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. 2026 Mar 17;22:580711. doi: 10.2147/NDT.S580711

Threshold Effect of Anxiety in the Comorbidity of Depression and Chronic Pain: A Cross-Sectional Study Based on Pain Sensitivity

Yongli Liu 1,2,*, Jian Cheng 2,*, Hua Gao 2, Yuanyuan Chen 2, He Chen 3, Qingrong Xia 4, Junwei Yan 4, Chao Li 4, Peijun Ju 5,✉, Cuizhen Zhu 3,✉
PMCID: PMC13005642  PMID: 41867698

Abstract

Purpose

The considerable co-prevalence of major depressive disorders (MDD), anxiety symptoms, and chronic pain (CP) indicates a complex link with significant treatment ramifications. This study elucidates the role of pain sensitivity as a potential etiological factor in anxiety and depression.

Methods

This cross-sectional study recruited 124 participants divided into a health control group (HC, n=48), patients with depression without CP group (MDD, n=38) and comorbidity group of patients with MDD and CP (COM, n=38). Clinical assessments included the Hamilton Depression Rating Scale-24 (HAMD-24), Beck Depression Inventory-II (BDI-II), Hamilton Anxiety Rating Scale-14 (HAMA-14), and Beck Anxiety Inventory (BAI) to evaluate depressive and anxiety symptoms. The 36-Item Short Form Health Survey (SF-36) quantified health-related quality of life, while the Pain Sensitivity Questionnaire (PSQ) and Pain Intensity Numerical Rating Scale (PI-NRS) assessed pain sensitivity and pain intensity, respectively. A piecewise regression model identified clinical thresholds.

Results

The COM group showed higher BAI (U=294.5, p<0.001) and PI-NRS scores (U=166.0, p<0.001) compared to the MDD group. Piecewise regression identified PSQ=4.14 as the critical breakpoint for anxiety’s influence on pain sensitivity. Beyond this threshold, BAI and Bodily Pain slopes reversed significantly (R2=0.2434, p<0.05; R2=0.2085, p<0.05). The high-PSQ group reported significantly more severe bodily pain (Cohen’s d = −0.81, p=0.042).

Conclusion

Our findings reveal that when pain sensitivity exceeds the threshold (PSQ≥4.14), anxiety significantly amplifies pain perception, forming a “pain-anxiety-depression” positive feedback loop. This finding provides an empirical foundation for personalized therapy.

Keywords: depression, chronic pain, anxiety, pain sensitivity, quality of life

Background

The notable co-occurrence of major depressive disorder (MDD), anxiety symptoms, and chronic pain (CP) has been extensively studied, indicating a complicated link with considerable treatment implications.1–3 Epidemiological studies indicate that 20% to 40% of adults with chronic pain concurrently have depression and anxiety,4 which are primary contributors to these complex symptoms, such as anhedonia, declining interest, agitation, slowed thinking, sleep and appetite disturbances, feelings of helplessness and hopelessness, and, in severe cases, suicidal ideation.5–7 The Global Burden of Disease (GBD) study identifies mental disorders as a major contributor to the global disease burden, accounting for 1566.2 disability-adjusted life years (DALYs) per 100,000 population in 2019, of them, MDD accounted for the largest percentage of DALYs for mental disorders (37.3%).8–10 As some of the world’s most significant health challenges, anxiety, depression, and chronic pain profoundly reduce quality of life (QoL) in social, psychological, physical, and functional spheres.11–14 Despite their global health impact, the intricate etiology of these interconnected conditions remains poorly understood.15

There is mounting evidence that pain and emotions have a reciprocal impact on quality of life. While emotional and anxiety disorders worsen pain perception, functional impairment, and a lower quality of life, chronic pain increases the likelihood of anxiety and depression. Although efficacy is usually modest and varies by condition, multimodal treatment (psychotherapy, exercise therapy, and selective pharmacotherapy such as Serotonin-Norepinephrine Reuptake Inhibitors medicines like duloxetine) can enhance quality of life and moderately reduce pain.16–19 Despite advancements, methodological, conceptual, and contextual variables continue to pose considerable problems and contradictions to research on the effects of anxiety, depression, and chronic pain on quality of life. The diversity of definitions and measurement instruments is a major problem. There is a great deal of variation in QoL tests, some, like the WHOQOL-BREF, emphasize subjective well-being,20 while others, like the EQ-5D,21 concentrate on health utility, producing results that are not always consistent. Due to scale sensitivity biases, for instance, some research emphasizes depression while others find greater QoL deficits from anxiety in chronic pain contexts.22

The majority of current research focuses on the clinical symptoms of depression and chronic pain, but it ignores studies that look at how these illnesses affect participants’ quality of life differently. Measurement validity concerns are brought to light by differences between self-reported and externally evaluated quality of life measures (such as in patients with major depressive illness). Thus, standardized cohort studies and sophisticated analytical methods that combine several modalities, like machine learning, are required for future research to solve these problems. Notwithstanding current inconsistencies, quality-of-life research results will have a significant impact on the necessity of comprehensive interventions to end vicious cycles. The purpose of this study is to assess how patients’ quality of life is affected by chronic pain linked to depression. A deeper understanding of the relationship between depressive and anxious mood, chronic pain, and general well-being will be possible by looking at how depression, anxiety, and pain persistence may affect one’s capacity to carry out daily tasks, lowering quality of life, and potential correlations with cognitive and psychological changes, such as attention, memory, and mood deficits.

