Metabolic syndrome and risk of pulmonary involvement

Respiratory Medicine (2010) 104, 47e51

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Metabolic syndrome and risk of pulmonary involvement Paola Rogliani, Giacomo Curradi, Marco Mura, Davide Lauro, Massimo Federici, Angelica Galli, Cesare Saltini, Mario Cazzola* Department of Internal Medicine, University of Rome ‘Tor Vergata’, Via Montpellier 1, 00133 Rome, Italy Received 21 April 2009; accepted 22 August 2009 Available online 1 October 2009

KEYWORDS Metabolic syndrome; Pulmonary involvement; Airflow limitation; HDL-C

Summary Metabolic syndrome (MS) is a complex disorder recognized clinically by the findings of abdominal obesity, elevated triglycerides, atherogenic dyslipidaemia, elevated blood pressure, high blood glucose and/or insulin resistance. It is associated with a pro-thrombotic and a pro-inflammatory state. A growing body of evidence suggests that individuals in the community with moderate airflow limitation may have co-existing systemic inflammation with this background. Therefore, we examined a population of 237 patients with metabolic disorder for the concomitant presence of functional pulmonary involvement, as assessed by FEV1 and FVC impairment. Criteria for the identification of the MS included 3 or more of the following: waist circumference: (>102 cm in men, >88 cm in women), triglycerides levels (150 mg/dl), high-density lipoprotein cholesterol levels (<40 mg/dl in men, <50 mg/dl in women), blood pressure (135/85 mm Hg), and fasting glucose levels (>100 mg/dl). 119 subjects were diagnosed MS. Non-smokers patients suffering from MS presented lower spirometric values, with a trend to ventilatory restrictive more than obstructive pattern. Also in smokers patients with MS there was a trend to harmonic decrease in FEV1 and FVC but not in FEV1/FVC ratio, although the changes did not reach statistical significance. Mainly abdominal circumference, and also insulin resistance were retained as independent predictors of both FEV1 and FVC changes. However, HDL-C was the strongest predictor of FEV1 and FVC changes, with an inverse association. ª 2009 Elsevier Ltd. All rights reserved.

Introduction Metabolic syndrome (MS) is a cluster of risk factors that increase the probability to develop type 2 diabetes (T2D) * Corresponding author. Tel.: þ39 06 20900631; fax: þ39 06 72596621. E-mail address: [email protected] (M. Cazzola).

and cardiovascular disease. The features of MS (abdominal obesity, atherogenic dyslipidaemia, mild elevated blood pressure, increased blood glucose concentrations and/or insulin resistance) are also associated with a pro-thrombotic and a pro-inflammatory state.1 A growing body of evidence suggests that individuals in the community with moderate airflow limitation may have co-existing systemic inflammation.2 In particular, systemic inflammation is

0954-6111/$ - see front matter ª 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.rmed.2009.08.009

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Table 1 Demographic and metabolic characteristics of the population included in the study. Age Sex Smoking history Waist circumference (cm) Triglycerides (mg/dl) HDL-C (mg/dl) Fasting glucose (mg/dl) Hypertension (yes/no) Insulin resistance (yes/no)

60.11  11.60 132 M and 105 F 27.82  18.65 101.46  11.80 159.75  141.40 48.53  14.11 102.38  18.81 168/70 117/121

present also in chronic obstructive pulmonary disease (COPD), a complex disorder characterized local pulmonary inflammation in response to inhalation of noxious particles or toxic gases, especially cigarette smoke.3 Currently there is growing recognition that the inflammatory response extends beyond the lung.4 In fact, it is likely that the inflammation in the lung ‘spills over’ into the systemic circulation to produce systemic effects such as muscle wasting, cachexia, atherosclerosis, and cardiac disease have been associated with COPD.5 Comorbidities are common in COPD and may become harder to manage when COPD is present, either because COPD adds to the total level of disability or because COPD therapy adversely affects the comorbid disorder.3 It is now accepted that patients with COPD often have one or more component of the MS, with a slightly lower frequency in severe COPD.6 However, we still do not know if individual with MS have an higher risk and incidence to develop COPD, although it has been reported that diabetes is independently associated with reduced lung function, and obesity in T2 diabetic patients could further worsen the severity of COPD.7 With this background, we investigated a population of patients with metabolic disorder for the concomitant presence of pulmonary involvement assessed by ordinary pulmonary function tests (PFTs).

