The Association Between Constipation or Stool Consistency and Pain Severity in Patients With Chronic Pain

AUTHORS

Young-Chang Arai 1 , 2 , Yukiko Shiro 1 , 3 , * , Yasushi Funak 4 , Kunio Kasugaii 4 , Yusuke Omichi 1 , Hiroki Sakurai 1 , Takako Matsubara 1 , Masayuki Inoue 1 , 2 , Kazuhiro Shimo 1 , 2 , Hironori Saisu 1 , Tatsunori Ikemoto 1 , 2 , Keiko Owari 1 , Makoto Nishihara 1 , Takahiro Ushida 1 , 2

1 Multidisciplinary Pain Center, Aichi Medical University, Nagakute, Japan

2 Institute of Physical Fitness, Sports Medicine and Rehabilitation, School of Medicine, Aichi Medical University, Nagakute, Japan

3 Department of Physical Therapy, Faculty of Rehabilitation Sciences, Nagoya Gakuin University, Seto, Aichi, Japan

4 Department of Gastroenterology, School of Medicine, Aichi Medical University, Nagakute, Japan

How to Cite: Arai Y, Shiro Y, Funak Y, Kasugaii K, Omichi Y, et al. The Association Between Constipation or Stool Consistency and Pain Severity in Patients With Chronic Pain, Anesth Pain Med. 2018 ; 8(4):e69275. doi: 10.5812/aapm.69275.

ARTICLE INFORMATION

Anesthesiology and Pain Medicine: 8 (4); e69275
Published Online: August 11, 2018
Article Type: Brief Report
Received: April 10, 2018
Revised: June 1, 2018
Accepted: August 3, 2018
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Abstract

Background: Bacteria can influence a variety of gut functions. Some studies showed that stool consistency and constipation were associated with gut microbiome (GM) composition, and enterotype, dysbiosis. Growing evidence indicates the significant role of GM in the homeostatic function of the host body. The GM may regulate multiple neurochemical and neurometabolic pathways. Chronicity of the pain is actively modulated at the molecular to the network level by means of several neurotransmitters. The GM to some extent can affect pain perception.

Objectives: The current study aimed at investigating the relationship between constipation state or usual stool form and pain severity of patients with chronic pain.

Methods: The current study was conducted on 365 patients with chronic pain. The participants were evaluated on their stool form (the Bristol stool form scale; BSFS), constipation state (the Cleveland clinic constipation score; CCCS), body mass index (BMI), and usual pain severity (numerical rating scale; NRS). In addition, the participants were assigned into five groups according to the pain region (i e, low back and/or lower limb, whole body, neck and/or upper back and/or upper limb, head and/or face, chest and/or abdominal).

Results: The CCS showed a significant and positive association with the pain severity of the total patients and patients with low back and/or lower limb pain. Simultaneous multiple linear regression analyses revealed that a predictor of the pain severity was the CCS for the total patients and patients with low back and/or lower limb, whole body pain.

Conclusions: Constipation displayed a significant and positive association with the pain severity of the total patients and patients with low back and/or lower limb pain, whole body.

Keywords

Gut Microbiome Pain Severity Stool Form Constipation Chronic Pain

Copyright © 2018, Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited

1. Background

Pain can become intractable and chronic when neural damage and inflammation are processed under disrupted psychosocial conditions. The transition from acute pain into intractable and chronic pain is actively modulated (plasticity) at the molecular to the network level since many neuromodulators invariably work for neuroplasticity of pain perception (1, 2).

Authors previously reported that stool consistency displays a significant association with the pain perception and anxiety status of healthy volunteers (3). Several researchers reported that stool consistency is profoundly linked to gut microbiome (GM) abundance and composition, enterotypes, and bacterial growth (4-7). In contrast, some studies show that constipation is deeply linked to microbial diversity and composition (8, 9). There is increasing evidence that changes in microbial diversity and composition are associated with several disease states including obesity and behavioral disorders. In the past few years, the human microbiome is recognized as a considerable contributor to human nutrition as well as health and disease (10). It was thus postulated that the GM dysbiosis might be associated with pain perception and anxiety states in healthy subjects. However, it is not clear if there is an association between GM and pain severity in patients with chronic pain in the authors’ previous study, since it was conducted on young healthy subjects.

