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. Author manuscript; available in PMC: 2025 Dec 1.
Published in final edited form as: J Pain. 2024 Oct 5;25(12):104691. doi: 10.1016/j.jpain.2024.104691

High body mass index disrupts the homeostatic effects of pain inhibitory control in the symptomatology of patients with fibromyalgia

Guilherme J M Lacerda 1,2, Kevin Pacheco-Barrios 1,3, Felipe Fregni 1
PMCID: PMC11959513  NIHMSID: NIHMS2030884  PMID: 39374799

Abstract

This study examines the influence of body mass index (BMI) on the relationship between quantitative sensory testing (QST) measures and clinical characteristics in fibromyalgia syndrome (FMS). Utilizing BMI as a categorical covariate (≥25 or ≥30kg/m2) in associations between QST metrics (Pain-60, conditioned pain modulation [CPM], and temporal summation of pain [TSP]) and FMS clinical features, we explored BMI’s role as both a confounder (change-in-estimate criterion – change equal or higher than 10%) and effect modifier (interaction term). Significant interactions revealed overweight/obese BMI as a modifier in the relationship between CPM and both depression and symptom impact, with a homeostatic relationship between better clinical profile and pain inhibitory response observed solely in the normal weight group. Similar results were found for Pain-60 and depression. Additionally, BMI ≥30kg/m2 modified TSP’s effect on pain, demonstrating lower pain with increased TSP, exclusively in the non-obese group. This study highlights the significant role of BMI in moderating the relationships of important pain inhibitory control processes and pain intensity, depression, and the overall impact of FMS symptoms. Our results suggest that high BMI states disrupt the homeostatic effects of pain inhibition, reducing its salutogenic response in FMS participants. We discuss the mechanistic and therapeutic implications of targeting BMI in FMS clinical trials and the potential impact of this important relationship.

Keywords: Fibromyalgia, Body mass index, BMI, quantitative sensory testing, pain

Perspective

This investigation highlights the disruptive influence of high BMI on pain inhibitory control in fibromyalgia, unbalancing clinical symptoms such as pain and depression. It underscores the necessity of integrating BMI considerations into therapeutic approaches to enhance pain management and patient outcomes.

Introduction

Fibromyalgia syndrome is a complex condition marked by persistent, widespread pain, affecting about 2% of the population, mainly women, typically during their working years. Beyond pain, patients with FMS often experience fatigue, stiffness, cognitive dysfunction, depression, and anxiety. These symptoms complicate diagnosis and treatment, significantly impacting quality of life and functional status, leading to substantial economic and societal costs1,2. FMS is associated with higher disability rates, work absences, and loss of labor force participation, highlighting the need for effective treatment and support strategies. Recent analyses show that patients with FMS incur significantly higher annual costs and have a markedly lower quality of life compared to the general population3,4.

As revealed in a meta-analysis, patients with FMS demonstrate altered quantitative sensory testing (QST) compared to healthy controls, characterized by increased temporal summation of pain (TSP) and reduced conditioned pain modulation (CPM)5. These findings suggest a shift in the pain processing mechanisms among individuals with FMS. Parallelly, research has demonstrated the significant role of BMI in influencing outcomes across pain conditions, with higher BMI levels indicating poorer outcomes. For instance, it has been shown that overweight and obese FMS patients experience lower quality of life, higher levels of anxiety and depression, and worse cardiorespiratory fitness compared to their normal-weight counterparts6. Moreover, a systematic review showed that among patients who suffer of fibromyalgia, obesity was linked to increased pain sensitivity, poorer sleep quality, and diminished physical abilities7.

The relationship between BMI and QST is complex, characterized by controversial and heterogeneous results across various studies. While the evidence is far from uniform, it is possible to observe a trend that higher BMI may indeed alter pain perception8,9. Despite these insights, the implications of how altered pain perception in individuals with high BMI affects clinical characteristics and treatment outcomes remain largely unexplored. This gap underscores the need for further research to investigate the complex mechanisms at play and to determine the potential clinical significance of the observed changes in pain sensitivity among obese populations.