Methods Patients We included in this study 237 subjects (132 males, 105 females) who were consecutively admitted in four months to the metabolic disorders outpatient office of our

Table 2 Comparison of PFTs between non-smokers with MS (54 subjects) and non-smokers without MS (58 subjects). Pulmonary Function Test

MET-SYN

No MET-SYN

p value

FEV1 (l) FEV1 (% pred) FVC (l) FVC (% pred) FEV1/FVC

2.42  0.73 99.92  16.42 2.86  0.87 96.20  15.46 0.845  0.061

2.90  0.93 107.26  14.88 3.44  16.20 104.43  16.20 0.844  0.069

0.0051 0.0174 0.0037 0.0082 0.892

Table 3 Comparison of PFTs between smokers with MS (65 subjects) and smokers without MS (60 subjects). Pulmonary Function Test

MET-SYN

No MET-SYN

p value

FEV1 (l) FEV1 (% pred) FVC (l) FVC (% pred) FEV1/FVC

2.62  0.84 94.92  18.29 3.19  0.95 93.35  15.87 0.813  0.065

2.81  0.80 98.02  19.80 3.45  0.93 97.79  16.98 0.808  0.065

0.218 0.176 0.166 0.169 0.411

university hospital because of suspected MS. All patients denied suffering from COPD or other identified pulmonary diseases. The NCEP ATP-III criteria that we used for the identification of the MS is the association of 3 or more of the following: waist circumference (>102 cm in men, >88 cm in women), triglycerides levels (150 mg/dl), high-density lipoprotein cholesterol (HDL-C) levels (<40 mg/dl in men, <50 mg/dl in women), blood pressure 130/85 mm Hg), and fasting glucose levels (>100 mg/dl).8 Insulin resistance was measured with the homeostasis model assessment for insulin resistance or HOMA-IR (HOMA-IR Z insulin [mU/ mL]  glucose [mmol/L]/22.5.9 All patients included in the present study were in a stable state at distance (minimum 8 weeks) from pulmonary infection or acute bronchitis. All subjects denied that they have suffered from an episode of right-heart failure (with peripheral oedema) or acute respiratory failure. None of them was suffering from muscular weakness.

Conventional spirometry FVC and FEV1 were measured with standard spirometric techniques (Masterlab, Jaeger, Wurzburg, Germany). All values obtained were related to age and gender and expressed as percentage of their predicted value.

Statistical analysis Subjects’ characteristics were summarized as mean and S.D. for continuous variables and frequency and percentage for categorical variables. Spearman rank correlation coefficients were estimated between the study variables and potential confounders including BMI, waist circumference, triglycerides levels, HDL-C levels, blood pressure, and fasting glucose levels. Multiple linear regression models were used to examine the association between waist circumference, triglycerides levels, HDL-C levels, blood pressure, and fasting glucose levels (as indicators of MS) and ventilatory function. Separate regression models with FVC, FEV1 and FEV1/FVC as the outcome variables, with waist circumference, triglycerides levels, HDL-C levels, blood pressure, and fasting glucose levels as predictor variables, were examined in turn. To exclude the possibility of confounding by smoking-induced respiratory disease, all multivariate models were repeated in the subgroup of nonsmokers. All statistical analyses were performed using the SPSS statistical software version 12.0 (SPSS, Inc., Chicago, IL, USA).

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Table 4 Relationships between metabolic parameters and PFTs observed in subjects affected by MET-SYN (119 subjects). Spearman’s Rank correlations are indicated.

Waist circumference (cm) Triglycerides (mg/dl) HDL-C (mg/dl) Fasting glucose (mg/dl)

FEV1 (l)

FVC (l)

FEV1/FVC

Rho Z 0.16 p Z 0.07 Rho Z 0.19 p Z 0.04 Rho Z 0.36 p Z 0.0003 Rho Z 0.20 p Z 0.20

Rho Z 0.14 p Z 0.12 Rho Z 0.20 p Z 0.03 Rho Z 0.39 p < 0.0001 Rho Z 0.16 p Z 0.30

