Mitochondria, Mood Lability, and Bone: mtSNPs’ Surprising Relationship to Mental Homeostasis

  1. Home
  2. Articles

Mitochondria, Mood Lability, and Bone: mtSNPs’ Surprising Relationship to Mental Homeostasis


Elizabeth Mingo* and Chelsea Stephens

Howard University Department of Biology and Quadgrid Data Lab Research Group

*Corresponding author: Howard University Department of Biology and Quadgrid Data Lab Research Group

Citation: Mingo E and Stephens C. (2024) Mitochondria, Mood Lability, and Bone: mtSNPs’ Surprising Relationship to Mental Homeostasis. Adv Clin Med Res. 5(2):1-43.

Received: February 04, 2024 | Published: March 08, 2024

Copyright© 2024 genesis pub by Mingo E, et al. CC BY-NC-ND 4.0 DEED. This is an open-access article distributedunder the terms of the Creative Commons Attribution-NonCommercial-No Derivatives 4.0 International License.,This allows others distribute, remix, tweak, and build upon the work, even commercially, as long as they credit the authors for the original creation.

DOI https://ttps://


Mitochondrial DNA profiles comprise some of the most inclusive and broadly representative genomic databases publicly available, containing diverse haplogroups from all over the world; however, there is less emphasis on mutations' biochemical and neurological impact. Mitochondria’s function in calcium regulation is often cited, but few weave in its roles in immunity, bone homeostasis, cytokinesis, and apoptosis. While this approach is apt for increasing statistical significance, it can miss the bigger picture. Currently, there are enough associations—such as the effects of calcium dysregulation, the role of ROS in circadian rhythm determination, and cytokines’ interaction with mitochondria—to speculate on causality. This systematic review re-contextualizes previously reported haplotypes and single nucleotide polymorphisms (SNPs) in their biochemical environment, reports on potential systemic effects of altered mitochondria, explores common setbacks for studying bipolar disorders, and suggests new technologies that could ameliorate some of them using a novel graphic representation of each study’s findings.


mtDNA; SNP; Mitochondria; Homeostasis; Bone homeostasis; Calcium; Calcium regulation; TNF-α; Cytokines; TFAM; P2X7R; ROS production; CACNA1C; RANKL; MCU; α-CaMKII; CAMK2II; IL-6; CRP; C-reactive protein; ROS; IL-1β; Mitochondrial morphology.


Although humans’ mtDNA is uniformly circular, everyone differs in the number of mtDNA molecules per mitochondrion (copy number) [5,6], the exact haplotype of the mtDNA, the level of heteroplasmy (intra-individual mtDNA haplotype diversity) [7], and even the shape of their mitochondria [8].

Such a varied structure suggests a multitude of functions, which proves correct. Mitochondria house the TCA cycle and the electron transport chain (ETC). Additionally, they engage in lipid metabolism, release ROS (signaling molecules on top of their damaging nature [9]), engage in calcium signaling [10,11], facilitate apoptosis [12], and regulate both nuclear and mitochondrial protein degradation [13].

This, together with mitochondria’s systemic roles in cytokinesis [14]], immunity [14-17], neurology [5] [6,18], and bone homeostasis [19,20], make it a versatile powerhouse within the body. Free-floating mitochondria have been found in both human and fetal bovine sera, expressing genes that regulate immune function [21]. However, this means that any downtick in utility has the potential to cause complex, messy disorders that do not fit nicely into any other box. Due to neurons’ steep energy demands, the brain is on the frontline of any metabolic disorder. This is seen clearly in altered mental statuses conferred by diabetic incidents [22] and the influence of eating disorders such as anorexia nervosa on confusion [23]. In other words, the symptoms of metabolic disorders bleed into psychiatry. Not everything that affects the brain occurs in the brain.

The etiologies of many mental illnesses, including bipolar, remain elusive. Bipolar is a group of mental disorders characterized by varying, prolonged, virtually unprompted periods of mania/hypomania, bipolar depression (as opposed to unipolar depression), and possibly mixed states. Psychosis and emotional blunting may occur throughout, and remission is known as euthymia. Euthymia is biochemically and mentally distinct from a normative mental state.

