Path Analysis Reveals the Direct Effect of PCB28 Exposure on Cognitive Dysfunction in Older Chinese Females
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Population
2.2. Cognitive Functions Outcomes
2.3. PCBs Concentrations in the Plasma Samples
2.4. Statistical Analysis
- (1)
- Model 0 was univariate logistic regression.
- (2)
- Model 1 adjusted for baseline covariates including age and sex.
- (3)
- Model 2 additionally adjusted for education level (formal education vs. without formal education), monthly income (≤1 k, 1.01–3 k, >3 k), marriage (living with a spouse vs. living without a spouse), offspring (yes vs. no), sleep quality (poor vs. general vs. well), and sleep duration.
3. Results
3.1. Basic Characteristics of the Participants
3.2. Plasma Concentrations of PCBs
3.3. Association between PCBs Burden and CoD
3.4. Path Analyses of PCBs Burden and CoD Association
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
CoD | Cognitive dysfunction |
PCBs | Polychlorinated Biphenyls |
dl-PCBs | dioxin-like polychlorinated biphenyls |
ndl-PCBs | non-dioxin-like polychlorinated biphenyls |
LPCBs | lower chlorinated polychlorinated biphenyls |
HPCBs | higher chlorinated polychlorinated biphenyls |
SEM | Structural Equation Modeling |
AMT | Abbreviated Mental Test |
NEQ | Neurotoxic Equivalent |
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Total | Normal (N = 211) | CoD a (N = 55) | pb | |
---|---|---|---|---|
Age (years) d | 67 [63, 74] | 66 [63, 71] | 75 [65, 81] | <0.001 |
Sex (%) | <0.001 | |||
Male | 123 (46.2) | 111 (52.6) | 12 (21.8) | |
Female | 143 (53.8) | 100 (47.4) | 43 (78.2) | |
Education level (%) | <0.001 | |||
Without formal education | 131 (49.2) | 89 (42.2) | 42 (76.4) | |
Primary school | 106 (39.8) | 94 (44.5) | 12 (21.8) | |
Middle school | 25 (9.4) | 24 (11.4) | 1 (1.8) | |
High school | 4 (1.5) | 4 (1.9) | 0 (0.0) | |
College | 0 (0.0) | 0 (0.0) | 0 (0.0) | |
Monthly income CNY (%) | 0.004 | |||
≤1 k | 166 (62.9) | 121 (57.9) | 45 (81.8) | |
1.01–3 k | 83 (31.4) | 74 (35.4) | 9 (16.4) | |
>3 k | 15 (5.7) | 14 (6.7) | 1 (1.8) | |
Living with spouse (%) | 0.058 | |||
Without | 43 (16.2) | 29 (13.7) | 14 (25.5) | |
With | 223 (83.8) | 182 (86.3) | 41 (74.5) | |
Offspring (%) | 0.222 | |||
No | 128 (48.1) | 97 (46.0) | 31 (56.4) | |
Yes | 138 (51.9) | 114 (54.0) | 24 (43.6) | |
Smoking status (%) | <0.001 | |||
Never smoker | 171 (64.3) | 124 (58.8) | 47 (85.5) | |
Current/former smoker | 95 (35.7) | 87 (41.2) | 8 (14.5) | |
Habitual alcohol drinker (%) | 0.020 | |||
No | 203 (76.3) | 154 (73.0) | 49 (89.1) | |
Yes | 63 (23.7) | 57 (27.0) | 6 (10.9) | |
Sleep quality (%) | 0.009 | |||
poor | 28 (10.5) | 16 (7.6) | 12 (21.8) | |
general | 35 (13.2) | 28 (13.3) | 7 (12.7) | |
well | 203 (76.3) | 167 (79.1) | 36 (65.5) | |
Sleep duration(h) d | 264 c | 210 c | 54 c | |
9.0 [8.0, 9.0] | 8.0 [8.0, 9.0] | 9.0 [8.0, 10.0] | <0.001 | |
Headache (%) | 0.043 | |||
No | 208 (78.2) | 171 (81.0) | 37 (67.3) | |
Yes | 58 (21.8) | 40 (19.0) | 18 (32.7) | |
Diabetes (%) | 0.057 | |||
No | 241 (90.6) | 187 (88.6) | 54 (98.2) | |
Yes | 25 (9.4) | 24 (11.4) | 1 (1.8) | |
Hypertension (%) | 0.916 | |||
No | 241 (90.6) | 187 (88.6) | 54 (98.2) | |
Yes | 141 (53.0) | 111 (52.6) | 30 (54.5) | |
AMT score d | 9.0 [8.0, 10.0] | 10.0 [8.0, 10.0] | 6.0 [5.0, 7.0] | <0.001 |
PCBs | Median [IQR] | Range | >MDL a (n) | >MDL (%) |
---|---|---|---|---|
PCB28 b | 8.95 [8.27, 10.10] | ND-127.07 | 37 | 13.91% |
PCB52 b | 162.74 | ND-162.74 | 1 | 0.38% |
PCB101 b | 11.30 [8.50, 16.10] | ND-247.46 | 110 | 41.35% |
PCB138 c | 15.45 [8.60, 30.67] | ND-309.88 | 25 | 9.40% |
