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Testing the self-medication hypothesis of depression and aggression in cannabis dependent subjects
- M. Arendt, R. Rosenberg, L. Fjordback, J. Brandholdt, L. Foldager, L. Sher, P. Munk-Jørgensen
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- Journal:
- European Psychiatry / Volume 22 / Issue S1 / March 2007
- Published online by Cambridge University Press:
- 16 April 2020, p. S183
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Background:
A self-medication hypothesis has been proposed to explain the association between cannabis use and a number of psychiatric and behavioral problems. However, there is little knowledge on reasons for use and reactions while intoxicated, in cannabis users who suffer from depression or problems controlling violent behavior.
Methods:We assessed 119 cannabis dependent subjects using the Schedules of Clinical Assessment in Neuropsychiatry (SCAN), parts of the Addiction Severity Index (ASI), and questionnaires on reasons for cannabis use and reactions to cannabis use while intoxicated. Participants with lifetime depression, and problems controlling violent behavior, were compared to subjects without such problems. Validity of the groupings was corroborated by use of a psychiatric treatment register, previous use of psychotropic medication, and convictions for violence.
Results:Subjects with lifetime depression used cannabis for the same reasons as others. While under the influence of cannabis, they more often experienced depression, sadness, anxiety and paranoia, and they were less likely to report happiness or euphoria. Participants reporting problems controlling violent behavior more often used cannabis to decrease aggression, decrease suspiciousness, and for relaxation; while intoxicated they more often reacted with aggression.
Conclusions:Subjects with prior depression do not use cannabis as a mean of self-medication. They are more likely to experience specific increases of adverse symptoms while under the influence of cannabis, and are less likely to experience specific symptom relief. There is some evidence that cannabis is used as a mean of self-medication for problems controlling aggression.
Early-adult outcome of child and adolescent mental disorders as evidenced by a national-based case register survey
- A.C. Castagnini, L. Foldager, E. Caffo, P.H. Thomsen
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- Journal:
- European Psychiatry / Volume 38 / October 2016
- Published online by Cambridge University Press:
- 23 March 2020, pp. 45-50
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Background
Mental disorders show varying degrees of continuity from childhood to adulthood. This study addresses the relationship of child and adolescent mental disorders to early adult psychiatric morbidity.
MethodsFrom a population at risk of 830,819 children and adolescents aged 6-16 years, we selected all those (n = 6043) who were enrolled for the first time in the Danish Psychiatric Register with an ICD-10 F00-99 diagnosis in 1995-1997, and identified any mental disorder for which they received treatment up to 2009.
ResultsNeurodevelopmental and conduct disorders were the principal diagnostic groups at 6-16 years and exhibited a characteristic male preponderance; while affective, eating, neurotic, stress-related and adjustment disorders were more common in girls. Over a mean follow-up period of 10.1 years, 1666 (27.6%) cases, mean age 23.4 years, were referred for treatment to mental health services, and they had a markedly higher risk than the general population (RR 5.1; 95% CI 4.9-5.4). Affective, eating, neurodevelopmental, obsessive-compulsive and psychotic disorders had the strongest continuity. Heterotypic transitions were observed for affective, eating, neurodevelopmental, personality and substance use disorders.
ConclusionsThese findings suggest that individuals with psychiatric antecedents in childhood and adolescence had a high risk of being referred for treatment in early adulthood, and many mental disorders for which they required treatment revealed both homotypic and heterotypic continuity.
Between- and within-herd variation in blood and milk biomarkers in Holstein cows in early lactation
- M. A. Krogh, M. Hostens, M. Salavati, C. Grelet, M. T. Sorensen, D. C. Wathes, C. P. Ferris, C. Marchitelli, F. Signorelli, F. Napolitano, F. Becker, T. Larsen, E. Matthews, F. Carter, A. Vanlierde, G. Opsomer, N. Gengler, F. Dehareng, M. A. Crowe, K. L. Ingvartsen, L. Foldager
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Both blood- and milk-based biomarkers have been analysed for decades in research settings, although often only in one herd, and without focus on the variation in the biomarkers that are specifically related to herd or diet. Biomarkers can be used to detect physiological imbalance and disease risk and may have a role in precision livestock farming (PLF). For use in PLF, it is important to quantify normal variation in specific biomarkers and the source of this variation. The objective of this study was to estimate the between- and within-herd variation in a number of blood metabolites (β-hydroxybutyrate (BHB), non-esterified fatty acids, glucose and serum IGF-1), milk metabolites (free glucose, glucose-6-phosphate, urea, isocitrate, BHB and uric acid), milk enzymes (lactate dehydrogenase and N-acetyl-β-D-glucosaminidase (NAGase)) and composite indicators for metabolic imbalances (Physiological Imbalance-index and energy balance), to help facilitate their adoption within PLF. Blood and milk were sampled from 234 Holstein dairy cows from 6 experimental herds, each in a different European country, and offered a total of 10 different diets. Blood was sampled on 2 occasions at approximately 14 days-in-milk (DIM) and 35 DIM. Milk samples were collected twice weekly (in total 2750 samples) from DIM 1 to 50. Multilevel random regression models were used to estimate the variance components and to calculate the intraclass correlations (ICCs). The ICCs for the milk metabolites, when adjusted for parity and DIM at sampling, demonstrated that between 12% (glucose-6-phosphate) and 46% (urea) of the variation in the metabolites’ levels could be associated with the herd-diet combination. Intraclass Correlations related to the herd-diet combination were generally higher for blood metabolites, from 17% (cholesterol) to approximately 46% (BHB and urea). The high ICCs for urea suggest that this biomarker can be used for monitoring on herd level. The low variance within cow for NAGase indicates that few samples would be needed to describe the status and potentially a general reference value could be used. The low ICC for most of the biomarkers and larger within cow variation emphasises that multiple samples would be needed - most likely on the individual cows - for making the biomarkers useful for monitoring. The majority of biomarkers were influenced by parity and DIM which indicate that these should be accounted for if the biomarker should be used for monitoring.
