In addition to the above-mentioned products, a dimer of uridine-phosphate-uridine (U-P-U) was also observed. The reactions were promoted by white quartz sand, Mg2+, and by using urea as a condensation agent. The reactions also proceeded without this mixture; however, the yields increased remarkably with the presence of the above-mentioned materials. The results suggest that a hot/evaporating-drying pool of water containing organics, salts, and reactive phosphorus could be sufficient to form significant phosphate esters.The availability of nucleotides on the early Earth is of great significance for the origin of a self-replicating system capable of undergoing evolution. We hereby report the successful phosphorylation reactions of the nucleoside uridine under heating in the “drying pool” prebiotic model at temperatures ranging from 60–75 °C, and by using pyrophosphate as a phosphorylation agent. Uridine monophosphates (UMP) such as uridine-5′-monophosphate (5′-UMP), 2′-UMP, and 3′-UMP, as well as cyclic 2′-3′-UMP, were identified by  During the process of origin of life on the Earth, it is believed that a self-replicating molecule must have been generated purely by chemical means and that this macromolecule could have been RNA, as suggested by the “RNA World” hypothesis ,2,3,4. TWater is the most-accepted, prebiotically relevant solvent for reactions on the early Earth, and would likely have made the phosphorylation of nucleosides even more challenging since phosphorylation is favored by anhydrous conditions . In ordeHowever, presuming that water was the most likely solvent for prebiotic phosphorylation has its own challenges, as discussed above. The challenge of performing nucleoside phosphorylation reactions in water has been attempted to have been overcome by various ways such as (1) heating the solutions, containing orthophosphate  and nucleosides, and a condensation agent , to dryness ,27,28,292O74−) is a noteworthy potentially prebiotic condensed phosphate. It is of great significance, as its proposed to be as a “plausible ancestor” of ATP molecule  at m/z 549 in the negative ion mode of MS. The other peaks confirmed were uridine-monophosphate  [C9N2O9PH13-H] at m/z 323.04 and cyclic-uridine-monophosphate [C9H11N2O8P-H] at m/z 305.0, respectively. The 31P-NMR yields of products were calculated based on the peak integration methods as previously reported [In our experiments, evaporation led to dryness of the reaction solutions and formed phosphorylated products of uridine, including uridine monophosphates (2′-UMP and 3′-UMP) along with 5′-UMP. No diphosphates of uridine were observed based on the analyses . One of reported ,13,30.31P-NMR yields represent the proportional areas under the peaks as measured by NMR, and are normalized to 100% of the dissolved phosphorus species. In addition, since the number of scans were constant between each analysis, the signal to noise ratio of the pyrophosphate peaks could provide a semiquantitative estimate of the molarities of individual ions [4, sand, and urea, indicating that the Mg2+ is complexed and thus not promoting precipitation  were obtained when the mixture of urea, white sand, and Mg2+ ion was employed with yields of organophosphates of up to 32.5%. Both temperature windows 60–65 °C and 70–75 °C produced almost produced similar results  in mild aqueous environments. However, such phosphate diesters would require activation before these would contribute to oligomerize towards an RNA or pre-RNA worlds . The pre2+ individually, which showed similar effects on the dimer formation.All samples, apart from samples 1 and 6  that coni from pyrophosphate, i.e., hydrolysis of pyrophosphate to Pi) and only samples 5 showed significant hydrolysis to Pi , either of which may have favored the hydrolysis of pyrophosphate. It is important to mention here that high temperature and condensation agents thermodynamically favor generation of pyrophosphate from Pi, as discussed by Keefe and Miller, who suggested that at high temperatures in the presence of urea and other condensation agents the generation of pyrophosphate from phosphate is favored [In the presented reactions, most of the pyrophosphate remained unreacted  employing water as a solvent (in contrast to nonaqueous solvents such as UAFW (urea. Ammonium formate and water), formamide, and other deep eutectic solvents discussed earlier) and (2) employing catalysts that are likely to have been found on the early Earth. Indeed, as discussed previously, urea has been identified in Miller–Urey’s gas discharge tube experiments ; silicatrelevant ,14,48,49A warm to mildly hot pool of water 60–70 °C) with dissolved nucleoside, coupled to a source of phosphate and minerals, clays, and simple condensation agents, could have given rise to the precursors of an RNA molecule through the formation of mono-phosphorylated nucleotides alongside nucleotide dimers. The present study also reveals that water is indeed challenging if it is considered as a solvent for phosphorylation. Furthermore, higher temperatures support phosphorylation reactions more, as suggested by Pasek , but thi0 °C with2+, urea, and quartz sand. Mg2+ likely serves as a bridge between the two phosphates of the pyrophosphate, activating it [We note here that this study is exploratory but demonstrates that phosphorylation of nucleosides and production of dimers is feasible at a temperature below the boiling point of water. This reaction necessitates an energetic phosphorus molecule (pyrophosphate), and a combination of at least two of the following: Mgating it ,48 for pating it ,27,28,29ating it . It is aPyrophosphate is considered here to be a prebiotically plausible phosphorylation agent, because it is not only ubiquitous in hydrothermal environments ,48 but i


Neurospora crassa is governed by multiple phosphorylations. We investigate how multiple, slow and random phosphorylations control delay and molecular switches. We model phosphorylations of intrinsically disordered clock proteins (IDPs) using conceptual models of sequential and distributive phosphorylations. Our models help to understand the underlying mechanisms leading to delays and ultrasensitivity. The model shows temporal and steady state switches for the free kinase and the phosphoprotein. We show that random phosphorylations and sequestration mechanisms allow high Hill coefficients required for self-sustained oscillations.Theory predicts that self-sustained oscillations require robust delays and nonlinearities (ultrasensitivity). Delayed negative feedback loops with switch-like inhibition of transcription constitute the core of eukaryotic circadian clocks. The kinetics of core clock proteins such as PER2 in mammals and FRQ in  Circadian clocks regulate a wide variety of molecular and physiological processes9.Life on earth in forms of cyanobacteria, algae , fungi, plants and animals has evolved 24 h periodicities called circadian clocks. This helps them to anticipate rhythmic environmental cues such as light, temperature and nutrients12. For example, the negative feedback loop of the fungal clock contains the negative element FREQUENCY [FRQ], which inhibits its own expression via inhibition of the circadian transcription factor White Collar Complex (WCC). FRQ is an intrinsically disordered protein (IDP) progressively hyperphosphorylated mainly by CK1a (Casein Kinase 1a). Hyperphosphorylation eventually leads to functional inactivation and degradation of FRQ allowing the WCC to reinitiate a new cycle14. This design principle is conserved in other frequently used model systems. Fruit flies and animal clocks are also made up of the inhibitors, kinases and transcriptional activators. In mammalian clocks, PERIOD proteins , and CRYPTOCHROME proteins ) inhibit their own expression. PER2 is also an IDP as FRQ and is phosphorylated by CK117.Circadian oscillators are based on a transcription-translation feedback loops (TTFLs). A delayed negative feedback loop is central to the gene regulatory network21. Moreover their respective activators also get phosphorylated23. Phosphorylations govern nuclear translocation24, complex formations26, inactivation of transcription and stability28.Recent experiments suggest that many sites on both FRQ (about 100 sites) and PER2 (about 60 sites) are phosphorylated over the course of many hours at seemingly random manner30. This raises two theoretical questions: (1) How can multiple random phosphorylations produce long delays? (2) What are the underlying switch mechanisms?Oscillator theory predicts that self-sustained circadian clocks require long delays and nonlinearities such as switches31. Under quite general assumptions the delay is in the range between a 1/4 and 1/2 of the oscillator period33. If gene expression and inhibitor formation last about an hour, periods of 2 to 4 h can be expected. Indeed, several TTFLs exhibit periods of a few hours including somite formation34, NF-kB rhythms36 and p53 pulses37. Circadian rhythms have much longer periods of about 24 h. This implies that the associate delays last at least 6 h. Several processes like transcription, translation, nuclear transport, post-translational modifications, mRNA decay, and proteasomal degradation may contribute to the needed delay in circadian rhythms24. It has been suggested that also the multiple phosphorylations contribute significantly to the required delay39.According to mathematical theory a delay of negative feedback loops governs the period41. In many models switch-like inhibitions are postulated43. Here we explore how multiple random phosphorylation contribute to the generation of switch-like behaviour.In order to generate self-sustained rhythms (“limit cycles”) nonlinearities are necessary in addition to delays44, in signaling48, and chronobiology50. Antagonistic enzymes may lead to zero-order ultrasensitivity51. Sequestration effects may enhance ultrasensitivity52. The identification of up to 100 phosphorylation sites on clock proteins motivated us to revisit the role of multiple random phosphorylations. Since quantitative details are not available we study conceptional models of processive and distributive phosphorylations with a focus on sequestration and amplitudes.The role of multiple phosphorylations has been studied earlier for cell cycle dynamics54. Figure Neurospora. FRQ is stabilized by the FRQ-interacting RNA Helicase (FRH) and forms the FFC complex with the kinase CK1a55. Moreover, FFC interact with the transcription factor WCC56. FRQ undergoes slow, seemingly random multiple phosphorylations predominantly by CK1a58. At an overcritical phosphorylation level the complex gets inactivated in a switch-like manner60.Figure Since detailed kinetic data of these processes are missing we explore in generic models how multiple random phosphorylations can reproduce long robust delays and switch-like behaviour. Figure 53. Recent in vitro and in vivo experiments show that in Neurospora about 100 FRQ sites are phosphorylated over more than a circadian day (up to 48 h) in a seemingly random manner18. However the detailed functions of increasing phosphorylation levels in circadian timekeeping are not well understood.As discussed above, PER2 and FRQ are core clock phosphoproteins with up to 100 phosphorylation sites62. It is known that phosphatases are tightly regulated, have specificity63 and that PP1 plays a critical role for the mammalian clock64. Furthermore, the competition of kinase and phosphatase could generate zero-order ultrasensitivity66. For simplicity, we focus in our paper on kinase dynamics as a first step. Fig. 54. Thus we can focus on delays, amplitudes and waveforms quantified by fits of Hill-functions . Alternatively, the kinase may bind and unbind, so that next phosphorylation first requires rebinding of a kinase molecule (distributive mechanism)54. We emphasize, however, that the following model versions do not reflect the full complexity of clock protein interactions as phosphorylations can affect stability and complex formation in a complicated manner27. If the kinase stays bound to the substrate it is termed processive phosphorylation  F, complexes with the kinase and turnover of C. The 5 kinetic parameters in the ODEs are enumerated as Pp and Pc, k1, k2 and kd  such as FRQ or PER2 might control cellular processes differently53. Hypothetically, the highly flexible FRQ protein can form almost random contacts to the active site of CK1a allowing phosphorylation72. Consequently we simulate now instead of processive phosphorylations 78.In order to keep simulations with differential equations feasible we introduce new variables F1, F2, etc. These variables lump all F molecules with 1, 2, ... phosphorylations. In other words, Fk represents the Our resulting nonlinear random model  and a tenfold increase of the enzyme/substrate ratio (green). It turns out that the main properties—huge amplitudes at intermediate phosphorylation levels, long delays, and Hill coefficients above 3—are robust with respect to large parameter variations.10. Such feedback loops are often modeled via Goodwin oscillators including gene X, protein Y, and inhibitor80. Most of these models, however, require ad hoc Hill coefficients40.So far we discussed conceptional models of phosphorylations leading to delays and ultrasensitivity. Phosphorylated clock proteins are core elements of transcriptional–translational feedback loops (TTFLs) leading to self-sustained oscillations50 the negative feedback is realized by increasing phosphorylation levels. Gene expression is driven only by “Fact”, i.e. by the sum of active protein forms. The fast turnover of Fk provides the required negative feedback.In Suppl. 