Solene Houlle

Françoise Charbonnier

Estelle Houivet

Julie Tinat

Marie-Pierre Buisine

Olivier Caron

Jacques Benichou

Stéphanie Baert-Desurmont

Thierry Frebourg
email: frebourg@chu-rouen.fr

Evaluation of Lynch syndrome modifier genes in 748 MMR mutation carriers

Keywords: Lynch syndrome, MMR genes, cancer risk, modifier factors

Several studies have reported that, in Lynch syndrome resulting from mutations of the mismatch repair (MMR) genes, a CA repeat ≤17 within the IGF1 promoter, SNPs within the xenobiotic metabolizing enzyme gene CYP1A1 and SNPs on 8q23.3 and 11q23.1 modify colorectal cancer (CRC) risk in MMR mutation carriers. We analysed the impact of these polymorphisms on CRC risk in 748 French MMR mutation carriers derived from 359 families. We also analysed the effect of the Novel 1 SNP (18q21), which has recently been shown to increase CRC risk in the general population. We observed a significant difference in the CRC-free survival time between males and females, between MSH2 and MSH6 mutation carriers and between MLH1 and MSH6 indicating that this series is representative of Lynch syndrome. In contrast, the univariate log-rank test, as well as multivariate Cox model analysis controlling for familial aggregation and mutated MMR gene, year of birth and gender showed that the polymorphic alleles tested were not associated with a significant CRC risk increase, neither on the entire sample nor among males and females. This discrepancy with previous reports might be explained both by the genetic heterogeneity between the different populations analysed and the allelic heterogeneity of the MMR mutations. We conclude that genotyping of these polymorphisms is not useful to evaluate CRC risk in MMR mutation carriers and to optimise their clinical follow-up.

INTRODUCTION

Lynch syndrome or Hereditary Non Polyposis Colorectal Cancer (HNPCC), the most common form of inherited colorectal cancer (CRC), results from germline mutations within the genes of the mismatch repair system (MMR), MSH2, MLH1, MSH6 and PMS2 (for review see reference 1). In MMR mutation carriers, the main tumour risks are colorectal and endometrial cancers. The cumulative risk at 70 years of CRC has been estimated to be 47-78% in males [START_REF] Vasen | MSH2 mutation carriers are at higher risk of cancer than MLH1 mutation carriers: a study of hereditary nonpolyposis colorectal cancer families[END_REF][START_REF] Ramsoekh | Cancer risk in MLH1, MSH2 and MSH6 mutation carriers; different risk profiles may influence clinical management[END_REF][START_REF] Alarcon | Estimating cancer risk in HNPCC by the GRL method[END_REF] and 30-57% in females [START_REF] Vasen | MSH2 mutation carriers are at higher risk of cancer than MLH1 mutation carriers: a study of hereditary nonpolyposis colorectal cancer families[END_REF][START_REF] Ramsoekh | Cancer risk in MLH1, MSH2 and MSH6 mutation carriers; different risk profiles may influence clinical management[END_REF][START_REF] Alarcon | Estimating cancer risk in HNPCC by the GRL method[END_REF] and the risk of endometrial cancer to be 25-61%. [START_REF] Vasen | MSH2 mutation carriers are at higher risk of cancer than MLH1 mutation carriers: a study of hereditary nonpolyposis colorectal cancer families[END_REF][START_REF] Ramsoekh | Cancer risk in MLH1, MSH2 and MSH6 mutation carriers; different risk profiles may influence clinical management[END_REF] Like in other Mendelian forms of cancer characterized by an incomplete penetrance, one of the main challenges is to identify modifier genetic factors able to modulate the mutation penetrance. Characterization of validated modifier genetic factors should have important clinical consequences in the future since it should be possible to adapt the follow-up of MMR gene mutation carriers, in terms of nature and timing of investigations, according to modifier alleles.

The modifier genes which have been reported so far in Lynch syndrome have been characterized according to two strategies: several studies have been focused on genes whose implication in CRC development had previously been suggested. One of the first modifier genetic factors identified in Lynch syndrome corresponds to a CA repeat polymorphism present within the IGF1 promoter, 1 kb upstream from the transcriptional initiation site. [START_REF] Zecevic | IGF1 gene polymorphism and risk for hereditary nonpolyposis colorectal cancer[END_REF][START_REF] Reeves | IGF1 is a modifier of disease risk in hereditary non-polyposis colorectal cancer[END_REF] In a study performed in 121 MMR mutation carriers from 59 families, mainly of Caucasian origin, Zecevic et al. [START_REF] Zecevic | IGF1 gene polymorphism and risk for hereditary nonpolyposis colorectal cancer[END_REF] reported that a CA repeat ≤17 was significantly associated to a higher CRC risk and an earlier age of tumour onset. This result was confirmed by an independent study performed in 443 Australian and Polish MMR mutation carriers originated from 269 distinct families. [START_REF] Reeves | IGF1 is a modifier of disease risk in hereditary non-polyposis colorectal cancer[END_REF] Another class of modifier genes reported in Lynch syndrome corresponds to genes encoding xenobiotic metabolizing enzymes involved in environmental carcinogen metabolism. In 129 subjects of South African origin and harbouring the same MLH1 missense mutation, males harbouring the null genotype for the GSTT1 and GSTM1 genes developed cancer earlier than the males harbouring the other genotypes. [START_REF] Felix | GSTM1 and GSTT1 polymorphisms as modifiers of age at diagnosis of hereditary nonpolyposis colorectal cancer (HNPCC) in a homogeneous cohort of individuals carrying a single predisposing mutation[END_REF] This effect of the GSTT1 and GSTM1 null genotypes was not confirmed in a second study including 257 MMR mutation carriers from 130 families. [START_REF] Pande | Genetic variation in genes for the xenobiotic-metabolizing enzymes CYP1A1, EPHX1, GSTM1, GSTT1, and GSTP1 and susceptibility to colorectal cancer in Lynch syndrome[END_REF] Nevertheless, the authors reported that subjects heterozygous for the CYP1A1 rs1048943 SNP (c.1384A>G; p.Ile462Val) developed CRC earlier than the individuals with the homozygous wild-type genotype and that subjects heterozygous for this polymorphism and an additional SNP rs4646903 (Msp1; g.6235T>C) had an increased CRC risk. [START_REF] Pande | Genetic variation in genes for the xenobiotic-metabolizing enzymes CYP1A1, EPHX1, GSTM1, GSTT1, and GSTP1 and susceptibility to colorectal cancer in Lynch syndrome[END_REF] The second strategy, which has recently allowed the detection of modifier genes in Lynch patients, originated from the numerous genome-wide association studies reporting SNPs associated with CRC risk in the general population. Starting from the hypothesis that SNPs acting as risk factors for CRC in the general population might act as risk modifiers in patients harbouring a highly penetrant mutation, Wijnen et al. [START_REF] Wijnen | Chromosome 8q23.3 and 11q23.1 variants modify colorectal cancer risk in Lynch syndrome[END_REF] recently reported a significant association of CRC risk with rs16892766 (8q23.3) and rs3802842 (11q 23.1) in 675 Dutch MMR mutation carriers from 127 families.

In this study, we investigated the impact of these different genetic factors on CRC risk in a large series of French MMR mutation carriers.

PATIENTS AND METHODS

Patients

The study included 748 unselected patients (Table 1) derived from 359 families with Lynch syndrome and recruited from Rouen (n = 494) and Lille (n = 254) University hospitals in France. All these individuals were confirmed carriers of a deleterious mutation of MSH2, MLH1 or MSH6 and were Caucasian. Among these 748 mutation carriers, 329 (44%) had been diagnosed with CRC prior to inclusion in the study, with a mean age of CRC onset of 43 years (range: 18-82 years). Additionally, 51 mutation carriers (6.8%) had been diagnosed with another tumour belonging to the Lynch syndrome spectrum (endometrial carcinoma, tumour of urinary tract, ovarian carcinoma, cancer of the stomach and of the small intestine) and 368 (49.2%) had developed no tumour at the time of inclusion.

