidence of MetS has been soaring over the past two decades [2]. In the European Union, the epidemics of overweight and obesity are associated with sedentary lifestyle and physical inactivity [3]. The aetiology of MetS is still unclear [4], but may involve a variety of factors such as modern lifestyle, environmental and hereditary factors, insulin resistance, growth hormone deficiency, inflammatory factors, and oxidative stress [2,4]. According to Martinez-Gonzalez et al. [3], MetS is an interplay of lifestyle and environmental factors, while other authors report genetic susceptibility in some populations as the most prominent factor in MetS development [5]. Early diagnosis of MetS can help to prevent complications of MetS such as type 2 diabetes, cardiovascular disease, cancer, and premature death. Therefore, the aim of our study was to identify the most
Abbreviations: ALT, alanine transaminase; AUC, area under curve; BMI, body mass index; CRP, C-reactive protein; FLI, fatty liver index; GGT, γ-glutamyltransferase; hOGG1, human 8-hydroxyguanine DNA-glycosylase; IL-6, interleukin-6; MetS, metabolic syndrome; NAFLD, non-alcoholic fatty liver disease; NECEP-ATP III, The National Cholesterol Education Program-Adult Treatment Panel III; NEFA, nonesterified fatty acids; ROC, Receiver–operating characteristic; ROS, reactive oxygen species; RNS, reactive nitrogen species; TAC, total antioxidant capacity; TNFα, tumor necrosis factor alfa. ⁎ Corresponding author at: Faculty of Pharmacy and Biochemistry, University of Zagreb, A. Kovačića 1, 10000 Zagreb, Croatia. Fax: + 385 1 63 94 400. E-mail address: [email protected] (A.-M. Domijan).
reliable parameter for early diagnosis of MetS among several anthropometric, biochemical, haematological, and oxidative damage parameters measured in a group of healthy middle-aged men diagnosed with MetS for the first time and in age-matched controls. Materials and methods Subjects The study included 128 non-smoking men, aged 35 to 55 (47.2 ± 5.3) years, all from Zagreb (Croatia). They were randomly recruited from patients having annual medical checkups at the polyclinic. The subjects were divided into two groups: patients for the first time diagnosed with MetS (n = 69) and age-matched healthy individuals (n = 59). The subjects completed a standardized questionnaire with information about age, dietary habits, smoking, alcohol consumption, and medical and occupational history. The exclusion criteria were: neoplastic diseases, heart failure, recent surgery, inflammatory diseases such as infections, autoimmune disorders, liver and kidney disease. Each subject gave informed consent to participate in this study. The study was approved by the institutional ethics committee and observed the ethical principles of the Declaration of Helsinki. Clinical examination and anthropometric measurements We measured sitting systolic and diastolic blood pressures in the right upper arm after 5 min of rest.
0009-9120/$ – see front matter © 2011 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved. doi:10.1016/j.clinbiochem.2011.10.014
D. Rogulj et al. / Clinical Biochemistry 45 (2012) 68–71
Anthropometric measurements included height, weight, body mass index (BMI), and waist circumference. Waist circumference was measured at midway between the lowest rib and the iliac crest. Hip circumference was measured over the widest part of the gluteal region. These two measurements served to calculate the waist-tohip and hip-to-waist ratio. We also measured the thickness of the abdominal subcutaneous fat tissue as a distance between the skin and linea alba with a linear 10 MHz probe (UZV, Philips, Eindhoven, The Netherlands). Intraabdominal fat thickness was measured as a distance between the anterior wall of the aorta and the posterior surface of the rectus abdominis muscle midway between the xiphoid process and the umbilicus. For this measurement we used a convex 3.5 MHz probe (UZV, Philips, Eindhoven, The Netherlands). Epicardial fat thickness was measured perpendicularly on the free wall of the right ventricle from shortaxis view at end-systole with cardiac 3.5 MHz probe (UZV, Philips, Eindhoven, The Netherlands) and corresponded to the echo-free distance between the outer myocardial wall and the visceral layer of the pericardium. Diagnosis of the metabolic syndrome The diagnosis of MetS was based on the National Cholesterol Education Program - Adult Treatment Panel III (NCEP-ATP III) guidelines [6] that are, according to Kuzmanić et al. [7], more appropriate for the Croatian climatic and ethnic setup than the WHO guidelines. Subjects having more than three of the following features were considered to have MetS: (I) abdominal obesity: waist circumference ≥102 cm; (II) hypertriglyceridaemia: ≥1.69 mmol/l; (III) HDL cholesterol: ≤1.04 mmol/l; (IV) blood pressure: ≥130/85 mmHg; (V) fasting blood glucose: ≥6.1 mmol/l. Biochemistry and haematology Plasma triglycerides, HDL cholesterol, alanine transaminase (ALT), γ-glutamyltransferase (GGT), C-reactive protein (CRP), urates, and fibrinogen were analysed with biochemical tests procured from Herbos Dijagnostika (Sisak, Croatia) or Siemens Healthcare Diagnostics (Munich, Germany) using an automated analyser Olympus AU 400 (Japan).