Considering the previously mentioned points, this study aims to investigate the role of body mass index (BMI) as a potential effect modifier and confounder in the relationships between quantitative sensory testing (QST) measures and clinical characteristics patients with FMS, including symptoms impact score, sleep quality, depression, and pain intensity. We hypothesize that higher BMI will be associated with a maladaptive state characterized by poor clinical and QST profiles.

Methods

Participants, Study Design and Sample Size

In this research, we analyzed data from 101 patients participating in an ongoing factorial clinical trial (NCT03371225)10. This study was approved by the IRB at the Mass General Brigham Human Research Committee (Protocol approval number: 2017P002524).

Although this was a cross-sectional analysis of an RCT, we also calculated whether our sample would be adequate to test this hypothesis. The effect size of a minimally clinically important difference (MCID) in pain and conditioned pain modulation (CPM) in chronic pain patients are our most crucial outcomes. For pain, the MCID is Cohen’s d = 0.5, requiring a sample size of 34 to detect this variability. For CPM, we consider a 30% effect or Cohen’s d = 0.30, necessitating a sample size of 90. Participants were recruited through a diversified approach, utilizing referrals from healthcare providers, advertisements in various media, online platforms, review of medical records, and outreach to support groups. Potential participants who expressed interest underwent an initial phone screening conducted by a study co-investigator. This screening collected preliminary information to determine initial eligibility. The data gathered during the phone screening was then reviewed by the principal investigator, who made the final determination of eligibility based on a predefined set of inclusion and exclusion criteria. Those considered eligible were then provided with information about the study, including its purpose, procedures, and potential risks and benefits. They were given ample opportunity to ask questions and address any concerns. Following this, eligible participants were required to provide informed consent, before proceeding further in the study. All participants then completed a pre-training visit, which involved a 30-minute treadmill walking assessment at their baseline heart rate. This step served to evaluate their physical readiness and ensure their comfort with the exercise component of the study. Only those participants who met all eligibility criteria, provided informed consent, and successfully completed the pre-training visit were officially enrolled in the study. The scope of this study was limited to the analysis of data obtained at the baseline assessment.

Inclusion Criteria

Patients enrolled included individuals of both sexes, aged between 18 and 65 years, diagnosed with Fibromyalgia according to the ACR 2010 criteria10, and experiencing pain that was resistant to standard analgesics and medications for chronic pain such as Tylenol, Aspirin, Ibuprofen, Soma, Parafon Forte DCS, Zanaflex, and Codeine. Participants could take medications other than the mentioned ones. Also, participants were required to have the ability to detect sensations using Von Frey fibers on the forearm to ensure they had intact sensory perception necessary for the study’s assessments. The Von Frey test involves using a set of filaments of varying thicknesses to apply precise pressure to the skin. The participant reports whether they can feel the sensation, which helps in assessing the sensitivity and functioning of their sensory system.

Exclusion Criteria

Patients were excluded if they had a clinically significant or unstable medical or psychiatric disorder, a history of substance abuse within the last 6 months based on self-report, a significant neurological history (e.g., traumatic brain injury) leading to deficits such as cognitive or motor impairments, as self-reported, a history of neurosurgical procedures involving craniotomy, severe depression, pregnancy, current use of opioids in high doses (opiate use in large doses -more than 30mg of oxycodone/hydrocodone or 7.5mg of hydromorphone (Dilaudid) or equivalent), or an increased risk for exercise (defined as (i) not fulfilling the American College of Sports Medicine (ACSM) criteria for exercise without medical clearance, indicating a potential risk of cardiovascular complications, and (ii) not having received clearance from a licensed physician.