The GM is known to influence host neuromodulatory, neurotransmission, and neuroimmune functions (11, 12). Since it was hypothesized that pathogenic bacteria indispensably work on neuroplasticity of pain perception in a maladaptive way, thereby exacerbating chronic pain, the current study aimed at investigating the relationship between stool form or constipation and pain severity in patients with chronic pain.

2. Methods

After receiving approval from the IRB (Aichi Medical University reference number: 12 - 067), a cross sectional survey was administered to a total of 365 patients with chronic pain that visited the pain center of Aichi Medical University Hospital to manage their chronic pain from March 2017 to April 2017. The demographics, medication, and course of pain in all patients were recorded on a regular basis. The exclusion criteria were digestive disease that may be cause constipation and diarrhea, stoma in situ, neurological diseases such as spinal cord injury and autonomic disturbance, or cognitive disease.

The participants were evaluated based on their usual stool consistency, constipation state, and degree of obesity and usual pain over a period of one week. The stool consistency was assessed by the Bristol stool form scale (BSFS). The BSFS is a graded visual scale of stool density from type 1 (hard to pass) to type 7 (the fluid kind). The relevance of this scale is that it shows the participant’s drawing stool shapes together with precise descriptions regarding form and consistency, and using easily recognizable examples (for example, in type 1, by an illustration of faces as separate balls, a description: “separate hard lumps, like nuts”). The stool types 1 and 2 (“sausage-shaped, but lumpy”) considered abnormally hard stools (designated as constipation symptoms), types 3, 4, and 5 are generally considered normal stool form, especially type 4 (“like a sausage or snake, smooth and soft”) is most common, and types 6 (“fluffy pieces with ragged edges, a mushy stool”) and 7 (“watery, no solid pieces”) are abnormally liquid stools (designated as diarrhea) (13). Constipation was rated with the Cleveland clinic constipation score (CCCS). This score is compatible with objective physiologic findings, provides standardized assessment of constipation, and is validated in clinical practice. CCCS consists of eight factors: frequency of bowel movements, difficulty (painful evacuation effort), completeness (incomplete evacuation), pain (abdominal pain), time (minutes in lavatory per attempt), assistance (type of assistance), failure (number of unsuccessful attempts of evacuation per 24 hours), and history (duration of constipation). The scoring of each factor ranges from 0 to 4 (with the exception of “type of assistance”, which is 0 to 2). Score ranges from 0 to 30, with 0 indicating normal and higher scores indicating more severity constipation (14, 15). The degree of obesity was assessed by using the body mass index (BMI). The scores of numerical rating scale (NRS) scores (0 indicates ‘no pain’ and 10 ‘the greatest pain possible’) were used to obtain the average severity of total pain over a period of one week.

2.1. Data Analyses

All data were analyzed with SPSS version 20 (IBM, New York, USA). Data were expressed as median and range, since each variable resulted in not only parametric but also non-parametric distribution. The participants were assigned into five groups according to the pain region (i e, low back and/or lower limb, whole body, neck and/or upper back and/or upper limb, head and/or face, chest and/or abdominal). First, G-power software was employed to determine the sample size for the current study. An effect size means the strength of correlation between two variables. In the magnitude of the effect size in correlation, 0.3 and 0.5 mean medium and large effect size, respectively. The sample size required a minimum of 60 subjects to show an effect size of 0.4 with a significance level of 0.05 (α = 0.05) and a power of 80% (β = 0.20) for each group; therefore, a total of more than 300 samples were needed for the study. Analysis of variance and the Fisher exact or the Kruskal-Wallis tests were performed for patients’ characteristics and medication where appropriate. The relationship among outcome measures was analyzed using Spearman correlation for bivariate regression analysis. Further analysis using a stepwise multiple linear regression analysis was performed to predict the pain severity of the independent variables (i e, gender, age, BMI, BSFS, CCCS). A P-value of < 0.05 was considered statistically significant.

3. Results

Three hundreds and three out of 365 patients with chronic pain completed the questionnaire. Their characteristics are presented in Table 1. No significant differences were observed in height (cm), weight (kg), BMI (kg/m2), BSFS, and CCCS among the pain regions. There were some differences in the gender ratio among the groups. However, BSFS and CCCS did not show gender differences (Figure 1). Patients of the low back and/or lower limb group were older. NRS score was statistically lower in the low back and/or lower limb and head and/or face groups than the whole body group. The BSFS showed a significant and negative association with age and BMI, but did not show association with the pain severity. On the other hand, CCCS showed a significant and positive association with the pain severity, but no association with age and BMI (Table 2).

