To effectively forage in natural environments, organisms must adapt to changes in the quality and yield of food sources across multiple timescales. Individuals foraging in groups act based on both their private observations and the opinions of their neighbours. How do these information sources interact in changing environments? We address this problem in the context of honeybee colonies whose inhibitory social interactions promote adaptivity and consensus needed for effective foraging. Individual and social interactions within a mathematical model of collective decisions shape the nutrition yield of a group foraging from feeders with temporally switching quality. Social interactions improve foraging from a single feeder if temporal switching is fast or feeder quality is low. When the colony chooses from multiple feeders, the most beneficial form of social interaction is direct switching, whereby bees flip the opinion of nest-mates foraging at lower-yielding feeders. Model linearization shows that effective social interactions increase the fraction of the colony at the correct feeder (consensus) and the rate at which bees reach that feeder (adaptivity). Our mathematical framework allows us to compare a suite of social inhibition mechanisms, suggesting experimental protocols for revealing effective colony foraging strategies in dynamic environments. The e sources ,7, and u sources  or curb  sources . While w sources . Thus, b sources ,12.Honeybee colonies live in dynamic environments, in which the best adjacent nest or foraging sites can vary across time ,14. BeesTo study how social inhibition shapes foraging yields, we focus on a task in which the nectar quality of feeders is switched periodically. Related situations probably occur in nature due to the dynamics of competitor and predator prevalence, crowding by nest-mates, and weather fluctuations –25. PrecForaging colonies appear to be able to adapt to change. In prior studies ,16,20, cconsensus (steady-state fraction of bees at the high-yielding feeder) and adaptivity (the rate of switching from low- to high-yielding feeders). This provides a clear means of determining the impact of social interactions on a colony’s foraging efficacy.We quantify the contribution of these positive and negative feedback interactions within a mathematical model of a foraging colony. Our study focuses on four potential inhibitory social interactions—discriminate and indiscriminate stop signalling ,26, dire2.The mathematical model of bee colony foraging decisions assumes potential foragers may be uncommitted or committed to one of the possible feeders . UncommiA and B) systems, in which the fraction of the foragers committed to either feeder is described by a pair of nonlinear differential equations in the limit of large groups (see figure 1a for a schematic)αA,B(t) are time-dependent food qualities at feeders A, B (see figure 1b for examples); β min−1 is the rate bees recruit nest-mates to their feeder via waggle dancing; γ min−1 is the rate bees spontaneously abandon a feeder3; and uA,B ≤ 1 and 0 ≤ uA + uB ≤ 1, the commitment  and recruitment (β) provide positive feedback and the abandonment (γ) and inhibition , αX(t) − c. Integrating this product and scaling by time yields the effective RR.αA,B(t) and total foraging time Tf, colonies with more efficient foraging strategies J.We assume feeders are large enough to accommodate all the bees in the colony and hence we neglect the effects of crowding. Foraging efficacy is thus quantified by the group reward rate (RR), assuming net nutrition is proportional to the fraction of the colony at a feeder Before studying how social inhibition shapes a bee colony foraging in two-feeder environments, we analyse the single-feeder model, finding that commitment and negative feedback from either abandonment or inhibition are usually sufficient for the group to rapidly adapt to feeder quality switches.2.1.self-inhibition, by which a foraging bee stops another based on a detected change in food quality (a). Since transit from the hive to the feeder takes time, we incorporate a delay of τ min, so the fraction of foraging bees u evolves asα(t) is the food quality schedule of the feeder that switches at time intervals T (min) between α(t) = 0 and b), Inhibitory social interactions in a single-feeder model can only take the form of  quality a. Since  α(t)=α¯ ,20 (figuα(t) and abandonment γ) and interactions (recruitment β and inhibition ρ) [c,d). Equation (α(t) = 0) the non-foraging (β < γ). When food becomes available (c and appendix A(a)). This fraction consensus of the group [adaptivity.Colony’s adaptivity and consensus is shaped by both individual behaviour changes (commitment ition ρ) . PeriodiEquation  admits ohe group , and the2.1.1.T. Colonies with stronger rates of abandonment γ and self-inhibition ρ more quickly leave the feeder once there is no food .The performance of a colony’s interaction strategies strongly depends on the feeder quality y feeder d.γ, recruitment β and self-inhibition ρ, we compute the long-term RR of the colony, measuring the foraging yield over a single period (2T min) once the group equilibrates to its periodic switching behaviour (see appendix A(b))To quantify the effect of abandonment γ, recruitment β and stop signalling ρ) within our set of possible strategies (see appendix A(c)) that maximizes the RR J (e). Here, private information is sufficient for individual bees to commit to foraging ), and recruitment does not benefit the colony (β = 0). Reinforcing the majority opinion via recruitment is detrimental once the environment changes, as opposed to static environments [T) or low food quality (ρ) is needed to swap group commitment when the environment changes . This nonlinear mechanism increases the adaptivity of the group, but tempers the initial stage of consensus after the feeder is switched on (see appendix A(a) for details). On the other hand, when food is plentiful (high e), inhibition should be weak . In intermediate environments, the best strategies interpolate these extremes.For each feeder quality level, γ, β, ρ) e. Here, ronments ,31,32. IT) with small delays (τ → 0), we can linearly approximate the evolving foraging fraction (see appendix A(d))λon/off are the rates the group arrives/departs the feeder once food is switched on/off. Plugging equation ). Clearly, J increases with γ tends to decrease consensus, so the most robust foraging strategies cannot use abandonment that is too rapid (appendix A(a)).Linearizing solutions to the model equation  providesequation , we estiT) and profitability : (a) direct switching: bees foraging at the superior feeder directly switch the preference of opposing foragers to the better feeder; (b) indiscriminate stop signalling: when two foraging bees meet, one will stop foraging; (c) self-inhibition: when two bees foraging at the same feeder meet, one will stop foraging; and (d) discriminate stop signalling: when bees foraging at different feeders meet, one stops foraging. These interactions are visualized in figure 3a–d and their evolution equations are given in appendix B(a) .Figuree). The colony will thus mostly forage at the higher-yielding feeder. On the other hand, strong discriminant stop signalling can produce colonies with bistable consensus behaviours (figure 3f). Such hysteresis in stop-signalling populations was also identified in [We can divide these forms of social inhibition into two classes, based on the group foraging dynamics they produce: monostable or bistable consensus behaviours. The first three forms of social inhibition yield groups with monostable consensus behaviours (see appendix B(b) and electronic supplementary material of ), tendinfigure 3ified in . As a reified in ,35. Inhi2.2.2.T (min) and the better feeder quality A and B. As in the single-feeder case, we tune interactions of each strategy (a–d) to maximize RR over a discrete set of strategies (see appendix B(c) for details). Comparing each social inhibition strategy type’s RR in different environments (a), we find direct switching generally yields higher RRs than other strategies. Differences in the effectiveness of distinct strategies are most pronounced at intermediate environmental timescales T. As expected, RRs increase with the maximal feeder quality b,c).To determine the most robust forms of social inhibition for foraging in dynamic environments, we studied how the rate of reward, equation , dependestrategy a–d to maronments a, we finality α¯ b,c.Figua), as opposed to other strategies which interrupt foraging with an uncommitted stage (b–d) and rely on recruitment β to restart foraging. To study how interactions should be balanced to yield effective foraging, we examined how to optimally tune  across environments in the direct switching model  and abandonment γ (b), and strong direct switching (c) yield the highest RRs in slow (large T) and high-quality  and low quality . Changes in the optimal recruitment strength are less systematic, and there are stratified regions in which the best β can change significantly for small shifts in environmental parameters. Overall, a mixture of abandonment and direct switching is more effective in more difficult environments , since the colony can forage more efficiently by keeping some bees uncommitted, and not risking the cost of foraging at the lower-yielding feeder. Strong self-inhibition ρ keeps more bees from foraging. Overall, both direct switching and self-inhibition can perform similarly, as recruitment interactions can be strengthened in self-inhibiting colonies, so more bees return to foraging after such inhibitory encounters . Focusing specifically on these measures, we demonstrate both how they shape foraging efficiency and how they distinguish each social inhibition strategy.Each interaction mechanism differentially shapes both the fraction of bees that forage at the better feeder in the long-time limit (consensus T (see appendix B(d) and λ (rate this fraction is approached):J increases with consensus λ (a). Efficient colonies rapidly recruit a high fraction of the colony to the better feeder. Consensus and adaptivity are approximated in each model using linear stability (appendix B(b)). The impact of varying abandonment γ and social inhibition ρ on λ is consistent with our optimality analysis of the full nonlinear model : social inhibition generates more robust switching between feeders than abandonment. While strengthening abandonment adaptivity γ can increase λ, it decreases consensus b). Such consensus–adaptivity trade-offs do not occur in most models, as social inhibition ρ is strengthened , but the other three models  do not. Rather, consensus We leverage our approach developed for the single-feeder model, and consider linear approximations of equation  in the ltivity λ a. Efficiar model b,c: sociommitted b. Such cst model .Figure λ  or indiscriminate stop signalling (figure 6c) increase adaptivity but limit consensus β leads to stronger consensus λ to environmental changes. Bee colonies probably use a combination of social inhibition and abandonment mechanisms [Direct switching sustains both high levels of consensus chanisms , which w3.Foraging animals constantly encounter temporal and spatial changes in their food supply . The sucEfficient group decision-making combines individual private evidence accumulation and information sharing across the cohort . HoweverWe find that social recruitment can speed along initial foraging decisions, but it can limit adaptation to change. This is consistent with experimental studies that show a reduction in positive feedback can help collectives steer away from lower value decisions. For example, challenging environmental conditions  are best managed by honeybee colonies whose individuals do not wait for recruitment but rely on their own individual scouting . Ants enIn concert with a reduction in recruitment, we predict that honeybee colonies foraging in volatile environments will benefit from strengthening inhibitory mechanisms at the individual and group level. Bees enacting social inhibition dissuade their nest-mates from foraging at opposing feeders. We found the most efficient form of social inhibition is direct switching whereby bees flip the opinion of committed bees to their own opinion. So do honeybees use this mechanism in dynamic environments? Observations of bee colonies making nest site decisions show scouts directly switching their dance allegiance ,51, but Most work studying the effects of social inhibition on honeybee colony decisions has focused on swarms choosing a place to build a nest from sites whose qualities are fixed in time ,28. SociOur simple parametrized model, developed from previously validated house-hunting models ,28,29,32Previous computational modelling studies of honeybee collective decisions primarily focused on groups solving house-hunting problems in static environments ,29, emphThere are a number of possible extensions of our work here. For instance, one could consider separate populations of scouts and foraging recruits as in some previous modelling studies ,53. Our d,f), so the stochasticity in the finite-sized model could allow colonies to break free from less-profitable feeders, similar to noise-driven escapes of particles in double potential well models [Collective decision strategies and outcomes can depend on group size ,62, thoufigure 3l models . Fluctual models ,66, and l models ,68.


