Type A behavior and metabolic syndrome precursors in young adults

J Clin Epidemiol Vol. 49, No. 3, pp. 335-343, Copynght 0 1996 Elsevier Science Inc.

1996

0895.4356/96/$15.00 SSDI 0895-4356(95)00524-8

ELSEVIER

Type A Behavior and Metabolic Syndrome Precursors in Young Adults Nikh

Rauaja, ‘, * Lisa Keltikangas-Jdwinen, ’ and Jorma Viikari’

‘DEPARTMENT AND

OF PSYCHOLOGY,

‘DEPARTMENT

OF

UNIVERSITY

MEDICINE,

OF HELSINKI,

UNIVERSITY

OF TURKU,

HELSINKI, TURKU,

FINLAND, FINLAND

ABSTRACT. The association between type A behavior and a cluster of parameters of the metabolic syndrome was studied in 919 randomly selected healthy young adults. Type A behavior was measured using the Type A Behavior Questionnaire for the Finnish Multicenter Study and the Hunter Wolf A-B Rating Scale. The results showed that type A men scored higher on the “Metabolic Syndrome Precursors Factor,” representing a metabolic entity, than did non-type A men. In addition, type A behavior had a moderating effect on the relationship between parameters of the metabolic syndrome, that is, interdependence of these somatic factors was stronger in type A men than in non-type A men. These findings were not true of women. It is discussed whether type A behavior might affect bodily functions through increased activity along the pituitary-adrenal system resulting in insulin resistance, compensatory hyperinsulinemia, and other characteristics of the metabolic syndrome. J CLIN EPIDEMIOL 49;3:335-343, 1996. KEY WORDS. Type A behavior,

metabolic

syndrome,

body mass index,

insulin,

human gender differences,

young adults.

INTRODUCTION The discovery of the type A behavior pattern (TABP), first described and measured by Friedman and Rosenman in the 195Os, gave impetus to efforts to connect behavioral factors with the research on the etiology of coronary heart disease (CHD) [1,2]. For the first 20 years, until about 1980, evidence concerning the significance of the TABP was strongly and consistently positive [3]. The TABP seemed to be associated with the development [4-81, incidence [9, lo], and prevalence (11,121 as well as poor prognosis [ 131 and recurrence of CHD [14]. Owing to the accumulation of positive evidence, investigations involving type A behavior predominated in the research of behavioral CHD risk factors for a long time. Since about 1980, however, an expanding body of negative findings on the role of type A behavior in the development of CHD has emerged from retrospective and prospective studies [15-171 as well as from angiographic comparisons [18,19]. Consequently, step by step this has led to the near rejection of the concept of the TABP. Total rejection of the type A behavior construct would, however, be premature considering the abundance of unequivocally positive findings. Particularly as new medical findings or hypotheses emerge, it is justifiable to examine whether type A behavior is of any significance in these new connections. A cluster of disorders called “metabolic syndrome” or “syndrome X” has received increasing attention as a potential contributing factor in the genesis of atherosclerotic cardiovascular disease. The most important aberrations included in the metabolic syndrome are resistance to insulin-stimulated glucose uptake followed by compensatory hyperinsulinemia, hyperglycemia, an increased plasma concentration of very low-density lipoprotein (VLDL) triglyceride, a decreased plasma concentration of high-density lipoprotein cholesterol (HDL Chol), and high blood pressure as well as obesity, especially upper body fat distribution [20,21]. The primary feature of the metabolic syndrome is insu‘Address for correspondence: Niklas Ravaja, Department of Psychology, P.O. Box 4, 00014 University of Helsinki, Helsinki, Finland. (Received in revrsed form 22 March 1995.)

lin resistance, and all other changes are supposed to be secondary to this basic abnormality [20-231, although with respect to insulin resistance and abdominal obesity there are causal paths leading in both directions 1241. All of the proposed consequences of insulin resistance have been shown to increase the risk of cardiovascular disease [22]. The separate relationships between type A behavior and each somatic risk factor included in the metabolic syndrome have been examined in several studies, but the results have been inconsistent. The most often studied relation is that between type A behavior and blood pressure. A positive association between global type A behavior or its subcomponents and systolic blood pressure (SBP) has been found in some studies both in adolescents 125-271 and adults 1281. It has also been shown that, in challenging or competitive situations, type A’s are likely to react with an increase in SBP [29,30]. However, gender differences in the association between type A behavior and SBP reactivity may well exist. Even though Lyness [31] observed no gender differences in a meta-analysis (with regard to heart rate reactivity, more men in the subject sample were, however, associated with larger effect sizes), the earlier meta-analysis of Harbin [32] suggested that type A behavior is associated with increased SBP reactivity only in males, not in females. Garrity et al. [33] have also reported that type A’s experienced significant increases in SBP and diastolic blood pressure over the s-year follow-up period while type B’s experienced no change. However, these findings have not been replicable in all studies [29] and inverse associations have even been found 127,341. Regarding HDL Chol, a negative association with type A behavior, that is, a low level of HDL Ch o I was related to high type A behavior, has been reported in boys (but no association was found in girls) [35,36] and in a pooled sample of men and women [37]. A positive association between serum triglyceride concentration and type A behavior has been found both in boys and girls [38,39]. Moreover, an increased level of serum triglycerides has been shown to be related to the subcomponents of type A behavior in girls [35], less pronouncedly in women [40], and in men [40,41]. Type A behavior has also been shown to be related to body mass index in males (but no association was found in females) [42,43] and in males and females [25,35]. How-

336 ever, there also exist null findings with respect to HDL Chol, serum triglycerides [34,44], and body mass index [33,45]. Overall, with regard to the gender differences, type A behavior appears to be more strongly and consistently related to these individual CHD risk factors in males than in females. As far as we know, the association between type A behavior and serum concentration of insulin has not been previously studied. The aim of the present study was to examine the association between type A behavior and the parameters of the metabolic syndrome in healthy young adults. It was asked (1) whether type A behavior is associated with the cluster of these physiological parameters and (2) whether type A behavior has a moderating effect on the relationship between the parameters of the metabolic syndrome, that is, does the interdependence of these physiological parameters differ at different levels of type A behavior.

