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Article

Does Screening for Bipolar Disorders Identify a “Dysregulation of Mood, Energy, and Social Rhythms Syndrome” (DYMERS)? A Heuristic Working Hypothesis

by
Mauro Giovanni Carta
1,
Goce Kalcev
1,*,
Michele Fornaro
2,
Samantha Pinna
1,
Cesar Ivan Aviles Gonzalez
3,
Antonio Egidio Nardi
4 and
Diego Primavera
1
1
Department of Medical Sciences and Public Health, University of Cagliari, Monserrato Blocco I (CA), 09042 Cagliari, Italy
2
Department of Psychiatry, Federico II University of Naples, 80126 Naples, Italy
3
Nursing Program, Faculty of Health Sciences, Universidad Popular del Cesar, Sede Sabanas, Valledupar 20002, Colombia
4
Laboratory Panic and Respiration, Institute of Psychiatry (IPUB), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 22725, Brazil
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2023, 12(15), 5162; https://doi.org/10.3390/jcm12155162
Submission received: 27 June 2023 / Revised: 31 July 2023 / Accepted: 5 August 2023 / Published: 7 August 2023
(This article belongs to the Section Mental Health)
Versions Notes

Abstract

:
The aim of this paper is to verify if people with a positive score on the Mood Disorder Questionnaire (MDQ) without comorbidity of mood disorders showed a worse level of Health-related Quality of life (HRQol) compared to a control-matched sample of MDQ negatives, identifying a specific syndrome. This is a case-control study based on a database from a community survey. Cases: MDQ-positive without mood disorders; Controls: MDQ negatives matched by sex, age, and psychiatric diagnosis according to the Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV) criteria. Tools: MDQ, the Advanced Neuropsychiatric Tools and Assessment Schedule (ANTAS) semi-structured interview for psychiatric diagnosis, and the Health Survey Short Form (SF-12) for measuring HRQol. People scoring positive on the MDQ without a diagnosis of mood disorders showed significantly lower scores on the SF-12 compared to people of the same age and of the same sex with an equal diagnosis of psychiatric disorders not related to mood disorders (35.21 ± 6.30 vs. 41.48 ± 3.39, p < 0.0001). In the debate whether a positive score on the MDQ selects an area of “malaise” due to the presence of disorders differing from Bipolar Disorders, or if a positive score on the MDQ may be considered a “subthreshold” form of bipolar disorder in people who may later develop bipolar disorder, a third hypothesis can be advanced, i.e., that a positive score on the MDQ identifies a specific “Dysregulation of Mood, Energy, and Social Rhythms Syndrome” (DYMERS), characterized by a considerable amount of suffering and not attributable to other disorders, and which might represent a trigger for the previously mentioned disorders with which a positive score on the MDQ is associated, probably including, in severe conditions, bipolar disorder.

1. Introduction

The interest in validated rating tools for the detection of hypomania among people endorsing a major depressive episode saw a surge some decades ago [1]. The Hypomania Checklist (HCL-32) represents a core tool primarily adopted in clinical settings [2,3,4], whereas the Mood Disorder Questionnaire (MDQ) [5,6,7,8] is particularly, but not only, employed in epidemiological studies: surveys carried out in the community found a (lifetime) prevalence of around 4% in the US [6], of 3% in Italy [9], of 3.6% in France [10], of 18.6% in a large sample of college students [11], and of 4.3% in overall adults in South Korea [12]. In contrast, the prevalence rate recorded in community surveys using structured interviews was approximately under 2% [4]. MDQ has been used to demonstrate, among other things, a greater prevalence of bipolar spectrum features (also subthreshold) in people without a diagnosis of bipolar disorders with Major Depressive Disorder [13,14,15], fibromyalgia [16,17], athletes engaging in extreme and high-risk sports [18], students of the arts [19], obese individuals and people with eating disorders [20], postpartum depression [21], and severe neurological diseases [22,23]. However, with respect to a diagnosis of bipolar disorder (BD) conducted according to the criteria of the international classifications in use at the time, a series of studies clarified that the screeners, specifically the MDQ, produced false positives [24]. In fact, individuals scoring positive on the MDQ did not frequently receive a diagnosis of bipolar disorder in clinical settings but, rather, of post-traumatic stress disorder, borderline personality disorders, specific phobias, attention deficit disorder, alcohol and drug use disorders, impulse control disorder, and eating disorders [24,25,26,27]. Therefore, the episodes of energy boost identified by BD screeners (i.e., answering positively to a series of short questions on the MDQ and other screeners) did not coincide with episodes of hypomania. However, although the screener was not accurate compared to the golden standard, it identified an area of “positives” of clinical interest. For this reason, it was observed that even if the individuals scoring positive on the MDQ did not fulfill all the criteria for a diagnosis of BD, they presented some homogeneity with BD [4], not only regarding sex and age distributions but also functioning, with a high level of distress, low social functioning, and a lower perception of quality of life [28,29,30].
It was also found that MDQ positives, consistent with people with BD, showed an elevated risk of suicide, particularly young people who were likely to develop a diagnosis of BD in the future [31,32]. The diagnoses (different from those of bipolar disorder) affecting MDQ positives were conditions well known to be frequently associated with BD [4] and notoriously clinically identified about ten years before the onset of the co-occurring BD [33]. There is a vast literature that focuses on co-morbidity with BD and, often, on the temporal sequence (according to the late recognition of diagnoses of BD) with the mental health disorders indicated by the research on the inefficacy of the screeners, specifically post-traumatic stress disorder [34,35,36], borderline personality disorders [37,38,39], specific phobia [40,41,42], attention deficit disorder [43,44,45], alcohol use disorders [46,47,48], substance use disorders [49,50,51], eating disorders [52,53,54], and impulse control disorder [55,56,57].
At that point, the opinions of the experts seemed divided. Most clinicians, even after the evidence of the low accuracy of the screeners and, therefore, of the low validity of the population studies suggesting the expansion of the area of BD, decided that the diagnosis of bipolar disorder should be related to a set of “severe” conditions” and separated from the depressive ones [58]. In contrast, according to a minority of “neo-Kraepelinian” experts, the focal issue consisted of starting to verify if the cases of bipolar disorder identified in clinical settings were the emerging tip of an iceberg with a significant amount of “submerged conditions” classified as depressive but with subthreshold features and treatment response profiles of BD, thus an area of the so-called “bipolar spectrum” [59,60,61,62].
However, new evidence could suggest the need to verify a new heuristic hypothesis. The results of a recent study confirm the poor validity of the MDQ as a screener since its scores do not correlate with the presence of genetic variants associated with bipolar disorder [63]. This evidence was challenged by new findings, which showed that some genetic characteristics associated with bipolar disorder were found to be frequent in older adults without bipolar disorder but with adaptive traits of hyperactivity and novelty seeking [64,65,66]. The frequency of the same gene in hyperactive people without BD does not contradict an association between BD and that genetic variant, but it makes a linear correlation between the MDQ score and genetic risk indices unlikely. This hypothesis was verified by studying the accuracy of screeners on both the MDQ score and, in parallel, the presence of a genetic variant known to be associated with bipolar disorder, keeping the clinical diagnosis of bipolar disorder as the Gold Standard [65]. Neither of the evaluated tools (the MDQ or the genetic test) had sufficient accuracy to be used as a screener, although both had some validity [67]. Interestingly, the level of concordance between the two screeners was shallow; in fact, each screener seems to have identified a component factor that was not associated with, or was even independent of, the other one [65]. The work on the absence of correlation between the MDQ score and the genetic risk [63] presented, however, another relevant element, i.e., the MDQ positivity, which was found to have a high genetic correlation with other conditions, such as post-traumatic stress disorder, anxiety disorders, and, specifically, insomnia, one of the most relevant elements of the so-called “social rhythm”, whose psychopathological relevance has been recently emphasized [68,69].
In essence, if the increase in energy (detected by the MDQ) does not reach the threshold to be identified with an episode of hypomania on a clinical level and does not give rise to depressive “rebounds,” it is closely associated with post-traumatic and anxiety disorders and is even positively correlated with the genetic variants associated with these disorders. This association is not found in bipolar disorder. Even more closely, MDQ positivity was associated with the presence of altered social rhythms.
Another interesting theme concerns the relationship between a positive score on the MDQ and an impaired perception of Health-related Quality of Life. A community survey found that the impairment in health-related quality of life in individuals who screened positive for Bipolar Disorder on the MDQ was largely only related to scoring positive on the screener, regardless of comorbid conditions and even when a diagnosis of Bipolar Disorder based on a semi-structured psychiatric interview was excluded [30]. Further evidence also confirmed that a positive score on the screener identifies an area of people with no pathologic mood status but with a low level of quality of life [29]. It has been underlined that QoL is more impaired in bipolar disorder than in other mood disorders and anxiety disorders. It was also found that, in people with bipolar disorder, QoL was negatively associated with depressive symptoms and episodes. On the other hand, QoL appears to be a meaningful and important indicator of outcome and recovery in Bipolar Disorder. For this reason, it was initially believed that the impairment of QoL in individuals with positive scores was an indication (if not proof) of the so-called spectrum of bipolar disorders, even in the absence of a diagnosis of bipolar disorder.
Thus, some questions arise:
(1)
Can the impairment be found to be associated with a positive score on the MDQ and, specifically, the impairment of quality of life, which is evidently not attributable to the presence of a bipolar disorder (given the high frequency of “false positives”), be attributed to the concomitance of associated disorders, or is it an “intrinsic” characteristic of people scoring positive on the MDQ?
(2)
If the impairment was not specifically associated with positivity with other disorders, would it be possible to think that a positive score on the MDQ (and the associated rhythm dysregulation) could be a stress factor constituting a trigger for many disorders and, therefore, a co-factor that triggers the eventual genetic disposition?
To test the hypothesis of an “intrinsic” property of the MDQ to identify people with a bad quality of life regardless of any associated disorders, we worked on the database of a previous epidemiological Italian nationwide survey, which measured the frequency of positive scores on the MDQ in the community as well as, in all the interviewees, the presence of mental health disorders and the individual impairment/well-being as a measure of the quality of life [9].
Our hypothesis is that people with a positive score on the MDQ and without the co-presence of mood disorders show a worse level of health-related quality of life (HRQoL) compared to a control sample of the same age and sex and with psychiatric comorbidity.

