Science of the Total Environment 548–549 (2016) 463–471

Contents lists available at ScienceDirect

Science of the Total Environment
journal homepage: www.elsevier.com/locate/scitotenv

Ionizing radiation, antioxidant response and oxidative damage:
A meta-analysis
D. Einor a, A. Bonisoli-Alquati a,b, D. Costantini c, T.A. Mousseau a,d, A.P. Møller e,⁎a

Department of Biological Sciences, University of South Carolina, Columbia, SC 29208, USA
School of Renewable Natural Resources, Louisiana State University AgCenter, Baton Rouge, LA 70803, USA
Department of Biology, University of Antwerp, Wilrijk, B-2610, Antwerp, BelgiumdFaculty of Bioscience and Biotechnology, Chubu University, Kasugai, Japane
Laboratoire d'Ecologie, Systématique et Evolution, CNRS UMR 8079, Université Paris-Sud, Bâtiment 362, F-91405 Orsay Cedex, Francebc

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T• There is interest in variation in metabolic effects of chronic low-dose ionizing radiation• A random effect meta-analysis of effectsizes of radioactive contamination was
performed• We found signiﬁcant effects of radiationon oxidative damage and antioxidant
response• We found signiﬁcant heterogeneity
among biological matrices, species andage classes

a r t i c l e

i n f oArticle history:
Received 21 July 2015
Received in revised form 6 January 2016
Accepted 6 January 2016
Available online xxxx
Editor: D. Barcelo
Keywords:
Chernobyl
Fukushima
Eastern Urals Trace
Antioxidant activity
Radioactive contamination
Oxidative stressROS

a b s t r a c t
One mechanism proposed as a link between exposure to ionizing radiation and detrimental effects on organisms
is oxidative damage. To test this hypothesis, we surveyed the scientiﬁc literature on the effects of chronic lowdose ionizing radiation (LDIR) on antioxidant responses and oxidative damage. We found 40 publications and
212 effect sizes for antioxidant responses and 288 effect sizes for effects of oxidative damage. We performed a
meta-analysis of signed and unsigned effect sizes. We found large unsigned effects for both categories (0.918
for oxidative damage; 0.973 for antioxidant response). Mean signed effect size weighted by sample size was
0.276 for oxidative damage and −0.350 for antioxidant defenses, with signiﬁcant heterogeneity among effects
for both categories, implying that ionizing radiation caused small to intermediate increases in oxidative damage
and small to intermediate decreases in antioxidant defenses. Our estimates are robust, as shown by very high failsafe numbers. Species, biological matrix (tissue, blood, sperm) and age predicted the magnitude of effects for oxidative damage as well as antioxidant response. Meta-regression models showed that effect sizes for oxidative
damage varied among species and age classes, while effect sizes for antioxidant responses varied among species
and biological matrices. Our results are consistent with the description of mechanisms underlying pathologicalAbbreviations: LDIR, low-dose ionizing radiation; NCRP, National Council on Radiation Protection and Measurements; INES, International Nuclear and Radiological Event Scale; NPP,
Nuclear Power Plant; ROS, reactive oxygen species; RNS, reactive nitrogen species.
⁎ Corresponding author.
E-mail addresses: daniel@einor.com (D. Einor), andreabonisoli@gmail.com (A. Bonisoli-Alquati), davidcostantini@libero.it (D. Costantini), mousseau@sc.edu (T.A. Mousseau), anders.
moller@u-psud.fr (A.P. Møller).

http://dx.doi.org/10.1016/j.scitotenv.2016.01.027
0048-9697/© 2016 Elsevier B.V. All rights reserved.

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D. Einor et al. / Science of the Total Environment 548–549 (2016) 463–471

Lipid peroxidation
Ecology
Evolutioneffects of chronic exposure to LDIR. Our results also highlight the importance of resistance to oxidative stress as
one possible mechanism associated with variation in species responses to LDIR-contaminated areas.
© 2016 Elsevier B.V. All rights reserved.

