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Article

Partner’s Perceived Social Support Influences Their Spouse’s Inflammation: An Actor–Partner Analysis

by
Joshua D. Landvatter
*,
Bert N. Uchino
,
Timothy W. Smith
and
Jos A. Bosch
Department of Psychology and Health Psychology Program, University of Utah, Salt Lake City, UT 84112, USA
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(2), 799; https://doi.org/10.3390/ijerph19020799
Submission received: 6 December 2021 / Revised: 7 January 2022 / Accepted: 9 January 2022 / Published: 12 January 2022
(This article belongs to the Special Issue Close Relationships and Health)
Review Reports Versions Notes

Abstract

:
Social support has been linked to lower cardiovascular morbidity and mortality. However, most studies have examined perceived support as an intrapersonal construct. A dyadic approach to social support highlights how interdependence between individuals within relationships, including partner perceptions and interactions, can influence one’s health. This study’s overall purpose was to test actor–partner models linking perceived social support to inflammation. Ninety-four cisgender married couples completed perceived support measures and had their blood drawn for CRP and IL-6 to produce an overall inflammatory index. The primary results indicate that only a partner’s level of perceived support was related to lower inflammation in their spouse. Our sample size, although moderate for inflammatory studies, was probably not large enough to detect actor influences. These data highlight the importance of taking a dyadic perspective on modeling perceived support and its potential mechanism.

1. Introduction

The quality of one’s social relationships is reliably related to physical health outcomes [1,2,3]. In perhaps the most compelling evidence to date, Holt-Lunstad, Smith, and Layton (2010) conducted a meta-analysis of 148 studies comprising over 308,000 participants. They found evidence that lower social support was related to a nearly 50% higher risk for future mortality [4]. Barth and colleagues [5] similarly found strong evidence linking social support to lower cardiovascular disease risk, which remains the leading cause of death in the United States and other industrialized countries [6].
Much of the prior work has focused on the biological mechanisms underlying the link between social support and health [7]. Of these biological mechanisms, inflammation has emerged as one of the more compelling pathways, considering its mechanistic links to cardiovascular disease morbidity and mortality [8,9,10]. A recent meta-analysis reported a small effect size linking social support to inflammatory cytokines [11]. Given the links between social support and broad-based mortality [4,7], an inflammatory pathway might explain part of its ties to health.
An important limitation in the literature is that all studies to date appear to examine if a person’s level of social support predicts their own inflammatory outcomes [12,13,14]. This intra-individual perspective highlights models in which social support processes perceived by the individual might influence their health and assumes that these perceptions, in part, reflect past support exchanges. However, research on invisible support and early life determinants of perceived support makes it clear that there are interpersonal determinants of support that have implications for its conceptualization [15,16]. Such interpersonal determinants of social support can be tested using actor–partner models, highlighting the interdependence between individuals within relationships [17]. These models examine if one’s own characteristics or those of a partner predict outcomes. In this case, the question is whether one’s own level of perceived support (actor influences) or a partner’s level of perceived support (partner influences) predicts inflammatory outcomes.
There is strong evidence that close social network members influence inflammation [18]. In a study by Donoho and colleagues, 2013, marital quality was seen as an important predictor of inflammation on the inflammatory biomarkers of C-reactive protein and interleukin-6 [19]. However, only limited work has examined dyadic influences on biomarkers to date, revealing links with cortisol, cardiovascular reactivity, and inflammation. Coregulation of actor–partner cortisol levels has been shown between couples and their mood states, with marital satisfaction acting as a possible buffer to the partner’s negative mood or stress state [20,21]. Furthermore, dyadic effects on cardiovascular reactivity measures have been observed [22,23,24,25,26,27], including partner influences of trait hostility and anger [28]. A review examining the immune system, marriage, and divorce also found that partners influence each other’s mood and health behaviors, producing both direct and indirect downstream effects on the immune system [29]. This evidence suggests that one’s perceived social support may influence inflammatory biomarkers in their partner.
When conceptualized as a dyadic or interpersonal process, there are several specific reasons to expect partner influences of perceived support on health. For example, being married to a person high in perceived support might simply be less stressful compared to being married to someone low in support [18,30]. Given that the partner has adequate support, they would have the resources needed to attend to their partner’s socio-emotional needs [31,32]. Additionally, perceived support is related to received support. Therefore, if a partner has higher levels of perceived support, this may be reflective of more responsive reciprocity in support processes [33]. Due to the reciprocal nature of dyadic relationships, one’s perception of support can ultimately affect partner responsiveness and self-disclosure and act as a buffer against stress or negative moods [20,21,24]. These processes (e.g., support reciprocity) between an actor and their partner may cushion the effects of stressors and consequent health issues [34,35].
This study’s main goal was to investigate actor–partner models linking perceived support to inflammation and explore the interpersonal components of perceived support. This interpersonal component “fills in the gaps” of prior work by highlighting the interpersonal context of dyads that one’s own perception of support may not reflect. These questions were explored in married couples, providing an ideal interpersonal context due to the importance of marriage in adulthood [36]. Based on prior work, it was predicted that higher actor levels of perceived support would be related to lower inflammation. Due to the interpersonal processes associated with support [16], it was also predicted that an increase in a partner’s level of support would be related to lower levels of inflammation in the spouse.

