Comparison of clinical features and inflammatory factors between patients with bipolar depression and unipolar depression

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And to further build a prediction model. Methods The inpatients of Shanghai Mental Health Center from June 2022 to June 2024 were selected as the study objects. According to the diagnosis of hospitalization records, 274 cases were divided into unipolar depression group and 128 cases were bipolar depression group. A total of 128 patients were enrolled in each of the two groups by the propensity score matching method. The demographic data, clinical characteristics and biological indicators of the two groups were compared. Biological markers include neutrophil/lymphocyte ratio (NLR), monocyte/lymphocyte ratio (MLR), platelet/lymphocyte ratio (PLR), C-reactive protein (CRP), serum triodothyronine (T3), thyroxin (T4), free thyroid hormone (fT3, fT4), and thyroid stimulating hormone (TSH) ), complement 3(C3), complement 4(C4), immunoglobulin A(IgA), immunoglobulin G(IgG), immunoglobulin M(IgM). Binomial Logistic regression analysis was used to control confounding factors to explore the predictors of bipolar depression. Receiver operating characteristic (ROC) curve was used to analyze the predictive value of clinical features and biological indicators in bipolar depression. Results There were statistical differences in life events (χ 2 = 15.397, P = 0.000), CRP (Z = 6.717, P = 0.000), TSH (Z = 1.988, P = 0.047), C3 (Z = 5.682, P = 0.000), C4 (Z = 4.216, P = 0.000), IgM (Z = 2.304, P = 0.021) between unipolar depression group and bipolar depression group. Logistic regression analysis showed that life events (OR = 4.552, 95%Cl = 2.238∼9.257), CRP (OR = 13.886, 95%Cl = 5.290∼36.452), IgM (OR = 0.561, 95%Cl = 0.325∼0.970) were associated with bipolar depression. ROC curve analysis showed that the AUC of Logistic regression model predicting bipolar depression was 0.806, with a sensitivity of 61.7%, and a specificity of 85.9%. Conclusions Compared with unipolar depression, no life events, higher levels of CRP and lower levels of IgM were related factors of bipolar depression, and the combined diagnosis model is more effective to distinguish unipolar depression from bipolar depression. Unipolar depression Bipolar depression Clinical features Inflammatory factors Differential diagnosis Figures Figure 1 1. Introduction Major Depressive disorder (MDD) is one of the most common major mental disorders characterized by significant and persistent mood in low out of proportion to the situation, accompanied by corresponding cognitive and behavioral changes. Clinically, according to the presence or absence of manic or hypomanic episodes, depressive episodes can be divided into unipolar depression and bipolar depression. In clinical practice, bipolar disorder is more difficult to diagnose early, with only 20% of bipolar patients who experience a depressive episode being diagnosed within the first year of seeking treatment, and the average delay from onset to correct diagnosis being 5–10 years(Phillips et al.,2013).Particularly, patients with bipolar disorder who are treated with a single antidepressant often experience poor symptom improvement, transition to manic episodes, and an increased risk of suicide(Rosso et al.,2023), making the identification of bipolar disorder extremely important. Some studies have found that unipolar depression and bipolar depression have different genetic basis, treatment plan and prognosis, suggesting that they may belong to different disease spectrum(Huang et al.,2011). Relevant studies suggest that the immune system and neuroendocrine system will be affected when patients are in the depressive phase, and patients with bipolar depression have more severe inflammatory responses(Maes et al.,2012; Bai et al.,2020). Therefore, looking for the differences between unipolar and bipolar depression in peripheral inflammatory factors will be helpful for early diagnosis and treatment. In terms of clinical characteristics of bipolar depression, some domestic and foreign studies have found that compared with unipolar depression, patients with bipolar disorder have earlier onset age, fewer life events, positive family history of bipolar disorder, atypical depressive symptoms (weight gain, lethargy), and psychotic symptoms(Galvão et al.,2013; Wang et al.,2015; Li et al.,2011; Oswald et al.,2007). The common underlying mechanism of abnormal thyroid function and inflammatory factors in patients with unipolar and bipolar depression may be the disturbance of the HPA axis and the activation of central and peripheral inflammatory responses when the body is faced with stressful events. The interaction between HPA axis and inflammatory response will interfere with neurotransmitter metabolism and affect nerve regeneration, or increase oxygen free radical release, and eventually lead to neurotransmitter depletion, neuron damage, synaptic function impairment and other pathological changes, which directly affect the neural circuit and cause depressive symptoms(Liu et al.,2024). In contrast to unipolar depression, existing evidence in bipolar disorder patients suggests that multi-system inflammation may be involved in the early stage, and pro-inflammatory cytokines have unique effects on central neurons and circuits, affecting the activation of microglia and it also affects signaling molecules in neurotransmission, memory, and glucocorticoid function, as well as activity control(Bai et al.,2020). Therefore, relevant studies at home and abroad all suggest that the impairment of thyroid hormone function and the expression of inflammatory factors in patients with bipolar disorder are higher than those with unipolar depression(Wang et al.,2015; Wysokiński et al.,2014; Liu,2018;Lv et al.,2023;Chen et al.2018;Han,2023;Zhao et al.,2022; Brunoni et al.,2020). For example, fT3, fT4 and TSH in patients with bipolar depression respectively showed differences in different studies compared with those in unipolar depression group (Wang et al.,2015; Liu,2018; Chen et al.2018). And differences in inflammatory factors such as neutrophil/lymphocyte ratio(NLR), monocyte/lymphocyte ratio (MLR), CRP, complement 3(C3), IL-1β, TNF-α, IL-6 and IgA have all been reported between patients with unipolar and bipolar depression (Lv et al.,2023; Han,2023; Brunoni et al.,2020;Mao et al.,2018). At present, relevant studies often compare the difference between patients with unipolar and bipolar depression only from the single dimension of thyroid function or inflammatory factors, and there are few studies on the multidimensional distinction between the two. Moreover, the probability value of the combined factor obtained by the Logistic regression model is low(Lv et al.,2023; Zhao et al.,2022). Based on previous studies have shown that patients with bipolar disorder have higher expression of inflammatory factors, and peripheral blood indicators are easily interfered by many factors. Therefore, the purpose of this study was to analyze and compare the differences between patients with unipolar and bipolar depression from three dimensions: clinical features, thyroid function, and inflammatory factors, in order to find potential indicators to distinguish unipolar depression from bipolar depression, and provide references for clinical decision-making. 2. Method 2.1.1 Subjects: The study subjects were all hospitalized patients admitted to Shanghai Mental Health Center from June 2022 to June 2024. Inclusion criteria: (1) Meeting the diagnostic criteria of the 10th edition of the International Classification of Diseases (ICD-10) (WHO,1993)for bipolar depressive episode and recurrent depressive disorder, the diagnosis was made by an attending physician and reviewed by another deputy chief physician or chief physician, and all enrolled patients were in the acute stage of depressive episode; (2) Age over 14 years old and under 60 years old, regardless of gender; (3) The duration of recurrent depressive disorder is greater than 2 years; (4) The medical records are reliable, and there is no lack of data information. Exclusion criteria: (1) Any hospitalization was diagnosed with schizophrenia, schizoaffective disorder and other mental disorders; (2) Endocrine diseases and serious physical diseases; (3) Patients with acute infection and patients with CRP > 10mg/L; (4) Women who are pregnant or breastfeeding. In this study, general demographic data, clinical characteristics and peripheral blood biomarkers were extracted from the electronic medical record system of Shanghai Mental Health Center. This study program was approved by the Ethics Committee of Shanghai Mental Health Center. All subjects were fully aware of the study program and procedures and participated voluntarily, with informed consent signed by themselves or their guardians. Ethics Lot Number: 2023-62. 2.1.2 Sample size calculating The calculation formula of sensitivity and specificity is n=( \(\:\frac{Z1-/2\times\:\sqrt{p\times\:(1-p)}}{}\) ) 2 , where n stands for the sample size for each group, α stands for significance level which is 0.05, δ stands for tolerance error which is 0.1, p stands for sensitivity (pse) or specificity (psp), By looking up the table, Z1-α/2 equals 1.96. According to the data of domestic research(Lv et al.,2023), sensitivity equals 0.784, specificity equals 0.539, substituted into the formula for calculating it, n se =( \(\:\frac{1.96\times\:\sqrt{0.784\times\:(1-0.784)}}{0.1}\) ) 2 \(\:\approx\:\) 65, n sp =( \(\:\frac{1.96\times\:\sqrt{0.539\times\:(1-0.539)}}{0.1}\) ) 2 \(\:\approx\:\) 95. Since this study is retrospective and does not consider the loss of follow-up, it is considered that at least 100 samples should be included in each group. 