1. Introduction(Azzam et al., 2011; Murphy et al., 2011). ROS activity is controlled bya number of enzymatic and non-enzymatic antioxidants. The inability
to balance the increased generation of ROS by antioxidant mechanisms
results in oxidative stress, a complex stressor for cells that manifests asincreased oxidative molecular damage to biomolecules, e.g. oxidation oflipids, oxidative modiﬁcation of nitrogenous bases etc. (Halliwell andGutteridge, 2007; Jones, 2006). In turn, oxidative damage may promote
the emergence of pathological states, accelerated cell aging and apoptosis (Halliwell and Gutteridge, 2007; Spitz et al., 2004). In numerous invertebrate and vertebrate species, oxidative damage may result in
reduced growth, fertility and survival (Costantini, 2014).
The association between LDIR and the generation of reactive species
has been widely described (Azzam et al., 2011; Smith et al., 2012). The
role of ionizing radiation in generation of ROS is well explained as thecorrelation between genetic damage and oxidative damage (e.g.
Costantini, 2014; Galván et al., 2014). Oxidative damage might be one
mechanism underlying several of the detrimental effects of radiation.
The root of the controversy relates to the manifestation of a given symptom or morbidity as a consequence of the three-way interaction of increased concentrations of ROS, decreased activity of antioxidant
enzymes, and genetic damage associated with increased background radiation (Spitz et al., 2004), especially when disease is followed by another medical condition, like malnutrition, inﬂammatory disease or
respiratory malfunction. However, it is important to note that radionuclides do not only generate damage through radiation, but also throughtheir catalytic activity (the Fenton reaction) (Halliwell and Gutteridge,2007). In addition, while several studies have documented increasedoxidative damage and reduced antioxidant defenses in humans andwild populations of animals chronically exposed to LDIR (e.g. Bonisoli
Alquati et al., 2010b), other studies have shown the potential for animals to adapt their antioxidant system to chronic exposure to LDIR
(Galván et al., 2014). In addition, theoretical calculations and lack of accurate dosimetry have called into question ﬁndings of increased oxidative stress from exposure to LDIR (Smith et al., 2012).
Here we assess the effects of chronic exposure to LDIR from radioactive contamination. We aim at exploring the insights of long-term metabolic processes, such as antioxidant function and oxidative damage, ofindividuals affected by chronic irradiation caused by radioactive contaminants. We collected exhaustive data from radiobiological studies
in the Russian and the English language scientiﬁc literature, and combined published evidence into a meta-analysis of the effects of chronic
radiation exposure on markers of oxidative damage and antioxidant
protection. Our aim was to test whether high environmental radioactivity would lead to higher oxidative damage and lower antioxidant defenses in exposed organisms. Meta-analysis is a powerful tool for
quantitatively summarizing research, especially when there is apparent
heterogeneity in research ﬁndings (Arnqvist and Wooster, 1995;
Hedges and Olkin, 1985; Koricheva et al., 2013).
We expected factors related to study design, age and model organism to explain variation across studies and species in the relationshipbetween LDIR exposure and oxidative damage. Hence, we also tested
whether different biological matrices and species differed in their response to radiation. Different organs and tissues can be differentially exposed and/or sensitive to radiation exposure, depending on themetabolic fate of radionuclides. Juveniles and adults can also differ intheir sensitivity, with individuals at early developmental stages generally being more sensitive to increased radiation because of their immature antioxidant system (Costantini, 2014; Lu and Finkel, 2008) and due
to potential hazardness of the damage being accumulated in their stem
cell progeny (Liu et al., 2014). Finally, variation across species in

