The insulin resistance syndrome and postprandial lipid intolerance in smokers

Atherosclerosis 129 (1997) 79 – 88

The insulin resistance syndrome and postprandial lipid intolerance in smokers Bjo¨rn Eliassona,*, Niina Merob, Marja-Riitta Taskinenb, Ulf Smitha a

The Lundberg Laboratory for Diabetes Research, Department of Internal Medicine, Sahlgrenska Uni6ersity Hospital, S-413 45 Go¨teborg, Sweden b Department of Medicine, Uni6ersity of Helsinki, Helsinki, Finland Received 1 May 1996; revised 17 September 1996; accepted 13 November 1996

Abstract Background: The effects of cigarette smoking on insulin resistance, postprandial lipemia following a mixed meal, lipoproteins and other aspects of the insulin resistance syndrome (IRS) were investigated in healthy middle-aged men. Methods: 36 smoking and 25 age- and body mass index (BMI)-matched non-smoking men participated. They were non-obese (BMI B 27), healthy and without any medication. The smokers had been smoking more than 10 cigarettes per day for more than 20 years; the non-smokers had never been habitual smokers. Body composition and several metabolic and cardiovascular risk factors were studied, including the prevalence of small dense LDL-particles, lipoprotein and hepatic lipase activity and triglyceride levels after a mixed test meal. For determination of degree of insulin sensitivity the euglycemic hyperinsulinemic clamp technique was used. Results: The smokers had lower HDL-cholesterol and lipoprotein A-I levels but higher fasting triglycerides, as well as an increased proportion of small dense LDL-particles and higher postheparin hepatic lipase activity. They also had higher levels of fibrinogen, plasminogen activator inhibitor 1 (PAI-1) activity and fasting and steady-state C-peptide levels during the clamp. The smokers were insulin resistant and lipid intolerant with an impaired triglyceride clearance after a mixed test meal. This lipid intolerance was not mirrored by fasting hypertriglyceridemia. Conclusions: This study, using the euglycemic hyperinsulinemic clamp technique, shows that smokers are both insulin resistant and lipid intolerant. The postprandial lipid intolerance is also seen in individuals with normal fasting triglyceride levels and is related to an increased prevalence of atherogenic small dense LDL. IRS is likely to be an important reason for the increased cardiovascular morbidity in smokers. © 1997 Elsevier Science Ireland Ltd. Keywords: Insulin resistance syndrome; Lipoproteins; LDL-particle size; Postprandial lipemia; Cigarette smoking; HDL-cholesterol; Lipoprotein A-I

1. Introduction Cigarette smoking is a major risk factor for cardiovascular disease (CVD) and one of the most important causes of premature death [1]. Although many pharmacological actions of cigarette smoking and nicotine have been demonstrated [2], the mechanisms for the relationship between smoking and CVD have not been clarified. * Corresponding author. Tel.: + 46 31 604243; fax: + 46 31 825330; e-mail: [email protected]

We have recently demonstrated that cigarette smoking can acutely impair insulin sensitivity [3], thus linking cigarette smoking with insulin resistance syndrome (IRS). Such an association was also demonstrated in a cross-sectional study where Facchini et al. showed that smokers are insulin resistant compared with non-smokers [4]. Furthermore, we recently reported that the degree of insulin resistance in smokers is related to smoking habits [5]. Other manifestations of IRS, such as hyperinsulinemia, elevated triglyceride levels, decreased HDL-cholesterol (HDL-C) and dysfibrinolysis,

0021-9150/97/$17.00 © 1997 Elsevier Science Ireland Ltd. All rights reserved. PII S 0 0 2 1 - 9 1 5 0 ( 9 6 ) 0 6 0 2 8 - 5

B. Eliasson et al. / Atherosclerosis 129 (1997) 79–88

80 Table 1 Subject characteristics Variable

Age (years) Body weight (kg) Lean body weight (kg) BMI (kg/m2) Body fat (%) WHR Systolic BP (mmHg) Diastolic BP (mmHg) Alcohol consumption (g/month) Smoking (years) Cigarettes/day

Smokers

Non-smokers

P-value

Mean

S.D.

Range

Mean

S.D.

