SOMATIC CO – MORBIDITIES AND FRAILTY IN PATIENTS WITH MENTAL DISORDERS

Somatic co-morbidities in patient with mental disorders represent an important public health issue due to their negative impact on quality of life and life expectancy, and also to high costs associated to care. Far from being an exception, presence of somatic co-morbidities represent the rule in patients with chronic mental disorders. Prevalence of chronic somatic disorders in patients with severe mental disorders reaches up to 74% (1). Factors contributing to association between mental and somatic disorders are multiple: stress, high prevalence of mood disorders, unhealthy behaviors and life-styles, high prevalence of smoking, alcohol dependence and drugs addictions, sedentariness, adverse reactions to drugs, particularly to antipsychotics (that present a high risk for developing metabolic syndrome and diabetes mellitus) (2-4).

Patients with schizophrenia present high risk of somatic morbidity and mortality (5). Life expectancy for patients with schizophrenia is 20-25 years smaller than for subjects in general population, and the excess of mortality cannot be explained only by increased incidence of suicide (6). Diabetes mellitus and metabolic syndrome are important complications in patients with schizophrenia. Prevalence of diabetes mellitus in these patients is estimated between 9-14%, and the risk of developing diabetes mellitus is 2-3 times higher in patients with schizophrenia as compared to general population (7). Incidence of diabetes mellitus following treatments with conventional (7.7/1000 years-patient) or atypical (9.8/1000 years-patient) antipsychotics was higher in comparison to general population (3.3/1000 years- patient) in a cohort from Great Britain (8). Adults or elderly patients with schizophrenia present a 79% increase in cardiovascular risk as compared to general population (9). Increased cardiovascular risk is due not only to diabetes mellitus, but also to dyslipidemia and smoking (10). Respiratory disorders, including chronic obstructive pulmonary disease and decreased values of pulmonary capacity are frequently encountered in patients with schizophrenia, that present an Odds Ratio for developing COPD of 1.88 in comparison to general population (11).
Majority of patients with type I or II bipolar disorder present at least one somatic co-morbidity and many present multimorbidity (3). Prevalence of cardiovascular disorders is increased in these patients. Higher prevalence of arterial hypertension represents an important risk factor for coronary and cerebro-vascular ischemic events (12). Prevalence of obesity, metabolic syndrome and diabetes mellitus are significantly increased in patients with bipolar disorder (13). Also patients with bipolar disorder present increased prevalence of various pulmonary disorders (bronchitis, COPD, asthma, thromboembolism), situations that can be explained by aggregation of risk factors for pulmonary disorders (smoking, obesity, sedentariness, musculo- skeletal trauma, hypercoagulability, diabetes mellitus, drugs addictions) (13, 14). Increasing weight is a side e f f e c t a s s o c i a t e d w i t h m o s t n e w g e n e r a t i o n antipsychotics, being present especially for olanzapine and clozapine and more moderate for risperidone and quetiapine (15). More than half of patients with bipolar disorders present different forms of addictions, between 6- 69% alcohol dependence and between 14-60% drug addictions (16, 17), with important impact on somatic state.
Although existence of co-morbidities in patients with depression represents the rule, most of therapeutic trials usually exclude patients with associated medical disorders.

This situation raises questions on the generalization of the results of these types of studies (4). On the other hand, in the study WHO World Health Survey between 9-23% of patients with chronic disorders presented also depression, values significantly higher in comparison to persons with no chronic disorders (18). In STAR*D, with a design that tried to reflect the real profile of co-morbidities, more that 2/3 patients with depression presented an associated somatic disorders (19). The severity of depression is correlated to the level of somatic co-morbidities (20), and the degree of somatic impairment is correlated to response to antidepressive treatment and to the incidence of relapses (21, 22). Older patients with higher number or severity of somatic co-morbidities, indicated by the score of Cumulative Illness Rating Scale for Geriatrics (CIRS-G), presented increased prevalence of depression and reduced response to the treatment with paroxetine in comparison to patients with less severe disorders (22). Depression presents an increased risk for occurrence and worsening of cardiac disorders. In a meta- analysis, depressive symptoms constituted a significant risk factor for incident ischemic heart disease with a Relative Risk (1.64) smaller than that of active smoking (2.5) but higher than passive smoking (1.25) (23). Also depressive symptoms present after an acute myocardial infarction are a risk factor for cumulated mortality (24) and depression post acute myocardial infarction is associated with a 2.25 times higher risk for unfavorable cardiovascular prognosis (25). Anxiety symptoms are also associated with increased incidence and with progression of cardiac impairment (26, 27).

Between 10% to 20% of patients with diabetes mellitus present depression and the percentage rises to 30% for patients with depression diagnosed prior to diabetes (28). Association of diabetes mellitus with depression determines a reduced compliance to treatment and increased risk of macro- and microvascular complications (29).
The relation between diabetes mellitus and depression is bidirectional. Presence of depression constitutes a risk factor for occurrence of diabetes mellitus and depression treatment is associated with decreased resistance to insulin, independent of other risk factors (obesity, alcohol abuse, smoking, family history) (30). Presence of complications of diabetes mellitus is associated with decreased answer to cognitive – behavioral therapy and persistence of depression is associated with increased levels of HbA1c (31). Similar studies support the connection between the depression or anxiety and stroke, Parkinson Disease, irritable bowel syndrome, cancer, fibromyalgia and pain syndromes (32).
All these data show the association between mental and somatic co-morbidities and support the fact that systematic approach of somatic co-morbidities can be decisive for the prognosis of patients with mental disorders.