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Leukocyte and neutrophil counts were measured with an automated Beckman Coulter HlX flow cytometer (Brea, CA, USA). As oxidative damage parameters, we determined glutathione (free-radical scavenger) and malondialdehyde (product of lipid peroxidation) in plasma. Glutathione was measured according to the Ellman's method [8] using a spectrophotometer set at 412 nm, and malondialdehyde was determined with the thiobarbituric acid assay, using an HPLC with fluorescence detector set at λex 527 nm and λem 551 nm [9]. Fatty liver index (FLI) was calculated according the following formula [10]: FLI ¼
eð0:953lnðtriglyceridesÞþ0:139BMIþ0:718lnðggtÞþ0:053waistcircumf erence−15:745Þ 100 1 þ eð0:953lnðtriglyceridesÞþ0:139BMIþ0:718lnðggtÞþ0:053waistcircumf erence−15:745Þ
Evaluation of DNA damage One of the most reliable and common methods to evaluate DNA damage on a single-cell level is the comet assay [11,12]. We evaluated the levels of primary and oxidative damage to DNA in peripheral blood leukocytes, as they reflect an overall level of DNA damage in the body. Primary DNA damage was assessed using the alkaline comet assay, which mostly detects single-strand DNA damage. The assay was performed according to Singh et al. [13], with minor modifications. Slides treated with 0.1 mM hydrogen peroxide solution for 5 min served as positive controls. Oxidative DNA damage was assessed using the hOGG1-modified comet assay, which detects oxidative lesions excised by human 8hydroxyguanine DNA-glycosylase (hOGG1). The assay was performed as described by Smith et al. [14]; and the hOGG1 enzyme was procured from Trevigen (MD, USA). All samples were analysed under epifluorescent microscope (Zeiss, Germany) with the excitation filter between 515 nm and 560 nm, connected to a black-and-white CCD camera (Cohu Inc., San Diego, CA). One hundred cells were analysed per slide using the Comet Assay II automatic digital analysis system (Perceptive Instruments Ltd., Suffolk, Halstead, UK) to determine tail length and tail intensity. Tail length (μm) is the distance to which DNA migrated from the centre of the body of the nuclear core when exposed to electric
Table 1 Parameters measured in the control and metabolic syndrome-diagnosed group, and the significance of the differences between the groups (p) analysed with Hotelling's T2 test. Parameter
Control (n)
MetS (n)
p value
Age (years) Waist circumference (cm) Waist-to-hip ratio Hip-to-waist ratio Lean muscle mass (% of body weight) Epicardial fat tissue (mm) Subcutaneous fat tissue (cm) Intraabdominal fat thickness (cm) HDL cholesterol (mmol/L) Triglycerides (mmol/L) CRP (mg/L) ALT (U/L) GGT (U/L) urates level (μmol/L) Fibrinogen (g/L) Leukocytes (109/L) Neutrophils (109/L) Fatty liver index (FLI) Alkaline comet test – tail length (μm) Alkaline comet test – tail intensity (%) hOGG1comet test - tail length (μm) hOGG1Comet test - tail intensity (%) Glutathione (μmol/L) Malondialdehyde (μmol/L)
46.9 ± 5.09 (n = 59) 88.9 ± 8.3 (n = 59) 0.88 ± 0.05 (n = 59) 1.14 ± 0.07 (n = 59) 37.17 ± 3.30 (n = 55) 4.09 ± 1.47 (n = 52) 1.31 ± 0.64 (n = 56) 3.75 ± 1.57 (n = 56) 1.58 ± 0.33 (n = 58) 1.14 ± 0.55 (n = 59) 1.18 ± 1.24 (n = 55) 24.14 ± 9.82 (n = 59) 23.42 ± 12.33 (n = 59) 302.08 ± 48.59 (n = 56) 3.46 ± 0.84 (n = 58) 5.75 ± 1.19 (n = 57) 3.22 ± 0.93 (n = 56) 30.23 ± 17.53 (n = 58) 16.87 ± 3.37 (n = 36) 2.22 ± 1.74 (n = 36) 15.31 ± 3.13 (n = 30) 2.25 ± 2.10 (n = 30) 4.38 ± 1.22 (n = 35) 2.26 ± 1.46 (n = 36)
47.4 ± 5.65 (n = 69) 111.49 ± 10.84 (n = 69) 0.98 ± 0.06 (n = 69) 1.02 ± 0.06 (n = 69) 31.15 ± 3.22 (n = 63) 7.71 ± 1.76 (n = 62) 1.81 ± 0.64 (n = 63) 7.33 ± 2.47 (n = 63) 1.23 ± 0.29 (n = 69) 2.98 ± 1.85 (n = 69) 3.47 ± 3.17 (n = 68) 51.77 ± 35.11 (n = 69) 61.16 ± 43.94 (n = 69) 393.98 ± 82.05 (n = 69) 3.41 ± 0.65 (n = 68) 7.19 ± 1.76 (n = 67) 3.96 ± 1.48 (n = 67) 87.83 ± 14.08 (n = 69) 16.22 ± 2.47 (n = 36) 1.90 ± 1.10 (n = 36) 15.00 ± 3.46 (n = 32) 2.51 ± 4.78 (n = 32) 4.13 ± 0.25 (n = 36) 3.49 ± 3.16 (n = 38)
0.663 b0.001 b0.001 b0.001 b0.001 b0.001 0.001 b0.001 b0.001 b0.001 0.004 b0.001 0.003 b0.001 0.672 0.004 0.142 b0.001 0.912 0.671 0.120 0.159 0.096 0.302
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D. Rogulj et al. / Clinical Biochemistry 45 (2012) 68–71
Table 2 The results of REPTree and SimpleCART algorithms of the Weka software for fatty liver index (FLI) as selected as the best discriminatory variable between the healthy and MetS group with 93.75% accuracy. IF FLI b 70.56 THEN CLASS = HEALTHY (42.5/4) [23/3] IF FLI >= 70.56 THEN CLASS = MetS (42.5/0.5) [20/0] Overall classification Leave-one-out cross-validation Correctly Classified Instances Correctly Classified Instances 120 (93.75%) 116 (90.625%) === Confusion Matrix === === Confusion Matrix === a b b– classified as a b b– classified as 62 7 | MetS = a 62 7 | MetS = a 1 58 | HEALTHY = b 5 54 | HEALTHY = b
field. Tail intensity (%) is the percentage of the genomic DNA that migrated during the electrophoresis from the nuclear core to the tail. Both tail length and tail intensity are measured automatically by image analysis software and are used to evaluate the extent of DNA damage.
