Personality and metabolic dysfunction in young adulthood: A cross-sectional study

Abstract

This research used the National Longitudinal Study of Adolescent to Adult Health (N = 15,359; age range 25–34) to examine the cross-sectional relation between personality and four components of metabolic syndrome (elevated glucose, blood pressure, cholesterol, and waist circumference) and a metabolic risk index in young adulthood. Consistent with research on older adults, higher Neuroticism and lower Conscientiousness were associated with greater risk of metabolic dysfunction; Agreeableness, however, was unrelated to it. The relation between personality and metabolic health may unfold across the lifespan, with the association between Neuroticism/Conscientiousness and metabolic dysfunction starting early and the association with Agreeableness emerging at older ages.

Keywords

Conscientiousness lifespan health metabolic dysfunction Neuroticism personality

The traits that define the five-factor model (FFM) of personality—Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness—are associated consistently with consequential health outcomes, including Alzheimer’s disease (Terracciano et al., 2014) and longevity (Jokela et al., 2013). Theoretical models of personality and health specify that both behavioral and physiological pathways account for the relation between personality and health and that these relations play out across the lifespan (Friedman et al., 2014). There is extensive evidence for the role of personality in health-risk behaviors, including smoking (Terracciano and Costa, 2004) and diet and physical activity (Sutin and Terracciano, 2016). And, indeed, such behavioral risk factors are one pathway through which personality contributes to premature mortality (Turiano et al., 2015).

A second pathway is through physiological dysregulation. A growing body of evidence has linked these traits to markers of inflammatory (Luchetti et al., 2014), cardiovascular (Cheng et al., 2016), and metabolic (Čukić et al., 2016) dysfunction. Personality has specifically been linked with higher risk of metabolic syndrome (Mommersteeg and Pouwer, 2012), a constellation of anthropometric, metabolic, and hemodynamic risk factors (Grundy et al., 2005). This constellation of risk factors is associated with an increased risk of cardiovascular disease, diabetes, and premature mortality (Ford, 2005). Metabolic syndrome is thus consequential for important health outcomes and may be one pathway through which diseases, such as cardiovascular disease and diabetes, develop (Wilson et al., 2005). Among the FFM traits, individuals who score higher in Neuroticism (the tendency to experience negative emotions) and lower in Agreeableness (the tendency to be trusting and modest) or Conscientiousness (the tendency to be organized and disciplined) tend to be at greater risk of metabolic syndrome (Sutin et al., 2010). Previous research on personality and components of metabolic syndrome has focused primarily on middle-aged and older adults (Human et al., 2013; Phillips et al., 2010; Sutin et al., 2010). This focus is reasonable given that the prevalence of metabolic syndrome increases at mid-life (Ford et al., 2002). Metabolic dysregulation, however, likely starts before middle age (Mattsson et al., 2007) and may be detectable in young adulthood before reaching the threshold for classification of metabolic syndrome. This research examines whether there is a relation between FFM personality traits and components of metabolic syndrome in a large national sample of younger adults. Based on previous research with older adults, we expect that higher Neuroticism will be associated with greater risk of metabolic dysregulation, whereas higher Conscientiousness and Agreeableness will be protective. With this large, diverse sample, we also test whether the association between personality and the metabolic risk factors are moderated by sex, race, or ethnicity and whether smoking, body mass index (BMI), physical inactivity, and sleep problems mediate the relation between personality and metabolic dysfunction.

Method

Participants and procedure

Participants were drawn from the fourth wave of the National Longitudinal Study of Adolescent to Adult Health (Add Health; http://www.cpc.unc.edu/projects/addhealth). The fourth wave of Add Health occurred in 2008–2009 and was the first to include an FFM measure of personality. A total of 15,359 participants from wave 4 had valid information on both personality and markers of metabolic health. At wave 4, participants’ age ranged from 25 to 34 (M = 29.10, standard deviation (SD) = 1.75), 53 percent of the sample was female, 21 percent of the sample was African American, and 16 percent of the sample was Hispanic.

