Levodopa-Induced Changes in Electromyographic Patterns in Patients with Advanced Parkinson's Disease

Levodopa medication is the most efficient treatment for motor symptoms of Parkinson's disease (PD). Levodopa significantly alleviates rigidity, rest tremor, and bradykinesia in PD. The severity of motor symptoms can be graded with UPDRS-III scale. Levodopa challenge test is routinely used to assess patients' eligibility to deep-brain stimulation (DBS) in PD. Feasible and objective measurements to assess motor symptoms of PD during levodopa challenge test would be helpful in unifying the treatment. Twelve patients with advanced PD who were candidates for DBS treatment were recruited to the study. Measurements were done in four phases before and after levodopa challenge test. Rest tremor and rigidity were evaluated using UPDRS-III score. Electromyographic (EMG) signals from biceps brachii and kinematic signals from forearm were recorded with wireless measurement setup. The patients performed two different tasks: arm isometric tension and arm passive flexion-extension. The electromyographic and the kinematic signals were analyzed with parametric, principal component, and spectrum-based approaches. The principal component approach for isometric tension EMG signals showed significant decline in characteristics related to PD during levodopa challenge test. The spectral approach on passive flexion-extension EMG signals showed a significant decrease on involuntary muscle activity during the levodopa challenge test. Both effects were stronger during the levodopa challenge test compared to that of patients' personal medication. There were no significant changes in the parametric approach for EMG and kinematic signals during the measurement. The results show that a wireless and wearable measurement and analysis can be used to study the effect of levodopa medication in advanced Parkinson's disease.Peer reviewe

Signal features of surface electromyography in advanced Parkinson's disease during different settings of deep brain stimulation

Objective: Electromyography (EMG) and acceleration (ACC) measurements are potential methods for quantifying efficacy of deep brain stimulation (DBS) treatment in Parkinson's disease (PD). The treatment efficacy depends on the settings of DBS parameters (pulse amplitude, frequency and width). This study quantified, if EMG and ACC signal features differ between different DBS settings and if DBS effect is unequal between different muscles. Methods: EMGs were measured from biceps brachii (BB) and tibialis anterior (TA) muscles of 13 PD patients. ACCs were measured from wrists. Measurements were performed during seven different settings of DBS and analyzed using methods based on spectral analysis, signal morphology and nonlinear dynamics. Results: The results showed significant within-subject differences in the EMG signal kurtosis, correlation dimension, recurrence rate and EMG-ACC coherence between different DBS settings for BB but not for TA muscles. Correlations between EMG feature values and clinical rest tremor and rigidity scores were weak but significant. Conclusions: Surface EMG features differed between different DBS settings and DBS effect was unequal between upper and lower limb muscles. Significance: EMG changes pointed to previously defined optimal settings in most of patients, which should be quantified even more deeply in the upcoming studies. (C) 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.Peer reviewe

Changes in elbow flexion EMG morphology during adjustment of deep brain stimulator in advanced Parkinson's disease

Publisher Copyright: © 2022 Ruonala et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Objective Deep brain stimulation (DBS) is an effective treatment for motor symptoms of advanced Parkinson's disease (PD). Currently, DBS programming outcome is based on a clinical assessment. In an optimal situation, an objectively measurable feature would assist the operator to select the appropriate settings for DBS. Surface electromyographic (EMG) measurements have been used to characterise the motor symptoms of PD with good results; with proper methodology, these measurements could be used as an aid to program DBS. Methods Muscle activation measurements were performed for 13 patients who had advanced PD and were treated with DBS. The DBS pulse voltage, frequency, and width were changed during the measurements. The measured EMG signals were analysed with parameters that characterise the EMG signal morphology, and the results were compared to the clinical outcome of the adjustment. Results The EMG signal correlation dimension, recurrence rate, and kurtosis changed significantly when the DBS settings were changed. DBS adjustment affected the signal recurrence rate the most. Relative to the optimal settings, increased recurrence rates (median ± IQR) 1.1 ± 0.5 (-0.3 V), 1.3 ± 1.1 (+0.3 V), 1.7 ± 0.4 (-30 Hz), 1.7 ± 0.8 (+30 Hz), 2.0 ± 1.7 (+30 μs), and 1.5 ± 1.1 (DBS off) were observed. With optimal stimulation settings, the patients' Unified Parkinson's Disease Rating Scale motor part (UPDRS-III) score decreased by 35% on average compared to turning the device off. However, the changes in UPRDS-III arm tremor and rigidity scores did not differ significantly in any settings compared to the optimal stimulation settings. Conclusion Adjustment of DBS treatment alters the muscle activation patterns in PD patients. The changes in the muscle activation patterns can be observed with EMG, and the parameters calculated from the signals differ between optimal and non-optimal settings of DBS. This provides a possibility for using the EMG-based measurement to aid the clinicians to adjust the DBS.Peer reviewe

Finnish Multi-Story Timber-Framed Apartment Buildings: Tampere Residents’ Perspectives

publishedVersionPeer reviewe

Characterization of metabolic interrelationships and in silico phenotyping of lipoprotein particles using self-organizing maps

Peer reviewe

A Partial Loss-of-Function Variant in AKT2 is Associated with Reduced Insulin-Mediated Glucose Uptake in Multiple Insulin Sensitive Tissues: a Genotype-Based Callback Positron Emission Tomography Study

Rare fully penetrant mutations in AKT2 are an established cause of monogenic disorders of glucose metabolism. Recently, a novel partial loss-of-function AKT2 coding variant (p.Pro50Thr) was identified that is nearly specific to Finns (frequency 1.1%), with the low-frequency allele associated with an increase in fasting plasma insulin level and risk of type 2 diabetes. The effects of p.Pro50Thr on insulin-stimulated glucose uptake (GU) in the whole body and in different tissues have not previously been investigated.  We identified carriers (N=20) and matched non-carriers (N=25) for this allele in the population-based METSIM study and invited these individuals back for positron emission tomography study with [18F]-fluorodeoxyglucose during euglycemic hyperinsulinemia. When we compared p.P50T/AKT2 carriers to non-carriers, we found a 39.4% reduction in whole body GU (P=0.006) and a 55.6% increase in the rate of endogenous glucose production (P=0.038). We found significant reductions in GU in multiple tissues: skeletal muscle (36.4%), liver (16.1%), brown adipose (29.7%), and bone marrow (32.9%), and increases of 16.8-19.1% in 7 tested brain regions. These data demonstrate that the P50T substitution of AKT2 influences insulin-mediated GU in multiple insulin sensitive tissues, and may explain, at least in part, the increased risk of type 2 diabetes in p.P50T/AKT2 carriers.</p

Genome-wide characterization of circulating metabolic biomarkers

Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1,2,3,4,5,6,7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8,9,10,11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases

New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk

To increase our understanding of the genetic basis of adiposity and its links to cardiometabolic disease risk, we conducted a genome-wide association meta-analysis of body fat percentage (BF%) in up to 100,716 individuals. Twelve loci reached genome-wide significance (P<5 × 10−8), of which eight were previously associated with increased overall adiposity (BMI, BF%) and four (in or near COBLL1/GRB14, IGF2BP1, PLA2G6, CRTC1) were novel associations with BF%. Seven loci showed a larger effect on BF% than on BMI, suggestive of a primary association with adiposity, while five loci showed larger effects on BMI than on BF%, suggesting association with both fat and lean mass. In particular, the loci more strongly associated with BF% showed distinct cross-phenotype association signatures with a range of cardiometabolic traits revealing new insights in the link between adiposity and disease risk

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms