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We investigated whether exposure to negative aspects of close relationships was associated with subsequent increase in body mass index (BMI) and waist circumference.
Data come from a prospective cohort study of 9425 civil servants aged 35–55 years at baseline (phase 1; 1985–1988), the Whitehall II study. Negative aspects of close relationships were assessed with the Close Persons Questionnaire (range 0–12) at phases 1 and 2 (1989–1990). BMI and waist circumference were measured at phases 3 (1991–1994) and 5 (1997–1999). Covariates at phase 1 included sex, age, marital status, ethnicity, BMI, employment grade, smoking, physical activity, fruit and vegetable consumption, and common mental disorder.
After adjustment for socio-demographic characteristics and health behaviors, participants with higher exposure to negative aspects of close relationships had a higher likelihood of ≥10% increase in BMI and waist circumference (OR per 1-unit increase 1.08 (95%CI: 1.02–1.14), P=0.007 and 1.09 (1.04–1.14), P<0.0001, respectively). Higher exposure to negative aspects of close relationships also predicted a transition from the overweight (25≤BMI<30) to obese (BMI>30) category.
Adverse social relationships may be a risk factor for weight gain.
Obesity is a major public health concern as it is associated with a number of ill health conditions, such as type-2 diabetes, coronary heart disease, hypertension, stroke, and certain forms of cancer (1). The rates of obesity have increased rapidly to epidemic proportions. In England, for example, 24% of men and 25% of women are obese (defined as body mass index above 30kg/m2) (2). The interplay between multiple factors - genetic factors, factors stemming from obesogenic environment, and individual, cultural, and racial factors - is seen to be behind the obesity epidemic (3). However, there is increasing evidence to suggest that social relations may also play a role in determining weight gain.
Stress associated with poor quality relationships may contribute to weight gain via various mechanisms. Negative aspects of close relationships may induce negative feelings (4), which can increase physiological arousal either through activation of the hypothalamic-pituitary-adrenal (HPA) axis or through the fight/flight response and the secretion of adrenal medullary hormones (5). Eating high fat and carbohydrate caloric content “comfort” food may reduce biological stress system activity and the concomitant negative emotions (6). There is also some evidence that suggests an association between chronic life stress and a greater preference for energy- and nutrient-dense foods, namely those that are high in fat and sugar (7). In addition, there may be further effects via other unhealthy coping mechanisms such as physical inactivity.
Childhood adversities related to close relationships, such as physical abuse, verbal abuse, humiliation, neglect, strict upbringing, physical punishment, conflict or tension, have been associated with increased risk of obesity in adulthood (8). However, there is limited and somewhat inconsistent evidence on the impact of negative aspects of close relationships in adulthood. One study found that heavier women had lower quality romantic relationships (9).
Poor marital quality has also been associated with a higher risk of metabolic syndrome (10) and obesity (11) in women. Strain in relations with family but not with spouse/partner was associated with weight gain in women with high initial body mass index (BMI) (12). In addition, some studies have found an association between reports of insufficient social support and increased risk of obesity (13,14) but other studies suggest no such association (15).
With a few exceptions (references 10–12), the above-reviewed evidence is cross-sectional or based on short follow-ups. Such data leave open the possibility of reverse causality (i.e., obesity negatively influencing close relationships). Since the development of obesity has a relatively long induction period, it is plausible that prolonged exposure to problems in social relationships affects weight more than short-term problems. Moreover, it might be more informative to look at weight gain rather than obesity status at one time point. We are not aware of prior studies examining the association between negative aspects of close relationships and weight gain. In addition, most of the earlier studies did not assess waist circumference (WC), a measure of central obesity. WC is probablya better indicator of health risk than BMI alone, especially when used in combination with BMI (16).
In this study from the Whitehall II cohort of British civil servants, we investigated the extent to which exposure to negative aspects of close relationships was associated with subsequent weight gain, as indicated by increase in BMI and WC over a long follow-up period.
The target population of the Whitehall II study was all office staff based in London, United Kingdom, in 20 civil service departments in 1985. The baseline cohort included 6895 men and 3413 women (age range 35–55; response rate 73%). Full details on study design and measures are reported elsewhere (17). Briefly, negative aspects of close relationships were assessed at phase 1 (1985–1988) and phase 2 (1989–1990). We measured change in BMI and WC between phase 3 (1991–1994) and phase 5 (1997–1999). Baseline covariates in our analysis are drawn from phase 1. Phases 1, 3 (N=8,815; 86% of phase 1 respondents) and 5 (N=7,870; 76% of phase 1 respondents) included a clinical examination and a questionnaire, whereas phase 2 (N=8,132; 79% of phase 1 respondents) included only a questionnaire. Phase 4 data did not include relevant variables and therefore were not used in the study. The median length of the follow-up from phase 1 to phase 5 was 11.2 years.
