A questionnaire-wide association study of personality and mortality: the Vietnam Experience Study

A bottom-up approach dramatically increases the predictability of body mass from personality traits

Personality traits consistently relate to and allow predicting body mass index (BMI), but these associations may not be adequately captured with existing inventories' domains or facets. Here, we aimed to test the limits of how accurately BMI can be predicted from and described with personality traits. We used three large datasets (combined N ≈ 100,000) with nearly 700 personality assessment items to (a) empirically identify clusters of personality traits linked to BMI and (b) identify relatively small sets of items that predict BMI as accurately as possible. Factor analysis revealed 14 trait clusters showing well-established personality trait-BMI associations (disorganization, anger) and lesser-known or novel ones (altruism, obedience). Most of items' predictive accuracy (up to r = .24 here but plausibly much higher) was captured by relatively few items. Brief scales that predict BMI have potential clinical applications-for instance, screening for risk of excessive weight gain or related complications.

The MMPI factor scales and risk of death in men during 45 years of follow-up: The Western Electric study

We examined associations between personality traits measured in 1958 and both all-cause and cause-specific mortality assessed 45 years later in 2003. Participants were 1,862 middle-aged men employed by the Western Electric Company. Outcomes were days to death from all causes, coronary heart disease, stroke, cancer, and causes other than circulatory diseases, cancer, accidents/homicide/suicides, or injuries (other causes). Measures in 1958 included age, education, health behaviors, biomedical risk factors, and nine content factors identified in the Minnesota Multiphasic Personality Inventory (MMPI). Four content factors-neuroticism, cynicism, extraversion, and intellectual interests-were related to the five-factor model domains of neuroticism, agreeableness, extraversion, and openness, respectively. The remaining five-psychoticism, masculinity versus femininity, religious orthodoxy, somatic complaints, and inadequacy-corresponded to the five-factor model's facets and styles (combinations of two domains) or were unrelated to the five-factor model. In age-adjusted and fully adjusted models, cynicism was associated with greater all-cause and cancer mortality. In fully adjusted models, inadequacy was associated with lower all-cause mortality and lower mortality from other causes. In age-adjusted models, religious orthodoxy was associated with lower cancer mortality. Further analyses revealed that the association between cynicism and all-cause mortality waned over time. Exploratory analyses of death from any disease of the circulatory system revealed no further associations. These findings reveal the importance of cynicism (disagreeableness) as a mortality risk factor, show that associations between cynicism and all-cause mortality are limited to certain periods of the lifespan, and highlight the need to study personality styles or types, such as inadequacy, that involve high neuroticism, low extraversion, and low conscientiousness. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Health risk prediction models incorporating personality data: Motivation, challenges, and illustration

The age of "big data" in health has ushered in an era of prediction models promising to forecast individual health events. Although many models focus on enhancing the predictive power of medical risk factors with genomic data, a recent proposal is to augment traditional health predictors with psychosocial data, such as personality measures. In this article we provide a general overview of the medical risk prediction models and then discuss the rationale for integrating personality data. We suggest three principles that should guide work in this area if personality data is ultimately to be useful within risk prediction as it is actually practiced in the health care system. These include (a) prediction of specific, priority health outcomes; (b) sufficient incremental validity beyond established biomedical risk factors; and (c) technically responsible model-building that does not overfit the data. We then illustrate the application of these principles in the development of a personality-augmented prediction model for the occurrence of mild cognitive impairment, designed for a primary care setting. We evaluate the results, drawing conclusions for the direction an iterative, programmatic approach would need to take to eventually achieve clinical utility. Although there is great potential for personality measurement to play a key role in the coming era of risk prediction models, the final section reviews the many challenges that must be faced in real-world implementation. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