Table 1. Participants’ Characteristicsa,b,c
TotalLow Back/Lower LimbWhole BodyNeck/Upper Back/Upper LimbHead/FaceChest/AbdominalP Value
No. (male: female)303 (121: 182)111 (53: 58)71 (24: 47)62 (27: 35)36 (6 : 30)15 (6: 9)0.014
Age (y)57 (11 - 90)64* (11 - 87)52 (14 - 90)52 (18 - 86)56 (15 - 80)49 (15 - 84)0.012
Height (cm)160 (131 - 184)160.0 (138 - 183)158.0 (147 - 182)164 (132 - 184)155 (146 - 171)158 (146 - 183)0.056
Weight (kg)55.0 (32 - 111)58.0 (37 - 105)53.0 (32 - 90)55.7 (40 - 111)53.0 (36 - 86)58.0 (44 - 77)0.582
BMI (kg/m2)21.7 (12.2 - 41.4)22.1 (14.5 - 41.4)20.8 (12.2 - 36.6)21.0 (15.8 - 36.2)21.0 (15.7 - 33.3)23.4 (17.6 - 26.1)0.203
Pain severity (NRS)6 (0 - 10)5* (0 - 10)6 (2 - 10)6 (0 - 10)5* (1 - 10)6 (2 - 8)0.008
BSFS4 (1 - 7)4 (1 - 7)4 (1 - 7)4 (1 - 6)4 (1 - 6)4 (2 - 5)0.681
CCCS4 (0 - 19)4 (0 - 17)4 (0 - 15)4 (0 - 19)3 (0 - 12)2 (0 - 16)0.108

Abbreviations: BMI, body mass index; BSFS, the Bristol stool form scale; CCCS, the Cleveland clinic constipation score; NRS, numerical rating scale.

a Value: median (range).

b*, vs. Whole body: P < 0.05.

c Others (n = 8), Cancer Pain (n = 1), Postherpetic Pain (n = 1), Endometriosis (n = 1), Arteriosclerosis Obliterans (n = 1), Anus Pain (n = 1), Pudendal pain (n = 3).

Gender differences of BSFS/ CCCS. BSFS, the Bristol stool form scale, CCCS: the Cleveland clinic constipation score.
Figure 1. Gender differences of BSFS/ CCCS. BSFS, the Bristol stool form scale, CCCS: the Cleveland clinic constipation score.
Table 2. Correlation of Outcomes Measurementsa
BMIPain SeverityBSFSCCCS
Age (y)0.184**-0.015-0.116*0.052
BMI (kg/m2)-0.118*-0.174**-0.047
Pain severity0.0140.227***
BSFS-0.175**

Abbreviations: BMI, body mass index; BSFS, the Bristol stool form scale;CCCS, the Cleveland clinic constipation score.

a Value: correlation coefficient, * P < 0.05, ** P < 0.01, *** P < 0.001.

For medication, there were some differences of the prescribed ratio only in acetaminophen (Table 3). Furthermore, there were no significant differences in BSFS and CCCS (P = 0.183, P = 0.292) among medications (Figure 2).

Table 3. Medicationa
Low Back/Lower LimbWhole BodyNeck/Upper Back/Upper LimbHead/FaceChest/AbdominalP Value
NSAIDs19 (17.1)13 (18.3)7 (11.3)5 (13.9)2 (13.3)0.808
Acetaminophen2 (1.8)7 (9.9)1 (1.6)1 (2.8)0 (0)0.040*
Steroid1 (0.9)4 (5.6)0 (0)0 (0)0 (0)0.062
Opioid22 (19.8)21 (29.6)17 (27.4)5 (13.9)2 (13.3)0.234
Antidepressant18 (16.2)8 (11.3)12 (19.4)8 (22.2)3 (20.0)0.599
Antiepileptic32 (28.8)34 (47.9)22 (35.5)12(33.3)3 (20.0)0.433
Antipsychotic1 (0.9)2 (2.8)2 (3.2)4 (11.1)1 (6.7)0.056
Muscle relaxant9 (8.1)10 (14.1)10 (16.1)3 (8.3)0 (0)0.242

Abbreviation: NSAIDs: nonsteroidal anti-inflammatory drugs.

a Values are available No. (%).