Does hut climate matter for piglet survival in organic production?
- S.-L. A. Schild, L. Foldager, M. K. Bonde, H. M.-L. Andersen, L. J. Pedersen
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Piglet mortality in outdoor production systems varies across the year, and a reason for this variation could be fluctuations in hut climate, as ambient temperature might influence piglet survival, both directly and indirectly. Therefore, the aim of the current study was to investigate the impact of farrowing hut climate and year variation on stillbirth and liveborn mortality. A large-scale observational study was conducted at five commercial organic pig-producing herds in Denmark from June 2015 to August 2016. Both year variation (F3,635=4.40, P=0.004) and farrowing hut temperature (F2,511=6.46, P=0.002) affected the rate of stillbirths. The risk of stillborn piglets was lowest in winter and during this season larger changes in hut temperature between day 1 prepartum and the day of farrowing increased the risk of stillbirths (F1,99=6.39, P=0.013). In addition, during the warm part of the year stillbirth rate increased at temperatures ⩾27°C. Year variation also affected liveborn mortality (F3,561=3.86, P=0.009) with a lower rate of liveborn deaths in spring. However, the hut climate did not influence liveborn deaths. Consequently, other factors than hut climate may explain the influence of year variation on liveborn mortality. These could be light differences causing seasonality in reproduction and lactation.
Potential of milk mid-IR spectra to predict metabolic status of cows through blood components and an innovative clustering approach
- C. Grelet, A. Vanlierde, M. Hostens, L. Foldager, M. Salavati, K. L. Ingvartsen, M. Crowe, M. T. Sorensen, E. Froidmont, C. P. Ferris, C. Marchitelli, F. Becker, T. Larsen, F. Carter, GplusE Consortium, F. Dehareng
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Unbalanced metabolic status in the weeks after calving predisposes dairy cows to metabolic and infectious diseases. Blood glucose, IGF-I, non-esterified fatty acids (NEFA) and β-hydroxybutyrate (BHB) are used as indicators of the metabolic status of cows. This work aims to (1) evaluate the potential of milk mid-IR spectra to predict these blood components individually and (2) to evaluate the possibility of predicting the metabolic status of cows based on the clustering of these blood components. Blood samples were collected from 241 Holstein cows on six experimental farms, at days 14 and 35 after calving. Blood samples were analyzed by reference analysis and metabolic status was defined by k-means clustering (k=3) based on the four blood components. Milk mid-IR analyses were undertaken on different instruments and the spectra were harmonized into a common standardized format. Quantitative models predicting blood components were developed using partial least squares regression and discriminant models aiming to differentiate the metabolic status were developed with partial least squares discriminant analysis. Cross-validations were performed for both quantitative and discriminant models using four subsets randomly constituted. Blood glucose, IGF-I, NEFA and BHB were predicted with respective R2 of calibration of 0.55, 0.69, 0.49 and 0.77, and R2 of cross-validation of 0.44, 0.61, 0.39 and 0.70. Although these models were not able to provide precise quantitative values, they allow for screening of individual milk samples for high or low values. The clustering methodology led to the sharing out of the data set into three groups of cows representing healthy, moderately impacted and imbalanced metabolic status. The discriminant models allow to fairly classify the three groups, with a global percentage of correct classification up to 74%. When discriminating the cows with imbalanced metabolic status from cows with healthy and moderately impacted metabolic status, the models were able to distinguish imbalanced group with a global percentage of correct classification up to 92%. The performances were satisfactory considering the variables are not present in milk, and consequently predicted indirectly. This work showed the potential of milk mid-IR analysis to provide new metabolic status indicators based on individual blood components or a combination of these variables into a global status. Models have been developed within a standardized spectral format, and although robustness should preferably be improved with additional data integrating different geographic regions, diets and breeds, they constitute rapid, cost-effective and large-scale tools for management and breeding of dairy cows.