Depression as a disorder of neurometabolic flexibility

Traditional accounts of MDD emphasize monoaminergic, neuroendocrine, inflammatory, cognitive-affective, and psychosocial mechanisms. These models remain important, but they do not fully explain clinical features such as fatigue, psychomotor retardation, low motivation, and impaired effort allocation. A complementary interpretation is that some depressive phenotypes involve reduced neurometabolic flexibility, defined here as the capacity of neural circuits to match changing cognitive, affective, and motivational demands with appropriate substrate supply, mitochondrial oxidation, vascular delivery, glial-neuronal metabolic cooperation, and recovery.

Glucose metabolism provides one entry point into this framework. A recent review synthesized evidence that MDD is associated with systemic and localized impairment of glucose metabolism across the illness course [14]. Earlier PET work also suggested regional alterations in cerebral glucose metabolism in depression, including prefrontal hypometabolism [17]. To provide more concrete context rather than relying on generalized statements, it is worth noting that a longitudinal fluorodeoxyglucose (FDG)-PET study by Kennedy and colleagues [18] examined 13 male patients with MDD before and after 6 weeks of paroxetine treatment and compared resting scans with 24 healthy male controls; successful treatment was associated with increased glucose metabolism in dorsolateral, ventrolateral, and medial prefrontal regions, parietal cortex, and dorsal anterior cingulate cortex (dACC). Although this study was small, male-only, and treatment-specific, it illustrates that depression-related symptoms can be accompanied by measurable changes in regional metabolic activity.

Mitochondrial dysfunction adds a second layer. Mitochondria regulate adenosine triphosphate production, redox balance, calcium signaling, apoptosis, and inflammatory responses, and abnormalities in these processes have been repeatedly implicated in MDD and related psychiatric disorders [15, 16]. If mitochondrial oxidative capacity is limited, neural circuits may struggle to meet the energetic demands of cognitive control, emotion regulation, reward pursuit, and physical effort. Under such conditions, lactate may have divergent meanings: it can serve as a useful oxidative substrate when transport and oxidation are efficient, but it may also accumulate when glycolytic flux exceeds mitochondrial use or when acid-base regulation is impaired.

Brain pH and lactate abnormalities further support a neurometabolic interpretation of depression. Post-mortem studies and animal model work suggest that reduced brain pH and altered lactate levels may occur across psychiatric conditions, but these findings are vulnerable to confounding by agonal state, medication exposure, brain activity before death, tissue handling, and post-mortem interval [13, 19]. These limitations do not invalidate the signal; they require cautious interpretation. The relevant point is that pH, lactate, glycolysis, mitochondrial oxidation, and excitation-inhibition balance form an interconnected metabolic system rather than independent variables.

Astrocytes are central to this system. They regulate glucose uptake, glycogen storage, lactate production, neurotransmitter recycling, potassium and proton buffering, BBB function, and neurovascular coupling [20, 21]. Post-mortem and experimental studies have reported reductions or abnormalities in astrocytic markers, glial density, and astrocyte-dependent support of prefrontal and hippocampal circuits in MDD [22–25]. Because astrocytes are major producers and distributors of brain lactate, glial pathology provides a plausible bridge between lactate metabolism and depressive symptoms. The key question is not whether lactate is simply “high” or “low,” but whether it is produced, transported, oxidized, and cleared at the right time, in the right region, and under a metabolic context that permits adaptation rather than burden.

Metabolism-linked depressive symptom dimensions

A symptom-dimensional approach is necessary because MDD is heterogeneous. Global scores on the Hamilton Depression Rating Scale, Montgomery-Asberg Depression Rating Scale, Patient Health Questionnaire-9, or Beck Depression Inventory can mix affective, cognitive, somatic, sleep, appetite, guilt, and motivational symptoms. Lactate-related mechanisms are unlikely to explain all depressive symptoms equally. They are most plausible for symptom dimensions that require sustained coordination of neural and bodily energy systems, including fatigue, anergia, psychomotor slowing, anhedonia, reduced motivation, and impaired effort-based decision-making [47–57].

Anhedonia should not be reduced to the absence of pleasure. Contemporary models distinguish consummatory pleasure, anticipatory pleasure, reward learning, motivational drive, and willingness to expend effort for reward [54, 55]. Effort-based decision-making studies indicate that individuals with MDD may be less likely to choose high-effort/high-reward options, suggesting altered cost-benefit valuation rather than a purely hedonic deficit [47–49]. Dopaminergic cortico-striatal and cingulate circuits are central to this process, but metabolic state is also relevant because effort valuation implicitly asks whether the organism can afford energetic investment [50, 51].

