Association between physical exercise, body mass index, and risk of ...

Arthritis Care & Research Vol. 62, No. 5, May 2010, pp 611– 617 DOI 10.1002/acr.20118 © 2010, American College of Rheumatology

ORIGINAL ARTICLE

Association Between Physical Exercise, Body Mass Index, and Risk of Fibromyalgia: Longitudinal Data From the Norwegian Nord-Trøndelag Health Study PAUL J. MORK, OTTAR VASSELJEN,

AND

TOM I. L. NILSEN

Objective. To examine the association between leisure time physical exercise, body mass index (BMI), and risk of fibromyalgia (FM). Methods. A longitudinal study with baseline assessment of physical exercise (frequency, duration, and intensity) and BMI was used to explore the risk of having FM at 11-year followup in a large, unselected female population (n ⴝ 15,990) without FM or physical impairments at baseline. Results. At followup, 380 cases of incident FM were reported. A weak dose-response association was found between level of physical exercise and risk of FM (for trend, P ⴝ 0.13) where women who reported the highest exercise level had a relative risk (RR) of 0.77 (95% confidence interval [95% CI] 0.55–1.07). BMI was an independent risk factor for FM (for trend, P < 0.001), and overweight or obese women (BMI >25.0 kg/m2) had a 60 –70% higher risk compared with women with normal weight (BMI 18.5–24.9 kg/m2). Overweight or obese women who exercised >1 hour per week had an RR of 1.72 (95% CI 1.07–2.76) compared with normal-weight women with a similar activity level, whereas the risk was >2-fold higher for overweight or obese women who were either inactive (RR 2.09, 95% CI 1.36 –3.21) or exercised 4 sessions

Women, no. Age, mean ⫾ SD years BMI, mean ⫾ SD kg/m2 Overweight or obese‡ Current smoker Higher education§

5,962 42.0 ⫾ 13.2 24.5 ⫾ 4.4 37.8 39.8 7.6

4,702 41.9 ⫾ 13.2 24.1 ⫾ 3.8 32.8 31.3 12.2

3,695 43.8 ⫾ 14.2 24.3 ⫾ 3.8 35.2 25.1 12.9

1,631 49.6 ⫾ 15.3 24.5 ⫾ 4.0 38.8 26.5 8.2

* Values are the percentage unless otherwise indicated. BMI ⫽ body mass index. † Reported no exercise or ⬍1 exercise session per week. ‡ BMI ⱖ25 kg/m2. § Reported an education of ⱖ13 years.

ber of months if symptoms had lasted ⬍1 year and the number of years if symptoms had lasted ⬎1 year. Statistical analyses. A generalized linear model for the binomial family was used to estimate relative risk (RR) of FM, in which women who reported different levels of exercise were compared with the reference group of inactive women, i.e., those who reported to exercise less than once per week. The RR for FM between categories of BMI was estimated in similar models. The precision of the estimated RRs was assessed by 95% confidence intervals (95% CIs), and trend tests across categories of exercise level and BMI were calculated by treating the categories as ordinal variables in the regression model. Our basic models were adjusted for age in 10-year categories (20 –29 years, 30 –39 years, 40 – 49 years, 50 –59 years, 60 – 69 years, and ⱖ70 years). In multivariable models, we controlled for potential confounding with smoking status (never, former, current, and unknown) and education (⬍10 years, 10 –12 years, ⱖ13 years, and unknown), as well as confounding with BMI (⬍18.5 kg/m2, 18.5–24.9 kg/m2, 25.0 –29.9 kg/m2, and ⱖ30.0 kg/m2) in the analysis of exercise, and frequency of exercise (inactive, 1 time per week, 2–3 times per week, and ⱖ4 times per week) in the analysis of BMI. In additional models, we also adjusted for a measure of psychological well-being at baseline (coded as depressed, somewhat happy, and happy). In a supplementary analysis, we examined the combined effect of different BMI and exercise levels on the reference category of women who were of normal weight and reported to exercise ⱖ1 hour per week. To assess potential statistical interaction between exercise and BMI, we conducted a likelihood ratio test after including the product term of these 2 factors in the regression model. All statistical tests were 2-sided, and all statistical analyses were performed using Stata for Windows, version 10.0 (StataCorp).

