Ph.D Vincent Derozier
email: vincent.derozier@mines-ales.fr

Ph.D Sylvie Arnavielhe
email: sylvie.arnavielhe@kyomed.com

Professor Eric Renard
email: renard@chu-montpellier.fr

Professor Gérard Dray
email: gerard.dray@mines-ales.fr

Ph.D Sophie Martin
email: sophie.martin@univ-montp3.fr

How Knowledge Emerges from Artificial Intelligence Algorithm and Data Visualization for Diabetes Management

Keywords: Artificial intelligence, blood glucose, demonstrator, diabetes, machine learning, self-monitoring, Shared Decision Making

AI) Artificial intelligence, (AL) After lunch, (Avg) Average, (BB) Before breakfast, (BL) Before lunch, (BG) Blood glucose, (CGM) Continuous glycemic measurement, (DNA) Deoxyribonucleic acid, (Glu) Glucose, (HbA1c) Glycated haemoglobin A1c, (IQR) Interquartile range, (NI) Night, (Q1) Fist quartile, (Q3) Third quartile, (SMBG) Self-monitoring blood glucose, (SD) Standard deviation, (TA) Temporal abstraction, (TS) Structural Analysis, (WU) Wake-up.

Introduction

Diabetes is a major problem [START_REF] Danaei | National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2.7 million participants[END_REF][START_REF]Global status report on non-communicable diseases 2010[END_REF] in terms of both public health and economic costs for all affected countries. This study focuses on patients suffering from type 1 diabetes. For this type, many studies recommend and demonstrate that the use of self-monitoring blood glucose (SMBG) meters offers significant progress for managing diabetes in terms of reducing hyperglycemic and hypoglycemic periods. Understanding the significance of self-monitoring data is greatest when glycemic data collection has been organized in a structured manner [START_REF] Durán | Benefits of self-monitoring blood glucose in the management of newonset Type 2 diabetes mellitus: the St Carlos Study, a prospective randomized clinic-based interventional study with parallel groups[END_REF][START_REF] Kempf | a self-monitoring of blood glucose structured 12-week lifestyle intervention significantly improves glucometabolic control of patients with type 2 diabetes mellitus[END_REF][START_REF] Bonomo | Evaluation of a simple policy for pre-and post-prandial blood glucose self-monitoring in people with type 2 diabetes not on insulin[END_REF][START_REF] Polonsky | Structured self-monitoring of blood glucose significantly reduces A1C levels in poorly controlled, noninsulin-treated type 2 diabetes: results from the Structured Testing Program study[END_REF][START_REF] Franciosi | ROSES: role of selfmonitoring of blood glucose and intensive education in patients with Type 2 diabetes not receiving insulin. A pilot randomized clinical trial[END_REF] . The structure of these data is enhanced by adding key information, such as daily life information.

The use of more recent personal glucometers makes it easier to record information (semiautomatic process). All these data should be taken into account upon each physician's visit in order to yield objective quantitative and qualitative information on glycemic events that occurred over the period prior to the visit, thus allowing the physician to establish a detailed diagnosis. The amount of data recorded over a 3-month period may however contain several thousand measurement points. This mass of information-considered as Big Data-must then be preprocessed with analysis software to provide the physician with a summary overview. Some manufacturers of self-monitoring tools provide the basic analysis and data visualization software with their glucometers. These software programs perform statistical analyses on various time slots during the day or on specific days of the week [START_REF]iBGStar Diabeties Manager softwareuser guide[END_REF][START_REF]Accu-Chek Smart Pix user guide[END_REF][START_REF]One Touch softwareuser guide[END_REF] . Statistical analyses are often in a conventional descriptive format and remain largely unused by practitioners during consultation and decision-making [START_REF] Rodbard | Optimizing display, analysis, interpretation and utility of self-monitoring of blood glucose (SMBG) data for management of patients with diabetes[END_REF] . In general, these calculations are restricted to the mean, median, standard deviation and inter-quartile (between 1 st and 3 rd quartiles); they are presented in tabular format.

These tools also feature graphical display modes that use conventional statistical representations; a few such modes will be described below. The time series of blood glucose values are often shown by a scatter plot Figure

1.

The scatter plot is often superimposed with other information, as is the case with several diabetes management software packages. For example, the average daily value gives an overview of the glycemic trend over an extended period. This value is often qualified by the standard deviation, which provides critical information on the variability of blood glucose. Hirsch [START_REF] Hirsch | Should minimal blood glucose variability become the gold standard of glycemic control[END_REF] and Kovatchev [START_REF] Kovatchev | Evaluation of a New Measure of Blood Glucose Variability in Diabetes[END_REF] used this information to indicate the level of glycemic control a long-term risks as a more reliable indicator than HbA1c taken on its own. Using the mean value and standard deviation has drawbacks however: very sensitive to extreme outliers and the interpretation of standard deviation is biased due to the non-Gaussian, yet not right-skewed, distribution of blood glucose values. asymmetry and then results in a normal type data distribution [START_REF] Kovatchev | Symmetrization of the Blood Glucose Measurement Scale and Its Applications[END_REF] . Moreover, this transformation allows for the use of many statistical tools, in assuming data normality: blood glucose variability constitutes an essential element.

One of the best evaluators for variability, according to Rodbard, is the IQR (or Inter-Quartile Range) is not sensitive to extreme values (outliers) and does not require a normal distribution. Boxplot provides a quick understanding of all components, as shown in Figure 3. Although helpful for its visual and synthetic layout, this representation remains non-intuitive for those without an extensive background in statistics. Other forms of graphs are often available, such as "pie". Additional materials include data sheets in tabular format with glucose measurements per time slot, medications, insulin injections and other background notes. Through various studies [START_REF] Rodbard | New Approches to Display of Self-Monitoring of Blood Glucose Data[END_REF][START_REF] Rodbard | Display of Glucode Distributions by Date, Tyme of Day, and Day of Week: New and improved Methods[END_REF] , Rodbard suggested to modify conventional methods of viewing data from SMBG in order to adapt to physicians' constraints during their quarterly consultations. Using a color code or "stacked bar graph", Figure 4, while facilitating and accelerating the interpretation of glycemic measurements over several months. This visualization requires a learning phase dedicated to the large, which prove to be less intuitive. Other studies make use of glucose measurements by adding a predictive characteristic. An alternative glucose index has been developed to predict abnormal glycemia as a means of optimizing diabetes treatment and monitoring. of hyper / hypoglycemia: ADRR, or the average daily risk range for associating a risk index with a given period :

Equation 1 shows that the expression of this variable does not use raw glucose values but instead a standardized form to output the same weight to hypoglycemia as to hyperglycemia. For each glucose value, an index is then calculated separately for hypoglycemia (L) and hyperglycemia (H) on each day i, for averaging over the entire period M. This variable is used to predict abnormal glycemia (both severe hyperglycemia and severe components are extracted from the data using various mathematical tools (Kalman filters, least squares, etc.). An artificial intelligence technique called temporal abstraction (TA) will then serve to define the temporal interval sets characterized by the occurrence of exceptional event(s). For example, the TA "state" will all be time intervals characterized by a state in {low, normal, high, very high} (in terms of blood glucose). For the TA "trend" among {ascending, descending}, the determination makes use of the decomposition step in Equation 1.

This structural analysis (TS) and the temporal abstractions (TA) applied to the analysis of blood glucose time-

series
C i from Equation 2.
It is straightforward to isolate the marked green area characterized by a TA "maximum sugar for breakfast" and the TA "minimum sugar at dinner", which might be viewed as a conjunctive rule. This relatively complex approach is far from intuitive, yet it allows extracting high-level information that is complementary with statistical approaches. More recent publications [START_REF] Bowen | A User-Centered Glucose-Insulin Data Display for the Inpatient Setting[END_REF][START_REF] Bellazzi | Data Mining Technologies for Blood Glucose and Diabetes Management[END_REF] This study seeks to develop a graphical representation that simplifies the monitoring of blood glucose levels over a long period (approx. 3 months) by highlighting, through data mining algorithms, recurrent glycemic patterns characteristic of the patient's behavior without requiring the use of calculation or prediction algorithms. This objective is achieved through adapting a visualization method used in other applications (temperature maps, DNA expression level maps) to glycemic data. To simplify the process with smart connected glucometer device we developed a prototype application embedded with the proprietary software of the glucometer. This visualization is an extension of the scale developed by Richard M. Bergenstal et al. [START_REF] Bergenstal | Recommendations for Standardizing Glucose Reporting and Analysis to Optimize Clinical Decision Making in Diabetes: The Ambulatory Glucose Profile[END_REF] In this article they suggested a dashboard called AGP (Ambulatory Glucose Profile) for glucose reporting and analysis with numerical indexes ( Avg. Glu, %HbA1c, SD Glu, IQR Glu, Freq. for different ranges of Glu, etc…) and nested time plots (for the hours of the day or days of the month). In this article we use the term profile in a different way than the one used by Bergenstal. We call profile a significant color pattern over time rather than an AGP report summarizing several metrics.

Materials and methods

We use anonymous glycemic data from a database made of 62 patients using several glucometer devices. The content collected herein included time-stamped measures of glycemia (with date, time and time slot), other contextual information and unrestricted comments. For this study, we only considered the values of timestamped glycemia for all days regardless of the number of data points they contain. The first data processing step consists of standardizing the data from different device brands in order to generate a unique output. For this purpose, we employed a matrix structure, whereby each row represents a day and each column a time slot number in chronological order. The correspondence between the time slot number and the time of day is synchronized with the glucometer definitions set by the patient though a default mapping table could be used.

