James A. Yonker

Predictors of Family Conflict at the End of Life: The Experience of Spouses and Adult Children of Persons with Lung Cancer

PURPOSE Guided by an explanatory matrix of family conflict at the end of life, the purpose of this article was to examine the correlates and predictors of family conflict reported by 155 spouses and adult children of persons with lung cancer. DESIGN AND METHODS A cross-sectional statewide survey of family members of persons who died from lung cancer was conducted as part of the larger study on the Assessment of Cancer CarE and SatiSfaction in Wisconsin. RESULTS Significant bivariate correlations were found between family conflict and family context variables (i.e., a history of conflict, younger respondent age, race, and specified end-of-life care wishes of the patient), conditions (i.e., greater physical and psychological clinical care needs of the patient), and contributing factors (i.e., communication constraints and family asserting control). In the multivariate model, significant predictors of family conflict included prior family conflict, race, communication constraints, and family members asserting control; the model explained 72% of the variance in conflict. IMPLICATIONS Implications for routine assessment and screening to identify families at risk and recommendations for the development and testing of interventions to facilitate shared decision making and enhance open communication among at-risk families are highlighted.

COMPLICATED GRIEF SYMPTOMS IN CAREGIVERS OF PERSONS WITH LUNG CANCER: THE ROLE OF FAMILY CONFLICT, INTRAPSYCHIC STRAINS, AND HOSPICE UTILIZATION *

Guided by a stress process conceptual model, this study examines social and psychological determinants of complicated grief symptoms focusing on family conflict, intrapsychic strains, and the potential moderating effect of care quality and hospice utilization. Relying on data from 152 spouse and adult child lung cancer caregiver survey respondents, drawn from an ancillary study of the Assessment of Cancer CarE and SatiSfaction (ACCESS) in Wisconsin, hierarchical multiple regression analysis was used to examine determinants of complicated grief. After controlling for contextual factors and time since death, complicated grief symptoms were higher among caregivers with less education, among families with lower prior conflict but higher conflict at the end-of-life, who had family members who had difficulty accepting the illness, and who were caring for patients with greater fear of death. Additionally, hospice utilization moderated the effect of fear of death on complicated grief. Findings suggest that family conflict, intrapsychic strains, and hospice utilization may help to explain the variability found in complicated grief symptoms among bereaved caregivers. Implications for enhancing complicated grief assessment tools and preventative interventions across the continuum of cancer care are highlighted.

Multigene interactions and the prediction of depression in the Wisconsin Longitudinal Study

Objectives Single genetic loci offer little predictive power for the identification of depression. This study examined whether an analysis of gene–gene (G × G) interactions of 78 single nucleotide polymorphisms (SNPs) in genes associated with depression and age-related diseases would identify significant interactions with increased predictive power for depression. Design A retrospective cohort study. Setting A survey of participants in the Wisconsin Longitudinal Study. Participants A total of 4811 persons (2464 women and 2347 men) who provided saliva for genotyping; the group comes from a randomly selected sample of Wisconsin high school graduates from the class of 1957 as well as a randomly selected sibling, almost all of whom are non-Hispanic white. Primary outcome measure Depression as determine by the Composite International Diagnostic Interview–Short-Form. Results Using a classification tree approach (recursive partitioning (RP)), the authors identified a number of candidate G × G interactions associated with depression. The primary SNP splits revealed by RP (ANKK1 rs1800497 (also known as DRD2 Taq1A) in men and DRD2 rs224592 in women) were found to be significant as single factors by logistic regression (LR) after controlling for multiple testing (p=0.001 for both). Without considering interaction effects, only one of the five subsequent RP splits reached nominal significance in LR (FTO rs1421085 in women, p=0.008). However, after controlling for G × G interactions by running LR on RP-specific subsets, every split became significant and grew larger in magnitude (OR (before) → (after): men: GNRH1 novel SNP: (1.43 → 1.57); women: APOC3 rs2854116: (1.28 → 1.55), ACVR2B rs3749386: (1.11 → 2.17), FTO rs1421085: (1.32 → 1.65), IL6 rs1800795: (1.12 → 1.85)). Conclusions The results suggest that examining G × G interactions improves the identification of genetic associations predictive of depression. 4 of the SNPs identified in these interactions were located in two pathways well known to impact depression: neurotransmitter (ANKK1 and DRD2) and neuroendocrine (GNRH1 and ACVR2B) signalling. This study demonstrates the utility of RP analysis as an efficient and powerful exploratory analysis technique for uncovering genetic and molecular pathway interactions associated with disease aetiology.

