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Monika Fekete Department of Public Health, Faculty of Medicine, Semmelweis University, Budapest, Hungary

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Gergo Szollosi Department of Family and Occupational Medicine, Faculty of Public Health, University of Debrecen, Debrecen, Hungary

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Stefano Tarantini Oklahoma Center for Geroscience and Healthy Brain Aging, Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73132, USA

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Andrea Lehoczki National Institute for Haematology and Infectious Diseases, Department of Haematology and Stem Cell Transplantation, South Pest Central Hospital, Budapest, Hungary

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Anna N Nemeth Department of Public Health, Faculty of Medicine, Semmelweis University, Budapest, Hungary

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Csenge Bodola Department of Public Health, Faculty of Medicine, Semmelweis University, Budapest, Hungary

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Luca Varga Department of Public Health, Faculty of Medicine, Semmelweis University, Budapest, Hungary

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Janos Tamas Varga Department of Pulmonology, Semmelweis University, Budapest, Hungary

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https://orcid.org/0000-0002-8552-1336
Open access

Abstract

Background

Decreased physical activity significantly increases the probability of prevalent metabolic syndrome (MetS) with substantial impact on the expected course of COPD.

Objective

Our research aims to assess the metabolic consequences of chronic obstructive pulmonary disease (COPD) and evaluate the prevalence of MetS and its interrelations with age, sex, comorbidities, drug intake, degree of decreased lung function, nutritional status, physical activity and quality of life.

Methods

A cross-sectional study was performed on a random sample (n = 401) at the Department of Pulmonary Rehabilitation of the National Koranyi Institute of Pulmonology from March 1, 2019 to March 1, 2020 in Budapest, Hungary. Anthropometric and respiratory function tests and laboratory parameters of all patients were registered.

Results

MetS occurred in 59.1% of COPD patients with significant gender difference (male: 49.7% female: 67.6%). Concerning BMI, the prevalence of MetS was higher with BMI≥25 kg m−2 (P < 0.0001). Patients with this syndrome had significantly worse FEV1%pred (43 (30–56) vs. 47 (36–61); P = 0.028), lower quality of life (CAT: 26 (21–32) vs. 24.5 (19–29); P = 0.049) and significantly more frequent exacerbations (2 (1–3) vs.1 (0–2); P < 0.05), than patients without MetS. The prevalence of comorbidities were higher in overweight/obese patients (BMI> 25 kg m−2).

Conclusions

In COPD patients MetS negatively affect respiratory function and quality of life and promotes exacerbations of the disease. MetS is related to nutritional status and the level of systemic inflammation in COPD patients.

Abstract

Background

Decreased physical activity significantly increases the probability of prevalent metabolic syndrome (MetS) with substantial impact on the expected course of COPD.

Objective

Our research aims to assess the metabolic consequences of chronic obstructive pulmonary disease (COPD) and evaluate the prevalence of MetS and its interrelations with age, sex, comorbidities, drug intake, degree of decreased lung function, nutritional status, physical activity and quality of life.

Methods

A cross-sectional study was performed on a random sample (n = 401) at the Department of Pulmonary Rehabilitation of the National Koranyi Institute of Pulmonology from March 1, 2019 to March 1, 2020 in Budapest, Hungary. Anthropometric and respiratory function tests and laboratory parameters of all patients were registered.

Results

MetS occurred in 59.1% of COPD patients with significant gender difference (male: 49.7% female: 67.6%). Concerning BMI, the prevalence of MetS was higher with BMI≥25 kg m−2 (P < 0.0001). Patients with this syndrome had significantly worse FEV1%pred (43 (30–56) vs. 47 (36–61); P = 0.028), lower quality of life (CAT: 26 (21–32) vs. 24.5 (19–29); P = 0.049) and significantly more frequent exacerbations (2 (1–3) vs.1 (0–2); P < 0.05), than patients without MetS. The prevalence of comorbidities were higher in overweight/obese patients (BMI> 25 kg m−2).

Conclusions

In COPD patients MetS negatively affect respiratory function and quality of life and promotes exacerbations of the disease. MetS is related to nutritional status and the level of systemic inflammation in COPD patients.

Introduction

Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory lung disease that causes obstructed airflow limitation from the lungs. COPD is currently the third leading cause of death worldwide [1]. In Hungary, about one fifth of smokers suffer from COPD, which is approximately 500,000 people, but most of them seek medical attention late [2]. In Organisation for Economic Co-operation and Development (OECD) countries, this chronic lung disease affects over 5–8% of the population, and its prevalence is unfortunately increasing rapidly year by year [1, 2]. In Hungary, 5–6,000 new cases are registered every year, one third of whom suffer from chronic respiratory failure [2].

The underlying cause of COPD is the inflammatory response to inhaled particles (eg. cigarette smoke, air pollution) which have a wide range of destructive consequences [3]. In that regard it is important that 31% of males and 23% of females in Hungary smoke regularly [4]. COPD is also a systemic disease, which has important manifestations beyond the lungs, including skeletal muscle atrophy and dysfunction, an increased risk of developing cardiovascular diseases, osteoporosis, anxiety and depression, anemia other chronic diseases [3]. The systemic effects of COPD are mediated by low-grade, chronic systemic inflammation [1–4].

The metabolic syndrome (MetS) is a cluster of cardiometabolic conditions frequently found among COPD patients (Table 1) [5–10]. Metabolic syndrome is closely linked to overweight or obesity, physical inactivity and insulin resistance. Recent observations indicate that obese COPD patients show higher prevalence of MetS compared to the healthy group of similar age and BMI [10, 11]. In affected patients MetS exacerbates progression of COPD by promoting inflammation [11]. Obesity rates have continued to surge in Hungary in the past decade, and recent data findicate that approximately 64 percent of the total adult population is overweight or obese [12]. However, the prevalence of MetS in COPD patients in Hungary and its effect of their general health and quality of life has not been documented.

Table 1.