2. Materials and Methods

2.1. Participants

All methods and procedures implemented in this study were pre-approved by the Institutional Review Board (IRB_00033677) of the University of Utah. Ninety-four relatively healthy married couples were recruited for this study. We included a wide age range (i.e., 42 to 78 years old, Mage = 56.2, SD = 7.30) to increase variability in inflammatory measures. Most participants were white (94.6%) and college educated (70.3%) and had an income of over USD 40,000 per year (87.6%). Because many middle-aged and older adults are on health-related medications, we only excluded individuals who (a) were undergoing strong immunosuppressive treatment (e.g., corticosteroid therapy) and/or (b) had cancer or HIV due to concerns about potential effects of treatment on inflammatory outcomes. Medication use was coded dichotomously (yes/no) based on its specific biological function (e.g., Lipitor as a statin). The presence of an acute illness that could influence inflammatory indices was determined by CRP values (>10). No participants had a CRP value over or close to 10. Additionally, participants were also screened for conditions related to cardiovascular disease, such as diabetes.

2.2. Procedure

To control for diurnal variations in inflammation, all eligible participants were scheduled together for an appointment at the University of Utah in a time window between 9 a.m. and 12 p.m. [37]. Following informed consent, participants were first rechecked against the exclusion criteria upon their arrival for their session. Participants then completed information on demographic factors, medication use, and perceived social support (see below). Blood (20 ccs) was then drawn and treated with EDTA to prevent clotting. Plasma was separated via centrifugation, and levels of IL-6 and CRP were determined during batch analyses at a later date (see below). Couples were then debriefed and received USD 60.00 each for their participation. All measures of demographics, medication use, and perceived social support were obtained at the time of the visit.

2.3. Measures

Interpersonal Support Evaluation List (ISEL). The ISEL subscales of appraisal, tangible support, and belonging were used for this study, omitting the subscale of self-esteem. This was carried out to better assess the perceived availability of global support. Cohen et al. (1985) reported that the scale’s internal consistencies were high, with a four-week test–retest reliability of 0.87. The reliability of the ISEL has also been established over a six-month period [38]. In the present study, the scale’s internal consistency was high (wives α = 0.91; husbands α = 0.93).
Inflammation Assessments. High-sensitivity CRP (hsCRP) was measured through immunonephelometry using a Behring Nephelometer II. The limit of detection for C-reactive protein was 0.015 mg/L (High Sensitivity CRP, Dade Behring). All samples were assayed in the same run, yielding a within-assay CV% of <4.5% for hsCRP. IL-6 was determined using a commercially available high-sensitivity ELISA (hsIL-6 Quantikine, R&D systems), which had a lower detection limit of 0.15 pg/mL and yielded an intra-assay CV% of <6%. Consistent with prior work, CRP and IL-6 were natural log transformed to normalize the distribution prior to analyses [39]. Significant Shapiro–Wilk tests confirmed the non-normality of the data for CRP (W = 0.67, p < 0.001) and IL-6 (W = 0.83, p < 0.001), which are among the most powerful tests of normality based on Monte Carlo simulations [40]. To create an overall index of inflammation (and reduce the number of statistical tests), CRP and IL-6 were standardized and then averaged. Similar to prior work, these separate indices were correlated at 0.48 (wives) and 0.60 (husbands).

2.4. Data Analysis

PROC MIXED (SAS Institute, Cary City, NC, USA) was utilized to examine actor–partner perceived support influences on inflammation following the recommendations of Campbell and Kashy [41]. Standard variables including age, gender, body mass, and medications linked to chronic conditions were statistically adjusted for [37,42,43]. The covariance structure for the dyad’s repeated-measures factors (i.e., husband, wife) was modeled using the compound symmetry structure [41]. The resulting actor–partner models allowed us to test if one’s own levels of perceived support (actor influences) and a partner’s level of perceived support were significantly related to one’s own outcomes [44].

