Self help bipolar

Posted on July 11, 2016 by JmaC

Self-help for bipolar disorder

While dealing with bipolar disorder isn’t always easy, it doesn’t have to run your life. But in order tosuccessfully manage bipolar disorder, you have to make smart choices. Your lifestyle and daily habits have a significant impact on your moods.

Get educated. Learn as much as you can about bipolar disorder. The more you know, the better you’ll be at assisting your own recovery.
Keep stress in check. Avoid high-stress situations, maintain a healthy work-life balance, and try relaxation techniques such as meditation, yoga, or deep breathing.
Seek support. It’s important to have people you can turn to for help and encouragement. Try joining a support group or talking to a trusted friend. Reaching out is not a sign of weakness and it won’t mean you’re a burden to others. In fact, most friends will be flattered that you trust them enough to confide in them, and it will only strengthen your relationship.
Make healthy choices. Healthy sleeping, eating, and exercising habits can help stabilize your moods. Keeping a regular sleep schedule is particularly important.
Monitor your moods. Keep track of your symptoms and watch for signs that your moods are swinging out of control so you can stop the problem before it starts.

Bipolar disorder and suicide

The depressive phase of bipolar disorder is often very severe, and suicide is a major risk factor. In fact, people suffering from bipolar disorder are more likely to attempt suicide than those suffering from regular depression. Furthermore, their suicide attempts tend to be more lethal.

The risk of suicide is even higher in people with bipolar disorder who have frequent depressive episodes, mixed episodes, a history of alcohol or drug abuse, a family history of suicide, or an early onset of the disease.

The warning signs of suicide include:

Talking about death, self-harm, or suicide
Feeling hopeless or helpless
Feeling worthless or like a burden to others

Acting recklessly, as if one has a “death wish”
Putting affairs in order or saying goodbye
Seeking out weapons or pills that could be used to commit suicide

Important

It’s very important to take any thoughts or talk of suicide seriously. If you or someone you care about is suicidal, call the National Suicide Prevention Lifeline in the U.S. at 1-800-273-TALK or visit IASP orSuicide.org to find a helpline in your country.

Bipolar disorder causes and triggers

Bipolar disorder has no single cause. It appears that certain people are genetically predisposed to bipolar disorder, yet not everyone with an inherited vulnerability develops the illness, indicating that genes are not the only cause. Some brain imaging studies show physical changes in the brains of people with bipolar disorder. Other research points to neurotransmitter imbalances, abnormal thyroid function, circadian rhythm disturbances, and high levels of the stress hormone cortisol.

External environmental and psychological factors are also believed to be involved in the development of bipolar disorder. These external factors are called triggers. Triggers can set off new episodes of mania or depression or make existing symptoms worse. However, many bipolar disorder episodes occur without an obvious trigger.

Stress – Stressful life events can trigger bipolar disorder in someone with a genetic vulnerability. These events tend to involve drastic or sudden changes—either good or bad—such as getting married, going away to college, losing a loved one, getting fired, or moving.
Substance Abuse – While substance abuse doesn’t cause bipolar disorder, it can bring on an episode and worsen the course of the disease. Drugs such as cocaine, ecstasy, and amphetamines can trigger mania, while alcohol and tranquilizers can trigger depression.
Medication – Certain medications, most notably antidepressant drugs, can trigger mania. Other drugs that can cause mania include over-the-counter cold medicine, appetite suppressants, caffeine, corticosteroids, and thyroid medication.
Seasonal Changes – Episodes of mania and depression often follow a seasonal pattern. Manic episodes are more common during the summer, and depressive episodes more common during the fall, winter, and spring.
Sleep Deprivation – Loss of sleep—even as little as skipping a few hours of rest—can trigger an episode of mania.

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Big data for bipolar disorder

Posted on July 11, 2016 by JmaC

Abstract

The delivery of psychiatric care is changing with a new emphasis on integrated care, preventative measures, population health, and the biological basis of disease. Fundamental to this transformation are big data and advances in the ability to analyze these data. The impact of big data on the routine treatment of bipolar disorder today and in the near future is discussed, with examples that relate to health policy, the discovery of new associations, and the study of rare events. The primary sources of big data today are electronic medical records (EMR), claims, and registry data from providers and payers. In the near future, data created by patients from active monitoring, passive monitoring of Internet and smartphone activities, and from sensors may be integrated with the EMR. Diverse data sources from outside of medicine, such as government financial data, will be linked for research. Over the long term, genetic and imaging data will be integrated with the EMR, and there will be more emphasis on predictive models. Many technical challenges remain when analyzing big data that relates to size, heterogeneity, complexity, and unstructured text data in the EMR. Human judgement and subject matter expertise are critical parts of big data analysis, and the active participation of psychiatrists is needed throughout the analytical process.

Keywords

Bipolar disorder Big data EMR Registries Claims Patient monitoring

Background

The frequency and importance of comorbid mental and chronic physical illness have emphasized the need for a change in the delivery of psychiatric care, including bipolar disorder (Melek et al. 2014, DeHert et al. 2011). Bipolar disorder is associated with poor functional outcome (Conus et al. 2014), considerable economic cost for society (Kleine-Budde et al. 2014; Young et al. 2011), and management is often complicated by medical comorbidity such as type II diabetes/insulin resistance (Calkin et al. 2015; Calkin and Alda 2015; Carney and Jones 2006). Responses to improve care delivery include integrating psychiatry with primary care (Butler et al.2008; Manderscheid and Kathol 2014; Cerimele and Strain 2010; Katon et al. 2010), collaborative care measures (Woltmann et al. 2012), implementing preventive programs and quality measurements consistent with a population health perspective (Rose 2001; Mabry et al. 2008), and increasing emphasis on the genetic and neuroscience basis of mental illness (Insel 2009; Reynolds et al. 2009). Additionally, precision medicine initiatives are accelerating interdisciplinary research with a goal of tailoring psychiatric care to the individual (Insel 2014).

Big data and advances in the ability to analyze these data are fundamental to this evolving perspective of psychiatry (Monteith et al. 2015; NRC 2013). Big data can be conceptualized as heterogeneous data, unprecedented in size and complexity, lacking in structure, and coming from many sources (Monteith et al. 2015). The scale of big data in size and complexity makes it difficult to process, analyze, and extract useful information (Burkhardt 2014). Today, the primary source of big data in medicine is from providers and payers including electronic medical records (EMR) created by physicians, claims records, pharmacy records, and imaging. However, the data for analysis will keep expanding from omics, such as genomic, epigenomic, proteomic, and metabolomic data. Today, about 95 % of the data for each patient is generated by imaging (Hamalka 2011), and genomic data requires 50-fold greater storage per patient than imaging (Starren et al. 2013). Data will also be coming from non-traditional sources including patients and non-providers, from smartphone applications, sensors, and Internet activities (Glenn and Monteith 2014a). With the addition of data from patient devices, it is estimated that every person will generate more than 1 petabyte (1 million gigabytes) of health information over a lifetime (IBM 2015a). IBM envisions a future in which 10 percent of medical data will be from medical records, 20 percent from genomics, and 70 % from patient-created sources (Slabodkin 2015). The amount of medical-related data in existence is expected to double in size every 2 years (IBM 2015b).

It is still early in the process of converting from paper to digital-based medicine. As with other industries, the main benefits will be related to future innovations and redefined work processes fostered by the technology, and increased software usability and usefulness (Fernald and Wang 2015; Landauer 1995). However, many initial benefits from digitizing data are already being seen today in the analysis of very large databases. The objective of this review is to discuss both the promises and challenges of using big data to improve the understanding and treatment of bipolar disorder.

Data sources from providers and payers

There are many public and private sources of big data from EMR, claims/administrative data, and registries that are available for secondary use in medical research. These data sources were not designed for research and each has strengths and weaknesses, with differences in quality, completeness, and potential for bias. In the US, claims or administrative encounter data that providers (physicians, hospitals, labs, and pharmacies) submit for payment to insurers and the government provide the most complete picture of patient involvement with the healthcare system. Although standardized diagnostic and procedure codes are used, claims data lacks clinical detail such as test results. The diagnosis on a claim is only for the services performed on that date, and may be incorrect, incomplete, differential, or driven by reimbursement policies (Sarrazin and Rosenthal 2012; Wilson and Bock 2012; West et al. 2014; Overhage and Overhage 2013). The time lag for claims processing is often several months. About 17 % of commercially insured people in the US switch coverage each year posing challenges for longitudinal analysis (Sung 2015; Marketscan 2011).

In contrast to claims, EMR provide timely clinical details from the providers who use the software, especially related to patient management. The clinical data may include patient history and symptoms, multiple diagnoses including those unrelated to the current visit, physician assessment and treatment plan, disease severity, lab results, vital signs, non-prescription drugs and results of screening tools such as PHQ-9. Government mandates in the US have dramatically increased the use of EMR. About half of EMR text is unstructured data (Davenport2014), and many challenges remain to automatically extract information from the rich but distinct vocabularies used throughout medicine (Dinov 2016; Ivanovic and Budimac 2014). Efforts are underway to address standardization with the goal of semantic interoperability of data from different providers and software systems (IHE 2015; HealthIT.gov 2015; Dinov 2016). There are other important quality issues in EMR data including inconsistency, redundancy, inaccuracy, missing data, interoperability between vendor products, and potential biases from measured and non-measured confounders (Monteith et al. 2015; Bayley et al. 2013; Kaplan et al.2014; Hersh et al. 2013; Hripcsak et al. 2011).

Outside the US, psychiatric register data may be based on a country population such as in the Nordic countries or Taiwan, or a geographical area such as the South London and Maudsley NHS Foundation Trust (SLAM) case register, or a provider (Munk-Jorgensen et al. 2014; Allebeck 2009; Stewart et al. 2009). These registries provide a longitudinal record of all psychiatric contacts, and have high coverage and low dropout rates in countries with a national health service. However, there are limitations to the validity and quality of data in psychiatric registries, including over-representation of severe cases or inpatient data, sparse clinical detail, exclusion of variables not available from all institutions reporting to the register, and insufficient linking to other registries such as cause of death (Munk-Jørgensen et al. 2014). There are also questions about the validity of psychiatric diagnoses in the register data (Byrne et al. 2005; Øiesvold et al. 2013), including bipolar disorder (Øiesvold et al. 2012). Psychiatric case registries do not include patients without a psychiatric diagnosis for comparison (Munk-Jørgensen et al.2014). Some other types of registries that can be linked to psychiatric registries include those for general health, prescription drugs, vital statistics, school registries, social insurance registries, and biobanks (Allebeck 2009), each of which has strengths and weaknesses.

Other sources of data include research databases and surveys, such as the US National Comorbidity Survey (Kessler et al. 1994) or the National Epidemiological Survey on Alcohol and Related Conditions (NESARC) (Grant et al. 2004), which may have a national scope but contain a subset of clinical information.

Even very large databases containing millions of individuals may not be representative of the general population (Riley 2009). For example, the US claims/administrative data from a Medicaid population will include more younger women and children, data from an employer-offered HMO may include more younger and healthier people, and data from Veterans Affairs (VA) will include mainly males and be older (Overhage and Overhage2013; Medicaid 2015). In a US multistate EMR database with 84 million patients, psychiatric and behavioral diagnoses were less frequent as compared to the US National Inpatient Sample, an established population estimate based on claims (HCUP 2015; DeShazo and Hoffman 2015). Population-based registries from small homogenous countries may not be representative of the population in larger diverse countries. Due to the heterogeneity among very large databases, the data source selected may challenge the results of observational studies, including even finding contradictory statistical significance (Madigan et al. 2013; Goldstein and Winkelmayer 2015; Crump et al. 2013a). However, with a clear understanding of the strengths and weaknesses of a database, some findings from observational analyses can now be verified in many national and regional settings. For example, in a systematic review of 25 international population or community-based studies using different diagnostic criteria, the prevalence of bipolar disorder type I and type II was consistently low (Clemente et al. 2015).

The addition of complementary data sources may improve the accuracy and usefulness of data from any one source. Even when using validated algorithms, it is difficult to determine an episodic diagnosis such as depression when analyzing US claims data, and combining another data source such as EMR may improve accuracy (Townsend et al. 2012; Fiest et al. 2014). However, in the US, linking of data from unrelated sources that were de-identified to meet privacy regulations is challenging (West et al. 2014, Li and Shen 2013). In contrast, many European countries have a unique person identifier that is present on all medical data (Allebeck 2009). The use of complementary linked databases may also expand the types of research questions that may be addressed. Examples of useful linkages include register population data linked with biobank data in a study that found no association between markers of prenatal infection and the risk of bipolar disorder (Mortensen et al. 2011), and in a study that found elevated C-reactive protein was associated with an increased risk of late-onset bipolar disorder (Wium-Andersen et al. 2015).

Uses for data from providers and payers

The analysis of very large databases has provided fundamental information about bipolar disorder including the incidence, prevalence, decreased life expectancy (Munk-Jørgensen et al. 2014; Allebeck 2009; Laursen et al.2007; Chang et al. 2011; Kessing et al. 2015c; Kessing et al. 2015d), and trends in prescribing medication (Baldessarini et al. 2007; Hayes et al. 2011; Bjorklund et al. 2015). Results from the analysis of large data sources are continuously being incorporated into patient care and research, and some key areas are discussed below.

Health policy decisions

Health policy decisions focus on outcome and cost. Big data is fundamental to the increasing importance of clinical guidelines, defining and measuring metrics that reflect the quality of care delivered, and meeting performance standards based on quality metrics. For the treatment of bipolar disorder, big data studies are helping to characterize problems and evaluate the results of policy changes. Of great concern are repeated findings of excess mortality in patients with bipolar disorder due primarily to physical illness, and of continuing disparities in the treatment of physical illness as compared with the general population (Roshanaei-Moghaddam and Katon 2009; McGinty et al. 2015). Some examples of suboptimal care for medical illness for people with bipolar disorder found using big data are shown in Table 1. In addition to health services and physical illness, socioeconomic factors and patient behaviors contribute to excess morbidity and mortality in bipolar disorder (Druss et al. 2011). The linking of psychiatric data with other databases, such as government financial databases, will help to clarify the complex, cumulative impacts of diverse socioeconomic factors, as shown in Table 2. Examples of studies directly related to health policy and bipolar disorder using big data are given in Table 3.