Basic and partially disputed components of metabolic syndrome and further factors in its development

Basic components Additional components Other factors
insulin resistance hyperuricemia Smoking
hyperinsulinaemia increased CRP oxidative stress
glucose intolerance hyperhomocysteinaemia increased resistin
hypertension PCOS increased TNF-α, IL-6
dyslipidaemia hyperfibrinogenemia endothelial dysfunction
diabetes mellitus microalbuminuria decreased adiponectin
obesity (cental type) sleep apnoea syndrome increased leptin

CRP: C-reactive protein; PCOS: Polycystic ovary syndrome; TNF-α: Tumor necrosis factor-alpha; IL-6: Interleukin-6.

The aim of our study was to assess the prevalence of MetS in Hungarian COPD patients and its interrelationship with age, sex, comorbidities, drug intake, degree of decreased lung function, nutritional status, physical activity, exacerbation of the disease in the previous year and quality of life. As direct indices of insulin resistance in routine clinical practice are usually cannot be readily measured, various simplified diagnostic criteria for MetS have been developed over the past 2 decades. In our study, we used the diagnostic criteria for MetS developed by the International Diabetes Federation (IDF) [13].

Methods

Study design and target population

Data collection was performed with volunteer participants, anonymously, using self-completed paper-based questionnaires between 1st March, 2019 and 1st March, 2020 among patients in the Department of Pulmonary Rehabilitation of the National Korányi Institute for Pulmonology, Budapest, Hungary. The study involved 401 COPD patients over 40 years of age in a pulmonary rehabilitation ward. No one refused to take part in the study. Participant recruitment was prematurely discontinued due to the COVID-19 pandemic, which resulted in strict lockdown measures in Hungary [14, 15]. Prior to completing the questionnaire, patients received detailed information about the purpose and time of the survey, anonymous and aggregated data processing, and the essence of the research. The inclusion criteria were: age ≥40 years and diagnosis of COPD (post-bronchodilation of FEV1/FVC<70%) [16]. Exclusion criteria included acute exacerbation, chronic oxygen therapy (resting oxygen saturation less than 89%), history of asthma, lung surgery or severe comorbidities such as severe heart failure or severe liver or kidney failure; acute coronary syndrome or acute cerebrovascular event. The study was approved by the Regional Institutional Scientific Research Ethics Committee of Semmelweis University (licence number: TUKEB 44402-2/2018/EKU) and complies with the Declaration of Helsinki. Participants in the research did not receive any financial, any remuneration or any other allowances.

Assessment of physiological parameters and measurements

We measured the post-bronchodilator FEV1 (forced expiratory volume in the first second) in each patient and registered all estimated data in percentage. Patients were classified in GOLD A-D stages according to the current and future risk parameters, like spirometric values, relevant symptoms and exacerbation rate [16]. COPD exacerbation was defined as a significant change of the patient’s initial symptoms (dyspnea, cough, sputum production), which is an acute event at a level exceeding the daily variability of symptoms, leading to a change in therapy [16].

Quality of life examination

We used the COPD Assessment Test (CAT test) to assess quality of life [17]. The patients responded to eight questions, scoring the symptoms from 0 to 5, where 0 indicates healthy condition, while 5 points indicate severe symptoms. Cough, the amount of sputum, hyperinflation, the load-bearing capacity when climbing stairs, and the level of energy were evaluated subjectively, as well as whether the patients dare to leave home, or whether their illness affects their sleep. Questionnaires were completed by the participants under the coordinators’ supervision in the Institute. Additional data were collected using a self-designed questionnaire assessing patients’ education, smoking and eating habits, and usage of inhaled medications to treat COPD. During the 6-minute walking test participants were asked to walk for six minutes indoors, while their walking distance was measured [18].

Assessment of nutritional status

To assess the nutritional status of the patients their weight and height were measured. To calculate the body mass index (BMI; kg m−2), the patients’ weight (kg) was divided by the square of their height (m). Patients were divided into three groups based on their BMI: normal weight (21–25 kg m−2), overweight/obese (BMI>25 kg m−2), and underweight (BMI<21 kg m−2). This classification is based on the NHLBI classification [19], because patients with COPD are at increased risk of death if the BMI<21 kg m−2, which is in line with the BMI used in the BODE index of COPD severity [20]. In COPD patients with a body weight of BMI<21 kg m−2 the initiation of nutritional therapy is indicated. Abdominal circumference and arm circumference were measured with a centimetric tape in all patients.

Blood tests

A fasting blood test was conducted in the central laboratory of National Koranyi Institute of Pulmonology. Serum CRP was measured with a high sensitivity (hs) immunoassay method and the lipid profile (total cholesterine, triglyceride, LDL and HDL) was obtained. Patients were in clinically stable condition, without fever and respiratory infection throughout the measurements.

Diagnostic criteria for metabolic syndrome

The diagnosis of MetS was made on the basis of a set of diagnostic criteria as defined by the International Diabetes Federation [13].

  • Central obesity: Waist circumference ≥94 cm in males, and ≥80 cm in females;

Plus at least two of the following:

  • Increased fasting plasma glucose: ≥ 5.6 mmol L−1, or previously diagnosed type 2 diabetes;

  • Increased serum triglycerides: ≥ 1.7 mmol L−1, or specific treatment for this lipid abnormality;

  • Reduced HDL cholesterol: <1.29 mmol L−1 (women), and <1.03 mmol L−1 (men), or specific treatment for this lipid abnormality;

  • Increased blood pressure: systolic values ≥130 Hgmm and/or diastolic values ≥85 Hgmm, or treatment of previously diagnosed hypertension [13].

Statistical analysis

Since most of the continuous data did not follow the normal distribution (verified by Sapphiro-Wilk test), non-parametric statistical methods were used. Continuous variables were interpreted and represented by medians and interquartile ranges. Categorical data were presented with case numbers and proportions. Mann-Whitney tests were used to detect the differences of continuous variables between the two groups; in case of more than two groups Kruskal-Wallis tests were conducted. Frequencies differences of categorical variables were examined by Fisher’s exact test. Spearman’s correlation was used to test the relationship between continuous variables, which were interpreted by Spearman’s rho and its P-values. All statistical analyses were conducted with STATA SE-10.0 (StataCorp, College Station, TX).