2.2 Data collection and assessment This study was conducted retrospectively with data collected from the electronic medical record system of Shanghai Mental Health Center. Data sets were established according to inclusion and exclusion criteria. The dataset included general demographic data, clinical characteristics and peripheral blood biomarkers. All subjects had peripheral blood drawn within 24h after admission. Biological indicators collected in this study included neutrophil/lymphocyte ratio (NLR) and monocyte/lymphocyte ratio (monocyte/lymphocyte ratio). MLR), platelet/lymphocyte ratio (PLR), CRP, thyroid hormone (T3, T4, fT3, fT4, TSH), complement 3(C3), complement 4(complement 4, C4), IgA, IgG, IgM. 2.3 Statistical analysis : SPSS version 26.0 was used for statistical analysis. The confounding factors in the unipolar depression group and the bipolar depression group were controlled by the propensity score matching method. The caliper value was set to 0.02, and the age and gender of the two groups were matched according to the method of 1:1 without putting back. 1–2 patients were included in each group. Shapiro-Wilk test was used to detect whether the data conformed to the normal distribution, and the measurement data of the normal distribution were represented by the mean value and standard deviation[‾x±s].The measurement data for non-normal distributions are expressed by median and quartile[M(P 25 ,P 75 )]. Independent sample t test was used to compare the measurement data with normal distribution, and Mann-Whitney U test was used to compare the measurement data with non-normal distribution. Counting data were expressed as frequency and percentage (%), and inter-group comparison was conducted by chi-square test. Binomial Logistic regression (forward method) was used to adjust the covariates and construct the regression model. SPSS26.0 software was used to analyze receiver operator characteristic (ROC) curve, and the prediction ability of different indicators and Logistic regression model was evaluated. The area under curve (AUC) was used to reflect the accuracy of the prediction, and the closer the value was to 1.0, the better the effect was(Obuchowski et al.,2018). 3. Result 3.1 Comparison of demographic and clinical characteristics of patients in unipolar depression group and bipolar depression group 274 patients in unipolar depression group and 128 patients in bipolar depression group were included. There was no significant difference in gender, psychotic symptoms, weight gain and lethargy symptoms between the two groups (P > 0.05). The age and onset age of bipolar depression group was younger than that of unipolar depression group, and the proportion of life events in bipolar depression group was less than that in unipolar depression group, and the proportion in family history was more, with statistical significance (P < 0.05). (Table 1 ) 3.2 Comparison of general data and peripheral biological indicators of patients in unipolar depression group and bipolar depression group A 1:1 propensity score matching method was used to analyze the data of 256 patients, including 128 patients in unipolar depression group and 128 patients in bipolar depression group. There were no statistically significant differences in age, age of onset, gender, family history, psychotic symptoms, weight gain, and lethargy symptoms between the two groups (P > 0.05). The proportion of life events in unipolar depression group was higher than that in bipolar depression group, with statistically significant differences (P < 0.05). The levels of CRP, TSH, C3 and C4 in bipolar depression group were higher than those in unipolar depression group, and the levels of IgM were lower than those in unipolar depression group, with statistical significance (P 0.05). (Table 2 ) 3.3 Predictors analysis of bipolar depression patients Patients with unipolar depression and bipolar depression were used as dependent variables (unipolar depression = 0, bipolar depression = 1), and variables with statistical significance in the univariate analysis were used as independent variables. The results showed that no life events, increased CRP and decreased IgM were risk factors for bipolar depression compared with unipolar depression (P < 0.05). (Table 3 ). Regression equation: Logit(P) = 1.515X inducement + 2.631X CRP -0.577 X IgM -1.865(X inducement , X CRP , X IgM represent inducement, CRP concentration and IgM concentration respectively). 3.4 ROC curve for distinguishing unipolar depression from bipolar depression Peripheral blood CRP and IgM alone had poor predictive effect on unipolar depression and bipolar depression, with an AUC of 0.743 and 0.583, respectively. The combined factor probability value obtained by Logistic regression model showed that the predictive effect of unipolar depression and bipolar depression was improved, with an AUC of 0.806. (Table 4 , Picture 1) Table1 Comparison of demographic and clinical characteristics between unipolar depression group and bipolar depression group Item Unipolar depression（n=274） Bipolar depression（n=128） Z/c2 P Age[years，M（P 25 ，P 75 ）] 27.50(18.00,44.00) 24.00(17.25,34.75) -2.321 0.020 Age of onset[years，M（P25，P75）] 21.00(15.00,37.00) 18.00(14.00,26.00) -2.729. 0.006 Male[rate（%）] 87（31.75） 42（32.81） 0.045 0.832 Life events[rate（%）] 113（41.24） 25（19.53） 18.239 0.000 Family history[rate（%）] 37（13.50） 30（23.44） 6.199 0.013 Psychotic symptoms[rate（%）] 78（28.47） 42（32.81） 0.787 0.375 Gain weight[rate（%）] 38（13.87） 25（19.53） 2.117 0.146 Lethargy symptoms[rate（%）] 17(6.20) 11(8.59) 0.769 0.381 Table2 Comparison of general data and peripheral blood biological indicators between unipolar depression group and bipolar depression group Item Unipolar depression（n=128） Bipolar depression（n=128） Z/t/c2 P Age[years，M（P 25 ，P 75 ）] 22.50(17.00,34.00) 24.00(17.25,34.75) -0.173 0.862 Age of onset[years，M（P25，P75）] 18.00(14.00,26.75) 18.00(14.00,26.00) -0.149 0.882 Male[rate（%）] 36（28.13） 42（32.81） 0.664 0.415 Life events[rate（%）] 54（42.19） 25（19.53） 15.397 0.000 Family history[rate（%）] 18（14.06） 30（23.44） 3.692 0.055 Psychotic symptoms[rate（%）] 45（35.16） 42（32.81） 0.157 0.692 Gain weight[rate（%）] 17（13.28） 25（19.53） 1.823 0.177 Lethargy symptoms[rate（%）] 9（7.03） 11（8.59） 0.217 0.641 CRP[mg/L,M（P25，P75）] 0.34(0.26,0.48) 0.63(0.36,1.58) -6.717 0.000 T3[nmol/L,`x±s] 1.62±0.31 1.67±0.36 -1.157 0.248 T4[nmol/L,M（P25，P75）] 98.40(86.12,109.05) 94.13(83.15,106.45) -1.212 0.225 fT3[pmol/L,M（P25，P75）] 4.60(4.02,5.11) 4.56(4.08,5.15) -0.180 0.857 fT4[pmol/L,M（P25，P75）] 15.88(14.61,17.77) 15.61(14.19,17.68) -1.302 0.193 TSH[uIU/mL,M（P25，P75）] 1.51(0.99,2.07) 1.66(1.15,2.59) -1.988 0.047 C3[g/L,`x±s] 1.10±0.16 1.22±0.19 -5.682 0.000 C4[g/L,M（P25，P75）] 0.25(0.21,0.31) 0.30(0.25,0.36) -4.216 0.000 IgA[g/L,M（P25，P75）] 2.02(1.51,2.53) 1.97(1.65,2.60) -0.504 0.614 IgG[g/L,`x±s] 11.66±2.08 11.68±2.41 -0.073 0.942 IgM[g/L,M（P25，P75）] 1.30(0.96,1.77) 1.17(0.82,1.52) -2.304 0.021 NLR[M（P25，P75）] 2.58(1.76,3.63) 2.46(1.77,3.36) -0.725 0.468 MLR[M（P25，P75）] 0.26(0.19,0.35) 0.26(0.21,0.32) -0.122 0.903 PLR[M（P25，P75）] 149.21(122.08,189.47) 139.36(116.25,175.25) -1.535 0.125 Table3 Binomial Logistic regression analysis of influencing factors of bipolar depression Variable Regression coefficient Standard error Wald x 2 值 P OR 95%Cl Low limit Upper limit No life events 1.515 0.362 17.506 0.000 4.552 2.238 9.257 CRP 2.631 0.492 28.547 0.000 13.886 5.290 36.452 IgM -0.577 0.279 4.287 0.038 0.561 0.325 0.970 Constant -1.865 0.540 11.902 0.001 0.155 Table4 ROC curve analysis results of single and bipolar depression Variable AUC Cutoff value 95%Cl P Sensitivity Specificity Positive predictive value Negative predictive value CRP 0.743 0.645 0.683-0.803 0.000 0.492 0.930 0.875 0.647 IgM 0.583 1.035 0.514-0.653 0.021 0.422 0.711 0.593 0.552 Logistic regression model 0.806 0.538 0.753-0.858 0.000 0.617 0.859 0.814 0.692 4. Discussion This study found that there were differences in age, onset age, inducement and family history between bipolar depression and unipolar depression. Due to the influence of age factors on peripheral blood indexes, 1:1 propensity score matching method was performed. After controlling age factors, there were differences in CRP, TSH, C3, C4 and IgM between the two groups. Logistic regression control for confounding factors suggested that life events, CRP and IgM were the influencing factors of bipolar depression. The accuracy of the above single index in the classification of bipolar depression and unipolar depression is poor, but the combined diagnostic model including life events, CRP and IgM is more effective in distinguishing bipolar depression and unipolar depression. The present study suggests that no life events before illness may be an effective predictor of bipolar depression. In this study, the age and onset age of patients with bipolar depression were younger than those with unipolar depression, and the patients with bipolar depression had fewer life events, which was consistent with the domestic studies of Xiaoquan Wang et al(Wang et al.,2015), Zezhi Li et al(Li et al.,2011)and Jianhong Liu(Liu,2018). At the same time, it was found that patients with bipolar depression had more positive family history, which was consistent with the studies of Zezhi Li et al(Li et al.,2011) and Shengjun Zhang(Zhang,2017), but no difference was found in the studies of Xiaoquan Wang et al(Wang et al.,2015) and Rong Yan(Yan,2017), which may be related to the selection of participants. However, the genetic tendency survey found that the heritability of bipolar disorder was up to 80% which much higher than the 40% heritability of severe depression. 