Fatigue and psychomotor slowing are also strongly aligned with a neurometabolic framework. Psychomotor retardation is associated with fronto-striatal and cingulate motor circuits [52, 53]. Fatigue, anergia, and reduced behavioral activation likely arise from interactions among inflammation, dopamine signaling, mitochondrial function, sleep disturbance, metabolic health, and substrate availability [50–57]. This matters for lactate because exercise-induced lactate is not merely a marker of external work rate; it may also index the ability to generate, tolerate, transport, oxidize, and recover from a controlled metabolic challenge.

Recent conceptual work on exercise, inflammation, dopamine, and motivation supports this symptom-level perspective, proposing that exercise may improve interest-activity symptoms through interactions among movement, inflammatory signaling, and motivational circuitry [58]. This does not imply uniform benefit across all depressed patients. A patient with fatigue-dominant, sedentary, metabolically unhealthy depression may have different exercise tolerance and lactate dynamics than a patient whose depression is dominated by guilt, rumination, grief, interpersonal stress, or severe insomnia. The lactate-window model should therefore be framed as most relevant to metabolic-motivational depressive phenotypes rather than to depression as a unitary syndrome.

Why lactate is a relevant exercise factor

Many exercise-induced molecules have been proposed as antidepressant-relevant mediators, including BDNF, irisin, kynurenine pathway metabolites, inflammatory cytokines, endocannabinoids, and myokines. Lactate is distinctive because its appearance in blood is tightly coupled to exercise intensity, muscle recruitment, glycolytic flux, training status, nutrition, and clearance capacity [4–12]. It therefore links an external prescription, such as session duration or target heart rate, with an internal metabolic response.

The lactate shuttle framework replaced the older view of lactate as useless waste. Lactate is now understood as a mobile substrate and signaling molecule exchanged across cells, tissues, and organs [4–6]. During exercise, skeletal muscle-derived lactate enters the circulation and can be taken up by the heart, liver, skeletal muscle, and brain [4–6, 41, 59–65]. Because lactate changes over time, a single value is less informative than a curve. Peak lactate, AUC, time-to-peak, clearance half-time, and recovery may carry different biological information, especially when interpreted with RPE, affective response, sleep, and next-day fatigue.

Lactate generation during exercise

Blood lactate concentration increases when lactate appearance exceeds lactate clearance. This occurs progressively as exercise intensity increases, particularly when glycolytic flux rises, fast-twitch fibers are recruited, sympathetic drive increases, and glycogenolysis accelerates [4–12]. Lactate accumulation is therefore not a simple marker of oxygen deficiency. Lactate can be produced under aerobic conditions and can be oxidized by active tissues during and after exercise [4–6].

Exercise modality strongly influences lactate dynamics. Moderate continuous exercise may produce a modest and relatively stable lactate elevation, whereas threshold-based training or high-intensity interval training (HIIT) can produce larger but shorter lactate pulses [8–12]. Lactate threshold and maximal lactate steady state have long been used to guide endurance training intensity [7, 8]. Interval training can improve oxidative capacity, mitochondrial biogenesis, lactate transport, and lactate clearance, meaning that the same external workload can produce different lactate profiles after training adaptation [9–12].

This individual variability is highly relevant to depression trials. Two patients can complete the same 30-minute session at the same target heart-rate range and yet show different lactate responses because of baseline fitness, sex, age, nutrition, sleep, medication, anxiety sensitivity, metabolic health, cardiovascular function, and exercise modality. Conversely, different activities can produce similar lactate curves. Lactate therefore offers a way to move from generic exercise exposure toward biologically anchored dosing, but only if interpreted as a dynamic response and not as a universal threshold.

This point should not be misread as a recommendation that HIIT is broadly superior for depression. Meta-analytic evidence supports exercise as an intervention for depressive symptoms, and some evidence suggests intensity may matter, but high-intensity protocols vary substantially in supervision, tolerability, dropout, and certainty of evidence [3, 66–69]. Low-intensity exercise can be clinically valuable for patients with severe fatigue, low fitness, cardiovascular risk, pain, anxiety sensitivity, obesity, or poor adherence. The lactate framework is meant to refine progression, not to force every patient toward maximal effort.

