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ORIGINAL RESEARCH COMMUNICATION |
1 From the Division of Epidemiology and Public Health, University of Nottingham, Nottingham, United Kingdom (AWF, CG, SAL, TMM, JRB), and the Department of Clinical Chemistry, Nottingham City Hospital, Nottingham, United Kingdom (SJ)
2 Supported by the Wellcome Trust (TMM), Asthma UK, the British Lung Foundation, and the University of Nottingham. 3 Address reprint requests to AW Fogarty, Division of Epidemiology and Public Health, University of Nottingham, Clinical Sciences Building, City Hospital, Nottingham NG5 1PB, United Kingdom. E-mail: andrew.fogarty{at}nottingham.ac.uk.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Objective: We tested the hypothesis that weight gain is associated with an increase in systemic inflammation during a 9-y period.
Design: In 1991 data on body weight and a blood sample were collected from a random sample of 2425 randomly selected adults from a community-based cohort in Nottingham, United Kingdom. In 2000, these measures were repeated in 1301 of these participants. The main outcome measure was change in systemic inflammation as measured by serum C-reactive protein (CRP) from the 1222 participants who provided paired samples.
Results: The mean change in weight from 1991 to 2000 was 2.9 kg (95% CI: 2.6, 3.2 kg). The geometric mean of CRP in 1991 was 1.22 mg/L (95% CI: 0.03, 125.0 mg/L), and it increased to 1.76 mg/L (95% CI: 0.09, 62.0 mg/L) in 2000 (P < 0.001). A linear association was observed between increase in weight and serum CRP, with a 1-kg increment in weight being associated with an additional increase in CRP of 0.09 mg/L (95% CI: 0.02, 0.16 mg/L) during this time period.
Conclusion: During a 9-y period, an increase in weight is associated with an increase in systemic inflammation. This provides a mechanism that may explain some of the previously reported association of weight gain with an increased risk of both cancer and cardiovascular disease.
Key Words: Inflammation • obesity • weight
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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This is an important area because the prevalence of obesity is increasing rapidly in many countries (2, 3). However, although a recent review concluded that weight loss may be an effective nonpharmacologic strategy for lowering CRP (4), it is uncertain whether an increase in body weight increases systematic inflammation during a long-term period. To our knowledge, there are no large prospective studies of the association between change in weight and its relation to changes in systemic inflammation in samples representative of a general adult population. We have therefore used data from a longitudinal community-based study to determine whether a change in body weight is associated with a change in systemic inflammation, as measured by CRP, in a large prospective population-based study of adults aged 18–70 y based in the United Kingdom during a 9-y period.
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SUBJECTS AND METHODS |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Data collection on anthropometry and serum CRP
In 1991 and 2000 participants completed a computer-administered lifestyle questionnaire that included questions on smoking and a semiquantitative food-frequency questionnaire (DietQ; Tinuviel Software, Warrington, United Kingdom) (5). They were also weighed, their height was measured, and a venous blood sample was taken. After venesection, serum samples were separated by centrifugation (room temperature, 3000 rpm, 
5 min), typically within 15 min, and stored at –80 °C. These samples were subsequently defrosted, and highly sensitive CRP was measured with the use of an automated, immunoturbidimetric assay on an Olympus AU5400 analyzer (Miami, FL). All analyses were performed by the same professional in 2006. The normal range in apparently healthy persons in the United States is <1.5 mg/dL (9).
Statistical analysis
For the statistical analysis, smoking status was defined in 4 categories: current smoker (those who had smoked within 1 mo of the study in 1991 or 2000), long-term ex-smoker (those who had not smoked for 1 mo before the appointment in 1991), shorter-term ex-smoker (those who were current smokers in 1991 but had not smoked for 1 mo in 2000), and never smokers. The number of pack-years of smoking exposure was estimated for participants from their reported age at starting smoking and the usual amount smoked during this period.
The cross-sectional data for serum CRP were not normally distributed and so are presented as the median values with interquartile ranges, whereas the cross-sectional data for weight were normally distributed and are presented as means with SDs. We calculated the change in weight and change in CRP between 1991 and 2000. Because these values were both normally distributed, parametric statistical tests were used for the analysis. Initially, we analyzed the association of the change in weight with change in serum CRP with the use of multiple linear regression with adjustment for a priori confounding factors of age, sex, smoking status, baseline body mass index (BMI; in kg/m2), and total of cigarette pack-years smoked. Potential confounding factors, including baseline weight, baseline CRP, vitamin C intake, vitamin E intake, and polyunsaturated fatty acid intake, were added to the model and were considered as potential confounding factors if their inclusion altered the size of the effect by >10%. Because none of these variables met this criterion, only a priori confounders were considered in the final model. We examined the linearity of the relation between change in weight and change in serum CRP by categorizing weight in deciles and assessed this association both graphically and by using the likelihood ratio test. Because the relation is linear, results are presented both as change in milligram per liter of serum CRP per kilogram increase in weight and graphically in deciles.
