The new Cryogenic Flux Capacitor (CFC) technology employs nano-porous aerogel composites to store large quantities of fluid molecules in a physisorbed solid-state condition at moderate pressures and cryogenic temperatures. By its design architecture, a CFC device can be “charged” and “discharged” quickly and on demand according to standby/usage requirements. One of three main application areas is the CFC-Life for breathing air or oxygen supply to meet new demands in life support systems. Through the Liquid Oxygen Storage Module (LOXSM) Project, the National Institute for Occupational Safety and Health, and Cryogenics Test Laboratory have partnered to test the feasibility of applying the CFC technology to Closed-Circuit Escape Respirators (CCER), or respirators operating on the closed-circuit principle in general. The envisioned Cryogenic Oxygen Storage Module (COSM) is an innovative concept to store oxygen in solid-state form, according to physisorption processes at any cryogenic temperature, and deliver it as a gas using the CFC as the core storage element. Gaseous oxygen would be admitted into the breathing loop of the CCER by introducing heat into the storage module. Potentially replacing the gaseous or chemical based oxygen supply used in today’s closed-circuit respirators, the COSM is a high capacity, conformal, small-size solution for future life support equipment of all kinds. In particular, are the CCER devices that must to be carried on the person, ready to be quickly deployed and used for escape in an emergency. Initial test data for physisorption of oxygen in aerogel materials and CFC core modules are presented. The basic operational parameters for charging and discharging are summarized through prototype testing of the cryogenic oxygen storage module. Cryogenic liquid tanks use vacuum-jacketing, low heat-leak supports, and carefully designed piping connections to enable storage of liquid with reasonably low boil-off losses. Such tanks cannot be made in conformal shapes and tend to be complex and heavy. Conversely, high pressure gas storage cylinders are not affected by tank orientation, operate at ambient temperature, and hence are much less complicated pieces of equipment. However, gas storage vessels, even at extreme pressures up to 700 bar, have limited capacity due to gas compressibility factors, especially at higher pressures, and require highly specialized components, materials, and cleaning specifications related to oxygen sensitivity. These two options are typically traded depending on the system requirements, but few practical options exist that provide all the benefits while limiting the downfalls.Storage of oxygen (O2) is removed by an absorber and oxygen is added to the system according to the user’s metabolic needs. Typical CCR designs use chemicals such as calcium hydroxide and lithium hydroxide as CO2 absorbents. Gaseous oxygen stored in high pressure cylinders or oxygen generating chemicals such as potassium superoxide serve as the oxygen source. Closed-circuit escape respirators (CCERs) are smaller versions of CCRs used for escape purposes and usually carried by the person at all times, ready to be used in an emergency. Next Generation CCERs need to be designed for comfortable and continuous wear by persons during their daily work routines, as well as for function as an escape breathing apparatus in the event of an emergency. Therefore, due consideration should be given in the design to minimize size and weight of these units. For use by the workforce in the United States, CCER designs must meet the applicable provisions within the Code of Federal Regulations Title 42, Part 84, including Subpart O. For example, the gaseous oxygen capacity (at standard conditions) of the units for shipboard escape, as required by the U.S. Navy is Cap 1–25 liters GO2, and for mine escape it is Cap 3–81 liters GO2, as defined in Subpart O, to meet mining regulatory requirements [Closed-circuit respirators (CCRs) are breathing apparatus that function in a re-circulatory mode whereby the user breathes the life support gas in a closed loop. Exhaled carbon dioxide   system t2 absorber, or even eliminate the need for it, by sequestering CO2 from the exhaled air. As part of the National Institute for Occupational Safety and Health (NIOSH) funded LOXSM Project to develop new oxygen breathing equipment, prototypes of the CFC-based modules have been produced and tested by the Cryogenics Test Laboratory at NASA’s Kennedy Space Center. The prototype units are designed with integral heat exchange capability to allow quick oxygen charging and controlled discharging within the specified range of flow rate. Future plans are to incorporate these modules into closed-circuit respirator test platforms for extensive testing. Key tests planned are oxygen static retention time, dosage control, and performance tests as detailed in regulation 42 CFR 84-O. The status of prototype module development and testing is reported here.Using CFC-based oxygen storage modules, the goal is to reduce the overall size and weight of CCERs , for the same oxygen capacity as a conventional apparatus that uses chemical or gaseous oxygen. Further advantages are that it is a high-capacity, small-size storage module that can be formed into conformal shapes for a compact respirator design. The new cryogenic oxygen storage module could potentially reduce the size of the CO2.The Cryogenic Flux Capacitor is an energy-based device for the storage (charging) and un-storage (discharging) of fluids in a practical way. The stored energy in this case is represented by fluid molecules accumulated in a solid-state manner. Solid-state in this context means that the fluid atoms or molecules are physically bonded within the pores of a meso-porous or nano-porous storage media—in other words, the fluid charged within a CFC is phase-less in the classical sense; however, the fluid is always discharged in its gas phase. This process of bonding or debonding is governed by principles of physical adsorption (physisorption) and thermodynamics. A thorough investigation into the physics and mechanisms of physisorption can be found in Boer ; but in 2 supply in conjunction focused refrigeration power to the core. Problems with liquid behavior such as sloshing or liquid level management are avoided because the fluid is stored in a physisorbed state that is not liquid, no matter the density or temperature. Due to the cryogenic temperatures, the core must be thermally protected from the ambient environment to a degree commensurate with the desired dormancy for the application. In a CFC-based oxygen storage module, the device can be charged by exposing the CFC core directly to liquid oxygen, or via a gaseous O3.The main requirements for the CCER  are listed as follows:3 of liquid O2.Flow: 1.35 liter per minute (lpm) average flow rate or 81 liters of oxygen gas supplied in one hour (minimum); which corresponds to a liquid volume equivalent (LVE) of approximately 102 cm2 gas density at standard temperature and pressure (STP) of 1.429 g/liter, the equivalent mass flow rate is 1.93 g/minute or a total mass of 116 g over a 1 hour periodWith a OWith a liquid expansion ratio of 860, the LO2 equivalent is 94.2 millilitersThe 1.35 lpm oxygen flow corresponds to an exhalation CO2 flow of 1.15 lpm. The respiratory coefficient is therefore 1.15/1.35 = 0.8 to 0.9 range on average.Maximum and minimum oxygen flow rates are: 3.00 lpm and 0.5 lpm, respectively.Duty cycle: respirator has to be available to work during a 10-hour shift (9-hour standby time)Charging: There will be a charging rack for multiple respirators.Standby: standby time depends on the heat leakage rate.2 scrubber material (e.g. lithium hydroxide) generates a lot of heat, future module designs may be able to take advantage of this heat to drive the discharge flow rate. The maximum inhalation temperature allowed for the user is 43 °C. The scrubber material itself can get up to 70°C or higher and ice or Phase Change Materials (PCM) are often used for cooling for the longer duration CCR units.Three chief design problems for the module are summarized as follows: 1) standby (ambient heat leak), 2) capacity for one hour at a nominal withdrawal rate of 1.35 lpm oxygen, and 3) regulation of demand from 0.5 to 3.0 lpm oxygen. In the standby mode the module must be as thermally isolated as possible, but in the discharge mode the heat exchange with the ambient process is needed. As the CO2, roughly 40-mm diameter by 100-mm length has adequate storage capacity to provide the one-hour minimum of breathable oxygen at 1.35 liters per minute to a CCER per 42 CFR 84 Subpart O requirements. Additional capacity must be added for standby or dormancy requirements up to nine hours to cover a full 10-hour work shift. Prototype modules would be designed around such a CFC, and provide the appropriate thermal protection/control, pressure containment, and flow metering. It is envisioned that this cryogenic oxygen storage module (per this LOXSM project) be part of the oxygen delivery system in future closed-circuit escape respirators (CCERs).Calculations show that a CFC charged using a supply of normal boiling point LO3), the average mass ratio (MR) is approximately 7.1 while the average volume ratio (VR) is approximately 1.03. For example, storing 116 g of oxygen requires 16.3 g of Cryogel, or about 100 cm3 in volume.From extensive testing of the aerogel composite materials in different fluids at cryogenic temperatures, the main design parameters can be established for a given material and fluid combination . For oxy4.2 scrubber material. Or the heat can be supplied from any combination of these three for optimum balancing effect to meet demand cycles from steady-state to high flow.To liberate (or “un-store”) the cryo-adsorbed molecules within the core of the CFC-based module, a heat source is required. Heating can be provided by a solid conduction path from the ambient environment and/or electrical heater, the exhaled breathing air, or the exothermic reaction of the associated COMost of the prior development work for the CFC technology was focused on the use of either embedded electrical heater which brings potential safety issues for an oxygen breathing apparatus.Therefore, several options were considered for heat supply or heat exchange with the ambient:Solid conduction system from external thermal mass via thermal switchTube-in-tube system with segregated pressure boundaries Flow-through system with segregated pressure boundariesFlow-through system with mixed fluids (open system)After consideration, the most promising design concepts to further explore were determined to be those that incorporate the human breath for heating in a combination of mixed and open boundaries. Conduction heating from the ambient environment is still a possibility to augment the process, but use of the heat available in the human breath is the desired method for delivering the necessary heat to drive the desorption process. In this way, the heat flow can potentially be made commensurate with the breathing rate (at rest or peak) such that a fully passive-acting heat exchanger system would be feasible.