Table 1

Examples of studies suggesting suboptimal treatment of medical illness in bipolar disorder

Country	Description	Primary finding	Data source	Number of subjects analyzed (N)	Reference
Denmark	Investigate cardiovascular (CV) drug use and the excess mortality in BP and schizophrenia (SCZ)	Under-prescription of most CV drugs to patients with BP or SCZ compared to general population	Population registries during 1995–1996 of those who used CV drugs	254 with BP, 609 with SCZ, 23,065 with no mental illness	Laursen et al. 2014
Denmark	Investigate hospital contact for CV disease by patients with BP or SCZ compared with general population	Despite excess mortality, rates of contact for those with BP or SCZ similar to general population and lower rates of invasive procedures	Register data from 1994 to 2007	4997 with heart disease and BP or SCZ, 566,071 with heart disease and no mental illness	Laursen et al. 2009
Scotland	Investigation of medical comorbidities in BP	Frequent wide ranging medical comorbidities. CV disease under-recognized and undertreated	Primary care registry for about 1/3 of Scottish population in 2007	2582 with BP and 1,421,796 without	Smith et al. 2013
Sweden	Estimate CV mortality in BP compared to general population	Mortality rate ratios for CV disease twice as high for BP than general population. People with BP died of CV disease about 10 years earlier than general population	National population register 1987–2006	17,101 patients diagnosed with BP in general population of 10.6 million	Westman et al. 2013
Sweden	Impact of physical health on mortality rate in BP	Frequent premature mortality is from chronic medical diseases. However, mortality from chronic diseases among those with prompt treatment approached that of general population	National population registries between 2001 and 2002, with follow-up 2003–2009	6618 diagnosed with BP	Crump et al. 2013b
Taiwan	Use of invasive diagnostic and revascularization procedures after acute myocardial infarction (AMI) in patients with SCZ or BP	Patients with BP and SCZ half as likely to receive catheterization or revascularization procedures after AMI	National register from 1996 to 2007	3661 patients with AMI of which 591 with SCZ and 243 with BP	Wu et al.2013
UK	Compare screening for CV risk in primary care of patients with SCZ or BP to patients with diabetes	Much less screening of patients with mental illness for CV risk (1/5 versus 96 %)	Five primary care centers in Northampton, England	368 with mental illness; 1875 with diabetes	Hardy et al. 2013
UK	Compare screening for metabolic risk in primary care of patients with SCZ or BP to patients with diabetes	Less screening of patients with mental illness for metabolic risk (74.7 versus 97.3 %)	NHS database between 2010 and 2011	2,488,948 patients with diabetes and 422,966 patients with mental illness	Mitchell and Hardy2013
US	Impact of guidelines released by American Diabetic Association (ADA) in 2004 on glucose monitoring in patients treated with second generation antipsychotics (SGA)	Low levels of monitoring despite small improvement after guidelines (just over 10 % lipid monitoring; just over 20 % glucose monitoring)	Managed care database of patients under age 65 between 2000 and 2006	5787 patients before guidelines; 17,832 after	Haupt et al. 2009
US	Investigate diabetes screening in patients with SCZ and BP who take antipsychotics over a 1 year period	Almost 70 % not screened for diabetes using validated screening measures. Those with at least one primary care visit more than twice as likely to be screened	CA Medicaid population during 1/2009–12/2009, and 10/2010–10/2011	50,915 patients with SCZ, BP and other severe mental illness	Mangurian et al. 2015
US	Investigate hospitals selected for patients with mental illness and acute myocardial infarction (AMI)	Comorbid mental illness was associated with an increased risk for admission to lower-quality hospitals. Both lower-quality hospital and mental illness predicted worse outcome	Medicare population in 2008, aged ≥65 years	287,881 patients with AMI, of which 41,044 also with mental illness	Cai and Li2013

Table 2

Examples of big data studies of socioeconomic factors in bipolar disorder

Country	Description	Primary finding	Data source	Number of subjects analyzed (N)	Reference
Denmark	Association of BP and schizophrenia (SCZ) with parent–child separation	Associations found but differed by type, developmental timing and family characteristics	Danish register between 1971 and 1991, followed to 2011	2821 with BP and 6469 with SCZ	Paksarian et al. 2015
Denmark	Association between mortality and lifetime substance use disorder in patients with BP, SCZ or unipolar depression	Mortality in people with mental illness far higher for those with substance use disorders; especially involving alcohol or hard drugs	Those born in Denmark in 1995 or later	41,470 with SCZ, 11,739 with BP, and 88,270 with unipolar depression	Hjorthoj et al. 2015
Israel	Percentage of patients with BP and SCZ and other psychosis, who earn at least minimum wage	For BP: with 1 hospital admission, only 24.2 % earned at least minimum wage; with multiple admissions, 19.9 %. Poor employment outcome in all cases	Israeli psychiatric hospitalization registry	35,673 total	Davidson et al. 2015
Sweden	Compare risks for suicidality and criminality in patients with BP and general population	22.2 % of BP engaged in suicidal or criminal acts after diagnosis. Combined risk of suicidality and criminality is elevated	Swedish national registries between 1973 and 2009	15,337 with BP, compared with 14,677 unaffected siblings	Webb et al. 2014
Sweden	Association of high intelligence and BP	High intelligence may be a risk factor for BP, but only in those without psychiatric comorbidity	Diagnosis of BP from Hospital Discharge Register from 1968 and 2004. IQ measure at military conscription	1,049,607 males. 3174 hospitalized with BP	Gale et al.2013
Sweden	Association of leadership traits with BP	Traits associated with BP may be linked to superior leadership qualities	Swedish population registries from 1973 and 2009	68,915 with BP, and healthy siblings	Kyaga et al. 2015
Sweden	Investigate disease burden in bipolar disorder	Compared to general population, patients had same education, more unemployment, less disposable income, and twice the mortality	Swedish population registries of all diagnosed with BP 1991–2010; cohort in 2006 versus 2009	4629 in 2006; 5644 in 2009	Carlborg et al. 2015
US	Association of BP and SCZ with criminal justice involvement	Males and females with BP disorder have higher risk for offending than those with SCZ; highest risk is BP plus substance use disorder	Connecticut mental health administrative records plus criminal justice records	25,133 adults, 5479 with BP and substance abuse; 7327 with BP alone	Robertson et al. 2014
US	Employment and functional limitations in BP and unipolar depressive disorders	Patients with BP significantly more unemployment and functional limitations than those with depressive disorders or controls	Nationally representative Medical Expenditure Panel survey 2004–2006	592 with BP, 5646 with depressive disorders, 53,905 controls	Shippee et al. 2011
UK	Childhood IQ and risk of BP	Higher childhood IQ may be a marker for risk of later BP	Avon birth cohort. IQ at age 8; lifetime manic features at age 22–23	1881 individuals	Smith et al. 2015

Table 3

Examples of big data projects related to health policy for patients with bipolar disorder

Country	Description	Primary finding	Data source	Number of subjects analyzed (N)	Reference
France	Impact of longitudinal continuity of care with the same community psychiatrist on mortality rate of patients with mental disorders	Higher the continuity of care the lower likelihood of death, especially in those with BP, major depressive disorder and schizophrenia (SCZ)	France national claims data 2007–2010	14,515 patients visiting psychiatrist at least once, tracked over 3 years	Hoertel et al. 2014
UK	Investigation of delay between first visit to a mental health service and a diagnosis of BP	Median diagnostic delay was 62 days; median treatment delay was 31 days	SLAM register data between 2007 and 2012	1364 diagnosed with BP	Patel et al.2015b
UK	Investigation of mortality after hospital discharge with principal diagnosis of BP or SCZ	Standardized mortality ratios about double general population. For BP, increased from 1.3 in 1999 to 1.9 in 2006. About 3/4 of all deaths from natural causes	English national hospital and death registries from 1999 and 2006	100,851 hospital discharges for patients with BP and 272,248 with SCZ	Hoang et al. 2011
US	Impact of state Medicaid formulary restrictions on total medical costs for patients with BP or SCZ	Medication adherence declined due to formulary restrictions. Total medical costs increased	Medicaid claims from 24 states 2001–2008	170,596 patients with BP and 117,908 with SCZ	Seabury et al. 2014
US	Impact of requiring prior authorization (PA) for more expensive medications on the discontinuation of antipsychotics and anticonvulsants	Higher rates of discontinuation of all medication treatment. No increase in use of preferred drugs (not requiring PA)	Medicaid and Medicare claims 2001–2004 in Maine	N = 5336 Maine N = 1376 New Hampshire (comparison state)	Zhang et al. 2009
US	Impact of prior authorization and copayments policy on medication continuity	Prior authorization and copayments decreased medication continuity. (High continuity in 54 % of those with BP and 64 % of those with SCZ)	Medicaid claims from 22 states in 2007	33,234 patients with BP and 91,451 with SCZ	Brown et al. 2013
US	Impact of adherence to and persistence with atypical antipsychotics on health care costs	Good adherence and persistence led to lower costs	Commercial health insurance claims 2007–2013	32,374 patients with diagnosis of BP or SCZ and prescription for oral antipsychotic	Jiang and Ni 2015
US	Association of frequent psychiatric interventions over 1 year on health care utilization and costs in patients with BP I	Patients needing frequent psychiatric interventions had higher psychiatric and general medical utilization and costs in following year	Commercial insurance claims 2004–2007	7260 patients with frequent psychiatric interventions and 11,571 without	Bagalman et al. 2011
US	Examine conformance to practice guidelines for children/adolescents with BP	Most received recommended therapy but only a minority received drug monitoring and/or recommended psychotherapy	Medicaid in Ohio 2006–2010	4047 youths aged 15–18 years with new episode of BP	Fontanella et al. 2015
US	Estimate number of emergency department (ED) visits by adults involving psychiatric medications	Antipsychotics and lithium involved in more visits relative to rate at which prescribed. Half of ED visits involving psychiatric medications were for patients 19–44 years	National surveillance database from 63 hospitals between 2009 and 2011	89,094 ED visits annually for therapeutic use of psychiatric medications in patients ≥19 years	Hampton et al. 2014
US	Evaluate if patients with SCZ and BP received comprehensive treatment by state	In each state, only 45 % with BP, and 47 % with SCZ had a continuous medication supply. About 25 % of beneficiaries had no mental health visit	Medicaid claims in 21 states + DC in 2007	40,609 with BP; 102,884 with SCZ	Brown et al. 2015
US	Drug utilization patterns for newly initiated atypical antipsychotic	Low adherence and persistence: 63.4 % discontinued index therapy, and majority of these (69.5 %) did not resume any antipsychotic	Commercial insurance between 2002 and 2008	16,807 patients ≥18 years with BP I	Chen et al.2013

Evaluation of rare events

Big data allows the study of rare events and outcomes that may require data from multiple sources to provide an adequate sample size for detection. Randomized controlled trials are not powered to detect rare events or long-term effects, and case control and retrospective cohort study designs of observational databases collected from clinical practice are often used (Chan et al. 2015; Rodriguez et al. 2001). For example, there have been several recent large or population-based studies of renal related events in patients who were treated with lithium, as shown in Table 4. Big databases are being used for pharmacovigilance of many drugs prescribed for bipolar disorder, such as studies of the potential for antipsychotics to increase risk of a seizure (Bloechliger et al. 2015), pulmonary embolism (Tournier 2015; Conti et al. 2015), and a Torsades de pointes ventricular arrhythmia (Poluzzi et al. 2013).

Table 4

Examples of big data projects related to lithium and renal function

Country	Description	Primary finding	Data source	Number of subjects analyzed (N)	Reference
Denmark	Examine association between long-term lithium use (≥5 years) and risk of renal and upper urinary tract cancers	Not associated with an increased risk	Danish Cancer Registry between 2000 and 2012	6447 cases matched to 259,080 controls	Pottegard et al. 2016
Denmark	Compare rates of chronic kidney disease (CKD) and end-stage CKD in patients taking lithium or other drugs for BP	Maintenance treatment with lithium or anticonvulsants increases rate of CKD, but lithium is not associated with increased rate of end-stage CKD	Danish population registries 1994–2012	1,500,000 randomly selected controls, 26,731 exposed to lithium and 420,959 to anticonvulsants for any reason. 10,591 with primary diagnosis of BP	Kessing et al. 2015a
Denmark	Assess risk of renal and upper urinary tract tumors among lithium users	Not associated with an increased risk	Danish population registries 1995–2012	1,500,000 randomly selected controls, 24,272 exposed to lithium and 386,255 to anticonvulsants for any reason. 9651 with primary diagnosis of BP	Kessing et al. 2015b
Italy	Examined glomerular filtration rate (GFR) in patients with long-term lithium treatment	Lithium is a risk factor for reduced GFR. Renal dysfunction tends to appear after decades of treatment and to progress slowly. Median time to enter G3a was 25 years	Lithium register from 1980 to 2012	953 patients. Patients treated up to 33 years	Bocchetta et al. 2015
Scotland	Comparison of estimated glomerular filtration rate (eGFR) in patients recently started on lithium therapy versus those taking other medications for affective disorders	No effect of stable lithium maintenance therapy, with lithium levels in the therapeutic range, on rate of change in eGFR over time	Population of patients started on lithium therapy in Tayside between 2000 and 2011	305 in lithium group; 815 in comparator group. Mean duration of exposure 55 months	Clos et al.2015
Sweden	Determine prevalence and extent of kidney damage during course of long-term lithium treatment	About one-third of patients treated for ≥10 years had evidence of chronic renal failure; only 5 % severe. Continuous monitoring of kidney function is required	Lab data from all Gothenburg area public hospitals and clinics	630 patients starting lithium after 1980 with ≥10 years of cumulative lithium treatment	Aiff et al.2015
UK	Compared lab measures of renal, thyroid and parathyroid function in those with at least two lithium measurements versus those with no lithium measurements	Lithium treatment associated with decline in renal function, hypothyroidism and hypercalcemia. Women <60 years with lithium concentrations higher than median at greatest risk. Long-term monitoring needed	Lab data from Oxfordshire area between 1985 and 2014	2795 ≥18 years with at least two lithium measurements; 689,228 controls	Shine et al. 2015
UK	Assess association between lithium use and renal failure in patients with bipolar disorder	Ever use of lithium was associated with an increased risk of renal failure (adjusted hazard ratio 2.5). Absolute risk of renal failure was age dependent and small	General practice research database from 418 practices between 1990 and 2007	6360 with BP; 2496 lithium users; 3864 non-users	Close et al. 2014
US	Possibility of stratifying risk for renal insufficiency among lithium treated patients	Use of lithium more than once daily; lithium levels >0.6 mEq/l, and use of first generation AP independently associated with risk	EMR records from large healthcare system 2006–2013	1445 lithium users with renal insufficiency; 4306 lithium users for comparison	Castro et al. 2015b

Exploration and hypothesis generation from large databases

The exploration of big data offers unique opportunities to find correlations that may trigger the investigation of new areas and generation of new hypotheses (Varian 2014; Khoury and Ioannidis 2014). These new correlations may or may not have meaning, do not measure causality, and may be further investigated by traditional or data-intensive experimental methods as appropriate. There are many computational and statistical challenges associated with the analysis of big data related to the number of patients, number of variables per patient, and the quality and technical complexity of the databases (Monteith et al. 2015; Fan et al. 2014; Grimes and Schulz2002). Both the variables included and the analytic techniques used may lead to variation in the associations detected in big data studies (Abrams et al. 2008; Fan et al. 2014; Patel et al. 2015a).

