Effects of serum inflammatory factors, health index and disease activity scores on ankylosing spondylitis patients with sleep disorder

Abstract

BACKGROUND

Patients with ankylosing spondylitis (AS) frequently suffer from comorbid sleep disorders, exacerbating the burden of the disease and affecting their quality of life.

AIM

To investigate the clinical significance of serum inflammatory factors, health index and disease activity scores in patients with AS complicated by sleep disorders.

METHODS

A total of 106 AS patients with comorbid sleep disorders were included in the study. The patients were grouped into the desirable and undesirable prognosis groups in accordance with their clinical outcomes. The serum levels of inflammatory factors, including C-reactive protein, erythrocyte sedimentation rate, interleukin (IL)-6, tumour necrosis factor-α and IL-1β, were measured. Disease activity scores, such as the Bath AS functional index, Bath AS disease activity index, Bath AS metrology index and AS disease activity score, were assessed. The health index was obtained through the Short Form-36 questionnaire.

RESULTS

The study found significant associations amongst serum inflammatory factors, health index and disease activity scores in AS patients with comorbid sleep disorders. Positive correlations were found between serum inflammatory factors and disease activity scores, indicating the influence of heightened systemic inflammation on disease severity and functional impairment. Conversely, negative correlations were found between disease activity scores and health index parameters, highlighting the effect of disease activity on various aspects of health-related quality of life. Logistic regression analysis further confirmed the predictive value of these factors on patient outcomes, underscoring their potential utility in risk assessment and prognostication.

CONCLUSION

The findings demonstrate the intricate interplay amongst disease activity, systemic inflammation and patient-reported health outcomes in AS patients complicated by sleep disorders. The results emphasise the need for comprehensive care strategies that address the diverse needs and challenges faced by these patients and underscore the potential relevance of serum inflammatory factors, health index and disease activity scores as prognostic markers in this patient population.

Key Words: Inflammatory factors; Disease activity scores; Health index; Ankylosing spondylitis; Sleep disorders

Core Tip: This study highlights the intricate associations between serum inflammatory factors, health index, and disease activity scores in ankylosing spondylitis (AS) patients with comorbid sleep disorders. Elevated serum inflammatory markers, including C-reactive protein, erythrocyte sedimentation rate, interleukin-6 (IL-6), tumor necrosis factor-alpha, and IL-1β, are linked to increased disease activity, while the health index is negatively correlated with disease activity scores. These findings underscore the complexity of managing AS patients with concurrent sleep disturbances and emphasize the need for comprehensive care strategies that integrate systemic inflammation, disease activity, and patient-reported health outcomes.

INTRODUCTION

Ankylosing spondylitis (AS) is a long-term inflammatory rheumatic condition marked by axial skeleton inflammation, frequently resulting in severe pain, rigidity and reduced functional ability[1,2]. It primarily affects the sacroiliac joints and the spine, resulting in structural and functional impairments that can significantly affect the quality of life of affected individuals[3]. Furthermore, AS is frequently associated with comorbidities, including sleep disorders, which can further exacerbate the burden of this disease[4-6]. Sleep disturbances in patients with AS have been attributed to various factors, including pain, discomfort and restricted physical mobility, leading to detrimental effects on physical and mental well-being[7,8].

The clinical management of AS has traditionally focused on alleviating inflammation, controlling symptoms and preserving physical functions[9,10]. However, the occurrence of sleep disorders in AS patients introduces an additional layer of complexity, because disrupted sleep patterns and quality can contribute to increased disease activity, augmented inflammatory responses and impaired health-related quality of life[10-13]. This multifaceted relationship emphasises the need to explore and delineate the clinical significance of serum inflammatory factors, health index and disease activity scores in AS patients with comorbid sleep disorders.

Serum inflammatory factors, including erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), interleukin (IL)-6, tumour necrosis factor-alpha (TNF-α) and IL-1β, serve as key biomarkers of disease activity and inflammation in AS[14,15]. Elevated levels of these inflammatory markers have been associated with increased disease severity, progression of structural damage and diminished functional capacity in AS patients[16]. Moreover, the presence of sleep disorders in AS may further potentiate the pro-inflammatory milieu, perpetuating a cycle of heightened disease activity and impaired well-being[17]. Thus, investigating the association between serum inflammatory factors and disease activity in AS patients with sleep disorders is pivotal for a comprehensive understanding of the disease pathophysiology and may offer insights into potential therapeutic targets to modulate the disease course.