Bipolar disorders are subject to the kindling effect [24], meaning that each episode is more extreme than the last. It shares this phenomenon (as well as potential treatment with lamotrigine) with epilepsy.

Bipolar is the sixth leading cause of disability worldwide [33]. However, it overlaps with other disorders and is difficult to diagnose, with 31.9% of probands suffering for 13 years before finding the true diagnosis [34] [25]. This lack of identification is a huge problem; 25% of bipolar probands will attempt suicide in their lifetime, and 11% will succeed [34]. This combines with other factors to decrease probands’ lifespan by 11-20 years [35].

The list of comorbidities is lengthy, even putting aside other mental disorders: diabetes [26,27], low bone mass [28], decreases in visual motor perception [29], atrial fibrillation [30], asthma [31], dyslipidemia [36], hypertension [36], CVD [36], T. gondii infection [36], myocardial infarction [36], systemic lupus erythematosus [36], and temperature fluctuations [32]. When compared to healthy controls, bipolar probands present with activation changes in the thalamus [28], dorsolateral prefrontal cortex (DLPFC) [531*], hippocampus [28], and decreased volume in the anterior cingulate cortex [110*]. Many cytokines, such as TNF-α [39], vary significantly between states, as do serum and CSF calcium levels [40]. 

This prevalence, severity, and suicide risk all demand a convincing etiology—and mitochondria almost certainly play a role. Mitochondria interact with cytokines such as TNF-α [113], their count and morphology change in bipolar [114], and they are one of the main players in calcium signaling. Calcium homeostasis interacts with many of these comorbidities and symptoms [115].  However, this illness is complex. Solving the problem of its etiology will take a greater sample size than is reasonable from a singular study.

Although there are excellent reviews on bipolar disorders and mitochondrial physiology [41], there is no review of the utility of available databases. This review aims to integrate the physiology of mitochondria with the immune and skeletal system, explain salient existing database options, and clarify where they fall short for describing bipolar disorders.


PubMed was searched for the combinations of: “bipolar disorder,” “mtDNA,” "mitochondrial DNA,” “cytokine,” "bone," "calcium,” and "mitochondria” on 12-21-2023. These searches provided 580 results, or 521 unique articles, which were then appraised by the methods outlined in Fig. 1A. Only primary sources were included. Other reviews and meta-analyses were excluded to avoid the possibility of evaluating outdated or duplicate studies. Studies were considered “relevant” if they met the following criteria.

In epidemiological studies, the bipolar group had to be the test group/cohort of at least 100 subjects. Thus, a study on the effect of alcohol dependence on bipolar was excluded due to the confounding variable of alcohol dependence. If the study investigating bipolar was one of a few cohorts, and the bipolar sample met the qualifications, it was included. However, the results for non-bipolar mental disorders (such as ASD) were not reported. Studies needed to evaluate a characteristic of bipolar and not suggest a clinical course. If this epidemiological study was a genetic analysis, it had to specify a gene, not a haplogroup.

Mitochondrial haplotypes are broad categories, and results based on haplogroups are inconsistent. This does not mean that mtDNA is irrelevant—rather, smaller subgroups can influence the neurological risk of conditions such as Alzheimer ’s disease [42]. While there are no studies directly addressing subgroup analysis in bipolar disorder, it would certainly make sense of the discrepancies in the literature. To ensure even reporting, only specific mutations were considered.

Mouse studies’ requirements were similar: to have an appropriate strain and sample size. The appropriate sample size was met if the difference between the total animals and the total test groups was greater than 10.