50% of patients with bipolar I disorder have at least one parent with a mood disorder(Jiang,2011) ] , still suggesting that patients with bipolar depression have an underlying positive family history. After 1:1 propensity score matching, family history was not statistically significant between the two groups, so it was not included in the Logistic regression equation. This study found that CRP levels in patients with bipolar depression were higher than those in patients with unipolar depression, which could be used as a predictor to distinguish between the two. This is the same as the findings of Nan Lv et al(Lv et al.,2023)and Chang HH et al(Chang et al.,2017), and the meta-analysis of Wei Guo et al also suggested that CRP concentrations in bipolar patients in manic, depressive and remission periods were higher than those in healthy controls(Guo et al.,2019; Orsolini et al.,2023). CRP is an acute phase protein that responds to inflammatory stimuli. High concentration of CRP in serum can increase the permeability of the blood-brain barrier, enable CRP to enter the central nervous system, promote the increased release of cytokines such as IL-6 and interferon-γ, and mediate the damage of neurons, which partly explains the more severe cognitive dysfunction in patients with bipolar disorder(Gorgulu et al.,2021). The results of this study showed that patients with bipolar depression had a higher TSH level than those with unipolar depression, which was consistent with the results of the meta-analysis conducted by Wenzhi CAI et al and Wysokiński A(Cai et al.,2016; Wysokiński et al.,2014). However, the results of a number of domestic studies all suggested that patients with bipolar depression had a lower TSH level than those with unipolar depression(Wang et al.,2015; Liu,2018;Yan,2017). This may relate to both the sample size and the drug. Treatment for bipolar disorder often uses lithium carbonate, which can cause hypothyroidism and affect thyroid hormone levels. Patients were not screened for drug use in this study, and the effect of treatment drugs on thyroid hormones could not be ruled out. At the same time, age also has an impact on thyroid hormone levels(Wysokiński et al.,2014), which may also lead to inconsistent results in the comparison between the bipolar depression group and the unipolar depression group in different studies. In binomial Logistic regression analysis, TSH index was not included, indicating that TSH has little effect on distinguishing unipolar and bipolar depression. C3 and C4 participate in inflammatory response and synaptic shear, which are related to the occurrence and development of mental disorders(Lv et al.,2023). The results of this study showed that C3 and C4 levels were elevated in patients with bipolar depression, which was consistent with multiple studies(Lv et al.,2023; Yu et al.,2023; Lyu et al.,2021). C3 belongs to the complement system, which is involved in brain development and the occurrence of mental diseases. Neurons, astrocytes and microglia all express complement receptors and synthesize complement proteins, regulate neurogenesis, migration and synaptic pruning, and participate in the regulation of emotions(Zhao et al.,2022). Although C3 and C4 were not included in the Logistic regression equation in this study, it is still suggested that these two indicators are potential predictors of bipolar depression. This study found that compared with unipolar depression, patients with bipolar depression had lower levels of IgM in their peripheral blood. Some previous studies have found that, compared with healthy people, the IgM level of people with bipolar depression, unipolar depression and schizophrenia is increased, and the IgM level of women is higher than that of men in people with single and bipolar depression(Legros et al.,1985). Domestic studies found that IgA levels were lower in patients with bipolar depression(Zhao et al.,2022). At present, more and more studies have found that unipolar depression and bipolar depression are related to potential infection and oxidative stress, in which humoral immunity plays an important role(Sigitova et al.,2017; Pape et al.,2019). Based on the above studies, it is suggested that there may be different humoral immune patterns between patients with bipolar depression and those with unipolar depression. This study ultimately included inflammatory factors (CRP, IgM) as predictors of bipolar depression, while thyroid related hormones were excluded. Studies have found that the thyroid hormone in patients with recurrent depressive episodes is often reduced, and based on the negative feedback mechanism of HTP axis, it has a significant inhibitory effect on the formation of TSH, which usually leads to a low level of serum TSH in the acute phase, and a high level of serum TSH in recurrent episodes(Zhao,2022). This may be due to the local deficiency of serotonin in the amygdala, which will adversely affect the neuroendocrine regulation function of the hypothalamus, and then significantly reduce the thyrotropin releasing hormone in the hypothalamus, resulting in a decrease in TSH secretion in the anterior pituitary(Joffe et al.,2000;Song et al.,2012). This also explains that in this study, the TSH level of unipolar depression was significantly lower than that of bipolar depression. However, in order to clarify the diagnosis and inclusion of patients with unipolar depression, it was strictly required that the disease course should be more than 2 years, which may lead to more recurrences in some patients with unipolar depression, and thus the distribution of TSH values in the unipolar group is more discrete. Therefore, in binomial Logistic regression analysis, the two groups could not be well distinguished. Recent studies have found that patients in inflammatory states can observe complex emotional and behavioral changes, including anxiety, depression, inattention, sleepiness and so on. In addition, a large proportion of patients with mood disorders are also accompanied by autoimmune diseases, such as rheumatoid arthritis, hepatitis, and Crohn's disease(Benedetti et al.,2020). In this study, it was found that CRP, C3, C4 and IgM in peripheral blood inflammatory factors were significantly different between the two groups with single and bipolar depression. A number of studies have shown that patients with bipolar disorder have higher levels of related inflammatory factors than those with unipolar depression, especially in the manic phase(Lv et al.,2023; Brunoni et al.,2020; Benedetti et al.,2020). This suggests that patients with bipolar disorder may have more serious systemic inflammatory response, and the secretion of related inflammatory factors acts on the corresponding brain regions of the brain to produce a series of emotional and behavioral changes. Therefore, clinically it can be observed that patients with bipolar disorder are more likely to have some atypical depressive symptoms and psychotic symptoms. In this study, the efficacy of CRP and IgM as biomarkers alone in distinguishing bipolar and unipolar depression was low, 74.3% and 58.3%, respectively. Although the pathogenesis of bipolar disorder is still unclear, a number of studies have suggested that it may be more effective to distinguish patients with bipolar depression by combining multiple clinical characteristics and biological indicators, and this study also found that combining multiple indicators can improve the diagnostic efficiency. In this study, the presence of life events, CRP level and IgM level were used as joint predictors to establish a Logistic regression model, and the ROC curve of the combined predictors was established. The AUC of the combined predictors was 0.806, which was higher than the prediction of other single indicators for bipolar depression. And these indicators are easy to obtain in clinical work, strong operability, and have certain application value. There are some limitations in this study: (1) No healthy control population was established. As this study was a retrospective study, the data were collected from previously hospitalized patients and did not include healthy people. (2) In the process of reviewing the medical history, the specific situation of drug use before admission was not adequately recorded in the medical history, so the therapeutic drugs of the two groups were not analyzed, which may have a potential impact on thyroid hormones and inflammatory factors. (3) All cases were hospitalized patients with severe overall depression, which may be deviated from the overall depressed population. (4) The dimensions of clinical symptoms were not quantified, and the severity of symptoms may also affect relevant biological indicators. 5. Conclusions This study revealed that compared with unipolar depression, no life events, CRP increase, IgM decrease are related factors of bipolar depression, and the combined diagnosis model is more effective in distinguishing unipolar depression from bipolar depression. Patients with bipolar depression have more severe inflammatory responses than those with unipolar depression. However, the pathway mechanism of how inflammatory factors affect each other and how inflammatory factors act on brain regions is still unclear, which needs to be explored and verified by further experiments. Declarations Ethics approval and consent to participate: This study program was approved by the Ethics Committee of Shanghai Mental Health Center. All subjects were fully aware of the study program and procedures and participated voluntarily, with informed consent signed by themselves or their guardians. Ethics Lot Number: 2023-62. Consent for publication: Not applicable Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Declaration of Competing Interest: The authors declare that they have no conflicts with any financial interests. Funding: This work was supported by multiple grants: STI 2030-Major Projects+2022ZD0208500; Elderly Health Project of Health Commission of Jiangsu Province of China(LKM2022062); Health Commission of Nantong City of China(grant number MSZ2023061 to Q.Y.); The fifth round’s subject construction of Health Commission of Qingpu District of China(grant number MY2023-9;LX2023-7). Authors’ contributions: Tianwei Zhang : Conceptualization,Data curation,Writing – original draft. Changjun Ji : Data curation,Writing – original draft. Jiayu Zhu : Data curation,Investigation. Xiaoxiao Wang , Chengjia Shen , Fei Liang , Yajun Hou , Yan Sun , Chongze Wang : Investigation. Peijuan Wang , Guoqiang Lu : Investigation,Funding acquisiton. Xiaohui Wang : Funding acquisition,Writing- Reviewing and Editing. Qinyu Lv and Zhenghui Yi : Funding acquisition,Methodology,Writing – review & editing. All authors read and approved the final manuscript. 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Oswald P, Souery D, Kasper S, Lecrubier Y, Montgomery S, Wyckaert S, Zohar J, Mendlewicz J. Current issues in bipolar disorder: a critical review. Eur Neuropsychopharmacol. 2007 Nov;17(11):687-95. Mengqi Liu, Fan He, Lanjun Tian, et al. The role of inflammatory factors in the pathogenesis of adolescent depressive disorder [J]. Chinese Journal of Mental Health, 2024,38(4):350-356. Wysokiński A, Kłoszewska I. Level of thyroid-stimulating hormone (TSH) in patients with acute schizophrenia, unipolar depression or bipolar disorder. Neurochem Res. 2014 Jul;39(7):1245-53. Jianhong Liu. Comparative study on clinical characteristics and serum thyroid hormone levels of single and bipolar depression [J]. International Medical Journal,2018,37(14):33-35. Nan Lv, Jinhong Li, Bingbing Fu, et al. The value of peripheral blood immune inflammation markers in distinguishing between unipolar and bipolar depression in the early stage of disease [J]. Neurological Diseases and Mental Health, 2023,23(4):234-239. Sai Chen, Bing Deng, Bin Bi, et al. Study on serum BDNF and TSH levels in bipolar depressive episode and unipolar depressive episode [J]. Journal of Taishan Medical University,2018,39(4):365-367. Dongsheng Han. Study on levels of peripheral blood inflammatory factors in patients with bipolar depressive episode and depression [D]. Anhui: Anhui Medical University,2023. Qian Zhao, Li Yin, Bingbing Fu, et al. Comparison of immune and stress-related factors in bipolar depression and unipolar depression [J]. Neurological Diseases and Mental Health,2022,22(3):177-183. Brunoni, Andre R.Supasitthumrong, ThitipornTeixeira, Antonio LucioVieira, Erica L. M.Gattaz, Wagner F.Bensenor, Isabela M.Lotufo, Paulo A.Lafer, BenyBerk, MichaelCarvalho, Andre F.Maes, Michael.Differences in the immune-inflammatory profiles of unipolar and bipolar depression[J].Journal of affective disorders, 2020, 262. Mao R, Zhang C, Chen J, Zhao G, Zhou R, Wang F, Xu J, Yang T, Su Y, Huang J, Wu Z, Cao L, Wang Y, Hu Y, Yuan C, Yi Z, Hong W, Wang Z, Peng D, Fang Y. Different levels of pro- and anti-inflammatory cytokines in patients with unipolar and bipolar depression. J Affect Disord. 