Range

50.6 78.0 60.0 23.7 22.9 0.924 116 69 379 32.7 20.8

6.3 6.0 4.6 1.7 6.0 0.044 10 7 304 6.4 6.8

40 – 60 64.2 – 92.8 51.5 – 73.4 19.2 – 26.4 11.2 – 33.3 0.790--1.000 100 – 140 58 – 86 0 – 960 20 – 45 10 – 45

52.5 79.2 62.2 24.0 22.0 0.891 122 72 129

5.6 7.0 5.5 1.2 5.2 0.040 8 7 134

42 – 60 69.0 – 92.8 52.0 – 72.4 21.6 – 26.1 11.0 – 31.7 0.830--0.970 106 – 140 60 – 85 0 – 480

NS NS NS NS 0.089 0.0041 0.023 NS 0.0003

NS, not significant.

are also seen in smokers [4 – 8] and the extent of these perturbations is related to smoking habits expressed as daily nicotine consumption [5]. In a recent pilot study we found that smokers also seem to have an impaired postprandial elimination of triglycerides from a standardized mixed meal even in the absence of a fasting hypertriglyceridemia [9]. The degree of postprandial triglyceridemia, expressed as area under the incremental curve (AUIC) was strongly and negatively related to fasting HDL-C levels in smokers [9]. Impaired postprandial triglyceride elimination has also been demonstrated in other insulin-resistant states, such as fasting hypertriglyceridemia and non-insulin dependent diabetes mellitus (NIDDM), and seems related to the progression of atherosclerosis [10,11]. Thus, lipid intolerance seems to be another facet of IRS and further experimental data to support this was recently reported [12]. The presence of small dense LDL-particles (SDLDL) is a risk factor for coronary heart disease [13], although not independent of other lipoprotein abnormalities [14]. SDLDL are susceptible to oxidation [15] and are also related to resistance of insulin-mediated glucose uptake and other metabolic disturbances characteristic of IRS, in particular hypertriglyceridemia [16 – 18]. The aim of this investigation was to do an extensive survey of smokers and non-smokers focused on the various aspects of IRS and to elucidate the potential mechanisms involved.

2. Subjects and methods

2.1. Subjects Non-obese men, 40 – 60 years of age, were recruited via a newspaper advertisement. Sixty-one individuals fulfilled the inclusion criteria and entered the study.

Thirty-six of these were habitual smokers who had been smoking more than 10 cigarettes per day for at least 20 years. The 25 non-smokers had never been regular smokers and had not had any regular nicotine or tobacco consumption for at least 20 years prior to the study. All participants were healthy, non-obese (body mass index (BMI)B 27), normotensive (blood pressure less than 140/90) and took no chronic medication. The clinical characteristics of the subjects are shown in Table 1. All subjects gave their informed consent to participation. The study was approved by the Ethics Committee of Go¨teborg University.

2.2. Methods The subjects were screened before they were selected and admitted to the study. At screening, the subjects were interviewed and underwent a physical examination. The number of cigarettes smoked per day was reported and the subjects were also asked about heredity for diabetes and/or hypertension in first degree relatives. Blood pressure was measured in the supine position after at least 10 min rest using a sphygmomanometer. Body weight was recorded to the nearest 0.1 kg with the subjects wearing only underwear and socks. Length was measured and BMI calculated. Waist and hip circumferences were measured using a non-elastic tape with the subjects standing. The waist circumference was measured in the midaxillary line midway between the lowest rib and the iliac crest and the hip circumference at the widest diameter around the buttocks according to the WHO criteria [19]. The waist:hip circumference ratio (WHR) was calculated from these measurements. Naturally occurring potassium-40 was measured in a whole body counter [20] and lean body mass (LBM) was calculated on the assumption that 1 kg contains

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81

Table 2 Metabolic and hemostatic variables in the fasting state and during the euglycemic hyperinsulinemic clamp Variable

Fasting state Glucose (mol/l) Insulin (mU/I) C-peptide (mg/l) FFA (mmol/l) Fibrinogen (g/l) PAI-1 activity (U/ml) Uric acid (mmol/l) During clamp Glucose (mmol/l) Insulin (mU/l) C-peptide (mg/l) FFA (mmol/l) GIR (mg/kg LBM×min)

Smokers

Non-smokers

Mean

S.E.M.