Low-dose ionizing radiation (LDIR) is the type of low-rate chronic irradiation that does not induce adverse toxic effects (National Council on
Radiation Protection (NCRP), 1987). However, exposure to LDIR can accelerate cellular senescence via increasing activity of reactive oxygen
species (ROS) and disruption of biopolymers (Loseva et al., 2014). Because the power of exposure decreases with the distance to the source,
a bigger potential hazard comes from radiological agents being ingested
or inhaled.
Industrial and military use of radioactive materials has led to theirrelease to ecosystems. Early monitoring studies of humans and biota following atomic bomb testing as well as radiation-related accidents (e.g.
in Kyshtym, Russian Federation), suggested elevated health risks andmortality rates in humans and some mammals associated with high acquired doses after chronic exposure to LDIR (Sakata et al., 2012;
Lushnikova et al., 1997; Mozolin et al., 2008; Shoikhet et al., 1999;
Grigorkina and Olenev, 2013; Grigorkina and Pashnina, 2007). Thesestudies also suggested that the effects of direct exposure to ionizing radiation were exacerbated by incorporation of soluble radioactive elements (Ivannikov et al., 2002). Recent ecological studies of theChernobyl and Fukushima catastrophes conﬁrmed this, and demonstrated high variability in such effects among taxa. Along with a highfrequency of morphological abnormalities (Akimoto, 2014; Hiyama
et al., 2012, 2013; Møller et al., 2007) and tumors (Møller et al., 2013),
and an overall decline in population abundances (Møller andMousseau, 2007a, 2007b, 2009; Møller et al., 2012), these effects included high rates of genetic aberrations in somatic (Alamri et al.,2012; Bonisoli Alquati et al., 2010a; Møller et al., 2013) and germline
cells (Ellegren et al., 1997). Moreover variation exists across species intheir biochemical and genetic responses to increasing environmental
radiation (Galván et al., 2014; Hinton et al., 2007).
Overall, attempts to rigorously monitor human populations in
Ukraine, Belarus and Russia following the Chernobyl accident havebeen scattered at best (Edwards et al., 2004; Yablokov et al., 2009).
Nonetheless, these studies showed that workers involved in the cleanup
operations (the so-called ‘liquidators’), who were exposed to muchhigher doses than evacuated civilians, demonstrated elevated frequencies of genetic abnormalities (Moysich et al., 2002; Sevan'kaev et al.,2005), solid cancers and cardio-vascular diseases (Cardis and Hatch,
2011; Serdiuk et al., 2011). An elevated fraction of evacuated adolescents and young adults suffered from thyroid cancer (Demidchik et al.,2007). At the same time, epidemiological studies with small cohorts
and small and non-representative control groups carried out years
after the catastrophe did not yield sufﬁcient evidence to support the hypothesis of radiation-associated mortality linked to the Chernobyl accident (Serdiuk et al., 2011; Weinberg et al., 2001).
These ﬁndings emphasize the need to study variation in health effects in the context of chronic exposure to LDIR. Results of suchstudies are important and may be used in radiation protection and fordeﬁning safety requirements, particularly given the current debates
about the shape of dose–response curves describing radiation-related
effects and the severity of radiation injury (Ryan, 2012).
When absorbed by living cells, ionizing radiation can induce direct
breakage in the chemical bonds of biological macromolecules. Ionizing
radiation can also affect proteins, nucleic acids and complex lipids as aresult of the generation of reactive oxygen species (ROS) via radiolysis
of water or alteration of mitochondrial functions (Kam and Banati,2014). ROS are a diverse group of chemical species, which naturally
occur in cells, where they perform important signaling functions

D. Einor et al. / Science of the Total Environment 548–549 (2016) 463–471

exposure and sensitivity to LDIR would allow us to identify species thatcan serve as sensitive bio-indicators of the effects of LDIR, and model
how such effects translate into potential risk for humans and other species (Møller and Mousseau, 2015).
2. Materials and methods
2.1. Literature search and data sets
We made an exhaustive literature search of both correlative and experimental studies on Web of Science, relying on the following keywords: “radioactive contamination”, “increased background radiation”,“occupation exposure”, “Chernobyl” in combination with “oxidative
stress”, “reactive oxidative species”, “lipid peroxidation”, “peroxide radicals” and “antioxidant”. Once we located these papers, we tested if they
fulﬁlled our inclusion criteria. We also searched the reference lists of all
identiﬁed publications in an attempt to locate additional publications.
Because many nuclear accidents have occurred in the former Soviet
Union, a large number of publications have appeared in Russian, Belorussian and Ukrainian. Although such publications are often neglected
or deliberately omitted from meta-analyses, we made a concerted effort
to identify such publications.
We included (1) ecological or biomedical surveys of individuals exposed to high environmental radiation levels; (2) studies examining therelationship between acquired dose (external; ingested etc.) andmarkers of ROS metabolism; (3) studies reporting at least one statisticaltest, which compared non-exposed and exposed individuals; and(4) studies from which those data could be extracted and converted
into effect sizes. Exclusion criteria were (1) studies that involved radiation therapy (e.g. for oncology treatment); (2) studies that concerned
treatment of radiation-affected people; and (3) studies that involved
short-term exposure to toxic doses of ionizing radiation. We present a465

