Assessment of Central Sensitisation in Rheumatoid Arthritis Patients Using Algometry: A Single-centre Case-control Study

Abstract

Background:

Central sensitisation (CS) is characterised by increased pain sensitivity and may contribute to the persistence of pain in rheumatoid arthritis (RA). This study aimed to evaluate the relationship between CS and various parameters, including age, gender, and sleep quality, by measuring pain thresholds and tolerance using algometry in RA patients.

Methods:

The study included 103 RA patients and 80 age- and sex-matched healthy controls. Pain thresholds and tolerance were measured using algometry. Patients were stratified into two groups based on their central sensitisation inventory (CSI) scores (CSI ≥40 vs. <40). Sleep quality was assessed using the Jenkins Sleep Scale (JSS).

Results:

Pain thresholds and tolerance at the second and third metacarpophalangeal joints, second distal interphalangeal joint, deltoid, suboccipital, and trapezius muscles were significantly lower in RA patients than in healthy controls (P < .001 for all). Among RA patients, those with CS (CSI ≥40) had significantly lower pain thresholds, were younger, had higher JSS scores, and were more frequently female compared to those without CS (P = .019, P = .047, and P < .001, respectively). In regression analysis, younger age and poor sleep quality were identified as independent associated for CS (P = .006, P < .001).

Conclusion:

This study demonstrates that CS is significantly associated with altered pain perception in patients with RA. CS was more prevalent among younger individuals, females, and patients with poor sleep quality. Identifying CS in clinical practice may help avoid overtreatment and facilitate more individualised pain management strategies.

Keywords

Algometer algometry central sensitisation dolorimeter rheumatoid arthritis

Introduction

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterised by persistent synovial inflammation, joint destruction, and systemic involvement.¹ Pain is the most prominent and debilitating symptom of RA, traditionally attributed to inflammatory processes.² However, clinical observations and patient reports indicate that pain may persist even when inflammation is well-controlled or absent, suggesting the involvement of alternative pain mechanisms, such as central sensitisation (CS).³

CS refers to an increased responsiveness of nociceptive neurons in the central nervous system (CNS) to normal or subthreshold stimuli. This phenomenon results in hypersensitivity to pain and may play a key role in the amplification and chronicity of pain in various rheumatic disorders.⁴ Studies have shown that CS contributes to non-inflammatory pain in approximately 15%-40% of patients with inflammatory rheumatic diseases, including RA, ankylosing spondylitis, Behçet’s disease, familial Mediterranean fever, and Sjögren’s syndrome.^2,5–8

Despite its clinical relevance, the diagnosis of CS remains challenging due to the lack of a universally accepted gold standard. Commonly used tools include questionnaires such as central sensitisation inventory (CSI), QST, and algometry.^9,10 Among these, pressure algometry is a practical and objective method for assessing pain threshold (the minimum stimulus perceived as painful) and pain tolerance (the maximum stimulus that can be tolerated).¹¹ In individuals with CS, both parameters are expected to be significantly reduced.

Several studies have investigated the relationship between CS and clinical parameters such as age, sex, disease activity, and sleep quality in RA and other rheumatic diseases. While female sex and poor sleep quality have consistently been associated with higher CS prevalence, the findings regarding age and disease activity remain inconsistent.^2,18,22–25 Moreover, most of these studies have relied predominantly on subjective questionnaires rather than objective measurement tools. This highlights the need for studies incorporating algometry to better clarify the interplay between CS and these clinical factors. This study aims to evaluate CS in RA patients through objective algometric measurements and to investigate its association with demographic and clinical variables, including sleep quality. By identifying patients at higher risk for CS, more tailored and effective pain management strategies can be developed, potentially reducing unnecessary escalation of anti-inflammatory treatments.

Methods

Study Design and Participants

This study was designed as a single-centre, cross-sectional study. A total of 103 patients diagnosed with RA according to the 2010 American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) classification criteria and 80 age and sex-matched healthy controls were included.¹² The study was conducted in accordance with the Declaration of Helsinki and was approved by the Çukurova University Faculty of Medicine Clinical Research Ethics Committee. The study protocol was submitted under document number 2024/147 and was approved on 6 September 2024. Written informed consent was obtained from all participants.