METHODS Subjects The subjects were a randomly selected sample of 919 healthy 18-, 21-, and 24-year-old Finnish young adults from the prospective epidemiological study “Cardiovascular Risk in Young Finns” (previously known as the Finnish Multicenter Study on Atherosclerosis Precursors in Childhood; FMS) [46]. The subjects were age cohorts 12, 15, and 18 of the FMS (n = 1790), who during the present study (6-year follow-up of the FMS [n = 12041) were 18, 21, and 24 years of age. Complete data (all somatic and psychological variables) were available in 921 cases. In addition, subjects with known diabetes were excluded. Those 919 cases who were left were the subjects of the present study. Regarding the variables used in the study, the 6-year follow-up sample has previously been shown to be representative when compared with the original one [25]. The number of subjects in different groups are shown in Table 4 in Results. SELECTION OF THE SAMPLE. The original sample of the FMS consisted of 3596 randomly selected, healthy Finnish children and adolescents. The sampling frame was the population register of the Social Insurance Institution which covers the whole population of Finland. From that register, boys and girls in age cohorts of 3, 6, 9, 12, 15, and 18 years and living in the area of five university cities with medical schools (i.e., Helsinki, Kuopio, Oulu, Tampere, and Turku), or in surrounding rural municipalities, were separately placed in a random order on the basis of their social security number. A random sample of 60 boys and 60 girls was selected in each age cohort in four areas and 120 boys and 120 girls were selected in each cohort in the area of the easternmost university city (Kuopio). To ensure equal and sufficiently large samples from east and west, and to include some communities in the extreme east, the sample size in Kuopio was twice that in other cities, with four instead of two rural municipalities included in the study.

Variables SOMATIC VARIABLES. It was not possible to assess the entire metabolic syndrome with all of its characteristics here. Instead, the following variables defined by Reaven 1201 and by DeFronzo and Ferrannini 1211 as the most essential parameters of the metabolic syndrome were measured: serum insulin (i.e., a biological marker of insulin resistance), serum high-density lipoprotein cholesterol (HDL Chol), serum triglyceride, systolic blood pressure (SBP), and body mass index (BMI, in kilograms per square meter, calculated from weight and height). Blood pressure was measured with a standard mercury gravity sphyg-

N. Ravaja et al. momanometer on the right arm after a rest of at least 3 minutes [47]. Weight and height were measured with each subject clothed (without shoes) [48]. Blood samples were taken after an overnight fast. Serum insulin was measured using a modification of the immunoassay method of Herbert et al. [49]. Serum HDL Chol concentrations were measured from the serum supematant after precipitation of VLDL and LDL lipoproteins with dextran sulfate 500,000 [50,51]. Serum triglyceride concentrations were determined enzymatically [52]. A more detailed description of the assessment protocol utilized by us has been previously reported [531. Validity of Somatic Risk Indicators. The subjects were healthy young adults for whom mean values of all physiological parameters fell within normal limits (only a few subjects had pathologically deviant values on most of the parameters). That differences in the levels of the present physiological parameters in young adulthood may, however, be indicative of the future risk from manifest metabolic syndrome and noninsulin-dependent diabetes mellitus (NIDDM, i.e., a disease outcome thought to be preceded by the metabolic syndrome) is evidenced by the following facts: (1) there is evidence that the insulin resistance phenomenon is already present in children and young adults [54,55]; (2) the individual factors that make up the metabolic syndrome, and even pairings of these variables, have been shown to exhibit considerable tracking of levels over time (i.e., the maintenance of relative rank of risk status over time compared with other individuals in the same birth cohort) [55-591; (3) all of these factors have shown considerable heritability (e.g., see Ref. 60) and nondiabetic children from populations at high risk for NIDDM (e.g., Pima Indians) have been shown to have a higher fasting insulin concentration than children at low risk of NIDDM [61]; and (4) fasting insulin levels in youth have been shown to predict NIDDM over an 1 l-year follow-up period [62]. In addition, we have previously found that, when adjustments were made for age, sex, and BMI, adolescents and young adults with a positive family history of NIDDM presented significantly higher fasting serum insulin levels than did subjects without a history of parental NIDDM (means = 14.5 2 5.7 mu/liter and 12.5 ? 5.0 mu/liter, respectively; p = 0.009; ages 12-21 years; N = 964) (N. Ravaja, unpublished observations). Type A behavior of the subjects was measured in two different ways: by means of the Type A Behavior Questionnaire for the Finnish multicenter Study (AFMS) [63] and using the Hunter Wolf A-B Rating Scale (HWolf) [64]. The AFMS is a combination of two well-known type A tests, that is, the Matthews Youth Test for Health (MYTH) [65] and the Swedish version of the Jenkins Activity Survey for students (JAS) [66], and consists of 17 items measured on a 5-point Likert-type scale. Some illustrative items are the following: I like to argue or debate; I get easily irritated; When I have to wait for others I become impatient; I frequently hurry even when there is plenty of time. Development of the AFMS is described in detail by Keltikangas-Jsrvinen and Raikktinen [63]. The test-retest stability [67,68], construct validity [36], and concurrent validity (as assessed using self-ratings and mothers’ evaluations) [63] of the test have previously been shown to be high. The reliability (general coefficient of reliability [69]) of the AFMS has been shown to be 0.79 for young adults [70]. Thus, the portion of error variance is low, indicating high internal consistency for &e AFMS. The scores ranged in the present population from 29 to 68, with a mean score of 45.5 and SD of 6.3. The HWolf consists of 24 items, which are composed of 2 opposite TYPE A BEHAVIOR.

Type A Behavior

and Metabolic

Syndrome

337

statements reflecting two kinds of contrasting behavior. Between the behaviors runs a seven-rung ladder and subjects are asked to mark where they are on the ladder most of the time. Illustrative items are the following: It takes very little to get me angry-It takes a lot to get me angry; I am hard-driving-I am easy-going; I eat fast-1 eat slowly. The test-retest stability (Pearson’s r) of the HWolf over a l-year period has previously been shown to be 0.70 in children (p < 0.001) [35]. The general reliability coefficient for the HWolf is shown to be 0.75 for young adults [70]. A complete description of the HWolf and validation of the measure is reported by Wolf et al. [64]. The scores ranged from 70 to 146, with a mean score of 102.6 and SD of 12.3. The total scores of the HWolf and the AFMS were used as indices of type A behavior in the following statistical analyses.