Genotyping

The IGF1 CA repeat was PCR-amplified using dye labelled primers and the length of the PCR products was determined after migration on an Applied Biosystems model 3130 Genetic Analyser (PE Applied Biosystems, Foster City, CA) and analysed using the GeneMapper Analysis Software version 4.0 (Applied Biosystems). To calibrate the results, we sequenced, after cloning into a plasmid, the most frequent allele (n=19) and used the corresponding genomic DNA as a reference DNA. The rs16892766 (8q23.3), rs3802842 (11q23.1) SNPs and the rs1048943 (c.1384A>G; p.Ile462Val) and rs4646903 SNPs, both located within the CYP1A1 gene (15q24.1), were genotyped using SNaPshot multiplex assays based on primer extension with dye labelled dideoxynucleotides (ABI PRISM SNaPshot Multiplex kit, Applied Biosystems, Foster, USA). We also analysed, by SNaPshot multiplex analysis, the Novel 1 SNP located on 18q21 which had been shown to be associated with CRC risk in the general population by altering SMAD7 expression. [START_REF] Pittman | The colorectal cancer risk at 18q21 is caused by a novel variant altering SMAD7 expression[END_REF] Labelled products were separated using a 25 min run on an ABI Prism 3100 DNA sequencer and data were analysed using the GeneMapper Analysis Software version 4.0 (Applied Biosystems). Primer sequences used for genotyping and conditions of the SNaPshot multiplex assays are available on request.

Statistical analyses

The genotype frequencies were tested for Hardy-Weinberg equilibrium. In all following analyses of time to CRC, first CRC onset was the event of interest, age (in years) was used at the time scale and retrospective follow-up started at birth for all subjects in the study and ended at either first CRC onset for incident CRC cases or age at last followup otherwise. These analyses were performed for all subjects and separately for males and females and were repeated for CRC cases only. The association of sex or mutated MMR gene with CRC risk was assessed using the log-rank test. The association of each polymorphism (i.e., five SNPs and IGF1 CA repeat length) and CRC risk was assessed using the log-rank test then the Cox proportional hazard model with stratification on gender (if applicable), mutated MMR gene (MSH2, MLH1, MSH6) and date of birth (<1940, 1940-1949, 1950-1959, 1960-1969, 1970-1979 and ≥ 1980) and with control for familial clustering. In the Cox model analyses of each of the five SNPs, an overall comparison of the three genotypes was performed and a hazard ratio (HR) and corresponding 95% confidence interval were estimated for homozygous and heterozygous subjects, relative to subjects homozygous for the wild-type allele.

Moreover, alternative Cox model analyses of each of the five SNPs considered the number of alleles with the SNP (0, 1, 2) and fitted a trend producing and alternative test of association with CRC risk and an estimate of the CRC hazard ratio per mutated allele. Statistical analyses were performed using the SAS software version 9.1 (SAS Institute, Cary NC).

RESULTS

Among the 748 MMR mutation carriers included into this study, 329 carriers (44%) developed CRC. As shown in Table 1, a significant difference was observed in the age at first CRC occurrence between males and females, with males developing CRC earlier, between MSH2 and MSH6 mutation carriers, between MLH1 and MSH6 mutation carriers, with MSH2 and MLH1 carriers developing CRC earlier, but not between MSH2 and MLH1 mutation carriers (log-rank test, P = 0.26). The five SNPs analysed, located on 8q23.3, 11q23.1, 18q21 and within CYP1A1 were in Hardy-Weinberg equilibrium. The distribution of the IGF1 CA repeat alleles was the following: 12 repeats (n=8), 13-15 repeats (n=0), 16 repeats (n=2), 17 repeats (n=28), 18 repeats (n=85), 19 repeats (n=976), 20 repeats (n=262), 21 repeats (n=114) and 22 repeats (n=21) and alleles were dichotomised in ≤17 and ≥ 18 repeats for the statistical analyses, like in the previous studies. [START_REF] Zecevic | IGF1 gene polymorphism and risk for hereditary nonpolyposis colorectal cancer[END_REF][START_REF] Reeves | IGF1 is a modifier of disease risk in hereditary non-polyposis colorectal cancer[END_REF] In univariate analysis, age at first CRC occurrence did not significantly vary for the five SNPs considered or according to number of IGF1 CA repeats whether for the whole sample or separately for males and females (Table 2). As indicated in Table 3, the multivariate Cox analysis performed in the total sample of mutation carriers revealed that, for rs16892766 (8q23.3), the homozygosity for the minor C allele present only in 3 mutation carriers was significantly associated to a decreased risk relative to subjects with AA alleles (HR = 0.267, P = 0.0271). For the CYPA1 SNPs, we observed a significant difference only in males for one of the SNPs analysed (rs4646903): the CC genotype, detected in 4 male mutation carriers, was associated with an increased risk relative to subjects with TT alleles (HR = 3.496, P = 0.0033). Considering that penetrance of MSH6 mutation was found in this study to be lower than that of MSH2 and MLH1 mutations, we restricted the log-rank and Cox analyses to MSH2 and MLH1 carriers and this restriction did not modify the results (data not shown).

Since colonoscopy had not been performed in all the unaffected mutation carriers at the time of this study, we then focused the statistical analyses on the 329 MMR mutation carriers with CRC, in order to avoid a bias in the phenotypic evaluation. As indicated in Table 4, the unrestricted multivariate Cox analysis showed that the only remaining significant association was a decreased risk associated with the 8q23.3 CC genotype but it should be emphasized that, because this genotype was only carried by two CRC cases, no HR estimate could be produced.

DISCUSSION

Like in other Mendelian predisposition to cancer with an incomplete penetrance, the characterization of modifier loci for CRC in Lynch syndrome might allow identification of a subset of mutation carriers who could benefit from a reinforced tumour detection program, such as annual chromocolonoscopy from 20 years of age.

This prospect prompted us to evaluate in a large series of 748 MMR carriers the risk conferred by the allelic variants which have recently been reported as risk modifiers for CRC in MMR mutation carriers. Within this series, 44% of the MMR mutation carriers had developed CRC at the time of the analysis, the median age of CRC onset was 43 years among them and we observed a significant difference of CRC penetrance between males and females and between MSH2 and MLH1 mutation versus MSH6 mutation carriers. These results are in agreement with the published studies which have estimated that the cumulative CRC risk at 70 years is higher in males than in females [START_REF] Vasen | MSH2 mutation carriers are at higher risk of cancer than MLH1 mutation carriers: a study of hereditary nonpolyposis colorectal cancer families[END_REF][START_REF] Ramsoekh | Cancer risk in MLH1, MSH2 and MSH6 mutation carriers; different risk profiles may influence clinical management[END_REF][START_REF] Alarcon | Estimating cancer risk in HNPCC by the GRL method[END_REF] and is lower in MSH6 mutation carriers as compared to MSH2 or MLH1 mutation carriers. [START_REF] Vasen | MSH2 mutation carriers are at higher risk of cancer than MLH1 mutation carriers: a study of hereditary nonpolyposis colorectal cancer families[END_REF][START_REF] Ramsoekh | Cancer risk in MLH1, MSH2 and MSH6 mutation carriers; different risk profiles may influence clinical management[END_REF][START_REF] Plaschke | Lower incidence of colorectal cancer and later age of disease onset in 27 families with pathogenic MSH6 germline mutations compared with families with MLH1 or MSH2 mutations: the German Hereditary Nonpolyposis Colorectal Cancer Consortium[END_REF][START_REF] Baglietto | Risks of Lynch syndrome cancers for MSH6 mutation carriers[END_REF] These findings are in favour of the representativeness of our series with respect to Lynch syndrome.

The distribution of IGF1 CA repeat length was similar to that reported by Zecevic et al. [START_REF] Alarcon | Estimating cancer risk in HNPCC by the GRL method[END_REF] in 121 MMR mutation carriers, the frequency of the major allele (19 repeats) being 65% and 58% in the two studies, respectively, although we found a lower proportion of alleles with length ≤17 (2.5% versus 5.1%). In the study published by Zecevic et al. [START_REF] Zecevic | IGF1 gene polymorphism and risk for hereditary nonpolyposis colorectal cancer[END_REF] on 121 MMR mutation carriers, the presence of a CA repeat ≤17, detected in 12 subjects, was found to be associated with a higher CRC risk (HR 2.36) and an earlier median age of CRC onset (44 versus 56.5 years), but our study, performed on a larger series, did not allow us to reproduce these results.

