Forecasting admissions in psychiatric hospitals before and during Covid-19: a retrospective study with routine data.

The COVID-19 pandemic has strong effects on most health care systems. Forecasting of admissions can help for the efficient organisation of hospital care. We aimed to forecast the number of admissions to psychiatric hospitals before and during the COVID-19 pandemic and we compared the performance of machine learning models and time series models. This would eventually allow to support timely resource allocation for optimal treatment of patients. We used admission data from 9 psychiatric hospitals in Germany between 2017 and 2020. We compared machine learning models with time series models in weekly, monthly and yearly forecasting before and during the COVID-19 pandemic. A total of 90,686 admissions were analysed. The models explained up to 90% of variance in hospital admissions in 2019 and 75% in 2020 with the effects of the COVID-19 pandemic. The best models substantially outperformed a one-step seasonal naïve forecast (seasonal mean absolute scaled error (sMASE) 2019: 0.59, 2020: 0.76). The best model in 2019 was a machine learning model (elastic net, mean absolute error (MAE): 7.25). The best model in 2020 was a time series model (exponential smoothing state space model with Box-Cox transformation, ARMA errors and trend and seasonal components, MAE: 10.44). Models forecasting admissions one week in advance did not perform better than monthly and yearly models in 2019 but they did in 2020. The most important features for the machine learning models were calendrical variables. Model performance did not vary much between different modelling approaches before the COVID-19 pandemic and established forecasts were substantially better than one-step seasonal naïve forecasts. However, weekly time series models adjusted quicker to the COVID-19 related shock effects. In practice, multiple individual forecast horizons could be used simultaneously, such as a yearly model to achieve early forecasts for a long planning period and weekly models to adjust quicker to sudden changes.

Genome-wide analysis of mitochondrial DNA copy number reveals loci implicated in nucleotide metabolism, platelet activation, and megakaryocyte proliferation.

Mitochondrial DNA copy number (mtDNA-CN) measured from blood specimens is a minimally invasive marker of mitochondrial function that exhibits both inter-individual and intercellular variation. To identify genes involved in regulating mitochondrial function, we performed a genome-wide association study (GWAS) in 465,809 White individuals from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (UKB). We identified 133 SNPs with statistically significant, independent effects associated with mtDNA-CN across 100 loci. A combination of fine-mapping, variant annotation, and co-localization analyses was used to prioritize genes within each of the 133 independent sites. Putative causal genes were enriched for known mitochondrial DNA depletion syndromes (p = 3.09 × 10-15) and the gene ontology (GO) terms for mtDNA metabolism (p = 1.43 × 10-8) and mtDNA replication (p = 1.2 × 10-7). A clustering approach leveraged pleiotropy between mtDNA-CN associated SNPs and 41 mtDNA-CN associated phenotypes to identify functional domains, revealing three distinct groups, including platelet activation, megakaryocyte proliferation, and mtDNA metabolism. Finally, using mitochondrial SNPs, we establish causal relationships between mitochondrial function and a variety of blood cell-related traits, kidney function, liver function and overall (p = 0.044) and non-cancer mortality (p = 6.56 × 10-4).

Whole-body R2∗ mapping to quantify tissue iron in iron storage organs: reference values and a genotype.

AIM: To define reference values for the transverse relaxation rate (R2∗) in iron storage organs and to investigate the role of human haemochromatosis protein (HFE) genotype on iron storage. MATERIALS AND METHODS: Whole-body magnetic resonance imaging (MRI) including a five-echo gradient-echo sequence was performed in 483 volunteers (269 men, mean age 59.3 ± 12.2 years) without clinical evidence of an iron storage disease at 1.5 T. R2∗ values were assessed for liver, spleen, pancreas, heart, bones, and brain parenchyma. The HFE genotype was determined regarding the single nucleotide polymorphisms (SNPs) rs74315324, rs1799945, rs41303501, rs1800562, rs1800730. R2∗ values were compared among participants without and with at least one mutation. R2∗ reference values were defined using volunteers without any mutation. RESULTS: Three hundred and one participants had no mutations in any HFE SNP, 182 had at least one mutation. HFE gene mutations were distributed as (heterozygous/homozygous) rs1799945:132/9, rs1800562:33/1, and rs1800730:11/0. Mean R2∗ values ± SD (per second) in the group without mutation were: liver: 33.4 ± 12.7, spleen: 24.1 ± 13.8, pancreas: 27.2 ± 6.6, heart: 32.7 ± 11.8, bone: 69.3 ± 21.0, brain parenchyma: 13.9 ± 1.2. No significant difference in R2∗ values were found between participants with and without the HFE gene mutation for any examined iron storage organ (pliver=0.09, pspleen=0.36, ppancreas = 0.08, pheart = 0.36, pbone = 0.98, pbrain=0.74). CONCLUSION: Reference values of R2∗ in iron storage organs are feasible to support the diagnosis of iron storage diseases. Non-specific mutations in HFE SNPs appear not to affect the phenotype of tissue iron accumulation.