Additional correlations detected include an association between epilepsy and bipolar disorder (Wotton and Goldacre 2014; Clarke et al. 2012), an increased risk of pneumonia in patients with bipolar disorder taking antipsychotics (Yang et al. 2013), an increased risk of bipolar disorder in those with a diagnosis of autism spectrum disorder (Selten et al. 2015), and finding that the premature risk of cardiovascular disease in bipolar disorder is not explained by traditional risk factors including cigarette smoking, obesity, or hypertension (Goldstein et al. 2015). In a study using medical records from 110 million patients, new associations were found between Mendelian diseases and complex psychiatric diseases, including bipolar disorder (Blair et al. 2013).

Defining phenotypes

There is considerable interest in using EMR to automate the process of defining phenotypic cohorts for genetic studies of bipolar disorder, since sample sizes of tens of thousands are needed (Pathak et al. 2013; Potash2015). In addition to the study of phenotype-genotype relationships and gene-disease associations, phenotypic cohorts will enable a wide range of clinical research. Despite many challenges, semi-automated methods are now being used to define phenotypes from EMR for psychiatric disorders, including bipolar disorder (Lyalina et al.2013; Castro et al. 2015a). The methodology used to automate phenotype detection in EMR is evolving, and includes data mining, natural language processing, statistical techniques, and human expertise (Hripcsak and Albers 2013; Pathak et al. 2013). More standardization is expected in the future.

Predictive models

Predictive models are widely used in medicine, such as cardiovascular risk prediction, to estimate the presence of a diagnosis or event, or if the diagnosis or event will occur in a specific time period (Moons et al. 2012). The results of validated predictive models may assist the physician and patient with decision making to mitigate risks, and help to limit spending on unnecessary procedures. Before adoption for clinical use, predictive models require considerable testing and re-adjustment, including internal validation, external validation with other populations, followed by determination if the validated model provides actionable information to the clinician and patient (Moons et al. 2012). Most predictive models are based on a small number of variables collected in cohort studies such as the Framingham Heart Study (D’Agostino et al. 2008). In general, models used in medicine today have limited predictive power, and access to the large number of variables and patients in EMR and other databases may improve their accuracy in the future (Berger and Doban 2014; de Lissovoy 2013). With the frequent use of heuristics in medical decision making, complex predictive models also need practical input requirements for routine use in clinical situations (Marewski and Gigerenzer 2012).

Many technical issues impede the development of predictive models from EMR data, including quality, multidimensional complexity, bias, comorbidities, and confounding medical interventions (Paxton et al. 2013; Wu et al. 2010; Wang et al. 2014). The temporal nature of EMR data also poses a significant challenge for prediction (Singh et al. 2015; Binder and Blettner 2015). In contrast to a controlled longitudinal study, data entries into an EMR only occur when a patient initiates or a physician recommends and documents care. There are great differences in the time between visits for one patient, and across all patients, in the number of visits and length of time each patient is tracked. New variables detected in EMR data may be associated with but not predictive of disease (Ware 2006). A variety of machine learning, data mining, classification algorithms, and statistical approaches are currently being researched for the future (Singh et al. 2015; Wu et al. 2010, Wang et al. 2014).

While the primary benefits of prediction will be in the future, in some recently developed models, bipolar disorder is a risk factor for readmission to a psychiatric hospital within 30 days of discharge (Vigod et al. 2015), readmission to a safety-net hospital within a year (Hamilton et al. 2015), and suicide by veterans (McCarthy et al.2015). The addition of variables relating to a diagnosis of bipolar disorder or schizophrenia improved the accuracy of a predictive model of cardiovascular risk for those with these diagnoses (Osborn et al. 2015).

Data sources from patients and non-providers

Digital technologies that are widely accepted by the general public are being integrated into the routine care of bipolar disorder to increase patient involvement and expand clinician oversight between visits. Many technologies are suitable platforms for active or passive patient monitoring including computers, smartphones, and even clothing with embedded sensors. Today, the patient-created data are not generally integrated into the EMR.

Data actively created by patients outside of medical settings

Many applications are available today to monitor bipolar disorder away from medical settings that require active patient participation. These include validated products for mood charting such as the ChronoRecord on a computer (Bauer et al. 2004; Bauer et al. 2008), the Life-Chart on a smartphone and web site (Scharer et al.2015), weekly text messaging of responses to Quick Inventory of Depressive Symptomatology and Altman self-rating manic scale (Bopp et al. 2010), and weekly or monthly use of an interactive voice response (IVR) system to complete the PHQ-9 (Piette et al. 2013). In all cases, the patients respond to questions or prompts directly related to their illness. In addition to clinical use, data collected from these systems is often aggregated for research (Bauer et al. 2013a, 2013b; Moore et al. 2014). A large number of parameters may be accumulated for each patient, such as from daily medications taken (Bauer et al. 2013a), but data are not routinely integrated into the EMR. Although challenges remain regarding the interpretation of self-reported data, much of the understanding about the long-term course of bipolar disorder is due to the daily recording efforts of patients worldwide, starting with paper-based instruments (Bauer et al. 1991; Kupka et al. 2007).

Data passively created by patients outside of medical settings

With passive monitoring, patients do not directly provide information about their illness, and much of the data collected are non-medical. For example, data from Internet and smartphone activities, and from sensors in smartphones and wearable technology, are routinely being used to monitor mental state and behavior for non-medical purposes such as behavioral advertising (Glenn and Monteith 2014b; Geller 2014; FTC 2009). There are a variety of passive monitoring projects for bipolar disorder, mostly in the pilot phase, with examples shown in Table 5. The implementation of routine passive monitoring for large numbers of patients faces many hurdles, including patient acceptance, physician usability, and processing large volumes of data from sensors (Redmond et al. 2014; Muench 2014). Many passive monitoring projects involve smartphones. Both the differing physical characteristics of the standard devices available to consumers such as sensor accuracy and memory size, and methods selected for analysis may impact the findings (Banaee et al. 2013; Redmond et al. 2014). The sales of smartphones are flat in developed countries with saturation reached, and usage patterns vary among countries (Thomas 2014, Waters 2015). In the US in 2015, 64 % of adults in the US use a smartphone with 7 % relying primarily on it for Internet access (Smith 2015).

Table 5

Examples of passive monitoring of patients with bipolar disorder related to smartphones, Internet activities, or wearables

Technology	Sensors	Aim	Primary measures	N	Findings	Study
Ingestible^a	Ingestible sensor in tablets. Wearable sensor on torso	Measure medication adherence	Adherence metrics. Logs date and time of tablet ingestion	28	System is feasible in patients with BP and SCZ	Kane et al.2013
Internet social media		Differentiate depression subgroups by language use	Analyze topics and linguistic features in 24 online communities interested in depression	5000 blog posts	Five distinct subgroups, one is BP. For those with BP, topics on medications and BP most important	Nguyen et al. 2015
Internet social media		Explore language differences among 10 mental health conditions	Using public Twitter posts 2008–2015, group by classifiers including self-reported diagnosis	>100 users/group; >100 posts/user	Language usage patterns differ by condition	Coppersmith et al. 2015
Smartphone	Accelerometer, GPS	Detect mood state	Daily mobility (physical motion), and travel patterns (number of locations visited, time outdoors)	12	Can detect a change in mood state. Less precise to detect mood state	Gruenerbl et al. 2014
Smartphone	Accelerometer; microphone	Detect mood state	Number of apps running; app usage patterns and selection. MONARCA software	18	Patterns of app usage vary with self-reported mood	Alvarez-Lozano et al. 2014
Smartphone	Accelerometer	Detect mood state	Overall activity levels	9	Substantial individual variation in activity levels, both daily and within intervals	Osmani et al. 2013
Smartphone		Detect mood state	Number and duration of ingoing and outgoing calls; number of text messages. MONARCA software	61	Patterns of calls and texts vary in manic and depressive mood states	Faurholt-Jepsen et al.2015
Smartphone	Microphone	Detect mood state	Phone call statistics; acoustic emotional analysis, and social signals from daily calls	12	Speaking length and call length among the most important predictors of mood	Muaremi et al. 2014
Smartphone	Recorder for outgoing speech	Detect mood state	Voice monitoring and acoustic analysis of speech patterns from continuously recorded outgoing calls	6	Can recognize manic and depressive mood states	Karam et al.2014
Wearable (T-shirts)	Electrodes and sensors integrated into garment	Detect mood state	ECG and respiration. Long term heart rate variability analysis. PSYCHE monitoring system	8	Can differentiate mood states (depressed, manic, mixed, euthymic)	Valenza et al. 2014

^a New drug application submitted to FDA by Otsuka pharmaceuticals and proteus digital health for sensor-embedded Abilify in September, 2015

Commercial processing of data

Provider-created data are traditionally processed by the provider or their contractors. In contrast, commercial firms unrelated to medicine may be involved in both active and passive patient monitoring. Many behavioral related apps are available for Apple and Android smartphones, and commercial firms may receive, store, and analyze data using proprietary and unvalidated algorithms. Any potential combination of data processed by commercial firms with EMR data needs to be carefully evaluated as the firms may not be covered by national privacy regulations (Glenn and Monteith 2014b). An analysis of 79 mobile health apps certified as trustworthy by the UK NHS found a multitude of privacy and security flaws (Huckvale et al. 2015).

Changing world of technology

Passive monitoring should be considered in the context of the ongoing changes in digital technology, especially in relation to mobile devices for consumers. First, the devices used to access the Internet will change the online activities of the public. For example, the use of a search engine is much lower from a smartphone than from a computer (Arthur 2015; MacMillan 2015). Second, the widespread use of mobile technology has triggered a push toward developing artificial intelligence (AI) interfaces for devices, as evidenced by the near simultaneous announcements of open source AI software tools from Google, Microsoft, IBM, and Facebook (Simonite 2015). The vision of Larry Page of Google is for Google to tell you what you want before you ask the question (Varian2014, Page 2013). In an international survey of 6600 smartphone users by Ericsson, half of all smartphone users expect AI interfaces to replace the smartphone screen within 5 years, and one-third want AI to keep them company (Boulden 2015). Messaging chatbots (computer-generated responses based on AI) are starting to replace search engines on mobile devices (Elgan 2015). In the future, consumer mobile devices will routinely incorporate voice and gesture input, and as hardware features change, the AI algorithms will also evolve. In the background, there is an industry-wide effort to develop AI algorithms based on massive databases to predict behavior and emotions for uses such as for targeted marketing.

Other provider data sources

Massive amounts of data will be coming from genomics, proteomics, and image processing, and the ongoing efforts of large-scale consortia will help to elucidate the neuropathology of bipolar disorder and define new treatment targets. The ENIGMA Consortium detected subcortical brain volumetric changes using brain structural MRI scans from 1710 patients with bipolar disorder and 2594 controls (Thompson et al. 2014, Hibar et al. 2016). The ConLiGen Consortium identified genetic variants associated with lithium response in a GWAS study of 2563 patients with bipolar disorder (Hou et al. 2016). The Psychiatric Genomics Consortium (PGC) found a new susceptibility locus in a GWAS study of 7481 individuals with bipolar disorder and 9250 controls (Sklar et al.2011). Recent technology allows large-scale comparison of proteome profiles (Gold et al. 2010; SomaLogic2016), and findings may improve predictive models for bipolar disorder. These data are not expected to be incorporated into the EMR or impact the routine care of bipolar disorder in the near future but suggest future directions for data integration.

General considerations

There are a wide range of anticipated and unanticipated complications related to the use of big data in the study of bipolar disorder some of which are mentioned briefly below.

Privacy and confidentiality

The privacy and confidentiality of big data are a major concern. Many technical issues affect the privacy and confidentiality of big data related to hardware and software implementations, mobile devices and wireless networks, shared resources, and shared control over monitoring systems (Ko et al. 2010). Breaches of provider medical data occur frequently with about 90 % of health care providers reporting at least one data breach over the last 2 years in an international study in 2015 (Experian 2015). The use of commercial apps for monitoring also complicates privacy issues. Patients may incorrectly assume that national medical privacy regulations apply to data collected and processed by non-providers (Glenn and Monteith 2014b). Patient posting of private medical data online, such as in support groups, is another complication, and online data cannot really be deleted due to the distributed and redundant storage of Internet data (President’s Council 2014). Preserving privacy in big data research is particularly difficult, since this often includes multiple international collaborators, and data are copied and shared around the world. The legal framework for medical privacy varies among countries (Dove and Phillips2015).

Ethical considerations

There is disagreement about the importance of informed consent for big data research (Rothstein 2015), with some wanting to ease regulations (Larson 2013). The consent process is of particular importance for bipolar disorder due to the highly sensitive information in the EMR (Clemens 2012), and since some patients have cognitive impairment (Daglas et al. 2015).

De-identification is frequently used to protect individual privacy. De-identified data are not covered by US federal privacy laws and are sold commercially. Yet the general public cares about using de-identified data without consent (McGraw 2013), and about the specific purpose for secondary use (Grande et al. 2013). The released data may be vulnerable to re-identification since current de-identification methods are inadequate for high-dimensional data (Narayanan et al. 2016). There is a growing confluence of the interests of academic and commercial organizations in big data projects, leading to questions about ownership of the data and any benefits created, and about disposition of data if a firm goes out of business or is purchased.

In countries without a national health service, predictive models of costs may increase coverage disparities of vulnerable groups (Wharam and Weiner 2012). Predictive models being developed by commercial, non-medical companies can create ethical conflicts (Glenn and Monteith 2014a). For example, privacy and non-discrimination laws in the US that impact decisions about credit, employment, or housing do not prohibit discrimination against the predisposition of disabilities (Horvitz and Mulligan 2015).

Unreasonable expectations for predictive models

The expectations of the general public regarding predictive models may be inappropriate. People are familiar with personalized recommendations from Netflix or Amazon, search results from Google, and advertising on Apple and Android smartphones. These predictive models are based solely on the available data, are unconnected to causal inference and underlying mechanisms, and focus on predicting the present rather than the future (Hand2013; Curtis 2014). The validity of predictive models in business is judged by increased overall sales and profits, not by accuracy of the prediction for individual customers (McAfee et al. 2012).

Physicians may also have unrealistic expectations for models that predict behavior based on big data. Big data is non-sampled, and from sources with a purpose other than statistical inference (Horrigan 2013). Data that are created and collected by humans reflect physical place and culture, and contain hidden biases (Pope et al. 2014, Crawford 2013). More data does not necessarily improve predictions over those made using smaller datasets as data must be relevant to the question at hand (Monteith et al. 2015; Guszcza and Richardson 2014). Big data is also without context (Boyd and Crawford 2012; Bilton 2013). Furthermore, malware or denial of service attacks occur frequently, change overall Internet behavior patterns, and further complicate interpretation of human behavior (NRC 2013). Predictive models can be wrong as shown repeatedly with Google Flu (Lazer et al. 2014a,b). Predictive models in medical and related settings can be inconsistent and biased (Singh et al. 2014), have little clinical impact (Hochster and Niedzwiecki 2016), and may be most appropriate for health policy and risk stratification rather than individual risk prediction (Harris et al. 2015; Wray et al. 2013; Wharam and Weiner 2012).