Table 3 The results of the receiver–operating characteristic (ROC) curve analysis of fatty liver index (FLI) in the prediction of MetS. Optimal cut-off value between the healthy and MetS group Area under the ROC curve (AUC) 95% confidence interval p value
68.53 0.985 0.946 to 0.998 b 0.0001
and intensity measured with the hOGG1 comet assay (modified comet assay that detects oxidative DNA lesions). RepTree and SimpleCART algorithms singled out FLI as the best discriminatory variable between patients with MetS and healthy persons (Table 2). The results of both algorithms were identical and show that when FLI is equal or over 70.56, then a person can be classified as MetS with an accuracy of 93.75% (Fig. 1). A parallel ROC curve analysis for the optimal cut-off FLI gave 68.53 as the threshold for discriminating between patients with MetS and healthy persons (see Table 3 and Fig. 2). Discussion
Statistics Data are presented as means and standard deviations (SD). Differences in parameters between the groups were analysed using Hotteling's T2 test, a multivariate version of the t-test. To find the variable that would best differentiate between healthy and MetS subjects, we used the REPTree and SimpleCART algorithms [15,16]. The results of these algorithms can be presented as a decision or regression tree. REPTree builds a decision or regression tree using information gain/variance reduction, while SimpleCART employs a minimal cost-complexity pruning strategy for classification. The optimal cutoff value of variable FLI, selected by REPTree and SimpleCART, was confirmed by receiver–operating characteristic (ROC) curve analysis [15,16]. Data were analysed using R (version 2.12.2) and Weka (version 3.6.2) software [16,17]. Results The results of measured parameters are presented in Table 1. Waist circumference, waist-to-hip ratio, hip-to-waist ratio, lean muscle mass, epicardial, intraabdominal, and subcutaneous fat thickness significantly differed between MetS patients and controls (p b 0.001; Hotelling's T2 Test). A similar difference was observed for HDL cholesterol, triglycerides, ALT, urates, CRP, GGT, and leukocytes. However, the two groups did not differ in fibrinogen and blood neutrophils. Neither did they differ in tail length and tail intensity measured with the alkaline comet assay, which suggests that there was no difference in primary DNA damage. The same is true for oxidative damage parameters glutathione, malondialdehyde, and tail length
Our study has shown a significant difference between healthy individuals and MetS patients in anthropometric, biochemical, and haematological parameters (save for fibrinogen and neutrophils). These findings are consistent with similar studies [18,19]. It is well known that visceral adipose tissue releases bioactive substances such as nonesterified fatty acids (NEFA), cytokines, and other pro-inflammatory factors [20]. It is responsible for inflammation, endothelial dysfunction, hyperlipidaemia, atherogenesis, progression of hypertension, and development of insulin resistance; all the processes characteristic of MetS [2]. A recent study on mice by Park et al. [21] demonstrated that chronic inflammatory response caused by obesity and enhanced production of cytokines such as interleukin-6 (IL-6) and tumour necrosis factor alfa (TNF-α) promote the development of hepatocellular carcinoma [21]. Some studies indicate that oxidative stress may play an important role in the pathogenesis of MetS [18,22-24]. Coppak [25] suggests that adipocytes stimulate reactive oxygen species/reactive nitrogen species (ROS/RNS) production, which leads to oxidative damage. In our study, however, the measured parameters of oxidative damage (hOGG1 comet test-tail length, hOGG1 comet test-tail intensity, glutathione and malondialdehyde) did not differ between control and MetS subjects. Similarly, Seet et al. [19] did not see any significant difference in several plasma or serum oxidative damage parameters as well as in urinary 8-OHdG between MetS-diagnosed and healthy subjects. Another similarity with our study is that Seet et al. recruited a
fatty liver index (FLI) 100
80
< 70.56
Sensitivity
fatty liver index
60
40
70.56 20
healthy
metabolic syndrome
0 0
20
40
60
80
100
100-Specificity Fig. 1. The decision-tree according to REPTree and SimpleCART algorithms. A cut-off value of 70.56 for fatty liver index (FLI) was found as the optimal value for discrimination between healthy and metabolic syndrome group.
