Open Access

Exercise and obesity in fibromyalgia: beneficial roles of IGF-1 and resistin?

  • Jan L Bjersing1, 2Email author,
  • Malin Erlandsson1,
  • Maria I Bokarewa1, 2 and
  • Kaisa Mannerkorpi1, 3, 4
Arthritis Research & Therapy201315:R34

DOI: 10.1186/ar4187

Received: 21 November 2012

Accepted: 21 February 2013

Published: 27 February 2013

Abstract

Introduction

Severe fatigue is a major health problem in fibromyalgia (FM). Obesity is common in FM, but the influence of adipokines and growth factors is not clear. The aim was to examine effects of exercise on fatigue, in lean, overweight and obese FM patients.

Methods

In a longitudinal study, 48 FM patients (median 52 years) exercised for 15 weeks. Nine patients were lean (body mass index, BMI 18.5 to 24.9), 26 overweight (BMI 25 to 29.9) and 13 obese. Fatigue was rated on a 0 to 100 mm scale (fibromyalgia impact questionnaire [FIQ] fatigue) and multidimensional fatigue inventory (MFI-20) general fatigue (MFIGF). Higher levels in FIQ fatigue and MFIGF indicate greater degree of fatigue. Free and total IGF-1, neuropeptides, adipokines were determined in serum and cerebrospinal fluid (CSF).

Results

Baseline FIQ fatigue correlated negatively with serum leptin (r = -0.345; P = 0.016) and nerve growth factor (NGF; r = -0.412; P = 0.037). In lean patients, baseline MFIGF associated negatively with serum resistin (r = -0.694; P = 0.038). FIQ Fatigue associated negatively with CSF resistin (r = -0.365; P = 0.073). Similarly, FIQ fatigue (r = -0.444; P = 0.026) and MFIGF correlated negatively with CSF adiponectin (r = -0.508; P = 0.01). In lean patients, FIQ fatigue (P = 0.046) decreased after 15 weeks. After 30 weeks, MFIGF decreased significantly in lean (MFIGF: P = 0.017), overweight (MFIGF: P = 0.001), and obese patients (MFIGF: P = 0.016). After 15 weeks, total IGF-1 increased in lean (P = 0.043) patients. ∆Total IGF-1 differed significantly between lean and obese patients (P = 0.010). ∆Total IGF-1 related negatively with ∆MFIGF after 15 weeks (r = -0.329; P = 0.050). After 30 weeks, ∆FIQ fatigue negatively correlated with ∆NGF (r = -0.463; P = 0.034) and positively with ∆neuropeptide Y (NPY) (r = 0.469; P = 0.032). Resistin increased after 30 weeks (P = 0.034). ∆MFIGF correlated negatively with ∆resistin (r = -0.346; P = 0.031), being strongest in obese patients (r = -0.815; P = 0.007). In obese patients, ∆FIQ fatigue after 30 weeks correlated negatively with ∆free IGF-1 (r = -0.711; P = 0.032).

Conclusions

Exercise reduced fatigue in all FM patients, this effect was achieved earlier in lean patients. Baseline levels of resistin in both serum and CSF associated negatively with fatigue. Resistin was increased after the exercise period which correlated with decreased fatigue. Changes in IGF-1 indicate similar long-term effects in obese patients. This study shows reduced fatigue after moderate exercise in FM and indicates the involvement of IGF-1 and resistin in these beneficial effects.

Trial registration

ClinicalTrials.gov: NCT00643006

Introduction

Severe fatigue, together with pain, is a major health problem in fibromyalgia (FM) [1, 2] and is considered to be equally important to pain [3] in causing impaired work ability and restricted social participation [4]. It is associated with depression, sleep quality and pain [5]. Obesity is common in FM, with a reported prevalence between 40 and 70% [68]. Increased body mass index (BMI) generally correlates with increased levels of pain and fatigue in FM [7, 911]. In chronic fatigue syndrome, symptom severity is suggested to be associated with metabolic syndrome [12]. Weight levels may affect neuroendocrine regulation of pain and fatigue through several pathways.

There is evidence for deregulation of the growth hormone/insulin-like growth factor (IGF-1) signaling in obesity [13, 14] and an inverse relationship between total IGF-1 levels and BMI has been reported [15, 16]. In FM patients we have recently reported a beneficial role of IGF-1 and exercise with regard to pain [17]. These results were in line with previous findings indicating that IGF-1 has a protective role in FM [18, 19] and that IGF-1 promotes resilience to stress and pain in the central nervous system (CNS) [20, 21]. Furthermore, growth hormone deficiency is shown to be associated with fatigue and reduced cognitive speed [22].

Recently, several factors, termed adipokines, which are produced in adipose tissue, have been found to have important regulatory roles in both inflammation and nutrition. Adiponectin is one of these adipokines and was initially isolated in adipocytes. Adiponectin regulates energy balance both in peripheral tissues and via the CNS [23, 24]. Adiponectin receptors are distributed widely in the brain, affecting appetite, metabolism and autonomic function [25, 26]. Adiponectin is negatively correlated with depression [27, 28] and has antidepressant-like effects in both lean and diet-induced obese mice [29].

Resistin is considered to be an adipokine with unusual properties, and a potential link between inflammation and metabolic disease [30]. It is expressed in human macrophages and has documented regulatory effects on metabolism, adipogenesis and inflammatory reactions [3133]. Peripheral levels of resistin are upregulated in subjects with insulin resistance and in obesity [34, 35], and resistin signaling involves both toll-like receptor (TLR)4 [36] and the IGF-1 receptor [37].

Leptin is another important adipokine. It is a major product of adipose tissue, is increased in obesity and is a central regulator of satiety and body weight [38, 39], as well as reproduction, mood and emotion [4042]. Induction of satiety is mediated by leptin receptors in hypothalamic neuropeptide Y (NPY), producing neurons [4346]. Leptin has anxiolytic effects in mice [47, 48] and is involved in allodynia in a neuropathic pain model [49]. NPY is an abundant neuropeptide, both in the peripheral and in the central nervous system. NPY is an important modulator of hippocampal and thalamic circuits, with the potential to affect a number of different functions in the brain. It is also involved in neuroprotection, neurogenesis and neuroinflammation [50]. NPY is altered in FM patients, possibly involving the hypothalamic-pituitary-adrenal axis [5154]. NPY is also altered in chronic fatigue syndrome [55, 56] and during stress and depression [57]. Disturbed neuropeptide levels with elevated substance P (SP) [5860] and nerve growth factor (NGF) [61] have previously been found in cerebrospinal fluid in FM. Recent evidence also implicate glial activation in FM with increased IL-6 and IL-8 in cerebrospinal fluid [62].

The aim of the study was to examine the long-term effects of aerobic exercise on fatigue, in lean, overweight, and obese women with FM. Changes in serum free bioactive IGF-1, total IGF-1, IGF binding protein (IGFBP)3, adipokines and neuropeptides were studied to gain a better understanding of the biological mechanisms involved in fatigue in FM.

Materials and methods

Study design

This study is a part of a previously reported randomized controlled exercise study, studying the effects of a moderate-to-high-intensity Nordic walking (NW) program and a supervised low-intensity walking (LIW) program. The effects of Nordic walking on body function were reported previously [63], showing that NW resulted in better improvement in the 6-minute walk test (6MWT) and aerobic capacity, when compared with LIW.

Subjects

The criteria for inclusion were as follows: women with FM, aged 20 to 60 years, with interest in exercising outdoors twice a week for 15 weeks, who agreed to undergo blood tests at baseline and after the exercise period. To ensure that the patients would manage the planned aerobic exercise, they were required to complete a bicycle test at 50 watts to fulfill the inclusion criteria. All included patients managed to perform the test. They were also invited to participate in an examination of cerebrospinal fluid; however, this was not a criterion for inclusion. FM was defined by the American College of Rheumatology (ACR) 1990 criteria [64]: a history of long-lasting generalized pain and pain in at least 11 of 18 tender points examined by manual palpation.

The criteria for exclusion were as follows: patients who could not speak or read Swedish; presence of other severe somatic or psychiatric disease; BMI <18.5; ongoing or planned physical therapy, including exercise, and inability to attend at the times of the planned exercise sessions.

Forty-nine patients, 26 of them undertaking NW and 23 undertaking LIW, had blood tests at baseline, after 15 weeks of exercise, and at 30 weeks of follow-up, as described in the previous report [17]. One of the patients had BMI <18.5 and was therefore excluded from this study. In total, 48 patients with FM formed the study population.