Comparison of BSFS/CCCS among medications. BSFS, the Bristol stool form scale, CCCS: the Cleveland clinic constipation score.
Figure 2. Comparison of BSFS/CCCS among medications. BSFS, the Bristol stool form scale, CCCS: the Cleveland clinic constipation score.

CCCS displayed a significant and positive association with the pain severity of the total patients and patients with low back and/or lower limb pain (Table 4, Figures 3 and 4). Simultaneous multiple linear regression analyses revealed that a predictor of pain severity was CCCS in the total patients and patients with low back and/or lower limb, and with whole body pain (Table 5). However, the BSFS showed no association with the pain severity at each pain region (Tables 4 and 5).

Table 4. A simple Linear Regression Analysis With Pain Severitya
TotalLow Back/Lower LimbWhole BodyNeck/Upper Back/Upper LimbHead/FaceChest/Abdominal
Age (y)-0.015-0.102-0.0770.259*-0.053-0.183
BMI (kg/m2)-0.118*-0.1840.007-0.1970.150-0.018
BSFS0.0140.062-0.081-0.0730.027-0.127
CCCS0.227***0.383***0.2070.0460.1610.056

Abbreviations: BMI, body mass index; BSFS, the Bristol stool form scale; CCS, the Cleveland clinic constipation score.

a Value: correlation coefficient with pain severity, * P < 0.05, *** P < 0.001.

Correlation between CCCS and pain severity at the total patients. CCCS, the Cleveland clinic constipation score, NRS: numerical rating scale.
Figure 3. Correlation between CCCS and pain severity at the total patients. CCCS, the Cleveland clinic constipation score, NRS: numerical rating scale.
Correlation between CCCS and pain severity in the participants with low back and/or lower limb pain. CCCS, the Cleveland clinic constipation score, NRS: numerical rating scale
Figure 4. Correlation between CCCS and pain severity in the participants with low back and/or lower limb pain. CCCS, the Cleveland clinic constipation score, NRS: numerical rating scale
Table 5. Multiple Regression Analysis With Pain Severity as a Dependent Variablea
VariableAdjusted R2BβP Value95%CI for B
Lower LimitUpper Limit
Total0.056
Constant4.9680.0004.5935.342
CCCS0.1270.2430.0000.0690.184
Low back/Lower limb0.148
Constant4.2370.0003.6184.855
CCCS0.2220.3940.0000.1240.320
Whole body0.057
Constant5.6980.0005.0006.396
CCCS0.1120.2650.0250.0140.210

Abbreviations: BMI, body mass index; CCCS, the Cleveland clinic constipation score.

a B, unstandardized coefficients; β, standardized coefficients.

4. Discussion

Authors previously reported that the stool consistency was associated with pain perception in healthy subjects. Specifically, the more watery their stool was, the more sensitive were the healthy subjects to painful stimuli (3). In contrast to healthy subjects, in the current study, patients with chronic pain showed that constipation was significantly and positively associated with the pain severity of the total patients and patients with low back and/or lower limb, and whole body pain. However, there were no significant associations between the stool consistency and the pain severity.

Constipation is associated with the GM composition. For example, the patients with constipation rigorously reduced abundance in Prevotella and increased representation in several genera of Firmicutes compared with the controls (8). Khalif et al. (16), reported lower amount of Lactobacillus and Bifidobacteria species in the stool sample of adults with chronic constipation. Moreover, short-chain fatty acids generated from the enteric bacterial fermentation of undigested carbohydrates may contribute to the pathophysiology of constipation (9).

