Toàn Nguyên
email: toan.nguyen@inria.fr

Jean-Antoine Désidéri

Laurentiu Trifan

Applications Resilience on Clouds

Keywords: Resilience, Cloud Computing, Scientific Applications, High-Performance Computing, Workflows. I

Cloud computing infrastructures support system and network fault-tolerance. They transparently repair and prevent communication and software errors. They also allow duplication and migration of jobs and data to prevent hardware failures. However, only limited work has been done so far on application resilience, i.e., the ability to resume normal execution after errors and abnormal executions in distributed environments and clouds. This paper addresses open issues and solutions for application errors detection and management. It also overviews a testbed used to to design, deploy, execute, monitor, restart and resume distributed applications on cloud infrastructures in cases of failures.

INTRODUCTION

This paper overviews open issues and the corresponding solutions for application errors detection and management when running on cloud infrastructures. A testbed is also presented relying on a workflow system interfaced with a grid infrastructure to model cloud environments. Section II addresses error detection. Section III details the corresponding solutions for error management. Section IV describes the testbed using a workflow management system to support these solutions on clouds. Section V is a conclusion.

Because many terms are used in the fault-tolerance area, a definition of various terms used in the domain is necessary. An interesting definition of errors, faults and failures is given in a system such as Apache's ODE [START_REF]The Apache Foundation[END_REF], where system failures and application faults address different types of errors.

A. Errors

The generic term error is used to characterize abnormal behavior, originating from hardware, operating systems and applications that do not follow prescribed protocols and algorithms. Errors can be fatal, transient and warnings, depending on their criticity level. Because sophisticated hardware and software stacks are operating on all production systems, there is a need to classify the corresponding concepts (Figure 1).

B. Failures

A failure to resolve a DNS address is different from a process fault, e.g., a bad expression. Indeed, a system failure does not impact the correct logics of the application process at work, and should not be handled by it, but by the system error-handling software instead: "failures are non-terminal error conditions that do not affect the normal flow of the process" [START_REF]The Apache Foundation[END_REF].

C. Faults

However, an activity can be programmed to throw a fault following a system failure, and the user can choose in such a case to implement a specific application behavior, e.g., a number of activity retries or its termination.

Application and system software usually raise exceptions when faults and failures occur. The exception handling software then handles the faults and failures. This is the case for the YAWL workflow management system [START_REF] Adams | Open source software for workflow management: the case of YAWL[END_REF] [START_REF] Russell | Surmounting BPM challenges: the YAWL story[END_REF], where specific exlets can be defined by the users [START_REF] Lachlan | Dimensions of coupling in middleware[END_REF]. They are components dedicated to the management of abnormal application or system behavior (Figure 2). The extensive use of these exlets allows the users to modify the behavior of the applications in real-time, without stopping the running processes. Further, the new behavior is stored as a component workflow which incrementally modifies the application specifications. The latter can therefore be modified dynamically to handle changes in the user requirements.

D. Fault Tolerance

Fault-tolerance is a generic term that has long been used to name the ability of systems and applications to handle errors. Transactional systems for example need to be faulttolerant [START_REF] Nguyên | A Workflow Platform for Simulation on Grids[END_REF]. Critical business and scientific applications need to be fault-tolerant, i.e., to resume consistently in case of internal or external errors.

E. Checkpoints

Therefore checkpoints need to be designed at specific intervals to backtrack the applications to consistent points in the application execution, and restart be enabled from there. They form the basis for recovery procedures.

In the following, we call checkpoint for a particular task the set including task definition, parameter specifications and data associated to the task, either input data or output data and the parameter values. This checkpoint definition does not include the tasks execution states or contexts, e.g., internal loop counters, current array indices, etc. Therefore, we assume that checkpointed tasks are stored stateless. This means that interrupted tasks, whatever the reasons and errors, cannot be restarted from their exact execution state immediately prior to the errors.

F. Recovery

We assume therefore that the recovery procedure must restart the failed tasks from previously stored elements in the set of existing tasks checkpoints. A consequence is that failed tasks cannot be restarted on the fly, following for example a transient non fatal error. They must be restarted from previously stored checkpoints.

Application robustness is the property of software that are able to survive consistently from data and code errors. This area is a major concern for complex numeric software that deal with data uncertainties. This is particularly the case for simulation applications [START_REF] Bachmann | Advances in Generalization and Decoupling of Software Parts in a Scientific Simulation Workflow System[END_REF].

G. Resilience

This is also a primary concern for the applications faced to system and hardware errors. In the following, we include both (application external) fault-tolerance and (internal) robustness in the generic term resilience [START_REF] Nguyên | A Distributed Workflow Platform for Simulation[END_REF].

Therefore we do not follow here the definition given in [START_REF] Capello | Toward Exascale Resilience[END_REF]: "By definition a failure is the impact of an error itself caused by a fault."

But we fully adhere to the following observation: "the response to a failure or an error depends on the context and the specific sensitivity to faults of the usage scenarios, applications and algorithms" [START_REF] Capello | Toward Exascale Resilience[END_REF].

II. ERROR DETECTION

A. Error Characterization

We address in this paper application errors, e.g., out of bounds data values, undefined parameters, execution timeouts, result discrepancies and unexpected values. We do not address communication, hardware and operating systems errors. We suppose that they are handled by the appropriate fault-tolerance sub-systems, which might automatically correct some of them or take appropriate corrective action, e.g., re-routing lost messages. We also suppose that these errors can be signaled to the application-level software by the appropriate raising of exceptions and posting of signals. Thus, the applications can take whatever actions are needed, e.g., re-executing tasks on other resources in case of network partition. This can be defined by the application designers and even by the application users at runtime.

The early characterization of errors is difficult because of the complex software stack involved in the execution of multi-discipline and multi-scale applications on clouds. The consequence is that errors might be detected long after the root cause that initiated them occurred. Also, the error observed might be a complex consequence of the root cause, possibly in a different software layer (Figure 2).

Similarly, the exact tracing and provenance data may be very hard to sort out, because the occurrence of the original fault may be hidden deep inside the software stack.

Without explicit data dependency information and realtime tracing of the components execution, the impacted components and associated results may be unknown. Hence there is a need for explicit dependency information [START_REF] Sindrilaru | Fault-Tolerance and Recovery in Grid Workflow Mangement Systems[END_REF].

B. Error Ranking

The ranking of errors is dependent on the application logic and semantics (e.g., default values usage). It is also dependent on the logics of each software layer composing the software stack. Some errors might be recoverable (unresolved address, resource unavailable…), some others not (network partition…). In each case, the actions to recover and resume differ: ignore, retry, reassign, suspend, abort... In all cases, resilience requires the application to include four components:

• a monitoring component for early error detection,

• an effective decision system, for provenance and impact assessment, • a low overhead checkpointing mechanism, • an effective recovery mechanism. Further, some errors might be undetected and transient. Without explicit data dependency information and real-time tracing of the components execution, the impacted components and associated results may be unknown. Hence there is a need for explicit dependency information between the component executing instances and between the corresponding result data [START_REF] Beckman | Facts and Speculations on Exascale: Revolution or Evolution?[END_REF].

III. ERROR MANAGEMENT

Many open issues are still the subject of active research concerning application resilience. The paradigm ranges from code and data duplication and migration, to the monitoring of application behavior, and this includes also quick correctness checks on partial data values, the design of error-aware algorithms, as well as hybrid checkpointing-message logging features (Figure 1). We focus here only on application errors. We do not address hardware, systems and communication errors. We suppose that these errors are fully treated by the appropriate system components [15][22]. We further suppose that they can be signaled to the applications by some exception events. This allows handling the consequences of the errors, e.g., communication failures, by the appropriate application resilience sub-system. A. Detection 1) Anomaly Detection: Hardware and system faulttolerance mechanisms can intercept errors [START_REF] Beckman | Facts and Speculations on Exascale: Revolution or Evolution?[END_REF]. Applications errors however must be explicitly taken into account in the code (Figure 3). This impacts severely the programming efforts and needs important design and re-programming efforts for existing codes [START_REF] Kovatch | The Malthusian Catastrophe is Upon Us! Are the Largest HPC Machines Ever Up?[END_REF].

2) Error Characterization:

Similarly, error characterization is heavily dependent on the application logics [START_REF] Riesen | Simulating Application Resilience at Exascale[END_REF]. It allows for error ranking, ranging from warnings to fatal. This is necessary to fine tune the faulttolerance and resilience capabiities to the application and user requirements.

3) Root Cause Detection: root cause characterization and provenance information is the most difficult part in complex applications and systems. Most of the time, even sophisticated tracing mechanisms will fail to provide an accurate characterization of the multiple root causes that provoke errors and abnormal application behavior [START_REF] Capello | Toward Exascale Resilience[END_REF].