Fig. 2. The receiver–operating characteristic (ROC) curve analysis of fatty liver index (FLI) in the prediction of MetS.
D. Rogulj et al. / Clinical Biochemistry 45 (2012) 68–71
younger population (43 ± 14 years). In contrast, Demirbag et al. [22], who found significant differences in oxidative damage parameters, investigated somewhat older population (57 ± 9 years). These discrepancies between the studies may be related to DNA repair capacity; Piperakis et al. [26] have found that it drops with age, as 60 to 70year-olds showed higher basal DNA damage than 40 to 50-year-olds. In other words, the absence of oxidative damage - because the defence system (ROS scavengers) is working properly at younger age, while other parameters are increased - points to an early stage of MetS. The absence of oxidative damage however does not mean that there is no oxidative stress. Our study revealed statistically higher levels of urates and GGT in the MetS group than in controls. Both parameters are associated with oxidative stress. Urate is catalysed by xanthine oxidase, but xanthine oxidase also catalyses the formation of ROS [27]. In fact, there is evidence that the urate molecule has free-radical scavenging properties in vitro[28]. GGT level, in turn, correlates well with the levels of prooxidants. In fact, GGT is an early and sensitive marker of oxidative stress that can serve as an indirect marker of the risk of future oxidative damage [29,30]. Therefore, increased levels of urates and GGT in the MetS group in our study suggest that MetS in our patients may be at an early stage, because at the early stage of MetS, ROS production is elevated but ROS scavenging is working properly and oxidative damage is not detected. We performed further statistical analysis of all our variables to identify or confirm predictors of MetS. Earlier research has shown that higher CRP correlates well with MetS [18,31] and that its application is simple in the clinical setting. In fact, two recent studies [19,32] have confirmed it as a superior predictor of MetS in health men and women. In our study, however, the REPTree and SimpleCART algorithms singled out FLI as the best independent predictor of MetS. This is not surprising, since FLI is a score derived from three parameters used for the diagnosis of MetS (BMI, waist circumference, and triglycerides) plus GGT. FLI has been developed by Bedogni et al. [10] as an accurate and easy-to-deploy routine tool which correlates well with ultrasonography of fatty liver disease [10]. The non-alcoholic fatty liver disease (NAFLD) is a hepatic manifestation of MetS, and its development is associated with obesity and its complications [33,34]. Our study confirmed the significance of FLI as an indicator of NAFLD and of the important role fatty liver plays in the development of MetS. We used the ROC curve analysis to check FLI for diagnostic utility, which is evaluated based on its specificity and sensitivity. The area under the curve (AUC) for FLI was 0.985, with 95% confidence interval 0.946 to 0.998. Judging by the ROC-based cut-off points, it seems that FLI has an excellent diagnostic utility for MetS in the terms of both sensitivity and specificity, which is superior to the one found for CRP by Stefanska et al. [32]. The treatment of the patients diagnosed with MetS includes changes in their lifestyle such as increasing their physical activity and improving their dietary habits. It is important to start treating it as early as possible because of its association with disorders such as diabetes, cardiovascular disease, cancer, and markedly lower life expectancy. Early treatment is not possible without early diagnosis. Our study has singled out signs which point to an early stage MetS: FLI as the most reliable early predictor of MetS and the absence of oxidative damage. Acknowledgments We wish to thank the Department of Biochemistry and Molecular Biology at the Zagreb University Faculty of Pharmacy and Biochemistry for financial support and the Mutagenesis Unit of the Institute for Medical Research and Occupational Health, Zagreb for the experimental part of the study. We also wish to thank Nevenka Kopjar and Davor Želježić for help in preparing the manuscript and Dado Čakalo for editing it to read better.
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Contents lists available at SciVerse ScienceDirect
Clinical Biochemistry journal homepage: www.elsevier.com/locate/clinbiochem
Fatty liver index as an indicator of metabolic syndrome Dinko Rogulj a, Paško Konjevoda b, Mirta Milić c, Marin Mladinić c, Ana-Marija Domijan d,⁎ a
Polyclinic “Sunce”, Zagreb, Croatia NMR Center, Institute Ruđer Bošković, Zagreb, Croatia c Mutagenesis Unit, Institute for Medical Research and Occupational Health, Zagreb, Croatia d Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia b
a r t i c l e
i n f o
Article history: Received 13 July 2011 Received in revised form 29 September 2011 Accepted 18 October 2011 Available online 26 October 2011 Keywords: Fatty liver index Metabolic syndrome Oxidative stress REPTree and SimpleCART algorithms ROC curve analysis
a b s t r a c t Objective: The aim of this study was to find an early indicator of metabolic syndrome (MetS). Design and methods: We measured several anthropometric, biochemical, haematological, and oxidative damage parameters in 128 middle-aged Caucasian men divided into two groups: patients with MetS (n = 69) and healthy controls (n = 59), and used Weka REPTree and SimpleCART algorithms to identify the most reliable predictor of MetS. Results: Oxidative damage parameters did not differ between the groups, suggesting that oxidative damage is less prominent at the early stage of MetS. The algorithms singled out fatty liver index (FLI) as the best variable for discriminating between healthy and MetS subjects. This finding was confirmed by the receiver–operating characteristic (ROC) curve analysis, which set FLI 68.53 as the threshold value for MetS diagnosis. Conclusions: FLI is the most reliable tool for diagnosing MetS. The absence of oxidative damage does not rule out oxidative stress but may indicate that MetS is at an early stage. © 2011 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.