3. Results

3.1. Descriptive Analyses

Table 1 lists the descriptive information on our primary measures of social support and inflammation. Overall perceived support levels were above the scale midpoint of 2.39 on a 0 to 4-point scale. Mean levels of inflammation were consistent with a healthy sample. Table 2 contains the raw correlation matrix for our main measures for wives and husbands separately, with the cross-diagonals indicating within-couple correlations. The use of statins and anti-inflammatory drugs was controlled for during the analyses shown in Table 2.

3.2. Actor and Partner Perceived Support Influences on Inflammation

Prior work has revealed relatively small effect sizes between one’s own perceived social support and inflammation [11]. Similar to this prior research, although the levels of one’s own perceived support (actor influences) were negatively related to inflammation, these links were not significant (p = 0.19). However, a spouse’s level of perceived support was related to lower levels of one’s own inflammation (b = −0.32, SE = 0.14, p = 0.02), as seen in Table 3 below. Ancillary analyses on hs-CRP and IL-6 separately revealed the same pattern of partner influences on both these measures of inflammation.

3.3. Ancillary Analyses

Several ancillary analyses were conducted to rule out alternative explanations. First, statistical controls for health behaviors, including weekly exercise, smoking, and alcohol consumption, were covaried during main model analyses. All main results remained unchanged. Due to socioeconomic status being a reliable predictor of inflammation [45], analyses that statistically adjusted for family income were completed, and all the main results remained unchanged.

4. Discussion

The main goal of this study was to test actor–partner models linking perceived social support to inflammation. The main results indicate that a partner’s level of perceived support was related to lower inflammation in their spouse. These results remained unchanged while statistically controlling for a number of demographic variables, including medication use and one’s own level of perceived support. These data highlight the importance of taking a dyadic perspective on perceived support, given the interdependence of individuals within relationships.
It was predicted that both actor and partner levels of perceived support would be related to lower levels of inflammation. Only partner levels of perceived support were associated with lower inflammation. There are a number of plausible reasons for the existence of such an association, and it requires future research. Given that marital relationships are among the most important in adulthood, a spouse with higher perceptions of support might be associated with smoother interpersonal interactions in daily life and over time. Importantly, such processes might not be captured by actor measures of support, which only reflect an individual’s own perception of support and their own interpersonal functioning. For instance, prior work indicated that social support is related to better social skills [46]. It is thus possible that partners high in perceived support may engage in more effective and responsive support, such that actors benefit from its positive influence [15,47]. Unfortunately, this study did not assess more specific interpersonal skills, and this requires future research. More generally, these explanations highlight the importance of a broader consideration of mechanisms that could be explored in future research.
It is also possible that this study captures the interpersonal and intrapersonal synchrony of perceived support within a relationship. At least one study has linked interpersonal mechanisms and close relationships to health [48] through the coregulation of emotion, cognition, behavior, and physiology within the dyad. This coregulation or psychophysiological homeostasis between partners has been seen to affect physical and emotional health [49]. Nonetheless, the interplay between intrapersonal and interpersonal perceptions within the dyad would need to be addressed in future research.
Although the directionality of effects cannot be investigated within cross-sectional data, there is some evidence suggesting a coregulation of inflammation and social behavior that could be an interesting avenue for future work. Two reviews on inflammation and social processes found that inflammatory processes regulated social behavior, inasmuch that neural sensitivity to positive and negative social feedback was seen to be enhanced [50,51]. Therefore, inflammation levels may have contributed to social behaviors that elicited support from partners. That is, when the need for support is recognized and perceived by the partner, it has the potential to influence actor levels of inflammation. Although this is speculative and requires future work, it is consistent with the growing literature documenting the bi-directional links between inflammation and social processes.
Significant relationships were found between actor–partner perceived social support, inflammation, and BMI, and between actor–partner perceived social support, inflammation, and gender. Research shows that there is a substantially increased risk of obesity when one’s partner is obese. It is thought that relationships can normalize the idea of obesity, making it more acceptable to become obese [52]. Additionally, studies have shown that increased obesity can then lead to an increase in inflammation [53,54]. In sum, the normalization of a higher BMI through one’s partner increases the likelihood of an increased actor BMI and the associated inflammation seen with increased adipose tissue. Regarding the relationship seen with gender, inflammation was seen to be lower in women than in men. This is thought to be due to the general anti-inflammatory properties associated with estrogen [55,56].
There are many limitations that should be raised. Although general perceived support has been linked to marital functioning [57,58], we need to be appropriately cautious in interpreting these results as reflecting specific interpersonal transactions within the context of marriage. As ancillary actor–partner analyses revealed that there were no significant partner effects on marital quality [59] or perceived stress [60], the precise mechanisms responsible for such a link need further exploration (e.g., social skills). This study was also cross-sectional, meaning causal inferences cannot be made. Future studies should include longitudinal designs or daily diary studies that examine support processes and health over short periods of time. In addition, our sample size, although moderate for inflammatory studies, was probably not large enough to detect actor influences. Furthermore, even though an inflammatory index was created from measures of CRP and IL-6, having just one assessment does not capture fluctuations that occur over time. Although inflammation is related to future health problems, other biological mechanisms would have bolstered our assessments. For instance, measures of cardiovascular reactivity, ambulatory blood pressure, and/or cellular aging might have improved the generalizability to health outcomes [61,62,63]. At least one study showed that perceived support influences other measures such as ambulatory blood pressure [24]. Nonetheless, none of the studies referenced above modeled actor–partner effects. Lastly, our findings may only be generalizable to populations with demographics similar to our own. These limitations notwithstanding, this is one of the first studies linking partner levels of support to a biological mechanism related to health. Hence, these results are novel and expand our thinking on antecedent processes and mechanisms linking perceived support to health that can be examined in future work.

