| BackgroundMajor depressive disorder(MDD)is a common mood disorder distinct from daily mood swings and transient emotional reactions.MDD is characterized by significant and persistent depression,loss of interest,and anhedonia.MDD has a variety of clinical manifestations,which often lead to continuous sadness and loss of interest in patients,affects the feelings,thinking,and behavior of patients,and leads to a variety of emotional and physical problems,even suicide attempts and behaviors.Approximate 350 million people around the world are currently affected by MDD,and domestic surveys indicate about 44 million.Correct diagnosis is an important prerequisite for the effective treatment of a disease.However,MDD still has many difficulties,such as unclear etiology,unknown pathogenesis,lack of clear diagnostic markers,and so on until now.In addition,the insidiousness of onset and lack of specificity of symptoms make it easy to misdiagnose only rely on clinical symptom criteria,thus delaying treatment and affecting prognosis.In fact,this is the biggest dilemma for MDD now.Therefore,finding biomarkers that are helpful for the diagnosis of MDD is the key to be urgently solved at present,and is also the current research hotspot in the neuropsychiatric field.Cerebrospinal fluid(CSF)is an ideal resource for studying pathogenesis and searching for reliable biomarkers for MDD.However,it is difficult to obtain CSF from MDD patients in clinical practice,and it is even more difficult to obtain brain tissue.Therefore,neither brain tissue nor CSF can be used as a routine source of clinical diagnostic biomarkers.Peripheral blood,which is easy to obtain and can reflect central function to a certain extent,has become an important resource forstudying MDD biomarkers instead of brain tissue and CSF.Furthermore,since genomic studies have failed to provide any reliable biomarkers for many years,and the proteins are more closely related to the individual phenotype,the study on the expression level of proteins may be more helpful for the objective diagnosis of MDD.Also,magnetic resonance imaging(MRI)becomes one of the main tools for the study of biomarkers for MDD due to its advantages of non-invasive,nonradiation,and high resolution.As a complex disease,clinical manifestations of MDD are diversiform and nonspecific,which may lead to many virtually different bio-heterogeneous subtypes with the same emotional symptoms being classified as an umbrella term for MDD.Searching for a single biomarker in such a mixed population may become meaningless,and difficult to solve clinical practical problems.Whereas,it is possible to develop a more effective objective method/tool for diagnosing MDD by combining multiple biological factors from different dimensions and constructing a multi-factor index joint model.Therefore,we will take a step-by-step approach to study the following three aspects in this study:(1)Research on the values of a single serum protein in the diagnosis of MDD,mainly aiming at the neurotrophic hypothesis of MDD.The classification efficacy of serum VGF and bicaudal C homolog 1(BICC1)levels in the diagnosis of MDD was investigated respectively;(2)Research on the values of the multi-indicator from single-dimension in the diagnosis of MDD.The classification efficacy of the combination of multi-protein levels from a single MDD pathogenesis and the combination of multi-protein levels from multiple pathogeneses in the diagnosis of MDD was explored respectively.In addition,the serum proteins involved in our previous studies were replicated in a multi-disease independent cohort including MDD.Machine learning(ML)technology was used to construct a composite diagnostic model containing multi-indicator from single-dimension for MDD to verify the results in our previous studies;(3)Research on the values of the multi-indicator from multi-dimension in the diagnosis of MDD.Based on the pathogeneses of MDD,the ML technology was used to combine neurophysiological(serum protein levels),neuroimaging(characteristics of brain function changes),neuropsychological(psychological measurements),and other related indicators from different dimensions to construct different MDD diagnostic models and explore their diagnostic values for MDD.The above three aspects of research will be comprehensively explored the powers of related parameters from serum levels ofproteins,neuroimaging,and psychological measurements,and their different combinations for diagnosing MDD,from the single-dimensional single indicator,the multi-indicator of singledimensional single pathogenesis,the multi-indicator of single-dimensional