4) Impact Assessment: The next important step is the assessment of the error impact. This includes the impact on the subsequent tasks, on the data, and the evaluation of error propagations. Further, a domino effect is that the errors can impact the messages exchanged and in transit between tasks as well as the advent of the pending tasks. This is detailed in the following sections (B.1, B.2, C.1 and C.2)

B. Impacted Tasks and Data 1) Impacted Tasks:

The application definition provides a detailed dependency relationship between tasks and data. It should therefore be straitghtforward to characterize the impacted tasks and data (Figure 4). However, the latency between error occurrence and their actual detection makes it difficult to precisely point out the exact time and location when an error occurred, particularly in distributed systems. Therefore, impacted tasks and data can bearly be defined without an undefined uncertainty. This paves the way for drastic backtracking policies and restarts. However, optimized checkpointing schemes, e.g., asymmetric [START_REF] Nguyên | A Distributed Workflow Platform for Simulation[END_REF], multi-level [START_REF] Moody | Modeling and evaluation of a Scalable Multi-level checkpointing System[END_REF] and encoded checkpoints [START_REF] Bautista-Gomez | FTI: high-performance Fault Tolerance Interface for hybrid systems[END_REF], alleviate somehow crude backtracking and checkpointing approaches by reducing their overhead, in both CPU and storage terms.

2) Corrupted Data: Similarly, corrupted data can originate from: a) Application errors: Computation errors on correct data will produce erroneous results, e.g., speciification, algorithmic, programming erros. They can be spotted and corrected with unpredictable delays. Performance and overhead issues are Performance and overhead issues are not necessarily fundamental here because CPU and data demanding tasks might have to be backtracked and reexecuted, incurring potentially very long delays.

b) Error propagation:

Correct computations performed on previously polluted data may generate random errors on data processed subsequently. Errors cannot in this case be pointed out immediately, if at all. Restarting the application componenents from ancestor tasks might be a necessary option here. The exact and most accurate restart location may in some cases be difficult to characterize. Policy requirements and implementations are in this case the last resort.

C. Impact 1) Transient Messages: Transient messages are potentially emitted before a component failure. Identifying such data might be very difficult in distributed computing and collaborative codes. Indeed, failed tasks might have sent unknown numbers of messages and data to a potentially unknown number of descendant tasks, depending on the point of failure. Time-outs might here be necessary to consider transient messages to reach their destinations.

Purging all these messages is necessary to backtrack to a previous consistent checkpoint. 2) Pending Tasks: Pending tasks are in contrast easily characterized since they are waiting for incoming data or events raised by ancestor tasks. Pause and resuming of such tasks is an option, without systematically calling for their cold restart from a previous checkpoint. Opportunistic checkpoints might here be interesting to store already produced data and application state. This is related to asymmetric checkpoints [START_REF] Nguyên | A Distributed Workflow Platform for Simulation[END_REF], where the users define points of interest in the application runs where checkpoints and snapshots must be stored in order to prevent potential catastrophic failures later. So, CPU, storage and communication demanding tasks will in such cases be saved without the need to restart them later in case of application errors.

D. Recovery 1) Termination of Non-faulty Tasks:

As mentionned above, recovery of non-faulty tasks is straightforward if they are not directly linked to faulty tasks, or if they are explicitly waiting for incoming data or events. If they are directly linked to failed tasks, i.e., processing data produced by failed tasks, restarting them with the failed tasks may be necessary. Indeed, without a sophisticated control of the data exchanged between tasks, it my be impossible to characterize the subsets of data already processed correctly by subsequent tasks. This is also the case when using data pipelining between tasks. In this case, restarting the tasks from the beginning is necessary. Further, resuming the subsequent tasks also requires the ancestor failed tasks to restart at their adequate execution locations when failed. This is most of the time impossible in current systems. It requires repetitive incremental and partial checkpoints of state dta and produced results, which can have an important overhead (Figure 5).

2) Secured Storage of Non-faulty Data: The secured storage of non-faulty data is essential for the optimization of the recovery process. Although, if it does not succeed, backtracking to a preceding checkpoint in the execution run is an option.

3) Restart Selection: There might be several options available for a single coordinated restart or local partial restarts (Figure 6). Depending on the situation, ranging from warnings to erros and fatal ones, the distributed configuration of the applications might render a global coordinated restart unrealistic. Several partial local restarts might be preferable, and in all cases, less expensive in terms of CPU and resource consumptions (Section D.5, below).

4) Checkpoint Selection: An adequate checkpoint selection mechanism must be devised, which supports local restart in parallel and/or partial restarts from distributed and coordinated checkpoints. Here again, the versatility of the checkpointing mechanism, i.e., the support for multi-level checkpoints, is of first importance for reducing the restart overhead (Figure 7). But the cost is of course, the checkpointing overhead, both in terms of CPU and storage capacity, incurred. Encoding mechanisms, "shadowed" and "cloned" virtual disk images have been proposed to answer these concerns [START_REF] Nicolae | BlobCR: Efficient Checkpoint-Retart for HPC Applications on IaaS Clouds using Virtual Disk Image Snapshots[END_REF].

5) Coordinated Restart: Coordinated local restarts is a middle term option, between global cold restarts and multiple local restarts. As mentioned previously (Section D.3), a global coordinated cold restart is unrealistic in distributed systems because it requires stopping all tasks and restarting the whole application, which might require large computing resources and days of CPU time. Coordination is fundamental here and related to distributed computations. It follows that coordinated restarts must be implemented by a specific mechanism that selects timestamped data and checkpoints.

IV. IMPLEMENTATION

A. Overview

Several proposals have emerged recently dedicated to resilience and fault management in HPC systems [START_REF] Riesen | Simulating Application Resilience at Exascale[END_REF][15] [START_REF]Proc. 5th Workshop INRIA-Illinois Joint Laboratory on Petascale Computing[END_REF].

The main components of such sub-systems are dedicated to the management of error, ranging from early error detections to error assessment, impact characterization, healing procedures concerning infected codes and data, choice of appropriate steps backwards and effective low overhead restart procedures.

General approaches which encompass all these aspects are proposed for Linux systems, e.g., CIFTS [START_REF] Gupta | CIFTS: a Coordinated Infrastructure for Fault-Tolerant Systems[END_REF]. More dedicated proposals focus on multi-level checkpointing and restart procedures to cope with memory hierarchy (RAM, SSD, HDD), hybrid CPU-GPU hardware, multi-core hardware topology and data encoding to optimize the overhead of checkpointing strategies, e.g., FTI [START_REF] Bautista-Gomez | FTI: high-performance Fault Tolerance Interface for hybrid systems[END_REF]. The goal is to design and implement low overhead, high frequency and compact checkpointing schemes. Also, new approaches take benefit of virtualization technologies to optimize checkpointing mechanisms using virtual disks images on cloud computing infrastructures [START_REF] Nicolae | BlobCR: Efficient Checkpoint-Retart for HPC Applications on IaaS Clouds using Virtual Disk Image Snapshots[END_REF].

Two complementary aspects are considered:

• The detection and management of faults inherent to the hardware and software systems used • The detection and management of faults emanating from the application code itself Both aspects are different and imply different system components to react. However, unforeseen or incorrectly handled application errors may have undesirable effects on the execution of system components. The system and hardware fault management components might then have drastic procedure to confine the errors, which can lead to the application aborting. This is the case for out of bound parameter and data values, incorrect service invocations, if not correctly taken care of in the application codes.

This raises an important issue in algorithms design. Parallelization of numeric codes on HPC platforms is today taken into account in an expanding move towards petascale and future exascale computers. But so far, only limited algorithmic approaches take into account fault-tolerance from the start.

B. Resilience Sub-system

Generic system components have been designed and tested for fault-tolerance. They include fault-tolerance backpanes [START_REF] Gupta | CIFTS: a Coordinated Infrastructure for Fault-Tolerant Systems[END_REF] and fault-tolerance interfaces [START_REF] Bautista-Gomez | FTI: high-performance Fault Tolerance Interface for hybrid systems[END_REF]. Both target general procedures to cope with systematic monitoring of hardware, system and applications behaviors. Performance consideration limit the design options of such systems where incremental and multi-level checkpoints become the norm, in order to alleviate the overhead incurred by checkpoints storage and CPU usage. These can indeed exceed 25% of the total wall time requirements for scientific applications [START_REF] Bautista-Gomez | FTI: high-performance Fault Tolerance Interface for hybrid systems[END_REF]. Other proposals take advantage of virtual machines technologies to optimize checkpoints storage using incremental ("shadowed" and "cloned") virtual disks images on virtual machines snapshots [START_REF] Nicolae | BlobCR: Efficient Checkpoint-Retart for HPC Applications on IaaS Clouds using Virtual Disk Image Snapshots[END_REF].

C. Distributed Platform

The distributed platform is built by the connection of two main components: 1) a workflow management system for application definition, deployment, execution and monitoring [START_REF] Deelman | Managing Large-Scale Scientific Workflows in Distributed Environments: Experiences and Challenges[END_REF][9]; 2) a middleware allowing for distributed resource reservation, and execution of the applications on a wide-area network.