5. Conclusions

Contrary to our predictions, actor levels of perceived support did not predict lower levels of inflammation. However, in the expected direction, the association was not significant. Based on a recent meta-analysis, the effect size linking social support to inflammation is small, at r = −0.07 [11]. Hence, it is likely that the current study was underpowered to detect such an association at a conventional level of significance. The nonsignificant finding for actor support also highlights the relatively larger effect sizes for partner levels of support. If similar results are confirmed in future studies, they may underscore the importance of dyadic approaches to psychosocial risk, even for psychosocial risk factors that have traditionally been conceptualized as intrapersonal in nature.

Author Contributions

Conceptualization, B.N.U., T.W.S., and J.A.B.; methodology, B.N.U., T.W.S. and J.A.B.; software, SAS Institute; validation, J.D.L., B.N.U., T.W.S., and J.A.B.; formal analysis, B.N.U. and J.D.L.; investigation, J.D.L., B.N.U., T.W.S. and J.A.B.; data curation, B.N.U. and T.W.S.; writing—J.D.L. and B.N.U.; writing—review and editing, J.D.L.; visualization, J.D.L., B.N.U., and T.W.S.; supervision, B.N.U.; project administration, B.N.U. and T.W.S.; funding acquisition, B.N.U. All authors have read and agreed to the published version of the manuscript.

Funding

This research was generously supported by grant numbers R01HL085106 and R01HL137606 from the National Heart, Lung, and Blood Institute (P.I.: Bert N. Uchino).

Institutional Review Board Statement

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed Consent Statement

Informed consent was obtained from all individual participants included in the study.

Data Availability Statement

All data can be provided upon request.

Acknowledgments

All authors provided critical feedback to the corresponding author and helped in the manuscript’s research design and/or analyses.

Conflicts of Interest

The authors declare that conflict of interest.

