Adiposity, body composition, and weight change in relation to organochlorine pollutant plasma concentrations

Abstract

We investigated cross-sectional associations of body composition and weight change with polychlorinated biphenyls (PCB) and organochlorine pesticides/pesticide metabolites measured in blood collected at the baseline of the Physical Activity for Total Health study of postmenopausal, overweight women living in the Seattle, Washington metropolitan area. Indicators of greater adiposity were associated with lower plasma concentrations of most PCBs with six or more chlorine atoms. This pattern was observed for current weight, body mass index, fat mass percent, subcutaneous abdominal fat, intra-abdominal fat, waist circumference, hip circumference, waist-to-hip ratio, and maximum adult weight. Conversely, PCB 105, PCB 118, and p,p′-DDE were generally increased or showed no association with these variables. Weight gain since age 35 was associated with lower concentrations of almost every organochlorine we studied, and past weight loss episodes of at least 20 pounds (≥9.1 kg) were associated with higher concentrations. Our results have implications for epidemiologic studies of organochlorines in terms of covariates that may be important to consider in statistical analyses, particularly as such considerations may differ importantly by specific analyte. Our finding of increased organochlorine concentrations with past weight loss episodes may have public health significance;however, this association requires confirmation in longitudinal studies.

INTRODUCTION

Organochlorine pollutants, including polychlorinated biphenyls (PCBs) and organochlorine pesticides, are lipid-soluble compounds that persist in the environment and bioaccumulate in the food chain. In humans, factors affecting the homeostasis between organochlorines stored in adipose tissue and circulating blood concentrations are not clear. Higher blood concentrations have been measured in obese vs lean individuals, even after the typical “correction” of the organochlorine concentration for blood lipids;^1-4 however, inverse associations between organochlorines and body mass index (BMI) have also been observed.^4,5 It is likely that the association between body composition and organochlorine concentration differs by the compound, due to differing chemical properties and excretion rates as well as patterns of historical use and current exposure,⁶ but there are few such published data on specific organochlorines. Circulating organochlorine concentrations are also affected by weight change in that weight loss results in reduction of the adipose tissue compartment and thus leads to increased organochlorine concentrations in both adipose tissue and blood — at least in the short term^7-9— however, it is not well described how weight change throughout the adult life or historical weight loss episodes affect in vivo organochlorine concentrations at older ages.

We investigated aspects of body composition and weight change in relation to organochlorines in postmenopausal, overweight, and obese women living in the Seattle, Washington metropolitan area. We measured a broad panel of organochlorine analytes in plasma samples to describe cross-sectional associations of body composition characteristics and weight change history, including weight loss episodes, with concentrations of specific individual PCB congeners and organochlorine pesticides/pesticide metabolites. We hypothesized that indicators of adiposity, including higher weight, BMI, and fat mass would be associated with lower organochlorine concentrations, as would weight gain since younger ages.

METHODS

Study Population

The study population included participants in a previously conducted exercise intervention trial and ancillary study of immune function. The Physical Activity for Total Health study,¹⁰ conducted at the Fred Hutchinson Cancer Research Center and the University of Washington (UW), was a randomized controlled trial comparing the effects of a yearlong moderate-intensity aerobic exercise intervention vs stretching control among 173 sedentary and overweight/obese postmenopausal women in the greater Seattle, WA area. Women were ages 50 to 75 years, non-smokers, with alcohol consumption of fewer than two drinks per day, sedentary (< 60 min per week of moderate-to-vigorous intensity exercise or maximal oxygen consumption [VO₂ max] < 25.0 ml/kg per min), overweight or obese (BMI ≥25.0 or between 24.0 and 24.9 with percentage body fat > 33%), weight stable for the past 3 months, postmenopausal and not taking hormone replacement therapy in the past 6 months, and with no clinical diagnosis of diabetes and fasting blood glucose levels < 140 mg/dl. Women were ineligible if they were volunteering for the study to lose weight, had a history of surgery for weight loss, or were currently attempting or planning to attempt weight loss by taking diet pills or entering a structured weight-loss program. The ancillary study of Immune Function and Exercise (IMEX) was conducted among 115 of these participants.¹¹ Additional eligibility criteria for IMEX were: no history of invasive cancer, cardiovascular disease, or asthma; no current serious allergies; no regular (≥2 times/week) use of aspirin or other non-steroidal anti-inflammatory medications; and not using corticosteroids or other medications known to affect immune function. Women were enrolled in 1998 through 2000.