In accordance with recent studies, key clinical tools, such as the Bath AS functional index (BASFI), Bath AS disease activity index (BASDAI), Bath AS metrology index (BASMI) and AS disease activity score (ASDAS), are crucial for assessing the overall disease burden and functional limitations in individuals with AS[18,19]. These composite indices reflect diverse aspects of disease activity, including pain, stiffness, fatigue, peripheral joint involvement and inflammatory markers, and thus, they offer a comprehensive representation of disease status. Understanding the correlations between serum inflammatory factors and disease activity scores in AS patients with sleep disorders can offer critical insights into the interplay amongst systemic inflammation, disease manifestations and patient-reported outcomes, guiding tailored therapeutic interventions and prognostication.

MATERIALS AND METHODS

A total of 106 AS patients with comorbid sleep disorders were admitted to the First Affiliated Hospital of Harbin Medical University from January 2022 to December 2023. The clinical data of these patients were retrospectively analysed. The study classified the patients into two prognosis groups: Desirable (n = 52) and undesirable (n = 54). This study was approved by the Ethics Committee of the First Affiliated Hospital of Harbin Medical University. The Ethics Committee has agreed to waive informed consent.

Inclusion and exclusion criteria

Inclusion criteria: (1) Subjects diagnosed with AS[20,21]; (2) patients with documented sleep disorders, including but not limited to obstructive sleep apnoea, insomnia, restless legs syndrome or other clinically diagnosed sleep disturbances[22]; and (3) availability of complete medical records.

Exclusion criteria: (1) Subjects with incomplete medical records; (2) patients with other chronic inflammatory conditions or systemic autoimmune diseases[23]; (3) patients with a history of significant psychiatric disorders, substance abuse or neurological conditions; (4) patients with recent acute infectious diseases or significant medical comorbidities; (5) patients currently receiving immunomodulatory or immunosuppressive therapies; and (6) pregnant or lactating women due to potential hormonal influences on the parameters under investigation[24].

Surgical procedures for AS

Surgical procedures for AS involve total hip arthroplasty via a lateral approach. The patient is positioned laterally with the affected side facing upwards, and the pelvic region is supported and immobilised using a body positioning frame at the pubic symphysis and sacrum. A posterior lateral curved incision, approximately 10 cm in length, is centred on the greater trochanter. An incision is then made layer by layer through the skin, i.e., superficial fascia, exposing the iliotibial band and gluteus maximus. The iliotibial band and gluteus maximus are then incised, and the external rotators are exposed and cut at the insertion on the greater trochanter. The joint capsule is exposed and incised to dislocate the femoral head. The femoral neck is cut with a saw at the preoperatively determined and planned level on the lesser trochanter, and the femoral head is removed. The acetabulum is exposed, and the labrum and surrounding osteophytes are excised. The acetabulum is reamed to an appropriate depth, and the acetabular prosthesis and liner are implanted at an appropriate angle. The hip joint is flexed, adducted and internally rotated to expose the proximal femur, which is reamed to an appropriate size, and the femoral stem is implanted. An appropriate femoral head is selected, the joint is reduced and the stability of the joint is examined. The external rotator muscles are reconstructed, and the incision is closed layer by layer.

Observation indicators

BASDAI is a clinical assessment tool used to evaluate disease activity level in patients with AS[25]. It consists of 6 questions that assess fatigue, back pain, peripheral joint pain and swelling, enthesitis, severity of morning stiffness and duration. All items are evaluated using a 0-10 cm visual analogue scale, with 0 indicating no corresponding discomfort and 10 indicating extremely severe pain. When calculating the final score, the last 2 items are firstly added together and averaged, and then, the result is added to the sum of the previous 4 items and divided by 5.

