| Literature DB >> 33180810 |
Violet Kayamba1, Paul Kelly1,2.
Abstract
INTRODUCTION: Persistent oxidative stress predisposes to various non-communicable diseases (NCDs), whose occurrence is increasing in sub-Saharan Africa. The aim of this study was to evaluate the link between markers of oxidative stress and some risk factors for NCDs in a Zambian cohort.Entities:
Year: 2020 PMID: 33180810 PMCID: PMC7660463 DOI: 10.1371/journal.pone.0242144
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Characteristics of enrolled participants in relation to markers of oxidative stress.
| Variable | n | 8-isoprostanes, ng/mg creatinine Median (IQR) | 8-hydroxydeoxyguanosine, ng/mg creatinine Median (IQR) | ||
|---|---|---|---|---|---|
| Sex | 0.07 | 0.08 | |||
| Males | 116 | 0.12 (0.08–0.21) | 3.5 (1.6–8.0) | ||
| Females | 128 | 0.15 (0.08–0.25) | 4.3 (2.4–11) | ||
| Age group (years) | |||||
| Less than 30 | 10 | 0.14 (0.10–0.28) | 1.7 (1.3–4.3) | ||
| 30–44 | 89 | 0.11 (0.07–0.22) | 3.7 (1.5–6.1) | ||
| 45–59 | 86 | 0.14 (0.09–0.21) | 3.5 (2.1–12) | ||
| 60 and above | 59 | 0.18 (0.10–0.39) | 7.0 (2.7–17) | ||
| Residence | 0.78 | 0.71 | |||
| Urban | 199 | 0.12 (0.08–0.24) | 3.7 (1.9–11) | ||
| Rural | 45 | 0.13 (0.08–0.20) | 4.4 (2.4–8.0) | ||
| Good accommodation | 0.65 | 0.40 | |||
| Yes | 199 | 0.12 (0.08–0.23) | 4.0 (2.0–12) | ||
| No | 45 | 0.15 (0.09–0.23) | 2.0 (4.4–6.8) | ||
| Basic household goods | 0.78 | 0.80 | |||
| Yes | 217 | 0.14 (0.09–0.20) | 3.9 (2.0–10) | ||
| No | 27 | 0.12 (0.08–0.24) | 4.7 (2.1–10.7) | ||
| Body mass index, kg/m2 | 0.28 | 0.05 | |||
| Less than 18 | 14 | 0.12 (0.06–0.21) | 8.5 (1.3–61) | ||
| 18–20 | 25 | 0.16 (0.10–0.24) | 11 (2.7–21) | ||
| 21–24 | 55 | 0.11 (0.07–0.20) | 3.1 (2.0–6.1) | ||
| 25–30 | 82 | 0.14 (0.09–0.25) | 4.4 (2.3–11) | ||
| Above 30 | 42 | 0.12 (0.07–0.22) | 3.4 (1.3–6.4) | ||
| HIV status | 0.05 | 0.09 | |||
| Positive | 44 | 0.10 (0.06–0.17) | 6.0 (2.0–20) | ||
| Negative | 176 | 0.14 (0.09–0.23) | 3.5 (1.9–8) | ||
| Smoking | 0.52 | 0.90 | |||
| Yes | 12 | 0.11 (0.08–0.21) | 3.3 (2-0-33) | ||
| No | 205 | 0.10 (0.08–0.21) | 3.9 (1.9–9.6) | ||
| Alcohol | 0.32 | ||||
| Yes | 58 | 0.10 (0.06–0.20) | 4.5 (2.2–12) | ||
| No | 172 | 0.14 (0.09–0.25) | 3.7 (1.8–9.3) |
*Residing in a brick house with a kitchen and piped water was considered as good accommodation.
**Being in possession of either a television, fridge, computer, car, cable connection or a microwave oven.
Fig 1Correlations between urinary 8-isoprostanes levels and; (a) Estimated 24 hour urine sodium by Tanaka method, (b) Estimated 24 hour urine sodium by Kawasaki method (c) Age, (d) 1-hydroxypyrene, (e) Aflatoxin M1, (f) Ochratoxin A. *denotes statistical significance.
Logistic regression for adjusted (a) and unadjusted (b) urinary 8-isoprostane dichotomized at the median.
| 1-hydroxypyrene, ng/mg creatinine | 13.7 | 1.6–115 | 0.02 |
| Estimated 24 hour urine sodium (Tanaka), g | 1.1 | 1.05–1.2 | 0.003 |
| 1-hydroxypyrene, ng/mg creatinine | 162 | 8.5–301 | 0.001 |
| Estimated 24 hour urine sodium (Tanaka), g | 1.3 | 1.2–1.5 | <0.001 |
| 1-hydroxypyrene, ng/mg creatinine | 12.5 | 1.8–85.7 | 0.01 |
| Age in years | 1.04 | 1.01–1.07 | 0.007 |
*Factors included in the regression model were 1-hydroxypyrene, body mass index, age, estimated 24-hour sodium excretion (by Tanaka methods), aflatoxin M1 and ochratoxin A
**Logistic regression model showing the output of significant results only.
Fig 2Correlation between the Tanaka and Kawasaki methods for estimation of 24-hour urine sodium from a single spot urine sample.