Results

The present study involved 401 stable COPD patients. The median age of the study participants was 67 (61–73) years. 47.6% of study participants were men and 52.4% were women. Demographic characteristics and data on tobacco smoking (Table 2) show that 5.2% of the patients have never smoked, 51.4% quit the habit and 43.4% were current smokers. The GOLD standardization indicated the following proportions: GOLD A: 7.5% GOLD B: 29.9% GOLD C: 45.4% and GOLD D: 17.2%.

Table 2.

Characteristics of the patients by BMI categories

Underweight BMI<21 kg m−2 n = 89 Normal weight BMI 21–25 kg m−2 n = 110 Overweight BMI>25 kg m−2 n = 202 All patiens n = 401 P-value
Median Age (years) (IQR) 66.0 (60–71) 69.5 (63–74) 67.0 (61–72) 67 (61–73) 0.026
Men (n, %) 47 (52.80) 56 (50.91) 88 (43.56) 191 (47.63) 0.232
Women (n, %) 42 (47.20) 54 (49.09) 114 (56.44) 210 (52.37)
Smoking status
 Current smokers (n, %) 51 (57.30) 40 (36.36) 83 (41.09) 174 (43.39) 0.006
 Former smokers (n, %) 36 (40.45) 60 (54.55) 110 (54.46) 206 (51.37)
 Never smokers (n, %) 2 (2.25) 10 (9.09) 9 (4.45) 21 (5.24)
SBP/DBP (mmHg) 136.1/80.6 140.9/81.3 146.8/86.6 144.8/83.8 <0.001
FEV1 (ref%) 36.0 (28–48) 45.5 (35–58) 49.0 (39–63) 46 (34–58) <0.001
FVC (%) 68.0 (54–79) 73.5 (60–85) 70.0 (58–83) 70 (58–83) 0.062
FEV1/FVC (%) 42.0 (38–52) 49.0 (42–62) 56.5 (48–66) 51 (42–63) <0.001
GOLD stage (n, %)
 A 3 (3.37) 12 (10.91) 15 (7.43) 30 (7.48) <0.001
 B 14 (15.73) 28 (25.45) 78 (38.61) 120 (29.92)
 C 47 (52.81) 49 (44.55) 86 (42.57) 182 (45.39)
 D 25 (29.09) 21 (19.09) 23 (11.39) 69 (17.21)
C-Reactive protein (mg L−1) 4 (1–21) 6 (2–16) 7 (2–18) 6 (1.9–18.1) 0.481
Metabolic variables
 Triglycerides (mmol L−1) 1.1 (0.9–1.7) 1.3 (1–2) 1.6 (1.2–2.1) 1.5 (1–2) <0.001
 HDL-cholesterol (mmol L−1) 1.5 (1.2–1.8) 1.4 (1.2–1.7) 1.3 (1.0–1.6) 1.4 (1.1–1.7) <0.001
 LDL-cholesterol (mmol L−1) 2.7 (2.0–3.3) 2.8 (2.3–3.4) 2.9 (2.2–3.5) 2.9 (2.2–3.4) 0.547
 Fasting glucose (mmol L−1) 5.1 (4–7) 6.0 (5–7) 6.6 (5–8) 6 (5–7.5) <0.001
 HbA1c (mmol mol−1) 37.1 (33.1–40.4) 37.4 (34.4–40.3) 39.0 (34.7–45.2) 37.6 (34.1–42) 0.005
Quality of Life
 CAT (points, IQR) 27 (22–33) 25.5 (19–31) 25.0 (18–30) 26 (20–31) 0.046
Comorbidity
 Hypertension (n, %) 51 (57.30) 76 (69.09) 166 (82.18) 293 (73.07) <0.001
 Diabetes (n, %) 6 (6.75) 16 (14.55) 51 (25.25) 73 (18.20) 0.003
 Metabolic syndrome (n, %) 20 (22.47) 57 (51.81) 161 (79.70) 238 (59.35) <0.001
 Ischemic heart disease (n, %) 43 (48.30) 44 (40.00) 119 (58.91) 206 (51.37) 0.014
 Psychiatric history (n, %) 10 (11.24) 11 (10.00) 29 (14.36) 50 (12.47) 0.390
Medication (n)
 SABA 30 41 74 145 (36.16) <0.001
 LAMA 5 5 19 29 (7.23) 0.216
 LABA 4 3 5 12 (2.99) 0.397
 LABA and LAMA 4 6 13 23 (5.74) 0.604
 ICS and LABA 9 19 26 54 (13.47) 0.136
 LABA and LAMA and ICS 22 26 50 98 (24.44) 0.837
 Supplemented with  Theophylline 36 39 61 136 (33.92) 0.178
 Supplemented with LTRA 5 7 11 23 (5.74) 0.239
 No data available 8 9 9 26 (6.48) -
 Influenza vaccination (n, %) 16 (17.98) 42 (38.18) 56 (27.72) 114 (28.43) 0.002
 Pneumococcal vaccination  (n, %) 8 (8.99) 22 (20.00) 20 (9.90) 50 (12.47) 0.010

Data are presented as median (IQR) or as frequency and percentage; GOLD: Global Initiative for Chronic Obstructive Lung Disease; CAT: COPD Assessment Test; mMRC: Modified Medical Research Council Dyspnoea Scale; FEV1: forced expiratory volume in 1 s post-bronchodilator; BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; 6MWD: six-minute walking distance; SD: Standard deviation; HDL: high density cholesterol level; LDL: low-density cholesterol level; HbA1c: glycated hemoglobin; SABA: short-acting bronchodilators; LABA: long-acting bronchodilators; LAMA: long-acting muscarinic antagonist; ICS: inhaled corticosteroids; LTRA: leukotriene receptor antagonists; P <0.05 means the two indicators were significantly correlated.