For the rs1048943 (c.1384A>G; p.Ile462Val) and rs4646903 SNPs, located within the CYP1A1 gene, we found respective allelic frequencies for the minor allele of 0.03 and 0.12, which correspond to the HapMap values estimated in the European population. Pande et al. [START_REF] Pande | Genetic variation in genes for the xenobiotic-metabolizing enzymes CYP1A1, EPHX1, GSTM1, GSTT1, and GSTP1 and susceptibility to colorectal cancer in Lynch syndrome[END_REF] had reported a higher MAF (0.074) for rs1048943 in 257 MMR mutation carriers, but it should be underlined that these carriers were from different ethnicities and included Hispanic and Asian subjects, in whom the allelic frequency of the G allele is known to be higher. In this series of 257 mutation carriers, CRC free survival statistically differed according to rs1048943 genotype, with a 1.78 fold increase of CRC hazard associated with the AG genotype, a result that we did not reproduce in our series of 748 MMR mutation carriers from Caucasian origin. For the second CYPA1 SNP (rs4646903), Pande et al. [START_REF] Pande | Genetic variation in genes for the xenobiotic-metabolizing enzymes CYP1A1, EPHX1, GSTM1, GSTT1, and GSTP1 and susceptibility to colorectal cancer in Lynch syndrome[END_REF] had reported that the TC genotype was associated with an increased hazard for earlier CRC and we detected no significant difference in our study for this genotype (Table 3). In contrast, we found that the CC genotype was associated in males with a 3.5 fold increased hazard ratio, but this observation should be interpreted with utmost caution, considering the limited number of male mutation carriers presenting this genotype (n=4). For rs16892766 (8q23.3) and rs3802842 (11q23.1) SNPs, we observed respective allele frequencies of 7% and 28%: these frequencies had been respectively estimated in the European population at 7% 13 and 29% 14 and, in 675 Dutch MMR mutation carriers at 10% and 24.9%. [START_REF] Wijnen | Chromosome 8q23.3 and 11q23.1 variants modify colorectal cancer risk in Lynch syndrome[END_REF] We did not detect in our series a modification of CRC risk in mutation carriers harbouring the 11q23.1 rs3802842 CC genotype, as previously reported in females. Among the 675 Dutch mutation carriers, the 8q23.3 rs16892766 CC genotype, detected in 9 subjects, was found to be associated with a 2.16 fold CRC risk increase. [START_REF] Wijnen | Chromosome 8q23.3 and 11q23.1 variants modify colorectal cancer risk in Lynch syndrome[END_REF] In contrast, we found in our study (Table 3) that this genotype was significantly associated to a decreased CRC risk (HR = 0.267), and this significant association was the only one remaining when we restricted the analysis to affected carriers, but the small number of subjects harbouring this genotype (n=3) and presenting a CRC (n=2) must be highlighted.

During the submission of this study, Talseth-Palmer et al. 15 reported that in MLH1 carriers, but not in MSH2 carriers, the 11q23.1 CC and 8q23.3 AC genotypes were associated with an increased risk, but this significant association detected in 373 Australian mutation carriers was not found in 311 Polish mutation carriers analysed in the same study. We did not replicate either of these associations in the French sample. Indeed, the only association, that we detected for these two SNPs in the 267 French MLH1 mutation carriers was a decrease of CRC risk with the 8q23.3 CC genotype (p<0.0001) but as this genotype was carried by only one CRC case, it was not possible to estimate the corresponding HR and due caution should be applied to interpret this finding.

To increase the power of the statistical analysis of the 8q23.3 and 11q23.1 SNPs impact on CRC risk in MMR mutation carriers, we performed a meta-analysis based on the study published by Wijnen et al. [START_REF] Wijnen | Chromosome 8q23.3 and 11q23.1 variants modify colorectal cancer risk in Lynch syndrome[END_REF] and our results, corresponding to 1423 MMR mutation carriers (Figure 1). In univariate analysis, age at first CRC occurrence significantly varied in male carriers according to the 8q23.3 genotype (AA, AC, or CC; P=0.0265 using Fisher's standard method to combine P-values from two log-rank tests), with increased CRC risk associated with the AC genotype and decreased risk associated with the CC genotype. In multivariate Cox analysis, as shown on the Forest plot for the 1 total sample (Figure 1), CRC risk did not significantly vary according to the 8q23.3 genotype. In males, the multivariate Cox analysis showed that the 8q23.3 CC genotype was associated with a CRC risk decrease (combined fixed-effect weighted sum HR = 0.61 relative to AA, P=0.0135). It remains however that there were very few males and even fewer male CRC cases carrying the CC genotype in both studies and thus the results of this meta-analysis should be interpreted with caution. Univariate and multivariate Cox analyses did not reveal any significant association between CRC risk and the 11q23.1 genotype (Figure 1).

In conclusion, the evaluation in a series of 748 MMR mutation carriers representative of Lynch syndrome of the IGF1 CA repeat, CYP1A1, 8q23.3 and 11q23.1 SNPs, previously reported as modifier factors for CRC risk in MMR mutation carriers, did not allow us to reproduce the previously published results. The only significant association that we detected both in the whole sample and in the unbiased affected mutation carrier sample, was between the 8q23.3 CC genotype and a decreased CRC risk in males, whereas previously published studies had reported an increased CRC risk associated either with this genotype [START_REF] Wijnen | Chromosome 8q23.3 and 11q23.1 variants modify colorectal cancer risk in Lynch syndrome[END_REF] or with the heterozygous AC genotype. 15 This discrepancy might be explained by the genetic heterogeneity between the different analysed populations. Considering that, in MMR mutation carriers, cancer development requires somatic mutation of several target genes containing repeated sequences, it is likely that numerous polymorphisms affecting either the DNA repair or target genes and / or that environmental factors playing a role in colorectal carcinogenesis will modify the penetrance of MMR mutations. It is also possible that a key factor modulating the penetrance is the allelic heterogeneity of the MMR mutations. Finally, another explanation of this discrepancy between our results and previous studies is that the previously reported associations were spurious associations due to too small samples. Therefore, we conclude that, from the medical point of view, genotyping of these polymorphisms is not useful to evaluate CRC risk in MMR mutation carriers and to optimise their clinical follow-up.

  13. Tomlinson IP, Webb E, Carvajal-Carmona L et al: A genome-wide association study identifies colorectal cancer susceptibility loci on chromosomes 10p14 and 8q23.3. Nat Genet 2008; 40: 623-630. 14. Tenesa A, Farrington SM, Prendergast JG et al: Genome-wide association scan identifies a colorectal cancer susceptibility locus on 11q23 and replicates risk loci at 8q24 and 18q21. Nat Genet 2008; 40: 631-637. 15. Talseth-Palmer BA, Brenne IS, Ashton KA et al: Colorectal cancer susceptibility loci on chromosome 8q23.3 and 11q23.1 as modifiers for disease expression in lynch syndrome. J Med Genet 2010 Dec 8. [Epub ahead of print]

Table 1 Clinical characteristics of MMR mutation carriers

 1

		Number	Number of incident Log-rank test
		of subjects (%)	CRC cases	P-value
		(n=748)	(n=329)
	Gender
	Male	349 (46.7)	177	<0.0001
	Female	399 (53.3)	152
	Index case
	Yes	290 (38.8)	259
	No	458 (61.2)	70
	MMR gene mutated
	MSH2	414 (55.4)	178	0.0495 (vs MSH6)
	MLH1	267 (35.7)	115	0.0069 (vs MSH6)
	MSH6	67 (8.9)	36
	Year of birth
	<1940	57 (7.6)	39 (11.8)
	1940-1949	106 (14.2)	73 (22.2)
	1950-1959	188 (25.1)	119 (36.2)
	1960-1969	165 (22.1)	66 (20.1)
	1970-1979	129 (17.2)	26 (7.9)
	≥1980	103 (13.8)	6 (1.8)
	Age		mean, min/max
	With CRC	329 (44)	43, 18/82
	Without CRC	419 (56)	38, 18/78

Table 3 Evaluation in 748 MMR mutation carriers of the CRC risk associated with 8q23.3, 11q23.1, 18q21, CYP1A1 and IGF1 variants