81.Figure 30. Recent experiments suggest that slow and seemingly random phosphorylations of intrinsically disordered clock proteins control stability and function of clock protein complexes such as FFC in Neurospora and PER:CRY in mammals83. Since only few detailed quantitative data are available we compared several generic models describing linear processive phosphorylation, nonlinear distributive phosphorylation, and random phosphorylation. We find that long delays are robustly achieved by long reaction chain lengths and slow degradation on Figs. , 3 and 4on Figs. . Thus ouNeurospora clock and mammalian clocks. In both cases intrinsically disordered proteins  with multiple random phosphorylations are central players within the negative transcriptional-translational feedback loop85. Interestingly, the control of the delay via phosphorylations is also a target of pharmacological interventions87. Below we focus on the Neurospora clock.Our theoretical approach is based on a few experimental observations that help to constrain our generic models. Interestingly, most of the data suggest quite similar principles in the 88. About 100 phosphorylation sites have been identified using isotope labelling and mass spectrometry. The priming-independent phosphorylation of non-consensus sites on FRQ by CK1a seems to be slower than five sites per hour62. Priming by other kinases is relatively fast and appears to be less essential for the principle function of the core clock. However, other kinases might be relevant for entrainment and temperature compensation not discussed in this paper18.The half-life of the FRQ protein is about 3–5 h88. Initial phosphorylation of the C-terminal part early in the circadian day has a stabilizing effect89. Subsequent phosphorylation of the N-terminal part destabilized the FFC complex allowing degradation. Thus, if charge repulsion were to govern stability and function, the number of phosphorylations would play a central role.Figure 89 and provide the framework of our models. We do not fit individual parameters to the sparse quantitative data but we adapt the models design to the observations listed above. For example, the number of phosphorylations, the degradation rates, and the central role of casein kinase are consistent with the data. Since quantitative details of binding and unbinding of CK1a to FRQ are not known, we simulated two mechanisms: linear processive phosphorylations  in humans24, phototactic sensitivity in green algae95, and reproductive fitness in cyanobacteria96.We emphasize, that our focus on multiple random phosphorylations neglects other essentials of the TTFL modeled in detail elsewhere97. Codes are available upon request.All the simulations have been performed on a Spyder Python 3.4 platform. Simulations resulting in Figs An ultrasensitive response is often sigmoidal and the curve can be well approximated by the Hill equation. The effective Hill equation is defined for the temporal curves in Figs n in Eq. .3\documeSupplementary information.


Compared to other Arctic ice masses, Svalbard glaciers are low-elevated with flat interior accumulation areas, resulting in a marked peak in their current hypsometry (area-elevation distribution) at  ~450 m above sea level. Since summer melt consistently exceeds winter snowfall, these low-lying glaciers can only survive by refreezing a considerable fraction of surface melt and rain in the porous firn layer covering their accumulation zones. We use a high-resolution climate model to show that modest atmospheric warming in the mid-1980s forced the firn zone to retreat upward by  ~100 m to coincide with the hypsometry peak. This led to a rapid areal reduction of firn cover available for refreezing, and strongly increased runoff from dark, bare ice areas, amplifying mass loss from all elevations. As the firn line fluctuates around the hypsometry peak in the current climate, Svalbard glaciers will continue to lose mass and show high sensitivity to temperature perturbations. Svalbard glaciers are among the lowest ice masses in the Arctic, with a peak in glacier area below 450 m elevation. Using a high-resolution climate model, here the authors show that a modest warming in the mid-1980s propagated meltwater runoff above the glacier area peak, amplifying Svalbard mass loss from all elevations. As a result of Arctic Amplification3, in which Arctic warming over the last two decades was twice the global average4, and being situated at the edge of retreating Arctic sea ice, Svalbard ice caps experience among the fastest warming on Earth. Compared to other Arctic ice caps, Svalbard glaciers have relatively low elevations  data, mass loss virtually stopped in 2005–2012, between two periods of sustained mass loss (2002–2004 and 2013–2016)10. Glacial mass balance (MB) expresses the difference between the surface mass balance (SMB) and solid ice discharge (D). Glacial mass loss can thus originate from increased D from accelerating marine-terminating glaciers13, and/or a decrease in SMB, the difference between mass accumulation from snowfall and ablation mainly from meltwater runoff. Surge-type glaciers strongly impact D and are widespread in Svalbard14, with more than 700 glaciers that likely surged in the past15. Although surge events can strongly influence mass loss locally16, these events are poorly understood and are only documented for a few glaciers19. Here we use a Svalbard-wide solid ice discharge estimate for the period 2000–200613, complemented by an increase in D after the surge of a major Austfonna (AF) glacier in 2012–201320.Combined in situ and remote-sensing measurements show that Svalbard land ice has been losing mass at strongly fluctuating rates since the early 2000s22, including internal accumulation of rain and meltwater in firn through refreezing (see “Methods” section). However, these models currently operate at relatively coarse spatial resolutions, typically 5–20 km, and do not resolve the narrow marginal ablation zones and outlet glaciers24. In previous studies, regional climate model outputs were refined to higher spatial resolution, e.g. 250 m to 1 km, using positive degree day25 or energy balance models27 to show that Svalbard recently lost mass following an increase in summer ablation  on a 500 m horizontal grid . The new product includes all individual SMB components  required to identify the drivers of the recent surface mass loss and its variability. Combined with discharge estimates20, our high-resolution SMB product enables us to estimate the spatially and temporally varying MB of Svalbard glaciers over the last six decades, including the high mass loss variability starting in the mid-1980s. We show that a modest atmospheric warming of 0.5 °C in the mid-1980s was sufficient to raise the firn line to the hypsometry peak at  ~450 m a.s.l., exposing large parts of the accumulation area to increased melt. The subsequent loss of refreezing capacity, i.e. the fraction of rain and meltwater retained or refrozen in firn (see “Methods” section), implies that Svalbard ice caps can no longer be sustained when the current climate persists or further warming occurs.Statistical downscaling to (sub-)km horizontal resolutionR2 = 0.63), with a small positive bias of 5 mm w.e. yr−1 . Note that significant deviations (RMSE) of up to 440 mm w.e. yr−1 remain locally  for the period 1958–2018 (see “Methods” section). Using data from the moderate resolution imaging spectroradiometer (MODIS) satellite over 2000–2018, we also evaluate the modelled bare ice area, i.e. the part of the ablation zone where bare ice is exposed after the seasonal snow has melted  annual balance measurements from 101 sites Fig. a. Good aD = 6.8 ± 1.8 Gt yr−1)13 is valid for the whole study period (1958–2018). In line with Dunse et al. (2015)20, we increase solid ice discharge by 4.2 ± 1.6 Gt yr−1 from 2012 onwards, following the surge of a major AF outlet glacier. Combining this with the downscaled SMB product, we reconstruct the mass change of Svalbard glaciers over the last six decades 10 and ICESat/CryoSat-2 altimetry (2003–2018) with R2 = 0.93 and 0.98, respectively  until the mid-1980s  by 25%. This proves that retention of surface meltwater in the firn through refreezing is crucial to sustain these ice caps. The refreezing capacity is defined as the fraction of liquid water (melt and rain) that is retained in the firn. Before 1985, the refreezing capacity was 54%, reducing meltwater runoff (16.3 Gt yr−1) and resulting in a positive SMB 13.Figure unoff 16. Gt yr−1 −1), initiating the post-1985 mass loss of Svalbard glaciers. We conclude that all regions in Svalbard experienced rapid ablation zone expansion and reduced firn refreezing capacity, resulting in strongly increased meltwater runoff (+55%), driving the post-1985 glacial mass loss , the average equilibrium line altitude  moved upwards by  ~100 m, from  ~350  to  ~450 m a.s.l. . Supplementary Fig. R = 0.82; Fig. 30.Compared to other Arctic ice masses.l. Fig. b. Before.l. Fig. a. In thi.l. Fig. a. Follow.l. Fig. b, nearly.l. Fig. d. This r.l. Fig. d, enhanc27. Since it has the largest accumulation zone, the strongest sensitivity to atmospheric warming is found for AF ice cap (AF sector), containing a third (~2500 km3)16 of the total ice volume in the archipelago. In contrast, for regions with smaller accumulation zones (NW and SS) or that had already lost most of their refreezing capacity before 1985  and AF +120 m), compared to southern sectors with an average of +85 m , Svalbard glaciers would completely melt within the next 400 years.The fact that the ELA now fluctuates around the hypsometry maximum makes Svalbard glaciers highly sensitive to changes in atmospheric temperature. During warm summers, the ablation zone now covers more than half of the surface area of most ice caps Fig. c. In the29 as input to the statistical downscaling procedure28. RACMO2.3 is run at 11 km spatial resolution for the period 1958–2018. The model incorporates the dynamical core of the high-resolution limited area model (HIRLAM)31 and the physics of the European Centre for Medium-Range Weather Forecasts-Integrated Forecast (ECMWF-IFS cycle CY33r1)32. RACMO2.3 includes a multi-layer snow module simulating melt, water percolation, retention and refreezing in firn, as well as runoff33. The model accounts for dry snow densification34, drifting snow erosion and sublimation35, and explicitly simulates snow albedo36. In this study, we refer to ‘SMB’ as both the local (kg m−2yr−1) and spatially integrated (Gt yr−1) sum of:We use the outputs of RACMO2.337, the local quantity that includes ’internal accumulation’ from refreezing and retention (RF) is referred to as ’climatic mass balance’. Firn refreezing capacity (RFcap), i.e. the fraction of rain and meltwater effectively retained or refrozen, is estimated asNote that in Cogley et al. (2011)38 and ERA-Interim (1979–2018)39 reanalyses on a 6-hourly basis within a 24 grid-cell wide relaxation zone at the 40 vertical atmospheric levels. The model also includes 40 active snow layers that are initialised in September 1957 using vertical temperature and density profiles derived from the Institute for Marine and Atmospheric research Utrecht-Firn Densification Model (IMAU-FDM)34. In RACMO2.3 Svalbard firn can be 30–40 m deep locally. Bare ice