Statistical Procedures Intercorrelations of the study variables r for men and women separately. LOW/HIGH

TYPE A BEHAVIOR

were computed

using Pearson’s

AND DIFFERENCES IN THE CLUSTER OF PARAM-

ETERS OF THE METABOLIC SYNDROME. The high type A and low type A groups (type A and non-type A groups, respectively) were constructed on the basis of quartile splits: The type A group consisted of those subjects who had total AFMS or HWolf scores in the fourth quartile (Q4) and the non-type A group consisted of those subjects whose scores were in the first three quartiles (Ql-Q3). This division method was adopted instead of a median split or Ql vs. Q4 for the following reasons: (1) subjects of the study were healthy, so as large a difference as possible between the groups in type A behavior was necessary; (2) it has been argued that somatic changes, for example, elevated psychophysiological reactivity, are typically seen only in extreme type A’s [34,71]; and (3) it was necessary in order to maintain an adequate number of subjects in all statistical analyses. Although both psychological and somatic variables of the study were continuous variables, quartile splits were used because associations between these variables are not necessarily linear, as pointed out above. Somatic variables of the study were subjected to factor analysis using the principal components analysis where orthogonal factors were rotated against the Varimax criterion. The resulting “metabolic syndrome precursors factor” is described in Results. Differences between the type A and non-type A groups on the metabolic syndrome precursors factor were studied with ANOVA (analysis of variance). MODERATING SOMATIC

EFFECT OF TYPE A BEHAVIOR:

VARIABLES

DEPENDENT

ASSOCIATION

BETWEEN

ON TYPE A BEHAVIOR

Interaction of Type A Behavior and Somatic Variables in Regression A&ysis. Because the potential effects of stress-induced endocrine aberrations along the corticotropin-releasing factor (CRF)-adrenocorticotropic hormone (ACTH)-adrenal cortex axis on the parameters of the metabolic syndrome have been suggested to be mediated, at least in part, by fat accumulation (e.g., see Ref. 72), we focused on the association between BMI and other somatic parameters. The most frequently recommended approach to assessing moderator influences of some variable is by moderated regression analysis involving a predictor variable, a moderator variable, and a product term [73,74]. Separate hierarchical regression analyses were carried out with BMI as the dependent variable and with triglycerides, HDL Chol, SBP, and insulin each being used in turn as the predictor variable.’ The main effect ‘We reversed the independent and dependent variables in these analyses. Because the distributions of insulin and triglyceride concentrations were slightly skewed, we used BMI as the dependent variable to increase the power of the

terms for the predictor variable and the dichotomized total AFMS score were entered in the first step of the equation, while the product of the predictor variable and the dichotomized total AFMS score were entered in the second step. The total AFMS score was dichotomized at the third quartile of the distribution and then dummy coded [74]. The predictor variable was centered to reduce possible multicollinearity among independent variables [73-751. Intercorrelations of Somatic Variables in Type A and Non-Type A Subjects. To study whether the intercorrelations of the parameters of the metabolic syndrome are stronger among type A’s when compared to other subjects, that is, whether the parameters of the metabolic syndrome are likely to accumulate in type A’s, factor analyses using the principal components analysis were computed for the somatic variables of the study separately for the type A and non-type A groups.

RESULTS The means and standard deviations of the somatic variables for the type A and the non-type A groups are given in Table 1. It can be seen that among men, type A’s had significantly higher BMIs than non-type A’s. With respect to other separate somatic variables no significant differences between the groups were found. Product-moment correlations among somatic variables and type A behavior are presented in Table 2. Type A behavior was significantly, although weakly, correlated only to BMI in men. Correlations between somatic variables included in the metabolic syndrome were in general also low. There was no statistically significant difference between the sexes on AFMS-defined type A behavior (means = 45.1 ? 6.4 and 45.9 + 6.0 for women and men, respectively) (F[l, 9171 = 3.22, not significant [NS]), but women scored somewhat higher on HWolf defined type A behavior (mean = 103.5 * 12.0) than did men (mean = 101.5 f 12.7) (F[l, 9171 = 6.09, p < 0.05).

Low/High Type A Behavior and Differences in the Cluster of Parameters of the Metabolic Syndrome Factor analysis of the somatic variables of the study resulted in one factor, which accounted for 35% of total variance of these somatic variables. However, HDL Chol operated separately from other variables and was omitted from this factor. The resulting factor was named the metabolic syndrome precursors factor and is presented in Table 3. The metabolic syndrome precursors factor accounted for 40% of the total variance in somatic variables. The metabolic syndrome precursors factor correlated only weakly, although significantly, with AFMS defined type A behavior in men [r(406) = 0.11, p < 0.051. The ANOVA revealed the following differences between the groups. Differentiation of AFMS defined type A and non-type A men on the basis of the metabolic syndrome precursors factor was statistically significant, with type A men scoring higher on this factor (Table 4). When different age groups were examined separately, statistically significant differences on the metabolic syndrome precursors factor between type A and non-type A men emerged only in the oldest age group. There were no significant differences on the metabolic syndrome precursors factor between type A and non-type A women. A

statistical analyses (we do not mean to imply that, e.g., insulin in fact “causes” BMI). Log-transformation was not appropriate in this connection because the

interpretation transformed transformed

of the results of moderate regression analysis performed on logvariables variables

is difficult. However, and obtained similar

we repeated results.

these analyses on log

338

N. Ravaja et al.

TABLE 1. Means and standard deviations groupsa

of somatic variables

Non-type A M

Variable

for type A and non-type

A

Type A

SD

M

SD

F ratio

Men n = 304 Triglyceride (mmol&ter) HDL Chol (mmol/liter) Insulin (mu/liter) SBP (mmHg) BMI (kg/m2)

0.94 1.33 9.2 I27 22.0

n = 102 0.46 0.23 4.4 11 2.5

1.02 1.32 9.3 127 23.2

0.57 0.24 4.4 I1 3.3

F(l, F(l, F(l, F(1, F(l,

404) 404) 404) 404) 404)

= = = = =

2.00 0.10 0.03 0.00 16.15h

0.38 0.28 5.9 11 2.6

F(l, F(1, F(l, F(l, F(1,

511) 511) 511) 511) 511)