 3 Forest plots of the meta-analysis of the CRC risk associated with 8q23.3 and 11q23.1 variants in 1423 MMR mutation carriers. HR: Hazard Rate; CI: Confidence Interval; LCL: Lower 95% Confidence Limit; UCL: Upper 95% Confidence Limit. For each panel, the last two columns display the number of subjects with colorectal cancer and the indicated genotype followed by the overall number of subjects with this genotype. Hazard Ratio and 95% CI

				Total sample (N = 748)				Males (N = 349)				Females (N = 399)
	Locus	Genotype	N	CRC cases	Hazard Ratio#	95% CI	P*	N	CRC Cases	HR	95% CI	P*	N	CRC cases	HR	95% CI	P*
	8q23.3 rs16892766
		AA	641	277	1			290	142	1			351	135	1
		AC	104	50	1.184 0.866-1.617 0.2892	57	34	1.266 0.836-1.916 0.2656	47	16	1.067 0.677-1.680	0.7802
		CC	3	2	0.267 0.083-0.861 0.0271	2	1	0.460 0.301-0.701 0.0003	1	1	/	/	<0.0001
							0.0462					0.0002					<0.0001
		Per allele			1.032 0.778-1.368 0.8283			1.117 0.770-1.621 0.5602			0.899 0.575-1.405	0.6397
	11q23.1 rs3802842
		AA	380	167	1			178	90	1			202	77	1
		AC	321	139	0.923 0.725-1.176 0.5165	141	71	0.844 0.611-1.167 0.3047	180	68	1.019 0.723-1.438	0.9125
		CC	47	23	1.107 0.684-1.791 0.6793	30	16	1.043 0.599-1.815 0.8814	17	7	1.222 0.543-2.750	0.6284
							0.6491					0.5079					0.8891
		Per allele			0.988 0.806-1.210 0.9058			0.947 0.735-1.220 0.6740			1.052 0.778-1.422	0.7428
	18q21	Novel 1
		CC	156	72	1			84	45	1			72	27	1
		CG	395	165	0.956 0.704-1.296 0.7702	185	87	1.021 0.675-1.546 0.9215	210	78	0.865 0.546-1.370	0.5359
		GG	197	92	1.006 0.727-1.390 0.9729	80	45	1.088 0.706-1.677 0.7007	117	47	0.903 0.564-1.447	0.6714
							0.9001					0.9087					0.8249
		Per allele			1.007 0.859-1.181 0.9326			1.044 0.841-1.295 0.6962			0.964 0.765-1.215	0.7570
	CYP1A1 rs1048943
		AA	708	313	1			331	168	1			377	145	1
		AG	40	16	1.026 0.601-1.752 0.9249	18	9	0.930 0.444-1.948 0.8480	22	7	1.168 0.583-2.338	0.6619
		GG	0	0	/	/	/	0	0	/	/	/	0	0	/	/	/
							0.9249					0.8480					0.6619
		Per allele			1.026 0.601-1.752 0.9249			0.930 0.444-1.948 0.8480
	CYP1A1 rs4646903
		TT	598	265	1			279	143	1			319	122	1
		TC	140	61	1.031 0.769-1.382 0.8400	66	32	0.909 0.601-1.375 0.6524	74	29	1.198 0.789-1.818	0.3956
		CC	10	3	1.163 0.387-3.495 0.7882	4	2	3.496 1.517-8.057 0.0033	6	1	0.507 0.071-3.640	0.4997
							0.9441					0.0090					0.5434
		Per allele			1.039 0.801-1.347 0.7730			1.001 0.680-1.474 0.9948			1.079 0.746-1.561	0.6874
	IGF1	CA repeat
		≥18; ≥18	710		1			329		1			381		1
		≥18; ≤17	38		0.731 0.447-1.196 0.2127	20		0.662 0.372-1.179 0.1611	18		0.979 0.450-2.132	0.9579

Decreased risk with AC Increased risk with AC Decreased risk with CC Increased risk with CC Forest plots for 11q23.1 Decreased risk with AC Increased risk with AC 0.1 1 10 Decreased risk with CC Increased risk with CC

ACKNOWLEDGMENTS

We thank our colleagues who referred to us Lynch patients. This study was supported by the French Canceropole Nord-Ouest and the French National Cancer Institute (INCa). We are grateful to Mario Tosi for critical review of the manuscript.

CONFLICT OF INTEREST

The authors declare no conflict of interest.


Dr Isabelle Niedhammer
email: isabelle.niedhammer@inserm.fr

Eve Bourgkard

Nearkasen Chau

Occupational and behavioural factors in the explanation of social inequalities in premature and total mortality: a 12.5-year follow-up in the Lorhandicap study

Keywords: occupational groups, mortality, occupational exposures, health behaviours

The respective contribution of occupational and behavioural factors to social disparities in allcause mortality has been studied very seldom. The objective of this study was to evaluate the role of occupational and behavioural factors in explaining social inequalities in premature and total mortality in the French working population. The study population consisted of a sample of 2189 and 1929 French working men and women, who responded to a self-administered questionnaire in mid-1996, and were followed up until the end of 2008. Mortality was derived from register-based information and linked to the baseline data. Socioeconomic status was measured using occupation. Occupational factors included biomechanical and physical exposures, temporary contract, psychological demands, and social support, and behavioural factors, smoking, alcohol abuse, and body mass index. Significant social differences were observed for premature and total mortality. Occupational factors reduced the hazard ratios of mortality for manual workers compared to managers/professionals by 72% and 41%, from 1.88 (95% CI: 1.17-3.01) to 1.25 (95% CI: 0.74-2.12) for premature mortality, and from 1.71 (95% CI: 1.18-2.47) to 1.42 (95% CI: 0.95-2.13) for total mortality. The biggest contributions were found for biomechanical and physical exposures, and job insecurity. The role of behavioural factors was very low. Occupational factors played a substantial role in explaining social disparities in mortality, especially for premature mortality and men. Improving working conditions amongst the lowest social groups may help to reduce social inequalities in mortality.

Introduction

Social inequalities in health have been reported for a long time. They refer to differences in morbidity and mortality between social groups, i.e. the lower the social position, the poorer the health status, and the measures of morbidity and mortality. These inequalities have been demonstrated for various chronic diseases such as cardiovascular diseases, and general measures of morbidity and mortality [START_REF] Dalstra | Socioeconomic differences in the prevalence of common chronic diseases: an overview of eight European countries[END_REF][START_REF] Huisman | Educational inequalities in cause-specific mortality in middle-aged and older men and women in eight western European populations[END_REF][START_REF] Mackenbach | Socioeconomic inequalities in health in 22 European countries[END_REF][START_REF] Van Lenthe | Material and behavioral factors in the explanation of educational differences in incidence of acute myocardial infarction: the Globe study[END_REF]. Several indicators may be used to measure social position or socioeconomic status (SES), education, occupation, and income being the most widely used of these indicators [START_REF] Galobardes | Indicators of socioeconomic position (part 1)[END_REF][START_REF] Galobardes | Indicators of socioeconomic position (part 2)[END_REF]. Besides the report of social inequalities in health, it appears crucial to better understand the mechanisms linking social position and health.

Consequently, identifying mediating factors that may contribute to explain social inequalities in health may be helpful to reduce the exposure to these factors in specific social groups, and thus to reduce social inequalities in health.

Various theories have been developed to explain the pathways and mechanisms underlying these inequalities [START_REF] Goldman | Social inequalities in health disentangling the underlying mechanisms[END_REF][START_REF] Mackenbach | Health inequalities: Europe in profile[END_REF][START_REF] Wilkinson | Social determinants of health: The solid facts[END_REF]. These theories include the materialist explanation, that put the emphasis on material conditions (access to goods/services, and exposures to material risk factors in the living and working environment), the psychosocial explanation, that focuses on psychosocial and stress related influences with a plethora of risk factors such as social support or sense of control, and the behavioural explanation, that emphasizes the importance of behavioural risk factors in explaining social inequalities in health. As mediating factors probably are interrelated, some authors have suggested simplified causal models to disentangle the direct (independent) effect of mediating factors, and their indirect effect through other factors [START_REF] Skalicka | Material, psychosocial, behavioural and biomedical factors in the explanation of relative socioeconomic inequalities in mortality: evidence from the HUNT study[END_REF][START_REF] Van Oort | Material, psychosocial, and behavioural factors in the explanation of educational inequalities in mortality in The Netherlands[END_REF].