PRISMA diagram showing the number of initial publications and thesubsequent publications excluded from the ﬁnal sample of 40 studies(Fig. 1). This resulted in 212 effect sizes for antioxidant response and288 effect sizes for oxidative damage. All studies found by March 31
2015 were included in the analyses, and all data are reported in Supplementary material Table S1. Copies of most of these papers, some ofwhich are difﬁcult to obtain electronically, have been posted on a
website (http://cricket.biol.sc.edu/oxi-stress).
2.2. Method of meta-analyses
A meta-analysis was performed using signed and unsigned effectsizes to estimate a direction and magnitude of LDIR effects on oxidative
status. The data were analyzed using the software Meta-Win(Rosenberg et al., 2000). We estimated effect sizes in terms of Hedges
g by using standard procedures. For data that contained estimatedmeans and standard deviations we used the formula by Hedges andOlkin (1985):

g¼

Mexperiment −Mcontrol
SDpooledwhere Hedges pooled standard deviation was estimated as:

SDpooled ¼

sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ðn1 −1ÞSD21 þ ðn2 −1ÞSD22
n1 þ n2 −2

where sample standard deviations were SD1 and SD2 and sample sizes
were n1 and n2 respectively for the two samples. If a standard error

Fig. 1. The PRISMA ﬂow diagram (http://prisma.thetacollaborative.ca/) of the initial publications and the subsequent number of papers retained for the meta-analysis.

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D. Einor et al. / Science of the Total Environment 548–549 (2016) 463–471

was reported, it was simply transformed into SD as follows:

3. ResultsSE
SD ¼ pﬃﬃﬃ :n

3.1. Markers of response to LDIR: response in antioxidants and oxidative
metabolism

For transformation of F-values and Pearson's correlation coefﬁcient r
we estimated effect sizes in terms of Cohen's d using equations in
Rosenthal (Rosenthal, 1994). In particular, we converted F-values froma one-way ANOVA (or the main effect of a two- or more way ANOVA;
here ionizing radiation) into Cohen's d:

In the meta-analysis we included enzymatic and non-enzymatic
markers of antioxidant response to LDIR. We extracted studies of effects
on catalase (CAT, n = 55), superoxide dismutase (SOD, n = 36), glutathione (GSH, n = 17), and glutathione peroxidase (GPX, n = 14), as for
non-enzymatic markers there were effects on vitamin A (VitA, n = 18),
vitamin E (VitE, n = 18), and carotenoids (n = 9). Overall, studies of enzymatic antioxidants accounted for 55% of all markers of antioxidant
response.
Markers of oxidative stress included effects on ROS concentration(n = 21) and their metabolites — diene and triene conjugates (n =157), thiobarbituric acid reactive species (TBARS, n = 81), and reduced
glutathione (GSSG, n = 17). Here, metabolic markers accounted for 89%
of all markers of oxidative stress (Supplementary material Table S1).

d¼

sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ﬃn þ ncni þ nc:F i
ni nc
ni þ nc −2

Pearson's r was converted into Cohen's d as:
2r
d ¼ pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ :1−r 2

3.2. Variation in effects between relatively high and low doses acquired byhumansLikewise, the variance of Cohen's d was deﬁned as follows:
σ 2d ¼!
2ni þ ncdni þ nc
þ
ni nc2ðni þ nc −2Þni þ nc −2

where ni and nc were the sample sizes of the two samples. Cohen's d canbe converted into Hedges g relying on Hedges and Olkin (1985):
g≈d 1−


