Serum Adiponectin Levels in Advanced-Stage Parkinson's Disease Patients

Patients with advanced Parkinson's disease (PD) experience body weight loss and reductions in the most common cardiovascular risk factors. At present, the pathogenetic mechanisms involved have not been elucidated. Increased serum concentrations of adiponectin, which possesses antiatherogenic and anti-inflammatory properties, are associated with a reduction in cardiovascular risk. The objective of this study was to determine adiponectin serum concentrations in PD patients. Thirty PD patients underwent a full nutritional status assessment, including the determination of adiponectin serum concentrations. Mean ± SD adiponectin concentrations were 9.59 ± 5.9 μg/mL (interquartile range: 5.92–12.9 μg/mL). In PD patients, adiponectin serum levels were similar to those in normal-weight, healthy, young subjects and significantly higher than that in an aged-matched group of morbidly obese subjects. Further studies are warranted to establish the role of adiponectin in the management of PD 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

Sex Dimorphism in the Metabolome of Metabolic Syndrome in Morbidly Obese Individuals

Adult morbid obesity is defined as abnormal or excessive fat accumulation, mostly resulting from a long-term unhealthy lifestyle. Between 10% and 30% of people with obesity exhibit low cardiometabolic risk. The metabolic syndrome has been suggested as an indicator of obesity-related metabolic dysregulation. Although the prevalence of obesity does not seem to be sex-related and metabolic syndrome occurs at all ages, in the last few years, sex-specific differences in the pathophysiology, diagnosis, and treatment of metabolic syndrome have received attention. The aim of this study was to determine the prevalence of metabolic syndrome and its components in different sex and age groups in people with metabolic unhealthy obesity and to compare them with people with metabolic healthy obesity. We analyzed the metabolome in 1350 well-phenotyped morbidly obese individuals and showed that there is a strong sex-dependent association of metabolic syndrome with circulating metabolites. Importantly, we demonstrated that metabolic dysregulation in women and men with severe obesity and metabolic syndrome is age-dependent. The metabolic profiles from our study showed age-dependent sex differences in the impact of MetS which are consistent with the cardiometabolic characterization. Although there is common ground for MetS in the metabolome of severe obesity, men older than 54 are affected in a more extensive and intensive manner. These findings strongly argue for more studies aimed at unraveling the mechanisms that underlie this sex-specific metabolic dysregulation in severe obesity. Moreover, these findings suggest that women and men might benefit from differential sex and age specific interventions to prevent the adverse cardiometabolic effects of severe obesity

Left ventricular hypertrophy revisited. cell and matrix expansion have disease-specific relationships

Left ventricular hypertrophy (LVH), a common pathway in health and disease, occurs because of cellular hypertrophy and expansion of extracellular matrix. Cardiovascular magnetic resonance (CMR) using T1 mapping can split LVMinto cellular and matrix components by measuring the extracellular volume fraction (ECV). Thisapproach was used to explore the biology of LVH. 190 subjects underwent CMR, including healthy volunteers (HV; n=30), and 160 subjects with LVH, defined as increased indexed LVM: athletes (AT; n=50), severe aortic stenosis awaiting valvereplacement (AS; n=30) Fabry disease (FD; n=20), hypertrophic cardiomyopathy (HCM; n=30), and cardiac amyloidosis (CA; n=30).We concluded that, for most causes of LVH, on average there is a proportional increase in cellular andmatrix components with 2 exceptions: physiological cell hypertrophy in AT (mainly cellular) and amyloidosis (almost exclusively matrix). Thus, ECV-derived volumes provide pathophysiological insights beyond quantifyingthe degree of hypertrophy

Mean non-esterified fatty acid (NEFA) concentrations (A) and mean glycerol concentrations (B) divided by fat mass (C) during the submaximal incremental test in lean (L: blue, n = 16) and obese (O: red, n = 14) individuals.

<p>Values are the means±SE. PPO: peak power output. * <i>p≤</i>0.05 for differences with lean; † <i>p≤</i>0.05 for significant group interaction effect.</p

Mean glycerol concentrations (A) divided by fat mass (B), mean non-esterified fatty acid (NEFA) concentrations (C) and whole-body fat oxidation kinetics in relative (D) [% of maximal fat oxidation (MFO)] values determined with the sinusoidal (SIN) model during the submaximal incremental test in lean (L: dark and light blue) and obese (O: dark and light red) individuals.

<p>In the sub-groups matched for aerobic fitness, O present similar MFO (O: 6.3±0.6; L: 5.5±0.4 mg^.FFM^−1.min⁻¹), left-shifted (O: 0.9±0.1; L: 1.2±0.1 for symmetry; <i>p</i><0.05) and less dilated (O: –0.1±0.1; L: 0.3±0.1 for dilatation; <i>p</i><0.05) curve, lower Fat<i>_max</i> (O: 46.5±5.0; L: 55.8±2.6 %) and lower Fat<i>_max</i> zone (O: 25.8±2.3; L: 30.1±1.2 %) although non-significant as a consequence of the small sample size. Values are the means±SE. PPO: peak power output. * <i>p≤</i>0.05 for differences between sub-groups; † <i>p≤</i>0.05 for significant group interaction effect between sub-groups; $ for significant group effect between sub-groups [2-way repeated-measures mixed design ANOVA (exercise intensity x group) followed by contrasts].</p

Mean epinephrine (A), norepinephrine (B), atrial natriuretic peptide (C) and insulin (D) concentrations during the submaximal incremental test in lean (L: blue, n = 16) and obese (O: red, n = 14) individuals.

<p>Values are the means±SE. PPO: peak power output. * <i>p≤</i>0.05 for differences with lean; † <i>p≤</i>0.05 for significant group interaction effect.</p

Maximal incremental ramp test and characteristics of whole-body fat oxidation kinetics during the submaximal incremental test (Incr).

<p>Values are the means±SE. : peak oxygen uptake; FFM: fat-free mass; PPO: peak power output; HR<i>_max</i>: maximal heart rate; Fat<i>_max</i>: exercise intensity at which maximal fat oxidation rate (MFO) occurs; Fat<i>_max</i> zone: range of exercise intensities with fat oxidation rates within 10% MFO; RER: respiratory exchange ratio; NS: non significant.</p