Social inequalities in all-cause mortality have been described extensively. Studies showed strong and persistent social inequalities in mortality in various countries, such as France [START_REF] Saurel-Cubizolles | Social inequalities in mortality by cause among men and women in France[END_REF], and other European countries [START_REF] Mackenbach | Widening socioeconomic inequalities in mortality in six Western European countries[END_REF], but the studies that attempted to explain these inequalities are still sparse. Most of them focused on behavioural factors, such as smoking, alcohol consumption, physical activity, body mass index, etc., and biologic factors (fibrinogen, cholesterol, triglycerides, blood pressure, etc.) as potential mediating factors [START_REF] Laaksonen | Health behaviours as explanations for educational level differences in cardiovascular and allcause mortality: a follow-up of 60 000 men and women over 23 years[END_REF][START_REF] Lantz | Socioeconomic factors, health behaviors, and mortality: results from a nationally representative prospective study of US adults[END_REF][START_REF] Lynch | Do cardiovascular risk factors explain the relation between socioeconomic status, risk of all-cause mortality, cardiovascular mortality, and acute myocardial infarction?[END_REF][START_REF] Pekkanen | Social class, health behaviour, and mortality among men and women in eastern Finland[END_REF][START_REF] Siahpush | The contribution of smoking to socioeconomic differentials in mortality: results from the Melbourne Collaborative Cohort Study, Australia[END_REF][START_REF] Woodward | Contribution of contemporaneous risk factors to social inequality in coronary heart disease and all causes mortality[END_REF]. These studies, in general, found that these factors, and especially smoking, explained a part (that may be small in some studies) of social inequalities in mortality, suggesting that a wider range of factors need to be considered to explain these inequalities.

Occupational factors, that included both material and psychosocial factors, such as physicochemical, biomechanical, and psychosocial exposures, are considered as major determinants of health, and they may be socially graded (the lower the social position, the higher the exposures). Consequently, they may be pertinent candidates to explain social inequalities in health, as underlined in a recent commentary [START_REF] Landsbergis | Assessing the contribution of working conditions to socioeconomic disparities in health: a commentary[END_REF]. Some studies have already mentioned the contribution of occupational factors, especially psychosocial work factors, in explaining at least partly social differences in various measures of morbidity [START_REF] Aldabe | Contribution of material, occupational, and psychosocial factors in the explanation of social inequalities in health in 28 countries in Europe[END_REF][START_REF] Borg | Social class and self-rated health: can the gradient be explained by differences in life style or work environment[END_REF][START_REF] Borrell | Social class and self-reported health status among men and women: what is the role of work organisation, household material standards and household labour[END_REF][START_REF] Hemstrom | Health inequalities by wage income in Sweden: the role of work environment[END_REF][START_REF] Niedhammer | The contribution of occupational factors to social inequalities in health: findings from the national French SUMER survey[END_REF][START_REF] Schrijvers | Socioeconomic inequalities in health in the working population: the contribution of working conditions[END_REF][START_REF] Sekine | Socioeconomic inequalities in physical and mental functioning of Japanese civil servants: explanations from work and family characteristics[END_REF][START_REF] Warren | Job characteristics as mediators in SEShealth relationships[END_REF]. To our knowledge, no previous study has attempted to evaluate the impact of both occupational and behavioural factors on social inequalities in all-cause mortality.

The objectives of this study were to analyse the association between SES as measured using occupation, and two measures of all-cause mortality, premature and total mortality, and to evaluate the contribution of occupational and behavioural factors in explaining social differences in mortality among a sample of men and women of the French working population.

Materials and methods

This study was based on the data from the Lorhandicap survey set up in 1996 in the nord-east of France. Several studies have already been published using this survey [START_REF] Chau | Strong association of physical job demands with functional limitations among active people: a population-based study in North-eastern France[END_REF][START_REF] Gauchard | Prevalence of sensory and cognitive disabilities and falls, and their relationships: a community-based study[END_REF][START_REF] Loos-Ayav | Functional disability in France and its relationship with health-related quality of life -a population-based prevalence study[END_REF]. The initial sample consisted of everyone aged 15 years or more living in 8000 randomly selected households in the Lorraine region of the north-east of France. Only households with a telephone were eligible. The investigation was approved by the Commission Nationale de l'Informatique et des Libertés (CNIL), and written informed consent was obtained from the respondents. The study protocol included: an application to participate to ascertain the number of persons in the household, and three self-administered questionnaires with a covering letter and a pre-paid envelope for the reply, were mailed at 1-month interval. When the number of individuals was unknown, two questionnaires were sent first, and a complementary one was sent later. The questionnaire included various sections covering socio-demographic characteristics, job characteristics, working conditions, health status, and behavioural factors.

If people were retired, they were asked about their main job during working life. SES was measured using occupational groups. Four occupational categories were considered following the international classification of occupation (ISCO): professionals/managers, associate professionals/technicians, service workers/clerks, and manual workers.

Professionals/managers were used as reference category. Occupation was studied as a marker of SES because it characterises adult SES, is available for all working people, and may reflect occupational exposures better than education [START_REF] Galobardes | Indicators of socioeconomic position (part 1)[END_REF][START_REF] Galobardes | Indicators of socioeconomic position (part 2)[END_REF].

Occupational factors were assessed by: biomechanical exposure (exposure to vibrationsmanual handling of vibrating tools or vibration from a fixed machine-, manual materials handling, postural and articular constraints such as standing/walking, awkward posture, handling objects or tools, working on a production line, or other constraints), physical exposure (exposure to noise, cold or hot temperatures, or outdoor work), work status (temporary versus permanent job) as a marker of job insecurity, psychological demands (exposure to high work pace, or mental load), and social support from colleagues (very unsatisfied, or unsatisfied versus neither satisfied nor unsatisfied, satisfied, or very satisfied).

Exposure to biomechanical and physical factors, and to psychological demands was defined by the presence of at least one item. Details on the formulation of the question and items, as well as the number and percentage of exposed people for each item separately may be found in the appendix. The factors that were the most prevalent were standing/walking, awkward posture, and manual materials handling for biomechanical factors, and noise, and hot and cold temperatures for physical factors. The study of the associations between the 5 occupational exposures studied (biomechanical and physical exposures, work status, psychological demands, and social support) showed three significant positive associations (p<0.001) between biomechanical exposure and physical exposure, between biomechanical exposure and psychological demands, and between temporary contract and low social support, as well as two significant negative associations between psychological demands and temporary contract (p<0.001) and between psychological demands and low social support (p<0.01) i.e. that people with high levels of psychological demands were less likely to have a temporary work contract and low levels of social support.

Behavioural factors included: smoking status (smoker, ex-smoker or non-smoker), body mass index (BMI) in kg/m 2 , and alcohol abuse measured using the French version of the Cut/Annoyed/Guilty/Eye-opener (CAGE) questionnaire [START_REF] Beresford | Comparison of CAGE questionnaire and computer-assisted laboratory profiles in screening for covert alcoholism[END_REF] and defined by at least two positive responses to four items: consumption considered excessive by the subject, consumption considered excessive by people around the subject, subject wishes to reduce consumption, and consumption on waking.

The cohort was followed up for mortality from 1st July 1996 to 31th December 2008. The vital status of all subjects was assessed by searching using the national computerised database listing all deceased subjects in France, contacting the registry offices of the birth places for people born in France, and the registry office devoted to foreign born French people (Ministry of Foreign Affairs). Two measures of mortality were considered: death, and premature death before the age of 70. Premature death focusses on deaths occurring at younger ages, and may be considered as a useful public health measure providing information on preventable deaths.

In addition, this outcome was retained because, as reported by Krieger et al. [START_REF] Krieger | The fall and rise of US inequities in premature mortality: 1960-2002[END_REF], unlike life expectancy and years of personlives lost, it is easy to understand, easy to compare, methodologically transparent, and a sensitive indicator of inequities in health.