® by Aspen Aerogels, Inc.) mounted on a center column of the counter-flow heat exchanger device. The GN2 gas was released utilizing warm GN2 to simulate “breath” from a breathing apparatus.A CFC concept using a counter-flow (tube-in-tube) heat exchanger was designed with a parallel-plate stack-up arrangement of the aerogel adsorption media as shown in 2 bath and allowed to cold soak for 30 minutes. After removing the module and shaking off any excess LN2, the module was again placed in the flask for a final “charged” mass. Measurements were recorded every 10 minutes for one hour. For Test 1, the initial flow rate was higher due to the flask not being precooled. The total mass uptake at the start (time zero) was 260 g. As the flask reached thermal equilibrium the flow rate became stable at approximately 1,200 standard cubic centimeters per minute (sccm), or 1.20 lpm, as shown in 2. After cooldown the same process for Test 1 was repeated and the charged mass was measured to be 329 g. The flow rate results for Test 2 are given in A one-liter capacity lab dewar flask was used to act as the vacuum-jacketed container for capacitor module. This arrangement allows for a small gap between the inner wall of the flask and the outer edge of the cap, ensuring that the vapor can properly escape. The entire assembly was placed inside the controlled environmental chamber to limit the amount of water frozen to the exposed metal components. The test procedure begins with measurement of the initial “dry” mass of the module (360g) and zeroing the scale. The module was then submerged in an LN5.2, L-Air, and LO2 for both the cooling and the molecular charging of the units. The physical characteristics of three CFC core module test articles are given in 2 after 9 hours of dormancy) for the rescue breathing pack application while Module II  would need to be increased in size by about 50%. There are many options to consider for optimization and reserve capacity, but the key part of meeting the dormancy requirement is the container heat leak.Testing of three prototype CFC modules was performed using LN6.2 supply (mass), the O2 supply flow rate, the dormancy period (mass loss rate), the container heat leakage rate, the overall shape/weight, and the on-demand heat supply. Another important factor includes balancing the heat supply depending on breathing load variations. The overall ruggedness (impact resistance) of the module is expected to be excellent due to the very high mechanical energy-absorbing nature of the aerogel composites.The primary design factors include physical configuration , the one hour O2) or circulation of LN2 through a heat exchanger could be used to conductivity cool the modules for concurrent supply of gaseous oxygen for adsorption and charging of the modules.Future or alternative systems for different methods of charging and discharging of the CFC core modules are also being investigated. Of particular interest is “liquid-free” charging. Charging by simple immersion in liquid or by liquid supply is quick and effective, but is not required. The concept for liquid-free charging is to thermally connect the CFC core to a cryocooler cold head, as suggested in 7.2 sequestration and longer usage durations for a relatively smaller envelope/weight.The technology of using the phenomena of cryo-adsorption afforded by the Cryogenic Flux Capacitor technique appears feasible for the breathing oxygen application. For the core modules tested, the liquid volume equivalent for oxygen is approximately 103% (that is, the solid-state molecular storage density is slightly higher than that of the liquid phase. The size and weight should be well within the current bounds. Promise is shown for additional features and capabilities such as CO


Can we help predict the future impact of researchers using early-career factors? We analyze early-career factors of the world’s 100 most prominent researchers across 8 scientific fields and identify four key drivers in researchers’ initial career: working at a top 25 ranked university, publishing a paper in a top 5 ranked journal, publishing most papers in top quartile (high-impact) journals and co-authoring with other prominent researchers in their field. We find that over 95% of prominent researchers across multiple fields had at least one of these four features in the first 5 years of their career. We find that the most prominent scientists who had an early career advantage in terms of citations and h-index are more likely to have had all four features, and that this advantage persists throughout their career after 10, 15 and 20 years. Our findings show that these few early-career factors help predict researchers’ impact later in their careers. Our research thus points to the need to enhance fairness and career mobility among scientists who have not had a jump start early on. These questions are of interest for hiring committees, funding bodies and university departments who make decisions by trying to predict the scientific trajectories of researchers often using limited information. The use of common bibliometric indicators, such as number of publications, journal impact factors and citations, as metrics for assessing research impact has been put into question by some researchers11. Other metrics such as open access publications and altmetrics have been proposed as complements or alternatives for improving the way we assess research12. Yet any measure of scientific impact and prominence faces constraints. A necessary step in identifying ways to evaluate research more fairly is to apply predictive models that help identify inherent biases to science’s current incentive and evaluation system. To this end, we comprehensively analyze the careers of prominent scientists to identify to what extent early-career factors help predict the success of researchers later on in their career.What drives high-impact science and how do scientists gain prominence? Can we help predict scientific success and especially the success of young researchers? And what would be the best metrics to do so? These are important questions in the science of science but that we still do not fully understand16, ranking of published papers in journals19, and collaborations29. Total citation counts and h-index of the world’s prominent scientists capture only past accomplishments, but not what has driven those achievements. Rarely are there studies conducted to identify the factors driving the production of high-impact research over time31, combining the different key factors in a single study to understand the relative importance of each factor18 and studying fields across the natural, behavioural and social sciences simultaneously29. Here, we do so by conducting a comparative analysis of these key factors to shed light on how early-career choices and factors shape the path to later become prominent researchers. To this end, we collected data on the scientific careers of the 100 most prominent scientists in eight different fields across science  to which we apply a set of descriptive statistics, as well as classification and regression analyses (Data and Methods sections). Specifically, we examine four key early-career factors  which we find capture the scientific achievements during the first 5 years of the career. We then assess how these key factors are related to their h-index later on in their career, while controlling for factors like their geographic location34, gender35 and scientific field35  are consistently at the top as their career progresses. Our results highlight how an 33). These eight fields include genetics, development economics, cognitive psychology, network science, social inequalities in public health, network ecology, metabolomics, and philosophy of science. We extracted all data for this study - publications, bibliometric data, university affiliations etc. for each author - using Scopus database in 2021 , with two exceptions–data for university rankings using QS World University Rankings 202136 and for journal rankings using Journal Citation Reports (JCR) 202137. All data used are publicly available via Scopus, QS World University Rankings and Journal Citation Reports (JCR).We collected data for several early-career factors, by building on a dataset we previously compiled that identified the 100 researchers with the highest h-index across eight fields that span across the natural, behavioural and social sciences  we adopt a longitudinal research design by collecting data over the entire scientific career of the 100 prominent researchers across these fields. We use the h-index as a metric for prominence as it is designed to capture the quantity and quality of researchers’ output40.All data presented throughout the paper reflect only the first 5 years of prominent researchers’ careers since their first publication, unless explicitly stated otherwise - i.e. with the exception of the number of accumulated citations and the h-index at 10, 15 and 20 years after the first publication of each scientist. All data are presented at the researcher level, i.e. only one aggregate value for each of the 100 prominent researchers across eight fields is provided for each variable. The 100 prominent researchers across these 8 fields have an average h-index of 64, meaning that researchers each have an average of 64 publications that have each received at least 64 citations. The median h-index is 49. In contrast, the average global h-index is approximated at 27 - 32 (median 14–25) as an upper bound estimate , while most moved within the first five years to a university in Europe or North America to which they have been classified.42 and, about 14% of junior researchers on average have co-authored a paper with a senior researcher in journals across scientific fields, including top multidisciplinary journals.We analyse the first 5 years of the academic career (starting at the first publication) of the 100 prominent researchers across these eight scientific fields, and we find overall that 47% were at a top 25 ranked university, 77% published a paper in a top 5 ranked journal in their field, 59% of their papers were published in top quartile (Q1) journals and 27% co-authored a paper with another prominent researcher in their field  while philosophy of science stands out as the least collaborative (in which 17% of prominent researchers have done so)  share of papers in the top quartile as males across fields, except in genetics. They are also more likely to have researched at a top 25 university than males across fields, except in network science and philosophy of science, and a larger fraction of women has also coauthored a paper with another prominent researcher  are those with the lowest 25% of citations received during the first five years, while researchers in quartile 4 (QIV) – the top cited quartile – are those with the highest 25% of citations). We find that there is a strong correlation between the four early-career drivers and the