3 resultados para Population Characteristics
em Helda - Digital Repository of University of Helsinki
Resumo:
Background. Evidence of cognitive dysfunction in depressive and anxiety disorders is growing. However, the neuropsychological profile of young adults has received only little systematic investigation, although depressive and anxiety disorders are major public health problems for this age group. Available studies have typically failed to account for psychiatric comorbidity, and samples derived from population-based settings have also seldom been investigated. Burnout-related cognitive functioning has previously been investigated in only few studies, again all using clinical samples and wide age groups. Aims. Based on the information gained by conducting a comprehensive review, studies on cognitive impairment in depressive and anxiety disorders among young adults are rare. The present study examined cognitive functioning in young adults with a history of unipolar depressive or anxiety disorders in comparison to healthy peers, and associations of current burnout symptoms with cognitive functioning, in a population-based setting. The aim was also to determine whether cognitive deficits vary as a function of different disorder characteristics, such as severity, psychiatric comorbidity, age at onset, or the treatments received. Methods. Verbal and visual short-term memory, verbal long-term memory and learning, attention, psychomotor processing speed, verbal intelligence, and executive functioning were measured in a population-based sample of 21-35 year olds. Performance was compared firstly between participants with pure non-psychotic depression (n=68) and healthy peers (n=70), secondly between pure (n=69) and comorbid depression (n=57), and thirdly between participants with anxiety disorders (n=76) and healthy peers (n=71). The diagnostic procedure was based on the SCID interview. Fourthly, the associations of current burnout symptoms, measured with the Maslach Burnout Inventory General Survey, and neuropsychological test performance were investigated among working young adults (n=225). Results. Young adults with depressive or anxiety disorders, with or without psychiatric comorbidity, were not found to have major cognitive impairments when compared to healthy peers. Only mildly compromised verbal learning was found among depressed participants. Pure and comorbid depression groups did not differ in cognitive functioning, either. Among depressed participants, those who had received treatment showed more impaired verbal memory and executive functioning, and earlier onset corresponded with more impaired executive functioning. In anxiety disorders, psychotropic medication and low psychosocial functioning were associated with deficits in executive functioning, psychomotor processing speed, and visual short-term memory. Current burnout symptoms were associated with better performance in verbal working memory and verbal intelligence. However, lower examiner-rated social and occupational functioning was associated with problems in verbal attention, memory, and learning. Conclusions. Depression, anxiety disorders, or burnout symptoms may not be associated with major cognitive deficits among young adults derived from the general population. Even psychiatric comorbidity may not aggravate cognitive functioning in depressive or anxiety disorders among these young adults. However, treatment-seeking in depression was found to be associated with cognitive deficits, suggesting that these deficits relate to increased distress. Additionally, early-onset depression, found to be associated with executive dysfunction, may represent a more severe form of the disorder. In anxiety disorders, those with low symptom-related psychosocial functioning may have cognitive impairment. An association with self-reported burnout symptoms and cognitive deficits was not detected, but individuals with low social and occupational functioning may have impaired cognition.