The median age of patients was 52 (48 to 56, interquartile range) years and their median duration of symptoms was 11 (7 to 15) years. The median number of tender points was 15 (13 to 16). Eighty-two percent of patients were taking analgesics during the study and 63% were taking antidepressants or sedatives. Nine patients were lean (BMI 18.5 to 24.9), 26 patients were overweight (BMI 25.0 to 29.9) and 13 were obese (BMI ≥30.0). After separating the patients into BMI groups we found a similar distribution in the NW and LIW group. In the lean group, four subjects participated in NW and five in LIW. In the overweight group fifteen participated in NW and eleven in LIW. In the obese group seven participated in NW and six in LIW.

Exercise intervention

The patients were randomized to either the moderate-to-high-intensity NW program (n = 26) or the supervised LIW program (n = 22). Both supervised aerobic exercise programs were conducted twice a week for 40 to 45 minutes for 15 weeks. Patients had blood tests before, after 15 weeks, and after 30 weeks. Pain and fatigue did not significantly change in any of the exercise groups after 15 weeks, while scores in the Multidimensional Fatigue Inventory (MFI-20) [66] subscale of General Fatigue (MFIGF) improved in both groups after 30 weeks. As no differences in fatigue or pain were found between the two exercise groups, and BMI was similarly distributed in both exercise groups, the analyses in this study were conducted on the total population (n = 48), irrespective of exercise intensity. Compliance was assessed as attendance at exercise sessions. It was slightly higher among the lean group, whose attendance was 71%, while it was 64% in the overweight group. Attendance in the obese group was 57%.

Clinical measurements

Fatigue was rated on a visual analog scale (0 to 100) of the Fibromyalgia Impact Questionnaire (FIQ) [65] which gives an estimation of global fatigue, as well as with the MFIGF [66], which estimates fatigue by questions related to feeling fit, tired and rested. Both instruments reflect fatigue during the last week, and a higher score indicates more severe fatigue.

Blood and cerebrospinal fluid (CSF) sampling

Serum was collected at rest (n = 48) at baseline, after 15 weeks in the exercise program, and at 30 weeks of follow-up (n = 41). Serum samples were acquired by venipuncture of the cubital vein. Twenty-six patients agreed to participate in an examination of cerebrospinal fluid (CSF) at baseline. CSF was collected through lumbar puncture through the lumbar vertebrae (L)3/L4 interspace. Collected blood and CSF samples were centrifuged at 800 g for 3 minutes, aliquoted, and stored frozen at -70°C until use.

Laboratory analyses

Samples were analyzed with enzyme-linked immunosorbent assay (ELISA) using commercially available kits. Assays specific for human adiponectin (DY1065, 62.5 pg/ml), human leptin (DY 398. 31 pg/ml), human resistin (DY1359, 31 pg/ml), free bioactive IGF-1 (DY291, 4 pg/ml) and IGFBP3 (DY675, 0.125ng/ml) were purchased from R and D Systems (Minneapolis, MN, USA). Serum total IGF1 was measured by solid-phase, enzyme-labeled chemoluminescent immunoassay (Immulite 2000 IGF1, L2KGF2) on an Immulite 2000 (Siemens Medical Solutions Diagnostics, Los Angeles, CA, USA). An assay specific for NPY (FEK-049-03, 1 pg/ml) was purchased from Phoenix Pharmaceuticals (Burlingame, CA, USA). The human NGF-specific assay was purchased from Promega (Madison, WI, USA; 4 pg/ml). All assays were run according to recommendations of the manufacturers. Ordinary colorimetric ELISA was read with a Spectramax 340 from Molecular Devices (Sunnyvale, CA, USA), and fluorescent ELISA assays were read with a Mithras LB940 from Berthold Technologies (Bad Wildbad, Germany).

Statistics

Descriptive data are presented as median and interquartile range. The Wilcoxon signed-rank test was used for comparisons of continuous variables within groups. Baseline data and differences in changes in lean patients were compared by the Mann-Whitney U-test with overweight and obese patients, respectively. Relationships between the variables were examined with the Spearman correlation coefficient. To control for possible Type I errors, the upper limit of the number of false significant results was calculated by the following formula:
Number of tests - Number of significant tests on level of alpha * Alpha / 1 -Alpha .

Ethics

The study was approved by the ethics committee of Sahlgrenska University Hospital. Written and verbal information was given to all patients, and written consent was obtained from all patients.

Results

Relationship between obesity, fatigue, adipokines and IGF-1

Several differences in fatigue and adipokines were found in relation to obesity. Patients with normal BMI (18.5 to 24.9) had higher baseline fatigue (97 mm) compared to overweight patients with BMI 25 to 29.9 (74 mm; P = 0.008), while no significant differences were found compared to obese patients with BMI ≥30 (88 mm; P-value not significant.) (Table 1). Baseline levels of adiponectin were higher in lean compared to overweight patients (P = 0.013) and compared to obese patients (P = 0.003) (Table 2). Leptin levels were lowest in lean patients and tended to be higher in overweight patients (P = 0.067) and were highest in obese patients (P <0.001). Resistin levels did not differ significantly between groups. Total IGF-1 was higher in lean patients compared to overweight patients (160.0 vs 113.0 ng/ml, P = 0.026) and obese patients (160.0 vs 106.5, P = 0.056), (Table 3). Serum free IGF-1 and IGFB3 did not differ between the groups.
Table 1

Clinical data in lean, overweight and obese patients with fibromyalgia

 

Lean (group 1)

Overweight (group 2)

Obese (group 3)

Lean vs overweight

Lean vs obese

 

Median (range)

n

Median (range)

n

Median (range)

n

P-valuea

Age, years

52.0 (33.5 to 54.0)

9

53.0 (48.0 to 56.0)

26

51.0 (47.0 to 55.5)

13

0.288

0.647

BMI, kg/m2

23.5 (21.5 to 24.0)

9

28.1 (27.1 to 29.5)

26

32.7 (30.8 to 35.7)

13

<0.001

<0.001

Tender points, n

14.0 (12.5 to 15.5)

9

15.0 (13.0 to 17.0)

26

15.0 (13.0 to 16.0)

13

0.224

0.393

Baseline FIQ fatigue, mm

97.0 (77.5 to 98.0)

9

74.0 (52.8 to 88.5)

26

88.0 (72.5 to 91.5)

13

0.008

0.110

Baseline MFIGF

20.0 (17.0 to 20.0)

9

16.5 (13.0 to 20.0)

26

17.0 (14.5 to 20.0)

13

0.061

0.209

Change in FIQ fatigue after 15 weeks, mm

-7.0 (-13.5 to 0.0)

P = 0.046

8

-0.5 (-16.3 to 8.3)

P = 0.485

26

-8.0 (-20.5 to 2.0)

P = 0.059

13

0.368

0.972

Change in FIQ fatigue after 30 weeks, mm

-4.5 (-12.8 to 0.3)

P = 0.161

8

-2.0 (-7.0 to 8.8)

P = 0.966

24

-2.5 (-6.8 to 4.5)

P = 0.345

12

0.254

0.473

Change in MFIGF after 15 weeks

-2.0 (-4.2 to 0.0)

P = 0.084

8

0.0 (-1.2 to 1.0)

P = 0.515

26

0.0 (-3.5 to 1.0)

P = 0.641

13

0.164

0.336

Change in MFIGF after 30 weeks

-3.0 (-5.5 to -2.0)

P = 0.017

8

-2.0 (-3.0 to 0.0)

P = 0.001

24

-3.0 (-4.0 to -1.0)

P = 0.016

12

0.147

0.624

Levels of fatigue (FIQ and MFIGF) at baseline (0 weeks), during training (15 weeks) and after training (30 weeks). Lean patients had BMI 18.5 to 24.9; overweight patients had BMI 25.0 to 29.9. Obese patients had BMI ≥30.0. Median values and interquartile range are indicated. aMann-Whitney U-test. n, number; BMI, body mass index; FIQ, Fibromyalgia Impact Questionnaire; MFIGF, Multidimensional Fatigue Inventory subscale of General Fatigue.