The GM has an influence on autism, major depression, and Parkinson disease (17). In a study on healthy volunteers, those who took specific probiotics (Lactobacillus belveticus and Bifidobacterium longum) displayed less anxiety and depression (18). The GM contributes to the modulation of multiple neurochemical and neuro-metabolic pathways (11, 12). These pathways involve the hypothalamic-pituitary-adrenal axis, chemokines and cytokines, and autonomic nervous and enteric nervous systems, which constitute the microbiota-gut-brain axis (10). Also, brain function and psychological makeup are considered to have a reciprocal relationship with GM. Furthermore, GM can release neuroactive molecules (such as acetylcholine, catecholamine, γ-aminobutyric acid, histamine melatonin, and 5-hydroxytryptramine (5-HT) similar to the host that may induce neuropeptide production in the brain, and increase gut-blood barrier and blood-brain barrier (BBB) permeability (19, 20). The 5-HT plays an important role in the regulation of peristalsis (21), pain perception (21, 22), mood, and cognition (23). Despite its well-known role in the central nervous system, while only 5% out of the whole human body 5-HT is found in the brain, the gut contains 95% of 5-HT (24). Since 5-HT is synthesized from essential amino acid tryptophan, the increasing microbiota deterioration reduces the functionality of the tryptophan absorption in the gut, thereby reducing 5-HT biosynthesis (25).

The endogenous pain modulatory mechanisms, involving both opioid and 5-HT signaling, are impaired in patients with chronic pain (26). The dysfunction of endogenous pain modulatory mechanisms is observed in patients with whole body pain (e g, widespread pain, fibromyalgia) rather than local pain (27). Previous research suggested that patients with fibromyalgia reduced 5-HT levels, and reduced tryptophan absorption (25). Low tryptophan absorption induces low 5-HT synthesis that causes fibromyalgia symptoms (25). The current study results indicated an association between constipation and pain severity in patients with chronic pain, especially in patients with whole body, low back and/or lower limb pain. Based on the current study results, it was thus postulated that dysbiosis might have disrupted pain-modulation systems, thereby leading to a vicious cycle in which biological factors could have aggravated the pain intensity of patients with low back and/or lower limb, and whole body pain.

The constipation was associated with insufficient physical activity and excessive sedentary behavior. The mild to moderate physical activity showed positive effects on constipation (28, 29). Also, it is well known that inactivity is a risk factor for development of chronic pain (30). Moreover, increase in physical activity attenuates the severity of symptoms in patients with chronic pain (31). One of the mechanisms by which the exercise induced hypoalgesia is thought to involve the endogenous pain modulatory system (32). Additionally, it is reported that the regular exercise influences the composition and function of human GM (33). Therefore, it is suspected that the physical activity, GM, and the endogenous pain modulatory function are correlated with patients with chronic pain.

The stool consistency and constipation may be affected by age, gender, and BMI (29, 34), but CCCS had no correlation with these factors. Although the BSFS was correlated with age and BMI, the correlation coefficients were small (rs = -0.116, -0.174). In addition, BSFS and CCCS did not show gender differences. Thus, it was thought that stool consistency and constipation had little influence on age, gender, and degree of obesity.

The pain severity was correlated with CCCS, but was not correlated with BSFS. There may be a relationship with CCCS and BSFS, since they had negative correlation. However, the correlation coefficient was small. Constipation does not necessary mean a hard stool. Furthermore, BSFS is a graded scale from 1 to 7. Therefore, it was thought that BSFS did not have a significant association with the pain severity. On the other hand, authors previously reported that BSFS was associated with the pain perception in healthy subjects (3). One of the reasons might be that the subjects of the authors’ previous study were younger than the subjects of the current study. Another reason might be that although BSFS was associated with pain perception in healthy subjects, the pain perception was induced by painful external stimuli. Therefore, further studies are necessary to investigate the difference between healthy subjects and patients with chronic pain.

Medication has several side effects, especially gastrointestinal effect. Although opioid, pregabalin, and antidepressant out of the drugs listed in the current study are known to cause constipation (35-38), there were some differences of the prescribed ratio only in acetaminophen. Furthermore, there were no significant differences in BSFS and CCCS among medications. It was thus postulated that medication would hardly have influenced the current study findings.

There were several limitations to the current study due to the inclusion of elements of a qualitative study. First, the study did not measure GM composition and richness, and blood levels of substances such as short-chain fatty acids. There is growing evidence that microbiota diversity can change variations in short-chain fatty acids (39). Secondly, authors’ previous study showed that stool form consistency was associated with pain perception (3), which was not consistent with the current study results. Authors’ previous study was conducted on young healthy subjects and, in contrast, the current study was conducted on older patients with chronic pain; therefore, these results could be inconsistent. Further studies should evaluate the relationship between GM and pain perception in older adults and patients with chronic pain using 16S rRNA analysis or by measuring short-chain fatty acids. Thirdly, the intensity of pain was affected by the dosage of the prescribed medications. Fourthly, the patients were classified into five groups based on the anatomical part of the body in which the patients felt pain. Even if the part with pain was the same, it included various diseases. Further studies are needed to investigate the influence of the dosage of the prescribed medications and the underlying disease. Finally, the current study did not evaluate the effects of endogenous pain modulatory molecules including the 5-HT.