We assessed negative aspects of close relationships at phase 1 and phase 2 with a 4-item scale from the Close Persons Questionnaire (18). The items refer to adverse exchanges and conflict within a relationship nominated by the respondents as their closest one. The items are as follows: “How much in the last 12 months did this person give you worries, problems and stress?”; “How much in the last 12 months would you have liked to have confided more in this person?”; “How much in the last 12 months did talking to this person make things worse?”; “How much in the last 12 months would you have liked more practical help with major things from this person?” Each of the 4 items was evaluated on a Likert scale from 1 to 4, with higher scores indicating more negative aspects. The Likert-scaled responses for the items were summed. Cronbach Alpha was 0.63 (18). At phase 1, 7384 participants completed the Close Persons Questionnaire. Only 74% of respondents were asked to complete it, as this measure was introduced after the start of the baseline survey. At phase 2, 7727 participants completed the questionnaire. Correlation coefficients of scores at phase 1 and phase 2 suggest a moderate degree of consistency (r=0.48, P<.001). Although the questionnaire assesses social relationships relative to a maximum of four close relationships, our analyses, similar to previous studies in the Whitehall II data, focused on the first close relationship only, for which the reliability was the highest (18).
The reliability and validity of the Close Persons Questionnaire was examined in a previous paper from this study (18). A re-test reliability study over a 4-week interval showed moderately high agreement for negative aspects of close relationships (r=0.72). To evaluate validity, the questionnaire was sent to the person closest to each of the last 60 interviewees who nominated a close relationship. Response from the person designated as the “close relationship” showed correlation with that reported by the participant (r=0.65 for female spouse and r=0.40 for male spouse).
Screenings at phases 3 and 5 included the measurement of height, weight and WC. BMI was calculated based on weight (kg) and height (m) assessed using standard protocols at the medical examination. WC was measured using a fiberglass tape measure at 600g tension as the smallest circumference at or below the costal margin (19). Test retest reliability of the WC measurement over one month, carried out on 490 participants, was 0.96 at the phase 3 clinical examination.
Several factors which have been associated with obesity or weight gain (20–22) were included in the analysis as covariates. All covariates were assessed at phase 1. Age, sex, ethnicity (white vs. non-white), marital status and BMI were measured. Employment grade was derived from a questionnaire asking details about job title and job characteristics. As in earlier studies in the Whitehall II cohort, the hierarchy of employment grades consisted of three levels (administrative, professional/executive, and clerical) based on salary, work role, and occupational seniority.
Health behaviors included self-reported smoking status (never smoker, ex-smoker, current smoker), daily fruit and vegetable consumption (yes vs. no), weekly moderate physical activity hours, and weekly vigorous physical activity hours. Physical activity was assessed with a standardized instrument. Participants were asked the average number of hours per week spent in “moderately energetic” (e.g., dancing, cycling, leisurely swimming) and “vigorous” (e.g., running, hard swimming, playing squash) physical activity (23).
The General Health Questionnaire-30 is a self-administered, well established screening instrument designed for community settings (24). It assesses common mental disorders such as depression and anxiety. As in previous studies, those with a total score of 5 or more were defined as cases and those scoring 0–4 as non-cases (25). The threshold scores are set to correspond to a case definition equivalent to that of the average patient referred to a psychiatrist (26). In the Whitehall II study, GHQ caseness was validated against a clinical interview schedule; the sensitivity (73%) and specificity (78%) measures indicate that the definition of “caseness” is acceptable (27).
The complete case analyses of the present study included 3703 (analyses on BMI increase) and 3224 (analyses on WC increase) participants with no missing data on any of the study variables. The median length of the follow-up from phase 1 to phase 5 was 11.2 years; 273 individuals died during this period.
To explore potential selection bias resulting from missing data, we ran a subsidiary analysis in which we used multiple multivariate imputation (28) using negative aspects of close relationships at phases 1 and 2, BMI at phases 1, 3 and 5, WC at phases 3 and 5 and all covariates at phase 1 (age, sex, ethnicity, marital status, employment grade, BMI, smoking, fruit and vegetable consumption, moderate and vigorous physical activity, and common mental disorder) to impute values for missing values for measures on the 9425 participants with at least one measurement of negative aspects of close relationships. We used switching regression in STATA as described by Royston(28) and carried out 10 cycles of regression switching and generated 10 imputation datasets. The multiple multivariate imputation approach creates a number of copies of the data (10 copies in this case), where the missing values are imputed with an appropriate level of randomness using chained equations. The estimates are obtained by averaging across the results from each of these 10 datasets using Rubin’s rules (28). The procedure takes into account the uncertainty in the imputation as well as the uncertainty due to random variation, as undertaken in all multivariable analyses.