2018 Sep;237:65-72. WHO. ICD-10 Classification of Mental & Behavioural Disorders ［M］. Geneva：World Health Organization,1993. Obuchowski NA, Bullen JA. Receiver operating characteristic (ROC) curves: review of methods with applications in diagnostic medicine. Phys Med Biol. 2018 Mar 29;63(7):07TR01. Shengjun Zhang. Comparative study on clinical and psychopathological characteristics of unipolar and bipolar depression [D]. Guangdong: Southern Medical University,2017. Rong Yan. Comparison of clinical features and serum thyroid hormone levels of unipolar and bipolar depression [J]. Journal of Health Care,2017(11):15. Kaida Jiang. Psychiatry 2nd Ed. [M]. People's Medical Publishing House,2011. Chang HH, Wang TY, Lee IH, Lee SY, Chen KC, Huang SY, Yang YK, Lu RB, Chen PS. C-reactive protein: A differential biomarker for major depressive disorder and bipolar II disorder. World J Biol Psychiatry. 2017 Feb;18(1):63-70. Wei Guo,Xingmei Zhao, Xiping Wang, et al. A meta-analysis of the correlation between C-reactive protein and bipolar disorder [J]. International Journal of Psychiatry,2019,46(5):817-821 Orsolini L, Ricci L, Pompili S, Cicolini A, Volpe U. Eveningness chronotype and depressive affective temperament associated with higher high-sensitivity C-reactive protein in unipolar and bipolar depression. J Affect Disord. 2023 Jul 1;332:210-220. Gorgulu Y, Uluturk MK, Palabiyik O. Comparison of serum BDNF, IL-1β, IL-6, TNF-α, CRP and leucocyte levels in unipolar mania and bipolar disorder. Acta Neuropsychiatr. 2021 Dec;33(6):317-322. Wenzhi Cai, Yanjun Jin, Wenze Chen, et al. A meta-analysis of the relationship between thyroid stimulating hormone and unipolar depression or bipolar depression in Chinese population [J]. Chinese Journal of Chronic Disease Prevention and Control,2016,24(5):387-391. Wysokiński A, Kłoszewska I. Level of thyroid-stimulating hormone (TSH) in patients with acute schizophrenia, unipolar depression or bipolar disorder. Neurochem Res. 2014 Jul;39(7):1245-53. Yu H, Ni P, Tian Y, Zhao L, Li M, Li X, Wei W, Wei J, Du X, Wang Q, Guo W, Deng W, Ma X, Coid J, Li T. Association of the plasma complement system with brain volume deficits in bipolar and major depressive disorders. Psychol Med. 2023 Oct;53(13):6102-6112. Lyu N, Xing G, Yang J, Zhu X, Zhao X, Zhang L, Wang G. Comparison of inflammatory, nutrient, and neurohormonal indicators in patients with schizophrenia, bipolar disorder and major depressive disorder. J Psychiatr Res. 2021 May;137:401-408. Legros S, Mendlewicz J, Wybran J. Immunoglobulins, autoantibodies and other serum protein fractions in psychiatric disorders. Eur Arch Psychiatry Neurol Sci. 1985;235(1):9-11. Sigitova E, Fišar Z, Hroudová J, Cikánková T, Raboch J. Biological hypotheses and biomarkers of bipolar disorder. Psychiatry Clin Neurosci. 2017 Feb;71(2):77-103. Pape K, Tamouza R, Leboyer M, Zipp F. Immunoneuropsychiatry - novel perspectives on brain disorders. Nat Rev Neurol. 2019 Jun;15(6):317-328. Lianhong Zhao. Clinical characteristics and serum thyroid hormone levels in patients with unipolar and bipolar depression [D]. Shandong: Qingdao University,2022. Joffe RT, Marriott M. Thyroid hormone levels and recurrence of major depression. Am J Psychiatry. 2000 Oct;157(10):1689-91. Liang Song, Yang Liu, Jimeng Zhang, et al. Relationship between depression-like behavioral changes and serum ACTH and TSH concentrations in 5-HT removed amygdala mice [J]. Journal of Xuzhou Medical College,2012,32(4):234-238. Benedetti F, Aggio V, Pratesi ML, Greco G, Furlan R. Neuroinflammation in Bipolar Depression. Front Psychiatry. 2020 Feb 26;11:71. Picture 1 Picture 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Figure.jpg Remark: ROC subject operating characteristics; Area under the AUC curve Picture1: ROC curve for predicting unipolar and bipolar depression based on logistic regression model Cite Share Download PDF Status: Published Journal Publication published 10 Feb, 2025 Read the published version in BMC Psychiatry → Version 1 posted Editorial decision: Revision requested 05 Nov, 2024 Reviews received at journal 29 Oct, 2024 Reviewers agreed at journal 25 Oct, 2024 Reviews received at journal 10 Oct, 2024 Reviews received at journal 09 Oct, 2024 Reviewers agreed at journal 08 Oct, 2024 Reviewers agreed at journal 06 Oct, 2024 Reviewers invited by journal 02 Oct, 2024 Editor invited by journal 24 Sep, 2024 Editor assigned by journal 22 Sep, 2024 Submission checks completed at journal 21 Sep, 2024 First submitted to journal 20 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5126282","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374408241,"identity":"e4bdfe68-dbc1-47f1-97a8-6331637dfefb","order_by":0,"name":"Tianwei Zhang","email":"","orcid":"","institution":"Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tianwei","middleName":"","lastName":"Zhang","suffix":""},{"id":374408242,"identity":"4cae2b08-f2e4-4d71-9e62-b660b2c87fe8","order_by":1,"name":"Changjun Ji","email":"","orcid":"","institution":"Shanghai 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23:24:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":167031,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5126282/v1/23641ae7adf9ef6b4e42a5be.png"},{"id":76487537,"identity":"a754d889-26c6-4445-b2b8-ede96e3cf869","added_by":"auto","created_at":"2025-02-17 16:08:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1118358,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5126282/v1/e9c0dfb1-a350-4451-ab9e-eace9ab1fd9e.pdf"},{"id":69850797,"identity":"318b33c8-97d8-4bf4-8b58-88fa4ad50898","added_by":"auto","created_at":"2024-11-25 23:32:31","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32697,"visible":true,"origin":"","legend":"\u003cp\u003eRemark: ROC subject operating characteristics; Area under the AUC curve\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePicture1:\u003c/strong\u003e \u003cstrong\u003eROC curve for predicting unipolar and bipolar depression based on logistic regression model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5126282/v1/4e9d0edd0a03d204b3c52349.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of clinical features and inflammatory factors between patients with bipolar depression and unipolar depression","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMajor Depressive disorder (MDD) is one of the most common major mental disorders characterized by significant and persistent mood in low out of proportion to the situation, accompanied by corresponding cognitive and behavioral changes. Clinically, according to the presence or absence of manic or hypomanic episodes, depressive episodes can be divided into unipolar depression and bipolar depression. In clinical practice, bipolar disorder is more difficult to diagnose early, with only 20% of bipolar patients who experience a depressive episode being diagnosed within the first year of seeking treatment, and the average delay from onset to correct diagnosis being 5\u0026ndash;10 years(Phillips et al.,2013).Particularly, patients with bipolar disorder who are treated with a single antidepressant often experience poor symptom improvement, transition to manic episodes, and an increased risk of suicide(Rosso et al.,2023), making the identification of bipolar disorder extremely important. Some studies have found that unipolar depression and bipolar depression have different genetic basis, treatment plan and prognosis, suggesting that they may belong to different disease spectrum(Huang et al.,2011). Relevant studies suggest that the immune system and neuroendocrine system will be affected when patients are in the depressive phase, and patients with bipolar depression have more severe inflammatory responses(Maes et al.,2012; Bai et al.,2020). Therefore, looking for the differences between unipolar and bipolar depression in peripheral inflammatory factors will be helpful for early diagnosis and treatment.\u003c/p\u003e \u003cp\u003eIn terms of clinical characteristics of bipolar depression, some domestic and foreign studies have found that compared with unipolar depression, patients with bipolar disorder have earlier onset age, fewer life events, positive family history of bipolar disorder, atypical depressive symptoms (weight gain, lethargy), and psychotic symptoms(Galv\u0026atilde;o et al.,2013; Wang et al.,2015; Li et al.,2011; Oswald et al.,2007). The common underlying mechanism of abnormal thyroid function and inflammatory factors in patients with unipolar and bipolar depression may be the disturbance of the HPA axis and the activation of central and peripheral inflammatory responses when the body is faced with stressful events. The interaction between HPA axis and inflammatory response will interfere with neurotransmitter metabolism and affect nerve regeneration, or increase oxygen free radical release, and eventually lead to neurotransmitter depletion, neuron damage, synaptic function impairment and other pathological changes, which directly affect the neural circuit and cause depressive symptoms(Liu et al.,2024). In contrast to unipolar depression, existing evidence in bipolar disorder patients suggests that multi-system inflammation may be involved in the early stage, and pro-inflammatory cytokines have unique effects on central neurons and circuits, affecting the activation of microglia and it also affects signaling molecules in neurotransmission, memory, and glucocorticoid function, as well as activity control(Bai et al.,2020). Therefore, relevant studies at home and abroad all suggest that the impairment of thyroid hormone function and the expression of inflammatory factors in patients with bipolar disorder are higher than those with unipolar depression(Wang et al.,2015; Wysokiński et al.,2014; Liu,2018;Lv et al.,2023;Chen et al.2018;Han,2023;Zhao et al.,2022; Brunoni et al.,2020). For example, fT3, fT4 and TSH in patients with bipolar depression respectively showed differences in different studies compared with those in unipolar depression group (Wang et al.,2015; Liu,2018; Chen et al.2018). And differences in inflammatory factors such as neutrophil/lymphocyte ratio(NLR), monocyte/lymphocyte ratio (MLR), CRP, complement 3(C3), IL-1β, TNF-α, IL-6 and IgA have all been reported between patients with unipolar and bipolar depression (Lv et al.,2023; Han,2023; Brunoni et al.,2020;Mao et al.,2018).