Questionnaire data at the study baseline included demographics, body weight history, medical history, smoking, medication use, and dietary intake over the past 3 months using a food frequency questionnaire.¹² During a clinic visit, study staff measured body weight, height, and waist and hip circumferences. Total body fat and body fat percentage were assessed using a DXA whole-body scanner. Intra-abdominal and subcutaneous fat areas were measured using computer tomography scan. Fasting blood samples took place at the UW Department of Laboratory Medicine between 0730 and 0830 hours, and were processed within 1 h.

Organochlorine Pollutant Measurement

PCB and organochlorine pesticides/metabolites were measured at the Combustion Products and Persistent Pollutants Biomonitoring Laboratory of the Centers for Disease Control and Prevention (CDC) in Atlanta, GA. We measured 36 PCB congeners (International Union of Pure and Applied Chemistry (IUPAC) scheme numbers 18, 28, 44, 49, 52, 66, 74, 87, 99, 101, 105, 110, 118, 128, 146, 149, 151, 153, 156, 157, 167, 170, 172, 177, 178, 180, 183, 187, 189, 194, 195, 199, 206, 209, 138/158, 196/203)¹³ and 9 organochlorine pesticides/metabolites (hexachlorobenzene (HCB), beta-hexachlorocyclohexane (BHCCH), γ-hexachlorocyclohexane (lindane), oxychlordane, trans-nonachlor, p,p′-DDE, o,p′-DDT, p,p′-DDT, and mirex) by high-resolution gas chromatography/isotope-dilution high-resolution mass spectrometry.¹⁴ The plasma sample amount available for the assay ranged from 0.225 to 0.843 g with a median of 0.382 g. The analytic results were reported on both a wet-weight basis and lipid-standardized basis (using previously measured triglyceride and total cholesterol values from the same blood draw).¹⁵

Measurements were successfully conducted on 109 of 111 samples from the study baseline that were shipped to the CDC laboratory (those available from the 115 IMEX participants). Detection rates for most of the PCBs and several of the pesticides/metabolites were high among this group of postmenopausal women; we list in Table 1 those analytes detected in > 80% of samples. For these organochlorines, we imputed values below the limit of detection (LOD) with the LOD divided by 2. We created a variable for the PCB sum (nmol/g-lipid), by adding the molar concentrations of PCBs with detection frequencies over 80% (Table 1). Repeatability of the measurements was excellent, with coefficients of variation between blinded QC replicate samples ranging from 2.4 to 11.2 for the organochlorine analytes in Table 1.

Table 1.

Organochlorine concentrations in plasma at the study reference date^a.

Organochlorine analyteb	N (of 109 total)c	Lipid-standardized concentration [ng/g-lipid]				Wet-weight concentration (pg/g)
		Mediand	Minimum detectede	Maximum detected	Average detection limitf	Mediand
PCB 74	102	13.7	4.9	68.6	5.0	100
PCB 99	108	9.6	2.2	105	1.9	64.5
PCB 105	93	3.8	1.5	63.1	1.9	26.1
PCB 118	109	19.4	4.1	185	2.7	136
PCB 138/158	109	34.5	12.4	235	1.9	239
PCB 146	102	5.2	2.2	41.8	1.9	36.2
PCB 153	109	45.1	15.1	258	1.9	322
PCB 156	105	6.1	2.6	41.0	1.9	44.5
PCB 170	109	12.6	5.1	37.0	1.9	88.5
PCB 180	109	33.7	13.6	111	1.9	235
PCB 183	90	3.6	2.0	12.3	1.9	24.5
PCB 187	107	10.7	4.3	33.6	1.9	74.5
PCB 194	100	8.7	2.9	35.0	1.9	59.3
PCB 196/203	109	8.5	3.5	31.6	1.9	60.7
PCB 199	108	8.7	2.9	27.3	1.9	58.5
PCB 206	107	5.6	2.0	16.1	1.9	39.0
PCB 209	93	4.0	1.5	26.4	1.9	28.0
p,p′-DDE	109	488	68.5	6540	5.1	3227
HCB	109	57.4	9.4	202	6.7	417
BHCCH	95	13.1	6.2	650	8.7	93.4
Oxychlordane	90	18.6	9.4	58.3	8.7	134
t’-Nonachlor	94	20.3	10.2	78.7	8.7	140

^aSamples with successful measurements for both organochlorines and lipids.

^bAnalytes shown if > 80% of samples had measured values above the limit of detection.

^cNumber of samples with measured values above the limit of detection.

^dMedian of all samples (N = 109 including those with non-detected values).

^eMinimum of detected (non-imputed) values.

^fAverage detection limit of all samples analyzed; note that minimum detected can be lower than average detection limit if the sample’s detection limit was lower than average.