justed associations for the BMI categories and each measure of physical exercise with the risk for FM. Analysis of BMI showed that women who were classified as overweight (25.0 –29.9 kg/m2) or obese (ⱖ30.0 kg/m2) had a higher risk of FM compared with women who were normal weight; RRs were 1.70 (95% CI 1.35–2.13) and 1.64 (95% CI 1.16 –2.33), respectively (for trend, P ⬍ 0.001). Overall, there was weak evidence of a dose-response effect for the different measures of physical activity (for trend, P ⫽ 0.13– 0.16) (Table 2). More specifically, women who reported exercising ⱖ4 times per week had a 29% lower risk of FM (adjusted RR 0.71, 95% CI 0.46 –1.09) compared with inactive women. Analysis of exercise hours per week showed that women who reported exercising ⱖ2 hours had an RR of 0.77 (95% CI 0.52–1.15) compared with inactive women. Similar results were found in the analysis of the summary score combining information on frequency, duration, and intensity of exercise; women with the highest exercise level had a somewhat lower risk than inactive women (RR 0.77, 95% CI 0.55–1.07). We examined the combined effect of exercise and BMI on the risk of FM (Table 3). Although there was no evidence of statistical interaction between BMI and exercise (from likelihood ratio test, P ⫽ 0.84), we observed that overweight or obese women who exercised ⱖ1 hour per week had an RR of 1.72 (95% CI 1.07–2.76) compared with normal-weight women with a similar activity level, whereas the risk was ⬎2-fold higher for overweight or obese women who were either inactive (RR 2.09, 95% CI 1.36 –3.21) or who reported exercising ⱕ1 hour per week (RR 2.19, 95% CI 1.39 –3.46). Among normal-weight women, we found no clear association between exercise and the risk of FM; inactive women had an RR of 1.18 (95% CI 0.84 –1.64) compared with the most physically active women.

DISCUSSION RESULTS The characteristics of the study population are presented in Table 1. During the ⬃11 years from HUNT 1 (1984 – 1986) to HUNT 2 (1995–1997), 380 cases of incident FM were reported among the 15,990 women. Table 2 gives the age-adjusted and multivariably ad-

The main objective of the present study was to investigate the association between levels of leisure time physical exercise and future risk of FM and, second, whether being overweight/obese represents an independent risk factor for future development of FM. We found a weak inverse doseresponse association between level of exercise at baseline

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Table 2. RR of FM associated with BMI and leisure time physical exercise*

BMI categories (kg/m2)§ Underweight (⬍18.5) Normal weight (18.5–24.9) Overweight (25.0–29.9) Obese (ⱖ30.0) Exercise sessions per week, no. Inactive¶ 1 2–3 ⱖ4 Exercise per week, hours Inactive¶ ⬍1.0 1.0–1.9 ⱖ2.0 Summary score of exercise# Inactive¶ Low Medium High

Women, no.

Cases, no.

Age-adjusted RR†

Multi-adjusted RR (95% CI)‡

P for trend

322 9,942 4,245 1,481

8 211 123 38

1.15 1.00 1.72 1.69

1.06 (0.53–2.13) 1.00 (reference) 1.70 (1.35–2.13) 1.64 (1.16–2.33)

⬍ 0.001

5,962 4,702 3,695 1,631

168 115 74 23

1.00 0.87 0.75 0.63

1.00 (reference) 0.99 (0.78–1.25) 0.89 (0.68–1.17) 0.71 (0.46–1.09)

0.13

5,962 4,432 3,534 1,760

168 109 69 28

1.00 0.88 0.72 0.67

1.00 (reference) 1.00 (0.79–1.27) 0.87 (0.66–1.16) 0.77 (0.52–1.15)

0.16

5,962 3,910 3,197 2,499

168 96 66 43

1.00 0.95 0.75 0.62

1.00 (reference) 1.03 (0.81–1.33) 0.90 (0.68–1.20) 0.77 (0.55–1.07)

0.13

* RR ⫽ relative risk; FM ⫽ fibromyalgia; BMI ⫽ body mass index; 95% CI ⫽ 95% confidence interval. † In 10-year age groups: 20 –29 years, 30 –39 years, 40 – 49 years, 50 –59 years, 60 – 69 years, and ⱖ70 years. ‡ Adjusted for age in 10-year age groups (see above), smoking status (never, former, current, unknown), psychological well-being (depressed, somewhat happy, happy), and education (⬍10 years, 10 –12 years, ⱖ13 years); exercise measures were adjusted for BMI and BMI categories were adjusted for exercise frequency. § By the World Health Organization cut points. ¶ Subjects who reported no exercise or ⬍1 exercise session per week. # Combining information on frequency, duration, and intensity of activity.

