Diabetes, but not hypertension and obesity, is associated with postoperative cognitive dysfunction

BACKGROUND/AIMS: Older people undergoing surgery are at risk of developing postoperative cognitive dysfunction (POCD), but little is known of risk factors predisposing patients to POCD. Our objective was to estimate the risk of POCD associated with exposure to preoperative diabetes, hypertension, and obesity. METHODS: Original data from 3 randomised controlled trials (OCTOPUS, DECS, SuDoCo) were obtained for secondary analysis on diabetes, hypertension, baseline blood pressure, obesity (BMI ≥30 kg/m(2)), and BMI as risk factors for POCD in multiple logistic regression models. Risk estimates were pooled across the 3 studies. RESULTS: Analyses totalled 1,034 patients. POCD occurred in 5.2% of patients in DECS, in 9.4% in SuDoCo, and in 32.1% of patients in OCTOPUS. After adjustment for age, sex, surgery type, randomisation, obesity, and hypertension, diabetes was associated with a 1.84-fold increased risk of POCD (OR 1.84; 95% CI 1.14, 2.97; p = 0.01). Obesity, BMI, hypertension, and baseline blood pressure were each not associated with POCD in fully adjusted models (all p > 0.05). CONCLUSION: Diabetes, but not obesity or hypertension, is associated with increased POCD risk. Consideration of diabetes status may be helpful for risk assessment of surgical patients

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Regional and stock-specific differences in contemporary growth of Baltic cod revealed through tag-recapture data

The use of growth estimation methods that depend on unreliable age data has previously hindered the quantification of perceived differences in growth rates between the two cod stocks inhabiting the Baltic Sea. Data from cod tagged in different regions of the Baltic Sea during 2007-2019 were combined, and general linear models were fit to investigate inter-regional (defined as area of release) and inter-stock (assigned to a subset of recaptures using genetic and otolith shape analyses) differences in individual growth. An average-sized cod (364 mm) caught in the western Baltic Sea and assigned to the western Baltic cod stock grew at more than double the rate (145 mm year-1) on average than a cod of the same size caught in the eastern Baltic Sea and assigned to the eastern Baltic cod stock (58 mm year-1), highlighting the current poor conditions for the growth of cod in the eastern Baltic Sea. The regional differences in growth rate were more than twice as large (63 mm year-1) as the stock differences (24 mm year-1). Although the relative importance of environmental and genetic factors cannot be fully resolved through this study, these results suggest that environmental experience may contribute to growth differences between Baltic cod stocks

Multidecadal changes in fish growth rates estimated from tagging data: A case study from the Eastern Baltic cod (Gadus morhua, Gadidae)

Long time series of reliable individual growth estimates are crucial for understanding the status of a fish stock and deciding upon appropriate management. Tagging data provide valuable information about fish growth, and are especially useful when age-based growth estimates and stock assessments are compromised by age-determination uncertainties. However, in the literature there is a lack of studies assessing possible changes in growth over time using tagging data. Here, data from tagging experiments performed in the Baltic Sea between 1971 and 2019 were added to those previously analysed for 1955\u20131970 to build the most extensive tagging dataset available for Eastern Baltic cod (Gadus morhua, Gadidae), a threatened stock with severe age-determination problems. Two length-based methods, the GROTAG model (based on the von Bertalanffy growth function) and a Generalized Additive Model, were used to assess for the first time the potential long-term changes in cod growth using age-independent data. Both methods showed strong changes in growth with an increase until the end of the 1980s (8.6\u201310.6 cm/year for a 40 cm cod depending on the model) followed by a sharp decline. This study also revealed that the current growth of cod is the lowest observed in the past 7 decades (4.3\u20135.1 cm/year for a 40 cm cod depending on the model), indicating very low productivity. This study provides the first example of the use of tagging data to estimate multidecadal changes in growth rates in wild fish. This methodology can also be applied to other species, especially in those cases where severe age-determination problems exist