Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Dioxin-like compounds and bone quality in Cree women of Eastern James Bay (Canada): a cross-sectional study

  • Alexandra-Cristina Paunescu1,
  • Éric Dewailly1,
  • Sylvie Dodin3,
  • Evert Nieboer4 and
  • Pierre Ayotte1, 2Email author
Environmental Health201312:54

DOI: 10.1186/1476-069X-12-54

Received: 27 February 2013

Accepted: 28 June 2013

Published: 2 July 2013

Abstract

Background

Aboriginal populations living in Canada’s northern regions are exposed to a number of persistent organic pollutants through their traditional diet which includes substantial amounts of predator fish species. Exposure to dioxin-like compounds (DLCs) can cause a variety of toxic effects including adverse effects on bone tissue. This descriptive cross-sectional study was conducted to investigate the relationship between plasma concentrations of DLCs and bone quality parameters in Cree women of Eastern James Bay (Canada).

Methods

Two hundred and forty-nine Cree women from seven communities in Eastern James Bay (Canada), aged 35 to 74 years old, participated in the study. In order to determine the total DLC concentration in plasma samples of participants, we measured the aryl hydrocarbon receptor-mediated transcriptional activity elicited by plasma sample extracts using a luciferase reporter gene assay. Plasma concentrations of mono-ortho-substituted dioxin-like polychlorinated biphenyls (DL-PCBs) 105, 118 and 156 were measured by gas chromatography–mass spectrometry. Bone quality parameters (speed of sound, m/s; broadband ultrasound attenuation, dB/MHz; stiffness index, %) were assessed by quantitative ultrasound at the right calcaneus with the Achilles InSight system. Several factors known to be associated with osteoporosis were documented by questionnaire. Multiple linear regression models were constructed for the three ultrasound parameters.

Results

DL-PCBs 105 and 118 concentrations, but not the global DLC concentration, were inversely associated with the stiffness index, even after adjusting for several confounding factors. The stiffness index (log) decreased by −0.22% (p=0.0414) and −0.04% (p=0.0483) with an increase of one μg/L in plasma concentrations of DL-PCB 105 and DL-PCB 118, respectively. Other factors, including age, height, smoking status, menopausal status and the percentage of omega-6 polyunsaturated fatty acids (PUFAs) in erythrocyte membranes were negatively associated with one of the ultrasound parameters, while the percentage of omega-3 PUFAs in these membranes and levels of physical activity and education were positively associated with them.

Conclusions

Our results show that an increase in plasma concentrations of DL-PCBs 105 and 118 was negatively associated with stiffness index, a measure of bone quality/strength, in women of this population. In addition to environmental contaminants, future studies should also consider PUFA intake as a factor influencing bone quality.

Keywords

Calcaneal Ultrasound Parameters Dioxin-Like Compounds Polychlorinated Biphenyls Cree Women Canada

Background

Osteoporosis is “a systemic skeletal disease characterized by low bone mass and micro-architectural deterioration of bone tissue, with a consequent increase in bone fragility and susceptibility to fractures” [1]. It is a multifactorial polygenic disease in which genetic determinants are modulated by hormonal, environmental, and nutritional factors [2], is asymptomatic and progress silently with age [3]. Eighty one percent of all fractures in women aged 50 years and older can be attributed to osteoporosis [4]. Osteoporotic fractures, such as those of the hip, spine and wrist, often appear in older people following minor trauma. Hip fractures lead to rehabilitation problems and greatly decrease the quality of life [5, 6].

Very little information is available on bone mineral density (BMD), fracture risk and their determinants among Canada’s First Nations people. The only existing publications focus on Manitoba’s Aboriginal population [712]. Retrospective studies using administrative health data have reported that members of First Nations in Manitoba had significantly higher osteoporotic fracture rates than non-Aboriginals [8, 9]. Factors such as low socioeconomic status, diabetes [8] or the number of comorbidities and alcohol and drug use [9] were associated with higher fracture rates in these populations. Low calcium and vitamin D intake among Aboriginals, particularly older women [13], may also play a role.

Moreover, Aboriginal populations living in the northern regions of the globe are exposed to persistent organic pollutants through their traditional diet [14]. These include dioxin-like compounds (DLCs), similar in structure to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), which are lipophilic, resistant to biodegradation and therefore bioaccumulate and bioamplify in aquatic food chains, where Aboriginals obtain part of their diet [15]. Yet the findings of in vitro studies [16] and experimental studies on rats [1719] show that TCDD, the most toxic compound in the DLC family, has multiple effects on bone tissue, resulting in increased bone fragility. Few human studies have focused on the relationship between exposure to DLCs and bone quality/strength. Furthermore, while populations are exposed to a complex mixture of DLCs, only the plasma concentrations of certain dioxin-like PCBs (DL-PCBs) have been measured in participants in order to study the relationship between their concentrations and BMD or quantitative ultrasound (QUS) parameters [2022].

The objective of this study was to investigate the relationship between plasma concentrations of DLCs or certain dioxin-like mono-ortho-substituted PCBs (DL-PCBs 105, 118 and 156) and calcaneal QUS parameters in Cree women of Eastern James Bay (Canada). Ultrasound bone measurement is a method used to assess bone strength that provides information that is complementary to BMD [23]. Recent studies demonstrate that QUS at the calcaneus can predict fractures as effectively as DXA in postmenopausal women and men over the age of 65 [24]. Values of QUS parameters are generally lower in osteoporotic patients than in healthy subjects [25]. This technique has several advantages: it is simple, fast, non-invasive, radiation-free and inexpensive. Furthermore, ultrasound bone measurement devices are portable and easy to use in remote, isolated regions, such as those inhabited by northern Aboriginal communities, where measurement of BMD cannot be performed by dual-energy x-ray absorptiometry (DXA).

Methods

Population

An environmental and health survey, entitled “Nituuchischaayihtitaau Aschii. Multi-community Environment- and Health Longitudinal Study in Eeyou Istchee”, was conducted from summer 2005 to summer 2009 in seven Cree communities located north of the 49th parallel north, in the Eastern James Bay region of the province of Quebec (Canada). The objective of this study was to investigate the health of these Aboriginal populations and the risk factors affecting them [26, 27].

Participants, stratified by age (0–7 years; 8–14 years; 15–39 years; ≥40 years) and gender, were selected using simple random sampling, without replacement, from the Beneficiary List in each community to create lists of potential participants to be contacted by recruiters. A weighting was attributed to each stratum to ensure that inferences could be drawn about the population. An initial list of subjects to be contacted was drawn up and all these people were contacted and asked to participate in the study. If they refused, a second list of participants was randomly created and recruitment continued in order to ensure the required number of subjects in each age group [28]. Owing to the low response rate (53.5%) in the first community visited, a correction to the predefined samples had to be taken into account in order to maintain the desired statistical power; the sample size in subsequent communities was therefore increased [29]. The total response rate of females (all age categories combined) varied between 34.4% and 70.9% depending on the community (Centre de Recherche du CHU de Québec, unpublished data). With respect to the non-participants, some could not be reached; some were undecided and withdrew, while others refused to participate. Others were excluded (unknown in the community, non-Cree, pregnant, disabled) [28, 29].

All participants (aged 8 years and over) completed a series of questionnaires in Eeyou Ayimuwin or English and administered by the research staff or interviewers (selected in local communities and provided with appropriate training) [30, 31]. They then underwent a clinical examination with biological samples and anthropometric measurements collected by research nurses.

All women aged 35 to 74 years who participated in the clinical examination were eligible for the calcaneal ultrasound measurement. A total of 254 women had QUS parameters measured. The volume of plasma collected from 249 women was sufficient to allow testing for DLCs and DL-PCBs.

Bone measurements

Two QUS parameters were measured at the right calcaneus of Cree women using a portable Achilles Insight system (GE Healthcare Lunar, Madison, WI, USA): 1) speed of sound (SOS, m/s) and 2) broadband ultrasound attenuation (BUA, dB/MHz) [32]. The Stiffness Index (SI, %) was automatically calculated by the system from the two parameters using the manufacturer’s formula [SI% = (0.67*BUA) + (0.28*SOS) – 420]. A research nurse inspected and calibrated the system’s membranes daily using the phantom provided by the manufacturer. In vitro accuracy was assessed by taking a number of repeated measurements with the manufacturer’s phantom: the mean coefficients of variation (CVs) recorded for the SOS parameter ranged from 0.05 to 0.12% depending on the community.

Anthropometric measurements

Body composition was measured using a bioelectric impedance analyzer (Tanita Corporation of America, IL, USA). Weight, lean body mass and body fat (kg), height, sitting height, waist and hip circumference (cm) were measured by research nurses using standardized techniques.

Laboratory analyses

DLCs and DL-PCBs

Plasma samples were tested for DLCs and DL-PCBs at INSPQ’s Toxicology Laboratory (Quebec City, Canada). The luciferase reporter gene cell assay described by Medehouenou et al. [33] was used to measure AhR-mediated transcriptional activity elicited by plasma extracts and provide an integrated measure of DLCs. The limit of detection (LOD) was 30 pg TCDD-equivalents (EQ)/L. For quality control purposes, we used human plasma spiked with TCDD to obtain a concentration of 322 pg/L. The mean concentration of this sample tested 22 times was 361 pg TCDD-EQ/L (bias=12.1%) and the CV was 14%.

DL-PCBs (DL-PCB 105: 2,3,3’,4,4’-pentachlorobiphenyl; DL-PCB 118: 2,3’4,4’,5-pentachlorobiphenyl and DL-PCB 156: 2,3,3’,4,4’,5-hexachlorobiphenyl) were measured by gas chromatography–mass spectrometry (GC-MS) as described extensively in the project reports [34, 35]. For samples from the first Cree community investigated, a liquid-liquid extraction was used followed by separation and quantification of PCB congeners by GC-MS using an Agilent 6890 Network GC system (Wilmington, DE, USA), fitted with an Agilent 7683 series automatic liquid sampler and an Agilent 5973 Network mass spectrometer. The recovery rate for the three DL-PCB congeners was over 90%. Coefficients of variation of 5.9%, 4.2% and 11.5% were obtained for DL-PCBs 105, 118 and 156 congeners respectively following repeated measures of standard reference material (SRM) 1589 (National Institute of Standards and Technology, Gaithersburg, MD, USA). The limit of detection for the three DL-PCBs was 0.02 μg/L [34]. For samples from the six other Cree communities investigated, a solid-phase extraction was used followed by GC-MS analysis with the same instrumentation as described above. Coefficients of variation of 7.4%, 7.7% and 9.7% were obtained for the three DL-PCB 105, 118 and 156 congeners respectively, following repeated measures of SRM 1589; the detection limit was 0.01 μg/L [35].

Lipids

Concentrations of total lipids in plasma samples were calculated using Phillips et al.’s formula [36]: [Total lipids (g/L) = (0.878*cholesterol mmol/L) + (0.885*triglycerides mmol/L) + 0.623]. Total cholesterol and triglyceride concentrations were determined by standard enzymatic methods. For the total cholesterol analysis, CVs of 1.5% and 1.3% were obtained for reference samples containing 2.87 and 6.67 mmol/L respectively. For the triglycerides analysis, CVs of 2.0% and 1.6% were noted for reference samples with concentrations of 1.07 and 2.32 mmol/L respectively [35].

Levels of omega-3 and omega-6 PUFAs were measured in erythrocyte membranes by liquid gas chromatography with flame ionisation detection at the Centre de Recherche sur les Maladies Lipidiques, CHU de Québec as previously described [34]. Omega-3 PUFAs refer to the following fatty acids: α-linolenic acid (C18:3n-3) + docosapentaenoic acid (C22:5n-3) + docosahexaenoic acid (C22:6n-3) + eicosapentaenoic acid (C20:5n-3) + (C18:4n-3) + (C20:3n-3) + (C20:4n-3). Omega-6 PUFAs refer to the following fatty acids: linoleic acid (C18:2n-6) + arachidonic acid (C20:4n-6) + (C18:3n-6) + (C20:2n-6) + (C20:3n-6) + (C22:2n-6) + (C22:4n-6) + (C22:5n-6).

Apolipoprotein B (Apo B) was measured by nephelometry using a BN ProSpec station (Dade Behring, Mississauga, ON). Control levels of 0.46 and 1.44 g/L for Apo B showed interassay CVs of 3.2% and 1.8% [34].

Metals/metalloids

Blood mercury levels (nmol/L) were measured by cold vapour atomic absorption spectrometry (Pharmacia) [34]. Cadmium (nmol/L), selenium (μmol/L) and lead (μmol/L) were measured by inductively coupled plasma mass spectrometry (ICP-MS). The blood samples were diluted in ammonium hydroxide and the metals converted to their elemental form by aspirating the sample into argon plasma before being identified and quantified by mass spectrometry. The samples were analysed using a Perkin Elmer Sciex Elan 6000 ICP-MS instrument). The LODs were 0.04 nmol/L for cadmium, 0.001 μmol/L for lead, 0.49 nmol/L for mercury and 0.09 μmol/L for selenium [35].

Other analyses

Glucose was measured by a hexokinase enzymatic assay employing the Roche Modular system. Interassay CVs were 1.6% and 1.4% for glucose at control values of 4.74 and 15.66 mmol/L respectively [35]. Alpha-tocopherol (vitamin E) was determined using a Waters high-pressure liquid chromatography system (Lachine, QC) equipped with an autosampler, a reverse phase column (Nucleosil ODS1) and a UV detector [35]. Serum levels of 25-hydroxyvitamin D (25OHD) were measured in the Biochemistry Laboratory at Montreal’s Hôpital St-Luc (CHUM) by a procedure that includes extraction and quantification by competitive radioimmunoassay using the IDS radioimmunoassay kit (Medicorp Inc., Montreal, QC) [35].

Questionnaires

Questionnaires were used to collect information about participants’ socio-demographic characteristics (date and place of birth; level of education: none or primary school/secondary and higher), their lifestyle habits (tobacco use: yes/no; use of calcium and vitamin D supplements in the last 12 months: yes/no; physical activity: active/inactive; alcohol intake in the last 12 months: yes/no; milk consumption in the last month: yes/no), their gynaecological history (menopausal status: menopausal/non-menopausal; parity: yes/no; number of children; use of hormonal contraceptives: yes/no; use of hormone replacement therapy, HRT: yes/no) and their personal fracture history (yes/no). Women were considered menopausal if they had had no menstrual periods for one year before recruitment. Sodium intake (≤ 2300 mg vs > 2300 mg/day) was compiled based on answers to the 24-hour dietary recall and data on the sodium content of foods [37].

We consulted the medical records of participants to document causes of secondary osteoporosis (CSO: yes/no), namely the use of certain medications in the last year (corticosteroids, heparin, Dilantin, Prednisone, chemotherapy) or the diagnosis of diseases (Cushing’s disease, rheumatoid arthritis, bone cancer, gastrectomy, kidney failure, liver failure, thyroid and parathyroid diseases) that may contribute to bone loss [38].

Physical activity (PA) was assessed using the short form of the International Physical Activity Questionnaire [39]. A dichotomous variable was created based on the median value of the Total MET (metabolic equivalent)-minutes/week, calculated using the algorithm in the IPAQ document [39]. Values at or below the median (3954 MET-minutes/week) define an inadequate level of PA, or subjects considered inactive, while values above the median define an adequate level of PA, or subjects considered active.

Statistical analyses

Our database comprised 249 participants. DLC concentrations were below the LOD (30 pg TCDD-EQ/L) for 16 participants. In these cases, we imputed a value between 0 and the LOD selected by simple random sampling with replacement. Of the 44 participants from the first community visited, four had DL-PCB 105 concentrations below the LOD, while one participant had a concentration of DL-PCB 156 below the LOD (LOD = 0.02 μg/L). Plasma concentrations of DL-PCBs 105, 118 and 156 below the LOD (0.01 μg/L) were obtained in 45, 7 and 12 participants respectively among the 205 residents of the other six communities. In these cases, we imputed a value equal to LOD/2, that is, 0.01 μg/L for participants from the first community and 0.005 μg/L for participants from the other communities.

Descriptive statistics (mean, standard error, minimum and maximum for quantitative variables; sample size and % per modality for qualitative variables) of the variables were generated for all participants.

The association between DLCs or each DL-PCB and QUS parameters was examined by simple and multiple linear regressions. Box-Cox procedures were used to resolve problems encountered with the hypotheses of normality and/or homoscedasticity in the multiple regression models for the dependent variables SOS and SI, which were subsequently log-transformed. Pearson’s correlation coefficients were calculated between the QUS parameters and the main exposure variables. The confounding effect of different factors on the relation between DLCs or DL-PCBs and QUS parameters was investigated. Secondary independent variables were tested to determine whether or not they should be included in the initial multiple regression models. Only those with a p value ≤ 0.20 (in simple linear regression analysis) were included. Multicollinearity between the variables of the initial regression models was tested to ensure that variables were not redundant. To avoid multicollinearity caused by the introduction of highly intercorrelated variables in the same linear regression model, we proposed for each dependent variable (SOS, BUA and SI) separate models that comprised only one of the main exposure variables (either DLCs, DL-PCB 105, DL-PCB 118 or DL-PCB 156). All potentially confounding variables were entered in the multiple regression model and those that did not change the value of β coefficient of the main exposure variable by more than 10% were removed one by one, starting with the variable that accounted for the least variation in the dependent variable. In the case of highly intercorrelated anthropometric variables, when one of these variables was not identified as a confounding factor, and therefore not retained in the final model, the initial model was rebuilt with the other variable.The final adjustment of the models was for the total plasma lipid concentration [40]. We also constructed a second set of multivariate models (models II in Additional files 1, 2 and 3) adjusted for the same confounders and covariates. A p value of < 0.05 in a bilateral situation is considered statistically significant. The software used was SAS version 9.2 (SAS Institute Inc., Cary, NC, USA).

The project was approved by the Research Ethics Committees of Université Laval and McGill University, in partnership with McMaster University, and by the Research Committee of the Cree Board of Health and Social Services of James Bay. Participation in the study was voluntary and a consent form was signed by each participant. All information concerning the participants was kept strictly confidential.

Results

With the weighting applied, the sample of 249 women (102 menopausal women and 147 non-menopausal women) could be extrapolated to a population of 2056 women (883 menopausal women and 1173 non-menopausal women).

QUS parameters, plasma DLC and DL-PCB concentrations as well as selected characteristics of the Cree women are shown in Table 1 and Additional file 4. Most participants were non-menopausal, had a secondary level education or higher, had had children, were sedentary, non-smokers and did not drink alcohol. A small proportion of women had used hormonal contraception or HRT, had taken supplements (calcium and vitamin D) in the 12 months prior to the study and had a history of fracture and conditions and/or medications identified as CSO.
Table 1

Characteristics of participants

Characteristic

N

AM(a)± SD(b)

Range(c)

GM(d)(95%-CI)(e)

Dependant variables

    

SOS (m/s)

249

1545.10 ± 39.21

1466.03-1665.22

 

BUA (dB/MHz)

249

118.05 ± 16.81

67.67-169.35

 

SI (%)

249

91.24 ± 20.59

46-151

 

Main exposition variables

    

DLC (pg TCDD-EQ/L)

249

234.13 ± 319.05

0.95-3115

124.31 (106.94-144.50)

DL-PCB 105 (μg/L)

249

0.09 ± 0.15

0.01-1.28

0.035 (0.03-0.04)

DL-PCB 118 (μg/L)

249

0.52 ± 0.90

0.01-8.35

0.19 (0.16-0.23)

DL-PCB 156 (μg/L)(f)

223

0.29 ± 0.42

0.01-3.15

0.12 (0.10-0.14)

Other variables

    

Age (years)

249

47.98 ± 10.72

35-74

 

Weight (kg)

248

91.46 ± 18.72

46.3-171.6

 

Lean mass (kg)

248

48.63 ± 6.48

25.8-75.2

 

Height (cm)

249

160.27 ± 6.03

134-175

 

Height in seated position (cm)

245

85.73 ± 3.24

74.7-93.85

 

Number of children

246

3.72 ± 2.77

0-13

 

Omega-3 PUFAs (%)

248

6.87 ± 1.38

2.16-13.9

 

Omega-6 PUFAs (%)

248

30.24 ± 1.73

22.44-33.76

 

Vitamin D (nmol/L)

249

59.03 ± 19.77

23-138

 

Glucose (mmol/L)

249

7.17 ± 8.98

3.0-21.8

 

Apolipoprotein B (g/L)

249

0.97 ± 1.17

0.31-2.62

 

Total lipid (g/L)

249

6.19 ± 1.28

3.49-14.64

 

Mercury (nmol/L)

249

51.09 ± 61.38

0.60-393.84

26.49 (22.67-30.94)

Selenium (μmol/L)

249

2.21 ± 0.42

1.3- 4.9

2.18 (2.14-2.23)

Lead (μmol/L)

249

0.29 ± 0.32

0.02-2.5

0.18 (0.16-0.21)

 

N

N weighted (%)

  

Menopausal status

249

2056.13

  

Menopausal

102

882.589 (42.92)

Non-menopausal

147

1173.54 (57.08)

Level of education

248

2044.31

  

None or primary school

74

620.02 (30.33)

Secondary or higher

174

1424.29 (69.67)

Supplements use(g)

232

1881.49

  

Yes

39

272.365 (14.48)

No

193

1609.13 (85.52)

Physical activity

243

2021.47

  

Active

118

986.11 (48.78)

Inactive

125

1035.37 (51.22)

Tobacco use

248

2042.24

  

Yes

94

726.82 (35.59)

No

154

1315.43 (64.41)

Sodium intake

249

2056.13

  

≤ 2300 mg

108

876.94 (42.65)

> 2300 mg

141

1179.19 (57.35)

  

a Unweighted arithmetic mean.

b Standard deviation;

c Minimum-maximum;

d Unweighted geometric mean;

e 95%-Confidence interval of geometric mean;

f Variable not measured in one community;

g Calcium and vitamin D supplements.

Most of the Cree women (96.9%) had a waist circumference ≥ 88 cm (abdominal obesity according to Health Canada) [41]; the median value was 112 cm. With respect to body mass index (BMI, kg/m2), according to Health Canada criteria [41], only 3.5% of Cree women had a normal BMI (between 18.5 and 24.9), 19.2% were overweight (BMI between 25.0 and 29.9 kg/m2) and 77.3% were obese (BMI ≥ 30 kg/m2). Among Cree women, 9.0% had a height of less than 153 cm, which has been associated with osteoporosis [42].

The maximum blood lead level of 0.32 μmol/L noted in women from the Quebec City area was exceeded by 26.9% of Cree women [43], while 14.2% exceeded Canada’s alert level of 0.48 μmol/L [44].

With regard to blood mercury levels, 64.5% of Cree women exceeded the maximum blood mercury level of 16 nmol/L observed in women from the Quebec City area [43], whereas 13.2% exceeded Canada’s alert level of 99.7 nmol/L [45].

Only 1.9% of participants exhibited a blood selenium concentration exceeding the maximum level (3.6 μmol/L) recorded in women from the Quebec City area [43] and 1.8% showed a blood cadmium concentration over the maximum value in the Quebec City inhabitants (55 nmol/L) [43].

With respect to serum levels of vitamin D (25OHD), 2.3% of Cree women had a level considered as critical (< 27.5 nmol/L) [46]; 14.6% displayed inadequate levels (< 37.5 nmol/L), while 39.5% showed a level considered minimal (< 50 nmol/L) [38].

Fasting glucose levels were considered normal (< 6.1 mmol/L) in 56.0% of Cree women, while 44.0% had levels corresponding to hyperglycemia (≥ 6.1 mmol/L). For 26.7% of Cree women, glucose levels could correspond to type 2 diabetes (≥ 7.0 mmol/L) according to the Canadian Diabetes Association [47].

Pearson’s r correlation coefficients revealed that QUS parameters were negatively and significantly correlated with DLCs, DL-PCBs and age, while the three DL-PCBs were positively and significantly correlated with one another and with age (see Additional file 5).