According to the power analysis results, n = 44 patients with a 5% margin of error, 80% power, and a standard effect size of 0.6 were considered appropriate. However, as many patients and controls as possible were included to increase the power of our study. Inclusion criteria for the RA group were: age ≥18 years, diagnosis of RA confirmed by a rheumatologist, no other rheumatic diseases, absence of fibromyalgia (patients were screened with the Fibromyalgia Rapid Screening Tool [FiRST], and those with positive results were excluded; thus, none of the RA patients had concomitant fibromyalgia), no active arthritis at the time of assessment, no history of hand surgery, and no severe neuropsychiatric disorders.

RA patients were consecutively recruited from the outpatient rheumatology clinic of Çukurova University Hospital between January and June 2024. Healthy controls were selected from hospital staff and volunteers from the general population. To minimise confounding, controls were matched to the RA group by sex and age (±3 years).

For the healthy control group, inclusion criteria were: age ≥18 years, absence of joint pain, no history of rheumatologic or chronic pain diseases, including fibromyalgia, and no use of analgesic medications on the day of algometric assessment. All control participants were also evaluated clinically to exclude tender points; therefore, none of the controls had fibromyalgia or tender points.

Study Variables and Clinical Assessments

The following data were collected: age, sex, disease duration, smoking status, presence of extra-articular involvement, autoantibody status [rheumatoid factor (RF) and anti-cyclic citrullinated peptide (anti-CCP)], medications (conventional synthetic disease-modifying anti-rheumatic drugs [csDMARDs] and biologics), Disease Activity Score 28 (DAS28), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), pain intensity via visual analogue scale (VAS), sleep quality via Jenkins Sleep Scale (JSS), hand function via Duruöz Hand Index, general functional status via Health Assessment Questionnaire (HAQ), and CS via CSI.

All assessments were performed on the same day, and laboratory data were verified from hospital records. Patients were advised not to take short-acting analgesics within 12 hours before algometric testing.

Disease Activity (DAS28)

The DAS28 is a composite index that assesses disease activity in RA using the number of tender and swollen joints (out of 28), patient global assessment (VAS), and CRP levels. DAS28 > 5.1 indicates high disease activity, 3.2–5.1 moderate activity, 2.6–3.2 low activity, and <2.6 remission.¹³

Pain Intensity (VAS)

Pain was assessed using a 10 cm visual analogue scale, where 0 represents ‘no pain’ and 10 ‘the worst pain imaginable’. Participants were asked to mark the number that best described their average pain intensity in the past week.^14,15

Sleep Quality (JSS)

Sleep quality was evaluated using the JSS, consisting of four items scored between 0 and 5. Total scores range from 0 to 20, with higher scores indicating poorer sleep quality over the previous four weeks.^16,17

Hand Function (Duruöz Hand Index)

This index assesses hand function through 18 daily activities, rated using a 5-point Likert scale. Higher scores reflect more severe functional impairment. It is validated for upper extremity functional limitations in RA.¹⁸ Since the hands are among the most commonly and severely affected sites in RA, evaluation of hand function was included to capture functional disability that may influence pain perception. Furthermore, reduced sensorimotor feedback from impaired hand use has been suggested to contribute to central pain processing, making hand function a clinically relevant parameter to examine in relation to CS.