= = = = =

0.07 0.11 0.00 0.48 0.55

Women n = 385 Triglyceride (mmol/liter) HDL Chol (mmol/liter) Insulin (mu/liter) SBP (mmHg) BMI (kg/m2)

0.91 1.52 10.0 116 21.9

“Non-type A and type A = Ql-Q3 bpC’b.001. ‘.

n = 128 0.37 0.27 5.1 11 2.9

and Q4 of the total AFMS

corresponding tendency emerged when type A behavior was HWolf defined: 24-year-old type A men scored significantly higher than nontype A men on the metabolic syndrome precursors factor (means = 0.88 ? 1.82 and 0.24 ? 1.15, respectively) (F[l, 1141 = 5.31, p < 0.05).2 In addition, men scored higher on the metabolic syndrome precursors factor (mean = 0.23 ? 1.27) than women (mean = -0.18 ? 1.23) (F[l, 9171 = 25.85, p < 0.001).

Moderating

Effect of Type A Behavior

INTERACTION

OF TYPE A BEHAVIOR

AND SOMATIC

0.92 1.53 10.0 117 21.7

VARIABLES

IN REGRES-

Moderated regression analysis revealed a significant type A/non-type A x insulin interaction for BMI in men (Table 5). Inclusion of the product term of insulin and type A behavior accounted for about 5% of the BMI variance, that is, the interdependence of insulin and BMI significantly differs at different levels of type A behavior. In addition, there was a significant interaction between type A behavior and both triglycerides and SBP when predicting BMI. A significant increase in R2 when the product term was included and positive b and /? coefficients of the product terms indicate that positive associations between insulin and BMI, between SBP and BMI, as well as between triglycerides and BMI were consistently stronger among type A men when compared to non-type A’s. Among women, there was a significant interaction between type A SION ANALYSIS.

‘We also found that men having high levels of serum insulin and at least three other somatic variables (values in the upper quartile [Haffner et al. have previously used quartile splits for insulin when examining the metabolic syndrome; see Ref. 761) scored higher on AFMS and HWolf defined type A behavior than did other subjects (means = 50.4 2 7.1 and 45.7 4 5.9 [AFMS], respectively, and 108.0 k 16.7 and 101.2 k 12.4 [HWolfl, respectively), [F(l, 404) = 10.30 and 4.69, p < 0.002 and 0.05, respectively, for AFMS and HWolfl. However, women having high levels of somatic parameters expressed a nonsignificant tendency to score lower on AFMS defined type A behavior than did other subjects (means = 43.4 k 6.6 and 45.2 k 6.4, respectively), [F(l, 511) = 1.92, NS].

score distribution,

respectively.

behavior and insulin when predicting BMI, but this interaction was in the opposite direction of that among men (Table 6). The positive relationship between insulin and BMI was less marked among type A women when compared to other women. The statistically significant interactions are also presented graphically in Fig. 1. INTERCORRELATIONS

OF SOMATIC

VARIABLES

IN TYPE A AND NON-TYPE

The question as to whether the intercorrelations of the parameters of the metabolic syndrome are higher among type A’s when compared to other subjects, that is, whether the parameters of the metabolic syndrome are likely to accumulate in type A’s, was studied using factor analysis. Somatic variables were factor analyzed separately in the type A and non-type A groups, where solutions of one factor were adopted and HDL Chol was omitted as before. Analyses showed that, among men, factor loadings were higher and the resulting metabolic syndrome precursors factor accounted for more of the variance of somatic variables in type A’s (AFMS defined) when compared to non-type A’s (variance accounted for = 51.0 and 35.6%, respectively). However, among women, an inverse tendency was found again: the variance accounted for by the metabolic syndrome precursors factor was smaller in type A’s when compared to non-type A’s (34.8 and 42.6%, respectively). A SUBJECTS.

DISCUSSION The present results may support the significance of type A behavior in the development of the metabolic syndrome in men. The principal finding was that although type A behavior was not correlated with the particular variables that here represented the metabolic syndrome, this behavior was consistently related to the entire cluster of the parameters constituting the metabolic syndrome. This conclusion was supported by different statistical facts. The intercorrelations between the parameters of the metabolic syndrome were higher among type A men than among non-type A’s, that is, the parameters of the metabolic syndrome are likely to accumulate in type A’s In addition, moderated regression analysis revealed a mod-

Type A Behavior

and Metabolic

TABLE

339

Syndrome

2. Correlations

among somatic variables and type A behavior

en 1 Triglyceride

HDL Chol

Insulin

SBP

for men’ and womenb BMI

Total AFMS

Total HWolf

Triglyceride HDL Chol Insulin SBP BMI Total AFMS Total HWolf

Abbreviations: See text for abbreviations.

‘p

“p <

< 0.05';

0.01;

"'p

<

0.001.

erating effect of type A behavior on the relationships between the factors of the metabolic syndrome studied here, that is, the interdependence of these somatic factors differed at different levels of type A behavior. We found that elevated BMI was markedly more strongly associated with increased serum insulin among type A men when compared to non-type A’s This finding clearly has relevance for the metabolic syndrome because it has been established that obesity and the endocrine aberrations associated with abdominal fat accumulation produce insulin resistance and resulting hyperinsulinemia [72,77]. In addition, insulin has known stimulating effects on energy deposition [24]. Furthermore, we found that the positive associations between BMI and serum triglycerides as well as between BMI and SBP were stronger among type A men when compared to non-type A’s Interestingly, Suarez et al. [78] have shown that for type A men, elevated total serum cholesterol was associated with larger catecholamine and cortisol responses to a mental arithmetic task while for type B men an inverse association emerged.