3:4ðni þ nc Þ−9Because there was considerable heterogeneity in the number of effect sizes per study, and because of differences in effect size among studies, we used random effect meta-analysis to account for such
heterogeneity (Raudenbush, 1994). Variables used in random effectmodels were subsequently entered in meta-regressions with study as
a random effect, and the three ﬁxed effects of species, biological matrix
and age as predictors. These models were subsequently reduced byeliminating all predictors that did not explain part of the variance at alevel of P b 0.10. We used P b 0.10 to avoid excluding factors with overall
weak effects. Full models including all three predictors all resulted insimilar conclusions. All analyses were made using JMP (SAS InstituteInc., 2012).
We interpreted our data as follows: positive ES for an oxidative
marker and negative ES for a marker of antioxidant response would indicate a negative impact of LDIR. Likewise, the effect of LDIR would beconsidered positive if oxidative damage decreased (negative ES for oxidative marker) and when enzymatic and non-enzymatic antioxidants
increased (positive ES for antioxidant marker), respectively(Costantini and Møller, 2009; Costantini et al., 2011). The analysis of
the unsigned effect sizes was performed to determine the magnitudeof effect regardless of its direction. Some biomarkers express radiation
effects by being up-regulated, while others are down-regulated. Thus,
using absolute effect sizes avoids effects on different biomarkers canceling each other out. Effect sizes were considered to be small (Hedges g =0.2, explaining 1% of the variance), intermediate (g = 0.5, explaining 9%of the variance) or large (g = 0.8, explaining 25% of the variance) as suggested by Cohen (1988).

Our analysis included studies on humans that were participating in
the cleanup operations after the Chernobyl accident. For some cleanup
workers maximum estimated exposure was 200 mSv (Lyashenko
et al., 2000, Supplementary material Table S1). We compared them tothe effects found in studies on other human individuals that were notdirectly involved in the event, but were also exposed to LDIR. Our analysis suggests none to very small difference between the two groups ofstudies (antioxidant response: F = 0.1, d.f. = 1, 124, P = 0.752; oxidative damage: F = 0.59, d.f. = 1, 97, P = 0.444).
3.3. Summary statistics, mean effect sizes and evidence for publication bias
Mean effect size for oxidative damage due to radiation and weighted
by sample size was 0.277, with a fail-safe number that exceeded 709(Table 1; Fig. F1). The absolute magnitude of oxidative damage waslarge, and estimated at 0.918. In comparison, mean effect size for antioxidant response weighted by sample size was − 0.350 (absolute ES =0.983), and the fail-safe number was more than 1600 (Table 1). In
total, sample size was 17,332 for 212 effect sizes from studies of antioxidant response, and 13,953 for 288 effect sizes from studies of oxidative
damage. There was signiﬁcant heterogeneity for effect sizes for both oxidative damage and antioxidant response (Table 2, Fig. F1).
Effect size was signiﬁcantly but weakly positively correlated withsample size for Kendall's rank order correlation for oxidative damage(τ = 0.171, P b 0.0001), but not for antioxidant response(τ = − 0.010, P = 0.84). The correlation between effect sizes andtheir variances was also signiﬁcant but weak for oxidative damage(τ = − 0.149, P = 0.0002), but not for antioxidants (τ = − 0.061,
P = 0.19). The correlation between effect size and publication year
was not signiﬁcant for oxidative damage (r = − 0.007, P = 0.87) orfor antioxidants (r = 0.021, P = 0.67).
3.4. Predictors of effect size
We ﬁrst used Generalized Linear Mixed Models (GLMM) to test for
the effects of marker (F = 32.67, d.f. = 1, 491.6, P b 0.0001, antioxidant:

Table 1
Effect size estimates weighted by sample size for antioxidant response and oxidative damage. The table also reports sample size, lower and upper 95% bootstrap conﬁdence intervals, testfor heterogeneity (QTotal), P-value for heterogeneity test and Rosenthal's failsafe number. Effect sizes shown in bold are signiﬁcantly different from zero.