\u003c/p\u003e \u003cp\u003eAt present, relevant studies often compare the difference between patients with unipolar and bipolar depression only from the single dimension of thyroid function or inflammatory factors, and there are few studies on the multidimensional distinction between the two. Moreover, the probability value of the combined factor obtained by the Logistic regression model is low(Lv et al.,2023; Zhao et al.,2022). Based on previous studies have shown that patients with bipolar disorder have higher expression of inflammatory factors, and peripheral blood indicators are easily interfered by many factors. Therefore, the purpose of this study was to analyze and compare the differences between patients with unipolar and bipolar depression from three dimensions: clinical features, thyroid function, and inflammatory factors, in order to find potential indicators to distinguish unipolar depression from bipolar depression, and provide references for clinical decision-making.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1.1 Subjects:\u003c/h2\u003e \u003cp\u003eThe study subjects were all hospitalized patients admitted to Shanghai Mental Health Center from June 2022 to June 2024. Inclusion criteria: (1) Meeting the diagnostic criteria of the 10th edition of the International Classification of Diseases (ICD-10) (WHO,1993)for bipolar depressive episode and recurrent depressive disorder, the diagnosis was made by an attending physician and reviewed by another deputy chief physician or chief physician, and all enrolled patients were in the acute stage of depressive episode; (2) Age over 14 years old and under 60 years old, regardless of gender; (3) The duration of recurrent depressive disorder is greater than 2 years; (4) The medical records are reliable, and there is no lack of data information. Exclusion criteria: (1) Any hospitalization was diagnosed with schizophrenia, schizoaffective disorder and other mental disorders; (2) Endocrine diseases and serious physical diseases; (3) Patients with acute infection and patients with CRP\u0026thinsp;\u0026gt;\u0026thinsp;10mg/L; (4) Women who are pregnant or breastfeeding. In this study, general demographic data, clinical characteristics and peripheral blood biomarkers were extracted from the electronic medical record system of Shanghai Mental Health Center.\u003c/p\u003e \u003cp\u003e This study program was approved by the Ethics Committee of Shanghai Mental Health Center. All subjects were fully aware of the study program and procedures and participated voluntarily, with informed consent signed by themselves or their guardians. Ethics Lot Number: 2023-62.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Sample size calculating\u003c/h2\u003e \u003cp\u003eThe calculation formula of sensitivity and specificity is n=(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{Z1-/2\\times\\:\\sqrt{p\\times\\:(1-p)}}{}\\)\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e2\u003c/sup\u003e, where n stands for the sample size for each group, α stands for significance level which is 0.05, δ stands for tolerance error which is 0.1, p stands for sensitivity (pse) or specificity (psp), By looking up the table, Z1-α/2 equals 1.96. According to the data of domestic research(Lv et al.,2023), sensitivity equals 0.784, specificity equals 0.539, substituted into the formula for calculating it, n\u003csub\u003ese\u003c/sub\u003e=(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{1.96\\times\\:\\sqrt{0.784\\times\\:(1-0.784)}}{0.1}\\)\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e2\u003c/sup\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\approx\\:\\)\u003c/span\u003e\u003c/span\u003e65, n\u003csub\u003esp\u003c/sub\u003e=(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{1.96\\times\\:\\sqrt{0.539\\times\\:(1-0.539)}}{0.1}\\)\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e2\u003c/sup\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\approx\\:\\)\u003c/span\u003e\u003c/span\u003e95. Since this study is retrospective and does not consider the loss of follow-up, it is considered that at least 100 samples should be included in each group.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data collection and assessment\u003c/h2\u003e \u003cp\u003eThis study was conducted retrospectively with data collected from the electronic medical record system of Shanghai Mental Health Center. Data sets were established according to inclusion and exclusion criteria. The dataset included general demographic data, clinical characteristics and peripheral blood biomarkers. All subjects had peripheral blood drawn within 24h after admission. Biological indicators collected in this study included neutrophil/lymphocyte ratio (NLR) and monocyte/lymphocyte ratio (monocyte/lymphocyte ratio). MLR), platelet/lymphocyte ratio (PLR), CRP, thyroid hormone (T3, T4, fT3, fT4, TSH), complement 3(C3), complement 4(complement 4, C4), IgA, IgG, IgM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.3 Statistical analysis\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eSPSS version 26.0 was used for statistical analysis. The confounding factors in the unipolar depression group and the bipolar depression group were controlled by the propensity score matching method. The caliper value was set to 0.02, and the age and gender of the two groups were matched according to the method of 1:1 without putting back. 1\u0026ndash;2 patients were included in each group. Shapiro-Wilk test was used to detect whether the data conformed to the normal distribution, and the measurement data of the normal distribution were represented by the mean value and standard deviation[\u0026oline;x\u0026plusmn;s].The measurement data for non-normal distributions are expressed by median and quartile[M(P\u003csub\u003e25\u003c/sub\u003e,P\u003csub\u003e75\u003c/sub\u003e)]. Independent sample t test was used to compare the measurement data with normal distribution, and Mann-Whitney U test was used to compare the measurement data with non-normal distribution. Counting data were expressed as frequency and percentage (%), and inter-group comparison was conducted by chi-square test. Binomial Logistic regression (forward method) was used to adjust the covariates and construct the regression model. SPSS26.0 software was used to analyze receiver operator characteristic (ROC) curve, and the prediction ability of different indicators and Logistic regression model was evaluated. The area under curve (AUC) was used to reflect the accuracy of the prediction, and the closer the value was to 1.0, the better the effect was(Obuchowski et al.,2018).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cp\u003e\u003cstrong\u003e3.1 Comparison of demographic and clinical characteristics of patients in unipolar depression group and bipolar depression group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e274 patients in unipolar depression group and 128 patients in bipolar depression group were included. There was no significant difference in gender, psychotic symptoms, weight gain and lethargy symptoms between the two groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The age and onset age of bipolar depression group was younger than that of unipolar depression group, and the proportion of life events in bipolar depression group was less than that in unipolar depression group, and the proportion in family history was more, with statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). (Table \u003cspan\u003e1\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Comparison of general data and peripheral biological indicators of patients in unipolar depression group and bipolar depression group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA 1:1 propensity score matching method was used to analyze the data of 256 patients, including 128 patients in unipolar depression group and 128 patients in bipolar depression group. There were no statistically significant differences in age, age of onset, gender, family history, psychotic symptoms, weight gain, and lethargy symptoms between the two groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The proportion of life events in unipolar depression group was higher than that in bipolar depression group, with statistically significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The levels of CRP, TSH, C3 and C4 in bipolar depression group were higher than those in unipolar depression group, and the levels of IgM were lower than those in unipolar depression group, with statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There was no statistical significance in T3, T4, fT3, fT4, IgA, IgG, NLR, MLR and PLR between the two groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). (Table \u003cspan\u003e2\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e3.3 Predictors analysis of bipolar depression patients\u003c/h2\u003e\n \u003cp\u003ePatients with unipolar depression and bipolar depression were used as dependent variables (unipolar depression\u0026thinsp;=\u0026thinsp;0, bipolar depression\u0026thinsp;=\u0026thinsp;1), and variables with statistical significance in the univariate analysis were used as independent variables. The results showed that no life events, increased CRP and decreased IgM were risk factors for bipolar depression compared with unipolar depression (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). (Table \u003cspan\u003e3\u003c/span\u003e). Regression equation: Logit(P)\u0026thinsp;=\u0026thinsp;1.515X\u003csub\u003einducement\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;2.631X\u003csub\u003eCRP\u003c/sub\u003e-0.577 X\u003csub\u003eIgM\u003c/sub\u003e-1.865(X \u003csub\u003einducement\u003c/sub\u003e, X\u003csub\u003eCRP\u003c/sub\u003e, X\u003csub\u003eIgM\u003c/sub\u003e represent inducement, CRP concentration and IgM concentration respectively).