Rho Z 0.06 p Z 0.49 Rho Z 0.099 p Z 0.31 Rho Z 0.10 p Z 0.32 Rho Z 0.17 p Z 0.28

Results 237 subjects were included in the study (132 males, 105 females). 125 of them were smokers (27.2  18.65 packyears). 119 subjects were diagnosed MS. Metabolic parameters are shown in Table 1. The subjects without MS were used as unexpected control group. The comparison of PFTs between non-smoker subjects with MS (54 subjects) and without MS (58 subjects) showed that the first group was characterized by a significantly lower FEV1 (l) (p Z 0.0051), FEV1 (% predicted) (p Z 0.0174), FVC (l) (p Z 0.0037), and FVC (% predicted) (0.0082), while no difference was observed regarding the FEV1/FVC ratio (Table 2). In contrast, considering smokers with MS (65 subjects) and smokers without MS (60 subjects), no significant difference was observed in regard to any PFT (Table 3). However, the MS group showed lower values of FEV1 and FVC. The correlations between metabolic parameters and PFTs observed in subjects affected by MS are shown in Table 4. Overall, abdominal circumference and triglycerides were positively associated with FEV1, whereas only triglycerides were positively associated with FVC. On the contrary, HDL-C was inversely associated with both FEV1 and FVC. Tables 5 and 6 illustrate the correlations between metabolic parameters and PFTs observed in smokers and non-smokers affected by MS, respectively. In smokers, abdominal circumference was positively associated with FEV1 and FVC, whereas in non-smokers, triglycerides were positively associated with FEV1 and FVC. In both groups, HDL-C was inversely associated with both FEV1 and FVC.

In order to predict the presence of a pulmonary involvement in patients with MS, we developed a stepwise multiple regression model by considering metabolic variables (HDL-C, triglycerides, hypertension, abdominal circumference, insulin resistance, fasting glucose) together with smoking history. These were inserted in the model as independent variables to predict FEV1 as dependent variable. The best model is shown. HDL-C, hypertension, abdominal circumference and insulin resistance were retained as independent predictors of FEV1, with a R2 of 0.178 and a p value of 0.0011 (Table 7). The best predictor of FEV1 was HDL-C (F ratio 11.19). Smoking history was excluded from the model. Stepwise multiple regression model was also applied to predict FVC as a dependent variable. HDL-C, hypertension, abdominal circumference, triglycerides and insulin resistance were retained as independent predictors, with a R2 of 0.230 and a p value of 0.0003 (Table 8). The best predictor of FVC was HDL (F-ratio 11.03). Again, smoking history was excluded from the model.

Discussion The most important finding of this study is that non-smokers patients suffering from MS present lower spirometric values, with a small but harmonic reduction in both FEV1 and FVC with an unchanged FEV1/FVC ratio that suggests ventilatory restrictive more than obstructive pattern. Consequently, our data seem to indicate that MS is not associated with airway obstruction and, likely, with COPD, at least as it is defined by GOLD guidelines.3 Although we completely agree that any attempts to impose FEV1 (or the

Table 5 Relationships between metabolic parameters and PFTs observed in smokers affected by MS (65 subjects). Spearman’s Rank correlations are indicated.

Waist circumference (cm) Triglycerides (mg/dl) HDL-C (mg/dl) Fasting glucose (mg/dl)

FEV1 (l)

FVC (l)

FEV1/FVC

Rho Z 0.27 p Z 0.03 Rho Z 0.03 p Z 0.79 Rho Z 0.32 p Z 0.01 Rho Z 0.30 p Z 0.15

Rho Z 0.25 p Z 0.04 Rho Z 0.06 p Z 0.65 Rho Z 0.37 p Z 0.005 Rho Z 0.27 p Z 0.19

Rho Z 0.059 p Z 0.64 Rho Z 0.10 p Z 0.45 Rho Z 0.069 p Z 0.61 Rho Z 0.39 p Z 0.06

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Table 6 Relationships between metabolic parameters and PFTs observed in non-smokers affected by MS (54 subjects). Spearman’s Rank correlations are indicated.