Peripheral-to-brain lactate communication and the proxy problem

During exercise, skeletal muscle-derived lactate enters the circulation and can become available to the brain. Human arteriovenous and MRS studies show brain lactate uptake or increased brain lactate-related signals during exercise and elevated arterial lactate [41, 59–64]. During vigorous exercise, lactate may contribute substantially to cerebral carbohydrate metabolism [60–63]. These findings establish a physiological foundation for considering peripheral lactate as a potential signal to the brain.

The BBB does not prevent lactate movement. MCTs, including MCT1, MCT2, and MCT4, mediate lactate transport across endothelial cells, astrocytes, neurons, and other cells [29, 30, 41]. Transport is influenced by concentration gradients, transporter expression, local metabolic state, pH gradients, blood flow, and tissue demand. Exercise can increase the arterial-to-brain lactate gradient [64], whereas endurance training may enhance brain lactate transport capacity in animal models [65].

However, blood lactate is an imperfect proxy for brain lactate exposure. Peripheral lactate concentration is shaped by muscle production, hepatic and renal clearance, cardiac and skeletal muscle uptake, plasma volume, nutrition, temperature, recent activity, sleep, and timing of sampling. Brain lactate depends additionally on local glycolysis, astrocytic metabolism, neuronal uptake, vascular delivery, transporter expression, regional neural activity, mitochondrial oxidation, and pH regulation [29–31, 41, 59–65]. Therefore, a single post-exercise blood lactate value should not be interpreted as a direct measure of brain lactate. This limitation is central to the translational model and should guide future trial design.

A more rigorous framework would treat blood lactate as a peripheral exposure signal requiring central and clinical bridging measures. These may include MRS-based estimates of regional brain lactate or related metabolic signals, neurovascular imaging, actigraphy, sleep metrics, inflammatory markers, metabolic markers, and symptom-dimensional outcomes. The aim is not to claim a linear blood-brain correspondence, but to test whether peripheral lactate kinetics covary with central neurometabolic adaptation and symptom improvement under standardized conditions.

Astrocyte-neuron lactate shuttle mechanisms

The ANLS model provides a mechanistic bridge between lactate metabolism and neural function. In this framework, neuronal activity increases glutamate uptake by astrocytes, which stimulates astrocytic glycolysis and lactate production. Lactate is then exported and taken up by neurons, where it can be oxidized to support activity-dependent energy demands [20, 21, 27–34]. Although aspects of the ANLS model remain debated, there is strong evidence that astrocyte-neuron metabolic cooperation is essential for neural activity, memory, and plasticity [20, 21, 28, 31–34].

The relevance to MDD is plausible but not clinically proven. If astrocytes are reduced, dysfunctional, or metabolically inflexible in depression, then lactate production, transport, oxidation, or clearance may be impaired [22–25]. Such impairment could reduce the ability of prefrontal, hippocampal, cingulate, and striatal circuits to sustain activity during cognitive control, emotion regulation, reward pursuit, and effortful action. Preclinical work supports this possibility: astrocytic lactate dehydrogenase A regulates neuronal excitability and depressive-like behaviors through lactate homeostasis in mice, and astrocyte-derived lactate has been implicated in passive coping and stress-related behavior [70–72].

This section prevents the hypothesis from becoming a peripheral exercise-metabolism argument only. Lactate is relevant to depression only if it can plausibly affect brain circuits. MCT transport, ANLS mechanisms, astrocytic pathology, and regional neurometabolic demands provide such a bridge. Still, the correct interpretation is conditional: lactate may be beneficial when it supports activity-dependent substrate availability and plasticity, but maladaptive when it reflects impaired mitochondrial oxidation, excessive glycolysis, local acidification, inflammation, or glial dysfunction [13, 19, 31, 73].

