High Throughput Genetic Characterisation of Caucasian Patients Affected by Multi-Drug Resistant Rheumatoid or Psoriatic Arthritis

Rheumatoid and psoriatic arthritis (RA and PsA) are inflammatory rheumatic disorders characterised by a multifactorial etiology. To date, the genetic contributions to the disease onset, severity and drug response are not clearly defined, and despite the development of novel targeted therapies, ~10% of patients still display poor treatment responses. We characterised a selected cohort of eleven non-responder patients aiming to define the genetic contribution to drug resistance. An accurate clinical examination of the patients was coupled with several high-throughput genetic testing, including HLA typing, SNPs-array and Whole Exome Sequencing (WES). The analyses revealed that all the subjects carry very rare HLA phenotypes which contain HLA alleles associated with RA development (e.g., HLA-DRB1*04, DRB1*10:01 and DRB1*01). Additionally, six patients also carry PsA risk alleles (e.g., HLA-B*27:02 and B*38:01). WES analysis and SNPs-array revealed 23 damaging variants with 18 novel “drug-resistance” RA/PsA candidate genes. Eight patients carry likely pathogenic variants within common genes (CYP21A2, DVL1, PRKDC, ORAI1, UGT2B17, MSR1). Furthermore, “private” damaging variants were identified within 12 additional genes (WNT10A, ABCB7, SERPING1, GNRHR, NCAPD3, CLCF1, HACE1, NCAPD2, ESR1, SAMHD1, CYP27A1, CCDC88C). This multistep approach highlighted novel RA/PsA candidate genes and genotype-phenotype correlations potentially useful for clinicians in selecting the best therapeutic strategy

Do demographic and clinical features and comorbidities affect the risk of spread to an additional body site in functional motor disorders?

The aim of this study is to assess changes in the body distribution and the semeiology of functional motor disorder (FMD) in patients who reported only one or more than one body site affected at FMD onset. Data were obtained from the Italian Registry of Functional Motor Disorders, which included patients with a diagnosis of clinically definite FMDs. The relationship between FMD features and spread to other body sites was estimated by multivariate Cox regression analysis. We identified 201 (49%) patients who reported only one body site affected at FMD onset and 209 (51%) who reported multiple body sites affected at onset. FMD spread from the initial site to another site in 43/201 (21.4%) patients over 5.7 ± 7.1 years in those with only one site affected at FMD onset; FMD spread to an another body site in 29/209 (13.8%) over 5.5 ± 6.5 years. The spread of FMD was associated with non-motor functional symptoms and psychiatric comorbidities only in the patients with one body site affected at FMD onset. Our findings provide novel insight into the natural history of FMD. The number of body sites affected at onset does not seem to have a consistent influence on the risk of spread. Furthermore, our findings suggest that psychiatric comorbidities and non-motor functional symptoms may predict the spread of FMD symptoms, at least in patients with one body site affected at onset

Do demographic and clinical features and comorbidities affect the risk of spread to an additional body site in functional motor disorders?

The aim of this study is to assess changes in the body distribution and the semeiology of functional motor disorder (FMD) in patients who reported only one or more than one body site affected at FMD onset. Data were obtained from the Italian Registry of Functional Motor Disorders, which included patients with a diagnosis of clinically definite FMDs. The relationship between FMD features and spread to other body sites was estimated by multivariate Cox regression analysis. We identified 201 (49%) patients who reported only one body site affected at FMD onset and 209 (51%) who reported multiple body sites affected at onset. FMD spread from the initial site to another site in 43/201 (21.4%) patients over 5.7 ± 7.1 years in those with only one site affected at FMD onset; FMD spread to an another body site in 29/209 (13.8%) over 5.5 ± 6.5 years. The spread of FMD was associated with non-motor functional symptoms and psychiatric comorbidities only in the patients with one body site affected at FMD onset. Our findings provide novel insight into the natural history of FMD. The number of body sites affected at onset does not seem to have a consistent influence on the risk of spread. Furthermore, our findings suggest that psychiatric comorbidities and non-motor functional symptoms may predict the spread of FMD symptoms, at least in patients with one body site affected at onset

Functional motor phenotypes: to lump or to split?