2. Materials and Methods

2.1. Design

A case-control study built on a database of community surveys.

2.2. Recruitment Methods and Study Sample

The database from which the study sample was extracted was randomly drawn after stratification by age and sex from selected urban and rural municipality records (adult population) in six different Italian regions (3 from the South, 1 from the Center, and two from the North of Italy). A sample of 4999 people was drawn. A more detailed description of the community survey has already been published [9]. From this database, we extracted all the people scoring positive on the MDQ (cases), and we excluded (exclusion criteria for cases) all the individuals who had co-morbidities with a diagnosis of mood disorder. We then searched, for each MDQ-positive person, for all MDQ-negative people of the same age and gender who had the same co-morbidity for non-mood DSM-IV psychiatric disorders (eligible controls). The exclusion criteria for controls were an MDQ borderline score of 6 or 7. For each MDQ-positive case, a cell with all the people eligible to be matched as a control for that case was created. Then we randomly selected a control for each case. We excluded from the analysis all the MDQ-positive people who had co-morbidities that did not allow for a match.

2.3. Study Tools

Basic demographic data were assessed on an ad hoc basis.
The presence of DSM-IV psychiatric disorders was confirmed by means of the Advanced Neuropsychiatric Tools and Assessment Schedule (ANTAS), a validated semi-structured clinical interview [70].
The Italian version of the Mood Disorder Questionnaire (MDQ) was used to assess lifetime hypomanic episodes or episodes of augmentation of energy [7].
The perceived HRQoL was assessed by means of the Health Survey Short Form (SF-12). The SF-12 explores dimensions such as physical functioning, emotional state, pain, general health, vitality, social activity, and mental health [71].

2.4. Ethics

The protocol for the Italian community survey was approved by the ethical committee of the Italian National Health Institute (Rome). The approved protocol envisaged the possibility of conducting case-control studies on the database to investigate specific working hypotheses. Informed consent was signed by each candidate.

2.5. Statistical Analysis

The comparison between means and standard deviations of the SF-12 scores between cases (MDQ+) and matched controls (MDQ−) was conducted by means of statistical parametric analyses (ANOVA). The comparison between the number of people with low levels of SF-12 cases and controls was conducted by means of the Chi-squared test.

3. Results

Applying the methodology described above, forty-one pairs of positive cases (MDQ+) and controls (MDQ) matched for sex (twenty-three males), age (42.95 ± 14.34), and psychiatric co-morbidity (eighteen without a psychiatric diagnosis, one with specific phobia, one with anorexia, and one with obsessive-compulsive disorder) were selected (see Table 1). The scores on the instrument that measured the health-related quality of life (SF-12) were clearly lower in cases compared to controls, F(1,81DF) = 31.492 p < 0.0001, with an average difference of about six points (see Table 1).
The differences remained largely unchanged for people aged 50 and over. A drastically low score (35 or less) on the SF-12 was markedly higher among cases compared to controls (19/41 or 46.3% versus 3/30 or 7.9%, OR = 10.93 (2.90–41.19). The difference remained significant in people aged 50 or more, but, in this case, the OR decreased fairly (9/17 = 52.94% against 3/17 = 17.6%, OR = 5.25 (1.09–25.21) (see Table 2).