Measures

Personality traits

Participants completed the Mini International Personality Item Pool (Mini-IPIP; Donnellan et al., 2006), a 20-item reliable and valid measure of the traits that define the FFM (Donnellan et al., 2006). Each domain was measured with four items rated on a scale from 1 (strongly agree) to 5 (strongly disagree); items were reverse coded such that higher ratings indicated greater agreement.

Blood pressure

After a 5-minute rest, three measures of systolic and diastolic blood pressures (mm Hg) were taken at 30-second intervals. The mean of the second and third measures was used to classify blood pressure. Participants were classified as having elevated blood pressure if their measured blood pressure was greater or equal to 140/90 or had a diagnosis of hypertension.

Waist circumference

Trained staff measured waist circumference to the nearest .5 cm at the superior border of the iliac crest. Elevated abdominal adiposity was defined as a waist circumference ⩾102 cm for males and ⩾88 cm for females.

Blood glucose

Whole blood spots were obtained during the in-home assessment through a finger prick administered by trained staff. The blood spots were shipped to a laboratory and analyzed for glucose. The presence of high blood glucose was defined as ⩾126 (fasting) or ⩾200 (non-fasting) mg/dL or a reported diagnosis of/medication for diabetes.

Dyslipidemia

The presence of elevated cholesterol was defined as a reported history of hyperlipidemia or use of an antihyperlipidemic medication in the past 4 weeks.

Metabolic risk index

Due to limitations in the available data (e.g. glucose was not assessed fasting), it was not possible to calculate metabolic syndrome with the standard definition. Thus, a metabolic risk index was calculated as the sum of individual risk factors (range 0–4) that took advantage of the available data (M = .90, SD = .86).

Analytic strategy

Logistic regression was used to examine the relation between each personality trait (entered separately and simultaneously) and elevated blood pressure, blood glucose, cholesterol, and waist circumference, controlling for age, sex, education, race, Hispanic ethnicity, and hours since last ate (for analysis of blood markers). We then tested whether any of these associations was moderated by sex, race, or Hispanic ethnicity. Follow-up analyses also controlled for BMI (M = 29.12, SD = 7.53), smoking (21% current smokers), physical inactivity (15% inactive who reported no to seven items on specific physical activities), and sleeping problems (11% participants with sleep problems who reported yes to any of three common problems with sleeping). Linear regression was used to examine the relation between the traits and the sum of the metabolic risk factors, controlling for the same set of covariates. Finally, we tested whether smoking, BMI, physical inactivity, and sleep problems mediated the relation between personality and the metabolic risk score using standard bootstrapping procedures (Preacher and Hayes, 2008).

Results

Of the metabolic risk factors, abdominal adiposity (51%) was the most prevalent, whereas the presence of elevated blood glucose (7%) was the least prevalent; 8 and 24 percent of the sample had high cholesterol and blood pressure, respectively.

Personality was associated with each marker of dysregulation (Table 1). Specifically, higher Neuroticism was associated with a greater risk of each individual risk factor (except elevated abdominal adiposity), whereas higher Conscientiousness was protective from each of the four markers. The higher risk associated with every SD higher in Neuroticism ranged from 9% for elevated blood pressure to 17% for elevated cholesterol. Likewise, each SD increase in Conscientiousness was associated with lower risk that ranged from 4 percent for elevated blood pressure to 15 percent for elevated waist circumference. Higher Agreeableness was associated with lower risk of an elevated waist circumference; there were no other associations between the traits and the markers of metabolic health. Some of these associations were moderated by demographic characteristics. Although there was no association in the full sample, sex moderated the association between both Neuroticism and Extraversion and elevated waist circumference. Specifically, Neuroticism was associated with elevated waist circumference among women but not men (OR_interaction = 1.10, 95% CI = 1.03, 1.18), whereas Extraversion was associated with elevated waist circumference among men but not women (OR_interaction = .93, 95% CI = .87, .99). The association between Neuroticism and both elevated blood glucose and blood pressure was stronger for White participants than African American participants (OR_interaction = .75, 95% CI = 65, .86 and OR_interaction = .78, 95% CI = .66, .92, respectively), whereas the association between Agreeableness and waist circumference was slightly stronger among African American participants than White participants (OR_interaction = 1.09, 95% CI = 1.00, 1.18). The association between Neuroticism and waist circumference was stronger among Asian participants than White participants (OR_interaction = 1.19, 95% CI = 1.01–1.40), whereas Openness and Agreeableness had stronger protective effects among Asian participants than White participants (OR_interaction = .74, 95% CI = .62, .87 and OR_interaction = .84, 95% CI = .71, .99, respectively). Finally, Neuroticism had a stronger association with elevated blood sugar among White participants than Asian participants (OR_interaction = .68, 95% CI = .50, .94). None of the associations was moderated by Hispanic ethnicity. Finally, the association between personality and each of the markers of metabolic dysfunction was generally similar after controlling for smoking, BMI, physical inactivity, and sleep problems. The inclusion of these covariates did not alter the association between Neuroticism and any of the risk factors, but it reduced the association between Conscientiousness and blood glucose and blood pressure to non-significance.