Binary logistic regression was used to examine whether exposure to negative aspects of close relationships was associated with ≥ 10% increase in BMI and WC in complete cases and in the imputed dataset. The 10% change has previously been used in Whitehall II papers to assess meaningful change over time (e.g. 29); this categorization for BMI and WC also provides sufficiently large groups for well-powered analyses. However, to ensure that our findings are robust and not attributable to a specific cut-off point, we ran sensitivity analyses. We repeated the analyses using ≥ 7.5% and ≥ 15% increases in BMI as outcomes in the complete case sample.
We ran separate analyses for negative aspects of close relationships at phase 1 and phase 2 as well as for the phase1-phase 2 mean score. Multinomial logistic regression was used to determine whether exposure to negative aspects of close relationships predicted transitions between BMI categories between phases 3 and 5. The models were used to assess the likelihood of: 1) recommended healthy weight (18.5≤BMI<25) at phases 3 and 5 (referent); 2) from recommended healthy weight at phase 3 to overweight (25≤BMI<30) or obese (BMI≥30) at phase 5; 3) from overweight at phase 3 to recommended healthy weight at phase 5 or from obese at phase 3 to overweight or recommended healthy weight at phase 5; 4) overweight at both phases or obese at both phases; and 5) from overweight at phase 3 to obese at phase 5. Underweight participants, that is, those with BMI<18.5 at phase 3 and/or phase 5 were excluded from this analysis (n=43 among complete cases).
In these analyses, adjustment for covariates was conducted in two steps to distinguish the different types of confounders and to assess potential pathways. First, the association of adverse close relationships and weight gain was adjusted for age, sex, ethnicity, and marital status. Second, employment grade, BMI at baseline, health behaviors (smoking, moderate and vigorous physical activity, and fruit and vegetable consumption) and common mental disorder were added to the model. All P values (2-tailed) below .05 were considered to be statistically significant.
There were no consistent differences in our results between men and women, so the data were pooled and sex-adjusted. Analyses were performed using the SAS 9.2 (SAS Institute, Cary, NC, USA) and STATA 11.0 software packages (College Station, TX, USA).
Table 1 shows the characteristics of the total Whitehall II baseline cohort, the participants with complete data on all study variables and the imputed sample. Any differences in baseline characteristics were small although differences were greater between complete cases and baseline cohort than between the imputed sample and the baseline cohort.
Tables 2 and and33 display the odds ratios of ≥ 10% increase in BMI and WC by exposure to negative aspects of close relationships (score range 0–12). In complete cases, a higher exposure to negative aspects of close relationships at phase 1 was associated with an increase in BMI (odds ratio per 1-unit increase in the negative aspects score 1.06, 95 percent confidence interval (95% CI): 1.02, 1.10, P=0.007) and a higher exposure to negative aspects of close relationships at phase 1 was also associated with an increase in WC (odds ratio, 1.06; 95% CI: 1.02, 1.10, P=0.002). However, the strongest association with both these outcomes were seen for the phase 1-phase 2 mean score of negative aspects of close relationships (odds ratios 1.08 (95% CI: 1.02, 1.14) P=0.007 and 1.08 (95% CI: 1.03, 1.13) P=0.001, respectively). Additional adjustment for baseline covariates affected these estimates very little. In the imputed sample, a similar pattern of results was found except that the effect size was slightly smaller (e.g. the odds ratio for mean score and WC was 1.04; 95% CI: 1.00, 1.07, p=0.03).
We repeated the analyses using ≥ 7.5% and ≥ 15% increases in BMI as outcomes in the complete case sample. The results were very similar to those with ≥ 10% increase. The odds ratios for the phase 1 negative aspects of close relationships were 1.04 (95% CI: 1.00, 1.07) P=0.03 for ≥ 7.5% increase, and 1.08 (95% CI: 1.00, 1.16) P=0.04 for ≥ 15% increase (data not shown).