5.1 6.9 2.5 0.50 3.3 13.9 368

0.1 0.4 0.1 0.02 0.1 1.4 13

4.9 61.8 2.0 0.039 8.58

0.02 1.8 0.1 0.003 0.23

Range

4.0 – 6.0 3.5 – 11.9 1.7 – 3.7 0.20 – 0.87 2.3 – 4.7 1.2 – 35.6 249 – 506 4.6 – 5.2 43.1 – 89.5 1.5 – 2.4 0.010 – 0.100 5.37 – 12.85

P-value

Mean

S.E.M.

5.1 5.9 1.7 0.44 2.6 8.0 338

0.1 0.4 0.1 0.02 0.2 1.3 12

4.9 58.6 1.1 0.032 9.51

0.03 2.1 0.1 0.004 0.31

Range

4.5 – 5.9 3.4 – 13.1 1.2 – 3.0 0.29 – 0.82 1.9 – 4.6 0 – 21.5 208 – 510

NS NS B0.0001 0.042 B0.0001 B0.0028 0.093

4.6 – 5.2 40.1 – 83.2 0.5 – 1.9 0.003 – 0.065 6.19 – 12.56

NS NS B0.0001 NS 0.034

NS, not significant.

68.1 mmol potassium-40 as reported by Forbes et al. [21]. Body fat was calculated by subtracting lean body mass from total body weight. Blood sampling, a euglycemic hyperinsulinemic clamp and a meal test, respectively, were performed in the subjects on separate occasions. The smokers were asked to refrain from smoking from midnight the evening before these examinations, and all subjects were told to eat normally until 20:00 and then to fast. They were also asked not to perform any vigorous exercises for 2 days before the clamp examinations. Samples for serum nicotine determinations were taken and levels less than 8 ng/ml were accepted as a confirmation of abstention from smoking during the night and morning before the investigation. Thirty-six smokers fulfilled these criteria and were used in the analysis. Routine biochemical tests for hematologic, renal and hepatic function were performed. Fasting samples were also taken for determination of LDL-particle size and the proportion of LDL-particles with a diameter smaller than 25.5 nm (%SDLDL). These samples were stored at −70°C before they were analyzed. Lipoprotein and hepatic lipase activities (LPL and HL) were determined before and 5 and 15 min after an intravenous injection of heparin, 100 U/kg body weight (5000 U/ml; Lo¨vens, Ballerup, Denmark). The euglycemic hyperinsulinemic clamp was performed for 2 h essentially as described by DeFronzo et al. [22]. Catheters were placed in the dorsal hand veins and arterialized blood was obtained by using heating pads as previously described [23]. The blood glucose was clamped at 5.0 mmol/l using an insulin infusion rate of 1.0 mU/kg × min. Human short-acting insulin (Actrapid®, Novo Nordisk, Bagsværd, Denmark) with albumin (Immuno, Wien, Austria) added to prevent

adhesion, was dissolved in isotonic saline. Potassium chloride (Kabi-Pharmacia, Uppsala, Sweden) was administered at a rate of 5.0 mmol/h to prevent hypokalemia. The glucose infusion rate (Glucose 200 mg/l, Kabi-Pharmacia) was adjusted every 5 min based on glucose determinations from whole blood with Hemocue® (Hemocue, A8 ngelholm, Sweden), a glucose dehydrogenase method. The excellent correlation between this method and other standard blood– glucose determinations has been documented [24]. The glucose infusion rate (GIR) was calculated during the last 30 min of the clamp, when steady-state had been reached, as amount of glucose infused per kg lean body mass and minute. On a third occasion the subjects were invited for a standardized breakfast and blood samples were drawn for 6 h essentially as described by Axelsen et al. [9]. The meal, which was prepared in the metabolic ward, consisted of a sandwich containing butter, cheese, ham and tomatoes, and a cup of hot chocolate with cream. The energy content, calculated from national food tables [25], was 900 kcal (3.78 MJ), where 47% was derived from fat (51 g), 38% from carbohydrate and 14% from protein. The total fatty acid and cholesterol contents of the test meal were 43.7 and 0.16 g, respectively.