Analytical challenges

In the future, data from all provider and patient sources will be integrated, creating massive datasets for analysis. Massive datasets have issues of scale, heterogeneity, multidimensional complexity, error handling, privacy, provenance, and many types of biases (NRC 2013; Monteith et al. 2015). If analysis of big data is based on the classical methods, underlying assumptions are likely to be violated. Researchers with different backgrounds tend to have different perspectives on data analysis, using either statistical (model-based focus on variability) or algorithmic (data mining for patterns and rules) (NRC 2013; Mahoney et al. 2008) techniques. New algorithms for big data are combining the complementary strengths of both approaches.

Human judgment is an absolutely critical component of big data analysis (Wyss and Stürmer 2014; NRC 2013). To optimize the studies of big data for bipolar disorder, participation of those with expertise in psychiatry is required throughout the analytical process, such as for parameter selection and exclusion, interpretation of results, and hypothesis generation. For example, just as Captcha demonstrates the difference between human and machine image resolution (Datta et al. 2009), psychiatrist input is needed during the development of algorithms to interpret the use of language by those with bipolar disorder.

Conclusions

Big data projects based on the data collected by providers in EMR, claims, registries, and active patient monitoring are providing valuable information on many aspects of bipolar disorder for research and clinical care. In the near future, data from passive patient monitoring will be available and integrated with the EMR, and diverse data sources from outside of medicine such as government financial data will be linked for research. This is only the beginning. Further on, data from genetics, other omics, and imaging will also be integrated with the EMR, and lead to new levels of understanding and improvement in routine care. Many significant challenges remain for big data projects, and the active collaboration of psychiatrists is required throughout the analytical process. Big data will provide the basis for transforming the understanding and management of bipolar disorder.

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Bipolar disorder and diabetes mellitus: evidence for disease-modifying effects and treatment implications

Posted on July 11, 2016 by JmaC

Abstract

Background

Bipolar disorder refers to a group of chronic psychiatric disorders of mood and energy levels. While dramatic psychiatric symptoms dominate the acute phase of the diseases, the chronic course is often determined by an increasing burden of co-occurring medical conditions. High rates of diabetes mellitus in patients with bipolar disorder are particularly striking, yet unexplained. Treatment and lifestyle factors could play a significant role, and some studies also suggest shared pathophysiology and risk factors.

Objective

In this systematic literature review, we explored data around the relationship between bipolar disorder and diabetes mellitus in recently published population-based cohort studies with special focus on the elderly.

Methods

A systematic search in the PubMed database for the combined terms “bipolar disorder” AND “elderly” AND “diabetes” in papers published between January 2009 and December 2015 revealed 117 publications; 7 studies were large cohort studies, and therefore, were included in our review.

Results

We found that age- and gender- adjusted risk for diabetes mellitus was increased in patients with bipolar disorder and vice versa (odds ratio range between 1.7 and 3.2).

Discussion

Our results in large population-based cohort studies are consistent with the results of smaller studies and chart reviews. Even though it is likely that heterogeneous risk factors may play a role in diabetes mellitus and in bipolar disorder, growing evidence from cell culture experiments and animal studies suggests shared disease mechanisms. Furthermore, disease-modifying effects of bipolar disorder and diabetes mellitus on each other appear to be substantial, impacting both treatment response and outcomes.

Conclusions

The risk of diabetes mellitus in patients with bipolar disorder is increased. Our findings add to the growing literature on this topic. Increasing evidence for shared disease mechanisms suggests new disease models that could explain the results of our study. A better understanding of the complex relationship between bipolar disorder and diabetes mellitus could lead to novel therapeutic approaches and improved outcomes.

Keywords

Bipolar disorder Diabetes Epidemiology Cohort studies Pathophysiology Evidence

Background

Bipolar disorder (BD) refers to a group of conditions that share the defining features of elated/euphoric or irritable mood accompanied by persistently increased activity or energy levels, also known as mania (American Psychiatric Association 2013). BD occurs worldwide with a lifetime prevalence of about 0.6 % for BD-I and 0.4 % for BD-II, with slightly higher rates reported in developed countries (Merikangas et al. 2007, 2011).

Evidence for an increase in chronic medical conditions in patients with BD has been described since the pretreatment era (Esquirol 1845; Swift 1907; Rennie 1942; Stenstedt 1952; Alvarez Ariza 2009). Several disorders are frequently diagnosed in patients with BD, including epilepsy, thyroid disorders, cardiovascular diseases, autoimmune–allergic disorders, and diabetes mellitus, especially in the elderly (Lala and Sajatovic2012; Perugi et al. 2015). Since symptoms of these somatic disorders overlap with those of BD, they could challenge the diagnostic process and delay treatment (Sajatovic and Chen 2011; Smith et al. 2013; Maina et al.2013). Chronic medical conditions in patients with severe mental illness also lead to increased risk of frequent hospitalizations and re-hospitalizations (Davydow et al. 2015). While recent reviews of this topic have identified comorbid medical conditions in the elderly with BD as a growing public health problem (Depp and Jeste 2004; Vasudev and Thomas 2010; Dols et al. 2014; Sajatovic et al. 2015a), this patient population is often not well represented in clinical trials (Beers et al. 2014). However, case reports suggest that co-occurring medical conditions have a significant effect on the disease onset, the disease course, treatment response, and outcome (Sami et al. 2015). Diabetes mellitus appears to take center stage among these disorders.

Recent reports and one meta-analysis have suggested a relationship between BD and diabetes mellitus. However, these studies could not disentangle the effects of ethnicity, medication use and age, which could have potentially confounded the results (Vancampfort et al. 2015). Especially, the variability in the prevalence of diabetes mellitus in the background population has been rarely considered. Small sample sizes and restricted mean age range were the main limitations in most studies. In a systematic review, we have attempted to address some of these shortcomings. In contrast to previous studies, we have focused on large population-based cohort studies from diverse ethnic backgrounds with special attention to those studies that included the elderly. Then, we reviewed the evidence for shared disease mechanisms between BD and diabetes mellitus. Finally, we explored the evidence for disease-modifying effects and treatment implications.

Methods

Using the combined terms “bipolar disorder” AND “elderly” AND “diabetes”, two independent researchers have carefully searched the PubMed database for large, observational cohort studies with retrospective, cross-sectional, or prospective design published between January 2009 and December 2015. We found 117 papers; 7 studies were large cohort studies from diverse populations (Table 1), and therefore, were suitable for our review. Two reviewers independently selected the studies and extracted the data in duplicate according to predefined criteria and a study protocol that could be provided on request. Studies were included if they were population based, contain patients diagnosed with BD based on Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) or International Classification of Diseases (ICD) criteria, and also included patients diagnosed with diabetes mellitus. Excluded were studies that had excluded elderly patients, studies that were not population based and studies that did not mention the inclusion of patients with diabetes mellitus in addition to BD (Fig. 1). Since the number of the identified studies was too small and too diverse for meta-analysis, we refrained from a statistical analysis.

Table 1

Large cohort studies provide evidence for a significant association between bipolar disorder and diabetes mellitus

Author	Year	Title	Design	Type of bipolar disorder (BD)	Method of assessment of BD	Type of diabetes mellitus (DM)	Method of assessment	Results for BD group	Age of participants (years)	N
Wändell et al.	2014	Diabetes and psychiatric illness in the total population of Stockholm	National cohort study Cross-sectional study	BD F30–F31	Electronic patient records	DM (ICD-10 codes E10–E14	Electronic patient records	Age adjusted odds ratio of BD among patients with DM 1.714 (1.540–1.905) for women and 1.600 (1.429–1.792) for men	0–85+	2058,408 96,103 with DM 6341 with BD
Crump et al.	2013	Comorbidities and mortality in bipolar disorder: a Swedish national cohort study	National cohort study Cross-sectional	BD ICD-10 code F31	Public health records	DM (ICD-10 codes E10–E14)	Public health records	Risk of DM (1.7-fold among women and 1.6-fold among men)	>20	6587,036 353,615 with DM 6618 with BD
Bai et al.	2013	Risk of developing diabetes mellitus and hyperlipidemia among patients with bipolar disorder, major depressive disorder, and schizophrenia: a 10-year nationwide population-based prospective cohort study	10-year nationwide population-based prospective matched control cohort study	BD (ICD-9-CM code: 296, except 296.2, 296.3)	National Health Insurance (NHI) program records	DM (ICD-9-CM code 250)	National Health Insurance (NHI) program records	Increased risk of initiation of anti-diabetic medications (10.1 vs. 6.3 %,p = 0.012) Age and gender adjusted risk [hazard ratio (HR) of 1.702, 95 % confidence interval (CI): 1.155–2.507]	Average age 45.3 ± 14.0	1000,000 367 patients with BD 37 with DM
Svendal et al.	2012	Co-prescription of medication for bipolar disorder and diabetes mellitus: a nationwide population-based study with focus on gender differences	Norwegian prescription database Case–control study	BD	Indicated by prescription of mood stabilizers	DM	Indicated by prescription of antidiabetic medication	Unadjusted odds ratio of 2.1 (CI 95 %: 1.9, 2.2) Sex and age adjusted odds ratio of 2.0 (CI 95 %: 1.8, 2.1)	20–69	2,929,065 77,669 with DM 17,007 with BD
Hsieh et al.	2012	Medical costs and vasculo-metabolic comorbidities among patients with bipolar disorder in Taiwan—a population-based and -matched control study	Matched case–control study	BD (ICD-9-CM code 296, except 296.2, 296.3)	Hospital admission	DM ICD-9-CM (250)	Medical records	DM prevalence ratio 3.19; [2.74, 3.70]; p < .0001	>20	About 23,000,000 4,067 with BD, 420 with DM
Kodesh et al.	2012	Epidemiology and comorbidity of severe mental illnesses in the community: findings from a computerized mental health registry in a large Israeli health organization	Publicly funded Health Maintenance Organization (HMO) records Case–control study	BD-I, BD-II, Mania ICD-9 codes 295.–298.	Medical records	DM	Computerized medical records	DM odds ratio of 1.6	>21	2,000,000 5,732 patients with BD
Chien et al.	2010	Prevalence of diabetes in patients with bipolar disorder in Taiwan: a population-based national health insurance study	National Health Research Institute Case–control study	BD	Medical records	DM	Medical records	Diabetes prevalence in BD patients versus controls 10.77 vs. 5.57 %, OR 2.01; 99 % CI 1.64–2.48	>18	1,000,000 1,848 with BD

https://static-content.springer.com/image/art%3A10.1186%2Fs40345-016-0054-4/MediaObjects/40345_2016_54_Fig1_HTML.gif — Fig. 1

Selection process for the inclusion in the systematic review

Results

Bipolar disorder and diabetes mellitus: is there a connection?

The results of the seven large population-based studies published between January 2009 and December 2015 provided strong evidence for a correlation between BD and diabetes mellitus (Table 1). When compared to the population background, odds ratios for diabetes mellitus in patient populations with BD were in the range of 1.7–3.2. Reciprocally, BD was more common among those with diabetes mellitus compared to the general population when adjusted for age and gender (Wändell et al. 2014). A nationwide, population-based longitudinal cohort study found that patients with BD, who had no diagnosis of diabetes mellitus at baseline, were more likely to begin anti-diabetic medications over the 10-year course of the study, even after controlling for gender, urbanization, and income (Bai et al. 2013). Across all ethnic and racial groups, females seem to have additional risk. Glucose and lipids were dysregulated at high rates in patients with BD, particularly in women over age 40 (Wysokinski et al.2015), and obesity, a major risk factor for diabetes mellitus, was highly prevalent (Goldstein et al. 2011).

The results of these very large studies are consistent with the results of previous literature reviews covering smaller studies up to 2012, which found that diabetes mellitus occurs up to three times as often among individuals with BD, as it does in the general population (Calkin et al. 2013; Janssen et al. 2015). However, some studies also indicated that metabolic dysfunctions in patients with BD are frequently underdiagnosed (Carliner et al. 2014; Konz et al. 2014).

Discussion

Bipolar disorder and diabetes mellitus: do these disorders share common disease mechanisms?

The results of our study suggest a relationship between BD and diabetes mellitus. Therefore, we reviewed the supporting evidence for shared disease mechanisms based on the broader literature.

A common explanation for the association between BD and diabetes mellitus focuses on the diabetogenic side effects of psychotropic medications, but evidence is also increasing for a medication-independent association (Foley et al. 2015). While diabetes mellitus in patients with BD has been associated with unintended medication effects (Correll et al. 2015), antipsychotics are more strongly linked to incident diabetes mellitus than other treatments, such as mood stabilizers and antidepressants. Among the antipsychotics, olanzapine and clozapine (both second generation antipsychotics) have been most strongly linked to diabetes mellitus, because they block insulin secretion as antagonists of acetylcholine muscarinic 3 receptors in the β-cells of the pancreas (Thakurathi and Henderson 2012; Weston-Green et al. 2013). A sedentary lifestyle has been claimed as another contributing factor to the increased risk of diabetes mellitus in patients with BD (Perseghin et al. 1996; Gomes et al. 2013; Janney et al. 2014; Conn et al. 2014). However, even after accounting for antipsychotic exposure and lifestyle factors, the higher incidence of diabetes mellitus among patients with BD remains unexplained, especially in treatment-naïve patients (Lilliker 1980; Cassidy et al. 1999; Regenold et al. 2002; Ruzickova et al. 2003; McIntyre et al. 2005; Maina et al. 2008; García-Rizo et al. 2014; Guha et al. 2014).

The observed association between BD and diabetes mellitus has inspired several hypotheses about shared disease mechanisms (Calkin et al. 2013). While some researchers have focused on dysregulations of the purine metabolism as a common link between energy homeostasis and neuro-regulation (Salvadore et al. 2010), others have proposed elevated cortisol levels related to imbalances in the hypothalamic–pituitary–adrenal axis, which consequently could result in obesity and derailment of the glucose metabolism (McElroy et al. 2004). A few researchers have hypothesized that insulin resistance in adipose tissue could be mediated by abnormalities in thyroid hormone receptor signaling pathways and gene regulation. Imbalances in thyroid hormones have long been suspected to be causally related to BD (Iwen et al. 2013). A new disease model hypothesizes that thyroid hormone receptor-associated protein 3 (Thrap3) could activate a diabetogenic gene cascade in adipose cells through interaction with cyclin-dependent kinase 5 (CDK5) leading subsequently to the phosphorylation of peroxisome proliferator-activated receptor γ (PPARγ) at Ser273 (Choi et al. 2014). An extension of this model included sleep abnormalities, which are frequently found in patients with psychiatric disorders, as a contributing factor to the manifestation of diabetes mellitus (Li et al. 2013). While thyroid hormone abnormalities have been convincingly linked to BD (Bauer et al. 2014), a causal link between thyroid abnormalities, diabetes mellitus, and mood symptoms continues to be a focus of intense investigations in cell culture and animal models (Wang 2013).

