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OBJECTIVES: This study aimed to evaluate effects of night and shift work patterns on type 2 diabetes (T2D) and hypertension in a longitudinal study, with detailed information on working hours. METHODS: The cohort comprised about 28 000 nurses and nursing assistants employed for more than one year 2008–2016 in Stockholm, Sweden. The employee register held detailed individual information on daily working hours. Information on diagnoses came from national and regional registers. Hazard ratios (HR) and confidence intervals (CI) were estimated by discrete-time proportional hazard models, adjusting for sex, age, country of birth, and profession. RESULTS: During follow-up in 2013–2017, we identified 232 cases of T2D and 875 of hypertension. We observed an increased risk of T2D, but not hypertension, among employees who worked only night shifts the previous year (HR 1.59, 95% CI 1.02–2.43) and those with intensive shift work (>120 afternoon and/or night shifts the previous year: HR 1.67, 95% CI 1.11–2.48) compared to only day work. There was a non-significantly increased risk of T2D related to mixed day and afternoon shifts (HR 1.34, 95% CI 0.97–1.88). We observed tendencies in increased risk of T2D related to frequent spells of ≥3 consecutive night shifts and with number of years with exclusive (but not mixed) night work. CONCLUSIONS: Permanent night work and frequent afternoon and/or night shifts were associated with an increased risk of T2D the following year, but not hypertension. The T2D risk was, to some extent, affected by frequent spells of several night shifts in a row and by cumulative years with permanent night work.

IntroductionNight and shift work have been suggested to increase the risk of chronic health issues like hypertension and metabolic disorders. The aim was to evaluate the effects of various aspects of night and shift work on the risk of incident type 2 diabetes (T2D) and hypertension, using detailed and registry-based data on working hours.Material and MethodsThe cohort comprised about 28 000 nurses and nursing assistants (88% women, 12% men) employed for one year or more 2008-2016 in Stockholm, Sweden. Information on daily working hours was obtained from employee registers and information on diagnoses from national and regional registers. Prescribed medication was not used as an outcome. Hazard ratios (HRs) were estimated by discrete-time proportional hazard models, as functions of working hour characteristics the preceding year, adjusted for sex, age, country of birth, and profession.ResultsWe observed 232 cases of T2D and 875 cases of hypertension during follow-up 2013-2017. The risk of T2D was increased among employees who the preceding year had permanent night shifts compared to those with permanent day work (HR=1.59, 95% confidence interval 1.02-2.43). The risk was non-significantly increased among employees with day and afternoon shifts only (HR=1.34, 95% confidence interval 0.97-1.88). No significantly elevated risks were found related to frequency of night shifts, frequent spells of ≥3 consecutive night shifts or frequent quick returns (<28 hours) from night shifts, compared to those who never worked night. There was no trend in risk with number of years of night work. No increased risks were observed for hypertension.ConclusionsWorking permanent night shifts is associated with an increased risk of T2D but not hypertension. The frequency of night shifts or quick returns from night shifts did not significantly affect the results. Organizing work schedules to minimize permanent night work may reduce the risk of T2D.

Here we describe the SweGen data set, a comprehensive map of genetic variation in the Swedish population. These data represent a basic resource for clinical genetics laboratories as well as for sequencing-based association studies by providing information on genetic variant frequencies in a cohort that is well matched to national patient cohorts. To select samples for this study, we first examined the genetic structure of the Swedish population using high-density SNP-array data from a nation-wide cohort of over 10 000 Swedish-born individuals included in the Swedish Twin Registry. A total of 1000 individuals, reflecting a cross-section of the population and capturing the main genetic structure, were selected for whole-genome sequencing. Analysis pipelines were developed for automated alignment, variant calling and quality control of the sequencing data. This resulted in a genome-wide collection of aggregated variant frequencies in the Swedish population that we have made available to the scientific community through the website https://swefreq.nbis.se. A total of 29.2 million single-nucleotide variants and 3.8 million indels were detected in the 1000 samples, with 9.9 million of these variants not present in current databases. Each sample contributed with an average of 7199 individual-specific variants. In addition, an average of 8645 larger structural variants (SVs) were detected per individual, and we demonstrate that the population frequencies of these SVs can be used for efficient filtering analyses. Finally, our results show that the genetic diversity within Sweden is substantial compared with the diversity among continental European populations, underscoring the relevance of establishing a local reference data set.