We tested for interactions between sex, age, smoking status, and baseline BMI and baseline CRP on the relation between change in weight and change in CRP, and considered these significant at the value of P < 0.05. The analyses were performed with STATA version 9 (Stata Corporation, College Station, TX). By using an estimate of within-subject SD of 7 mg/L for the change in CRP over 9 y derived from our own data, the available maximum sample size of 1222 participants provides >90% power to detect a linear trend between change in weight and change in CRP with a difference in change in CRP of 2 mg/L between the lowest and highest quintiles of change in weight (NQUERY, 4.0; Statistical Solutions, Saugus, MA).
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RESULTS |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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). Compared with all participants who responded in 2000, there was a slightly lower response rate of those in the lowest quintile for weight in 1991, with 47% of this subgroup providing data in 2000 (Figure 1). A similar differential in the response rate was seen in those who were in the top quintile for CRP in 1991, of whom 47% provided samples in 2000 (Figure 1). The geometric mean for serum CRP increased significantly (P < 0.001) from 1.22 mg/L (95% CI: 0.03, 125.0 mg/L) in 1991 to 1.76 mg/L (95% CI: 0.09, 62.0 mg/L) in 2000. Change in serum CRP was normally distributed, and the mean increase in serum CRP was 0.85 mg/dL (95% CI: –91.1, 44.0 mg/dL). Mean weight also increased significantly from 72.8 ± 13.3 kg to 75.7 ± 14.2 kg between 1991 and 2000 (mean increase: 2.9 kg; 95% CI: 2.6, 3.2 kg). The increase in weight was generally higher in women, smokers, and younger participants (Table 2).
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). Eliminating outlying values by restricting the analysis to those with a CRP of <50 mg/L had a small effect when outliers from 1991 were excluded, such that a 1-kg increase in weight was associated with an additional increment of 0.12 mg/L (95% CI: 0.07, 0.17 mg/L) in serum CRP, but there was no substantial change after excluding outliers from 2000 (0.10 mg/L; 95% CI: 0.03, 0.16 mg/L). No interactions were observed with sex, age, smoking status, or baseline weight or serum CRP in 1991 (Table 3).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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30% for those in the lowest quintile of baseline serum CRP who gain 4 kg, assuming a causal relation between inflammation and cardiovascular disease and ignoring any additional effects of weight gain that are independent of inflammation.
Methodologic considerations
Our findings are based on a randomly selected population from the electoral register of a Local Authority Area in Nottingham and are therefore likely to be representative of the general population. Storage of the serum samples at –80 °C and testing the assays by the same professional in the same time period permits confidence in the accuracy of the highly sensitive CRP assay (10). Although participation in the current study was potentially biased by survival, nonmigration, and motivation to participate, our data suggest that the participants in 2000 were broadly similar to the original population in terms of diet, smoking history, initial lung function, and history of respiratory disease (8). Participants who donated a blood sample in both surveys were generally similar to participants who did not, with the exception that participants who did not give a sample in 1991 were slightly younger than participants who did, and we do consider this unlikely to influence our findings, although it may slightly modify the generalizability of our findings. Our response rate of 51% of the original study population raises concerns about response bias, particularly because persons having higher BMI values (11) or serum CRP concentrations (12) have increased rates of mortality. In our study population we were able to account for 85% of the original cohort, of whom 4% had died. A small differential was observed in response rates for baseline weight with 47% of participants in the lowest quintile for weight in 1991 responding in 2000 and also for baseline CRP with 47% of participants in the highest quintile for CRP in 1991 participating again in the second survey in 2000. However, for our association between change in weight and change in CRP to be a consequence of differential response rates leading to bias, participants who did not respond in the follow-up study would have to have had a strong inverse association between weight gain and CRP. This is known to occur, particularly in the context of clinical disease such as infection and advanced malignancy. However, we would anticipate that, although advanced clinical disease will have affected some of our study population, it would have been necessary for this to have affected the majority of the nonresponders to create an artifactual association between change in weight and change in CRP which we consider unlikely in our population who were aged 27–79 y in 2000. Because we do not have validated data available on the incidence of inflammatory diseases, the concurrent use of medications, or lifestyle exposures such as exercise (13) in our dataset, we are unable to exclude the possibility that these may constitute unadjusted confounding factors for the association between change in weight and change in systemic inflammation. Finally, our use of a cohort study design means that we are unable to eliminate the possibility that our results are a consequence of reverse causality, ie, increased systemic inflammation results in weight gain, although the short-term weight-loss studies suggest otherwise (4).
