***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: BACKGROUND Depression is one of the most disabling diseases , and causes a significant burden both to the individual and to society . WHO data suggests that depression causes 6 % of the burden of all diseases in Europe in terms of disability adjusted life years ( DALYs ) . Yet , the knowledge of the economic impact of depression has been relatively little research ed in Europe . AIMS OF THE STUDY The present study aims at estimating the total cost of depression  in Europe based on published epidemiologic and economic evidence . METHODS A model was developed to combine epidemiological and economic data on depression in Europe to estimate the cost . The model was populated with data collected from extensive literature review s of the epidemiology and economic burden of depression in Europe . The cost data was calculated as annual cost per patient , and epidemiologic data was reported as 12-month prevalence estimates . National and international statistics for the model were retrieved from the OECD and Eurostat data bases . The aggregated annual cost estimates were presented in Euro for 2004 . RESULTS In 28 countries with a population of 466 million , at least 21 million were affected by depression . The total annual cost of depression in Europe was estimated at Euro 118 billion in 2004 , which corresponds to a cost of Euro 253 per inhabitant . Direct costs alone totalled dollar 42 billion , comprised of outpatient care ( Euro 22 billion ) , drug cost ( Euro 9 billion ) and hospitalization ( Euro 10 billion ) . Indirect costs due to morbidity and mortality were estimated at Euro 76 billion . This makes depression the most costly brain disorder in Europe , accounting for 33 % of the total cost . The cost of depression corresponds to 1 % of the total economy of Europe ( GDP ) . DISCUSSION Our cost results are in good agreement with previous research findings . The cost estimates in the present study are based on model simulations for countries where no data was available . The predictability of our model is limited to the accuracy of the input data employed . As there is no earlier cost-of-illness study conducted on depression in Europe , it is , however , difficult to evaluate the validity of our results for individual countries and thus further research is needed . CONCLUSION The cost of depression poses a significant economic burden to European society . The simulation model employed shows good predictability of the cost of depression in Europe and is a novel approach to estimate the cost-of-illness in Europe . IMPLICATION S FOR HEALTH CARE PROVISION AND POLICIES : Health and social care policy and commissioning must be evidence -based . The empirical results from this study confirm previous findings , that depression is a major concern to the economic welfare in Europe which has consequences to both healthcare providers and policy makers . One important way to stop this explosion in cost is through increased research efforts in the field . Moreover , better detection , prevention , treatment and patient management are imperatives to reduce the burden of depression and its costs . Mental healthcare policies and better access to healthcare for mentally ill are other challenges to improve for Europe . IMPLICATION S FOR FURTHER RESEARCH This study has identified several research gaps which are of interest for future research . In order to better underst and the impact of depression to European society long-term prospect i ve epidemiology and cost-of-illness studies are needed . In particular data is lacking for Central European countries . On the basis of our findings , further economic evaluations of treatments for depression are necessary in order to ensure a cost-effective use of European healthcare budgets Identifying data -driven subtypes of major depressive disorder ( MDD ) is an important topic of psychiatric research . Currently , MDD subtypes are based on clinical ly defined depression symptom patterns . Although a few data -driven attempts have been made to identify more homogenous subgroups within MDD , other studies have not focused on using human genetic data for MDD subtyping . Here we used a computational strategy to identify MDD subtypes based on single-nucleotide polymorphism genotyping data from MDD cases and controls using Hamming distance and cluster analysis . We examined a cohort of Mexican-American participants from Los Angeles , including MDD patients ( n=203 ) and healthy controls ( n=196 ) . The results in cluster trees indicate that a significant latent subtype exists in the Mexican-American MDD group . The individuals in this hidden subtype have increased common genetic substrates related to major depression and they also have more anxiety and less middle insomnia , depersonalization and derealisation , and paranoid symptoms . Advances in this line of research to vali date this strategy in other patient groups of different ethnicities will have the potential to eventually be translated to clinical practice , with the tantalising possibility that in the future it may be possible to refine MDD diagnosis based on genetic data Background Due to a lack of evidence , there is no consistent age of onset to define early onset ( EO ) versus later onset ( LO ) major depressive disorder ( MDD ) . Fractional anisotropy ( FA ) , derived from diffusion tensor imaging ( DTI ) , has been widely used to study neuropsychiatric disorders by providing information about the brain circuitry , abnormalities of which might facilitate the delineation of EO versus LO MDD . Method In this study , 61 pairs of untreated , non-elderly , first-episode MDD patients and healthy controls ( HCs ) aged 18–45 years old received DTI scans .  The voxel-based analysis method ( VBM ) , classification analysis , using the Statistical Package for the Social Sciences ( SPSS ) , and regression analyses were used to determine abnormal FA clusters and their correlations with age of onset and clinical symptoms . Results Classification analysis suggested in the best model that there were two subgroups of MDD patients , delineated by an age of onset of 30 years old , by which MDD patients could be divided into EO ( 18–29 years old ) and LO ( 30–45 years old ) groups . LO MDD was characterized by decreased FA , especially in the white matter ( WM ) of the fronto-occipital fasciculus and posterior limb of internal capsule , with a negative correlation with the severity of depressive symptoms ; in marked contrast , EO MDD showed increased FA , especially in the WM of the corpus callosum , corticospinal midbrain and inferior fronto-occipital fasciculus , while FA of the WM near the midbrain had a positive correlation with the severity of depressive symptoms . Conclusion Specific abnormalities of the brain circuitry in EO vs. LO MDD were delineated by an age of onset of 30 years old , as demonstrated by distinct abnormal FA clusters with opposite correlations with clinical symptoms . This DTI study supported the evidence of an exact age for the delineation of MDD , which could have broad multidisciplinary importance . Trial Registration Clinical Trials.gov This paper describes the application of prospect i ve longitudinal data from an epidemiological sample of young adults to define subtypes of major depression . Depression was classified on a spectrum from subthreshold manifestation of symptoms and duration at one end , to cases with recurrent episodes of depression meeting duration criteria for major depressive episodes at the other . There was a direct relationship between the severity of depression over the longitudinal course and both duration and recurrence of depressive episodes . The subgroup of depression with recurrence of both brief and longer duration episodes could be discriminated on most of the indicators of validity including symptoms , impairment , family history , and suicide attempts . In light of the young age of this cohort , the strong history of suicide attempts and other complications of depression among the subjects with recurrent depression was striking . These findings underscore the importance of employing course as a classification criterion of depression , and the inclusion of subthreshold episodes of depression in the characterization of course 
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These symptom-based subtypes were associated with differences in biomarker profiles and functional connectivity , but results have not sufficiently been replicated .
Although the review ed work provides many leads for future research , the method ological differences across studies and lack of replication preclude definitive conclusions about the existence of clinical ly useful and generalizable biological subtypes