Lactate as an adaptive neurometabolic signal

Lactate may influence antidepressant-relevant biology through several mechanisms. First, lactate can serve as an oxidative substrate during increased energy demand [29–33, 41, 59–65]. This role is relevant for prefrontal, anterior cingulate, hippocampal, and striatal circuits that support emotion regulation, memory, reward processing, motivated behavior, and effort valuation. Second, lactate can participate in synaptic plasticity. Astrocyte-neuron lactate transport is required for long-term memory formation, and lactate can regulate plasticity-related genes such as Arc, c-Fos, and Zif268 through mechanisms involving N-methyl-D-aspartate receptor signaling and redox-sensitive pathways [28, 34, 36, 37]. Third, exercise-induced lactate has been linked to hippocampal BDNF signaling through SIRT1-dependent mechanisms [35, 38]. BDNF is not the only downstream pathway of exercise, but it is a major plasticity-related mechanism that provides a plausible link between metabolic challenge and neural adaptation. Fourth, lactate may engage receptor-mediated neurovascular and neurogenic pathways. Exercise-induced lactate has been shown to signal through HCAR1 to induce cerebral VEGF and angiogenesis in preclinical work [39], while HCAR1 signaling has also been linked to neurogenesis and microglial activation in another experimental context [42]. Fifth, lactate may influence glial and immune-metabolic states through microglial activity, inflammatory signaling, phagocytosis, and histone lactylation [43–46]. Direct antidepressant relevance is supported mainly by preclinical studies. Peripheral L-lactate administration produced antidepressant-like effects in rodent models and was associated with hippocampal lactate changes and gene expression patterns related to neurogenesis, astrocytic function, serotonergic signaling, nitric oxide synthesis, and cAMP signaling [40, 74]. These findings are important because they suggest lactate can influence depression-relevant behaviors, but the term “antidepressant-like” should be used in preclinical contexts. These studies do not demonstrate that lactate-guided exercise improves clinical MDD outcomes in humans.

Table 1 summarizes the evidence map and distinguishes established physiological findings, plausible mechanistic inferences, and hypothesis-generating translational applications.

Figure 1 integrates these evidence levels into the proposed lactate-window framework, emphasizing dynamic exposure and recovery rather than a universal lactate threshold.

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Figure 1. Lactate-window hypothesis linking exercise intensity, peripheral lactate kinetics, peripheral-to-brain communication, brain neurometabolic mechanisms, and putative depression-relevant symptom targets. The figure deliberately avoids displaying symptom improvement as established clinical fact. Instead, fatigue, anhedonia, low motivation, psychomotor slowing, and effort valuation are presented as hypothesized target domains requiring prospective validation. Blood lactate is represented as a dynamic exposure signal defined by peak, AUC, time-to-peak, clearance, recovery, and exploratory lactate-to-RPE index, not as a single static biomarker. The model distinguishes low lactate stimulus, adaptive lactate window, and maladaptive lactate burden. The clinical aim is not to maximize lactate but to identify a tolerable and recoverable metabolic challenge. ANLS: astrocyte-neuron lactate shuttle; AUC: area under the curve; BBB: blood-brain barrier; HCAR1: hydroxycarboxylic acid receptor 1; MCT: monocarboxylate transporter; MDD: major depressive disorder; RPE: rating of perceived exertion; VEGF: vascular endothelial growth factor.

The lactate paradox in depression

The strongest version of the lactate-window hypothesis must explicitly include a paradox. On one side, exercise-induced lactate and experimentally administered lactate can support substrate availability, synaptic plasticity, BDNF-related signaling, neurovascular remodeling, neurogenesis, and antidepressant-like behavior in preclinical models [34–40, 42–46, 74]. On the other side, depression and neuropsychiatric models can show reduced brain pH and altered lactate levels, suggesting that lactate accumulation may sometimes index metabolic stress, mitochondrial limitation, acid-base disturbance, or abnormal neural activity [13, 19].

A recent human 7T MRS and functional MRI study sharpened this paradox by reporting that higher lactate concentrations in the dorsomedial prefrontal cortex (dmPFC)/dACC were associated with reduced motivation for physical effort, and that plasma lactate correlated with lactate in this cortical region [73]. This finding is important because the dmPFC/dACC is implicated in effort valuation and action selection. However, the result is correlational. It does not prove that lactate causes reduced motivation, nor does it establish that regional lactate has the same meaning during exercise recovery, depressive episodes, inflammatory states, or clinical intervention.

Confounding variables must therefore be considered. Regional lactate may be influenced by task demand, recent activity, sleep, inflammatory state, systemic metabolic health, medication exposure, arousal, anxiety, vascular delivery, mitochondrial oxidative capacity, lactate-pyruvate balance, pH context, and baseline fitness. The same lactate concentration may have different meanings depending on whether it occurs as a transient physiological pulse after tolerable exercise or as persistent accumulation in a stressed or inefficient circuit. This state-dependence is the reason lactate should be modeled dynamically and contextually.

Operational definition of the lactate window

To address ambiguity, the revised model defines the lactate window operationally rather than by a universal blood lactate concentration. A single threshold is unlikely to apply across patients because lactate kinetics are shaped by fitness, sex, age, exercise modality, nutrition, sleep, medication, metabolic disease, anxiety sensitivity, and cardiovascular capacity. The window should instead be defined by the pattern of exposure and recovery.