Increased insulin resistance is commonly considered an intermediate phenotype to the manifestation of diabetes mellitus. In patients with BD, an alternative pathomechanism has been explored in the context of the metabolic syndrome, a combination of obesity, diabetes mellitus, dyslipidemia and hypertension. The metabolic syndrome is very common in the general population, but it occurs at even higher rates in patients with BD (Fagiolini et al.2005). While insulin resistance was not increased in patients with BD and metabolic syndrome compared to age, gender, and body mass index (BMI)-matched controls, patients with BD had a reduced capacity to utilize fat as an energy source. This abnormality could predispose BD patients to exacerbated weight gain and increased risk for diabetes mellitus and cardiovascular disease (Fleet-Michaliszyn et al. 2008).

Perhaps the most intriguing hypothesis linking BD and diabetes mellitus has focused on underlying immune dysfunctions paired with a chronic inflammatory state, which could confer risk for both BD and diabetes mellitus (Leboyer et al. 2012; Hamdani et al. 2013; Sharma et al. 2014; Rosenblat and McIntyre 2015; Kim et al. 2015). This argument is supported by findings of increased susceptibility to allergies and elevated pre-inflammatory markers in BD and in diabetes mellitus (Goldstein et al. 2009; Wang et al. 2013; Chen et al. 2014). Oxidative stress could also lead to cell damage and apoptosis in the pancreas and in the brain, suggesting shared environmental risk factors for BD and diabetes mellitus (Reininghaus et al. 2014; Wright et al. 2006; Chang and Chuang 2010). This disease mechanism has been convincingly demonstrated in rat pancreatic β-cells, in which increased β-cell apoptosis was initiated by endoplasmic reticulum (ER) stress, mediated by abnormal glycogen synthase kinase-3β (GSK-3β) and caspase-3 activity. Valproic acid inhibited GSK-3β, which resulted in a cytoprotective effect. While this disease mechanism still awaits confirmation in patients with BD, the striking results suggest abnormal GSK-3β activity as a common link between BD and diabetes mellitus supported by a potentially similar drug effect of valproic acid on GSK-3β in the pancreas and in the brain (Huang et al. 2014).

Bipolar disorder and diabetes mellitus: what are the outcomes?

The impacts of BD and diabetes mellitus on each other appear to be substantial. Recent work by Calkin et al. found that patients with BD and diabetes mellitus or insulin resistance had three times higher risk of having a chronic course of BD compared to euglycemic BD patients; patients with either type of insulin dysregulation also had three times higher risk of rapid cycling and were more likely to be refractory to lithium (Calkin et al. 2015). In a study of 82,060 patients with diabetes mellitus admitted to community hospitals over a 2-year period in Washington State, having a serious mental illness significantly increased the odds of rehospitalization for non-mental conditions within 1 month of discharge (odds ratio 1.24, 95 % confidence interval 1.07–1.44), even after controlling for demographics, medical co-morbidity, and index hospitalization (Chwastiak et al. 2014). Among the 2.2 % with comorbid serious mental illness, 60 % had a diagnosis of BD, which was consistent with previous studies (Callaghan and Khizar 2010). Other studies confirmed that diabetes mellitus increased hospital-based mortality in patients with BD (Schoepf and Heun 2014; Sylvia et al. 2015).

Worryingly, BD and diabetes mellitus are each independently associated with increased risk of dementia and reduced cognitive performance (Biessels et al. 2006; Xu et al. 2009; Wu et al. 2013; Zilkens et al. 2014; Depp et al. 2014). After controlling for vascular risk factors, patients with diabetes mellitus show increased evidence for global brain atrophy relative to age- and gender-matched controls (Wisse et al. 2014; Biessels and Reijmer 2014), including reduced gray matter density, reduced cerebral glucose metabolism in frontotemporal regions (García-Casares et al. 2014), increased ventricular volume (De Bresser et al. 2010), and white matter hyper-intensities (Reijmer et al. 2011). When compared to euglycemic BD patients and non-psychiatric controls, the BD patients with insulin resistance or glucose intolerance and diabetes mellitus had significantly more neurochemical changes in the prefrontal cortex, indicating reduced neuronal health (Hajek et al. 2015). In one study, patients with BD and diabetes mellitus or insulin resistance also had significantly smaller hippocampal and cortical volumes than either euglycemic BD patients or controls (Hajek et al. 2014).

Separately, each disease is associated with increased mortality. Diabetes mellitus is the seventh leading cause of death (Center for Disease Control 2014). Among adults 18 years and older during the years 2003–2006 in the US, a diagnosis of diabetes mellitus increased all-cause mortality about 1.5 times over non-diabetics. For BD, a Swedish national cohort study has shown that, relative to the general population, men and women with BD died on average 8.5 and 9.0 years earlier, respectively, and for each gender, having BD increased the risk of death by twofold (Crump et al. 2013). BD patients have a 20-fold greater risk of suicide relative to the general population (Jann 2014). Meanwhile, those with BD in addition to diabetes mellitus have increased mortality rates of 1.47 (95 % CI 1.07–2.02) versus those with diabetes mellitus but not BD (Vinogradova et al. 2010).

Outlook

Investigations into treatment implications

Both diabetes mellitus and BD are highly refractory: less than half of the participants in the National Health and Nutrition Examination Survey (NHANES) met glycemic control goals (Koro et al. 2004). BD patients in general have high rates of treatment non-adherence and recurrence. Furthermore, a strong association between HbA1c levels and symptoms of depression has been described in patients with BD (Bajor et al. 2015; Sajatovic et al.2015b). Because of the difficulties in arresting progression of diabetes mellitus, achieving lifetime remission from BD, and the high stakes involved in both diseases, new treatment avenues, especially those that treat the potentially shared disease mechanisms of diabetes mellitus and BD, are desirable.

In the search for new drug targets, glycogen synthase 3 (GSK-3) has taken center stage for its known involvement in several pathways linked to both BD and diabetes mellitus (Gould et al. 2004; Ronai et al. 2014; Huang et al. 2014; Iwahashi et al. 2014). In the rat, lithium, a standard treatment for BD, reduces the enzyme’s activity in the hippocampus and improves memory and learning (Qu et al. 2014). Novel GSK-3 inhibitors are now in preclinical testing (Datusalia and Sharma 2014; King et al. 2013).

In addition to the GSK-3 pathway, dysregulation of noradrenaline signaling could potentially be a shared disease mechanism between BD and diabetes mellitus, which has led to investigations into prophylactic use of noradrenaline modulators (Fitzgerald 2015). With the intention to target inflammatory pathways, toll-like receptor (TLR)-modifying agents have been tried in diabetes mellitus and BD among others (Ladefoged et al. 2013; McKernan et al. 2011; Lucas and Maes 2013). Last, but not least, treatment with the antidiabetic drug pioglitazone as an adjunct to lithium improved symptoms of depression in patients with BD even in the absence of diabetes mellitus (Zeinoddini et al. 2015).

Bipolar disorder in the elderly: does age of onset hint a distinct disease phenotype?

BD in the elderly poses specific challenges for diagnosis and treatment (Préville et al. 2008, 2010; Volkert et al.2013; Sajatovic et al. 2015a). Although the usual gender ratio for BD is 1:1, in elderly patients, more women than men receive treatment for BD. Lower overall cognitive and executive functioning have been reported in older patients with BD compared to both younger patients and normal controls in some studies (Tsai et al. 2009; Sheeran et al. 2012). However, not all studies have supported these conclusions (Delaloye et al. 2011). Age of onset of BD might be a confounding factor.

While BD usually presents with an age of onset during adolescence and early adulthood, some individuals experience a first episode of mania in and beyond the 5th decade of life (Bellivier et al. 2001, 2003; Kennedy et al. 2005). Most studies on BD in the elderly have not distinguished between early-onset and late-onset cases, but the evidence for a separate subtype of BD distinguished by age of onset is growing, if complex. Late-onset mania appears to have a distinctive phenotype, pathophysiology, and risk factors (Leboyer et al. 2005; Vasudev and Thomas 2010; Sheeran et al. 2012; Schouws et al. 2009, 2012; Sajatovic et al. 2005; Sajatovic and Chen 2011; Sajatovic et al. 2015a). In several studies, the late-onset group differed in psychiatric comorbidities, including lower rates of lifetime alcohol and substance abuse, and lower rates of anxiety disorders. In some studies, elderly patients with late-onset BD performed particularly worse on tests of psychomotor function and mental flexibility compared to those with BD who had an earlier age of onset, though elderly patients with BD from both groups performed more poorly than age-matched controls (Schouws et al. 2009, 2012). An increasing burden of chronic health problems has been related to the risk of late-onset BD including diabetes mellitus, hyperlipidemia, and other cardiovascular conditions (Préville et al. 2010; Sylvia et al. 2015), whereas in BD individuals with younger age of onset the risk is much less.

Investigations into the relationship between BD and diabetes mellitus have generally focused on all ages of patients. Even though late-onset cases of BD were not explicitly excluded in most studies, we noticed that few studies clearly distinguished between early-onset and late-onset cases of BD. However, this distinction could be quite relevant to treatment and outcome. Reports that particularly focused on late-onset BD and diabetes mellitus were sparse, and large studies were non-existing.

Gaps in knowledge and limitations of our study

Even though diabetes mellitus and BD in the elderly are growing public health problems, clinical studies on these topics are sparse. In general, available studies still suffer from methodological problems including small sample size, limitations of retrospective chart review, lack of standardized measures, overemphasis on inpatients, and lack of longitudinal data. Several studies have addressed not only the increasing healthcare utilization in elderly patients with BD and medical comorbidity, pointing to a need for integrated medical and psychiatric care in this vulnerable population (Hendrie et al. 2013), but also to existing healthcare disparities for patients with mental illness (Gierisch et al. 2014; McGinty et al. 2015).

In our literature review, we have been unable to identify published large-scale, multi-center studies on the prevalence, the etiology, or the clinical features of late-onset BD. To our knowledge, no double-blind, randomized, controlled trials of pharmacologic treatments have been performed in this specific patient population. Therefore, we recommend to increase emphasis on research in BD during the late stages of the disease, which could inform about the disease course and risk factors across the lifespan. It is hoped that this knowledge will not only assist in enhancing services and improving outcomes, but it might also lead to the discovery of potentially new pathophysiological pathways and risk factors for BD and diabetes mellitus, as well as to novel treatments and interventions.

Conclusions and recommendations

Increasing evidence supports the association between BD and diabetes mellitus and suggests shared risk factors and disease mechanisms. This public health problem deserves focused attention, especially in the elderly, to improve diagnosis, treatment and outcome. A stronger integration of medical and psychiatric care could help prevent the negative effects of these co-occurring disorders on the long-term outcome of patients with BD. Therefore, we recommend to increase research efforts on late-life BD and diabetes mellitus to better understand the complex relationship that exists between these disorders. A better understanding of risk factors in BD and diabetes mellitus could lead to novel treatment approaches, early intervention and prevention.

Abbreviations

BD:: bipolar disorder

DSM-IV:: Diagnostic and Statistical Manual of Mental Disorders, 4th Edition

ICD:: International Classification of Diseases

PPARγ:: peroxisome proliferator-activated receptor γ

CDK5:: cyclin-dependent kinase 5

Thrap3:: thyroid hormone receptor-associated protein 3

ER:: endoplasmatic reticulum

GSK-3β:: glycogen synthase kinase-3β

NHANES:: National Health and Nutrition Examination Survey

TLR:: toll-like receptor

Declarations

Authors’ contributions

EFC and BK participated equally in the conception and design of the study. They selected and reviewed the literature and drafted the manuscript. CGL participated in the conception of the study and writing the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Ethical standards

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.

Financial support

This research was not supported by any specific grant from any funding agency, commercial or not-for-profit sectors.

Abstract

Background

Objective

Methods

Results

We found that age- and gender- adjusted risk for diabetes mellitus was increased in patients with bipolar disorder and vice versa (odds ratio range between 1.7 and 3.2).

Discussion

Conclusions

Keywords

Bipolar disorder Diabetes Epidemiology Cohort studies Pathophysiology Evidence

Background

Methods

Table 1

Large cohort studies provide evidence for a significant association between bipolar disorder and diabetes mellitus

Author	Year	Title	Design	Type of bipolar disorder (BD)	Method of assessment of BD	Type of diabetes mellitus (DM)	Method of assessment	Results for BD group	Age of participants (years)	N
Wändell et al.	2014	Diabetes and psychiatric illness in the total population of Stockholm	National cohort study Cross-sectional study	BD F30–F31	Electronic patient records	DM (ICD-10 codes E10–E14	Electronic patient records	Age adjusted odds ratio of BD among patients with DM 1.714 (1.540–1.905) for women and 1.600 (1.429–1.792) for men	0–85+	2058,408 96,103 with DM 6341 with BD
Crump et al.	2013	Comorbidities and mortality in bipolar disorder: a Swedish national cohort study	National cohort study Cross-sectional	BD ICD-10 code F31	Public health records	DM (ICD-10 codes E10–E14)	Public health records	Risk of DM (1.7-fold among women and 1.6-fold among men)	>20	6587,036 353,615 with DM 6618 with BD
Bai et al.	2013	Risk of developing diabetes mellitus and hyperlipidemia among patients with bipolar disorder, major depressive disorder, and schizophrenia: a 10-year nationwide population-based prospective cohort study	10-year nationwide population-based prospective matched control cohort study	BD (ICD-9-CM code: 296, except 296.2, 296.3)	National Health Insurance (NHI) program records	DM (ICD-9-CM code 250)	National Health Insurance (NHI) program records	Increased risk of initiation of anti-diabetic medications (10.1 vs. 6.3 %,p = 0.012) Age and gender adjusted risk [hazard ratio (HR) of 1.702, 95 % confidence interval (CI): 1.155–2.507]	Average age 45.3 ± 14.0	1000,000 367 patients with BD 37 with DM
Svendal et al.	2012	Co-prescription of medication for bipolar disorder and diabetes mellitus: a nationwide population-based study with focus on gender differences	Norwegian prescription database Case–control study	BD	Indicated by prescription of mood stabilizers	DM	Indicated by prescription of antidiabetic medication	Unadjusted odds ratio of 2.1 (CI 95 %: 1.9, 2.2) Sex and age adjusted odds ratio of 2.0 (CI 95 %: 1.8, 2.1)	20–69	2,929,065 77,669 with DM 17,007 with BD
Hsieh et al.	2012	Medical costs and vasculo-metabolic comorbidities among patients with bipolar disorder in Taiwan—a population-based and -matched control study	Matched case–control study	BD (ICD-9-CM code 296, except 296.2, 296.3)	Hospital admission	DM ICD-9-CM (250)	Medical records	DM prevalence ratio 3.19; [2.74, 3.70]; p < .0001	>20	About 23,000,000 4,067 with BD, 420 with DM
Kodesh et al.	2012	Epidemiology and comorbidity of severe mental illnesses in the community: findings from a computerized mental health registry in a large Israeli health organization	Publicly funded Health Maintenance Organization (HMO) records Case–control study	BD-I, BD-II, Mania ICD-9 codes 295.–298.	Medical records	DM	Computerized medical records	DM odds ratio of 1.6	>21	2,000,000 5,732 patients with BD
Chien et al.	2010	Prevalence of diabetes in patients with bipolar disorder in Taiwan: a population-based national health insurance study	National Health Research Institute Case–control study	BD	Medical records	DM	Medical records	Diabetes prevalence in BD patients versus controls 10.77 vs. 5.57 %, OR 2.01; 99 % CI 1.64–2.48	>18	1,000,000 1,848 with BD

Results

Bipolar disorder and diabetes mellitus: is there a connection?