Materials and Methods

Participants

From January 2023 to September 2025, this cross-sectional cohort study was carried out at Anhui Mental Health Center (AMHC) with permission from the AMHC Medical Ethics Committee. According to the Declaration of Helsinki, written informed consent was given by each participant. The Mini-International Neuropsychiatric Interview (MINI) in Chinese was used to evaluate individuals by two board-certified psychiatrists.23 The trial clinical registration number was ChiCTR2500099258.

Initially, 218 participants were screened: 68 healthy people from the hospital’s physical examination center and 150 depressed inpatients and outpatients from AMHC. In the healthy group, two were ineligible, two withdrew, 13 rejected consent, and three did not finish assessments. Of patients with depression, 18 withdrew consent, 25 did not satisfy inclusion criteria, 15 rejected informed consent, and 16 did not complete assessments. In the end, 124 people were recruited and divided into three groups: the healthy control group (HC, n = 48), the MDD group (MDD without chronic pain, n = 38), and the COM group (MDD with chronic pain, n =38).

Two independent senior psychiatrists had to confirm the concurrent diagnosis of MDD in accordance with the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and chronic pain in accordance with the International Association for the Study of Pain (IASP) criteria in order for the COM group to be included, with pain duration exceeding three months. (2) 18 to 65 years of age. (3) Prolonged discomfort that is not caused by serious physical illnesses, inflammatory conditions, or traumatic injuries. (4) ≥3 on the Pain Intensity Numerical Rating Scale (PI-NRS). The MDD group’s inclusion requirements were: (1) a DSM-5-recognized MDD diagnosis from two senior, independent psychiatrists. (2) 18–65 years old. (3) Participants were either first-onset, untreated patients, or had discontinued all psychotropic medications for at least four weeks. (4) PI-NRS score <3 indicates that there is no clinically significant pain. Healthy people who were screened from the AMHC physical examination center met the requirements to be included in the HC category. All participants must meet the following exclusion criteria: (1) Have a history of severe head or bodily injuries. (2) A history of neurological, inflammatory, or cancerous conditions. (3) Bipolar disorder, schizophrenia, substance use disorder, or other serious mental illnesses. (4) Serious comorbidities of the body. (5) Lactation or pregnancy. (6) Modified electroconvulsive therapy (MECT) within the past 3 months. (7) Failure to follow prescription guidelines.

Mini International Neuropsychiatric Interview (MINI) 7.0.2

In order to verify initial clinical diagnoses, psychiatrists and clinicians from the US and Europe collaborated to create the Mini-International Neuropsychiatric examination (MINI) 7.0.2, a quick, validated, and trustworthy structured diagnostic examinational patients underwent MINI examinations. To enable experts to collect correct data, all patients underwent MINI examinations.24

Demographic Characteristics

Demographic variables, including gender, education level, occupational status, marital status, income level, body mass index (BMI), and smoking/alcohol use history, were collected for all three groups.

Hamilton Depression Rating Scale-24 (HAMD-24)

HAMD-24 is a commonly used scale for evaluating the severity of depression symptoms, which is given by clinicians. Higher total scores indicate more severe symptoms, which is correlated with the severity of the illness. The following are the severity thresholds: total score <8 (no depression), 20–35 (mild/moderate depression), and >35 (severe depression) (Cronbach’s α = 0.85).25

Beck Depression Inventory (BDI-II)

The BDI-II is a 21-item self-report questionnaire measuring the presence and severity of depressive symptoms. Each item is scored 0–3 based on symptom intensity, with higher total scores reflecting greater depression severity (Cronbach’s α = 0.83).26

Hamilton Anxiety Rating Scale-14 (HAMA-14)

The 14 items on the HAMA are broken down into two categories: somatic anxiety (muscular, sensory, cardiovascular, respiratory, gastrointestinal, genitourinary, and autonomic symptoms) and psychological anxiety (anxious mood, tension, fears, insomnia, concentration/memory difficulties, depressed mood, and interview behavior). Every item has a value between 0 and 4, where higher overall scores denote more severe anxiety.27

Beck Anxiety Inventory (BAI)

A self-report questionnaire consisting of 21 items, the BAI evaluates subjective symptoms of anxiety. Items are graded 0–3 across two dimensions: physical anxiety (13 items) and cognitive anxiety (8 items). More acute anxiety is indicated by higher overall scores (Cronbach’s α = 0.83).28