1) Workflow. The applications are defined using a workflow management system, i.e., YAWL [START_REF] Russell | Surmounting BPM challenges: the YAWL story[END_REF]. This allows for dataflow and control flow specifications. It allows parameter definition and passing between application tasks. The tasks are defined incrementally and hierarchically. They can bear constraints that trigger appropriate code to cope with exceptions, i.e., exlets, and user-defined real-time runtime branchings. This allows for situational awareness at runtime and supports user interventions, when required. This is a powerful tool to deal with fault-tolerance and application resilience at runtime [START_REF] Nguyên | A Distributed Workflow Platform for Simulation[END_REF].

2) Middleware. The distribution of the platform is designed using an open-source middleware, i.e., Grid5000 [START_REF] Nguyên | A Workflow Platform for Simulation on Grids[END_REF]. This alows for reservation, deployment and execution of customized systems and application configurations. The Grid5000 nationwide infrastructure currently includes 12 sites in France and abroad, 19 research labs, 15 clusters, 1500 nodes, 8600 cores, connected by a 10Gb/s network. The resource reservations, deployment and running of the applications are made through standardized calls to specific system libraries. Because the infrastructure is shared between many research labs, resource reservation and job executions, i.e., applications, are queued with specific priority considerations.

3) Applications. Applications are defined, run and monitored using the standard workflow interface [START_REF] Abramson | Embedding Optimization in Computational Science Workflows[END_REF] [START_REF] Adams | Open source software for workflow management: the case of YAWL[END_REF]. They invoke automatically or manually the tasks, as defined in the specification interface. Tasks in turn invoke the various executable components tranparently through the middleware, using Web services [START_REF] Lachlan | Dimensions of coupling in middleware[END_REF]. They are standard in YAWL and used to invoke executable codes specific to each task. The codes are written in any programming language, ranging from Python to Java and C++. Script invocations with parameters are also possible. Parameter passing and data exchange between the executable codes are standardized in the workflow interface. Data structures are extendible user-defined templates to cope with all potential applications. As mentioned in the previous sections (Section "Workflow", above), constraints are defined and rules trigger component tasks based on data values, conditional checking. The testcases are distributed on a network of HPC clusters using the Grid5000 infrastructure. The hardware characteristics of the clusters are different. The application performance when running on various clusters are therefore different.

We use this infrastructure to deploy the application tasks on the various clusters and take advantage of the different cluster performance characteristics to benefit from loadbalancing techniques combined with error management.

The automotive testcase presented here includes 17 different rear-mirror models tested for aerodynamics refinement. They are attached to a vehicle mesh of 22 million cells. A reference simulation was performed in 2 days on a 48 CPU non-distributed cluster with a total of 144 GB RAM. The result was a 2% drag reduction for the complete vehicle. The mesh will be eventually refined to include 35 million cells. A DES (Detached Eddy Simulation) flow simulation model is used.

The tasks include, from left to right in Figure 6:

• An initialization task for configuring the application (data files, optimization codes among which to choose…) • A mesh generator producing the input data to the optimizer from a CAD file • An optimizer producing the optimized data files (e.g., variable vectors) • A partitioner that decomposes the input mesh into several sub-meshes for parallelization • Each partition is input to a solver, several instances of which work on particular partitions • A cost function evaluator, i.e., a drag evaluation • A result gathering task for output and data visualization • Error handlers in order to process the errors potentially raised by the solvers

The optimizer and solvers are all implemented using MPI. This allows highly parallel software executions. Combined with the parallelization made possible by the various mesh partitions, and the different geometry configurations of the testcase, it follows that there are three complementary parallelization levels in this testcase, which allow to fully benefit from the HPC clusters infrastructure.

Should an error occur during the solver processes, an exception is raised by the faulty tasks and they transfer the control to the corresponding error handler. This one will process the errors and trigger the appropriate actions, including: • Transfer automatically the solver task and data to another cluster, in case of CPU time limit or memory overflow: this is a load-balancing approach • Retry the optimizer task with new input parameters requested from the user (number of iterations, switch optimizer code…) • Ignore the error, if applicable, and resume the solver task

This approach merges two different and complementary techniques:

• Application-level error handling • A load-balancing approach to take full benefit of the various cluster characteristics, for best application performance Finally, the testbed implements the combination of a user-friendly workflow system with a grid computing infrastructure. It includes automatic load-balancing and resilience techniques. It therefore provides a powerful cloud infrastructure, compliant with the "Infrastructure as a Service" approach (IaaS).

V. CONCLUSION

High-performance computing and cloud infrastructures are today commonly used for running large-scale e-Science applications.

This has raised concerns about system fault-tolerance and application resilience. Because exascale computers are emerging, the need for supporting resilience becomes even more stringent.

Sophisticated and optimized functionalities are therefore required in the hardware, systems and application codes to support effectively error detection and recovery.

This paper defines concepts, details current issues and sketches solutions to support application resilience. Our approach is currently implemented and tested on simulation testcases using a distributed platform that operates a workflow management system interfaced with a grid infrastructure, altogether providng a seamless cloud computing environment.

The platform provides functionalities for application specification, deployment, execution and monitoring. It features resilience capabilities to handle runtime errors. It implements the cloud computing "Infrastructure as a Service" paradigm using a user-friendly application workflow interface.

Figure 1 .

 1 Figure 1. Error management.

Figure 2 .

 2 Figure 2. Error detection and assessment. The baseline is: • the early detection of errors • root cause characterization • characterization of transient vs. persistent errors • the tracing and provenance of faulty data • the identification of the impacted components and their associated corrupted results • the ranking of the errors (warnings, fatal, medium) and associated actions (ignore, restart, backtrack) • the identification of pending components • the identification and purge of transient messages • the secured termination of non-faulty components • the secure storage of partial and consistent results • the quick recovery of faulty and impacted components • the re-synchronization of the components and their associated data • the properly sequenced restart of the components Each of these items needs appropriate implementation and algorithms in order to orchestrate the various actions required by the recovery of the faulty application components.

Figure 3 .

 3 Figure 3. Error impact.

Figure 4 .

 4 Figure 4. Error recovery.

Figure 5 .

 5 Figure 5. Restart.

Figure 6 .

 6 Figure 6. Distributed resilience testcase.

ACKNOWLEDGMENT

The authors wish to thank Alain Viari, Directeur de Recherche with INRIA, for many fruitful discussions concerning the testcase design and deployment.

This work is supported by the European Commission FP7 Cooperation Program "Transport (incl. aeronautics)", for the GRAIN Coordination and Support Action ("Greener Aeronautics International Networking"), grant ACS0-GA-2010-266184.

It is also supported by the French National Research Agency ANR (Agence Nationale de la Recherche) for the OMD2 project (Optimisation Multi-Discipline Distribuée), grant ANR-08-COSI-007, program COSINUS (Conception et Simulation).


Cécile Vors

Gaëlle Pineau

Laure Gabert

Jocelyne Drai

Corinne Louche-Pélissier

Catherine Defoort

Denis Lairon

Michel Désage

Sabine Danthine

Stéphanie Lambert-Porcheron

Hubert Vidal

Martine Laville

Marie-Caroline Michalski
email: marie-caroline.michalski@insa-lyon.fr

Defoort, Lairon, Désage, Danthine Lambert-Porcheron Vidal

Michalski Laville

Modulating absorption and postprandial handling of dietary fatty acids by structuring fat in the meal: a randomized

Keywords: anhydrous milk fat, AP: atom percent, APE: atom percent excess, AUC: area under curve, iAUC: incremental area under curve, CMRF: chylomicron-rich fraction, CRNH-RA: Human Nutrition Research d 32 : volume-surface mean diameter, dARF: acetate recovery factor, DSC: differential scanning calorimetry, FA: fatty acid, FAMEs: fatty acid methyl esters, NEFA intestinal absorption, chylomicron, emulsion, stable isotopes, obesity

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

The metabolic importance of intestinal absorption and transport of nutrients in the postprandial period is recognized as important in the context of metabolic diseases such as obesity and type 2 diabetes [START_REF] Lopez-Miranda | Dietary, physiological, genetic and pathological influences on postprandial lipid metabolism[END_REF]. Regarding lipid metabolism, plasma kinetics, timing of peak of lipemia as well as chylomicron size are recognized as factors determining metabolic complications that are still an open field of research [START_REF] Nordestgaard | Nonfasting triglycerides and risk of myocardial infarction, ischemic heart disease, and death in men and women[END_REF][START_REF] Chapman | Triglyceride-rich lipoproteins and high-density lipoprotein cholesterol in patients at high risk of cardiovascular disease: evidence and guidance for management[END_REF][START_REF] Maggi | Lipoprotein remnants and endothelial dysfunction in the postprandial phase[END_REF]. For this reason, control of intestinal lipid absorption, the resulting chylomicron transport dynamics and ultimate dietary lipid fate may be an effective tool in the management of metabolic diseases. Recent studies have shown differential effects of oral sensory stimulation with high vs low amounts of dietary fat on intestinal lipid absorption [START_REF] Chavez-Jauregui | Dynamics of fat absorption and effect of sham feeding on postprandial lipema[END_REF]. The possible effects of fatty acid (FA) profile of an oral fat load on chylomicron size have been suggested [START_REF] Sakr | Fatty acid composition of an oral load affects chylomicron size in human subjects[END_REF][START_REF] Lai | Gastric digestion modifies absorption of butterfat into lymph chylomicrons in rats[END_REF][START_REF] Renner | Effect of saturated and unsaturated lipid on the composition of mesenteric triglyceride-rich lipoproteins in the rat[END_REF][START_REF] Feldman | Intestinal lymph lipoproteins in rats fed diets enriched in specific fatty acids[END_REF][START_REF] Xiao | Regulation of chylomicron production in humans[END_REF]. While both fat load and composition can affect postprandial lipid absorption, few studies have investigated the effects of fat structure on the postprandial metabolism of an identical lipid load. In diabetes, the concept of -slow/low glycemic index carbohydrates and fast/high glycemic index carbohydrates‖ is well established and has facilitated the development of specific foods and/or cooking methods to control postprandial glycemia [START_REF] Willett | Glycemic index, glycemic load, and risk of type 2 diabetes[END_REF]. We thus raised the question of whether a similar concept may be applicable to dietary fat according to the way it is structured in the meal.