Functional Status (HAQ)

The Health Assessment Questionnaire includes 20 items covering eight domains of daily living (e.g., dressing, walking, hygiene). Each item is rated on a scale from 0 (no difficulty) to 3 (unable to do). The average score ranges from 0 to 3, with higher scores indicating greater disability.¹⁹

Central Sensitisation

The CSI consists of 25 items rated from 0 to 4, with a total score range of 0 to 100. In line with previous validation studies (Mayer et al., 2012; Düzce Keleş et al., 2021), a cutoff score of ≥40 was used to classify participants as having clinically relevant CS. The CSI was analysed both as a continuous variable (for correlation analyses) and as a dichotomised variable (≥40 vs. <40) for subgroup comparisons and logistic regression modelling, to allow both quantitative evaluation and clinically meaningful interpretation.^20,21

Algometric Evaluation

Pain thresholds and pain tolerances were measured using a digital pressure algometer (JTECH Medical Commander Echo, serial number: 1310200). Measurements were taken on the dominant side at six anatomical sites: the second and third metacarpophalangeal (MCP) joints, the second distal interphalangeal (DIP) joint (using a 0.5 cm² tip), and the deltoid muscle, upper trapezius muscle, and suboccipital region (using a 1 cm² tip). At each site, two measurements were taken at 30-second intervals and averaged.

Algometric measurements were performed at both commonly studied muscular sites (deltoid, trapezius, suboccipital), as supported by previous literature,¹¹ and at the second and third MCP and second DIP joints. While no standardised algometric references exist for these joints, they were deliberately selected due to their clinical relevance as the most commonly affected sites in RA.

Pain threshold was defined as the minimum pressure at which pain was first perceived, and pain tolerance was defined as the maximum pressure the participant could tolerate. Measurements were performed by the same examiner using a standardised protocol. There are no established normative values for pain threshold or tolerance at these sites; therefore, the same procedure was applied in both the RA and control groups for comparison.

Statistical Analysis

All statistical analyses were performed using SPSS version 25.0 (IBM Corp., Armonk, NY, USA). Normality of continuous variables was assessed using both Kolmogorov-Smirnov and Shapiro-Wilk tests, as the former is commonly applied in larger samples, whereas the latter has greater power in smaller samples. Using both tests allowed us to ensure robust evaluation across groups of varying sample sizes; since most variables failed normality in both tests, non-parametric methods were chosen. For normally distributed variables, comparisons between groups were performed using Student’s t-test; otherwise, the Mann-Whitney U test was used. Categorical variables were analysed using the chi-square test. Correlation analyses were conducted using Spearman’s rho, since most clinical and algometric variables did not meet the normality assumption according to the Shapiro-Wilk test. Spearman correlation was therefore preferred as a conservative approach for consistency across variables. Correlation between variables was assessed using Spearman’s correlation coefficient (rho), interpreted as follows: <0.25 = very weak, 0.25–0.5 = weak to moderate, 0.5–0.75 = strong, >0.75 = very strong correlation. To account for multiple testing in correlation analyses, P values were adjusted using the Benjamini-Hochberg false discovery rate (FDR) method. A multivariate logistic regression analysis was performed to identify independent associated factors of CS (defined as CSI ≥40) in RA patients. Variables entered into the multivariate logistic regression model included age, sex, JSS score, and Duruöz Hand Index score, based on their clinical relevance and significant associations in univariate analyses. Statistical significance was defined as P < .05.

Results

A total of 103 patients with RA (91% female; mean age ± standard deviation [SD]: 55 ± 12 years) and 80 healthy controls (75% female; mean age ± SD: 49.6 ± 9.2 years) were included in the study. There were no statistically significant differences between the groups in terms of age and sex (P = .78 and P = .82, respectively).

Demographic and clinical characteristics of the RA group, including disease duration, smoking status, extra-articular involvement, autoantibody status, treatment types, DAS28, ESR, CRP, VAS, JSS, Duruöz Hand Index, HAQ, and CSI scores, are presented in Table 1.

Table 1.

Demographic, Clinical and Laboratory Characteristics of Rheumatoid Arthritis Patients.