Statistical Analysis

Statistical analyses were conducted using SAS version 9.3 (Cary, NC). Associations of body composition and weight-related characteristics with plasma organochlorine concentrations at the study baseline were evaluated using linear regression, with the natural log of the measured organochlorine concentration as the dependent variable. We modeled each specific organochlorine analyte and the PCB sum separately. We present results as the average percentage change in the organochlorine concentration per unit change in the body composition or weight history characteristic.

Characteristics evaluated included measured current (reference date) weight (kg), height (cm), BMI (kg/m²), fat mass as percentage of total body mass (%), subcutaneous abdominal fat area (cm²), intra-abdominal fat area (cm³), waist circumference (cm), hip circumference (cm), and waist-to-hip ratio, as well as historical weight (kg) at ages 18, 35, and 50 years, and maximum adult weight (kg). We also calculated net weight changes from ages 18 and 35 years to the reference date. Where appropriate, we categorized continuous variables using percentiles or predefined cut-points. Participants were asked about weight loss episodes with the question, “Within the last 20 years, when you were not pregnant or sick, did you ever lose 10 pounds or more on purpose?”, and were then asked to specify the number of episodes in amounts of 10–19 pounds (4.5 to < 9.1 kg), 20–49 pounds (9.1 to < 22.7 kg), and 50 or more pounds (22.7 kg or more). We combined the eight women who reported a weight loss episode of ≥50 pounds with the next-lowest category to examine weight loss episodes of 20 or more pounds (≥9.1 kg). The timing of these weight loss episodes within the last 20 years was not specified. However, an additional question asked, “How long have you been within 10 pounds of your current weight (do not count times when you were pregnant or sick)?”, and we conducted a subanalysis of our main results stratified by the number years a woman reported she was within 10 pounds (4.5 kg) of her current weight, in categories of ≤5 years and > 5 years.

Covariates selected a priori as potential confounders of the cross-sectional associations between body composition/weight-related characteristics and plasma organochlorine concentrations were age (years), education (≤high school diploma; vocational school, some college, or associate’s degree; bachelor’s degree, master’s degree or doctorate), race/ethnicity (white, non-Hispanic vs other), annual household income (< $20,000, $20,000 to < $35,000, $35,000 to < $50,000K, $50,000 to < $75,000, $75,000 to < $100,000, ≥$100,000), marital status (presently married vs not), smoking status (ever vs never), number of live births (0, 1, 2, 3, 4, ≥5), and lifetime total months of breastfeeding (0, 1–12, > 12). In secondary models, we estimated the associations of interest with adjustment for additional potential confounders expected to be more proximal in time to the observed associations of interest, and therefore potentially intermediate in the causal pathway and questionable in terms of true confounding: typical physical activity (MET minutes per week), alcohol consumption (drinks per week), and dietary intakes in the past year of fish (< 1.5, 1.5 to < 2.5, and ≥2.5 servings per week), and total fat, animal protein, and vegetables (categorized by quartiles).

RESULTS

Our study population organochlorine concentrations (Table 1) were similar to the National Health and Nutrition Examination Survey (NHANES) medians of participants age 60 years and older for several analytes (in ng/g-lipid) including PCB 74 (13.7 (our study) vs 13.0 (NHANES)) and PCB 118 (19.4 vs 14.7).¹⁶ However, median levels were somewhat lower in our study for other analytes including PCB 180 (33.7 vs 49.6). Per the inclusion criteria of the parent study, the 109 women in our study (Table 2) were postmenopausal (mean age 60.6 years) and overweight or obese (mean BMI 30.3). Participants were also frequently highly educated (41% with a bachelor’s degree or higher), had relatively high incomes (29% with annual incomes ≥$75,000), and were of white race (89%). Age was the covariate most consistently associated with plasma organochlorine concentrations; the levels of PCB 118, PCB 180, and p,p′-DDE were increased by 3.9% (95% CI: 1.2, 6.7), 3.1% (95% CI: 1.8, 4.5), and 2.3% (95% CI: −0.41, 5.0), respectively, per 1-year increase of age, when modeled with the full set of a priori covariates. Married status, higher education, and higher income were associated with higher concentrations of several organochlorine analytes. Breastfeeding longer than a year (vs no breastfeeding) was associated higher concentrations of PCBs 74, 99, 105, 118, and p,p′-DDE, but breastfeeding was not associated with higher-chlorinated PCBs. Women with two or three live births (vs none) had significantly lower concentrations of several organochlorines, but there were no associations with greater number of births. Neither race/ethnicity nor smoking was associated with organochlorines with any consistency; these variables were dropped from further consideration as potential confounders.

Table 2.