Dietary lipids are incorporated in food products with different physicochemical structures, e.g., in dispersed lipid droplets in oil-in-water emulsions like ice cream or in a continuous lipid phase in butter and margarine. Emulsions are the most widespread fat structures, in processed foods and enteral formulas, and are therefore of interest regarding their role in lipid digestion and absorption [START_REF] Hur | Influence of initial emulsifier type on microstructural changes occurring in emulsified lipids during in vitro digestion[END_REF]. Indeed, we have previously demonstrated in rodents the importance of lipid emulsified structure on FA absorption and -oxidation [START_REF] Michalski | The supramolecular structure of milk fat influences plasma triacylglycerols and fatty acid profile in the rat[END_REF][START_REF] Michalski | The dispersion state of milk fat influences triglyceride metabolism in the rat--a 13CO2 breath test study[END_REF][START_REF] Laugerette | Emulsified lipids increase endotoxemia: possible role in early postprandial low-grade inflammation[END_REF]. However, the impact of fat structure on the kinetics of lipid absorption and dietary FA handling in humans remains to be elucidated. Such effects also deserve to be elucidated in obese subjects that present altered storage function of dietary FA in the postprandial state [START_REF] Mcquaid | Downregulation of adipose tissue fatty acid trafficking in obesity: a driver for ectopic fat deposition?[END_REF].

We therefore hypothesized that the physicochemical structure of the fat in a meal could modulate postprandial lipemia and fat partitioning: storage vs oxidation, and that the effect would be more pronounced in obese subjects. We investigated the metabolic response to fatcontaining meals (40 g) differing only in the structuring of fat, emulsified or not, in healthy young normal weight and obese men. Measurements included chylomicron number and size, FA β-oxidation and FA excretion in feces. The aim of this study was to define the contribution of fat structure and subject BMI on the postprandial lipemia and metabolism of dietary FA.

SUBJECTS AND METHODS

Study design

The study was an open label trial with a cross-over randomized controlled design involving 2 days of metabolic testing separated by at least three weeks (Supplemental Figure 1). It was conducted at the Human Nutrition Research Center Rhône-Alpes (CRNH-RA; Lyon, France) according to the Second Declaration of Helsinki and the French Huriet-Serusclat law. The LIPINFLOX study was approved by the Scientific Ethics Committee of Lyon Sud-Est-II and AFSSAPS and registered at Clinical Trials (#NCT01249378).

Volunteers received written and oral information and their medical history was reviewed. In addition they underwent a physical examination and fasting clinical analysis were assessed before enrolment. Informed written consent was obtained from all subjects. Volunteers performed trial in the period from April 2010 to July 2011. During the protocol, all subjects were asked to continue their regular diet and activity except for the week before and the 3-d period following each test day. Subjects were told to avoid foods naturally rich in 13 C and were given a list. For 48 h prior to testing subjects were asked to refrain from consuming alcohol and to avoid exercise. In addition, subjects were provided with a standardized dinner the evening prior to testing. Compliance was checked through diet records, 5 days before and 3 days after each test day.

After an overnight of fast, subjects ingested one of the two test breakfasts. The primary outcome measured was the effect of fat structure on postprandial lipemia. Secondary outcomes measured were the effect of fat structure and BMI on postprandial lipid metabolism.

Previous studies on lipemia [START_REF] Armand | Digestion and absorption of 2 fat emulsions with different droplet sizes in the human digestive tract[END_REF] and lipid oxidation [START_REF] Binnert | Influence of human obesity on the metabolic fate of dietary long-and medium-chain triacylglycerols[END_REF] were used for the power analysis: a minimum sample size of 8 subjects per BMI group was calculated to be necessary to detect significant changes in these parameters. The treatments were randomized according to a random allocation sequence performed by a CRNH-RA biostatistician using Stat® v.11; two randomization lists were generated and stratified over BMI. Subjects were anonymized using a number corresponding to randomization sequence order.

Subjects

Twenty-two healthy men were recruited, 11 normal-weight (NW) and 11 obese, and 20 completed the study (see Flow Diagram online). One subject in each group was not included in data analyses due to abnormal postprandial lipid metabolism; therefore 18 healthy subjects divided in two groups, 9 NW and 9 obese with comparable mean age were finally tested for the primary outcome, (Table 1). Volunteers were required to be non-smokers, sedentary or having <4h per week of physical activity and non-claustrophobic. We excluded persons under medication interfering with lipid metabolism, with psychological illness, or those with eating/metabolic disorders. In addition, subjects were required to have had stable weight, to be free of diabetes and to have not made blood donation for 3 months prior to the start of the study. Data were collected at CRNH-RA.

Test meals

The test breakfasts were isoenergetic, equal in nutrient composition (Table 2) and both consisted of bread (50 g), skimmed milk (160 mL) and anhydrous milk fat (AMF; 40 g), containing 600 mg of tracers, either spread on bread or emulsified in skim milk. Both meals had the same composition as no additional emulsifier was added because milk proteins are sufficient to provide a submicronic milk fat emulsion. Prior to the test day, a mixture of labeled triacylglycerols (TAG) proportionally representing each FA type present in test fat was first incorporated into melted milk fat: 300 mg of [1,1,1-13 C3] tripalmitin for long-chain saturated FA, 210 mg of [1,1,1-13 C3] triolein for unsaturated FA and 90 mg of [1,1,1-13 C3] trioctanoin for short-and medium-chain FA (99 atom% 13 C, Eurisotop, Saint-Aubin, France).

For the emulsion test, melted labeled milk fat was coarsly pre-mixed in skimmed milk (ProScientific Inc., Oxford, USA) and then further finely emulsified (4x 1 min, Vibra-cell TM Ultrasonic Processor, Sonics, Newtown, USA) (Supplemental Figure 2). Test products were then kept at 4°C overnight.

A second meal was served 5 hours after breakfast, containing pasta (200 g), turkey (100 g), butter (10 g), olive oil (10 g), bread (50 g), stewed fruit (100 g) which provided 713 kcal (2985 kJ) with 29%, 51% and 20% of energy as lipids (22.7 g), carbohydrates (91.5 g) and proteins (35.7 g) respectively. All subjects were given 10 minutes to eat breakfast and 30 minutes for lunch. During the test, participants were allowed to drink 200 mL of water.

Test fat characterization

Emulsion droplet size was measured by Dynamic Light Scattering (Zetasizer Nano S, Malvern, France). Specific surface area of emulsion droplets was calculated using Laser Light Scattering (Mastersizer 2000, Malvern, France). The melting temperature and crystalline state of the fat was characterized by Differential Scanning Calorimetry (DSC) using a Q1000 DSC (TA Instruments, New Castle, USA) and by powder X-ray Diffraction (XRD) using a D8 Advance diffractometer (Bruker, Germany).

Hunger assessment

Subjective assessment of hunger was measured on a 10-cm visual analogue scale 2 min before breakfast and 2 min before lunch. Specific question to assess hunger was -How hungry do you feel?‖.

Metabolic explorations

Blood samples were obtained at baseline and at regular intervals after the meal, from an antecubital arm vein through a catheter and collected in vacutainer sterile tubes (with EDTA when necessary). Plasma was separated by centrifugation (1500 g, 10 min, 4°C) and stored at -20°C until analysis or at 4°C for separation of the chylomicron-rich fraction (CMRF).

Metabolic tests were divided into postprandial phases including a first period of 5 hours (0 to 300 min) post-breakfast in the morning, a second period of 3 hours post-lunch (300 to 480 min) and the entire exploration day (0 to 480 min).

Indirect calorimetry was performed during metabolic testing using a Deltatrac II™ calorimeter (Sensormedics, Yorba Linda, CA, USA). Respiratory exchanges (VO 2 and VCO 2 ) were recorded for periods of 30 or 60 minutes during the 8-h test period. Substrate oxidations were calculated using Ferrannini's equations [START_REF] Ferrannini | The theoretical bases of indirect calorimetry: a review[END_REF]. Urine was collected at 0, 300 and 480 min to determine nitrogen excretion for oxidation calculations. For breath test, expired gas samples were obtained at baseline, each 30 min for 8-h and then at 720 and 1440 min to check return to baseline. Subjects had to collect and freeze their stools individually over 72 h after the test day.