BASFI is a tool used to assess the level of functional impairment in patients with AS[26]. It comprises 10 questions, and all items are assessed using a 0-10 cm visual analogue scale, and the final score is the average of the responses to the 10 questions. The calculation formula for ASDAS-D is as follows: 0.152 × back pain + 0.069 × duration of morning stiffness + 0.078 × fatigue + 0.224 × sqrt (ESR) + 0.400 × In (CRP + 1). Here, back pain, duration of morning stiffness and fatigue correspond to the 2^nd, 6^th and 1^st questions of BASDAI, respectively. ‘Sqrt’ denotes the square root, and ‘In’ denotes the natural logarithm. ESR is measured in mm/1 h by using the Westergren method, with normal values being ≤ 15 mm/1 h for males and ≤20 mm/1 h for females. CRP is tested using the immunoturbidimetric method, with normal values of ≤ 5 mg/L.

BASMI is a clinical tool used to assess the disease activity and level of functional impairment in AS[27]. It includes 5 items: Cervical flexion, lumbar side flexion, tragus-to-wall distance, thoracic expansion and fingertip-to-floor distance. Each item is scored in accordance with specific criteria, resulting in a comprehensive scoring system to assess a patient’s spinal mobility and functional impairment. Each item is scored from 0 to 10, with higher scores indicating more severe disease.

ASDAS is an assessment tool for evaluating the disease activity of AS[28]. ASDAS-CRP combines CRP in its scoring, whilst ASDAS-ESR combines ESR in its scoring. Both scoring methods include considerations of morning stiffness, intensity of back pain, overall fatigue, chronic peripheral joint inflammatory disease activity and serum inflammatory biomarkers. Clinical assessments are conducted using a 10-point scale. For the 6 aforementioned components, a score of 0 indicates no symptoms, whilst the most severe score is 10. Morning stiffness that lasts 15 min scores 1 point, 2-4 h scores 4-6 points, 4-6 h scores 8 points and more than 6 h scores 10 points. (Duration of morning stiffness + severity of morning stiffness)/2 = morning stiffness score.

The health index in this study was derived from the Short Form-36 questionnaire, a widely used tool for evaluating health status and quality of life. It includes 8 domains that encompass different aspects of health-related quality of life. Each domain is scored from 0 to 100 based on respondents’ answers, with higher scores reflecting better health status for the respective domain. This questionnaire has been widely recognised for its comprehensive assessment of health-related quality of life[29].

Statistical analysis

The data were analysed using SPSS 25.0. Descriptive statistics for categorical data were presented as counts and percentages [n (%)]. For sample sizes ≥ 40 and theoretical frequencies T ≥ 5, the chi-squared test was used with the basic formula, with the test statistic being χ². If the sample size was ≥ 40 but the theoretical frequency was 1 ≤ T < 5, then the chi-squared test was adjusted using the correction formula. If the sample size was < 40 or the theoretical frequency was T < 1, then statistical analysis was conducted using Fisher’s exact test. Normally distributed continuous data were expressed as the mean plus or minus the standard deviation (mean ± SD). Non-normally distributed data were transformed to achieve normal distribution before conducting statistical analysis. For continuous data, the t-test was used, and Spearman’s correlation analysis was employed for assessing correlations. Variables that presented statistically significant differences between the two groups were selected for binary logistic regression analysis.

RESULTS

The study compared the demographic characteristics, disease activity scores and health index of AS patients with sleep disorders (Table 1). A total of 106 patients were included, with 52 in the desirable prognosis group and 54 in the undesirable prognosis group. No statistically significant differences were found in age (44.13 ± 5.67 vs 45.28 ± 6.54, t = 0.962, P = 0.338), sex distribution (male/female: 35/17 vs 38/16, t = 0.017, P = 0.896), disease duration (8.81 ± 2.34 vs 9.17 ± 3.12, t = 0.67, P = 0.504), body mass index (26.45 ± 3.57 vs 27.89 ± 4.21, t = 1.897, P = 0.061), smoking status (13.46% vs 20.37%, t = 0.474, P = 0.491), alcohol consumption (19.23% vs 25.93%, t = 0.35, P = 0.554), family history of cancer (23.08% vs 20.37%, t = 0.01, P = 0.919), hypertension (48.08% vs 38.89%, t = 0.575, P = 0.448), diabetes (26.92% vs 33.33%, t = 0.257, P = 0.612) and hyperlipidemia (44.23% vs 37.04%, t = 0.309, P = 0.578) between the two groups, indicating that the demographic characteristics were largely similar between the two groups. The above results indicate no statistically significant differences between the two groups, suggesting comparability and laying the foundation for subsequent research.