References

  1. Berkman, L.F.; Glass, T.; Brissette, I.; Seeman, T.E. From social integration to health: Durkheim in the new millennium. Soc. Sci. Med. 2000, 51, 843–857. [Google Scholar] [CrossRef]
  2. De Vogli, R.; Chandola, T.; Marmot, M.G. Negative aspects of close relationships and heart disease. Arch. Intern. Med. 2007, 167, 1951–1957. [Google Scholar] [CrossRef] [Green Version]
  3. Uchino, B. Social Support and Physical Health: Understanding the Health Consequences of Relationships; Yale University Press: New Haven, CT, USA, 2004. [Google Scholar]
  4. Holt-Lunstad, J.; Smith, T.B.; Layton, J.B. Social Relationships and Mortality Risk: A Meta-analytic Review. PLoS Med. 2010, 7, e1000316. [Google Scholar] [CrossRef] [PubMed]
  5. Barth, J.; Schneider, S.; Von Känel, R. Lack of Social Support in the Etiology and the Prognosis of Coronary Heart Disease: A Systematic Review and Meta-Analysis. Psychosom. Med. 2010, 72, 229–238. [Google Scholar] [CrossRef] [PubMed]
  6. Benjamin, E.J.; Muntner, P.; Alonso, A.; Bittencourt, M.S.; Callaway, C.W.; Carson, A.P.; Chamberlain, A.M.; Chang, A.R.; Cheng, S.; Das, S.R.; et al. Heart disease and stroke statistics—2019 update: A report from the American heart association. Circulation 2019, 139, e56–e528, Erratum in Circulation 2020, 141, e33. [Google Scholar] [CrossRef] [PubMed]
  7. Reblin, M.; Uchino, B.N. Social and emotional support and its implication for health. Curr. Opin. Psychiatry 2008, 21, 201–205. [Google Scholar] [CrossRef] [Green Version]
  8. Libby, P.M.; Ridker, P.; Maseri, A. Inflammation and Atherosclerosis. Circulation 2002, 105, 1135–1143. [Google Scholar] [CrossRef] [PubMed]
  9. Guo, Y.-Z.; Pan, L.; Du, C.-J.; Ren, D.-Q.; Xie, X.-M. Association between C-reactive Protein and Risk of Cancer: A Meta-analysis of Prospective Cohort Studies. Asian Pac. J. Cancer Prev. 2013, 14, 243–248. [Google Scholar] [CrossRef] [Green Version]
  10. Hunter, C.; Jones, S. IL-6 as a keystone cytokine in health and disease. Nat. Immunol. 2015, 16, 448–457. [Google Scholar] [CrossRef]
  11. Uchino, B.N.; Trettevik, R.; De Grey, R.G.K.; Cronan, S.; Hogan, J.; Baucom, B.R.W. Social support, social integration, and inflammatory cytokines: A meta-analysis. Health Psychol. 2018, 37, 462–471. [Google Scholar] [CrossRef]
  12. Loucks, E.B.; Berkman, L.F.; Gruenewald, T.L.; Seeman, T.E. Relation of Social Integration to Inflammatory Marker Concentrations in Men and Women 70 to 79 Years. Am. J. Cardiol. 2006, 97, 1010–1016. [Google Scholar] [CrossRef]
  13. McDade, T.W.; Hawkley, L.C.; Cacioppo, J.T. Psychosocial and Behavioral Predictors of Inflammation in Middle-Aged and Older Adults: The Chicago Health, Aging, and Social Relations Study. Psychosom. Med. 2006, 68, 376–381. [Google Scholar] [CrossRef] [Green Version]
  14. Bajaj, B.; Gupta, R.; Pande, N. Self-esteem mediates the relationship between mindfulness and well-being. Personal. Individ. Differ. 2016, 94, 96–100. [Google Scholar] [CrossRef]
  15. Bolger, N.; Amarel, D. Effects of social support visibility on adjustment to stress: Experimental evidence. J. Pers. Soc. Psychol. 2007, 92, 458–475. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Uchino, B.N. Understanding the Links Between Social Support and Physical Health: A Life-Span Perspective with Emphasis on the Separability of Perceived and Received Support. Perspect. Psychol. Sci. 2009, 4, 236–255. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Reed, R.G.; Butler, E.A.; Kenny, D.A. Dyadic Models for the Study of Health. Soc. Pers. Psychol. Compass 2013, 7, 228–245. [Google Scholar] [CrossRef]
  18. Dehle, C.; Larsen, D.; Landers, J.E. Social Support in Marriage. Am. J. Fam. Ther. 2001, 29, 307–324. [Google Scholar] [CrossRef]
  19. Donoho, C.J.; Crimmins, E.M.; Seeman, T.E. Marital Quality, Gender, and Markers of Inflammation in the MIDUS Cohort. J. Marriage Fam. 2013, 75, 127–141. [Google Scholar] [CrossRef] [PubMed]
  20. Saxbe, D.; Repetti, R.L. For better or worse? Coregulation of couples’ cortisol levels and mood states. J. Pers. Soc. Psychol. 2010, 98, 92–103. [Google Scholar] [CrossRef] [PubMed]