In vitro studies needed slightly different inclusion/exclusion criteria. A list of comorbidities [43], medications, and biochemical markers associated with bipolar disorder were compiled. Relevant studies either investigated bipolar and one relevancy term or investigated three (or more) of the relevancy terms. The relevancy terms were as follows: childhood maltreatment/trauma, CVD, mitochondrial dysfunction, diabetes, dyslipidemia, senescence/aging, blood-brain barrier, metabolic syndrome, obesity, HPA disruption, bone mass, CKD, sleep deprivation, hippocampus, T. gondii, dentate gyrus, dorsolateral prefrontal cortex, prefrontal cortex, ROS/oxidative stress, mitochondrial copy number, mania/ hyperactivity (in mice), NF-κB, TNF-α, IL-6, IL-8, BRPF2, α-CaMKII, CRP, P2X7R, mitophagy, apoptosis, IL-1β, Lithium Lamotrigine, Valproate/valproic acid, Quetiapine, Olanzapine, Aripiprizole, Risperidone, any SSRIs, calcium, Complex 1, Complex 2, Polg1, LPS. iPSC studies claimed the correct significance, outlined in this review [44].

All relevant studies reported no author bias and had accurate abstracts. All were peer-reviewed.

Results from the relevant studies were tabulated in the supplementary table, which is the source for all the following figures to create ‘etiological fingerprints’ for each given variable—including comorbidities, cytokine elevations, ROS, cell cycle alterations, calcium alterations, and mitochondrial alterations. These were the columns of our table, with the exposure variables on the x-axis and our dependent variables on the y-axis. We then screened the following biometric databases for the qualities outlined in (Figure 1B)  MITOMAP, MitBASE, MSeqDR, GnomA, PGC, and GWAS. The benefits and drawbacks of each were tabulated and compared to the literature review to estimate the efficacy of the available sources.


Figure 1: The results of the literature and database review.

Diagnoses and Symptoms

Findings Map 1: A summary of dependent variables associated with bipolar and vetted symptoms of bipolar.

The finalists confirmed the existing notion that bipolar is an inflammatory disease. TNF-α, CRP, IL-1β, monocyte activators, and Complex I mutations all showed symptom profiles similar to bipolar. Both early and late onset bipolar were associated with mutations in Cytochrome B and Complex I, but the mutations differed. ND4 affected both phenotypes, whereas NDUFV2 was only associated with late onset [45] [46]. They also showed different epistasis—with late-onset preferring MGAM and early onset interacting with IL34 [46].

Although studies show consistent associations with TNF-α, IL-6, and IL-1β, these are dramatically affected by sample processing time, which may differ between collection sites [47]. While in vitro studies still support the involvement of these three cytokines, sample processing time remains a silent confounding variable for all epidemiological studies. The only cytokine that survived the correction was a decrease in IL-8. The effects of sample processing time on CRP were not tested. Even with this confounding variable, TNF-α, IL-6, CRP, and MCP-1 are all associated with the duration of bipolar and are differentially affected by depressive and manic states [48].


Findings Map 2: A summary of dependent variables associated with common medications for bipolar.

The etiological fingerprints of many medications prescribed for bipolar were also surveyed. Lithium and d-amphetamine were the most investigated drugs—lithium, because it is widely recognized as the gold standard for the treatment of bipolar (mania, in particular); d-amphetamine, because it can induce mania. These drugs are appealing proxies for euthymia/mania in vitro. No drug seemed to simulate mixed states, rapid cycling, or bipolar (as opposed to unipolar) depressive episodes.

All investigated drugs (except valproate) act on cytokines—especially IL-6, IL-1β, TNF-α, and IL-2. However, each has a unique profile. Lamotrigine is more effective against bipolar depression than mania and is exceptionally effective against rapid cycling [49]. Therefore, building a unique etiological fingerprint for each drug could illuminate subtle differences between mood disorders. Valproate, the drug with the least measured anti-inflammatory action, was the only drug found to independently increase fracture risk.

Lithium demonstrated anti-inflammatory properties, even in cerebral and cardiac ischemia [50]. It partially diminished the etiological fingerprint of bipolar—and lithium response correlated with an innate ability to ‘fill in the gaps.’ For example, one study found lithium increased cardiac IL-6; lithium-responsive rats showed a decreased concentration of IL-6 in the orbitofrontal cortex [51]. These are different tissues, but the larger pattern suggests the potential of a findings map—even when the findings from one study are unilluminating, it is easy to see that lithium responsivity protects against a potential inconsistency in lithium’s action. A findings map can identify intermittent problems quickly because they are all recorded in the same place.

Biochemical Findings