Table 1 Demographic characteristics of ankylosing spondylitis patients with sleep disorders, n (%).

Parameters	Desirable prognosis group (n = 52)	Undesirable prognosis group (n = 54)	t	P value
Age (yr)	44.13 ± 5.67	45.28 ± 6.54	0.962	0.338
Sex (male/female)	35/17	38/16	0.017	0.896
Disease duration (yr)	8.81 ± 2.34	9.17 ± 3.12	0.67	0.504
Body mass index (kg/m2)	26.45 ± 3.57	27.89 ± 4.21	1.897	0.061
Smoking status	7 (13.46)	11 (20.37)	0.474	0.491
Alcohol consumption	10 (19.23)	14 (25.93)	0.35	0.554
Family history of cancer	12 (23.08)	11 (20.37)	0.01	0.919
Hypertension	25 (48.08)	21 (38.89)	0.575	0.448
Diabetes	14 (26.92)	18 (33.33)	0.257	0.612
Hyperlipidemia	23 (44.23)	20 (37.04)	0.309	0.578

Sleep quality

The comparison of sleep quality parameters between the desirable and undesirable prognosis groups revealed no statistically significant differences (Table 2). In particular, the Pittsburgh sleep quality index (6.82 ± 1.68 vs 7.49 ± 1.98, t = 1.887, P = 0.062), Epworth sleepiness scale (8.67 ± 1.42 vs 9.28 ± 2.15, t = 1.739, P = 0.085), insomnia severity index (8.81 ± 2.03 vs 9.46 ± 3.19, t = 1.247, P = 0.216), sleep efficiency (85.46% ± 4.72% vs 84.28% ± 5.64%, t = 1.162, P = 0.248) and total sleep time (6.89 ± 1.32 h vs 6.48 ± 1.14 h, t = 1.729, P = 0.087) did not demonstrate statistically significant differences between the two groups.

Table 2 Sleep quality in the desirable and undesirable prognosis groups.

Parameters	Desirable prognosis group (n = 52)	Undesirable prognosis group (n = 54)	t	P value
Pittsburgh sleep quality index	6.82 ± 1.68	7.49 ± 1.98	1.887	0.062
Epworth sleepiness scale	8.67 ± 1.42	9.28 ± 2.15	1.739	0.085
Insomnia severity index	8.81 ± 2.03	9.46 ± 3.19	1.247	0.216
Sleep efficiency (%)	85.46 ± 4.72	84.28 ± 5.64	1.162	0.248
Total sleep time (h)	6.89 ± 1.32	6.48 ± 1.14	1.729	0.087

Inflammatory factors

The serum levels of inflammatory factors were compared between the desirable and undesirable prognosis groups in patients with AS complicated by sleep disorders (Table 3). The desirable prognosis group exhibited significantly lower CRP levels (6.17 ± 2.34 mg/L) compared with the undesirable prognosis group (7.52 ± 3.21 mg/L) (t = 2.482, P = 0.015). Similarly, ESR levels were significantly lower in the desirable prognosis group (16.56 ± 3.78 mm/h) compared with in the undesirable prognosis group (18.32 ± 4.91 mm/h) (t = 2.076, P = 0.04). TNF-α levels were also significantly lower in the desirable prognosis group (45.21 ± 9.33 pg/mL) compared with in the undesirable prognosis group (50.79 ± 12.54 pg/mL) (t = 2.605, P = 0.011). In addition, IL-6 Levels were significantly lower in the desirable prognosis group (28.14 ± 6.75 pg/mL) compared with in the undesirable prognosis group (32.79 ± 8.21 pg/mL) (t = 3.195, P = 0.002). Finally, IL-1β levels were significantly lower in the desirable prognosis group (12.45 ± 3.88 pg/mL) compared with in the undesirable prognosis group (14.67 ± 4.21 pg/mL) (t = 2.82, P = 0.006). These findings suggest an association between lower serum inflammatory factor levels and improved prognosis in AS patients with comorbid sleep disorders.