Quantitative sensory testing (QST)

Pain-60 and Temporal Summation of Pain (TSP)

Initially, we determined the test temperature (defined as Pain-60) that induces a pain level of 60 on a Numerical Pain Scale (NPS) ranging from 60 to 100 by applying a Peltier thermode (Medoc Advanced Medical Systems, Ramat Yishai, Israel) to the participants’ right forearm. For this purpose, we delivered three short heat stimuli (at 43, 44, and 45 °C), each lasting 7 seconds from the moment the temperature reaches the set point. Participants were asked to assess their pain intensity on an NPS where 0 represents “no pain” and 100 signifies “the worst pain imaginable”. When the initial 43°C stimulus was excessively painful (exceeding a pain score of 60), we instead applied stimuli at reduced temperatures of 41 and 42 °C. If none of the temperatures (43, 44, and 45°C) succeed in reaching the Pain-60 threshold, we increased the stimuli to 46, 47, and 48°C until the target pain intensity of 60 is achieved. In the cases that even these temperatures failed to induce the Pain-60 level, we designated 48°C as the Pain-60. Subsequently, using the Pain-60 temperature, heat pulses produced by TSA-II Stimulator (Medoc Advanced Medical Systems, Ramat Yishai, Israel) were applied to the right proximal volar forearm with a HP-thermode. The procedure followed a protocol where the HP-thermode was set to emit pulses with a rise/fall time of 1–2 seconds, tailored to the participant’s threshold for heat-induced pain, transitioning from initial to peak temperatures over a 0.7-second plateau11. The patients received a series of 15 consecutive heat stimuli at a frequency of 0.4 Hz targeted at the same region. Finally, TSP was determined by calculating the difference in pain intensity (visual analog scale) reported right after the 15th stimulus minus the pain intensity reported just after the 1st one.

Conditioned Pain Modulation (CPM)

Roughly after 2 minutes after TSP, we started the CPM which is composed by two parts: the test-stimulus and the conditioned-stimulus12,13. The procedure began with the test stimulus, where the Pain-60 temperature was sustained for 30 seconds on the participants’ right forearm. At this stage, participants were requested to assess their pain intensity at three different intervals: 10, 20, and 30 seconds after the thermode’s attainment of the Pain-60 temperature, with the average of these three pain ratings being calculated. Five minutes after the test stimulus administration, as part of the conditioned stimulus procedure, the participant’s left hand will be submerged in a water bath maintained in a temperature range from 10°C to 12°C for 30 seconds. Following this, the predetermined Pain-60 temperature was reapplied to the right forearm while the left hand remained immersed, for another 30 seconds. Once again, participants evaluated their pain intensity on their right arm at the 10, 20, and 30-second marks after reaching the Pain-60 temperature, with the mean of these three evaluations being computed. The CPM response was determined by subtracting the average pain rating during the conditioned stimulus from the average pain rating elicited by the test stimulus.

Clinical variables

We chose to cover important aspects related to fibromyalgia, namely pain, depression, and quality of life1,3,14. For this purpose, we selected the following variables as outcomes:

Beck Depression Inventory (BDI)

At the onset and during follow-up of the study, a 21-item, multiple-choice questionnaire was used to evaluate the presence and intensity of depression in adults. This approach is rooted in research within chronic pain, which has identified that depression can influence the perception of pain15.

Revised Fibromyalgia Impact Questionnaire (FIQ-R)

At the start of the study and during follow-up, participants were given a 21-item multiple-choice questionnaire designed to evaluate function, overall impact, and symptoms16.

Pain (visual analog scale – VAS)

We also assessed subject’s pain using the visual analog scale, which has been a reliable instrument as shown by systematic literature review. The participant was instructed to indicate their pain level by moving a marker that goes from the left side, signifying “no pain at all,” to the right side, representing “the worst pain imaginable”.

The Pittsburgh Sleep Quality Index

This self-assessment tool measures adults’ sleep quality and patterns, evaluating seven aspects of sleep over the past month: subjective quality, time to fall asleep, total sleep time, efficiency, disturbances, medication usage, and daytime impairment. Responses are rated on a Likert scale from “0” (not experienced in the past month) to “3” (experienced three or more times a week), with a total score of “5” or above classifying an individual as a “poor” sleeper. This tool is useful for monitoring sleep quality changes across various stages of an intervention, from initial evaluation to subsequent follow-ups17,18.