Plasma metabolite and hormone measurements

Non esterified fatty acid (NEFA) concentrations were determined by an enzymatic method Wako® (Neuss, Deutschland). ApoB48 was measured by ELISA (Gentaur, France).

Insulin concentration was determined by RIA (CISBIO Bi insuline IRMA, France).

Isolation and analysis of chylomicron-rich fractions

To collect the CMRF, containing chylomicrons and their large remnants, 250 µL of plasma was deposited below a layer of 850 µL of distilled/deionized water and centrifuged at 80 000 rpm for 30 min using a Sorvall Kendro ultracentrifuge (Asheville, NC, USA). The floating layer was collected and stored at -80°C. TAG and cholesterol concentrations of CMRF were measured with a lipase glycerokinase and a cholesterol esterase/oxidase method, respectively, on a AU 2700 Beckman Coulter® (O' Cllagan's mils, Ireland) and expressed as differences in concentration over baseline. Hydrodynamic diameter of CMRF was measured by dynamic light scattering at 25°C using a ZetaSizer NanoS (Malvern, UK) using 1.0658 cP and 1.33 as viscosity and refractive index of the aqueous phase, respectively. Gravityseparated fraction containing chylomicrons and their large remnants are reported to size in the range 70-450 nm [START_REF] Hussain | Signposts in the assembly of chylomicrons[END_REF][START_REF] Pang | Postprandial dyslipidaemia and diabetes: mechanistic and therapeutic aspects[END_REF][START_REF] Su | Methods to assess impaired post-prandial metabolism and the impact for early detection of cardiovascular disease risk[END_REF], and postprandial increase in cholesterol content of such fraction is about <0.2 mM [START_REF] Dubois | Effects of graded amounts (0-50 g) of dietary fat on postprandial lipemia and lipoproteins in normolipidemic adults[END_REF]. Our fraction collected by ultracentrifugation contained particles of mean diameter up to 200-300 nm and with postprandial increase in cholesterol of <0.15 mM in NW and <0.25 mM in obese subjects. Our CMRF is thus typical of fractions that are rich in chylomicrons and that also contain large remnants. Plasma processing. Plasma samples were submitted to direct methylation as described previously [START_REF] Gabert | 13C tracer recovery in human stools after digestion of a fat-rich meal labelled with [1,1,1-13C3]tripalmitin and [1,1,1-13C3]triolein[END_REF].

NEFA processing. Total lipids were extracted from plasma aliquots at 120 min after breakfast consumption (700µL) with 3 mL of a mixture of chloroform/methanol (2:1 v/v) according to Folch method [START_REF] Folch | A simple method for the isolation and purification of total lipides from animal tissues[END_REF]. NEFA fractions were obtained therefrom by TLC on silicagel plates with a mobile phase of hexane/diethyl ether/acetic acid (80:20:1 v/v/v). NEFA were derivatized to Fatty Acid Methyl Esters (FAMEs) [START_REF] Gabert | 13C tracer recovery in human stools after digestion of a fat-rich meal labelled with [1,1,1-13C3]tripalmitin and [1,1,1-13C3]triolein[END_REF].

CMRF processing. Lipids were extracted from CMRF at 120 min after breakfast according to Folch method [START_REF] Folch | A simple method for the isolation and purification of total lipides from animal tissues[END_REF]. TAG fractions were then processed as the NEFA fractions above, to obtain the FAMEs from CMRF.

Stool processing. Fecal collections were weighed, homogenized and a precisely weighed aliquot was collected. Total lipids were extracted according to a modification of the Folch method and derivatized to obtain FAMEs [START_REF] Gabert | 13C tracer recovery in human stools after digestion of a fat-rich meal labelled with [1,1,1-13C3]tripalmitin and [1,1,1-13C3]triolein[END_REF]. Sample analysis. The amounts of FA in stools, plasma and NEFA were assessed by GC/MS using a quadrupole mass spectrometer connected to a gas chromatograph (MS 5975 and GC6890, Agilent Technologies, Massy, France). The isotopic enrichment of palmitic and oleic acids was determined using GC/C/IRMS (Isoprime, GV Instruments, Manchester, UK) [START_REF] Gabert | 13C tracer recovery in human stools after digestion of a fat-rich meal labelled with [1,1,1-13C3]tripalmitin and [1,1,1-13C3]triolein[END_REF]. The 13 C enrichments were expressed as atom percent excess (APE). The plasma concentrations of non-esterified labelled palmitic and oleic acids (called [ 13 C-NEFA]) and non-esterified unlabelled palmitic and oleic acids (called [ 12 C-NEFA]) were also obtained from these analyses.

Calculations associated with apparent dietary fatty acid "spillover"

NEFA analysis at 120 min after breakfast was used to calculate 13 C enrichment in plasma NEFA as: [ 13 C-NEFA] / ( [ 13 C-NEFA] + [ 12 C-NEFA] ) (expressed in % enrichment). The proportion of exogenous NEFA in total plasma NEFA, expressed in %, was estimated by the ratio of the 13 C enrichment in plasma NEFA to the 13 C enrichment of corresponding FA in the ingested milk fat. The proportion of exogenous fatty acids in plasma that was present in nonesterified form in the sum of pools NEFA+CMRF was calculated as: [ 13 C-NEFA] plasma / ([ 13 C-NEFA] plasma + [ 13 C-FA CMRF ] plasma ), where [ 13 C-FA CMRF ] plasma is the plasma concentration of 13

C-FA esterified in CMRF-TAG = [ 13 C-FA] CMRF / ( [ 13 C-FA] CMRF + [ 12 C-FA] CMRF ) x 3 x [CMRF-TAG] plasma .

Calculations of exogenous lipid oxidation from indirect calorimetry & breath tests

Exogenous lipid oxidation was calculated according to Binnert et al. [START_REF] Binnert | Influence of human obesity on the metabolic fate of dietary long-and medium-chain triacylglycerols[END_REF] from data of indirect calorimetry and breath tests. Here the formula was adapted to our use of 3 labeled triglycerides as follows:

Exogenous lipid oxidation (% of ingested fat) =                     100 4 . 22 100 / ) ( 2 / 2 0 2 30 2 2           dARF C B A CO V t CO AP t CO AP t CO AP With: (A)=         27 66 . 473 / 09 . 0 100 / 0 : 8 13   C TG AP (B)=         51 30 . 810 / 30 . 0 100 / 0 : 16 13   C TG AP (C)=         57 40 . 888 / 21 . 0 100 / 1 : 18 13   C TG AP
Where AP CO 2 (t) is the AP value of the expired CO 2 at time t, AP CO 2 (t 0 ) is the AP value of the expired CO 2 at time t 0 , AP tracers is the calculated AP value of the labeled mixture of TAG (tracers) and  V CO 2 is the production rate of expired CO 2 (indirect calorimetry). Mean molecular weights of trioctanoin, tripalmitin and triolein are 473.66 g/mol, 810.30 g/mol and 888.40 g/mol, respectively. Mean number of carbons in trioctanoin, tripalmitin and triolein are 27, 51 and 57, respectively. dARF (Acetate Recovery Factor) is the correction factor for incomplete recovery of 13 C bicarbonate (0.505 for NW; 0.453 for obese [START_REF] Antoun | The [1-13C]acetate recovery factor to correct tracer-derived dietary fat oxidation is lower in overweight insulin-resistant subjects[END_REF]) and 22.4 is the molar volume (L) of CO 2 .

Kinetic parameters

We calculated the incremental area under curve (iAUC); maximum postprandial concentration, delta and diameter (C max , ∆ max , d max ); time for appearance of these maximum parameters (t max ) and appearance/enlargement-rates between 0 and 60 min.

Statistical analysis

Each subject served as his own control. All data are presented as means ±SEM (n=9 per group) and were analyzed with Statview 5.0 software (Abacus Concept, Berkeley, CA).

Postprandial data were compared by analysis of variance (ANOVA) for repeated measures followed by post-hoc test (Fisher PLSD) for statistical effects of (i) time alone (P time ) over the first postprandial period (0-300min), (ii) meal alone (P meal ) independently of the time in the postprandial period and (iii) interaction of both factors, time and meal (P timexmeal ). Kinetic parameters were compared by two-way ANOVA followed by Fisher PLSD according to meal and BMI (P meal , P BMI , P mealxBMI ) and time period before/after lunch (P mealxBMIxtime ). Multiple comparisons regarding tracer excretion in feces were performed using ANOVA followed by Bonferroni post-hoc test. Comparisons between meals within subject groups were performed using a paired Student's t-test and comparisons between subject groups within meals with an unpaired Student's t-test. Differences were considered significant at the P< 0.05 level.