The final multivariate models of each QUS parameter, constructed for each of the main exposure variables (DLCs or DL-PCBs), are presented in Tables 2, 3 and 4. DL-PCBs 105 and 118 were significantly and negatively associated with SI (log); these associations persisted even after adjustment for several confounding factors. Hence the SI (log) decreased by −0.22% (p=0.0414) and −0.04% (p=0.0483) for every one-unit increase (1 μg/L) in plasma concentrations of DL-PCBs 105 and 118, respectively (Table 4). In QUS parameters models built for each of the main exposure variables and adjusted for the same factors (see Additional files 1, 2 and 3, models II), DL-PCB 105 was negatively and significantly associated with BUA (Additional file 2), whereas DL-PCBs 105 and 118 were negatively and significantly associated with SI (log) (Additional file 3).
Table 2

Results of explanatory multivariate analysis: SOS (log) models (I)

SOS (log) model

Main exposure variable

 

DLCs (pg TCDD-EQ/L)

DL-PCB 105 (μg/L)

DL-PCB 118 (μg/L)

DL-PCB 156 (μg/L)

N

240

 

220

 

241

 

202

 

R 2 (Adjusted R 2 )

0.3086 (0.2721)

 

0.3470 (0.2990)

 

0.3070 (0.2737)

 

0.3123 (0.2725)

 

Regression coefficient (SE) (a)

p-value

Regression coefficient (SE) (a)

p-value

Regression coefficient (SE) (a)

p-value

Regression coefficient (SE) (a)

p-value

Variables

DLCs

0.4012

DL-PCB 105

0.2008

DL-PCB 118

0.1440

DL-PCB 156

0.2534

 

0.009 (0.01)

 

−18.66 (14.54)

 

−3.40 (2.32)

 

−6.38 (5.57)

Age (years)

−1.02 (0.30)

0.0007

−1.03 (0.29)

0.0005

−1.26 (0.19)

<0.0001

−1.44 (0.22)

<0.0001

Weight (kg)

0.007 (0.09)

0.9409

NR

 

NR

 

NR

 

Height (cm)

−0.49 (0.28)

0.0852

−0.84 (0.28)

0.0027

−0.54 (0.27)

0.0466

−0.75 (0.29)

0.0118

Number of children

NR

 

0.09 (0.63)

0.8854

NR

 

−0.10 (0.61)

0.8749

Omega-3 PUFAs (%)

1.27 (1.27)

0.3176

2.19 (1.30)

0.0928

1.79 (1.28)

0.1620

2.11 (1.33)

0.1143

Vitamin D (nmol/L)

NR

 

0.07 (0.10)

0.4498

NR

 

NR

 

Mercury (nmol/L)

0.02 (0.03)

0.6270

0.03 (0.04)

0.4081

0.03 (0.03)

0.3461

0.05 (0.04)

0.2162

Selenium (μmol/L)

2.13 (3.86)

0.5820

0.39 (0.53)

0.4626

4.30 (3.70)

0.2463

2.74 (3.79)

0.4696

Glucose (mmol/L)

NR

 

3.51 (3.64)

0.3354

0.46 (0.51)

0.3637

NR

 

Apolipoprotein B (g/L)

−5.70 (3.90)

0.1454

NR

 

−5.81 (3.86)

0.1340

NR

 

Menopausal status

−8.62 (4.89)

0.0791

−10.34 (4.92)

0.0368

NR

 

NR

 

Level of education

9.41 (3.86)

0.0156

10.15 (3.91)

0.0102

7.62 (3.84)

0.0486

8.06 (4.13)

0.0526

Smoking status

NR

 

−6.75 (3.39)

0.0480

NR

 

−6.44 (3.59)

0.0741

Physical activity

5.49 (2.92)

0.0610

6.10 (3.03)

0.0454

5.97 (2.91)

0.0416

NR

 

Supplements use (b)

NR

 

−5.50 (4.82)

0.2550

NR

 

−0.54 (5.19)

0.9177

Total lipid(c) (g/L)

0.51 (1.17)

0.6661

0.94 (1.27)

0.4582

0.47 (1.19)

0.6927

1.52 (1.33)

0.2532

a Regression coefficients and standard errors values are multiplied by 103;

b Calcium and vitamin D supplements;

c Final adjustment for the total plasma lipid concentration;

NR – Variable not retained in the model (non-confounding factor; proportional change of the regression coefficient of the main exposure variable < 10%).

Table 3

Results of explanatory multivariate analysis: BUA models (I)

BUA model

Main exposure variable

 

DLCs (pg TCDD-EQ/L)

DL-PCB 105 (μg/L)

DL-PCB 118 (μg/L)

DL-PCB 156 (μg/L)

N

226

 

239

 

244

 

203

 

R 2 (Adjusted R 2 )

0.3680 (0.3324)

 

0.3579 (0.3356)

 

0.3496 (0.3303)

 

0.3424 (0.3188)

 
 

Regression coefficient (SE)

p-value

Regression coefficient (SE)

p-value

Regression coefficient (SE)

p-value

Regression coefficient (SE)

p-value

Variables

DLCs

0.4275

DL-PCB 105

0.1060

DL-PCB 118

0.0841

DL-PCB 156

0.2649

 

0.005 (0.007)

 

−13.23 (8.15)

 

−2.42 (1.40)

 

−3.79 (3.39)

Age (years)

−0.73 (0.19)

0.0002

−0.78 (0.13)

<0.0001

−0.83 (0.12)

<0.0001

−0.74 (0.13)

<0.0001

Weight (kg)

0.04 (0.06)

0.5404

NR

 

NR

 

NR

 

Lean mass (kg)

NR

 

0.18 (0.17)

0.2924

NR

 

NR

 

Height in seated position (cm)

0.19 (0.37)

0.6181

0.13 (0.36)

0.7163

NR

 

NR

 

Omega-6 PUFAs (%)

−1.43 (0.66)

0.0317

−0.72 (0.63)

0.2521

−0.75 (0.62)

0.2268

NR

 

Mercury (nmol/L)

NR

 

NR

 

0.01 (0.02)

0.4820

0.01 (0.02)

0.6159

Selenium (μmol/L)

−1.998 (2.41)

0.4075

NR

 

NR

 

NR

 

Lead (μmol/L)

2.78 (3.66)

0.4487

NR

 

NR

 

NR

 

Number of children

NR

 

0.42 (0.39)

0.2882

0.48 (0.38)

0.2144

0.43 (0.38)

0.2663

Menopausal status

−2.63 (3.20)

0.4134

NR

 

NR

 

NR

 

Level of education

5.95 (2.43)

0.0150

4.63 (2.42)

0.0573

5.55 (2.37)

0.0202

5.28 (2.54)

0.0386

Supplements use(a)

−3.58 (2.82)

0.2063

NR

 

NR

 

−2.53 (3.25)

0.4369

Sodium intake

2.56 (2.02)

0.2062

NR

 

NR

 

NR

 

Total lipid(b) (g/L)

−0.93 (0.72)

0.1971

−0.02 (0.79)

0.9810

−0.24 (0.78)

0.7558

0.001 (0.83)

0.9991

a Calcium and vitamin D supplements;

b Final adjustment for the total plasma lipid concentration;

NR – Variable not retained in the model (non-confounding factor; proportional change of the regression coefficient of the main exposure variable < 10%).