4. Discussion

The study shows that people who live in the community and have a positive score on the MDQ screener, even in the absence of a diagnosis of mood disorders, show significantly lower scores on the health-related quality of life questionnaire SF-12 compared to people of the same age and of the same sex with an equal diagnosis of psychiatric disorders not related to mood disorders. These differences remain high in the sub-sample of people over the age of 50, in whom the onset of a mood disorder is more unlikely. The frequency of extremely low SF-12 scores (less than or equal to 35) is markedly higher in cases compared to controls. This difference remains statistically significant for people aged 50 or older.
In the debate whether a positive score on the MDQ selects an area of “malaise” attributable to a series of disorders often present in people with positive scores on the MDQ who do not have BD disorders, (post-traumatic stress disorder, borderline personality disorders, specific phobia, attention deficit disorder, alcohol, substance use disorders, eating disorders, and impulse control disorders) or if a positive score on the MDQ may be considered a “subthreshold” form of bipolar disorder in people who may later develop bipolar disorder, then a third hypothesis can be advanced, i.e., that a positive score on the MDQ does not identify a generic area of malaise linked to “other” disorders, but a specific syndrome characterized by a considerable amount of suffering and not attributable to other disorders, which might represent a trigger for the previously mentioned disorders with which a positive score on the MDQ is associated, probably including, in the most serious forms and in people with genetic risk, bipolar disorder.
It must be kept in mind that a difference of six points on the total score of the health-related quality of life scale, i.e., in relation to dimensions such as physical functioning, emotional state, pain, general health, vitality, social activity, and, specifically, mental health [72], is a notable difference, even greater than that highlighted between those people suffering from certain psychiatric and non-psychiatric disorders and people that do not have these diagnoses. Comparing studies taken from the same database, the “burden” of MDQ positivity, understood as a difference in the health-related quality of life against controls without MDQ positivity, is lower than that highlighted with a similar methodology in disorders such as multiple sclerosis [73], fibromyalgia [74], and atherosclerosis [75], but is even higher than the “burden” of pathologies such as specific phobia [76] and panic disorder [77], and is comparable to the burden of major depression [78] or eating disorders [79]. It is therefore a condition that is associated with a considerable impairment not attributable to other associated psychiatric diagnoses. However, what other features could be hypothesized to be associated with this MDQ positivity in addition to the lifetime episodes of increased energy identified by the instrument? Importantly, other studies have found that a positive score on the MDQ is closely associated with sleep disturbances, regardless of the presence of bipolar disorder [53]. It is also well known that sleep dysregulation and energy dysregulation, as identified by the MDQ, are often associated with other social rhythms, such as the rhythms of eating and social relationships [79,80]. In support of this hypothesis of the consistency of a “Dysregulation of Mood, Energy, and Social Rhythms Syndrome” (DYMERS), some evidence was accumulated in the COVID era. The emergency induced by the pandemic and, above all, the consequent lockdown have, in fact, shown, on a clinical level, how the dysregulation of social rhythms such as energy, sleep, food rhythm, and social rhythms could influence negatively (or positively, in the case of maintaining good rhythms) not only mood disorders [69], but also work-related stress [81] and general well-being in people without previous disorders [82,83].
The heuristic hypothesis that produces the reading of these data is that three levels of energy increase can be identified:
(1)
Episodic increase in adaptive energy (typical, for example, of the sports stars that perform excellently even if they do not sleep the day before the race due to the hyper-activation [84]), which a previous study identified in healthy and well-adapted hyperactive elderly people who possessed one of the genetic variants also found to be associated with bipolar disorder [64,65,66].
(2)
Episodic increase in energy that enters the stress area and is associated with the dysregulation of social rhythms (sleep, eating, and relational). This condition is typical of MDQ positivity and is associated with anxiety disorders and post-traumatic stress disorder (more closely than bipolar disorder), but not exclusively, as it is even found alone in MDQ positives without co-morbidity. The increase in energy is still finalized, but inefficacy in resolving issues (hence short-circuiting the stress as, for example, seen paradoxically in some cases of burnout/occupational stress syndromes [85]) may impair the health-related quality of life.
(3)
Hypomania with mood dysregulation and bipolar disorder implies a strong out-of-control hyperactivation that loses relationships with adaptive goals and often rebounds into depression. In this case, the increase in energy is such that it can be clinically identified as a hypomanic episode. This condition does not only require greater stress and environmental factors but also, possibly, a specific and additional genetic risk component. In fact, it has been shown that genetic risk and a positive score on the MDQ could be independent components of BD [67]. A predisposition to hyperactivity could be the substrate of the disorder if activated by great stress.
From this point of view, the neo-Kraepelinian concept that sees the primacy of mania and considers depression to be like the ashes after the fire of mania in bipolar disorders is not totally in contradiction with this hypothesis [60].
Indeed, if it can be hypothesized that a serious fire can cause the ash (i.e., severe and chronic depression) and that a fire can re-explode from the ash as in a volcano after a really important stimulus (a switch from drugs or considerable stress), it can also be conceivable that, in predisposed individuals, a less violent fire can cause consequences such as rhythm dysregulation and vulnerability to stress, even without necessarily depressive symptoms.
The limits of this study lie in the fact that the aim was to preliminarily verify a heuristic hypothesis on a historical sample, whose aim was to accumulate preliminary data for supporting specific ad hoc research on the actual consistency of the “Dysregulation of Mood, Energy, and Social Rhythms Syndrome” (DYMERS). The methodology of exclusion of people with multiple co-morbidities due to the absence of matched controls may have generated a bias because the cases with greater impairment were excluded from the analysis, but, at the same time, it allowed for a perfect balance between cases and controls matching all potential psychiatric diseases influencing the health-related quality of life. Having excluded the most serious cases with the presence of a positive score on the MDQ also goes against the working hypothesis, according to which a positive score on the MDQ is associated with a serious impairment since a greater impact could be assumed in people who are sick for other reasons. The value of the results was, therefore, not affected.
Although QoL can also be impaired by other confounders, such as education, employment status, and marital status, only age and sex were taken into account as matching variables, as it is difficult to hypothesize that, on the basis of literature data, the influence of these confounders—which always interact with age and sex—can be crucial in determining the large difference that emerged from our analysis [86,87].
A case-control study built on a historical database did not obviously make it possible to conduct specific analyses to identify the direction of the association between the MDQ and the HRQoL scores. In fact, it would be theoretically possible that people with a specific personality trait that manifests itself in low HRQoL are predisposed to episodes of stress and rhythm dysregulation. This limited research cannot obviously answer this question, which would require the conduct of longitudinal studies. However, this aspect should be correctly reported. The study, given the importance of the health burden of MDQ positivity independent of other psychiatric diagnoses, suggests an urgent need for ad hoc research.

5. Conclusions

In the debate whether a positive score on the MDQ selects an area of “malaise” due to the presence of disorders differing from Bipolar Disorders, or if a positive score on the MDQ may be considered a “subthreshold” form of bipolar disorder in people who may later develop bipolar disorder, a third hypothesis can be advanced, i.e., that a positive score on the MDQ identifies a specific “Dysregulation of Mood, Energy, and Social Rhythms Syndrome” (DYMERS), characterized by a considerable amount of suffering and not attributable to other disorders, and which might represent a trigger for the previously mentioned disorders with which a positive score on the MDQ is associated, probably including, in severe conditions, bipolar disorder.

Author Contributions

Conceptualization: M.G.C., G.K., M.F., S.P., C.I.A.G., A.E.N. and D.P.; methodology: M.G.C., G.K., M.F. and D.P.; software: M.G.C.; validation: M.G.C., G.K. and D.P.; formal analysis: M.G.C.; investigation: M.G.C.; resources: M.G.C.; data curation: M.G.C. and G.K.; writing-original draft preparation; M.G.C.; writing-review and editing: M.G.C., G.K., M.F., S.P., C.I.A.G., A.E.N. and D.P.; visualization: M.G.C.; supervision: M.G.C.; project administration: M.G.C.; funding acquisition: M.G.C. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded through a grant from AIFA (Agenzia Italiana del Farmaco, Italian Drug Agency, Number FARM54S73S, approved in 2005); AIFA had no further role in study design; in the collection, analysis, and interpretation of data; in the writing of the report, or in the decision to submit the paper for publication.