The findings also showed that type A men scored higher on the metabolic syndrome precursors factor representing a metabolic entity when compared to non-type A’s. There was only a weak linear association between type A behavior and the metabolic syndrome precursors factor, suggesting that especially among healthy people type A behavior has to reach a certain level to become significant. There seems to be qualitative rather than quantitative difference at different levels of type A behavior. The present associations were statistically not very strong, but this is not surprising considering the fact that the subjects were young and healthy. Restriction of range in type A behavior and somatic parameters is likely to reduce statistical power and decrease associations between these variables in young healthy people. To summarize, all this indicates that the clustering of metabolic syndrome precursors was more manifest in type A men than in non-type A’s. As opposed to men, the association between BMI and serum insulin was weaker among type A women when compared to non-type A’s. What may account for this inverse type A/non-type A difference in the association between these physiological factors among women is unknown at the present time. The factor structure of type A behavior was the same for both sexes and there was no level difference on AFMS defined type A behavior. This supports the previous findings that behavioral risk for CHD is different in men and women [79,80], as is somatic risk. The etiology of CHD may differ in men and women [81] and there is evidence that abdominal obesity or a factor highly correlated with it may help to explain the sex differences in CHD [82]. In addition, it has previously been shown that women respond to stress with a less marked increase in catecholamine and cortisol excretion when compared to men [83,84]. Regarding the possible causal role of type A behavior in the develop-

TABLE

precursors factor for type A and non-type

TABLE

3. Metabolic

syndrome

precursors factor Factor loading

Somatic variable

0.61

Triglyceride Insulin SBP BMI Variance

0.63 0.53 0.74 accounted

40.0%

for:

Abbreviations: See text.

4. Means and standard deviations

of metabolic Non-type

Age Sex

(years)

Men

18 Total:

122 95 87 304

Women

18

154

21 24 Total:

115 116 385

n

syndrome A

Type A

Mean

(SW

n

Mean

(SD)

0.03 0.17 0.18 0.14 -0.06 -0.31 -0.23 -0.19

(1.13) (1.13)

41 32 29 102 52 38 38 128

0.26 0.26 1.07 0.50 -0.38 -0.30 0 07 -0.

(1.40) (1.30)

(1.32) (1.08)

“Non-type A and type A = Ql-Q3 and (24 of the total AFMS scoredistribution, respectively. bp = 0.012. 'p = 0.002.

A groupsa

(1.09) (1.12)

F ratio F(l, F(l, F(l, F(1, F(l, F(l, F(l, F(l,

161) 125) 114) 404) 204) 151) 152) 511)

= = = = = = = =

1.03 0.14 10.31’ 6.41b 2.18 0.00 1.79 0.00

N. Ravaja et al. TABLE

5. Interaction

of type A behavior

Abbreviations:

variables

in moderated

regression

analysis

for men-

RZ

Change in R2

0.117

0.012

23.7"' 5.46'

2, 403

0.14'

0.05" 0.81' 0.08"

0.22"' 0.13' 0.17"

0.123 0.017

28.3"' 7.85"

2, 403

0.140

0.05’

0.11’

1.42”’

0.22"' 0.23"'

0.124

0.077 0.047

16.9"' 21.3"'

2, 403 1, 402

b

s

1.01” 1.06”’ 1.31’

0.18” 0.17”’

Variable BMI Triglyceride Total AFMS Triglyceride x total AFMS BMI SBP Total AFMS SBP x total AFMS BMI Insulin Total AFMS Insulin x total AFMS

and somatic

0.29"'

0.105

F

df

1. 402

1, 402

See text.

‘n = 406. bb and p coefficwnts are those computed at the final step of each analysis. ‘Only statistically significant findings ate reported. *p < 0.05; **p < 0.01; “‘p < 0.001.

ment of the metabolic syndrome, this pattern has been defined as an action-emotion complex that is manifested as a stressful life style. This life style may be characterized, among other things, by competitiveness, continuous striving for achievement, and an inability to relax [3]. An exaggerated psychophysiological reactivity, especially a high level of activity of the sympathetic nervous system, is also a determinant of this behavior pattern [29,85,86]. In addition, type A’s have been shown to be more aroused than type B’s not only in challenge or competitive situations but also during inactivity, at least in terms of catecholamine and cortisol excretion and heart rate [87]. Catecholamines have been shown to decrease rapidly the number of insulin receptors in fat cells and in this way to produce insulin Bj6mtorp has hypothesized that resistance [88,89]. Furthermore, chronic stress results in hypothalamic arousal syndrome and neuroendocrine reactions in the CRF-ACTH-cortisol axis [72,77]. This arousal is expressed as a high rate of secretion of cortisol and a low rate of secretion of sex steroid hormones followed by abdominal fat accumulation, insulin resistance, and compensatory hyperinsulinemia [77,90,91]. Thus findings suggesting arousal of those particular physio-

TABLE

6. Interaction

Variable BMI Insulin Total AFMS Insulin x total AFMS

of type A behavior

and somatic

variables

b

P

0.22”’ -0.19 -0.17”’

0.41"' -0.03 -0.18”’

Abbreviations: See text. % = 513. bb and p coefficients are those computed at the final step of each analysis. ‘Only statistically si&icant findings are reported. “‘t, i 0.001.

logical and neuroendocrine systems among type A’s might indicate that this life style operates as a signal that activates both the sympathetic-adrenal and pituitary-adrenal systems, which are the comerstones of psychoneuroendocrine stress reaction [92], and in this way may increase CHD risk. The present findings suggest how coronary-prone behaviors may interact with physiological risk factors in the etiology of CHD. These findings are of considerable importance because our subjects were healthy young people who potentially have metabolic syndrome in its early developmental phase, although it remains to be seen which of them will actually have a manifest metabolic syndrome later in life. The cross-sectional nature of this study precludes causal inferences; however, it is unlikely that the physiological variables studied would have effects on type A behavior. Nevertheless, the present findings are in need of replication in future studies. Despite the limitations of the study, these findings may support the significance of type A behavior in the development of the metabolic syndrome in men and so may bring us one step forward in understanding the early development of this disorder.

in moderated RL

0.119

regression

analysis Change in R2

for womendf

F

0.097

27.3"'

0.022

12.9”

2, 510 *

1, 509

341

Type A Behavior and Metabolic Syndrome

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-

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ww

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Women BMI 27.9 Wm3 25.1

I

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FIGURE 1. The relationship between BMI and other somatic parameters in type A and non-type on the x- and y-axes is mean f 2 SD).

References 1. Friedman M, Rosenman RH. Association of the specific overt behavior pattern with increases in blood cholesterol, blood clotting time, incidence of arcus senilis and clinical coronary heart disease. J Am Med Assoc 1959;

169: 1286-1296. 2. Rosenman RH, Chesney 3. 4.

5.

6.