Table 2

Adipokines in lean, overweight and obese patients

 

Lean (group 1)

Overweight (group 2)

Obese (group 3)

Comparison of groups

 

Baseline

∆15 wks

∆30 wks

Baseline

∆15 wks

∆30 wks

Baseline

∆15 wks

∆30 wks

Groups

At baseline

Change after 15 weeks

Change after 30 weeks

 

Median (range)

Median (range)

P -valuea

Median (range)

P -valuea

Median (range)

Median (range)

P -valuea

Median (range)

P -valuea

Median (range)

Median (range)

P -valuea

Median (range)

P -valuea

 

P -valueb

P -valueb

P -valueb

Adipo-nectin

ng/ml

18620.8 (5707.0 to 23038.0)

783.3

(-3501.6 to 4270.7)

488.5 (-1047.7 to 6069.1)

4580.4 (2939.7 to 9634.0)

400.0

(-438.0 to 1681.0)

578.0

(-1764.5 to 1281.3)

4745.5 (2864.9 to 5879.0)

1011.3

(-382.6 to 3273.8)

1206.9

(-1157.4 to 3811.5)

Lean vs overweight

0.013

0.591

0.346

 

n = 9

P = 0.441

n = 9

P = 0.398

n = 7

n = 26

P = 0.122

n = 25

P = 0.861

n = 25

n = 13

P = 0.133

n = 13

P = 0.214

n = 9

Lean vs obese

0.003

0.794

1.000

Leptin

ng/ml

16012.4 (10250.4 to 25627.3)

2136.2

(-4133.4 to 5400.4)

-4455.8 (-10473.7 to 2250.7)

27658.2 (19222.8 to 45504.1)

1417.0

(-6055.4 to 6858.3)

1760

(-9912.1 to 9778.1)

45300.0 (34456.5 to 86307.4)

5269.7

(-5534.8 to 9055.5)

1446.4

(-3059.4 to 8129.1)

Lean vs overweight

0.067

0.939

0.161

 

n = 9

P = 0.515

n = 9

P = 0.128

n = 7

n = 26

P = 0.778

n = 25

P = 0.443

n = 25

n = 13

P = 0.422

n = 13

P = 0.594

n = 9

Lean vs obese

<0.001

0.471

0.142

NPY

pg/ml

114.5 (86.6 to 624.6)

19.0

(-10.7 to 98.5)

6.7

122.8 (80.5 to 164.9)

-7.7

(-27.4 to 31.3)

9.0 (-8.2 to 27.3)

113.7 (76.0 to 553.8)

21.8 (1.3 to 225.0)

44.4 (18.3 to 1272.7)

Lean vs overweight

0.82

0.178

0.824

 

n = 4

P = 0.465

n = 4

P = 0.18

n = 2

n = 16

P = 0.959

n = 16

P = 0.256

n = 15

n = 6

P = 0.075

n = 6

P = 0.043

n = 5

Lean vs obese

0.914

0.762

0.095

Resistin

pg/ml

14768.2 (12312.3 to 27191.5)

927.7

(-3165.5 to 3897.5)

1006.1

(-8555.9 to 4448.6)

14419.1 (13786.6 to 16531.2)

437.3

(-1698.2 to 2634.9)

701.1

(-573.9 to 3872.6)

14951.6 (12671.2 to 20016.0)

1189.2

(-1110.2 to 2292.4)

3110.7

(-534.1 to 4311.0)

Lean vs overweight

0.67

1.000

0.789

 

n = 9

P = 0.678

n = 9

P = 1.000

n = 7

n = 26

P = 0.465

n = 23

P = 0.051

n = 25

n = 13

P = 0.221

n = 13

P = 0.214

n = 9

Lean vs obese

0.794

0.845

0.837

Serum levels of adiponectin, leptin, NPY and resistin at baseline (0 weeks), change (∆) during training (15 weeks) and change after training (30 weeks). Median values and interquartile range are indicated. aWilcoxon signed rank test. bMann-Whitney U-test. NPY, neuropeptide Y.

Table 3

Serum free IGF-1, total IGF-1 and IGFB3 in lean, overweight and obese patients

 

Lean (group 1)

Overweight (group 2)

Obese (group 3)

Comparison of groups

 

Baseline

Median (range)

∆15 wks

Median (range)

P-valuea

∆30 wks

Median (range)

P-valuea

Baseline

Median (range)

∆15 wks

Median (range)

P-valuea

∆30 wks

Median (range)

P-valuea

Baseline

Median (range)

∆15 wks

Median (range)

P-valuea

∆30 wks

Median (range)

P-valuea

Groups

At baseline

P-valueb

Change after 15 weeks

P-valueb

Change after 30 weeks

P-valueb

Free

IGF-1

ng/ml

4.3 (2.3 to 4.6)

0.8 (-1.1 to 1.4)

0.1 (-5.5 to 0.9)

3.1 (1.7 to 6.3)

-0.4 (-1.5 to 0.5)

-0.7 (-1.6 to 0.6)

5.7 (3.4 to 8.0)

-1.2 (-4.7 to 0.4)

-2.1 (-4.7 to -0.1)

Lean vs overweight

0.697

0.171

0.900

 

n = 9

P = 0.515

n = 9

P = 0.753

n = 6

n = 26

P = 0.174

n = 26

P = 0.053

n = 24

n = 13

P = 0.173

n = 13

P = 0.017

n = 10

Lean vs obese

0.164

0.051

0.368

IGFB3

ng/ml

1605.6 (1179.4 to 2397.9)

-260.7 (-578.6 to 36.3)

-568.5 (-899.7 to 123.5)

1492.2 (1079.2 to 1999.4)

106.0 (-370.3 to 942.3)

349.2 (-282.8 to 767.0)

1645.2 (1420.6 to 2002.8)

-19.9 (-622.3 to 544.8)

282.0 (-869.5 to 1558.2)

Lean vs overweight

0.563

0.107

0.095

 

n = 8

P = 0.093

n = 8

P = 0.249

n = 6

n = 26

P = 0.397

n = 25

P = 0.128

n = 25

n = 10

P = 0.878

n = 10

P = 0.735

n = 7

Lean vs obese

0.897

0.460

0.628

Total

IGF-1

ng/ml

160.0 (123.0 to 187.0)

33.0 (0.0 to 51.0

65.0 (17.8 to 94.3)

113.0 (90.3 to 151.8

10.0 (-13.5 to 19.0)

34.0 (-0.9 to 48.0)

106.5 (95.8 to 157.8)

-1.0 (-21.8 to 4.5)

19.0 (6.0 to 29.0)

Lean vs overweight

0.026

0.065

0.125

 

n = 7

P = 0.043

n = 7

P = 0.116

n = 24

P = 0.309

n = 17

P = 0.006

n = 12

P = 0.255

n = 12

P = 0.017

Lean vs obese

0.056

0.010

0.036

Levels of serum free IGF-1, total serum IGF-1 and serum IGFB3 at baseline (0 weeks), change () during training (15 weeks) and change after training (30 weeks). Median values and interquartile range are indicated. aWilcoxon signed rank test. bMann-Whitney U-test. IGF-1, insulin-like growth factor-1; IGFB3, insulin-like growth factor-binding protein-3.

Fatigue was also related to adipokine levels and IGF-1 levels. Baseline fatigue was negatively correlated with serum levels of leptin (r = -0.345, P = 0.016, n = 48) and NGF (r = -0.412, P = 0.037, n = 26) (Table 4). Leptin correlated negatively with total IGF-1 (r = -0.354, P = 0.020, n = 43) and positively with NPY (r = 0.472, P = 0.015, n = 26) and NGF. Serum free IGF-1 correlated with total IGF-1 (r = 0.366; P = 0.016; n = 43) and IGFB3 (r = 0.361; P = 0.016; n = 44) and with NGF (r = 0.401; P = 0.042; n = 26). In lean patients, baseline fatigue was negatively associated with resistin levels (r = -0.694; P = 0.038; n = 9).
Table 4

Fatigue versus adipokines, IGF-1 and neuropeptides

  

FIQ fatigue

Adiponectin

Leptin

Resistin

Free IGF-1

Total IGF-1

IGFB3

NPY

NGF

MFIGF

r

P

n

0.623

<0.001

48

0.107

0.468

48

-0.074

0.618

48

-0.125

0.398

48

0.024

0.871

48

0.254

0.100

43

0.039

0.801

44

0.150

0.464

26

0.141

0.493

26

FIQ Fatigue

r

P

n

1.000

48

0.143

0.333

48

-0.345

0.016

48

-0.111

0.451

48

-0.032

0.829

48

0.198

0.203

43

0.155

0.315

44

0.004

0.984

26

-0.412

0.037

26

Adiponectin

r

P

n

 

1.000

48

0.032

0.827

48

0.123

0.405

48

-0.200

0.172

48

0.148

0.343

43

-0.143

0.355

44

0.245

0.227

26

-0.151

0.460

26

Leptin

r

P

n

  

1.000

48

0.153

0.300

48

0.131

0.374

48

-0.354

0.020

43

0.082

0.595

44

0.472

0.015

26

0.426

0.030

26

Resistin

r

P

n

   

1.000

48

-0.103

0.486

48

0.036

0.817

43

-0.043

0.782

44

0.023

0.912

26

0.148

0.470

26

Free IGF-1

r

P

n

    

1.000

48

0.366

0.016

43

0.361

0.016

44

0.167

0.414

26

0.401

0.042

26

Total IGF-1

r

P

n

     

1.000

43

0.188

0.250

39

0.085

0.706

22

0.233

0.296

22

IGFB3

r

P

n

      

1.000

44

0.395

0.046

26

0.198

0.332

26

Neuropeptide Y

(NPY)

r

P

n

       

1.000

26

0.340

0.089

26

Correlation between baseline fatigue (FIQ and MFIGF), serum free IGF-1, serum levels of total IGF-1, IGFB3, adipokines and neuropeptides. r, Spearman's correlation coefficient; P, P-value; n, number; MFIGF, Multidimensional Fatigue Inventory subscale of General Fatigue; FIQ, Fibromyalgia Impact Questionnaire; IGF, insulin-like growth factor; IGFB3, insulin-like growth factor-binding protein-3; NGF, nerve growth factor.