The associations between SES (i.e. occupation) and occupational and behavioural factors were tested using the Chi-Square test to determine the significance and direction of these associations. Cox proportional hazard model, which yields hazard ratios (HRs), was used to examine the association between SES and mortality. The duration of follow-up for each subject was calculated for each subject from 1st July 1996 to 31th December 2008, or earlier in the case of death, or 70 th birthday for premature mortality. The associations between occupational and behavioural factors and mortality were also examined using Cox regression models. Occupational and behavioural factors that displayed inverse social gradients were excluded in subsequent analyses. Several models were performed: a basic model (model 1) measuring the association between SES and mortality after adjustment for age (and sex), behavioural factors added to model 1 (model 2), occupational factors added to model 1 (model 3), and behavioural and occupational factors added simultaneously to model 1 (model 4). The contribution of behavioural and/or occupational factors to the explanation of the social differences in mortality was estimated by the change in the HRs for occupational groups after inclusion of the variable(s) in the model, i.e. explained fraction calculated by the formula: (HR model 1 -HR extended model )/(HR model 1 -1) [START_REF] Lynch | Do cardiovascular risk factors explain the relation between socioeconomic status, risk of all-cause mortality, cardiovascular mortality, and acute myocardial infarction?[END_REF]. Positive % values indicate reductions in HRs, and negative % values increases in HRs. The contribution was calculated only if the HR for a given occupational group was significant in model 1. The proportional hazard assumption was checked based on Schoenfeld residuals for the global model and for each covariate. Analyses were also done with additional adjustment for chronic disease at baseline, the results were unchanged. Results are presented for men and women separately, and for the total sample for Cox regression models. The statistical analyses were performed using STATA software.

Results

Of the 8000 households included in the sample, mailings to 193 (2%) were lost (due to addressing error or death). Of the 7807 households contacted, 3460 (44.3%) participated (all eligible members of the family took part in 86% of those). In total, 6235 subjects filled in the questionnaire, 19 were of unknown sex or age, leaving 6216 subjects who were similar in age and sex distribution to the overall population of the north-east of France [START_REF] Gauchard | Prevalence of sensory and cognitive disabilities and falls, and their relationships: a community-based study[END_REF]. During the follow-up, 143 subjects (2.3%) were lost and excluded. The subjects with unknown smoking habit or alcohol abuse were excluded (296 subjects, i.e. 4.8%). Only the subjects who had been working, were alive, and aged 70 years or less at baseline (1st July 1996) were retained for this study i.e. 4118 subjects, 2189 men and 1929 women. In total, 291 deaths (206 and 85 among men and women) occurred, and 165 deaths before age 70 (115 and 50 among men and women). Almost all behavioural and occupational factors displayed strong and significant associations with SES, except alcohol abuse for men and women, and smoking for women (Table 1). A trend towards increasing alcohol abuse with lower SES was observed for men. Biomechanical and physical exposures, temporary contract, and low social support were strongly socially graded, the lower the occupational group, the higher the exposure. High psychological demands displayed a significant inverse social gradient, managers/professionals being more likely to be exposed. Psychological demands were consequently omitted from subsequent analyses.

A significant association was found between SES and premature mortality (Table 2), which was confirmed after adjustment for age and sex (model 1), manual workers being at increased risk of mortality. This association was observed for men and women separately, although non significant for women. Male gender and all behavioural and occupational factors displayed significant crude associations with premature mortality (not all significant for each gender separately). Adding behavioural factors to model 1 did not change the HRs for manual workers very much (model 2). Additional analyses (not shown) exploring the separate effects of each behavioural factor showed that the biggest contribution was found for alcohol abuse (7%) in the total sample. The inclusion of occupational factors to model 1 led to a substantial decrease in the HRs for manual workers, by 72% for the total sample, 74% for men, and 61% for women. The HRs for manual workers were no longer significant after adjustment for occupational factors. The occupational factors that contributed to decrease the social difference in premature mortality were in the total sample (not shown): biomechanical exposure (35%), job insecurity (28%), physical exposure (24%), and social support (14%).

Model 4 that included behavioural and occupational factors simultaneously provided a similar explanation of social differences in premature mortality than model 3.

Significant social differences for total mortality were observed (Table 3). Manual workers were at higher risk for mortality, a similar trend was observed for service workers/clerks after adjustment for age and sex (model 1). Social differences were also found for men and women separately, manual workers being at significant higher risk of mortality for men. Male gender and almost all behavioural and occupational factors were found to be predictive factors of mortality in crude associations. The inclusion of behavioural factors to model 1 did not modify the HRs for manual workers very much (model 2). Studying each factor separately showed that the biggest contributions were found for BMI (6%) and alcohol abuse (4%) in the total sample. Adding occupational factors to model 1 contributed to decrease the social differences between manual workers and managers/professionals by 41% for the total sample, 44% for men, and 31% for women (model 3). The HRs for manual workers were no longer significant after adjustment for occupational factors. The contributions of each occupational factor separately were as follows in the total sample: job insecurity (23%), social support (11%), biomechanical exposure (10%), and physical exposure (8%). Adding behavioural and occupational factors simultaneously increased only slightly the explained fractions (model 4) compared to model 3.

Discussion

Significant social differences were observed for premature and total mortality in this 12.5year follow-up study among the French working population. Manual workers were at increased risk of total and premature mortality compared to managers/professionals with HRs reaching almost 2. Occupational factors played a substantial role in explaining social differences in mortality. Their contributions were 31-74%, and were more pronounced for men and for premature mortality. The contribution of behavioural factors was very low.

Manual workers were the occupational group that displayed a significant excess of mortality compared to managers/professionals. Other previous studies showed social disparities in mortality in France and in other countries, using various SES markers [START_REF] Saurel-Cubizolles | Social inequalities in mortality by cause among men and women in France[END_REF][START_REF] Mackenbach | Widening socioeconomic inequalities in mortality in six Western European countries[END_REF]. Our study also underlined social inequalities in occupational exposures, with the lowest occupational groups, especially manual workers, being more likely to be exposed to negative working conditions.

Other previous studies reported the accumulation of unfavourable working conditions in the lowest occupational categories [START_REF] Borg | Social class and self-rated health: can the gradient be explained by differences in life style or work environment[END_REF][START_REF] Borrell | Social class and self-reported health status among men and women: what is the role of work organisation, household material standards and household labour[END_REF][START_REF] Niedhammer | The contribution of occupational factors to social inequalities in health: findings from the national French SUMER survey[END_REF][START_REF] Schrijvers | Socioeconomic inequalities in health in the working population: the contribution of working conditions[END_REF][START_REF] Sekine | Socioeconomic inequalities in physical and mental functioning of Japanese civil servants: explanations from work and family characteristics[END_REF]. One major exception was psychological demands, that displayed a strong inverse social gradient, managers/professionals being more likely to be exposed, something already reported [START_REF] Borg | Social class and self-rated health: can the gradient be explained by differences in life style or work environment[END_REF][START_REF] Borrell | Social class and self-reported health status among men and women: what is the role of work organisation, household material standards and household labour[END_REF][START_REF] Niedhammer | The contribution of occupational factors to social inequalities in health: findings from the national French SUMER survey[END_REF][START_REF] Schrijvers | Socioeconomic inequalities in health in the working population: the contribution of working conditions[END_REF].

Although there were a number of studies describing social inequalities in mortality in various populations, studies that tried to explain these inequalities were less numerous. This study is one of the seldom studies evaluating the contribution of occupational factors to social inequalities in all-cause mortality, and suggested that these factors may play a substantial role.