These times of day must naturally be tied to the patient's eating habits to ensure maximum relevance for the analysis (see Table 1). The second step entails transforming the numerical scale of blood glucose levels into a qualitative scale that highlights information on the patient's glycemic status rather than the numerical value of glycemia. To be successful, we used a color scale encoded on 6 levels (see Table 2 and Figure 7).

No measure

In Table 2 physicians suggest 5 categories of glycemia according to their severity. Bergenstal used a similar scale with 6 levels of severity. In our demonstrator these levels can be modified, if necessary, by the physician.

The core of our process is based on artificial intelligence algorithm (AI involves machines that can perform tasks that are characteristic of human intelligence) to make emerge high-level meaningful information (i.e.

knowledge).

Table 1:

Table of time slot vs. time of day / hours #time slot Time of day Hours

Time slot1

Waking up, WU Before 7 am

Time slot2

Before breakfast, BB

-10 am

Time slot3

After breakfast, AB 10 am -noon

Time slot4

Before lunch, BL

-3 pm

Time slot5

After lunch, AL

-6 pm

Time slot6

Before dinner, BD

-9 pm

Time slot7

After dinner, AD 9 -11:30 pm

Time slot8

Nighttime, NI After 11:30

Machine learning is simply a way of achieving AI, it is a process based on analysis of some training data sets.

That will allow us to highlight relevant information. Machine learning is not here used to generate a predictive model since it has been often described that diabetes is quite complex to predict. We focus our effort in understanding the recorded information instead of setting any prediction. In this perspective adding more data samples would not necessarily improve our algorithm. For this task, we applied an algorithm that consists of grouping a set of multidimensional vectors according to their similarity. Each group will thus be of a homogeneous composition and suitable for characterization by a single consensus vector. In our case for example, all vectors will be the days, and each time slot will represent one dimension of the vector. In comparing daily returns to evaluate their similarities in terms of glycemic profile (i.e. time slot values), we obtain groups of similar days and can proceed to identify patients' "glycemic pattern" characteristics (depending on group sizecluster). With a more formal expression, we can model this problem and solve it using the K-means algorithm [START_REF] Hartigan | A K-means clustering algorithm[END_REF] :

Let 𝑥 𝑛 = {𝑥 1 , 𝑥 2 , ⋯ , 𝑥 𝑛 } be the set of vectors modeling n days within a space of m dimensions (time slots), as:

𝑥 𝑖 = (𝑥 𝑖 1 , 𝑥 𝑖 2 ⋯ 𝑥 𝑖 𝑚 )
, with c j being the vector representing the center of each cluster, the problem then is to find k centers of c j that minimize the following function:

𝑗 = ∑ ∑‖𝑥 𝑖 𝑗 -𝑐 𝑗 ‖ 2 𝑛 𝑖=1 𝑘 𝑗=1 (3)
where ‖𝑥 𝑖 𝑗 -𝑐 𝑗 ‖ 2 denotes the Euclidean distance between two vectors x i and c j and moreover corresponds to our similarity measure. Minimizing j will now be defined by an iterative calculation sequence described in the box below: The main parameter to challenge is K, the number of cluster that we choose to be understandable according to the data by experience.

The second parameter is the type of the distance between objects to compare to achieve classification (objects are vectors of glycemia per day denoted by 𝑥 𝑖 𝑗 ). Without specific a priori knowledge we chose Euclidian distance which in a glance gives meaningful colored patterns.

Results

To develop a demonstrator, the algorithm has been implemented in an Android smartphone. We analyzed data file that we transferred to the phone one by one. The analysis was performed one set of data at a time. For all of them, a visualization map was processed then using K=3 for the K-mean algorithm, we had some clusters defined for every of the 62 samples. But only 19 were clearly understandable. We chose one of them as an example for the demonstration 1. The analysis of raw glycemic values data in the digital matrix is obtained by means of a qualitative conversion according to the 6 color code, it may now be used to identify consensus glycemic pattern during the period between two quarterly consultations (i.e. approx. 90 days) applying k-mean algorithm.

One example is provided in the Figure 9. This glycemic map with high variability does not, at first sight, reveal any general color trend. Use of the K-means algorithm on all days for k=3 (this parameter was found by iterative test with incremental values for k. The value used is the one that gives the most interpretable patterns) yields the two following groups, with each representing a dominant weekly trend.

The first cluster, which covers 34% of days, shows that the patient is hypoglycemic before and after breakfast and hyperglycemic before and after dinner and then hyperglycemic and highly hyperglycemic at night. This glycemic map also emphasizes missing data that could also be interpreted.

2. The second cluster-made of 24% of days-indicates that the same patient is hypoglycemic when waking up as opposed to hyperglycemic both before and after dinner but does not measure glycemia at night.

These two clusters provide useful information for the physician to complete his diagnosis, which is considerably more difficult when just looking at glycemic raw data. From another data file we could use parameters of the application to restrict the analysis for only post and preprandial timeslots, the result of the clustering on the Figure 10 identify 2 dominant clusters: one for hypoglycemia during wake-up (45% of the days) and one for hyperglycemia during dinner period (59% of the days).

Discussion

We initiated this study after the physicians' feedback of an unmet need for data visualization. Through their practices, recovering glucometer data was already a technical challenge. Using the device provided interface or previously described method for data visualization required technical options that often they could not install in the Hospital network. Our main goal was then to provide an easy-to use tool, interoperable, or at least used on a very friendly interface that would provide, in a glance, as much as much information as possible. This is why we did not compare our process to any other existing ones. Then the expertise of the physician would complement the analysis. The algorithm was supposed to pre-analyze more information than a regular human brain can do, in a short time and provide a visual scale according to the thresholds asked by physicians themselves.

Through our methodology, we follow a two steps process. The first one is a global intuitive visualization of data through a color-code overview. Three technical advantages : (1) reading and interpretation are instantaneous (compared to a numerical value in tabular format), (2) no need for pretreating the blood glucose values to compensate for the "asymmetry" of hypoglycemia since the color code is neutral, (3) the reading step is independent of the blood glucose measurement unit (either mg/dL, mmol/L or g/L).

The second step results in applying an machine learning algorithm that provides the physician with a thorough analysis of the patient's most frequent glycemic pattern based on several significant periods (e.g. weekly, pre / post-prandial, day of week, time slots, waking up in the morning / nighttime). This post-processed information of glycemic data is based on heat maps visualization; interpretation can therefore be processed very quickly with minimal training.

We can anticipate three levels of benefits of this methodology once deployed as a mobile appli Figure 11 (through the pilot study; the app has been deployed on Android phone. The demo version is available upon request):

1-For the physicians: During the visit, more data will be globally analyzed to overcome a specific behavior we noticed through patient data: the "white coat effect". The patient behavior in monitoring his glycemia could be different the month before the visit in comparison with the months before.

Through our process, a bigger time-scale of data is considered avoiding any "cheating" or "misleading" on data collection. Furthermore, data are post-processed for physician convenience, to emerge the knowledge on glycemic repetitive motifs and patterns that could not be qualified otherwise.

2-For the patient:

The visual representation of data should be more intuitive to understand the daily data.

That could bring a new insight on the diabetes understanding and management. Furthermore, it should become a decision-making tool to help and improve any decision about daily treatment. The wide-scale data visualization should become for the patient an education tool to appreciate his pathology on the long-term.

3-Shared decision making: the data could be contextualized on the long-term emphasizing, for example treatment doses, life events, and quality of life. In the long-term it would be an efficient way to prevent comorbidities that are quite silent and asymptomatic for patients. The algorithm is already multiparametric (multi dimensions) and allow the integration of various complementary variables. This Method is consistent with other methods, since we based our analysis on usual thresholds, usual metrics and usual physician's practice. Moreover this methodology could be used as a complement of any other method.

This methodology could be adjusted to any glycemic data collection device such as continuous glucose measurement [START_REF] Danne | International Consensus on Use of Continuous Glucose Monitoring[END_REF] (CGM) devices that generated huge amount of data. This algorithm could, eventually, be adjusted to the use of several glucometers by one patient.

Our visualization process seems to be more intuitive than the ones described by Rodbard and al. that could be evaluated with patients in upcoming studies. Another tool proposed by Bergenstal called AGP provide a dashboard with standardized metrics summarize glycemic patient profile. But the amount of information on one panel is not easy to read : the overall cognitive charge and perception of such tools, as already discussed by

Bowen and al., should be of priority concern to ensure that technology by itself does not create a limitation of the software use. Finally, our tool does not provide -so far-any prediction process, as described by Bellazi et al. [START_REF] Bellazzi | Data Mining Technologies for Blood Glucose and Diabetes Management[END_REF] since this field seem to require a lot of contextual data to be optimized.

Conclusion

Through this work we have highlighted characteristic patterns that underlie information and potential benefits that must be validated by physicians in an upcoming clinical trial. Usually, one month of glycemic data is analyzed during the visit. With this method, we can easily analyze three months of data and even much more.

The overall temporality of the knowledge is increased. We can anticipate that seasonal changes should appear in sort of glycemic patterns that impact the overall pathology evolution understanding. The next step will be to scale up the use of this app for Android device and to analyze the impact of this technology on patients, physicians and the process of shared decision making and expand our methodology for the huge data sets generated by continuous glycemic measurement devices.

Figure 1 .

 1 Figure 1. Graph of temporal variation of glycemia superposed with daily mean values (critical hypoglycemia threshold set to 50mg/dl) (17) Accu-Chek Smart Pix software V3.0 Roche

Figure 2 .