ASSESSMENT OF IMPACT OF CO-MORBIDITIES
Co-morbidities can delay diagnosis, can influence treatment, are related to occurrence of complications and can influence survival (33). Due to these reasons the quantifying of co-morbidities is becoming a necessity both in research and clinical practice.
One method for assessment of co-morbidities is represented by co-morbidity indices. Co-morbidity indices reduce all coexistent disorders to a single numerical score and allow comparison between patients with different disorders. These scores offer a global assessment of severity of coexistent disorders in one patient. Co-morbidity indices are research instrument used in prospective or retrospective studies that need stratifying patients in risk groups.
They have three components in their structure: items represented by co- morbidities, a severity scale for assessment of co- morbidities and a scoring system.
There are many co-morbidity indices used in clinical research, depending on the destination they have been developed for (33,34). Cumulative Illness Rating Scale (CIRS) was developed to assess co-morbidities load and probability of survival (35). Kaplan-Feinstein Classification is an index developed in a group of adults with diabetes mellitus to prove the impact of co- morbidities on prognosis (36). Charlson Co-morbidity Index is an index used for prediction of short term mortality (37) and INDEX of Coexistent Disease (ICED) is an index developed to prove that disorders other then the primary disorder influence prognosis (38). As a general rule, co-morbidity indices exclude the primary disorder from the analyses of impact and are limited to assessment of cumulated effects of coexistant disorders, that is co- morbidities. Co-morbidity indices are not, however, direct methods of assessment for the impact of disorders on health status, performance scales (Activities of Daily Living, Karnofsky etc.) being used for these purposes. In addition co-morbidity indices are quite coarse to replace clinical reasoning for specific cases.

THE CONCEPT OF FRAILTY
Numerous studies show that social demographic parameters and co-morbidities cannot fully explain the different prognosis of elderly patients. The concept of frailty aims to reflect different evolutions in patients with comparable co-morbidity load (39). Frailty is referring to a state of global vulnerability to stressors, vulnerability which determines an unfavorable prognosis. Frailty, a major research theme in geriatrics, is considered a consequence of multi-systemic physiologic decline encountered in elderly, representing a transitional state during a dynamic process of progression from functional fitness to dependency and death (40). Frail patients are characterized by functional decline, disabilities, increased incidence of fractures, frequent hospital admissions and increased mortality (39).
Currently frailty is distinguished from co- morbidities or disabilities, which can coexist with frailty but are considered distinct phenomena. This situation is illustrated in Cardiovascular Health Study, in which the presence of frailty phenotype is partially superposed to the presence of co-morbidities or disabilities. (41)
The link between frailty, co-morbidities and disability is complex. Both frailty and co-morbidities predict onset of disability, disability may worsen frailty and co-morbidities and co-morbidities can contribute to frailty onset (42). Similarly, the onset of progression of chronic disorders and poly-pathology may represent symptoms of frailty (41), (43).
The defining characteristics of frailty concept are global-multi-systemic physiological impairment and increased vulnerability to stressors (39). Frailty represents a multi-systemic vulnerability associated with accumulation of deficits (dysfunctions), which can be quantified by a frailty index with predictive value for different types of outcomes (44).
Multi-systemic impairment translates into a global alteration of homeostasis and into the occurrence of decompensation in the presence of reduced level of stressors (39). It is important to note that frailty characterizes patients with limited functional reserves, fact that could explain why relative minor burdens (stress, infections, dehydration, extreme temperature) are poor tolerated by these patients.
Among the symptoms of frailty are mobility decline, increased prevalence of falls, loss of capacity of self care and functional impairment, poor nutritional state, sensory deficits, fatigue, decline of muscular strength (39, 42, 43, 44). There is still a debate regarding the inclusion of cognitive function impairment among the domains of frailty, some authors emphasizing the physical aspects of frailty while other having a multidimensional approach (45, 46).

The consequences of frailty are usually d i s c u s s e d i n t e r m s o f m o r t a l i t y, m o r b i d i t y, institutionalization, incidence of dependency, sarcopenia, decrease in quality of life and frequent hospital admissions (39, 42, 43). Frailty represents a risk factor for incident Alzheimer’s dementia and cognitive decline (47, 48). Other cross-sectional studies show the existence of a positive correlation between frailty and mood disorders (43).