Introduction The metabolic syndrome (MetS) is a modern disease, characterised by excess weight, high blood sugar, blood pressure, and triglycerides, and low HDL cholesterol [1]. The incidence of MetS has been soaring over the past two decades [2]. In the European Union, the epidemics of overweight and obesity are associated with sedentary lifestyle and physical inactivity [3]. The aetiology of MetS is still unclear [4], but may involve a variety of factors such as modern lifestyle, environmental and hereditary factors, insulin resistance, growth hormone deficiency, inflammatory factors, and oxidative stress [2,4]. According to Martinez-Gonzalez et al. [3], MetS is an interplay of lifestyle and environmental factors, while other authors report genetic susceptibility in some populations as the most prominent factor in MetS development [5]. Early diagnosis of MetS can help to prevent complications of MetS such as type 2 diabetes, cardiovascular disease, cancer, and premature death. Therefore, the aim of our study was to identify the most
Abbreviations: ALT, alanine transaminase; AUC, area under curve; BMI, body mass index; CRP, C-reactive protein; FLI, fatty liver index; GGT, γ-glutamyltransferase; hOGG1, human 8-hydroxyguanine DNA-glycosylase; IL-6, interleukin-6; MetS, metabolic syndrome; NAFLD, non-alcoholic fatty liver disease; NECEP-ATP III, The National Cholesterol Education Program-Adult Treatment Panel III; NEFA, nonesterified fatty acids; ROC, Receiver–operating characteristic; ROS, reactive oxygen species; RNS, reactive nitrogen species; TAC, total antioxidant capacity; TNFα, tumor necrosis factor alfa. ⁎ Corresponding author at: Faculty of Pharmacy and Biochemistry, University of Zagreb, A. Kovačića 1, 10000 Zagreb, Croatia. Fax: + 385 1 63 94 400. E-mail address: [email protected] (A.-M. Domijan).
reliable parameter for early diagnosis of MetS among several anthropometric, biochemical, haematological, and oxidative damage parameters measured in a group of healthy middle-aged men diagnosed with MetS for the first time and in age-matched controls. Materials and methods Subjects The study included 128 non-smoking men, aged 35 to 55 (47.2 ± 5.3) years, all from Zagreb (Croatia). They were randomly recruited from patients having annual medical checkups at the polyclinic. The subjects were divided into two groups: patients for the first time diagnosed with MetS (n = 69) and age-matched healthy individuals (n = 59). The subjects completed a standardized questionnaire with information about age, dietary habits, smoking, alcohol consumption, and medical and occupational history. The exclusion criteria were: neoplastic diseases, heart failure, recent surgery, inflammatory diseases such as infections, autoimmune disorders, liver and kidney disease. Each subject gave informed consent to participate in this study. The study was approved by the institutional ethics committee and observed the ethical principles of the Declaration of Helsinki. Clinical examination and anthropometric measurements We measured sitting systolic and diastolic blood pressures in the right upper arm after 5 min of rest.
0009-9120/$ – see front matter © 2011 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved. doi:10.1016/j.clinbiochem.2011.10.014
D. Rogulj et al. / Clinical Biochemistry 45 (2012) 68–71
Anthropometric measurements included height, weight, body mass index (BMI), and waist circumference. Waist circumference was measured at midway between the lowest rib and the iliac crest. Hip circumference was measured over the widest part of the gluteal region. These two measurements served to calculate the waist-tohip and hip-to-waist ratio. We also measured the thickness of the abdominal subcutaneous fat tissue as a distance between the skin and linea alba with a linear 10 MHz probe (UZV, Philips, Eindhoven, The Netherlands). Intraabdominal fat thickness was measured as a distance between the anterior wall of the aorta and the posterior surface of the rectus abdominis muscle midway between the xiphoid process and the umbilicus. For this measurement we used a convex 3.5 MHz probe (UZV, Philips, Eindhoven, The Netherlands). Epicardial fat thickness was measured perpendicularly on the free wall of the right ventricle from shortaxis view at end-systole with cardiac 3.5 MHz probe (UZV, Philips, Eindhoven, The Netherlands) and corresponded to the echo-free distance between the outer myocardial wall and the visceral layer of the pericardium. Diagnosis of the metabolic syndrome The diagnosis of MetS was based on the National Cholesterol Education Program - Adult Treatment Panel III (NCEP-ATP III) guidelines [6] that are, according to Kuzmanić et al. [7], more appropriate for the Croatian climatic and ethnic setup than the WHO guidelines. Subjects having more than three of the following features were considered to have MetS: (I) abdominal obesity: waist circumference ≥102 cm; (II) hypertriglyceridaemia: ≥1.69 mmol/l; (III) HDL cholesterol: ≤1.04 mmol/l; (IV) blood pressure: ≥130/85 mmHg; (V) fasting blood glucose: ≥6.1 mmol/l. Biochemistry and haematology Plasma triglycerides, HDL cholesterol, alanine transaminase (ALT), γ-glutamyltransferase (GGT), C-reactive protein (CRP), urates, and fibrinogen were analysed with biochemical tests procured from Herbos Dijagnostika (Sisak, Croatia) or Siemens Healthcare Diagnostics (Munich, Germany) using an automated analyser Olympus AU 400 (Japan).