impact of research output early on in researchers’ career. The fraction of prominent researchers in the top citation quartile in the first five years are, in general, more likely than expected by chance to have any of the four early-career features than other prominent researchers in lower citation quartiles Fig. .Figure 321. Our analysis is consistent with these findings: co-authorship is strongly correlated with higher citations across all fields, and the relationship is particularly strong in the natural sciences including genetics and network science  with another prominent researcher in the first 5 years of their career. The papers co-authored by two (or more) prominent researchers have a much higher number of citations than other papers. The effect, intensity and size of collaborations, however, is not homogeneous across geographic locations44, and provides visibility early on in a scientist’s career26. In fact, junior scientists at less recognised universities are most likely to benefit from co-authorship with leading researchers26. Young scientists can also apply what they learn from high-impact, established researchers in their own career45, providing them with a competitive advantage relative to their peers46.Our findings are thus in line with previous studies that analyzed the advantages of co-authoring with leading researchers in one’s field. Working under leading researchers can boost career development through greater citations and mentorship22. Here, we assess the relationship between being at a top university and early-career impact. The share of researchers who have spent part of their early career in such institutions is not homogeneous across fields, with traditional disciplines having much larger shares, as outlined earlier. Not surprisingly, we find that for these disciplines – genetics, development economics and cognitive psychology – being at a top university is strongly correlated with early-on research impact. Nonetheless, across most fields we find that researchers most cited early on in their career are more likely to be in a top institution , 72% published more than half of all papers in top quartile journals (compared to 64%) and 29% co-authored with another prominent researcher (compared to 25%)  Fig. . To thisModels  , the classifier is more accurate than if we only include the early-career factors. Nonetheless, the classifier for Model 2 is still able to correctly predict overall 40% of the researchers that fall into the lowest quartile (QI) and 38% who fall into the top quartile (QIV) at 20 years from the start of their career—significantly higher than the expected 25% for random quartile assignment. Our results show that the early-career factors we study can explain h-index quartiles as early as 5 years after the first publication . As all variables are binary (0 or 1), this facilitates comparing the relative importance of each factor. Collaborating with other prominent researchers is the most important factor, followed by publishing a paper in a top 5 journal. Working at a top 25 university and publishing more than half of one’s papers in Q1 journals have less explanatory power; and gender and geographic location appear to have little predictive power . This finding is very insightful, especially because classification models are able to accurately predict the citation and h-index quartiles after 10, 15 and 20 years for researchers falling into the top and lowest quartiles: what scientists do early on largely determines their impact later on in their careers.43, and raises the question of whether hierarchies in the hiring system pose a threat to innovation and the emergence of new fields of science. Indeed, we also find that in disciplines in which university affiliation is not such an important driver, publishing with other prominent scientists becomes especially important44.We also find that in traditional areas of science, being at a top-ranked institution can be an important driver, but in younger disciplines it is less important. This finding is especially interesting in light of recent findings about graduates from top-ranked US universities occupying the majority of faculty positions in the US university ecosystem19 or, in relevant cases, the traits of a PhD advisor45 that have not been considered here. While it can be a limitation, our results also explain that the success of individual researchers cannot be attributed to a single factor but involve a combined set of early-career factors.Our analysis shows that these four key factors are important as a general strategy for young researchers across science and that an early-career jump start gives scientists an advantage that is sustained throughout their career. At the same time, our results suggest that there are also other factors influencing the h-index at 5 years such as individual, more qualitative or psychological traits of researchersGiven that these four attributes of ultra-successful scientists are predictable, the findings suggest that the scientific system is presently relatively closed. The results also illustrate limitations of using highly-influential metrics of success, such as citations and h-index. This is because early career advantages on these metrics are so strong that they predefine ‘highly-prominent scientists’, independent of the content of their research. More generally, the findings point to the need for a reform among the scientific community: As some scientists produce good science but are not successful in the ‘metrics game’, decision makers evaluating the work of researchers should also use additional metrics such as policy and social impact of research, developing new research tools, and the like. Decision makers should thus by no means take this as an opportunity to just use citation and h-index metrics to evaluate scientific prominence.jump-start hypothesis here can, by integrating multiple early-career factors and not focusing on an individual factor in isolation, better explain the Matthew effect in science47, namely how the most cited researchers get more cited just because they became highly cited early on in their career. The central implication for researchers is that early-career factors can be fostered through deliberate choices and hard work.Overall, our 48 divided by the total number of researchers (university staff) at the same top 25 universities (using QS World University Rankings) at 56,900. For comparison, the top 25 universities account for 1.8% of the total 1396 universities in the Times World University Rankings49. Secondly, an estimated 3−14% of all researchers worldwide have published a paper ranked in the top 5% in their field. This share is calculated by using data on the total number of all publications ranked top 5% in researchers’ field at 267,966 publications indexed in Web of Science using the Leiden Ranking50 divided by the total number of researchers worldwide at 8,854,28848 or by the total number of researchers (university staff) at 1,914,14949 that results in a 3% (lower bound) or 14% (upper bound) estimate, respectively. Thirdly, about one third of all articles worldwide (upper bound estimate) are published in top quartile journals indexed in Web of Science;42 and as many individual researchers publish multiple articles in quartile 1 journals it is likely that the share is significantly lower for the average researchers to publish at least half of their papers in quartile 1 journals. Fourthly, about 14% of junior researchers on average have co-authored a paper with a senior researcher between 1990 and 2012 in a global study covering about 1000 journals across the sciences , with the shares varying across the fields of biology (15%), physics (13%), chemistry (13%), medicine (16%) and mathematics (6%), including the top three multidisciplinary journals  at about 19% for each journal44. Fifth, the average h-index using university-level data is estimated at about 27 (median 25) as an upper bound estimate that includes only the top 500 universities40. The average h-index using all journal-level data from the Scimago Institutions Ranking39 via Scopus is estimated at about 32 (median 14). Note that both the mean university-level and journal-level h-indexes are upper bound estimates - i.e. higher than the mean researcher-level h-index given that researchers with lower h-indices are not represented in such estimates. These averages for researchers globally provide the baseline comparisons for our analysis.The calculations for the average researchers globally—the comparison group—for the four factors analysed here have been made as follows. Firstly, less than 1% of all researchers worldwide—an estimated 0.6%—are at one of the top 25 universities. This share is calculated using UNESCO data on the total number of researchers worldwide at 8,854,288We use two statistical approaches, a Random Forest classifier and a linear regression, to understand the role that different early-career variables play in the evolution of the h-indices and accumulated citations over the duration of scientists’ career.Our goal is to assess how well different factors help predict researchers’ h-index/citation counts (the dependent variables). We thereby consider four different groups of independent variables that we denote as Models 1, 2, 3 and Q5. Formally, we will refer to the sets of variables as For these three Models, all independent variables are binary 0 or 1). Descriptive data for all variables used in the models are provided in Table . DescripModel 1. This model considers as independent variables solely the four key early-career factors we study, namely working at a top 25 ranked university or not 19 and co-authoring with other top 100 researchers or not , In order to quantify the predictive power of the models and the different variables, we performed a classification experiment using a Random Forest Classifier. Our goal was to assess whether we could correctly predict the h-index/citation quartile at 5, 10, 15 and 20 years of career using only indicators from the first 5 years since the first publication.A Random Forest Classifier (RFC) behaves similarly to a Random Forest Regressor but produces a categorical output instead of a continuous one. In this sense, the classifier iteratively evaluates several decision trees over different parts of the data and averages the resulting outputs.Y we want to predict, and M. We then select the best model from Models 1, 2 and 3 as the one with the best overall precision and recall, in our case Model 2  and logistic regression analyses.y is the normalized h-index/number of citations and The OLS results illustrate the mean change in the dependent variable  given a one-unit change in each independent variable . All independent variables are binary (0 or 1). Specifically, the model is We perform OLS regression analysis to assess the predictors of h-index in the first 5, 10, 15 and 20 years for the world’s prominent researchers (see Supplementary Fig. Second, we conduct a logistic regression analysis in which the binary dependent variable Supplementary Information 1.