 2 Figure2. This representation with histograms clearly highlights the asymmetric distribution curve (i.e. non-Gaussian) "sliding" to the right. One consequence of this asymmetry in the time series representation Figure1is a graphical minimization of hypoglycemia as compared to hyperglycemia, which appears to be more accentuated. Correcting this bias by applying an operator, such as the logarithm (log10) may compensate for the

Figure 2 .

 2 Figure 2. Density of distribution of blood glucose values associated with a color code (software CoPilot V4.2 Abbott)

Figure 3 .Figure 4 .

 34 Figure 3. Representation of the dispersion by IRQ (A) tabular form, (B) graphic boxplot type.

Figure 5

 5 Figure 5 Structural analysis and temporal abstraction proposed by Bellazzi et al. 2009

  data are shown in Figure 5, where Bellazzi et al. applies a blood glucose time series on two time slots (+ = breakfast, O = dinner). The left-hand panels display an extraction of the T i component from Equation 2 of the raw data with a red line. These trends are indicated by the superposition of yellow rectangles (upward trend) and white rectangles (downward trend). The graph in the upper right shows the filtration of the cyclical component

  use pentagon to display multiparameters (5) with synthetic view Figure 6 or group different information (from different institutions, different devices) in a optimally organized view to minimize cognitive load and maximize effectiveness of diagnosis.

Figure 6 .

 6 Figure 6. Glucose pentagon representation with 5 parameters

Figure 7 .

 7 Figure 7. Process raw device data (~ 400 rows) to standard glycemic matrix representation transcoded with 6 color level

3 .

 3 Once all dots are assigned to a cluster, re-calculate the position of the center for each cluster found (we use the mean operator here) 4. Repeat steps 1 through 3 until the centers no longer move.The execution of this algorithm is illustrated inFigure 8 for m = 2 dimensions (time slots) To test and select clustering algorithms (unsupervised classifier) we have used the data mining workbench of machine learning WEKA developed by the Waikato University.

Figure 8 .

 8 Figure 8. K-means algorithm step by step. To simplify we used only 2 time slots (2Dgraph). We identify 3 groups of points in the scatter plot that represents 3 characteristic glycemic patterns

WUFigure 9 .Figure 10 .

 910 Figure 9. Highlighting weekly behavior by applying the K-mean algorithm

Figure 11 .

 11 Figure 11. Two steps process of glucometer data analysis

Table 2 : Six color-coded levels Glycemia (mg/dL)

 2

		Glycemic status	Color
	>250	High hyperglycemia
	[140, 250[	Hyperglycemia
	[80, 140[	Target
	]80, 60]	Hypoglycemia
	<60	High hypoglycemia

TABLE 1 TABLE 2 1

 12 . Set k dots initializing the center of each cluster cj 2. Assign each dot xi to the nearest center cj (using the similarity measure) and define each cluster


Moussa El Yahyaoui

Ali El Moussati

Ghaïs El Zein

Kamel Haddadi

New millimeter wave generation scheme for MIMO-OFDM based Radio-over-Fiber system

Keywords: 60GHz, MIMO, OFDM, Radio over Fiber, MMW generation

This paper proposes a new scheme for the generation and transmission of a MIMO-OFDM signal over optical fiber using a single local oscillator (LO) at 15GHz. This technique is based on subcarrier multiplexing and Optical Carrier Suppression (OCS) modulation using both Dual-Parallel Mach-Zehnder Modulator (DP-MZM) and Dual-Drive Mach-Zehnder Modulator (DD-MZM). DP-MZM is used to multiplex the MIMO-OFDM signals while DD-MZM is used to generate the 90GHz signal by quadrupling. The realistic and global simulation of the complete system is performed to predict the behavior of a Radio over Fiber (RoF) system prior to its realization. The optical and wireless channels are based on Single Mode Fiber (SMF) and Triple-S-and-Valenzuela (TSV) models, respectively. We have exploited the allocated 7GHz in the 60GHz band for unlicensed use and we have successfully achieved 50km SMF followed by 3m wireless link at 70Gb/s. In comparison with traditional methods, the technique proposed combines better performance at relatively low-cost.

Introduction

Over the last decade, there has been a lot of interest to achieve the highest data throughput. In this context, Multiple-Input Multiple-Output (MIMO) technology has proven its ability to improve data rate. However, the implementation of MIMO technology has been usually restricted to the microwave range (< 6GHz) resulting in limited data rate below few Gb/s. Therefore, the transition to higher frequency range, specifically the 60GHz band, is considered by standardization organizations as a viable alternative to respond to the need of high data rate. In particular, new standards foreseen in this frequency range are capable of delivering data rate up to 7Gb/s (802.11.ad and 802.15.3c) [START_REF] Bhusal | Qualitative and Quantitative Comparison of IEEE 802.15.3c and IEEE 802.11ad for Multi-Gbps Local Communications[END_REF]. However, the signals transmitted in this band suffer from a strong attenuation related to free-space propagation losses and multiple reflections due to the possible presence of obstacles. Thus, the radio coverage is generally limited to a few meters in the indoor environment. To extend this coverage to an entire building or a large area, Radio over Fiber (RoF) technology is considered.

Radio over Fiber has been widely described in the literature as a viable solution for transporting MIMO signals. Indeed, RoF allows the transport of multiple RF signals over single optical fiber, from central office to base station. Various techniques have been described to transport MIMO signals over fiber [START_REF] Elmagzoub | Polarization multiplexing of two MIMO RoF signals and one baseband signal over a single wavelength[END_REF][START_REF] Huang | Direct-Detection PDM-OFDM RoF System for 60-GHz 2 × MIMO Wireless Transmission Without Polarization Tracking[END_REF][START_REF] Ho | Performance Evaluation of a 60 GHz Radio-over-Fiber System Employing MIMO and OFDM Modulation[END_REF][START_REF] Lin | 60-GHz optical/wireless MIMO system integrated with optical subcarrier multiplexing and 2 × 2 wireless communication[END_REF][START_REF] Yahyaoui | On the capacity of MIMO-OFDM based diversity and spatial multiplexing in Radio-over-Fiber system[END_REF][START_REF] Yu | Faster than fiber: over 100-Gb/s signal delivery in fiber wireless integration system[END_REF]. Among them, we find the polarization-division-multiplexing (PDM) [START_REF] Elmagzoub | Polarization multiplexing of two MIMO RoF signals and one baseband signal over a single wavelength[END_REF][START_REF] Huang | Direct-Detection PDM-OFDM RoF System for 60-GHz 2 × MIMO Wireless Transmission Without Polarization Tracking[END_REF], optical subcarriers multiplexing [START_REF] Ho | Performance Evaluation of a 60 GHz Radio-over-Fiber System Employing MIMO and OFDM Modulation[END_REF][START_REF] Lin | 60-GHz optical/wireless MIMO system integrated with optical subcarrier multiplexing and 2 × 2 wireless communication[END_REF][START_REF] Yahyaoui | On the capacity of MIMO-OFDM based diversity and spatial multiplexing in Radio-over-Fiber system[END_REF], or PDM with optical heterodyne up-converter at base station [START_REF] Yu | Faster than fiber: over 100-Gb/s signal delivery in fiber wireless integration system[END_REF]. However, these techniques use more than one high-frequency local oscillator (up to 40GHz). In this work, we introduce a new technique that makes use of only one oscillator operating at 15GHz. In addition, this technique offers better performances compared to those used in [START_REF] Lin | 60-GHz optical/wireless MIMO system integrated with optical subcarrier multiplexing and 2 × 2 wireless communication[END_REF] and [START_REF] Yahyaoui | On the capacity of MIMO-OFDM based diversity and spatial multiplexing in Radio-over-Fiber system[END_REF].

The MIMO channel model used in this work is based on TSV model [START_REF] Shoji | A Modified SV-Model Suitable for Line-of-Sight Desktop Usage of Millimeter-Wave WPAN Systems[END_REF]. This model was proposed by the National Institute of Information and Communication Technology (NICT) -Japan to the 802.15.3c Channel model subgroup. TSV is a statistical model whose basic assumption is that multipath components arrive in clusters in both the temporal and spatial domains [START_REF] Hirokazu | Proposal of novel statistic channel model for millimeter wave WPAN[END_REF]. This model incorporates some of the antenna parameters within the channel accounting for their effects. We have used the model of Desktop environment which is a typical office desktop and computer cluster [START_REF] Sk | TG3c Channel Modeling Sub-committee Final Report[END_REF]. Compared to previous works [START_REF] Lin | 60-GHz optical/wireless MIMO system integrated with optical subcarrier multiplexing and 2 × 2 wireless communication[END_REF][START_REF] Yahyaoui | On the capacity of MIMO-OFDM based diversity and spatial multiplexing in Radio-over-Fiber system[END_REF] and considering in this paper a more precise channel model, we propose in this work a global architecture including a hybrid system in the optical and wireless regimes for communications around 60 GHz. The behavior prediction of such system is carried out by a global simulation using the real characteristics of the functions: baseband, optical/RF and transmission environment. To validate our approach, we have evaluated and compared our technique that uses one LO with the method based on two LOs, in function of bit error rate (BER) performance and optical power spectrum.

The rest of this paper is organized as follows: Section 2 presents the 2 × 2 MIMO-RoF proposed system. Section 3 describes its theoretical analysis. Next, Section 4 presents the results obtained by simulation. Finally, some concluding remarks are given in Section 5.