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Leukocyte and neutrophil counts were measured with an automated Beckman Coulter HlX flow cytometer (Brea, CA, USA). As oxidative damage parameters, we determined glutathione (free-radical scavenger) and malondialdehyde (product of lipid peroxidation) in plasma. Glutathione was measured according to the Ellman's method [8] using a spectrophotometer set at 412 nm, and malondialdehyde was determined with the thiobarbituric acid assay, using an HPLC with fluorescence detector set at λex 527 nm and λem 551 nm [9]. Fatty liver index (FLI) was calculated according the following formula [10]: FLI ¼
eð0:953lnðtriglyceridesÞþ0:139BMIþ0:718lnðggtÞþ0:053waistcircumf erence−15:745Þ 100 1 þ eð0:953lnðtriglyceridesÞþ0:139BMIþ0:718lnðggtÞþ0:053waistcircumf erence−15:745Þ
Evaluation of DNA damage One of the most reliable and common methods to evaluate DNA damage on a single-cell level is the comet assay [11,12]. We evaluated the levels of primary and oxidative damage to DNA in peripheral blood leukocytes, as they reflect an overall level of DNA damage in the body. Primary DNA damage was assessed using the alkaline comet assay, which mostly detects single-strand DNA damage. The assay was performed according to Singh et al. [13], with minor modifications. Slides treated with 0.1 mM hydrogen peroxide solution for 5 min served as positive controls. Oxidative DNA damage was assessed using the hOGG1-modified comet assay, which detects oxidative lesions excised by human 8hydroxyguanine DNA-glycosylase (hOGG1). The assay was performed as described by Smith et al. [14]; and the hOGG1 enzyme was procured from Trevigen (MD, USA). All samples were analysed under epifluorescent microscope (Zeiss, Germany) with the excitation filter between 515 nm and 560 nm, connected to a black-and-white CCD camera (Cohu Inc., San Diego, CA). One hundred cells were analysed per slide using the Comet Assay II automatic digital analysis system (Perceptive Instruments Ltd., Suffolk, Halstead, UK) to determine tail length and tail intensity. Tail length (μm) is the distance to which DNA migrated from the centre of the body of the nuclear core when exposed to electric
Table 1 Parameters measured in the control and metabolic syndrome-diagnosed group, and the significance of the differences between the groups (p) analysed with Hotelling's T2 test. Parameter
Control (n)
MetS (n)
p value
Age (years) Waist circumference (cm) Waist-to-hip ratio Hip-to-waist ratio Lean muscle mass (% of body weight) Epicardial fat tissue (mm) Subcutaneous fat tissue (cm) Intraabdominal fat thickness (cm) HDL cholesterol (mmol/L) Triglycerides (mmol/L) CRP (mg/L) ALT (U/L) GGT (U/L) urates level (μmol/L) Fibrinogen (g/L) Leukocytes (109/L) Neutrophils (109/L) Fatty liver index (FLI) Alkaline comet test – tail length (μm) Alkaline comet test – tail intensity (%) hOGG1comet test - tail length (μm) hOGG1Comet test - tail intensity (%) Glutathione (μmol/L) Malondialdehyde (μmol/L)
46.9 ± 5.09 (n = 59) 88.9 ± 8.3 (n = 59) 0.88 ± 0.05 (n = 59) 1.14 ± 0.07 (n = 59) 37.17 ± 3.30 (n = 55) 4.09 ± 1.47 (n = 52) 1.31 ± 0.64 (n = 56) 3.75 ± 1.57 (n = 56) 1.58 ± 0.33 (n = 58) 1.14 ± 0.55 (n = 59) 1.18 ± 1.24 (n = 55) 24.14 ± 9.82 (n = 59) 23.42 ± 12.33 (n = 59) 302.08 ± 48.59 (n = 56) 3.46 ± 0.84 (n = 58) 5.75 ± 1.19 (n = 57) 3.22 ± 0.93 (n = 56) 30.23 ± 17.53 (n = 58) 16.87 ± 3.37 (n = 36) 2.22 ± 1.74 (n = 36) 15.31 ± 3.13 (n = 30) 2.25 ± 2.10 (n = 30) 4.38 ± 1.22 (n = 35) 2.26 ± 1.46 (n = 36)
47.4 ± 5.65 (n = 69) 111.49 ± 10.84 (n = 69) 0.98 ± 0.06 (n = 69) 1.02 ± 0.06 (n = 69) 31.15 ± 3.22 (n = 63) 7.71 ± 1.76 (n = 62) 1.81 ± 0.64 (n = 63) 7.33 ± 2.47 (n = 63) 1.23 ± 0.29 (n = 69) 2.98 ± 1.85 (n = 69) 3.47 ± 3.17 (n = 68) 51.77 ± 35.11 (n = 69) 61.16 ± 43.94 (n = 69) 393.98 ± 82.05 (n = 69) 3.41 ± 0.65 (n = 68) 7.19 ± 1.76 (n = 67) 3.96 ± 1.48 (n = 67) 87.83 ± 14.08 (n = 69) 16.22 ± 2.47 (n = 36) 1.90 ± 1.10 (n = 36) 15.00 ± 3.46 (n = 32) 2.51 ± 4.78 (n = 32) 4.13 ± 0.25 (n = 36) 3.49 ± 3.16 (n = 38)
0.663 b0.001 b0.001 b0.001 b0.001 b0.001 0.001 b0.001 b0.001 b0.001 0.004 b0.001 0.003 b0.001 0.672 0.004 0.142 b0.001 0.912 0.671 0.120 0.159 0.096 0.302
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Table 2 The results of REPTree and SimpleCART algorithms of the Weka software for fatty liver index (FLI) as selected as the best discriminatory variable between the healthy and MetS group with 93.75% accuracy. IF FLI b 70.56 THEN CLASS = HEALTHY (42.5/4) [23/3] IF FLI >= 70.56 THEN CLASS = MetS (42.5/0.5) [20/0] Overall classification Leave-one-out cross-validation Correctly Classified Instances Correctly Classified Instances 120 (93.75%) 116 (90.625%) === Confusion Matrix === === Confusion Matrix === a b b– classified as a b b– classified as 62 7 | MetS = a 62 7 | MetS = a 1 58 | HEALTHY = b 5 54 | HEALTHY = b