The electrochemical decoration of CVD monolayer graphene with MoO2 is shown to originate from the edge plane like- sites/defects. Edge plane decoration of MoO2 nanowires upon monolayer graphene is observed via electrochemical deposition over short time periods only (ca. −0.6 V for 1 second (vs. Ag/AgCl)). At more electrochemically negative potentials (ca. −1.0 V) or longer deposition times, a large MoO2 film is created/deposited on the graphene sheet, originating and expanding from the original nucleation points at edge plane like- sites/defects/wrinkles. Nanowire fabrication along the edge plane like- sites/defects of graphene is confirmed with Cyclic Voltammetry, Scanning Electron Microscopy (SEM), Atomic Force Microscopy (AFM) and Raman Spectroscopy. Monitoring the electrochemical response towards [Ru(NH3)6]3+/2+ and comparing the heterogeneous electron transfer (HET) kinetics at CVD grown monolayer graphene prior and post nanowire fabrication reveals key understandings into the fundamental electrochemical properties of carbon materials. The HET kinetics  grown monolayer graphene using a polymer-free transfer methodology is explored through the selective electrodeposition of Molybdenum (di)oxide (MoO Typically, graphene is fabricated via one of two routes; a bottom-up (BU) or a top-down (TD) approach. TD methods, such as chemical/thermal reduction of graphene oxide or using physical/chemical exfoliation, can give rise to a large quantity of graphene sheets; however, the fabricated graphene is generally highly defective and abundant with residual C/O groups or other contamination such as surfactants or metals24. Bottom-up (BU) fabrication routes typically lead to higher quality graphene, but in smaller quantities25. Chemical Vapour Deposition (CVD) growth of graphene onto a copper catalyst is a BU route that facilitates the study of high quality monolayer graphene sheets/films28, which can then either be transferred (commonly using Poly(methyl methacrylate), PMMA) onto a suitable substrate31. PMMA has previously been reported to affect the physical and electrical properties of CVD grown graphene samples transferred via this route38, therefore, in this work we address the fundamental properties and conduct performance studies on CVD grown monolayer graphene transferred via a polymer-free transfer method39.Graphene is extensively studied due to its reported unique electronic, mechanical and optical propertiesvia a BU approach), the reactivity of such electrodes is commonly assigned to the graphene edge planes, which are reported to exhibit ca. 4 orders of magnitude greater specific capacitance, faster electron transfer rates and higher electrocatalytic activity when compared to the graphene basal planes40. Recent work involving the electrochemical behaviour of CVD grown pristine graphene has shown a correlation in the structure of graphene, in terms of a dependence upon its number of layers and the macroscopic electrochemical response/performance42. Moreover, an identical relationship in terms of graphene’s geometric structure  has recently been reported with respect to the lateral flake size, in which smaller graphitic flake sizes  exhibited improved electrochemical properties when compared to the inverse43. Indeed, there have been numerous other studies reporting that, in comparison to the edge plane like- sites/defects45, the basal planes of graphitic and carbon-based materials are effectively inert47. However, regardless of the vast number of recent reports concerning graphene-based electrodes, researchers still debate the real contributions of edge and basal plane like- sites/defects at the macroscopic scale, in future experiments researchers should clarify the experimental configuration of their studies, ie. macro- vs microscopic voltammetry48 with respect to their observed heterogeneous electron transfer (HET) kinetics52; further work is required to explore this edge vs basal argument for carbon surfaces48. It is these aforementioned research considerations that we investigate herein, through the utilisation of a MoO2 deposition technique to decorate CVD grown monolayer graphene (transferred using a polymer-free method) so that one can determine the true electrochemical contributions arising from its structure.In terms of understanding the electrochemical response of CVD grown monolayer graphene  are responsible for the voltammetric signal, and Rowley-Neale et al.59 extended these insights with screen-printed graphitic electrodes (SPEs) with similar results. In these approaches, the selective deposition of metals and metal oxides onto graphite is called ‘step edge decoration’56, and has been utilised to demonstrate that electrochemical reactions at edge plane graphite are “anomalously fast” in comparison to the basal plane, which is effectively inert in comparison60. Through the selective coating and blockage method of the edge plane like- sites/defects with MoO2 nanowires , a reduction in the electrochemical response towards [Ru(NH3)6]3+/2+ 47 was reported; clearly demonstrating that the electrochemical reactivity of the HOPG is due to edge plane like- sites/defects. To the best of our knowledge, the above elegant approach has not previously been applied towards CVD grown monolayer graphene sheets (in addition to the polymer-free transfer method utilised herein) contributing to the understanding of graphene and all akin carbon-based electrode materials.Molybdenum oxide(s) and other metal oxides have been shown to electrochemically nucleate specifically onto the edge plane like- sites/defects of highly oriented pyrolytic graphite (HOPG)2 onto HOPG and SPEs59, where MoO2 selectively deposits upon the available edge plane like- site/defects, allowing their electrochemical contributions to be deduced. In consideration of these approaches, herein we adapt this method towards CVD grown monolayer graphene sheets. Presented within the Experimental Section and ESI (1.1–1.4) are details of the fabrication process, optimisation and physicochemical characterisation of the CVD grown monolayer graphene sheets and their MoO2 decorated counterparts. Briefly, Figure 2 decorated monolayer graphene, displaying the typical monolayer graphene D (1350 cm−1), G (1580 cm−1), 2D (2700 cm−1) and 2D’ (3250 cm−1) peaks, the presence of MoO2 (308 cm−1)61 and the presence of the underlying Si (514 and 985 cm−1) wafer . Additionally, AFM images were collected in order to characterise the MoO2 nucleation upon the edge plane like- sites/defects as depicted in Figure selective nucleation characterised by Rowley-Neale et al.59.Previously reported methodologies have utilised the electrochemical decoration of MoO3)6]3+/2+, which exhibits a peak-to-peak separation (ΔEp) of 160 mV (at 50 mV s−1vs. Ag/AgCl) and a HET . Note that this value is similar but slightly faster than other CVD monolayer graphene sheets previously reported by our research group41, which is due to a higher exposure of the edge plane like- sites/defects upon the graphene sheet. Furthermore, it is worth noting at this stage that each individual graphene sheet is unique in terms of its level of edge plane like- site/defects, so there is natural variation in the reported HET rates throughout the literature; thus, it is important to characterise each monolayer graphene sample62 prior to modification with MoO2. Figure 2 electrochemically decorated monolayer graphene sheet following applying chronoamperometry at a set voltage (−0.6 V vs. Ag/AgCl) for 1 second, where it is readily observed that the MoO2 nucleates upon the edge plane like- sites/defects/wrinkles of the monolayer graphene surface. Figure 3)6]3+/2+, which exhibits a ΔEp of 200 mV (at 50 mV s−1vs. Ag/AgCl) and a −4 cm s−1. Such decreased voltammetric behaviour implies that the insulating MoO2 nanowires have coated some of the available edge plane like- sites/defects, with some edge sites remaining unmodified. Figure θedge) values) the changes observed as one electrodeposits MoO2 onto the graphene electrode’s surface. Such voltammetric profiles show that the decorated electrode (dashed line) still exhibits relatively fast HET kinetics when compared to the unmodified electrode (solid line), but presents a lower current intensity due to the limited availability of edge plane like- sites/defects on the electrode surface. This is corroborated with the SEM presented within Fig. Figure vs. Ag/AgCl). Presented within Fig. 2 electrochemically decorated monolayer graphene sheet that has been applied at −0.6 V (vs. Ag/AgCl) for 10 seconds via chronoamperometry, where it is clear that electrodeposition is no longer isolated upon the edge plane like- sites/defects. Instead, the MoO2 has coated/blocked the entire electroactive surface. Upon electrodeposition at more electrochemically negative potentials (ca. −1.0 V) or longer deposition times, a large MoO2 film is created/deposited on the graphene sheet, originating and expanding from the original edge plane like- sites/defects (non-selective deposition), as shown in Figures 3)6]3+/2+, exhibits a ΔEp of 820 mV and a −6 cm s−1. As expected, the monolayer graphene sheets coated with insulating MoO2 (upon the edge plane like- sites/defects), give rise to an electrochemical response with a larger ΔEp (slower electron transfer) than the unmodified monolayer graphene. The aforementioned MoO2 coverage dependant electrochemical behaviour has been illustrated within the schematic presented within Fig. Next we investigate the effect of increased deposition times at −0.6 V (2 wires that nucleate onto the graphene’s edge plane (and or defect/wrinkle) sites when a electrodeposition for 1 second at −0.6 V is performed, resulting in a decrease in the reversibility of the [Ru(NH3)6]3+/2+ redox probe. Longer deposition times will increase the nucleation of the MoO2 onto remaining edge sites and towards the basal plane of the graphene sheet (deposition of 5 seconds at −0.6 V). Eventually, a complete coverage of the surface of the graphene is confirmed by the absence of cyclic voltammetric redox peaks of [Ru(NH3)6]3+/2+, for example, when a electrodeposition of 10 total seconds at −0.6 V is performed.Figure 2 wires are indeed being electrodeposited selectively onto the graphene edge plane like- sites/defects, at a set potential of −0.6 V (vs. Ag/AgCl) for 1 second and this results in a decrease in the 2 selectively electrodeposits upon the edge plane like- sites/defects, which is confirmed by physicochemical characterisation, indicating that these are the active sites for electron transfer. If no changes in the voltammetry were observed, this would indicate that basal planes sites are the origin of electron transfer, as this is not the case, our work provides convincing evidence that it is the edge plane like- sites/defects of the CVD grown monolayer graphene that are the origin. When utilising an electrodeposition of 10 seconds, an insulating film is created, which largely decreases the amount of available edge plane like- sites/defects by up to 99.9%. Note that in the case of sample B, however, due to the initial low θedge and hence 2 has fewer edge plane like- sites/defects to deposit upon. This demonstrates again that each individually