Institutional Review Board Statement

The protocol of the Italian community survey was approved by the ethical committee of the Italian National Health Institute (Rome). The approved protocol envisaged the possibility of conducting case-control studies on the database to investigate specific working hypotheses. Ethic Committee Name: IFA (Agenzia Italiana del Farmaco, Italian Drug Agency), approval Code: FARM54S73S, approval Date: 2005.

Informed Consent Statement

Informed consent was signed by each candidate.

Data Availability Statement

The approved protocol envisaged the possibility of conducting case-control studies on the database to investigate specific working hypotheses.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Carta, M.G.; Angst, J. Screening for bipolar disorders: A public health issue. J. Affect. Disord. 2016, 205, 139–143. [Google Scholar] [CrossRef] [PubMed]
  2. Angst, J.; Adolfsson, R.; Benazzi, F.; Gamma, A.; Hantouche, E.; Meyer, T.D.; Skeppar, P.; Vieta, E.; Scott, J. The HCL-32: Towards a self-assessment tool for hypomanic symptoms in outpatients. J. Affect. Disord. 2005, 88, 217–233. [Google Scholar] [CrossRef] [PubMed]
  3. Angst, J.; Meyer, T.D.; Adolfsson, R.; Skeppar, P.; Carta, M.; Benazzi, F.; Lu, R.B.; Wu, Y.H.; Yang, H.C.; Yuan, C.M.; et al. Hypomania: A transcultural perspective. World Psychiatry 2010, 9, 41–49. [Google Scholar] [CrossRef] [Green Version]
  4. Ouali, U.; Zgueb, Y.; Jouini, L.; Aissa, A.; Jomli, R.; Ouertani, A.; Omrani, A.; Nacef, F.; Carta, M.G.; Preti, A. Accuracy of the Arabic HCL-32 and MDQ in detecting patients with bipolar disorder. BMC Psychiatry 2023, 23, 70. [Google Scholar] [CrossRef]
  5. Hirschfeld, R.M.; Williams, J.B.; Spitzer, R.L.; Calabrese, J.R.; Flynn, L.; Keck, P.E., Jr.; Lewis, L.; McElroy, S.L.; Post, R.M.; Rapport, D.J.; et al. Development and validation of a screening instrument for bipolar spectrum disorder: The Mood Disorder Questionnaire. Am. J. Psychiatry 2000, 157, 1873–1875. [Google Scholar] [CrossRef]
  6. Hirschfeld, R.M.; Calabrese, J.R.; Weissman, M.M.; Reed, M.; Davies, M.A.; Frye, M.A.; Keck, P.E., Jr.; Lewis, L.; McElroy, S.L.; McNulty, J.P.; et al. Screening for bipolar disorder in the community. J. Clin. Psychiatry 2003, 64, 53–59. [Google Scholar] [CrossRef] [PubMed]
  7. Hardoy, M.C.; Cadeddu, M.; Murru, A.; Dell’Osso, B.; Carpiniello, B.; Morosini, P.L.; Calabrese, J.R.; Carta, M.G. Validation of the Italian version of the “Mood Disorder Questionnaire” for the screening of bipolar disorders. Clin. Pract. Epidemiol. Ment. Health 2005, 1, 8. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Ouali, U.; Jouini, L.; Zgueb, Y.; Jomli, R.; Omrani, A.; Nacef, F.; Preti, A.; Carta, M.G. The Factor Structure of the Mood Disorder Questionnaire in Tunisian Patients. Clin. Pract. Epidemiol. Ment. Health 2020, 16 (Suppl. 1), 82–92. [Google Scholar] [CrossRef] [PubMed]
  9. Carta, M.G.; Aguglia, E.; Balestrieri, M.; Calabrese, J.R.; Caraci, F.; Dell’osso, L.; Di Sciascio, G.; Drago, F.; Faravelli, C.; Lecca, M.E.; et al. The lifetime prevalence of bipolar disorders and the use of antidepressant drugs in bipolar depression in Italy. J. Affect. Disord. 2012, 136, 775–780. [Google Scholar] [CrossRef]
  10. Carta, M.G.; Zairo, F.; Saphino, D.; Sevilla-Dedieu, C.; Moro, M.F.; Massidda, D.; Kovess, V. MDQ positive people’s searching for effective and ineffective treatments for Bipolar Disorders: A screening study in France. J. Affect. Disord. 2013, 149, 84–92. [Google Scholar] [CrossRef]
  11. Bae, S.O.; Kim, M.D.; Lee, J.G.; Seo, J.S.; Won, S.H.; Woo, Y.S.; Seok, J.H.; Kim, W.; Kim, S.J.; Min, K.J.; et al. Prevalence of bipolar spectrum disorder in Korean college students according to the K-MDQ. Neuropsychiatr. Dis. Treat. 2013, 9, 869–874. [Google Scholar]
  12. Kim, J.H.; Chang, S.M.; Hong, J.P.; Bae, J.N.; Cho, S.J.; Hahm, B.J.; Lee, D.W.; Park, J.I.; Lee, J.Y.; Jeon, H.J. Lifetime prevalence, sociodemographic correlates, and diagnostic overlaps of bipolar spectrum disorder in the general population of South Korea. J. Affect. Disord. 2016, 203, 248–255. [Google Scholar] [CrossRef] [PubMed]
  13. Kamat, S.A.; Rajagopalan, K.; Pethick, N.; Willey, V.; Bullano, M.; Hassan, M.J. Prevalence and humanistic impact of potential misdiagnosis of bipolar disorder among patients with major depressive disorder in a commercially insured population. J. Manag. Care Pharm. 2008, 14, 631–642. [Google Scholar] [CrossRef] [Green Version]
  14. Tafalla, M.; Sanchez-Moreno, J.; Diez, T.; Vieta, E. Screening for bipolar disorder in a Spanish sample of outpatients with current major depressive episode. J. Affect. Disord. 2009, 114, 299–304. [Google Scholar] [CrossRef] [PubMed]
  15. Amin-Esmaeili, M.; Motevalian, A.; Rahimi-Movaghar, A.; Hajebi, A.; Sharifi, V.; Mojtabai, R.; Gudarzi, S.S. Bipolar features in major depressive disorder: Results from the Iranian mental health survey (IranMHS). J. Affect. Disord. 2018, 241, 319–324. [Google Scholar] [CrossRef]
  16. Carta, M.G.; Cardia, C.; Mannu, F.; Intilla, G.; Hardoy, M.C.; Anedda, C.; Ruggero, V.; Fornasier, D.; Cacace, E. The high frequency of manic symptoms in fibromyalgia does influence the choice of treatment? Clin. Pract. Epidemiol. Ment. Health 2006, 2, 36. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Wilke, W.S.; Gota, C.E.; Muzina, D.J. Fibromyalgia and bipolar disorder: A potential problem? Bipolar Disord. 2010, 12, 514–520. [Google Scholar] [CrossRef]