This review considers the hypothesis that a small portion of plasma membrane cholesterol regulates reverse cholesterol transport in coordination with overall cellular homeostasis. It appears that almost all of the plasma membrane cholesterol is held in stoichiometric complexes with bilayer phospholipids. The minor fraction of cholesterol that exceeds the complexation capacity of the phospholipids is called active cholesterol. It has an elevated chemical activity and circulates among the organelles. It also moves down its chemical activity gradient to plasma HDL, facilitated by the activity of ABCA1, ABCG1, and SR-BI. ABCA1 initiates this process by perturbing the organization of the plasma membrane bilayer, thereby priming its phospholipids for translocation to apoA-I to form nascent HDL. The active excess sterol and that activated by ABCA1 itself follow the phospholipids to the nascent HDL. ABCG1 similarly rearranges the bilayer and sends additional active cholesterol to nascent HDL, while SR-BI simply facilitates the equilibration of the active sterol between plasma membranes and plasma proteins. Active cholesterol also flows downhill to cytoplasmic membranes where it serves both as a feedback signal to homeostatic ER proteins and as the substrate for the synthesis of mitochondrial 27-hydroxycholesterol (27HC). 27HC binds the LXR and promotes the expression of the aforementioned transport proteins. 27HC-LXR also activates ABCA1 by competitively displacing its inhibitor, unliganded LXR. § Considerable indirect evidence suggests that active cholesterol serves as both a substrate and a feedback signal for reverse cholesterol transport. Direct tests of this novel hypothesis are proposed. Cell cholesterol is held at its physiologic setpoint by a battery of molecular mechanisms . Excess We first describe the behavior of active cholesterol and then review literature concerning the three proteins and functions related to them. Direct evidence supporting our hypothesis is sparse. The presentation is therefore speculative; sometimes contentions are expressed without qualification to facilitate the flow of ideas. The argument is basic and general; it does not consider a variety of activities related to reverse cholesterol transport that do not bear directly on the premise such as the complex management of the body’s cholesterol by the liver , 7. A siIt is a strong hypothesis that almost all the unesterified cholesterol in animal cell membranes is associated with phospholipids , 8. ThesActive cholesterol spontaneously exits lipid bilayers to acceptors , 16, 17.Active cholesterol equilibrates down its chemical activity gradient throughout the cell on a time scale of several minutes facilitated by a battery of transport proteins , 28, 29.Active cholesterol is the substrate for the synthesis of 27HC by sterol 27-hydroxylase, and a small excess of cell sterol acutely drives the production of the oxysterol , 32. 27Hβ associates with ABCA1 and inhibits its function. 27HC bound to LXRβ reverses this effect. That is, 27HC-LXRβ outcompetes the unliganded LXRβ inhibitor for ABCA1, thereby activating it  membrane-intercalating amphiphiles that displace cholesterol from its complexes and thereby activate it; and (b) amphiphiles such as short-chain phospholipids that complex cholesterol and thereby suppress its activity , 12, 17.The hypothesis that active cholesterol regulates reverse cholesterol transport is in need of direct experimental support. Missing are complete cholesterol concentration-dependence curves for the relevant functions, such as illustrated in If the basic premise is affirmed, it will be worth considering whether active cholesterol coordinates the particularly complex handling of cholesterol in hepatocytes and enterocytes. These cells utilize not only ABCA1, ABCG1, and SR-BI but other sterol transporters and effectors to maintain the balance of their cholesterol along with that of the whole body , 149.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Prognosticating the clinical outcome of neurological diseases is essential to guide treatment and facilitate decision-making. It usually depends on clinical and radiological findings. Biomarkers have been suggested to support this process, as they are deemed objective measures and can express the extent of tissue damage or reflect the degree of inflammation. Some of them are specific, and some are not. Few of them, however, reached the stage of daily application in clinical practice. This mini review covers available applications of the S100B protein in prognosticating clinical outcome in patients with various neurological disorders, particularly in those with traumatic brain injury, spontaneous subarachnoid hemorrhage and ischemic stroke. The aim is to provide an understandable picture of the clinical use of the S100B protein and give a brief overview of the current limitations that require future solutions. Modern medical practice increasingly depends on laboratory and instrumental diagnostic procedures. This can be attributed to rapid technological advances and often huge workloads  that limS100B is a calcium-binding protein, widely expressed in the brain predominantly by astrocytes, but also by maturing oligodendrocytes, neural progenitor cells, dendritic cells and specific lymphocyte subpopulations. S100B can be released extracellularly and is considered as a marker of brain damage . Many stThe potential use of S100B as a biomarker ranges from determining the severity of a certain disease and predicting the clinical outcome to monitoring disease progression and response to treatment. The type of application differs according to the disease features being chronic such as neurodegenerative diseases and other neurological and psychiatric disorders, or acute such as subarachnoid hemorrhage, trauma, and stroke. It is noteworthy that S100B has been suggested to be involved in the pathogenesis of some neurological diseases, such as Parkinson’s disease  and multThere is evidence for a significant increase in serum S100B levels following the onset of symptoms in patients with migraine and epilepsy –16. HoweIn cases of suspected neurodegenerative disorders, reliance on S100B concentrations for differential diagnostic purposes is not recommended due to lack of disease specificity. Moreover, there is no consistent evidence for a correlation between disease severity and concentrations of S100B in serum. Therefore, S100B has limited usefulness for monitoring disease progression . In addiMany studies have provided evidence that peripheral S100B concentrations are elevated in individuals diagnosed with schizophrenia when compared with healthy controls S100B is the only single biomarker with a validated clinically useful cutoff value in mild traumatic brain injury (TBI) . The ScaThe relatively low specificity of S100B limits its role in patients with moderate or severe brain trauma or even with multiple traumas. On the one hand, Müller et al. reported that fractures and thoracic injuries appeared to be the main factors associated with increased S100B levels and that head injury may only play a minor role in S100B serum elevation in multiple trauma patients . On the Spontaneous subarachnoid hemorrhage (SAH) is usually associated with damage to the central nervous system. Although other organs can be involved in the acute onset of the disease, primary cell damage is expected to take place at the level of neural tissue. Based on that, an elevation in biomarker levels is usually specific to neural damage. This was the case for many biomarkers that were investigated in the context of SAH including H-FABP, S100B, troponin I, NDKA, UFD-1, neuron-specific enolase and neurofilament light chain , 37–39. Patients with severe SAH, such as those with severe TBI, face a significant short- and long-term mortality risk and pose a similar diagnostic and decision-making dilemma. However, secondary complications, mainly vasospasm and delayed cerebral ischemia, occur later in the course of SAH, which might reduce the sensitivity of biomarkers for prognostication of long-term clinical outcome when measured within the first days after hemorrhage but would not affect their specificity regarding prediction of poor outcome . AineskoSerum S100B has been extensively investigated in ischemic stroke patients. This includes acute diagnostic, infarct volume, development of intracranial hemorrhage, monitoring of treatment success and outcome prediction. Regarding acute diagnostic, S100B has a low specificity for ischemic stroke and its serum levels doe not seem to increase immediately after acute stroke and might reach the highest values at day 3 after symptom onset . S100B sSimilar to traumatic brain injury and subarachnoid hemorrhage, functional recovery of patients with ischemic stroke can be predicted by measuring serum S100B. Its concentrations at days 2 to 4 after acute stroke may predict neurological status as well as functional impairment at discharge .Because S100B elevation in serum can have extra-cranial sources , S100B iSimilar to serum S100B, CSF S100B is elevated in frontotemporal lobe dementia  and earlhttps://www.aesnet.org/clinical-care/clinical-guidance/guidelines). For S100B to be adopted by clinical guidelines as a surrogate marker, a higher level of evidence will be needed. Large-scale studies and national registries might provide the basis for such evidence. Moreover, because there are other markers that compete with S100B, the use of artificial intelligence might be helpful to build a marker hierarchy or perhaps to combine different markers to reach a higher sensitivity and specificity in prognosticating clinical outcome.Many aspects limit the usability of S100B as a surrogate marker. One is that S100B is not brain-specific because of the wide extra-astrocytic and extracerebral expression of S100B . In addi