grown CVD graphene sheet is unique in terms of edge plane like- sites/defects and i.e. too long deposition times and high deposition potentials, physicochemical characterisation would indicate a large film is created, which would give the false impression that the basal plane of monolayer graphene is as equally electrochemically active as the edge plane sites, which is not the case. However, there has been a great deal of elegant work published on the micro scale response of electron transfer at carbon materials using novel nanoscale techniques64, therefore it is intended that the data reported herein be considered in-conjunction with such other studies for the electrochemical community to better understand this phenomena as a whole.The experiments performed above confirm that the MoO2 nanowire arrays have been electrodeposited onto the edge plane like- sites/defects of CVD grown monolayer graphene using a polymer-free transfer method, which have been confirmed via physicochemical characterisation. The electrochemical activity pre and post MoO2 electrochemical deposition has been assessed using a near-ideal redox probe and in the latter case indicates that the HET kinetics (MoO3)6]3+/2+ (in 0.1 M KCl), 0.5 M H2SO4 and 1 mM Na2MoO4 (in 1 M NaCl).All chemicals used were of analytical grade and were used as received from the supplier  without any further purification. All solutions were prepared with deionised water of resistivity no less than 18.2 MΩ cm and were vigorously degassed prior to electrochemical measurements with high purity, oxygen free nitrogen. Test solutions were: 1 mM 3+/2+ (in 0.1 M KCl) using the well-known66 and utilised Nicholson method67, for quasi-reversible electrochemical reactions via the following formula59:φ is a kinetic parameter, D is the diffusion coefficient for [Ru(NH3)6]3+/2+ (D = 9.1 × 10−6 cm2 s−1)66, n is the number of electrons that are taking part in the process, F is the faraday constant, vis the scan rate, R is the gas constant and T is the temperature in Kelvin. In order to calculate the HET rate constant, we use the peak to peak separation (ΔEp) to deduce φ, where ΔEp is obtained at various voltammetric scan rates52. The standard heterogeneous constant 6]3+/2+, has been widely determined with a commercial simulation package providing a value of 0.4 cm s−1 for HOPG electrodes58.allowing the coverage of edge plane like- sites/defects upon the graphite electrode to be deduce; we use this and adapt this to the voltammetry observed at the CVD grown monolayer graphene sheets. %θ−1 . Scans were carried out at 40 µm/s with a force set point of 6 nN. Samples were attached to glass microscope slides using double sided tape.Raman Spectroscopy was performed using a DXR Raman Microscope  fitted with a 532 nm excitation laser at a low power of 3 mW to avoid any heating effects. Spectra were recorded using a 3 second exposure time for 3 accumulations at each point. Scanning electron microscope (SEM) images were obtained using a JSM-5600LV  model SEM equipped with an energy-dispersive X-ray microanalysis (EDS) package. Atomic force microscopy (AFM) images were obtained using a DualScope C26 , carried out using AC mode using DS 95 AC probes with a spring constant of 42 N mNote that the area studied is in the macroscale range which is not considered too large because indeed it averages the actual phenomena occurring locally . Such that this makes this study applicable for real applications. We are reporting on the average response of the macro electrode with varying contributions of the two micro features , which is relevant because the likely end use/application of such systems will be on that of the macro scale.Supplementary information


Weighting methods offer an approach to estimating causal treatment effects inobservational studies. However, if weights are estimated by maximum likelihood,misspecification of the treatment assignment model can lead to weighted estimators withsubstantial bias and variance. In this paper, we propose a unified framework forconstructing weights such that a set of measured pretreatment covariates is unassociatedwith treatment assignment after weighting. We derive conditions for weight estimation byeliminating the associations between these covariates and treatment assignmentcharacterized in a chosen treatment assignment model after weighting. The momentconditions in covariate balancing weight methods for binary, categorical and continuoustreatments in cross-sectional settings are special cases of the conditions in ourframework, which extends to longitudinal settings. Simulation shows that our method givestreatment effect estimates with smaller biases and variances than the maximum likelihoodapproach under treatment assignment model misspecification. We illustrate our method withan application to systemic lupus erythematosus data. Thepropensity function, the conditional probability of receiving treatment given a set ofmeasured pretreatment covariates , featureIn this paper, we introduce covariate association eliminating weights, a unifiedframework for constructing weights with the goal being that a set of measured pretreatmentcovariates will be unassociated with treatment assignment after weighting. Our method can beused to estimate causal effects for semicontinuous, count, ordinal, or even multivariatetreatments, and it extends to longitudinal settings. An example of estimating the directeffect of a time-varying treatment on a longitudinal outcome is provided in § Our method has several attractive characteristics. First, it encompasses existingcovariate balancing weight methods and provides a unified framework for weighting withtreatments of any distribution; see § 22·1iX, iT andiY be respectively a set of measured pretreatmentcovariates, the possibly multivariate treatment variable and the outcome for theith unit  in asimple random sample of size n. Following iT | iX;βtrue), whereβtrue parameterizes this distribution. The parameterβtrue is assumed to be unique and finite-dimensional,and is such that pr(iT | iX)depends on iX only through a subset ofβtrue; this is the uniquely parameterized propensityfunction assumption of iT |iX) follows a regression model, thenβtrue would include regression coefficients thatcharacterize the dependence of iT oniX and intercept terms that describe the baselinedistribution of iT. In addition, we make the strongignorability of treatment assignment assumption (iT |iY(tP),iX} = pr(iT |iX) whereiY(tP) is a random variable thatmaps a potential treatment tP to a potential outcome, and thepositivity assumption (pr(Ti∈𝒜|Xi)>0 for all iX and any set 𝒜 with positive measure. Finally, the distribution of potential outcomes for oneunit is assumed to be independent of the potential treatment value of another unit giventhe set of pretreatment covariates; this is the stable unit treatment value assumption.Throughout the paper, we make the above assumptions; otherwise our method may result inseverely biased causal effect estimates, even compared with an unadjusted analysis. Forexample, when unconfoundedness holds without conditioning oniX, adjusting for iX can induceM-bias  =pr(iT)/pr(iT |iX) , will be equivalentto pr(iT) and hence does not depend oniX, as shown in the iW also preserves thecausal effect of tP onE{iY(tP)} inthe original population } canbe consistently estimated by modelling E(iY |iT) in the weighted data  . The idepulation , and so ted data .2·4iT; α) andiX, and then estimating the unknown parametersα and β by solving the score equationsiT;α) are correctly specified, then the weightsα and β, are equivalent toiW will result in iT), i.e., iT does not dependon iT) but alsodoes not even guarantee that the association between iT is reduced in theweighted data relative to the observed data. Researchers are therefore encouraged to checkfor the absence of this association in the weighted data before proceeding to causaleffect estimation. For nonbinary treatments, correctly specifying the propensity functionmodel will generally also entail correct specification of the distribution and thedependence structure on covariates for higher-order moments of the treatment variable.Therefore, model misspecification for nonbinary treatments is likely to be worse.Estimating the weights by maximum likelihood involves specifying parametricmodels pr(33·1iT) by fitting a modeliT characterized by achosen propensity function iT as characterized byiT to be independent of iT after weighting, whileour method will eliminate their association in finite samples.Maximum likelihood estimation indirectly aims to achieve the asymptoticequivalence of W =, we can fita parametric propensity function model W by solving thescore equations β(W) is avector of parameters. Here we write β(W) as afunction of W because the resulting maximum likelihood estimates,W. We use the uniquelyparameterized propensity function assumption in § β(W) into{βb(W),βd(W)}, whereβd(W) are the unique parameters thatcharacterize the dependence of iT on βb(W) are parameters thatcharacterize the baseline distribution, e.g., the intercept terms. Here the subscripts‘d’ and ‘b’ stand for dependence and baseline, respectively.Without loss of generality, we assume W satisfy the equations iT;α) to the observed data. Our method therefore sets the goal ofweighting as attaining iW,(i) iT is unassociated with iT is preserved from the observed data, as described bypr. Here (ii) is astatement concerning the projection function, the treatment assignment distribution in theweighted data. The choice of projection function, as long as it does not depend onβb(W) in  andβb(W) in  in  to be, riT is a binary treatmentvariable, i.e., iT = 1 if the ith unitreceived treatment and iT = 0 otherwise, the followingcovariate balancing conditions for the estimation of the propensity score have beenproposed (Let proposed :(6)1n∑iW. This model can be fitted to the weighted data by solving the scoreequations W: These conditions can be derived using our framework. Suppose that we specify alogistic regression model for the propensity function/score,  between  and 7)  pr{Ti=1|4·2iT is continuous on the real line, W that satisfy iT. The first set of conditions in  andσβ(W) to zero in the scoreequations. This corresponds to solving the equations iT. Moredetails can be found in § When  satisfy ∑i=1nWiXial line, 1n∑i=1nWiquations ∑i=1nWiXi5Having demonstrated that our framework encompasses previously proposed work, wenow widen its applicability by considering semicontinuous treatments, motivated by ourapplication in § I (·) is an indicator function. Here g(·)is a monotonic function included to make the normal assumption for the positive values ofiT more tenable. The likelihoods for the binary andcontinuous components of the two-part model are separable, so the results in § iπ, iμ andiσ obtained by fitting  = 1/[1 + exp{−(0·5 +Xi1 +Xi2 +Xi3)}]. Then ifiT > 0, iT is drawn from anormal distribution with mean 1 + 0·5Xi1+ 0·2Xi2 +0·4Xi3 and standard deviationexp(0·3 + 0·3Xi1 +0·1Xi2 +0·2Xi3). The outcomeiY follows a negative binomial distribution θ = 1 andiλ =E = exp[−1 +0·5iT + 2/{1 +exp(−3Xi1)} +0·2Xi2 − 0·2exp(Xi3)]. Using this set-up, we generatefour sets of 2500 simulated