Antioxidant response
Oxidative damage

Effect sizeN

Lower B-strap CI

Upper B-strap CI

Absolute ES

QTotalP

Rosenthal's failsafe number

−0.35
0.276

212
288

−0.53
0.086

−0.17
0.37

0.97270.9184454.33735.88

0.001
0.001

1698.2708.7

D. Einor et al. / Science of the Total Environment 548–549 (2016) 463–471

467

Table 2
Effect size estimates and absolute estimates weighted by sample size for antioxidant response and oxidative damage. The table also reports sample size, lower and upper 95% bootstrap
conﬁdence intervals. Effect sizes shown in bold are signiﬁcantly different from zero.

Antioxidant response by biological matrix
Blood
Liver
Brain
Heart
Eggs
Sperm
Oxidative damage by biological matrix
Blood
Liver
Brain
Lungs
Heart
SpleenKidneyMuscleTestesAntioxidant response by species
Homo sapiens
Rattus norvegicus
Hirundo rustica
Parus major
Apodemus agrarius
Mus musculus
Microtus oeconomus
Microtus arvalis
Myodes rutilus
Apodemus uralensis
Apodemus sylvaticus
Myodes glareolus
Oxidative damage by species
Rattus norvegicus
Homo sapiens
Microtus arvalis
Bos taurus
Myodes rutilus
Apodemus agrarius
Apodemus uralensis
Hirundo rustica
Antioxidant response by age
Adult
Juvenile
Oxidative damage by age
Adult
Juvenile

Effect sizeN

Lower B-strap CI

Upper B-strap CI

Absolute effect size

Lower B-strap CI

Upper B-strap CI−0.4558
0.4574−1.0769−0.7009−0.70480.5558

160
32
4
2
9
3

−0.6467−0.1518−3.5724−3.171−1.5757
0.1251−0.2536
1.13380.45070.43370.07490.98570.92371.33731.38991.10171.1296
0.55540.79740.95930.3616
0.43370.55690.12511.0871
1.79774.2475
3.171
1.84720.84280.4936−0.1426−0.6542−0.5896
0.8035−0.5653−0.31420.764
0.1756

136
32
20
17
18
17
17
15
15

0.3533−1.0279−1.6221−1.16710.1717−1.1006−1.0505
0.2166−0.1659

0.63280.64980.134−0.0752
1.4084−0.07820.40971.28850.52770.6705
1.78371.5866
0.96671.45180.919
1.3763
1.10810.56540.58561.31361.118
0.63541.03920.6431
0.945
0.71840.36570.778
2.48972.13661.38331.84091.2882
1.75231.51260.8158−0.5179−0.0369
1.0128−1.3696
0.8725−1.413−1.2427
2.10332.74420.2167−0.1701−0.0366

149
19
11
6
5
5
4
4
2
2
2
2

−0.7161−0.9505
0.6021−2.2902−0.7928−1.616−4.10360.56001.47130−0.3994−0.1693−0.32650.85491.4296−0.5828
3.4625−1.25640.07495.05184.66990.43370.05880.09610.89281.69661.00031.3562
1.68961.4121
1.19411.93572.63590.21660.22890.13270.7543
1.2086
0.61470.61000.5651
1.26160.11090.62651.471300.05880.0961

1.04222.33451.46642.17493.62491.6065
3.25364.84084.66990.43370.39940.1693−0.04280.523−0.51890.4457−2.11442.81642.17560.1737

155
110
9
3
3
3
2
2

−0.21140.3922−3.58420.3406−7.4000
1.9291
1.9291−0.57110.16900.66291.34702.48550.23108.72242.42730.9516

1.05940.62712.64920.44563.56822.64762.17360.75410.934
0.52651.56160.34061.5603
1.99371.92910.5711