Principle of the proposed system

The proposed 2 × 2 MIMO-RoF architecture is presented in Fig. 1. The OFDM complex baseband signals, I j -OF DM and Q j -OF DM (with j=1,2), were generated using Matlab/Simulink and then sampled at 10 GS/s. These signals are obtained by mapping binary data into QPSK, 16QAM, or 64QAM.The mapped data is coded using a Vertical-Bell Laboratories Layered Space-Time (V-BLAST) coder, which corresponds to spatial multiplexing technique, to form two OFDM signals. Two training sequences are inserted before the Inverse Fast Fourier Transform (IFFT) operation for channel estimation at receiver. The OFDM signal is obtained by using FFT of 512 points with a cyclic prefix (CP) of 1/8 symbol time, and 355 subcarriers which corresponds to 6.93GHz bandwidth. The principle of the used RoF downlink, with an optical carrier (f c ) transmitting two RF OFDM (RF 1 and RF 2 ) signals, is based on dual parallel MZM (DP-MZM) and dual drive MZM (DD-MZM). The OFDM baseband signals are up-converted electrically to f LO =15GHz using I/Q mixers. The converted signals are coupled through a 90 • hybrid coupler. The DP-MZM is polarized to its minimum transmission bias point to realize optical carrier suppression (OCS) modulation and the used parameters are shown in Table 1. This MZM is driven by the combined signals to modulate the optical carrier and to multiplex the subcarriers as depicted in inset (b) [START_REF] Yahyaoui | On the capacity of MIMO-OFDM based diversity and spatial multiplexing in Radio-over-Fiber system[END_REF][START_REF] Zheng | Optical single sideband millimeter-wave signal generation and transmission using 120hybrid coupler[END_REF].

In order to generate 60GHz (4 × f LO ) from optical heterodyne in photodetector, a DD-MZM is used to generate two subcarriers, spaced by 90GHz (inset d'), to be combined with the multiplexed RF-OFDM subcarriers (inset c) [START_REF] Xue | Optical Single-Sideband Modulation Based on a Dual-Drive MZM and a 120 • ; Hybrid Coupler[END_REF]. The multiplexed subcarriers, depicted in inset (e), are transported from central station to base station through a single mode optical fiber. The received optical signal at base station is interleaved to separate the two RF-OFDM components of 2 × 2 MIMO signal. Finally, the interleaved signals are converted in the electrical domain using two photodiodes. An interleaver is used after DD-MZM to delete the unused subcarriers of frequency f c + f LO and f cf LO (an optical filter can be used).

f LO f LO f c f LO f c - (a) (c) (a') (a') BPF1 BPF2 60 GHz BPF1 I1 OFDM Q1 OFDM x 60 GHz BPF2 I2 OFDM Q2 OFDM x Wireless Channel (d) (d') f c (d') f c (d) f c f LO + f c f LO - f c +3.f LO f c -3.f LO f c +3.f LO f c +3.f LO f c -3.f LO f c -3.f LO x x f c f LO + f LO Q1 OFDM I1 OFDM Q2 OFDM I2 OFDM

Theoretical analysis

The signal generated from the DD-MZM, inset (d) of Fig. 1 , has the optical field E out (t) defined as

E out (t) = E in (t)exp [jβ cos (ω m t + ϕ)] + exp [jβ cos ω m t + jφ 0 ] (1)
where E in (t) = E c exp (jω c t) is the optical carrier, j 2 = -1, E c represents the amplitude, ω c is the angular frequency of the optical carrier (ω c = 2πf c ), β is the modulation index of the DD-MZM, ∆ϕ is the phase difference of RF signal applied to two arms of DD-MZM, φ 0 is the phase obtained by adjusting the DC bias of DD-MZM, ω m is the angular frequency for the RF signal. From Jacobi-Anger expansion [START_REF] Xue | Optical Single-Sideband Modulation Based on a Dual-Drive MZM and a 120 • ; Hybrid Coupler[END_REF], Eq. ( 1) has the form of

E out (t) = E c +∞ n=-∞ j n J n (β) exp [j(ω c + nω m )t]exp (jn∆ϕ) + exp (jφ 0 ) (2)
where J n (β) is the nth Bessel function of the first kind.

Since the sidebands of order 3 rd , 2 nd , 1 st , 0 th , +1 st , +2 nd , and +3 rd are of the most importance for our modulation, we list the optical fields of these sidebands as follows:

E -3 = E c exp (j 3π 2 )[J 3 (β 1 ) exp (-j3ϕ) + J 3 (β 2 ) exp (jφ 0 )] exp [j(ω c t -3ω e t)] E -2 = E c exp (jπ)[J 2 (β 1 ) exp (-j2ϕ) + J 2 (β 2 ) exp (jφ 0 )] exp [j(ω c t -2ω e t)] E -1 = E c exp (j π 2 )[J 1 (β 1 ) exp (-jϕ) + J 1 (β 2 ) exp (jφ 0 )] exp [j(ω c t -ω e t)] E 0 = E c [J 0 (β 1 ) + J 0 (β 2 ) exp (jφ 0 )]exp(jω c t) E +1 = E c exp (j π 2 )[J 1 (β 1 ) exp (jϕ) + J 1 (β 2 ) exp (jφ 0 )] exp [j(ω c t -ω e t)] E +2 = E c exp (jπ)[J 2 (β 1 ) exp (j2ϕ) + J 2 (β 2 ) exp (jφ 0 )] exp [j(ω c t -2ω e t)] E +3 = E c exp (j π 2 )[J 3 (β 1 ) exp (j3ϕ) + J 3 (β 2 ) exp (jφ 0 )] exp [j(ω c t -3ω e t)] (3)
The corresponding optical powers are:

P -3 = E 2 c [J 2 3 (β 1 ) + J 2 3 (β 2 ) + 2J 3 (β 1 )J 3 (β 2 ) cos (3ϕ + φ 0 )] P -2 = E 2 c [J 2 2 (β 1 ) + J 2 2 (β 2 ) + 2J 2 (β 1 )J 2 (β 2 ) cos (2ϕ + φ 0 )] P -1 = E 2 c [J 2 1 (β 1 ) + J 2 1 (β 2 ) + 2J 1 (β 1 )J 1 (β 2 ) cos (ϕ + φ 0 )] P 0 = E 2 c [J 2 0 (β 1 ) + J 2 0 (β 2 ) + 2J 0 (β 1 )J 0 (β 2 ) cos (φ 0 )] P +1 = E 2 c [J 2 1 (β 1 ) + J 2 1 (β 2 ) + 2J 1 (β 1 )J 1 (β 2 ) cos (ϕ -φ 0 )] P +2 = E 2 c [J 2 2 (β 1 ) + J 2 2 (β 2 ) + 2J 2 (β 1 )J 2 (β 2 ) cos (2ϕ -φ 0 )] P +3 = E 2 c [J 2 3 (β 1 ) + J 2 3 (β 2 ) + 2J 3 (β 1 )J 3 (β 2 ) cos (3ϕ -φ 0 )] (4)
In order to obtain an OCS modulation with the even sidebands order suppressed (in our case 2 nd , 0 th , +2 nd order sidebands), the following conditions should be verified

cos (φ 0 ) = -1 cos (2ϕ -φ 0 ) = -1 cos (2ϕ + φ 0 ) = -1 β 1 = β 2 (5)
From ( 5), we have β 1 = β 2 , ϕ = π(mod2π), and φ 0 = π(mod2π). Thus, the OCS modulation can be realized by means of a 180 • hybrid coupler and a dual-drive MZM by a proper DC bias, which introduces an additional phase difference of π between the two arms of the dual-drive MZM.

Simulation results and discussion

The simulation of the proposed system is performed with a cosimulation technique using MATLAB/Simulink and OptiSystem. The simulations results are depicted in this section. We have started by fixing the optimal optical power injected in the optical modulators to avoid operation in the nonlinearity regime of optical components. Indeed, we have calculated the EVM performance at the receiver (Rx) for each value of optical power at the transmitter (Tx), as shown in Fig. 2. We have obtained 10dBm as optimal EVM is calculated for 50km SMF with, high efficiency, 64QAM modulation scheme. Fig. 3 shows the optical spectrum of generated subcarriers with 90GHz separation (a), the multiplexed subcarriers of OFDM-MIMO signals (b), the combined optical signal (c) to be transported throught optical fiber, and the frequency spectrum of millimeter wave at reception after photodetector.

For our simulations, the 2 × 2 MIMO channel is developed in our previous research [START_REF] Yahyaoui | On the capacity of MIMO-OFDM based diversity and spatial multiplexing in Radio-over-Fiber system[END_REF], which is based on TSV model [START_REF] Sk | TG3c Channel Modeling Sub-committee Final Report[END_REF]. To create sufficient antenna decorrelation, the transmitting and receiving antennas were separated by 9 cm and 5 cm, respectively [START_REF] Yahyaoui | On the capacity of MIMO-OFDM based diversity and spatial multiplexing in Radio-over-Fiber system[END_REF]. The considered radio link has 3m of distance between Tx and Rx. In Fig. 4, we show the variation of BER in function of optical fiber length at fixed signal-to-noise ratio Eb/No of 7dB. From this value, performance of 70Gb/s using a 64QAM modulation scheme is achieved over, up to, 42km of SMF length with a BER of 10 -3 . Then, we have calculated the performance of the system for 50km SMF, using different modulation schemes. The modulations used are QPSK, 16QAM, and 64QAM, which correspond to bit rates of 23.32Gb/s, 46.66Gb/s, and 70Gb/s, respectively. These results are obtained without using channel coding. Fig. 5 shows the performance of 2 × 2 MIMO-RoF for different modulations. These results are obtained for 50km SMF followed by 3m wireless For comparison purpose, we have also simulated a system based on two LO (25GHz and 35GHz) as in [START_REF] Lin | 60-GHz optical/wireless MIMO system integrated with optical subcarrier multiplexing and 2 × 2 wireless communication[END_REF]. This comparison is made in the same conditions and with the same parameters in terms of modulation, data rate, and optical power. As we can see in Fig. 6, the optical spectrum obtained with the proposed system, using a single LO (15GHz) (in blue), shows low attenuation of the optical power. Indeed, a difference of 10dB between the generated spectra is noticed. We have also compared the transmission performance of a 2 × 2 MIMO system at 70Gb/s with SMF fiber of 25km, as shown in Fig. 7. This comparison is performed by using 64QAM, 16QAM, and QPSK modulation schemes, which allows us to see easily the difference between the performances of studied systems. As we can see, our system offers better BER performance. Indeed, considering BER = 10 -3 , a gain of 10dB in term of Eb/No is noticed. Moreover, by comparing these results with those obtained experimentally in [START_REF] Lin | 60-GHz optical/wireless MIMO system integrated with optical subcarrier multiplexing and 2 × 2 wireless communication[END_REF], where they have used two LOs, we concluded that our system has better performance. Indeed, the authors in [START_REF] Lin | 60-GHz optical/wireless MIMO system integrated with optical subcarrier multiplexing and 2 × 2 wireless communication[END_REF] have transmitted 61.5Gb/s data rate signal with BER of 10 -3 over 25km SMF transmission followed by 3m wireless transmission, while we have transmitted 70Gb/s over 50km SMF followed by a 3m wireless transmission.