datasets with n = 500, 1000, 2500 and 4000.We consider the set-up where there are three independent standard normalpretreatment covariates iT conditional on iT> 0 to depend on covariates, whereas the incorrect model structure B restrictsiσ to be a constant σ. Thefirst set of covariates are the correct covariates, n. Then, using the estimated weights, wefit a weighted negative binomial model (iλ =exp(γ0 +γ1iT), whereγ0, γ1 andθ are parameters to be estimated. The true causal treatment effectof interest is γ1 = 0·5.For each of the four sets of simulations, we use the proposed method forsemicontinuous treatments in § al model to the oThe left panels of The middle panels of n ≥ 1000.Because Approach 2 performs so poorly relative to Approach 1 when the transformedcovariates are used, it is difficult to distinguish between the performances of Approach 1under model structures A and B in In summary, in all examined scenarios, estimates based on the proposed method havesmaller biases and variances than the maximum likelihood estimates.7Steroids are effective and low-cost treatments for relieving disease activity inpatients with systemic lupus erythematosus, a chronic autoimmune disease that affectsmultiple organ systems. However, there is evidence that steroid exposure could be associatedwith permanent organ damage that might not be attributable to disease activity. Motivated bya recent study , we aim iY is the number of items accrued in theSystemic Lupus International Collaborating Clinics/American College of Rheumatology DamageIndex during the period iO, defined as the time interval betweenthe two initial assessments in years. The semicontinuous treatment variableiT is the average steroid dose per day in milligrams overthe same time period. Based on clinical inputs, we consider the following pretreatmentcovariates iX: the numerical scoring version of the BritishIsles Lupus Assessment Group disease activity index  =log(x + 1) to reduce right skewness in the positive steroid dose. ForiX. We use the same four models as in the simulations toestimate weights. Further details can be found in the We consider the model  forTi wiY with a weighted negative binomial model =iO exp{γ0 +γ1I(iT> 0)}, and for examining the dose-response relationship we specifyiλ =E =iO exp{ξ0 +ξ1 log(iT + 1)}, withγ0, γ1,ξ0, ξ1 andθ parameters to be estimated. We construct 95% bootstrappercentile confidence intervals for parameter estimates using 1000 nonparametric bootstrapsamples.We model al model, where fThe results of the outcome regression models based on four sets of estimatedweights are reported in I (iT> 0) and I (iT > 0)log(iT + 1) included in the outcome regression. Thisprovides the dose-response relationship after removing the patients unexposed to steroids, in each time interval[sij−1,ijs)  of alongitudinal study, we observe in chronological order a vector of covariatesijX, a time-varying treatmentijT that can be of any distribution, and a longitudinaloutcome ijY. The units are not necessarily followed up at thesame time-points. Let the random variable sij−1,ijs). We consider the setting where interest lies inestimating the direct causal effect of Ȳij−1. Here an overbarrepresents the history of a process; for example, Our framework can be extended to the longitudinal setting. Here we give an exampleusing a similar setting to that in Ȳij−1 and for any set 𝒜 with positive measure. Under these assumptions, the effect ofith unit’s data in the interval[sij−1,sij) with Ȳij−1, andα and β.In order to identify the direct causal effect of ijW, the time-varyingpropensity function in the time interval[sij−1,ijs) follows a model β(W) into{βb(W),βd (W)}, whereβd (W) are regression coefficientsthat characterize the association between ijT over time, andβb(W) are parameters that characterizethe baseline distribution, e.g., the intercept terms. Similarly to before, conditions forweight estimation can be derived by inverting the weighted score equations ijT; α) to the observed data forthe time-varying treatment ijT. Thus these conditions aredesigned to eliminate the association between ijT and to preserve theobserved marginal distribution of ijT after weighting. Otherprojection functions that can help to further stabilize the weights, such as those thatdepend on some of the covariates, can also be considered in the proposed framework with onlyminor modifications. This would be useful when the interactions between these covariates andtreatment are of interest in the outcome regression model. Finally, the convex optimizationapproach in § An alternative approach to weight estimation is to generalize our proposed method.Following the same strategy as in § quations ∑i=1n∑j=1quations ∑i=1n∑j=19c is the sample mean ofThe proposed method has some limitations. First, both our method and existingcovariate balancing weight methods treat covariates equally and balance them simultaneously.This can lead to poor performance in high-dimensional settings, so it would be of interestto incorporate different weights for the covariates when estimating the weights for theunits. Second, it can be hard to detect near violations of the positivity assumption withour method, because it generally results in small variance of the causal effect estimates byexactly balancing the covariates. In such circumstances, e.g., when there is strongconfounding, the results from our method can hide the fact that the observed data alonecarry little information about the target causal effect and can have large bias under modelmisspecification because our method will almost entirely rely on extrapolation. It istherefore important to assess the positivity assumption when applying our framework. Third,our method does not necessarily estimate the causal effect for the population of interest,such as the target population of a health policy. This can be remedied by supplementing theconditions  with thepulation .Supplementary material available at Biometrika onlineincludes properties of stabilized inverse probability of treatment weights, derivations ofconditions for other treatment distributions, simulation results and details of theapplication.Supplementary information


TRPM8 expression in humans and human physiologic responses to cold challenge. Here we show that TRPM8 expression is decreased from the chromosome harboring the rs10166942[C] allele in the human dorsal root ganglia. Moreover, carriers of rs10166942[C] required significantly lower temperatures and longer duration of exposure to reach a cold pain threshold (CPTh), which correlated with decreased TRPM8 expression expected in the carriers. This study provides evidence for a genotype-dependent influence on cold pain sensation suggesting that carriers of the reduced migraine risk allele have reduced sensitivity to cold stimuli and that TRPM8 acts as a cold thermosensor and cold pain transducer in humans. Reduced TRPM8 expression and function underpins the migraine protection in carriers of rs10166942[C]; thus, the evaluation of TRPM8 antagonists as migraine therapeutics is warranted. Furthermore, these results provide mechanistic insights for evolutionary positive selection of rs10166942[T] allele in adaptation along latitudinal cline to colder climates.Multiple genome-wide association studies have identified non-coding single-nucleotide variants (SNVs) near  or within (rs17862920[T]) the TRPM8 gene that encodes a cold thermosensor is associated with reduced migraine risk. Furthermore, rs10166942[C]) and rs10166942[T]) are more prevalent in populations that reside in hotter and colder climates, respectively. Here we assessed whether these alleles affect  Knockout mice studies indicated that TRPM8 is required for sensing innocuous ambient cold temperatures5. Role of thermosensory channels in thermoregulation have been demonstrated initially with agonists: TRPV1 activation caused a drop whereas TRPM8 activation caused an increase in body temperature7. Further, antagonists demonstrated a basal tone of activation because antagonists by themselves altered body temperature: TRPV1 antagonists increased body temperature in rodents, dogs, non-human primates and humans whereas TRPM8 antagonists decreased body temperature10.Transient receptor potential melastatin 8 (TRPM8) is a non-selective cation channel that is activated by cool temperatures and compounds that elicit cool sensation, such as menthol and icilin (AG-3-5), and that serves as a cold thermosensor 4. Multiple genome-wide association studies have identified non-coding single-nucleotide polymorphisms (SNP) in cis or within the TRPM8 gene (rs10166942[C] is located 950 bp 5′ to the start codon and rs17862920[T] is located within the first intron) are associated with reduced migraine risk 13. In this study, we have assessed the extent to which reduced migraine risk alleles affect TRPM8 expression by evaluating allelic expression imbalance (AEI) at the transcript level using next generation sequencing. We have also investigated if reduced TRPM8 expression is associated with alterations in physiologic responses to cold challenge.TRPM8 is highly expressed in migraine and pain neuronal circuitry, such as trigeminal and dorsal root ganglia (DRG)TRPM8 expression by evaluating allelic expression imbalance (AEI) at the transcript level using next generation sequencing. To measure allele-specific mRNA expression, we identified three marker SNPs  showing sufficient heterozygosity within a coding region of TRMP8 (het-mSNP) 14. Therefore, alterations in physiologic responses to cold challenge may reflect an association with reduced TRPM8 function.Because TRPM8 is a cold thermosensor, we hypothesized that carriers of rs10166942[C] may be less responsive to cold challenge. It has been shown that pharmacologic blockade of TRPM8 attenuates autonomic and behavioral cold defense mechanismsTo investigate this further, we enrolled 38 healthy male volunteers in a Phase 0 study, and evaluated the impact of rmrSNP alleles on cardiovascular reactivity and cold sensitivity based on cold pain threshold [CPTh] and tolerance [CPTo]) using CPT and quantitative sensory testing.n = 18), or non-carriers who were homozygous for the common SNP allele (n = 20). Reduced migraine risk genotype distribution and SNP group status (whether they were carriers or non-carriers of the aforementioned SNP) did not have any bearing on their CPT eligibility criteria, suggesting that enrollment was genetically unbiased relative to the test alleles  were separated into two cohorts: carriers who had rmrSNP alleles . Individuals homozygous for the common allele and heterozygotes generally shared a similar response suggesting the allele associated with reduced migraine risk is recessive. Similarly, individuals homozygous for rs10166942[C] took longer to reach CPTh than homozygotes for the common allele after QST challenge . In the CPT, homozygous carriers of rs10166942[C] had delayed onset of cold pain compared with individuals homozygous for the common allele (T/T), although a statistically significant difference was not detected  were reported in one participant (2.6%) in the non-carrier group.We found that Likert pain scores were insensitive to genotypic differences in the CPT and QST at the