Pain Intensity Numerical Rating Scale (PI-NRS)

The PI-NRS is a validated self-report tool measuring pain intensity on an 11-point scale (0–10). Scores ≥4 indicate moderate pain.29

Pain Sensitivity Questionnaire (PSQ)

The PSQ assesses pain sensitivity in daily life using 17 items, comprising 14 pain-related items across the PSQ-minor (mild pain), PSQ-moderate (moderate pain), and PSQ-total (Cronbach’s α = 0.89), as well as three non-pain reference items. Greater sensitivity to pain is reflected in higher scores.30

36-Item Short Form Health Survey (SF-36)

The SF-36 evaluates health-related quality of life in eight areas: mental health, role-emotional, role-physical, physical functioning, bodily pain, general health, vitality, and social functioning. Higher scores indicate a higher quality of life. The scores are combined into two summaries: the Physical Component Summary (PCS) and the Mental Component Summary (MCS).31

Pittsburgh Sleep Quality Index (PSQI)

The PSQI uses five non-scored observer-rated measures and 19 self-rated items to assess sleep quality. A total score of 0–21 is obtained by rating each scored item from 0–3. Poorer sleep quality is indicated by higher scores (Cronbach’s α = 0.82).32,33

Statistical Analysis

Statistical analyses were performed using SPSS 27.0 and Python 3.x. Frequencies and percentages (n [%]) are used to present categorical data, while chi-square tests are used to compare groups. The Kolmogorov–Smirnov test was used to determine whether continuous variables were normal. Whereas non-normally distributed data are given as median and interquartile range (IQR: 25th–75th percentiles), normally distributed data are stated as mean ± standard deviation (SD). In order to eliminate biases between the data, we use normalization to process the data. For two-group comparisons, independent samples t-tests were employed. The Kruskal–Wallis H-test was utilized for nonparametric analyses, while one-way ANOVA was applied to normally distributed data for multigroup comparisons. Significant differences were found in multigroup comparisons. Type I error was controlled by applying False Discovery Rate correction. The relationships between possible factors and pain sensitivity were investigated using Chatterjee correlation analysis. The regression slopes of the variables affecting BAI scores were analyzed using a piecewise regression model before and after the breakpoint. Python was used to calculate the clinical threshold (Threshold = 4.14). Standardized mean difference, or Cohen’s d, was computed to measure the effect size differences between the high- and low-PSQ groups. The threshold for statistical significance was set at p < 0.05, and all tests were two-tailed.

Results

Comparison of Demographic and Clinical Characteristics Across Control, Depressive Disorder, and Comorbidity Groups

Demographic characteristics and clinical symptoms of the MDD, COM, and HC groups are summarized in Table 1. A total of 124 participants were included in the final analysis. We recruited 38 depression patients (17 males, 21 females), 38 depression with pain patients (12 males, 26 females), and 48 healthy controls (HC, 28 males, 20 females). No significant intergroup differences were observed in gender, education level, occupational status, marital status, income level, BMI, or smoking/alcohol use history (p> 0.05). The SF-36 quality of life assessment revealed that the COM group scored significantly lower than both the HC and MDD groups across various dimensions. These dimensions included physical functioning, role-physical, role-emotional, bodily pain, vitality, mental health, social functioning, general health perception, and both physical and mental health summary scores (H=36.618, p<0.001; H=72.058, p<0.001; H=85.132, p<0.001; H=60.967, p<0.001; H=78.961, p<0.001; H=79.226, p<0.001; H=75.258, p<0.001; H=67.661, p<0.001; H=71.556, p<0.001; H=84.732, p<0.001). Furthermore, post-hoc tests indicated that the COM group exhibited significantly greater impairment in quality of life specifically in the Bodily Pain, Social Functioning, and Physical Component Summary (PCS) dimensions compared to the MDD group (all p<0.05). Additionally, the Pain Sensitivity Questionnaire (PSQ) uncovered that there were significant differences among the three groups (H=8.290, p=0.022). Post-hoc analysis confirmed that the COM group reported higher PSQ scores than the control group (p=0.003).

Table 1.