FIQ Fatigue was negatively associated with resistin levels in CSF (r = -0.365, P = 0.073, n = 25) (Table 5). A similar pattern was seen for CSF levels of adiponectin with negative correlations to FIQ fatigue (r = -0.444, P = 0.026, n = 25) and MFIGF (r = -0.508, P = 0.01, n = 25).
Table 5

Correlation between baseline fatigue and cerebrospinal fluid levels of adipokines and neuropeptides

 

MFIGF

Adiponectin CSF

Leptin CSF

Resistin CSF

NPY CSF

NGF CSF

FIQ fatigue

r = 0.623

P <0.001

n = 48

r = -0.444

P = 0.026

n = 25

r = -0.233

P = 0.263

n = 25

r = -0.365

P = 0.073

n = 25

r = -0.111

P = 0.607

n = 24

r = -0.243

P = 0.243

n = 25

MFIGF

 

r = -0.508

P = 0.01

n = 25

r = -0.189

P = 0.365

n = 25

r = -0.316

P = 0.123

n = 25

r = 0.014

P = 0.947

n = 24

r = 0.219

P = 0.293

n = 25

Correlation between baseline fatigue (FIQ and MFIGF) and cerebrospinal fluid levels of adiponectin, leptin, resistin, NPY and NGF. r, Spearman's correlation coefficient; P, P-value; n, number; FIQ, Fibromyalgia Impact Questionnaire; MFIGF, Multidimensional Fatigue Inventory subscale of General Fatigue; CSF, cerebrospinal fluid; NPY, neuropeptide Y; NGF, nerve growth factor.

Influence of exercise on fatigue

In the group as a whole, FIQ fatigue was decreased after 15 weeks (median -4, interquartile range -15 to 4, P = 0.024, n = 47), and after 30 weeks both FIQ fatigue (-2, -7 to 4.5; P = 0.252, n = 44) and MFIGF were decreased (-2, -4 to -1, P <0.001, n = 44). In lean patients, FIQ fatigue (-7, 13.5 to 0, P = 0.046) and MFIGF (-2, -4.2 to 0, P = 0.084) were decreased after 15 weeks (Table 1). After 30 weeks, MFIGF decreased significantly in lean patients, (-3, -5.5 to -2, P = 0.017), overweight patients (-2, -3 to 0, P = 0.001) and obese patients (-3, -4 to -1, P = 0.016), and the direction of change in FIQ fatigue was the same although not significant.

Influence of exercise on levels of IGF-1, adipokines and neuropeptides

As mentioned above, total IGF-1 was highest in lean patients and lower in overweight and obese patients (Table 3). After 15 weeks, total IGF-1 was further increased in lean patients (33 ng/ml, 0 to 51, P = 0.043, n = 7), but was unchanged in overweight patients (10, -13.5 to 19, P = 0.309, n = 17) and in obese patients (-1, -22 to 4.5, P = 0.255). The change in total IGF-1 differed significantly between lean and obese patients (P= 0.010). After 30 weeks, serum free IGF-1 was significantly decreased in obese patients (-2.1 ng/ml, -4.7 to -0.1, P = 0.017, n = 10) and in overweight patients (-0.7, -1.6 to 0.6, P = 0.053, n = 24) but was unchanged in lean FM patients. Resistin increased in the group as a whole after 30 weeks (944 pg/ml, -819 to 4299, P = 0.034, n = 41), while adiponectin and leptin were unchanged after 30 weeks. NPY levels were increased after 30 weeks (11.1, 0.6 to 33, P = 0.017, n = 22), this increase was only significant in obese patients (44.4, 18.3 to 1272, P = 0.043, n = 5). Adiponectin levels were increased in the whole group of FM patients after 15 weeks (695, -432 to 1891, P = 0.022, n = 47) but not after 30 weeks.

Changes in fatigue in relation to IGF-1, adipokines and neuropeptides

Change in MFIGF (∆MFIGF) after 15 weeks was negatively correlated with ∆total IGF-1 (r = -0.329, P = 0.050, n = 36). In lean patients, ∆total IGF-1 was correlated with ∆resistin in serum (r = 0.829, P = 0.021, n = 7) after 15 weeks; ∆free IGF-1 after 15 weeks of exercise correlated positively with ∆NGF in serum (r = 0.428, P = 0.029, n = 26).

After 30 weeks, ∆free IGF-1 was negatively correlated with ∆NPY (r = -0.563, P = 0.006) (Table 6). ∆FIQ fatigue was correlated negatively with ∆NGF (r = -0.463, P = 0.034, n = 21) and positively with ∆NPY (r = 0.469, P = 0.032, n = 21). ∆MFIGF correlated negatively with ∆resistin (r = -0.346, P = 0.031, n = 39); this negative correlation was strong in obese patients (r = -0.815, P = 0.007, n = 9) (Table 7) but was much weaker in lean and overweight patients. In obese patients, ∆FIQ fatigue after 30 weeks was negatively correlated with ∆free IGF-1 (r = -0.711, P = 0.032, n = 9) and ∆adiponectin (r = -0.753, P = 0.019) (Table 7).
Table 6

Change in fatigue versus change in adipokines, IGF-1 and neuropeptides

  

∆MFIGF

30 wks

∆Adiponectin

30 wks

∆Leptin

30 wks

∆Resistin

30 wks

∆Free IGF-1

30 wks

∆Total IGF-1

30 wks

∆IGFB3

30 wks

∆NGF

30 wks

∆NPY

30 wks

∆FIQ fatigue

30 wks

r

P

n

0.415

0.005

44

-0.227

0.164

39

-0.111

0.502

39

-0.016

0.923

39

-0.147

0.385

37

0.106

0.549

34

0.250

0.141

36

-0.463

0.034

21

0.469

0.032

21

∆MFIGF

30 wks

r

P

n

1.000

44

-0.096

0.561

39

0.280

0.084

39

-0.346

0.031

39

-0.201

0.232

37

-0.075

0.672

34

0.382

0.022

36

0.043

0.852

21

0.209

0.364

21

∆Adiponectin

30 wks

r

P

n

 

1.000

41

-0.016

0.922

41

0.011

0.943

41

0.002

0.989

39

0.091

0.600

36

-0.241

0.145

38

0.097

0.669

22

-0.361

0.099

22

∆Leptin

30 wks

r

P

n

  

1.000

41

-0.009

0.954

41

-0.211

0.196

39

0.051

0.765

36

-0.058

0.730

38

0.199

0.374

22

-0.091

0.687

22

∆Resistin

30 wks

r

P

n

   

1.000

41

0.048

0.770

39

-0.186

0.277

36

-0.143

0.393

38

0.103

0.647

22

-0.278

0.210

22

∆Free IGF-1

30 wks

r

P

n

    

1.000

40

0.103

0.556

35

0.178

0.299

36

0.356

0.104

22

-0.563

0.006

22

∆Total IGF-1

30 wks

r

P

n

     

1.000

40

0.043

0.812

33

-0.115

0.639

19

-0.128

0.601

19

∆IGFB3

30 wks

r

P

n

      

1.000

38

-0.089

0.695

22

-0.242

0.277

22

∆NGF

30 wks

r

P

n

       

1.000

22

-0.215

0.336

22

Correlation after 30 weeks between change (∆) in fatigue (∆FIQ and ∆MFIGF) and change in serum free IGF-1, serum levels of IGFB3, adipokines and neuropeptides. r, Spearman's correlation coefficient; P, P-value; n, number; FIQ, Fibromyalgia Impact Questionnaire; MFIGF, Multidimensional Fatigue Inventory subscale of General Fatigue; IGF, insulin-like growth factor; IGFB3, insulin-like growth factor-binding protein-3; NGF, nerve growth factor; NPY, neuropeptide Y.

Table 7

Change in fatigue in lean, overweight and obese patients versus change in adipokines, IGF-1 and neuropeptides

Lean patients

∆MFIGF

30 wks

∆Adiponectin

30 wks

∆Leptin

30 wks

∆Resistin

30 wks

∆Free IGF-1

30 wks

∆Tot IGF-1

30 wks

∆IGFB3

30 wks

∆NPY

30 wks

∆NGF

30 wks

∆FIQ Fatigue

30 weeks

r

P

n

0.268

0.521

8

0.541

0.210

7

-0.432

0.333

7

-0.685

0.090

7

0.145

0.784

6

0.812

0.05

6

0.086

0.872

6

1.000

2

1.000

2

∆MFIGF

30 weeks

r

P

n

1.000

8

0.275

0.550

7

-0.220

0.635

7

-0.239

0.606

7

0.059

0.912

6

0.706

0.117

6

0.177

0.738

6

1.000

2

1.000

2

Overweight patients

∆MFIGF

30 wks

∆Adiponectin

30 wks

∆Leptin

30 wks

∆Resistin

30 wks

∆Free IGF-1

30 wks

∆Tot IGF-1

30 wks

∆IGFB3

30 wks

∆NPY

30 wks

∆NGF

30 wks

∆FIQ Fatigue

30 wks

r

P

n

0.530

0.008

24

-0.219

0.315

23

-0.099

0.654

23

0.270

0.213

23

-0.021

0.924

22

0.213

0.380

19

0.260

0.232

23

0.394

0.164

14

-0.475

0.086

14

∆MFIGF

30 wks

r

P

n

1.000

24

-0.089

0.685

23

0.127

0.562

23

-0.265

0.222

23

-0.233

0.297

22

-0.012

0.962

19

0.104

0.635

23

0.384

0.176

14

-0.047

0.874

14

Obese patients

∆MFIGF

30 wks

∆Adiponectin

30 wks

∆Leptin

30 wks

∆Resistin

30 wks

∆Free IGF-1

30 wks

∆Tot IGF-1

30 wks

∆IGFB3

30 wks

∆NPY

30 wks

∆NGF

30 wks

∆FIQ Fatigue

30 wks

r

P

n

0.283

0.373

12

-0.753

0.019

9

0.084

0.831

9

-0.326

0.391

9

-0.711

0.032

9

-0.261

0.498

9

0.072

0.878

7

0.154

0.805

5

-0.205

0.741

5

∆MFIGF

30 wks

r

P

n

1.000

12

-0.210

0.587

9

0.647

0.060

9

-0.815

0.007

9

-0.025

0.949

9

-0.418

0.263

9

0.786

0.036

7

-0.900

0.037

5

0.500

0.391

5

Correlation after 30 weeks between change (∆) in fatigue (∆FIQ and ∆MFIGF) and change in serum free IGF-1, serum levels of IGFB3, adipokines and neuropeptides in lean patients, overweight and obese patients. r, Spearman's correlation coefficient; P, P-value; n, number; FIQ, Fibromyalgia Impact Questionnaire; MFIGF, Multidimensional Fatigue Inventory subscale of General Fatigue; IGF, insulin-like growth factor; IGFB3, insulin-like growth factor-binding protein-3; NGF, nerve growth factor; NPY, neuropeptide Y.

Type 1 error

Analyses of baseline data, changes in fatigue, levels of adipokines and IGF levels (Tables 1, 2, 3, and text) comprised a total of 121 comparisons and the upper level of the number of false significant results was 5.10, which means that five of the significant results might be false.

Correlations at baseline (Tables 4 and 5, and text), comprised a total of 57 comparisons and the upper level of the number of false significant results was 2.26, which means that two significant results might be false. Correlations with regard to change (Tables 6 and 7, and text), comprised a total of 93 comparisons and the upper level of number of false significant results was 4.21, which means that four significant results might be false.

Discussion

Fatigue is a debilitating and common health problem in FM and in many autoimmune rheumatic diseases, influencing quality of life, work ability and motivation to exercise. The cause of fatigue is multifactorial and poorly understood. Suggested causes of chronic fatigue include central and peripheral neuropeptides and cytokines, endocrine dysregulation and secondary effects due to pain, depression and sleep disturbance[3, 18, 55, 56, 67].

Aerobic exercise, together with pharmacological treatment, is one of the cornerstones of treatment for FM [68], and many patients with FM report lower levels of fatigue after a lengthy exercise period [63, 69]. In this group of women with FM, the response to aerobic exercise on fatigue was related to levels of BMI. Lean patients already reported significantly reduced fatigue after 15 weeks of exercise. The response to exercise in overweight and obese patients was delayed, but a significant reduction in fatigue was found after six months. An association between BMI and fatigue in FM has previously been reported [70], and a high BMI together with inactivity also increases the risk for development of FM [71]. In our material, the overweight group reported lower levels of fatigue than the lean group. Fatigue levels between the lean and obese groups did not significantly differ.

We used two different instruments to rate fatigue [72]. Both ratings of general fatigue reflect symptom severity, but somewhat different aspects. The FIQ rates the global feeling of fatigue, possibly including a feeling of pain, and the MFIGF estimates fatigue in relation to feeling fit, tired and rested.

We found evidence of a positive role for total and free bioactive IGF-1 on fatigue. This is in line with previous reports that IGF-1 has a protective role in FM [1719, 73] promoting adaptation and neuroplasticity in the central nervous system [20, 21]. Baseline levels of resistin in CSF were negatively correlated with fatigue. The same pattern, although not significant, was seen for resistin in serum. Increased resistin after 6 months correlated with reduced fatigue. Thus, the increase in resistin during exercise appears to improve fatigue, and the positive effects may be especially important in obese patients. Resistin represents a potential link between inflammation and metabolism and can stimulate TLR4 [36] as well as promote IGF-1 receptor signaling [37]. To the best of our knowledge, resistin has not previously been studied in relation to fatigue.

We also found evidence of a role for adiponectin, leptin and NPY in the reduced fatigue after exercise. Serum leptin and cerebrospinal adiponectin were both associated with low fatigue at baseline, and change in adiponectin correlated with reduced fatigue. NPY correlated with increased fatigue. Serum leptin is taken up via the blood-brain barrier and is a central regulator of energy levels with behavioral effects [43, 42, 48]. The arcuate nucleus of the hypothalamus is believed to be important in mediating these effects [43, 74]. Different peripheral energy signals such as leptin and insulin [74] were found to activate different but overlapping subpopulations of arcuate NPY neurons. In line with this, the IGF-1-receptor is expressed in arcuate neurons and glial cells [75], and IGF-1 receptor activation is important for neuroplasticity in the arcuate hypothalamus [76]. Similarly, resistin can activate hypothalamic neurons and induce NPY expression in the hypothalamus [77]. Based on our findings, the roles of leptin and NPY in fatigue and the long-term effects of exercise merit further study.

This is an exploratory longitudinal study. Since we aimed to investigate the interaction of IGF-1 and adipokines in relation to BMI, the study includes many analyses. Due to multiple analyses, the significance level should be interpreted with caution, and the upper limit of the expected number of false sigificant results is presented in the Results section.

Conclusions

Aerobic exercise reduced fatigue in all FM patients; this effect was achieved early in lean patients. In overweight and obese patients the reduction of fatigue was most pronounced after 6 months. Fatigue in FM patients is inversely correlated to resistin in serum and CSF, indicating a beneficial role of resistin. The long-term reduction of fatigue following exercise correlated with increased levels of resistin. The inverse correlation of resistin with reduced fatigue was more pronounced in obese FM patients. Changes in IGF-1 indicate a similar beneficial role on fatigue in obese patients. The results also indicate the involvement of leptin, adiponectin and NPY, although it is not clear how these signals may interact with each other in chronic fatigue.

Abbreviations

ACR: 

American College of Rheumatology

BMI: 

body mass index

CNS: 

central nervous system

CSF: 

cerebrospinal fluid

ELISA: 

enzyme-linked immunosorbent assay

FM: 

fibromyalgia

FIQ: 

Fibromyalgia Impact Questionnaire

IL: 

interleukin

IGF-1: 

insulin-like growth factor-1

IGFBP3: 

insulin-like growth factor-binding protein-3

L3/L4: 

lumbar vertebrae 3 to 4

LIW: 

low-intensity walking

MFI-20: 

Multidimensional Fatigue Inventory

MFIGF: 

Multidimensional Fatigue Inventory subscale of General Fatigue

6MWT: 

6-minute walking test

NGF: 

nerve growth factor

NPY: 

neuropeptide Y

NW: 

Nordic walking

SP: 

substance P

TLR: 

toll-like receptor.