Table 1.

Association between personality traits and metabolic dysfunction.

	Blood glucose		Blood pressure		Cholesterol		Waist circumference		Metabolic risk index
	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI	β
Neuroticism	1.10**^a,b	1.03–1.18	1.09**^a,b	1.04–1.13	1.17**^a,b	1.11–1.25	1.02	.99–1.06	.05**^a,b
Extraversion	.96	.90–1.02	1.00	.97–1.05	.95	.90–1.01	1.00	.97–1.04	−.01
Openness	1.02	.95–1.09	.98	.94–1.02	1.02	.96–1.09	.97	.94–1.00	−.01
Agreeableness	1.01	.94–1.08	1.00	.96–1.04	1.04	.98–1.11	.93**^b	.90–.97	−.02
Conscientiousness	.92*^b	.86–.98	.96*	.92–.99	.88**^a,b	.83–.93	.85**^a,b	.82–.88	−.07**^a,b

N = 15,359. Odds ratio (OR) and 95 percent confidence interval (CI) from logistic regression predicting each metabolic component from each personality entered separately, controlling for age, sex, race, ethnicity, and education. β is the standardized beta from linear regression predicting the metabolic dysfunction index from personality, controlling for the same covariates.

Association remained significant after controlling for smoking, body mass index, physical inactivity, and sleep problems.

Association remained significant when all five traits were entered into the analysis simultaneously.

p<.05. **p<.01.

Similar to the individual risk factors, higher Neuroticism was associated positively with the metabolic risk index, whereas there was a negative association for Conscientiousness.

The association between both Neuroticism and Conscientiousness and the metabolic index remained significant after inclusion of smoking, BMI, physical inactivity, and sleep problems. Mediation analyses revealed that the relation between Neuroticism and the metabolic risk score was mediated by smoking (point estimate = .0008, 95% CI = .0001, .0018), physical inactivity (point estimate = .0006, 95% CI = .0001, .0013), and sleep problems (point estimate = .0052, 95% CI = .0032, .0073); BMI was not a significant mediator (point estimate = .0059, 95% CI = −.0021, .0137). Similarly, the relation between Conscientiousness and the metabolic risk score was mediated by smoking (point estimate = −.0007, 95% CI = −.0016, −.0001), BMI (point estimate = −.0411, 95% CI = −.0489, −.0336), physical inactivity (point estimate = −.0009, 95% CI = −.0017, −.0002), and problems sleeping (point estimate = −.0026, 95% CI = −.0038, −.0016).

Discussion

Lifespan models of personality and health hypothesize that the pathway from personality to disease unfolds across the lifespan. Much of the literature on personality and metabolic dysfunction has been on middle-aged and older adults (Human et al., 2013; Phillips et al., 2010), which may miss the development of metabolic dysfunction that occurs earlier in life. These results showed that by early adulthood, individuals already had metabolic dysfunction, and that individual differences in Neuroticism and Conscientiousness were implicated in this dysfunction. These associations remained similar after accounting for smoking, BMI, physical inactivity, and sleep problems, which are common risk factors for metabolic dysfunction that are also associated with Neuroticism and Conscientiousness.