Hypothalamic-pituitary-gonadal axis homeostasis predicts longevity

The reproductive-cell cycle theory of aging posits that reproductive hormone changes associated with menopause and andropause drive senescence via altered cell cycle signaling. Using data from the Wisconsin Longitudinal Study (n = 5,034), we analyzed the relationship between longevity and menopause, including other factors that impact “ovarian lifespan” such as births, oophorectomy, and hormone replacement therapy. We found that later onset of menopause was associated with lower mortality, with and without adjusting for additional factors (years of education, smoking status, body mass index, and marital status). Each year of delayed menopause resulted in a 2.9% reduction in mortality; after including a number of additional controls, the effect was attenuated modestly but remained statistically significant (2.6% reduction in mortality). We also found that no other reproductive parameters assessed added to the prediction of longevity, suggesting that reproductive factors shown to affect longevity elsewhere may be mediated by age of menopause. Thus, surgical and natural menopause at age 40, for example, resulted in identical survival probabilities. These results support the maintenance of the hypothalamic–pituitary–gonadal axis in homeostasis in prolonging human longevity, which provides a coherent framework for understanding the relationship between reproduction and longevity.

Assessment of Genetic and Nongenetic Interactions for the Prediction of Depressive Symptomatology: An Analysis of the Wisconsin Longitudinal Study Using Machine Learning Algorithms

OBJECTIVES We examined depression within a multidimensional framework consisting of genetic, environmental, and sociobehavioral factors and, using machine learning algorithms, explored interactions among these factors that might better explain the etiology of depressive symptoms. METHODS We measured current depressive symptoms using the Center for Epidemiologic Studies Depression Scale (n = 6378 participants in the Wisconsin Longitudinal Study). Genetic factors were 78 single nucleotide polymorphisms (SNPs); environmental factors-13 stressful life events (SLEs), plus a composite proportion of SLEs index; and sociobehavioral factors-18 personality, intelligence, and other health or behavioral measures. We performed traditional SNP associations via logistic regression likelihood ratio testing and explored interactions with support vector machines and Bayesian networks. RESULTS After correction for multiple testing, we found no significant single genotypic associations with depressive symptoms. Machine learning algorithms showed no evidence of interactions. Naïve Bayes produced the best models in both subsets and included only environmental and sociobehavioral factors. CONCLUSIONS We found no single or interactive associations with genetic factors and depressive symptoms. Various environmental and sociobehavioral factors were more predictive of depressive symptoms, yet their impacts were independent of one another. A genome-wide analysis of genetic alterations using machine learning methodologies will provide a framework for identifying genetic-environmental-sociobehavioral interactions in depressive symptoms.

Myocardial infarction in the Wisconsin Longitudinal Study: the interaction among environmental, health, social, behavioural and genetic factors

Objectives This study examined how environmental, health, social, behavioural and genetic factors interact to contribute to myocardial infarction (MI) risk. Design Survey data collected by Wisconsin Longitudinal Study (WLS), USA, from 1957 to 2011, including 235 environmental, health, social and behavioural factors, and 77 single- nucleotide polymorphisms were analysed for association with MI. To identify associations with MI we utilized recursive partitioning and random forest prior to logistic regression and chi-squared analyses. Participants 6198 WLS participants (2938 men; 3260 women) who (1) had a MI before 72 years and (2) had a MI between 65 and 72 years. Results In men, stroke (LR OR: 5.01, 95% CI 3.36 to 7.48), high cholesterol (3.29, 2.59 to 4.18), diabetes (3.24, 2.53 to 4.15) and high blood pressure (2.39, 1.92 to 2.96) were significantly associated with MI up to 72 years of age. For those with high cholesterol, the interaction of smoking and lower alcohol consumption increased prevalence from 23% to 41%, with exposure to dangerous working conditions, a factor not previously linked with MI, further increasing prevalence to 50%. Conversely, MI was reported in <2.5% of men with normal cholesterol and no history of diabetes or depression. Only stroke (4.08, 2.17 to 7.65) and diabetes (2.71, 1.81 to 4.04) by 65 remained significantly associated with MI for men after age 65. For women, diabetes (5.62, 4.08 to 7.75), high blood pressure (3.21, 2.34 to 4.39), high cholesterol (2.03, 1.38 to 3.00) and dissatisfaction with their financial situation (4.00, 1.94 to 8.27) were significantly associated with MI up to 72 years of age. Conversely, often engaging in physical activity alone (0.53, 0.32 to 0.89) or with others (0.34, 0.21 to 0.57) was associated with the largest reduction in odds of MI. Being non-diabetic with normal blood pressure and engaging in physical activity often lowered prevalence of MI to 0.2%. Only diabetes by 65 (4.25, 2.50 to 7.24) and being exposed to dangerous work conditions at 54 (2.24, 1.36 to 3.69) remained significantly associated with MI for women after age 65, while still menstruating at 54 (0.46, 0.23 to 0.91) was associated with reduced odds of MI. Conclusions Together these results indicate important differences in factors associated with MI between the sexes, that combinations of factors greatly influence the likelihood of MI, that MI-associated factors change and associations weaken after 65 years of age in both sexes, and that the limited genotypes assessed were secondary to environmental, health, social and behavioral factors.