and incidence of FM at the 11-year followup, and a high BMI (i.e., BMI ⱖ25 kg/m2) represented an independent risk factor for the future development of FM. When examining the combined effect of BMI and physical exercise, we found the highest risk among overweight and obese women who reported low exercise levels, and a somewhat lower risk in overweight and obese women who exercised ⱖ1 hour per week. To the best of our knowledge, this is the first study to document BMI and level of leisure time physical exercise as risk factors for future development of FM. The current study shows that a high BMI (i.e., being

overweight or obese) is a strong and independent risk factor for future development of FM. Moreover, the higher RRs for the combined effect of being overweight/obese and inactive, relative to being overweight/obese alone, point to a further disadvantage for overweight women who do not exercise. Several studies have shown an association between obesity and widespread musculoskeletal pain, including nonweight-bearing body sites (26 –28); however, the causality between high body mass and pain is undecided due to the cross-sectional nature of these studies. There are currently no known mechanisms that can explain a causal relationship between being overweight/

Table 3. Combined effect of BMI and leisure time physical exercise on the risk of FM* BMI and exercise categories Normal weight (18.5–24.9 kg/m2) ⱖ1 hour per week 0.1–0.9 hour per week Inactive Overweight or obese (ⱖ25.0 kg/m2) ⱖ1 hour per week 0.1–0.9 hour per week Inactive

Women, no.

Cases, no.

Age-adjusted RR†

Multi-adjusted RR (95% CI)‡

3,362 2,860 3,558

57 60 90

1.00 1.15 1.37

1.00 (reference) 1.10 (0.77–1.58) 1.18 (0.84–1.64)

1,846 1,494 2,251

39 46 74

1.63 2.19 2.24

1.72 (1.07–2.76) 2.19 (1.39–3.46) 2.09 (1.36–3.21)

* BMI ⫽ body mass index; FM ⫽ fibromyalgia; RR ⫽ relative risk; 95% CI ⫽ 95% confidence interval. † In 10-year age groups: 20 –29 years, 30 –39 years, 40 – 49 years, 50 –59 years, 60 – 69 years, and ⱖ70 years. ‡ Adjusted for age in 10-year age groups (see above), smoking status (never, former, current, unknown), psychological well-being (depressed, somewhat happy, happy), education (⬍10 years, 10 –12 years, ⱖ13 years), and BMI (continuous).

Exercise, Body Mass Index, and Risk of Fibromyalgia obese and widespread pain in FM, although FM and obesity do seem to share some etiologic factors that may explain this relationship. First, elevated serum levels of proinflammatory cytokines have been observed in both subjects with FM (31) and obese subjects (32). Proinflammatory cytokines have previously been shown to induce or facilitate both inflammatory and neuropathic pain as well as hyperalgesia (33). A recent longitudinal study reported an elevated baseline level of the proinflammatory cytokine interleukin-8 (IL-8) in patients with FM compared with healthy controls (31). IL-8 level was significantly reduced among patients with FM after 6 months of multidisciplinary treatment, but was still higher than in controls. Another study, including only patients with FM, reported a moderate association (i.e., ⬃27% explained variance) between the level of IL-6 and BMI in patients with FM (34). Therefore, proinflammatory cytokines seem to play a role in FM and in the relationship between FM and obesity, but further studies are needed to elucidate the exact mechanism. Second, dysregulation of the HPA axis has been observed in both FM (5) and obesity (35). A recent study reported increased norepinephrine/cortisol and norepinephrine/epinephrine secretion ratios, as well as higher waist-to-hip ratio in patients with FM compared with healthy controls (36). The relative increase in norepinephrine secretion among patients with FM lends support to the notion that FM is maintained by chronic hyperactivity of the sympathetic nervous system (7). Finally, increased sympathetic tonus and reduced sympathetic reactivity, as recorded by heart rate variability, has been observed in patients with FM (37) as well as in overweight and obese subjects (38). An autonomic dysfunction may therefore contribute to enhanced pain and other symptoms associated with FM (e.g., disturbed sleep, fatigue) by alterations of the physiologic responses required for adequate stress management and pain inhibition (39). Of interest is the observation that weight loss is associated with reduced symptoms in patients with FM. A pilot study investigating the effect of a 20-week weight loss program in overweight and obese women with FM reported that an average weight reduction of 4.4% was associated with a significant reduction in FM symptoms and pain interference (40). Another study found that overweight and obese women with FM who underwent bariatric surgery reported significantly less FM symptoms at followup 6 –12 months after surgery (41). Together with the findings in the current study, the abovementioned studies indicate that maintenance of normal body weight is important in both primary and secondary prevention of FM. However, prospective studies are required to elucidate whether a disturbed autonomic regulation is a risk factor for both obesity and FM or whether it develops secondary to these pathologic states. The pathogenesis of FM is only partially understood, but a hallmark is altered pain processing, indicated by increased sensitivity to painful stimuli (hyperalgesia) and lowered pain threshold (allodynia). Therefore, the potential preventive effect of physical exercise on the development of FM may in particular relate to the preservation or enhancement of the endogenous pain inhibitory capacity.