The current best membrane-protein topology-prediction methods are typically based on sequence statistics and contain hundreds of parameters that are optimized on known topologies of membrane proteins. However, because the insertion of transmembrane helices into the membrane is the outcome of molecular interactions among protein, lipids and water, it should be possible to predict topology by methods based directly on physical data, as proposed >20 years ago by Kyte and Doolittle. Here, we present two simple topology-prediction methods using a recently published experimental scale of position-specific amino acid contributions to the free energy of membrane insertion that perform on a par with the current best statistics-based topology predictors. This result suggests that prediction of membrane-protein topology and structure directly from first principles is an attainable goal, given the recently improved understanding of peptide recognition by the translocon.

Aims Glomerular filtration rate is an important factor in management of heart failure (HF). Our objective was to validate eight creatinine‐based equations for estimating glomerular filtration rate (eGFR) in an HF population against measured glomerular filtration rate. Methods and results One hundred forty‐six HF patients (mean age 68 ± 13 years, mean left ventricular ejection fraction 45% ± 15) within a single‐centre hospital that underwent 51Cr‐EDTA clearance between 2010 and 2018 were included in this retrospective study. eGFR was estimated by means of Cockcroft–Gault ideal and actual weight, the Modification of Diet in Renal Disease Study (MDRD), simplified MDRD with isotope dilution mass spectroscopy traceable calibration, the Chronic Kidney Disease Epidemiology Collaboration, revised Lund–Malmö, full age spectrum, and the Berlin Initiative Study 1. Mean measured glomerular filtration rate was 42 mL/min/1.73 m2. Pearson's correlation coefficient (r) had the highest precision for MDRD (r = 0.9), followed by revised Lund–Malmö (r = 0.88). All equations except MDRD (mean difference −4.8%) resulted in an overestimation of the renal function. The accuracy was below 75% for all equations except MDRD. Conclusions None of the exclusively creatinine‐based methods was accurate in predicting eGFR in HF patients. Our findings suggest that more accurate methods are needed for determining eGFR in patients with HF.

We examine the impact of offshoring on patenting and total factor productivity using a panel of 7,000 mainly small Swedish manufacturing firms over the period 2001-2014. We apply the United Nations Broad Economic Categories (BEC)system to identify offshoring-related intermediate imports. The results show that the link between offshoring on the one hand and innovation and productivity on the other is largely explained by self-selection and reverse causality. We find a positive but statistically weak impact of offshoring on innovation, and no effect on productivity.

This paper examines the impact of offshoring on total factor productivity (TFP) and innovation measured by patent applications. It applies instrumental variable and matching approaches on a panel of about 7,500 Swedish manufacturing firms over the period 2001--2014, and identifies offshoring-related intermediate imports by the United Nations Broad Economic Categories system. Accounting for selection and simultaneity bias, no causal impact on TFP can be established, while the estimated positive effect on innovation is found to be weakly significant.

We exploit increased access to detailed employer-employee data to assess whether outside board members affect innovation performance among start-up firms. Using data for all new limited companies in Sweden born during 1999–2013 which have no more then 10 employees when formed, we provide structural equation estimates that deal with the endogenous selection of board directors. Our empirical findings show that an increase in the board’s expertise, measured by the relative productivity of the firms where outsiders are employed, has a significant and positive impact on the new firm’s propensity to apply for both patents and trademarks.

Here we describe the SweGen dataset, a high-quality map of genetic variation in the Swedish population. This data represents a basic resource for clinical genetics laboratories as well as for sequencing-based association studies, by providing information on the frequencies of genetic variants in a cohort that is well matched to national patient cohorts. To select samples for this study, we first examined the genetic structure of the Swedish population using high-density SNP-array data from a nation-wide population based cohort of over 10,000 individuals. From this sample collection, 1,000 individuals, reflecting a cross-section of the population and capturing the main genetic structure, were selected for whole genome sequencing (WGS). Analysis pipelines were developed for automated alignment, variant calling and quality control of the sequencing data. This resulted in a whole-genome map of aggregated variant frequencies in the Swedish population that we hereby release to the scientific community.