Introduction

Stress-related neuropsychiatric disorders such as major depressive disorder (MDD), anxiety disorders, and post-traumatic stress disorder (PTSD) are common and associated with high levels of comorbidity and within-disorder heterogeneity [1,2,3]. High disorder comorbidity and symptom heterogeneity suggest that approaches focusing on DSM5 diagnostic categories or on a circumscribed biomarker set could limit identification of likely complex relationships between clinically heterogeneous neuropsychiatric symptoms and their underlying biological signatures [4]. This is consistent with the concept that neuropsychiatric disorders are not distinct disorders, but instead are comprised of sets of neurobiological mechanisms across several units of analysis [5,6,7]. Since it is unlikely that any single biological mechanism that operates in isolation can explain the full range of symptoms of a given disorder, there is a need for development of alternative analytic approaches that address the dimensional nature of psychiatric symptoms and the array of neurobiological mechanisms that are likely contributors [8]. Such approaches can help identify sources of heterogeneity within a disorder or reveal comprehensive phenotype profiles to explain transdiagnostic symptom patterns. Use of unbiased data-driven approaches have begun to yield biological signatures of discrete profiles of stress-related neuropsychiatric symptoms [9,10,11].

Leverage of multivariate analytic approaches is increasingly used to identify interrelationships among multiple units of analysis, including between psychiatric symptoms and biological markers that univariate approaches are unable to capture [12,13,14]. One analytic technique that has received renewed interest in addressing psychiatric and neurobiological heterogeneity in neuropsychiatric disorders is canonical correlation analysis (CCA) [48]. We randomly scrambled subjects’ psychophysiological and biochemical data columns to break the association between subjects’ psychophysiology/biochemical measures with their clinical psychiatric symptom measures. To reduce potential inflation of significance testing caused by dependence in datasets [49], we restricted the permutations to within battalion cohort due to the shared military experience within each battalion. We re-ran mCCA for 5000 permutations to create a null distribution of mCCA values. We compared the original mCCA values to these re-aligned distributions. Any significant mCCA values would have to be greater than the correlations in the permuted datasets. Permuted P values were computed by determining the number of permuted canonical correlation values (mCCperm) that were greater than or equal to the observed canonical correlation (mCCobs) divided by the number of permutations: Pperm = (# mCCperm ≥ mCCobs) / 5000. We conducted permutations for each mCC triplet and each pair-wise CC. To reduce Type-I error, the permuted P values were corrected using Benjamini-Hochberg False Discovery Rate (FDR < 0.05). We additionally computed a bootstrap resampling procedure. We performed 5,000 random resamples and estimated the means, standard errors, and 95% confidence intervals for each mCCA value (See Supplementary Table 4 for the bootstrapped mean mCCs and the standard errors).

Results

Multi-set canonical correlation analysis

A multi-set canonical correlation analysis (mCCA) of psychiatric symptoms and biological measures revealed four significant canonical correlations (Table 2). In the remaining sections, we focus only on the first two mCCs. The first two mCCAs have moderately robust pairwise CCs, whereas the remaining mCCs were considerably weaker and potentially not scientifically meaningful [16, 51]. As shown in the current investigation, we observed a robust relationship between psychiatric symptoms and psychophysiology CV (CC 1 = 0.46), whereas the highest univariate correlation was substantially weaker (r = –0.19). These results provide compelling support for the utility of the mCCA approach to identify novel and robust findings in complex multimodal datasets.

Using the mCCA approach, we derived psychiatric symptom phenotypes that were based on patterns between individual differences of psychophysiology and psychiatric symptoms. In other words, the psychiatric dimension of dysphoric arousal (characterized by anhedonia, dysphoric arousal, and anxiety) observed in mCC 1 is represented as a mixture of these symptoms and psychophysiology measures. The psychophysiology dimension was comprised of blunted measures of arousal, including low general startle reactivity and low blood pressure. The finding of a negative relationship between general startle response and low blood pressure with self-reported arousal may seem counterintuitive [52,53,54]. However, both startle hyporeactivity [55,56,57,58,59] and low blood pressure [60,61,62] are documented in individuals with a history of anxiety, dysphoria, and stress-related neuropsychiatric symptoms (see Lang et al., 2014 for a review [63]). Epidemiological studies support a link between low blood pressure and depression, particularly anhedonia symptoms [64]. In a large cohort (n = 60,799), individuals with comorbid anxiety and depression were more likely to have low blood pressure than individuals without these symptoms [65] and high baseline levels of anxiety and depression predicted low blood pressure 22 years later [62]. Furthermore, low blood pressure is associated with suicidal ideation [66] and is a risk-factor for late life depression [61]. The CCA also showed blunted startle associated with dysphoric arousal and anhedonia. Blunted startle responding can occur after chronic or long-standing stress [63, 67,68,69] and in adults endorsing early-life adversity [59, 70, 71]. In a recent review of psychophysiological phenotypes across anxiety and mood disorders, blunted heart rate and startle responses either at baseline or in response to threat is observed in populations with high chronic distress and depression symptoms [72]. Blunted psychophysiological reactivity may also reflect elevated dissociative symptoms in individuals experiencing chronic traumatic stress symptoms [73, 74]. Pre-deployment dissociative symptoms were not assessed in this investigation; therefore, it will be important for future studies to include a measure of dissociation to determine how it links to multimodal physiological markers. These previous findings have focused on traditional diagnostic categories (i.e., Major Depressive Disorder, PTSD), whereas our findings are focused on transdiagnostic phenotypes. Therefore, direct comparison of our findings to previous work needs to acknowledge this distinction. However, these prior findings are thus very much in line with the CCA identified in the current study that used an unbiased, data-driven approach across wide range of symptom measures and physiological and peripheral signaling markers. Taken together these findings suggest that there is a clear subpopulation of individuals with blunted physiological responses associated with combined dysphoric arousal and anhedonia, which may suggest specific mechanisms underlying this symptom pattern. The biological mechanism(s) for this association is unclear, however there is some data to suggest an inflexible corticolimbic-response including blunted amygdala reactivity or that poor amygdala synchrony to emotional stimuli contribute to the blunted physiological reactivity [75, 76] as well as dysregulation of the hypothalamic-pituitary-adrenal axis and renin-angiotensin systems that influence blood pressure [65].