MA. The relationship of type A behavior to coronary heart disease. Acta Nerv Super 1980; 22: l-45. Jenkins CD. Epidemiology of cardiovascular diseases. J Consult Clin Psychol 1988; 56: 324-332. Blumenthal JA, Williams R, Kong Y, Schanberg SM, Thompson LW. Type A behavior and angiographically documented coronary disease. Circulation 1978; 58: 634-639. Zyzanski SJ, Jenkins CD, Ryan TJ, Fl essas A, Everist M. Psychological correlates of coronary angiographic findings. Arch Intern Med 1976; 136: 1234-1237. Frank KA, Heller SS, Komfeld DS, Spom AA, Weiss MB. Type A behavior pattern and coronary angiographic findings. J Am Med Assoc 1978;

240: 761-763. 7. Williams RB, Haney TL, Lee KL, Kong Y, Blumenthal

]A,

Whalen

RE.

8.

9.

10.

11.

A subjects (the range of the scale

Type A behavior, hostility, and coronary atherosclerosis. Psychosom Med 1980; 42: 539-549. Krantz DS, Sanmarco MI, Selvester RH, Matthews KA. Psychological correlates of progression of atherosclerosis in men. Psychosom Med 1979; 41: 467-475. Rosenman RH. Brand RJ, Jenkins CD, Friedman M, Straus R, Wurm M. Coronary heart disease in the western collaborative group study: Final follow-up experience of 8% years. J Am Med Assoc 1975; 233: 872-877. Haynes SC, Feinleib M, Kannel WB. The relationship of psychosocial factors to coronary heart disease in the Framingham study. III. Eight-year incidence of coronary heart disease. Am J Epidemiol 1980; 111: 37-58. Haynes SG, Feinleib M, Levine S, Scotch N, Kannel WB. The relationship of psychosocial factors to coronary heart disease in the Framingham study. 11. Prevalence of coronary heart disease. Am J Epidemiol 1978; 107:

348-402. 12. Komitzer M, Kittell F, DeBacker G, Dramaix M. The Belgian Heart Disease Prevention Project: Type A behavior pattern and the prevalence of coronary heart disease. Psychosom Med 1981; 43: 133-145. 13. Defoumy M, DeLandsheere CH, Paturiaux G. Influence prognostique du

342

14. 15.

16. 17.

18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29.

30.

31. 32. 33. 34. 35. 36. 37. 38.

N. Ravaja et al. modele de comportement de type A. [Prognostic influence of the Type A behavior pattern]. Acta Psychiat Belg 1986; 86: 30-35. Jenkins CD, Zyzanski SJ, Rosenman RH. Risk of new myocardial infarction in middle-aged men with manifest coronary heart disease. Circulation 1976; 53: 342-347. Shekelle RB, Hulley SB, Neaton JD, Billings JH, Borhani NO, Gerace TA, Jacobs DR, Lasser NL, Mittlemark MB, Stamler J. The MRFIT Behavioral Pattern Study. II. Type A behavior and incidence of coronary heart disease. Am J Epidemiol 1985; 122: 559-570. Shekelle RB, Gale M, Norusis M. Type A score (Jenkins Activity Survey) and risk of recurrent coronary heart disease m the Asprin Myocardial Infarction Study. Am J Cardiol 1985; 56: 221-225. Dembroski TM, Czajkowski SM. Historical and current developments in coronary-prone behavior. In: In Search of Coronary-Prone Behavior: Beyond Type A (Siegman AW, Dembroski TM, eds.). Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1989, pp. 21-39. Matthews K, Haynes SC;. Type A behavior pattern and coronary risk: Update and critical evaluation. Am J Epidemiol 1986; 123: 923-960. Krantz DS, Contrada RJ, Hill DR, Friedler E. Environmental stress and biobehavioral antecedents of coronary heart disease. J Consult Clin Psychol 1988; 56: 333-341. Reaven GM. Banting Lecture 1988: Role of insulin resistance in human disease. Diabetes 1988; 37: 1595-1607. DeFronzo RA, Ferrannini E. Insulin resistance: A multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidemia, and atherosclerotic vascular disease. Diabetes Care 1991; 14: 173-194. Ferrannini E. Haffner SM, Mitchell BD, Stem MP. Hyperinsulinaemia: The key feature of a cardiovascular and metabolic syndrome. Diabetologia 1991; 34: 416-422. Karam JH. Type 11 diabetes and syndrome X. Pathogenesis and glycemic management. Endocrinol Metab Clin North Am 1992; 21: 329-350. Brindley DN. Neuroendocrine regulation and obesity. Int J Obes 1992; 16(Suppl. 3): S73-S79. Keltikangas-Jirvinen L, Rnikktinen K. Type A factors as predictors of somatic risk factors of coronary heart disease in young Finns-a six-year follow-up study. J Psychosom Res 1990; 34: 89-97. Siegel JM, Leitch CJ. Behavioral factors and blood pressure in adolescence: The Tacoma Study. Am J Epidemiol 1981; 113: 171-181. Keltikangas-JBrvinen L, Jokinen J. Type A behavior, coping mechanisms and emotions related to somatic risk factors of coronary heart disease in adolesceqts. J Psychosom Res 1989;33:17-27. Contrada RJ, Wright RA, Glass DC. Psychophysiological correlates of type A behavior: Comments on Houston (1983) and Holmes (1983). J Res Pers 1985; 19: 12-30. Suls J, Glenn SS. Why do some behaworal styles place people at coronary risk? In: In Search of Coronary-Prone Behavior: Beyond Type A (Siegman AW, Dembroski TM, eds.). Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1989, pp. l-20. Manuck SB, Kasprowtcz AL, Monroe SM, Larkin KT, Kaplan JR. Psychophysiologic reactivity as a dimension of individual differences. In: Handbook of Research Methods in Cardiovascular Behavioral Medicine (Schneiderman N, Weiss S, Kaufmann P, eds.). Plenum, New York, 1989. Lyness SA. Predictors of differences between type A and B individuals in heart rate and blood pressure reactivity. Psychoi Bull 1993; 114: 266-295. Harbm TJ. The relationshlp between the type A behavior pattern and physiological responsivity: A quanntative review. Psychophysiology 1989; 26: 110-119. Garrity TF, Kotchen JM, McKean HE, Gurley D, McFadden M. The association between type A behavior and change m coronary risk factors among young adults. Am J Public Health 1990; 80: 1354-1357. Lundberg U, Hedman M, Melin B, Frankenhaeuser M. Type A behavior m healthy males and females as related to physiological reactivity and blood lipids. Psychosom Med 1989; 51: 113-122. Weidner G, McLellam R, Sexton 0, lstvan J, Connor S. Type A behavior and physiologic coronary risk factors in children of the Family Heart Study: Results from a l-year follow-up. Psychosom Med 1986; 48: 480-488. Keltikangas-Jsrvinen L, Rnikkiinen K. Pathogenic and protective factors of type A behavior in adolescents. J Psychosom Res 1989; 33: 591-602. Manson JE, O’Connor NJ, O’Connor CT, Buring JE, Hennekens CH. Type A personality, decreased HDL cholesterol, and risk of myocardial infarction. Circulation 1992; 86(Suppl. 4): Abstract 1295. Hunter SM, Wolf TM, Sklov MC, Webber LS, Watson RM, Berenson GS. Type A coronary prone behavior pattern and cardiovascular risk factor