Discussion

Bipolar disorder and diabetes mellitus: do these disorders share common disease mechanisms?

The results of our study suggest a relationship between BD and diabetes mellitus. Therefore, we reviewed the supporting evidence for shared disease mechanisms based on the broader literature.

Bipolar disorder and diabetes mellitus: what are the outcomes?

Outlook

Investigations into treatment implications

Bipolar disorder in the elderly: does age of onset hint a distinct disease phenotype?

Gaps in knowledge and limitations of our study

Conclusions and recommendations

Abbreviations

BD:: bipolar disorder

DSM-IV:: Diagnostic and Statistical Manual of Mental Disorders, 4th Edition

ICD:: International Classification of Diseases

PPARγ:: peroxisome proliferator-activated receptor γ

CDK5:: cyclin-dependent kinase 5

Thrap3:: thyroid hormone receptor-associated protein 3

ER:: endoplasmatic reticulum

GSK-3β:: glycogen synthase kinase-3β

NHANES:: National Health and Nutrition Examination Survey

TLR:: toll-like receptor

Declarations

Authors’ contributions

Competing interests

The authors declare that they have no competing interests.

Ethical standards

Financial support

This research was not supported by any specific grant from any funding agency, commercial or not-for-profit sectors.

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Bipolar disorder

Posted on July 11, 2016 by JmaC

Do you go through intense moods?

Do you feel very happy and energized some days, and very sad and depressed on other days? Do these moods last for a week or more? Do your mood changes make it hard to sleep, stay focused, or go to work?

Some people with these symptoms have bipolar disorder, a serious mental illness. This brochure will give you more information.

What is bipolar disorder?

Bipolar disorder is a serious brain illness. It is also called manic-depressive illness or manic depression. People with bipolar disorder go through unusual mood changes. Sometimes they feel very happy and “up,” and are much more energetic and active than usual. This is called a manic episode. Sometimes people with bipolar disorder feel very sad and “down,” have low energy, and are much less active. This is called depression or a depressive episode.

Bipolar disorder is not the same as the normal ups and downs everyone goes through. The mood swings are more extreme than that and are accompanied by changes in sleep, energy level, and the ability to think clearly. Bipolar symptoms are so strong that they can damage relationships and make it hard to go to school or keep a job. They can also be dangerous. Some people with bipolar disorder try to hurt themselves or attempt suicide.

People with bipolar disorder can get treatment. With help, they can get better and lead successful lives.

Who develops bipolar disorder?

Anyone can develop bipolar disorder. It often starts in a person’s late teen or early adult years. But children and older adults can have bipolar disorder too. The illness usually lasts a lifetime.

Why does someone develop bipolar disorder?

Doctors do not know what causes bipolar disorder, but several things may contribute to the illness. Family genes may be one factor because bipolar disorder sometimes runs in families. However, it is important to know that just because someone in your family has bipolar disorder, it does not mean other members of the family will have it as well. Another factor that may lead to bipolar disorder is the brain structure or the brain function of the person with the disorder. Scientists are finding out more about the disorder by studying it. This research may help doctors do a better job of treating people. Also, this research may help doctors to predict whether a person will get bipolar disorder. One day, doctors may be able to prevent the illness in some people.

What are the symptoms of bipolar disorder?

Bipolar “mood episodes” include unusual mood changes along with unusual sleep habits, activity levels, thoughts, or behavior. People may have manic episodes, depressive episodes, or “mixed” episodes. A mixed episode has both manic and depressive symptoms. These mood episodes cause symptoms that last a week or two or sometimes longer. During an episode, the symptoms last every day for most of the day.

Mood episodes are intense. The feelings are strong and happen along with extreme changes in behavior and energy levels.

People having a manic episode may:

Feel very “up” or “high”
Feel “jumpy” or “wired”
Have trouble sleeping
Become more active than usual
Talk really fast about a lot of different things
Be agitated, irritable, or “touchy”
Feel like their thoughts are going very fast
Think they can do a lot of things at once
Do risky things, like spend a lot of money or have reckless sex

People having a depressive episode may:

Feel very “down” or sad
Sleep too much or too little
Feel like they can’t enjoy anything
Feel worried and empty
Have trouble concentrating
Forget things a lot
Eat too much or too little
Feel tired or “slowed down”
Have trouble sleeping
Think about death or suicide

Can someone have bipolar disorder along with other problems?

Yes. Sometimes people having very strong mood episodes may have psychotic symptoms. Psychosis affects thoughts and emotions as well as a person’s ability to know what is real and what is not. People with mania and psychotic symptoms may believe they are rich and famous, or have special powers. People with depression and psychotic symptoms may believe they have committed a crime, they have lost all of their money, or that their lives are ruined in some other way.

Sometimes behavior problems go along with mood episodes. A person may drink too much or take drugs. Some people take a lot of risks, like spending too much money or having reckless sex. These problems can damage lives and hurt relationships. Some people with bipolar disorder have trouble keeping a job or doing well in school.

Is bipolar disorder easy to diagnose?

No. Some people have bipolar disorder for years before the illness is diagnosed. This is because bipolar symptoms may seem like several different problems. Family and friends may notice the symptoms but not realize they are part of a bigger problem. A doctor may think the person has a different illness, like schizophrenia or depression.

People with bipolar disorder often have other health problems as well. This may make it hard for doctors to recognize the bipolar disorder. Examples of other illnesses include substance abuse, anxiety disorders, thyroid disease, heart disease, and obesity.

How is bipolar disorder treated?

Right now, there is no cure for bipolar disorder, but treatment can help control symptoms. Most people can get help for mood changes and behavior problems. Steady, dependable treatment works better than treatment that starts and stops. Treatment options include:

1. Medication. There are several types of medication that can help. People respond to medications in different ways, so the type of medication depends on the patient. Sometimes a person needs to try different medications to see which works best.

Medications can cause side effects. Patients should always tell their doctors about these problems.Also, patients should not stop taking a medication without a doctor’s help. Stopping medication suddenly can be dangerous, and it can make bipolar symptoms worse.

2. Therapy. Different kinds of psychotherapy, or “talk” therapy, can help people with bipolar disorder. Therapy can help them change their behavior and manage their lives. It can also help patients get along better with family and friends. Sometimes therapy includes family members.

3. Other treatments. Some people do not get better with medication and therapy. These people may try electroconvulsive therapy, or ECT. This is sometimes called “shock” therapy. ECT provides a quick electric current that can sometimes correct problems in the brain.

Sometimes people take herbal and natural supplements, such as St. John’s wort or omega-3 fatty acids. Talk to your doctor before taking any supplement. Scientists aren’t sure how these products affect people with bipolar disorder. Some people may also need sleep medications during treatment.

Personal Story

James has bipolar disorder.

Here’s his story.

Four months ago, James found out he has bipolar disorder. He knows it’s a serious illness, but he was relieved when he found out. That’s because he had symptoms for years, but no one knew what was wrong. Now he’s getting treatment and feeling better.

James often felt really sad. As a kid, he skipped school or stayed in bed when he was down. At other times, he felt really happy. He talked fast and felt like he could do anything. James lived like this for a long time, but things changed last year. His job got very stressful. He felt like he was having more “up” and “down” times. His wife and friends wanted to know what was wrong. He told them to leave him alone and said everything was fine.

A few weeks later, James couldn’t get out of bed. He felt awful, and the bad feelings went on for days. Then, his wife took him to the family doctor, who sent James to a psychiatrist. He talked to this doctor about how he was feeling. Soon James could see that his ups and downs were serious. He was diagnosed with bipolar disorder, and he started treatment.

These days, James takes medicine and goes to talk therapy. Treatment was hard at first, and recovery took some time, but now he’s back at work. His mood changes are easier to handle, and he’s having fun again with his wife and friends.

Getting Help

If you’re not sure where to get help, call your family doctor. You can also check the phone book for mental health professionals. Hospital doctors can help in an emergency. Finally, the Substance Abuse and Mental Health Services Administration (SAMHSA) has an online tool to help you find mental health services in your area. You can find it here: https://findtreatment.samhsa.gov .

How can I help myself if I have bipolar disorder?

You can help yourself by getting treatment and sticking with it. Recovery takes time, and it’s not easy. But treatment is the best way to start feeling better. Here are some tips:

Talk with your doctor about your treatment.
Stay on your medication.
Keep a routine for eating and sleeping.
Make sure you get enough sleep.
Learn to recognize your mood swings.
Ask a friend or relative to help you stick with your treatment.
Be patient with yourself. Improvement takes time.

How can I help someone I know with bipolar disorder?

Help your friend or relative see a doctor to get the right diagnosis and treatment. You may need to make the appointment and go to the doctor together. Here are some helpful things you can do:

Be patient.
Encourage your friend or relative to talk, and listen carefully.
Be understanding about mood swings.
Include your friend or relative in fun activities.
Remind the person that getting better is possible with the right treatment.

I know someone who is in crisis. What do I do?

If you know someone who might hurt himself or herself, or if you’re thinking about hurting yourself, get help quickly. Here are some things you can do:

Do not leave the person alone.
Call your doctor.
Call 911 or go to the emergency room.
Call the National Suicide Prevention Lifeline, toll-free:
1-800-273-TALK (8255). The TTY number is 1-800-799-4TTY (4889).

How does bipolar disorder affect friends and family?

When a friend or relative has bipolar disorder, it affects you too. Taking care of someone with bipolar disorder can be stressful. You have to cope with the mood swings and sometimes other problems, such as drinking too much. Sometimes the stress can strain your relationships with other people. Caregivers can miss work or lose free time.

If you are taking care of someone with bipolar disorder, take care of yourself too. Find someone you can talk to about your feelings. Talk with the doctor about support groups for caregivers. If you keep your stress level down, you will do a better job, and it might help your loved one stick to his or her treatment.

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Social Phobia (Social Anxiety Disorder): Always Embarrassed

Posted on July 11, 2016 by JmaC

Introduction

Are you afraid of being judged by others or of being embarrassed all the time? Do you feel extremely fearful and unsure around other people most of the time? Do these worries make it hard for you to do everyday tasks like run errands, or talk to people at work or school?

If so, you may have a type of anxiety disorder called social phobia, also called social anxiety disorder.

What is social phobia?

Social phobia is a strong fear of being judged by others and of being embarrassed. This fear can be so strong that it gets in the way of going to work or school or doing other everyday things.

Everyone has felt anxious or embarrassed at one time or another. For example, meeting new people or giving a public speech can make anyone nervous. But people with social phobia worry about these and other things for weeks before they happen.

People with social phobia are afraid of doing common things in front of other people. For example, they might be afraid to sign a check in front of a cashier at the grocery store, or they might be afraid to eat or drink in front of other people, or use a public restroom. Most people who have social phobia know that they shouldn’t be as afraid as they are, but they can’t control their fear. Sometimes, they end up staying away from places or events where they think they might have to do something that will embarrass them. For some people, social phobia is a problem only in certain situations, while others have symptoms in almost any social situation.

Social phobia usually starts during youth. A doctor can tell that a person has social phobia if the person has had symptoms for at least 6 months. Without treatment, social phobia can last for many years or a lifetime.

What are the signs and symptoms of social phobia?

People with social phobia tend to:

Be very anxious about being with other people and have a hard time talking to them, even though they wish they could
Be very self-conscious in front of other people and feel embarrassed
Be very afraid that other people will judge them
Worry for days or weeks before an event where other people will be
Stay away from places where there are other people
Have a hard time making friends and keeping friends
Blush, sweat, or tremble around other people
Feel nauseous or sick to their stomach when with other people.

What causes social phobia?

Social phobia sometimes runs in families, but no one knows for sure why some people have it, while others don’t. Researchers have found that several parts of the brain are involved in fear and anxiety. Some researchers think that misreading of others’ behavior may play a role in causing social phobia. For example, you may think that people are staring or frowning at you when they truly are not. Weak social skills are another possible cause of social phobia. For example, if you have weak social skills, you may feel discouraged after talking with people and may worry about doing it in the future. By learning more about fear and anxiety in the brain, scientists may be able to create better treatments. Researchers are also looking for ways in which stress and environmental factors may play a role.

How is social phobia treated?

First, talk to your doctor about your symptoms. Your doctor should do an exam to make sure that an unrelated physical problem isn’t causing the symptoms. The doctor may refer you to a mental health specialist.

Social phobia is generally treated with psychotherapy, medication, or both.

Psychotherapy. A type of psychotherapy called cognitive behavioral therapy (CBT) is especially useful for treating social phobia. It teaches a person different ways of thinking, behaving, and reacting to situations that help him or her feel less anxious and fearful. It can also help people learn and practice social skills.

Medication. Doctors also may prescribe medication to help treat social phobia. The most commonly prescribed medications for social phobia are anti- anxiety medications and antidepressants. Anti-anxiety medications are powerful and there are different types. Many types begin working right away, but they generally should not be taken for long periods.

Antidepressants are used to treat depression, but they are also helpful for social phobia. They are probably more commonly prescribed for social phobia than anti-anxiety medications. Antidepressants may take several weeks to start working. Some may cause side effects such as headache, nausea, or difficulty sleeping. These side effects are usually not a problem for most people, especially if the dose starts off low and is increased slowly over time. Talk to your doctor about any side effects you may have.

A type of antidepressant called monoamine oxidase inhibitors (MAOIs) are especially effective in treating social phobia. However, they are rarely used as a first line of treatment because when MAOIs are combined with certain foods or other medicines, dangerous side effects can occur.

It’s important to know that although antidepressants can be safe and effective for many people, they may be risky for some, especially children, teens, and young adults. A “black box”—the most serious type of warning that a prescription drug can have—has been added to the labels of antidepressant medications. These labels warn people that antidepressants may cause some people to have suicidal thoughts or make suicide attempts.

Anyone taking antidepressants should be monitored closely, especially when they first start treatment.

Another type of medication called beta-blockers can help control some of the physical symptoms of social phobia such as excessive sweating, shaking, or a racing heart. They are most commonly prescribed when the symptoms of social phobia occur in specific situations, such as “stage fright.”

Some people do better with CBT, while others do better with medication. Still others do best with a combination of the two. Talk with your doctor about the best treatment for you.

What is it like having social phobia?

“In school I was always afraid of being called on, even when I knew the answers. When I got a job, I hated to meet with my boss. I couldn’t eat lunch with my co-workers. I worried about being stared at or judged, and worried that I would make a fool of myself. My heart would pound and I would start to sweat when I thought about meetings. The feelings got worse as the time of the event got closer. Sometimes I couldn’t sleep or eat for days before a staff meeting.”