  21. Slatcher, R.B.; Robles, T.F.; Repetti, R.L.; Fellows, M.D. Momentary Work Worries, Marital Disclosure, and Salivary Cortisol Among Parents of Young Children. Psychosom. Med. 2010, 72, 887–896. [Google Scholar] [CrossRef] [Green Version]
  22. Nealey-Moore, J.B.; Smith, T.W.; Uchino, B.N.; Hawkins, M.W.; Olson-Cerny, C. Cardiovascular Reactivity During Positive and Negative Marital Interactions. J. Behav. Med. 2007, 30, 505–519. [Google Scholar] [CrossRef]
  23. Smith, T.W.; Cribbet, M.R.; Nealey-Moore, J.B.; Uchino, B.N.; Williams, P.G.; MacKenzie, J.; Thayer, J.F. Matters of the variable heart: Respiratory sinus arrhythmia response to marital interaction and associations with marital quality. J. Pers. Soc. Psychol. 2011, 100, 103–119. [Google Scholar] [CrossRef] [PubMed]
  24. Bowen, K.S.; Birmingham, W.; Uchino, B.N.; Carlisle, M.; Smith, T.W.; Light, K.C. Specific dimensions of perceived support and ambulatory blood pressure: Which support functions appear most beneficial and for whom? Int. J. Psychophysiol. 2013, 88, 317–324. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Smith, T.W.; Uchino, B.N.; MacKenzie, J.; Hicks, A.M.; Campo, R.A.; Reblin, M.; Grewen, K.M.; Amico, J.A.; Light, K.C. Effects of couple interactions and relationship quality on plasma oxytocin and cardiovascular reactivity: Empirical findings and methodological considerations. Int. J. Psychophysiol. 2013, 88, 271–281. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Birditt, K.S.; Newton, N.; Hope, S. Implications of Marital/Partner Relationship Quality and Perceived Stress for Blood Pressure Among Older Adults. J. Gerontol. Ser. B Psychol. Sci. Soc. Sci. 2012, 69, 188–198. [Google Scholar] [CrossRef] [Green Version]
  27. Birditt, K.S.; Newton, N.J.; Cranford, J.; Ryan, L.H. Stress and Negative Relationship Quality among Older Couples: Implications for Blood Pressure. J. Gerontol. Ser. B 2016, 71, 775–785. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Smith, T.W.; Uchino, B.N.; Bosch, J.A.; Kent, R.G. Trait hostility is associated with systemic inflammation in married couples: An actor–partner analysis. Biol. Psychol. 2014, 102, 51–53. [Google Scholar] [CrossRef] [Green Version]
  29. Kiecolt-Glaser, J.K. Marriage, divorce, and the immune system. Am. Psychol. 2018, 73, 1098–1108. [Google Scholar] [CrossRef]
  30. Ryan, L.H.; Wan, W.H.; Smith, J. Spousal social support and strain: Impacts on health in older couples. J. Behav. Med. 2014, 37, 1108–1117. [Google Scholar] [CrossRef] [Green Version]
  31. Fitzsimons, G.M.; Finkel, E.J. Interpersonal Influences on Self-Regulation. Curr. Dir. Psychol. Sci. 2010, 19, 101–105. [Google Scholar] [CrossRef]
  32. Coan, J.A.; Sbarra, D.A. Social Baseline Theory: The social regulation of risk and effort. Curr. Opin. Psychol. 2015, 1, 87–91. [Google Scholar] [CrossRef] [Green Version]
  33. Haber, M.G.; Cohen, J.L.; Lucas, T.; Baltes, B. The relationship between self-reported received and perceived social support: A meta-analytic review. Am. J. Community Psychol. 2007, 39, 133–144. [Google Scholar] [CrossRef]
  34. Jou, Y.H.; Fukada, H. Stress, Health, and Reciprocity and Sufficiency of Social Support: The Case of University Students in Japan. J. Soc. Psychol. 2002, 142, 353–370. [Google Scholar] [CrossRef]
  35. Robles, T.F.; Slatcher, R.B.; Trombello, J.M.; McGinn, M.M. Marital quality and health: A meta-analytic review. Psychol. Bull. 2014, 140, 140–187. [Google Scholar] [CrossRef]
  36. Moskowitz, D.; Morris, A.; Schmidt, L. Givers & Getters: Reciprocity in Social Support, Poverty and Health. J. Gen. Intern. Med. 2010, 25, 286. [Google Scholar]
  37. O’Connor, M.F.; Bower, J.E.; Cho, H.J.; Creswell, J.D.; Dimitrov, S.; Hamby, M.E.; Hoyt, M.A.; Martin, J.L.; Robles, T.F.; Sloan, E.K.; et al. To assess, to control, to exclude: Effects of biobehavioral factors on circulating inflammatory markers. Brain Behav. Immun. 2009, 23, 887–897. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Cohen, S. Perceived stress in a probability sample of the United States. In The Claremont Symposium on Applied Social Psychology. The Social Psychology of Health; Spacapan, S., Oskamp, S., Eds.; Sage Publications Inc.: Thousand Oaks, CA, USA, 1988; pp. 31–67. [Google Scholar]