group and genotype levels  or within (rs17862920[T]) the TRPM8 expression is reduced from the chromosome harboring rs10166942[C] in all the carriers in the range of 47% to 99% with the maximum decrease observed from the chromosome that harbors both reduced risk migraine alleles (rs10166942[C] and rs17862920[T]). The factors that underpin the variability in the magnitude of reduction in TRPM8 expression in carriers of rs10166942[C] are unknown. Since most of the effect on cold pain sensation driven by the homozygotes for the rs10166942[C], we expect a large effect size for migraine protection with full antagonism of TRPM8 supporting an evaluation of TRPM8 antagonists as migraine therapeutics.21. For example, both TRPM8 and TRPV1 have a level of basal activation (tone) that can balance thermoregulation22. Basal TRPV1 activation contributes to a suppressive tone on body temperature and further activation of TRPV1 by chemical agonists like capsaicin and resiniferatoxin (RTX) cause a decrease in body temperature (towards hypothermia). Basal TRPM8 activation contributes to an enhancing tone on body temperature and further activation of TRPM8 by cold or chemical agonists cause an increase in body temperature (towards hyperthermia). Conversely, TRPV1 antagonists cause an increase in body temperature23, whereas TRPM8 antagonists cause a reduction in body temperature  are linked  expression that results in less heat production in hotter environments thus improved homeostatic management of body temperature , University of California Santa Cruz Genome Browser26 and Array Studio . Selection criteria to determine the suitability of an mSNP were as follows: the mSNP must be located in the coding region of TRPM8; the mSNP must be within 30 kb of the non-coding reduced migraine risk SNP rs10166942 because downstream linkage analysis (phasing) techniques have certain limitations ; the mSNP must have high heterozygosity in the CEU population; and the mSNP must be amenable to PCR amplification, so that the mSNP-containing amplicon would fall within a single exon. mSNPs meeting these criteria are shown in Supplementary Table Tissue specimens were collected within 4 hours postmortem and flash frozen in liquid nitrogen. To assess allele-specific mRNA expression, candidate mSNPs were identified using the database of SNPs (Database of Single Nucleotide Polymorphisms (dbSNP). Bethesda (MD): National Center for Biotechnology Information, National Library of Medicine (dbSNP Build ID: 141); available at: To extract gDNA, frozen DRG tissue (~15 mg) was suspended in DNA lysis buffer containing proteinase K at 55 °C overnight, and then purified using the Gentra Puregene Core Kit A and treated with RNase A according to the manufacturer’s instructions . gDNA samples were stored in TE buffer  at −80 °C.To isolate total RNA, frozen DRG tissue (~20 mg) was homogenized using a RNeasy Mini Kit (Qiagen) buffer and a rotor-stator homogenizer. Total RNA was then purified according to the manufacturer’s instructions. In brief, following resuspension in 50 µl of RNase-free water, contaminating gDNA was removed by incubating with 2 U of RNase-free DNase (Qiagen) at 37 °C for 60 minutes. The RNA was then purified using a Qiagen RNeasy Mini Kit and stored at −80 °C. gDNA and total RNA were quantified using a NanoDrop™ UV- Vis spectrophotometer .Genotyping of gDNA samples for SNPs rs11562975, rs13004520 and rs28901637 was performed using pyrosequencing on a PyroMark Q24 instrument (Qiagen). A single genotyping assay was used to test heterozygosity for these SNPs because of their position on the same 77-bp amplicon. The genomic region was amplified using the following primers: forward primer 5′-CCTCAATATCCCCTTCCC-3′, a biotinylated reverse primer 5′-GCAGCAACACTTGAAGCACTT-3′ and a sequencing primer 5′-ATCCCCTTCCCCTTCAT-3′. The template sequence used to generate a reagent-dispensing order was 5′-(C/G)AGATCC(A/T)CT(C/G)T(A/G)TATCCTGGACAACAACCACACACATTTG-3′.For the pyrosequencing reactions, 15 ng of gDNA was mixed with forward and reverse primers and Phusion High-Fidelity PCR Master Mix (Thermo Fisher Scientific), and was amplified using the following thermocycling program: 1 cycle of 95 °C for 3 minutes; 35 cycles of 95 °C for 15 seconds, 62 °C for 15 seconds and 72 °C for 20 seconds; and followed by a final cycle of 72 °C for 5 minutes. The PCR products were then run on the pyrosequencer using the pre-programmed standard protocol, assay-specific sequencing primer and the supplied PyroMark Q24 template sequence to generate a reagent-dispensing order. The percentage of base calls at each position was identified using the pyrosequencing trace files in the PyroMark Q24 software v2.0.7, and genotyping calls were made based on the ratio of the expected alleles. Heterozygous calls were made if both alleles were present at 25% or greater ratios.® qPCR assays on the Applied Biosystems 7900HT Fast Real-Time PCR System . The assays were purchased from Applied Biosystems . A total of 15 ng of gDNA from each sample were loaded into the recommended qPCR reaction master mix and amplified for 40 cycles using the following standard qPCR thermocycling program: 1 cycle of 95 °C for 2 minutes; 40 cycles of 95 °C for 30 seconds and 60 °C for 1 minute. The data were collected after each cycle. A common threshold was set for all samples and the Cq values were recorded.Genotyping of SNPs rs10166942 and rs17862920 was performed using allele-specific TaqManThe ratio of alleles was calculated using the equation (2^(40-CqSNP))/((2^(40-CqREF)) + (2^(40-CqSNP)) with the CqSNP value corresponding to the Cq value of the minor SNP allele, and the CqREF value corresponding to the Cq value of the reference allele giving a value between 0  and 2 . Heterozygous calls were made if the value calculated was between 0.5 and 1.5. Genotypes of samples used in this study are shown in Supplementary Table Total RNA (1 µg) was treated with DNase I using the TURBO™ DNase Kit (Thermo Fisher Scientific). The RNA was diluted to 43 µl with nuclease-free water. A total of 5 µl of 10 × DNase I buffer and 2 µl of DNase I enzyme were added, and the samples were incubated at 37 °C for 1 hour. For the reaction inhibitor, 6 µl were added and mixed thoroughly at room temperature. DNase-treated RNA was purified using an RNeasy MinElute Cleanup Kit (Qiagen) and then eluted in 20 µl of nuclease-free water.RNA was reverse transcribed using the SuperScript II Reverse Transcriptase kit  following the manufacturer’s protocol. In brief, 2 µl of oligo d(T) and dNTP mix were added to each RNA sample, heated to 65 °C for 5 minutes in a thermocycler and then placed on ice for 2 minutes. For each reaction, 8 µl of 5× first-strand buffer, 4 µl of 0.1 M DTT and 2 µl of RNAseOUT™ were added. Of the reaction mixture, 10 µl were removed and set aside as a negative template control (RT−). The remaining reaction mixture (RT+) was placed in a pre-heated thermocycler at 42 °C for 2 minutes. A total of 2 µl of SuperScript II Reverse Transcriptase were added to each reaction directly and the samples were maintained at 42 °C for 50 minutes. The temperature was increased to 70 °C for 15 minutes; the samples were then removed and placed on ice. Of nuclease-free water, 45 µl and 15 µl were added to the RT + and RT− samples, respectively.The regions of gDNA containing the reduced migraine risk SNPs and mSNPs were amplified using PCR. Approximately 5–20 ng of gDNA isolated from the DRG samples were amplified using Phusion High-Fidelity PCR Master Mix (Thermo Fisher Scientific) following the manufacturer’s instructions. To reduce any potential for imbalance differences owing to PCR amplification bias, the total number of amplifications was limited to a maximum of 12 cycles. The oligonucleotide primers and PCR amplicon size are shown in Supplementary Table ® System as described in the Illumina MiSeq cluster generation and sequencing kits. The minimum number of reads required to assess allelic imbalance was no less than 10,000 reads per barcoded DNA.gDNA and cDNA amplicons were end-repaired using the End-It™ DNA End-Repair Kit (Epicentre Biotechnologies (an Illumina company), Madison, Wisconsin, USA) following the manufacturer’s instructions. Barcoded adapters from the TruSeq DNA-Seq Kit  were ligated to the ends of the gDNA and cDNA amplicons following the manufacturer’s instructions. Barcoded amplicons were further amplified using limited-cycle PCR (7 cycles), and purified using Agencourt AMPure XP beads  following the manufacturer’s instructions. Barcoded DNAs, along with a 20% PhiX loading control, were sequenced using an Illumina MiSeqSequencing data were mapped to the human genome (version B37.3) and further analyzed for SNP annotation and frequency using the Array Studio software package (OmicSoft).Digital counts for each nucleotide variant were matched to the barcoded amplicon and mapped back to the gDNA and cDNA samples and individual DRG samples from which they were originally derived. Ratios of allelic imbalance were calculated as the number of reads for the minor allele divided by the number of reads for the common allele.® Long PCR Master Mix  and 0.5 M betaine. Thermocycling conditions for PCR were as follows: preheat at 65 °C for 5 minutes; followed by 95 °C for 2 minutes; then 18–26 cycles of 93 °C for 30 seconds and 65 °C for 29 minutes. A final extension was carried out at 72 °C for 15 minutes. The LR PCR products were subcloned into a phagemid using the GC Cloning and Amplification Kit  following the manufacturer’s instructions. Alleles were assigned to chromosomes by performing Sanger sequencing at both ends of the LR PCR–cloned fragments using SP6 and T7 primers.rs10166942(C) and rs17862920(T) alleles with reduced migraine risk were phased with the mSNP alleles on the maternal and paternal chromosomes using LR PCR. Approximately 150 ng of gDNA from each donor were mixed with 0.2 µM oligonucleotide primers (5′-CACAGCCCCCACCCTTCCCTACTTAC-3′ and 5′-TGGCCGTATTTATCATTGCCTTTAGCATG-3′), GoTaqhttps://anabios.com/contact/). are fully traceable and periodically reviewed by US Federal authorities. DRGs were acquired from fifteen  human adult organ donors (19–59 years of age) during the removal of the vital organs for transplantation. No information about the medication use or disease diagnoses were available.All human dorsal root ganglia that were used for this study were obtained by legal consent from organ donors or next of kin in the US. Policies for donor screening and consent are the ones established by the United Network for Organ Sharing. Organizations supplying human tissues to AnaBios follow the standards and procedures established by the US Centers for Disease Control and are inspected biannually by the Department of Health and Human Services. Tissue distribution is governed by internal IRB procedures and compliance with HIPAA regulations regarding patient privacy. All organ donor transfers to AnaBios  that were clinically acceptable to the investigator and Amgen physician; screening and baseline blood pressures for all participants were 90–130 mm Hg for their systolic reading and 50–85 mm Hg for their diastolic reading; screening and baseline heart rate were 50–100 bpm; screening