“I’m taking medicine and working with a counselor to cope better with my fears. I had to work hard, but I feel better. I’m glad I made that first call to my doctor.”

For More Information

For more information on conditions that affect mental health, resources, and research, go toMentalHealth.gov at http://www.mentalhealth.gov , or the NIMH website at http://www.nimh.nih.gov. In addition, the National Library of Medicine’s MedlinePlus service has information on a wide variety of health topics, including conditions that affect mental health.

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Obsessive-Compulsive Disorder: When Unwanted Thoughts Take Over

Posted on July 11, 2016 by JmaC

Introduction: Obsessive-Compulsive Disorder

Do you feel the need to check and re-check things over and over? Do you have the same thoughts constantly? Do you feel a very strong need to perform certain rituals repeatedly and feel like you have no control over what you are doing?

If so, you may have a type of anxiety disorder called obsessive-compulsive disorder (OCD).

What is OCD?

Everyone double checks things sometimes. For example, you might double check to make sure the stove or iron is turned off before leaving the house. But people with OCD feel the need to check things repeatedly, or have certain thoughts or perform routines and rituals over and over. The thoughts and rituals associated with OCD cause distress and get in the way of daily life.

The frequent upsetting thoughts are called obsessions. To try to control them, a person will feel an overwhelming urge to repeat certain rituals or behaviors called compulsions. People with OCD can’t control these obsessions and compulsions.

For many people, OCD starts during childhood or the teen years. Most people are diagnosed by about age 19. Symptoms of OCD may come and go and be better or worse at different times.

What causes OCD?

OCD sometimes runs in families, but no one knows for sure why some people have it, while others don’t. Researchers have found that several parts of the brain are involved in obsessive thoughts and compulsive behavior, as well as fears and anxiety associated with them. By learning more about fear and anxiety in the brain, scientists may be able to create better treatments. Researchers are also looking for ways in which stress and environmental factors may play a role.

What are the signs and symptoms of OCD?

People with OCD generally:

Have repeated thoughts or images about many different things, such as fear of germs, dirt, or intruders; acts of violence; hurting loved ones; sexual acts; conflicts with religious beliefs; or being overly tidy
Do the same rituals over and over such as washing hands, locking and unlocking doors, counting, keeping unneeded items, or repeating the same steps again and again
Can’t control the unwanted thoughts and behaviors
Don’t get pleasure when performing the behaviors or rituals, but get brief relief from the anxiety the thoughts cause
Spend at least 1 hour a day on the thoughts and rituals, which cause distress and get in the way of daily life.

How is OCD treated?

First, talk to your doctor about your symptoms. Your doctor should do an exam to make sure that another physical problem isn’t causing the symptoms. The doctor may refer you to a mental health specialist.

OCD is generally treated with psychotherapy, medication, or both.

Psychotherapy. A type of psychotherapy called cognitive behavioral therapy (CBT) is especially useful for treating OCD. It teaches a person different ways of thinking, behaving, and reacting to situations that help him or her better manage obsessive thoughts, reduce compulsive behavior, and feel less anxious. One specific form of CBT, exposure and response prevention, has been shown to be helpful in reducing the intrusive thoughts and behaviors associated with OCD.

Medication. Doctors may also prescribe medication to help treat OCD. The most commonly prescribed medications for OCD are antidepressants. Although antidepressants are used to treat depression, they are also particularly helpful for OCD. They may take several weeks—10 to 12 weeks for some—to start working. Some of these medications may cause side effects such as headache, nausea, or difficulty sleeping. These side effects are usually not severe for most people, especially if the dose starts off low and is increased slowly over time. Talk to your doctor about any side effects you may have.

It’s important to know that although antidepressants can be safe and effective for many people, they may be risky for some, especially children,teens, and young adults. A “black box”—the most serious type of warning that a prescription drug can have—has been added to the labels of antidepressant medications. These labels warn people that antidepressants may cause some people to have suicidal thoughts or make suicide attempts.Anyone taking antidepressants should be monitored closely, especially when they first start treatment with medications.

In addition to prescribing antidepressants, doctors may prescribe other medications such as benzodiazepines to address the anxiety and distress that accompany OCD. Not all medications are effective for everyone. Talk to your doctor about the best treatment choice for you.

Combination. Some people with OCD do better with CBT, especially exposure and response prevention. Others do better with medication. Still others do best with a combination of the two. Many studies have shown that combining CBT with medication is the best approach for treating OCD, particularly in children and adolescents. Talk with your doctor about the best treatment for you.

What is it like having OCD?

“I couldn’t do anything without rituals. They invaded every aspect of my life. Counting really bogged me down. I would wash my hair three times as opposed to once because three was a good luck number and one wasn’t. It took me longer to read because I’d count the lines in a paragraph. When I set my alarm at night, I had to set it to a number that wouldn’t add up to a ‘bad’ number.”

“Getting dressed in the morning was tough, because I had a routine, and if I didn’t follow the routine, I’d get anxious and would have to get dressed again. I always worried that if I didn’t do something, my parents were going to die. I’d have these terrible thoughts of harming my parents. I knew that was completely irrational, but the thoughts triggered more anxiety and more senseless behavior. Because of the time I spent on rituals, I was unable to do a lot of things that were important to me.”

“I knew the rituals didn’t make sense, and I was deeply ashamed of them, but I couldn’t seem to overcome them until I got treatment.”

For More Information

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Generalized Anxiety Disorder (GAD): When Worry Gets Out of Control

Posted on July 11, 2016 by JmaC

Introduction

Are you extremely worried about everything in your life, even if there is little or no reason to worry? Are you very anxious about just getting through the day? Are you afraid that everything will always go badly?

If so, you may have an anxiety disorder called generalized anxiety disorder (GAD).

What is GAD?

All of us worry about things like health, money, or family problems. But people with GAD are extremely worried about these or other things, even when there is little or no reason to worry about them. They are very anxious about just getting through the day. They think things will always go badly. At times, worrying keeps people with GAD from doing everyday tasks.

GAD develops slowly. It often starts during the teen years or young adulthood. Symptoms may get better or worse at different times, and often are worse during times of stress.

People with GAD may visit a doctor many times before they find out they have this disorder. They ask their doctors to help them with headaches or trouble falling asleep, which can accompany GAD but they don’t always get the help they need right away. It may take doctors some time to be sure that a person has GAD instead of something else.

What causes GAD?

GAD sometimes runs in families, but no one knows for sure why some people have it, while others don’t. Researchers have found that several parts of the brain are involved in fear and anxiety. Research suggests that the extreme worries of GAD may be a way for a person to avoid or ignore some deeper concern. If the person deals with this concern, then the worries of GAD would also disappear. By learning more about fear and anxiety in the brain, scientists may be able to create better treatments. Researchers are also looking for ways in which stress and environmental factors may play a role.

What are the signs and symptoms of GAD?

A person with GAD may:

Worry very much about everyday things
Have trouble controlling their constant worries
Know that they worry much more than they should
Have trouble relaxing
Have a hard time concentrating
Be easily startled
Have trouble falling asleep or staying asleep
Feel tired all the time
Have headaches, muscle aches, stomach aches, or unexplained pains
Have a hard time swallowing
Tremble or twitch
Be irritable, sweat a lot, and feel light-headed or out of breath
Have to go to the bathroom a lot.

How is GAD treated?

GAD is generally treated with psychotherapy, medication, or both.

Psychotherapy. A type of psychotherapy called cognitive behavioral therapy (CBT) is especially useful for treating GAD. It teaches a person different ways of thinking, behaving, and reacting to situations that help him or her feel less anxious and worried.

Medication. Doctors also may prescribe medication to help treat GAD. Two types of medications are commonly used to treat GAD—anti-anxiety medications and antidepressants. Anti-anxiety medications are powerful and there are different types. These side effects are usually not severe for most people, especially if the dose starts off low and is increased slowly over time.

Antidepressants are used to treat depression, but they also are helpful for GAD. They may take several weeks to start working. These medications may cause side effects such as headache, nausea, or difficulty sleeping. These side effects are usually not a problem for most people, especially if the dose starts off low and is increased slowly over time. Talk to your doctor about any side effects you may have.

Some people do better with CBT, while others do better with medication. Still others do best with a combination of the two. Talk with your doctor about the best treatment for you.

What is it like to have GAD?

“I was worried all the time about everything. It didn’t matter that there were no signs of problems, I just got upset. I was having trouble falling asleep at night, and I couldn’t keep my mind focused at work. I felt angry at my family all the time.

“I saw my doctor and explained my constant worries. My doctor sent me to someone who knows about GAD. Now I am taking medicine and working with a counselor to cope better with my worries. I had to work hard, but I feel better. I’m glad I made that first call to my doctor.”

For More Information

National Institute of Mental Health
Office of Science Policy, Planning, and Communications
Science Writing, Press, and Dissemination Branch
6001 Executive Boulevard
Room 6200, MSC 9663
Bethesda, MD 20892–9663
Phone: 301-443-4513 or 1-866-615-NIMH (6464) toll-free
TTY: 301-443-8431 or1-866-415-8051 toll-free
Fax: 301-443-4279
Email: nimhinfo@nih.gov
Website: http://www.nimh.nih.gov

Reprints

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Using government materials inappropriately, however, can raise legal or ethical concerns, so we ask you to follow these guidelines:

NIMH does not endorse or recommend any commercial products, processes, or services. You may not use NIMH publications for advertising or endorsement purposes.
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Bipolar TV WEB MD

Posted on July 11, 2016 by JmaC

http://www.webmd.com/bipolar-disorder/bipolar-tv/default.htm

Posted in Mental Health & Mindful Videos | Leave a comment

Mental Illness

Posted on July 10, 2016 by JmaC

Mental illness

Definition

Mental illness refers to a wide range of mental health conditions — disorders that affect your mood, thinking and behavior. Examples of mental illness include depression, anxiety disorders, schizophrenia, eating disorders and addictive behaviors.

Many people have mental health concerns from time to time. But a mental health concern becomes a mental illness when ongoing signs and symptoms cause frequent stress and affect your ability to function.

A mental illness can make you miserable and can cause problems in your daily life, such as at school or work or in relationships. In most cases, symptoms can be managed with a combination of medications and talk therapy (psychotherapy).

Symptoms

Signs and symptoms of mental illness can vary, depending on the disorder, circumstances and other factors. Mental illness symptoms can affect emotions, thoughts and behaviors.

Examples of signs and symptoms include:

Feeling sad or down
Confused thinking or reduced ability to concentrate
Excessive fears or worries, or extreme feelings of guilt
Extreme mood changes of highs and lows
Withdrawal from friends and activities
Significant tiredness, low energy or problems sleeping
Detachment from reality (delusions), paranoia or hallucinations
Inability to cope with daily problems or stress
Trouble understanding and relating to situations and to people
Alcohol or drug abuse
Major changes in eating habits
Sex drive changes
Excessive anger, hostility or violence
Suicidal thinking

Sometimes symptoms of a mental health disorder appear as physical problems, such as stomach pain, back pain, headache, or other unexplained aches and pains.

When to see a doctor

If you have any signs or symptoms of a mental illness, see your primary care provider or mental health specialist. Most mental illnesses don’t improve on their own, and if untreated, a mental illness may get worse over time and cause serious problems.

If you have suicidal thoughts

Suicidal thoughts and behavior are common with some mental illnesses. If you think you may hurt yourself or attempt suicide, get help right away:

Call 911 or your local emergency number immediately.
Call your mental health specialist.
Call a suicide hotline number — in the U.S., call the National Suicide Prevention Lifeline at 1-800-273-TALK (1-800-273-8255).
Seek help from your primary care doctor or other health care provider.
Reach out to a close friend or loved one.
Contact a minister, spiritual leader or someone else in your faith community.

Suicidal thinking doesn’t get better on its own — so get help.

Helping a loved one

If your loved one shows signs of mental illness, have an open and honest discussion with him or her about your concerns. You may not be able to force someone to get professional care, but you can offer encouragement and support. You can also help your loved one find a qualified mental health provider and make an appointment. You may even be able to go along to the appointment.

If your loved one has done self-harm or is considering doing so, take the person to the hospital or call for emergency help.

Causes

Mental illnesses, in general, are thought to be caused by a variety of genetic and environmental factors:

Inherited traits. Mental illness is more common in people whose blood relatives also have a mental illness. Certain genes may increase your risk of developing a mental illness, and your life situation may trigger it.
Environmental exposures before birth. Exposure to environmental stressors, inflammatory conditions, toxins, alcohol or drugs while in the womb can sometimes be linked to mental illness.
Brain chemistry. Neurotransmitters are naturally occurring brain chemicals that carry signals to other parts of your brain and body. When the neural networks involving these chemicals are impaired, the function of nerve receptors and nerve systems change, leading to depression.

Risk factors

Certain factors may increase your risk of developing mental health problems, including:

Having a blood relative, such as a parent or sibling, with a mental illness
Stressful life situations, such as financial problems, a loved one’s death or a divorce
An ongoing (chronic) medical condition, such as diabetes
Brain damage as a result of a serious injury (traumatic brain injury), such as a violent blow to the head
Traumatic experiences, such as military combat or being assaulted
Use of alcohol or recreational drugs
Being abused or neglected as a child
Having few friends or few healthy relationships
A previous mental illness

Mental illness is common. About 1 in 5 adults has a mental illness in any given year. Mental illness can begin at any age, from childhood through later adult years, but most begin earlier in life.

The effects of mental illness can be temporary or long lasting. You also can have more than one mental health disorder at the same time. For example, you may have depression and a substance use disorder.

Complications

Mental illness is a leading cause of disability. Untreated mental illness can cause severe emotional, behavioral and physical health problems. Complications sometimes linked to mental illness include:

Unhappiness and decreased enjoyment of life
Family conflicts
Relationship difficulties
Social isolation
Problems with tobacco, alcohol and other drugs
Missed work or school, or other problems related to work or school
Legal and financial problems
Poverty and homelessness
Self-harm and harm to others, including suicide or homicide
Weakened immune system, so your body has a hard time resisting infections
Heart disease and other medical conditions

Preparing for your appointment

Whether you schedule an appointment with your primary care provider to talk about mental health concerns or you’re referred to a mental health provider, such as a psychiatrist or psychologist, take steps to prepare for your appointment.

If possible, take a family member or friend along. Someone who has known you for a long time may be able to share important information with your heath care provider, with your permission.

What you can do

Before your appointment, make a list of:

Any symptoms you or people close to you have noticed, and for how long
Key personal information, including traumatic events in your past and any current, major stressors
Your medical information, including other physical or mental health conditions
Any medications, vitamins, herbal products or other supplements you take, and their doses
Questions to ask your doctor or mental health provider

Questions to ask include:

What type of mental illness might I have?
Why can’t I get over mental illness on my own?
How do you treat my type of mental illness?
Will talk therapy help?
Are there medications that might help?
How long will treatment take?
What can I do to help myself?
Do you have any brochures or other printed material that I can have?
What websites do you recommend?