Characteristics of participants at the study reference date (N = 109)^a.

	Mean (SD) or N (%)
Age (years)	60.6 (6.9), mean (SD)
Education
High school diploma or less	16 (14.7%)
Vocational school, some college, or associate’s degree	48 (44.0%)
Bachelor’s degree	25 (22.9%)
Master’s degree or doctorate	20 (18.4%)
White, non-Hispanic race/ethnicityb	96 (88.9%)
Annual household incomeb
< $20,000	12 (11.4%)
$20,000 to < $35,000	23 (21.9%)
$35,000 to < $50,000	18 (17.2%)
$50,000 to < $75,000	21 (20.0%)
$75,000 to < $100,000	16 (15.2%)
≥$100,000	15 (14.3%)
Presently married	60 (55.1%)
Ever smoked	55 (50.5%)
Cigarettes/day among ever-smokersb	14.8 (13.6), mean (SD)
Body mass index (kg/m2)	30.3 (3.9), mean (SD)
< 25	7 (6.4%)
25 to < 30	51 (46.8%)
30 to < 35	36 (33.0%)
≥ 35	15 (13.8%)
Ever pregnant	96 (88.1%)
Number of live birthsb
0	15 (13.7%)
1	13 (11.9%)
2	27 (24.8%)
3	28 (25.7%)
4	16 (14.7%)
≥5	10 (9.2%)
Ever breastfedb	66 (61.1%)
Months breastfed
0	42 (39.3%)
1–12	37 (34.5%)
> 12	28 (26.2%)

^aStudy population includes participants with successful measurements for both organochlorines and lipids.

^bVariable contains missing values for our study population.

Associations of body composition and weight change characteristics with plasma organochlorine concentrations differed by specific organochlorine analyte. Results for all the analytes in relation to fat mass percent, weight gain since age 35, and weight loss episodes ≥20 pounds (≥9.1 kg) are shown in Figures 1-3, and detailed results for PCB 180, PCB 118, and p,p′-DDE are shown in Table 3. The patterns of association were very similar when modeling the wet-weight organochlorine concentrations (pg/g-plasma) with serum lipids as covariates (results not shown).

Figure 1.

Association of fat mass (% of total body mass) with plasma organochlorines at the study reference date (% difference in mean organochlorine concentration [ng/g-lipid] per unit increase in fat mass, adjusted for age, education, marital status, income, number of live births, and total months breastfeeding).

Figure 3.

Association of weight loss episode of ≥20 pounds within the last 20 years with plasma organochlorines at the study reference date (% difference in mean organochlorine concentration [ng/g-lipid] in women reporting at least one episode vs none, adjusted for age, education, marital status, income, number of live births, and total months breastfeeding).

Table 3.

Associations of body composition and weight history characteristics with plasma organochlorine concentrations at the study reference date (percent difference in plasma organochlorine concentration [ng/g-lipid] per unit increase in characteristic)^a.