albedo is prescribed from a down-sampled version of the 500 m MODIS albedo 16-day product (MCD43A3) as the 5% lowest surface albedo records for the period 2000–2015, minimised at 0.30 for dark bare ice and maximised at 0.55 for bright ice beneath perennial firn.RACMO2.3 is forced by ERA-40 (1958–1978)40 version 6.0 and the 20 m spatial resolution S0 Terreng DEM of Svalbard (Norwegian Polar Institute) down-sampled onto a 500 m grid σ) is estimated at an average of 1.6 Gt yr−1 for the period 1958–2018. The uncertainty is obtained by integrating the conservative 10% and 20% SMB uncertainty in RACMO2.341 over the accumulation  and ablation zones  of Svalbard, respectively. A similar uncertainty is estimated for individual sectors , we used the down-sampled S0 Terreng DEM of Svalbard at 500 m to average the surface elevation of grid cells showing an annual cumulative SMB ranging from −50 to 50 mm w.e. for each specific year. The procedure was conducted separately for the six sectors and the whole of Svalbard over the periods 1958–1984 . For a meaningful comparison, modelled SMB was integrated between September 15 of two consecutive years. The in situ data set is made available by the World Glacier Monitoring Service (WGMS) and was compiled by the University of Oslo, the Norwegian Polar Institute, the Polish Academy of Sciences, the University of Uppsala and Utrecht University27. For consistency, we rejected four sites with  >100 m height difference relative to the S0 Terreng DEM of Svalbard at 500 m spatial resolution. For comparison with stake measurements, we selected the downscaled grid cell with the smallest elevation bias among the closest pixel and its eight adjacent neighbours.We use 1611 local (in situ) annual balance measurements covering the period 1967–2015 and collected at 101 sites  and CryoSat-2 (2010–2018). Following the method described in Gardner et al. (2013)7 and Wouters et al. (2015)46, ICESat records were grouped every 700 m along repeated ground tracks, whereas for CryoSat-2, neighbouring observations are collected within 1 km of each individual echo location. A model is fitted to these clusters of elevation observations in order to estimate the local surface topography and elevation rate at the central point, where outliers are removed in an iterative procedure. For full details, we refer the reader to Wouters et al. (2015)46. After estimating the local topography and elevation rate for the ICESat and CryoSat-2 periods, local elevation anomalies at the echo locations can be estimated by adding the elevation rate of the fitted model to the residuals. These anomalies are used to compute monthly volume anomalies for  Svalbard ice caps. Elevation anomalies are parameterised as a function of absolute elevation using a third-order polynomial. The resulting fit is used to derive regional volume anomalies within 100 m elevation intervals, by multiplying the polynomial value at each interval’s midpoint with the total glacier area within this elevation bin1. Finally, volume anomalies are converted to mass anomalies by assuming a constant density profile, using the density of ice below the ELA, and a density of 600 ± 250 kg m−3 above the ELA46.We use a combination of GRACE mass change time series for the period 2002–2016Annual modelled bare ice area is estimated for six sectors and the whole of Svalbard (Supplementary Tables Supplementary InformationPeer Review File


The emergency medical service (EMS) provides rapid pre-hospital diagnosis and transportation in ST-elevation myocardial infarction (STEMI) systems of care. Aim of the study was to assess temporal and regional characteristics of EMS-related delays in a metropolitan STEMI network.Patient call-to-arrival of EMS at site , transportation time from site to hospital (site-to-door), call-to-door, patient’s location, month, weekday, and hour of EMS activation were recorded in 4751 patients referred to a tertiary center with suspicion of STEMI.p < 0.001 between-group difference for call-to-site, site-to-door, and call-to-door). After multivariable adjustment, patient’s site of call was an independent predictor of call-to-site delay (p < 0.001). Moreover, age and hour of EMS activation were the strongest predictors of call-to-site, site-to-door, and call-to-door delays (p < 0.05).Median call-to-site, site-to-door, and call-to-door times were 9 (7–12), 39 (31–48), and 49 (41–59) minutes, respectively. The shortest transportation times were noted between 08:00 and 16:00 and in general on Sundays. They were significantly prolonged between midnight and 04:00, whereby the longest difference did not exceed 4 min in median. Patient’s site of call had a major impact on transportation times, which were shorter in Central and Western districts as compared to Southern and Eastern districts of Vienna (In our Viennese STEMI network, the strongest determinants of pre-hospital EMS-related transportation delays were patient’s site of call, patient’s age, and hour of EMS activation. Due to the significant but small median time delays, which are within the guideline-recommended time intervals, no impact on clinical outcome can be expected. Timely reperfusion therapy is the cornerstone of successful treatment for ST-elevation myocardial infarction (STEMI) and is known to improve patient’s outcome –3. SpeciWith about 1.9 million inhabitants, the city of Vienna is the 12th largest city of Europe –12. HoweHence, the aim of this study was (1) to test for potential circadian, day-of-week and seasonal variability in transportation times and (2) to investigate the association between transportation delay and patient’s site  in our metropolitan Viennese STEMI network.The study population consisted of 4751 consecutive patients within a previous period of 8 years who called into the Viennese ambulance service due to ongoing chest pain, in whom STEMI was suspected after the initial onsite ECG, who were immediately transferred to a tertiary hospital for primary PCI, and in whom all necessary variables had been collected. The Vienna-STEMI network was established in 2003 and is composed of the Viennese EMS and six alternating tertiary cardiac centers offering a 24-h primary PCI service for patients with acute STEMI. The EMS is equipped with a 12-lead ECG, devices for advanced life support, and is staffed either with physicians or with paramedics, who will call for an emergency physician if required who arrives at the patient’s site by car in parallel. Usually, the emergency physicians of the Viennese ambulance system  are responsible for diagnosis of STEMI and for choice of reperfusion strategy . The folU test was used to test for differences in transportation times between genders and elderly versus younger patients. Kruskal–Wallis test was used to test for between-group difference in transportation times concerning patient’s site, daytime, weekday, and month. Linear regression modelling was performed by entering gender, age, patient’s site, month, weekday, and hour into three models with call-to-site, site-to-door, and call-to-door as the dependent variables, respectively. As logistic regression is a mean-based operation, logarithmic transformation was performed for the variables call-to-site, site-to-door, and call-to-door using the log to base 2 [computed by log2delay = ln(delay)/ln(2)]. The level of significance used for all tests was a p value of 0.05. Calculations were performed using SPSS 23, IBM for windows.Transportation times are presented as minutes [median with interquartile range (IQR)]. Mann–Whitney p = 0.114], but significantly longer site-to-door  and call-to-door delays  as compared to men. Elderly patients (≥ 75 years) had slightly, but significantly shorter call-to-site times , but prolonged site-to-door  and call-to-door  delays compared to their younger counterparts.Transportation times were recorded in 4751 patients with suspected STEMI . Baseline characteristics are shown in Table p < 0.001). A significant between-group difference in transportation times was found between weekdays, with the shortest call-to-site [9 (7–11) min], site-to-door [38 (31–47) min], and call-to-door [47 (40–57) min] on Sundays, and the longest call-to-site [9 (7–12) min], site-to-door [41 (33–51) min], and call-to-door [51 (43–61) min] on Wednesdays , site-to-door , and call-to-door , while weekday was a determinant of call-to-site only . Month did not independently influence transportation times . Moreover, after multivariable adjustment, patient’s site was the strongest predictor of call-to-site delay  and a non-significant determinant of call-to-door delay , site-to-door , and call-to-door time , while gender did not impact on call-to-site and site-to-door, but was an independent predictor of call-to-door delay .Location of patient’s site (defined as city district) had a significant impact on transportation times, which were shorter in Central and Western districts of Vienna as compared to Southern and Eastern districts , and on Sundays. Moreover, after multivariable adjustment, hour of activation and age was the strongest predictors of EMS delay. Although median transportation times were guideline conform in all city districts, the location of patient’s site was a key predictor of the transportation delay even after adjustment for age and gender.The ESC STEMI guidelines recommend timely transport to a PCI-capable hospital with a goal of 90− 120) min delay between diagnosis of STEMI and reperfusion therapy 20 min de.Several studies found significant circadian variation associated with the incidence and mortality of acute coronary syndromes –17 and oAs discussed elsewhere, longer pre-hospital transportation delay is usually associated with shorter door-to-balloon times, which can be explained by pre-information of the catheter laboratory about an arriving STEMI case, which allows a faster and better preparation of catheter facilities and catheter personnel . Such exApart from circadian variation, we observed a significant difference in transportation times between different districts within the city. It is well known that bigger distance between patient’s site and primary PCI centers directly translates into transportation delay in STEMI, as has been shown in a rural STEMI network . IdeallyIn the present analysis, women had similar call-to-site, but significantly longer site-to-door and call-to-door times compared to men. After multivariable adjustment , age and gender remained independent predictors of call-to-door delay. Female patients are typically older, and elderly patients exhibit more comorbidities , 25. MyoWe only included patients in whom primary PCI after direct transportation to a PCI-capable hospital was the reperfusion strategy of choice. Therefore, our results cannot be transformed to STEMI patients who receive pharmacological reperfusion (< 3% in our network) or no reperfusion at all (if contra-indications exist against both reperfusion strategies), or to those who were initially admitted to referral hospitals, which usually prolongs onset of pain-to-door times unacceptably. Moreover, it was our goal to investigate in this trial potential factors that influence the EMS-related transportation part of the pre-hospital delay exclusively. Of note, we were able to adjust for gender and age, but not for other clinical variables or comorbidities that might have influenced our results, as they were not documented by the EMS system on a regular basis.In the Vienna-STEMI network, median transportation times are within the international guidelines independent of weekday and daytime. Within these guideline-conform delay times, hour of activation, patient’s site, and age were the strongest determinants of small, but significant transportation delays following an emergency call. The data show that (1) it is unlikely that the existent median transportation delay times have an impact on clinical outcome in the Viennese STEMI system of care and (2) further improvements of the quality of this network might rather concern the reduction of the patient-related delay  and/or a further improvement of in-hospital organization, e.g., a direct transfer of presumable STEMI patients into the catheter laboratory by bypassing emergency rooms or intensive care units. This analysis also demonstrates the importance of performing a registry to optimize patient care where possible. These data from the Viennese STEMI network might also stimulate control of out-of-hospital STEMI management in other cities and networks.