Don’t hesitate to ask any other questions.

What to expect from your doctor

During your appointment, your doctor or mental health provider is likely to ask you several questions about your mood, thoughts and behavior, such as:

When did you first notice symptoms?
How is your daily life affected by your symptoms?
What treatment, if any, have you had for mental illness?
What have you tried on your own to feel better or control your symptoms?
What things make you feel worse?
Have family members or friends commented on your mood or behavior?
Do you have blood relatives with a mental illness?
What do you hope to gain from treatment?
What medications or over-the-counter herbs and supplements do you take?
Do you drink alcohol or use recreational drugs?

Tests and diagnosis

To determine a diagnosis and check for related complications, you may have:

A physical exam. Your doctor will try to rule out physical problems that could cause your symptoms.
Lab tests. These may include, for example, a check of your thyroid function or a screening for alcohol and drugs.
A psychological evaluation. A doctor or mental health provider talks to you about your symptoms, thoughts, feelings and behavior patterns. You may be asked to fill out a questionnaire to help answer these questions.

Determining which mental illness you have

Sometimes it’s difficult to find out which mental illness may be causing your symptoms. But taking the time and effort to get an accurate diagnosis will help determine the appropriate treatment.

The defining symptoms for each mental illness are detailed in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), published by the American Psychiatric Association. This manual is used by mental health providers to diagnose mental conditions and by insurance companies to reimburse for treatment.

Classes of mental illness

The main classes of mental illness are:

Neurodevelopmental disorders. This class covers a wide range of problems that usually begin in infancy or childhood, often before the child begins grade school. Examples include autism spectrum disorder, attention-deficit/hyperactivity disorder (ADHD) and learning disorders.
Schizophrenia spectrum and other psychotic disorders. Psychotic disorders cause detachment from reality — such as delusions, hallucinations, and disorganized thinking and speech. The most notable example is schizophrenia, although other classes of disorders can be associated with detachment from reality at times.
Bipolar and related disorders. This class includes disorders with alternating episodes of mania — periods of excessive activity, energy and excitement — and depression.
Depressive disorders. These include disorders that affect how you feel emotionally, such as the level of sadness and happiness, and they can disrupt your ability to function. Examples include major depressive disorder and premenstrual dysphoric disorder.
Anxiety disorders. Anxiety is an emotion characterized by the anticipation of future danger or misfortune, along with excessive worrying. It can include behavior aimed at avoiding situations that cause anxiety. This class includes generalized anxiety disorder, panic disorder and phobias.
Obsessive-compulsive and related disorders. These disorders involve preoccupations or obsessions and repetitive thoughts and actions. Examples include obsessive-compulsive disorder, hoarding disorder and hair-pulling disorder (trichotillomania).
Trauma- and stressor-related disorders. These are adjustment disorders in which a person has trouble coping during or after a stressful life event. Examples include post-traumatic stress disorder (PTSD) and acute stress disorder.
Dissociative disorders. These are disorders in which your sense of self is disrupted, such as with dissociative identity disorder and dissociative amnesia.
Somatic symptom and related disorders. A person with one of these disorders may have physical symptoms with no clear medical cause, but the disorders are associated with significant distress and impairment. The disorders include somatic symptom disorder (previously known as hypochondriasis) and factitious disorder.
Feeding and eating disorders. These disorders include disturbances related to eating, such as anorexia nervosa and binge-eating disorder.
Elimination disorders. These disorders relate to the inappropriate elimination of urine or stool by accident or on purpose. Bedwetting (enuresis) is an example.
Sleep-wake disorders. These are disorders of sleep severe enough to require clinical attention, such as insomnia, sleep apnea and restless legs syndrome.
Sexual dysfunctions. These include disorders of sexual response, such as premature ejaculation and female orgasmic disorder.
Gender dysphoria. This refers to the distress that accompanies a person’s stated desire to be another gender.
Disruptive, impulse-control and conduct disorders. These disorders include problems with emotional and behavioral self-control, such as kleptomania or intermittent explosive disorder.
Substance-related and addictive disorders. These include problems associated with the excessive use of alcohol, caffeine, tobacco and drugs. This class also includes gambling disorder.
Neurocognitive disorders. Neurocognitive disorders affect your ability to think and reason. These acquired (rather than developmental) cognitive problems include delirium, as well as neurocognitive disorders due to conditions or diseases such as traumatic brain injury or Alzheimer’s disease.
Personality disorders. A personality disorder involves a lasting pattern of emotional instability and unhealthy behavior that causes problems in your life and relationships. Examples include borderline, antisocial and narcissistic personality disorders.
Paraphilic disorders. These disorders include sexual interest that causes personal distress or impairment or causes potential or actual harm to another person. Examples are sexual sadism disorder, voyeuristic disorder and pedophilic disorder.
Other mental disorders. This class includes mental disorders that are due to other medical conditions or that don’t meet the full criteria for one of the above disorders.

Treatments and drugs

Your treatment depends on the type of mental illness you have, its severity and what works best for you. In many cases, a combination of treatments works best.

If you have a mild mental illness with well-controlled symptoms, treatment from one health care provider may be sufficient. However, often a team approach is appropriate to make sure all your psychiatric, medical and social needs are met. This is especially important for severe mental illnesses, such as schizophrenia.

Your treatment team

Your treatment team may include your:

Family or primary care doctor
Nurse practitioner
Physician assistant
Psychiatrist, a medical doctor who diagnoses and treats mental illnesses
Psychotherapist, such as a psychologist or a licensed counselor
Pharmacist
Social worker
Family members

Medications

Although psychiatric medications don’t cure mental illness, they can often significantly improve symptoms. Psychiatric medications can also help make other treatments, such as psychotherapy, more effective. The best medications for you will depend on your particular situation and how your body responds to the medication.

Some of the most commonly used classes of prescription psychiatric medications include:

Antidepressants. Antidepressants are used to treat depression, anxiety and sometimes other conditions. They can help improve symptoms such as sadness, hopelessness, lack of energy, difficulty concentrating and lack of interest in activities. Antidepressants are not addictive and do not cause dependency.
Anti-anxiety medications. These drugs are used to treat anxiety disorders, such as generalized anxiety disorder or panic disorder. They may also help reduce agitation and insomnia. Long-term anti-anxiety drugs typically are antidepressants that also work for anxiety. Fast-acting anti-anxiety drugs help with short-term relief, and they also have the potential to cause dependency, so ideally they’d be used short term.
Mood-stabilizing medications. Mood stabilizers are most commonly used to treat bipolar disorders, which involves alternating episodes of mania and depression. Sometimes mood stabilizers are used with antidepressants to treat depression.
Antipsychotic medications. Antipsychotic drugs are typically used to treat psychotic disorders, such as schizophrenia. Antipsychotic medications may also be used to treat bipolar disorders or used with antidepressants to treat depression.

Psychotherapy

Psychotherapy, also called talk therapy, involves talking about your condition and related issues with a mental health provider. During psychotherapy, you learn about your condition and your moods, feelings, thoughts and behavior. With the insights and knowledge you gain, you can learn coping and stress management skills.

There are many types of psychotherapy, each with its own approach to improving your mental well-being. Psychotherapy often can be successfully completed in a few months, but in some cases, long-term treatment may be needed. It can take place one-on-one, in a group or with family members.

When choosing a therapist, you should feel comfortable and be confident that he or she is capable of listening and hearing what you have to say. Also, it’s important that your therapist understands the life journey that has helped shape who you are and how you live in the world.

Brain-stimulation treatments

Brain-stimulation treatments are sometimes used for depression and other mental health disorders. They’re generally reserved for situations in which medications and psychotherapy haven’t worked. They include electroconvulsive therapy, transcranial magnetic stimulation, an experimental treatment called deep brain stimulation and vagus nerve stimulation.

Make sure you understand all the risks and benefits of any recommended treatment.

Hospital and residential treatment programs

Sometimes mental illness becomes so severe that you need care in a psychiatric hospital. This is generally recommended when you can’t care for yourself properly or when you’re in immediate danger of harming yourself or someone else.

Options include 24-hour inpatient care, partial or day hospitalization, or residential treatment, which offers a temporary supportive place to live. Another option may be intensive outpatient treatment.

Substance abuse treatment

Substance abuse commonly occurs along with mental illness. Often it interferes with treatment and worsens mental illness. If you can’t stop using drugs or alcohol on your own, you need treatment. Talk to your doctor about treatment options.

Participating in your own care

Working together, you and your health care provider can decide which treatment may be best, depending on your symptoms and their severity, your personal preferences, medication side effects, and other factors. In some cases, a mental illness may be so severe that a doctor or loved one may need to guide your care until you’re well enough to participate in decision-making.

Lifestyle and home remedies

In most cases, a mental illness won’t get better if you try to treat it on your own without professional care. But you can do some things for yourself that will build on your treatment plan:

Stick to your treatment plan. Don’t skip therapy sessions. Even if you’re feeling better, don’t skip your medications. If you stop, symptoms may come back. And you could have withdrawal-like symptoms if you stop a medication too suddenly. If you have bothersome drug side effects or other problems with treatment, talk to your doctor before making changes.
Avoid alcohol and drug use. Using alcohol or recreational drugs can make it difficult to treat a mental illness. If you’re addicted, quitting can be a real challenge. If you can’t quit on your own, see your doctor or find a support group to help you.
Stay active. Exercise can help you manage symptoms of depression, stress and anxiety. Physical activity can also counteract the effects of some psychiatric medications that may cause weight gain. Consider walking, swimming, gardening or any form of physical activity that you enjoy. Even light physical activity can make a difference.
Don’t make important decisions when your symptoms are severe. Avoid decision-making when you’re in the depth of mental illness symptoms, since you may not be thinking clearly.
Determine priorities. You may reduce the impact of your mental illness by managing time and energy. Cut back on obligations when necessary and set reasonable goals. Give yourself permission to do less when symptoms are worse. You may find it helpful to make a list of daily tasks or use a planner to structure your time and stay organized.
Learn to adopt a positive attitude. Focusing on the positive things in your life can make your life better and may even improve your health. Try to accept changes when they occur, and keep problems in perspective. Stress management techniques, including relaxation methods, may help.

Coping and support

Coping with a mental illness is challenging. Talk to your doctor or therapist about improving your coping skills, and consider these tips:

Learn about your mental illness. Your doctor or therapist can provide you with information or may recommend classes, books or websites. Include your family, too — this can help the people who care about you understand what you’re going through and learn how they can help.
Join a support group. Connecting with others facing similar challenges may help you cope. Support groups for mental illness are available in many communities and online. One good place to start is the National Alliance on Mental Illness.
Stay connected with friends and family. Try to participate in social activities, and get together with family or friends regularly. Ask for help when you need it, and be upfront with your loved ones about how you’re doing.
Keep a journal. Keeping track of your personal life can help you and your mental health provider identify what triggers or improves your symptoms. It’s also a healthy way to explore and express pain, anger, fear and other emotions.

Prevention

There’s no sure way to prevent mental illness. However, if you have a mental illness, taking steps to control stress, to increase your resilience and to boost low self-esteem may help keep your symptoms under control. Follow these steps:

Pay attention to warning signs. Work with your doctor or therapist to learn what might trigger your symptoms. Make a plan so that you know what to do if symptoms return. Contact your doctor or therapist if you notice any changes in symptoms or how you feel. Consider involving family members or friends to watch for warning signs.
Get routine medical care. Don’t neglect checkups or skip visits to your health care provider, especially if you aren’t feeling well. You may have a new health problem that needs to be treated, or you may be experiencing side effects of medication.
Get help when you need it. Mental health conditions can be harder to treat if you wait until symptoms get bad. Long-term maintenance treatment also may help prevent a relapse of symptoms.
Take good care of yourself. Sufficient sleep, healthy eating and regular physical activity are important. Try to maintain a regular schedule. Talk to your health care provider if you have trouble sleeping or if you have questions about diet and physical activity.

Last updated: October 13th, 2015

Posted in News & updates | Leave a comment

Mental health: Overcoming the stigma of mental illness

Posted on July 10, 2016 by JmaC

Stigma is when someone views you in a negative way because you have a distinguishing characteristic or personal trait that’s thought to be, or actually is, a disadvantage (a negative stereotype). Unfortunately, negative attitudes and beliefs toward people who have a mental health condition are common.

Stigma can lead to discrimination. Discrimination may be obvious and direct, such as someone making a negative remark about your mental illness or your treatment. Or it may be unintentional or subtle, such as someone avoiding you because the person assumes you could be unstable, violent or dangerous due to your mental health condition. You may even judge yourself.

Some of the harmful effects of stigma can include:

Reluctance to seek help or treatment
Lack of understanding by family, friends, co-workers or others you know
Fewer opportunities for work, school or social activities or trouble finding housing
Bullying, physical violence or harassment
Health insurance that doesn’t adequately cover your mental illness treatment
The belief that you’ll never be able to succeed at certain challenges or that you can’t improve your situation

Steps to cope with stigma

Here are some ways you can deal with stigma:

Get treatment. You may be reluctant to admit you need treatment. Don’t let the fear of being labeled with a mental illness prevent you from seeking help. Treatment can provide relief by identifying what’s wrong and reducing symptoms that interfere with your work and personal life.
Don’t let stigma create self-doubt and shame. Stigma doesn’t just come from others. You may mistakenly believe that your condition is a sign of personal weakness or that you should be able to control it without help. Seeking psychological counseling, educating yourself about your condition and connecting with others with mental illness can help you gain self-esteem and overcome destructive self-judgment.
Don’t isolate yourself. If you have a mental illness, you may be reluctant to tell anyone about it. Your family, friends, clergy or members of your community can offer you support if they know about your mental illness. Reach out to people you trust for the compassion, support and understanding you need.
Don’t equate yourself with your illness. You are not an illness. So instead of saying “I’m bipolar,” say “I have bipolar disorder.” Instead of calling yourself “a schizophrenic,” say “I have schizophrenia.”
Join a support group. Some local and national groups, such as the National Alliance on Mental Illness (NAMI), offer local programs and Internet resources that help reduce stigma by educating people with mental illness, their families and the general public. Some state and federal agencies and programs, such as those that focus on vocational rehabilitation or the Department of Veterans Affairs (VA), offer support for people with mental health conditions.
Get help at school. If you or your child has a mental illness that affects learning, find out what plans and programs might help. Discrimination against students because of a mental health condition is against the law, and educators at primary, secondary and college levels are required to accommodate students as best they can. Talk to teachers, professors or administrators about the best approach and resources. If a teacher doesn’t know about a student’s disability, it can lead to discrimination, barriers to learning and poor grades.
Speak out against stigma. Consider expressing your opinions at events, in letters to the editor or on the Internet. It can help instill courage in others facing similar challenges and educate the public about mental illness.

Others’ judgments almost always stem from a lack of understanding rather than information based on the facts. Learning to accept your condition and recognize what you need to do to treat it, seeking support, and helping educate others can make a big difference.

Last updated: May 17th, 2014

Posted in News & updates | Leave a comment

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