Characteristic	N	PCB 180		PCB 118		p,p′-DDE
		Association	P-value	Association	P-value	Association	P value
Current body composition
Height (cm)	103	−0.29 (−1.2, 0.67)	0.55	−0.40 (−2.4, 1.6)	0.70	0.62 (−1.4, 2.7)	0.55
Weight (kg)	103	−0.82 (−1.3, −0.35)	0.0007	0.36 (−0.70, 1.4)	0.51	0.19 (−0.87, 1.3)	0.73
Body mass index (kg/m2)	103	−3.3 (−4.8, −1.7)	< 0.0001	2.0 (−1.6, 5.8)	0.28	0.20 (−3.4, 3.9)	0.91
≤27.5	26 (25.2%)	0 (Referent)		0 (Referent)		0 (Referent)
> 27.5–29.6	24 (23.3%)	−18.1 (−30.9, −2.9)	0.02	−5.4 (−35.9, 39.6)	0.78	−8.8 (−38.3, 34.6)	0.64
> 29.6–32.8	25 (24.3%)	−22.0 (−34.3, −7.5)	0.004	15.4 (−21.9, 70.5)	0.47	2.0 (−31.1, 50.9)	0.92
> 32.8	28 (27.2%)	−31.5 (−42.6, −18.1)	< 0.0001	14.0 (−24.1, 71.2)	0.53	−2.3 (−35.0, 46.8)	0.91
Fat mass (% of total body mass)	103	−5.0 (−7.0, −3.0)	< 0.0001	2.7 (−2.3, 7.8)	0.29	0.43 (−4.4, 5.5)	0.86
Subcutaneous fat (cm3), per 100 unitsb	101	−6.3 (−11.7, −0.60)	0.03	19.4 (5.6, 35.0)	0.005	1.4 (−10.7, 15.1)	0.83
Intra-abdominal fat (cm3), per 100 unitsb	102	−16.2 (−26.6, −4.2)	0.01	37.4 (3.5, 82.5)	0.03	36.5 (2.8, 81.2)	0.03
Waist circumference (cm)	103	−0.99 (−1.6, −0.43)	0.0006	1.2 (−0.04, 2.5)	0.06	0.54 (−0.72, 1.8)	0.40
Hip circumference (cm)	103	−1.3 (−2.0, −0.54)	0.0008	0.54 (−1.1, 2.3)	0.53	−0.14 (−1.8, 1.6)	0.87
Waist-to-hip ratio, per 0.1 unit	103	−11.0 (−19.5, −1.5)	0.02	28.5 (3.8, 59.2)	0.02	18.9 (−4.2, 47.6)	0.12
Weight history
Weight (kg), age 18 years	103	−0.27 (−0.63, 0.08)	0.13	−0.03 (−0.78, 0.73)	0.94	0.45 (−0.30, 1.2)	0.24
Weight (kg), age 35 years	103	−0.14 (−0.40, 0.13)	0.32	0.65 (0.09, 1.2)	0.02	0.35 (−0.21, 0.92)	0.22
Weight (kg), age 50 years	103	−0.24 (−0.50, 0.01)	0.06	0.68 (0.15, 1.2)	0.01	0.58 (0.05, 1.12)	0.03
Weight (kg), maximum	103	−0.26 (−0.46, −0.06)	0.01	0.42 (−0.01, 0.86)	0.06	0.13 (−0.32, 0.57)	0.58
Net weight change since age 35 years (kg)	103	−0.93 (−1.5, −0.34)	0.002	−1.0 (−2.3, 0.28)	0.13	−0.56 (−1.9, 0.74)	0.40
Net change < 10 kg (loss or gain)	17 (16.5%)	0 (Referent)		0 (Referent)		0 (Referent)
Net gain 10–20 kg	47 (45.6%)	−13.7 (−28.4, 4.1)	0.12	−32.9 (−55.4, 1.1)	0.06	−11.0 (−41.1, 34.5)	0.58
Net gain 20–30 kg	25 (24.3%)	−18.9 (−34.3, 0.01)	0.05	−19.2 (−48.9, 27.7)	0.36	−9.4 (−43.0, 43.8)	0.67
Net gain ≤30 kg	14 (13.6%)	−31.6 (−45.8, −13.8)	0.001	−14.5 (−48.5, 42.0)	0.55	−27.5 (−56.5, 21.0)	0.22
Weight loss episodes in past 20 years (vs no loss ≥10 pounds (≥4.5kg))b,c
Ever lost 10–19 lbs (4.5 to < 9.1 kg)	69 (67.7%)	−27.7 (−39.5, −13.7)	0.0003	−14.7 (−42.8, 27.2)	0.43	−6.7 (−37.3, 38.9)	0.73
Ever lost ≥20 lbs (≥9.1 kg)	43 (42.2%)	27.9 (9.3, 49.6)	0.002	25.7 (−11.7, 79.0)	0.20	31.4 (−7.5, 86.7)	0.13
Number of weight loss episodes ≥20 pounds (≥9.1 kg) in past 20 yearsb
0	59 (57.8%)	0 (Referent)		0 (Referent)		0 (Referent)
1–2	25 (24.5%)	25.0 (4.7, 49.3)	0.01	23.9 (−16.9, 84.9)	0.29	22.8 (−17.4, 82.6)	0.31
≥3	18 (17.7%)	31.7 (8.8, 59.5)	0.005	28.1 (−16.7, 97.0)	0.26	43.6 (−6.3, 120)	0.10

^aAdjusted for age, education, marital status, income, number of live births, and total months breastfeeding.

^bVariable contains missing values for our study population.

^cCategories are not mutually exclusive and were therefore modeled together.

Higher body weight and other body composition indicators of increased adiposity were generally associated with lower baseline plasma concentrations of PCBs with six or more chlorine atoms (IUPAC numbers 146 and higher). This pattern was observed for current weight, BMI, fat mass percent (Figure 1), subcutaneous abdominal fat, intra-abdominal fat, waist circumference, hip circumference, waist-to-hip ratio, and maximum adult weight. For example, each unit increase in BMI (kg/m²) was associated with 3.3% lower average PCB 180 concentration, and obese women with BMI > 32.8 kg/m² had, on average, 31.5% lower PCB 180 than women who were only modestly overweight (BMI ≤27.5 kg/m²; Table 3). Furthermore, each 1% increase in total body mass as fat was associated with 5% lower average PCB 180 (Table 3). Of the pesticides/metabolites, BHCCH and trans-nonachlor showed similar directions of association as PCB 180 with BMI and fat mass percent (Figure 1), but were not associated with intra-abdominal fat or waist-to-hip ratio.