Waist circumference (cm) Triglycerides (mg/dl) HDL-C (mg/dl) Fasting glucose (mg/dl)

FEV1 (l)

FVC (l)

FEV1/FVC

Rho Z 0.027 p Z 0.88 Rho Z 0.38 p Z 0.0059 Rho Z 0.28 p Z 0.06 Rho Z 0.004 p Z 0.98

Rho Z 0.003 p Z 0.97 Rho Z 0.37 p Z 0.0083 Rho Z 0.29 p Z 0.05 Rho Z 0.02 p Z 0.91

Rho Z 0.15 p Z 0.27 Rho Z 0.08 p Z 0.57 Rho Z 0.23 p Z 0.12 Rho Z 0.16 p Z 0.51

ratio of FEV1 to FVC) limits in defining COPD are bound to be arbitrary and contentious, we must also consider that according to the current GOLD guidelines, only a FEV1/FVC ratio lower than 0.7 indicates airflow obstruction, thus allowing a COPD diagnosis.3 We must highlight that our definition of restricted spirometric pattern is not completely adequate, but it is an indication for additional lung function tests to understand the kind of pulmonary disorder. In fact, the precise determination of restriction requires the measurement of total lung capacity (TLC) 10 but also that most patients with restriction on spirometry have this confirmed when the TLC is measured.11 However, measuring TLC is unfeasible in large studies as it is time consuming, expensive, and requires special facilities and trained technicians. Our finding fits well with the recent documentation that the prevalence of MS is independently associated with restrictive lung impairment.12e14 A cross-sectional study of 159 consecutive nondiabetic elderly persons attending two social centres showed that restrictive, but not obstructive, respiratory pattern was associated with MS, at least in older people, and did not only reflect a limitation of ventilation due to visceral obesity.12 In fact, restriction was an independent correlate of MS, also after adjustment for waist circumference and body mass index (BMI). Lin et al.13 demonstrated an association between MS and restrictive lung impairment also after adjustment for age, gender, BMI, smoking, alcohol drinking, and physical activity. Recently, Nakajima et al.14 confirmed that impaired restrictive pulmonary function might be associated with metabolic disorders and MS and documented a severity dependent association in an apparently healthy population. Restriction on spirometry can be related to a variety of etiologies, including diseases such as muscular weakness, Table 7 Significant independent predictors of FEV1 (l) selected by stepwise multiple regression analysis in patients affected by MS (119 subjects). p value of the model Z 0.0011. 2

Variable

Cumulative R

F-Ratio

HDL-C (mg/dl) Hypertension (yes/no) Abdominal circumference (cm) Insulin resistance (yes/no)

0.123 0.147 0.165

11.19 3.07 2.91

0.178

1.42

congestive heart failure, interstitial lung disease, diabetes mellitus and obesity, all of which can also be associated with increased limitation and morbidity.15e19 The subjects examined in our study were not suffering from muscular weakness, right-heart failure and interstitial lung disease, whereas diabetes mellitus and obesity were features of MS. Obesity has been shown to be inversely related to lung function.20e24 Fasting serum insulin levels are negatively correlated with FVC and FEV1.25 Furthermore, insulin resistance assessed by HOMA and prevalence of T2D are inversely associated with FEV1 and FVC.26 Actually, in our patients, abdominal circumference and also insulin resistance were retained as independent predictors of both FEV1 and FVC. Surprisingly, HDL-C was the strongest predictor of FEV1 and FVC, with an inverse association that has been observed both in smokers and in non-smokers. This was a novel, but unexpected finding, considering that numerous epidemiological studies have associated HDL-C with an inverse risk for coronary artery disease,27 whereas reduced pulmonary function, as assessed by FEV1 and FVC, is associated with increased incidences of cardiovascular disease and death in both smokers and non-smokers.28 The mechanisms underlying this association are unknown. It must be mentioned that several patients presented low levels of HDL-C (<40 mg/dl in men, <50 mg/dl in women) and, consequently, the inverse association between HDL-C and FEV1 and FVC does not mean high levels of HDL-C and low levels of FEV1 and FVC. In any case, we must mention that, based on our reported data, the presence of lung function impairment itself is not clearly demonstrated as the changes in lung function possibly reflect changes in thoracic and abdominal wall compliance. Table 8 Significant independent predictors of FVC (l) selected by stepwise multiple regression analysis in patients affected by MS (119 subjects). p value of the model Z 0.0003. Variable

Cumulative R2

F-Ratio

HDL-C (mg/dl) Hypertension (yes/no) Abdominal circumference (cm) Triglycerides (mg/dl) Insulin resistance (yes/no)

0.147 0.183 0.200

11.03 4.90 2.60

0.217 0.230

1.56 1.55

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Consequently, although statistically significant, the inverse association between HDL-C and FEV1 and FVC could not be expression of a pulmonary involvement.

13.

Conflict of interest

14.

The authors declare that no significant conflicts of interest exist with any companies/organizations whose products or services may be discussed in this article.

15.

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