Conclusion

This paper presents the design and simulation of a new 2 × 2 MIMO OFDM-RoF system architecture based on a single oscillator at 15GHz. In addition to the simplicity and relatively low-cost of the proposed solution, the results obtained demonstrate better performance compared to those obtained using an architecture based on two oscillators. In particular, the solution proposed can achieve data rate of 70Gb/s over 50km SMF followed by a 3m wireless transmission. This performance improvement is due to the gain provided in terms of optical power generated.

Figure 1 :

 1 Figure 1: Schematic principle of the proposed system Parameter Value Extinction ratio 30 dB Switching bias voltage 4V Switching RF voltage 4V Bias voltage 1 0V Bias voltage 2 4V

Figure 2 :Figure 3 :

 23 Figure 2: EVM variation in function of injected optical power

Figure 4 :

 4 Figure 4: BER in function of SMF length for Eb/No = 7dB

Figure 5 :

 5 Figure 5: Variation of BER in function of modulation and Eb/No

Figure 6 :

 6 Figure 6: Optical spectra of the multiplexed subcarriers in both cases: with one LO (blue) and with two LOs (red)

  BER comparison for QPSK modulation

Figure 7 :

 7 Figure 7: BER comparison between using one LO and two LOs

Table 1 :

 1 Parameters of DP-MZM modulator


Laurent Brureau

Elise Emeville

Carole Helissey

Jean Pierre Thome

Luc Multigner
email: luc.multigner@inserm.fr

Pascal Blanchet

Endocrine Disrupting-Chemicals and Biochemical Recurrence of Prostate Cancer after Prostatectomy: A cohort study in Guadeloupe (French West Indies)

Keywords: Biochemical recurrence, Endocrine-disrupting chemicals, Prostate cancer DDE, p,p'-dichlorodiphenyldichloroethylene, PCB-153, polychlorinated biphenyl congener 153, EDCs, endocrine-disrupting chemicals, POPs, persistent organic pollutants, LOD, limit of detection, HR, hazard ratio, CI, confidence interval, PSA, prostatespecific antigen

Novelty and Impact: Environmental chemicals with hormonal properties, also called endocrine-disrupting chemicals are suspected to favor the occurrence of prostate cancer.

These chemicals are obviously still present at the beginning of therapy, and beyond, and consequently they could influence the treatment outcome. Here we report for the first time that exposure to the estrogenic insecticide chlordecone, determined at the time of diagnosis, increases the risk of biochemical recurrence after radical prostatectomy of organ-confined disease.

Introduction

Prostate cancer is the most common hormone-sensitive malignancy in men. [START_REF] Center | International variation in prostate cancer incidence and mortality rates[END_REF] Endogenous steroid sex hormones play a key role in the initiation and progression of this disease. [START_REF] Bosland | The role of steroid hormones in prostate carcinogenesis[END_REF] In vitro and in vivo experimental research has increasingly suggested that environmental chemicals with hormonal properties, also called endocrine-disrupting chemicals (EDCs), particularly those that mimic steroid sex hormones, may modulate the risk of developing prostate cancer. [START_REF] Prins | Endocrine disruptors and prostate cancer risk[END_REF][START_REF] Soto | Environmental causes of cancer: endocrine disruptors as carcinogens[END_REF][START_REF] Sweeney | Environmental endocrine disruptors: Effects on the human male reproductive system[END_REF] . Among these, persistent organic pollutants (POPs) have attracted attention because of their long-term presence in the environment and for some of them their ability to interfere with steroid sex hormone-regulated processes. [START_REF] Cooke | Disruption of steroid hormone signaling by PCBs[END_REF][START_REF] Hammond | Estrogenic activity of the insecticide chlordecone (Kepone) and interaction with uterine estrogen receptors[END_REF][START_REF] Kelce | Persistent DDT metabolite pp'-DDE is a potent androgen antagonist[END_REF][START_REF] Oh | Antiestrogenic potentials of ortho-PCB congeners by single or complex exposure[END_REF][START_REF] Plísková | Impact of polychlorinated biphenyls contamination on estrogenic activity in human male serum[END_REF] Several epidemiological studies using exposure biomarkers have been conducted to investigate the relationship between human exposure to POPs with hormonal properties during adulthood and the risk of prostate cancer. [START_REF] Aronson | Plasma organochlorine levels and prostate cancer risk[END_REF][START_REF] Emeville | Associations of plasma concentrations of dichlorodiphenyldichloroethylene and polychlorinated biphenyls with prostate cancer: a case-control study in Guadeloupe (French West Indies)[END_REF][START_REF] Koutros | Prediagnostic Serum Organochlorine Concentrations and Metastatic Prostate Cancer: A Nested Case-Control Study in the Norwegian Janus Serum Bank Cohort[END_REF][START_REF] Multigner | Chlordecone exposure and risk of prostate cancer[END_REF][START_REF] Pi | Associations of serum organohalogen levels and prostate cancer risk: Results from a case-control study in Singapore[END_REF][START_REF] Ritchie | Organochlorines and risk of prostate cancer[END_REF][START_REF] Ritchie | Comparison of proposed frameworks for grouping polychlorinated biphenyl congener data applied to a case-control pilot study of prostate cancer[END_REF][START_REF] Sawada | Plasma organochlorines and subsequent risk of prostate cancer in Japanese men: a nested case-control study[END_REF][START_REF] Xu | Associations of serum concentrations of organochlorine pesticides with breast cancer and prostate cancer in U.S. adults[END_REF] Conclusions have been divergent, sometimes even demonstrating associations in opposite directions, depending on the chemical considered or the aggressiveness of the disease at the time of diagnosis. Such disparities may be explained by the different hormonal properties of these compounds, agonistic or antagonistic, on different members of the steroid sex receptor superfamily, the clinical and molecular heterogeneity of the disease, or differences in exposure windows.

The influence of human exposure to EDCs on the development of prostate cancer is still an open question. Nonetheless, patients are still exposed at the time of diagnosis and subsequent treatment. This raises the question of the influence of EDCs on disease progression after treatment. Surgical resection of the entire gland, radical prostatectomy, is a common therapeutic option, for curative purposes, for men with organ confined prostate cancer. However, approximately 20-40% of patients will present biochemical recurrence (rising prostate-specific antigen, PSA) of the disease. Many studies, have identified clinical and pathological features to be significant risk factors of biochemical recurrence [START_REF] Srigley | Prognostic and predictive factors in prostate cancer: historical perspectives and recent international consensus initiatives[END_REF][START_REF] Punnen | Multi-institutional validation of the CAPRA-S score to predict disease recurrence and mortality after radical prostatectomy[END_REF][START_REF] Brureau | Predictors of biochemical recurrence after radical prostatectomy in an Afro-Caribbean population in Guadeloupe (French West Indies)[END_REF] but nothing is known about the influence of environmental chemicals We sought to analyze the association between blood concentrations of various POPs at the time of diagnosis and the risk of biochemical recurrence after radical prostatectomy, a common surgical procedure for the complete removal of the prostate for men with clinically organ-confined prostate cancer.

Among POPs, we have focused our attention on the most prevalent ones such as the estrogenic insecticide chlordecone, the anti-androgenic p, p'dichlorodiphenyldichloroethylene (DDE, the main and most stable metabolite of the insecticide p, p'-dichlorodiphenyltrichloroethane, DDT), and the non-dioxin-like polychlorinated biphenyl congener 153 (PCB-153), which displays both estrogenic and antiestrogenic properties.

Material and Methods

Study population

This study took place in Guadeloupe (French West Indies), a Caribbean archipelago, where most of the inhabitants are of African descent. It was prospectively conducted on incident prostate cancer patients who initially participated in a population-based case-control study between June 2004 and December 2007, [START_REF] Multigner | Chlordecone exposure and risk of prostate cancer[END_REF] subsequently underwent radical prostatectomy in one single center (Urology Department of the University Hospital of Guadeloupe) (n = 392), and from whom we were able to obtain plasma samples and determine the POPs concentrations one to three months before surgery (n = 340). All patients were followed by serial PSA determinations and clinical visits every six months for the first three years after surgery and annually thereafter. Biochemical recurrence was defined as two consecutive (usually four weeks apart) PSA measurements above 0.2 ng/ml, with the date of the first increase after nadir as the recurrence date. [START_REF] Heidenreich | EAU Guidelines on Prostate Cancer. Part 1: Screening, Diagnosis, and Treatment of Clinically Localised Disease[END_REF] Fourteen patients were excluded because they were treated with neoadjuvant hormonal therapy or radiotherapy or because the PSA value did not return below 0.2 ng/ml six weeks after surgery. The study was approved by the Guadeloupean ethics committee for studies involving human subjects. Each participant received, completed, and signed written informed consent.