Table 4

Results of explanatory multivariate analysis: SI (log) models (I)

SI (log) model

Main exposure variable

 

DLCs (pg TCDD-EQ/L)

DL-PCB 105 (μg/L)

DL-PCB 118 (μg/L)

DL-PCB 156 (μg/L)

N

245

 

246

 

244

 

203

 

R 2 (Adjusted R 2 )

0.3935 (0.3675)

 

0.3631 (0.3471)

 

0.3932 (0.3725)

 

0.3766 (0.3475)

 

Regression coefficient (SE) (a)

p-value

Regression coefficient (SE) (a)

p-value

Regression coefficient (SE) (a)

p-value

Regression coefficient (SE) (a)

p-value

Variables

DLCs

0.3806

DL-PCB 105

0.0414

DL-PCB 118

0.0483

DL-PCB 156

0.1379

 

0.07 (0.08)

 

−219.24 (106.90)

 

−37.84 (19.07)

 

−69.81 (46.87)

Age (years)

−13.87 (2.58)

<0.0001

−12.74 (1.49)

<0.0001

−12.55 (1.57)

<0.0001

−12.21 (1.76)

<0.0001

Weight (kg)

0.11 (0.75)

0.8815

NR

 

NR

 

NR

 

Height (cm)

−2.09 (2.36)

0.3762

−0.75 (2.18)

0.7321

−2.38 (2.23)

0.2860

−2.60 (2.50)

0.2994

Number of children

NR

 

6.22 (5.09)

0.2232

4.71 (5.00)

0.3477

25.2 (5.17)

0.6264

Omega-3 PUFAs (%)

18.36 (9.89)

0.0646

NR

 

23.90 (9.94)

0.0170

17.64 (11.36)

0.1221

Vitamin D (nmol/L)

0.57 (0.72)

0.4298

NR

 

NR

 

NR

 

Mercury (nmol/L)

NR

 

0.22 (0.25)

0.3769

NR

 

0.31 (0.31)

0.3215

Glucose (mmol/L)

NR

 

NR

 

2.88 (4.18)

0.4914

NR

 

Menopausal status

−26.05 (40.99)

0.5256

NR

 

NR

 

NR

 

Level of education

95.19 (31.70)

0.0030

NR

 

71.25 (31.37)

0.0240

72.72 (34.94)

0.0388

Smoking status

−36.58 (27.40)

0.1831

NR

 

NR

 

NR

 

Supplements use(b)

NR

 

NR

 

NR

 

−12.67 (43.91)

0.7733

Total lipid(c) (g/L)

−5.28 (9.51)

0.5796

7.31 (10.41)

0.4829

3.04 (10.38)

0.7696

7.19 (11.28)

0.5244

a Regression coefficients and standard errors values are multiplied by 103;

b Calcium and vitamin D supplements;

c Final adjustment for the total plasma lipid concentration;

NR – Variable not retained in the model (non-confounding factor; proportional change of the regression coefficient of the main exposure variable < 10%).

Several variables were identified as confounders of associations between exposure variables and QUS parameters. Some confounding factors were significantly associated with SOS (log): age (in all models), level of education (DLC, DL-PCB 105 and DL-PCB 118 models), height (all DL-PCB models), PA (DL-PCB 105 and DL-PCB 118 models), smoking status and menopausal status (DL-PCB 105 model) (Table 2). In general, SOS (log) decreased with increasing age, height, menopausal status and smoking status and SOS (log) increased with increasing level of education and physical activity.

Age (in all models), level of education (DLC, DL-PCB 118 and DL-PCB 156 models) and % of omega-6 PUFAs (DLC model) were significantly associated with BUA (Table 3). In general, BUA decreased with increasing age and % of omega-6 PUFAs, and increased with increasing level of education.

Lastly, age (in all models), level of education (DLC, DL-PCB 118 and DL-PCB 156 models) and % of omega-3 PUFAs (DL-PCB 118 model) were significantly associated with SI (Table 4). In general, SI (log) decreased with increasing age, and increased with an increase in the total % of omega-3 PUFAs and with increasing level of education.

Discussion

This is the first population study to investigate the relationship between total DLC concentration measured with an AhR responsive reporter gene assay and bone quality parameters [48]. In Cree women aged 35 to 74 years of Eastern James Bay, DL-PCBs 105 and 118 were negatively and significantly associated with SI. However, we did not find any association between total DLC concentrations measured by the reporter gene bioassay and QUS parameters, which suggests that the effect of PCBs is not linked to activation of the AhR signalling pathway. Alternatively, the lack of association could also be due to the lower precision of the bioassay measurement compared to that of GC-MS measurements.

Three studies have been conducted on the relation between DL-PCBs and QUS bone parameters, with quite controversial findings. In a group of Swedish men from the general population (n = 115, age 40–75 years), Glynn et al. [20] reported a positive association at the limit of significance (β = 0.0044; p = 0.05) between serum concentrations of DL-PCB 167 and whole-body BMD (measured by DXA), after adjustment for several confounding factors. However, no association was observed between DL-PCB 167 and BUA or SOS parameters measured by ultrasound at the left calcaneus [20]. No association was observed between DL-PCBs 105, 118, 156 and QUS parameters or lumbar spine, femoral neck or whole-body BMD [20].

Exploratory multivariate analyses conducted by Côté et al. [21] revealed that plasma concentrations of DL-PCB 156 were negatively associated with QUS parameters measured in 153 peri- and postmenopausal Inuit women from Nuuk in Greenland (SOS model: β = −22.68, p = 0.014; BUA model: β = −8.12, p = 0.028; SI model: β = −11.95, p = 0.009) [21]. However, DL-PCBs 105 and 118 were not associated with QUS parameters [21].

Hodgson et al. [22] found a negative association between serum DL-PCB 118 concentration and forearm BMD measured using DXA (β = −0.00024, p = 0.002) in the dominant arm of 154 men (60–81 year old) from the Swedish OSCAR cohort study. The odds ratio for low BMD of 1.06 per 10 pg/mL DL-PCB 118 was significant (95% CI: 1.01-1.12) [22]. However, in women (n = 167), DL-PCB 118 was positively associated with BMD (β = 0.00008, p = 0.045). In this study, no association was observed between DL-PCBs 105, 156, 157, 167 and BMD; the authors considered the statistical power to be limited and a large proportion of variance was not explained by regression models [22].

In additional analyses, we performed logistic regression using medians for SI (91%), DL-PCB 105 (0.03 μg/L) and DL-PCB 118 (0.18 μg/L) as cut-off values between “low” and “high” categories. Statistically-significant odds ratios (OR) of 1.69 for low SI at high DL-PCB 105 exposure (95% CI: 1.34-2.13) and 1.36 for low SI at high DL-PCB 118 exposure (95% CI: 1.06-1.73) were obtained in models adjusted for all variables indicated in Table 4, confirming the associations obtained by multiple linear regression. However, the choice of medians as a cut-off values to define “low” and “high” categories of SI and the main exposure variables is arbitrary and does not convey the same meaning in different populations. Cut-off values for QUS parameters based on pathophysiological considerations are not available in the literature.

The geometric means (GMs) of plasma DL-PCB concentrations among Cree women were higher than those reported in the 2007–2009 Canadian Health Measures Survey for women aged 40 to 79 years and representative of the general Canadian population [49]. However, mean plasma concentrations of DLCs in Cree women estimated by the reporter gene assay were lower than those of women of childbearing age (Inuit, Dene, Caucasian) in other regions in Northern Canada [14].

Explanatory multivariate analyses revealed that several other factors were significantly associated with QUS parameters. Negative associations were found with age, height, smoking status, menopausal status and omega-6 PUFAs, whereas positive associations were noted with level of education, PA and % of omega-3 PUFAs.

Omega-3 PUFAs measured as a % of total fatty acids in erythrocyte membrane phospholipids in Cree women were positively and significantly associated with SI, while the % of omega-6 PUFAs was negatively associated with the BUA parameter. As observed following additional analyses, the omega-3/omega-6 PUFA ratio was positively and significantly associated with BUA and SI (log). To our knowledge, associations between omega-3 and omega-6 PUFAs and calcaneal QUS parameters have not been reported previously. A high ratio of omega-6 to omega-3 PUFAs has been associated with low BMD [50], while a higher dietary intake of omega-3 PUFAs had a protective effect against bone loss [51, 52].

Our study has several strengths. Firstly, the representativeness of our population sample was assured by the recruitment strategy and the weighting scheme of the study, which took non-response and refusal to participate rates into account. Hence the findings can be generalized to the entire female Cree population aged 35 to 74 years living in Eastern James Bay communities. Secondly, we took into consideration a large number of potential confounding factors of the association between the main exposure variables (DLCs, DL-PCBs) and QUS parameters. Several of these factors were investigated for the first time (% omega-3 and omega-6 PUFAs, mercury, selenium, lead, glucose, apolipoprotein B). Thirdly, measurement biases in the dependent variables or exposure variables are relatively unlikely. Data collection in the field (ultrasound measurements, anthropometric measurements, biological samples) was performed by research nurses using standardized techniques. Completed questionnaires were reviewed by members of the research team to ensure that all the questions had been completely and properly answered [30, 31]. Individual levels of exposure to contaminants were measured using standardized methods in the same laboratory (INSPQ) which has a quality control system accredited according to ISO/CEI 17025, CAN-P-43 and ILAC G-13 standards. All laboratory analyses (plasma or blood) were carried out, with two exceptions (PUFAs and 25OHD), at the INSPQ laboratory. Measurement accuracy complied with applicable standards.