A GWAS meta-analysis from 5 population-based cohorts implicates ion channel genes in the pathogenesis of irritable bowel syndrome.

BACKGROUND: Irritable bowel syndrome (IBS) shows genetic predisposition, however, large-scale, powered gene mapping studies are lacking. We sought to exploit existing genetic (genotype) and epidemiological (questionnaire) data from a series of population-based cohorts for IBS genome-wide association studies (GWAS) and their meta-analysis. METHODS: Based on questionnaire data compatible with Rome III Criteria, we identified a total of 1335 IBS cases and 9768 asymptomatic individuals from 5 independent European genotyped cohorts. Individual GWAS were carried out with sex-adjusted logistic regression under an additive model, followed by meta-analysis using the inverse variance method. Functional annotation of significant results was obtained via a computational pipeline exploiting ontology and interaction networks, and tissue-specific and gene set enrichment analyses. KEY RESULTS: Suggestive GWAS signals (P ≤ 5.0 × 10-6 ) were detected for 7 genomic regions, harboring 64 gene candidates to affect IBS risk via functional or expression changes. Functional annotation of this gene set convincingly (best FDR-corrected P = 3.1 × 10-10 ) highlighted regulation of ion channel activity as the most plausible pathway affecting IBS risk. CONCLUSION & INFERENCES: Our results confirm the feasibility of population-based studies for gene-discovery efforts in IBS, identify risk genes and loci to be prioritized in independent follow-ups, and pinpoint ion channels as important players and potential therapeutic targets warranting further investigation.

Exome Variant Analysis of Chronic Periodontitis in 2 Large Cohort Studies.

Periodontitis is characterized by inflammation of the gingival tissue. The main risk factors are socioeconomic factors, sex, age, smoking, and diabetes, but periodontal disease has also a genetic background. Previous genome-wide association studies failed to reveal genome-wide significant associations of single common single-nucleotide polymorphisms with chronic periodontitis. Using the Illumina ExomeChip data of 6,576 participants of the German population-based cohort studies Study of Health in Pomerania (SHIP) and SHIP-Trend, the authors performed single variant and also gene-based association studies of rare and common exonic variations on different periodontal case definitions. Although our study comprised the largest sample size to date to assess genetic predisposition for chronic periodontitis, the authors found no significant association. This study emphasizes that for chronic periodontitis, large sample sizes will be necessary to find genetic associations, even when examining rare genetic variants.

Gene transcripts associated with muscle strength: a CHARGE meta-analysis of 7,781 persons.

Lower muscle strength in midlife predicts disability and mortality in later life. Blood-borne factors, including growth differentiation factor 11 (GDF11), have been linked to muscle regeneration in animal models. We aimed to identify gene transcripts associated with muscle strength in adults. Meta-analysis of whole blood gene expression (overall 17,534 unique genes measured by microarray) and hand-grip strength in four independent cohorts (n = 7,781, ages: 20-104 yr, weighted mean = 56), adjusted for age, sex, height, weight, and leukocyte subtypes. Separate analyses were performed in subsets (older/younger than 60, men/women). Expression levels of 221 genes were associated with strength after adjustment for cofactors and for multiple statistical testing, including ALAS2 (rate-limiting enzyme in heme synthesis), PRF1 (perforin, a cytotoxic protein associated with inflammation), IGF1R, and IGF2BP2 (both insulin like growth factor related). We identified statistical enrichment for hemoglobin biosynthesis, innate immune activation, and the stress response. Ten genes were associated only in younger individuals, four in men only and one in women only. For example, PIK3R2 (a negative regulator of PI3K/AKT growth pathway) was negatively associated with muscle strength in younger (<60 yr) individuals but not older (≥ 60 yr). We also show that 115 genes (52%) have not previously been linked to muscle in NCBI PubMed abstracts. This first large-scale transcriptome study of muscle strength in human adults confirmed associations with known pathways and provides new evidence for over half of the genes identified. There may be age- and sex-specific gene expression signatures in blood for muscle strength.