Body Mass Index (BMI)

Body Mass Index is a simple index of weight-for-height that is commonly used to classify underweight, normal weight, overweight, and obesity in adults. According to the World Health Organization (WHO), it is calculated by dividing a person’s weight in kilograms by the square of their height in meters (kg/m2)19.

Statistical Analysis

We conducted separate linear regressions between the QST metrics (independent variables) and all the clinical variables (dependent variables). Then, we included a categorical BMI covariate as a single term to investigate confounding effect; or an interaction term (BMI categorical*QST metric) to investigate BMI’s role as an effect modifier. We considered BMI as a potential effect modifier when the interaction term was statistically significant (p-value < 0.05). Subsequently, following the World Health Organization cutoff of 25kg/m2 for BMI, we stratified the sample into two groups, namely normal weight (BMI < 25kg/m2) and overweight/obese (BMI ≥ 25kg/m2). If the interaction term was statistically significant, we performed linear regression analyses to examine the relationship between the dependent and independent variables within each group. It is noteworthy that we also conducted a sensitivity analysis employing the World Health Organization’s BMI threshold of 30 kg/m2.

To investigate BMI as potential confounder, we initially performed linear regression analysis between the dependent and independent variables, extracting the β-coefficients specifically from regressions yielding a P-value less than 0.05. Subsequently, we repeated these regressions incorporating BMI to obtain revised β-coefficients. By comparing the original and revised β-coefficients, BMI was considered a confounder when a shift of more than 10% in either direction occurred20.

R-Studio Version 2023.06.0+421 (2023.06.0+421) was used for all the statistical analyses. P-values lower than 0.05 were considered statistically significant.

Results

Sample Characteristics

Table 1 summarizes the characteristics of the sample, consisting of 101 patients with FMS. The median age of the participants is 50 years, with an interquartile range (IQR) of 39 to 57 years. The sample includes 11 males (10.89%) and 89 females (88.12%). Educational level varies, with 82 (81.89%) having completed college, 2 (1.98%) reaching middle school level, 11 (10.89%) completing high school, and 6 (5.94%) holding a doctorate degree. Racially, the majority, 76 (75.25%), identify as white, followed by 1 (0.99%) as American Indian or Alaska Native, 3 (2.97%) as Asian, 9 (8.91%) as Black or African American, 7 (6.93%) reporting multiple races, and 5 (4.95%) not specifying their race. The duration of fibromyalgia among the participants has a median of 10 months, with an IQR of 4 to 17 months. Additionally, the Body Mass Index median is 28.3, with an IQR of 22.8 – 32.8.

Table 1:

Sample Demographic Characteristics

Age years: median, IQR 50.0 39.0 – 57.0
Gender: number, % Males: 11 10.89%
Females: 89 88.12%
Native language: number, % English: 98 97.03%
Spanish: 3 2.97%
Education: number, % Middle School = 2 1.98%
High School = 11 10.89%
College = 82 81.89%
Doctorate Degree = 6 5.94%
Ethnicity: number, % American Indian or Alaska Native: 1 0.99%
Asian: 3 2.97%
Black or African American: 9 8.91%
Native Hawaiian or Other Pacific Islander: 0 0%
White: 76 75.25%
More than one race: 7 6.93%
Unknown or not reported: 5 4.95%
Fibromyalgia duration (months): median, IQR 10 4 – 17
Fibromyalgia Impact Questionnaire: Mean, SD 54.51 18.1
Body Mass Index: Median, IQR 28.32 22.86 – 32.92
Body Mass Index number of subjects BMI < 18.5 kg/m2 3
18.5 kg/m2 ≥ BMI > 25 kg/m2 29
25 kg/m2 ≥ BMI > 30 kg/m2 28
BMI ≥ 30 kg/m2 40
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SD: Standard Deviation; IQR: Inter Quartile Range.