Demographic, Clinical, and Quality of Life Profiles of the Comorbidity, Depression, and Control Groups

Control Group Depression Group Comorbidity Group χ2/F/H/t/U FDR-Adjusted P A vs B A vs C B vs C
n=48 (A Group) n=38 (B Group) n=38 (C Group)
Gender 6.146 0.460
 Male, n (%) 28 (58.3) 17 (44.7) 12 (31.6)
 Female, n (%) 20 (41.7) 21 (55.3) 26 (68.4)
Education years 4.207 0.122
 ≦9, n (%) 13 (27.1) 7 (18.4) 15 (39.5)
 >9, n (%) 35 (72.9) 31 (81.6) 23 (60.5)
Employment 3.806 0.149
 Yes, n (%) 26 (54.2) 15 (39.5) 13 (34.2)
 No, n (%) 22 (45.8) 23 (60.5) 25 (65.8)
Marital status 2.764 0.598
 Single, n (%) 21 (43.8) 20 (52.6) 18 (47.4)
 Married, n (%) 27 (56.3) 18 (47.4) 20 (52.6)
Monthly income 2.505 0.286
 ≦5000, n (%) 32 (66.7) 20 (52.6) 26 (68.4)
 >5000, n (%) 16 (33.3) 18 (47.4) 12 (31.6)
History of alcohol consumption 5.249 0.252
 Never drinks alcohol, n (%) 33 (68.8) 32 (84.2) 28 (73.7)
 Drinking, n (%) 15 (31.3) 6 (15.8) 10 (26.3)
Current Alcohol Consumption 2.674 0.072
 Never drinks alcohol, n (%) 33 (68.8) 34 (89.5) 29 (76.3)
 Drinking, n (%) 15 (31.3) 4 (10.5) 9 (23.7)
Current Smoking Status 0.612 0.537
 No smoking, n (%) 43 (89.6) 32 (84.2) 35 (92.1)
 Smoking, n (%) 5 (10.4) 6 (15.8) 3 (7.9)
BMI (kg/m2, Inline graphic) 23.24±4.38 22.21±4.29 22.16±3.06 1.026 0.362
SF-36
 PF 100.00 (95.00, 100.00) 85.00 (76.25, 100.00) 82.50 (71.25, 90.00) 36.618 <0.001*** <0.001*** <0.001*** 0.161
 RP 100.00 (100.00, 100.00) 0.00 (0.00, 50.00) 0.00 (0.00, 0.00) 72.058 <0.001*** <0.001*** <0.001*** 0.131
 BP 84.00 (84.00, 100.00) 74.00 (64.00, 100.00) 52.00 (31.00, 62.00) 60.967 <0.001*** 0.002** <0.001*** <0.001***
 GH 72.00 (66.50, 82.00) 35.00 (25.00, 53.75) 30.00 (16.25, 40.00) 67.661 <0.001*** <0.001*** <0.001*** 0.102
 VT 80.00 (70.00, 85.00) 27.50 (20.00, 40.00) 25.00 (15.00, 35.00) 78.961 <0.001*** <0.001*** <0.001*** 0.273
 SF 112.50 (100.00, 112.50) 62.50 (37.50, 84.38) 37.50 (25.00, 62.50) 75.258 <0.001*** <0.001*** <0.001*** 0.021*
 RE 100.00 (66.67, 100.00) 0.00 (0.00, 25.00) 0.00 (0.00, 25.00) 85.132 <0.001*** <0.001*** <0.001*** 0.952
 MH 74.00 (68.00, 84.00) 28.00 (24.00, 36.00) 28.00 (20.00, 36.00) 79.226 <0.001*** <0.001*** <0.001*** 0.531
 PCS 90.38 (87.38, 92.75) 51.12 (41.88, 66.88) 44.00 (35.25, 46.75) 71.556 <0.001*** <0.001*** <0.001*** 0.002**
 MCS 88.00 (78.73, 93.57) 31.29 (24.53, 41.19) 25.50 (19.62, 32.37) 84.732 <0.001*** <0.001*** <0.001*** 0.053
PSQ 2.09 (1.53, 2.94) 2.38 (1.54, 3.29) 2.88 (2.18, 3.91) 8.290 0.022* 0.336 0.003** 0.101
PSQI 4.29±1.76 11.21±4.60 13.29±3.60 83.588 <0.001*** <0.001*** <0.001*** 0.024*
 Sleep Quality 1.00 (1.00, 1.00) 2.00 (1.00, 2.00) 2.00 (2.00, 3.00) 53.815 <0.001*** <0.001*** <0.001*** 0.022*
 Sleep Onset Time 1.00 (0.00, 1.00) 2.00 (1.00, 2.75) 2.00 (2.00, 3.00) 26.050 <0.001*** <0.001*** <0.001*** 0.164
 Sleep Duration 1.00 (0.00, 1.00) 1.00 (0.00, 2.00) 2.00 (0.00, 3.00) 10.508 0.008** 0.037* <0.001*** 0.252
 Sleep Efficiency 0.00 (0.00, 0.00) 2.00 (0.00, 3.00) 2.50 (1.00, 3.00) 41.780 <0.001*** <0.001*** <0.001*** 0.329
 Sleep Disturbances 1.00 (1.00, 1.00) 1.00 (1.00, 2.00) 2.00 (2.00, 2.00) 45.949 <0.001*** <0.001*** <0.001*** <0.001***
 Hypnotic Medications 0.00 (0.00, 0.00) 2.00 (0.00, 3.00) 2.50 (0.00, 3.00) 49.953 <0.001*** <0.001*** <0.001*** 0.616
 Daytime Dysfunction 0.00 (0.00, 0.00) 1.00 (0.00, 2.00) 1.00 (0.00, 2.00) 28.703 <0.001*** <0.001*** <0.001*** 0.511
BDI-II 23.37±9.77 28.47±12.32 −2.001 0.058
BAI Total Score 30.00 (26.00, 35.50) 41.50 (34.25, 52.75) 294.500 <0.001***
BAI Standardized Score 35.00 (30.00, 41.50) 49.00 (40.25, 62.50) 294.500 <0.001***
HAMD-24 21.00 (18.00, 30.25) 28.00 (23.25, 35.00) 446.500 0.007**
 Anxiety and Somatization 4.00 (2.00, 5.75) 6.00 (4.00, 8.00) 472.500 0.014*
 Body Weight 0.00 (0.00, 0.00) 0.00 (0.00, 1.00) 659.000 0.408
 Cognitive Impairment 1.00 (0.00, 3.75) 3.00 (0.25, 5.00) 629.000 0.338
 Day-Night Variation 0.00 (0.00, 1.00) 1.00 (0.00, 2.00) 588.500 0.140
 Psychomotor Retardation 6.50 (6.00, 8.00) 7.00 (6.00, 9.00) 586.000 0.165
 Sleep Disturbance 4.50 (3.00, 6.00) 6.00 (5.00, 6.00) 541.000 0.056
 Hopelessness 5.32±2.76 6.18±2.50 −1.436 0.165
HAMA-14 12.00 (8.00, 18.00) 15.00 (13.00, 25.50) 484.000 0.019*
 Psychic Anxiety 9.00 (7.00, 12.00) 11.00 (10.00, 15.00) 499.000 0.026*
 Somatic Anxiety 2.00 (0.00, 5.75) 4.00 (2.00, 9.75) 518.000 0.041*
PI-NRS 0.00 (0.00, 2.00) 5.00 (4.00, 7.00) 166.000 <0.001***
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Notes: A: control group; B: depression group; C: comorbidity group. FDR-adjusted P: *p<0.05; **p<0.01; ***p<0.001.