2.3. Laboratory analyses All blood samples were drawn in appropriate tubes, kept on ice until centrifuged and stored at − 20°C. The fasting blood glucose values shown in Table 2 were analyzed with a chemical glucose dehydrogenase method. Free fatty acids (FFA) were determined with an enzymatic colorimetric method using reagents from Wako (Neuss, Germany). Serum free insulin (Pharma-

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cia Insulin RIA 100, Pharmacia, Uppsala, Sweden) and C-peptide levels (Behringwerke, Marburg/Lahn, Germany) were determined with radioimmunochemical analyses. The coefficients of variation for insulin and C-peptide determinations were 6.0% (at 38 mU/l) and 7.0% (at 1.7 mg/l), respectively. Serum triglycerides and cholesterol were determined with an automated Cobas Mira analyzer (HoffmanLaRoche, Basel, Switzerland) by enzymatic methods. The concentration of HDL-C was measured by the phosphotungstic acid – magnesium chloride precipitation method. Apolipoproteins A-I, A-II (Boehringer, Mannheim, Germany) and Apoprotein B (Orion Diagnostica, Espoo, Finland) were measured by immunoturbidometric methods. Interassay variations for Apo A-I and for Apo A-II were 3.6 and 3.7%, respectively. LPL activity was measured by an immunochemical assay using specific antiserum raised against HL in rabbits [26]. HL activity was determined as lipase activity in the presence of 1 M NaCl, when LPL is inactivated. Intra-assay variation for LPL was 4.6 and for HL 5.1%. The interassay variations were 5.1 and 8.4%, respectively. LipoproteinA-I (LpA-I) was quantified by using electroimmunophoretic methods as described by Kahri et al. [27]. Interassay variation for LpA-I particle concentration was 7.3%. LDL-particle size was analyzed with gradient gel electrophoresis essentially as reported by Nichols et al. [28]. Coefficients of variation for intragel and intergel precisions for the used control sample were 1.8 and 1.2%, respectively. Fibrinogen was analyzed according to Clauss [29]. The total coefficient of variation was 4.1% (at 2.3 g/l). PAI-1 activity was measured using Spectrolyse pL kit (Biopool, Umea˚, Sweden). The total coefficient of variation was 12.0% (at 11.9 U/ml) and 6.3% (at 43.6 U/ml). Nicotine was analyzed by capillary gas chromatography [30].

2.4. Statistical analyses Data are presented as mean 9S.D. and mean 9 S.E.M. as indicated. Each variable was tested for normality and, accordingly, unpaired t-tests or Mann-Whitney U-tests were used to compare smokers and non-smokers. Triglyceride elimination after the test meals were individually calculated as the AUIC, according to Matthews et al. [31]. Linear regression analysis was used to analyze relations between data. Multiple regression analysis was performed to evaluate the independent relations between variables. Two-tailed P5 5% is considered statistically significant. StatView® 4.5 (Abacus Concepts, Berkeley, CA) was used for all statistical calculations.

3. Results

3.1. Subject characteristics and metabolic 6ariables The smoking and non-smoking groups were well matched for age, BMI, body fat and LBM. However, the smokers had higher WHR but lower blood pressure (Table 1). Family history of diabetes and/or hypertension was similar in the 2 groups (non-smokers 7/25, smokers 7/36; x 2 test, NS). All individuals had normal fasting blood glucose (B 6.7 mmol/l). Two smokers had moderately elevated triglyceride values (\ 2.3 mmol/l) when included in the study. Eight smokers and one non-smoker had elevated cholesterol values (\6.5 mmol/l). The mean values of the biochemical analyses and clamp data are shown in Tables 2 and 3. Smokers and non-smokers had similar fasting glucose and insulin levels. However, the smokers had significantly higher levels of fasting C-peptide, FFA, Apolipoprotein A-II, fibrinogen and PAI-1 activity (Table 2 and 3). Apolipoprotein B as well as uric acid levels also tended to be higher in the smokers (PB 0.1). Both HDL-C and LpA-I were significantly lower in the smokers (Table 3). Fasting triglyceride levels were significantly higher in the smokers (Table 3). This difference was slightly less when the 2 hypertriglyceridemic subjects were excluded from the calculations but still remained statistically significant (non-smokers 1.190.1 versus smokers 1.3 9 0.1 mmol/l; P= 0.048).

