Journal of Traditional Chinese Medicine ›› 2026, Vol. 46 ›› Issue (4): 972-981.DOI: 10.19852/j.cnki.jtcm.2026.04.017
• Original Articles • Previous Articles Next Articles
Construction of a nomogram for anxiety and depression in patients with Sjögren’s syndrome by combining tongue and pulse characteristics: a cross-sectional study based on a prospective cohort
SHAN Xueyan1,2, HE Jiale1, GUO Zilin1, PANG Fengtao1, LIU Ruihua3, LIU Ying4, JIANG Ping4, JI Wei5, YU Jing6, ZHOU Xinyao1(
), TANG Xiaopo1()
- 1
Department of Rheumatology ,Guang’anmen Hospital, China Academy of Chinese Medical Sciences Beijing 100053, China
2Graduate School ,Beijing University of Chinese Medicine Beijing 100029, China
3Department of Rheumatology ,Shaanxi Provincial People's Hospital Xi’an 710068, China
4Department of Rheumatology ,Affiliated Hospital of Shandong University of Traditional Chinese Medicine Jinan 250011, China
5Department of Rheumatology ,Jiangsu Provincial Hospital of Traditional Chinese Medicine Nanjing 210029, China
6Department of Rheumatology ,Affiliated Hospital of Liaoning University of Traditional Chinese Medicine Shenyang 110031, China
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Received:2025-07-27Accepted:2025-11-18Online:2026-08-15Published:2026-08-08 -
Contact:Prof. ZHOU Xinyao, Prof. TANG Xiaopo, Department of Rheumatology, Guang’anmen Hospital, Chinese Academy of Chinese Medical Sciences, Beijing 100053, China. xyz_1102@126.com;
tangxiaopo@163.com,Telephone: +86-138-10380455; +86-131-2158-1066
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Supported by:Scientific and Technological Innovation Project of China Academy of Chinese Medical Sciences: Development of a Stratified Prevention and Treatment Strategy and Chronic Disease Management Model Based on Traditional Chinese Medicine for Multisystem Comorbidities in Sjögren’s Syndrome(CI2026A01503)
Cite this article
SHAN Xueyan, HE Jiale, GUO Zilin, PANG Fengtao, LIU Ruihua, LIU Ying, JIANG Ping, JI Wei, YU Jing, ZHOU Xinyao, TANG Xiaopo. Construction of a nomogram for anxiety and depression in patients with Sjögren’s syndrome by combining tongue and pulse characteristics: a cross-sectional study based on a prospective cohort[J]. Journal of Traditional Chinese Medicine, 2026, 46(4): 972-981.
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Figure 1 Research Flowchart ROC: receiver operating characteristic; DCA: decision curve analysis. The flowchart was generated using R version 3.6.3 (R Foundation for Statistical Computing, Vienna, Austria).
Figure 1 Research Flowchart ROC: receiver operating characteristic; DCA: decision curve analysis. The flowchart was generated using R version 3.6.3 (R Foundation for Statistical Computing, Vienna, Austria).
Table 1 Comparison of immunological markers between groups of anxiety and non-anxiety patients [n (%)]
| Item | Non-anxiety (n = 242) | Anxiety (n = 202) | P value |
|---|---|---|---|
| ANA | 199 (82.2) | 163 (80.7) | 0.677 |
| SSA | 165 (68.2) | 151 (74.8) | 0.128 |
| SSB | 72 (29.8) | 76 (37.6) | 0.080 |
| Ro-52 | 149 (61.6) | 148 (73.3) | 0.009a |
Table 1 Comparison of immunological markers between groups of anxiety and non-anxiety patients [n (%)]
| Item | Non-anxiety (n = 242) | Anxiety (n = 202) | P value |
|---|---|---|---|
| ANA | 199 (82.2) | 163 (80.7) | 0.677 |
| SSA | 165 (68.2) | 151 (74.8) | 0.128 |
| SSB | 72 (29.8) | 76 (37.6) | 0.080 |
| Ro-52 | 149 (61.6) | 148 (73.3) | 0.009a |
Table 2 Comparison of laboratory test results between depression and non-depression patients
| Item | Non-depression (n =257) | Depression (n =187) | P value |
|---|---|---|---|
| WBC (×1012/L) | 5.05±1.91 | 7.75±3.17 | 0.248 |
| RBC (×1012/L) | 4.15±0.53 | 4.08±0.52 | 0.119 |
| Hb (g/L) | 129.72±75.69 | 122.11±15.30 | 0.176 |
| PLT (×109/L) | 213.07±73.34 | 193.81±16.30 | 0.006a |
| ESR (mm/h) | 26.97±22.46 | 29.59±17.30 | 0.226 |
| CRP (mg/L) | 3.36±11.03 | 4.87±18.30 | 0.187 |
| IgG (g/L) | 17.52±6.01 | 26.26±19.30 | 0.205 |
| IgM (g/L) | 1.19±0.73 | 1.34±20.30 | 0.205 |
| IgA (g/L) | 3.10±1.21 | 3.41±21.30 | 0.376 |
| C3 (g/L) | 1.31±4.68 | 1.64±22.30 | 0.595 |
| C4 (g/L) | 0.25±0.11 | 0.34±23.30 | 0.264 |
| ALB(g/L) | 42.46±3.34 | 42.60±24.30 | 0.646 |
Table 2 Comparison of laboratory test results between depression and non-depression patients
| Item | Non-depression (n =257) | Depression (n =187) | P value |
|---|---|---|---|
| WBC (×1012/L) | 5.05±1.91 | 7.75±3.17 | 0.248 |
| RBC (×1012/L) | 4.15±0.53 | 4.08±0.52 | 0.119 |
| Hb (g/L) | 129.72±75.69 | 122.11±15.30 | 0.176 |
| PLT (×109/L) | 213.07±73.34 | 193.81±16.30 | 0.006a |
| ESR (mm/h) | 26.97±22.46 | 29.59±17.30 | 0.226 |
| CRP (mg/L) | 3.36±11.03 | 4.87±18.30 | 0.187 |
| IgG (g/L) | 17.52±6.01 | 26.26±19.30 | 0.205 |
| IgM (g/L) | 1.19±0.73 | 1.34±20.30 | 0.205 |
| IgA (g/L) | 3.10±1.21 | 3.41±21.30 | 0.376 |
| C3 (g/L) | 1.31±4.68 | 1.64±22.30 | 0.595 |
| C4 (g/L) | 0.25±0.11 | 0.34±23.30 | 0.264 |
| ALB(g/L) | 42.46±3.34 | 42.60±24.30 | 0.646 |
Figure 2 Prediction nomograms for anxiety and depression in SS patients A: diagnosed anxiety; B: diagnosed depression. SS: Sjögren’s syndrome.