Penta-PCBs (five chlorine atoms) and PCBs with lower chlorination generally showed either no association with the body composition characteristics, or associations in the opposite direction than PCBs with higher chlorination. For example, greater subcutaneous abdominal fat, intra-abdominal fat, waist circumference, waist-to-hip ratio, weight at ages 35 and 50 years, and maximum adult weight were associated with higher PCB 118 concentration. Each 100-cm³ increase in intra-abdominal fat was associated with 37% higher PCB 118 concentration, and each 0.1-unit increase in waist-to-hip ratio was associated with 29% higher PCB 118 concentration (Table 3). We observed similar patterns of association for PCB 105 and p,p′-DDE. Weight at age 50 years was significantly associated with both PCB 118 and p,p′-DDE (Table 3), whereas these organochlorines were not associated with current weight. Given the differing results among the different PCB congeners, the PCB sum was not surprisingly unassociated with many of the body composition characteristics we studied.

Weight gain since age 35 in this group of postmenopausal women was associated (significantly or non-significantly) with lower plasma concentrations of almost every organochlorine we studied (except HCB, Figure 2). Each kilogram of weight gained since age 35 in this group of postmenopausal women was associated with ~0.9% lower average PCB 180 concentration (Table 3), and net gain of 30 or more kilograms was associated with 32% lower PCB 180 compared with little weight change (< 10 kg change). Consistent decreases in organochlorine concentrations across increasing categories of weight gain since age 35 were observed for PCBs 170, 180, 187, 196, 199, and 206. Similar associations were observed for net weight change since age 18 (not shown), except the magnitudes of effect were less often statistically significant, and PCBs 99, 105, and 118 showed non-significant, positive associations with weight gain.

Figure 2.

Association of net weight change since age 35 (kg) with plasma organochlorines at the study reference date (% difference in mean organochlorine concentration [ng/g-lipid] per unit increase in weight, adjusted for age, education, marital status, income, number of live births, and total months breastfeeding).

Weight loss episodes of at least 20 pounds (≥9.1 kg) in the last 20 years were generally associated with higher organochlorine concentrations at the study reference date (except for HCB and BHCCH, Figure 3), and concentrations increased with the frequency of such episodes. For example, women who had lost 20 or more pounds (≥9.1 kg) at least once in the past 20 years had 28% higher average PCB 180 concentration than those reporting no weight loss (< 10 pounds only (< 4.54 kg)), and this varied by whether the woman reported one or two such weight loss episodes (25% higher PCB 180) or three or more episodes (32% higher PCB 180). Statistically significant organochlorine increases with at least one ≥20-pound (≥9.1 kg) weight loss episode ranged from 25% (PCB 199) to 46% (PCB 146), and with three or more episodes ranged from 28% (PCB 170) to 71% (PCB 146). These associations remained after adjustment for current BMI.

Adjustment beyond the a priori covariates for physical activity, alcohol consumption, and dietary intakes of fish, total fat, animal protein, and vegetables did not notably change our results (not shown). For example, we observed similar associations for fat mass percent with PCB 180 (−5.2% with full adjustment vs −5.0% with a priori adjustment, per unit increase in fat mass), for waist-to-hip ratio with PCB 118 (29.6% vs 28.5%), for net weight change since age 35 with trans-nonachlor (−1.7% vs −1.2%), and for ever lost ≥20 pounds (≥9.1 kg) with the PCB sum (30.3% vs 32.9%). Of the secondary covariates, fish consumption was most consistently associated with plasma organochlorine concentrations. Consumption of ≥2.5 servings of fish per week (vs < 1.5 servings per week) was significantly associated with higher concentrations of several organochlorines, such as a 35% increase in the PCB sum and a 55% increase in trans-nonachlor, when modeled with the other covariates. Despite this, it was not an important confounder of the associations we studied.