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e3.4 ROC curve for distinguishing unipolar depression from bipolar depression\u003c/h2\u003e\n \u003cp\u003ePeripheral blood CRP and IgM alone had poor predictive effect on unipolar depression and bipolar depression, with an AUC of 0.743 and 0.583, respectively. The combined factor probability value obtained by Logistic regression model showed that the predictive effect of unipolar depression and bipolar depression was improved, with an AUC of 0.806. (Table \u003cspan\u003e4\u003c/span\u003e, Picture 1)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable1\u0026nbsp;\u003c/strong\u003eComparison of demographic and clinical characteristics between unipolar depression group and bipolar depression group\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnipolar depression（n=274）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBipolar depression（n=128）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eZ/c2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge[years，M（P\u003csub\u003e25\u003c/sub\u003e，P\u003csub\u003e75\u003c/sub\u003e）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.50(18.00,44.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e24.00(17.25,34.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge of onset[years，M（P25，P75）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.00(15.00,37.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e18.00(14.00,26.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.729.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale[rate（%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e87（31.75）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e42（32.81）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLife events[rate（%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e113（41.24）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e25（19.53）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFamily history[rate（%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37（13.50）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e30（23.44）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePsychotic symptoms[rate（%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78（28.47）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e42（32.81）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGain weight[rate（%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38（13.87）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e25（19.53）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLethargy symptoms[rate（%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17(6.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e11(8.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003eTable2\u0026nbsp;\u003c/strong\u003eComparison of general data and peripheral blood biological indicators between unipolar depression group and bipolar depression group\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnipolar depression（n=128）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBipolar depression（n=128）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eZ/t/c2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge[years，M（P\u003csub\u003e25\u003c/sub\u003e，P\u003csub\u003e75\u003c/sub\u003e）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.50(17.00,34.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.00(17.25,34.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge of onset[years，M（P25，P75）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.00(14.00,26.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.00(14.00,26.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale[rate（%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36（28.13）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42（32.81）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLife events[rate（%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54（42.19）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25（19.53）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFamily history[rate（%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18（14.06）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30（23.44）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePsychotic symptoms[rate（%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45（35.16）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42（32.81）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGain weight[rate（%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17（13.28）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25（19.53）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLethargy symptoms[rate（%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9（7.03）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11（8.59）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.641\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCRP[mg/L,M（P25，P75）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.34(0.26,0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.63(0.36,1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-6.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT3[nmol/L,`x\u0026plusmn;s]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.62\u0026plusmn;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.67\u0026plusmn;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT4[nmol/L,M（P25，P75）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e98.40(86.12,109.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e94.13(83.15,106.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003efT3[pmol/L,M（P25，P75）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.60(4.02,5.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.56(4.08,5.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003efT4[pmol/L,M（P25，P75）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.88(14.61,17.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.61(14.19,17.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTSH[uIU/mL,M（P25，P75）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.51(0.99,2.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.66(1.15,2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC3[g/L,`x\u0026plusmn;s]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.10\u0026plusmn;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.22\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-5.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC4[g/L,M（P25，P75）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.25(0.21,0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.30(0.25,0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-4.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIgA[g/L,M（P25，P75）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.02(1.51,2.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.97(1.65,2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIgG[g/L,`x\u0026plusmn;s]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.66\u0026plusmn;2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.68\u0026plusmn;2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIgM[g/L,M（P25，P75）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.30(0.96,1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.17(0.82,1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNLR[M（P25，P75）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.58(1.76,3.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.46(1.77,3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMLR[M（P25，P75）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.26(0.19,0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.26(0.21,0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePLR[M（P25，P75）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e149.21(122.08,189.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e139.36(116.25,175.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003eTable3\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eBinomial Logistic regression analysis of influencing factors of bipolar depression\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRegression\u003c/p\u003e\n \u003cp\u003ecoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eStandard error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eWald x\u003csup\u003e2\u003c/sup\u003e值\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e95%Cl\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003cp\u003elimit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003cp\u003elimit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo life events\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36.452\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIgM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cstrong\u003eTable4\u0026nbsp;\u003c/strong\u003eROC curve analysis results of single and bipolar depression\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"565\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCutoff value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95%Cl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePositive predictive value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNegative predictive value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.683-0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.647\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIgM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.514-0.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLogistic regression model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.753-0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\u003cbr\u003e\u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study found that there were differences in age, onset age, inducement and family history between bipolar depression and unipolar depression. Due to the influence of age factors on peripheral blood indexes, 1:1 propensity score matching method was performed. After controlling age factors, there were differences in CRP, TSH, C3, C4 and IgM between the two groups. Logistic regression control for confounding factors suggested that life events, CRP and IgM were the influencing factors of bipolar depression. The accuracy of the above single index in the classification of bipolar depression and unipolar depression is poor, but the combined diagnostic model including life events, CRP and IgM is more effective in distinguishing bipolar depression and unipolar depression.