multi-pathogenesis,and the multi-indicator of multi-dimensional multi-pathogenesis,respectively.Part 1 Research on the values of a single serum protein in the diagnosis of major depressive disorderChapter 1 Classification value of serum VGF level in the diagnosis of major depressive disorderObjective: Misdiagnosis between major depressive disorder(MDD)and bipolar depression(BD)is quite common in clinical.Here,we aimed to investigate the changes of serum VGF levels in MDD and BD patients,and its classification value in the diagnosis and differentiation for MDD and BD.Methods: General information,clinical data such as scores of 17-item Hamilton Depression Rating Scale(HAMD-17),and fasting blood samples of all participants including 30 MDD patients,20 BD patients,and 30 healthy controls(HC)were collected.Serum VGF levels were measured by Enzyme-linked immunosorbent assay(ELISA)kits.One-way analysis of variance(ANOVA)was applied to compare serum VGF levels among the three groups.Receiver operating characteristic(ROC)curve and likelihood ratios(LRs)were applied to analyze the classification potential of serum VGF level.Results: Serum VGF levels were significantly lower in MDD patients but higher in BD patients compared with HC(both P < 0.01).ROC curve analysis showed that the optimal cutoff value for serum VGF in discriminating MDD patients from HC subjects was 968.19 pg/m L,the area under the curve(AUC)was 0.732 with a sensitivity of 100% and specificity of 46.7%.While the optimal cutoff value for serum VGF in classifying BD patients from MDD patients was 1093.85 pg/m L,AUC was 0.990 with a sensitivity of 95%,specificity of 100%,and accuracy of 95%.LRs further confirmed the differential efficiency of serum VGF level in distinguishing BD and MDD patients.Conclusion: The results suggest that serum VGF levels significantly decreased in MDD and increased BD patients,and serum VGF levels may be an indicator for differentiating BD patients from MDD patients.However,it should be used with caution to diagnose MDD for its low specificity.Chapter 2 Classification value of serum BICC1 level in the diagnosis of major depressive disorderObjective: Misdiagnosis between MDD and other mood disorders is common when based solely on clinical interviews because of overlapping symptoms.This study aims to evaluate the ability of serum BICC1 level to discriminate between various mood disorders,including MDD and the manic and depressive phases of bipolar disorder,namely bipolar mania(BM)and BD.Methods: Serum BICC1 levels in drug-free patients with MDD(n=30),BM(n=30),and BD(n=13),and well-matched HC(n=30)were measured with ELISA kits.One-way ANOVA was employed to compare serum BICC1 levels.ROC curve analysis was used to analyze the differential discriminative potential of serum BICC1 level for different mood disorders.Results: One-way ANOVA indicated that serum BICC1 levels were significantly increased in all patient groups compared with the HC group and significantly different between each pair of patient groups(all P < 0.01).ROC curve analysis showed that the AUC of serum BICC1 level in the diagnosis of MDD,BM,and BD was 0.954,0.714,and 1.0,sensitivity was 100%,36.7%,and 100%,specificity was 76.7%,100%,and 100%,respectively.Also,serum BICC1 level could discriminate among all three mood disorders from each other accurately with the AUC from 0.787 to 1.0.Conclusion: The findings of this preliminary study indicated significant differences in serum BICC1 levels in patients with different mood disorders,which could help to distinguish different mood disorders including MDD,BM,and BD.However,the specificity is relatively low when classifying MDD and HC.Part 2 Research on the values of the multi-indicator from singledimension in the diagnosis of major depressive disorderChapter 3 Classification value of serum multi-protein levels in the tPA-BDNF pathway in the diagnosis of major depressive disorderObjective: Mental disorders are severe,disabling conditions with unknown etiology and are commonly misdiagnosed when clinical symptomology criteria are solely used.This study aimed to analyze serum level changes and the classification efficacy of multiple proteins in tissue plasminogen activator(t PA)-brain-derived neurotrophic factor(BDNF)pathway of patients with different common mental disorders.Methods: Thirty-four patients with schizophrenia(SZ),30 patients with MDD,30 patients with BM,22 patients with BD,and 30 patients with panic disorder(PD)as well as 30 HC subjects were recruited.The corresponding scales were used to evaluate the severity of disease in different disease groups,and blood samples were employed to measure serum levels of t PA,plasminogen activator inhibitor-1(PAI-1),BDNF,precursor-BDNF(pro BDNF),tropomyosin-related kinase B(Trk B),and neurotrophin receptor p75(p75NTR).ROC curve was applied to analyze the classification powers of these proteins alone and their combination.Results: We found,compared with HC,that serum t PA,and pro BDNF were lower in SZ,BM,and BD;Trk B was lower in SZ and BD;p75NTR was declined in SZ and BM(all P < 0.05).ROC curve analysis showed that serum levels of single t PA,PAI-1,BDNF,pro BDNF,Trk B,and p75 NTR in the t PA-BDNF pathway could not distinguish all the five common mental disorders;however,the combination of multiple serum proteins can not only distinguish the above five common mental disorders but also significantly better than any single protein in the accuracy of diagnosis and differentiation.Conclusion: The combination of serum multi-protein levels in the t PA-BDNF pathway can distinguish a variety of common mental disorders,and this kind of combination may be a potential diagnostic panel for common mental disorders including MDD.Chapter 4 Classification value of serum multi-protein levels based on multihypotheses in the diagnosis of major depressive disorderObjective: Misdiagnosis and ineffective treatment are common in MDD in current clinical practice,while the combination of various serum proteins may assist the correct diagnosis.The study aimed to explore the classification value of the combination of serum inflammatory,stress,and neurotrophic factors in the diagnosis of MDD and to investigate the predictors associated with earlysymptom improvements.Methods: Baseline serum levels of C-reactive protein(CRP),interleukin(IL)-6,IL-10,IL-1beta,tumor necrosis factor(TNF)-alpha,interferon(INF)-gamma,cortisol,and BDNF were detected in 30 MDD patients and 30 age-and gender-matched healthy HC.HAMD-17 and Hamilton Anxiety Rating Scale(HAMA)were applied to assess symptoms both at baseline and two weeks after antidepressant treatment.Stepwise multiple linear regression was employed to identify the early efficacy predictors,and a Logistic regression model was built with the above serum proteins.The area under the receiver operating characteristic(AUC)curve was calculated to evaluate the model’s diagnostic power.Results: Multiple linear regression revealed that baseline scores of retardation(β =-0.432,P = 0.012)and psychological anxiety(β =-0.423,P = 0.014)factors were negatively associated with the reduction rate of HAMD-17.A simple and efficient diagnostic model using serum BDNF,cortisol,and IFN-gamma levels was established by the forward stepwise Logistic regression,and the model achieved an AUC of 0.884,with 86.7% sensitivity and 83.3% specificity.Conclusion: The results showed that combining serum BDNF,cortisol,and IFN-gamma levels could aid the diagnosis of MDD,while baseline retardation and psychological anxiety may negatively predict early symptom improvement.Chapter 5 Classification value of the linear discriminant model based on multiprotein in the diagnosis of major depressive disorderObjective: MDD,SZ,bipolar disorder(BPD),and PD are all common mental disorders and are easily misdiagnosed only based on clinical symptomology.So,it is essential and urgent to develop easily accessible and useable biomarkers.The study aimed to develop a blood-based multi-proteins model to identify patients with different mental disorders mentioned above.Methods: A total of 255 serum samples were obtained from patients with MDD,SZ,BPD,and PD,and HC.Serum levels of BDNF,VGF,BICC1,CRP,and cortisol were measured.The linear discriminant analysis(LDA)was employed to extract features from these blood-based proteins to build the classification model to classify these mental disorders.Both leave-one-out cross-validation(LOOCV)and 5-fold cross-validation methods were applied to validate the accuracy and stability of the LDA model.Results: Compared with the HC group,serum levels of BICC1,CRP,and cortisol were significantly increased and serum BDNF levels were significantly decreased in all disease groups except for cortisol in the SZ group(all P < 0.05).Serum VGF levels in MDD,SZ,and PD groups were significantly decreased,while serum VGF levels in BPD group were significantly increased(P < 0.05).In addition to no significant differences in serum CRP and cortisol levels between MDD and BPD groups and serum BICC1 levels between MDD and SZ groups,there were significant differences in the levels of the above five serum proteins in any two disease groups(All P < 0.05).The established LDA model based on these above five proteins displayed a high