Binge Drinking (BD), a highly prevalent drinking pattern among youth, has been linked with anomalies in inhibitory control. However, it is still not well characterized whether the neural mechanisms involved in this process are compromised in binge drinkers (BDs). Furthermore, recent findings suggest that exerting inhibitory control to alcohol-related stimuli requires an increased effort in BDs, relative to controls, but the brain regions subserving these effects have also been scarcely investigated. Here we explored the impact of BD on the pattern of neural activity mediating response inhibition and its modulation by the motivational salience of stimuli .Sixty-seven  first-year university students, classified as BDs (n = 32) or controls (n = 35), underwent fMRI as they performed an alcohol-cued Go/NoGo task in which pictures of alcoholic or non-alcoholic beverages were presented as Go or NoGo stimuli.During successful inhibition trials, BDs relative to controls showed greater activity in the bilateral inferior frontal gyrus (IFG), extending to the anterior insula, a brain region usually involved in response inhibition tasks, despite the lack of behavioral differences between groups. Moreover, BDs displayed increased activity in this region restricted to the right hemisphere when inhibiting a prepotent response to alcohol-related stimuli.The increased neural activity in the IFG/insula during response inhibition in BDs, in the absence of behavioral impairments, could reflect a compensatory mechanism. The findings suggest that response inhibition-related activity in the right IFG/insula is modulated by the motivational salience of stimuli and highlight the role of this brain region in suppressing responses to substance-associated cues. European School Survey Project on Alcohol and Other Drugs)    and the tration) . The acttration) , 4 and itration) –9. For etration)  informedtration) ], has betration) –13 and itration) , 14, 15.tration)  and humatration)  studies tration) , 19. In tration) , 21 and tration) , 23. Unitration) , 25, plaNeuroscientific models of addictive behaviors have proposed that impairments of two related processes—response inhibition and salience attribution—may underlie the development of substance use disorders –29. In lResponse inhibition, usually defined as the ability to withhold or suppress a prepotent response , is consAn extensive body of work in alcohol dependence has shown alterations in inhibitory control at behavioral and neural levels  revealed greater accuracy for alcohol-related than for non-alcohol-related stimuli irrespective of the participant’s consumption pattern. There was also a significant gender by group interaction for the percentage of correct responses to Go stimuli , which was explained by higher accuracy in males of the BD group (95.57 ± .96) relative to the CN group (92.88 ± .76) (p = .032), with no significant group differences in females . Behavioral data are summarized in There were no significant differences between groups (BDs vs. CN), neither in the response to Go stimuli (i.e. percentage of hits or RTs) nor in the number of commission errors (i.e. percentage of FA) to NoGo stimuli. A significant main effect of stimulus type for the percentage of hits [Whole-brain analysis for the whole sample revealed significant BOLD activations during successful inhibition (NoGo > Go) in different areas of the right hemisphere including the precentral gyrus, IPL, IFG, MFG and SFG see –35. ThesROI analyses revealed significant differences between groups in BOLD response during successful response inhibition (NoGo > Go). Specifically, BDs showed greater activity, in comparison with CN, in the bilateral BA47 (IFG extending to the anterior insula) during NoGo relative to Go trials  with any of the variables explored .The main objective of this study was to investigate the link between BD and inhibitory control differences, with special consideration to the relationship between response inhibition and alcohol-related processing. We first analyzed the association between BD and behavioral performance in an alcohol-cued Go/NoGo task. We then explored the task-related neural correlates for both BDs and controls. Finally, we considered if there were differences in neural activity in BDs, relative to controls, when exerting inhibitory control to alcohol-related cues.Consistent with most of the previous studies using Go/NoGo tasks in BDs , 67–72, via the “hyperdirect” pathway and the STN sends excitatory output to the globus pallidus, which results in thalamus inhibition , in BDs compared to controls during successful inhibition trials, a region usually involved in response inhibition tasks , 73–75. hibition , 77. Loohibition , 56.It is worth noting that while BD tends to be associated with increased neural response, AUD has been associated with reduced response in fronto-parietal regions  , 79. FurConsistent with our second hypothesis and the findings reported by Ames et al. , we obseThe current research extends prior results about BD-related anomalies in inhibitory control mechanisms and provides new evidence about increased IFG/insula activity, a key region for inhibitory control, during response inhibition to alcohol-related stimuli. Some limitations of the present study should be noted. First, the lack of assessment before participants engaged in BD prevents us from establishing potential pre-existing differences between groups that may explain the observed results, as indicated by previous investigations , 88, 89.In summary, our results revealed that young BDs showed increased frontal activity, relative to controls, during successful inhibition trials in an alcohol-cued Go/NoGo task, despite a lack of behavioral differences between groups. These findings provide new evidence about the role of the IFG, extending to the anterior insula, as an important region to explore neural differences associated with BD, as well as suggest a specific involvement of this region in withholding a prepotent response to stimuli with alcohol-related content. At a more clinical level, our study provides subclinical information in a healthy population, without evidence of behavioral problems, which highlights the risk of this form of consumption. In addition, this investigation emphasizes the relevance of assessing the cognitive processes of interest using specific substance-related cues to better understand the relationship between those processes and the alcohol consumption pattern, and provides useful information to contribute to the development of future prevention strategies as suggested by studies focused on inhibitory control training and cognitive bias modification , Ministerio de Economía y Competitividad (PSI2015-70525-P) co-funded for European Regional Development Fund and Xunta de Galicia (GRC ED431C 2017/06). SS-S was supported by a predoctoral fellowship from the Spanish Ministerio de Economía y Competitividad (BES-2016-076298). JP-G was supported by the FPU program from the Spanish Ministerio de Educación, Cultura y Deporte (FPU16/01573).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We developed a method to make Inference about Causation from Examination of FAmiliaL CONfounding (ICE FALCON) using observational data for related individuals and considering changes in a pair of regression coefficients. ICE FALCON has some similarities to Mendelian randomization (MR) but uses in effect all the familial determinants of the exposure, not just those captured by measured genetic variants, and does not require genetic data nor make strong assumptions. ICE FALCON can assess tracking of a measure over time, an issue often difficult to assess using MR due to lack of a valid instrumental variable.We describe ICE FALCON and present two empirical applications with simulations.ABCG1 locus, the same conclusion as from MR analyses but providing about 2.5 times more information per subject. We found evidence that tracking of BMI is consistent with longitudinal causation, as well as familial confounding. The simulations supported the validity of ICE FALCON.We found evidence consistent with body mass index (BMI) having a causal effect on DNA methylation at the There are conceptual similarities between ICE FALCON and MR, but empirically they are giving similar conclusions with possibly more information per subject from ICE FALCON. ICE FALCON can be applied to circumstances in which MR cannot be applied, such as when there is no a priori genetic knowledge and/or data available to create a valid instrumental variable, or when the assumptions underlying MR analysis are suspect. ICE FALCON could provide insights into causality for a wide range of public health questions. Inference about Causation from Examination of FAmiliaL CONfounding (ICE FALCON) uses observational data for related individuals, in particular twin pairs, to assess causality between measured variables.ICE FALCON has some similarities to Mendelian randomization (MR) but uses in effect all the familial determinants of the exposure, not just those captured by measured genetic variants, and does not make strong assumptions. ICE FALCON and MR empirically are giving similar conclusions with possibly more information per subject from ICE FALCON.ICE FALCON does not require genetic data and can be applied to circumstances in which MR cannot be applied, such as when there is no a priori genetic knowledge and/or data available to create a valid instrumental variable for the exposure of interest, or when the assumptions underlying MR analysis are suspect.,Mendelian randomization (MR) uses measured genetic variants as instrumental variables for exposures to make inference about causation from observational data. The methodology behind MR has developed substantially over the past decade,Twin studies have special properties for understanding the causes of variation of traits. Traditionally, twin studies have been used to test hypotheses about unmeasured causes. For example, comparing the similarity of monozygotic (MZ) twin pairs with the similarity of dizygotic twin pairs for a particular trait is a way of testing the null hypothesis that genetic factors do not influence variation in that trait. Under certain assumptions, these twin studies can also be used to estimate the proportion of variation due to genetic and shared non-genetic factors.,The within-twin pair designs, using pairs discordant for outcome or exposure,,We have developed a new method, Inference about Causation from Examination FAmiliaL CONfounding (ICE FALCON), which applies to data for related individuals and enables an assessment of evidence for causality and causal direction between measured factors—using statistical analysis to try to detect the signal from the noise. ICE FALCON has been applied to try to understand the causes of several traits including mammographic density,In this paper we describe the methodology of ICE FALCON. To illustrate how it can be applied, we present simulation studies and two examples.cg06500161 of the ABCG1 locus. MR analyses have suggested that this association is due to a causal effect of BMI on methylation: Wahl et al.P = 6.4 × 10–5), by analysing data for 4034 individuals; Mendelson et al.P = 7.1 × 10–3).For the first example, we considered the cross-sectional association between body mass index (BMI) and blood DNA methylation level at site For the second example, we considered the issue of causality between BMI measures repeated in time. This addresses whether a correlation between BMI at two different time points is due to: (i) BMI at the earlier time having a causal effect on BMI at the later time; (ii) familial factors (genetic and/or non-genetic) that operate at both time points; or (iii) non-familial factors that operate at both time points. For MR to assess these explanations of tracking with time, there would need to be genetic variants associated with BMI at the earlier time which are not associated with BMI at the later time. Genetic variants for adulthood BMI found to date by GWAS appear to apply to BMI across the whole of adulthood;X and Y, measured for related individuals. Here we assume, for simplicity of exposition and the examples, that the related individuals are twin pairs but other pairs of related individuals, such as sibling pairs, could also be used. We consider here only one exposure variable, but the extension to multiple exposures is straightforward.Suppose there are two traits, X and Y are positively associated within an individual. X and Y, in which circles are unmeasured causes and squares are measured traits following the original convention.S denote all the combination of unmeasured causes (genetic and/or non-genetic) that affect both twins; XS represents those causes that influence X only, YS those causes that influence Y only and XYS those causes  that influence