Introduction Functional motor disorders (FMDs) are usually categorized according to the predominant phenomenology; however, it is unclear whether this phenotypic classification mirrors the underlying pathophysiologic mechanisms. Objective To compare the characteristics of patients with different FMDs phenotypes and without co-morbid neurological disorders, aiming to answer the question of whether they represent different expressions of the same disorder or reflect distinct entities. Methods Consecutive outpatients with a clinically definite diagnosis of FMDs were included in the Italian registry of functional motor disorders (IRFMD), a multicenter data collection platform gathering several clinical and demographic variables. To the aim of the current work, data of patients with isolated FMDs were extracted. Results A total of 176 patients were included: 58 with weakness, 40 with tremor, 38 with dystonia, 23 with jerks/facial FMDs, and 17 with gait disorders. Patients with tremor and gait disorders were older than the others. Patients with functional weakness had more commonly an acute onset (87.9%) than patients with tremor and gait disorders, a shorter time lag from symptoms onset and FMDs diagnosis (2.9 ± 3.5 years) than patients with dystonia, and had more frequently associated functional sensory symptoms (51.7%) than patients with tremor, dystonia and gait disorders. Patients with dystonia complained more often of associated pain (47.4%) than patients with tremor. No other differences were noted between groups in terms of other variables including associated functional neurological symptoms, psychiatric comorbidities, and predisposing or precipitating factors. Conclusions Our data support the evidence of a large overlap between FMD phenotypes

Data-driven clustering of combined Functional Motor Disorders based on the Italian registry.

INTRODUCTION: Functional Motor Disorders (FMDs) represent nosological entities with no clear phenotypic characterization, especially in patients with multiple (combined FMDs) motor manifestations. A data-driven approach using cluster analysis of clinical data has been proposed as an analytic method to obtain non-hierarchical unbiased classifications. The study aimed to identify clinical subtypes of combined FMDs using a data-driven approach to overcome possible limits related to "a priori" classifications and clinical overlapping. METHODS: Data were obtained by the Italian Registry of Functional Motor Disorders. Patients identified with multiple or "combined" FMDs by standardized clinical assessments were selected to be analyzed. Non-hierarchical cluster analysis was performed based on FMDs phenomenology. Multivariate analysis was then performed after adjustment for principal confounding variables. RESULTS: From a study population of n = 410 subjects with FMDs, we selected n = 188 subjects [women: 133 (70.7%); age: 47.9 ± 14.4 years; disease duration: 6.4 ± 7.7 years] presenting combined FMDs to be analyzed. Based on motor phenotype, two independent clusters were identified: Cluster C1 (n = 82; 43.6%) and Cluster C2 (n = 106; 56.4%). Cluster C1 was characterized by functional tremor plus parkinsonism as the main clinical phenotype. Cluster C2 mainly included subjects with functional weakness. Cluster C1 included older subjects suffering from anxiety who were more treated with botulinum toxin and antiepileptics. Cluster C2 included younger subjects referring to different associated symptoms, such as pain, headache, and visual disturbances, who were more treated with antidepressants. CONCLUSION: Using a data-driven approach of clinical data from the Italian registry, we differentiated clinical subtypes among combined FMDs to be validated by prospective studies

A saturated map of common genetic variants associated with human height

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes(1). Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel(2)) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.A large genome-wide association study of more than 5 million individuals reveals that 12,111 single-nucleotide polymorphisms account for nearly all the heritability of height attributable to common genetic variants

A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries

Self-Biasing Dynamic Start-up Circuit for Current-Biased Class-C Oscillators

This work presents a very compact self-biasing dynamic startup circuit for class-C voltage-controlled oscillators (VCOs). Using a referenceless nonlinear inverting stage, the solution has been implemented in a 28-nm CMOS technology 14-GHz oscillator, leading to a VCO startup time better than 20 ns, at par with the fastest startup circuits in literature, with an extremely compact area of 0.003 mm2