Patients, n	103
Age (years), mean ± SD	55 ± 12
Female, n (%)	91 (88.3)
Duration of disease, year, median (min-max)	10 (0–39)
Smoking, n (%)	27 (26)
Extra-articular involvement	15 (14.5)
Seropositive, n (%)	65 (63)
Treatment, n (%)
▒csDMARD ▒Biological	75 (73) 28 (27)
DAS28 (CRP), median (min-max)	1.3 (0–3)
Erythrocyte sedimentation rate, mean ± SD	22 ± 17
C-reactive protein, mean ± SD (mg/L)	4.2 ± 2.3
Visual analogue scale, mean ± SD	1.1 ± 0.9
JSS, mean ± SD	7.2 ± 6.4
Duruöz, mean ± SD	8.4 ± 14.4
HAQ, mean ± SD	0.84 ± 1.5
CSI, mean ± SD	31.3 ± 17

Note: CSI = Central sensitisation inventory, DAS28 = Disease Activity Score 28, DMARD = Disease-modifying anti-rheumatic drug, HAQ = Health assessment questionnaire, JSS = Jenkins Sleep Evaluation Scale, SD = Standard deviation.

Algometric measurements demonstrated that pain thresholds and pain tolerances at all six measurement points—namely the second and third MCP joints, second DIP joint, deltoid muscle, suboccipital region, and trapezius muscle—were significantly lower in the RA group compared to healthy controls (P < .001 for all).

Based on CSI scores, RA patients were categorised into two subgroups: those with CS (CSI ≥ 40, n = 35; 34%) and those without (CSI < 40, n = 68; 66%). A comparison of these subgroups revealed significant age differences (51 ± 12 vs. 57 ± 11 years, P = .019), sex distribution (97% vs. 83% female, P = .047), and sleep quality as measured by the JSS (10.7 ± 5.9 vs. 5.5 ± 6.0, P < .001). However, no significant differences were observed between the groups in terms of disease duration, smoking status, extra-articular involvement, autoantibody status (seropositive vs. seronegative), treatment type (csDMARD vs. biologic), DAS28, ESR, CRP, VAS, HAQ, or Duruöz Hand Index (all P > .05). Detailed results are provided in Table 2.

Table 2.

Comparison of Pain Thresholds and Tolerances Between Rheumatoid Arthritis Patients With and Without Central Sensitisation.

	CSI < 40, n = 68, mean ± SD	CSI > 40, n = 35, mean ± SD	P Value
Second MCP pain threshold	4.3 ± 2.2	3.2 ± 1.6	.003
Second MCP pain tolerance	6.5 ± 2	5.2 ± 2	.004
Third MCP pain threshold	4.5 ± 1.7	3.3 ± 1.6	<.001
Third MCP pain tolerance	6.4 ± 2.4	4.9 ± 2	.003
Second DIP pain threshold	4 ± 1.5	3.5 ± 1.4	.043
Second DIP pain tolerance	5.7 ± 1.8	5 ± 1.9	.03
Deltoid pain threshold	5.8 ± 1.8	4.6 ± 1.4	<.001
Deltoid pain tolerance	8.3 ± 2.8	6.5 ± 2.1	.001
Suboccipital pain threshold	4.8 ± 1.7	3.4 ± 1.2	<.001
Suboccipital pain tolerance	6.8 ± 2.2	5 ± 1.7	<.001
Trapezius pain threshold	6 ± 2	4.3 ± 1.5	<.001
Trapezius pain tolerance	8.2 ± 2.4	6 ± 1.8	<.001

Note: CSI = Central sensitisation inventory, DIP = Distal interphalangeal joint, MCP = Metacarpophalangeal joint, SD = Standard deviation.

Spearman correlation analysis showed significant negative correlations between CSI scores and all pain threshold and pain tolerance measurements (P < .001 for all). CSI scores showed a moderate correlation with JSS (ρ = 0.574, P < .001) and a modest correlation with the Duruöz Hand Index (ρ = 0.350, P < .001). No significant correlations were observed with other clinical parameters (Table 3).

Table 3.

Correlation of the CSI Score with Sociodemographic, Clinical and Algometric Variables.