Metabolic dysfunction may be one mechanism that links Neuroticism and Conscientiousness to long-term health outcomes such as Alzheimer’s disease and premature mortality. Individuals higher in Neuroticism and lower in Conscientiousness tend to engage in behaviors that increase risk of metabolic syndrome, such as physical inactivity (Sutin et al., 2016). Individuals with these traits also have unhealthy inflammatory profiles (Mõttus et al., 2013) that may lead to the development of metabolic dysfunction. The lifestyle and physiological factors associated with Neuroticism and Conscientiousness may culminate in early metabolic dysfunction, which has significant downstream correlates with risk for morbidity and mortality later in adulthood. Similar to other psychological factors (Susin et al., 2016), these traits may also play an important role in adherence to intervention programs that aim to improve outcomes in individuals with metabolic syndrome. Tailoring intervention programs based on knowledge of the psychological characteristics associated with metabolic syndrome may help in improve treatment outcomes.

In contrast to Neuroticism and Conscientiousness, there was little evidence that Agreeableness was associated with metabolic dysfunction. This finding is in contrast to the literature on middle-aged and older adults that typically finds that individuals who are more antagonistic are at greater risk of metabolic syndrome (Mommersteeg and Pouwer, 2012; Sutin et al., 2010). This difference may be due to a more cumulative effect of this trait. That is, the processes associated with low Agreeableness (e.g. a proneness to anger) may take more years to develop than for Neuroticism and Conscientiousness and thus only becomes apparent in middle to later life (Boylan and Ryff, 2015). Such differences underscore the importance of addressing the relation between personality and physiological markers of health across the entire lifespan.

We also examined whether the association between personality and metabolic dysfunction was moderated by demographic factors and mediated by other risk factors for dysfunction. Similar to previous research on BMI (Brummett et al., 2006), Neuroticism had a stronger relation with elevated waist circumference among women, whereas Extraversion had a stronger relation with this risk factor among men. Interestingly, personality was more strongly associated with elevated blood glucose and blood pressure for White participants but more strongly associated with waist circumference among African American and Asian American participants. The mediation analyses revealed that common behavioral risk factors associated with both personality and metabolic dysfunction—smoking, physical inactivity, and sleep problems—partially mediated the relation between both Neuroticism and Conscientiousness and the metabolic risk score. Behavioral factors are regularly implicated in the relation between personality and health (Turiano et al., 2015) and are likely one factor in the pathway from personality to disease. Of note, however, in this study, the relation between personality and metabolic dysfunction persisted after inclusion of these behavioral factors in the analysis. This pattern indicates that behavioral risk factors account for part, but not all, of the association between personality and metabolic dysfunction.

This research has several strengths, including a large sample, indicators of metabolic dysfunction, and a validated FFM measure of personality. Future research could address some of the limitations of this study, such as an assessment of all components of metabolic syndrome (e.g. triglycerides), measures of blood markers when fasting, and longitudinal assessments that may capture the potential reciprocal nature of personality and metabolic dysfunction. The cross-sectional nature of the data, in particular, makes it difficult to assess the timing and course of the relation between personality and metabolic dysregulation. That is, it would be of interest to examine whether personality predicts the development of metabolic syndrome over time and whether there are reciprocal relations between the two. In addition, the indicators of metabolic dysfunction were measured in a slightly different way than is traditionally done. Although the results were consistent with our hypotheses and with what is typically found in older samples, it is a limitation that needs to be addressed in future research. Despite these limitations, it is of note that the associations generally match what is found in older samples (Human et al., 2013; Phillips et al., 2010; Sutin et al., 2010) and suggest that individuals high on Neuroticism and low on Conscientiousness are at higher risk of metabolic dysfunction from an early age.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

References

Boylan

Ryff

(2015) High anger expression exacerbates the relationship between age and metabolic syndrome. Journals of Gerontology: Series B: Psychological Sciences and Social Sciences 70: 77–82.

Brummett

Babyak

Williams

, et al. (2006) NEO personality domains and gender predict levels and trends in body mass index over 14 years during midlife. Journal of Research in Personality 40: 222–236.