  39. Mezuk, B.; Roux, A.V.; Seeman, T. Evaluating the buffering vs. direct effects hypotheses of emotional social support on inflammatory markers: The Multi-Ethnic Study of Atherosclerosis. Brain Behav. Immun. 2010, 24, 1294–1300. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Razali, N.M.; Wah, Y.B. Power comparisons of shapiro-wilk, kolmogorov-smirnov, lilliefors and anderson-darling tests. J. Stat. Model. Anal. 2011, 2, 21–33. [Google Scholar]
  41. Campbell, L.; Kashy, D.A. Estimating Actor, Partner, and Interaction Effects for Dyadic Data Using PROC MIXED and HLM: A User-Friendly Guide. Pers. Relatsh. 2002, 9, 327–342. [Google Scholar] [CrossRef]
  42. Libby, P. Inflammation in atherosclerosis. Nature 2002, 420, 868–874. [Google Scholar] [CrossRef] [PubMed]
  43. Sbarouni, E.; Georgiadou, P.; Koutelou, M.; Sklavainas, I.; Panagiotakos, D.; Voudris, V. Ischaemia-modified albumin in dilated cardiomyopathy. Ann. Clin. Biochem. Int. J. Lab. Med. 2009, 46, 241–243. [Google Scholar] [CrossRef] [Green Version]
  44. Kenny, D.A.; Kashy, D.A.; Cook, W.L. Methodology in the social sciences. In Dyadic Data Analysis; Kenny, D.A., Ed.; Guilford Press: New York, NY, USA, 2006. [Google Scholar]
  45. Muscatell, K.A.; Brosso, S.N.; Humphreys, K.L. Socioeconomic status and inflammation: A meta-analysis. Mol. Psychiatry 2020, 25, 2189–2199. [Google Scholar] [CrossRef]
  46. Sarason, B.R.; Sarason, I.G.; Hacker, T.A.; Basham, R.B. Concomitants of Social Support: Social Skills, Physical Attractiveness, and Gender. J. Personal. Soc. Psychol. 1985, 49, 469–480. [Google Scholar] [CrossRef]
  47. Zee, K.S.; Cavallo, J.V.; Flores, A.J.; Bolger, N.; Higgins, E.T. Motivation moderates the effects of social support visibility. J. Pers. Soc. Psychol. 2018, 114, 735–765. [Google Scholar] [CrossRef]
  48. Pietromonaco, P.R.; Collins, N.L. Interpersonal mechanisms linking close relationships to health. Am. Psychol. 2017, 72, 531–542. [Google Scholar] [CrossRef] [PubMed]
  49. Sbarra, D.A.; Hazan, C. Coregulation, Dysregulation, Self-Regulation: An Integrative Analysis and Empirical Agenda for Understanding Adult Attachment, Separation, Loss, and Recovery. Pers. Soc. Psychol. Rev. 2008, 12, 141–167. [Google Scholar] [CrossRef] [PubMed]
  50. Eisenberger, N.I.; Moieni, M.; Inagaki, T.K.; Muscatell, K.A.; Irwin, M.R. In Sickness and in Health: The Co-Regulation of Inflammation and Social Behavior. Neuropsychopharmacology 2017, 42, 242–253. [Google Scholar] [CrossRef]
  51. Moieni, M.; Eisenberger, N.I. Effects of inflammation on social processes and implications for health. Ann. N. Y. Acad. Sci. 2018, 1428, 5–13. [Google Scholar] [CrossRef]
  52. Christakis, N.A.; Fowler, J.H. The Spread of Obesity in a Large Social Network over 32 Years. N. Engl. J. Med. 2007, 357, 370–379. [Google Scholar] [CrossRef] [Green Version]
  53. McArdle, M.A.; Finucane, O.M.; Connaughton, R.M.; McMorrow, A.M.; Roche, H.M. Mechanisms of Obesity-Induced Inflammation and Insulin Resistance: Insights into the Emerging Role of Nutritional Strategies. Front. Endocrinol. 2013, 4, 52. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Burhans, M.S.; Hagman, D.K.; Kuzma, J.N.; Schmidt, K.A.; Kratz, M. Contribution of Adipose Tissue Inflammation to the Development of Type 2 Diabetes Mellitus. Compr. Physiol. 2018, 9, 1–58. [Google Scholar] [CrossRef]
  55. Monteiro, R.; Teixeira, D.; Calhau, C. Estrogen Signaling in Metabolic Inflammation. Mediat. Inflamm. 2014, 2014, 615917. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Villa, A.; Rizzi, N.; Vegeto, E.; Ciana, P.; Maggi, A. Estrogen accelerates the resolution of inflammation in macrophagic cells. Sci. Rep. 2015, 5, 15224. [Google Scholar] [CrossRef] [PubMed]
  57. Reis, H.T.; Collins, N. Measuring Relationship Properties and Interactions Relevant to Social Support. In Social Support Measurement and Intervention: A Guide for Health and Social Scientists; Cohen, S., Underwood, L.G., Gottlieb, B.H., Eds.; Oxford University Press (OUP): New York, NY, USA, 2000; pp. 136–192. [Google Scholar]
  58. Qadir, F.; Khalid, A.; Haqqani, S.; Medhin, G. The association of marital relationship and perceived social support with mental health of women in Pakistan. BMC Public Health 2013, 13, 1150. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  59. Locke, H.J.; Wallace, K.M. Short Marital-Adjustment and Prediction Tests: Their Reliability and Validity. Marriage Fam. Living 1959, 21, 251. [Google Scholar] [CrossRef]