1.19250.73344.90910.54527.4
8.72242.42730.9516−0.315−0.4668

155
55−0.5616−0.7373−0.0822−0.2288

1.11090.65850.9627
0.4831

1.27760.90510.1866
1.343

275
11

0.0586
0.66380.31562.30290.8481
1.32260.769
0.65340.93512.1804−0.393 (0.146); oxidative damage: 0.458 (0.155)) and study (varianceratio = 0.002, 95% CI: 0.026, 0.639, 0.227% of total variance; oxidative
damage: variance ratio = 0.054, 95% CI: − 0.126, 0.233, 0.042% of
total variance).
Levels of antioxidant response differed signiﬁcantly among biological matrices (Fig. 3, Table 2). Two out of ﬁve matrices had effect sizes(ES) that differed signiﬁcantly from zero (Table 2). While blood had
an intermediate negative effect, sperm had a signiﬁcant intermediate
positive effect (Table 1). Interestingly, the positive effect for sperm differed signiﬁcantly from that for eggs, as shown by non-overlapping conﬁdence intervals, and even the sign of mean effect sizes for sperm andeggs was different (Table 1). Biological matrices differed signiﬁcantly
in terms of oxidative damage with signiﬁcant heterogeneity (Fig. 2,
Table 1). However, signiﬁcant large absolute effect (i.e. magnitude) ofantioxidant response and oxidative damage was demonstrated formost matrices (Figs. 2-3, Table 2).
There was signiﬁcant interspeciﬁc variation in antioxidant response(Fig. 5, Table 2). Among the 12 species presented here, six showed effectsizes signiﬁcantly different from zero, with Homo sapiens, Parus majorand Mus musculus showing negative effect sizes, while Myodes rutilus,
Microtus arvalis and Hirundo rustica showing positive effect sizes(Table 2). For oxidative damage, three out of seven species (Apodemussp., Bos taurus and H. sapiens) showed signiﬁcant positive intermediate

to large effect sizes (Fig. 4, Table 2). When we used absolute effect sizes,
rather than signed ES, all species had effect sizes signiﬁcantly different
from zero for markers of antioxidant response, while for oxidative damage three species (Apodemus uralensis, Apodemus sylvaticus, Myodes
glareolus) did not yield a signiﬁcant ES.
Age was signiﬁcantly related to antioxidant response, being 35%
weaker in juveniles than in adults (Fig. 6, Table 2). Oxidative damage

Fig. 2. Forest plot for mean and absolute effect sizes of oxidative damage in biologicalmatrices. Values are means and 95% bootstrap conﬁdence intervals.

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D. Einor et al. / Science of the Total Environment 548–549 (2016) 463–471

Fig. 3. Forest plot for mean and absolute effect sizes of antioxidant response in biologicalmatrices. Values are means and 95% bootstrap conﬁdence intervals.

in juveniles was six-fold larger than in adults (Table 2). Thus juveniles
were more susceptible than adults, likely as a consequence of theweaker antioxidant system of juveniles.
We developed meta-regression models weighted by sample size
using study as a random effect. However, none of the random effects
accounted for an amount of variance deviating from zero, so instead
we used ordinary least squares models. The model for oxidative damage, accounting for 8% of the variance, included signiﬁcant effects of species and age, but no signiﬁcant effect of biological matrix (Table 3). The
effect of oxidative damage was larger for juveniles than for adults(Table 3). The best-ﬁt model for antioxidant response, accounting for
17% of the variance, included signiﬁcant effects of species, biological matrix and age, with a larger effect in juveniles than in adults (Table 4).

4. Discussion
The main ﬁndings of this study were signiﬁcant effects of LDIR on theantioxidant status of exposed organisms, with a large magnitude of themean effect (Table 1). We found signiﬁcant heterogeneity in effect sizes
among species and biological matrices. There were several-fold stronger
effects of oxidative damage in juveniles than in adults. There was little
or no indirect evidence to suggest publication bias.
We found mean weighted effect sizes for oxidative damage of 0.237
and for antioxidant response of −0.350. The absolute effect sizes were0.918 and 0.973 respectively. Cohen (1988) regarded a d = 0.50 to represent an intermediate effect (equivalent to 6% of the variance). Thus
the raw effect sizes that we have estimated here can be considered tobe small to intermediate, and the magnitude of effects can beinterpreted as large. Møller and Jennions (2002) reported when using
Hedges' d an average effect size weighted by sample size across metaanalyses in the biological sciences of 0.721, while the median was
0.595 (in this case the sign of d was also disregarded).