Declarations

Acknowledgements

We thank Lena Nordeman, Åsa Cider, Gunilla Jonsson and all other members of the GAU-study group for recruiting, examining or supervising the exercise groups. This work has been funded by grants from the Swedish Research Council (KM, MBo), the Medical Society of Göteborg (MBo), the Swedish Association against Rheumatism (KM, MBo), the King Gustaf V:s 80-year Foundation (MBo), Professor Nanna Swartz Foundation (MBo), Torsten Söderberg's Foundation (MBo), Rune and Ulla Amlövs Trust, the Swedish Research Agency for Innovation Systems (VINNOVA), the Swedish Foundation for Strategic Research, the Ingabritt and Arne Lundberg's Foundation, Magnus Bergwall Foundation (MBo), the University of Göteborg, the Family Thölen and Kristlers Foundation, the Regional agreement on medical training and clinical research between the Western Götaland county council and the University of Göteborg (LUA/ALF) (JB, KM, MBo), the Health and Medical Care Executive Board of Västra Götaland Region (KM). The funding sources have no involvement in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

Authors’ Affiliations

(1)
Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg
(2)
Sahlgrenska University Hospital, Rheumatology
(3)
Sahlgrenska University Hospital, Physiotherapy and Occupational therapy
(4)
University of Gothenburg Centre for Person-centered Care (GPCC), Sahlgrenska Academy