Before surgery, we collected information on the demographic characteristics, Caribbean origin (French West Indies, Haiti or Dominica), education (primary, secondary, high school or higher), weight and height allowing the calculation of body mass index (kilograms per meter squared), smoking (never, former or current), alcohol consumption (never, former, or current), diabetes type 2 (no, yes), and family history of prostate cancer (first degree relatives: no, yes, not known), and preoperative PSA level. After surgery, we collected data on the pathological stage, prostatectomy ISUP Gleason grade, [START_REF] Who | Pathology and Genetics of Tumours of the Urinary System and Male Genital Organs[END_REF] surgical margins, tumour density, and follow-up PSA levels.

Laboratory Assays.

A non-dioxin-like (18, 28, 52, 101, 110, 128, 138, 143, 149, 153, 170, 180, 183, 187, 194, 195, 206, and 209). The limit of detection (LOD) was 0.05 µg/L for all POPs except 0.06 µg/L for chlordécone (0.06 µg/L). [START_REF] Emeville | Associations of plasma concentrations of dichlorodiphenyldichloroethylene and polychlorinated biphenyls with prostate cancer: a case-control study in Guadeloupe (French West Indies)[END_REF] . Detailed information about sampling, analysis, and quality assurance and control has been provided elsewhere. [START_REF] Multigner | Chlordecone exposure and risk of prostate cancer[END_REF][START_REF] Debier | Dynamics of PCB transfer from mother to pup during lactation in UK grey seals Halichoerus grypus: differences in PCB profile between compartments of transfer and changes during the lactation period[END_REF] Plasma total cholesterol and total triglyceride concentrations were determined enzymatically (DiaSys Diagnostic Systems GmbH; Holzheim, Germany) and total lipid concentration was calculated as previously described. [START_REF] Bernert | Calculation of serum "total lipid" concentrations for the adjustment of persistent organohalogen toxicant measurements in human samples[END_REF] Statistical Analysis.

We restricted our analysis to chemicals detected at a rate of more than 80% (DDE; PCB congeners 138, 153, and 180; and chlordecone). Correlations between concentrations of the frequently detected PCBs were explored by Spearman's rank correlation analysis (Supporting information, supplemental Table 1). The concentrations of the various PCBs were highly correlated (Spearman's rho ≥ 0.75; all p-values < 0.001), so we restricted further analysis to PCB-153.

Plasma POPs concentrations were categorized into quartiles, based on their distribution among patients. Time to event was defined as the duration between the date of surgery and the PSA value that defined the recurrence event. Patients who did not relapse, or were lost to follow-up, were censored at the last normal post-operative PSA measurement before October 30, 2017. Five-year BCR-free survival analysis were carried out using the Kaplan-Meier method and log-rank test for trend. The Cox proportional hazards method was used to compute adjusted survival curves and test for trend were assessed with quartiles of exposure scored as ordinal variables in ascending order. We also used multivariable Cox proportional hazards regression models and 95% confidence intervals (CI) to estimate the hazard ratio (HR) of biochemical recurrence associated with POPs exposure. Potential confounders were included in all statistical models if they predicted (p < 0.05) biochemical recurrence status: body mass index, family history of prostate cancer, pathological Gleason grade, pathological stage, surgical margins, tumor density (Table 1) and then selected by applying a backward selection procedure at p < 0.1. Missing data for covariates varied from none to two (0.6 %) for education, two (0.6 %) for alcohol, two (0.6 %) for family history of prostate cancer, three (0.9 %) for smoking, and eight (2.5 %) for diabetes. Missing data were handled using missing value indicator categories. The proportional hazards assumption was verified by the log-negative-log survival distribution function of all variables. Tests for linear trend across exposure categories were performed, with the natural log transformed POPs concentration treated as a continuous variable. Exposure levels equal to or below the LOD were estimated by a maximum likelihood estimation method. [START_REF] Jin | Analysis of lognormally distributed exposure data with repeated measures and values below the limit of detection using SAS[END_REF] Sensitivity analyses were conducted by excluding patients with positive surgical margins or prostatectomy ISUP Gleason grade 3 or higher, or advanced pathological stage (pT3 or pT4 or N+). Statistical analyses were carried out using SAS software version 9.3 (SAS Institute, Cary, NC, USA).

All tests were two-sided, and p < 0.05 were considered statistically significant.

Results

The results presented here were obtained from a study population of 326 patients. During recurrence are presented in Table 1. Detection levels and plasma concentrations of POPs are presented in Table 2.

The crude five-year BCR-free survival rates were 83.8%, 79.8%, 86.6%, and 65% for men in Adjusted Cox analysis showed that men in the highest quartile of chlordecone plasma concentration had a significantly higher risk of biochemical recurrence than those in the lowest quartile (HR= 2.51, 95% CI: 1.39-4.56) (Table 3). The linear relationship between exposure and response was significant (p-trend = 0.002). We obtained comparable results if DDE and PCB-153 concentrations were additionally included in the multivariable model (HR= 2.62, 95% CI: 1.40-4.90 for the highest quartile relative to the lowest quartile; p-trend = 0.002). DDE and PCB-153 exposure were not associated with a risk of BCR, irrespective of the model (Table 3).

Sensitivity analysis revealed that chlordecone exposure was still significantly associated with a risk of BCR after excluding patients with positive surgical margins or prostatectomy ISUP Gleason grade 3 or higher, or advanced pathological stage (Table 4).

Men who were in the highest quartile of chlordecone plasma concentration had a higher risk of BCR than those in the lowest quartile (Adjusted HR = 2.98, 95% CI: 1.06-8.38) and the linear relationship between exposure and response was significant (p-trend = 0.003). Here again, DDE and PCB-153 exposure were not associated with a risk of BCR (Table 4).

Discussion

This is the first study to investigate the association of POPs plasma concentrations, measured at the time of diagnosis, with biochemical recurrence following primary curative surgical treatment of prostate cancer.

In our study population, the highest quartile of exposure to chlordecone was consistently associated with a more than two-fold increased risk of biochemical recurrence, with a significant exposure-response relationship. This was still true after taking into account well-known prognostic clinic-pathological features of biochemical recurrence as confounding factors and in sensitivity analysis restricted to patients who did not present these adverse clinic-pathological features. In contrast, exposure to DDE or PCB-153 was not associated with BCR.

Chlordecone was extensively used from 1973 to 1993 in the French West Indies, to control the banana root borer. This pesticide undergoes no significant biotic or abiotic degradation in the environment, so permanently polluted soils and waters have remained the primary source of foodstuffs contamination, and human beings continue to be exposed to this chemical [START_REF] Multigner | Chlordecone exposure and adverse effects in French West Indies populations[END_REF] . In our study population, chlordecone plasma concentration was in the range of values observed for residents in the neighborhoods near the chlordecone factory in Hopewell (VA, U.S.). [START_REF] Cannon | Epidemic kepone poisoning in chemical workers[END_REF] Such exposure resulted from the air pollution and illegal dumping of waste laden into Hopewell's sewer system which in turn contaminated the James River and fish resources. There is currently no data on exposure to chlordecone in other populations.

Concerning other POPs, DDE and PCB congeners 138, 153, and 180 were the most prevalent found in the blood in our population, as expected and found in most populations worldwide.

Moreover, blood concentrations of these pollutants are in the range of background environmental levels currently found in the US population of similar age range [START_REF]Fourth National Report on Human Exposure to Environmental Chemicals. Updated Tables[END_REF] . This is not surprising, because the use of DDT in agricultural supplies or for disease vector control in the French West Indies was sporadic and industrial activities involving the significant use or emission of PCBs has only been very limited. Consequently, exposure to these chemicals is likely to be associated with background contamination of the food chain.

Biomonitoring of human tissues is considered to be a gold standard approach for assessing aggregated environmental chemical exposure, because it integrates all sources of exposure from different absorption pathways. [START_REF] Pirkle | Using biological monitoring to assess human exposure to priority toxicants[END_REF] The exposure to POPs observed in this study was assessed based on plasma concentrations at the time of diagnosis, prior to surgery, and before the outcome occurrence. These chemicals have a long half-life in the body, varying from six months for chlordecone to several years for DDE and PCB-153. Although the use of POPs is mostly banned worldwide, they are still present in the environment and populations are continuously exposed through food consumption. Thus, a single measurement of POPs concentration in the blood reflects the body burden under steady-state conditions and provides a sufficiently confident estimation of exposure over an extended period. However, the levels of each POPs varies depending on the type of foodstuffs (i.e. chlordecone is preferentially found in root vegetables, fish, and meat of local origin, DDE in food crops, fish and meat, and PCB in meat and milk). Thus, any significant changes in dietary habits may result in changes in the body burden over several years. However, no specific dietary recommendations have been made and we are not aware of any changes in the dietary habits of our patients after surgery.

Although radical prostatectomy is considered as a definitive therapy for organconfined prostate cancer, an increase in PSA levels after a variable period of undetectable or low detectable-stable PSA indicates the presence of residual prostatic cells in the body, which may be benign or malignant and in the surgical bed or disseminated. We do not know whether the positive association between chlordecone exposure and biochemical recurrence reflects a direct effect on the PSA production of residual cells or the proliferation of these cells, which in turn secrete PSA. PSA secretion is generally under androgen regulation, but chlordecone shows low affinity binding to the androgen receptor (AR) and acts as an AR antagonist rather than agonist. [START_REF] Kelce | Persistent DDT metabolite pp'-DDE is a potent androgen antagonist[END_REF] Moreover, there is currently no data supporting the involvement of chlordecone in AR-dependent or AR-independent mechanisms driving PSA mRNA expression or protein secretion.