Our study has also certain limitations. Firstly, the main methodological limitation is its cross-sectional design, with exposure and the dependent variable measured at the same time (a single measurement for each subject), such that the temporal sequence of cause and effect cannot be determined. However, due to the fact that DLCs and DL-PCBs accumulate in the body with age, plasma concentrations in Cree women reflect their lifelong exposure. Secondly, the participation rate in the study varied largely between communities, which may suggest a selection bias. However, this bias is quantitatively unimportant, since it is unlikely that the characteristics of the subjects included in the study are different from those of all eligible persons. The lists of beneficiaries which were used to build the list of contact persons were completed as necessary with information from other local listings; moreover, the investigated communities are small and live in reserves, which facilitated the identification and location of their members. Regarding the non-response and refusal to participate in the study, it is unlikely that these subjects have different levels of plasma DL-PCBs congeners or QUS parameter values, compared with participants; the recruitment of participants was made for a survey of general health and not specifically for the purpose of our study. Thirdly, the number of participants with QUS measurements was relatively small, limiting our capacity to observe an association between DLC concentrations determined by the reported gene assay and QUS parameters, especially considering that this measure is relatively imprecise compared to analytical chemistry based data. Fourthly, while we took a maximum number of potential confounding factors into account given the size of our sample, a residual confounding effect cannot be ruled out. The determinants of bone strength are multigenic and multifactorial and other factors (genetic, nutritional, environmental) not measured in our study could have decreased the residual variance in multiple regression models. Lastly, two variables that are known to influence bone quality could not be thoroughly assessed. The assessment of dietary calcium intake considered only milk consumption. Physical activity was evaluated using the short form of the IPAQ [39], an instrument that proved difficult to administer in Cree communities, even though its feasibility and validity has been demonstrated in general populations (adults aged 15 to 69 years) of 12 countries [53]. Participants in our study had trouble quantifying their activities, especially the duration and intensity of activities during the week prior to the study [54]. We believe that the proportion of active Cree women was probably overestimated.

Conclusions

We observed negative associations between SI values and plasma concentrations of two dioxin-like PCB congeners in women from this Aboriginal population. Our study does not allow causal inference and residual confounding may still be present. Therefore, we do not believe that dietary recommendations to reduce PCB exposure are warranted at this time. In multivariate models, associations were also noted with dietary and lifestyle factors, suggesting avenues to improve bone quality in this population, such as smoking cessation and increases in omega-3 PUFA intake and physical activity.

Abbreviations

AhR: 

aryl hydrocarbon receptor

AM: 

arithmetic mean

Apo B: 

Apolipoprotein B

BMD: 

bone mineral density

BMI: 

body mass index

DL-PCB: 

dioxin-like polychlorinated biphenyl

BUA: 

broadband ultrasound attenuation

CHUM: 

Centre Hospitalier de l’Université de Montréal

CHU de Québec: 

Centre Hospitalier Universitaire de Québec

95% CI: 

95% confidence interval

CSO: 

causes of secondary osteoporosis

CV: 

coefficient of variation

dB/MHz: 

decibels per microhertz

DLCs: 

dioxin-like compounds

DL-PCBs: 

dioxin-like polychlorinated biphenyls

DXA: 

dual-energy x-ray absorptiometry

GC-MS: 

gas chromatography–mass spectrometry

GM: 

geometric mean

g/L: 

grams per litre

HRT: 

hormone replacement therapy

ICP-MS: 

inductively coupled plasma mass spectrometry

INSPQ: 

Institut National de Santé Publique de Québec

MET: 

metabolic equivalent

m/s: 

metres per second

μg/L: 

micrograms per litre

μmol/L: 

micromoles per litre

mmol/L: 

millimoles per litre

nmol/L: 

nanomoles per litre

NR: 

not retained

LOD: 

limit of detection

OR: 

odds ratio

PUFA: 

polyunsaturated fatty acid

PA: 

physical activity

%: 

percentage

pg TCDD-EQ/L: 

picograms TCDD-equivalents per litre

QC: 

quality control

QUS: 

Quantitative UltraSonography

RR: 

relative risk

SOS: 

speed of sound

SRM: 

standard reference material

SI: 

Stiffness Index

TCDD: 

2,3,7,8-tetrachlorodibenzo-p-dioxin

UV: 

ultra violet

25OHD: 

25-hydroxyvitamin D.

Declarations

Acknowledgements

We would like to thank the Cree women of Eastern James Bay who participated in this health study. We would also like to thank the medical and research staff and the Cree interpreters for their assistance and for data collection. This scientific communication is a report from the Nituuchischaayihtitaau Aschii Multi-Community Environment and Health Longitudinal Study in Iiyiyiu Aschii supported by the Cree people of northern Québec, the Cree First Nations and the Cree Board of Health and Social Services of James Bay through financial contributions from Niskamoon Corporation. ACP is the recipient of a Doctoral Training Award from FRQ-S and a doctoral scholarship from Nasivvik - Centre for Inuit Health and Changing Environments.

Authors’ Affiliations

(1)
Axe santé des populations et pratiques optimales en santé, Centre de recherche du CHU de Québec
(2)
Laboratoire de toxicologie, INSPQ
(3)
Axe santé des populations et pratiques optimales en santé, Centre de Recherche du CHU de Québec, Hôpital Saint-François-d'Assise
(4)
Department of Biochemistry and Biomedical Sciences, McMaster University