3.2. Insulin sensiti6ity The degree of insulin sensitivity quantitated with the euglycemic hyperinsulinemic clamp technique (GIR) was significantly lower in smokers (Table 2). Both blood glucose and insulin levels were similar during the clamps in both groups (Table 2). However, the C-peptide levels remained higher in the smokers during clamp and the relative suppression in C-peptide levels was higher in the non-smokers (37.99 3.4 and 22.595.7% respectively; P= 0.028), although delta-values (fasting minus steady-state levels) were not significantly different between the groups (Fig. 1).

3.3. Lipase acti6ities There were no differences in fasting or postheparin LPL activity (Table 3). However, the postheparin HL activity was markedly increased in the smokers. This difference remained even when the two hypertriglyceridemic subjects were excluded (P= 0.0018). Postheparin LPL activity was significantly and positively correlated to fasting HDL-C (smokers r=0.49, P= 0.0070; non-smokers r= 0.66, P= 0.0030; Fig. 2) and GIR (P=0.004).

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83

Table 3 Lipo- and apolipoprotein levels and LPL and HL activities Variable

Smokers

Non-smokers

Mean

S.E.M.

Lipo- and apolipoproteins Triglycerides (mol/l) Cholesterol (mol/l) LDL-cholesterol (mmol/l) Apolipoprotein B (mg%) HDL-cholesterol (mmol/l) Apolipoprotein A-I (mg%) Apolipoprotein A-II (mg%) LpA-I (mg/dl)

1.4 5.5 3.9 103 1.0 119 37 40

0.1 0.2 0.2 4 0.04 2 1 2

LPL and HL activities LPL (mU/ml) Posthepa LPL (mU/ml) HL, mU/ml Posthepa (mU/ml) Posthepa LPL/HL

5.1 267 1.7 356 0.86

0.4 19 0.2 25 0.09

Range

P-value

Mean

S.E.M.

Range

0.5 – 3.2 3.3 – 7.4 2.0 – 5.8 52 – 162 0.6 – 1.3 84 – 148 27 – 57 23 – 57

1.1 5.2 3.6 91 1.1 124 34 52

0.1 0.2 0.2 4 0.05 3 1 3

0.6 – 1.9 3.2 – 7.0 1.9 – 5.8 50 – 122 0.7 – 1.6 99 – 168 25 – 44 29 – 73

0.014 NS NS 0.067 0.049 NS 0.042 0.0002

1.0 – 14.5 126 – 546 0 – 4.0 163 – 650 0.26 – 2.18

4.7 283 1.3 268 1.32

0.3 20 0.3 25 0.18

2.4 – 6.6 167 – 447 0 – 3.1 134 – 472 0.47 – 2.48

NS NS NS 0.031 0.041

NS, not significant. Posthep, lipase activity 15 min after i.v. heparin bolus.

a

3.4. Postprandial lipemia Triglyceride elimination after the standardized mixed test meal is shown in Fig. 3. The fasting triglyceride level was slightly higher in the smokers than in the non-smokers but still in the normal range (1.4 and 1.1 mmol/l). The individually calculated peak postprandial rise after the meal was markedly increased in the smokers (non-smokers 1.089 0.11 versus smokers 1.699 0.16 mmol/l; P = 0.0052), as well as the AUIC (non-smokers 11798 versus smokers 1749 14 arbitrary units; P = 0.0052; Fig. 3). These differences between the groups still remained highly significant even when the two individuals with fasting triglyceride level \2.3 mmol/l were excluded from the calculations. The postprandial triglyceride levels, expressed as AUIC, were significantly and negatively correlated to postheparin LPL activity (Fig. 4) as well as to fasting

Fig. 1. Fasting and clamp steady-state C-peptide levels 9S.E.M. in smokers and non-smokers. PB 0.0001 on both comparisons, i.e. at baseline and at clamp steady-state. The relative C-peptide suppression during the clamp was higher in the non-smokers (P= 0.028).

HDL-C (r= − 0.39, P= 0.0096). Degree of insulin sensitivity (GIR) was also correlated to AUIC but this only reached statistical significance for the non-smokers (r= − 0.52; P=0.017). Insulin sensitivity (GIR) and fasting triglycerides were also negatively correlated (r= − 0.30; P= 0.021).