  18. Dudek, D.; Siwek, M.; Jaeschke, R.; Drozdowicz, K.; Styczeń, K.; Arciszewska, A.; Chrobak, A.A.; Rybakowski, J.K. A web-based study of bipolarity and impulsivity in athletes engaging in extreme and high-risk sports. Acta Neuropsychiatry 2016, 28, 179–183. [Google Scholar] [CrossRef]
  19. Siwek, M.; Dudek, D.; Arciszewska, A.; Filar, D.; Rybicka, M.; Cieciora, A.; Pilecki, M.W. The analysis of the bipolarity features in students of arts and the students of technology. Psychiatr. Pol. 2013, 47, 787–797. [Google Scholar] [CrossRef]
  20. Nagata, T.; Yamada, H.; Teo, A.R.; Yoshimura, C.; Kodama, Y.; van Vliet, I. Using the mood disorder questionnaire and bipolar spectrum diagnostic scale to detect bipolar disorder and borderline personality disorder among eating disorder patients. BMC Psychiatry 2013, 13, 69. [Google Scholar] [CrossRef] [Green Version]
  21. Masters, G.A.; Brenckle, L.; Sankaran, P.; Person, S.D.; Allison, J.; Moore Simas, T.A.; Ko, J.Y.; Robbins, C.L.; Marsh, W.; Byatt, N. Positive screening rates for bipolar disorder in pregnant and postpartum women and associated risk factors. Gen. Hosp. Psychiatry 2019, 61, 53–59. [Google Scholar] [CrossRef] [PubMed]
  22. Carta, M.; Mura, G.; Sorbello, O.; Farina, G.; Demelia, L. Quality of Life and Psychiatric Symptoms in Wilson’s Disease: The Relevance of Bipolar Disorders. Clin. Pract. Epidemiol. Ment. Health 2012, 8, 102–109. [Google Scholar] [CrossRef] [Green Version]
  23. Özkan, A.; Altinbaş, K.; Koç, E.R.; Şen, H.M.; Özişik Karaman, H.I. Affective Temperament Profiles in Patients with Multiple Sclerosis: Association with Mood Disorders. Noro Psikiyatr. Ars. 2016, 53, 311–316. [Google Scholar] [CrossRef] [PubMed]
  24. Zimmerman, M.; Galione, J.N.; Chelminski, I.; Young, D.; Dalrymple, K. Psychiatric diagnoses in patients who screen positive on the Mood Disorder Questionnaire: Implications for using the scale as a case-finding instrument for bipolar disorder. Psychiatry Res. 2011, 185, 444–449. [Google Scholar] [CrossRef] [PubMed]
  25. Zimmerman, M.; Galione, J.N. Screening for bipolar disorder with the Mood Disorders Questionnaire: A review. Harv. Rev. Psychiatry 2011, 19, 219–228. [Google Scholar] [CrossRef]
  26. Zimmerman, M.; Galione, J.N.; Ruggero, C.J.; Chelminski, I.; Young, D.; Dalrymple, K.; McGlinchey, J.B. Screening for bipolar disorder and finding borderline personality disorder. J. Clin. Psychiatry 2010, 71, 1212–1217. [Google Scholar] [CrossRef] [Green Version]
  27. Zimmerman, M.; Galione, J.N.; Ruggero, C.J.; Chelminski, I.; Dalrymple, K.; Young, D. Are screening scales for bipolar disorder good enough to be used in clinical practice? Compr. Psychiatry 2011, 52, 600–606. [Google Scholar] [CrossRef]
  28. Carvalho, A.F.; Takwoingi, Y.; Sales, P.M.; Soczynska, J.K.; Köhler, C.A.; Freitas, T.H.; Quevedo, J.; Hyphantis, T.N.; McIntyre, R.S.; Vieta, E. Screening for bipolar spectrum disorders: A comprehensive meta-analysis of accuracy studies. J. Affect. Disord. 2015, 172, 337–346. [Google Scholar] [CrossRef] [Green Version]
  29. Fava, G.A.; Bech, P. The Concept of Euthymia. Psychother. Psychosom. 2016, 85, 1–5. [Google Scholar] [CrossRef]
  30. Carta, M.G.; Norcini-Pala, A.; Moro, M.F.; Balestrieri, M.; Caraci, F.; Dell’Osso, L.; Sciascio, G.D.; Faravelli, C.; Hardoy, M.C.; Aguglia, E.; et al. Does Mood Disorder Questionnaire identify sub-threshold bipolarity? Evidence studying worsening of quality of life. J. Affect. Disord. 2015, 183, 173–178. [Google Scholar] [CrossRef]
  31. Merikangas, K.R.; Cui, L.; Kattan, G.; Carlson, G.; Youngstrom, E.A.; Angst, J. Mania with and without depression in a community sample of U.S. adolescents. Arch. Gen. Psychiatry 2012, 69, 943–951. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Hughes, T.; Cardno, A.; West, R.; Marino-Francis, F.; Featherstone, I.; Rolling, K.; Locker, A.; McLintock, K.; House, A. Unrecognised bipolar disorder among UK primary care patients prescribed antidepressants: An observational study. Br. J. Gen. Pract. 2016, 66, e71–e77. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Serra, G.; Koukopoulos, A.; De Chiara, L.; Napoletano, F.; Koukopoulos, A.E.; Curto, M.; Manfredi, G.; Faedda, G.; Girardi, P.; Baldessarini, R.J. Features preceding diagnosis of bipolar versus major depressive disorders. J. Affect. Disord. 2015, 173, 134–142. [Google Scholar] [CrossRef]
  34. Chen, Q.; Kumar, V.; Mummini, S.; Pato, C.N.; Pato, M.T. Traumatic events in childhood and adulthood in a diverse-ancestry sample and their role in bipolar disorder. Psychiatry Res. 2023, 326, 115259. [Google Scholar] [CrossRef] [PubMed]
  35. Quarantini, L.C.; Miranda-Scippa, A.; Nery-Fernandes, F.; Andrade-Nascimento, M.; Galvão-de-Almeida, A.; Guimarães, J.L.; Teles, C.A.; Netto, L.R.; Lira, S.B.; de Oliveira, I.R.; et al. The impact of comorbid posttraumatic stress disorder on bipolar disorder patients. J. Affect. Disord. 2010, 123, 71–76. [Google Scholar] [CrossRef]
  36. Otto, M.W.; Perlman, C.A.; Wernicke, R.; Reese, H.E.; Bauer, M.S.; Pollack, M.H. Posttraumatic stress disorder in patients with bipolar disorder: A review of prevalence, correlates, and treatment strategies. Bipolar Disord. 2004, 6, 470–479. [Google Scholar] [CrossRef]
  37. You, J.S.; Lee, C.W.; Park, J.Y.; Jang, Y.; Yu, H.; Yoon, J.; Kwon, S.S.; Oh, S.; Park, Y.S.; Ryoo, H.A.; et al. Borderline Personality Pathology in Major Depressive Disorder, Bipolar I and II Disorder, and Its Relationship with Childhood Trauma. Psychiatry Investig. 2022, 19, 909–918. [Google Scholar] [CrossRef]