	▒CSI Score
	Rho	Original P Value	FDR-corrected P Value	Significant After FDR
Second MCP pain threshold	−0.366	<.001	.0015	True
Second MCP pain tolerance	−0.357	<.001	.0015	True
Third MCP pain threshold	−0.506	<.001	.0015	True
Third MCP pain tolerance	−0.448	<.001	.0015	True
Second DIP pain threshold	−0.350	<.001	.0015	True
Second DIP pain tolerance	−0.371	<.001	.0015	True
Deltoid pain threshold	−0.394	<.001	.0015	True
Deltoid pain tolerance	−0.365	<.001	.0015	True
Suboccipital pain threshold	−0.396	<.001	.0015	True
Suboccipital pain tolerance	−0.417	<.001	.0015	True
Trapezius pain threshold	−0.413	<.001	.0015	True
Trapezius pain tolerance	−0.451	<.001	.0015	True
Duruöz	0.350	<.001	.0015	True
JSS	0.574	<.001	.0015	True
VAS	0.470	.640	.7074	False
HAQ	0.306	.102	.1428	False
Age	−0.145	.146	.1916	False
Duration of disease	−0.019	.852	.8946	False
DAS28	0.121	.225	.2779	False
Erythrocyte sedimentation rate	0.153	.249	.2905	False
C-reactive protein	0.04	.976	.9760	False

Note: CSI = Central sensitisation inventory, DAS28 = Disease Activity Score 28, DIP = Distal interphalangeal joint, FDR = False discovery rate, HAQ = Health assessment questionnaire, JSS = Jenkins Sleep Evaluation Scale, MCP = Metacarpophalangeal joint.

In multivariate analysis, including age, sex, JSS, and Duruöz Hand Index, higher JSS scores (P < .001) and younger age (P = .006) were independent associated factors of CS in RA patients. Neither sex (P = .304) nor the Duruöz Hand Index (P = .908) was a significant associated factor. Regression results are shown in Table 4.

Table 4.

Regression Analysis Results Between Groups With and Without Central Sensitisation.

	CSI > 40, n = 35
	Age	Gender	JSS	Duruöz
B	−0.058	1.149	0.152	−0.002
SE	0.021	1.118	0.041	0.016
OR (95% CI)	0.943 (0.905–0.983)	3.155 (0.352–28)	1.164 (1.074–1.263)	0.998 (0.967–1.031)
P	.006	.304	<.001	.908

Note: B = Regression coefficient, CI = Confidence interval, CSI = Central sensitisation inventory, JSS = Jenkins sleep evaluation scale, SE = Standard error, OR = Odds ratio.

Discussion

In this study, 103 patients with RA and 80 healthy control individuals were evaluated using algometric assessments. The results revealed that all pain thresholds and tolerances were significantly lower in the RA group compared to healthy controls. RA patients were further classified into two subgroups based on CSI scores: those with CS (CSI ≥40) and those without (CSI <40). It was found that patients with CS had significantly lower pain thresholds and tolerances than those without CS. Additionally, individuals in the CS group were more frequently female, younger, and had higher JSS scores. A significant negative correlation was identified between CSI scores and all algometric measurements, while a positive correlation was observed with both the Duruöz Hand Index and JSS. Regression analysis identified younger age and higher JSS scores as independent associated factors of CS.

The absence of correlation between CSI and disease duration, ESR, CRP, VAS, and DAS28 scores aligns with previous studies.^2,22 These findings suggest that CS may develop independently of disease activity and inflammatory markers. While some studies have reported an association between HAQ scores and CSI, no such relationship was found in our analysis. This may be due to HAQ primarily reflecting mechanical and functional limitations rather than central pain processing.^2,8

Notably, although no direct correlation was observed between age and CSI scores, regression analysis identified younger age as an independent associated factor of CS. This discrepancy may reflect the influence of confounding variables that affect bivariate relationships.² It implies that earlier exposure to persistent pain or chronic inflammation might predispose younger individuals to develop CS, even with comparable disease durations.