4.1. Conclusions

The results of the current study showed that constipation was significantly and positively associated with the pain severity in the total patients and patients with low back and/or lower limb, and whole body pain.

Acknowledgements

References

  • 1.

    Ultsch A, Kringel D, Kalso E, Mogil JS, Lotsch J. A data science approach to candidate gene selection of pain regarded as a process of learning and neural plasticity. Pain. 2016;157(12):2747-57. doi: 10.1097/j.pain.0000000000000694. [PubMed: 27548044].

  • 2.

    Jutzeler CR, Curt A, Kramer JL. Relationship between chronic pain and brain reorganization after deafferentation: A systematic review of functional MRI findings. Neuroimage Clin. 2015;9:599-606. doi: 10.1016/j.nicl.2015.09.018. [PubMed: 26740913]. [PubMed Central: PMC4644246].

  • 3.

    Shiro Y, Arai YC, Ikemoto T, Hayashi K. Stool consistency is significantly associated with pain perception. PLoS One. 2017;12(8). e0182859. doi: 10.1371/journal.pone.0182859. [PubMed: 28793322]. [PubMed Central: PMC5549932].

  • 4.

    Tigchelaar EF, Bonder MJ, Jankipersadsing SA, Fu J, Wijmenga C, Zhernakova A. Gut microbiota composition associated with stool consistency. Gut. 2016;65(3):540-2. doi: 10.1136/gutjnl-2015-310328. [PubMed: 26276682].

  • 5.

    Vandeputte D, Falony G, Vieira-Silva S, Tito RY, Joossens M, Raes J. Stool consistency is strongly associated with gut microbiota richness and composition, enterotypes and bacterial growth rates. Gut. 2016;65(1):57-62. doi: 10.1136/gutjnl-2015-309618. [PubMed: 26069274]. [PubMed Central: PMC4717365].

  • 6.

    Zhao Y, Yu YB. Intestinal microbiota and chronic constipation. Springerplus. 2016;5(1):1130. doi: 10.1186/s40064-016-2821-1. [PubMed: 27478747]. [PubMed Central: PMC4951383].

  • 7.

    Strati F, Cavalieri D, Albanese D, De Felice C, Donati C, Hayek J, et al. Altered gut microbiota in Rett syndrome. Microbiome. 2016;4(1):41. doi: 10.1186/s40168-016-0185-y. [PubMed: 27473171]. [PubMed Central: PMC4967335].

  • 8.

    Zhu L, Liu W, Alkhouri R, Baker RD, Bard JE, Quigley EM, et al. Structural changes in the gut microbiome of constipated patients. Physiol Genomics. 2014;46(18):679-86. doi: 10.1152/physiolgenomics.00082.2014. [PubMed: 25073603].

  • 9.

    Kang DW, DiBaise JK, Ilhan ZE, Crowell MD, Rideout JR, Caporaso JG, et al. Gut microbial and short-chain fatty acid profiles in adults with chronic constipation before and after treatment with lubiprostone. Anaerobe. 2015;33:33-41. doi: 10.1016/j.anaerobe.2015.01.005. [PubMed: 25617726].

  • 10.

    Mayer EA. Gut feelings: the emerging biology of gut-brain communication. Nat Rev Neurosci. 2011;12(8):453-66. doi: 10.1038/nrn3071. [PubMed: 21750565]. [PubMed Central: PMC3845678].

  • 11.

    Bravo JA, Julio-Pieper M, Forsythe P, Kunze W, Dinan TG, Bienenstock J, et al. Communication between gastrointestinal bacteria and the nervous system. Curr Opin Pharmacol. 2012;12(6):667-72. doi: 10.1016/j.coph.2012.09.010. [PubMed: 23041079].

  • 12.

    Mayer EA, Tillisch K, Gupta A. Gut/brain axis and the microbiota. J Clin Invest. 2015;125(3):926-38. doi: 10.1172/JCI76304. [PubMed: 25689247]. [PubMed Central: PMC4362231].