Obesity is a major risk factor for cardiovascular disease, stroke, and type 2 diabetes (T2D). Excessive accumulation of fat in the abdomen further increases T2D risk. Abdominal obesity is measured by calculating the ratio of waist-to-hip circumference adjusted for the body-mass index (WHRadjBMI), a trait with a significant genetic inheritance. Genetic loci associated with WHRadjBMI identified in genome-wide association studies are predicted to act through adipose tissues, but many of the exact molecular mechanisms underlying fat distribution and its consequences for T2D risk are poorly understood. Further, mechanisms that uncouple the genetic inheritance of abdominal obesity from T2D risk have not yet been described. Here we utilize multi-omic data to predict mechanisms of action at loci associated with discordant effects on abdominal obesity and T2D risk. We find six genetic signals in five loci associated with protection from T2D but also with increased abdominal obesity. We predict the tissues of action at these discordant loci and the likely effector Genes (eGenes) at three discordant loci, from which we predict significant involvement of adipose biology. We then evaluate the relationship between adipose gene expression of eGenes with adipogenesis, obesity, and diabetic physiological phenotypes. By integrating these analyses with prior literature, we propose models that resolve the discordant associations at two of the five loci. While experimental validation is required to validate predictions, these hypotheses provide potential mechanisms underlying T2D risk stratification within abdominal obesity. Metabolic syndrome (MetSyn) is a cluster of dysregulated metabolic conditions that tend to occur together to increase the risk for cardiometabolic disorders such as type 2 diabetes (T2D) . This clDespite the promise of these obesity-centered therapeutic strategies, there has also been a growing body of evidence describing a rare phenotype known as metabolically healthy obesity (MHO) . MHO desMost of the genetic mechanisms of MHO described have been associated with increased BMI without increased disease risk. For example, the missense variant rs373863828 in CREB3 regulatory factor has been shown to increase BMI without a corresponding increase in HOMA-IR and circulating TG, or a decrease in circulating adiponectin . Ob/ob mAs complex traits with both environmental and genetic risk factors, abdominal obesity and T2D have been the subject of multiple genome-wide association studies (GWAS). While GWAS have identified hundreds of genetic loci associated with abdominal obesity and T2D, moving from association to mechanism at a locus is not trivial. The use of colocalization analysis (COLOC), which identifies loci that contain shared genetic architecture for multiple traits of interest, can inform mechanistic hypotheses moving from association to function by integrating data from multiple studies . For exaTo advance the understanding of mechanisms linking body fat distribution to T2D risk, independent of overall obesity, we used COLOC and TACTICAL to predict the mechanisms of action at genetic loci associated with both T2D and WHR, both adjusted for the BMI. Using the most recent GWAS summary statistics, QTL summary statistics, tissue-specific gene expression data, and high-resolution epigenetic annotations, we predicted the shared genetic architecture of T2DadjBMI and WHRadjBMI at 79 genetic loci. Here, we present the identification of five loci that contained association signals with discordant effects on abdominal fat and T2D risk, meaning that the allele of the lead variant associated with protection from T2D was associated with increased abdominal fat accumulation. We predicted the eGenes and tissues of action at these five loci and explored the relationship between adipose eGene expression with cellular and physiological phenotypes. Here, we provide data-driven hypotheses about predicted candidate causal eGenes at GWAS loci with associations that recall metabolically healthy abdominal obesity.To identify genetic loci which contained pleiotropic association signals for both T2DadjBMI and WHRadjBMI, we performed colocalization analysis . This anWe next investigated the genetic and physiological consequences of discordant variants. We performed a phenome-wide association study (PheWAS) for anthropometric and glycemic traits with the most highly powered GWAS available . We usedWe then queried the variant effect predictor (VEP) to discover genetic variant annotations . VEP preTo investigate the role of eGenes in physiological phenotypes and cellular phenotypes, we evaluated the correlation of adipose tissue eGene expression and T2D-relevant phenotypes since these correlations can reveal biologically relevant functional relationships . To predTHADA-AS  to be the sole eGene. At the 5q21.1 locus, we predicted GIN1 (SAT), PAM (SAT & SKM), and PPIP5K2 (SAT) to be the eGenes. The association signal at rs6860588 was associated with a novel alternative splicing isoform of PAM in SAT, which skips the 14th exon. Using SMR, we predicted four eGenes at two discordant loci. At the 5q21.1 locus, we predicted that the genetic association signal represented by rs6860588 was also associated with the regulation of EIF3KP1 , PPIP5K2 (SAT), and GIN1 . At the discordant association signal in the 19q13.11 locus, we predicted that the genetic association signal represented by variant rs3786897 was also associated with the regulation of PEPD . As the colocalization transcripts GIN1 and PPIP5K2 were replicated with both methods (To predict effector genes (eGenes) regulated by discordant variants, we next predicted the colocalization of QTLs with the WHRadjBMI and T2DadjBMI GWAS. Colocalization of a GWAS association signal with a genetic regulatory association signal can be used to prioritize mechanisms underlying association. We obtained expression QTL (eQTL) and splicing QTL (sQTL) summary statistics from multiple cohorts and tissue groups . We extr methods , these r methods  for disc methods . Because methods , we reasPAM. We then computed the biweight midcorrelation of transcript counts or splice junction counts with 23 metabolic phenotypes. We found significant  correlations of adipose tissue gene expression of three genes with 13 phenotypes  and C-reactive protein (CRP). IL-1RA plays a protective role in resolving inflammation  on the discordant variants.We obtained the 99% credible set of SNPs from the results of Bayesian factor analysis implemented through Coloc.abf at each locus. We calculated the Z-scores for the association test of each genetic variant and the GWAS trait. If the Z-score associated with SNP had the opposite sign for association with WHRadjBMI and T2DadjBMI with respect to the same allele and the p-value for the association with both traits was less than 1e-05, we considered the variant discordant. We then identified in which loci the SNPs were located, and queried haploReg  LD data We queried the GWAS meta-analysis associations of glycemic and anthropometric traits for each lead discordant variant in the Type 2 Diabetes Knowledge Portal (T2DKP) . We addiWe obtained GWAS summary statistics for WC, hip circumference (HC), WHR, WHRadjBMI, T2D, and T2DadjBMI. We extracted summary statistics of variants within genetic loci containing a discordant association signal  and perfWe performed variable selection in multiple regression as implemented in the R package SuSiE . This meWe obtained eQTL data from the Genotype-Tissue Expression (GTEx) for 49 tissues , the StoWe used the functional GWAS (fGWAS)  command-We conducted TOA score analysis using the credible set of SNPs from each of the 79 colocalized loci. TACTICAL computes the TOA score with the SNP-level Bayesian probabilities, the SNP annotation maps, and the annotation enrichment scores. We used the Coloc.abf PPH4 scores for the SNP-level Bayesian probability, the fGWAS annotation enrichment scores, and the SNP annotation maps to compute the TOA score at all colocalized loci. We separated independent association signals in the same loci (LD <0.5) with HaploReg . With TAFor each eGene, we computed the biweight midcorrelation and its significance, as implemented by the Weighted Genetic Coexpression Network Analysis (WGCNA) package , betweenWe obtained Simpson-Golabi-Behmel syndrome preadipocyte adipogenesis time series gene expression data from GEO (accession number GSE76131) . We eval This study presents a valuable finding on five genome-wide association study loci displaying discordant effects on T2D and abdominal obesity. The evidence supporting the claims of the authors is solid, although further experiments are required to describe the mechanisms through which a genetic variant can confer increased abdominal obesity while offering protection from T2D risk. The work will be of interest to researchers working within the fields of variant-to-function analysis and endocrinology. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, including Tune H Pers as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by David James as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Marcel den Hoed (Reviewer #2).Thank you for submitting your article "Predicting mechanisms of action at genetic loci associated with discordant effects on type 2 diabetes and abdominal fat accumulation" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1) Please carefully address, through additional analyses, the comments related to whether the proposed effector genes are likely "healthy obesity genes", or whether there are at these loci either multiple causal genes per locus, or single causal genes acting on different traits via different tissues and mechanisms.2) Please carefully address the comments on WHRadjBMI being a trait that is complicated to interpret due to its composite structure, ideally by including analyses focusing on the two different fat depots that the WHR measure is based on.3) Please address, where appropriate through additional analyses, all remaining comments raised by the three referees.Reviewer #2 (Recommendations for the authors):I read your manuscript with great interest and think the findings are interesting. Nevertheless, I hope the points raised below can help make the manuscript even better, and help support some of the conclusions drawn.1. The trait that is WHRadjBMI has puzzled me for a while. Ratios can be problematic per se if two traits have a non-zero intercept (I am not sure if this is the case for WC and HC?), and adjusting for a heritable trait (BMI) may introduce collider bias, as was elegantly shown by Day et al. (https://pubmed.ncbi.nlm.nih.gov/26849114/). As a result, an association with WHRadjBMI may not reflect the same biological phenomenon for each locus. Hence, in addition to associations with WHRadjBMI and T2DadjBMI, it would be informative to also show summary statistics for associations of these loci with waist circumference, hip circumference, BMI and T2D in Figure 1.2. By running a co-localization analysis with sets of SNPs within 200kb of a genome-wide significant SNP in either GWAS, the number of truly co-localizing signals may be underestimated due to secondary association signals in either or both GWASs. It could be worthwhile to additionally run conditional analyses for each outcome to identify such loci and run the co-localization analysis for each association signal separately.3. The authors state that associations were considered discordant if the z-score