and baseline oral temperatures were 36.1–37.8 °C; respiratory rate at screening and baseline were 12–18 respirations/min; participants exhibited a mean increase in systolic and diastolic blood pressures of at least 10% above their baseline values in response to each cold pressor test (CPT) session; following completion of the screening CPT assessments, participant’s blood pressure returned to the normal range (90–130 mm Hg for their systolic reading and 50–85 mm Hg for their diastolic reading) within 5 minutes; participant understood study procedures and requirements, and provided written informed consent approved by an Independent Ethics Committee/Institutional Review Board .Inclusion criteria were as follows: healthy males and females of non-childbearing potential , between the ages of 18 and 55 years, inclusive; body mass index between 18.0 and 32.0 kg/mExclusion criteria were as follows: history or evidence of a clinically significant disorder, condition or disease that, in the opinion of the investigator and Amgen physician, significantly impaired pain perception  or interfered with evaluation, procedures or study completion; prior or current history of peripheral neuropathy, paresthesias or dysesthesias; a history of hypertension, hypotension, symptomatic or asymptomatic coronary artery disease, percutaneous transluminal coronary angioplasty, coronary artery bypass surgery, peripheral vascular disease and any cerebrovascular event, including but not limited to cerebrovascular accident or transient ischemic attack; prior or current history of pulmonary or metabolic diseases , or primary or secondary Raynaud’s disease or any other condition related to severe cold intolerance; participants exhibiting systolic blood pressure above 180 mm Hg or diastolic blood pressure above 110 mm Hg during the CPT at screening or baseline; CPTo of less than 120 seconds following the CPT during screening; participants unable to refrain from ingesting solid food for at least 2 hours before the start of the quantitative sensory testing (QST) and/or CPT ; participants unable to refrain from ingesting caffeinated, decaffeinated and/or alcoholic beverages during the 24 hours preceding testing; women who are pregnant; participants unable to refrain from exercise during the 24 hours prior to testing; one or both parents diagnosed with hypertension; prior or current history of psychiatric illness, such as anxiety, depression or schizophrenia; evidence of any current illness ; evidence of any disturbance of the autonomic nervous system, including significant orthostatic hypotension and/or heart rate changes at screening exam (orthostatic hypotension defined as a systolic blood pressure decrease of at least 20 mm Hg or a diastolic blood pressure decrease of at least 10 mm Hg within 3 minutes of standing compared with blood pressure from the sitting or supine position); evidence of frostbite or bruising, cut, sore or fracture of the hand to be immersed; prior or current history of syncope or seizures; history of chronic or frequent clinically significant pain; current use of any prescription or non-prescription medication, including but not limited to opioids, benzodiazepines or other anxiolytics, tricyclic antidepressants or other antidepressants, anticonvulsants or botanical or herbal remedies; use of any investigational drug  or device within 60 days prior to study enrollment; use of any investigational or therapeutic biologic  within <6 months prior to study enrollment; current or prior history of tobacco use or use of any other nicotine-containing product; active substance abuse within 12 months prior to study enrollment; positive for human immunodeficiency virus antibodies, hepatitis B surface antigen, hepatitis B core antibody or hepatitis C antibodies at screening; or individual previously had entered this study. Individuals provided written informed consent before participating in any study-specific activities or procedures.This was a phase 0, parallel-group, method validation study conducted at a single center in the United States between May 20, 2013 (first participant enrolled) and June 16, 2013 (last participant completed study). The study planned to enroll approximately 40 participants.The study comprised a 28-day screening period (day –28 to day –1), followed by a two- day assessment period. Each participant who entered the screening period received a unique identification number via manual assignment. During the screening period, SNP genotyping was performed after an initial screening CPT assessment had been completed.5. During the two-day assessment period, QST and CPT were performed three times per day. The total planned study duration was 30 days.Participants who met the screening criteria were enrolled and separated into two groups: carriers (+SNP) included participants with either SNP (rs10166942 or rs17862920) or both SNPs, and non-carriers (−SNP) included participants without either SNP. In Caucasian populations, T is considered the common allele at rs10166942 and C is the reduced risk allele. Conversely, C is considered the common allele at rs17862920, whereas T is the reduced risk alleleAll study tests and procedures were conducted at a room temperature maintained at 20 ± 2 °C. Participants refrained from ingesting solid food for at least 2 hours prior to screening and study procedures on days 1 and 2. Participants avoided alcohol, caffeinated and decaffeinated beverages during the 24 hours prior to assessments. Tobacco and nicotine use were not permitted throughout the screening period and for the duration of the study. Participants also avoided exercise during the 24 hours prior to assessments. Between study assessments on days 1 and 2, participants were allowed water, juice and light snacks.Baseline vital signs were measured at screening and prior to both QST and CPT. Clinical laboratory assessments, ECGs and physical examinations were performed at screening.Concomitant medications, procedure-related events and safety reporting were recorded throughout the study.Written informed consent was received from participants prior to inclusion in the study.https://www.quorumreview.com/). The study was conducted in accordance with the International Conference on Harmonization Tripartite Guideline for Good Clinical Practice and applicable regulations and guidelines.For the studies of allelic imbalance, all human dorsal root ganglia (DRG) specimens were collected under Institutional Review Board approval with appropriate informed consent. In all cases, materials obtained were surplus to standard clinical practice. Patient identity and protected health information/identifying information were redacted from tissues and clinical data. Donors were between the ages of 19 and 37 years. The study protocol and any amendments for the Phase 0 study to evaluate responsiveness of carriers of rs10166942[C] to cold challenge were approved by an Independent Ethics Committee/Institutional Review Board  were purchased from the Coriell Institute for Medical Research . These samples served as blinded controls during assay validation and subsequently as process controls during clinical testing.TRPM8 SNPs rs10166942 and rs17862920 using TaqMan® SNP genotyping assay ID C__30341627_10 and C__2641482_10 on the ViiA™ 7 Real-Time PCR system (Applied Biosystems). gDNA (10–15 ng) was amplified by TaqMan® Genotyping Master Mix using the following thermocycling conditions: 1 cycle of 95 °C for 10 minutes; followed by 40 cycles of 95 °C for 15 seconds and 60 °C for 90 seconds. Data were analyzed by allele discrimination using the TaqMan® software.Clinical samples in the form of EDTA-preserved whole blood were collected at screening and stored frozen at −70 °C until shipped on dry ice for DNA extraction and genotyping . gDNA was extracted from 200 µl EDTA- preserved whole blood using the QIAcube system (Qiagen). DNA with an A260/280 ratio in the range of 1.7–2.0 was accepted as pure. DNA was genotyped for 28.The QST protocol of the German Research Network on Neuropathic Pain has been validated in 180 healthy volunteers and used to assess the somatosensory function in patients with neuropathic pain2) against the skin (forearm) of the non-dominant arm. Thermal stimuli were delivered through the Peltier thermode using a computerized system28. The thermode was set at a baseline temperature of 32 °C  and was cooled at a rate of 1 °C/s until a temperature of 0 °C was reached. This temperature was maintained for the remainder of the test (maximum duration 120 seconds). Participants pushed a button when their cold pain threshold (CPTh) and CPTo levels were reached. The times at which the participant experienced CPTh and CPTo were recorded, along with the intensity of the pain using the Likert scale.QST was performed by placing a Peltier thermode . The CPT was performed by immersing the participant’s dominant hand into an ice bath (1–5 °C) for up to 6 minutes, while blood pressure and heart rate were measured.The CPT has been widely used to measure cardiovascular reactivity to environmental stressCPT was performed once during screening. Blood pressure and heart rate were measured every 60 seconds during the CPT and again 5 minutes after the participant removed their hand from the ice bath. After immersing their hand into the ice bath, participants were asked to indicate when the sensation first became painful (CPTh) by saying “painful,” and state when they were no longer able or willing to tolerate the pain (CPTo) by saying “stop.”On assessment days 1 and 2, CPT was performed three times per day within 40 minutes of QST administration and with 2 hours between each set of assessments. Blood pressure and heart rate were measured every 60 seconds during the CPT and, when possible, immediately after the participant reached CPTo. Blood pressure and heart rate were also measured 5 minutes after the participant removed their hand from the ice bath in the first and second CPT assessments, and 10 minutes after the third CPT assessment. The time at which the participant experienced CPTh and CPTo was recorded, along with the intensity of the pain using the Likert scale.An oral temperature was taken prior to starting QST and the CPT, immediately after the participant reached CPTo for both tests and 5 minutes after the completion of both tests.After the final CPT of each day, every participant had a 10-minute minimum recovery period.The primary endpoint was the change in systolic and diastolic blood pressures in response to CPT. Secondary endpoints were CPTh and CPTo, as measured by the Likert scale (0–10) for the CPT and QST.We planned to enroll approximately 40 participants in this study. This sample size was based on practical considerations; however, sufficient power existed (80%) to detect a difference between the 20 participants with either SNP (rs10166942 or rs17862920) or both SNPs, and the 20 participants without either SNP when the difference in systolic blood pressure between the two groups is 8.45 mm Hg following CPT (assuming a standard deviation of 9.5 mm Hg).All participants with at least one post-baseline CPT or QST measure were included in the primary analysis set. For the analysis of the