QST and clinical characteristics

Table 2 outlines the QST and clinical characteristics, namely Beck Depression Inventory with a mean of 16.0 (95% CI: 14.3 – 17.6, SD: 8.5); Fibromyalgia Impact Questionnaire with a mean of 54.5 (95% CI: 51.0 – 58.0, SD: 18.1); Pain (VAS) with a median of 6.5 (95% CI: 6.0 – 6.6, IQR: 5.0 – 7.1); Conditioned pain modulation with a median of 1.0 (95% CI: 0.7 – 1.5, IQR: 0.0 – 2.3); Temporal summation of pain with a median of 0.0 (95% CI: 0.0 – 0.0, IQR: [−1] –[+1]); Pain-60 with a median of 47 (95% CI: 47 – 48, IQR: 46 – 48).

Table 2:

QST and clinical characteristics

Variable Mean or Median (95% CI) SD or IQR
Beck Depression Inventory: Mean, SD 16.0 (14.3 – 17.6) 8.5
Revised Fibromyalgia Impact Questionnaire:
Mean, SD		 54.5 (51.0 – 58.0) 18.1
Pain (VAS): Median, IQR 6.5 (6.0 – 6.6) 5.0 – 7.1
Conditioned Pain Modulation (CPM): Median,
IQR		 1.0 (0.7 – 1.5) 0.0 – 2.3
Temporal Summation of Pain (TSP): Median, IQR 0.0 (0.0 – 0.0) (−1) - (+1)
Test Temperature (P60): Median, IQR 47 (47 – 48) 46 – 48
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CI Confidence Interval; SD Standard Deviation; IQR Interquartile Range; VAS (Visual Analog Scale)

BMI as an effect modifier

We found a statistically significant P-value for the interaction term of CPM and BMI affecting the dependent variables BDI and FIQ-R. Similarly, the interaction of TSP and BMI showed a statistically significant effect on the dependent variable Pain (VAS). These results are summarized on table 3.

Table 3:

Interaction Term between BMI and Independent Variables

P-Value β-coefficient Adjusted R2
Beck Depression Inventory (BDI) *
BMI_cat25*CPM 0.0061 2.8762 0.0677
BMI_cat25*P60 0.0233 2.3488 0.0265
Revised Fibromyalgia Impact Questionnaire (FIQ-R) *
BMI_cat25*CPM 0.0300 4.8932 0.0655
Pain (VAS)*
BMI_cat30*TSP 0.0067 0.6351 0.0452
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BMI_cat25: BMI binary with a cutoff of 25kg/m2; BMI_cat30: BMI binary with a cutoff of 30kg/m2

*

Dependent Variable; VAS (Visual Analog Scale)

BMI as an effect modifier in the relationship between CPM and BDI

We chose the World Health Organization cutoff of 25kg/m2 to stratify our sample into two groups, namely normal weight (BMI < 25kg/m2) and overweight/obese (BMI ≥ 25kg/m2). The normal weight group showed a negative association (β-coefficient = − 2.67; P-value = 0.0024) (Figure 1A), indicating that the higher the CPM, the lower the BDI score. It is noteworthy that in the overweight/obese group, no statistically significant association was found (β-coefficient = − 2.67; P-value = 0.6041).

Figure 1. BMI Effect modification.

Figure 1.

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depicts BMI as an effect modifier. 1A: BMI <25kg/m2 group, as CPM increases, BDI decreases. BMI ≥25kg/m2 group is not statistically significant; 1B: BMI <25kg/m2 group, as Pain-60 increases, BDI decreases. BMI ≥25kg/m2 group is not statistically significant; 1C: BMI <25kg/m2 group, as CPM increases, FIQ-R decreases. BMI ≥25kg/m2 group is not statistically significant; 1D: BMI 30kg/m2 group, as TSP increases, Pain(VAS) decreases. BMI ≥25kg/m2 group is not statistically.