RESULTS

Properties of emulsion vs spread fat

The emulsion droplet size (Table 3; Supplemental Figure 3) indicates the homogenization was effective in producing the emulsions. The emulsion had an approximately ~70000-fold greater surface area than the spread fat. To control for the possibility that the different metabolic effects could be attributed to the fat melting properties, we measured melting profiles and crystalline structures in all conditions (with or without tracers, emulsified or not, Supplemental Figure 4). According to these analyses, the test fat differed only by their structure (Table 3).

Hunger feeling

At the end of the first postprandial period (0-300 min) just before lunch, normal-weight subjects felt similarly hungry regardless of breakfast type (Supplemental figure 5). In contrast, obese subjects felt hungrier after emulsion than spread fat (P< 0.05; Supplemental figure 5).

Of note, before breakfasts, all subjects felt equally hungry (not shown).

Postprandial concentration profile and size of chylomicron-rich fraction

Figure 1A-B shows that in both groups, CMRF-TAG rapidly increased (60 min) after ingestion of emulsified fat and peaked at 3-4 h (t max in Table 4). The emulsion induced a significantly earlier and sharper increase in CMRF-TAG than the spread fat (Table 4: t max and appearance-rate 0-60min ; P< 0.001). These differences were dramatically marked in the obese subjects, with a significant delay in absorption of the spread fat from 0 to 300 min compared to NW subjects (P< 0.01, Table 4). At the end of the test, CMRF-TAG of NW subjects returned to lower values regardless of fat structure. The obese subjects showed different profiles, with CMRF-TAG remaining elevated at the end of the spread fat test: e.g. at 480 min, 0.61 ± 0.15 mmol/L for spread vs 0.27 ± 0.06 mmol/L for emulsion above fasting baseline (P< 0.05). These differences in profile before and after lunch according to obese state and meal type are supported by different BMI x meal x time interactions for the Δ max and iAUC of CMRF-TAG (Table 4).

Mean CMRF size sharply increased in both groups from the first hour after emulsion (Figure 1 C-D; P meal < 0.05 for enlargment-rate 0-60min , Table 4). For NW subjects, CMRF diameters were similar for spread fat and emulsion all along the test. In obese subjects, CMRF diameters became equal for both meals at 300 min. We can note that in the period from 0 to 240 min, CMRF diameter in obese subjects was higher after emulsion vs spread fat from 0 to 240 min (P meal < 0.05 and P time < 0.001). Altogether, obese subjects presented larger CMRF than NW subjects (P< 0.01 for d max 0-480 min, Table 4) with persistence of large CMRF after the second meal for spread fat.

Plasma concentration profile of ApoB48

Figure 1E-F shows that plasma ApoB48 changed over time in both groups after both breakfasts (P time < 0.0001) and differently according to the type of breakfast for NW subjects (P timexmeal =0.001). At 120 min, NW subjects accumulated more ApoB48 after consumption of emulsion than spread fat (7.08 ±0.86 mg/L for spread fat vs 9.73 ±0.69 mg/L for emulsion, P< 0.05) and compared with obese subjects for emulsion (7.47 ±0.78 mg/L, P< 0.05).

Plasma concentration profile of 13 C-fatty acid tracers and fecal loss

Figure 2A-B-C-D shows a change in plasma 13 C-palmitic and 13 C-oleic acids over time in both groups after both breakfasts (P time < 0.0001). 13 C-palmitic acid appeared earlier and sharper in plasma when it was in emulsion, differences between breakfasts being greater for obese (P meal = 0.007). Plasma concentrations of 13 C-oleic acid were higher during 5h of emulsion digestion, especially for obese subjects (P meal = 0.018 and P timexmeal = 0.0002). For both tracers, a second peak was observed at 360 min, after ingestion of the second meal.

During the first 300 min for obese subjects, the iAUC for plasma 13 C-FA were significantly higher after consumption of emulsion vs spread fat (P< 0.05, Figure 2B-D).

iAUC after spread fat were lower for obese vs NW subjects (P< 0.05, Figure 2B-D).

Fecal excretion of 13 C-palmitic acid was higher than that of 13 C-oleic acid (Figure 2E).

There was no effect of breakfast type on fecal excretion of 13 C-palmitic acid or 13 C-oleic acid in the two groups.

Plasma concentration profile of insulin and NEFA and apparent dietary FA spillover Figure 3A-B-C-D shows a significant change in plasma insulin and NEFA over time after the two breakfasts in both groups (P time < 0.0001). Over the first 300 min, the NEFA profile indicated a meal type x time interaction in both groups, with the decrease in plasma NEFA at 120 min being lower for emulsion than spread (Figure 3C-D). Therefore, we measured 13 C enrichment in plasma NEFA at 120 minutes (Figure 3E) to estimate whether this would result from the contribution of exogenous FA, so-called apparent fatty acid -spillover‖. We observed higher apparent -spillover‖ during the postprandial phase of emulsion vs spread fat, in both groups (P< 0.05 for NW and P< 0.01 for obese subjects, Figure 3E). The contribution of exogenous FA to total NEFA in NW subjects was 42% for spread vs 79% for emulsion, and was lower in obese, 4% for spread vs 50% for emulsion (meal effect, P< 0.01; BMI effect, P< 0.01; no meal x BMI interaction). Moreover, in NW subjects, the proportion of exogenous FA being in non-esterified form in plasma at 120 min was 10.6 % for spread vs 18.7 % for emulsion, i.e. 1.8-fold increase, and in obese 10.1 % for spread vs 15.0% for emulsion, i.e. 1.5-fold increase (P<0.05 for spread vs emulsion; no significant effect of BMI nor meal x BMI interaction). In the same time emulsification increased total plasma NEFA by 2.2-fold in NW and by 1.6-fold in obese subjects (Figure 3C-D). Altogether, this means that more than 80 % of the increase of plasma NEFA due to emulsification may be explained by an increased amount of exogenous fatty acids being released non-esterified in plasma (spillover).

Postprandial appearance of label in expired CO 2

The 13 C appearance in expired CO 2 represents the final product of FA -oxidation. A significant change in APE occurred over time in both groups (P time < 0.0001; Figure 4A-B). In both groups, APE was higher after consumption of emulsion vs spread fat over the first 300 min, indicating improvement of dietary fat -oxidation using emulsified form. APE returned to baseline after 720 min. An effect of BMI was also observed with higher appearancerate 0-60min in NW than obese subjects (P< 0.01; Table 4).

Inserts in Figure 4A-B show that AUC of expired 13 CO 2 after 300 min for the emulsion was significantly higher than for spread fat in both groups (P< 0.01 for NW subjects and P< 0.001 for obese subjects). Besides, over 0 to 720 min, obese subjects presented higher AUC of expired 13 CO 2 after consuming emulsion vs spread fat (P< 0.05, Figure 4B insert).

Altogether, the structuring of fat in the meal significantly affected the kinetic parameters of 13 CO 2 air enrichment regardless of BMI while BMI affected AUC and appearance-rate 0-60min (Table 4).

Exogenous lipid fate

We studied the metabolic handling of exogenous lipids by evaluating the fractions of ingested lipids that have been either oxidized or lost in feces, and so estimated the remaining fraction stored in body pools. For the same quantity and composition of ingested fat, all subjects -oxidized FA better when fat was emulsified (Figure 4C). In turn, the calculated fraction of ingested lipids oriented towards storage in body pools was lower after emulsion vs spread consumption. After accounting for the part of exogenous lipids lost in feces (Figure 4C), the percentage of exogenous lipid oxidation according to the fraction that has been intestinally absorbed was higher for NW and obese subjects after emulsion vs spread fat (P< 0.05 and P< 0.01, respectively, Figure 4D). Total lipid oxidation was unchanged with the two test meals in both groups but with a greater relative contribution of exogenous lipids ingested at breakfast as emulsion vs spread fat (Figure 4E).

DISCUSSION

Postprandial triglyceridemia is the first step in the metabolization of dietary lipids.