Abbreviations: BMI, Body Mass Index; SF-36, Health-Related Quality of Life-36; PF, physical function; RP, role physical; BP, bodily pain; GH, general health; VT, vitality; SF, social functioning; RE, role emotional; MH, metal health; PCS, physical component summary; MCS, mental component summary; PSQ, Pain Sensitivity Questionnaire; PSQI, Pittsburgh Sleep Quality Index; BDI-II, Beck Depression Inventory-II; BAI, Beck Anxiety Inventory; HAMD-24, Hamilton Depression Rating Scale-24; HAMA-14, Hamilton Anxiety Rating Scale-14; PI-NRS, Pain Intensity Numerical Rating Scale.

Based on sleep assessment results, significant differences were observed among the three groups in terms of sleep quality, sleep onset time, sleep duration, sleep efficiency, sleep disturbance, daytime dysfunction, hypnotic medication use, and PSQI total scores (H=53.815, p<0.001; H=26.050, p<0.001; H=10.508, p=0.008; H=41.780, p<0.001; H=45.949, p<0.001; H=28.703, p<0.001; H=49.953, p<0.001; F=83.588, p<0.001). In addition, compared to the HC and MDD groups, the COM group showed considerably more severe decrease in sleep quality, more sleep disruptions, and higher global PSQI scores (all p<0.05). We thoroughly evaluated patients’ anxiety and depression symptoms using the self-report measures BDI-II and BAI in addition to the clinician-rated measures HAMD and HAMA. Compared to the MDD group, the COM group showed a trend toward higher depressive symptoms on the self-report BDI-II score (t = −2.001, p = 0.058), and demonstrated significantly higher scores on BAI, HAMD, and HAMA (U = 294.5, p < 0.001; U = 446.5, p = 0.007; U = 484.0, p = 0.019). On particular subscales, such as the anxiety and somatization factor of HAMD (U= 472.5, p= 0.014), the psychic anxiety factor of HAMA (U= 499.0, p=0.026) and the somatic anxiety dimension of HAMA (U=518.0, p=0.041), the COM group displayed significantly more severe symptoms than the MDD group. Pain intensity was also significantly higher in the comorbidity group (U =166.0, p < 0.001). See Figure 1 and Table 1.

Figure 1.

Figure 1

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Clinical characteristics of participants included in this study. All variables are standardized by 0–1, so that they can be directly compared in different measuring ranges. The vertical line diagram of symptom comparison among the three groups, the average value is represented by different markers (the control group is round, the MDD group is square, and the COM group is triangle). Color schemes using different tones to distinguish individual variables represent significant variables (FDR) corrected p<0.05), while non-significant variables are displayed in gray.