Sex-related differences in CS have been widely reported. Hormonal factors, particularly oestrogen, are known to modulate pain perception by influencing neurotransmitters and ion channels in the CNS. Oestrogen may enhance the sensitivity of nociceptive pathways, contributing to a lower pain threshold and higher CS prevalence among women.^23,24 Although the CS group included a higher proportion of females, regression analysis did not identify gender as an independent associated factor. While hormonal influences, such as oestrogen’s effect on central nociceptive processing, may contribute to the increased prevalence of CS in women, the lack of statistical significance suggests the need for larger, stratified studies to better understand this association.

Sleep quality also plays a significant role in CS. Poor sleep can disrupt neuroplasticity and increase inflammatory cytokine levels, which in turn may heighten pain sensitivity.²⁵ Sleep disturbances were strongly associated with CS, as evidenced by a robust correlation between JSS and CSI scores and the identification of JSS as an independent associated factor. Poor sleep may disrupt neuroplasticity and elevate inflammatory cytokines, thereby heightening pain perception. Routine screening for sleep disturbances in RA patients may facilitate early recognition of CS and more targeted pain management.

The positive correlation between the Duruöz Hand Index and CSI scores suggests that functional limitations may influence pain centralisation. Reduced sensorimotor feedback from impaired hand function might contribute to heightened central pain processing.^26,27 However, due to limited supporting evidence, this relationship warrants further investigation in future studies.

Algometric measurements confirmed that RA patients, particularly those with CS, had significantly lower pain thresholds and tolerances. These findings suggest that CS not only amplifies pain perception at clinically affected joints but also alters pain sensitivity in more generalised areas such as the deltoid, suboccipital, and trapezius regions. This generalised hypersensitivity reflects the central nature of CS, similar to findings in other chronic pain conditions like subacromial pain syndrome.²⁸

This study has several limitations. First, while the CSI is a validated screening instrument, it is not a definitive diagnostic tool for CS. The lack of QST, the current gold standard, may limit the objectivity of our findings.²⁹ Second, the absence of an inflammatory control group prevents us from assessing whether the results are specific to RA. Third, although the sample size was adequate for the primary analyses, it may have limited power to detect more subtle associations. Fourth, sleep quality data were collected only from RA patients, limiting comparative analysis. Finally, the cross-sectional design precludes causal inference. In addition, while previous algometry studies have supported the muscular sites (deltoid, trapezius, suboccipital), there are no standardised references for the MCP and DIP joints. These joints were chosen based on their clinical relevance in RA; however, the absence of established normative values may limit the generalizability of our findings.

Despite these limitations, this study contributes meaningful data to the limited body of literature on CS in RA. By including a healthy control group and utilising objective algometric measurements, the study offers valuable insights into the role of central mechanisms in RA-related pain. Incorporating CS assessment into routine rheumatologic evaluations may enhance individualised pain management and reduce the risk of overtreatment.

In conclusion, this study demonstrated that pain thresholds and tolerance levels assessed by algometry were significantly lower in RA patients compared to healthy controls, particularly in those with CS. The CS subgroup exhibited higher JSS scores and a greater proportion of female participants. The significant negative correlation between CSI scores and both pain thresholds and tolerances supports the role of CS in amplifying pain perception. Additionally, the positive association with sleep disturbances underscores the importance of addressing sleep quality in the management of RA-related pain. These findings suggest that CS is a relevant, non-inflammatory contributor to pain in RA and should be systematically considered during clinical evaluation. Incorporating CS assessment into routine practice may help individualise pain management strategies and reduce the unnecessary use of anti-inflammatory therapies.

Footnotes

Authors’ Contributions

Elif Altunel Kılınç contributed to conceptualisation, data curation, formal analysis, methodology, software, validation, and writing—original draft, review and editing.

Ayşegül Yetişir contributed to conceptualisation, methodology, supervision, and writing—review and editing.

Öner Kılınç contributed to conceptualisation, methodology, and supervision.

Süleyman Özbek contributed to methodology, and writing—original draft, review and editing.

Data Availability

Not applicable.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Ethical Approval and Informed Consent

The study was approved by the Ethics Committee of Çukurova University (Approval date/number: 2024/147).

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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