Pain mechanisms in the transgender individual: a review.

Specific Aim: Provide an overview of the literature addressing major areas pertinent to pain in transgender persons and to identify areas of primary relevance for future research. Methods: A team of scholars that have previously published on different areas of related research met periodically though zoom conferencing between April 2021 and February 2023 to discuss relevant literature with the goal of providing an overview on the incidence, phenotype, and mechanisms of pain in transgender patients. Review sections were written after gathering information from systematic literature searches of published or publicly available electronic literature to be compiled for publication as part of a topical series on gender and pain in the Frontiers in Pain Research. Results: While transgender individuals represent a significant and increasingly visible component of the population, many researchers and clinicians are not well informed about the diversity in gender identity, physiology, hormonal status, and gender-affirming medical procedures utilized by transgender and other gender diverse patients. Transgender and cisgender people present with many of the same medical concerns, but research and treatment of these medical needs must reflect an appreciation of how differences in sex, gender, gender-affirming medical procedures, and minoritized status impact pain. Conclusions: While significant advances have occurred in our appreciation of pain, the review indicates the need to support more targeted research on treatment and prevention of pain in transgender individuals. This is particularly relevant both for gender-affirming medical interventions and related medical care. Of particular importance is the need for large long-term follow-up studies to ascertain best practices for such procedures. A multi-disciplinary approach with personalized interventions is of particular importance to move forward.

Duration of subsequent episodes and periods of recovery in recurrent major depression.

BACKGROUND: Little is known about the duration of subsequent depressive episodes and periods of recovery, and much is based on potentially biased retrospective data. We therefore prospectively assessed whether duration of depressive episodes and recoveries is correlated within subjects and across episodes, and whether duration of subsequent depressive episodes and recoveries increases or decreases over time. METHODS: From a sample of 267 depressed primary care patients enrolled in a RCT, we identified 279 depressive episodes and 455 recovery periods during a 3-year follow-up. We correlated durations of depressive episodes and of recovery within subjects, and compared within subjects the duration of first depressive episodes after index depression with second and third episodes, and similarly with recovery periods. RESULTS: No significant correlations were found between duration of depressive episodes or between recovery periods within subjects (Rs ranging from -0.17 to 0.08; all Ps >0.05). Median duration of first and second depressive episodes was 11 (IQR 6-19) and 9 weeks (IQR 5-14). Median duration of first and second recovery periods was 16.5 (IQR 7-31) and 17.5 weeks (IQR 9-32). No significant increase or decrease was observed in duration of consecutive depressive episodes, nor in recovery periods across episodes (all Ps >0.05). CONCLUSIONS: In this prospective study, we found no correlation between duration of depressive episodes or between recovery periods within subjects. Moreover, we found no support for an increase or decrease in subsequent duration of depressive episodes or a decrease in recovery periods across episodes. These findings do not support the notion that experiencing multiple depressive episodes results in a growing vulnerability.

Random-mood interpretation of determinants for major depression.

BACKGROUND: It has recently been proposed that major depression disorder (MDD) may, in a heterogeneous population-based cohort, be interpreted in terms of a random-mood model. Mood fluctuations are thought to result from stressors that occur randomly in time. We have investigated whether this concept also holds for more homogeneous groups, defined by known determinants for MDD, and whether the model's parameters, susceptibility (Z) and relaxation time (T), may be evaluated and used to differentiate between subcohorts. METHOD: From a large epidemiological survey, the Netherlands Mental Health Survey and Incidence Study (NEMESIS), data on the duration of MDD were obtained for subcohorts, based on gender, severity of depression, recurrence and co-morbidity with dysthymia, anxiety and somatic disorder, and were compared with random-mood simulation calculations. RESULTS: Susceptibility, Z, is empirically found to be proportional to incidence and may be identified with a risk ratio. A second scaling rule states the proportionality of mean duration with the product of Z and T. This Z-T classification proves to be more sensitive than conventional significance tests. Notably for men/women and for co-morbid anxiety, differences are seen that have previously gone unnoticed. CONCLUSIONS: Depression may be conceptualized as a disorder resulting from random-mood fluctuations, the response to which is influenced by a large variety of determinants or risk factors. The model's parameters can be evaluated and may be used in differentiating between risk factor-defined subgroups.

Course of depressive symptoms after myocardial infarction and cardiac prognosis: a latent class analysis.

OBJECTIVE: The presence of depressive symptoms after myocardial infarction (MI) is a risk factor for new cardiovascular events. The importance of the course of post-MI depressive symptoms for cardiac prognosis is not clear. We therefore set out to investigate whether different courses of post-MI depressive symptoms can be identified and determine their associations with cardiac events. METHODS: Data were derived from the Depression after Myocardial Infarction (DepreMI) study, a naturalistic follow-up study of patients admitted for an MI in four hospitals in The Netherlands (N = 475). Scores on the Beck Depression Inventory (BDI) during hospitalization and at 3, 6, and 12 months post-MI were analyzed. Using latent class analysis (LCA), we identified classes characterized by distinctive courses of depressive symptoms and then examined their link to cardiac prognosis. RESULTS: The prevalence of significant depressive symptoms ranged from 22.7% to 25.5% throughout the post-MI year. Five distinct courses were found: no depressive symptoms (56.4%), mild depressive symptoms (25.7%), moderate and increasing depressive symptoms (9.3%), significant but decreasing depressive symptoms (4.6%), and significant and increasing depressive symptoms (4.0%). Subjects in this last class had, statistically, a significantly higher risk for a new cardiovascular event compared with subjects without depressive symptoms (hazard ratio (HR) = 2.73; p = .01). Controlling for baseline cardiac status and sociodemographic data did not alter the association (HR = 2.46; p = .03). CONCLUSIONS: Post-MI depressed subjects with significant and increasing depressive symptoms are at particular risk of new cardiac events. This subgroup may be most suited for evaluation of the effects of antidepressant treatment on cardiac prognosis.

Major depressive episodes and random mood.

CONTEXT: Mathematical models describing changes in mood in affective disorders may assist in the identification of underlying pathologic and neurobiologic mechanisms and in differentiating between alternative interpretations of psychiatric data. OBJECTIVE: Using time-to-event data from a large epidemiologic survey on recovery from major depression, to model the survival probability, in terms of an underlying process, with parameters which might be recognized and influenced in clinical practice. DESIGN: We present a sequential-phase model for survival analysis, which describes depression as a state with or without an additional incubation phase. Recovery is seen as the transition to a nondepressive state. We show that this sequential-phase model finds a microscopic realization in a dynamic description, the random-mood model, which depicts mood as governed by an Ornstein-Uhlenbeck type of stochastic process, driven by intermittent gaussian noise. RESULTS: For reversible depression (80%), the fractional probability of recovery is remarkably independent of the history of the depression. Analysis with the sequential-phase model suggests single exponential decay in this group, possibly with a short incubation phase. Within the random-mood model, the data for this reversibly depressed cohort are compatible with an intermittent noise pattern of stimuli with average spacing of 4 months and incompatible with nonintermittent noise. CONCLUSIONS: Time-to-event data from psychiatric epidemiologic studies can be conceptualized through modeling as intrasubject processes. The proposed random-mood model reproduces the time-to-event data and explains the incubation phase as an artifact due to the inclusion criterion of 14 days in most current psychiatric diagnostic systems. Depression is found to result more often from pileup of negative stimuli than from single life events. Time sequences, generated using the random-mood model, produce power plots, phase-space trajectories, and pair-correlation sums, similar to recent results for individual patients. This suggests possible clinical relevance along with the model's use as a tool in survival analysis.