\u003c/p\u003e \u003cp\u003eThe present study suggests that no life events before illness may be an effective predictor of bipolar depression. In this study, the age and onset age of patients with bipolar depression were younger than those with unipolar depression, and the patients with bipolar depression had fewer life events, which was consistent with the domestic studies of Xiaoquan Wang et al(Wang et al.,2015), Zezhi Li et al(Li et al.,2011)and Jianhong Liu(Liu,2018). At the same time, it was found that patients with bipolar depression had more positive family history, which was consistent with the studies of Zezhi Li et al(Li et al.,2011) and Shengjun Zhang(Zhang,2017), but no difference was found in the studies of Xiaoquan Wang et al(Wang et al.,2015) and Rong Yan(Yan,2017), which may be related to the selection of participants. However, the genetic tendency survey found that the heritability of bipolar disorder was up to 80% which much higher than the 40% heritability of severe depression. 50% of patients with bipolar I disorder have at least one parent with a mood disorder(Jiang,2011)\u003csup\u003e]\u003c/sup\u003e, still suggesting that patients with bipolar depression have an underlying positive family history. After 1:1 propensity score matching, family history was not statistically significant between the two groups, so it was not included in the Logistic regression equation.\u003c/p\u003e \u003cp\u003eThis study found that CRP levels in patients with bipolar depression were higher than those in patients with unipolar depression, which could be used as a predictor to distinguish between the two. This is the same as the findings of Nan Lv et al(Lv et al.,2023)and Chang HH et al(Chang et al.,2017), and the meta-analysis of Wei Guo et al also suggested that CRP concentrations in bipolar patients in manic, depressive and remission periods were higher than those in healthy controls(Guo et al.,2019; Orsolini et al.,2023). CRP is an acute phase protein that responds to inflammatory stimuli. High concentration of CRP in serum can increase the permeability of the blood-brain barrier, enable CRP to enter the central nervous system, promote the increased release of cytokines such as IL-6 and interferon-γ, and mediate the damage of neurons, which partly explains the more severe cognitive dysfunction in patients with bipolar disorder(Gorgulu et al.,2021).\u003c/p\u003e \u003cp\u003eThe results of this study showed that patients with bipolar depression had a higher TSH level than those with unipolar depression, which was consistent with the results of the meta-analysis conducted by Wenzhi CAI et al and Wysokiński A(Cai et al.,2016; Wysokiński et al.,2014). However, the results of a number of domestic studies all suggested that patients with bipolar depression had a lower TSH level than those with unipolar depression(Wang et al.,2015; Liu,2018;Yan,2017). This may relate to both the sample size and the drug. Treatment for bipolar disorder often uses lithium carbonate, which can cause hypothyroidism and affect thyroid hormone levels. Patients were not screened for drug use in this study, and the effect of treatment drugs on thyroid hormones could not be ruled out. At the same time, age also has an impact on thyroid hormone levels(Wysokiński et al.,2014), which may also lead to inconsistent results in the comparison between the bipolar depression group and the unipolar depression group in different studies. In binomial Logistic regression analysis, TSH index was not included, indicating that TSH has little effect on distinguishing unipolar and bipolar depression.\u003c/p\u003e \u003cp\u003eC3 and C4 participate in inflammatory response and synaptic shear, which are related to the occurrence and development of mental disorders(Lv et al.,2023). The results of this study showed that C3 and C4 levels were elevated in patients with bipolar depression, which was consistent with multiple studies(Lv et al.,2023; Yu et al.,2023; Lyu et al.,2021). C3 belongs to the complement system, which is involved in brain development and the occurrence of mental diseases. Neurons, astrocytes and microglia all express complement receptors and synthesize complement proteins, regulate neurogenesis, migration and synaptic pruning, and participate in the regulation of emotions(Zhao et al.,2022). Although C3 and C4 were not included in the Logistic regression equation in this study, it is still suggested that these two indicators are potential predictors of bipolar depression.\u003c/p\u003e \u003cp\u003eThis study found that compared with unipolar depression, patients with bipolar depression had lower levels of IgM in their peripheral blood. Some previous studies have found that, compared with healthy people, the IgM level of people with bipolar depression, unipolar depression and schizophrenia is increased, and the IgM level of women is higher than that of men in people with single and bipolar depression(Legros et al.,1985). Domestic studies found that IgA levels were lower in patients with bipolar depression(Zhao et al.,2022). At present, more and more studies have found that unipolar depression and bipolar depression are related to potential infection and oxidative stress, in which humoral immunity plays an important role(Sigitova et al.,2017; Pape et al.,2019). Based on the above studies, it is suggested that there may be different humoral immune patterns between patients with bipolar depression and those with unipolar depression.\u003c/p\u003e \u003cp\u003eThis study ultimately included inflammatory factors (CRP, IgM) as predictors of bipolar depression, while thyroid related hormones were excluded. Studies have found that the thyroid hormone in patients with recurrent depressive episodes is often reduced, and based on the negative feedback mechanism of HTP axis, it has a significant inhibitory effect on the formation of TSH, which usually leads to a low level of serum TSH in the acute phase, and a high level of serum TSH in recurrent episodes(Zhao,2022). This may be due to the local deficiency of serotonin in the amygdala, which will adversely affect the neuroendocrine regulation function of the hypothalamus, and then significantly reduce the thyrotropin releasing hormone in the hypothalamus, resulting in a decrease in TSH secretion in the anterior pituitary(Joffe et al.,2000;Song et al.,2012). This also explains that in this study, the TSH level of unipolar depression was significantly lower than that of bipolar depression. However, in order to clarify the diagnosis and inclusion of patients with unipolar depression, it was strictly required that the disease course should be more than 2 years, which may lead to more recurrences in some patients with unipolar depression, and thus the distribution of TSH values in the unipolar group is more discrete. Therefore, in binomial Logistic regression analysis, the two groups could not be well distinguished. Recent studies have found that patients in inflammatory states can observe complex emotional and behavioral changes, including anxiety, depression, inattention, sleepiness and so on. In addition, a large proportion of patients with mood disorders are also accompanied by autoimmune diseases, such as rheumatoid arthritis, hepatitis, and Crohn's disease(Benedetti et al.,2020). In this study, it was found that CRP, C3, C4 and IgM in peripheral blood inflammatory factors were significantly different between the two groups with single and bipolar depression. A number of studies have shown that patients with bipolar disorder have higher levels of related inflammatory factors than those with unipolar depression, especially in the manic phase(Lv et al.,2023; Brunoni et al.,2020; Benedetti et al.,2020). This suggests that patients with bipolar disorder may have more serious systemic inflammatory response, and the secretion of related inflammatory factors acts on the corresponding brain regions of the brain to produce a series of emotional and behavioral changes. Therefore, clinically it can be observed that patients with bipolar disorder are more likely to have some atypical depressive symptoms and psychotic symptoms. In this study, the efficacy of CRP and IgM as biomarkers alone in distinguishing bipolar and unipolar depression was low, 74.3% and 58.3%, respectively. Although the pathogenesis of bipolar disorder is still unclear, a number of studies have suggested that it may be more effective to distinguish patients with bipolar depression by combining multiple clinical characteristics and biological indicators, and this study also found that combining multiple indicators can improve the diagnostic efficiency. In this study, the presence of life events, CRP level and IgM level were used as joint predictors to establish a Logistic regression model, and the ROC curve of the combined predictors was established. The AUC of the combined predictors was 0.806, which was higher than the prediction of other single indicators for bipolar depression. And these indicators are easy to obtain in clinical work, strong operability, and have certain application value.\u003c/p\u003e \u003cp\u003eThere are some limitations in this study: (1) No healthy control population was established. As this study was a retrospective study, the data were collected from previously hospitalized patients and did not include healthy people. (2) In the process of reviewing the medical history, the specific situation of drug use before admission was not adequately recorded in the medical history, so the therapeutic drugs of the two groups were not analyzed, which may have a potential impact on thyroid hormones and inflammatory factors. (3) All cases were hospitalized patients with severe overall depression, which may be deviated from the overall depressed population. (4) The dimensions of clinical symptoms were not quantified, and the severity of symptoms may also affect relevant biological indicators.