both X and Y. Let U denote all the unmeasured individual-specific confounders between X and Y which are not shared by twins. For the purposes of explanation, let ‘self’ refer to an individual and ‘co-twin’ refer to the individual’s twin, but recognize that these labels can be swapped and both twins within a pair are included in the analysis.Without loss of generality, assume that selfY and co-twinX, or between selfX and co-twinY) exists, it might be due to the following: (i) the effects of familial confounders XYS  caue to SY ; or . Three models are fitted:The trait ,For the purpose of illustration, the models are simplified without including any covariates, though covariates can be easily included. Given that both twins within a pair are included in the regression, the correlation in the outcome variable between twins needs to be accounted for. This can be handled by explicitly estimating the correlation, using for example the package FISHER,selfY and selfX , and between selfY and co-twinX . Adjusting for selfX (Model 3), there will still be a conditional association between selfY and co-twinX (co-twinβ'), but it will be attenuated towards the null compared with co-twinβ. Similarly, adjusting for co-twinX (Model 3), the conditional association between selfY and selfX (selfβ') will be attenuated towards the null compared with selfβ. Both the attenuations will be of a similar magnitude.If there is familial confounding only (i.e. no direct causation) , there wX to Y only . In Model 2, selfY and co-twinX are associated through two paths: the confounder XS, and conditioning on the collider co-twinY (accounting for the correlation in Y between twins in effect conditions on co-twinY). Conditioning on co-twinY induces a negative correlation between co-twinX and selfY, so that co-twinβ depends on the within-pair correlations in X (Xρ) and in Y (Yρ): if Xρ>Yρ, co-twinβ is expected to be positive; otherwise to be negative. Conditioning on selfX (Model 3), both paths are closed and the conditional association between selfY and co-twinX (co-twinβ') will be null, and therefore attenuated compared with co-twinβ. However, conditioning on co-twinX (Model 3), the association between selfY and selfX (selfβ') will be similar to selfβ.If there is a causal effect from o Y only , there wY to X only (selfY and selfX (selfβ). In Model 2, there is no open path between selfY and co-twinX—the path through selfX is closed due to selfX being a collider, and the path through YS is closed due to the fact that co-twinY is conditioned on, so co-twinβ is null. Conditioning on selfX (Model 3), both paths are open and there will be a conditional association between selfY and co-twinX (co-twinβ'), which depends on Xρ and Yρ: if Xρ>Yρ, co-twinβ' is expected to be negative; otherwise to be positive.If there is a causal effect from o X only , there wIJE online.The mathematical formulae for the theoretical arguments above can be found in the If there is both familial confounding and causation , the resIn summary, for the different causal diagrams showed by X and Y can be reversed, i.e. let Y be the predictor variable and X be the outcome variable and fit the same three regression models. For X as the predictor variable are expected. For X as the predictor variable; similarly, the results for X as the predictor variable. Therefore, if there is a causal association, the results from the analyses using Y as the predictor variable are expected to differ in distinct ways from those from the analyses using X as the predictor variable.To provide additional evidence, X or Y, as in a classic twin model that assumes no casual effect between X and Y. ICE FALCON investigates the changes in regression coefficients under different causal scenarios, and thereby has the potential to uncover novel information about the source of the data than could be found from fitting a model based only on correlations.Note that, ICE FALCON does not model the within-pair correlations in X has a causal effect on Y; (ii) Y has a causal effect on X. For each scenario, we simulated X and Y to have various within-pair correlations, from 0.1 to 0.9 with a step of 0.1, and applied ICE FALCON. Details can be found in the IJE online.To test the validity of ICE FALCON in general, we simulated two causal scenarios: (i) IJE online.To show how the simulation studies can be used to help make inference about causation, for each of the two examples above we simulated three different causal scenarios, each based on the observed correlational structure of the data, and applied ICE FALCON. For Example 1, we simulated data consistent with BMI and DNA methylation level being associated due to: (i) familial confounding; (ii) BMI having a causal effect on DNA methylation level; and (iii) DNA methylation level having a causal effect on BMI. For Example 2, we simulated data being consistent with longitudinal BMI measures being associated due to: (i) familial confounding;(ii) the baseline measure having a causal effect on the follow-up measure; and (iii) a mixture of (i) and (ii), with weights of 36% and 64% as suggested by the emprical data analysis, respectively. We created a test statistic for each scenario based on the observed changes in regression coefficient estimates with those expected based on the simulations, taking into account the expected variation in estimates, and thereby derived empirical tests of fit with statistical significance. Null hypothesis of the test is that the simulated scenario is consistent with the observed results from empirical data analysis. Details can be found in the For the two examples, we used data from the Australian Mammographic Density Twins and Sisters Study, a twin and family cohort.2 and average methylation beta-value at cg06500161 was 0.62 (0.03). We used the residuals in BMI after adjusting for age and smoking status, and residuals in methylation level after adjusting for age, smoking status and cell proportions in analysis. The within-pair correlations in BMI and in methylation level residuals were 0.79  and 0.37 , respectively.For Example 1, we used follow-up data for 65 MZ pairs whose average age was 55.4 (standard deviation: 8.3) years, average BMI was 26.3 (5.4) kg/m2 at baseline, and whose average age was 57.3 (7.3) years and average BMI was 25.6 (4.3) kg/m2 at follow-up. We used the residuals in the two BMI measures after adjusting for age in analysis.For Example 2, we used baseline and follow-up data for 250 MZ pairs whose average age was 49.6 (standard deviation: 7.4) years and average BMI was 24.8 (4.2) kg/mThe regression analyses were conducted using the GEE model with the exchangeable correlation structure. Standard error for the change in regression coefficient estimates between models was estimated using a nonparametric bootstrap method, in which the included twin pairs were randomly sampled with replacement to generate 1000 new datasets with the same sample size as the original dataset. ICE FALCON was then applied to each dataset to calculate the change in regression coefficient for that dataset. The standard error was the standard deviation of the change across the 1000 datasets.X having a causal effect on Y (IJE online), selfX was associated with selfY in Model 1. co-twinX was associated with selfY in Model 2, and the estimate of co-twinβ was positive when Xρ > Yρ, and negative when Xρ < Yρ. Conditioning on selfX (Model 3), co-twinX was not associated with selfY, and the estimate of co-twinβ’ was close to null, regardless of Xρ or Yρ. That is, there was a marginal association between co-twinX and selfY, and the association attenuated to the null after conditioning on selfX.Under the scenario of Y having a causal effect on X (IJE online), selfX was associated with selfY in Model 1. co-twinX was not associated with selfY in Model 2, and the estimate of co-twinβ was close to the null, regardless of Xρ or Yρ. Conditioning on co-twinX (Model 3), co-twinX was associated with selfY, and the estimate of co-twinβ’ was negative when Xρ > Yρ, and positive when Xρ < Yρ. That is, there was no marginal association between co-twinX and selfY, but there was an association after conditioning on selfX.Under the scenario of Therefore, this simulation study showed that ICE FALCON gives distinct patterns of regression coefficients consistent with our theoretical arguments and the mathematical expressions.selfβ = 0.13, 95% CI: 0.05, 0.22) and with her co-twin’s BMI . Conditioning on her co-twin’s BMI (Model 3), the association between her methylation level and her own BMI remained unchanged (P = 0.49). On the other hand, conditioning on her own BMI (Model 3), the association between her methylation level and her co-twin’s BMI attenuated by 97% to become null . This attenuation was marginally significant (P = 0.08). The findings that there was an association between a woman’s methylation level and her co-twin’s BMI in Model 2, and that the association disappeared after conditioning on her own BMI, are consistent with the expectation of selfβ = 26.6, 95% CI: 0.1, 51.1), but not with her co-twin’s methylation level . However, after conditioning on her own methylation level, there was a marginally significant association between her own BMI and her co-twin’s methylation level , and there was a significant change (P = 1.4 × 10–3) when comparing co-twinβ' with co-twinβ. Given there was no association between a woman’s BMI and her co-twin’s methylation level in Model 2, but a change in association from conditioning on her own methylation level, these results are inconsistent with the expectation of We reversed BMI and methylation level, i.e. methylation level became the predictor variable and BMI was the outcome variable, and fitted the same three regression models . A womenTherefore, the data are inconsistent with methylation level having a causal effect on BMI, but consistent with BMI having a causal effect on methylation level.P = 0.74), whereas the other two scenarios were inconsistent with the observed results (both P <0.05) (IJE online).This inference is also supported by the simulation study; no evidence was found that the scenario in which BMI has a causal effect on DNA methylation level was inconsistent with the observed results , and with her co-twin’s baseline BMI . In Model 3, there remained a strong association between a woman’s follow-up BMI with her own baseline BMI , and a weak association with her co-twin’s baseline BMI . Both the associations attenuated compared with the estimates from Models 1 and 2, but to different extents (P = 1.8 × 10–14), being 9.8% (P = 0.02) and 79.9% (P = 1.5 × 10–30), respectively.The findings that there were associations for both a woman’s baseline BMI and her co-twin’s baseline BMI in Model 3, and that both the associations attenuated from comparing with Models 1 and 2 but to different extents, are consistent with the expectations of P = 0.35), whereas the other two scenarios were highly inconsistent with the observed results (both P = 0) (IJE online).This inference is also supported by the simulation study; no evidence was found that the scenario in which longitudinal BMI measures are associated due to a mixture of familial confounding and longitudinal causation was inconsistent with the observed results  for the association between PRS and outcome, in proportion to the square root of the sample size (n). Similar for ICE FALCON, a measure of the amount of information comes from the test statistic (IFZ) for change in cross-trait cross-pair regression coefficient. The study by Wahl et al.n = 4034 and MRZ = 4.00, the study by Mendelson et al.n = 2170 and MRZ = 2.69 and our ICE FALCON analysis had n = 130 and IFZ = 1.75. Therefore, MR/nZ1/2 = 0.063 and 0.058 respectively when using MR, whereas IF/nZ1/2 = 0.153 when using ICE FALCON. That is, in this example ICE FALCON appears to be extracting about 2.5 times more information on causality per subject than MR. Given these Z scores capture the main driver of decision making for each method, we think Z/n1/2 is a good starting point for comparing the power per subject between the two methods, and this is an issue for further research.One measure of the amount of information on causality assessment from MR can be derived from consideration of the test statistic  for outcome (selfY) and the exposure (selfX) is also investigated.ICE FALCON is analogous in some ways to MR. Consider the scenario in which ect on Y ; SX is aX are all within XS, and that none are within XYS. ICE FALCON allows for XYS to exist. MR tests for causality by fitting a single parameter, whereas ICE FALCON considers a pair of parameters and how their estimates change between whether the parameters are estimated together or alone.Although sharing some similarities, a major differentiating point is that ICE FALCON does not rely on the strong assumptions of MR, and