We previously reported that chlordecone exposure was associated with an increased risk of prostate cancer occurrence and suggested that the estrogenic properties of the molecule may explain this association. [START_REF] Multigner | Chlordecone exposure and risk of prostate cancer[END_REF] Chlordecone interferes with estradiol signaling through binding to the nuclear estrogen receptors α (ERα) and β (ERβ), eliciting agonistic and antagonistic effects, respectively. [START_REF] Hammond | Estrogenic activity of the insecticide chlordecone (Kepone) and interaction with uterine estrogen receptors[END_REF][START_REF] Kuiper | Interaction of estrogenic chemicals and phytoestrogens with estrogen receptor beta[END_REF][START_REF] Lemaire | Activation of alpha-and betaestrogen receptors by persistent pesticides in reporter cell lines[END_REF] The ERα mediates the adverse effects of estrogen, such as aberrant proliferation, inflammation, and malignancy, whereas the ERβ exerts opposite and beneficial effects, such as anti-proliferative, pro-apoptotic, anti-inflammatory, and, potentially, anticarcinogenic effects. [START_REF] Ellem | The dual, opposing roles of estrogen in the prostate[END_REF] Thus, the combined interaction between the agonistic effects of chlordecone on the ERα and the antagonistic effects on the ERβ could lead to an imbalance, promoting cell proliferation. It is possible that such interplay could also play a role in the proliferation of residual prostatic tumor cells, if present, after radical prostatectomy.

Although biochemical recurrence is never followed by disease progression for some patients, it is almost certainly an indicator of persistent disease and an unquestionable risk factor for the development of subsequent distant metastases. Indeed, chlordecone has been found to promote angiogenesis through ERα activation, [START_REF] Clere | Estrogen receptor alpha as a key target of organochlorines to promote angiogenesis[END_REF] and both chlordecone and estradiol increase expression of the homeostatic chemokine receptor type 4 and enhance vascular cell adhesion protein 1 levels. [START_REF] Wang | Acceleration of autoimmunity by organochlorine pesticides: a comparison of splenic B-cell effects of chlordecone and estradiol in (NZBxNZW)F1 mice[END_REF] Moreover, chlordecone disrupts cell-adherence junctions by modulating E-cadherin and β-catenin expression. [START_REF] Starcevic | Kepone (chlordecone) disrupts adherens junctions in human breast epithelial cells cultured on matrigel[END_REF] Collectively, these effects participate in the tumor cell-microenvironment signaling involved in tumor and metastasis progression for several types of cancers, including prostate cancer. [START_REF] Bonkhoff | Estrogen receptor signaling in prostate cancer: Implications for carcinogenesis and tumor progression[END_REF][START_REF] Schlesinger | Vascular cell adhesion molecule-1 (VCAM-1)-an increasing insight into its role in tumorigenicity and metastasis[END_REF][START_REF] Sun | CXCL12 / CXCR4 / CXCR7 chemokine axis and cancer progression[END_REF][START_REF] Tai | Osteoblast-derived WNT-induced secreted protein 1 increases VCAM-1 expression and enhances prostate cancer metastasis by down-regulating miR-126[END_REF][START_REF] Taichman | Use of the stromal cell-derived factor-1/CXCR4 pathway in prostate cancer metastasis to bone[END_REF] Whether biochemical recurrence associated with chlordecone exposure is predictive of an increased risk of metastasis remains to explored through the long-term follow up of patients.

Several epidemiological studies in different locations, including in French West Indies, have suggested that exposure to DDE is associated with a higher risk of prostate cancer occurrence. 12,15,19 However, we did not find a significant association between DDE exposure and the risk of biochemical recurrence. DDE is known to inhibit AR-mediated effects through direct binding to the AR. [START_REF] Kelce | Persistent DDT metabolite pp'-DDE is a potent androgen antagonist[END_REF] A recent study has also shown that DDE lowers PSA mRNA and protein levels in human prostate cancer cell lines in a dose-dependent manner. [START_REF] Wong | p,p'-Dichlorodiphenyltrichloroethane (p,p'-DDT) and p,p'-dichlorodiphenyldichloroethylene (p,p'-DDE) repress prostate specific antigen levels in human prostate cancer cell lines[END_REF] DDE exposure of healthy men is negatively associated with serum concentration of 5α-dihydrotestosterone, [START_REF] Emeville | Persistent organochlorine pollutants with endocrine activity and blood steroid hormone levels in middle-aged men[END_REF] the most potent ligand and activator of the AR. Given these properties, DDE would have been expected to be associated with a decreased risk of biochemical recurrence due to a possible direct effect on PSA production or prostate cell growth, itself an AR-mediated effect. DDE may also have an agonistic effect on ERα. [START_REF] Li | In vitro profiling of the endocrine disrupting potency of organochlorine pesticides[END_REF] It is therefore difficult to predict the net effect of DDE on the prostate, given the potential effects on both the AR and ERα. [START_REF] Carruba | Estrogen and prostate cancer: an eclipsed truth in an androgen-dominated scenario[END_REF][START_REF] Ellem | Aromatase and regulating the estrogen:androgen ratio in the prostate gland[END_REF] The literature concerning PCBs is less clear, as there are contradictory and even opposite observations regarding their association with the risk of PCa occurrence. [START_REF] Aronson | Plasma organochlorine levels and prostate cancer risk[END_REF][START_REF] Emeville | Associations of plasma concentrations of dichlorodiphenyldichloroethylene and polychlorinated biphenyls with prostate cancer: a case-control study in Guadeloupe (French West Indies)[END_REF][START_REF] Koutros | Prediagnostic Serum Organochlorine Concentrations and Metastatic Prostate Cancer: A Nested Case-Control Study in the Norwegian Janus Serum Bank Cohort[END_REF][START_REF] Ritchie | Organochlorines and risk of prostate cancer[END_REF][START_REF] Ritchie | Comparison of proposed frameworks for grouping polychlorinated biphenyl congener data applied to a case-control pilot study of prostate cancer[END_REF][START_REF] Sawada | Plasma organochlorines and subsequent risk of prostate cancer in Japanese men: a nested case-control study[END_REF] Here, we did not observe a significant association between PCB-153 exposure and the risk of biochemical recurrence, in either direction. Unlike dioxin-like PCBs, non-dioxin-like PCBs (including PCB-153), which are the most highly prevalent PCBs in the environment, [START_REF] Mcfarland | Environmental occurrence, abundance, and potential toxicity of polychlorinated biphenyl congeners: considerations for a congener-specific analysis[END_REF] do not interact substantially with the aryl hydrocarbon receptor and may act through other pathways, such as steroid hormone signaling. [START_REF] Cooke | Disruption of steroid hormone signaling by PCBs[END_REF] Non-dioxin-like PCBs may interact with genomic and non-genomic ER pathways and exhibit both estrogenic and anti-estrogenic properties, depending on the in vivo or in vitro experimental models. [START_REF] Cooke | Disruption of steroid hormone signaling by PCBs[END_REF][START_REF] Oh | Antiestrogenic potentials of ortho-PCB congeners by single or complex exposure[END_REF][START_REF] Plísková | Impact of polychlorinated biphenyls contamination on estrogenic activity in human male serum[END_REF] The multiple potential steroidal mechanisms of non-dioxin-like PCBs are yet to be totally deciphered and it is thus difficult to have a clear picture of their actions.

In conclusions, our results shown that exposure to chlordecone, an environmental EDCs with estrogenic properties, at the time of prostate cancer diagnosis is significantly associated with a risk of biochemical recurrence after prostatectomy. Increasing evidence suggests that estrogens are critical players in human prostate cancer. [START_REF] Cussenot | Combination of polymorphisms from genes related to estrogen metabolism and risk of prostate cancers: The hidden face of estrogens[END_REF][START_REF] Dobbs | Estrogens and prostate cancer[END_REF] Our findings add support to the hypothesis that human exposure to environmental estrogens may influence prostate cancer progression

  high-resolution gas chromatograph (Thermo Quest Trace 2000, Milan, Italy) equipped with a Ni63 electron capture detection system was used to determine the blood concentration of chlordecone, p,p´-DDT, p,p´-DDD (dichlorodiphenyldichloroethane), p,p´-DDE, the α, β, and γ isomers of hexa-chlorocyclohexane (HCH), and 24 PCB congeners (International Union of Pure and Applied Chemistry number): 6 dioxin-like (77, 105, 118, 126, 156, and 169) and 18

1 ,

 1 923 person-years of follow-up after surgery, 93 men (28.5%) experienced biochemical recurrence. The median follow-up time was 6.1 years and among men without progression 7.3 years. The baseline characteristics of the study population and HR for risk of biochemical

  the 1 st , 2 rd , 3 rd , and 4 th quartile of chlordecone exposure, respectively (Log-rank test for trend, p = 0.01). For DDE and PCB-153 exposure, the five-year biochemical recurrence-free survival rates were 78.7, 83.8, 70.7, and 81.1 and 81.5, 79.0, 79.7, and 75.0 for men in the 1 st to 4 th quartile, respectively (DDE: Log-rank test for trend, p = 0.85; PCB-153: Log-rank test for trend, p = 0.83). Crude and adjusted survival curves are presented in supporting information (supplemental figures 1 to 3).