BMI as an effect modifier in the relationship between CPM and FIQ-R

Also using WHO’s cutoff of 25kg/m2 to stratify our sample, we found that the normal weight group showed a negative association (β-coefficient = − 4.20; P-value = 0.0316) (Figure 1B), showing that an increase in CPM leads to a reduction in FIQ-R score. Similarly, to the earlier BDI findings, this analysis also showed no statistical significance for the overweight/obese group (β-coefficient = 0.70; P-value = 0.5738).

BMI as an effect modifier in the relationship between Pain-60 and BDI

Also using WHO’s cutoff of 25kg/m2 to stratify our sample, we found that the normal weight group showed a negative association (β-coefficient = − 1.62; P-value = 0.0437) (Figure 1C), showing that an increase in the Pain-60 leads to a reduction in BDI score. Consistent to our previous findings, this analysis also showed no statistical significance for the overweight/obese group (β-coefficient = 0.70; P-value = 0.2890).

BMI as an effect modifier in the relationship between TSP and Pain

Given our finding that the interaction term between TSP and BMI has a P-value equal to 0.0075 and yet no significance was found using the BMI cutoff of 25kg/m2, we performed a sensitivity analysis using WHO’s cutoff of 30kg/m2. This created two groups, namely normal weight/overweight (BMI <30kg/m2) and obese (BMI ≥30kg/m2). Thus, the weight/overweight group showed a negative association (β-coefficient = − 0.37; P-value = 0.0132) (Figure 1D), indicating that the higher the TSP, the lower the Pain. Mirroring the prior observations pertaining to the BDI and FIQ-R outcomes, this analysis did not show statistical significance for the obese group, as evidenced by a P-value of 0.1536 (β-coefficient = 0.27).

BMI as a confounder

Initially, we performed linear univariate analyses to examine the relationship between each of the eight selected outcomes and all independent variables, aiming to determine the statistical significance of these relationships and, if confirmed, to extract the β coefficients. Subsequently, for each statistically significant association, we conducted additional linear regression analyses incorporating BMI to evaluate its potential role as a confounder. It is noteworthy to highlight that, in this study, BMI did not act as a positive or negative confounder in any of the associations examined (QST metrics and clinical characteristics).

Discussion

Main findings

In our study, we investigated the potential of BMI to act as an effect modifier or a confounder in patients with FMS. Our objective was to delve into the ways in which body mass index might influence the relationships between QST measures and clinical variables. The first finding is the role of BMI as an effect modifier of CPM on both depression and on the impact of fibromyalgia symptoms in patients’ lives. Specifically, we found that in the normal BMI group, there is a statistically significantly improvement in those outcomes as CPM levels rise, suggesting a homeostatic effect of pain inhibition to control FMS symptoms, which is disrupted in overweight/obese patients. Furthermore, our analysis revealed that high BMI also modifies the effect of TSP on pain intensity, indicating that an increase in TSP corresponds to a decrease in reported pain, only in the normal/overweight BMI group. Similarly, this exemplifies the important role of high BMI status on the pain sensitization influence in pain levels, supporting the hypothesis of a disrupted symptoms adaptation in patients with FMS with high BMI.

BMI as an effect modifier for Conditioned Pain Modification (CPM)

After stratifying the sample into two groups with a BMI cutoff of 25kg/m2, within the normal weight group we found negative association between CPM and both, BDI and FIQ-R. No statistical significance was found in the overweight/obese group. This indicates that within the normal BMI group, higher CPM leads to reductions in both BDI and FIQ-R. In other words, as endogenous pain control improves, as measured by CPM, there is a corresponding decrease in symptoms of depression and a diminished impact of FMS on patients’ lives. In fact, endogenous pain modulation has shown to be defective in patients with FMS5. Moreover, poorer outcomes have been found within higher BMI group in many contexts. For instance, studies have identified the critical impact of BMI on the outcomes of various pain conditions. Higher BMI levels are associated with more poorer outcomes. Specifically, patients with FMS who are overweight or obese report a lower quality of life, higher instances of anxiety and depression, and reduced cardiorespiratory fitness than those with a normal weight6. Furthermore, a systematic review highlighted that obesity in people with FMS is associated with greater pain sensitivity, reduced sleep quality, and lower physical functionality7. Our study aligns with trends observed in existing literature, highlighting the important interplay between BMI and CPM in the context of fibromyalgia and its psychological impacts. This pattern suggests that among individuals with a normal BMI, an increase in CPM, indicative of more effective endogenous pain control, correlates with decreased depression symptoms and a lesser impact of FMS on patient lives. This finding is particularly relevant, given that patients with FMS often exhibit compromised endogenous pain modulation capabilities.