field. Tail intensity (%) is the percentage of the genomic DNA that migrated during the electrophoresis from the nuclear core to the tail. Both tail length and tail intensity are measured automatically by image analysis software and are used to evaluate the extent of DNA damage.
Table 3 The results of the receiver–operating characteristic (ROC) curve analysis of fatty liver index (FLI) in the prediction of MetS. Optimal cut-off value between the healthy and MetS group Area under the ROC curve (AUC) 95% confidence interval p value
68.53 0.985 0.946 to 0.998 b 0.0001
and intensity measured with the hOGG1 comet assay (modified comet assay that detects oxidative DNA lesions). RepTree and SimpleCART algorithms singled out FLI as the best discriminatory variable between patients with MetS and healthy persons (Table 2). The results of both algorithms were identical and show that when FLI is equal or over 70.56, then a person can be classified as MetS with an accuracy of 93.75% (Fig. 1). A parallel ROC curve analysis for the optimal cut-off FLI gave 68.53 as the threshold for discriminating between patients with MetS and healthy persons (see Table 3 and Fig. 2). Discussion
Statistics Data are presented as means and standard deviations (SD). Differences in parameters between the groups were analysed using Hotteling's T2 test, a multivariate version of the t-test. To find the variable that would best differentiate between healthy and MetS subjects, we used the REPTree and SimpleCART algorithms [15,16]. The results of these algorithms can be presented as a decision or regression tree. REPTree builds a decision or regression tree using information gain/variance reduction, while SimpleCART employs a minimal cost-complexity pruning strategy for classification. The optimal cutoff value of variable FLI, selected by REPTree and SimpleCART, was confirmed by receiver–operating characteristic (ROC) curve analysis [15,16]. Data were analysed using R (version 2.12.2) and Weka (version 3.6.2) software [16,17]. Results The results of measured parameters are presented in Table 1. Waist circumference, waist-to-hip ratio, hip-to-waist ratio, lean muscle mass, epicardial, intraabdominal, and subcutaneous fat thickness significantly differed between MetS patients and controls (p b 0.001; Hotelling's T2 Test). A similar difference was observed for HDL cholesterol, triglycerides, ALT, urates, CRP, GGT, and leukocytes. However, the two groups did not differ in fibrinogen and blood neutrophils. Neither did they differ in tail length and tail intensity measured with the alkaline comet assay, which suggests that there was no difference in primary DNA damage. The same is true for oxidative damage parameters glutathione, malondialdehyde, and tail length
Our study has shown a significant difference between healthy individuals and MetS patients in anthropometric, biochemical, and haematological parameters (save for fibrinogen and neutrophils). These findings are consistent with similar studies [18,19]. It is well known that visceral adipose tissue releases bioactive substances such as nonesterified fatty acids (NEFA), cytokines, and other pro-inflammatory factors [20]. It is responsible for inflammation, endothelial dysfunction, hyperlipidaemia, atherogenesis, progression of hypertension, and development of insulin resistance; all the processes characteristic of MetS [2]. A recent study on mice by Park et al. [21] demonstrated that chronic inflammatory response caused by obesity and enhanced production of cytokines such as interleukin-6 (IL-6) and tumour necrosis factor alfa (TNF-α) promote the development of hepatocellular carcinoma [21]. Some studies indicate that oxidative stress may play an important role in the pathogenesis of MetS [18,22-24]. Coppak [25] suggests that adipocytes stimulate reactive oxygen species/reactive nitrogen species (ROS/RNS) production, which leads to oxidative damage. In our study, however, the measured parameters of oxidative damage (hOGG1 comet test-tail length, hOGG1 comet test-tail intensity, glutathione and malondialdehyde) did not differ between control and MetS subjects. Similarly, Seet et al. [19] did not see any significant difference in several plasma or serum oxidative damage parameters as well as in urinary 8-OHdG between MetS-diagnosed and healthy subjects. Another similarity with our study is that Seet et al. recruited a
fatty liver index (FLI) 100
80
< 70.56
Sensitivity
fatty liver index
60
40
70.56 20
healthy
metabolic syndrome
0 0
20
40
60
80
100
100-Specificity Fig. 1. The decision-tree according to REPTree and SimpleCART algorithms. A cut-off value of 70.56 for fatty liver index (FLI) was found as the optimal value for discrimination between healthy and metabolic syndrome group.