Ingested FA are firstly present in plasma TAG in the form of intestinally secreted chylomicrons, which further lead to large remnants after hydrolysis by lipoprotein lipase [START_REF] Xiao | Regulation of chylomicron production in humans[END_REF][START_REF] Su | Methods to assess impaired post-prandial metabolism and the impact for early detection of cardiovascular disease risk[END_REF]. The next step concerns trafficking of FA towards β-oxidation or storage that is of utmost importance regarding the metabolic impact of these dietary FA. We therefore investigated whether structuring fat in the meal could modify postprandial lipid metabolism, from the amount and size of chylomicrons to -oxidation, including fecal loss. To this aim, labeled breakfasts containing either spread or emulsified fat were fed to NW and obese subjects. Test meals were designed to be of equal composition. Thus, factors like FA composition or protein content cannot be involved in the presently observed differences in lipid metabolism, which can be uniquely attributed to the fat physico-chemical structure in the meal. The postprandial chylomicron TAG profile after emulsion consumption differed from that of the spread fat, with the peak being more rapidly achieved, more pronounced and more quickly cleared, especially in obese subjects. This is consistent with reports of enhanced FA absorption when a simple bolus of vegetable oil was emulsified in humans [START_REF] Garaiova | A randomised cross-over trial in healthy adults indicating improved absorption of omega-3 fatty acids by pre-emulsification[END_REF] and rodents [START_REF] Michalski | The dispersion state of milk fat influences triglyceride metabolism in the rat--a 13CO2 breath test study[END_REF][START_REF] Laugerette | Emulsified lipids increase endotoxemia: possible role in early postprandial low-grade inflammation[END_REF][START_REF] Couedelo | Lymphatic absorption of alpha-linolenic acid in rats fed flaxseed oil-based emulsion[END_REF]. One explanation is that our emulsion had ~70000-times greater surface area available for lipases than spread fat, which is reported to enhance lipolysis and absorption [START_REF] Delorme | Effects of surfactants on lipase structure, activity, and inhibition[END_REF]. Enteral emulsions of different droplet sizes, ~1 vs ~10 µm (14.5-fold difference in fat surface area), were shown to result in small differences only in postprandial lipemia in humans [START_REF] Armand | Digestion and absorption of 2 fat emulsions with different droplet sizes in the human digestive tract[END_REF]. The dramatic differences observed in the present work are due to the greatest differences in fat structure. Of note, postprandial lipid metabolization was previously found faster using unemulsified than emulsified milk fat in rats [START_REF] Michalski | The dispersion state of milk fat influences triglyceride metabolism in the rat--a 13CO2 breath test study[END_REF]. Differences with the present results can be explained by (i) rodent physiology of bile flow that is different from humans [START_REF] Martins | Surgical anatomy of the liver, hepatic vasculature and bile ducts in the rat[END_REF], and (ii) unemulsified melted milk fat being force-fed intragastrically, prior to the proteinaceous phase. This could have favored lipid emptying in the upper intestine and a rapid rise of plasma TAG in rats.

Our study also provides a proof of concept that effects of fat structure in the meal can occur in a real mixed meal, while previous studies used oil or emulsion bolus fed orally or intragastrically [START_REF] Armand | Digestion and absorption of 2 fat emulsions with different droplet sizes in the human digestive tract[END_REF][START_REF] Delorme | Effects of surfactants on lipase structure, activity, and inhibition[END_REF]. Regarding emulsifier type, our fat was emulsified by the proteins naturally present in skim milk. Emulsions stabilized with caseins and monoacylglycerols were recently reported to result in lower postprandial plasma TAG than those formulated with lecithin in non-obese humans [START_REF] Keogh | Slowly and rapidly digested fat emulsions are equally satiating but their triglycerides are differentially absorbed and metabolized in humans[END_REF], which can be explained by lower in vitro digestive lipolysis [START_REF] Vors | Coupling in vitro gastrointestinal lipolysis and Caco-2 cell cultures for testing the absorption of different food emulsions[END_REF]. Because obese men were the most affected by emulsification, further work should test the effect of emulsifiers on postprandial lipid metabolism. The importance of sensory exposure to lipids on postprandial metabolism in humans was also recently revealed [START_REF] Chavez-Jauregui | Dynamics of fat absorption and effect of sham feeding on postprandial lipema[END_REF]. Therefore, we cannot exclude a contribution of oral fat perception in our results. TAG-rich lipoproteins remaining elevated all along the postprandial phase are an independent CVD risk factor [START_REF] Chapman | Triglyceride-rich lipoproteins and high-density lipoprotein cholesterol in patients at high risk of cardiovascular disease: evidence and guidance for management[END_REF][START_REF] Maggi | Lipoprotein remnants and endothelial dysfunction in the postprandial phase[END_REF], which can be mechanistically related to the atherogenic potential of small chylomicron remnants [START_REF] Pang | Postprandial dyslipidaemia and diabetes: mechanistic and therapeutic aspects[END_REF][START_REF] Su | Methods to assess impaired post-prandial metabolism and the impact for early detection of cardiovascular disease risk[END_REF]. In this study, we collected CMRF fractions containing chylomicrons and their large remnants. The latter do not contribute significantly to the formation of small atherogenic remnants because of direct hepatic clearance [START_REF] Karpe | Chylomicron/chylomicron remnant turnover in humans: evidence for margination of chylomicrons and poor conversion of larger to smaller chylomicron remnants[END_REF].

However, the role of TAG-rich particles is still debated, underlining that this is still an open field needing further studies [START_REF] Lopez-Miranda | Dietary, physiological, genetic and pathological influences on postprandial lipid metabolism[END_REF][START_REF] Nordestgaard | Nonfasting triglycerides and risk of myocardial infarction, ischemic heart disease, and death in men and women[END_REF]. In this context, our study shows for the first time in NW and obese men that postprandial lipemia profile can be modulated by structuring the fat in a mixed meal. Therefore, the study of atherogenic small particles in the postprandial phase after consumption of differently structured lipids in the meal should now be performed.

Obese men presented a delayed increase of CMRF-TAG after spread fat. Overweight men were also reported to present delayed TAG-rich lipoprotein metabolism after a high-fat load [START_REF] Nabeno-Kaeriyama | Delayed postprandial metabolism of triglyceride-rich lipoproteins in obese young men compared to lean young men[END_REF]. Our observed differences between the two breakfasts in obese men could be explained by their lower pancreatic secretion and lower levels of gallbladder emptying compared with lean men [START_REF] Wisen | Gastrointestinal function in obesity: motility, secretion, and absorption following a liquid test meal[END_REF]. Therefore, obese subjects can better hydrolyze fat when it is pre-emulsified. Moreover, fine stable emulsions were reported to be emptied faster and to cause greater release of cholecystokinin than those that broke and layer in the stomach [START_REF] Marciani | Enhancement of intragastric acid stability of a fat emulsion meal delays gastric emptying and increases cholecystokinin release and gallbladder contraction[END_REF][START_REF] Marciani | Effect of intragastric acid stability of fat emulsions on gastric emptying, plasma lipid profile and postprandial satiety[END_REF]. We can thus suggest that the fat absorption delay observed with spread fat can be due to layering in the stomach and thus delayed emptying. Of note, immediately after lunch, a peak of 13 C-FA appeared in plasma. This so-called -second-meal effect‖ is known as the contribution of lipids from a meal to lipemia after the next meal [START_REF] Silva | Meal ingestion provokes entry of lipoproteins containing fat from the previous meal: possible metabolic implications[END_REF]. For obese men, the marked delay in lipemia appearance after spread fat cumulated with the second meal effect, causing high lipemia until the test ended. In contrast, obese subjects (without fasting hyperlipidemia herein) did not have difficulties in absorbing the emulsion, with a final return of lipemia to baseline.

In NW subjects, higher CMRF-TAG after the emulsion corresponded transiently to an increased number of particles, as shown by the similar CMRF-size with an increased ApoB48 level at 120 min. In obese subjects however, ApoB48 levels remained similar, i.e., the increase in lipemia after emulsion was due to an increased CMRF size. High particle numbers estimated by ApoB48 level are reported to lead to increased chylomicron remnant numbers, hence potentially increased atherosclerotic risk [START_REF] Xiao | Regulation of chylomicron production in humans[END_REF][START_REF] Pang | Postprandial dyslipidaemia and diabetes: mechanistic and therapeutic aspects[END_REF]. It would now be useful to explore the chronic metabolic impact of fat structure, especially regarding ApoB48-containting particles.

The few reports about metabolic effects of emulsions have solely studied lipemia or plasma FA concentrations as endpoints. For the first time to our knowledge, our study shows that fat emulsification further affects the metabolic handling of exogenous FA, including oxidation. Early appearance of 13 CO 2 was due to the rapid -oxidation of short-chain FA that are directly absorbed in the portal vein and oxidized by the liver [START_REF] Michalski | Specific molecular and colloidal structures of milk fat affecting lipolysis, absorption and postprandial lipemia[END_REF]. Obesity is associated with a defect in the -oxidation of dietary FA [START_REF] Binnert | Influence of human obesity on the metabolic fate of dietary long-and medium-chain triacylglycerols[END_REF][START_REF] Schutz | Failure of dietary fat intake to promote fat oxidation: a factor favoring the development of obesity[END_REF][START_REF] Giacco | Insulin sensitivity is increased and fat oxidation after a high-fat meal is reduced in normal-weight healthy men with strong familial predisposition to overweight[END_REF][START_REF] Bessesen | Dietary fat is shunted away from oxidation, toward storage in obese Zucker rats[END_REF]. Hodson et al. recently challenged this idea by showing greater FA -oxidation in obese men and attributed this to specific FA acid partitioning [START_REF] Hodson | Greater dietary fat oxidation in obese compared with lean men: an adaptive mechanism to prevent liver fat accumulation?[END_REF]. We highlight that exogenous FA oxidation can be enhanced in obese men by emulsifying fat. Discrepancies between reports can thus be explained by the present -fast vs slow lipid‖ notion. Indeed, lower -oxidation in obese vs lean subjects was observed using a single oil bolus (18) whereas higher -oxidation in obese was observed when the tracer was dispersed into an emulsion [START_REF] Hodson | Greater dietary fat oxidation in obese compared with lean men: an adaptive mechanism to prevent liver fat accumulation?[END_REF]. This aspect had not been taken into account by previous authors. Moreover, emulsification is now advised to enhance the intestinal absorption of essential fatty acids [START_REF] Garaiova | A randomised cross-over trial in healthy adults indicating improved absorption of omega-3 fatty acids by pre-emulsification[END_REF]. However, our results highlight the risk that such essential FA quickly absorbed can be lost in the -oxidation process rather than being bioavailable for cell membrane turnover. Therefore, further studies on the structuring of oils rich in essential PUFA should now investigate their final postprandial metabolic fate.