primary endpoint, change from baseline in blood pressure following CPT was analyzed using a repeated measures analysis of covariance.P values for the null hypothesis of no difference are presented. All repeated measures analyzed used the mixed-effects model repeated measures method to account for missing data.Independent variables included SNP type, time (from CPT initiation) and the SNP type-by- time interaction. SNP type comprised the following three levels: homozygous for the risk allele; heterozygous with a risk allele and a protective allele; or homozygous for the protective allele. The baseline measures were used as a covariate and subject was used as a random effect. For each SNP combination, SNP type-by-time baseline-adjusted least squares mean with 95% confidence intervals and P value were estimated by fitting a Cox regression model for the null hypothesis of no difference.For the analysis of the secondary endpoints, time to event analyses for both CPTh and CPTo in response to CPT and QST challenge were carried out using Kaplan–Meier survival and Cox regression models. Within-subject repeated measures of each event were first averaged prior to the analyses. For QST challenge, the maximal duration extended to 152 seconds and these events were right-censored. For CPT challenge, the maximal duration extended to 300 seconds and these events were right-censored. Time to event curves were generated for each of the three SNP types, and hazard ratios with corresponding P value achieved significance, subsequent comparisons between SNP groups were made using the Dunn–Sidak method for multiple test corrections. Likert scores for both CPTh and CPTo in response to CPT and QST challenge were analyzed similar to temperature measurements. These analyses were performed in those participants that had either SNP or both SNPs (carriers) and those participants that had neither SNP (non-carriers), as well as separately for the SNP loci rs10166942 and rs17862920.Temperature measurements for CPTh and CPTo in response to QST challenge were analyzed using a non-parametric Kruskal–Wallis test to compare mean ranks of CPTh or CPTo temperatures between SNP groups. In the event that the Kruskal–Wallis omnibus Supplementary Information


With increasing age, having multiple chronic conditions is the norm. It is of importance to study how co-existence of diseases affects functioning and mortality among older persons. Complex multimorbidity may be defined as three or more conditions affecting at least three different organ systems. The aim of this study was to investigate how complex multimorbidity affects activities of daily living and mortality amongst older Norwegians.n = 9058. Multinomial logistic regression models were used to investigate the association between complex multimorbidity in HUNT2, basic and instrumental activities of daily living in HUNT3 (2006–2008) and mortality during follow-up (n = 5819/5836). Risk ratios (RR) and risk differences (RD) in percentage points (pp) with 95% confidence intervals (CI) were reported.Participants were 60–69-year-olds at baseline in the Nord-Trøndelag Health Study 1995-1997 (HUNT2) 47.8% of 60–69-year-olds met the criteria of complex multimorbidity at baseline (HUNT2). Having complex multimorbidity was strongly associated with the need for assistance in IADL in HUNT3 11 years later (RR = 1.80 (1.58–2.04) and RD = 8.7 (6.8–10.5) pp) and moderately associated with mortality during the follow-up time (RR = 1.22 (1.12–1.33) and RD = 5.1 (2.9–7.3) pp). Complex multimorbidity was to a lesser extent associated with basic activities of daily living 11 years later (RR = 1.24 (0.85–1.83) and RD = 0.4 (− 0.3–1.1) pp).This is the first study to show an association between complex multimorbidity and activities of daily living. Complex multimorbidity should receive more attention in order to prevent future disability amongst older persons. The world is experiencing population aging where the number of people over 60 years is expected to more than double and to be over 2 billion in 40 years . Health-gap between personal capability and environmental demand” [Disability may be defined as the “ demand”  and meas demand” . Activit demand” , 25. Bas demand” . However demand”  and to oThe aim of this study was to investigate the association between complex multimorbidity, basic and instrumental activities of daily living and mortality among older participants of the Nord-Trøndelag Health Study, HUNT2 (1995–97) and HUNT3 (2006–2008) in a prospective cohort study.The HUNT Study is a population-based health study where all participants aged 20 years and older in the county of Nord-Trøndelag were invited to participate. There have been four waves of data collection comprising HUNT1 (1984–96), HUNT2 (1995–97), HUNT3 (2006–08) and a fourth wave completed in 2019 . The participants filled out questionnaires and undertook a clinical screening test. All participants signed a written consent to participate and the Regional Ethical Committee approved the HUNT Study  as well The main predictor of interest in this study was complex multimorbidity measured at baseline in HUNT2 1995–97). Complex multimorbidity was measured by compiling a complete list of 38 conditions in HUNT2 . Basic activities of daily living included to walk, go to the toilet, wash oneself, shower, dress, go to bed and get up, and eat. Instrumental activities of daily living included preparation of meals, light and heavy housework, do laundry, go shopping, take the bus, take medicines, and go out. Since population health change can be regarded as a continuum of disease, disability, loss of function and mortality , ADL and006–08. Badjrr in Stata was performed to attain risk ratios (RR) and risk differences (RD) with corresponding 95% confidence intervals (95% CI) [Multinomial logistic regression models were used to investigate the association between complex multimorbidity at baseline in HUNT2 (1995–97) and basic and instrumental activities of daily living in HUNT3 (2006–08) and mortality during follow-up. In a sensitivity analysis non-participation in HUNT3 was included as a competing outcome in the multinomial logistic regression analysis to evaluate its effect on the results. The analyses were adjusted for relevant confounders . The postestimation command (95% CI) . All ana(95% CI) .n = 4327). 91.4% (n = 8277) of the participants had primary and secondary education. The prevalence of complex multimorbidity varied by education; 52.1% with primary education fulfilled the criteria for complex multimorbidity compared to 35.5% with tertiary education. There was a 11.9 percentage points gender difference in prevalence of complex multimorbidity between women and men.Table Among the 60–69-year-olds in HUNT2 2.4% reported needing assistance from another person in any of the basic activities of daily living in HUNT3, whereas 19.9% needed assistance in instrumental activities of daily living (Table Table Table In this prospective cohort study 47.8% of 60–69-year-olds met the criteria of complex multimorbidity at baseline in HUNT2 (1995–97). Having complex multimorbidity was strongly associated with the need for assistance in IADL in HUNT3 11 years later (2006–08) and moderately associated with mortality during the follow-up time.Few comparable studies have reported prevalence of complex multimorbidity, even though it is proposed to better identify patients in high need of care than multimorbidity . In an AThis is to our knowledge the first study showing to what degree complex multimorbidity increases the future risk for disability in instrumental activities of daily living, adding to previous research on multimorbidity and disability. A systematic review from 2015 concluded that multimorbidity predicts future functional decline in adults, but comparisons between studies are hampered by the heterogeneity in definitions and operationalizations of multimorbidity and functional decline and the included cohort studies had short follow-up time (1–3 years) . Later sThe declining association between complex multimorbidity and IADL disability, ADL disability and mortality during follow-up, could indicate a hierarchical relationship between instrumental and basic activities of daily living and mortality , 46, andThe main limitations of this study are healthy survivor and participant bias and the lack of information about activities of daily living at baseline. Institutionalized older persons and those not able to attend the HUNT Study are not included. Non-participants in HUNT3 have been found to have higher mortality, lower socioeconomic status and higher prevalence of several chronic diseases but also lower prevalence of some conditions . FurtherSince we did not have information about activities of daily living in HUNT2 (questions were only asked to participants older than 70 years) we were unable to control for this at baseline. Abilities to perform basic activities such as walking and eating are fundamental for independent living and may also be determinants for participation in the HUNT Study. Therefore, the HUNT data may represent the healthier part of the older adult population. If participants with ADL/IADL disabilities were included at baseline this would have introduced differential misclassification bias where those with ADL/IADL disabilities at baseline were more likely to be classified as ADL/IADL disabled in HUNT3 compared with those who were ADL/IADL independent in HUNT2. This could have led to both over- and underestimation of the results . DespiteThe HUNT2 questionnaire did not include common conditions such as chronic obstructive pulmonary disease, alcohol misuse, health failure and only one gastro-esophageal and respiratory disorder . However, defining complex multimorbidity from a complete list of the available conditions, as we did in HUNT2, should identify individuals with a high need of care . The valA high prevalence of complex multimorbidity was found in this Norwegian population with older persons and this is the first study to show to what degree complex multimorbidity is associated with instrumental activities of daily living. This could indicate that the load of having several diseases itself is important and should receive attention in addition to treatment of the individual conditions. Focusing on complex multimorbidity could be instrumental in order to prevent future functional decline amongst older persons.Additional file 1. Variables, question texts, answer categories and operationalization of conditions included in complex multimorbidity variable (HUNT2).Additional file 2. Question texts, answer categories and operationalization of confounders (HUNT2).Additional file 3. Question texts, answer categories and operationalization of outcome variables in HUNT3.Additional file 4. Exposures and confounders in HUNT2 (1995–97) by outcomes in HUNT3 (2006–08).Additional file 5 Association between complex multimorbidity (HUNT2) and ADL (HUNT3), mortality and non-participation (HUNT3), multinomial logistic regression.* n = 8357.Additional file 6 Association between complex multimorbidity (HUNT2) and IADL (HUNT3), mortality and non-participation (HUNT3), multinomial logistic regression.* n = 8340.