Table 3 Comparison of serum inflammatory factors between the desirable and undesirable prognosis groups.

Parameters	Desirable prognosis group (n = 52)	Undesirable prognosis group (n = 54)	t	P value
CRP (mg/L)	6.17 ± 2.34	7.52 ± 3.21	2.482	0.015
ESR (mm/h)	16.56 ± 3.78	18.32 ± 4.91	2.076	0.04
TNF-α (pg/mL)	45.21 ± 9.33	50.79 ± 12.54	2.605	0.011
IL-6 (pg/mL)	28.14 ± 6.75	32.79 ± 8.21	3.195	0.002
IL-1β (pg/mL)	12.45 ± 3.88	14.67 ± 4.21	2.82	0.006

CRP: C-reactive protein; ESR: Erythrocyte sedimentation rate; TNF-α: Tumour necrosis factor-alpha; IL: Interleukin.

Disease activity scores

Disease activity scores were compared between the desirable and undesirable prognosis groups in AS patients with sleep disorders, demonstrating significant differences (Table 4). The desirable prognosis group displayed lower scores across all parameters compared with the undesirable prognosis group. In particular, BASDAI scores were 3.21 ± 0.87 vs 4.67 ± 1.45 (t = 6.304, P < 0.001), BASFI scores were 2.98 ± 0.75 vs 3.72 ± 1.02 (t = 4.267, P < 0.001), BASMI scores were 2.34 ± 0.65 vs 3.78 ± 0.98 (t = 8.957, P < 0.001), ASDAS-CRP scores were 2.56 ± 0.68 vs 3.28 ± 1.21 (t = 3.771, P < 0.001) and ASDAS-ESR scores were 2.78 ± 0.72 vs 4.21 ± 1.14 (t = 7.776, P < 0.001). These results indicate that lower disease activity scores are associated with improved prognosis in AS patients with comorbid sleep disorders.

Table 4 Comparison of disease activity scores between the desirable and undesirable prognosis groups.

Parameters	Desirable prognosis group (n = 52)	Undesirable prognosis group (n = 54)	t	P value
BASDAI	3.21 ± 0.87	4.67 ± 1.45	6.304	P < 0.001
BASFI	2.98 ± 0.75	3.72 ± 1.02	4.267	P < 0.001
BASMI	2.34 ± 0.65	3.78 ± 0.98	8.957	P < 0.001
ASDAS-CRP	2.56 ± 0.68	3.28 ± 1.21	3.771	P < 0.001
ASDAS-ESR	2.78 ± 0.72	4.21 ± 1.14	7.776	P < 0.001

BASDAI: Bath ankylosing spondylitis disease activity index; BASFI: Bath ankylosing spondylitis functional index; BASMI: Bath ankylosing spondylitis metrology index; ASDAS: Ankylosing spondylitis disease activity score; CRP: C-reactive protein; ESR: Erythrocyte sedimentation rate.

Health index

The health index parameters were compared between the desirable and undesirable prognosis groups in AS patients with sleep disorders, revealing notable differences (Table 5). Physical functioning scores were higher in the desirable prognosis group compared with in the undesirable prognosis group (62.78 ± 8.52 vs 58.92 ± 9.74, t = 2.175, P = 0.032), as were the role-physical scores (48.73 ± 12.45 vs 43.62 ± 11.39, t = 2.203, P = 0.03) and general health scores (60.94 ± 10.27 vs 56.33 ± 11.68, t = 2.161, P = 0.033). Vitality scores were also higher in the desirable prognosis group compared with in the undesirable prognosis group (54.17 ± 7.94 vs 49.78 ± 9.63, t = 2.565, P = 0.012). However, no significant difference was found in bodily pain scores (55.88 ± 9.76 vs 54.13 ± 8.92, t = 0.967, P = 0.336). These findings suggest that higher health index scores, particularly in the domains of physical functioning, role-physical, general health and vitality, may be indicative of better prognosis in AS patients with comorbid sleep disorders, highlighting the clinical relevance of these parameters in assessing patient well-being and outcomes.