Other studies explored the contribution of occupational factors to social inequalities in several measures of morbidity. Our study is in agreement with some previous studies underlying the role of physical and biomechanical exposure [START_REF] Borg | Social class and self-rated health: can the gradient be explained by differences in life style or work environment[END_REF][START_REF] Hemstrom | Health inequalities by wage income in Sweden: the role of work environment[END_REF][START_REF] Niedhammer | The contribution of occupational factors to social inequalities in health: findings from the national French SUMER survey[END_REF][START_REF] Schrijvers | Socioeconomic inequalities in health in the working population: the contribution of working conditions[END_REF][START_REF] Warren | Job characteristics as mediators in SEShealth relationships[END_REF], job insecurity [START_REF] Aldabe | Contribution of material, occupational, and psychosocial factors in the explanation of social inequalities in health in 28 countries in Europe[END_REF][START_REF] Borrell | Social class and self-reported health status among men and women: what is the role of work organisation, household material standards and household labour[END_REF], and low social support [START_REF] Niedhammer | The contribution of occupational factors to social inequalities in health: findings from the national French SUMER survey[END_REF][START_REF] Sekine | Socioeconomic inequalities in physical and mental functioning of Japanese civil servants: explanations from work and family characteristics[END_REF] in explaining social inequalities in health outcomes such as selfreported health. Furthermore, the occupational factors, that were the most prevalent, may play a substantial role in explaining social differences in mortality, i.e. standing/walking, awkward posture, and manual materials handling among the biomechanical factors, and noise, and hot and cold temperatures among the physical factors, supporting previous results on the explanation of social inequalities in morbidity outcomes in France [START_REF] Niedhammer | The contribution of occupational factors to social inequalities in health: findings from the national French SUMER survey[END_REF]. Our results are also in agreement with another study showing that the role of occupational factors in explaining social inequalities in health was not modified very much when behavioural factors were taken into account [START_REF] Borg | Social class and self-rated health: can the gradient be explained by differences in life style or work environment[END_REF]. The issue of independent (direct) and indirect (through behavioural factors) effects of occupational factors is consequently less important in our study as we did not observe any major role of behavioural factors. Consequently, the contribution of occupational factors remained almost the same with or without adjusting for behavioural factors.

Behavioural factors did not play an important role in explaining social inequalities in mortality in our study. Other authors demonstrated that behavioural factors may explain only a modest proportion of social inequalities in mortality [START_REF] Skalicka | Material, psychosocial, behavioural and biomedical factors in the explanation of relative socioeconomic inequalities in mortality: evidence from the HUNT study[END_REF][START_REF] Van Oort | Material, psychosocial, and behavioural factors in the explanation of educational inequalities in mortality in The Netherlands[END_REF][START_REF] Lantz | Socioeconomic factors, health behaviors, and mortality: results from a nationally representative prospective study of US adults[END_REF]. Several hypotheses may be assumed to explain this. Behavioural factors were evaluated only at baseline, and as the follow-up was long, people might change their behaviours, which is likely to lead to misclassification and dilution of their effects. Behavioural factors were based on self-reported data, that may lead to an underreporting bias of the most negative health behaviours. For example, the heaviest drinkers may be underrepresented in our sample, because of both selection and underreporting bias. Evaluation of alcohol consumption was done using the CAGE instrument that may be adequate to measure alcohol-related problems, but may neglect some specific ones that may be more strongly related to SES, such as binge drinking.

Gender differences were also of interest in our study, this was why genders were studied separately. Women were more likely to be service workers/clerks, and men manual workers.

The prevalence of occupational and behavioural factors was found to be different between genders. Men were more likely to be exposed to physical exposure, and women to job insecurity. Men were more likely to be smokers and overweight, and to have alcohol-related problems. The risk of mortality was also higher for men than for women. These results confirm the different patterns of occupation and occupational exposures between genders, related to the strong sexual division of labour, as well as the differences in health behaviours and mortality between genders. Similar social inequalities in mortality were observed for men and women, but the contribution of occupational factors was found to be higher in men than in women. This result is in agreement with other studies [START_REF] Niedhammer | The contribution of occupational factors to social inequalities in health: findings from the national French SUMER survey[END_REF]. Strong gender differences were observed for the associations between behavioural factors and mortality; smoking and alcohol abuse were found to be strong predictors of premature and total mortality for men, but not for women. Nevertheless, the contribution of behavioural factors was very modest and appeared to be similar in explaining social inequalities in mortality in both genders.

Limitations of our study may be mentioned. A selection bias may have occurred, as the response rate was about 44%. However, this response rate is similar to those of other studies using postal self-administered questionnaires in France [START_REF] Goldberg | Socioeconomic, demographic, occupational, and health factors associated with participation in a long-term epidemiologic survey: a prospective study of the French GAZEL cohort and its target population[END_REF]. Furthermore, the gender and age distributions of the initial sample were close to those of the census population. Nevertheless, previous studies showed that non-respondents may be more likely to have lower SES, poorer health-related and behavioural factors [START_REF] Goldberg | Socioeconomic, demographic, occupational, and health factors associated with participation in a long-term epidemiologic survey: a prospective study of the French GAZEL cohort and its target population[END_REF]. Consequently, it is likely that such a bias may lead to underestimate social inequalities in health. A limitation was related to sample size especially for women, and led to more uncertainty in the estimation of HRs and explained fractions for this group. Another limitation was that behavioural and occupational factors were not based on lifetime exposures. Other authors demonstrated that this may lead to underestimate the contributions of behavioural and occupational factors to social inequalities in health [START_REF] Monden | Current or lifetime smoking? Consequences for explaining educational inequalities in self-reported health[END_REF][START_REF] Monden | Current and lifetime exposure to working conditions. Do they explain educational differences in subjective health?[END_REF]. The contribution of these factors may also be underestimated because some behavioural and occupational factors were not explored, such as diet or physical activity, as well as chemical/biological exposures, decision latitude at work, reward, or workplace violence. Thus, inclusion of more mediators might result in different estimates of the contributions of mediators. Finally, the generalisation of our results to other populations should be made with caution because of cultural and socioeconomic differences between countries.

Strengths of the study also deserve to be mentioned. The sample was derived from the general population, making generalisation possible for the population in the nord-east of France.

Sample size allowed us to study men and women separately, which may be crucial in occupational epidemiology [START_REF] Niedhammer | How is sex considered in recent epidemiological publications on occupational risks?[END_REF]. The study was based on a 12.5-year follow-up, i.e. a rather long period. Mortality was measured using national database (an exhaustive and independent source of data). Mortality is also an objective outcome measure, consequently no reporting bias may be suspected. Occupational groups were used in this study as a marker of social position, and are a well-known measure of social position in the working population.

Although results may differ somewhat using other measures of social position (such as education or income) [START_REF] Galobardes | Indicators of socioeconomic position (part 1)[END_REF][START_REF] Galobardes | Indicators of socioeconomic position (part 2)[END_REF], relatively similar conclusions have been provided by others [START_REF] Hemstrom | Health inequalities by wage income in Sweden: the role of work environment[END_REF][START_REF] Monden | Current and lifetime exposure to working conditions. Do they explain educational differences in subjective health?[END_REF]. We performed additional analyses that included the presence of chronic disease at baseline in our models to make sure that no previous chronic disease may introduce a confounding effect in our results. These results confirmed the robustness of our findings. We also performed the analyses for premature mortality before 65 and found similar results, but statistical power was lower because of a smaller number of premature deaths.

To conclude, occupational factors may play a substantial role in explaining social inequalities in mortality, especially premature mortality. Preventive actions focusing on these factors and specific social groups may be useful to reduce social inequalities in mortality. More research is needed to better understand the role of these factors, over the life course, on social inequalities in various health outcomes.