had an opposite direction for the same allele. Presumably, the association with both traits also needed to be significant?4. In a best-case scenario, WHRadjBMI indeed reflects abdominal obesity, as intended. However, associations with WHRadjBMI then reflect the net association with adiposity across all individual abdominal fat depots. This does not tell us which lipid depot(s) drive(s) the association with WHRadjBMI. Could the authors explore associations with e.g., liver fat and pancreas fat as derived by MRI in UK biobank data for these six loci?5. Associations don't provide insights into causal pathways or directions of effect. Hence, I suggest refraining from using causal language, e.g., in line 75: "protecting against T2D but increasing abdominal fat accumulation". Even if a causal variant acts on both outcomes through a single causal gene and pathway (both are debatable at this stage), then there are still four possible courses of action. Similarly, in the discussion (line 237) the authors state that: "increased expression of THADA-AS results in increased abdominal fat accumulation but decreased T2D risk". No evidence was provided that justifies this conclusion based on an association.6. Even if there is only one causal gene, its effects on different outcomes may be mediated by multiple tissues and cell types . Since all six loci showing directionally discordant associations with WHRadjBMI and T2DadjBMI are located in non-coding regions, these loci likely have a regulatory role, e.g., as enhancers. Hence, the seemingly directionally discordant associations may well be mediated by more than one causal gene. I would argue that neither the "one gene, multiple pathways" nor the "multiple genes" scenarios reflect uncoupling.7. Co-localization of credible variants with sQTLs and eQTLs does not mean that such genes are causal for the traits of interest, as the association between the expression of a gene X and the outcome of interest can be confounded by the expression of the truly causal gene Y, with the expression of both genes X and Y influenced by the enhancer that is flagged by the causal variant. To get one step closer to such inference, it would be informative if the authors could show chromatin-chromatin interaction data for credible variants in all six discordant loci, across all relevant cell types.8. Gene prioritization is a fast-moving field, and a range of bioinformatics approaches – but no gold standard – are available to prioritize candidate genes. Results using proof-of-concept loci have not shown very convincing results for the predictive ability of eQTL associations to flag causal genes, even in the presence of co-localization. Hence, it would be informative to see which genes are prioritized in these loci by other available approaches – including but not limited to chromatin accessibility QTLs in relevant cell types – and if there is consensus across those approaches as to which gene(s) are likely causal.9. Even if there are loci that uncouple genetic effects on abdominal adiposity and risk of type-2 diabetes, the causal beneficial effect of weight loss is so large in comparison that it would no doubt remain advisable for most if not all individuals to lose weight to reduce the risk of T2D and its complications.10. Why was ATAC-sec data provided for only one of six discordant loci? In the discussion the authors state that tissues and mechanisms of action are predicted for six loci, but only one or two are described in the paper.Reviewer #3 (Recommendations for the authors):1. The rationale for the study is unclear. The authors introduce the study as an investigation into factors explaining individual differences in the relationship between obesity and cardiometabolic health, particularly the "metabolically healthy obesity" and "lean, insulin resistance" phenotypes. However, the analyses use outcome phenotypes that are adjusted for BMI (i.e. obesity): WHRadjBMI and T2DadjBMI. Thus, the results do not explain differences in obesity status and metabolic health, but rather tell about differences in body fat distribution and T2D risk, independent of obesity. Furthermore, a relatively low abdominal fat accumulation is one of the key characteristics of the "metabolically healthy obesity" phenotype associated with lower T2D risk, while the "lean, insulin resistance" phenotype associated with increased T2D risk, is characterized by relative increase in abdominal adiposity. Thus, the discordant association pattern of relatively higher abdominal adiposity but lower risk of T2D, investigated by the authors, does not exemplify either of these two phenotypes. Could the authors think of a biological or clinical example that could represent the discordant genetic association pattern they are aiming to characterize, in order to create a more plausible rationale for the present analyses?2. Overall, it is difficult to imagine a plausible biological explanation for a relationship where higher WHRadjBMI would mediate a lower risk of T2DadjBMI. It would be really interesting is such biology exists, but at the moment the paper does not give much help to the reader in interpreting what biological mechanism could underlie such a relationship. An alternative explanation for the findings is the possibility that the genetic effects are driven by independent effects of the same genes on biological pathways in two separate tissues (e.g. adipose tissue and pancreas), which would then generate the seemingly discordant genetic association pattern between WHRadjBMI and T2DadjBMI.3. A clear weakness in the study is the lack of any consideration to the subcomponents of the WHR phenotype: waist and hip circumference. As WHR is calculated as the ratio of waist circumference to hip circumference, WHR can increase if waist circumference increases or if hip circumference decreases, reflecting an increase in abdominal fat or a decrease in gluteofemoral fat, respectively. Waist and hip circumferences have very different implications for cardiometabolic health and T2D risk. Thus, is it critical to include an analysis on genetic effects on the two different fat depots, to understand the biological underpinnings of the findings. In addition, further GWAS results could be used to look into more detailed fat distribution phenotypes, such as the amount of visceral fat and subcutaneous fat in different body compartments.4. The results show that two of the independent discordant loci (represented by rs3776705 and rs459193), associated with lower risk of T2DadjBMI, show genome-wide significant associations with higher 2-hour glucose levels, and one of the loci (rs459193) also shows a genome-wide significant association with higher fasting glucose levels, despite their protective effect on T2D risk. The findings appear counter-intuitive, considering that T2D is diagnosed based on elevated fasting and 2-hour glucose values. Particularly for these two loci, it would be critical to make sure that the directions of effects and the reported reference alleles are validated in a further set of data, to make sure that the discordant effects are not due to mismatched alleles.5. The paper includes several overstated claims about the clinical impact of the findings and use of the findings for personalized risk stratification . The authors should revise the manuscript to avoid misleading the readers, by overstating the implications of their findings. Essential revisions:1) Please carefully address, through additional analyses, the comments related to whether the proposed effector genes are likely "healthy obesity genes", or whether there are at these loci either multiple causal genes per locus, or single causal genes acting on different traits via different tissues and mechanisms.We appreciate the suggestions for additional analysis to determine the proposed effector disease genes. To increase confidence in effector genes, we performed summary-based randomization (SMR) using T2DadjBMI and WHRadjBMI with QTLs. This provided an orthogonal method for colocalization that infers the causality of QTLs for GWAS associations. To control for false positives associated with MR methods, we implemented a false-discovery rate threshold of 5e-02 and performed a visual inspection of the association signal as visualized by LocusCompare. Implementing a requirement that a single variant that is associated with both the eQTL and trait of interest yielded 20 significant eQTL colocalizations, which have now been reported in Supplementary File 7. Additionally, we updated the table labels in the supplementary files to differentiate between colocalization results achieved using Coloc.abf and using SMR (Supplementary Files 6 & 7). We have updated our interpretation in the results to reflect the potential causal genes as either single effector genes, or multiple causal genes per locus acting on different traits .2) Please carefully address the comments on WHRadjBMI being a trait that is complicated to interpret due to its composite structure, ideally by including analyses focusing on the two different fat depots that the WHR measure is based on.We appreciate the comments on the choice of WHRadjBMI as a trait. To increase the interpretability of our analysis, we performed a multi-trait colocalization analysis for each of the loci containing a discordant association signal in the SNP set using the summary statistics for Waist Circumference (WC), Hip Circumference (HC), the ratio of Waist-to-Hip circumference (WHR), Body-Mass Index (BMI), WHR adjusted for BMI (WHRadjBMI), Type 2 Diabetes (T2D), and T2D adjusted for BMI (T2DadjBMI). In this recursive multi-trait colocalization analysis, traits are pairwise analyzed, and then summary statistics from traits are successively added and removed until the highest likelihood is achieved. This is repeated until all the colocalization of genetic association signals for each trait is tested and the maximal number of high-confidence colocalized groups of traits at each locus is achieved. This analysis was able to replicate colocalization at all six discordant loci. Further, at three of the six discordant loci the WHRadjBMI association signal was colocalized with the WC association signal. The colocalization analysis with the additional traits is now summarized in Supplementary File 3. The text has been updated to reflect these results (lines 123-127) and the methods (lines 353-358). In addition, we performed a comprehensive phenome-wide association study (PheWAS) of the individual abdominal fat depots underlying WHRadjBMI as well as the molecular glycemic phenotypes underlying T2DadjBMI . The text has been updated to reflect the results of this PheWAS (lines 108-144).3) Please