Falsifiable predictions of the lactate-window hypothesis

The lactate-window hypothesis is valuable only if it generates testable predictions. The model therefore states the following falsifiable predictions.

• Prediction 1: In exercise trials for MDD, antidepressant response will correlate more strongly with lactate dynamics, including peak, AUC, clearance, and recovery half-time, than with external exercise dose alone.

• Prediction 2: Peak lactate alone will be less predictive than combined indices that include recovery kinetics, RPE, affective response, next-day fatigue, sleep, and adherence.

• Prediction 3: Lactate-related benefits will be stronger for fatigue, anergia, motivational anhedonia, psychomotor slowing, and effort-based decision-making than for all depressive symptoms equally.

• Prediction 4: Blood lactate kinetics will show stronger relationships with symptom change when central bridging markers, such as MRS-derived regional metabolism or neurovascular indices, indicate adaptive brain response.

• Prediction 5: Slow clearance, disproportionate RPE, negative affect during exercise, worsened sleep, or prolonged next-day fatigue will predict poor adherence or weaker symptom improvement, even if peak lactate is high.

• Prediction 6: Baseline phenotype will moderate response. Patients with sedentary behavior, fatigue, anhedonia, metabolic dysfunction, or impaired effort valuation may show stronger lactate-linked symptom changes than patients whose depressive symptoms are dominated by non-metabolic dimensions.

From generic exercise prescription to physiologically informed dosing

Current exercise prescriptions for depression often specify frequency, duration, modality, and target intensity using heart rate, percentage of maximal oxygen uptake (VO₂max), or perceived exertion. These parameters are useful but do not fully capture internal metabolic response. A lactate-informed framework would supplement external dose with physiological response. The practical question becomes not only whether a patient completed 30 minutes of exercise, but what metabolic challenge the session produced and how well the patient recovered.

This framework remains a research agenda. It should not be presented as an already validated clinical tool, and it should not be interpreted as recommending high-intensity exercise for all patients with depression. Its translational value is to generate hypotheses about why exercise intensity may matter, why some patients respond poorly to fixed prescriptions, and how future trials could personalize exercise dose using recovery-sensitive biomarkers.

Baseline assessment should include physical activity level, cardiorespiratory fitness, fatigue severity, anhedonia and motivational impairment, sleep quality, medication exposure, metabolic health, cardiovascular risk, pain, anxiety sensitivity, history of post-exertional symptom exacerbation, exercise preference, and feasibility. Exercise could then be progressed toward a tolerable metabolic stimulus rather than a fixed high-intensity target. For some patients, the first therapeutic goal is adherence and behavioral activation. For others, especially those already capable of exercise, the research target may be a controlled near-threshold lactate pulse with rapid recovery.

Figure 2 translates the hypothesis into a prospective research workflow spanning baseline phenotyping, stepwise exercise dosing, serial lactate sampling, response characterization, and multidimensional outcomes.

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Figure 2. Lactate-informed exercise research framework for depression. The figure reframes lactate-informed exercise prescription as a prospective research framework rather than a validated clinical algorithm. Baseline phenotyping should include activity level, VO₂max or cardiorespiratory fitness, fatigue, anhedonia, motivation, sleep, metabolic health, medication, cardiovascular risk, and exercise preference. A candidate serial sampling schedule may include baseline, immediate post-exercise (5–10 minutes and 20–30 minutes), with selected next-day recovery indicators when relevant. Outcomes should include global depression severity but should prioritize mechanistic symptom dimensions such as fatigue, anhedonia, psychomotor speed, effort-based decision-making, adherence, sleep, and functional recovery. Standardization of meals, sleep, time of day, medication, sex, age, training status, metabolic health, exercise modality, temperature, and sampling method is required before clinical interpretation. MRS: magnetic resonance spectroscopy; RPE: rating of perceived exertion; VO₂max: maximal oxygen uptake.

Candidate populations and safety boundaries

The lactate-window hypothesis is unlikely to apply equally across all forms of depression. It may be most relevant to patients with fatigue-dominant depression, anhedonia, low motivation, effort avoidance, sedentary behavior, obesity, insulin resistance, metabolic syndrome, or inflammatory/metabolic signatures [47–58]. In these patients, lactate dynamics might help distinguish deconditioning, poor recovery, and abnormal metabolic tolerance. Combining lactate measures with effort-based decision-making tasks may clarify whether changes in metabolic response track willingness to work for reward.