3.5. LDL-particle size Mean LDL-particle diameter of the major LDL peak was 26.869 0.18 nm among the non-smokers and 26.319 0.27 nm in the smoking group (P= 0.10). However, the proportion of LDL-particles with a diameter B 25.5 nm (%SDLDL) was higher among the smokers than the non-smokers (27.49 4.9 and 16.19 2.7%, respectively; P= 0.050).

Fig. 2. Regression plot: postheparin lipoprotein lipase (LPL) activity vs. fasting HDL-cholesterol. (“), smokers; (), non-smokers. r= 0.55, R2 = 0.32, PB 0.0001.

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B. Eliasson et al. / Atherosclerosis 129 (1997) 79–88

Fig. 3. Triglycerides after a standardized meal test 9 S.E.M. in smokers and non-smokers. AUIC: non-smokers 117 9 8; smokers 1749 14 arbitrary units. P =0.0052.

LDL-particle diameters were strongly negatively correlated to fasting (r = − 0.53, P=0.0003) as well as postprandial triglyceride levels in the total study population (r = − 0.45, P = 0.0043) and also in both study groups when analyzed separately. The relationships between %SDLDL and fasting as well as postprandial triglyceride levels were equally strong (data not shown). Mean LDL-particle diameters also correlated positively to insulin sensitivity, GIR (P =0.014).

status and GIR were both independently related to fasting triglyceride levels (borderline significance), PAI1 activity and they were both strongly correlated to fasting C-peptide levels. When alcohol consumption was also included in the regression models as a third independent variable the results were similar, but alcohol consumption was positively correlated to postheparin LPL (model R2=0.20, P=0.026, r= 0.47). HDL-cholesterol levels were independently associated to GIR, smoking status and alcohol consumption (model R2= 0.22, P= 0.0032; r values 0.33, −0.26 and 0.31, respectively). Finally, backward stepwise multiple linear regressions were performed to find the factors most strongly and independently correlated to the metabolic variables. In these models age, smoking status, GIR, alcohol consumption, percent body fat and postheparin LPL and HL levels were used as independent variables and the metabolic variables as dependents. As shown in Table 5 the analyses essentially corroborate the previous results. Both smoking status and insulin sensitivity were independently related to both insulin release and lipid levels.

3.6. Multiple regression analysis

4. Discussion

In order to examine the effects of smoking and degree of insulin sensitivity per se on the various risk factors, multiple linear regression analyses were performed, when smoking status (smoker or non-smoker), reported alcohol consumption and GIR were addressed as independent variables and each major feature of IRS was included as a dependent variable (Table 4). These showed that WHR, systolic blood pressure, fibrinogen, LpA-I, postheparin HL, test meal triglyceride AUIC and clamp steady-state C-peptide levels were independently determined by smoking status. GIR was independently correlated to SDLDL, HDL-C, steady-state FFA and fasting insulin levels. Smoking

In the present study we examined insulin sensitivity and cardiovascular risk factor profile in healthy nonobese, middle-aged male smokers and in a group of non-smoking men who were selected to have similar ranges of age and BMI. The smokers were insulin resistant and exhibited a number of perturbations related to IRS. These included higher fasting levels of triglycerides, PAI-1 activity and fibrinogen, while HDLC was lower. Fasting insulin and blood glucose levels were not significantly higher in the smokers but C-peptide levels were, which is consistent with a compensatory increase in insulin release as a result of the insulin resistance. The present study also shows that smokers, even when they exhibit fasting normotriglyceridemia, are lipid intolerant and have an impaired elimination of triglycerides from a mixed meal. This corroborates and extends our recent findings in a pilot study of nine smoking middle-aged men [9]. In addition, we now show that normotriglyceridemic but lipid intolerant smokers have smaller and more dense LDL-particles. Thus, modifiable environmental factors are related to this lipoprotein abnormality which, in turn, is strongly associated with cardiovascular disease [13–15]. The relationship between smoking and IRS has recently come into focus. We have shown that smoking can acutely impair insulin sensitivity [3]. Furthermore, Facchini et al. [4], using the insulin suppression test, showed in a cross-sectional study that smokers exhib-

Fig. 4. Regression plot: postheparin lipoprotein lipase activity vs. postprandial triglycerides after standardized meal test. AUIC: (“), smokers; (), non-smokers. r= − 0.40, R2 = 0.16, P= 0.023.

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85

Table 4 Multiple linear regression analyses of the relationships between cardiovascular riskfactors, insulin sensitivity (GIR) and smoking status Dependent variables

Independent variables Smoking statusa

GIR

WHR Systolic BP Fibrinogen Lipoprotein A-I Postheparin HL Meal test triglyceride AUIC ssb-C-peptide LDL-diameter HDL-C ssb-FFA fasting Insulin fasting Triglyceride PAI-1 activity fasting C-peptide

Model

Standard coefficient

P

Standard coefficient

P

R2

P

−0.15 −0.05 −0.19 0.12 −0.10 −0.16 −0.04 0.36 0.36 −0.36 −0.45 −0.23 −0.23 −0.42

NS NS NS NS NS NS NS 0.026 0.0026 0.0055 0.0004 0.077 0.019 B0.0001

0.32 −0.30 0.43 −0.47 0.32 0.45 0.63 −0.08 −0.15 0.09 0.08 0.25 0.29 0.51

0.013 0.023 0.0005 0.0013 0.033 0.0027 B0.0001 NS NS NS NS 0.050 0.020 B0.0001

0.15 0.09 0.27 0.27 0.12 0.26 0.41 0.16 0.19 0.16 0.23 0.15 0.21 0.56

0.0086 0.071 0.0001 0.0010 0.060 0.0027 0.0002 0.029 0.0047 0.0073 0.0005 0.010 0.0009 B0.0001

NS, not significant. a Smoking status, smoker or non-smoker. b ss, Clamp steady-state.

ited an impaired insulin sensitivity when compared to non-smoking individuals. We have also recently shown that the degree of insulin resistance and various aspects of IRS are functions of smoking habits in healthy middle-aged men [5]. Taken together, these studies show that smoking is an important environmental factor for insulin resistance and that IRS is likely to be a major reason for the increased cardiovascular morbidity and mortality in smokers. One reason for the impaired insulin sensitivity in smokers may be increased levels of counterregulatory hormones and increased sympathetic nervous system activity [2,3,9,32]. Our recent finding [9] that norepinephrine levels are raised in smokers, even after 48 h smoking abstinence, is in agreement with this. Catecholamines are potent insulin-antagonistic hormones and also have long-term effects on cellular synthesis of insulin-regulated proteins, including the glucose transporting proteins GLUT 4 (reviewed in [33]). Adipose tissue distribution, expressed as waist:hip ratio, showed a preponderance for abdominal sites in smokers. This finding is in agreement with other results [34,35] and is associated with an increased risk for cardiovascular disease. Although there may be several reasons for this finding, it is interesting to note that smokers, when compared to non-smokers, tended to have a larger adipose mass at the expense of lean body tissue. Thus, for a given BMI, smokers seem to be relatively more ‘obese’ which may contribute to their higher WHR. Smokers are lipid intolerant and eliminate the triglycerides more slowly after a mixed meal. Since we did not

quantitate the endogenous VLDL-triglycerides from the intestinally derived lipids it is unclear whether these lipid pools were differentially eliminated. However, these data further support the concept that lipid intolerance is another facet of IRS as also recently suggested by Axelsen et al. [9] and Jeppesen et al. [12]. One likely mechanism for lipid intolerance would be an impaired LPL activity. In a recent study, Knudsen et al. [36] found a lower LPL activity in healthy insulin-resistant offspring to diabetic patients. In agreement with this, we found correlations between triglyceride AUIC following the mixed meal and postheparin lipase activity as well as a degree of insulin sensitivity (GIR). However, there was no significant difference between the smokers and the non-smokers in LPL activity suggesting that differences in lipoprotein composition may play a role. Since we did not measure LPL activity of skeletal muscle or adipose tissue our data do not exclude specific alterations of LPL activities in these tissues, which is possible since increased adipose tissue LPL activity has been described in one previous study [37]. Although an increased prevalence of small, dense LDL is related to insulin resistance and IRS [16–18], the link for this relationship seems to be the ambient triglyceride levels [16]. The present data are supportive of this conclusion and show a close relationship between %SDLDL and fasting triglyceride levels as well as triglyceride AUIC. Likewise Griffin et al. [38] found that the excess of small dense LDL in smokers was explained by serum triglycerides. Thus, even though the smokers were normotriglyceridemic, defined from the

a

ss, Clamp steady-state.