Concerning BMI, we assessed the risk groups and found that 22.2% had a BMI less than 21 kg m−2, 27.4% had values between 21 and 25 kg m−2 and more than half of the sample (50.4%) fell into the overweight/obese category (>25 kg m−2). These values showed a significant correlation with smoking status (P = 0.006), blood pressure (136.1/80.6 vs. 146.8/86.6; P < 0.001), respiratory function (FEV1 (%pred) 36.0 (28–48) vs. 49.0 (39–63); P < 0.0001), metabolic status (LDL, HDL cholesterin and tryglicerides) (P < 0.0001), quality of life (P = 0.046), and with comorbidities as hypertension (P < 0.0001), diabetes mellitus (P=0.003) and ischemic heart disease (P = 0.014). The MetS prevalence (P < 0.0001) and the rate of administered drugs (SABA, LAMA, LABA, ICS, Theophylline, LTRA) was higher among the overweight/obese (BMI > 25 kg m−2) patients (Table 2). Overweight/obese patients had higher LDL (mmol L−1) serum levels (2.7 vs. 2.9, P = 0.547) and significantly lower HDL (mmol L−1) levels (1.5 vs. 1.3, P < 0.0001). Serum trygliceride (mmol L−1) levels also significantly differed between overweight/obese patients (1.1 vs. 1.6, P < 0.0001) and the adequately nourished group of patients (Table 2).

The prevalence of MetS in the GOLD staging groups is presented in Table 3. The serum levels of C-reactive protein were elevated in 55.1% of patients and the highest values were measured in GOLD C and GOLD D. The prevalence of MetS in the entire cohort was 59.1%. It was significantly more common in women than in men (67.6% vs. 49.7%; P < 0.001). The highest MetS prevalence was measured in groups B (65.0%) and C (60.4%).

Table 3.

Patient severity classification and comparison of parameters according to GOLD stages

GOLD A GOLD B GOLD C GOLD D P-value
n (%) n = 30 (7.48) n = 120 (29.93) n = 182 (45.39) n = 69 (17.21)
CRP
Normal (≤5 mg L−1) 17 (56.67) 62 (51.67) 79 (43.41) 22 (31.88) 0.048
High (>5 mg L−1) 13 (43.33) 58 (48.33) 103 (56.59) 47 (68.12)
Metabolic syndrome
Present 17 (56.66) 78 (65.00) 110 (60.44) 32 (46.38) 0.180
Absent 13 (43.33) 42 (35.00) 72 (39.56) 37 (53.62)
BMI (kg m−2) 24.8 (22.2–26.8) 28.0 (23.7–32.6) 24.4 (20.8–29.7) 22.2 (19.3–27.4) <0.001
6MWD (m) 287 (200–400) 300 (177–365) 250 (150–325) 235 (130–300) 0.003
C-Reactive protein (mg L−1) 3.7 (1.7–13.4) 4.8 (1.7–16.1) 6.3 (2.2–15.7) 12.1 (4.4–27.7) 0.014
FEV1 (ref%) 92 (84–100) 62 (54–69) 42 (36–46) 24 (21–26) <0.001
FVC (%) 108 (100–116) 81 (70–90) 67 (59–76) 49 (43–55) <0.001

Data are presented as median (IQR) or as frequency and percentage. GOLD: Global Initiative for Chronic Obstructive Lung Disease; CRP: C-reactive protein; BMI: body mass index; 6MWD: six-minute walking distance; FEV1: forced expiratory volume in 1 s post-bronchodilator; FVC: forced vital capacity; P <0.05 means the two indicators were significantly correlated.

The IDF criteria of MetS are shown in Table 4. The most typical indicator of MetS was the greater waist circumference (95.4%). Further indicators were hypertension (89.8%) and hyperglicemia (79.3%), however elevated levels of serum tryglicerides (48.5%) and lower levels of serum HDL (38.8%) were not significantly related (Table 4).

Table 4.

Fulfilled criteria for Metabolic Syndrome

MetS criteria With MetS Without MetS All patients
n = 237 n = 164 n = 401
Elevated waist circumference ≥94 cm in males, ≥80 cm in females (n, %) 226 (95.36) 78 (47.56) 304 (75.81)
Elevated blood pressure: systolic ≥130 and/or diastolic ≥85 mmHg (or on therapy) (n, %) 213 (89.78) 80 (48.78) 293 (73.07)
Triglycerides ≥1.7 mmol L−1 (or on therapy) (n, %) 115 (48.52) 24 (14.63) 139 (34.66)
Fasting glucose≥ 5.6 mmol L−1 (or on therapy) (n, %) 188 (79.32) 51 (31.10) 239 (59.60)
HDL <1.03 mmol L−1 in males, <1.29 mmol L−1 in females (or on therapy) (n, %) 92 (38.82) 10 (6.01) 102 (25.44)

MetS: Metabolic Syndrome; HDL: high-density lipoprotein; The IDF (International Diabetes Federation) consensus worldwide definition of the metabolic syndrome. 2006.

A fraction of MetS patients showed impaired respiratory function, performed shorter 6-minute walking distance (m) (250 (150–330) vs. 277 (162–360); P = 0.235). Additionally, they had lower quality of life (CAT (points): 26 (21–32) vs. 24.5 (19–29); P = 0.049) and had significantly more exacerbations in the previous years (2 (1–3) vs.1 (0–2); P < 0.05) if compared with non-MetS patients (Table 5).

Table 5.

Characteristics of COPD patients with and without metabolic syndrome

With MS Without MS P-value
n = 237 n = 164
Median Age (years) (IQR) 67 (61–72) 67 (62–73) 0.852
Men (n, %) 95 (40.08) 96 (58.54) <0.001
Women (n, %) 142 (59.92) 68 (41.46)
Smoking status
 Current smokers (n, %) 100 (42.19) 74 (45.12) 0.192
 Former smokers (n, %) 127 (53.59) 79 (48.17)
 Never smokers (n, %) 10 (4.22) 11 (6.70)
Quality of Life
 CAT (points) 26 (21–32) 24.5 (19–29) 0.049
FEV1 (ref%) 43 (30–56) 47 (36–61) 0.028
FVC (%) 71 (55–84) 70 (60–83) 0.608
FEV1/FVC (%) 50 (39–58) 54 (44–64) <0.001
C-Reactive protein (mg L−1) 7.0 (2–18) 5.1 (1–17) 0.064
BMI (kg m−2) 28.0 (24–32) 21.6 (18–24) <0.0001
6MWD (m) 250 (150–330) 277 (162–360) 0.235

Data are presented as median (IQR) or as frequency and percentage. CAT: COPD Assessment Test; FEV1: forced expiratory volume in the first second; FVC: forced vital capacity; BMI: body mass index; 6MWD: six minute walking distance; mMRC: modified Medical Research Council; P < 0.05 means the two indicators were significantly correlated.