Table 5 Health index in the desirable and undesirable prognosis groups.

Parameters	Desirable prognosis group (n = 52)	Undesirable prognosis group (n = 54)	t	P value
Physical functioning	62.78 ± 8.52	58.92 ± 9.74	2.175	0.032
Role-physical	48.73 ± 12.45	43.62 ± 11 .39	2.203	0.03
Bodily pain	55.88 ± 9.76	54.13 ± 8.92	0.967	0.336
General health	60.94 ± 10.27	56.33 ± 11.68	2.161	0.033
Vitality	54.17 ± 7.94	49.78 ± 9.63	2.565	0.012

Correlation analysis

The correlation analysis revealed significant associations amongst serum inflammatory factors, health index and disease activity scores in patients with AS complicated by sleep disorders (Table 6). The serum levels of CRP, ESR, TNF-α, IL-6 and IL-1β exhibited positive correlations with disease activity scores (BASMI, BASDAI, ASDAS-CRP, BASFI and ASDAS-ESR), indicating a relationship between heightened inflammatory markers and increased disease activity. Conversely, the health index parameters (role-physical, general health, bodily pain, physical functioning and vitality) exhibited negative correlations with disease activity scores, suggesting that lower disease activity scores were associated with better health index outcomes. These findings underscore the interconnectedness of inflammatory factors, disease activity and health index in AS patients with comorbid sleep disorders, highlighting the clinical relevance of these associations in the management and prognosis of this patient population.

Table 6 Correlation analysis of serum inflammatory factors, health index and disease activity scores in patients with as complicated by sleep disorders.

Parameters	r	R2	P value
CRP	0.235	0.055	0.015
ESR	0.199	0.039	0.041
TNF-α	0.246	0.061	0.011
IL-6	0.298	0.089	0.002
IL-1β	0.266	0.071	0.006
BASDAI	0.522	0.273	P < 0.001
BASFI	0.384	0.148	P < 0.001
BASMI	0.657	0.432	P < 0.001
ASDAS-CRP	0.344	0.118	P < 0.001
ASDAS-ESR	0.603	0.364	P < 0.001
Physical functioning	-0.208	0.043	0.032
Role-physical	-0.211	0.045	0.03
Bodily pain	-0.095	0.009	0.335
General health	-0.207	0.043	0.033
Vitality	-0.243	0.059	0.012

BASDAI: Bath ankylosing spondylitis disease activity index; BASFI: Bath ankylosing spondylitis functional index; BASMI: Bath ankylosing spondylitis metrology index; ASDAS: Ankylosing spondylitis disease activity score; CRP: C-reactive protein; ESR: Erythrocyte sedimentation rate; TNF-α: Tumour necrosis factor-alpha; IL: Interleukin.

Logistic regression analysis

The logistic regression analysis demonstrated significant associations amongst serum inflammatory factors, health index and disease activity scores in patients with AS complicated by sleep disorders (Table 7). The serum levels of CRP, ESR, TNF-α, IL-6 and IL-1β, and the disease activity scores (BASDAI, BASFI, BASMI, ASDAS-CRP and ASDAS-ESR), were found to have positive coefficients and odds ratios, signifying their potential predictive value in the presence of the outcome of interest. Conversely, the health index parameters (physical functioning, role-physical, bodily pain, general health and vitality) exhibited negative coefficients and odds ratios, suggesting their roles in predicting positive outcomes in this patient population. These findings emphasise the predictive significance of these factors in patient outcomes, underscoring their utility in informing clinical decision-making and patient management strategies in AS patients with comorbid sleep disorders.

Table 7 Logistic regression analysis of serum inflammatory factors, health index and disease activity scores in patients with ankylosing spondylitis complicated by sleep disorders.