References

  1. Aaron LA, Burke MM, Buchwald D: Overlapping conditions among patients with chronic fatigue syndrome, fibromyalgia, and temporomandibular disorder. Arch Intern Med. 2000, 160: 221-227. 10.1001/archinte.160.2.221.View ArticlePubMedGoogle Scholar
  2. Mease P, Clauw D, Christensen R, Crofford L, Gendreau R, Martin SA, Simon LS, Strand V, Williams DA, Arnold LM, OMERACT Fibromyalgia Working Group: Toward development of a fibromyalgia responder index and disease activity score: OMERACT module update. J Rheumatol. 2011, 38: 1487-1495. 10.3899/jrheum.110277.PubMed CentralView ArticlePubMedGoogle Scholar
  3. Staud R: Peripheral and central mechanisms of fatigue in inflammatory and noninflammatory rheumatic diseases. Curr Rheumatol Rep. 2012, 14: 539-548. 10.1007/s11926-012-0277-z.PubMed CentralView ArticlePubMedGoogle Scholar
  4. Mannerkorpi K, Gard G: Hinders for continued work among persons with fibromyalgia. BMC Muskuloskeletal Disorders. 2012, 13: 96-10.1186/1471-2474-13-96.View ArticleGoogle Scholar
  5. Nicassio P, Moxham E, Schumand C, Gevirtz R: The contribution of pain, reported sleep quality, and depressive symptoms to fatigue in fibromyalgia. Pain. 2002, 100: 271-279. 10.1016/S0304-3959(02)00300-7.View ArticlePubMedGoogle Scholar
  6. Bennet RM, Jones J, Turk DC, Russel IJ, Matallana L: An internet survey of 2,596 people with fibromyalgia. BMC Musculoskelet Disorders. 2007, 8: 27-10.1186/1471-2474-8-27.View ArticleGoogle Scholar
  7. Neumann L, Lerner E, Glazer Y, Bolotin A, Shefer A, Buskila D: A cross-sectional study of the relationship between body mass index and clinical characteristics, tenderness measures, quality of life and physical functioning in fibromyalgia patients. Clin Rheumatol. 2008, 27: 1543-1547. 10.1007/s10067-008-0966-1.View ArticlePubMedGoogle Scholar
  8. Okifuji A, Bradshaw DH, Olson C: Evaluating obesity in fibromyalgia: neuroendocrine biomarkers, symptoms and functions. Clin Rheumatol. 2009, 28: 475-478. 10.1007/s10067-009-1094-2.PubMed CentralView ArticlePubMedGoogle Scholar
  9. Kim CH, Luedtke CA, Vincent A, Thompson JM, Oh TH: Association of body mass index with symptom severity and quality of life in patients with fibromyalgia. Arthritis Care Res. 2012, 64: 222-228.View ArticleGoogle Scholar
  10. Mork PJ, Vasseljen O, Nilsen TI: Association between physical exercise, body mass index and risk of fibromyalgi: longitudinal data from the Norwegian Nord-Trondelag Health Study. Arthritis Care Res. 2010, 62: 611-617. 10.1002/acr.20118.View ArticleGoogle Scholar
  11. Okifuji A, Donaldson GW, Barck L, Fine PG: Relationship between fibromyalgia and obesity in pain function, mood and sleep. J Pain. 2010, 11: 1329-1337. 10.1016/j.jpain.2010.03.006.PubMed CentralView ArticlePubMedGoogle Scholar
  12. Maloney EM, Boneva RS, Lin JM, Reeves WC: Chronic fatigue syndrome is associated with metabolic syndrome: results from a case-control study in Georgia. Metabolism. 2010, 59: 1351-1357. 10.1016/j.metabol.2009.12.019.View ArticlePubMedGoogle Scholar
  13. Vahl N, Jørgensen JO, Skjaerbaek C, Veldhuis JD, Orskov H, Christiansen JS: Abdominal adiposity rather than age and sex predicts mass and regularity of GH secretion in healthy adults. Am J Physiol. 1997, 272: E1108-E1116.PubMedGoogle Scholar
  14. Pijl H, Langendonk JG, Burggraaf J, Frölich M, Cohen AF, Veldhuis JD, Meinders AE: Altered neuroregulation of GH secretion in viscerally obese premenopausal women. J Clin Endocrinol Metab. 2001, 86: 5509-5515. 10.1210/jc.86.11.5509.View ArticlePubMedGoogle Scholar
  15. Faupel-Badger JM, Berrigan D, Ballard-Barbash R, Potischman N: Anthropometric corelates of insulin-like growth factor 1 (IGF-1) and IGF-1 binding protein-3 (IGFBP-3) levels by race/ethnicity and gender. Ann Epidemiol. 2009, 19: 841-849. 10.1016/j.annepidem.2009.08.005.PubMed CentralView ArticlePubMedGoogle Scholar
  16. Friedrich N, Jørgensen T, Juul A, Spielhagen C, NaucK M, Wallaschofski H, Linneberg A: Insulin-like growth factor I and anthropometric parameters in a Danish population. Exp Clin Endocrinol Diabetes. 2012, 120: 171-174.View ArticlePubMedGoogle Scholar
  17. Bjersing JL, Dehlin M, Erlandsson M, Bokarewa MI, Mannerkorpi K: Changes in pain and insulin-like growth factor 1 in fibromyalgia during exercise: the involvement of cerebrospinal inflammatory factors and neuropeptides. Arthr Res Ther. 2012, 14: R162-10.1186/ar3902.View ArticleGoogle Scholar
  18. Jones KD, Deodhar P, Lorentzen A, Bennett RM, Deodhar AA: Growth hormone perturbations in fibromyalgia: a review. Semin Arthritis Rheum. 2007, 36: 357-379. 10.1016/j.semarthrit.2006.09.006.View ArticlePubMedGoogle Scholar
  19. Cuatrecasas G, Alegre C, Fernandez-Sola J, Gonzalez MJ, Garcia-Fructuoso F, Poca-Dias V, Nadal A, Navarro F, Mera A, Lage M, Peino R, Casanueva F, Linan C, Sesmilo G, Coves MJ, Izquierdo JP, Alvarez I, Granados E, Puig- Domingo M: Growth hormone treatment for sustained pain reduction and improvement in quality of life in severe fibromyalgia. Pain. 2012, 153: 1382-1389. 10.1016/j.pain.2012.02.012.View ArticlePubMedGoogle Scholar
  20. Garcia-Segura LM, Diz-Chaves Y, Perez-Martin M, Darnaudéry M: Estradiol, insulin-like growth factor-I and brain aging. Psychoneuroendocrinology. 2007, 32: S57-S61.View ArticlePubMedGoogle Scholar
  21. Morgado C, Silva L, Pereira-Terra P, Tavares I: Changes in serotonergic and noradrenergic descending pain pathways during painful diabetic neuropathy: the preventive action of IGF1. Neurobiol Dis. 2011, 43: 275-284. 10.1016/j.nbd.2011.04.001.View ArticlePubMedGoogle Scholar
  22. Arwert LI, Veltman DJ, Deijen JB, van Dam PS, Delemarre-van deWaal HA, Drent ML: Growth hormone deficiency and memory functioning in adults visualized by functional magnetic resonance imaging. Neuroendocrinology. 2005, 82: 32-40. 10.1159/000090123.View ArticlePubMedGoogle Scholar
  23. Thundyil J, Pavloski D, Sobey CG, Arumugam TV: Adiponectin receptor signalling in the brain. Br J Pharmacol. 2012, 165: 313-327. 10.1111/j.1476-5381.2011.01560.x.PubMed CentralView ArticlePubMedGoogle Scholar
  24. Yamauchi T, Kamon J, Minokoshi Y, Ito Y, Waki H, Uchida S, Yamashita S, Noda M, Kita S, Ueki K, Eto K, Akanuma Y, Froguel P, Foufelle F, Ferre P, Carling D, Kimura S, Nagai R, Kahn BB, Kadowaki T: Adiponectin stimulates glucose utilization and fatty-acid oxidation by activating AMP-activated protein kinase. Nature Medicine. 2002, 8: 1288-1295. 10.1038/nm788.View ArticlePubMedGoogle Scholar
  25. Bassi M, do Carmo JM, Hall JE, da Silva AA: Chronic effects of centrally administered adiponectin on appetite, metabolism and blood pressure regulation in normotensive and hypertensive rats. Peptides. 2012, 37: 1-5. 10.1016/j.peptides.2012.06.013.PubMed CentralView ArticlePubMedGoogle Scholar
  26. Hoyda TD, Samson WK, Ferguson AV: Adiponectin depolarizes parvocellular paraventricular nucleus neurons controlling neuroendocrine and autonomic function. Endocrinology. 2009, 150: 832-840.PubMed CentralView ArticlePubMedGoogle Scholar
  27. Barbosa IG, Rocha NP, de Miranda AS, Magalhaes PV, Huguet RB, de Souza LP, Kapczinski F, Teixeira AL: Increased levels of adipokines in bipolar disorder. J Psychiatr Res. 2012, 46: 389-393. 10.1016/j.jpsychires.2011.11.010.View ArticlePubMedGoogle Scholar
  28. Diniz BS, Teixeira AL, Campos AC, Miranda AS, Rocha NP, Talib LL, Gattaz WF, Forlenza OV: Reduced serum levels of adiponectin in elderly patients with major depression. J Psychiatr Res. 2012, 46: 1081-1085. 10.1016/j.jpsychires.2012.04.028.View ArticlePubMedGoogle Scholar
  29. Liu J, Guo M, Zhang D, Cheng S-Y, Liu M, Ding J, Scherer PE, Liu F, and Xin-Yun Lu: Adiponectin is critical in determining susceptibility to depressive behaviors and has antidepressant-like activity. Proc Natl Acad Sci USA. 2012, 109: 12248-12253. 10.1073/pnas.1202835109.PubMed CentralView ArticlePubMedGoogle Scholar
  30. Schwartz DR, Lazar MA: Human resistin: found in translation from mouse to man. Trends Endocrinol Metab. 2011, 22: 259-265.PubMed CentralPubMedGoogle Scholar
  31. Krysiak R: The role of adipokines in connective tissue diseases. Eur J Nutr. 2012, 51: 513-528. 10.1007/s00394-012-0370-0.PubMed CentralView ArticlePubMedGoogle Scholar
  32. Patel L, Buckels AC, Kinghorn IJ, Murdock PR, Holbrook JD, Plumpton C, Macphee CH, Smith SA: Resistin is expressed in human macrophages and directly regulated by PPAR gamma activators. Biochem Biophys Res Commun. 2003, 300: 472-476. 10.1016/S0006-291X(02)02841-3.View ArticlePubMedGoogle Scholar
  33. Nagaev I, Bokarewa M, Tarkowski A, Smith U: Human resistin is a systemic immune-derived proinflammatory cytokine targeting both leukocytes and adipocytes. PLoS One. 2006, 1: e31-10.1371/journal.pone.0000031.PubMed CentralView ArticlePubMedGoogle Scholar
  34. Kusminski CM, Scherer PE: The road from discovery to clinic: adiponectin as a biomarker of metabolic status. Nature. 2009, 86: 592-595.Google Scholar
  35. Lau CH, Muniandy S: Novel adiponectin-resistin (AR) and insulin resistance (IRAR) indexes are useful integrated diagnostic biomarkers for insulin resistance, type 2 diabetes and metabolic syndrome: a case control study. Cardiovascular Diabetology. 2011, 10: 8-10.1186/1475-2840-10-8.PubMed CentralView ArticlePubMedGoogle Scholar
  36. Tarkowski A, Bjersing J, Shestakov A, Bokarewa MI: Resistin competes with lipopolysaccharide for binding to toll-like receptor 4. J Cell Mol Med. 2010, 14: 1419-1431.PubMed CentralView ArticlePubMedGoogle Scholar
  37. Boström EA, Svensson M, Andersson S, Jonsson I-M, Ekwall A-K H, Eisler T, Dahlberg LE, Smith U, Bokarewa MI: Resistin and insulin/insulin-like growth factor signaling in rheumatoid arthritis. Arthritis Rheum. 2011, 63: 2894-2904. 10.1002/art.30527.View ArticlePubMedGoogle Scholar
  38. Zhang Y, Proenca P, Maffei M, Barone M, Leopold L, Friedman JM: Positional cloning of the mouse obese gene and its human homologue. Nature. 1994, 372: 425-432. 10.1038/372425a0.View ArticlePubMedGoogle Scholar
  39. Kamohara S, Burcelin R, Halaas JL, Friedman JM, Charron MJ: Acute stimulation of glucose metabolism in mice by leptin treatment. Nature. 1997, 389: 374-377. 10.1038/38717.View ArticlePubMedGoogle Scholar