Cheng

Montgomery

Treglown

, et al. (2016) Emotional stability, conscientiousness, and self-reported hypertension in adulthood. Personality and Individual Differences. Epub ahead of print 2 March 2016. DOI: 10.1016/j.paid.2016.02.034

Čukić

Mõttus

Realo

, et al. (2016) Elucidating the links between personality traits and diabetes mellitus: Examining the role of facets, assessment methods, and selected mediators. Personality and Individual Differences 94: 377–382.

Donnellan

Oswald

Baird

, et al. (2006) The mini-IPIP scales: Tiny-yet-effective measures of the Big Five factors of personality. Psychological Assessment 18: 192–203.

Ford

(2005) Risks for all-cause mortality, cardiovascular disease, and diabetes associated with the metabolic syndrome: A summary of the evidence. Diabetes Care 28: 1769–1778.

Ford

Giles

Dietz

(2002) Prevalence of the metabolic syndrome among US adults: Findings from the third national health and nutrition examination survey. Journal of the American Medical Association 287: 356–359.

Friedman

Kern

Hampson

, et al. (2014) A new life-span approach to conscientiousness and health: Combining the pieces of the causal puzzle. Developmental Psychology 50: 1377–1389.

Grundy

Cleeman

Daniels

, et al. (2005) Diagnosis and management of the metabolic syndrome: An American Heart Association/National Heart, Lung, and Blood Institute scientific statement. Circulation 112: 2735–2752.

10.

Human

Biesanz

Miller

, et al. (2013) Is change bad? Personality change is associated with poorer psychological health and greater metabolic syndrome in midlife. Journal of Personality 81: 249–260.

11.

Jokela

Batty

Nyberg

, et al. (2013) Personality and all-cause mortality: Individual-participant meta-analysis of 3,947 deaths in 76,150 adults. American Journal of Epidemiology 178: 667–675.

12.

Luchetti

Barkley

Stephan

, et al. (2014) Five-factor model personality traits and inflammatory markers: New data and a meta-analysis. Psychoneuroendocrinology 50: 181–193.

13.

Mattsson

Rönnemaa

Juonala

, et al. (2007) The prevalence of the metabolic syndrome in young adults. The Cardiovascular Risk in Young Finns Study. Journal of Internal Medicine 261: 159–169.

14.

Mommersteeg

Pouwer

(2012) Personality as a risk factor for the metabolic syndrome: A systematic review. Journal of Psychosomatic Research 73: 326–333.

15.

Mõttus

Luciano

Starr

, et al. (2013) Personality traits and inflammation in men and women in their early 70s: The Lothian Birth Cohort 1936 study of healthy aging. Psychosomatic Medicine 75: 11–19.

16.

Phillips

Batty

Weiss

, et al. (2010) Neuroticism, cognitive ability, and the metabolic syndrome: The Vietnam experience study. Journal of Psychosomatic Research 69: 193–201.

17.

Preacher

Hayes

(2008) Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods 40: 879–891.

18.

Susin

de Melo Boff

Ludwig

MWB

, et al. (2016) Predictors of adherence in a prevention program for patients with metabolic syndrome. Journal of Health Psychology 21: 2156–2167.

19.

Sutin

Terracciano

(2016) Personality traits and body mass index: Modifiers and mechanisms. Psychology & Health 31: 259–275.

20.

Sutin

Costa

Uda

, et al. (2010) Personality and metabolic syndrome. Age 32: 513–519.

21.

Sutin

Stephan

Luchetti

, et al. (2016) The five factor model of personality and physical inactivity: A meta-analysis of 16 samples. Journal of Research in Personality 63: 22–28.

22.

Terracciano

Costa

(2004) Smoking and the five-factor model of personality. Addiction 99: 472–481.

23.

Terracciano

O’Brien

, et al. (2014) Personality and risk of Alzheimer’s disease: New data and meta-analysis. Alzheimer’s & Dementia 10: 179–186.

24.

Turiano

Chapman

Gruenewald

, et al. (2015) Personality and the leading behavioral contributors of mortality. Health Psychology 34: 51–60.

25.

Wilson

D’Agostino

Parise

, et al. (2005) Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus. Circulation 112: 3066–3072.