Notes: FDR-adjusted P: False Discovery Rate adjusted P-value. Asterisks denote statistically significant differences in post hoc pairwise comparisons between groups after False Discovery Rate (FDR) correction: *FDR < 0.05, **FDR < 0.01, ***FDR < 0.001.

Abbreviations: PSQ, Pain Sensitivity Questionnaire; PSQI, Pittsburgh Sleep Quality Index; BAI, Beck Anxiety Inventory; BDI, Beck Depression Inventory; HAMD, Hamilton Depression Rating Scale; HAMA, Hamilton Anxiety Rating Scale; PI-NRS, Pain Intensity Numerical Rating Scale; MDD, depression group; COM, comorbidity group.

Correlation Among Pain Sensitivity and Clinical Symptoms

We performed Chatterjee correlation analyses to investigate if pain and quality of life are linked to negative emotions in order to address the possible risk factors of anxiety and depressive symptoms on chronic pain and quality of life. The BAI total score showed significant positive correlations with Physical Functioning, Physical Role Functioning, General Health, and the Pain Sensitivity Questionnaire (PSQ) (r=0.22, p=0.018; r=0.16, p=0.044; r=0.16, p<0.001; r=0.04, p=0.007). The HAMA total score was positively correlated with Physical Role Functioning, Emotional Role Functioning, PSQI Sleep Quality, Sleep Onset Time, and Sleep Efficiency (r=0.21, p=0.014; r=0.20, p=0.029; r= 0.24, p=0.023; r=0.20, p=0.039; r=0.20, p=0.037). The HAMD total score demonstrated broader significant positive correlations with Physical Role Functioning, Bodily Pain, Emotional Role Functioning, Sleep Duration, Sleep Efficiency, and Daytime Dysfunction (r=0.17, p=0.036; r=0.17, p=0.049; r=0.31, p=0.001; r=0.23, p=0.037; r=0.27, p=0.007; r=0.27, p=0.048). Furthermore, the anxiety-somatization factor of HAMD was positively correlated with Physical Functioning, Physical Role Functioning, Bodily Pain, Energy, Emotional Role Functioning, and Sleep Onset Time (r=0.26, p=0.007; r=0.28, p=0.002; r=0.19, p=0.029; r=0.19, p=0.039; r=0.27, p=0.005; r=0.21, p=0.033), while its somatic anxiety factor correlated with Physical Role Functioning, Bodily Pain and the Emotional Role Functioning (r=0.25, p=0.004; r=0.18, p=0.039; r=0.27, p=0.005). Complete results are presented in Figure 2.

Figure 2.

Figure 2

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Correlation of clinical symptoms in patients with COM. The results of correlation analysis between symptoms in the co-morbid group showed significant correlation (p<0.05). Color intensity indicates the size of Chatterjee correlation coefficient (R), warm color indicates positive correlation, and cool color indicates negative correlation.

Note: Asterisks indicate statistically significant correlations between variables: *p < 0.05, **p < 0.01, ***p < 0.001.

Abbreviations: PF, physical function; RP, role physical; BP, bodily pain; GH, general health; VT, vitality; SF, social functioning; RE, role emotional; MH, metal health; PCS, physical component summary; MCS, mental component summary; PSQ, Pain Sensitivity Questionnaire; PSQI, Pittsburgh Sleep Quality Index; BDI-II, Beck Depression Inventory-II; BAI, Beck Anxiety Inventory; HAMD, Hamilton Depression Rating Scale; HAMA, Hamilton Anxiety Rating Scale; PI-NRS, Pain Intensity Numerical Rating Scale.

Piecewise Regression Analysis of Pain Sensitivity and Clinical Features

Building on these results, we looked into the inherent connection between pain sensitivity and clinical outcomes using polynomial regression analysis. The anxiety scale total scores and physical discomfort were found to have a substantial nonlinear association (R2=0.313, p=0.001; R2=0.156, p=0.051). Additionally, we used piecewise regression analysis to investigate the relationship between physical pain sensitivity and different clinical features in order to identify high-risk individuals for early anxiety management and establish the best time for therapy. The forest plot results for piecewise regression slopes show that BAI scores demonstrated a significant negative association with PSQ scores (slope = −11.10, 95% CI: [−20.02, −2.18], standardized score slope = −13.14, 95% CI: [−23.74, −2.54]). However, beyond this breakpoint, the relationship reversed significantly, with BAI scores showing a positive association with increasing PSQ scores (slope = 5.92, 95% CI: [0.61, 11.24], standardized score slope = 7.12, 95% CI: [0.80, 13.44], R2=0.2434, p<0.05). Similarly, the SF-36 Physical Pain dimension score showed a significant negative correlation with the PSQ score (slope = −16.83, 95% CI: [−34.60, 0.94], coefficient of determination, R2= 0.2085, p< 0.05). These findings indicate that when PSQ scores exceed the critical threshold, both BAI total scores and physical pain scores significantly increase, suggesting that anxiety levels rise once the body’s pain intensity reaches a certain threshold (Figure 3A).