Safety boundaries are equally important. Evidence on high-intensity exercise in depression and mental illness remains heterogeneous, and adherence barriers are common [68, 69, 75, 76]. As proposed safety considerations for future trials, patients with severe fatigue, panic symptoms, cardiovascular disease, severe obesity, post-viral fatigue, unstable medical illness, chronic pain, low baseline fitness, or high anxiety sensitivity should undergo appropriate clinical screening and stepwise progression rather than default early high-intensity protocols. Low-intensity walking, cycling, or appropriately dosed resistance exercise can be used to establish routine and confidence before progression toward moderate or threshold-based sessions. In severe depression, suicidality, or marked functional impairment, exercise should complement, not replace, standard psychiatric care.

Adherence is a core endpoint, not an afterthought. Barriers to exercise in depression include low energy, impaired motivation, negative affect, fear of failure, discomfort, lack of social support, cost, and difficulty organizing behavior [75, 76]. A lactate profile that looks physiologically interesting but produces aversion, symptom worsening, or dropout has no practical antidepressant value. The success criterion is better metabolic adaptability and sustained participation, not higher lactate.

Measurement standardization and future trial design

Future trials should directly test whether lactate kinetics predict antidepressant response and whether lactate-informed progression adds explanatory or clinical value beyond standard exercise prescription. Practical designs could compare low-intensity continuous exercise, moderate-to-vigorous continuous exercise, lactate-threshold-based training, interval training, and resistance circuits while matching or statistically adjusting for session duration, energy expenditure, supervision, expectancy, social contact, and adherence.

Blood lactate should be measured as a curve. Candidate time points include pre-exercise baseline, immediately after exercise, 5–10 minutes post-exercise, 20–30 minutes post-exercise, and selected next-day recovery measures in studies focused on fatigue. Sampling density should be sufficient to capture delayed post-exercise peaks. Prespecified kinetic variables may include peak lactate, baseline-corrected AUC over a defined interval, time-to-peak, clearance indices when sampling density permits, recovery measures, and an exploratory lactate-to-RPE index. Measurements should be standardized for meal timing, carbohydrate intake, hydration, caffeine, sleep, time of day, menstrual cycle or hormonal status when relevant, medication, recent activity, ambient temperature, exercise modality, sampling site, and device calibration.

Clinical endpoints should include global depressive symptom severity, but mechanistically relevant outcomes should include fatigue, anhedonia, psychomotor speed, effort-based decision-making, affective valence during exercise, next-day fatigue, sleep, actigraphy, and functional recovery [47–58]. Adherence should be treated as a core outcome and interpreted with literature on exercise barriers and adherence in psychiatric populations [75, 76]. Candidate biomarker panels may include BDNF, inflammatory markers, metabolic indices, cortisol, mitochondrial measures, and sleep or activity measures, selected according to prespecified hypotheses [14–16, 38, 42–46, 56–57]. When feasible, neuroimaging studies should use MRS or related methods to assess regional brain lactate, pH, glutamate/glutamine, or metabolic signals in regions such as the prefrontal cortex, anterior cingulate cortex, hippocampus, and striatum [31, 59, 62, 73]. However, MRS lactate measurement is technically difficult and should not be treated as a simple clinical readout.

The primary trial question should be explicit: does antidepressant response depend on achieving an adaptive lactate response rather than simply completing a prescribed exercise volume? Secondary questions should test whether lactate dynamics predict changes in fatigue, anhedonia, motivation, psychomotor slowing, or effort avoidance. A negative result would also be informative. If lactate dynamics do not predict symptom changes when measured rigorously, then the lactate-window hypothesis should be revised or rejected.

Dynamic biomarker and PK-PD concepts

A conceptual extension involves pharmacokinetic-pharmacodynamic (PK-PD) thinking. Although exercise-induced lactate is not a drug exposure, PK-PD principles provide a useful analogy for distinguishing time-dependent exposure, downstream response, recovery, and interindividual variability. Recent discussion of mechanistic clinical pharmacology emphasizes that PK-PD modeling and dynamic biomarkers can link exposure, target engagement, and downstream biological effects in a translational framework [77]. Applied cautiously, this perspective supports treating lactate as a dynamic exposure signal rather than a static biomarker.

The analogy has limits. Exercise induces multiple simultaneous signals, including temperature, catecholamines, myokines, hemodynamic changes, ventilation, affective experience, and behavioral reinforcement. Peripheral lactate kinetics do not define brain lactate exposure in the way plasma drug concentration might approximate systemic drug exposure. Therefore, PK-PD concepts should be used to improve study design and temporal modeling, not to imply that lactate alone is the active “dose” of exercise.