The presumed cause of frailty is physiological decline that occurs in some older people. Multiple mechanisms are incriminated in frailty occurrence (49): hormonal deficits, inflammation, oxidative stress, sarcopenia (decline of muscular mass and strength associated with ageing). Interestingly, these pathogenic mechanisms are also present in patients with mental disorders.
Beyond the debate regarding frailty concept, today it is accepted that frailty represents an increased risk state for mortality and other types of poor outcomes: functional decline, dependency, fracture, hospital admission (39).
The modalities proposed for frailty assessment are multiple, reflecting the lack of consensus on defining this concept (46). Some instruments are focused on the identification of a clinical pattern (Fried phenotype), others use global clinical impression (Clinical Frailty Scale); and others use a multidimensional approach (including domains like mobility, physical activity, nutritional status, cognition, social support, patient’s perception on his health) or quantify the number of deficits (Frailty Index) (45).

The Frailty Phenotype proposed by Fried, an assessment instrument frequently used in research, is focused on parameters of physical functionality. Fried phenotype is a construct that comprises five dimensions:

1. Involuntary weight loss (>5% weight loss in last year);
2. Decreased grip strength;
3.Exhaustion, assessed by questions derived from a depression questionaire (Center for Epidemilogic Studies Depression Scale);
4. Decreased walking speed at a normal pace on 5m distance (<=1m/s); 5. Decreased level of physical activities. For each criterion specific cut-offs derived from population studies are used. At >=3 criteria patients are considered frail, pre-frail at 1-2 criteria, absence of any criterion defining fit patients. These criteria have been derived from Cardiovascular Health Study (CHS) and thereafter validated in numerous studies, frail patients presenting an increased risk of mortality, functional decline, dependency and hospitalizations (50).
Clinical Frailty Scale (CFS) is a simple instrument developed for use by clinicians (51). Clinical Frailty Scale uses clinician’s reasoning on co-morbidities, cognitive impairment and disability. Patients are divided in seven categories: fit, well (without signs of active disease); well with treated co-morbidities; apparently vulnerable (symptomatic co-morbidities); mildly frail (partial dependency in instrumental activities of daily living – IADL); moderately frail (partial dependency in activities of daily living -ADLs- and instrumental activities of daily living – IADLs); severely frail (total dependency). CFS is easy to administer and from this point of view it has an advantage over other more complex instruments. Hence, clinical judgment on frailty can bring useful information on patient`s prognosis. In Canadian Study of Health and Aging, CFS has been proved to be a useful instrument for prediction of mortality and institutionalization at five years, having performances comparable to Frailty Index and Cumulative Illness Rating Scale (51). In the same study, in a multivariate analysis, every increase with one stage on CFS presented an increase in middle term (70 m o n t h s ) o f m o r t a l i t y w i t h 2 1 , 2 % a n d o f institutionalization with 23,9% (51).
Frailty Index. Rockwood et al (51, 52), developed a frailty index based on identification of deficits in domains like cognition, mood, ability to communicate, balance, continence, activities of daily living (ADLs), presence of co-morbidities. These deficits have been identified during a population study that selected a group of prognostic factors (70 factors) for mortality and institutionalization. Frailty Index is expressed as a ratio between number of identified deficits and maximum number of possible deficits. Frailty Index is an argument in favor of the theory of accumulation of deficits as mechanism for frailty occurrence, mortality increasing proportionally to the number of deficits. Useful in research, Frailty Index is difficult to use in practice due to its complexity.

MODELS WITH MULTIPLE DOMAINS
Many investigators describe a broader frailty model, including domains like cognition, functionality and social factors. Most of these models with multiple domains are the result of statistical analysis and do not propose pathophysiological explanations for the relationship between measured parameters at the beginning of the study and prognostic parameters (45, 46). In addition, correlation between the results of these models is only partial, sometimes even quite reduced, which can be explained by the different instruments used and also by the fact that they detect different groups of frail patients with different evolution trajectories (53). There is also the problem of inclusion of disability in the structure of instruments for frailty assessement disability being considered by expert groups a complication of frailty and not one of its components.
Among the models with multiple domains, we mention Frailty Index – Comprehensive geriatric Assessment (FI- CGA), a frailty index based on comprehensive geriatric assessment (54). Comprehensive geriatric assessment is a multidimensional, multidisciplinary diagnostic process, used for assessment of medical, functional and psycho- social problems encountered in older patients. Several components of comprehensive geriatric assessment are described in the specialty literature: biological, nutritional state, polymedication, functionality, risk of falls, mood, cognition, social network and social support, quality of life and spirituality. FI–CGA is an instrument that includes t h e s e c o m p o n e n t s a n d c o – m o r b i d i t i e s i n a multidimensional scale for assessment of frailty. Increased levels of frailty assessed by FI-CGA are associated with an increased risk of mortality and institutionalization.

CONCLUSIONS
In the present review we presented a series of data that highlight the somatic dimension of mental disorders, classically represented by somatic co-morbidities. A new category of somatic impairment described especially in older patients is represented by frailty. The existence of an association between mental disorders, somatic co- morbidities and frailty remains to be established by future studies, such studies assuming the utilization of standardized instruments.

ACKNOWLEDGEMENT
This paper is supported by the Sectorial Operational
Programme Human Resources Development (SOP HRD)
2007-2013, financed from the European Social Fund and by the Romanian Government under the contract number POSDRU/107/1.5/S/82839.

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