Table 6 shows the Spearman rank correlation coefficients of serum cholesterin levels related to age, specific anthropometric, functional parameters in different stages of COPD. The serum levels of cholesterin was positively correlated to the serum levels of tryglicerides in each GOLD group.

Table 6.

The correlation of cholesterol with age and anthropometric, functional parameters

GOLD A GOLD B GOLD C GOLD D
Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-value
Age (years) −0.064 0.752 −0.087 0.355 −0.078 0.309 −0.040 0.746
FEV1 (ref%) −0.158 0.431 −0.049 0.601 0.041 0.587 0.028 0.821
FVC (%) 0.150 0.455 0.176 0.062 0.014 0.856 0.037 0.769
FEV1/FVC (%) −0.121 0.546 −0.236 0.011 0.034 0.661 −0.155 0.212
BMI (kg m−2) 0.052 0.797 −0.072 0.447 0.085 0.267 0.166 0.182
6MWD (m) 0.078 0.696 0.070 0.761 −0.043 0.567 −0.031 0.800
Triglycerides (mmol L−1) 0.279 0.157 0.243 0.009 0.361 <0.001 0.287 0.019

ρ: Spearman Correlation Coefficients; FEV1: forced expiratory volume in 1 s post-bronchodilator; FVC: forced vital capacity; 6MWD: six-minute.

Discussion

The aim of our study was to determine the prevalence of MetS among COPD patients with different nutritional status and GOLD stages. We also determined the extent of systemic inflammation and its association with prevalent MetS by measuring CRP levels. Abdominal obesity, hypertension, hyperglycemia and metabolic syndrome were also more common in the overweight/obese group (BMI ≥ 25 kg m−2; P < 0.0001). MetS showed a significant association with several cardiovascular comorbidities and type 2 diabetes. MetS was prevalent in 59.1% of COPD patients. This prevalence is close to those published in previous studies, ranging from 21% to 58% [21]. Prevalence of MetS in COPD patients appears to depend on the severity of COPD, the geographical location of the study, and also the diagnostic criteria of MetS used. In general, MetS is more common in patients with increasing severity of COPD. Patients with both MetS and COPD have worse physical status than the group of patients who have only COPD [21].

MetS as defined by IDF criteria, depends mainly on abdominal obesity. More studies indicated that the amount of abdominal - visceral - adipose tissue is increased in patients with COPD [8–11, 21]. The underlying mechanisms are multifaceted and likely include an unhealthy diet combined with an inactive lifestyle. It is remarkable that MetS patients with normal nutritional status had decreased physical exercising (6MWD) compared to those without MetS but with COPD. Our findings extend previous results that MetS reduces muscle mass, which may explain, at least in part, the aforementioned observation. The link between COPD and MetS is likely bi-directional as lower physical activity observed in COPD patients per se may contribute to the development of MetS [21, 22].

Numerous studies have shown that GOLD stage II patients have the highest MetS prevalence compared to more severe GOLD stage patients [8, 21, 23]. First, this observation may be due to the fact that in less advanced cases of COPD lifestyle has a greater effect on metabolism than other factors promoting the progression of the disease. Second, it is also possible that MetS has a higher cardiovascular risk in COPD and some of the patients may die earlier because of their comorbid cardiovascular disease and may not reach end-stage COPD.

Drugs can also directly affect the prevalence of MetS. For example, oral glucocorticoids used to treat COPD can increase LDL cholesterol levels, blood glucose levels, and appetite, and can lead to abdominal obesity and muscle atrophy [9]. Other medications commonly used to treat COPD, such as antidepressants, may also cause impaired glucose tolerance and thus contribute to the development of MetS [24].

We detected a significantly increased triglyceride level and significantly decreased HDL cholesterol level among overweight/obese patients and MetS patients had significantly higher BMI values than those without MetS. Our results are in line with the findings of many, but not all, previous studies [25–28]. Xuan et al. found that serum HDL levels were significantly lower, while triglyceride levels were significantly higher in stable COPD patients than in the control groups [25]. Can et al. described that there was no difference in serum total cholesterol and triglyceride levels between COPD patients and controls [26]. Breyer et al. reported that MetS was more common among obese COPD patients [27]. Reed and colleagues found that HDL cholesterol levels were significantly lower in the more severe stages of COPD than in controls [28]. Systemic inflammation in COPD is associated with elevated triglyceride levels and reduced HDL cholesterol levels [29]. It has also been shown that inflammatory cytokines disrupt lipid metabolism, and there is an inverse correlation between serum HDL and IL-6 levels [30]. COPD patients are physically inactive, which further increases the risk of dyslipidemia [2, 8, 30]. They often use corticosteroids, mainly in severe, and very severe COPD (GOLD C, D) which also increases the occurrence of dyslipidemia and obesity [31]. In addition, smoking as well as oxidative stress are possible mechanisms for the development of dyslipidemia, and altogether they contribute to the MetS development [32].

MetS appears to be more common in females with higher BMI values. In older female patients there is likely a significant decline in circulating estrogen levels, which may increase adiposity, alter lipid metabolism, and promote a prothrombotic state. Female patients usually exhibit higher prevalence of abdominal obesity [12].

MetS and increased adiposity increases the the incidence of cardiovascular diseases in COPD patients [3]. The underlying mechanisms by which MetS and increased adiposity promotes the pathogenesis of cardiovascular diseases include vascular oxidative stress and inflammation [7], which promote accelerated atherogenesis [2, 5] mediated, in part, by pro-inflammatory adipokines secreted from the adipose tissue. These factors also increase blood pressure, exacerbate thrombogenesis and promote insulin resistance [5]. Decreased activity of fibrinolytic mechanisms contribute to the prothrombogenic state [2, 5, 7].