Figure 2 Prediction nomograms for anxiety and depression in SS patients A: diagnosed anxiety; B: diagnosed depression. SS: Sjögren’s syndrome.
Figure 3 ROC curves and AUC for Nomogram A A: calibration curves for Nomogram A; A1: training cohort; A2: validation cohort; B: decision curve analysis for Nomogram A; B1: training cohort; B2: validation cohort; C: receiver operating characteristic curves for Nomogram A; C1: training cohort; C2: validation cohort; Nomogram A was developed to predict anxiety. The training cohort included 324 patients and was used for model development, and the validation cohort included 120 patients and was used for model validation. Calibration curves were used to evaluate agreement between predicted and observed probabilities. Decision curve analysis was used to assess clinical net benefit across a range of threshold probabilities. Receiver operating characteristic curves and the corresponding areas under the curves were used to evaluate model discrimination. The diagonal line in the calibration plots represents perfect calibration; the “All” and “None” lines in the decision curves represent the assumptions that all or no patients would experience the outcome, respectively. AUC: area under the curve; DCA: decision curve analysis; ROC: receiver operating characteristic.
Figure 3 ROC curves and AUC for Nomogram A A: calibration curves for Nomogram A; A1: training cohort; A2: validation cohort; B: decision curve analysis for Nomogram A; B1: training cohort; B2: validation cohort; C: receiver operating characteristic curves for Nomogram A; C1: training cohort; C2: validation cohort; Nomogram A was developed to predict anxiety. The training cohort included 324 patients and was used for model development, and the validation cohort included 120 patients and was used for model validation. Calibration curves were used to evaluate agreement between predicted and observed probabilities. Decision curve analysis was used to assess clinical net benefit across a range of threshold probabilities. Receiver operating characteristic curves and the corresponding areas under the curves were used to evaluate model discrimination. The diagonal line in the calibration plots represents perfect calibration; the “All” and “None” lines in the decision curves represent the assumptions that all or no patients would experience the outcome, respectively. AUC: area under the curve; DCA: decision curve analysis; ROC: receiver operating characteristic.
Figure 4 ROC curves and AUC for Nomogram B A: calibration curves for Nomogram B; A1: training cohort; A2: validation cohort; B: decision curve analysis for Nomogram B; B1: training cohort; B2: validation cohort; C: receiver operating characteristic curves for Nomogram B; C1: training cohort; C2: validation cohort. Nomogram B was developed to predict depression. The training cohort included 324 patients and was used for model development, and the validation cohort included 120 patients and was used for model validation. Calibration curves were used to evaluate agreement between predicted and observed probabilities. Decision curve analysis was used to assess clinical net benefit across a range of threshold probabilities. Receiver operating characteristic curves and the corresponding areas under the curves were used to evaluate model discrimination. The diagonal line in the calibration plots represents perfect calibration; the “All” and “None” lines in the decision curves represent the assumptions that all or no patients would experience the outcome, respectively. AUC: area under the curve; DCA: decision curve analysis; ROC: receiver operating characteristic.
Figure 4 ROC curves and AUC for Nomogram B A: calibration curves for Nomogram B; A1: training cohort; A2: validation cohort; B: decision curve analysis for Nomogram B; B1: training cohort; B2: validation cohort; C: receiver operating characteristic curves for Nomogram B; C1: training cohort; C2: validation cohort. Nomogram B was developed to predict depression. The training cohort included 324 patients and was used for model development, and the validation cohort included 120 patients and was used for model validation. Calibration curves were used to evaluate agreement between predicted and observed probabilities. Decision curve analysis was used to assess clinical net benefit across a range of threshold probabilities. Receiver operating characteristic curves and the corresponding areas under the curves were used to evaluate model discrimination. The diagonal line in the calibration plots represents perfect calibration; the “All” and “None” lines in the decision curves represent the assumptions that all or no patients would experience the outcome, respectively. AUC: area under the curve; DCA: decision curve analysis; ROC: receiver operating characteristic.
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