When Is Higher Neuroticism Protective Against Death? Findings From UK Biobank

We examined the association between neuroticism and mortality in a sample of 321,456 people from UK Biobank and explored the influence of self-rated health on this relationship. After adjustment for age and sex, a 1- SD increment in neuroticism was associated with a 6% increase in all-cause mortality (hazard ratio = 1.06, 95% confidence interval = [1.03, 1.09]). After adjustment for other covariates, and, in particular, self-rated health, higher neuroticism was associated with an 8% reduction in all-cause mortality (hazard ratio = 0.92, 95% confidence interval = [0.89, 0.95]), as well as with reductions in mortality from cancer, cardiovascular disease, and respiratory disease, but not external causes. Further analyses revealed that higher neuroticism was associated with lower mortality only in those people with fair or poor self-rated health, and that higher scores on a facet of neuroticism related to worry and vulnerability were associated with lower mortality. Research into associations between personality facets and mortality may elucidate mechanisms underlying neuroticism's covert protection against death.

Statistical learning theory for high dimensional prediction: Application to criterion-keyed scale development

Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in "big data" problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how 3 common SLT algorithms-supervised principal components, regularization, and boosting-can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach-or perhaps because of them-SLT methods may hold value as a statistically rigorous approach to exploratory regression. (PsycINFO Database Record

Somatic concerns, depressive traits, atherosclerosis and the incidence of cardiovascular disease in ageing Finnish men

OBJECTIVE

To examine the impact of somatic concerns and depressive traits on carotid artery intima-media thickness (IMT) and on the incidence of cardiovascular disease (CVD).

METHODS

In the cross-sectional study of 2682 Finnish men aged 42 to 61years, a subsample of 1333 men had their carotid artery IMT recorded. In the prospective part of the study participants (n=1453) were followed up for an average of 20years. Data on incident CVD (n=766) were obtained from the National Population Register. For both subsamples, the Minnesota Multiphasic Personality Inventory (MMPI) was used to measure depressive traits and somatic concerns. In addition, composite scales of general ill-being, sleep and pain problems, psychological well-being, energy level, and cheerfulness were formed.

RESULTS

In the final corrected models there were no significant associations between somatic concerns or depressive traits and IMT. Both somatic concerns and depressive traits predicted a higher CVD incidence after adjustments for age and risk factors. The association between depressive traits and CVD incidence was highest among the subgroup of men with the highest levels of hsCRP, but this result did not reach statistical significance. Of the composite scales, general ill-being predicted a higher CVD incidence in the fully adjusted model, even when all the composite scales were entered into the model simultaneously.

CONCLUSION

Somatic concerns and depressive traits predict a higher CVD incidence. In particular, general ill-being contributes to this association. The association between depressive traits and CVD may be moderated by low-grade inflammation.

Trait anxiety and somatic concerns associate with increased mortality risk: a 23-year follow-up in aging men

PURPOSE

We aimed to examine the impact of anxiety and somatic concerns on the mortality risk during a 23-year follow-up of a representative sample of men.

METHODS

Finnish men aged 42-61 years (n = 2388) were followed up for a median of 23.4 years. Anxiety was assessed using baseline scores for the Minnesota Multiphasic Personality Inventory Psychasthenia subscale and somatic concerns were measured with the Hypochondriasis subscale. Mortality data were obtained from the National Population Register.

RESULTS

All-cause, injury, disease, cardiovascular, and cancer mortalities were examined as endpoints. Adjustments were performed for age, smoking, alcohol consumption, physical activity, low- and high-density lipoprotein cholesterol, body mass index, systolic blood pressure, a history of cardiovascular disease, marital status, socioeconomic status, the Framingham Type A Behavior Pattern Scale, and life events during the 12 months before the baseline examination. Anxiety and somatic concerns predicted the all-cause mortality risk after full adjustments for sociodemographic background, lifestyle factors, and descriptors of somatic health. Regarding other forms of mortality, the risk ratios were significant after full adjustments in anxiety for injury and in somatic concerns for disease death.

CONCLUSIONS

This study supported previous findings of anxiety predicting the all-cause mortality risk in men. Somatic concerns are a novel factor that needs to be taken into account while examining associations between personality and the risk of increased mortality.