Parameters	Coefficient	Odds ratio	B	β	P value
CRP (mg/L)	0.174	1.19	2.353	0.174	0.019
ESR (mm/h)	0.094	1.098	1.999	0.094	0.046
TNF-α (pg/mL)	0.046	1.047	2.47	0.046	0.014
IL-6 (pg/mL)	0.084	1.087	2.938	0.084	0.003
IL-1β (pg/mL)	0.138	1.148	2.638	0.138	0.008
BASDAI	0.992	2.698	4.708	0.992	P < 0.001
BASFI	0.924	2.518	3.687	0.924	P < 0.001
BASMI	2.219	9.2	5.303	2.219	P < 0.001
ASDAS-CRP	0.749	2.115	3.343	0.749	P < 0.001
ASDAS-ESR	1.591	4.907	5.054	1.591	P < 0.001
Physical functioning	0.047	0.954	2.098	-0.047	0.036
Role-physical	0.037	0.964	2.132	-0.037	0.033
Bodily pain	0.021	0.98	0.969	-0.021	0.333
General health	0.038	0.962	2.093	-0.038	0.036
Vitality	0.057	0.945	2.441	-0.057	0.015

BASDAI: Bath ankylosing spondylitis disease activity index; BASFI: Bath ankylosing spondylitis functional index; BASMI: Bath ankylosing spondylitis metrology index; ASDAS: Ankylosing spondylitis disease activity score; CRP: C-reactive protein; ESR: Erythrocyte sedimentation rate; TNF-α: Tumour necrosis factor-alpha; IL: Interleukin.

DISCUSSION

This study aimed to find associations amongst serum inflammatory factors, health index and disease activity scores in patients with AS and comorbid sleep disorders. Notably, the findings revealed significant correlations amongst these parameters, indicating a complex interrelation amongst disease activity, systemic inflammation and patient-reported health status. The positive correlations of the serum levels of CRP, ESR, TNF-α, IL-6 and IL-1β with disease activity scores (BASDAI, BASFI, BASMI and ASDAS) highlight the influence of heightened systemic inflammation on disease severity and functional impairment in AS patients with sleep disorders. These findings are aligned with previous research that demonstrates the central role of inflammatory markers in driving disease activity and structural damage in AS. Eichberger et al[30] also confirmed a similar effect[30]. In addition, the present study extends this understanding by elucidating the relevance of serum inflammatory factors in the context of comorbid sleep disorders, implicating their potential contribution to the multifaceted burden experienced by these patients.

Moreover, this study demonstrated negative correlations between disease activity scores and health index parameters, particularly in domains related to physical functioning, role-physical, general health and vitality. Kaneko et al[31] mentioned similar viewpoints[31]. The current study highlights the effect of disease activity on various aspects of health-related quality of life, underscoring the pervasive influence of AS and sleep disorders on patient well-being. The findings are aligned with the multifactorial nature of AS, where disease activity encompasses not only physical symptoms but also exerts profound effects on emotional and social functioning, vitality and overall health perception. Moreover, the findings of the study underscore the integrated nature of disease activity and health-related quality of life, emphasising the need for comprehensive patient care that addresses inflammatory disease manifestations and their effect on various dimensions of patient-reported outcomes.

Furthermore, logistic regression analysis provided insights into the predictive value of serum inflammatory factors, health index and disease activity scores in AS patients with comorbid sleep disorders. The positive coefficients and odds ratios associated with serum inflammatory factors and disease activity scores signify their potential as predictive markers for adverse outcomes in this patient population, reflecting their utility in prognostication and risk stratification. Similar findings were reported by Sigmund et al[32]. Conversely, the negative coefficients and odds ratios observed for health index parameters imply their role as predictors of favourable outcomes, highlighting their importance in identifying patients with better health-related quality of life and functional status. These findings hold clinical significance, offering valuable insights for the risk assessment, treatment planning and monitoring of AS patients with concurrent sleep disorders.