PCB153 c | 10.96 [6.95, 15.92] | ND-283.12 | 22 | 8.27% |
PCB180 c | 18.25 [5.68, 96.63] | ND-294.27 | 4 | 1.50% |
d | 12.69 [8.91, 21.89] | ND-1424.54 | 140 | 52.63% |
Number of | Model 0 a | Model 1 b | Model 2 c,e | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Variable | Normal | CoD | OR | 95% CI | p | OR | 95% CI | p | pd | OR | 95% CI | p | pd |
PCB28 | |||||||||||||
Not detected | 186 | 43 | 1.00 | - | ref | 1.00 | - | ref | 1.00 | - | ref | ||
Detected | 25 | 12 | 2.08 | (0.94–4.39) | 0.061 | 2.92 | (1.23–6.81) | 0.014 | 0.041 | 3.12 | (1.19–8.11) | 0.019 | 0.171 |
PCB101 | |||||||||||||
Not detected | 121 | 35 | 1.00 | - | ref | 1.00 | - | ref | 1.00 | - | ref | ||
Detected | 90 | 20 | 0.77 | (0.41–1.41) | 0.400 | 0.95 | (0.48–1.87) | 0.884 | 1.000 | 0.98 | (0.46–2.04) | 0.951 | 1.000 |
PCB138 | |||||||||||||
Not detected | 189 | 52 | 1.00 | - | ref | 1.00 | - | ref | 1.00 | - | ref | ||
Detected | 22 | 3 | 0.50 | (0.11–1.50) | 0.269 | 0.46 | (0.1–1.56) | 0.261 | 0.782 | 0.53 | (0.10–2.00) | 0.391 | 1.000 |
PCB153 | |||||||||||||
Not detected | 191 | 53 | 1.00 | - | ref | 1.00 | - | ref | 1.00 | - | ref | ||
Detected | 20 | 2 | 0.36 | (0.06–1.29) | 0.178 | 0.43 | (0.06–1.72) | 0.294 | 0.883 | 0.41 | (0.06–1.84) | 0.301 | 1.000 |
LPCBs f | |||||||||||||
Not detected | 101 | 25 | 1.00 | - | ref | 1.00 | - | ref | 1.00 | - | ref | ||
Detected | 110 | 30 | 1.10 | (0.61–2.01) | 0.750 | 1.52 | (0.78–3.04) | 0.224 | 0.672 | 1.55 | (0.75–3.3) | 0.241 | 1.000 |
HPCBs f | |||||||||||||
Not detected | 175 | 52 | 1.00 | - | ref | 1.00 | - | ref | 1.00 | - | ref | ||
Detected | 36 | 3 | 0.28 | (0.07–0.82) | 0.041 | 0.31 | (0.07–0.99) | 0.077 | 0.230 | 0.33 | (0.07–1.16) | 0.117 | 1.000 |
∑PCBs f | |||||||||||||
Not detected | 101 | 25 | 1.00 | - | ref | 1.00 | - | ref | 1.00 | - | ref | ||
Detected | 110 | 30 | 1.10 | (0.61–2.01) | 0.750 | 1.52 | (0.78–3.04) | 0.224 | 0.672 | 1.55 | (0.75–3.3) | 0.241 | 1.000 |
Country/Regions | Year | Sample | Median (Mean) | Reference |
---|---|---|---|---|
China | ||||
Weitang | 2015–2016 | Plasma | 12.7 a | this study |
Weifang | 2012 | Serum | (7.1) b | [33] |
Weifang | 2014 | Serum | 11 c | [34] |
Yitong | 2014 | Serum | 15 c | [34] |
Lingshui | 2014 | Serum | 14 c | [34] |
Huaihua | 2014 | Serum | 10 c | [34] |
Ganzi | 2014 | Serum | 5.9 c | [34] |
Canada | 2011–2013 | Serum | 39.8 d | [35] |
US | 2005–2007 | Serum | 444.9 a | [36] |
US | 1999–2002 | Serum | 235 c | [37] |
UK | 2003 | Serum | 103.0 a | [38] |
Lebanon | 2018 | Serum | 18.9 a | [39] |
Iran | 2016–2017 | Serum | 344.6 a | [40] |
Japan | 2012 | Blood | 21.0 d | [41] |
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Pan, C.; Zhao, H.; Du, Q.; Xu, Y.; Tian, D.; Xiao, S.; Wang, H.; Wei, X.; Wu, C.; Ruan, Y.; et al. Path Analysis Reveals the Direct Effect of PCB28 Exposure on Cognitive Dysfunction in Older Chinese Females. Int. J. Environ. Res. Public Health 2022, 19, 6958. https://doi.org/10.3390/ijerph19126958
Pan C, Zhao H, Du Q, Xu Y, Tian D, Xiao S, Wang H, Wei X, Wu C, Ruan Y, et al. Path Analysis Reveals the Direct Effect of PCB28 Exposure on Cognitive Dysfunction in Older Chinese Females. International Journal of Environmental Research and Public Health. 2022; 19(12):6958. https://doi.org/10.3390/ijerph19126958
Chicago/Turabian StylePan, Chenwei, Huijuan Zhao, Qiaoling Du, Yong Xu, Dajun Tian, Shuo Xiao, Haiyin Wang, Xiao Wei, Chunfeng Wu, Yuanyuan Ruan, and et al. 2022. "Path Analysis Reveals the Direct Effect of PCB28 Exposure on Cognitive Dysfunction in Older Chinese Females" International Journal of Environmental Research and Public Health 19, no. 12: 6958. https://doi.org/10.3390/ijerph19126958