It is known that obesity is linked with chronic inflammation, a state that contributes significantly to the onset of metabolic diseases and a range of other health complications21. The chronic inflammation associated with obesity is particularly notable in the brain, affecting areas involved in visceral interception and the dopaminergic-serotoninergic pathways. This inflammation impacts the brain’s ability to control the descending networks between the brain stem and the spinal cord. Moreover, in obese patients with Fibromyalgia, reduced physical activity and diminished motor cortex activation impair the cortical governance over the Periaqueductal Gray and Rostral Ventromedial Medulla, key regions in the endogenous pain modulation system. This downregulation contributes to a compromised ability to modulate pain internally22. Additionally, obesity contributes significantly to increased disease burden through various comorbidities, suggesting the need for targeted multimorbidity prevention. The distribution of these diseases among individuals with obesity might be uneven, indicating a subset with obesity-related multimorbidity. Stratifying obesity into risk-based categories could help identify those with “high-risk obesity” more effectively23.

The utility of this finding for prediction models in clinical trials and personalized FMS treatments is significant, offering an approach to patient care based on individual BMI status. By stratifying patients according to their BMI, clinicians can tailor interventions more effectively, enhancing treatment outcomes and patient satisfaction. For instance, patients with a BMI close to the normal range, treatments targeting pain inhibition mechanisms, such as transcranial Direct Current Stimulation (tDCS) and transcutaneous auricular vagus nerve stimulation (taVNS), could be beneficial. These therapies focus on neuroplastic changes and the modulation of pain pathways, which might be more effective in individuals without the added factor of obesity-related inflammation and its potential to diminish the effectiveness of pain inhibition mechanisms. Conversely, for patients with a higher BMI, a preliminary focus on behavioral changes and weight loss before implementing other pain management strategies could be more beneficial. This approach recognizes the complex interplay between obesity, inflammation, and FMS’ pain.

These stratified treatment approaches underscore the importance of a personalized treatment plan in managing FMS. It acknowledges the variability in patient responses to treatment based on individual physiological differences and comorbid conditions. Future research should explore these approaches in clinical trials, focusing on how BMI influences the effectiveness of different treatments for FMS. Such trials are crucial for developing evidence-based, personalized treatment plans that can significantly improve the quality of life for patients with FMS.

BMI as an effect modifier for Temporal Pain Summation(TSP)

Regarding BMI and TSP, no significant significance was observed when we stratified groups by a BMI cutoff of 25kg/m2, then we performed a sensitivity analysis using the WHO’s 30kg/m2 BMI cutoff. Thus, we found a notable result in the <30kg/m2 BMI group, indicating that higher TSP levels correlate with fewer pain symptoms in this group. No such correlation was seen in the obese group.