Nearly half of COPD patients in pulmonary rehabilitation programs are overweight or obese, which negatively affects their respiration and exercise tolerance, especially while walking [3]. Interestingly, the paradox phenomenon that patients with higher BMIs tend to live longer than patients with low or normal BMI has been well-documented in the literature [33]. This so called “obesity paradox” is not manifested in obese patients (BMI> 30 kg m−2). Physical inactivity and comorbidities clearly negatively affect the survival of individuals with COPD [3, 6, 8, 33].

Recent studies have identified a so-called “co-morbidity predominant subtype” among COPD patients, characterized by a group of metabolic comorbidities, including CVD, T2DM, and obesity [6, 11, 31]. Patients with hypertension, T2DM, have been shown to be at increased risk of morbidity and mortality. Particular attention should be given to a sedentary life-style or decreased physical activity since these show a significant correlation with blood sugar levels and abdominal circumference. In Park and Larson’s study [31], COPD patients were recorded as sitting in one place for more than 11 h per day. This is also harmful because prolonged sitting around or sedentary working per se have a strong impact on the MetS development. Even low-intensity physical activity can create a more favorable metabolic situation [31, 34].

In our study, we found significantly higher exacerbation rate (2 (1–3) vs.1 (0–2); P < 0.05) in COPD patients with MetS, which accord with the lower quality of life found in this patient group. Our findings extend the results of Kupeli et al. demonstrating that the prevalent MetS in COPD patients increases the number (2.4 vs. 0.7) and duration (7.5–8.0 vs. 5.0–5.5 days) of exacerbations [35]. However, it is unclear whether early diagnosis and treatment of MetS can improve long-term clinical outcomes. Weight loss alone affects a number of risk factors including hypertension, dyslipidemia, and insulin resistance. There are a number of studies demonstrating the beneficial effects of exercise in overweight and obese individuals on blood pressure, lipid profiles and glucose levels [36–38]. Improvement in glycated hemoglobin (HbA1c) and insulin sensitivity has been also described, and the total duration of exercise appears to be more important than the mode of its execution [39]. The available data suggest that in patients with COPD, a significant increase in abdominal fat is directly associated with the likelihood of developing acute myocardial infarction [6]. The physical workload and quality of life of COPD patients with MetS is also reduced [31]. Taken together, MetS in COPD patients should be considered by pulmonologists and treated as soon as possible.

Limitations of the study

Our study relied on a self-reported history of exacerbations, which may introduce a significant bias. Our present study was a single-centre, cross-sectional study, and therefore the cause and effect of relationship cannot be established. Further longitudinal prospective studies are needed to examine the long-term effects of MetS on the cardiovascular system, the physical activity, quality of life, number of exacerbations and other comorbidities in COPD patients.

Conclusion

Among COPD patients, MetS is more likely to develop, especially in obese patients, with a prevalence of 59.1% in our study. The most common components of MetS are abdominal obesity, hypertension, hyperglycemia and hyperlipidemia, more common in women with high BMI values. COPD patients with MetS use more inhaled medications and have more co-morbidities, with a poorer quality of life and poorer physical capacity than those without MetS. Future follow-up and interventional studies are needed to determine the best treatment options. Also, additional studies are warranted to determine how MetS, obesity, COPD and COVID-19 morbidity and mortality relate [40, 41].

Ethics approval and consent to participate

The study was approved by the TUKEB Ethical Committee (Licence Number: TUKEB 44402-2/2018/EKU) and it complies with the Helsinki Declaration.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Availability of data and materials

The data that support the findings of this study are available on request from the corresponding author [JTV].

Competing interests

The authors declare that they have no competing interests.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Acknowledgements

We would like to thank our coordinator Anita Kecskés, the physicians, physiotherapists and dieticians of the National Koranyi Institute of Pulmonology who actively participated in the study, and the patients of the National Koranyi Institute of Pulmonology who contributed with their valuable answers to the research.

List of abbreviations

BMI

body mass index

COPD

Chronic Obstructive Pulmonary Disease

CRP

C-reactive protein

CVD

cardiovascular disease

FEV1

forced expiratory volume in 1 s

FVC

forced vital capacity

GOLD

Global Initiative for Chronic Obstructive Lung Disease

HDL

high-density lipoprotein

HbA1c

Hemoglobin A1c

IDF

International Diabetes Federation

IL-6

interleukin-6

IL-8

interleukin-8

ICS

inhaled corticosteroids

LABA

long-acting bronchodilators

LAMA

long-acting muscarinic antagonist

LDL

low-density lipoprotein

LTRA

leukotriene receptor antagonists

MetS

Metabolic syndrome

6MWT

six-minute walk test

OECD

Organisation for Economic Co-operation and Development

SABA

short-acting bronchodilators

T2DM

Type 2 diabetes mellitus

TG

triglyceride

TNF-α

tumor necrosis factor alpha

TUKEB

Regional Institutional Scientific Research Ethics Committee of Semmelweis University

VLDL

very low-density lipoproteins

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  • 1.

    Egészségügyi Szakmai Kollégium . Egészségügyi szakmai irányelv - A krónikus obstruktív tüdőbetegség (chronic obstructive pulmonary disease - COPD) diagnosztikájáról, kezeléséről és gondozásáról [Health professionals’ directive - About the diagnosis, treatment and management of chronic obstructive pulmonary disease]. Emberi Erőforrások Minisztériuma–Egészségügyért Felelős Államtitkárság; 2017. [Budapest][cited 2021 Jan 21];[63 p.] Directive No. 001049. Hungarian. Available at: https://kollegium.aeek.hu/Download/Download/2253.

    • Search Google Scholar
    • Export Citation
  • 2.

    Varga JT . Smoking and pulmonary complications: respiratory prehabilitation. J Thorac Dis 2019; 11(Suppl 5): S639S644. https://doi.org/10.21037/jtd.2018.12.11.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 3.

    Varga J. , editor. A pulmonológiai rehabilitáció kézikönyve (Hungarian). Budapest: SpringMed Kiadó; 2018.

  • 4.

    European Commission . Special Eurobarometer 458. Attitudes of Europeans towards tobacco and electronic cigarettes. Brussels; 2017. Available from https://data.europa.eu/data/datasets/s2146_87_1_458_eng?locale=en [accessed 21 December 2021].