  • 13.

    Martinez AP, de Azevedo GR. The Bristol Stool Form Scale: its translation to Portuguese, cultural adaptation and validation. Rev Lat Am Enfermagem. 2012;20(3):583-9. [PubMed: 22991122].

  • 14.

    Agachan F, Chen T, Pfeifer J, Reissman P, Wexner SD. A constipation scoring system to simplify evaluation and management of constipated patients. Dis Colon Rectum. 1996;39(6):681-5. [PubMed: 8646957].

  • 15.

    Wu J, Liu B, Li N, Sun J, Wang L, Wang L, et al. Effect and safety of deep needling and shallow needling for functional constipation: a multicenter, randomized controlled trial. Medicine (Baltimore). 2014;93(28). e284. doi: 10.1097/MD.0000000000000284. [PubMed: 25526462]. [PubMed Central: PMC4603109].

  • 16.

    Khalif IL, Quigley EM, Konovitch EA, Maximova ID. Alterations in the colonic flora and intestinal permeability and evidence of immune activation in chronic constipation. Dig Liver Dis. 2005;37(11):838-49. doi: 10.1016/j.dld.2005.06.008. [PubMed: 16169298].

  • 17.

    Dinan TG, Cryan JF. The impact of gut microbiota on brain and behaviour: implications for psychiatry. Curr Opin Clin Nutr Metab Care. 2015;18(6):552-8. doi: 10.1097/MCO.0000000000000221. [PubMed: 26372511].

  • 18.

    Dinan TG, Cryan JF. Melancholic microbes: a link between gut microbiota and depression? Neurogastroenterol Motil. 2013;25(9):713-9. doi: 10.1111/nmo.12198. [PubMed: 23910373].

  • 19.

    Barrett E, Ross RP, O'Toole PW, Fitzgerald GF, Stanton C. gamma-Aminobutyric acid production by culturable bacteria from the human intestine. J Appl Microbiol. 2012;113(2):411-7. doi: 10.1111/j.1365-2672.2012.05344.x. [PubMed: 22612585].

  • 20.

    Petra AI, Panagiotidou S, Hatziagelaki E, Stewart JM, Conti P, Theoharides TC. Gut-Microbiota-Brain Axis and Its Effect on Neuropsychiatric Disorders With Suspected Immune Dysregulation. Clin Ther. 2015;37(5):984-95. doi: 10.1016/j.clinthera.2015.04.002. [PubMed: 26046241]. [PubMed Central: PMC4458706].

  • 21.

    Talley NJ. Serotoninergic neuroenteric modulators. Lancet. 2001;358(9298):2061-8. doi: 10.1016/S0140-6736(01)07103-3. [PubMed: 11755632].

  • 22.

    McLean PG, Borman RA, Lee K. 5-HT in the enteric nervous system: gut function and neuropharmacology. Trends Neurosci. 2007;30(1):9-13. doi: 10.1016/j.tins.2006.11.002. [PubMed: 17126921].

  • 23.

    Cryan JF, Leonard BE. 5-HT1A and beyond: the role of serotonin and its receptors in depression and the antidepressant response. Hum Psychopharmacol. 2000;15(2):113-35. doi: 10.1002/(SICI)1099-1077(200003)15:2<113::AID-HUP150>3.0.CO;2-W. [PubMed: 12404340].

  • 24.

    Burokas A, Moloney RD, Dinan TG, Cryan JF. Microbiota regulation of the Mammalian gut-brain axis. Adv Appl Microbiol. 2015;91:1-62. doi: 10.1016/bs.aambs.2015.02.001. [PubMed: 25911232].

  • 25.

    Lattanzio SM. Fibromyalgia Syndrome: A Metabolic Approach Grounded in Biochemistry for the Remission of Symptoms. Front Med (Lausanne). 2017;4:198. doi: 10.3389/fmed.2017.00198. [PubMed: 29250522]. [PubMed Central: PMC5715322].

  • 26.

    Lewis GN, Rice DA, McNair PJ. Conditioned pain modulation in populations with chronic pain: a systematic review and meta-analysis. J Pain. 2012;13(10):936-44. doi: 10.1016/j.jpain.2012.07.005. [PubMed: 22981090].

  • 27.