address, where appropriate through additional analyses, all remaining comments raised by the three referees.We appreciate the comments raised by referees and feel they have significantly improved the quality of the manuscript. We aimed to respond to each comment raised by referees comprehensively and have included responses to all comments below.Reviewer #2 (Recommendations for the authors):I read your manuscript with great interest and think the findings are interesting. Nevertheless, I hope the points raised below can help make the manuscript even better, and help support some of the conclusions drawn.1. The trait that is WHRadjBMI has puzzled me for a while. Ratios can be problematic per se if two traits have a non-zero intercept (I am not sure if this is the case for WC and HC?), and adjusting for a heritable trait (BMI) may introduce collider bias, as was elegantly shown by Day et al. (https://pubmed.ncbi.nlm.nih.gov/26849114/). As a result, an association with WHRadjBMI may not reflect the same biological phenomenon for each locus. Hence, in addition to associations with WHRadjBMI and T2DadjBMI, it would be informative to also show summary statistics for associations of these loci with waist circumference, hip circumference, BMI and T2D in Figure 1.We appreciate the comments on the choice of WHRadjBMI as a trait. To increase the interpretability of our analysis, we performed a multi-trait colocalization analysis for each of the loci containing a discordant association signal in the SNP set using the summary statistics for Waist Circumference (WC), Hip Circumference (HC), the ratio of Waist-to-Hip circumference (WHR), Body-Mass Index (BMI), WHR adjusted for BMI (WHRadjBMI), Type 2 Diabetes (T2D), and T2D adjusted for BMI (T2DadjBMI). In this recursive multi-trait colocalization analysis, traits are pairwise analyzed, and then summary statistics from traits are successively added and removed until the highest likelihood is achieved. This is repeated until all the colocalization of genetic association signals for each trait is tested and the maximal number of high-confidence colocalized groups of traits at each locus is achieved. This analysis was able to replicate colocalization at all six discordant loci. Further, at three of the six discordant loci the WHRadjBMI association signal was colocalized with the WC association signal. The colocalization analysis with the additional traits is now summarized in Supplementary File 3. The text has been updated to reflect these results (lines 123-127) and the methods (lines 353-358). In addition, we performed a comprehensive phenome-wide association study (PheWAS) of the individual abdominal fat depots underlying WHRadjBMI as well as the molecular glycemic phenotypes underlying T2DadjBMI . The text has been updated to reflect the results of this PheWAS (lines 108-144). Further, the point is excellently taken about the collider bias, and this suggestion has increased the quality of our manuscript.2. By running a co-localization analysis with sets of SNPs within 200kb of a genome-wide significant SNP in either GWAS, the number of truly co-localizing signals may be underestimated due to secondary association signals in either or both GWASs. It could be worthwhile to additionally run conditional analyses for each outcome to identify such loci and run the co-localization analysis for each association signal separately.We apologize for the confusion, and the Methods section has been updated to reflect this. SNP windows of 200kb were constructed and then merged with nearby windows to form the loci that underwent colocalization analysis. This was to include all variants in linkage disequilibrium with the lead signal at the locus, as well as to include secondary association signals. To determine if the initial SNP window of 200kb significantly altered results, we altered the initial SNP window to 500kb and did not discover new loci containing discordant association signals, or find that previously identified loci were no longer significant. We amended the text in the Methods (line 327) to reflect this change.Additionally, we implemented the sum of single effects (SuSiE) model to relax the single causal variant assumption and parse the effects of potential multiple causal signals at a locus. In conjunction with colocalization analysis, this found a separate discordant association signal in the 5q21.2 locus that was even more strongly associated with genetic regulation than the primary signal identified in our earlier analysis . We report our results in Supplementary File 3 and throughout the Results section. We again thank the reviewers for this particularly helpful suggestion that has improved the quality of our manuscript.3. The authors state that associations were considered discordant if the z-score had an opposite direction for the same allele. Presumably, the association with both traits also needed to be significant?This is an excellent point, and our methods section has been updated to clarify this (lines 332 – 341). We additionally added all of the p-values for the association to Supplementary File 1 to further clarify the significance of association within the 99% credible set of SNPs. The association with both traits needed to reach at least a nominal, locus-wide significance of 5e-04. The signals for both WHRadjBMI and T2DadjBMI at four of the five discordant loci reached a genome-wide significance level of 5e-08. At the locus represented by variant rs74567345, the association p-value for WHRadjBMI was 1.599e-05. At the locus represented by the variant rs3786897, the association p-value for T2DadjBMI was 1.6e-07. Because these loci passed visual inspection as well as having significant Bayesian factors (>0.70) we considered them for further analysis. Additionally, while genome-wide significance is traditionally used to prioritize variant candidates for further investigation, work done in the laboratory of Brian W. Parks (PMID: 32197071) demonstrates that even variants with only nominal, local significance in GWAS can also have functional relevance to GWAS traits. We additionally used SuSiE to parse the colocalization of multiple causal signals in loci and found the association signal represented by variant rs6860588 that did reach genome-wide significance for association with both traits, as described in the Methods section and the response to reviewers above.4. In a best-case scenario, WHRadjBMI indeed reflects abdominal obesity, as intended. However, associations with WHRadjBMI then reflect the net association with adiposity across all individual abdominal fat depots. This does not tell us which lipid depot(s) drive(s) the association with WHRadjBMI. Could the authors explore associations with e.g., liver fat and pancreas fat as derived by MRI in UK biobank data for these six loci?We thank the reviewers for an excellent suggestion here. We followed this line of inquiry and reported the results in Figure 1C in response to the concerns raised above. This suggestion improved our results. The text has been updated to reflect these changes (lines 349-352).5. Associations don't provide insights into causal pathways or directions of effect. Hence, I suggest refraining from using causal language, e.g., in line 75: "protecting against T2D but increasing abdominal fat accumulation". Even if a causal variant acts on both outcomes through a single causal gene and pathway (both are debatable at this stage), then there are still four possible courses of action. Similarly, in the discussion (line 237) the authors state that: "increased expression of THADA-AS results in increased abdominal fat accumulation but decreased T2D risk". No evidence was provided that justifies this conclusion based on an association.We thank the reviewers for their comments on this topic in particular. We have changed the language we used in those specific lines and throughout the manuscript to reflect greater caution with causal language. We have moved to language that proposes a model that reconciles discordant effects and emphasized in the Discussion the need for experimental validation and further studies to conclusively show that discordant loci eGene expression or splicing are causal (lines 312-324).6. Even if there is only one causal gene, its effects on different outcomes may be mediated by multiple tissues and cell types . Since all six loci showing directionally discordant associations with WHRadjBMI and T2DadjBMI are located in non-coding regions, these loci likely have a regulatory role, e.g., as enhancers. Hence, the seemingly directionally discordant associations may well be mediated by more than one causal gene. I would argue that neither the "one gene, multiple pathways" nor the "multiple genes" scenarios reflect uncoupling.We thank the reviewer for this comment, and we added it to the Discussion section (lines 360-380) to reflect this uncertainty of interplay between variants, genes, and tissues. We also removed causal language throughout the manuscript for metabolically healthy obesity. Because of the directionally discordant associations, we do still believe the variants confer protection from diabetes but increased abdominal obesity and propose models to reconcile this.7. Co-localization of credible variants with sQTLs and eQTLs does not mean that such genes are causal for the traits of interest, as the association between the expression of a gene X and the outcome of interest can be confounded by the expression of the truly causal gene Y, with the expression of both genes X and Y influenced by the enhancer that is flagged by the causal variant. To get one step closer to such inference, it would be informative if the authors could show chromatin-chromatin interaction data for credible variants in all six discordant loci, across all relevant cell types.We thank the reviewers for this suggestion– we thought following these lines of inquiry would significantly improve the quality of our manuscript. We obtained the chromatin-chromatin interaction data from Hi-C experiments performed on adipocytes, skeletal myocytes, and pancreatic islets. We were unable to obtain interaction data from Hi-C experiments performed on hepatocytes. We then searched for the presence of variants in our credible set in the “bait” and “prey” regions of chromatin and were unable to discover the presence of discordant variants in the credible set within regions containing significant chromatin-chromatin interactions. While this revealed significant associations in the loci of interest, none of the discordant variants were in “bait” or “prey” regions in any cell line or tissue of interest. In summary, we concluded that