Finding genetic variants that cause functional disruption or regulatory change among the many implicated GWAs variants remains a key challenge to translating the findings from GWAs to therapeutic treatments. Defining the causal mechanisms behind the variants require functional screening experiments that can be complex and costly. Prioritizing variants for functional characterization using techniques that capture important functional and regulatory elements can assist this. The genetic architecture of complex traits such as cardiovascular disease and type II diabetes comprise an enormously large number of variants of small effect contributing to heritability and spread throughout the genome. This makes it difficult to distinguish which variants or core genes are most relevant for prioritization and how they contribute to the regulatory networks that become dysregulated leading to disease. Despite these challenges, recent GWAs for CAD prioritized genes associated with lipid metabolism, coagulation and adhesion along with novel signals related to innate immunity, adipose tissue and, vascular function as important core drivers of risk. We focus on three examples of novel signals associated with CAD which affect risk through missense or UTR mutations indicating their potential for therapeutic modification. These variants play roles in adipose tissue function vascular function and innate immunity which form the cornerstones of immuno-metabolism. In addition we have explored the putative, but potentially important interactions between the environment, specifically food and nutrition, with respect to key processes. CVD is increasing and the distribution of risk factors is changing with increasing prevalence of obesity and type II diabetes (T2D), particularly among young adults aged 18–45) in developed countries 8–45 in d. The burGWAs provided unique insights into the genetic architecture of complex diseases. Genetic architecture considers the overall composition of variants influencing a trait in terms of number, frequency and magnitude of effect and potential interactions, and can vary over traits . With inIn theory, the set of core genes must have a more pronounced effect on disease traits and proteins derived from these genes will drive pharmaceutical development and therapeutic strategies. However, how this works at the cellular regulatory network is incompletely understood. To understand the relevance of the variants for therapeutic development, it is crucial to understand their effect on protein level, activity or function. Even if a variant has a small effect on protein level and disease risk this protein may still be a suitable target for disease prevention if considered in context with its disease architecture . For insGWAs has reproducibly associated over 160 variants with cardiovascular disease –20. By cTo prioritize core CAD-related genes, they fine mapped the regions characterizing the functional, cellular and regulatory contribution of the variants  and prioSeveral major challenges stand in the way to understanding how GWAs associations could become therapeutic targets. Most GWAs associations lie within non-coding regions making it difficult to predict their functions and identify targets/genes. Loci can be linked to multiple genes and the likely causal variant requires detailed investigation to elucidate the underlying mechanism. Functional follow up of important GWAs candidate loci now shows that multiple variants of small effect can synergistically drive dysfunction in regulatory networks, for example risk related to FTO , ANGPTL4p = 2.1 × 10−12, OR 1.09 . Chromatin interactions between this variant and eQTLs in the promotors/enhancers of three other genes  suggest these genes enhance the expression of TRIM5. Chromatin interactions reveal relationships of chromatin organization in 3D space that may indicate biological function such as promotor-enhancer interactions. The evidence used to support rs11601507 is from a range of Hi-C experimental cell lines  (The variant rs11601507 causes a missense mutation in TRIM5 and increases CAD risk (ll lines  38). rs. rsp = 2ll lines  and showll lines ]. Ingenull lines . Interesctively)  which isGiven the enormous dimensionality of the phenome, it is unlikely that functional variants exist without pleiotrophic effects . PleiotrTRIM5 promotes Interferon γ (IFNG) in macrophages, this forms part of the innate immune system . It has The genetic mechanism associated with the TRIM5 variant suggests enhanced olfactory signaling enhances TRIM5  to reduce lipolysis which enhances adiposity and increases risk of CAD. OR52B6, and OR52S1 are G-protein coupled olfactory signaling receptors (ORs) . These rp = 6.8 × 10−12) reaching GWAs significance are related to inflammation, which are relatively newly identified, show convergence between biological and genetic determinants of CVD and add to this inflammatory hypothesis , this SNP is an eQTL for the protein coding gene Fibronectin type III domain containing 3B (FNDC3B) (p = 1 × 10−21) in the overall analysis (rs12897 is a common variant (MAF 0.41) showing a protective association with CAD; OR 0.96  ((FNDC3B)  occurrinanalysis . IPA® prp = 3 × 10−39) (p = 8 × 10−10) and HIP adjusted BMI (p = 3 × 10−12) (p = 1 × 10−9)  , waist-t× 10−12)  and hear × 10−9) . Interes × 10−48  and p =  × 10−50 , howeverp = 3.3 × 10−14), CEBPA, PPARγ, HOXC-mir196, TBX15, and PEMT but these variants had no apparent regulatory links/eQTLs  have shown distinct genetic components , 67. WHRks/eQTLs . While Cks/eQTLs . Adipocyks/eQTLs .de-novo-adipocyte differentiation of precursor cells which FNDC3B may play a role) or more commonly trans-differentiation  and enervated with b-adrenoreceptors  compared to white adipose tissue (WAT) which expands in response to lipid excess by hypertrophy, hyperplasia and inflammation and upon reaching a certain size become dysfunctional and necrotic, promoting macrophage infiltration. The conversion of WAT to the more functional energy dispersing BAT adipocytes would be a valuable approach to the treatment of obesity and its metabolic complications and is becoming the focus of anti-obesity research . Converseceptors . BAT dereceptors  and BMP4eceptors , irisin/eceptors , FGF21, eceptors  and is aeceptors . FNCD5B The strongest obesity variant associated to date, FTO, can act through a complex regulatory network also affecting pre-adipocyte differentiation highlighting the importance of this pathway . InteresPRDM16 and TWIST1 recently associated with CAD risk play key roles in adipogenic transdifferentiation of WAT to BAT which highlights this pathway as relevant to disease risk and therapeutic exploration. From a biological perspective PRDM16 is one of the most effective molecular targets to induce white-to-brown adipocyte trans-differentiation  with the promotors of three genes that act as enhancers , 6 × 10−8 . A varia6 × 10−8 . MYOG1 i6 × 10−8 . Inherit6 × 10−8 –80. Thes6 × 10−8 . CCM2 re6 × 10−8 , 82. A l6 × 10−8  with sev6 × 10−8 , 83, 84 p = 1.9 × 10−12) (p = 3 × 10−27 (Increased vascular permeability correlates with neo-angiogenesis . CCM pro× 10−12)  and wais × 10−27 . CCM pro × 10−27  [typical × 10−27 ]. Lipid  × 10−27 . VasculaIn summary, the three novel GWAs signals implicated in CAD risk play putative roles in immuno-metabolism Figure . TRIM5 hSince the recognition that fatty acids can modulate an inflammatory response, e.g., via lipid induced re-programming of macrophage metabolism and inflammation  or the NEfforts to explain this inter individual variability in response, has focused on the interaction between the genes, metabolites and diet. As diet is the exogenous source of many metabolites, as well as affecting the generation of endogenous metabolites, interactions with the nutritional environment are plausible. However, many putative gene/variant-diet associations have failed to replicate in large studies  with varp = 7 × 10−28) had trans regulatory effects on several genes associated with platelet function and this module had diverse effects on 55 metabolites. Other important core immunometabolic associations related to neutrophil activation and viral response. A subset of the cohort measured repeatedly over 7 years, demonstrated the gene-metabolite effects were temporally stable (Attention is shifting to large scale studies integrating transcriptomic and metabolomic data to understand the interplay between genes and metabolites . To exply stable . As longin vivo and interventional studies can be developed to modulate these interactions through existing lipid lowering medications, gut microbe effects or dietary changes. In this way, the immune system itself can be harnessed to reduce the burden of cardiovascular and metabolic disorders (Identifying the genetic basis to these interactions can be useful therapeutically to modulate the variant itself for individualized treatment or modulate the pathway the variant functions in, which can have much wider implications for population treatment e.g., PCSK9 inhibitors for individual and population level treatment of hypercholesterolemia and CVD risk . With beisorders , 71. Witisorders , it is iProgress has been made to determine the tissues and cell types underlying disease through the GTex consortium . GTex, RTo identify cell type specific gene regulation, grouped cell types or deconvolution methods have been used, but the methods tend to be biased to specific cell types or difficult-to-identify less abundant cell types . It is pWe have highlighted three novel variants associated with CAD risk which have been prioritized and annotated based on systems genetics approaches including expression quantitative trait analysis and network analysis to infer their functional relevance. These core variants play roles in innate immunity, adipogenesis and endothelial function which drive coronary artery disease and principally in the role that obesity and T2D shape the pathogenesis of CAD through immuno-metabolism. Core variants representing these pathways provide a starting point to potential mechanism that may lead to therapeutic manipulation with further understanding of the regulatory networks connecting these is needed. Given that CAD is a multifactorial disease, it may be possible in the future to develop individual treatment strategies based on these variants or design relevant population level interventions based on the pathways these variants highlight for subsets of people in the population with subtypes of CAD risk related to obesity or T2D.MH wrote the paper. YL, CG, and HR discussed and wrote sections, refined and edited the text. All agree with the submission.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.