The focus on psychopathology towards the end of the lifespan puts a sharp focus on an issue germane to all of psychiatry, namely characterizing heterogeneity (i.e., diversity) at multiple levels: etiopathogenesis, clinical phenotypes, and variability in response to interventions (prophylactic, short-term and long-term). The puzzle of heterogeneity addresses the key question of “What are we trying to solve?”. Indeed, the impact of accumulated life experiences can significantly contribute to the heterogeneity of psychopathology that we are trying to disentangle in later life. To tackle this question, our issue on the Trajectories of Mental Illness from Mid-to-Late Life highlights several themes also explored in the Part I of the Trajectory of Mental Illness focusing on adolescence through young adulthood. These concepts cover etiopathogenesis and the role of the exposome in the development of psychopathology across the lifespan, variable clinical presentation and course, unique considerations for intervention (i.e., what works for whom, and how does effective intervention work?) and, finally, potential solutions to and ways of addressing the challenges of heterogeneity.

Etiopathogenetic factors and the exposome represent major contributors to heterogeneity of mental illness across the lifespan

Related to etiopathogenesis, in Part I, Robinson and colleagues [1] detail the role of the exposome, namely non-genetic exposures, experiences and external environments, on developing brain health and cognition. Lean et al. also describe the impact on the developing brain of social determinants of health [2]. We continue this examination in Part II in work from Barzilay and Jeste (a child psychiatrist and geriatric neuropsychiatrist, respectively) examining how the exposome encapsulates social determinants of mental health in later life [3], such as social isolation, frailty and life-stage challenges (such as typical bereavement and complicated or prolonged grief [4]. Another example of cross-cutting themes that unites trajectories of mental illness across the lifespan is the concept of resilience. As covered in Part I by Torres-Berrio et al. [5], resilience represents an adaptive behavioral, molecular and cellular response to early life stress. In Part II, Lavretsky and colleagues extend this basic neurobiological concept of resilience to include social and psychological factors that can contribute to longevity and extended brain health throughout the lifespan (“healthspan”) [6]. All of the factors, from what we are exposed to in our internal and external environments to how we respond to these exposures at the molecular, cellular, behavioral, social and psychological levels, contribute significantly to the heterogeneity of psychopathology across the lifespan. Our attempts to detail these factors can aid in how we characterize psychopathology to better manage variability in clinical presentations and treatment response. In Part II, we also deal with bridging science and service in a review of health care delivery, capturing issues pertinent to both early and later life [7].

Attempts to parse heterogeneity across the trajectories of mental illness involves detailed characterizations of variability in clinical phenotypes, disease course and treatment response, particularly within traditional diagnostic categories

One promising approach highlighted in Part II by Beekman et al. discusses the role of clinical staging [8]. Clinical staging, a standard method of parsing heterogeneity in clinical medicine (especially in oncology and in cardiovascular medicine) can assist with prognosis and treatment selection. The importance of clinical staging is also discussed in Part I by Holt et al. [9] and DeTore et al. [10] in their discussion of transdiagnostic preventative approaches to youth mental health. Prevention is the focus of a perspective by Okereke in which she utilizes the National Academies of Medicine (NAM) framework (indicated, selective, and universal prevention) to discuss the latest findings in depression prevention [11]. The notion of tertiary prevention (to prevent down-stream complications of neuropsychiatric illness) is addressed in Part II, by Taylor et al., examining characteristics of stable remission and predictors of recurrence [12]. Linking all of this work is the compelling idea that we could use clinical staging to identify people who would benefit from either an indicated, selective or universal approach to prevention of later-life mental illness per the NAM framework. For example, indicated prevention can be used effectively in persons with sub-threshold symptoms of depression, while a selective approach could be used to deploy social support-based interventions for those identified to be at risk of depression recurrence, as reviewed by Taylor et al. [12]; or a more universal approach of nutritional supplementation as described by Okereke [11].

We present here additional crosstalk concepts under clinical phenotypes, disease course and treatment response variability. For example, one of these clinical phenotypes is related to the trajectories of mental illness involved in the complicated risk factors for suicide. In Part I, Lewis et al. present work relevant to developmental and neural risk factors for suicidal thoughts and behavior [13]. Similarly, Galfalvy and colleagues detail the complex risk factors associated with suicide in late-life depression in this issue [14].

An additional source of heterogeneity is variability of treatment response which complicates the treatment of psychiatric conditions in later life. This is not unique to any given treatment modality and applies across a range of therapies addressing the needs of older adults including psychotherapy highlighted by Gum and Crawford [15], psychopharmacology [16, 17] and neuromodulation [18]. This is akin to the work of Conelea and Lieske examining considerations for pediatric TMS in Part I [19]. In all, these papers highlight the importance of parsing heterogeneity to formulate mechanistically informed targeted interventions in the service of tailored treatment and prevention.

Finally, Part II addresses potential solutions to the puzzle of heterogeneity by leveraging recent developments in computational psychiatry and implementation science

One aspect of computational psychiatry is the use of artificial intelligence/machine learning (AI/ML) as described by Mizuno et al. [20]. These AI tools provide enough methodological flexibility to incorporate multifactorial contributors to disease heterogeneity such as genetic/epigenetic markers, social determinants of health, digital biomarkers to determine clinical subtypes and/or predictors (moderators) of intervention response variability. Another potential solution that could incorporate solutions derived from AI/ML is implementation science, which describes best practices for improving systems of care to address issues of access and quality of care. Thus, in Part I, Wolitzky-Taylor et al. describe a technological-enabled stepped care program which could serve as a model for treating psychopathology across the lifespan [21]. This concept is further elaborated in the review by Colenda and colleagues providing an important systems-level perspective on how to integrate science into real-world settings [7]. In many ways, “artificial” is a misnomer. We are really talking about the use of “augmented” intelligence in the service of our patients, their families, the health care systems in which they live, and policy makers. “Intelligence” is related to but not the same thing as “wisdom,” which connotes prosocial activity in the ethics of science and its application to the betterment of the human condition.

In summary, we suggest to our ACNP colleagues that the whole (Parts I and II) is truly greater than the sum of their parts. We are grateful to all authors in Parts I and II, reviewers, and senior editors at NPP (Lisa Monteggia, Chuck Zorumski, Carolyn Rodriguez, and Tony George), for the privilege of doing this work; and to NPP staff for hugely important help along the way. Finally, we remain most deeply grateful to the patients (from the Latin verb, “pati”, to suffer) who are our partners in the scientific enterprise.