    • Search Google Scholar
    • Export Citation
  • 5.

    Breyer MK , Spruit MA , Hanson CK , Franssen FM , Vanfleteren LE , et al. Prevalence of metabolic syndrome in COPD patients and its consequences. PLoS One 2014; 9: e98013. https://doi.org/10.1371/journal.pone.0098013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 6.

    Chan SMH , Selemidis S , Bozinovski S , Vlahos R . Pathobiological mechanisms underlying metabolic syndrome (MetS) in chronic obstructive pulmonary disease (COPD): clinical significance and therapeutic strategies. Pharmacol Ther 2019; 198: 160188. https://doi.org/10.1016/j.pharmthera.2019.02.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7.

    Varga J , Casaburi R , Ma S , Hecht A , Hsia D , Somfay A , et al. Relation of concavity in the expiratory flow-volume loop to dynamic hyperinflation during exercise in COPD. Respir Physiol Neurobiol 2016; 234: 7984. https://doi.org/10.1016/j.resp.2016.08.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 8.

    Clini E , Crisafulli E , Radaeli A , Malerba M . COPD and the metabolic syndrome: an intriguing association. Intern Emerg Med 2013; 8: 283289. https://doi.org/10.1007/s11739-011-0700-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9.

    Varga J , Munkacsi A ,Mathe Cs , Somfay A , Balint B , Lovasz O , et al. The effect of the inspiratory muscles training on physical condition in COPD. [A belégző izmok tréningjének hatása a betegek fizikai állapotára COPDben]. Med Thor 2018; 71: 96102.

    • Search Google Scholar
    • Export Citation
  • 10.

    Piazzolla G , Castrovilli A , Liotino V , Vulpi MR , Fanelli M , Mazzocca A , et al. Metabolic syndrome and Chronic Obstructive Pulmonary Disease (COPD): the interplay among smoking, insulin resistance and vitamin D. PLoS One 2017; 12: e0186708. https://doi.org/10.1371/journal.pone.0186708.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 11.

    Choi HS , Rhee CK , Park YB , Yoo KH , Lim SY . Metabolic syndrome in early chronic obstructive pulmonary disease: gender differences and impact on exacerbation and medical costs. Int J Chron Obstruct Pulmon Dis 2019; 14: 28732883. https://doi.org/10.2147/COPD.S228497.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 12.

    Nutrition . Physical activity and obesity Hungary. Available from https://www.euro.who.int/__data/assets/pdf_file/0014/243302/Hungary-WHO-Country-Profile.pdf [accessed 30 December 2021].

    • Search Google Scholar
    • Export Citation
  • 13.

    Alberti KG , Zimmet P , Shaw J , IDF Epidemiology Task Force Consensus Group . The metabolic syndrome--a new worldwide definition. Lancet 2005; 366(9491): 10591062. https://doi.org/10.1016/S0140-6736(05)67402-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 14.

    Merkely B , Szabo AJ , Kosztin A , Berenyi E , Sebestyen A , Lengyel C , et al. Novel coronavirus epidemic in the Hungarian population, a cross-sectional nationwide survey to support the exit policy in Hungary. Geroscience 2020; 42: 10631074. https://doi.org/10.1007/s11357-020-00226-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 15.

    Voko Z , Pitter JG . The effect of social distance measures on COVID-19 epidemics in Europe: an interrupted time series analysis. Geroscience 2020; 42: 10751082. https://doi.org/10.1007/s11357-020-00205-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 16.

    Singh D , Agusti A , Anzueto A , Barnes PJ , Bourbeau J , Celli BR , et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease: the GOLD science committee report 2019. Eur Respir J 2019; 53(5): 1900164. https://doi.org/10.1183/13993003.00164-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 17.

    Jones PW , Tabberer M , Chen WH . Creating scenarios of the impact of COPD and their relationship to COPD Assessment Test (CAT™) scores. BMC Pulm Med 2011; 11: 42. https://doi.org/10.1186/1471-2466-11-42.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 18.

    ATS Committee on Proficiency Standards for Clinical Pulmonary Function Laboratories . ATS statement: guidelines for the six-minute walk test [published correction appears in Am J Respir Crit Care Med. 2016 May 15;193(10):1185]. Am J Respir Crit Care Med 2002; 166(1): 111117. https://doi.org/10.1164/ajrccm.166.1.at1102.

    • Search Google Scholar
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Editor-in-Chief

László ROSIVALL (Semmelweis University, Budapest, Hungary)

Managing Editor

Anna BERHIDI (Semmelweis University, Budapest, Hungary)

Co-Editors

  • Gábor SZÉNÁSI (Semmelweis University, Budapest, Hungary)
  • Ákos KOLLER (Semmelweis University, Budapest, Hungary)
  • Zsolt RADÁK (University of Physical Education, Budapest, Hungary)
  • László LÉNÁRD (University of Pécs, Hungary)
  • Zoltán UNGVÁRI (Semmelweis University, Budapest, Hungary)

Assistant Editors

  • Gabriella DÖRNYEI (Semmelweis University, Budapest, Hungary)
  • Zsuzsanna MIKLÓS (Semmelweis University, Budapest, Hungary)
  • György NÁDASY (Semmelweis University, Budapest, Hungary)

Hungarian Editorial Board

  • György BENEDEK (University of Szeged, Hungary)
  • Zoltán BENYÓ (Semmelweis University, Budapest, Hungary)
  • Mihály BOROS (University of Szeged, Hungary)
  • László CSERNOCH (University of Debrecen, Hungary)
  • Magdolna DANK (Semmelweis University, Budapest, Hungary)
  • László DÉTÁRI (Eötvös Loránd University, Budapest, Hungary)
  • Zoltán GIRICZ (Semmelweis University, Budapest, Hungary and Pharmahungary Group, Szeged, Hungary)
  • Zoltán HANTOS (Semmelweis University, Budapest and University of Szeged, Hungary)
  • Zoltán HEROLD (Semmelweis University, Budapest, Hungary) 
  • László HUNYADI (Semmelweis University, Budapest, Hungary)
  • Gábor JANCSÓ (University of Pécs, Hungary)
  • Zoltán KARÁDI (University of Pecs, Hungary)
  • Miklós PALKOVITS (Semmelweis University, Budapest, Hungary)
  • Gyula PAPP (University of Szeged, Hungary)
  • Gábor PAVLIK (University of Physical Education, Budapest, Hungary)
  • András SPÄT (Semmelweis University, Budapest, Hungary)
  • Gyula SZABÓ (University of Szeged, Hungary)
  • Zoltán SZELÉNYI (University of Pécs, Hungary)
  • Lajos SZOLLÁR (Semmelweis University, Budapest, Hungary)
  • Gyula TELEGDY (MTA-SZTE, Neuroscience Research Group and University of Szeged, Hungary)
  • József TOLDI (MTA-SZTE Neuroscience Research Group and University of Szeged, Hungary)
  • Árpád TÓSAKI (University of Debrecen, Hungary)