Fig. 2. The receiver–operating characteristic (ROC) curve analysis of fatty liver index (FLI) in the prediction of MetS.
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younger population (43 ± 14 years). In contrast, Demirbag et al. [22], who found significant differences in oxidative damage parameters, investigated somewhat older population (57 ± 9 years). These discrepancies between the studies may be related to DNA repair capacity; Piperakis et al. [26] have found that it drops with age, as 60 to 70year-olds showed higher basal DNA damage than 40 to 50-year-olds. In other words, the absence of oxidative damage - because the defence system (ROS scavengers) is working properly at younger age, while other parameters are increased - points to an early stage of MetS. The absence of oxidative damage however does not mean that there is no oxidative stress. Our study revealed statistically higher levels of urates and GGT in the MetS group than in controls. Both parameters are associated with oxidative stress. Urate is catalysed by xanthine oxidase, but xanthine oxidase also catalyses the formation of ROS [27]. In fact, there is evidence that the urate molecule has free-radical scavenging properties in vitro[28]. GGT level, in turn, correlates well with the levels of prooxidants. In fact, GGT is an early and sensitive marker of oxidative stress that can serve as an indirect marker of the risk of future oxidative damage [29,30]. Therefore, increased levels of urates and GGT in the MetS group in our study suggest that MetS in our patients may be at an early stage, because at the early stage of MetS, ROS production is elevated but ROS scavenging is working properly and oxidative damage is not detected. We performed further statistical analysis of all our variables to identify or confirm predictors of MetS. Earlier research has shown that higher CRP correlates well with MetS [18,31] and that its application is simple in the clinical setting. In fact, two recent studies [19,32] have confirmed it as a superior predictor of MetS in health men and women. In our study, however, the REPTree and SimpleCART algorithms singled out FLI as the best independent predictor of MetS. This is not surprising, since FLI is a score derived from three parameters used for the diagnosis of MetS (BMI, waist circumference, and triglycerides) plus GGT. FLI has been developed by Bedogni et al. [10] as an accurate and easy-to-deploy routine tool which correlates well with ultrasonography of fatty liver disease [10]. The non-alcoholic fatty liver disease (NAFLD) is a hepatic manifestation of MetS, and its development is associated with obesity and its complications [33,34]. Our study confirmed the significance of FLI as an indicator of NAFLD and of the important role fatty liver plays in the development of MetS. We used the ROC curve analysis to check FLI for diagnostic utility, which is evaluated based on its specificity and sensitivity. The area under the curve (AUC) for FLI was 0.985, with 95% confidence interval 0.946 to 0.998. Judging by the ROC-based cut-off points, it seems that FLI has an excellent diagnostic utility for MetS in the terms of both sensitivity and specificity, which is superior to the one found for CRP by Stefanska et al. [32]. The treatment of the patients diagnosed with MetS includes changes in their lifestyle such as increasing their physical activity and improving their dietary habits. It is important to start treating it as early as possible because of its association with disorders such as diabetes, cardiovascular disease, cancer, and markedly lower life expectancy. Early treatment is not possible without early diagnosis. Our study has singled out signs which point to an early stage MetS: FLI as the most reliable early predictor of MetS and the absence of oxidative damage. Acknowledgments We wish to thank the Department of Biochemistry and Molecular Biology at the Zagreb University Faculty of Pharmacy and Biochemistry for financial support and the Mutagenesis Unit of the Institute for Medical Research and Occupational Health, Zagreb for the experimental part of the study. We also wish to thank Nevenka Kopjar and Davor Želježić for help in preparing the manuscript and Dado Čakalo for editing it to read better.
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