Resumo:
Hereditary leiomyomatosis and renal cell cancer (HLRCC) is a rare, dominantly inherited tumor predisposition syndrome characterized by benign cutaneous and uterine (ULM) leiomyomas, and sometimes renal cell cancer (RCC). A few cases of uterine leiomyosarcoma (ULMS) have also been reported. Mutations in a nuclear gene encoding fumarate hydratase (FH), an enzyme of the mitochondrial tricarboxylic acid cycle (TCA cycle), underlie HLRCC. As a recessive condition, germline mutations in FH predispose to a neurological defect, FH deficiency (FHD). Hereditary paragangliomatosis (HPGL) is a dominant disorder associated with paragangliomas and pheochromocytomas. Inherited mutations in three genes encoding subunits of succinate dehydrogenase (SDH), also a TCA cycle enzyme, predispose to HPGL. Both FH and SDH seem to act as tumor suppressors. One of the consequences of the TCA cycle defect is abnormal activation of HIF1 pathway ( pseudohypoxia ) in the HLRCC and HPGL tumors. HIF1 drives transcription of genes encoding e.g. angiogenetic factors which can facilitate tumor growth. Recently hypoxia/HIF1 has been suggested to be one of the causes of genetic instability as well. One of the aims of this study was to broaden the clinical definers of HLRCC. To determine the cancer risk and to identify possible novel tumor types associated with FH mutations eight Finnish HLRCC/FHD families were extensively evaluated. The extension of the pedigrees and the Finnish Cancer Registry based tumor search yielded genealogical and cancer data of altogether 868 individuals. The standardized incidence ratio-based comparison of HLRCC/FHD family members with general Finnish population revealed 6.5-fold risk for RCC. Moreover, risk for ULMS was highly increased. However, according to the recent and more stringent diagnosis criteria of ULMS many of the HLRCC uterine tumors previously considered malignant are at present diagnosed as atypical or proliferative ULMs (with a low risk of recurrence). Thus, the formation of ULMS (as presently defined) in HLRCC appears to be uncommon. Though increased incidence was not observed, interestingly the genetic analyses suggested possible association of breast and bladder cancer with loss of FH. Moreover, cancer cases were exceptionally detected in an FHD family. Another clinical finding was the conventional (clear cell) type RCC of a young Spanish HLRCC patient. Conventional RCC is distinct from the types previously observed in this syndrome but according to these results, FH mutation may underlie some of young conventional cancer cases. Secondly, the molecular pathway from defective TCA cycle to tumor formation was intended to clarify. Since HLRCC and HPGL tumors display abnormally activated HIF1, the hypothesis on the link between HIF1/hypoxia and genetic instability was of interest to study in HLRCC and HPGL tumor material. HIF1α (a subunit of HIF1) stabilization was confirmed in the majority of the specimens. However, no repression of MSH2, a protein of DNA mismatch repair system, or microsatellite instability (MSI), an indicator of genetic instability, was observed. Accordingly, increased instability seems not to play a role in the tumorigenesis of pseudohypoxic TCA cycle-deficient tumors. Additionally, to study the putative alternative functions of FH, a recently identified alternative FH transcript (FHv) was characterized. FHv was found to contain instead of exon 1, an alternative exon 1b. Differential subcellular distribution, lack of FH enzyme activity, low mRNA expression compared to FH, and induction by cellular stress suggest FHv to have a role distinct from FH, for example in apoptosis or survival. However, the physiological significance of FHv requires further elucidation.
Resumo:
Population dynamics are generally viewed as the result of intrinsic (purely density dependent) and extrinsic (environmental) processes. Both components, and potential interactions between those two, have to be modelled in order to understand and predict dynamics of natural populations; a topic that is of great importance in population management and conservation. This thesis focuses on modelling environmental effects in population dynamics and how effects of potentially relevant environmental variables can be statistically identified and quantified from time series data. Chapter I presents some useful models of multiplicative environmental effects for unstructured density dependent populations. The presented models can be written as standard multiple regression models that are easy to fit to data. Chapters II IV constitute empirical studies that statistically model environmental effects on population dynamics of several migratory bird species with different life history characteristics and migration strategies. In Chapter II, spruce cone crops are found to have a strong positive effect on the population growth of the great spotted woodpecker (Dendrocopos major), while cone crops of pine another important food resource for the species do not effectively explain population growth. The study compares rate- and ratio-dependent effects of cone availability, using state-space models that distinguish between process and observation error in the time series data. Chapter III shows how drought, in combination with settling behaviour during migration, produces asymmetric spatially synchronous patterns of population dynamics in North American ducks (genus Anas). Chapter IV investigates the dynamics of a Finnish population of skylark (Alauda arvensis), and point out effects of rainfall and habitat quality on population growth. Because the skylark time series and some of the environmental variables included show strong positive autocorrelation, the statistical significances are calculated using a Monte Carlo method, where random autocorrelated time series are generated. Chapter V is a simulation-based study, showing that ignoring observation error in analyses of population time series data can bias the estimated effects and measures of uncertainty, if the environmental variables are autocorrelated. It is concluded that the use of state-space models is an effective way to reach more accurate results. In summary, there are several biological assumptions and methodological issues that can affect the inferential outcome when estimating environmental effects from time series data, and that therefore need special attention. The functional form of the environmental effects and potential interactions between environment and population density are important to deal with. Other issues that should be considered are assumptions about density dependent regulation, modelling potential observation error, and when needed, accounting for spatial and/or temporal autocorrelation.