Table 1 Baseline characteristics of patients and HR (95% CIs) of biochemical recurrence of prostate cancer

 1

	Characteristic	n (%)	HR (95%CI)
	Age (years), [mean (interquartile range)]	63.7 (58.5-67.9) 1.01 (0.98-1.04)
	Caribbean origin
	French West Indies	313 (96.0)	1.0 (reference)
	Haiti or Dominica	13 (4.0)	1.43 (0.58-3.43)
	Education
	Primary	165 (60.0)	1.0 (reference)
	Secondary	97 (29.9)	0.64 (0.39-1.04)
	High school and higher	62 (19.1)	0.98 (0.38-1.20)
	Missing data	2
	Body mass index (kg/m²)
	< 25	141 (43.3)	1.0 (reference)
	25 to 30	154 (47.2)	1.19 (0.76-1.85)
	> 30	31 (9.5)	2.18 (1.15-4.14)
	Smoking
	Never	202 (62.5)	1.0 (reference)
	Former or current	121 (37.5)	0.91 (0.59-1.40)
	Missing data	3
	Alcohol consumption
	Never	49 (15.1)	1.0 (reference)
	Ever	275 (84.9)	1.02 (0.58-1.80)
	Missing data	2
	Type 2 diabetes
	No	266 (83.7)	1.0 (reference)
	Yes	52 (16.3)	1.03 (0.59-1.79)
	Missing data	8
	Family history of prostate cancer
	No	180 (55.6)	1.0 (reference)
	Yes	89 (27.5)	1.60 (1.01-2.54)
	Do not know	55 (16.9)	1.47 (0.85-2.55)
	Missing data	2
	Pre-operative PSA (ng/mL) [median (interquartile range)]	7.3 (5.5-10.5)	1.32 (0.90-1.94)
	Total lipids (g/L) [median (interquartile range]	5.2 (4.6-5.6)	0.96 (0.78-1.18)
	Pathological Gleason grade
	ISUP Gleason grade 2 or lower	280 (85.9)	1.0 (reference)
	ISUP Gleason grade 3 or higher	46 (14.1)	3.68 (2.36-5.74)
	Pathological stage
	pT2 and N0	269 (82.5)	1.0 (reference)
	pT3a or pT3b, or N +	57 (17.5)	2.90 (1.88-4.49)
	Surgical margins
	Negative	233 (71.5)	1.0 (reference)
	Positive	93 (28.5)	2.72 (1.80-4.10)
	Tumor density (%) [median (interquartile range)]	9.0 (5.0-17.0)	2.62 (1.60-4.29)
	Biochemical recurrence status
	No	233 (71.5)
	Yes	93 (28.5)
	Follow-up time (years) [median (interquartile range)]	6.1 (2.8-8.9)

Table 2 Detection and concentrations of persistent organic pollutants in plasma samples from the study population (µg/L)

 2

	POPs a		Detection frequency (%)			Percentile
							10th	25th	50th	75th Maximum
	Chlordecone		80.1		<LOD	0.16	0.38	0.69	19.1
	DDE			94.7			0.38	0.93	2.33	4.68	40.1
	DDD			22.1		<LOD <LOD <LOD <LOD	0.95
	DDT			28.0		<LOD <LOD <LOD	0.06	2.3
	PCB-28			47.7		<LOD <LOD <LOD	0.24	2.94
	PCB-52			50.0			<LD	<LD	0.05	0.39	6.72
	PCB-101		45.7		<LOD <LOD <LOD	0.12	0.83
	PCB-118		57.3		<LOD <LOD	0.07	0.16	2.38
	PCB-138		98.3			0.16	0.29	0.53	0.82	3.60
	PCB-153		97.0			0.20	0.38	0.76	1.17	5.96
	PCB-180		96.3			0.23	0.35	0.59	0.86	3.16
	-HCH			28.0		<LOD <LOD <LOD <LOD	1.1
	-HCH			35.3		<LOD <LOD <LOD	0.05	2.2
	-HCH			19.5		<LOD <LOD <LOD <LOD	0.60
	a Chlordecone was measured in 326 plasma samples, and other POPs in 300 plasma samples.
	PCB congeners 18, 77, 101, 105, 110, 126, 128, 143, 149, 156, 169, 170, 183, 187, 194, 195,
	206,	and	209	were	below	the	LOD	in	all	plasma	samples.

Table 3 HR (95% CIs) of biochemical recurrence of prostate cancer according to quartile of chlordecone, DDE and PCB-153 exposure

 3 For chlordecone: adjusted for pathological Gleason grade, pathological stage, surgical margins, body mass index, and family history of prostate cancer; For DDE or PCB-153: adjusted for pathological Gleason grade, pathological stage, surgical margins, and body mass index. For chlordecone: adjusted for pathological Gleason grade, pathological stage, surgical margins, family history of prostate, body mass index, DDE, and PCB-153; For DDE: adjusted for pathological Gleason grade, pathological stage, surgical margins, body mass index, chlordecone, and PCB-153; For PCB-153: adjusted for pathological Gleason grade, pathological stage, surgical margins, body mass index, chlordecone, and DDE.

	Exposure	Without	With	Crude HR (95% CI)	Adjusted HR (95% CI) a	Adjusted HR (95% CI) b
		biochemical	biochemical
		recurrence	recurrence
		(n)	(n)
	Chlordecone (µg/L)
	< 0.16	62	18	1.0 (reference)	1.0 (reference)	1.0 (reference)
	0.16 -0.37	57	27	1.50 (0.83-2.73)	1.63 (0.89-2.98)	1.55 (0.81-2.93)
	0.38 -0.68	64	18	0.97 (0.50-1.86)	1.03 (0.53-2.00)	0.94 (0.46-1.90)
	> 0.69	50	30	1.96 (1.09-3.52)	2.51 (1.39-4.56)	2.62 (1.40-4.90)
	P trend			0.01	0.002	0.002
	DDE (µg/L)
	< 0.93	54	21	1.0 (reference)	1.0 (reference)	1.0 (reference)
	0.93 -2.31	53	21	0.84 (0.46-1.54)	0.54 (0.29-1.01)	0.54 (0.27-1.04)
	2.32 -4.66	48	27	1.18 (0.67-2.09)	0.91 (0,50-1.66)	0.87 (0.45-1.71)
	> 4.67	59	17	0.71 (0.37-1.35)	0.55 (0.29-1.06)	0.50 (0.24-1.05)
	P trend			0.89	0.51	0.35
	PCB-153 (µg/L)
	< 0.38	54	20	1.0 (reference)	1.0 (reference)	1.0 (reference)
	0.38 -<0.74	51	25	1.28 (0.71-2.30)	1.09 (0.59-1.99)	1.19 (0.62-2.28)
	0.75 -1.16	55	20	0.94 (0.50-1.74)	0.88 (0.47-1.64)	1.05 (0.53-2.06)
	> 1.17	60	21	1.04 (0.56-1.92)	0.93 (0.49-1.76)	1.12 (0.55-2.28)
	P trend			0.81	0.41	0.82

a This article is protected by copyright. All rights reserved Accepted Article b This article is protected by copyright. All rights reserved Accepted Article

Table 4 Sensitivity analysis among patients with negative surgical margins, low-grade pathological Gleason score, and organ-confined disease. HRs (95% CIs) of biochemical recurrence of prostate cancer according to quartile of chlordecone, DDE and PCB-153 exposure

 4

	Exposure	Without	With	Crude HR (95% CI)	Adjusted HR (95% CI) a
		biochemical	biochemical
		recurrence	recurrence
		(n)	(n)
	Chlordecone (µg/L)
	< 0.16	35	6	1.0 (reference)	1.0 (reference)
	0.16 -0.37	43	6	0.87 (0.28-2.70)	0.61 (0.18-2.09)
	0.38 -0.68	42	3	0.44 (0.11-1.75)	0.42 (0.10-1.79)
	> 0.69	34	15	2.48 (0.96-6.39)	2.98 (1.06-8.38)
	P trend			0.008	0.003
	DDE (µg/L)
	< 0.93	38	6	1.0 (reference)	1.0 (reference)
	0.93 -2.31	31	7	1.24 (0.42-3.70)	0.99 (0.30-3.32)
	2.32 -4.66	34	8	1.24 (0.43-3.58)	1.32 (0.39-4.48)
	> 4.67	39	8	1.25 (0.43-3.62)	1.21 (0.36-4.05)
	P trend			0.66	0.96
	PCB-153 (µg/L)
	< 0.38	42	7	1.0 (reference)	1.0 (reference)
	0.38 -<0.74	31	8	1.49 (0.54-4.10)	1.36 (0.48-3.85)
	0.75 -1.16	30	8	1.33 (0.48-3.68)	0.92 (0.29-2.92)
	> 1.17	33	6	0.98 (0.33-2.93)	0.82 (0.25-2.72)
	P trend			0.94	0.89

This article is protected by copyright. All rights reserved Accepted Article

Acknowledgments: This work was supported by the French National Health Directorate. E Emeville was supported by a PhD fellowship from the Ligue Nationale Contre le Cancer. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors declare they have no actual or potential competing financial interest related to this work.

Some environmental chemicals can mimic human hormones, and may spur cancer progression. Here, the authors studied the effect of three prevalent organic chemicals on prostate cancer recurrence: the estrogenic insecticide chlordecone; DDE, a stable metabolite of DDT which has anti-androgenic properties; and PCB-153, which has estrogenic and anti-estrogenic properties. No effect was associated with DDE or PCB-153. However, those in the highest exposure quartile for chlordecone had 2-fold increased risk of biochemical recurrence, defined as two consecutive elevated PSA measurements, after prostatectomy. These findings add to the evidence that environmental estrogens could promote cancer progression.