Previous studies of weight change and serum CRP
The size of effect found in our study of an increase in weight of 1 kg resulting in an increment of CRP of 0.09 mg/L is similar to that seen in the recent meta-analysis by Selvin et al (4), which examined 28 lifestyle interventions that aimed at reducing weight. The investigators concluded that weight loss was associated with a decline in CRP concentrations across all types of interventions, with an overall mean change in CRP of –0.13 mg/L per 1-kg loss of weight. Although this size of effect for change in CRP per kilogram decrease in weight is within our CIs of 0.02 to 0.16 mg/L, and thus consistent with our data, the data from the weight-loss studies have necessarily looked at the effect of a decrease in weight on CRP, whereas our data from a community population over 9 y showed that most persons gained weight with an average change in weight of 2.9 kg (95% CI: 2.6, 3.2 kg). Furthermore, Figure 2
shows that even participants who lost weight over the 9 y of our study did not, on average, experience a decrease in serum CRP. This discrepancy between our data and the weight-loss intervention studies may be a consequence of a variety of factors. The most notable factor is the period of follow-up, because we resurveyed our original population after 9 y, whereas the longest follow-up available in the weight-loss intervention studies was 24 mo, with most of the studies being less than a year in duration (4). Alternatively, other environmental or temporal factors in addition to an increase in weight may also result in the higher CRP values observed over the 9 y of our study. However, the consistency of the size of effect between our data and the weight-loss intervention studies suggest that adipose tissue is an important factor in modulating systemic inflammation, possibly against the background of as yet unidentified environmental exposures, although we cannot discount the possibility that a change in lean mass may also influence systemic inflammation. Our data complement previous studies showing that baseline markers of systemic inflammation are elevated in those who both gain (14-16) and lose weight (16), suggesting that the association between systemic inflammation and body weight is complex. However, consideration should be given to maintaining body weight in adults after they have attained an appropriate weight based on existing recommendations for BMI (17).
Our observation that participants who increase their weight have an associated linear increase in systemic inflammation is consistent with the observation that women who have even a modest weight gain during adulthood have a greater risk of death independent of physical activity. In the cohort from the Nurses' Heath Study, an increase in weight was associated with an excess of death from cardiovascular disease and death from cancer, compared with those who did not increase their weight (11). Similarly, in a population of Swedish men whose BMI increased by 15%, an excess of pancreas and renal cell cancers were seen (18). Chronic low-grade inflammation provides a unifying explanation for both of these observations having been implicated in the pathogenesis of both cancer (19, 20) and cardiovascular disease (9, 21). Although the mechanisms that drive the link between adiposity and chronic inflammation are not fully delineated, the production by fat cells of both proinflammatory cytokines, such as interleukin-6 and tumor necrosis factor-
, and adipocytokines, such as adiponectin and lectin, may be important for many aspects of inflammation and immunity (22, 23).
Conclusion
In conclusion, we have shown for the first time in a prospective population-based study that an increase in weight in adults is associated with an increase in systemic inflammation during a 9-y interval, after correction for likely confounding factors.
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ACKNOWLEDGMENTS |
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The author's responsibilities were as follows—AWF (guarantor): developed the hypothesis, performed statistical analyses, and wrote the final version of the manuscript; JRB, SAL, and TMM collected the data and reviewed and critiqued the manuscript; SJ did the biochemical analyses and reviewed and critiqued the manuscript; CG performed the initial statistical analyses and reviewed and critiqued the manuscript. None of the authors had a personal or financial conflict of interest.
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REFERENCES |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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  R. C.M. van Kruijsdijk, E. van der Wall, and F. L.J. Visseren Obesity and Cancer: The Role of Dysfunctional Adipose Tissue Cancer Epidemiol. Biomarkers Prev., October 1, 2009; 18(10): 2569 - 2578. [Abstract] [Full Text] [PDF]
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 A. W Fogarty, S. A Lewis, T. M McKeever, and J. R Britton Is higher sodium intake associated with elevated systemic inflammation? A population-based study Am. J. Clinical Nutrition, June 1, 2009; 89(6): 1901 - 1904. [Abstract] [Full Text] [PDF]
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H. K. Neilson, C. M. Friedenreich, N. T. Brockton, and R. C. Millikan Physical Activity and Postmenopausal Breast Cancer: Proposed Biologic Mechanisms and Areas for Future Research Cancer Epidemiol. Biomarkers Prev., January 1, 2009; 18(1): 11 - 27. [Abstract] [Full Text] [PDF]
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M. Hamer and A. Steptoe Prospective study of physical fitness, adiposity, and inflammatory markers in healthy middle-aged men and women Am. J. Clinical Nutrition, January 1, 2009; 89(1): 85 - 89. [Abstract] [Full Text] [PDF]
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