chromatin-chromatin interactions are not a significant driver at the QTL colocalizations. We report the studies we obtained chromatin-chromatin interaction data from in Figure 2 – source data 2 and the results of our analysis in lines 156-157.8. Gene prioritization is a fast-moving field, and a range of bioinformatics approaches – but no gold standard – are available to prioritize candidate genes. Results using proof-of-concept loci have not shown very convincing results for the predictive ability of eQTL associations to flag causal genes, even in the presence of co-localization. Hence, it would be informative to see which genes are prioritized in these loci by other available approaches – including but not limited to chromatin accessibility QTLs in relevant cell types – and if there is consensus across those approaches as to which gene(s) are likely causal.We appreciate this comment from the reviewer. We used Summary-Based Mendelian randomization to prioritize eGenes with a direct association between expression levels and traits of interest. This orthogonal method to Coloc.abf provided us with more significant associations and replicated a limited set from Coloc.abf (Supplementary Files 6 and 7). We considered looking into chromatin accessibility QTLs (caQTLs) in relevant cell types as well, but we were only able to obtain liver caQTL data. Because we did not propose mechanisms of action in the liver for our discordant loci, we decided this would not represent a further prioritization or more association data to support or discredit our hypotheses and did not move forward with this. Nonetheless, we thank the reviewer for this suggestion as this did prompt us to look more critically at publicly available data to support and prioritize our candidate genes. We decided instead to look at time series preadipocyte differentiation data to see if our candidate eGenes played a role in adipogenesis . This revealed significant associations with dynamic expression across adipogenesis. Because adipogenesis is known to be a significant molecular regulator of adipose tissue distribution, this helped us to prioritize candidate genes and form hypotheses at the 2p21 and 5q21.2 loci.9. Even if there are loci that uncouple genetic effects on abdominal adiposity and risk of type-2 diabetes, the causal beneficial effect of weight loss is so large in comparison that it would no doubt remain advisable for most if not all individuals to lose weight to reduce the risk of T2D and its complications.We agree with the reviewer, and thank them for this comment. We have added clarifying language in the introduction (lines 53-106) and Discussion section (lines 277-291) to further reflect the value of our investigation in the proper context of basic science research into mechanisms and not clinical advice.10. Why was ATAC-sec data provided for only one of six discordant loci? In the discussion the authors state that tissues and mechanisms of action are predicted for six loci, but only one or two are described in the paper.We thank the reviewer for this suggestion, and have removed the ATAC-Seq results from our manuscript.Reviewer #3 (Recommendations for the authors):1. The rationale for the study is unclear. The authors introduce the study as an investigation into factors explaining individual differences in the relationship between obesity and cardiometabolic health, particularly the "metabolically healthy obesity" and "lean, insulin resistance" phenotypes. However, the analyses use outcome phenotypes that are adjusted for BMI (i.e. obesity): WHRadjBMI and T2DadjBMI. Thus, the results do not explain differences in obesity status and metabolic health, but rather tell about differences in body fat distribution and T2D risk, independent of obesity. Furthermore, a relatively low abdominal fat accumulation is one of the key characteristics of the "metabolically healthy obesity" phenotype associated with lower T2D risk, while the "lean, insulin resistance" phenotype associated with increased T2D risk, is characterized by relative increase in abdominal adiposity. Thus, the discordant association pattern of relatively higher abdominal adiposity but lower risk of T2D, investigated by the authors, does not exemplify either of these two phenotypes. Could the authors think of a biological or clinical example that could represent the discordant genetic association pattern they are aiming to characterize, in order to create a more plausible rationale for the present analyses?We thank the reviewer for their suggestion. We aimed to incorporate this into our Discussion section in lines 254-264 and 277-291 and reflect caution with strong claims. We additionally have amended our introduction to put our findings in the context of the field, describing progress made to describe MHO loci that act on overall obesity, but not on loci associated with abdominal obesity but with protection from type 2 diabetes (lines 76-83). Finally, we found literature evidence supporting our hypothesis at the 2p21 locus containing the gene encoding THADA, which we expand upon in the discussion (lines 292-305).2. Overall, it is difficult to imagine a plausible biological explanation for a relationship where higher WHRadjBMI would mediate a lower risk of T2DadjBMI. It would be really interesting is such biology exists, but at the moment the paper does not give much help to the reader in interpreting what biological mechanism could underlie such a relationship. An alternative explanation for the findings is the possibility that the genetic effects are driven by independent effects of the same genes on biological pathways in two separate tissues (e.g. adipose tissue and pancreas), which would then generate the seemingly discordant genetic association pattern between WHRadjBMI and T2DadjBMI.We agree with the reviewer, and have attempted to interpret our results and propose a biological explanation for two of our five loci where we were able to reconcile our results. The text has been amended to include this, and our Figure 4 and Figure 4 – supplementary figure 2 are focused on this goal. The text has been amended in lines 297-321 and 361-381. We hope this addresses the concerns about biological plausibility. We additionally include literature evidence supporting our hypothesis at the 2p21 locus containing the gene encoding THADA, which we expand upon in the discussion (lines 292-305).3. A clear weakness in the study is the lack of any consideration to the subcomponents of the WHR phenotype: waist and hip circumference. As WHR is calculated as the ratio of waist circumference to hip circumference, WHR can increase if waist circumference increases or if hip circumference decreases, reflecting an increase in abdominal fat or a decrease in gluteofemoral fat, respectively. Waist and hip circumferences have very different implications for cardiometabolic health and T2D risk. Thus, is it critical to include an analysis on genetic effects on the two different fat depots, to understand the biological underpinnings of the findings. In addition, further GWAS results could be used to look into more detailed fat distribution phenotypes, such as the amount of visceral fat and subcutaneous fat in different body compartments.We appreciate the comments on the choice of WHRadjBMI as a trait. To increase the interpretability of our analysis, we performed a multi-trait colocalization analysis for each of the loci containing a discordant association signal in the SNP set using the summary statistics for Waist Circumference (WC), Hip Circumference (HC), the ratio of Waist-to-Hip circumference (WHR), Body-Mass Index (BMI), WHR adjusted for BMI (WHRadjBMI), Type 2 Diabetes (T2D), and T2D adjusted for BMI (T2DadjBMI). In this recursive multi-trait colocalization analysis, traits are pairwise analyzed, and then summary statistics from traits are successively added and removed until the highest likelihood is achieved. This is repeated until all the colocalization of genetic association signals for each trait is tested and the maximal number of high-confidence colocalized groups of traits at each locus is achieved. This analysis was able to replicate colocalization at all six discordant loci. Further, at three of the six discordant loci the WHRadjBMI association signal was colocalized with the WC association signal. The colocalization analysis with the additional traits is now summarized in Supplementary File 3. The text has been updated to reflect these results (lines 123-127) and the methods (lines 353-358). In addition, we performed a comprehensive phenome-wide association study (PheWAS) of the individual abdominal fat depots underlying WHRadjBMI as well as the molecular glycemic phenotypes underlying T2DadjBMI . The text has been updated to reflect the results of this PheWAS (lines 108-144). Further, the point is excellently taken about the collider bias, and this suggestion has increased the quality of our manuscript. We appreciate the thoughtful suggestions.4. The results show that two of the independent discordant loci (represented by rs3776705 and rs459193), associated with lower risk of T2DadjBMI, show genome-wide significant associations with higher 2-hour glucose levels, and one of the loci (rs459193) also shows a genome-wide significant association with higher fasting glucose levels, despite their protective effect on T2D risk. The findings appear counter-intuitive, considering that T2D is diagnosed based on elevated fasting and 2-hour glucose values. Particularly for these two loci, it would be critical to make sure that the directions of effects and the reported reference alleles are validated in a further set of data, to make sure that the discordant effects are not due to mismatched alleles.We thank the reviewer for this suggestion. We found an error in the code that did flip the effect for fasting glucose for this locus and corrected it by pulling our PheWAS from the Type 2 Diabetes Knowledge Portal (T2DKP) directly instead of from individual manuscript summary statistics files. The effects now make sense in the context of protection from type 2 diabetes. We thank the reviewers for this suggestion and report our new results in Figure 1C.5. The paper includes several overstated claims about the clinical impact of the findings and use of the findings for personalized risk stratification . The authors should revise the manuscript to avoid misleading the readers, by overstating the implications of their findings.We thank the reviewer for this suggestion, and have changed the specific language in lines 77-78 and 259-263. We have additionally revised the manuscript throughout to use greater caution with the implications of our findings.