In analyses stratified by the number years a woman reported she was at her current weight (within 10 pounds, or 4.5 kg), associations between current weight-related characteristics and organochlorines were generally stronger among women who had been at their current weight for 5 years or less (n = 59 women) compared with those within their current weight for > 5 years (n = 42 women). For example, each increase in fat mass percent was associated with a 6.6% decrease (95% CI: −9.6%, −3.4%) in PCB 180 concentration among women at their current weight for ≤5 years, and a 3.2% decrease (95% CI: −7.4%, −0.26%) among women at their current weight for > 5 years (Supplemental Figures 1a and 1b). Net weight gain since age 35 was associated with significantly lower plasma organochlorine concentrations among women at their current weight for ≤5 years, for the PCB sum and PCBs 138/158, 153, 156, 170, 180, 196/203, 199, and 206. In contrast, only PCB 206 was significantly decreased in association with weight gain since age 35 among women who were at their current weight for longer than 5 years, and there were non-significant increases for many of the organochlorines in this analysis (Supplemental Figures 2a and 2b). Results for weight loss episodes of ≥20 pounds (9.1 kg) in the past 20 years did not depend strongly on the number of years within current weight. One or more such episodes was associated with significantly higher concentrations of PCBs 170, 187, 206, and 209 among women at their current weight for ≤5 years, and with increases in PCBs 146, 153, 170, 180, 196/203 among women who reported being at their current weight for > 5 years (Supplemental Figures 3a and 3b).

DISCUSSION

Our study is the first to present detailed data on in vivo plasma concentrations of multiple, specific organochlorine pollutants in relation to body composition characteristics and weight change history. Our findings indicating lower concentrations of circulating organochlorines with higher current adiposity (as reflected by higher weight, BMI, fat mass percent, etc.) are in our hypothesized direction, based on a scenario of a greater adipose tissue compartment with higher adiposity allowing in vivo dilution of these lipid-soluble compounds. We found the most consistent support of this hypothesis for PCBs with six or more chlorine atoms. In addition, weight gain since age 35 was associated (although not always statistically significant) with lower concentrations of nearly every organochlorine analyte examined (except HCB and oxychlordane), indicating that the principle of in vivo dilution may apply fairly universally across the different compounds. However, several of our observations of higher organochlorine concentrations in relation to higher adiposity (such as for PCB 118 and p,p′-DDE) are counter to our hypothesis.

Our analyte-specific findings are in agreement with the existing literature — of inverse associations of BMI with PCB 180^17-19 and summed PCBs,^5,18 and a positive association between BMI and PCB 118.^5,19 Obesity has also been associated in previous studies with higher serum concentrations PCB 138/158,⁵ BHCCH,¹⁸ and oxychlordane,¹⁹ in contrast to our null findings for these compounds. Our results for weight gain since early adulthood agree with a study of women in Québec, Canada, in which weight gain since age 18 was inversely associated with plasma PCB 153 concentration measured postmenopause,⁴ and with two longitudinal studies in which BMI that increased over time was associated with lowering of PCB 153, summed PCBs, and p,p′-DDE, but not HCB.^20-23 We found that the associations of net weight gain in adulthood with lower organochlorine concentrations were mostly limited to women who reported that they had been at their current weight for 5 years or less, suggesting that an increase in the amount of adipose tissue with weight gain may initially provide a dilution effect for these pollutants, but that other competing factors affect the organochlorine concentrations as time passes since the weight gain occurred.

Based on our hypothesis, we had no a priori reason to expect that measures of adiposity would be inversely associated with certain organochlorine analytes and not associated, or positively associated, with others. Differences observed between the organochlorines are at least partially due to differing chemical properties affecting their propensity for storage in adipose tissue, as reflected by the octanol-water partition coefficients (K_ow values) for these chemicals, which for PCBs tend to increase with the number of chlorine atoms.²⁴ A lower propensity for PCB 118 to dissolve in lipids (reflected by logK_ow of 6.7970) than PCB 180 (logK_ow of 7.2070) would contribute to less dependence of its in vivo concentration on the amount of body fat, as reflected in the results shown in Figure 1. Another reason for different results between organochlorine analytes could be the timing of maximum exposure. Although DDT and PCBs were banned in the United States during a similar time period (1970s), we can assume that the use of DDT as a pesticide would have ended almost immediately, whereas PCBs continued to be present as a component of dielectric fluids in older industrial equipment such as transformers, and therefore even today present a source of exposure with leakage from such equipment. Therefore, DDT exposure declined more rapidly than PCB exposure,^25,26 and may be less likely to show associations with current body composition and weight characteristics.