  38. Friborg, O.; Martinsen, E.W.; Martinussen, M.; Kaiser, S.; Overgård, K.T.; Rosenvinge, J.H. Comorbidity of personality disorders in mood disorders: A meta-analytic review of 122 studies from 1988 to 2010. J. Affect. Disord. 2014, 152–154, 1–11. [Google Scholar] [CrossRef]
  39. Wolfenberger, T.; Diaz, A.P.; Bockmann, T.; Selvaraj, S.; Sanches, M.; Soares, J.C. Predominant polarity and associated post-traumatic stress disorder in patients with comorbid bipolar disorder and borderline personality disorder: A cross-sectional study. Rev. Bras. Psiquiatr. 2022, 44, 557–558. [Google Scholar] [CrossRef]
  40. Nabavi, B.; Mitchell, A.J.; Nutt, D.A. Lifetime Prevalence of Comorbidity Between Bipolar Affective Disorder and Anxiety Disorders: A Meta-analysis of 52 Interview-based Studies of Psychiatric Population. eBioMedicine 2015, 2, 1405–1419. [Google Scholar] [CrossRef]
  41. Lieb, R.; Miché, M.; Gloster, A.T.; Beesdo-Baum, K.; Meyer, A.H.; Wittchen, H.U. Impact of Specific Phobia on the Risk of Onset of Mental Disorders: A 10-Year Prospective-Longitudinal Community Study of Adolescents and Young Adults. Depress. Anxiety 2016, 33, 667–675. [Google Scholar] [CrossRef] [PubMed]
  42. Henry, C.; Van den Bulke, D.; Bellivier, F.; Etain, B.; Rouillon, F.; Leboyer, M. Anxiety disorders in 318 bipolar patients: Prevalence and impact on illness severity and response to mood stabilizer. J. Clin. Psychiatry 2003, 64, 331–335. [Google Scholar] [CrossRef] [PubMed]
  43. Fahrendorff, A.M.; Pagsberg, A.K.; Kessing, L.V.; Maigaard, K. Psychiatric comorbidity in patients with pediatric bipolar disorder—A systematic review. Acta Psychiatr. Scand. 2023; advance online publication. [Google Scholar] [CrossRef] [PubMed]
  44. Jerrell, J.M.; McIntyre, R.S.; Park, Y.M. Correlates of incident bipolar disorder in children and adolescents diagnosed with attention-deficit/hyperactivity disorder. J. Clin. Psychiatry 2014, 75, e1278–e1283. [Google Scholar] [CrossRef] [PubMed]
  45. Parker, G. Comorbid attention deficit hyperactivity disorder and bipolar disorder. Aust. N. Z. J. Psychiatry 2023, 57, 789–792. [Google Scholar] [CrossRef]
  46. Gordon-Smith, K.; Lewis, K.J.S.; Vallejo Auñón, F.M.; Di Florio, A.; Perry, A.; Craddock, N.; Jones, I.; Jones, L. Patterns and clinical correlates of lifetime alcohol consumption in women and men with bipolar disorder: Findings from the UK Bipolar Disorder Research Network. Bipolar Disord. 2020, 22, 731–738. [Google Scholar] [CrossRef]
  47. Balanzá-Martínez, V.; Crespo-Facorro, B.; González-Pinto, A.; Vieta, E. Bipolar disorder comorbid with alcohol use disorder: Focus on neurocognitive correlates. Front. Physiol. 2015, 6, 108. [Google Scholar]
  48. Johnson, J.C.S.; Byrne, G.J.; Pelecanos, A.M. The prevalence of subthreshold psychiatric symptoms and associations with alcohol and substance use disorders: From a nationally representative survey of 36,309 adults. BMC Psychiatry 2022, 22, 270. [Google Scholar] [CrossRef]
  49. Sengupta, G.; Jena, S. Psychiatric comorbidity and quality of life in patients with bipolar disorder. Ind. Psychiatry J. 2022, 31, 318–324. [Google Scholar] [CrossRef]
  50. Oliva, V.; De Prisco, M.; Pons-Cabrera, M.T.; Guzmán, P.; Anmella, G.; Hidalgo-Mazzei, D.; Grande, I.; Fanelli, G.; Fabbri, C.; Serretti, A.; et al. Machine Learning Prediction of Comorbid Substance Use Disorders among People with Bipolar Disorder. J. Clin. Med. 2022, 11, 3935. [Google Scholar] [CrossRef]
  51. Yoon, Y.H.; Chen, C.M.; Yi, H.Y.; Moss, H.B. Effect of comorbid alcohol and drug use disorders on premature death among unipolar and bipolar disorder decedents in the United States, 1999 to 2006. Compr. Psychiatry 2011, 52, 453–464. [Google Scholar] [CrossRef]
  52. Álvarez Ruiz, E.M.; Gutiérrez-Rojas, L. Comorbidity of bipolar disorder and eating disorders. Rev. Psiquiatr. Salud Ment. 2015, 8, 232–241. [Google Scholar] [CrossRef]
  53. McElroy, S.L.; Kotwal, R.; JrKeck, P.E. Comorbidity of eating disorders with bipolar disorder and treatment implications. Bipolar Disord. 2006, 8, 686–695. [Google Scholar] [CrossRef] [PubMed]
  54. Khoubaeva, D.; Dimick, M.K.; Roane, J.L.; Timmins, V.H.; Mitchell, R.H.B.; Goldstein, B.I. Comorbid Eating Disorders in a Sample of Youth with Bipolar Disorder: Elevated Burden of Dimensional and Categorical Psychopathology. J. Clin. Psychiatry 2022, 83, 21r14201. [Google Scholar] [CrossRef] [PubMed]
  55. Karakus, G.; Tamam, L. Impulse control disorder comorbidity among patients with bipolar I disorder. Compr. Psychiatry 2011, 52, 378–385. [Google Scholar] [CrossRef]
  56. Woodward, D.; Wilens, T.E.; Yule, A.M.; DiSalvo, M.; Taubin, D.; Berger, A.; Stone, M.; Wozniak, J.; Burke, C.; Biederman, J. Examining the clinical correlates of conduct disorder in youth with bipolardisorder. J. Affect. Disord. 2023, 329, 300–306. [Google Scholar] [CrossRef] [PubMed]
  57. Dunsieth, N.W., Jr.; Nelson, E.B.; Brusman-Lovins, L.A.; Holcomb, J.L.; Beckman, D.; Welge, J.A.; Roby, D.; Taylor, P., Jr.; Soutullo, C.A.; McElroy, S.L. Psychiatric and legal features of 113 men convicted of sexual offenses. J. Clin. Psychiatry 2004, 65, 293–300. [Google Scholar] [CrossRef]
  58. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th ed.; Text Revision (DSM-5-TR); American Psychiatric Association: Washington, DC, USA, 2022. [Google Scholar]
  59. Koukopoulos, A.; Ghaemi, S.N. The primacy of mania: A reconsideration of mood disorders. Eur. Psychiatry 2009, 24, 125–134. [Google Scholar] [CrossRef]