Figure 3.

Figure 3

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Patients with high/low pain sensitivity differed in somatic pain and anxiety symptoms. (A) Forest plot illustrating piecewise regression slopes (with 95% CI) for clinical measures against PSQ scores, before and after the breakpoint. A notable reversal of slope direction was observed for BAI total scores and SF-36 Bodily Pain beyond the breakpoint. Circles and squares represent slopes before and after the breakpoint, respectively. (B) Distribution of PSQ scores across the study cohort. The dashed vertical line indicates the optimal threshold (PSQ=4.14) for classifying patients into high and low pain-sensitivity subgroups. (C) Comparison of anxiety and pain levels in High vs Low PSQ groups. (D) Comparison of effect sizes for anxiety and pain between high and low PSQ groups.

Note: The asterisk denotes a statistically significant difference: *p < 0.05.

Abbreviations: PSQ, Pain Sensitivity Questionnaire; PSQI, Pittsburgh Sleep Quality Index; BDI-II, Beck Depression Inventory-II; BAI, Beck Anxiety Inventory; HAMD, Hamilton Depression Rating Scale; HAMA, Hamilton Anxiety Rating Scale; PI-NRS, Pain Intensity Numerical Rating Scale.

Furthermore, we used a piecewise linear regression model to find the ideal threshold for PSQ scores in order to measure the association between pain sensitivity and anxiety. The PSQ value that minimized the piecewise regression model’s sum of squared residuals was found iteratively. In the end, it was determined that a PSQ score of 4.14 was the ideal cutoff point for clearly identifying patterns in anxiety levels (Figure 3B). Furthermore, based on a PSQ threshold of 4.14, patients were categorized into a high pain sensitivity group (PSQ≥4.14) and a low pain sensitivity group (PSQ < 4.14). An independent samples t-test revealed that the high pain sensitivity group scored numerically higher on the BAI Total Score (t=0.96, p=0.342), while showing significantly lower SF-36 bodily pain subscale scores (t= −2.11, p= 0.042) (Figure 3C). To quantify the clinical impact of the identified pain sensitivity threshold, we calculated the effect sizes (Cohen’s d) for the differences between the high- and low-PSQ groups on key outcomes, as illustrated in Figure 3D. The analysis revealed a large effect of PSQ stratification on bodily pain (Cohen’s d = −0.81, p= 0.042), indicating substantially worse pain in the high-PSQ group. In contrast, a small and non-significant effect was observed for anxiety symptoms (Cohen’s d = 0.37, p=0.342). These results not only demonstrate a distinct dose-response association between anxious symptoms and pain, but they also show that when pain sensitivity beyond a particular threshold, clinical deterioration quickens.

Discussion

This study examines the connections between MDD, CP and anxiety symptoms, with an emphasis on how these factors collectively affect patients’ quality of life. Our findings show that people with comorbid depression and chronic pain (COM group) had significantly worse quality of life, higher pain sensitivity, and more noticeable sleep and affective disturbances than people with depression alone or healthy controls, which is consistent with previous evidence that depression, anxiety, and pain frequently co-occur and interact bidirectionally. These results emphasize the need for integrated diagnostic and treatment strategies as well as the intricate cognitive and psychosomatic interactions that underlie affective-pain comorbidity.

Our findings showed that COM group had significantly reduced SF-36 scores across both physical and psychological domains, which suggests that pain-associated mood symptoms exert a pervasive impact on functional and emotional well-being.34 Post-hoc analyses further revealed that, compared to the MDD group, the COM group exhibited particularly severe impairments in bodily pain, general health, and social functioning, indicating that pain not only exacerbates physical disability but also amplifies the emotional and cognitive burden associated with depression. This observation supports the “mutual maintenance model,” in which chronic pain and emotional dysregulation reinforce one another through maladaptive neurocognitive and neuroendocrine pathways.35,36 Additionally, individuals in the chronic pain group showed higher levels of pain sensitivity (as assessed by PSQ scores) in this study, which may imply that their central sensitization and increased experience of discomfort are caused by defective sensory-emotional integration. Furthermore, segmented regression analysis showed a nonlinear relationship between pain sensitivity and anxiety and revealed a critical threshold at a PSQ score of 4.14, at which anxiety symptoms sharply increase. This “tipping point” suggests that when pain processing exceeds a crucial neurophysiological threshold, compensatory emotional control may fail, resulting in elevated anxiety and a further deterioration in quality of life.37,38

The biopsychosocial feedback loop of pain and mood is further illuminated by the sleep difficulties noted among COM participants.39,40 Increased pain perception, cognitive impairment, and emotional dysregulation have been associated with poor sleep quality, longer sleep latency, Daytime dysfunction and frequent awakenings—all of which are reflected in higher PSQI scores.41 Our study’s substantial associations between PSQI, PI-NRS, and HAMD/HAMA scores support the idea that sleep dysregulation both mediates and results from pain-affective comorbidity.42 Clinically, this emphasizes the significance of all-encompassing management techniques that concurrently address pain management, mood stabilization, and sleep quality.43,44