Increased organochlorine concentrations with greater adiposity could occur if adiposity affects the rate of metabolism and excretion of organochlorines, as hypothesized by Wolff et al.,⁶ particularly if this effect differs by analyte. Thus, the positive associations we observed for PCB 118 and p,p′-DDE measured at the study reference date may reflect slowed excretion of these compounds in obese women following maximum exposure. This idea is supported by our data in that positive associations were limited to weight at younger ages (age 50 for both analytes and age 35 for PCB 118) and did not appear with current (reference date) weight. Alternatively, although the interpretation relating to our main hypothesis is that body composition characteristics affected the organochlorine concentration, the opposite direction of effect — that organochlorines affected body composition — is also possible and may underlie some of the positive cross-sectional associations we observed. For example, PCBs 118 and 105 and p,p′-DDE were positively associated with several factors indicating current central adiposity. Few previous studies have examined the influence of organochlorines on development of obesity or changes in body composition, although these chemicals have been suspected as obesogens²⁷ due to their endocrine-disrupting properties in addition to an observed negative association between PCB 153 and the metabolism-modulating hormone, adiponectin.²⁸ A recent study reported that maternal serum DDE levels were associated with rapid infant weight gain and elevated 14-month BMI in their offspring.²⁹ Certainly, more research in adults is needed from longitudinal studies of the effects of these chemicals on weight gain and on patterns of weight gain.

A striking finding for most of the organochlorine analytes was that women who reported at least one weight loss episode of 20 pounds or more (≥9.1 kg) during the past 20 years had higher average organochlorine concentrations at the study reference date. These results did not differ substantially according to the number of years a woman was at her current weight, suggesting that weight loss episodes that occurred more than 5 years in the past (and perhaps up to 20 years in the past) affected current organochlorine concentrations. Furthermore, organochlorine increases were greatest when such weight loss episodes were frequent (three times or more). These findings may illustrate the converse of the dilution scenario with a reduced adipose tissue compartment from weight loss resulting in increased organochlorine concentrations; however, the associations remained with additional adjustment for current (reference date) BMI, suggesting that any organochlorine increase from past weight loss episodes did not decline to previous levels with weight regain. This could occur if weight-loss-associated organochlorine increases may in fact promote weight regain, as proposed by Tremblay and Chaput;²⁷ however, this scenario was not investigated in our study because we did not have data on the timing of weight loss episodes or subsequent weight regain. An alternative explanation for observed associations is that frequent weight loss (“weight cycling”) does not affect in vivo organochlorine levels, and that observed associations may be due confounding by a related factor such as diet. Nevertheless, these associations were robust to adjustment for recent dietary factors. Although there are well-demonstrated benefits of weight reduction such as improvement of cardiovascular risk factors (e.g., blood pressure, lipids) and decreased diabetes incidence among high-risk individuals,^30-32 increases in organochlorine concentrations are an apparent understudied consequence of weight loss. Increased pollutant exposure with weight loss is a potentially important issue because of the high frequency of weight loss episodes (usually with regain) among adults,³³ however, the public health significance of the small increases we observed is unknown.

Although the current body composition variables we studied were measured in the clinic, the weight history variables were self-reported, and thus subject to misreporting. A comparison in NHANES of self-reported historical weight to measured values from an earlier survey found that elapsed time contributed to greater misreporting;however, the degree of bias due to recall was modest, estimated as 1.8 kg (4 pounds) after 20 years.³⁴ There was a tendency of women to underreport weight, and this bias increased with BMI. It is unclear how such reporting bias would have affected our results;however, we assume that the results for historical weight are less accurate than those for current weight, and results may be particularly biased for women of higher BMI (either historically or currently). In addition, the questions we used on weight loss episodes and years at current weight have not been validated for accuracy of recall. These are issues for more detailed examination in longitudinal studies with careful tracking of weight over time.

Our study findings have implications for interpretation of epidemiologic research on persistent pollutants. Our study population consisted of postmenopausal women — a group in which body burden of organochlorine pollutants may have health consequences including increased risk of certain cancers such as non-Hodgkin lymphoma^35-38 and non-cancer outcomes such as diabetes.^39-41 Given that diabetes etiology is clearly associated with obesity, and NHL may be also be influenced by obesity (particularly for certain NHL histologic types⁴²), understanding how organochlorine pollutant levels vary with body composition and historical weight change is critical in studies of pollutant health effects — particularly when exposure is characterized by a single measurement, often at the time of diagnosis. For example, our data suggest that epidemiologic studies showing positive associations between p,p′-DDE and health outcomes such as diabetes may be spurious findings due to confounding by a possible positive correlation between p,p′-DDE and indicators of adiposity. Conversely, positive associations between PCB 180 and health outcomes may be subject to negative confounding from obesity-related characteristics due to PCB 180s generally inverse association with obesity. It is important to note that our exclusive focus on postmenopausal women limits the generalizability of our findings, since associations may differ in men or younger women. Nevertheless, our data illustrate the importance of further characterization of how past and current body composition affect (and are potentially affected by) in vivo pollutants, and the need for analyte-specific consideration of such relationships when interpreting epidemiologic data.