615 It is well known that aerobic exercise provides temporary pain relief, often named exercised-induced analgesia (42). This effect likely involves the endogenous opioid system, although the precise mechanism is still unclear. There is some evidence that temporary pain relief after exercise can accumulate and translate to a more persistent buffer against pain development. One study showed that pregnant women who performed regular aerobic exercise had consistently higher plasma levels of ␤-endorphin and less labor pain compared with pregnant women who did not exercise (43). A recent study showed that rats systematically bred to have high aerobic capacity had a higher pain threshold and a shorter postexercise period with hyperalgesia than rats bred to have low aerobic capacity (44). Therefore, regular exercise seems to be directly associated with reduction in pain perception, possibly due to an elevated tonic activation of the endogenous opioid system. The beneficial effect of exercise on pain perception has also been demonstrated in studies with sleep deprivation. Both total sleep deprivation and selective stage 4 (slow wave sleep) interruption in healthy subjects has been associated with development of similar symptoms as observed in patients with FM, i.e., reduced pain threshold to mechanical pressure at multiple body sites (45– 47). Conversely, selective stage 4 interruptions in a small group of well-trained long-distance runners did not induce the same symptoms (45). This may indicate that regular physical exercise and improved aerobic fitness can enhance the endogenous pain inhibitory capacity (48). Therefore, the findings in the current study, together with the findings in the abovementioned studies, indicate that regular physical exercise, and thereby improved physical fitness, may serve as a buffer against the perpetuation of musculoskeletal symptoms that eventually lead to the development of FM. However, the results of this study do not indicate a strong effect of physical exercise on the prevention of FM. Despite the obvious strengths of the current study, such as the prospective design and the large, population-based sample size, some limitations should be considered in the interpretation of the results. First, incident cases of FM were assessed at the followup survey (i.e., HUNT 2) among those who were able to and chose to participate in both studies. Hence, if women who were overweight/obese or physically inactive at baseline were less likely to participate at the followup survey, the estimated RR may be underestimated. Second, misclassification of leisure time physical exercise cannot be ruled out. Physical exercise was assessed by a single baseline measure without followup information, and the questionnaire-based nature of the data allows for subjective interpretation of the questions and individual perception of the activity (49). It should also be noted that the HUNT data do not allow assessment of the relative importance of different exercise types or fitness components (e.g., cardiorespiratory fitness, strength, and flexibility) in preventing development of FM. However, validation studies have shown that questionnaires may be useful in classifying people into broad categories of physical exercise (e.g., low, moderate, or highly active) but less appropriate for quantifying energy expenditure (50). Nonetheless, biased estimates due to confounding by unmeasured or unknown factors cannot be

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ruled out in this type of study. Adjustments for variables commonly associated with FM, such as genetic predisposition, sociopsychological factors, adverse life events, and occupational exposures (e.g., work stress), could be of importance. To summarize, BMI was identified as an independent risk factor for the development of FM 11 years later. A weak inverse dose-response association was indicated between level of leisure time physical exercise and future risk of FM. Being overweight or obese was associated with an increased risk of FM, especially among women who also reported low levels of leisure time physical exercise. Therefore, community-based measures aimed at reducing the incidence of FM should emphasize the importance of regular physical exercise and maintenance of normal body weight. AUTHOR CONTRIBUTIONS All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be submitted for publication. Dr. Mork had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study conception and design. Mork, Vasseljen, Nilsen. Analysis and interpretation of data. Mork, Vasseljen, Nilsen.

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