After many years of effort, researchers have not yet constructed a prediction model for discriminating between BD and MDD with clinical utility. In the present study, we preliminarily developed and validated a diagnostic nomogram, with a composite of biomarkers from routinely tested blood results, to distinguish MDD and BD. The model was constructed using the best subset selection method and then verified and adjusted using multiple imputations and the inclusion of psychotropic medication use. The final model consisted of five variables: age, LDL, TC, Eos, and PRL. The model could discriminate between MDD and BD with an AUC of 0.858, with a sensitivity of 0.716 and a specificity of 0.890.
During the construction of the model, 47 features were reduced to 22 potential predictors at the first step by univariant analysis, then the best subset selection method was managed to select seven prominent markers. Of the 721 patients in the study, only 495 without missing data were used for the primary multivariable selections. Then 700 were used for adjustment and evaluation of the model after deleting cases with incomplete values of the prominent variables, which made the findings relatively more robust than constructing and validating the model using the same population. Moreover, repeated cross validations were subsequently used to verify the model when the training dataset and test dataset did not overlap, and subset validations were used to test the effectiveness of the model in drug naïve patients and patients of different age groups.
The findings of the present study were somewhat consistent with previous studies. For example, age is one of the most profound distinguishing factors between MDD and BD, as it had been broadly accepted that the onset age of MDD is generally later than that of BD [15, 16]. However, we wanted to see how the performance of the composite biomarkers would change if the effect of age was minimized. The study divided patients into three groups, 14–29 years, 30–44 years group, and 45 + years respectively. Within each group, age became insignificant different between MDD and BP patients (data not shown). Unsurprisingly, the model discrimination had varying degrees of deteriorations, and the AUC were 0.688, 0.671, and 0.739 respectively, indicating that the model still had mild to moderate diagnostic efficiency in patients of same age group.
Moreover, eosinophil could also help discriminate the two disorders, which was consistent with previous studies. For example, it has been demonstrated that eosinophil counts were reduced in MDD patients [17], while the increased eosinophil function could be found in the late-stage of BD [18].
In addition, the inclusion of PRL in the model, one of the hormones secreted by pituitary gland, suggested that pituitary function might play a role in differentiating MDD and BD. However, previous studies on the pituitary gland mainly focused on the gland volume changes in mental disorders and their association with hyperactivity in the Hypothalamic-Pituitary-Adrenal axis [19–21]. Other hormones provided by pituitary gland besides adrenocorticotropic hormone could also have potential effects on mental disorders. In this study, TSH and FSH were tested statistically significant but excluded after preliminary adjustment for psychotropic medication use, which was in accordance with clinical consensus that endocrine is greatly influenced during the drug treatment for affective disorders [22]. Interestingly, PRL remained in the model after medication adjustment. However, these findings require further confirmation in drug-free patients.
In addition, LDL and TC were also included in the final model. These findings did not contradict previous findings that abnormal lipid metabolism was more prevalent in MDD and BD patients compared to health controls [23, 24]. However, few studies have compared the differences in lipid profile distribution between MDD and BD. Our study showed that BD patients had relatively higher LDL levels, while MDD patients had higher TC levels. Although these findings indicated different lipid profiles in MDD and BP patients, but both were consistent with the findings that patients with severe mental illnesses had increased risks for cardiovascular diseases [25, 26].
Moreover, like endocrine functions, lipid metabolism is also seriously affected by some kinds of psychotropic drugs, especially antipsychotics and mood stabilizers, such as clozapine, olanzapine, and valproate [27], which can ultimately result in hyperlipidemia or even obesity. As it was demonstrated in Table 1, the proportion of BD patients using antipsychotics and mood stabilizers was significantly higher than that of MDD patients, however, the coefficients of TC and LDL in the regression model remained significant after the adjustment of medication use in this study, indicating that the pharmacological effect was not the only reason for the differences in the lipid levels between the two groups. In other words, abnormal lipid metabolism may underlie the mental disorders. However, since the cholesterol level can be greatly influenced by living habits, such as diet and physical activities [28], the significance could not be applied to a population with different lifestyles.
Emerging studies have confirmed the potential roles of inflammation or immune-based biomarkers as predictive biomarker panels to differentiate MDD and BD, usually including C-reactive protein (CRP), interleukins, and complement components [5, 29–31]. However, the above related potential biomarkers were surprisingly excluded during model development, which was inconsistent with previous findings. For example, Chang et.al demonstrated that baseline C-reactive protein could serve as a discrimination biomarker for MDD and bipolar II disorder in drug naïve patients (cutoff value: 621.6 ng/mL; AUC value: 0.816), and patients with baseline C-reactive protein greater than 621.6 ng/mL had 28.2 higher odds of bipolar II disorder [32]. However, in our study, C-reactive protein level showed no statistical difference between MDD and BD and was excluded at the first step. The possible reason might be treatment effects as indicated by Chang’s work itself: the difference of C-reactive protein level would become narrower between MDD and bipolar II disorder after treatment. Another possible reason may be bias from concentrated missing values on inflammation and immune factors; although the multiple imputations had indicated that the missingness of the selected variables in the model was at random, it may not represent the same missing pattern of the potential predictors in question [33].
There were several limitations to this study. Firstly, behavior characteristics and psychological assessments failed to be included in the analysis process. Secondly, BD patients were not specifically classified into different clinical phases including (hypo)manic or depressive phase, or rapid cycling BD. Moreover, although the diagnostic biomarker panel constructed by this study was preliminarily unlikely to be affected by medications as cofounding factors but should be tested further in drug naïve patients. At last, the data were collected from one hospital, the generalizability of the prediction panel needs further testing.