39. 40. 41. 42 43 44 45. 46. 47. 48.

49. 50. 51. 52. 53. 54. 55. 56. 57. 58.

59.

60.

61. 62.

variables in children and adolescents: The Bogalusa Heart Study. J Chron Dis 1982; 35: 613-621. Higginbotham JC, Baranowski T, Carroll RM, Greaves KA. Lipids and lipoproteins in a triethnic sample of 5- or 6-year-old type A or type B children. Behav Med 1990; 16: 133-139. Fowkes FGR, Leng GC, Donnan PT, Deary IJ, Riemersma RA, Housley E. Serum cholesterol, triglycerides, and aggression in the general population. Lancet 1992; 340: 995-998. Johnson EH, Collier P, Nazzaro P, Gilbert DC. Psychological and physiological predictors of lipids in black males. J Behav Med 1992; 15:285-298. Cohen JB, Reed D. The type A behavior pattern and coronary heart disease among Japanese men in Hawaii. J Behav Med 1985; 8: 343-352. Keltikangas-Jgrvinen L, RPikkijnen K. Developmental trends in type A behavior as predictors for the development of somatic coronary heart disease risk factors. Psychother Psychosom 1989; 5 1: 210-215. Weidner G, Sexton G, McLellarn R, Conner SL, Matarazzo JD. The role of type A behavior and hostility in an evaluatton of plasma lipids in adult women and men. Psychosom Med 1987; 49: 136-145. Hayman LL, Meininger JC, Stashinko EE, Gallagher PR. Type A behavior and physiological cardiovascular risk factors in school-age twin children. Nursing Res 1988; 37: 290-296. Akerblom HK, Uhari M, Pesonen E, et al. Cardiovascular risk in young Finns. Ann Med 1991; 23: 35-40. Uhari M, Nuutinen M, Turtinen J, et al. Blood pressure in children, adolescents and young adults. Ann Med 1991; 23: 47-51. Dahlstrom S, Viiikari J, Akerblom HK, et nl. Atherosclerosis precursors in Finnish children and adolescents. II. Height, weight, body mass Index, and skinfolds and their correlation to metabolic variables. Acta Paediatr Stand 1985; (Supp1.)318: 65-78. Herbert V, Lau KS, Gottlieb CW, Bleicher SJ. Coated charcoal immunoassay of insulin. J Clin Endocrinol Metab 1965; 25: 1375-1384. Kostner GM. Enzymatic determination of cholesterol in high density lipo protem fractions prepared by polyanion precipitation. Clin Chem 1976; 22: 695. Siegel J, Schlumberger H, Klose S, Ziegenhorn J, Wahlefeld AW. Improved reagent for the enzymanc determination of serum cholesterol. J Clin Chem Clin Biochem 1981; 19: 838-839. Wahlefeld AW. Triglycerides-determination after enzymatic hydrolysis. In: Methods of Enzymatic Analysis, 2nd Ed. (Bergmeyer HV, ed.). Verlag ChemieiAcademic Press, New York, 1974, pp. 1831-1835. Vlikari J, RBnnemaa T, Sepp%inenA, et al. Serum lipids and lipoproteins m children, adolescents and young adults in 1980-1986. Ann Med 1991; 23: 53-59. Caprio S, Plewe G, Diamond MP, et ai. Increased insulin secretion in puberty: A compensatory response to reductions in insulin sensitivity. J Pediatr 1989; 114: 963-967. RZinnemaa T, Knip M, Lautala P, et al. Serum insulin and other cardiovascular risk indicators in children, adolescents and young adults. Ann Med 1991; 23: 67-72. Porkka KV, Viikari JS, Akerblom HK. Tracking of serum HDL-cholesterol and other lipids in children and adolescents: The Cardiovascular Risk in Young Finns Study. Prev Med 1991; 20: 713-724. Casey VA, Dwyer JT, Coleman KA, Valadian 1. Body mass index from chlldhood to middle age: A 50-y follow-up. Am J Clin Nutr 1992; 56: 14-18. Burke GL, Webber LS, Srinivasan SR, Radhakrishnamurthy 8, Freedman DS, Berenson GS. Fasting plasma glucose and insulin levels and their relationship to cardiovascular risk factors in children: The Bogalusa Heart Study. Metabolism 1986; 35: 441-446. Rolland-Cachera MF, Bellisle F, Deheege; M, Pequignot F, Sempe M. Influence of body fat distribution during childhood on body fat distribution m adulthood: A two-decade follow-up study. Int J Obes 1990; 14: 473-481. Warram JH, Martin BC, Krolewski AS, Soeldner JS, Kahn CR. Slow glucose removal rate and hyperinsulinemia precede the development of type II diabetes in the offspring of diabetic parents. Ann Intern Med 1990; 113: 909-915. Petntt DJ, Nelson RG, Mall PP, Saad MF, Knowler WC, Bennett BH, Mott DM, Kottke BA. Insuhnemia in children at low and high risk of NIDDM. Diabetes Care 1993; 16: 608-615. Zimmet PZ, Collins VR, Dowse GK, Knight IT. Hyperinsulinaemia in youth is a predictor of type 2 (non-insulin-dependent) diabetes mellitus. Diabetologia 1992; 35: 534-541.