International Editorial Board

  • Dragan DJURIC (University of Belgrade, Serbia)
  • Christopher H.  FRY (University of Bristol, UK)
  • Stephen E. GREENWALD (Blizard Institute, Barts and Queen Mary University of London, UK)
  • Osmo Otto Päiviö HÄNNINEN (Finnish Institute for Health and Welfare, Kuopio, Finland)
  • Helmut G. HINGHOFER-SZALKAY (Medical University of Graz, Austria)
  • Tibor HORTOBÁGYI (University of Groningen, Netherlands)
  • George KUNOS (National Institutes of Health, Bethesda, USA)
  • Massoud MAHMOUDIAN (Iran University of Medical Sciences, Tehran, Iran)
  • Tadaaki MANO (Gifu University of Medical Science, Japan)
  • Luis Gabriel NAVAR (Tulane University School of Medicine, New Orleans, USA)
  • Hitoo NISHINO (Nagoya City University, Japan)
  • Ole H. PETERSEN (Cardiff University, UK)
  • Ulrich POHL (German Centre for Cardiovascular Research and Ludwig-Maximilians-University, Planegg, Germany)
  • Andrej A. ROMANOVSKY (University of Arizona, USA)
  • Anwar Ali SIDDIQUI (Aga Khan University, Karachi, Pakistan)
  • Csaba SZABÓ (University of Fribourg, Switzerland)
  • Eric VICAUT (Université de Paris, UMRS 942 INSERM, France)
  • Nico WESTERHOF (Vrije Universiteit Amsterdam, The Netherlands)

 

Editorial Correspondence:
Physiology International
Semmelweis University
Faculty of Medicine, Institute of Translational Medicine
Nagyvárad tér 4, H-1089 Budapest, Hungary
Phone/Fax: +36-1-2100-100
E-mail: pi@semmelweis-univ.hu

Indexing and Abstracting Services:

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  • Medline
  • Referativnyi Zhurnal
  • SCOPUS
  • WoS - Science Citation Index Expanded

 

2022  
Web of Science  
Total Cites
WoS
335
Journal Impact Factor 1.4
Rank by Impact Factor

Physiology (Q4)

Impact Factor
without
Journal Self Cites
1.4
5 Year
Impact Factor
1.6
Journal Citation Indicator 0.42
Rank by Journal Citation Indicator

Physiology (Q4)

Scimago  
Scimago
H-index
33
Scimago
Journal Rank
0.362
Scimago Quartile Score

Physiology (medical) (Q3)
Medicine (miscellaneous) (Q3)

Scopus  
Scopus
Cite Score
2.8
Scopus
CIte Score Rank
Physiology 68/102 (33rd PCTL)
Scopus
SNIP
0.508

2021  
Web of Science  
Total Cites
WoS
330
Journal Impact Factor 1,697
Rank by Impact Factor

Physiology 73/81

Impact Factor
without
Journal Self Cites
1,697
5 Year
Impact Factor
1,806
Journal Citation Indicator 0,47
Rank by Journal Citation Indicator

Physiology 69/86

Scimago  
Scimago
H-index
31
Scimago
Journal Rank
0,32
Scimago Quartile Score Medicine (miscellaneous) (Q3)
Physiology (medical) (Q3)
Scopus  
Scopus
Cite Score
2,7
Scopus
CIte Score Rank
Physiology (medical) 69/101 (Q3)
Scopus
SNIP
0,591

 

2020  
Total Cites 245
WoS
Journal
Impact Factor
2,090
Rank by Physiology 62/81 (Q4)
Impact Factor  
Impact Factor 1,866
without
Journal Self Cites
5 Year 1,703
Impact Factor
Journal  0,51
Citation Indicator  
Rank by Journal  Physiology 67/84 (Q4)
Citation Indicator   
Citable 42
Items
Total 42
Articles
Total 0
Reviews
Scimago 29
H-index
Scimago 0,417
Journal Rank
Scimago Physiology (medical) Q3
Quartile Score  
Scopus 270/1140=1,9
Scite Score  
Scopus Physiology (medical) 71/98 (Q3)
Scite Score Rank  
Scopus 0,528
SNIP  
Days from  172
submission  
to acceptance  
Days from  106
acceptance  
to publication  

2019  
Total Cites
WoS
137
Impact Factor 1,410
Impact Factor
without
Journal Self Cites
1,361
5 Year
Impact Factor
1,221
Immediacy
Index
0,294
Citable
Items
34
Total
Articles
33
Total
Reviews
1
Cited
Half-Life
2,1
Citing
Half-Life
9,3
Eigenfactor
Score
0,00028
Article Influence
Score
0,215
% Articles
in
Citable Items
97,06
Normalized
Eigenfactor
0,03445
Average
IF
Percentile
12,963
Scimago
H-index
27
Scimago
Journal Rank
0,267
Scopus
Scite Score
235/157=1,5
Scopus
Scite Score Rank
Physiology (medical) 73/99 (Q3)
Scopus
SNIP
0,38

 

Physiology International
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Physiology International
Language English
Size B5
Year of
Foundation
2006 (1950)
Volumes
per Year
1
Issues
per Year
4
Founder Magyar Tudományos Akadémia
Founder's
Address
H-1051 Budapest, Hungary, Széchenyi István tér 9.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2498-602X (Print)
ISSN 2677-0164 (Online)

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