  60. Salazar de Pablo, G.; Guinart, D.; Cornblatt, B.A.; Auther, A.M.; Carrión, R.E.; Carbon, M.; Jiménez-Fernández, S.; Vernal, D.L.; Walitza, S.; Gerstenberg, M.; et al. Demographic and Clinical Characteristics, Including Subsyndromal Symptoms Across Bipolar-Spectrum Disorders in Adolescents. J. Child Adolesc. Psychopharmacol. 2020, 30, 222–234. [Google Scholar] [CrossRef]
  61. Carta, M.G.; Colom, F.; Erfurth, A.; Fornaro, M.; Grunze, H.; Hantouche, E.; Nardi, A.E.; Preti, A.; Vieta, E.; Karam, E. In Memory of Hagop Akiskal. Clin. Pract. Epidemiol. Ment. Health 2021, 17, 48–51. [Google Scholar] [CrossRef]
  62. Carta, M.G.; Nardi, A.E.; Pinna, S.; Cossu, G.; Gureje, O. Multidisciplinary contributions towards an evolutive interpretation of bipolar disorders: Could it be the pathological drift of a potentially adaptive condition? Braz. J. Psychiatry 2023, in press. [Google Scholar] [CrossRef]
  63. Mundy, J.; Hübel, C.; Adey, B.N.; Davies, H.L.; Davies, M.R.; Coleman, J.R.I.; Hotopf, M.; Kalsi, G.; Lee, S.H.; McIntosh, A.M.; et al. Genetic examination of the Mood Disorder Questionnaire and its relationship with bipolar disorder. Am. J. Med. Genet. Part B Neuropsychiatr. Genet. 2023; advance online publication. [Google Scholar] [CrossRef] [PubMed]
  64. Carta, M.G.; Kalcev, G.; Scano, A.; Primavera, D.; Orrù, G.; Gureye, O.; Cossu, G.; Nardi, A.E. Is Bipolar Disorder the Consequence of a Genetic Weakness or Not Having Correctly Used a Potential Adaptive Condition? Brain Sci. 2022, 13, 16. [Google Scholar] [CrossRef] [PubMed]
  65. Kalcev, G.; Cossu, G.; Preti, A.; Littera, M.T.; Frau, S.; Primavera, D.; Zaccheddu, R.; Matza, V.; Ermellino, M.; Pintus, E.; et al. Development and validation of the questionnaire for adaptive hyperactivity and goal achievement (AHGA). Clin. Pract. Epidemiol. Ment. Health 2023, 19, e174501792303281. [Google Scholar] [CrossRef]
  66. Kalcev, G.; Scano, A.; Orrù, G.; Primavera, D.; Cossu, G.; Nardi, E.A.; Carta, M.G. Is a genetic variant associated with bipolar disorder frequent in people without bipolar disorder but with characteristics of hyperactivity and novelty seeking? Clin. Pract. Epidemiol. Ment. Health 2023, 19, e174501792303280. [Google Scholar] [CrossRef]
  67. Carta, M.G.; Kalcev, G.; Scano, A.; Pinna, S.; Aviles Gonzalez, C.I.; Nardi, A.E.; Orrù, G.; Primavera, D. Screening, Genetic Variants, and Bipolar Disorders: Can Useful Hypotheses Arise from the Sum of Partial Failures? Clin. Pract. 2023, 13, 853–862. [Google Scholar] [CrossRef]
  68. Carta, M.G.; Cossu, G.; Pintus, E.; Zoccheddu, R.; Callia, O.; Conti, G.; Pintus, M.; Gonzalez, C.; Massidda, M.V.; Mura, G.; et al. Moderate Exercise Improves Cognitive Function in Healthy Elderly People: Results of a Randomized Controlled Trial. Clin. Pract. Epidemiol. Ment. Health 2021, 17, 75–80. [Google Scholar] [CrossRef] [PubMed]
  69. Carta, M.G.; Ouali, U.; Perra, A.; Ben Cheikh Ahmed, A.; Boe, L.; Aissa, A.; Lorrai, S.; Cossu, G.; Aresti, A.; Preti, A.; et al. Living with Bipolar Disorder in the Time of COVID-19: Biorhythms During the Severe Lockdown in Cagliari, Italy, and the Moderate Lockdown in Tunis, Tunisia. Front. Psychiatry 2021, 12, 634765. [Google Scholar] [CrossRef]
  70. Carta, M.G.; Aguglia, E.; Bocchetta, A.; Balestrieri, M.; Caraci, F.; Casacchia, M.; Dell’osso, L.; Sciascio, G.D.; Drago, F.; Faravelli, C.; et al. The use of antidepressant drugs and the lifetime prevalence of major depressive disorders in Italy. Clin. Pract. Epidemiol. Ment. Health 2010, 6, 94–100. [Google Scholar] [CrossRef]
  71. Ware, J.; Jr Kosinski, M.; Keller, S.D. A 12-item short-form health survey: Construction of scales and preliminary tests of reliability and validity. Med. Care 1996, 34, 220–233. [Google Scholar] [CrossRef] [Green Version]
  72. Bolton, S.; Warner, J.; Harriss, E.; Geddes, J.; Saunders, K. E/A. Bipolar disorder: Trimodal age-at-onset distribution. Bipolar Disord. 2021, 23, 341–356. [Google Scholar] [CrossRef] [PubMed]
  73. Carta, M.G.; Moro, M.F.; Lorefice, L.; Trincas, G.; Cocco, E.; Del Giudice, E.; Fenu, G.; Colom, F.; Marrosu, M.G. The risk of Bipolar Disorders in Multiple Sclerosis. J. Affect. Disord. 2014, 155, 255–260. [Google Scholar] [CrossRef] [PubMed]
  74. Carta, M.G.; Moro, M.F.; Pinna, F.L.; Testa, G.; Cacace, E.; Ruggiero, V.; Piras, M.; Romano, F.; Minerba, L.; Machado, S.; et al. The impact of fibromyalgia syndrome and the role of comorbidity with mood and post-traumatic stress disorder in worsening the quality of life. Int. J. Soc. Psychiatry 2018, 64, 647–655. [Google Scholar] [CrossRef] [PubMed]
  75. Carta, M.G.; Lecca, M.E.; Saba, L.; Sanfilippo, R.; Pintus, E.; Cadoni, M.; Sancassiani, F.; Moro, M.F.; Craboledda, D.; Lo Giudice, C.; et al. Patients with carotid atherosclerosis who underwent or did not undergo carotid endarterectomy: Outcome on mood, cognition and quality of life. BMC Psychiatry 2015, 15, 277. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Sancassiani, F.; Romano, F.; Balestrieri, M.; Caraci, F.; Di Sciascio, G.; Drago, F.; Hardoy, M.C.; Moro, M.F.; Roncone, R.; Piras, M.; et al. The Prevalence of Specific Phobia by Age in an Italian Nationwide Survey: How Much Does it Affect the Quality of Life? Clin. Pract. Epidemiol. Ment. Health 2019, 15, 30–37. [Google Scholar] [CrossRef] [PubMed]
  77. Carta, M.G.; Moro, M.F.; Aguglia, E.; Balestrieri, M.; Caraci, F.; Dell’Osso, L.; Di Sciascio, G.; Drago, F.; Hardoy, M.C.; D’Aloja, E.; et al. The attributable burden of panic disorder in the impairment of quality of life in a national survey in Italy. Int. J. Soc. Psychiatry 2015, 61, 693–699. [Google Scholar] [CrossRef]