Type A Behavior

and Metabolic

Syndrome

63. Keltikangas-Jiirvinen L, R%ikk(inen K. Healthy and maladjusted type A behavior in adolescents. J Youth Ado1 1990; 19: 1-18. 64. Wolff TM, Sklov MC, Wenzl PA, Hunter SM, Berenson GS. Validation of a measure of type A behavior pattern in children: Bogalusa Heart Study. Child Dev 1982; 53: 126-135. 65. Matthews KA, Angulo J. Measurement of the type A behavior pattern in children: Assessment of children’s competitiveness, impatience-anger and aggression. Child Dev 1980; 51: 466-475. 66. Lundberg U. Type A behavior and its relation to personality variables in Swedish male and female university students. Stand J Psycho1 1980; 21: 133-138. 67. Keltikangas-JBrvinen L. Continuity of type A behavior during childhood, preadolescence and adolescence. J Youth Ado1 1990; 19: 221-232. 68. Keltikangas-Jiirvinen L. Stability of type A behavior during adolescence, young adulthood and adulthood. J Behav Med 1989; 12: 387-396. 69. Tarkkonen L. On reliability of composite scales (Statistical Studies, Report No. 7). Department of Statistics, University of Helsinki, Helsinki, Finland, 1987. 70. RBikkGnen K, Keltikangas-Jsrvinen L. Prevalence and sociodemographic variance of type A behavior in Finnish preadolescents, adolescents, and young adults. J Gen Psycho1 1989; 116:271-283. 71. Dembroski TM, MacDougall JM, Williams RB, Haney TL, Blumenthal JA. Components of type A behavior, hostility and anger-in: Relationship to angiographic findings. Psychosom Med 1985; 47: 219-233. 72. Bj&-ntorp P. Visceral fat accumulation: The missing link between psychosocial factors and cardiovascular disease? J Int Med 1991; 230: 195-201. 73. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J Pers Sot Psycho1 1986; 51: 1173-1182. 74. Aiken LS, West SG. Multiple regression: Testing and interpreting interactions. Sage Pubhcations, Newbury Park, California, 1992. 75. Cohen J, Cohen P. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 2nd Ed. Erlbaum, Hillsdale, New Jersey, 1983. 76. Haffner SM, Valdez SA, Hazuda HP, MItchelI BD, Morales PA, Stem MP. Prospective analysis of the insulin-resistance syndrome (syndrome X). Diabetes 1992; 41: 715-722. 77. Bjtimtorp P. Metabolic implications of body fat distribution. Diabetes Care 1991; 14: 1132-1143. 78. Suarez EC, Williams RB, Kuhn CM, Zimmerman EH, Schanberg SM. Biobehavioral basis of coronary-prone behavior in middle-aged men. II. Serum cholesterol, the type A behavior pattern, and hostility as interactive modulators of physiological reactivity. Psychosom Med 1991; 53: 528-537. 79. RBikkiinen K, Keltikangas-JCrvmen L. Hostility and its association with behaviorally induced and somatic coronary risk indicators in Finnish adolescents and young adults. Sot Sci Med 1991; 33: 1171-1178.

343 80. Greenglass ER, Julkunen J. Construct validity and sex differences in CookMedley hostility. Person lndiv Diff 1989; 10: 209-218. 81. Manuck SB, Muldoon MF, Kaplan JR, Adams MR, Polefrone JM. Coronary artery atherosclerosis and cardiac response to stress in cynomolgus monkeys. In: In Search of Coronary-Prone Behavior: Beyond Type A (Siegman AW, Dembroski TM, eds.). Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1989, pp. 207-227. 82. Larsson B, Bengtsson C, BjGmtorp P, Lapidus L, Sjtistrtim L, Svardsudd K, Tibblin G, Wedel H, Welin L, Wilhelmsen L. Is abdominal body fat distribution a major explanation for the sex difference in the incidence of myocardial infarction? The study of men born in 1913 and the study of women, Gdteborg, Sweden. Am J Epidemiol 1992; 135: 266-273. 83. Frankenhaeuser M. The sympathetic-adrenal and pituitary-adrenal response to challenge: Comparison between the sexes. In: Biobehavioral Bases of Coronary Heart Disease (Dembroski TM, Schmidt TH, Bliimchen G, eds.). S. Karger, New York, 1983, pp. 91-105. 84. Kirschbaum C, Wiist S, Hellhammer D. Consistent sex differences in cortisol responses to psychological stress. Psychosom Med 1992; 54: 648-657. 85. Williams RB. Biological mechanisms mediating the relationship between behavior and coronary heart disease. In: In Search of Coronary-Prone Behavior: Beyond Type A (Siegman AW, Dembroski TM, eds.). Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1989, pp. l95205. 86. Williams RB, Suarez EC, Kuhn CM, Zimmerman EA, Schanberg SM. Biobehavioral basis of coronary-prone behavior in middle-aged men. 1. Evidence for chronic SNS activation in type As. Psychosom Med 1991; 53: 517-527. 87. Frankenhaeuser M, Lundberg U, Forsman L. Dissociation between sympathetic-adrenal and pituitary-adrenal responses to an achievement situation characterized by high controllability: comparison between type A and type B males and females. Biol Psycho1 1980; 10: 79-91. 88. Diebert DC, DeFronzo RA. Epinephrine-induced insulin resistance in man: A beta receptor mediated phenomenon. J Clin Invest 1980; 65: 717721. 89. Sacca L, Vigorito C, Cicala M, Ungaro B, Sherwin KS. Mechanisms of epinephrine-induced glucose intolerance in normal human. J Clin Invest 1982; 69: 284-293. 90. Rebuffe-Strive M, Walsh UA, McEwen B, Rodin J. Effect of chronic stress and exogenous glucocorticoids on regional fat distribution and metabolism. Physiol Behav 1992; 52: 583-590. 91. Marin P, Darin N, Amemiya T, Andersson B, Jem S, Bjtirntorp P. Cortisol secretion in relation to body fat distribution in obese premenopausal women. Metabolism 1992; 41: 882-886. 92. Frankenhaeuser M. Current issues in psychobiological stress research. Paper presented at the III European Congress of Psychology, Tampere, Finland, July, 1993.