The Mitochondrial Stress, Brain Imaging, and Epigenetics (MiSBIE) study (Kelly et al., 2024, doi: 10.1016/j.tem.2024.08.006) was designed to investigate how mitochondria regulate multisystemic processes involved in resilience and disease vulnerability across the lifespan. Specifically, the study assessed emotional, cognitive, neuroendocrine, cardiovascular, metabolic, and immune functioning in both healthy individuals (n = 70) and patients with Mitochondrial Disease (MitoD) (n = 40). Participants underwent both home-based assessments reflecting normative physiological conditions, and clinic-based protocols that included fasting and non-fasting baseline measurements as well as exposure to an acute psychological stressor. This dataset and documentation pertain to the MiSBIE Stress Reactivity Subproject, which focuses on responses to the acute stressor. In this arm of the study, we aimed to examine the reactivity and recovery of key stress response systems following a simplified, 5-minute version of the Trier Social Stress Test (TSST, cf. Kelly et al., 2024, for details). Study Design and Measurements: Participants provided self-report affect ratings and biofluid samples (blood and saliva) over a 2-hour time period, beginning with a baseline measurement 5 minutes before stress exposure and continuing across seven post-stressor time points (at 5, 10, 30, 60, 90, and 120 minutes). Various affective states (i.e., Stressed, Nervous, Angry, Calm, Relaxed, Energetic, and Worn-out) were assessed on Likert scales. From the biofluids, we analyzed hypothalamic-pituitary-adrenal (HPA) axis hormones (ACTH and cortisol), and sympathetic-adreno-medullary (SAM) axis catecholamines (norepinephrine and epinephrine), as well as other steroids produced in the mitochondria (testosterone, cortisone, corticosterone, progesterone, DHEA, and DHEAs), and other catecholamines relevant to affect regulation (serotonin and dopamine). Additionally, physiological measures included peripheral body temperature (hand, tongue, and neck) and continuously assessed breathing rate (BR), heart rate (HR), blood pressure (BP), and electrodermal activity (EDA). At the time of data upload (Aug 2025), BR, HR, BP and EDA raw data are still undergoing technical pre-processing and will be made available for analysis later. Planned Analyses: Our main objective is to assess whether patients with mitochondrial disease (MitoD) differ from healthy controls in their emotional, hormonal, and physiological stress reactivity. To evaluate this, we will apply longitudinal mixed-effects models to model the time courses of each outcome variable. Therefore, models will include: (i) Fixed effects of interest for evaluating our research questions, i.e., main effects of Time, Group (MitoD vs. Control), and their interaction; (ii) random effects to map the study design, i.e., COVID-19 timing (i.e., pre vs. post pandemic visits), analytical batch (to map technical variance) and participant ID (to account for repeated within-subject measures), and (iii) potential fixed covariates, i.e., sex, BMI, and age. We will construct multiple candidate mixed-effects models that consistently include our hypothesis-relevant fixed effects and design-relevant random effects (i.e., Time x Group + (1|COVID19) + (1|Batch) + (1|Subject)) and vary in their inclusion of additional covariates. Model selection will be based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The model with the lowest criterion values will be selected for interpretation. Distributional Properties and Special Considerations: Due to the strictly positive and typically right-skewed nature of hormonal concentration data, we anticipate using log-normal distributions (or log-transformation of outcomes) in our modeling. In addition, many biofluid markers are also subject to technical detection limits, resulting in: (i) Left-censoring (values below the lower limit of detection, LLOD); (ii) Semi-quantitative measurements (values detectable but below the lower limit of quantification, LLOQ); and (iii) Ceiling effects (values above the upper limit of quantification, ULOQ). We plan to address these properties through: (i) Tobit mixed-effects models for left-censored outcomes; (ii) Generalized rank-based models (e.g., aligned rank transform (ART) mixed-effects models) for outcome with relevant proportions of semi-quantitative data; and (iii) more complex censoring-aware modeling approaches that account for the simultaneous presence of three tiers of data quality (LLOD-censored, semi-quantitative, fully quantitative). Model adequacy will be evaluated using standard criteria such as information criteria, posterior predictive checks, and assumption diagnostics. Post-Hoc Analysis and Interpretation: Significant main or interaction effects relevant to our hypotheses will be explored through post hoc comparisons, with correction for multiple testing where appropriate. Figures will visualize the model-derived estimates on the backdrop of individual data trajectories.