The effect of emulsification on exogenous lipid oxidation cannot be due to differences in intestinal absorption because of similar fecal excretion. Total lipid oxidation during the test day was unchanged by fat structuring, as well as total energy expenditure and diet-induced thermogenesis. However, the source of -oxidized FA was different: using emulsion, exogenous FA ingested at breakfast were shunted towards -oxidation pathways. Using spread fat, more endogenous FA and/or exogenous FA ingested at lunch were oxidized so that exogenous FA ingested at breakfast were more oriented towards storage. Therefore, regardless of energy balance, FA metabolism is changed by lipid structure. This is consistent with the greater FA spillover after emulsion. It can be explained by the faster intestinal absorption, resulting in enhanced lipolysis of chylomicrons that generates exogenous NEFA [START_REF] Fielding | Tracing the fate of dietary fatty acids: metabolic studies of postprandial lipaemia in human subjects[END_REF]. Their early influx can serve as fuel for tissues and explain their higher contribution to total FA oxidation with emulsion. However, high NEFA can also constitute a risk for ectopic fat accumulation [START_REF] Fielding | Tracing the fate of dietary fatty acids: metabolic studies of postprandial lipaemia in human subjects[END_REF]. Another aspect in obesity research concerns energy balance and satiety regulation [START_REF] Halford | Satiety-enhancing products for appetite control: science and regulation of functional foods for weight management[END_REF][START_REF] Simpson | CCK, PYY and PP: the control of energy balance[END_REF]. Just before lunch, our obese subjects felt hungrier after emulsion than spread fat (visual analog scales, Supplemental figure 5). Further trials could test the impact of fat structuring at breakfast on satiety regulation at lunch and energy balance.

In summary, we demonstrate that the postprandial metabolic handling of dietary FA can be significantly modified by emulsifying the fat in the meal, especially in obese subjects. The clinical perspectives of this first study should thus not be underrated. This study supports the further exploration of a possible dietary concept of -fast vs slow lipid‖ for the nutritional management of metabolic diseases through food formulation. Our results in the postprandial phase raise the questions of whether (i) daily ingestion of -fast vs slow fat‖ would result in different lipid metabolisms, adiposity and/or cardiovascular risk markers in the long term and (ii) the composition and structuring of dietary lipids could be optimized to this aim. data; SLP: designed research, administrative support; HV: critical revision of the manuscript for important intellectual content; ML: critical revision of the manuscript for important intellectual content; MCM: designed research, study supervision, analyzed data, wrote paper, had primary responsibility for final content. All authors read and approved the final manuscript. No conflict of interest exists for this manuscript. 13 C-labelled triglycerides.

1 Identical nutrient composition for both spread and emulsion breakfasts.

2 Fatty acid profile of TAG includes 68.6% SFA, 28.1% MUFA and 3.3% PUFA. 3 Natural vanillia-flavoured.

Breakfast composition

13 C

 13 -fatty acids in plasma lipids, NEFA, CMRF and stools Sample preparation. Internal standards were added according to the fraction analyzed (heptadecanoic acid or glycerol triheptadecanoate).

Figure 1 .

 1 Figure 1. Postprandial profile after consuming spread fat (□, ) or emulsion (○, ): CMRF-

Figure 2 .

 2 Figure 2. Postprandial concentration profile and iAUC of total plasma lipids of 13 C-palmitic acid (mM) and 13 C-oleic acid (mM) in NW (A & C, respectively) and obese subjects (B & D,

Figure 3 .

 3 Figure 3. Postprandial concentration profile of insulin (mUI/L) and NEFA (µM) in NW (A &

Figure 4 .

 4 Figure 4. Postprandial profile of 13 C-appearance in breath in NW subjects (A) and obese

Table 1 .

 1 Anthropometric and fasting metabolic subject parameters.Data are means ± SEM. Groups are compared using unpaired Student's t-test.

		Normal weight	Obese	P value
		(n=9)	(n=9)
	Anthropometric parameters
	Age (years)	28.3 ± 1.4	30.2 ± 2.2	ns
	Body weight (kg)	72.0 ± 2.1	101.2 ± 1.9	< 0.0001
	BMI (kg.m -²)	22.3 ± 0.5	31.7 ± 0.3	< 0.0001
	Waist circumference (cm)	83.3 ± 1.6	105.9 ± 0.8	< 0.0001
	Fasting metabolic parameters
	Glucose (mM)	4.94 ± 0.16	5.19 ± 0.15	ns
	Insulin (mIU/L)	3.75 ± 0.59	7.14 ± 0.95	0.008
	HOMA	0.85 ± 0.14	1.69 ± 0.25	0.009
	Total cholesterol (mM)	4.85 ± 0.22	4.89 ± 0.24	ns
	HDL cholesterol (mM)	1.51 ± 0.10	1.09 ± 0.06	0.004
	LDL cholesterol (mM)	3.03 ± 0.27	3.11 ± 0.21	ns
	Triacylglycerols (mM)	0.85 ± 0.06	1.39 ± 0.18	0.017

Table 2 .

 2 Nutritional composition of the test breakfasts containing either spread or emulsified fat enriched with

Table 4 .

 4 Kinetic parameters after digestion of test breakfasts in NW vs obese subjects.Data are means ± SEM, n=9 per group. Parameters calculated over the indicated time period: C max indicates maximum concentration; d max indicates maximum diameter; ∆ max indicates maximum concentration delta; iAUC, incremental area under the curve; AUC, area under the curve. P values (P meal , P BMI and P mealxBMI ) obtained by ANOVA followed by post hoc Fisher PLSD.1 P values of two-way ANOVA for meal and BMI effects and their interactions.2 P values of two-way ANOVA for repeated measures regarding both time periods (before and after 300 min), for meal x BMI x time period interactions.

			1
		Quantity	Carbohydrates	Proteins	Lipids
		(g or mL)	(g)	(g)	(g)
	Anhydrous Milk Fat 2	40	-	-	40
	Skimmed milk 3	160	7.5	5.3	0.3
	Bread	50	28	4	0.5
	[1,1,1-13 C 3 ] trioctanoin	0.09	-	-	0.09
	[1,1,1-13 C 3 ] tripalmitin	0.30	-	-	0.30
	[1,1,1-13 C 3 ] triolein	0.21	-	-	0.21
	Total (g)	250.6	35.5	9.3	41.4
	% caloric intake		26	7	67

Acknowledgements

We thank volunteers for their involvement. J Peyrat, C Maitrepierre, N Torche are acknowledged for technical assistance, M Sothier for dietary survey analysis, and C Louche-Pelissier, A Faure, M Cervantes, C Norbert and S Gonin for management of lipid analyses. B Morio, JM Antoine, C Boyer and C Marmonier are acknowledged for useful discussions. We thank RJ Ward for editing the English language. F Bellisle is acknowledged for advice on visual analog scales for hunger responses. Corman SA (N Vaghefi) is acknowledged for kindly providing AMF, Lactalis R&D (P Le Ruyet) for providing skimmed milk powder, and Sodiaal-Candia R&D (O Cousin, G Vialaret) for preparing and processing bottled flavoured skimmed milk. The authors' responsibilities were as follows -CV: designed research, conducted research, analyzed data, wrote paper, statistical analysis; GP: conducted research, technical support; LG: conducted research, technical support, analyzed data; JD: conducted research, technical and material support, analyzed data; CLP: conducted research, technical support; CD: acquisition of data, technical support; DL: critical revision of the manuscript for important intellectual content; MD: conducted research, analyzed data; SD: technical support, analyzed

This work was funded by the Centre National Interprofessionnel de l'Economie Laitière (CNIEL). CV received funding for PhD study by INRA and CNIEL. CV received a Research Prize from the Foundation Nestlé France. HV received financial support from Ezus-Lyon1.

The funding agencies had no role in the data analysis.

Table 3. Physico-chemical properties of fat used in the formulation of test breakfasts. 1 Mixture of milk fat + 13 C-TAG tracers. 2 Diameter of the peak of maximum intensity measured by DLS. 3 Surface averaged diameter measured by LLS. 4 For Spread fat: calculated as the surface of an equivalent sphere of 40 g. For Emulsion: calculated from the specific surface area (m 2 per g fat) calculated by the software, further multiplied by fat content in the meal. 5 Temperature at which the entire fat amount is in liquid form.

Fat properties