We also identified a biomarker associated with anxious fatigue, high anxiety and reexperiencing/avoidance symptoms—a symptom profile that covaried with elevated norepinephrine and CRP, elevated blood pressure, and high startle threshold. Physiologically, autonomic imbalance, such as excess sympathetic drive, particularly under periods of stress, are known to contribute to increased blood pressure and inflammation [77,78,79,80,81,82]. Thus, our observed relationship between CRP, norepinephrine and blood pressure levels is not un-expected. The physiologic profile of high inflammation/sympathetic drive, associated with reexperiencing/avoidance symptoms and overall high anxiety/fear symptoms in humans is consistent with previous work, and may be exacerbated in those with more severe illness [79, 83,84,85]. These findings are also in line with descriptions of both high CRP and peripheral norepinephrine across anxiety and trauma disorders as well as during chronic stress (e.g. [86,87,88,89,90]) and fatigue [91]. A benefit of CCA and mCCA is that they can extract multiple components of latent variable patterns—relationships that exist in a high-dimensional dataset that would not otherwise be apparent if a non-latent variable approach was used [47]. The high sympathetic drive phenotype is a unique linear combination of psychiatric, biochemical, and psychophysiological variables that is orthogonal to the phenotype observed in mCC 1.

There are limitations to this study that should be considered. First, although the CCA approach may allow for detection of complex relationships, it requires a number of a-priori choices in model development and interpretation, including definition of the phenotype and delimitation of the variety and array of biological and behavioral markers to be included in the analytic model. This is a feature of most unbiased analytic approaches however, not just CCA [13]. Related, creation of robust data-driven and latent psychiatric phenotypes helps address clinical heterogeneity and aids in linking complex constructs across multiple domains. But such approaches also raise important questions about interpretability and applications in new datasets [92]. Enhancing the generalizability and clinical utility of latent variable psychiatric phenotypes will be a critical step for future investigations. Second, the sample consisted exclusively of active-duty male Marine/Navy servicemen and thus is not necessarily generalizable to civilians and females [93, 94]. Third, this population had good variance in symptoms and biomarker phenotypes, however it was predominantly a relatively healthy population, thus other or additional symptom-marker relationships may be detectable in a more clinically impaired sample. Fourth, the sample was predominately White and the results may not generalize to individuals from other racial groups. Future work will be needed to examine racial and ethnic differences in multimodal phenotypes since recent work has found racial differences in phenotypes associated with neuropsychiatric symptoms [95]. Fifth, another drawback of CCA is that it is based on the assumption that the relationships between the features are linear and therefore do not measure higher-order relationships [15]. Applying kernelized-CCA, other multivariate/machine learning approaches (e.g., independent components analysis [ICA], neural nets), or their combination (mCCA + ICA, deep CCA) may better identify non-linear relationships between variable sets than CCA alone [9, 21, 96, 97]. However, these limitations are balanced by notable strengths, including the large sample (N = 2024), the deeply phenotyped dataset, and the relative physical health of the population—reducing confounds of comorbid physical illnesses and other extraneous variables (note all peripheral biomarkers were controlled for the effects of age, time of assessment, cohort, and ethnicity).

Conclusion

High psychiatric disorder comorbidity and symptom heterogeneity suggests that the current diagnostic system is not capturing the range of patient’s symptom experience, which may hinder the identification of clinically useful biomarkers to guide treatment development [2, 98]. Work linking a single neurobiological measure to a single diagnostic disorder has had limited clinical utility [99], motivating the field to shift to a framework where the focus is on dimensional psychiatric symptoms, not diagnostic categories, and on multiple, not single, biological markers [5, 11, 100,101,102]. Clearly, new data-driven analytic strategies are needed to address the complex multivariate relationship between psychiatric symptoms and multiple biological markers [8, 103], with mCCA being well-suited to handle this challenge [47]. The current findings support the mission towards a dimensional model of neuropsychiatric symptoms grounded in neurobiology and highlight the potential of multivariate statistics to reveal important psychiatric symptom biomarkers derived from several psychophysiological and biochemical measures. Future work will be required to apply this approach with other high-dimensional datasets that are an inherent part of biological assays (e.g., neuroimaging, genome, epigenome), and to test how multimodal biomarkers relate to other measures (e.g., trauma history, psychosocial functioning), and to determine their predictive utility.