References

  1. WHO: Assessment of fracture risk and its application to screening for postmenopausal osteoporosis. 1994, Geneva: Report of a WHO Study Group, 130-Google Scholar
  2. Gennari L, Merlotti D, De Paola V, Calabrò A, Becherini L, Martini G, Nuti R: Estrogen receptor gene polymorphisms and the genetics of osteoporosis: a HuGE review. Am J Epidemiol. 2005, 161: 307-320. 10.1093/aje/kwi055.View ArticleGoogle Scholar
  3. Saag KG, Geusens P: Progress in osteoporosis and fracture prevention: focus on postmenopausal women. Arthritis Res Ther. 2009, 11: 251-10.1186/ar2815.View ArticleGoogle Scholar
  4. Bessette L, Ste-Marie LG, Jean S, Davison KS, Beaulieu M, Baranci M, Bessant J, Brown JP: The care gap in diagnosis and treatment of women with a fragility fracture. Osteoporos Int. 2008, 1: 79-86.View ArticleGoogle Scholar
  5. Adachi JD, Ioannidis G, Pickard L, Berger C, Prior JC, Joseph L, Hanley DA, Olszynski WP, Murray TM, Anastassiades T, Hopman W, Brown JP, Kirkland S, Joyce C, Papaioannou A, Poliquin S, Tenenhouse A, Papadimitropoulos EA: The association between osteoporotic fractures and health-related quality of life as measured by the Health Utilities Index in the Canadian Multicentre Osteoporosis Study (CaMos). Osteoporos Int. 2003, 14: 895-904. 10.1007/s00198-003-1483-3.View ArticleGoogle Scholar
  6. Cummings SR, Melton LJ: Epidemiology and outcomes of osteoporotic fractures. Lancet. 2002, 359: 1761-1767. 10.1016/S0140-6736(02)08657-9.View ArticleGoogle Scholar
  7. Leslie WD, Derksen S, Metge C, Lix LM, Salamon EA, Wood SP, Roos LL: Fracture risk among First Nations people: a retrospective matched cohort study. CMAJ. 2004, 171: 869-873.View ArticleGoogle Scholar
  8. Leslie WD, Derksen SA, Metge C, Lix LM, Salamon EA, Steiman PW, Roos LL: Demographic risk factors for fracture in First Nations people. Can J Public Health. 2005, 96: 45-50.Google Scholar
  9. Leslie WD, Derksen S, Prior HJ, Lix LM, Metge C, O‘neil J: The interaction of ethnicity and chronic disease as risk factors for osteoporotic fractures: a comparison in Canadian Aboriginals and non-Aboriginals. Osteoporos Int. 2006, 17: 1358-1368. 10.1007/s00198-006-0111-4.View ArticleGoogle Scholar
  10. Leslie WD, Metge CJ, Weiler HA, Doupe M, Steiman PW, O’Neil JD: Bone density and bone area in Canadian Aboriginal women: the First Nations Bone Health Study. Osteoporos Int. 2006, 17: 1755-1762. 10.1007/s00198-006-0184-0.View ArticleGoogle Scholar
  11. Weiler HA, Leslie WD, Bernstein CN: Parathyroid hormone is predictive of low bone mass in Canadian Aboriginal and White women. Bone. 2008, 42: 498-504. 10.1016/j.bone.2007.11.013.View ArticleGoogle Scholar
  12. Leslie WD, Weiler HA, Lix LM, Nyomba BL: Body composition and bone density in Canadian White and Aboriginal women: the First Nations Bone Health Study. Bone. 2008, 42: 990-995. 10.1016/j.bone.2008.01.018.View ArticleGoogle Scholar
  13. Weiler HA, Leslie WD, Krahn J, Steiman PW, Metge CJ: Canadian Aboriginal women have a higher prevalence of vitamin D deficiency than non-Aboriginal women despite similar dietary vitamin D intakes. J Nutr. 2007, 137: 461-465.Google Scholar
  14. Van Oostdam J, Donaldson SG, Feeley M, Arnold D, Ayotte P, Bondy G, Chan L, Dewailly E, Furgal CM, Kuhnlein H, Loring E, Muckle G, Myles E, Receveur O, Tracy B, Gill U, Kalhok S: Human health implications of environmental contaminants in Arctic Canada: A review. Sci Total Environ. 2005, 351–352: 165-246.View ArticleGoogle Scholar
  15. Lorber M, Patterson D, Huwe J, Kahn H: Evaluation of background exposures of Americans to dioxin-like compounds in the 1990s and the 2000s. Chemosphere. 2009, 77: 640-651. 10.1016/j.chemosphere.2009.08.016.View ArticleGoogle Scholar
  16. Carpi D, Korkalainen M, Airoldi L, Fanelli R, Hakansson H, Muhonen V, Tuukkanen J, Viluksela M, Pastorelli R: Dioxin-sensitive proteins in differentiating osteoblasts: effects on bone formation in vitro. Toxicol Sci. 2009, 108: 330-343. 10.1093/toxsci/kfp021.View ArticleGoogle Scholar
  17. Lind PM, Wejheden C, Lundberg R, Alvarez-Lloret P, Hermsen SA, Rodriguez-Navarro AB, Larsson S, Rannug A: Short-term exposure to dioxin impairs bone tissue in male rats. Chemosphere. 2009, 75: 680-684. 10.1016/j.chemosphere.2008.12.024.View ArticleGoogle Scholar
  18. Herlin M, Kalantari F, Stern N, Sand S, Larsson S, Viluksela M, Tuomisto JT, Tuomisto J, Tuukkanen J, Jämsä T, Lind PM, Håkansson H: Quantitative characterization of changes in bone geometry, mineral density and biomechanical properties in two rat strains with different Ah-receptor structures after long-term exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin. Toxicology. 2010, 273: 1-11. 10.1016/j.tox.2010.04.006.View ArticleGoogle Scholar
  19. Finnilä MA, Zioupos P, Herlin M, Miettinen HM, Simanainen U, Håkansson H, Tuukkanen J, Viluksela M, Jämsä T: Effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin exposure on bone material properties. J Biomech. 2010, 43: 1097-1103. 10.1016/j.jbiomech.2009.12.011.View ArticleGoogle Scholar
  20. Glynn AW, Michaëlsson K, Lind PM, Wolk A, Aune M, Atuma S, Darnerud PO, Mallmin H: Organochlorines and bone mineral density in Swedish men from the general population. Osteoporos Int. 2000, 11: 1036-1042.View ArticleGoogle Scholar
  21. Côté S, Ayotte P, Dodin S, Blanchet C, Mulvad G, Petersen HS, Gingras S, Dewailly E: Plasma organochlorine concentrations and bone ultrasound measurements: a cross-sectional study in peri-and postmenopausal Inuit women from Greenland. Environ Health. 2006, 5: 33-10.1186/1476-069X-5-33.View ArticleGoogle Scholar
  22. Hodgson S, Thomas L, Fattore E, Lind PM, Alfven T, Hellström L, Håkansson H, Carubelli G, Fanelli R, Jarup L: Bone mineral density changes in relation to environmental PCB exposure. Environ Health Perspect. 2008, 116: 1162-1166. 10.1289/ehp.11107.View ArticleGoogle Scholar
  23. Padilla F, Laugier P: Recent developments in trabecular bone characterization using ultrasound. Curr Osteoporos Rep. 2005, 3: 64-69. 10.1007/s11914-005-0006-x.View ArticleGoogle Scholar
  24. Krieg MA, Barkmann R, Gonnelli S, Stewart A, Bauer DC, Del Rio BL, Kaufman JJ, Lorenc R, Miller PD, Olszynski WP, Poiana C, Schott AM, Lewiecki EM, Hans D: Quantitative ultrasound in the management of osteoporosis: the 2007 ISCD Official Positions. J Clin Densitom. 2007, 2008 (11): 163-187.Google Scholar
  25. Danese RD, Licata AA: Ultrasound of the skeleton: review of its clinical applications and pitfalls. Curr Rheumatol Rep. 2001, 3: 245-248. 10.1007/s11926-001-0025-2.View ArticleGoogle Scholar
  26. Bonnier-Viger Y, Dewailly E, Egeland GM, Nieboer E, Pereg D: Nituuchischaayihtitaau Aschii. Multi-community Environment- and Health longitudinal study in IIyiyiu Aschii: Mistissini. Technical report: summary of activities, results and recommendations. Edited by: Pereg D, Nieboer E. 2007, Montreal, QC: Cree Board of health and Social Services of James Bay, 389-http://www.creehealth.org/sites/default/files/Environmental%20Health%20Study-%20Technical%20Report%20of%20Mistissini.pdf,Google Scholar
  27. Bonnier-Viger Y, Château-Degat ML, Dewailly E, Egeland GM, Nieboer E: Nituuchischaayihtitaau Aschii multi-community environment and health longitudinal study in Eeyou Istchee: Eastmain and Wemindji Technical Report: summary of 2007 activities, results and recommendations. Public Health Report Series 4 on the Health of the Population. Edited by: Nieboer E, Robinson E, Petrov K. 2011, Chisasibi, QC: Cree Board of health and Social Services of James Bay, 376-http://www.creehealth.org/sites/default/files/Eastmain-Wemindji%20report%20pdf%2015-06-11_0.pdf,Google Scholar
  28. Château-Degat ML, Pereg D, Louchini R: Study Population, Recruitment, Ethics and Confidentiality. Nituuchischaayihtitaau Aschii Multi-community Environment- and Health longitudinal study in IIyiyiu Aschii: Mistissini Technical report: summary of activities, results and recommendations. Edited by: Pereg D, Nieboer E. 2007, Montreal, QC: Cree Board of health and Social Services of James Bay, 45-46.Google Scholar