Table 1

 1 Associations between SES (occupation) and age, behavioural and occupational factors

		Total sample	Managers, professionals	Associate professionals,	Service workers, clerks	Manual workers	P
						technicians
		N	%	N	%	N	%	N	%	N	%
	MEN	N=2189		N=433	19.8	N=467	21.3	N=448	20.5	N=841	38.4
	Age (y)											***
	<40	981	44.8	176	40.7	165	35.3	232	51.8	408	48.5
	40-59	829	37.9	188	43.4	201	43.0	150	33.5	290	34.5
	≥60	379	17.3	69	15.9	101	21.6	66	14.7	143	17.0
	Smoking											***
	Non-smoker	582	26.6	130	30.0	126	27.0	118	26.3	208	24.7
	Ex-smoker	864	39.5	180	41.6	201	43.0	189	42.2	294	35.0
	Smoker	743	33.9	123	28.4	140	30.0	141	31.5	339	40.3
	Alcohol abuse	290	13.2	48	11.1	54	11.6	65	14.5	123	14.6	NS
	BMI (kg/m²)											***
	<25	990	45.2	213	49.2	190	40.7	229	51.1	358	42.6
	25-30	829	37.9	178	41.1	181	38.8	157	35.0	313	37.2
	>30	370	16.9	42	9.7	96	20.6	62	13.8	170	16.9
	Biomechanical exposure	975	44.5	63	14.5	201	43.0	154	34.4	557	66.2	***
	Physical exposure	1177	53.8	94	21.7	277	59.3	180	40.2	626	74.4	***
	Temporary contract	820	37.5	116	26.8	194	41.5	157	35.0	353	42.0	***
	High psychological demands	782	35.7	234	54.0	166	35.5	156	34.8	226	26.9	***
	Low social support	721	32.9	122	28.2	140	30.0	145	32.4	314	37.3	**
	WOMEN	N=1929		N=278	14.4	N=140	7.3	N=1161	60.2	N=350	18.1
	Age (y)											***
	<40	950	49.2	141	50.7	65	46.4	617	53.1	127	36.3
	40-59	690	35.8	111	39.9	49	35.0	409	35.2	121	34.6
	≥60	289	15.0	26	9.4	26	18.6	135	11.6	102	29.1
	Smoking											NS
	Non-smoker	949	49.2	140	50.4	64	45.7	555	47.8	190	54.3
	Ex-smoker	483	25.0	76	27.3	31	22.1	294	25.3	82	23.4
	Smoker	497	25.8	62	22.3	45	32.1	312	26.9	78	22.3
	Alcohol abuse	67	3.5	13	4.7	6	4.3	40	3.4	8	2.3	NS
	BMI (kg/m²)											***
	<25	1259	65.3	218	78.4	99	70.7	762	65.6	180	51.4
	25-30	347	18.0	34	12.2	22	15.7	210	18.1	81	23.1
	>30	323	16.7	26	9.4	19	13.6	189	16.3	89	25.4
	Biomechanical exposure	813	42.2	59	21.2	71	50.7	466	40.1	217	62.0	***
	Physical exposure	567	29.4	58	20.9	42	30.0	279	24.0	188	53.7	***
	Temporary contract	933	48.4	900	32.4	67	47.9	541	46.6	235	67.1	***
	High psychological demands	602	31.2	157	56.5	45	32.1	300	25.8	100	28.6	***
	Low social support	697	36.1	70	25.2	52	37.1	397	34.2	178	50.9	***

Chi-Square test to test the association between SES (occupation) and each mediator, *p<0.05, **p<0.01, ***p<0.001

Table 2

 2 Contribution of behavioural and occupational factors to social differences in premature mortality (<70y) Model 1 (adjusted for SES, gender, and age) (2) Model 2 = Model 1 + smoking, alcohol abuse, and BMI (3) Model 3 = Model 1 + biomechanical exposure, physical exposure, temporary contract, and social support (4) Model 4 = Model 1 + Model 2 + Model 3 % = Reduction (positive %) or increase (negative %) in HR computed with the following formula: (HR model 1 -HR extended model)/(HR model 1 -1)

	TOTAL SAMPLE	Crude	95% CI	Adjusted	95% CI	Adjusted	95% CI	% Adjusted	95% CI	%	Adjusted	95% CI	%
	N=4118	HR		HR (1)		HR (2)			HR (3)			HR (4)
	SES (occupation)
	Managers, professionals	1		1		1			1			1
	Associate professionals, technicians	1.25	0.70-2.22	1.02	0.57-1.82	1.02	0.57-1.82		0.78	0.43-1.43		0.78	0.43-1.45
	Service workers, clerks	0.96	0.59-1.57	1.40	0.84-2.32	1.37	0.82-2.28		1.13	0.67-1.90		1.12	0.66-1.88
	Manual workers	1.92**	1.20-3.08	1.88**	1.17-3.01	1.88**	1.17-3.03	0	1.25	0.74-2.12	72	1.27	0.75-2.17	69
	Men	2.08***	1.49-2.90	1.96***	1.38-2.82	1.59*	1.08-2.33		2.08***	1.44-3.00		1.68**	1.13-2.49
	Smoking
	Non-smoker	1				1						1
	Ex-smoker	2.14***	1.46-3.13			1.52*	1.02-2.28					1.49*	1.00-2.23
	Smoker	1.40	0.93-2.12			1.57*	1.01-2.43					1.57*	1.01-2.44
	Alcohol abuse	2.63***	1.80-3.85			2.01***	1.35-2.98					1.90**	1.28-2.83
	BMI (kg/m²)
	<25	1				1						1
	25-30	1.71**	1.22-2.41			0.90	0.63-1.28					0.91	0.63-1.31
	>30	1.58*	1.04-2.39			0.90	0.59-1.38					0.91	0.60-1.40
	Biomechanical exposure	1.52**	1.12-2.06						1.35 §	0.96-1.90		1.31	0.93-1.84
	Physical exposure	1.59**	1.17-2.15						1.19	0.83-1.70		1.18	0.82-1.68
	Temporary contract	2.81***	2.05-3.85						1.86***	1.28-2.70		1.80**	1.24-2.63
	Low social support	2.21***	1.62-2.99						1.39*	1.00-1.94		1.39*	1.00-1.93
	*p<0.05, **p<0.01, ***p<0.001, §p<0.10.
	(1)

Table 3

 3 (continued) Model 2 = Model 1 + smoking, alcohol abuse, and BMI (3) Model 3 = Model 1 + biomechanical exposure, physical exposure, temporary contract, and social support (4) Model 4 = Model 1 + Model 2 + Model 3 % = Reduction (positive %) or increase (negative %) in HR computed with the following formula: (HR model 1 -HR extended model)/(HR model 1 -1)

	WOMEN	Crude	95% CI	Adjusted	95% CI	Adjusted	95% CI	%	Adjusted	95% CI	%	Adjusted	95% CI	%
	N=1929	HR		HR (1)		HR (2)			HR (3)			HR (4)
	SES (occupation)
	Managers, professionals	1		1		1			1			1
	Associate professionals, technicians	3.24*	1.06-9.91	2.63 §	0.86-8.06	2.57 §	0.84-7.92		2.35	0.76-7.29		2.30	0.74-7.17
	Service workers, clerks	2.27 §	0.90-5.71	2.24 §	0.89-5.64	2.19 §	0.87-5.55		2.05	0.81-5.19		1.99	0.78-5.09
	Manual workers	4.09**	1.57-10.68	2.48 §	0.94-6.52	2.41 §	0.90-6.42	5	2.02	0.74-5.52	31	1.96	0.71-5.43	35
	Smoking
	Non-smoker	1				1						1
	Ex-smoker	0.86	0.52-1.42			1.12	0.67-1.86					1.15	0.68-1.91
	Smoker	0.49*	0.27-0.90			1.21	0.54-2.31					1.24	0.65-2.36
	Alcohol abuse	0.67	0.16-2.70			0.80	0.20-3.28					0.79	0.19-3.26
	BMI (kg/m²)
	<25	1				1						1
	25-30	1.89*	1.12-3.19			1.04	0.61-1.78					1.05	0.61-1.80
	>30	2.23**	1.34-3.71			1.21	0.72-2.05					1.21	0.71-2.05
	Biomechanical exposure	1.12	0.73-1.71						1.17	0.74-1.85		1.17	0.74-1.86
	Physical exposure	1.00	0.63-1.60						1.13	0.69-1.87		1.14	0.69-1.88
	Temporary contract	3.13***	1.93-5.09						1.17	0.63-2.18		1.17	0.63-2.19
	Low social support	2.72***	1.76-4.20						1.40	0.86-2.29		1.41	0.86-2.30
	*p<0.05, **p<0.01, ***p<0.001, §p<0.10.
	(1) Model 1 (adjusted for SES and age)
	(2)

Acknowledgements

Lorhandicap study group: N Chau, F Guillemin, JF Ravaud, J Sanchez, S Guillaume, JP Michaely, C Otero Sierra, B Legras, A Dazord, M Choquet, L Méjean, N Tubiana-Rufi, JP Meyer, Y Schléret, and JM Mur.

The authors would like to thank D Saouag, M Weiss, M Depesme-Cuny, and B Phélut for their help during the survey. The work was granted by the Pôle Européen de Santé.

Appendix. Description and prevalence (No. of exposed, % exposed) of occupational exposures among the population studied (N=4118)

The question was: please indicate the occupational exposures you have (had)