overall accuracy of 96.9% when classifying MDD,SZ,BPD,PD,and HC subjects.And the LOOCV and 5-fold crossvalidation showed that the classification accuracy of the LDA model could achieve 96.9% and 96.5%,respectively.Conclusion: Serum levels of the proteins related to different pathogenesis of MDD were changed specifically in common mental disorders.LDA model constructed based on serum multi-protein levels is helpful for the diagnosis and differential diagnosis of various common mental disorders including MDD.Part 3 Research on the values of the multi-indicator from multidimension in the diagnosis of major depressive disorderChapter 6 Classification value of social-psychological factors combined with brain functional changes in the diagnosis of major depressive disorderObjective: Childhood emotional neglect(EN)is a risk factor for brain development and neurocognitive impairment and subsequent MDD,while social support is a protective factor.However,the exact links among them remain unclear.This study aimed to investigate whether the regional homogeneity(Re Ho)of spontaneous brain activity or social support mediates the association between EN and MDD and to explore the classification value of the combination of Re Ho,EN,and social support for diagnosing MDD.Methods: A total of 33 MDD patients and 36 HC were recruited to complete resting-state fMRI(rsf MRI)scans and clinical assessments.Two-sample t test was used to compare the differences in spontaneous brain activity between the two groups.Mediation analysis was employed to explore whether social support or the distinct Re Ho mediates the association between EN and MDD.The LDA model was constructed and applied to distinguish MDD patients from HC subjects.Results: Compared with HC,MDD patients experienced severer EN and poorer social support and exhibited decreased Re Ho in the left middle occipital gyrus(MOG.L)and bilateral postcentral gyrus,and elevated Re Ho in right cerebellum crus1.The results of mediation analysis revealed that EN could affect MDD not only directly,but also indirectly through Re Ho in the above four brain regions as well as social support.The LDA model combing EN,objective support,and these four regional Re Ho had an AUC of 0.96 with a sensitivity of 84.8% and specificity of 97.2% for diagnosing MDD,and the LOOCV accuracy was 84.1%.Conclusion: There were significant differences in EN,social support,and spontaneous brain activity between MDD and HC.EN can influence the occurrence of MDD through the mediating effect of social support and Re Ho in regions related to mood regulation.Combining EN,objective support,and Re Ho of spontaneous brain activity could help the diagnosis of MDD.Chapter 7 Classification value of serum multi-protein combined with brain functional changes in the diagnosis of major depressive disorderObjective: MDD is a high disabling disease with high misdiagnosis.Abnormal peripheral protein levels and brain functions are often observed in MDD and they are even considered to be involved in the pathogenesis of MDD.This study mainly explored the classification value of integrating serum multi-protein levels and the indicators of functional neuroimaging for diagnosing MDD.Methods: A total of 63 MDD patients and 82 HC underwent rs-f MRI to examine amplitudes of low-frequency fluctuation(ALFF)and Re Ho of spontaneous activity using a two-sample t-test.Blood samples of 37 MDD and 45 HC subjects among them were provided for detecting serum levels of BDNF,cortisol,and multiple cytokines.LDA models based on imaging indicators,levels of serum proteins,and indicators of imaging proteomics were constructed with ML,and thediagnostic powers of the models and each component indicator for MDD were evaluated respectively.Results: Compared with HC,higher ALFF in the right caudate nucleus,lower ReHo in MOG.L and left superior frontal gyrus,higher serum cortisol,and IL-6 levels,and lower serum BDNF,IL-4,and IL-10 levels were observed in MDD patients(all P < 0.05).The LDA model integrating neuroimaging indicators,serum BDNF,IL-4,IL-6,IL-10,and cortisol levels displayed an excellent classification efficiency.The 5-fold cross-validated accuracy of the LDA model was 96.3%(AUC = 0.99,sensitivity = 0.92,specificity = 1.00).Conclusion: These results demonstrated that regional spontaneous activities and serum protein levels changed significantly in MDD patients.The LDA model combining the indicators from imaging proteomics has excellent power in distinguishing MDD patients from HC subjects,which has the potential to be a diagnostic biomarker of MDD. |
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