Surrogate Markers of Insulin Resistance in Predicting Major Depressive Disorder: Metabolism Metastasizes to the Brain

The study by Watson et al. (1), in this issue of the Journal, is very thoughtful and prescient in its integration of prediction, prevention, deep in vivo phenotyping, the parsing of disease mechanisms (i.e., the role of insulin in brain health and disease), and models of major depressive disorder. Using data from the Netherlands Study of Depression and Anxiety (NESDA) cohort (N=601; 18–65 years old), the investigators sought to determine whether surrogate measures of insulin resistance were predictive of incident DSM-IV-defined major depressive disorder in a well-characterized cohort of individuals without a current or lifetime history of depression or anxiety disorders.

The early age at onset, high prevalence, chronicity, and debilitating nature of major depressive disorder invites the need for scalable, cost-effective, and implementable prevention/preemption interventions (2). Toward the foregoing aim, the field urgently needs to identify modifiable risk (and resiliency) factors. A cross-sectional association between impaired glucose tolerance, type 2 diabetes mellitus, obesity, and metabolic syndrome with major depressive disorder is amply documented (3). Moreover, there are a cornucopia of cross-sectional studies that have identified pathophysiologic mechanisms associated with depression (e.g., inflammation, oxidative stress) (4). A limitation, however, is the relatively few longitudinal studies with well-characterized samples and biologic measures, which are essential to establish causality.

Watson et al. focused on three surrogate measures of insulin resistance: 1) the triglyceride-high-density lipoprotein (HDL) ratio (the primary exposure, with sex-specific cut-points to define high triglyceride-HDL ratio in female and male subjects of ≥0.83 and ≥1.22, respectively); 2) prediabetes (i.e., a fasting plasma glucose level ≥5.54 mmol/L); and 3) obesity (i.e., waist circumference >100 cm). The rationale for focusing on insulin as a putative causal mechanism in major depressive disorder is supported by a confluence of preclinical and clinical data underscoring insulin’s critical role in brain health and disease.

For example, a replicated finding in animal models is the association between insulin resistance and measures of behavioral despair, helplessness, and cognitive impairment (5). It has been known for decades that insulin modulates a surfeit of neurotransmitters relevant to the phenomenology and/or treatment of major depressive disorder (e.g., dopamine) (6). Additionally, insulin is implicated in neuroplasticity, neurotrophism, and long-term potentiation (7). A separate line of evidence has also shown that insulin is associated with resting-state brain connectivity with consequent effects on functional segregation, integration, and reciprocity (8, 9). All the foregoing observations comport with contemporary disease models in major depressive disorder.

Parenthetically, I’ve also found it intriguing that insulin, a peptide so critical to peripheral metabolic homeostasis, also plays a critical role in general cognitive and reward processes, two of the most common phenomenological disturbances in major depressive disorder (10, 11). Disturbances in each of these two domains are prevalent, persistent, and overrepresented in mediational models as reasons for impaired psychosocial functioning in major depressive disorder (12).

In their study, Watson et al. reported that all three surrogate markers of insulin resistance were moderately correlated, indicating that they were not orthogonal variables. In total, 14.0% (N=84) of the sample experienced incident major depressive disorder during the 9-year follow-up. The Cox proportional hazards model indicated a positive association between higher triglyceride-HDL ratio and incident major depressive disorder (hazard ratio=1.89, 95% CI=1.15–3.11), higher fasting plasma glucose (hazard ratio=1.37, 95% CI=1.05–1.77), and higher waist circumference (hazard ratio=1.11, 95% CI=1.01–1.21). A separate analysis revealed that prediabetes was positively associated with incident major depressive disorder in the first 2 years of follow-up (hazard ratio=2.66, 95% CI=1.13–6.27).

What further information would we like to know and how do we translate these findings? As I read the study, I was curious as to whether childhood adversity represented a “cascade event” resulting in alterations in insulin signaling. In other words, are the surrogate markers of insulin resistance also a surrogate marker of social determinants and exposure to environmental stressors? I was also curious as to what longitudinal, noninvasive imaging evaluating brain circuits and connectivity might reveal in this sample. For example, among those with surrogate markers of insulin resistance, were they more likely to manifest abnormalities in functional connectivity across discrete brain circuits subserving aspects of cognitive function or reward?

Can we speculate that the findings herein extrapolate to other mental disorders? It’s known that many domains of pathology in psychiatry are transdiagnostic. Could these surrogate markers also be predictive of impairment in general cognitive processes (13)? For example, it has been documented that impairment in cognition may precede the onset of syndromal major depressive disorder (14). What about predicting the onset of reward-based disturbances (e.g., anhedonia) also known to be phenomenological antecedents to major depressive disorder? Could these surrogate markers be predictive of other mental disorders known to be associated with impaired insulin function (e.g., bipolar disorder, Alzheimer’s disease [15, 16])?

I was also curious as to whether integrating the surrogate markers with other inexpensive scalable markers could facilitate prediction. For example, although accounting for a relatively small variability in risk, it is known that elevated C-reactive protein associates with incident major depressive disorder (17, 18). Can inflammatory and metabolic markers be integrated to create a biosignature with greater predictive utility? What if we integrated the foregoing biosignature with a history of childhood adversity (19)? What about resiliency? It seems to me that any coherent, comprehensive, and highly clinically valid prediction model with clinical utility could integrate psychosocial risk and resiliency factors with multiomic biosignatures. Would modern computational models and machine learning be able to assist in this process?

It’s long overdue that psychiatry needs to prioritize implementing the prevention of many of our common and severe disorders. Obviously, cost effectiveness and clinical utility of any biomarker/signature would need to be compelling before it can be recommended. In the interim, it seems reasonable to hypothesize that interventions (e.g., behavioral, lifestyle, diet) targeting surrogate markers of insulin may be implemented and possibly preventive. I’ve often wondered how does exercise at the general population level “vaccinate” people from major depressive disorder (20)? Does this occur by improving the surrogate markers of insulin resistance? For those who evince insulin resistance that have not yet developed major depressive disorder, could pharmacologic strategies known to enhance insulin sensitivity be protective? If interventions are not protective against the full syndrome of major depression, could they possibly preserve functions relevant to major depressive disorder (e.g., cognitive functions)? Replicated evidence indicates that insulin moderates hedonic function; could this imply that improving insulin sensitivity could translate into improved patient-reported outcomes (e.g., quality of life and function) (10)?

The data from Watson et al.’s study instantiate a critical effector system (i.e., insulin) that appears to be relevant to the major depressive disorder disease process and possibly also to its prevention and therapeutics. Psychiatry has unfortunately had innovation stasis for quite some time from the point of view of improving treatment outcomes for persons affected. Glutamate and GABA therapeutics represent innovative new pathways in treating major depressive disorder, providing hope to persons already affected (21). Watson et al.’s data give reasons to believe that targeting metabolics could play a role in the prevention and possibly therapeutics of major depressive disorder (22). The extant literature now augmented by Watson et al.’s longitudinal analysis reminds me yet again that “metabolism metastasizes to the brain.”