We exposed above that the higher BMI groups have poorer outcomes21–23, and yet we found counterintuitive relationship compared to the typical understanding of TSP. Typically, higher TSP is expected to correlate with increased pain perception, as it reflects greater pain sensitization5. However, a negative beta coefficient indicates that within the BMI < 30 group, an increase in TSP is associated with lower reported pain levels. This interesting finding can be explored in different ways. For instance, it might indicate that in individuals with a BMI < 30, the pain processing mechanisms differ from those in individuals with higher BMI. This could be related to less pronounced central sensitization or more effective endogenous pain inhibitory mechanisms that become more active or efficient in the context of repetitive noxious stimuli. Another approach would be that the observed inverse correlation between TSP and pain in individuals with a BMI under 30 might indicate an adaptive response, where repeated exposure to noxious stimuli could lead to a habituation effect, thereby diminishing pain perception over time. Alternatively, the intricate interplay between BMI and pain perception could suggest that a lower BMI is linked with variables such as enhanced overall health status or reduced inflammation, which in turn may modulate pain differently and influence the impact of TSP on perceived pain levels. It’s also crucial to acknowledge that this relationship might be shaped by the specific methodologies employed within the study, including how TSP was quantified, the demographic and clinical profile of the participants, or the precise conditions under which the data were gathered, all of which could potentially affect the observed outcomes. Moreover, TSP varies greatly from person to person, making the relationship between TSP levels and pain intensity a subject of debate. Studies have reported no significant correlation between the two11.

BMI as an effect modifier for pain sensitivity:Pain-60

By using BMI cutoff of 25kg/m2 to stratify the sample, we found a negative association between Pain-60 and BDI within the normal weight group. No statistical significance was found on overweight/obese group. This suggests that within the normal BMI group, an increase in Pain-60 diminishes BDI scores. In other words, as the response to pain improves, evidenced by higher Pain-60, reported depression symptoms decreases. In fact, it has been shown that chronic pain patients show a decreased pain sensitivity24, although no correlation with depression has been mentioned.

Strengths and limitations

This study, conducted on a cohort of 101 patients enrolled in an ongoing clinical trial employs cross-sectional analysis, which does not allow the establishment of causality. While significant findings, particularly regarding BMI’s role as an effect modifier and a confounder have emerged, the intricacies of causal relationships and underlying mechanisms demand further investigation through longitudinal and interventional studies.

Furthermore, it’s essential to acknowledge the specificity of our sample to certain demographics, possibly limiting the generalizability of our results. The cohort consists of individuals participating in a specialized fibromyalgia treatment program, and the majority is female, white and college educated, which may not reflect the broader population’s experiences. This specificity, however, underlines the importance of our findings, offering valuable insights into fibromyalgia management and the potential impact of BMI on treatment outcomes. Another limitation of this study is that, for the pain-60 assessment, the temperatures were delivered in an increasing order, which could have influenced participant expectations and affected their responses.

Additionally, our analysis incorporates a stratification based on the World Health Organization’s BMI cutoffs, providing a nuanced understanding of the interaction between BMI, pain, and depression in patients with FMS. However, the sensitivity analysis using a different BMI threshold suggests the need for caution in interpreting these results, highlighting the complexity of BMI’s influence on disease outcomes.

Conclusion

Our study brings insights on the intricate role of Body Mass Index in modifying the relationship between QST metrics and various clinical outcomes. Our results suggests that high BMI states disrupt the homeostatic effects of pain inhibition, reducing its salutogenic response in patients with FMS. By highlighting BMI’s impact as both an effect modifier, our findings underscore the importance of considering patient-specific factors such as weight status in treatment planning. These insights not only contribute to a deeper understanding of fibromyalgia management but also highlight for more personalized and effective therapeutic approaches. The observed associations encourage further investigation into how BMI influences treatment efficacy, advocating for a holistic approach to fibromyalgia care that integrates physical health considerations with targeted interventions.

Highlights.

  • BMI modifies pain processing in fibromyalgia, influencing clinical outcomes significantly

  • Normal BMI correlates with better fibromyalgia symptoms and higher pain inhibition efficacy

  • In normal weight fibromyalgia patients, better pain modulation can reduce depression and symptom impact

  • Temporal summation of pain and pain intensity relationship varies by BMI

  • BMI’s role in fibromyalgia treatment necessitates tailored interventions for better outcomes

Funding Statement:

This work is supported by the National Institutes of Health (NIH) grant R01 AT009491–01A1.

Footnotes

Disclosures

All authors declare no conflicts of interest. This manuscript has not been submitted elsewhere.

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References

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