Objective: To examine associations between IgA responses to Gram-negative gut-commensal bacteria and peri-menstrual symptoms and sex hormone levels during the menstrual cycle in women with and without premenstrual symptoms. Methods: Forty women aged 18-45 years completed the Daily Record of Severity of Problems (DRSP) during all 28 consecutive days of the menstrual cycle. We assayed, in plasma,, IgA responses to 6 Gram-negative bacteria, i.e. Hafnei alvei, Pseudomonas aeruginosa, Morganella morganii, Klebsiella pneumoniae, Pseudomonas putida and Citrobacter pylori, progesterone and oestradiol at days 7, 14, 21 and 28 of the menstrual cycle. Results: Significant changes in Δ (actual minus one week earlier) IgA to LPS of the 6 Gram-negative bacteria during the menstrual cycle were observed with peak IgA levels at T4 (day 28) and lows at T1 or T2 (day 7 or 14). The Δ IgA changes in H. alvei, M. Morganii, P. putida during the menstrual cycle were significantly and positively associated with changes in the total DRSP score, and severity of physio-somatic, anxiety and breast-craving, but not depressive, symptoms. The changes in IgA responses to LPS were largely predicted by changes in progesterone and steady-state levels of progesterone averaged over the luteal phase. Discussion: Menstrual cycle-associated changes in IgA directed against LPS and by inference bacterial translocation may be driven by effects of progesterone on transcellular, paracellular and vascular pathways thereby contributing to the severity of physio-somatic and anxiety symptoms as well as fatigue, breast swelling and food cravings.

Rationale: Major depressive disorder (MDD) is characterized by signaling aberrations in interleukin (IL)-6, IL-10, beta-endorphins as well as mu (MOR) and kappa (KOR) opioid receptors. Here we examined whether these biomarkers may aid in the classification of unknown subjects into the target class MDD.Methods: The aforementioned biomarkers were assayed in 60 first-episode, drug-naïve depressed patients and 30 controls. We analyzed the data using joint principal component analysis (PCA) performed on all subjects to check whether subjects cluster by classes; support vector machine (SVM) with 10-fold validation; and linear discriminant analysis (LDA) and SIMCA performed on calibration and validation sets and we computed the figures of merit and learnt from the data. Results: PCA shows that both groups were well separated using the first three PCs, while correlation loadings show that all 5 biomarkers have discriminatory value. SVM and LDA yielded an accuracy of 100% in validation samples. Using SIMCA there was a highly significant discrimination of both groups (model-to-model distance=87.5); all biomarkers showed a significant discrimination and modeling power, while 10% of the patients were identified as outsiders and no aliens could be identified.Discussion: We have delineated that MDD is a distinct class with respect to neuro-immune and opioid biomarkers and that future unknown subjects can be authenticated as having MDD using this SIMCA fingerprint. Precision psychiatry should employ SIMCA a) to authenticate patients as belonging to the claimed target class and identify other subjects as outsiders, members of another class or aliens; and b) to acquire knowledge through learning from the data by constructing a biomarker fingerprint of the target class.