it makes inference based on changes in a pair of regression estimates, rather than estimation of a single parameter alone. MR makes the essential assumption that the measured genetic variants associated with ICE FALCON, therefore, does not assume inference can be made based on a single ‘causal parameter’ related to a  variable that is designated to be ‘instrumental’ by making strong assumptions which presume biological knowledge. Instead, ICE FALCON uses the existence of the familial similarity of an exposure to make causal inference using a new approach to hypothesis testing based on changes in pairs of regression coefficients. It is not even necessary to know the causes of the familial similarity of the exposure or decompose the expsoure's variance into genetic and/or non-genetic components, though the potential for obtaining new knowledge by doing so will be explored in future publications.ICE FALCON is based on regression, so the method can be applied to continuous and binary outcomes using ordinary and logistic regression, respectively, and potentially to survival data using Cox regression. There are no restrictions on the measurement scale of exposures. ICE FALCON can also be used to assess the causes of tracking in a trait over time, as in Example 2, an issue that cannot be easily assessed using MR due to the difficulty of finding a valid instrumental variable.The validity of an MR analysis is subject to three key assumptions.XS includes all causes of familial correlation in X that are specific to X, which is theoretically stronger than a limited number of genetic variants, so ICE FALCON will not be giving biased results in the way MR can.Regarding the relevance assumption, MR requires the exposure to have been extensively studied, and measured, for genetic determinants. ICE FALCON, however, does not explicitly require genetic data and can be applied to all measured exposures of interest. There is a weak instrumental variable bias in MR estimates if the studied genetic variants do not explain a substantial variation in the exposure. By contrast, co-twinX is unrelated to selfU, the individual-specific confounders of the relationship between selfX and selfY. Any relationship with unmeasured confounders shared between twins is captured by XYS. Should co-twinX be related to XYS, as in the scenario showed by co-twinX and selfY is still expected to attenuate towards the null after adjusting for selfX, given that the path co-twinX←XS →selfX →selfY is closed. Example 2 shows the validity of ICE FALCON in this scenario.Regarding the independence assumption, co-twinX has directional pleiotropy, i.e. is related to selfY not through selfX only but also through a shared mediator between twins, a change in the association between co-twinX and selfY is still expected after adjusting for selfX.Similarly, regarding the exclusion restriction assumption, if There are, nonetheless, limitations in the usage of ICE FALCON. It requires data for related individuals with both members of the same pair having been measured in the same way for the variables of interest. ICE FALCON uses measured variables which can be subject to measurement error, whereas the genetic data used in MR typically have little measurement error.Note that the measured exposure variables in ICE FALCON could also include measured genetic variants, so in principle ICE FALCON could be used to address the causality of polygenic risk scores and in theory even individual genetic variants. ICE FALCON and MR analyses could also be combined given a suitable dataset. The combination could be used to test the validity of some MR assumptions, such as whether the genetic variants for the exposure have directional pleiotropy. Methodology for the combination needs to be developed.As with MR analyses, results from ICE FALCON should of course be interpreted appropriately, given that they are both statistical modelling approaches which allow consideration of the extent to which the analysed data are consistent with different causal models. Neither approach can prove that a consistent model is a true representation of nature, and we are not proposing that ICE FALCON can ‘prove causality’. All that can be said is whether or not the data ‘are consistent with’ a particular causal hypothesis. The results of these observational analyses should be considered with other evidence as well when making interpretations, as pointed out by the guidelines (not criteria) developed by Bradford Hill for addressing causation based on assuming that a factor is causal and thinking through the consequences.XYS does not exist). They also only use marginal correlations to make inference. Our analyses show that this assumption is not substantiated in either Example 1 or 2, because the observed regression coefficients clearly differ from those expected under the classical twin model. This calls into question the results of multivariate twin analyses that assume that the only reason why variables are correlated within pairs is due to shared familial factors, in effect excluding the potential for intervention studies.Nonetheless, statistical modelling is an attempt to identify the plausible and implausible explanations of data. ICE FALCON can be used to test hypotheses and thereby has the potential to falsify model(s). For example, classic bivariate twin models assume there are no causal effects between variables of interest. Attempts have been made to include causation,X and Y; (iii) quantifying the causal effect ; and (iv) as mentioned above, how to combine ICE FALCON with MR.Several issues need to be investigated to further develop ICE FALCON: (i) the statistical power of ICE FALCON; (ii) the change in regression coefficient in relation to the within-pair correlations in vice versa. By providing causality evidence in multiple ways, ICE FALCON, perhaps together with other causality assessing methods, should be useful in deciding, for example, whether to pursue intervention studies of the measured factor in relation to influencing the trait in question. Given ICE FALCON does not rely on genetic knowledge or measurement of those genetic factors, but instead uses the almost universal fact that siblings  are correlated in exposures, it could provide insights into causality for a wide range of public health questions.To conclude: we have developed ICE FALCON, a statistical modelling approach to observational data for related individuals, to assess causality between measured variables of interest. There are some conceptual similarities and differences between ICE FALCON and MR, and empirically they are giving similar conclusions, with possibly more information per subject from ICE FALCON. ICE FALCON can be applied to circumstances in which MR cannot be applied, such as when there is no a priori genetic knowledge and/or data available to create a valid instrumental variable for the exposure of interest, or when the assumptions underlying MR analysis are suspect. ICE FALCON can also be used as an independent method to replicate the findings from MR analysis, and The Australian Mammographic Density Twins and Sisters Study (AMDTSS) was supported by the National Health and Medical Research Council , Cancer Australia and National Breast Cancer Foundation (grant number 509307). S.L. is a Victorian Cancer Agency Early Career Research Fellow (grant number ECRF19020). J.L.H. is a NHMRC Senior Principal Research Fellow. The work was supported by the NHMRC Program grant (grant number 1074383).dyaa065_Supplementary_DataClick here for additional data file.


It has been suggested that low concentrations of erythromycin may have a protective effect against hERG block and associated drug‐induced arrhythmia by reducing the affinity of the pore‐binding site for high potency hERG inhibitors. This study aimed to explore further the notion of a potentially protective effect of erythromycin. Whole‐cell patch‐clamp experiments were performed in which hERG‐expressing mammalian  cells were preincubated with low to moderate concentrations of erythromycin (3 or 30 µM) prior to whole‐cell patch clamp recordings of hERG current (IhERG) at 37°C. In contrast to a previous report, exposure to low concentrations of erythromycin did not reduce pharmacological sensitivity of hERG to the antipsychotic thioridazine and antihistamine terfenadine. The IC50 value for IhERG tail inhibition by terfenadine was decreased by ~32‐fold in the presence of 3 µM erythromycin (p < .05 vs. no preincubation). Sensitivity to thioridazine remained unchanged (p > .05 vs. no preincubation). The effects of low concentrations of erythromycin were investigated for a series of pore blocking drugs, and the results obtained were consistent with additive and/or synergistic effects. Experiments with the externally acting blocker BeKm‐1 on WT hERG and a pore mutant (F656V) were used to explore the location of the binding site for erythromycin. Our data are inconsistent with the use of erythromycin for the management of drug‐induced QT prolongation.The macrolide antibiotic erythromycin has been associated with QT interval prolongation and inhibition of the hERG‐encoded channels responsible for the rapid delayed rectifier K In contrast to an earlier study that suggested a protective effect of the macrolide antibiotic erythromycin against pharmacological block of recombinant hERG channels overexpressed in Human Embryonic Kidney (HEK) cells, the present report showed that pretreatment with low concentrations of erythromycin did not reduce hERG channel sensitivity to inhibitors. In the presence of erythromycin, hERG displayed increased sensitivity to pharmacological inhibition by three drugs: the antihistamine terfenadine, the antimalarial chloroquine and antiarrythmic dofetilide. hERG block by antipsychotic thioridazine and antifungal ketoconazole was unaffected. This complex pharmacological profile of erythromycin actions may be mediated through two distinct binding sites on the hERG channel. The res) Crumb,  and it i) Crumb, . However22.1α Escherichia coli (Invitrogen) were transformed using standard procedures. The mutation was confirmed by sequencing of the entire open reading frame (Eurofins MWG Operon).The F656V hERG mutation  and maintained as previously described  according to the manufacturer's instructions. Expression plasmid encoding CD8 was also added as a transfection marker . All experimental data for the F656V hERG mutant channel were obtained from cells from a minimum of two transfections.Human Embryonic Kidney (HEK‐293) cells stably expressing WT hERG were kindly donated by Prof Craig January. HEK 293 cells used for transient transfection were obtained from ECACC . Cells were passaged using a nonenzymatic agent . Patch‐pipettes  were pulled and heat‐polished (Narishige MF83) to 2.5–4 MΩ; pipette dialysate contained (in mM): 130 KCl, 1 MgCl2, 5 EGTA, 5 MgATP, 10 HEPES (titrated to pH 7.2 using KOH). Recordings of hERG current (IhERG) and were made using an Axopatch 200 amplifier (Axon Instruments) and a CV201 head‐stage. Between 70%–80% of pipette series resistance was compensated. Voltage‐clamp commands were generated using “WinWCP” .For whole‐cell patch‐clamp recording cells were continuously superfused at physiological temperature (37°C) with an external solution containing (in mM): 140 NaCl, 4 KCl, 2.5 CaCl2.450)   is an antimalarial agent that exhibits a fast‐open block for the hERG channel  is an imidazole relative of clotrimazole  is a class Ia antiarrythmic agent inhibitor that binds low in the hERG channel's inner cavity, which renders the inhibitory effects of disopyramide poorly dependent on inactivation  Crumb,  were sels Crumb, . Dofetiln Crumb, , but unln Crumb, . DisopyrhERG amplitude  to produce a stock solution of 50 mM. Chloroquine and disopyramide‐diphosphate (Sigma‐Aldrich) were dissolved in deionized water  at a stock concentration of 50 mM. Terfenadine, ketoconazole and thioridazine (Sigma‐Aldrich) were dissolved in methanol to produce stock solutions of 10 mM. All stock solutions were diluted to produce stock solutions ranging down to 1 mM with at least 1:1000‐fold final dilution with Tyrode's solution to achieve concentrations stated in the Results. At such dilutions, the stock solution vehicles used have been shown not to significantly affect I Himmel, . In hous Himmel,  was diss Himmel, . Externa Himmel, .2.550) and Hill‐coefficient (nH) values (GraphPad Prism v.7) of the form:Concentration–response relations were fitted with a standard Hill equation to obtain half‐maximal inhibitory concentration 1( and Bx)11( and Bx)1 values were calculated to measure and quantify the effects on Ion Chou, :(2)DCI=AhERG inhibition were fitted using either a single  as mean ± 95% confidence intervals (CI) from n = 4 to 10 cells per experiment. Statistical analysis was performed using unpaired t tests