  29. Côté S, Annasour-Laouan-Sidi E, Château-Degat ML, Nieboer E: Study Population, Recruitment, Ethics and Confidentiality. Nituuchischaayihtitaau Aschii multi-community environment and health longitudinal study in Eeyou Istchee: Eastmain and Wemindji Technical Report: summary of 2007 activities, results and recommendations. Edited by: Nieboer E, Robinson E, Petrov K. 2011, Chisasibi, QC: Public Health Report Series 4 on the Health of the Population. Cree Board of health and Social Services of James Bay, 26-Google Scholar
  30. Côté S: Questionnaires. Nituuchischaayihtitaau Aschii Multi-community Environment- and Health longitudinal study in IIyiyiu Aschii: Mistissini Technical report: summary of activities, results and recommendations. Edited by: Pereg D, Nieboer E. 2007, Montreal, QC: Cree Board of health and Social Services of James Bay, 47-48.Google Scholar
  31. Côté S: Questionnaires. Nituuchischaayihtitaau Aschii multi-community environment and health longitudinal study in Eeyou Istchee: Eastmain and Wemindji Technical Report: summary of 2007 activities, results and recommendations. Edited by: Nieboer E, Robinson E, Petrov K. 2011, Chisasibi, QC: Public Health Report Series 4 on the Health of the Population. Cree Board of health and Social Services of James Bay, 29-30.Google Scholar
  32. Madison, Wisconsin. Achilles InSight - Manuel de l’opérateur.http://www3.gehealthcare.com/en/Products/Categories/Bone_Health/Quantitative_Ultrasound/~/media/Downloads/us/Product/Product-Categories/Bone-Health/Quantitative%20Ultrasound/Achilles/GEHealthcare-Brochure_Achilles-Bone-Ultrasonometers.pdf,
  33. Medehouenou TC, Larochelle C, Dumas P, Dewailly E, Ayotte P: Determinants of AhR-mediated transcriptional activity induced by plasma extracts from Nunavik Inuit adults. Chemosphere. 2010, 80: 75-82. 10.1016/j.chemosphere.2010.04.017.View ArticleGoogle Scholar
  34. Pereg D, Ayotte P, Château-Degat ML, Lamarche B, Julien P: Laboratory Analyses. Nituuchischaayihtitaau Aschii Multi-community Environment- and Health longitudinal study in IIyiyiu Aschii: Mistissini Technical report: summary of activities, results and recommendations. Edited by: Pereg D, Nieboer E. 2007, Montreal, QC: Cree Board of health and Social Services of James Bay, 52-60.Google Scholar
  35. Ayotte P, Château-Degat ML, Côté S, Bertrand F, Julien P, Lamarche B: Laboratory Analyses. Nituuchischaayihtitaau Aschii multi-community environment and health longitudinal study in Eeyou Istchee: Eastmain and Wemindji Technical Report: summary of 2007 activities, results and recommendations. Edited by: Nieboer E, Robinson E, Petrov K. 2011, Chisasibi, QC: Public Health Report Series 4 on the Health of the Population. Cree Board of health and Social Services of James Bay, 34-40.Google Scholar
  36. Phillips DL, Pirkle JL, Burse VW, Bernert JT, Henderson LO, Needham LL: Chlorinated hydrocarbon levels in human serum: effects of fasting and feeding. Arch Environ Contam Toxicol. 1989, 18: 495-500. 10.1007/BF01055015.View ArticleGoogle Scholar
  37. Votre santé et vous. Sodium.http://www.hc-sc.gc.ca/hl-vs/alt_formats/pdf/iyh-vsv/food-aliment/sodium-fra.pdf,
  38. The Journal of The North American Menopause Society: Management of osteoporosis in postmenopausal women: 2010 Position Statement of the North American Menopause Society. Menopause. 2010, 17: 25-54. 10.1097/gme.0b013e3181c617e6.View ArticleGoogle Scholar
  39. Guidelines for data processing and Analysis of the International Physical Activity Questionnaire (IPAQ) – Short and Long Forms. 2005,http://www.ipaq.ki.se/scoring.pdf, November ,
  40. Schisterman EF, Whitcomb BW, Louis GM, Louis TA: Lipid adjustment in the analysis of environmental contaminants and human health risks. Environ Health Perspect. 2005, 113: 853-857. 10.1289/ehp.7640.View ArticleGoogle Scholar
  41. Lignes directrices canadiennes pour la classification du poids chez les adultes.http://www.hc-sc.gc.ca/fn-an/alt_formats/hpfb-dgpsa/pdf/nutrition/cg_quick_ref-ldc_rapide_ref-fra.pdf,
  42. Newton JL, Jones DEJ, Wilton K, Pairman J, Parry SW, Francis RM: Calcaneal bone mineral density in older patients who have fallen. Q J Med. 2006, 99: 231-236. 10.1093/qjmed/hcl026.View ArticleGoogle Scholar
  43. LeBlanc A, Lapointe S, Beaudet A, Côté I, Dumas P, Labrecque F, Lamy C, Larochelle J, Lepage L, Pelletier F, Weber JF, Levallois P, Gingras S: Étude sur l'établissement de valeurs de référence d'éléments traces et métaux dans le sang, le sérum et l‘urine de la population de la grande région de Québec. 2004, Institut National de Santé Publique du Québec: Direction toxicologie humaine, Direction risques biologiques, environnementaux et occupationnels,http://www.inspq.qc.ca/pdf/publications/289-ValeursReferenceMetaux.pdf,Google Scholar
  44. Santé Québec: A Health Profile of the Inuit. Report of the Santé Québec Health Survey Among the Inuit of Nunavik, 1992. Edited by: Jetté M. 1994, Montréal: Ministère de la Santé et des Services sociaux, Gouvernement du QuébecGoogle Scholar
  45. Mercury. Your health and the environment. A resource tool.http://www.acpo.org.br/MPF_PAPA_LAMPADAS/06_ANEXO_06_health_canada.pdf,
  46. Intaraprasert S, Taneepanichskul S, Theppisai U, Chaturachinda K: Bone density in women receiving Nortplant implants for contraception. J Med Assoc Thai. 1997, 80: 738-741.Google Scholar
  47. Association canadienne du diabète: Lignes directrices de pratique clinique 2008 de l’Association canadienne du diabète pour la prévention et le traitement du diabète au Canada. Can J Diab. 2008, 32: 239-http://www.diabetes.ca/documents/about-diabetes/CPG_FR.pdf,Google Scholar
  48. Paunescu AC: PhD thesis. Les composés dioxin-like mesurés par DR-CALUX et les paramètres osseux évalués par ultrasonographie chez les femmes cries et inuites du Nord-du-Québec et du Groenland. 2012, Université Laval: Département de Médecine Sociale et PréventiveGoogle Scholar
  49. Santé Canada: Rapport sur la biosurveillance humaine des substances chimiques de l’environnement au Canada. Résultats de l’Enquête canadienne sur les mesures de la santé Cycle 1 (2007 à 2009). 2010, 300-http://www.hc-sc.gc.ca/ewh-semt/alt_formats/hecs-sesc/pdf/pubs/contaminants/chms-ecms/report-rapport-fra.pdf,Google Scholar
  50. Weiss LA, Barrett-Connor E, von Mühlen D: Ratio of n-6 to n-3 fatty acids and bone mineral density in older adults: the Rancho Bernardo Study. Am J Clin Nutr. 2005, 81: 934-938.Google Scholar
  51. Kruger MC, Coetzer H, de Winter R, Gerike G, van Papendorp DH: Calcium, gamma-linolenic acid and eicosopentaenoic acid supplementation in senile osteoporosis. Aging Clin Exp Res. 1998, 10: 385-394.View ArticleGoogle Scholar
  52. Fernandes G, Bhattacharya A, Rahman M, Zaman K, Banu J: Effects of n-3 fatty acids on autoimmunity and osteoporosis. Front Biosci. 2008, 13: 4015-4020.View ArticleGoogle Scholar
  53. Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, Pratt M, Ekelund U, Yngve A, Sallis JF, Oja P: International Physical Activity Questionnaire: 12-country reliability and validity. Medicine and Science in Sports and Exercise. Med Sci Sports Exerc. 2003, 35: 1381-1395. 10.1249/01.MSS.0000078924.61453.FB.View ArticleGoogle Scholar
  54. Egeland G, Denomme D, Johnson-Down L: Physical Activity. Nituuchischaayihtitaau Aschii Multi-community Environment- and Health longitudinal study in IIyiyiu Aschii: Mistissini Technical report: summary of activities, results and recommendations. Edited by: Pereg D, Nieboer E. 2007, Montreal, QC: Cree Board of health and Social Services of James Bay, 64-66.Google Scholar

Copyright

© Paunescu et al.; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement