Hypoinsulinemia is strongly associated with coronary artery calcification (CAC) assessed by multislice computed tomography

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1 Hypoinsulinemia is strongly associated with coronary artery calcification (CAC) assessed by multislice computed tomography Yohei Oda 1, Muhei Tanaka 2, Michiaki Fukui 2, Sei Tsunoda 1, Satoshi Akabame 3, Ki-ichiro Tomiyasu 3, Goji Hasegawa 2, Koji Nakano 3, Naoto Nakamura 2 1 Division of Cardiology, Department of Internal Medicine, Social Insurance Kyoto Hospital 2 Department of Endocrinology and Metabolism, Kyoto Prefectural University of Medicine, Graduate School of Medical Science 3 Department of Cardiology, Yamashiro Public Hospital Kyoto, JAPAN

2 Introduction In recent years, it is widely known that hyperinsulinemia is associated with an unfavorable risk profile for atherosclerosis and predicts the development of metabolic and hemodynamic abnormalities associated with high cardiovascular risk. The coronary artery calcification (CAC) score has recently been developed as a marker of coronary atherosclerosis, which can be assessed noninvasively using multislice computed tomography (MSCT). CAC scores have been shown to be related to CHD risk factors and cardiovascular events.

3 Aim The purpose of this study was to assess the relationship between serum insulin level and coronary artery calcification (CAC) determined by MSCT in the nondiabetic participants.

4 Methods We performed a cross-sectional study in 582 consecutive and nondiabetic participants with clinical suspicion of CHD, who visited at the Department of Cardiology, Yamashiro Public Hospital (Kyoto, Japan) from April 2006 to November A standard 75-g OGTT was performed to evaluate their glucose-load response and venous blood was collected at time 0, 30, 60 and 120 min for the measurement of plasma glucose and serum insulin levels. The product of glucose and insulin levels and time duration in whole OGTT study was expressed as glucose area under the concentration time curve (GluAUC) and insulin area under the concentration time curve (InsAUC). They were divided into 4 subgroups according to fasting insulin quartiles and InsAUC quartiles.

5 Statistical analysis Categorical and continuous variables were compared among the groups by a chi-square analysis and analysis of variance (ANOVA), respectively. A Tukey Kramer post-hoc analysis was performed to determine significant differences among each group. Analysis of covariance (ANCOVA) was used to assess differences of groups, adjusted for variable sets of covariates. Model 1 adjusted for age, sex and BMI; model 2 adjusted for age, sex, BMI, hypertension, hyperlipidemia, smoking status and egfr; model 3 adjusted for age, sex, BMI, hypertension, hyperlipidemia, smoking status, egfr and aspirin, statin, angiotensin II receptor blocker, angiotensin-converting enzyme inhibitor, betablocker, alpha-blocker, calcium channel blocker and diuretic uses. Association between InsAUC quartiles and the prevalence of CAC > 0, CAC > 100 and CAC > 400 was assessed with a chi-square analysis. Skewed variables were preliminarily log-transformed to improve the approximation to a Gaussian distribution and CAC scores were log-transformed after adding 1. Continuous variables are presented as the mean value + 1 SD and categorical variables are presented as number (percentage). A P value < 0.05 was considered statistically significant. The statistical analyses were performed using the JMP version 8.0 software (SAS Institute Inc., Cary, North Carolina).

6 Participant Characteristics n 582 Glucose (mmol/l) Age (y) 70.0 (10.1) 0 min 4.9 (0.5) Male (%) 360 (61.8) 30 min 8.2 (1.4) BMI (Kg/m 2 ) 23.3 (3.8) 60 min 8.6 (2.3) Smoking (%) 41 (7.0) 120 min 6.7 (1.5) Hyperlipidemia (%) 215 (36.6) AUC (mmol/l hr) 15.1 (2.5) Hypertension (%) 168 (28.9) Insulin (pmol/l) egfr (ml/s/m 2 ) 65.4 (18.3) 0 min 41.0 (27.9) HbA1c (%) 5.1 (0.3) 30 min (315.5) HOMA-IR 1.3 (0.9) 60 min (375.3) CAC 39.8 (12.6) 120 min (422.0) AUC (pmol/l hr) (532.7)

7 Unadjusted mean characteristics according to fasting insulin and insulin area under the concentration time curve levels (ANOVA) Fasting insulin (pmol/l) < < P-value n Age (y) 73.2 (9.0) 71.3 (6.9) 69.3 (10.1) 66.2 (12.2) < Male (%) 119 (82.1) 84 (57.5) 64 (43.8) 93(64.1) < BMI (Kg/m 2 ) 21.1 (2.1) 23.4 (3.1) 22.8 (3.7) 26.0 (4.4) < Smoking (%) 16 (11.0) 6 (4.1) 16 (11.0) 3 (2.1) Hyperlipidemia (%) 50 (34.4) 51 (34.9) 53 (36.3) 59 (40.7) N.S. Hypertension (%) 39 (26.9) 44 (30.1) 46 (31.5) 39 (26.9) N.S. egfr (ml/s/m 2 ) 64.9 (15.8) 67.0 (19.0) 65.2 (22.2) 64.4 (15.2) N.S. HbA1c (%) 5.1 (0.3) 5.1 (0.4) 5.2 (0.3) 5.1 (0.3) N.S. HOMA-IR 0.5 (0.1) 0.9 (0.2) 1.2 (0.1) 2.5 (1.2) < Log (CAC+1) 1.8 (1.1) 1.6 (1.0) 1.4 (1.2) 1.7 (0.9)

8 Unadjusted mean characteristics according to fasting insulin and insulin area under the concentration time curve levels (ANOVA) Fasting insulin (pmol/l) < < P-value Glucose (mmol/l) 0 min 4.9 (0.3) 4.9 (0.5) 4.8 (0.4) 5.0 (0.5) min 8.1 (1.3) 8.2(1.6) 8.2 (1.1) 8.3 (1.4) N.S. 60 min 8.2 (2.1) 8.2 (2.2) 8.7 (2.5) 9.4 (2.0) < min 6.4 (1.6) 6.4 (1.2) 6.7 (1.5) 7.2 (1.6) < AUC (mmol/l hr) 14.6 (2.4) 14.7 (2.4) 15.2 (2.6) 16.1 (2.5) < Insulin (pmol/l) 0 min 17.2 (2.6) 28.9 (3.7) 40.1 (3.3) 77.9 (32.1) < min (260.0) (276.4) (366.7) (279.2) < min (307.7) (218.8) (259.7) (452.1) < min (123.6) (232.0) (352.4) (608.7) < AUC (pmol/l hr) (335.4) (282.0) (381.3) (672.8) <

9 Unadjusted mean characteristics according to fasting insulin and insulin area under the concentration time curve levels (ANOVA) InsAUC (pmol/l hr) < < P-value n Age (y) 74.1 (10.3) 70.1 (7.9) 69.7 (7.0) 66.1 (12.5) < Male (%) 103 (71.0) 71 (48.6) 89 (61.0) 97 (66.9) BMI (Kg/m 2 ) 21.4 (3.3) 22.8 (3.2) 24.1 (3.1) 25.0 (4.6) < Smoking (%) 7 (4.8) 13 (8.9) 9 (6.2) 12 (8.2) N.S. Hyperlipidemia (%) 58 (40.0) 51 (34.9) 51 (34.9) 53 (36.6) N.S. Hypertension (%) 39 (26.9) 44 (30.1) 42 (28.8) 43 (29.7) N.S. egfr (ml/s/m 2 ) 63.8 (17.0) 68.1 (21.9) 65.8 (13.2) 63.7 (19.6) N.S. HbA1c (%) 5.1 (0.3) 5.1 (0.4) 5.1 (0.3) 5.1 (0.3) N.S. HOMA-IR 0.8 (0.4) 1.0 (0.4) 1.5 (1.3) 1.9 (0.9) < Log (CAC+1) 2.0 (1.0) 1.3 (1.1) 1.4 (1.2) 1.8 (1.0) <

10 Unadjusted mean characteristics according to fasting insulin and insulin area under the concentration time curve levels (ANOVA) InsAUC (pmol/l hr) < < P-value Glucose (mmol/l) 0 min 4.8 (0.3) 4.8 (0.4) 4.9 (0.5) 5.1 (0.5) min 8.1 (1.3) 8.2 (1.3) 8.3 (1.3) 8.3 (1.6) N.S. 60 min 8.1 (1.8) 8.3 (2.2) 8.9 (2.1) 9.3 (2.4) < min 6.6 (1.4) 6.7 (1.4) 6.6 (1.6) 6.8 (1.7) N.S. AUC (mmol/l hr) 14.6 (1.9) 14.9 (2.4) 15.4 (2.4) 15.8 (2.9) Insulin (pmol/l) 0 min 24.6 (11.1) 30.8 (11.5) 47.8 (35.0) 60.8 (29.2) < min (73.7) (121.9) (282.3) (329.6) < min (60.2) (78.3) (144.0) (411.8) < min (94.5) (99.3) (216.9) (610.3) < AUC (pmol/l hr) (61.7) (72.8) (109.0) (486.8) <

11 Prevalence of CAC score > 0, 100 and 400 across quartile of insulin area under the concentration time curve levels. (%) CAC > 0 CAC > 100 CAC > 400 P < P = P < st Quartile 2nd Quartile 3rd Quartile 4th Quartile Quartile of insulin area under the concentration time curve levels. 1st, < 52.1; 2nd, ; 3rd, ; 4th, > (pmol/l hr)

12 Adjusted least square mean log (CAC+1) according to insulin area under the concentration time curve levels (ANCOVA) Fasting insulin (pmol/l) Model 1 < < P-value Log (CAC+1) 1.78 ( ) a 1.56 ( ) 1.44 ( ) b 1.71 ( ) Model 2 Log (CAC+1) 1.79 ( ) a 1.49 ( ) 1.45 ( ) b 1.73 ( ) Model 3 Log (CAC+1) 1.86 ( ) a 1.49 ( ) b 1.43 ( ) b 1.68 ( ) The Post Hoc analysis indicated a significant difference between these two quartiles. a,b, P < 0.05; c,d, P < 0.005; e,f, P < Model 1 adjusted for age, sex and body mass index; model 2 adjusted for age, sex, body mass index, hypertension, hyperlipidemia, smoking status and estimated glomerular filtration rate; model 3 adjusted for age, sex, body mass index, hypertension, hyperlipidemia, smoking status, estimated glomerular filtration rate and statin and antihypertensive drugs uses. Data are expressed as mean (95%CI).

13 Adjusted least square mean log (CAC+1) according to insulin area under the concentration time curve levels (ANCOVA) InsAUC (pmol/l hr) Model 1 < < P-value Log (CAC+1) 2.01 ( ) e 1.31 ( ) c,f 1.42 ( ) a,f 1.75 ( ) b,d < Model 2 Log (CAC+1) 2.06 ( ) e 1.28 ( ) c,f 1.35 ( ) c,f 1.77 ( ) d < Model 3 Log (CAC+1) 2.01 ( ) e 1.32 ( ) c,f 1.35 ( ) c,f 1.79 ( ) d < The Post Hoc analysis indicated a significant difference between these two quartiles. a,b, P < 0.05; c,d, P < 0.005; e,f, P < Model 1 adjusted for age, sex and body mass index; model 2 adjusted for age, sex, body mass index, hypertension, hyperlipidemia, smoking status and estimated glomerular filtration rate; model 3 adjusted for age, sex, body mass index, hypertension, hyperlipidemia, smoking status, estimated glomerular filtration rate and statin and antihypertensive drugs uses. Data are expressed as mean (95%CI).

14 Discussion Hyperinsulinemia is associated with a high risk of atherosclerosis due to the adverse concerted action of metabolic, hemodynamic, and hemocoagulative atherogenic factors. In our study, the lowest InsAUC quartile was lower glucose level than other quartiles and maintained glucose homeostasis. Nevertheless, our study pointed out that the relationship between CAC and the insulin AUC was nonlinear. Moreover, the lowest InsAUC quartile was independently associated with CAC. Interestingly, InsAUC seems to be more associated with CAC than fasting insulin level.

15 Discussion Insulin has several direct vascular actions that contribute to either vascular injury or protection. Insulin has been proposed to be potentially atherogenic in itself. Several experimental studies in animal models or in arterial tissue in vitro suggested that insulin has a direct atherogenic effect. Insulin has also been proposed to be a vasodilator and stimulate nitric oxide (NO) production. Physiological concentrations of insulin possess a dose-dependent inhibitory action on platelet aggregation, and inhibit intracellular uptake of calcium. Intracellular calcium have pleiotropic effects on monocytes, macrophages, fibroblasts and vascular muscle cells.

16 Discussion Some studies suggested an alternative hypothesis, according to which the atherogenic factor might be a state of insufficient cell insulinization. In the case of low insulin level, despite the expected higher insulin sensitivity, the insufficient insulinization would result from low ambient insulin, whereas in the case of hyperinsulinemia, despite the higher ambient insulin, it would be the consequence of insulin resistance. The insufficient insulinization, whether caused by low insulin level or insulin resistance, may adversely affect the function of one cell type that plays a significant role in atherogenesis. The insufficient insulinization may act in concert to target endothelial cells, resulting in oxidative stress and endothelial dysfunction. Accordingly, those facts may provide evidence that CAC and vascular calcification process may actually begin in the spectrum of the insufficient insulinization, whether caused by insulin resistance or low insulin level (i.e. high insulin sensitivity).

17 Conclusions The lowest InsAUC quartile was related to CAC, though the lowest InsAUC quartile maintained glucose homeostasis, in participants with clinical suspicion of CHD. Not only hyperinsulinemia but also low insulin level is independently associated with CAC.

egfr > 50 (n = 13,916)

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