Table 4 summarizes the results from multinomial logistic regression analyses on the associations between negative aspects of close relationships and changes in BMI. Participants with high negative aspects of close relationships were more likely to experience a transition from overweight to obese BMI category than stay in the recommended healthy weight category throughout the study period compared to those who did not report negative aspects of close relationships (odds ratio 1.05; 95% CI: 1.00, 1.10 in the imputed sample). In contrast, a higher exposure to negative aspects of close relationships was not associated with transition from recommended weight to overweight or obesity; nor was the lack of negative close relationships associated with weight reduction among obese and overweight participants.
This prospective study suggests that negative interactions in close relationships may – albeit modestly - contribute to increases in BMI and waist circumference. These effects were not accounted for by socio-demographic characteristics, health behaviors, and common mental disorder. Analyses in repeat data indicated that a long-term exposure to negative aspects (indicated by mean score across 2 study phases) had a slightly stronger effect on weight gain than a single measurement of the exposure.
Our results are in line with previous studies that have suggested a link between poor relationship quality or insufficient social support and obesity; although these studies are limited as they did not specifically measure negative aspects of close relationships, did not assess cumulative exposure, and/or did not measure weight gain (9, 10, 13, 14). In contrast, in a previous longitudinal study, strain in relations with spouse/partner was not associated with weight gain (12).
Potential explanations for the associations between negative aspects of close relationships and weight gain can involve neuroendocrine effects of chronic stress via psychological processes as well as behavioral effects, or both (18). More specifically, the presence of negative aspects of close relationships can induce psychological processes that are linked to negative appraisals and emotions or low mood. Dysfunctional social relationships may provoke negative feelings (4), which can increase physiological arousal (5). Marital strain has been shown to have deleterious effects on cardiovascular, endocrine and immune functions (30). Dallman et al. (6) proposed that people might eat high fat and carbohydrate caloric content “comfort food” in an attempt to reduce activity in the corticotrophin-releasing factor (CRF) –driven central chronic stress-response network with its attendant anxiety. Chronic life stress has been associated with a greater preference for energy-dense foods (7), possibly leading to weight gain in those experiencing chronic stress (31).
Furthermore, there may be effects via health behaviors and adherence to medical regimens. For example, the individual may use unhealthy eating and physically inactive lifestyle as adverse coping mechanisms. Psychological and behavioral pathways can also influence each other (32). However, in the present study an adjustment for health behaviors had little effect on estimates, suggesting that the association between negative aspects of close relationships and weight change may primarily be explained by mechanisms other than those related to health behaviors.
The strengths of this study include the assessment of repeated exposure to negative aspects of close relationships and simultaneous inclusion of a number of covariates. Our study is based on a large well-characterized cohort of British employees and a prospective study design with a median follow-up of 11.2 years. A further strength is that weight, height and waist circumference were directly measured at both examinations (phases 3 and 5) and were not based on questionnaires, thus minimizing the potential of recall bias and misclassification that occur when using self-reports.
However, several limitations need to be taken into account when interpreting the findings. First, our measure of negative aspects of close relationships was self-reported and may thus be influenced by personality traits or specific characteristics of respondents (18). For example, levels of social support are lower than normal in hostile individuals due to less effective coping strategies in psychosocial stress situations, increasing the likelihood of breakdown of intimate relationships and unhealthy lifestyle (33, 34). However, it can be argued that it is exactly the subjective experience that gives meaning and significance to social environmental characteristics and that these subjective experiences finally get under one’s skin and/or cause adverse behavioral changes. Therefore, self-rated measures, such as the one being used here, are relevant indicators of social relationships, expressly due to their subjectivity.
Second, our complete case sample included less than half of the original cohort. Loss to follow-up is inevitable in all long-term prospective studies and may lead to biased estimates. We examined potential bias by performing subsidiary analyses with imputed datasets. These analyses suggested that incompleteness of data might have contributed to an overestimation rather than an underestimation of the association between negative aspects of close relationships and weight gain. This finding is important as sample attrition in prospective studies is often speculated to attenuate the effect estimates.
Third, even though we adjusted for a number of possible confounders, the possibility of residual confounding cannot be excluded in observational studies. For example, information was not available on childhood factors (35) or individual differences in genetic predisposition (36). Finally, the participants were mostly white, middle-aged civil servants based in the southeast of England, limiting the generalizability of our findings. In other words, there is a need for more diverse samples to extend the validity of our findings.
Despite these limitations, our results suggest that exposure to negative aspects of close relationships is associated with an increased risk of weight gain. The present study adds to the evidence that the development of obesity may be related to social environment in which people live. Future research is needed to study the specific biological, behavioral and psychological mechanisms linking social environmental factors to weight gain and whether interventions designed to improve social relationships could decrease obesity risk.