  60. Cohen, S.; Mermelstein, R.; Kamarck, T.; Hoberman, H.M. Measuring the Functional Components of Social Support. In Social Support: Theory, Research and Applications; Sarason, I.G., Sarason, B.R., Eds.; NATO ASI Series (D: Behavioral and Social Sciences); Springer Science and Business Media LLC: New York, NY, USA, 1985; pp. 73–94. [Google Scholar]
  61. O’Donovan, A.; Hughes, B.M. Access to Social Support in Life and in the Laboratory: Combined Impact on Cardiovascular Reactivity to Stress and State Anxiety. J. Health Psychol. 2008, 13, 1147–1156. [Google Scholar] [CrossRef]
  62. Uchino, B.; Bowen, K.; Kent, R. Social support and mental health. In Encyclopedia of Mental Health, 2nd ed.; Yale University Press: New Haven, CT, USA, 2015; pp. 189–195. [Google Scholar]
  63. Ettehad, D.; Emdin, C.A.; Kiran, A.; Anderson, S.G.; Callender, T.; Emberson, J.; Chalmers, J.; Rodgers, A.; Rahimi, K. Blood pressure lowering for prevention of cardiovascular disease and death: A systematic review and meta-analysis. Lancet 2016, 387, 957–967. [Google Scholar] [CrossRef] [Green Version]
Table
Table 1. Mean and standard deviation (SD) for main study variables.
Table 1. Mean and standard deviation (SD) for main study variables.
VariableMean (Range)SD
Perceived Support2.39 (1–4)0.39
Interleukin-6 (pg/mL)1.571.05
C-reactive protein (mg/L)0.19 (0–10)0.25
Body mass (kg/m2)26.25 (18.5–40)4.61
Age (years)56.27.30
Frequency
Ethnicity (% white)94.6%
College educated70.3%
Annual income over USD 40,000 per year87.6%
Statin use7.75%
Anti-inflammatory use25.75%
Note. n = 94.
Table
Table 2. Zero-order correlations among main study variables for women (top panel) and men (bottom panel), with diagonals representing cross-spouse correlations.
Table 2. Zero-order correlations among main study variables for women (top panel) and men (bottom panel), with diagonals representing cross-spouse correlations.
Variable1. ISEL2. BMI3. Age4. Statin5. Anti-Inflam.6. HRT7. Inflam. Ind
1. ISEL0.04−0.050.060.06−0.14−0.060.02
2. BMI−0.160.35 **−0.050.100.16−0.040.58 **
3. Age−0.02−0.020.90 **0.20−0.200.10−0.06
4. Statin−0.010.000.25 *0.34 **−0.020.090.05
5. Anti-Inflam.−0.050.02−0.090.080.200.010.09
6. HRT------0.06
7. Inflam. Index−0.200.42 ***0.050.060.01-0.05
Note. BMI = body mass index, Inflam. = inflammatory, HRT = hormone replacement therapy, * p < 0.05, ** p < 0.01, *** p < 0.001. n = 88 for women, 91 for men.
Table
Table 3. Actor and partner perceived support and inflammation.
Table 3. Actor and partner perceived support and inflammation.
VariableInflammatory Index
bS.E.p
Body Mass0.090.010.00 **
Age0.000.010.87
Gender (male–female)0.240.120.05 *
Statin0.090.220.69
Anti-inflammatory0.030.120.80
Hormone replacement0.120.240.50
Actor ISEL−0.050.140.72
Partner ISEL−0.320.140.02 *
* p < 0.05; ** p < 0.01; n = 179.
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Landvatter, J.D.; Uchino, B.N.; Smith, T.W.; Bosch, J.A. Partner’s Perceived Social Support Influences Their Spouse’s Inflammation: An Actor–Partner Analysis. Int. J. Environ. Res. Public Health 2022, 19, 799. https://doi.org/10.3390/ijerph19020799

AMA Style

Landvatter JD, Uchino BN, Smith TW, Bosch JA. Partner’s Perceived Social Support Influences Their Spouse’s Inflammation: An Actor–Partner Analysis. International Journal of Environmental Research and Public Health. 2022; 19(2):799. https://doi.org/10.3390/ijerph19020799

Chicago/Turabian Style

Landvatter, Joshua D., Bert N. Uchino, Timothy W. Smith, and Jos A. Bosch. 2022. "Partner’s Perceived Social Support Influences Their Spouse’s Inflammation: An Actor–Partner Analysis" International Journal of Environmental Research and Public Health 19, no. 2: 799. https://doi.org/10.3390/ijerph19020799

APA Style

Landvatter, J. D., Uchino, B. N., Smith, T. W., & Bosch, J. A. (2022). Partner’s Perceived Social Support Influences Their Spouse’s Inflammation: An Actor–Partner Analysis. International Journal of Environmental Research and Public Health, 19(2), 799. https://doi.org/10.3390/ijerph19020799

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