or one‐way and two‐way analysis of variance as appropriate (Sigmaplot v13) with Bonferroni post hoc test. Tests used are specified in the relevant figure legends. P values of less than 0.05 were taken as statistically significant.Mean values in the text are presented either as mean ± 33.1hERG inhibition by erythromycin, terfenadine and thioridazine under the recording conditions of this study, with representative traces of effects of single drug concentrations shown in Figure hERG traces were elicited by the voltage protocol shown in the lower panels of Figure hERG pharmacology from our laboratory  depolarizing step that preceded the voltage command to +20 mV in the absence and presence of the drug. Consistent with a previous report from our laboratory  and 42.6 ± 9.3% (n = 6 cells) within 6‐min of drug superfusion. A range of erythromycin concentrations between 0.3 and 600 µM was then tested and a concentration response relation constructed. The half maximal inhibitory concentration (IC50) derived from a standard Hill equation was 94 μM  with a Hill coefficient (nH) of 0.40  and an nH of 0.57 (CI 0.36–0.77 nM). Finally, thioridazine was found to inhibit IhERG tails with an IC50 of 62 nM  and an nH of 0.55 (CI 0.50–0.61). We also assessed the effects of drug block on IhERG time constants of deactivation. Tail currents on repolarization to −40mV were fitted using a biexponential to derive the fast τ1 and slow τ2 time constants of deactivation in control and after drug application. The estimated fast time constant τ1 and slow time constant τ2 were, respectively, 158.0 ± 20.5 ms and 1,196.1 ± 103.1 ms in control (n = 6 cells). At 6‐min of exposure to 30 µM erythromycin, the fast component of deactivation was slowed but the slow component remained unchanged: τ1 was 232.4 ± 33.6 ms  and τ2 was 1,335.1 ± 185.4 ms in erythromycin . The proportion of deactivating current fitted by the fast component 1τ was ∼0.6 and ~0.4 for the slow component τ2. None of these values was significantly different between control and following drug application (p > .05 vs. in control). No change to the time constants of deactivation was observed following exposure to 100 nM terfenadine or 100 nM thioridazine .Figure 3.2hERG at higher concentrations (≥30 μM) would preclude the use of such erythromycin concentrations to protect against hERG channel block by other drugs, we predominantly focused our investigation on the effects of a low concentration of 3 μM erythromycin, as reported in . Two other terfenadine concentrations were tested in the continuous presence of 3 µM erythromycin and a concentration–response relation was constructed, yielding an IC50 of 4 nM  with a Hill coefficient (nH) of 0.3 (CI 0.14–0.47). Superimposition (as dashed line) of the concentration–response relation for inhibition of IhERG tail in the absence of 3 µM erythromycin was made to allow comparison between the two conditions. These data indicate that, under the conditions of this study, exposure to a low concentration of erythromycin resulted in a ~32‐fold increase in IhERG sensitivity to terfenadine as shown in Figure Since the substantial inhibitory effect on In Crumb, . Figure hERG block by thioridazine. The IC50 for IhERG tail inhibition by thioridazine in the presence of erythromycin was 46.4 nM  which does not differ from the calculated IC50 value for IhERG inhibition in the absence of erythromycin as represented by the respective overlapping concentration–response curves in Figure p > .05).In contrast to its effect on terfenadine, 3 µM erythromycin exposure did not affect I3.3hERG sensitivity to terfenadine and thioridazine suggests that results of erythromycin exposure could vary between different pore‐blocking drugs. Additional experiments were therefore conducted to assess the effects of low concentrations of erythromycin  on IhERG tail block by a series of hERG pore inhibitors exhibiting varying blocking/interaction properties . In addition to terfenadine and thioridazine, dofetilide, chloroquine, and ketoconazole were tested. Results obtained under conventional voltage clamp for all five drugs are summarized in Table hERG sensitivity to a concentration close to the IC50 value for each of the different blockers selected. These experiments showed that increasing erythromycin concentration from 3 to 30 µM had limited effect on terfenadine and thioridazine block. IhERG block by dofetilide was significantly increased in the presence of erythromycin. Thus, 10 nM dofetilide reduced IhERG tails by 52.1 ± 3.0% (n = 6). In the presence of 3 µM or 30 µM erythromycin, IhERG block by dofetilide was significantly increased, respectively, to 66.0 ± 4.8%  and 68.9 ± 2.8% . Similarly, exposure to 3 µM erythromycin or 30 µM erythromycin was associated with increased hERG block by chloroquine  and 70.0 ± 11.7%  of hERG block, respectively, in the presence of 10 nM dofetilide or 100 nM thioridazine. In the presence of 1 µM chloroquine, the percentage of IhERG tail inhibition was 43.7 ± 3.0% . Overall, our data suggest that under the conditions of this study low concentrations of erythromycin had no protective effect against pharmacological blockade of hERG. For some inhibitors results were consistent with additive and/or possibly synergistic interactions that vary with the order of application and the nature of the pore inhibitor tested.The differences in effects of preincubation with 3 µM erythromycin on I3.4hERG measurement in high external [K+] to obtain sufficiently large tail currents. In contrast, the valine mutation of F656 (hERG F656V) preserves the current profile of WT hERG in normal K+  and 45.4 ± 3.8% . This modest reduction in channel sensitivity to erythromycin is in good agreement with previously reported data obtained for alanine mutation of residue F656 . No change to the time constant τ2 was observed. In contrast, both time constants of deactivation were slowed for currents recorded from the mutant channel. The time constant τ1 was 27.9 ± 1.5 ms in control and 64.0 ± 11.9 ms in erythromycin , whereas τ2 was 286.5 ± 54.2 ms in control and 665.0 ± 81.8 ms in erythromycin . This change to the time constants of deactivation resulted in a crossing‐over of F656V IhERG currents as displayed in Figure It has been suggested that erythromycin may bind to an external site that would participate in the pharmacological modulation of hERG’s block by pore inhibitors Crumb, . In prevhERG tail was assessed in the absence and in the presence of 3 µM erythromycin. Figure hERG tail in the absence and following superfusion of 3 µM erythromycin; representative traces of effects of 30 nM BeKm‐1 are shown in Figure half) of IhERG, respectively, shown as bar graphs in Figure hERG exhibited inverse dependence on duration of depolarization (data not shown). Consequently, to ensure adequate levels of IhERG block, drug effects for all experiments utilizing BeKm‐1 were assessed against IhERG tail elicited by a short (400 ms) depolarizing step protocol    of IhERG tail by 30 nM BeKm‐1 in the absence and in the presence of 3 µM erythromycin. In the absence of 3 µM erythromycin, evaluation of the averaged time course of IhERG inhibition obtained for 10 cells yielded a thalf of 15.1 ± 2.1 s. In the presence of 3 µM erythromycin, thalf was 69.6 ± 20.4 s . These experiments showed that erythromycin was able to interfere with BeKm‐1 block of IhERG, suggesting that erythromycin may have an external‐binding site. However, when we tested sensitivity of F656V IhERG tail to 30 nM BeKm‐1 in the absence and presence of 3 µM erythromycin, we found that erythromycin had no effect on F656V hERG block by BeKm‐1.Without a clear pattern of effects for erythromycin on hERG pharmacological sensitivity to its pore inhibitors, probing the existence of an external‐binding site by way of mutagenesis was challenging. To attempt to address this issue we used the externally acting blocker BeKm‐1, which has a known external‐binding site  Figure . While thERG block by 30 nM BeKm‐1  with a Thalf of IhERG inhibition of 14.2 ± 3.3 s ; that is, the F656V mutation enhanced BeKm‐1 block of hERG. Preapplication of 3 µM erythromycin had no effect on F656V IhERG block by 30 nM BeKm‐1. In the maintained presence of 3 µM erythromycin, BeKm‐1 block of F656V IhERG at steady state was 69.4 ± 5.1%  and a thalf of IhERG inhibition of 20.7 ± 4.6 s . The enhanced sensitivity of the F656V mutant channel to 30 nM BeKm‐1 ) suggests an effect of the pore mutation on binding of BeKm‐1 to the external turret. Each of these observations may be encompassed within a model where the effect of the F656V mutation on the conformational properties of the external turret results in an impairment of erythromycin binding to its putative external site and an enhancement of BeKm‐1 binding to its external site. External binding of erythromycin affects (slows) the binding of BeKm‐1 and this effect is attenuated by the disruption of erythromycin binding resulting from the F656V mutation. However, an allosteric modulation by erythromycin of hERG sensitivity to BeKm‐1 through an internal‐binding site involving residue F656, cannot be ruled out.Figure 1 Figure  and corrn Figure  obtained50 = 7–10 µM; [El Harchi et al., hERG of 30 nM BeKm‐1 in the continuous presence of 3 µM disopyramide, with representative traces of effects shown in Figure half of inhibition obtained in the absence and presence of 3 µM erythromycin shown in Figure n = 7; p < .01 vs. in the absence of 3 µM disopyramide). However, there was no significant effect of disopyramide on BeKm‐1 potency at steady‐state block . These data showed that preapplication of 3 µM disopyramide delayed the time course of WT hERG inhibition by 30 nM BeKm‐1 to a similar extent to that of 3 µM erythromycin, which: (a) demonstrates communication between the distinct disopyramide  and BeKm‐1  binding sites; (b) by analogy, indicates that erythromycin could affect the time‐course of BeKm‐1 block without necessarily competing for a single‐binding site.To address this issue, we performed additional experiments with the Class Ia antiarrhythmic agent disopyramide . This significant shift in sensitivity was associated with a DCI calculated value of 0.06 which indicates strong synergism . Increasing the erythromycin concentration to 30 µM did not produce any further effect on either terfenadine or thioridazine potencies. Overall, the results from our investigation of the effects of low concentrations of erythromycin on recombinant channels were consistent with additive and/or synergistic effects that vary with the order of application, the concentration and nature of the pore inhibitors tested.In clear contrast to an earlier study reporting a protective effect of macrolides against block of hERG overexpressed in HEK cells Crumb, , the ressm Chou, . The syn4.190‐ prolongation mediated through IKr block by the concomitant application of single concentrations of erythromycin or ketoconazole with dofetilide was less than the addition of the effects of each drug alone  with diverse pore and nonpore hERG blockers. However, we did find, consistent with previous reports . 100 nM thioridazine reduced WT IhERG tail by 52.0 ± 4.4% with a 500 ms test pulse  and 65.1 ± 4.1% , respectively, in the absence and presence of 3 µM erythromycin. These results argue against a major role for voltage protocol in underlying the different findings of the two studies. The basis for the differences in findings of the two studies is therefore not clear at the present time.The present work and the previous study by Crumb Crumb,  each hign Crumb,  are sparn Crumb,  versus −n Crumb,  and +20 n Crumb, . At nearn Crumb, . The lonn Crumb,  may not Kr/hERG pharmacology in relation to diLQTS is enhanced safety of drug use in patients and in drug development . Our study provides further understanding of hERG blocking drug interactions, in particular involving erythromycin, and could potentially contribute to better preclinical assessment of the arrhythmogenic risk related to polypharmacy. It also suggests that caution should be exercised in considering administration of more than one hERG interacting drug to mitigate drug‐induced LQTS, as the preponderance of data suggest that protection is unlikely to occur.The ultimate aim of a comprehensive understanding of IThis study scrutinized the effects of erythromycin modulation of hERG sensitivity to a series of pore  and external inhibitors at the channel level. It therefore excluded any interference from metabolic pathways mediated by the enzymes of the cytochrome P450 system known to metabolize erythromycin extensively. Erythromycin potency against isoenzymes of the cytochrome P450 is well documented to cause increased serum concentration of terfenadine, and thereby enhanced sensitivity to drug‐induced QT interval prolongation  drug sites in hERG.None.