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study revealed that compared with unipolar depression, no life events, CRP increase, IgM decrease are related factors of bipolar depression, and the combined diagnosis model is more effective in distinguishing unipolar depression from bipolar depression. Patients with bipolar depression have more severe inflammatory responses than those with unipolar depression. However, the pathway mechanism of how inflammatory factors affect each other and how inflammatory factors act on brain regions is still unclear, which needs to be explored and verified by further experiments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study program was approved by the Ethics Committee of Shanghai Mental Health Center. All subjects were fully aware of the study program and procedures and participated voluntarily, with informed consent signed by themselves or their guardians. Ethics Lot Number: 2023-62.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts with any financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by multiple grants: STI 2030-Major Projects+2022ZD0208500; Elderly Health Project of Health Commission of Jiangsu Province of China(LKM2022062); Health Commission of Nantong City of China(grant number MSZ2023061 to Q.Y.); The fifth round\u0026rsquo;s subject construction of Health Commission of Qingpu District of China(grant number MY2023-9;LX2023-7).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTianwei Zhang\u003c/strong\u003e: \u0026nbsp;Conceptualization,Data curation,Writing \u0026ndash; original draft. \u003cstrong\u003eChangjun Ji\u003c/strong\u003e: Data curation,Writing \u0026ndash; original draft. \u003cstrong\u003eJiayu Zhu\u003c/strong\u003e: Data curation,Investigation.\u003cstrong\u003eXiaoxiao Wang\u003c/strong\u003e,\u003cstrong\u003eChengjia Shen\u003c/strong\u003e,\u003cstrong\u003eFei Liang\u003c/strong\u003e,\u003cstrong\u003eYajun Hou\u003c/strong\u003e,\u003cstrong\u003eYan Sun\u003c/strong\u003e,\u003cstrong\u003eChongze Wang\u003c/strong\u003e: Investigation. \u003cstrong\u003ePeijuan Wang\u003c/strong\u003e,\u003cstrong\u003eGuoqiang Lu\u003c/strong\u003e: Investigation,Funding acquisiton. \u003cstrong\u003eXiaohui Wang\u003c/strong\u003e: Funding acquisition,Writing- Reviewing and Editing. \u003cstrong\u003eQinyu Lv\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003eZhenghui Yi\u003c/strong\u003e: Funding acquisition,Methodology,Writing \u0026ndash; review \u0026amp; editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to all the physicians and subjects that participated in this study.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePhillips ML, Kupfer DJ. Bipolar disorder diagnosis: challenges and future directions. Lancet. 2013 May 11.\u003c/li\u003e\n\u003cli\u003eRosso G, Maina G, Teobaldi E, Balbo I, Di Salvo G, Montarolo F, Rizzo Pesci N, Tempia F, Hoxha E. Differential diagnosis of unipolar versus bipolar depression by GSK3 levels in peripheral blood: a pilot experimental study. Int J Bipolar Disord. 2023 Oct 8.\u003c/li\u003e\n\u003cli\u003eJia Huang, Chengmei Yuan, Yiru Fang. Research progress in early recognition of bipolar disorder [J]. 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Level of thyroid-stimulating hormone (TSH) in patients with acute schizophrenia, unipolar depression or bipolar disorder. Neurochem Res. 2014 Jul;39(7):1245-53.\u003c/li\u003e\n\u003cli\u003eYu H, Ni P, Tian Y, Zhao L, Li M, Li X, Wei W, Wei J, Du X, Wang Q, Guo W, Deng W, Ma X, Coid J, Li T. Association of the plasma complement system with brain volume deficits in bipolar and major depressive disorders. Psychol Med. 2023 Oct;53(13):6102-6112.\u003c/li\u003e\n\u003cli\u003eLyu N, Xing G, Yang J, Zhu X, Zhao X, Zhang L, Wang G. Comparison of inflammatory, nutrient, and neurohormonal indicators in patients with schizophrenia, bipolar disorder and major depressive disorder. J Psychiatr Res. 2021 May;137:401-408.\u003c/li\u003e\n\u003cli\u003eLegros S, Mendlewicz J, Wybran J. Immunoglobulins, autoantibodies and other serum protein fractions in psychiatric disorders. Eur Arch Psychiatry Neurol Sci. 1985;235(1):9-11. \u003c/li\u003e\n\u003cli\u003eSigitova E, Fi\u0026scaron;ar Z, Hroudov\u0026aacute; J, Cik\u0026aacute;nkov\u0026aacute; T, Raboch J. Biological hypotheses and biomarkers of bipolar disorder. Psychiatry Clin Neurosci. 2017 Feb;71(2):77-103. \u003c/li\u003e\n\u003cli\u003ePape K, Tamouza R, Leboyer M, Zipp F. Immunoneuropsychiatry - novel perspectives on brain disorders. Nat Rev Neurol. 2019 Jun;15(6):317-328. \u003c/li\u003e\n\u003cli\u003eLianhong Zhao. Clinical characteristics and serum thyroid hormone levels in patients with unipolar and bipolar depression [D]. Shandong: Qingdao University,2022.\u003c/li\u003e\n\u003cli\u003eJoffe RT, Marriott M. Thyroid hormone levels and recurrence of major depression. Am J Psychiatry. 2000 Oct;157(10):1689-91. \u003c/li\u003e\n\u003cli\u003eLiang Song, Yang Liu, Jimeng Zhang, et al. Relationship between depression-like behavioral changes and serum ACTH and TSH concentrations in 5-HT removed amygdala mice [J]. Journal of Xuzhou Medical College,2012,32(4):234-238.\u003c/li\u003e\n\u003cli\u003eBenedetti F, Aggio V, Pratesi ML, Greco G, Furlan R. Neuroinflammation in Bipolar Depression. Front Psychiatry. 2020 Feb 26;11:71.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Picture 1","content":"\u003cp\u003ePicture 1 is available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Unipolar depression, Bipolar depression, Clinical features, Inflammatory factors, Differential diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-5126282/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5126282/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTo compare the differences in clinical features and inflammatory factors of unipolar depression and bipolar depression, and to explore potential clinical features and peripheral blood biological markers are used to distinguish unipolar depression and bipolar depression. And to further build a prediction model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe inpatients of Shanghai Mental Health Center from June 2022 to June 2024 were selected as the study objects. According to the diagnosis of hospitalization records, 274 cases were divided into unipolar depression group and 128 cases were bipolar depression group. A total of 128 patients were enrolled in each of the two groups by the propensity score matching method. The demographic data, clinical characteristics and biological indicators of the two groups were compared. Biological markers include neutrophil/lymphocyte ratio (NLR), monocyte/lymphocyte ratio (MLR), platelet/lymphocyte ratio (PLR), C-reactive protein (CRP), serum triodothyronine (T3), thyroxin (T4), free thyroid hormone (fT3, fT4), and thyroid stimulating hormone (TSH) ), complement 3(C3), complement 4(C4), immunoglobulin A(IgA), immunoglobulin G(IgG), immunoglobulin M(IgM). Binomial Logistic regression analysis was used to control confounding factors to explore the predictors of bipolar depression. Receiver operating characteristic (ROC) curve was used to analyze the predictive value of clinical features and biological indicators in bipolar depression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThere were statistical differences in life events (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;15.397, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000), CRP (Z\u0026thinsp;=\u0026thinsp;6.717, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000), TSH (Z\u0026thinsp;=\u0026thinsp;1.988, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047), C3 (Z\u0026thinsp;=\u0026thinsp;5.682, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000), C4 (Z\u0026thinsp;=\u0026thinsp;4.216, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000), IgM (Z\u0026thinsp;=\u0026thinsp;2.304, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021) between unipolar depression group and bipolar depression group. Logistic regression analysis showed that life events (OR\u0026thinsp;=\u0026thinsp;4.552, 95%Cl\u0026thinsp;=\u0026thinsp;2.238\u0026sim;9.257), CRP (OR\u0026thinsp;=\u0026thinsp;13.886, 95%Cl\u0026thinsp;=\u0026thinsp;5.290\u0026sim;36.452), IgM (OR\u0026thinsp;=\u0026thinsp;0.561, 95%Cl\u0026thinsp;=\u0026thinsp;0.325\u0026sim;0.970) were associated with bipolar depression. ROC curve analysis showed that the AUC of Logistic regression model predicting bipolar depression was 0.806, with a sensitivity of 61.7%, and a specificity of 85.9%.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eCompared with unipolar depression, no life events, higher levels of CRP and lower levels of IgM were related factors of bipolar depression, and the combined diagnosis model is more effective to distinguish unipolar depression from bipolar depression.\u003c/p\u003e","manuscriptTitle":"Comparison of clinical features and inflammatory factors between patients with bipolar depression and unipolar depression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-25 23:24:26","doi":"10.21203/rs.3.rs-5126282/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-05T14:05:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-29T07:50:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257521715107835862061998321122753876637","date":"2024-10-26T01:00:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-10T11:44:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-09T23:03:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8226902469427518842666101137814900152","date":"2024-10-08T04:13:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23384465275994104372656516393098646282","date":"2024-10-06T08:05:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-03T00:35:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-09-24T19:07:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-22T17:54:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-21T12:37:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2024-09-21T02:46:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cd35e948-113d-427d-883c-63960f978d96","owner":[],"postedDate":"November 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-17T16:03:21+00:00","versionOfRecord":{"articleIdentity":"rs-5126282","link":"https://doi.org/10.1186/s12888-025-06516-w","journal":{"identity":"bmc-psychiatry","isVorOnly":false,"title":"BMC Psychiatry"},"publishedOn":"2025-02-10 15:57:49","publishedOnDateReadable":"February 10th, 2025"},"versionCreatedAt":"2024-11-25 23:24:26","video":"","vorDoi":"10.1186/s12888-025-06516-w","vorDoiUrl":"https://doi.org/10.1186/s12888-025-06516-w","workflowStages":[]},"version":"v1","identity":"rs-5126282","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5126282","identity":"rs-5126282","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}