  40. Chehab FF: Leptin as a regulator of adipose mass and reproduction. Trends Pharmacol Sci. 2000, 21: 309-314. 10.1016/S0165-6147(00)01514-5.View ArticlePubMedGoogle Scholar
  41. Lu XY, Kim CS, Frazer A, Zhang W: Leptin: a potential novel antidepressant. Proc Natl Acad Sci USA. 2006, 103: 1593-1598. 10.1073/pnas.0508901103.PubMed CentralView ArticlePubMedGoogle Scholar
  42. Liu J, Garza JC, Bronner J, Kim CS, Zhang W, Lu XY: Acute administration of leptin produces anxiolytic-like effects: a comparison with fluoxetine. Psychopharmacology. 2010, 207: 535-545. 10.1007/s00213-009-1684-3.PubMed CentralView ArticlePubMedGoogle Scholar
  43. Satoh N, Ogawa Y, Katsuura G, Tsuji T, Masuzaki H, Hiraoka J, Okazaki T, Tamaki M, Hayase M, Yoshimasa Y, Nishi S, Hosoda K, Nakao K: Pathophysiological significance of the obese gene product, leptin, in ventromedial hypothalamus (VMH)-lesioned rats: evidence for loss of its satiety effect in VMH-lesioned rats. Endocrinology. 1997, 138: 947-954. 10.1210/en.138.3.947.PubMedGoogle Scholar
  44. Delgado TC, Violante IR, Nieto-Charques L, Cerdan S: Neuroglial metabolic compartmentation underlying leptin deficiency in the obese ob/ob mice as detected by magnetic resonance imaging and spectroscopy methods. Journal of Cerebral Blood Flow & Metabolism. 2011, 31: 2257-2266. 10.1038/jcbfm.2011.134.View ArticleGoogle Scholar
  45. Gao S, Zhu G, Gao X, Wu D, Carrasco P, Casals N, Hegardt FG, Moran TH, Lopaschuk GD: Important roles of brain-specific carnitine palmitoyltransferase and ceramide metabolism in leptin hypothalamic control of feeding. Proc Natl Acad Sci USA. 2011, 108: 9691-9696. 10.1073/pnas.1103267108.PubMed CentralView ArticlePubMedGoogle Scholar
  46. Stephens TW, Basinski M, Bristow PK, Bue-Valleskey JM, Burgett SG, Craft L, Hale J, Hoffmann J, Hsiung HM, Kriauciunas A: The role of neuropeptide Y in the antiobesity action of the obese gene product. Nature. 1995, 377: 530-532. 10.1038/377530a0.View ArticlePubMedGoogle Scholar
  47. Haque Z, Akbar N, Yasmeen F, Haleem MA, Haleem DJ: Inhibition of Immobilization Stress-induced Anorexia, Behavioral Deficits and Plasma Corticosterone Secretion by Injected Leptin in Rats. Stress. 2012, doi:10.3109/10253890.2012.73604Google Scholar
  48. Guo M, Huang TY, Garza JC, Chua SC, Lu XY: Selective deletion of leptin receptors in adult hippocampus induces depression-related behaviours. Int J Neuropsychopharmacol. 2012, 29: 1-11.Google Scholar
  49. Maeda T, Kiguchi N, Kobayashi Y, Ikuta T, Ozaki M, Kishioka S: Leptin derived from adipocytes in injured peripheral nerves facilitates development of neuropathic pain via macrophage stimulation. Proc Natl Acad Sci USA. 2009, 106: 13076-13081. 10.1073/pnas.0903524106.PubMed CentralView ArticlePubMedGoogle Scholar
  50. Malva JO, Xapelli S, Baptista S, Valero J, Agasse F, Ferreira R, Silva AP: Multifaces of neuropeptide Y in the brain - neuroprotection, neurogenesis and neuroinflammation. Neuropeptides. 2012, 46: 299-308. 10.1016/j.npep.2012.09.001.View ArticlePubMedGoogle Scholar
  51. Anderberg UM, Liu Z, Berglund L, Nyberg F: Elevated plasma levels of neuropeptide Y in female fibromyalgia patients. Eur J Pain. 1999, 3: 19-30. 10.1016/S1090-3801(99)90185-4.View ArticlePubMedGoogle Scholar
  52. Crofford LJ, Pillemer SR, Kalogeras KT, Cash JM, Michelson D, Kling MA, Sternberg EM, Gold PW, Chrousos GP, Wilder RL: Hypothalamic-pituitary-adrenal axis perturbations in patients with fibromyalgia. Arthritis Rheum. 1994, 37: 1583-1592. 10.1002/art.1780371105.View ArticlePubMedGoogle Scholar
  53. Di Franco M, Iannuccelli C, Alessandri C, Paradiso M, Riccieri V, Libri F, Valesini G: Autonomic dysfunction and neuropeptide Y in fibromyalgia. Clin Exp Rheumatol. 2009, 27: S75-78.PubMedGoogle Scholar
  54. Griep EN, Boersma JW, Lentjes EG, Prins AP, van der Korst JK, de Kloet ER: Function of the hypothalamic-pituitary-adrenal axis in patients with fibromyalgia and low back pain. J Rheumatol. 1998, 25: 1374-1381.PubMedGoogle Scholar
  55. Fletcher MA, Rosenthal M, Antoni M, Ironson G, Zeng XR, Barnes Z, Harvey JM, Hurwitz B, Levis S, Broderick G, Klimas NG: Plasma neuropeptide Y: a biomarker for symptom severity in chronic fatigue syndrome. Behav Brain Funct. 2010, 6: 76-10.1186/1744-9081-6-76.PubMed CentralView ArticlePubMedGoogle Scholar
  56. Klimas NG, Broderick G, Fletcher MA: Biomarkers for chronic fatigue. Brain, Behaviour, and Immunity. 2012, 26: 1202-1210. 10.1016/j.bbi.2012.06.006.View ArticleGoogle Scholar
  57. Irwin MR: Human psychoneuroimmunology: 20 years of discovery. Brain behav. 2008, 22: 129-139.Google Scholar
  58. Russel IJ, Orr MD, Litman B, Vipraio GA, Alboukrek D, Michalek JE, Lopez Y, MacKillip F: Elevated cerebrospinal fluid levels of substance P in patients with the fibromyalgia syndrome. Arthritis Rheum. 1994, 37: 1593-1601. 10.1002/art.1780371106.View ArticleGoogle Scholar
  59. Vaeroy H, Helle R, Forre O, Kass E, Terenius L: Elevated CSF levels of substance P and high incidence of Raynaud phenomen in patients with fibromyalgi: new features for diagnosis. Pain. 1988, 32: 21-26. 10.1016/0304-3959(88)90019-X.View ArticlePubMedGoogle Scholar
  60. Liu Z, Welin M, Bragee B, Nyberg F: A high-recovery extraction procedure for quantitative analysis of substance P and opioid peptides in human cerebrospinal fluid. Peptides. 2000, 21: 853-860. 10.1016/S0196-9781(00)00219-9.View ArticlePubMedGoogle Scholar
  61. Giovengo SL, Russel IJ, Larson AA: Increased concentrations of nerve growth factor in cerebrospinal fluid of patients with fibromyalgia. J Rheumatol. 1999, 26: 1564-1569.PubMedGoogle Scholar
  62. Kadetoff D, Lampa J, Westman M, Andersson M, Kosek E: Evidence of central inflammation in fibromyalgia - increased cerebrospinal fluid interleukin-8 levels. J Neuroimmunol. 2012, 242: 33-38. 10.1016/j.jneuroim.2011.10.013.View ArticlePubMedGoogle Scholar
  63. Mannerkorpi K, Nordeman L, Cider Å, Jonsson G: Does moderate-to-high aerobic exercise result in better improvement of body impairments and pain than low-intensive exercise in FM? A prospective randomised controlled trial. Arthritis Res Ther. 2010, 12: R189-10.1186/ar3159.PubMed CentralView ArticlePubMedGoogle Scholar
  64. Wolfe F, Smythe HA, Yunus MB, Bennett RM, Bombardier C, Goldenberg DL, Tugwell P, Campbell SM, Abeles M, Clark P, Fam AG, Farber SJ, Fiechtner JJ, Franklin CM, Gatter RA, Hamaty D, Lessard J, Lichtbroun AS, Masi AT, McCain GA, Reynolds WJ, Romano TJ, Russell IJ, Sheon RP: The American College of Rheumatology 1990 criteria for the classification of fibromyalgia: report of the Multicenter Criteria Committee. Arthritis Rheum. 1990, 33: 160-172. 10.1002/art.1780330203.View ArticlePubMedGoogle Scholar
  65. Burckhardt CS, Clark SR, Bennett RM: The fibromyalgia impact questionnaire: development and validation. J Rheumatol. 1991, 18: 728-733.PubMedGoogle Scholar
  66. Ericsson A, Mannerkorpi K: Assessment of fatigue in patients with fibromyalgia and chronic widespread pain. Reliability and validity of the Swedish version of the MFI-20. Disabil Rehabil. 2007, 30: 1665-1670.View ArticleGoogle Scholar
  67. Srikuea R, Symons TB, Long DE, Lee JD, Shang Y, Chomentowski PJ, Yu G, Crofford LJ, Peterson CA: Fibromyalgia is associated with altered skeletal muscle characteristics which may contribute to post-exertional fatigue in post-menopausal women. Arthritis Rheum. 2012, 65: 519-528.View ArticleGoogle Scholar
  68. Carville S, Arendt-Nielsen S, Bliddal H, Blotman F, Branco JC, Buskila D, Da Silva JA, Danneskiold-Samsøe B, Dincer F, Henriksson C, Henriksson KG, Kosek E, Longley K, McCarthy GM, Perrot S, Puszczewicz M, Sarzi-Puttini P, Silman A, Späth M, Choy EH; EULAR: EULAR evidence-based recommendations for the management of fibromyalgia syndrome. Annals Rheum Dis. 2008, 67: 536-541.View ArticleGoogle Scholar
  69. Mannerkorpi K, Nordeman L, Ericsson A, Arndorw M, GAU-Study-Group: Pool-exercise for patients with fibromyalgia or chronic widespread pain. A randomized controlled trial and subgroup analyses. J Rehabil Med. 2009, 41: 751-760. 10.2340/16501977-0409.View ArticlePubMedGoogle Scholar
  70. Kim CH, Luedtke CA, Vincent A, Thompson JM, Oh TH: Association of body mass index with symptom severity and quality of life in patients with fibromyalgia. Arthritis Care Res. 2012, 64: 222-228.View ArticleGoogle Scholar
  71. Mork PJ, Vasseljen O, Nilsen TI: Association between physical exercise, body mass index and risk of fibromyalgi: longitudinal data from the Norwegian Nord-Trondelag Health Study. Arthritis Care Res. 2010, 62: 611-617. 10.1002/acr.20118.View ArticleGoogle Scholar
  72. Choy E, Arnold L, Clauw D, Crofford L, Glass J, Simon L, Martin SA, Strand CV, Williams DA, Mease PJ: Content and criterion validity of the preliminary core dataset for clinical trials in fibromyalgia syndrome. J Rheumatol. 2009, 36: 2330-2334. 10.3899/jrheum.090368.PubMed CentralView ArticlePubMedGoogle Scholar
  73. Bennett RM: Adult growth hormone deficiency in patients with fibromyalgia. Curr Rheumatol Rep. 2002, 4: 306-312. 10.1007/s11926-002-0039-4.View ArticlePubMedGoogle Scholar
  74. Kohno D, Yada T: Neuropeptides. Arcuate NPY neurons sense and integrate peripheral metabolic signals to control feeding. Neuropeptides. 2012, 46: 315-319. 10.1016/j.npep.2012.09.004.View ArticlePubMedGoogle Scholar
  75. Garcia-Segura LM, Rodriguez JR, Torres-Aleman I: Localization of the insuli-like growth factor I receptor in the cerebellum and hypothalamus of adult rats: an electron microscopic study. J Neurocytology. 1997, 26: 479-490. 10.1023/A:1018581407804.View ArticleGoogle Scholar
  76. Fernandez-Galas MC, Naftolin F, Garcia-Segura LM: Phasic synaptic remodeling of the rat arcuate nucleus during the estrous cycle depends on insulin-like growth factor-I receptor activation. J Neurosci Res. 1999, 55: 286-292. 10.1002/(SICI)1097-4547(19990201)55:3<286::AID-JNR3>3.0.CO;2-4.View ArticleGoogle Scholar
  77. Singhal NS, Lazar MA, Ahima RS: Central resistin induces insulin resistance via neuropeptide Y. J Neurosci. 2007, 27: 12924-12932. 10.1523/JNEUROSCI.2443-07.2007.View ArticlePubMedGoogle Scholar

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