It is worth noting that the severity of anxiety/depression symptoms is positively correlated with the physical function, emotional role, overall health and physical pain of patients in the comorbid group in this study. According to earlier research, “increased stress” causes patients’ mentalities to shift, and causes adaptive coping mechanisms and behavioral adjustments. Anxiety urges patients to actively maintain or even improve their daily functions and health management.45–47 Our research results also indirectly show that anxiety may have dual functions. Despite being a known pathological factor, it might occasionally serve as a motivator to mobilize one’s own psychological resources in order to preserve functional capacity.48,49 This reciprocal pattern suggests that physical and emotional suffering are not separate comorbidities but rather integral parts of an integrated psychopathological process.50,51 From a methodological standpoint, multidimensional evaluation of quality of life, emotional symptoms, and pain sensitivity is made possible by the use of standardized scales (such as SF-36, PSQI, PSQ, BDI-II, BAI, HAMD, and HAMA). Differences between self-reported and clinically measured results underline the need for a multimodal strategy, as previously noted. This method can help predict treatment response and further clarify the underlying mechanisms of pain-mood interactions.52

However, there are a few restrictions that should be taken into account. The cross-sectional design made it impossible to draw conclusions about the directionality of pain and emotional symptoms. The sample size may restrict the generalizability of the nonlinear relationships found in segmented regression, even while it was enough for group-level analysis. Moreover, mechanistic interpretation is limited by the lack of neurological or inflammatory biomarkers. Future multimodal and longitudinal neuroimaging research should look at how changes in affective processing and pain perception change over time and whether early identification of high pain sensitivity is a predictor of long-term affective decline.

In summary, our research offers solid proof that the co-occurrence of depression and anxiety with chronic pain has a synergistic negative impact on quality of life, which is mediated by increased pain sensitivity, sleep disturbances, and affective dysregulation. Finding a pain threshold linked to an increase in anxiety provides new information for risk assessment and tailored treatment. In the future, combining computational modeling, neuroimaging, and closed-loop neurostimulation may open the door to precision psychiatry techniques that can break the cycle of pain and mood dysregulation, thereby enhancing functional outcomes and the general well-being of those who are impacted.

Conclusion

This study advances our understanding of the depression-anxiety-chronic pain complex by elucidating the central role of pain sensitivity. The clinical presentation of comorbid patients reflects not merely additive symptoms, but a vicious cycle where heightened pain sensitivity, sleep disturbances, and emotional dysregulation mutually reinforce one another. The identification of PSQ 4.14 as a critical threshold provides an objective tool for recognizing patients at imminent risk of entering the “pain-anxiety-depression” positive feedback loop.

These findings strongly advocate for the integration of pain sensitivity assessment into routine clinical practice for comorbid patients. Management strategies should transition from traditional siloed approaches toward integrated interventions simultaneously targeting pain perception, emotional regulation, and sleep quality. For patients exceeding this pain sensitivity threshold, intensive interventions should be implemented promptly to disrupt the symptom interaction network.

Future research should prioritize uncovering the neurobiological mechanisms underlying heightened pain sensitivity, utilizing neuroimaging to identify central sensitization markers. Concurrently, intervention studies are needed to validate whether treatment strategies stratified by this threshold can effectively improve long-term patient outcomes.

Acknowledgments

We express our sincere gratitude to all the participants who participated in our research.

Funding Statement

This study was supported by funding from Anhui Provincial Health Research Project (grant number: AHWJ2024Aa30308). Applied Medicine Research Project of Hefei Health Committee (grant number: Hwk2024zd004). National Clinical Key Specialty Construction Project of China (grant number: None). Hefei Innovation Procurement Project (grant number: None). The funding sources were not involved in the study design, data collection, data analysis, drafting the paper, or the decision to publish the paper.

Data Sharing Statement

The data that support the findings of this study are available from the corresponding author, Cuizhen Zhu, upon reasonable request.

Ethics Approval and Consent to Participate

This study was approved by the Medical Ethics Committee of the Hefei Fourth People’s Hospital (certificate number: HFSY-IRB-YJ-KYXM-CH (2025-042-001)). All participants provided written informed consent prior to participation in the study, in accordance with the principles of the Declaration of Helsinki.

Author Contributions

All authors substantially contributed to the conception and design of the study, acquisition of data, or analysis and interpretation of data, or all of these aspects. All authors were involved in drafting the manuscript or revising it critically for important intellectual content, approved the final version to be published, agreed on the target journal, and accept responsibility for the integrity and accuracy of the work in all its aspects.

Disclosure

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

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, Cuizhen Zhu, upon reasonable request.

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