This study also compared demographic characteristics, sleep quality, serum inflammatory factors, health index and disease activity scores between the desirable and undesirable prognosis groups in AS patients with sleep disorders. The analysis revealed no statistically significant differences in demographic characteristics and sleep quality parameters between the two groups, underscoring the relevance of serum inflammatory factors, health index and disease activity scores as potential determinants of prognosis in this patient population. Moreover, the study findings contribute to the understanding of prognostic factors in AS patients with comorbid sleep disorders, emphasising the need for tailored approaches that address the complex interplay of disease activity, systemic inflammation and patient-reported outcomes in clinical decision-making.

Overall, the study findings illuminate the intricate associations amongst serum inflammatory factors, health index and disease activity scores in AS patients complicated by sleep disorders, providing valuable insights into the multifaceted burden experienced by this patient population. These findings have significant clinical implications, emphasising the need for holistic approaches that integrate the assessment of systemic inflammation, disease activity and patient-reported health outcomes in the management of AS patients with concurrent sleep disorders. The multifaceted nature of AS, compounded by the presence of sleep disturbances, underscores the importance of comprehensive care strategies that extend beyond traditional disease-modifying therapies to address the diverse needs and challenges faced by these patients.

The study findings also underscore the potential relevance of serum inflammatory factors, health index and disease activity scores as prognostic markers in AS patients with comorbid sleep disorders, offering valuable insights for risk assessment and patient stratification. These findings complement existing knowledge on the pathophysiology and clinical manifestations of AS, shedding light on the complexities of managing a chronic inflammatory rheumatic disease in the context of sleep disturbances. Furthermore, the findings highlight the need for multidisciplinary care models that encompass rheumatological, sleep medicine and psychological support to optimise patient outcomes and well-being.

Despite the contributions of this study, several limitations should be acknowledged. The findings may have limited generalisability due to the retrospective design and relatively small sample size. In addition, the study did not explore specific interventions that target sleep disorders or systemic inflammation, warranting further research to elucidate the potential effects of targeted therapies on disease activity and health outcomes in this patient population. Future prospective studies with larger cohorts and longitudinal follow-up are warranted to validate the findings and explore the longitudinal associations amongst serum inflammatory factors, health index and disease activity scores in AS patients with sleep disorders.

In consideration of our study’s implications for clinical decision-making and patient care, underscoring the role of incorporating the comprehensive assessments of sleep quality and inflammatory status into the routine clinical evaluation of AS patients is imperative. Our findings underscore the importance of a multidimensional approach to patient care, emphasising the need for rheumatologists and healthcare providers to consider the bidirectional relationships amongst AS, systemic inflammation and sleep disturbances when formulating tailored treatment strategies. Furthermore, the integration of validated patient-reported outcome measures that assess sleep patterns and perceived sleep quality can offer valuable insights into disease management and treatment response, informing the development of personalised care plans for AS patients with concurrent sleep disorders.

We recognise the importance of confirming and expanding upon the current results through prospective, larger-scale studies to establish the robustness and generalisability of our findings. Future research endeavours will involve the implementation of prospective cohort studies with larger patient populations to validate the associations amongst serum inflammatory factors, health index and disease activity scores in AS patients complicated by sleep disorders. By increasing sample size and employing a longitudinal approach, these studies will provide a comprehensive understanding of the longitudinal associations and predictive value of these parameters in informing clinical decision-making and patient management strategies. In particular, the incorporation of longitudinal follow-up will enable the assessment of the temporal relationships amongst serum inflammatory factors, disease activity and patient-reported health outcomes, shedding light onto the dynamic nature of these interrelations over time.

CONCLUSION

In conclusion, the present study provides valuable insights into the clinical significance of serum inflammatory factors, health index and disease activity scores in patients with AS complicated by sleep disorders. The findings underscore the intricate interplay amongst systemic inflammation, disease activity and patient-reported health outcomes in this patient population, highlighting the multifaceted burden experienced by AS patients with concurrent sleep disturbances. These findings hold implications for tailored therapeutic interventions, risk assessment and patient stratification, emphasising the importance of a comprehensive approach to address the diverse needs and challenges faced by these patients.

ACKNOWLEDGEMENTS

I would like to express my gratitude to all those who helped me during the writing of this thesis. I acknowledge the help of my colleagues, Jian-Yang Zhuang, who gave me advice on academic research.