  78. Carta, M.G.; Tondo, L.; Balestrieri, M.; Caraci, F.; Dell’osso, L.; Di Sciascio, G.; Faravelli, C.; Hardoy, M.C.; Lecca, M.E.; Moro, M.F.; et al. Sub-threshold depression and antidepressants use in a community sample: Searching anxiety and finding bipolar disorder. BMC Psychiatry 2011, 11, 164. [Google Scholar] [CrossRef]
  79. Carta, M.G.; Preti, A.; Moro, M.F.; Aguglia, E.; Balestrieri, M.; Caraci, F.; Dell’Osso, L.; Di Sciascio, G.; Drago, F.; Faravelli, C.; et al. Eating disorders as a public health issue: Prevalence and attributable impairment of quality of life in an Italian community sample. Int. Rev. Psychiatry 2014, 26, 486–492. [Google Scholar] [CrossRef]
  80. Cossu, G.; Agus, M.; Atzori, L.; Aviles Gonzales, C.I.; Minerba, L.; Ferreli, C.; Puxeddu, R.; Orrù, G.; Scano, A.; Romano, F.; et al. Principal Component Analysis of the Social and Behavioral Rhythms Scale in elderly. J. Public Health Res. 2021, 11, 2546. [Google Scholar] [CrossRef]
  81. Pirkola, S.; Eriksen, H.A.; Partonen, T.; Kieseppä, T.; Veijola, J.; Jääskeläinen, E.; Mylläri-Figuerola, E.M.; Salo, P.M.; Paunio, T. Seasonal variation in affective and other clinical symptoms among high-risk families for bipolar disorders in an Arctic population. Int. J. Circumpolar Health 2015, 74, 29671. [Google Scholar] [CrossRef]
  82. Okechukwu, C.E.; Griffiths, M.D.; Carta, M.G.; Nwobodo, E.; Shariful Islam, S.M.; Forbes, M.; Berk, M.; Potenza, M.N.; Banach, M.; Amamihechineke Grace, O.; et al. Biological and practical considerations regarding circadian rhythm and mental health relationships among nurses working night shifts: A narrative review and recommendations. Riv. Psichiatr. 2022, 57, 67–79. [Google Scholar]
  83. Carta, M.G.; Fornaro, M.; Minerba, L.; Pau, M.; Velluzzi, F.; Atzori, L.; Aviles Gonzalez, C.I.; Romano, F.; Littera, R.; Chessa, L.; et al. Previous functional social and behavioral rhythms affect resilience to COVID-19-related stress among old adults. J. Public Health Res. 2022, 11, 2768. [Google Scholar] [CrossRef] [PubMed]
  84. Gaudette, J. Will a Bad Night’s Sleep before a Race Affect Performance? RunnersConnect, Inc.: San Luis, AZ, USA, 2023; Available online: https://runnersconnect.net/coach-corner/pre-race-sleep/ (accessed on 26 June 2023).
  85. Roy, I. Burnout syndrome: Definition, typology and management. Soins Psychiatr. 2018, 39, 12–19. [Google Scholar] [CrossRef] [PubMed]
  86. Han, K.T.; Park, E.C.; Kim, J.H.; Kim, S.J.; Park, S. Is marital status associated with quality of life? Health Qual. Life Outcomes 2014, 12, 109. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  87. Sagayadevan, V.; Lee, S.P.; Ong, C.; Abdin, E.; Chong, S.A.; Subramaniam, M. Quality of Life across Mental Disorders in Psychiatric Outpatients. Ann. Acad. Med. Singap. 2018, 47, 243–252. [Google Scholar] [CrossRef]
Table 1. Comparing SF-12 scores in cases and controls.
Table 1. Comparing SF-12 scores in cases and controls.
AgeSexMDQ+
Score		MDQ−
Score		Case-SF12 ScoreControl-
SF12 Score		Diagnosis
121M904545N (None)
222F1033940N
323M934342N
424F804040Specific Phobia
525M803541N
625F1103446N
726F803842Anorexia
827M812744N
927M804042N
1028F1112442N
1129F932736Obsessive-compulsive disorder (OCD)
1230M854543N
1331M1302343N
1432F813938N
1533M803744N
1635M1403845N
1736M1313438N
1838F1004344N
1941F1203541N
2042F1004042N
2143M1052943N
2245M1102944N
2349M814246N
2449F1003342N
2550M1002546N
2650M804744N
2750F1113934N
2852F803644N
2954M804241N
3054F813644N
3154F943132N
3257F1354043N
3359M1102333N
3459F803541N
3560M903339N
3661M1003339N
3762M803138N
3863M813246N
3963F902942N
4065M804343N
41 (23M)67M913039N
42.95 ± 14.34 9.48 ± 1.680.90 ± 1.5135.21 ± 6.3041.48 ± 3.39F(1,81DF) = 31.492
p < 0.0001
≥50
N = 17		57.64 ± 5.34 34.68 ± 6.2640.47 ± 4.17F(1,33)= 15.511
p < 0.0001
<50
N = 24		32.54 ± 8.37 35.79 ± 6.3342.20 ± 2.46F(1,47) = 21.828
p < 0.0001
Table 2. Frequency of people with low SF-12 scores by age.
Table 2. Frequency of people with low SF-12 scores by age.
MDQ+MDQ−Chi-SquarepOR (CI 95%)
People with SF-12 score ≤ 3519315.903<0.000110.93 (2.90–41.19)
People with SF-12 score > 352238
People with SF-12 score ≤ 35 (Only ≥50)934.6360.0315.25 (1.09–25.21)
People with SF-12 score > 35 (Only ≥50)814
People with SF-12 score ≤ 35 (Only <50)10011.007<0.0001Inf (NC)
People with SF-12 score > 35 (Only <50)1424
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Carta, M.G.; Kalcev, G.; Fornaro, M.; Pinna, S.; Gonzalez, C.I.A.; Nardi, A.E.; Primavera, D. Does Screening for Bipolar Disorders Identify a “Dysregulation of Mood, Energy, and Social Rhythms Syndrome” (DYMERS)? A Heuristic Working Hypothesis. J. Clin. Med. 2023, 12, 5162. https://doi.org/10.3390/jcm12155162

AMA Style

Carta MG, Kalcev G, Fornaro M, Pinna S, Gonzalez CIA, Nardi AE, Primavera D. Does Screening for Bipolar Disorders Identify a “Dysregulation of Mood, Energy, and Social Rhythms Syndrome” (DYMERS)? A Heuristic Working Hypothesis. Journal of Clinical Medicine. 2023; 12(15):5162. https://doi.org/10.3390/jcm12155162

Chicago/Turabian Style

Carta, Mauro Giovanni, Goce Kalcev, Michele Fornaro, Samantha Pinna, Cesar Ivan Aviles Gonzalez, Antonio Egidio Nardi, and Diego Primavera. 2023. "Does Screening for Bipolar Disorders Identify a “Dysregulation of Mood, Energy, and Social Rhythms Syndrome” (DYMERS)? A Heuristic Working Hypothesis" Journal of Clinical Medicine 12, no. 15: 5162. https://doi.org/10.3390/jcm12155162

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