    Gerhardt A, Eich W, Treede RD, Tesarz J. Conditioned pain modulation in patients with nonspecific chronic back pain with chronic local pain, chronic widespread pain, and fibromyalgia. Pain. 2017;158(3):430-9. doi: 10.1097/j.pain.0000000000000777. [PubMed: 27902566].

  • 28.

    Simren M. Physical activity and the gastrointestinal tract. Eur J Gastroenterol Hepatol. 2002;14(10):1053-6. [PubMed: 12362093].

  • 29.

    Tantawy SA, Kamel DM, Abdelbasset WK, Elgohary HM. Effects of a proposed physical activity and diet control to manage constipation in middle-aged obese women. Diabetes Metab Syndr Obes. 2017;10:513-9. doi: 10.2147/DMSO.S140250. [PubMed: 29276399]. [PubMed Central: PMC5734236].

  • 30.

    Landmark T, Romundstad P, Borchgrevink PC, Kaasa S, Dale O. Associations between recreational exercise and chronic pain in the general population: evidence from the HUNT 3 study. Pain. 2011;152(10):2241-7. doi: 10.1016/j.pain.2011.04.029. [PubMed: 21601986].

  • 31.

    Pinto RZ, Ferreira PH, Kongsted A, Ferreira ML, Maher CG, Kent P. Self-reported moderate-to-vigorous leisure time physical activity predicts less pain and disability over 12 months in chronic and persistent low back pain. Eur J Pain. 2014;18(8):1190-8. doi: 10.1002/j.1532-2149.2014.00468.x. [PubMed: 24577780].

  • 32.

    Sluka KA, O'Donnell JM, Danielson J, Rasmussen LA. Regular physical activity prevents development of chronic pain and activation of central neurons. J Appl Physiol (1985). 2013;114(6):725-33. doi: 10.1152/japplphysiol.01317.2012. [PubMed: 23271699]. [PubMed Central: PMC3615604].

  • 33.

    Zhao X, Zhang Z, Hu B, Huang W, Yuan C, Zou L. Response of Gut Microbiota to Metabolite Changes Induced by Endurance Exercise. Front Microbiol. 2018;9:765. doi: 10.3389/fmicb.2018.00765. [PubMed: 29731746]. [PubMed Central: PMC5920010].

  • 34.

    Munch L, Tvistholm N, Trosborg I, Konradsen H. Living with constipation--older people's experiences and strategies with constipation before and during hospitalization. Int J Qual Stud Health Well-being. 2016;11:30732. doi: 10.3402/qhw.v11.30732. [PubMed: 27121271]. [PubMed Central: PMC4848391].

  • 35.

    Hartling L, Ali S, Dryden DM, Chordiya P, Johnson DW, Plint AC, et al. How Safe Are Common Analgesics for the Treatment of Acute Pain for Children? A Systematic Review. Pain Res Manag. 2016;2016:5346819. doi: 10.1155/2016/5346819. [PubMed: 28077923]. [PubMed Central: PMC5203901].

  • 36.

    Cooper TE, Wiffen PJ, Heathcote LC, Clinch J, Howard R, Krane E, et al. Antiepileptic drugs for chronic non-cancer pain in children and adolescents. Cochrane Database Syst Rev. 2017;8. CD012536. doi: 10.1002/14651858.CD012536.pub2. [PubMed: 28779491].

  • 37.

    Shanthanna H, Gilron I, Rajarathinam M, AlAmri R, Kamath S, Thabane L, et al. Benefits and safety of gabapentinoids in chronic low back pain: A systematic review and meta-analysis of randomized controlled trials. PLoS Med. 2017;14(8). e1002369. doi: 10.1371/journal.pmed.1002369. [PubMed: 28809936]. [PubMed Central: PMC5557428].

  • 38.

    Richards BL, Whittle SL, Buchbinder R. Muscle relaxants for pain management in rheumatoid arthritis. Cochrane Database Syst Rev. 2012;1. CD008922. doi: 10.1002/14651858.CD008922.pub2. [PubMed: 22258993].

  • 39.

    Pozuelo M, Panda S, Santiago A, Mendez S, Accarino A, Santos J, et al. Reduction of butyrate- and methane-producing microorganisms in patients with Irritable Bowel Syndrome. Sci Rep. 2015;5:12693. doi: 10.1038/srep12693. [PubMed: 26239401]. [PubMed Central: PMC4523847].

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