Left ventricular non-compaction (LVNC) is characterized by a 2-layered myocardium composed of a noncompacted (NC) and a compacted (C) layer. The echocardiographic NC:C ratio is difficult to assess in many patients. The aim of the study was to assess the value of cardiac computed tomography (CCT) for the diagnosis of LVNC.In this prospective controlled study, segmental analysis of transthoracic echocardiography (TTE) and prospective ECG-triggered CCT was performed in 17 patients with LVNC and 19 healthy controls. In TTE maximal NC and C thickness was measured at enddiastole and endsystole in the segment with most prominent trabeculation in short axis views. In CCT, maximal segmental NC and C thickness was measured during diastole, and NC:C ratio was determined. Spearman’s correlation coefficient and receiver operating characteristic curves were calculated.σ = 0.8) and endsystole (σ = 0.9). Using a CCT NC:C ratio ≥1.8, all LVNC patients could be identified.The median [IQR] radiation dose was 1.3[1.2–1.5]mSv. The CCT thickness of the C layer was significantly lower in patients with LVNC as compared to controls in the inferolateral, midventricular, lateral-, inferior-, and septal-apical segments. The CCT NC:C ratio differed significantly between LVNC and controls in the inferior-midventricular and all the apical segments. NC:C ratio correlated significantly between TTE and CCT at enddiastole (LVNC can be diagnosed with ECG-triggered low-dose CCT and discriminated from normal individuals using a NC:C ratio of ≥1.8 in diastole. There is a very good correlation of NC:C ratio in TTE and CCT. Isolated left ventricular noncompaction (LVNC) is characterized by a two-layered myocardium consisting of a noncompacted (NC) and a compacted layer (C). Echocardiographic criteria with a maximal endsystolic ratio of NC:C > 2 and deep intertrabecular recesses with a predominant location in the apical segments and absence of coexisting cardiac abnormalities have been established and validated against anatomical examination of the heart . HoweverCT offers a high spatial resolution and is independent of any acoustic window. Moreover, the acquisition of three-dimensional datasets allows interpretation and measurements of any angle post scanning. Additionally, technical refinements, such as prospective ECG-triggering and iterative reconstruction algorithms have led to substantial radiation dose reduction , 3 and h2. The diagnosis of LVNC was established by TTE with strict adherence to previously published criteria comprising the following four conditions: (1) absence of coexisting cardiac abnormalities; (2) a two-layered myocardial wall with a C subepicardial layer and a NC subendocardial layer, with a maximal end-systolic ratio of NC:C > 2 in parasternal short-axis view; (3) a predominant mid-lateral, mid-inferior, and apical location of affected segments; and (4) colour Doppler with adjustment of the colour scale for evidence of deep intertrabecular recesses filled with blood from the left ventricular cavity. Additionally, compacted wall thickness ≤ 8.1 mm was found to be very specific for myocardial thickening in LVNC compared to normal controls . The statistical software package SPSS 20.0  was used for analysis. Segmental CCT NC:C ratio was calculated and compared between LVNC and control. Intra and inter-rater agreement for the CCT based assessment of NC:C ratio were measured using Bland Altmann analysis for each segment in a representative sample of 12 patients (6 NC and 6 controls). The mean difference (bias) and Bland-Altman (BA) limits of agreement  were calculated. The data were tested for normal distribution by Shapiro-Wilk test. Comparison of variables with no normal distribution was performed with Mann-Whitney U Test. Spearman’s correlation coefficient was calculated between TTE and CCT segmental ratios and BA limits of agreement were calculated. Receiver operating characteristic curves were used to generate cut-off values for distinguishing LVNC from control on a per-segment and a per-patient basis. The optimal cut-point were determined by the Youden-Index. Patients with at least one segment fulfilling the established cut-off value were considered as LVNC patients. P values of less than 0.05 were considered statistically significant. A sample size of 411 segments was calculated in order to provide a desired precision of 6%.A total of 17 patients with LVNC and 19 controls underwent low-dose CCT without any adverse events and were included in the final analysis. Previous to CCT the patients received 3 [0–10] mg metoprolol and the median [IQR] radiation exposure of low-dose CCT was 1.3 [1.2–1.5] mSv. The median [IQR] heart rate during the scan was 56 [50–61] beats per minute. Patient baseline characteristics of both groups are listed in Based on echocardiographic myocardial layer thickness , the maxThe CCT median value for maximal diastolic NC thickness per patient was significantly higher in LVNC vs controls. This resulted in a significantly higher median NC:C ratio in LVNC vs controls . Intra- σ = 0.9; p<0.001) with narrow Bland-Altman limits of agreement (-2.6 to 1.0) and at enddiastole  with broader Bland-Altman limits of agreement (-4.4 to 1.8) (There was a significant correlation of the NC:C ratio between CCT and TTE both at endsystole ( to 1.8) .The CCT segmental median [IQR] thickness of the C layer was significantly lower in LVNC compared to controls in the inferolateral-midventricular (7.8 [6.8–9.8] mm vs 8.8 [7.8–9.5] mm), lateral-apical (6.2 [5.9–8.0] mm vs 8.3 [7.5–9.3] mm), inferior-apical (6.3 [5.7–6.7] mm vs 7.8 [6.9–8.4] mm), and septal-apical (6.9 [6.0–7.5] mm vs 7.9 [7.2–8.8] mm) segments, whereas it did not differ significantly in the remaining segments .The CCT segmental median [IQR] thickness of the NC layer was significantly higher in LVNC compared to controls in the inferior-midventricular (7.6 [0–12.9] mm vs 0 [0–0] mm), anterior-apical (12.4 [3.1–14.2] mm vs 6.2 [0–8.2] mm), septal-apical (7.3 [0–13.1] mm vs 0 [0–0] mm), and inferior-apical (12.9 [11.1–15.3] mm vs 3.2 [0–8.2] mm), and lateral-apical (13.1 [11.3–15.0] mm vs 7.7 [4.9–8.4] mm), segments, whereas it did not differ significantly in the remaining segments .The CCT median NC:C ratio differed significantly between LVNC and controls in the inferior-midventricular (0.9 [0–2.1] vs 0 [0–0]), lateral-apical (2.1 [1.8–2.3] vs 0.8 [0.6–1.1]), inferior-apical (2.2 [1.8–2.5] vs 0.4 [0.0–1.0]), septal-apical (1.1 [0–2.1] vs 0 [0–0]), and anterior-apical (1.6 [0.3–2.1] vs 0.8 [0–1.0]) segments . AccordiBased on echocardiography, 54 affected segments were identified in the 17 LVNC patients and 0 affected segments in the 19 controls, respectively. The quantitative segmental NC:C CCT analysis revealed a optimal cut-off value of ≥1.8 for detecting an affected LVNC segment with an area under the curve of 0.997 .This study demonstrates the feasibility to diagnose LVNC with prospective ECG triggered low-dose CCT in a prospective controlled trial with a median [IQR] radiation dose of 1.3 [1.2–1.5] mSv.Since its first description by Engberding et al in 1984, LVNC has gained increasing attention . Based oThe low prevalence of LVNC indicated between 0.02% to 0.26% in adults –21 may hA maximal endsystolic ratio of NC:C >2 has been established as one of the major criteria to diagnose LVNC by TTE and validated versus anatomical examination of the heart . CompareSeveral different methods have been proposed to diagnose LVNC by CMR. Peterson et al applied an end-diastolic NC:C Ratio ≥2.3 measured in long axis cine views at the site with most pronounced trabeculations . Stacey Any diagnostic tool should offer a positive balance of harms and benefits. This is particularly true for CCT, which has in the past evoked a vivid discussion on its potential carcinogenic risk and its justification for a purely diagnostic test. Melendez-Ramirez et al and Sidhu et al reported a high accuracy to detect LVNC in CCT; however, their CCT resulted in a high radiation exposure for the patients of >12 mSv, whereas this value averaged 1.3 [1.2–1.5]mSv despite high accuracy in our study. This was enabled by prospective ECG triggering, e.g. limiting the beam to a narrow diastolic phase, and a BMI adapted scanning protocol, with adjective tube current and voltage [Based on our results CCT can be used complementary to previously established imaging techniques. However, overdiagnosis of LVNC is an issue of concern. Zemrak et al demonstrated in the MESA (Multi-Ethnic Study of Atherosclerosis) study that a huge number of asymptomatic patients fulfilled the Peterson CMR criteria for LVNC without exhibiting any adverse events . This obKip1 [Recently, iodine staining enhanced micro CT enabled the differentiation between the NC and C layer in mice with deletion of the cell cycle inhibitor protein p27Kip1  and tissThere are some potential limitations of this study. Despite the prospectively controlled study design, the cohort size was small and the results were obtained in a single center. Multi-center studies are thus needed to confirm the results in a larger and unselected population. In this study, we have established the NC:C threshold only in LVNC patients versus healthy controls. Thus, the applicability of the proposed criterion will have to be validated in the differentiation of LVNC from other cardiac diseases such as patients with hypertrophic or dilated cardiomyopathy. Furthermore, African-Americans and athletes were not included in the study as it is known that they are more likely to fulfill the LVNC criteria. Finally, clinical history regarding arrhythmia was not available.To conclude, this study demonstrates that CCT allows to discriminate LVNC patients from healthy persons with low radiation exposure. CCT might be used complementary to echocardiography in patients with equivocal results or poor image quality to provide further evidence whether the patient is affected or not, particularly when there is an indication to exclude the presence of coronary artery disease in such an individual. Furthermore, as patients undergo CCT for other reasons, this threshold may be used for answering the question whether accidentally seen trabeculations are consistent with the diagnosis of LVNC.S1 Checklist(PDF)Click here for additional data file.S1 File(DOC)Click here for additional data file.