Model R2 P-value

Age Smoking status GIR Alcohol consumption Percent body fat Postheparin LPL Postheparin HL

Independent variables

0.41 B0.0001

0.35

−0.41

fasting Insulin

0.60 B0.0001

0.53 −0.49

fasting C-peptide

0.38 0.0013

0.62

ssa-C-peptide

Dependent variables (standardized coefficients)

Table 5 Backward stepwise multiple linear regression analyses

0.23 0.0043

0.39 0.31

fasting FFA

0.080 0.050

−0.28

Triglycerides

0.36 B0.0001

0.51

0.26

HDL-cholesterol

0.24 0.0007

0.49

Apo A-I

0.17 0.0046

0.42

Apo A-II

0.42

LDL size

0.35 0.17 0.0013 0.020

0.44

−0.37

LpA-I

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levels in the fasting state, they clearly showed prolonged and elevated postprandial triglyceride levels and this may strongly influence %SDLDL. Furthermore, meal test triglyceride AUIC was strongly correlated to fasting triglyceride levels but when AUIC was corrected for fasting triglycerides there was no significant difference between smokers and nonsmokers (data not shown). However, there were significant differences between smokers and non-smokers in SDLDL and %SDLDL even after correction for fasting triglyceride levels P = 0.042 and 0.041, respectively). In the present study, as well as in many previous reports (reviewed in [6]), the smokers were found to have lower HDL-C levels, which repeatedly has been demonstrated to be a strong cardiovascular risk factor [39,40]. Although postheparin LPL activity was normal, HL activity was elevated. This is in agreement with another report [41]. Taken together, smokers have a lower LPL:HL ratio which would be consistent with their lower HDL-C. The co-ordinate opposite action of these enzymes would determine HDL-particle size, the fractional removal rate and, thus, the plasma concentration [42,43]. The finding that LpA-I was significantly lower in the smokers is consistent with this concept and suggests that HDL2 is reduced, which has been demonstrated in smokers previously [7]. Since small dense LDL is derived from triglyceride-rich VLDL particles by the action of HL, the increased activity of this enzyme in smokers may also contribute to the excess of SDLDL. Furthermore, it is also possible that a disturbance in the action of CETP (cholesteryl ester transfer protein) can explain abnormalities in HDL-C metabolism in smokers to some extent, but this is not yet fully elucidated [7,44]. The participants in this study were invited via a newspaper advertisement. Except for alcohol consumption we did not evaluate their degree of physical activity, fitness or diet. Thus, it is possible that these factors, or other environmental factors contribute to the differences between the smokers and non-smokers. Although the alcohol consumption generally was low to moderate in the subjects in this study, it was significantly higher in the smokers (mean9 S.D.: non-smokers 4.39 4.5 versus smokers 12.69 10.1 g/day). Moderate alcohol consumption has been found to improve insulin sensitivity [45], HDL-cholesterol levels [46], fibrinolysis [47], and the cardiovascular risk is increased in non-alcohol consumers [46]. In this study HDL-cholesterol levels was positively correlated with alcohol consumption and GIR but negatively correlated with smoking status, and, thus, it is possible that the HDL-cholesterol levels and the impaired insulin sensitivity in the smokers was underscored because of their higher alcohol intake. This study documents a strong relationship between cigarette smoking and IRS. Recent epidemiological studies have also shown that smoking is an independent

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risk factor not only for CVD but also for NIDDM (non-insulin dependent diabetes mellitus) [48–50]. Thus, we are now beginning to understand the pathogenesis for the increased risk for cardiovascular disease in smokers. It is crucial to determine whether nicotine is the responsible constituent in cigarette smoke, since nicotine replacement during smoking cessation and non-smoke nicotine use are increasingly recommended throughout the world.

Acknowledgements The excellent technical assistance by M. Lande´n and R. Marjanen is gratefully acknowledged. Sources of support: The Swedish Medical Research Council (grant B-3506), the Inga Britt and Arne Lundberg Foundation, King Gustaf V and Queen Victoria’s Fund, the Sigrid Juselius Foundation and the Go¨teborg Medical Society.

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