The majority of our studies were extracted from Russian and Ukrainian sources, which are usually not included in Western bibliographicalsources. Thus we were able to test for a difference in effect size between
these two categories of effects with the eastern literature being more
likely to be under-represented than the western literature, which is
fully indexed in Web of Science as well as in other search engines. However, we found little or no evidence consistent with expectations for
publication bias (Møller and Jennions, 2001). Jennions and Møller
(2002) reported a general temporal decline in effect size over time.
Here we found a weak and non-signiﬁcant Pearson correlation between
effect size and publication year. We also showed a non-signiﬁcant correlation between absolute effect sizes and publication year. Begg and
Mazumdar (1995) proposed a non-parametric correlation between
raw effect size and sample as a test of publication bias, and although
we showed signiﬁcant correlations, they differed in sign and were ofsmall magnitude. Funnel plots arise from the reduction in variance in effect size with increasing sample size (Light and Pillemer, 1984). Again,
we found little and inconsistent relationships between variance in effectsize and sample size (Fig. F1). Thus there was little evidence suggestiveof publication bias.
Antioxidant responses varied among species, biological matrices andage classes. However, a meta-regression only showed a signiﬁcant effectof species and biological matrices, when the effects of age classes were
excluded as an independent predictor. Blood and eggs had intermediate
to large negative effects for oxidative damage and antioxidant responses, while there were weak effects for liver, spleen and brain. The
direction of the effect was difﬁcult to evaluate due to high variability
in markers studied within biological categories. However, this problem
can be alleviated when the sign of the effect size is disregarded in thesame model. Such analysis showed that all biological matrices had signiﬁcant effects for individuals exposed to LDIR compared to controls(Figs. 2-3). It was not surprising that brain cells had a large effect.
Brain cells have high metabolic activities and signiﬁcant antioxidant defenses as inferred from the high contents of carotenoids and polyunsaturated fatty acids, which are targets of lipid peroxidation (Barja, 2004;
Agostinho et al., 2010; Johnson et al., 2013). The importance of antioxidant defense is conﬁrmed in studies of cancer radiotherapy, where theadditional intake of antioxidants decreased the effectiveness of radiation treatment (Lawenda et al., 2008).
Our results also suggest that there might be priority of protection ofsome biological matrices at the cost for others. For example, we foundthat radiation caused increased antioxidant levels in sperm, resulting
in a reduction of oxidative damage. This might indicate that, when exposed to chronic LDIR, males invest more in protection of sperm inorder to limit reduction in fertility and so in the ability to successfully reproduce. Interestingly, previous studies of sperm motility in barn swallows showed that the relationship with individual oxidative statusdepended on environmental radiation, suggesting individuals prioritize
protection of sperm when exposed to LDIR, at the expense of theirFig. 4. Forest plots for mean and absolute effect sizes of oxidative damage for different species. Values are means and 95% bootstrap conﬁdence intervals.

D. Einor et al. / Science of the Total Environment 548–549 (2016) 463–471

469

Fig. 5. Forest plots for mean and absolute levels of antioxidant response for different species. Values are means and 95% bootstrap conﬁdence intervals.

plasma oxidative status (Bonisoli Alquati et al., 2011). The effect for eggs
was opposite to that for sperm. It is possible that eggs and sperm differ
in susceptibility to LDIR, and that this affects differences in the level ofinvestment in antioxidant protection under LDIR.
Species varied signiﬁcantly in level of antioxidant response and oxidative damage. Three species (M. rutilus, M. arvalis and H. rustica)
showed an increase in antioxidant defenses after exposure to ionizing
radiation, while three others (M. musculus, P. major and H. sapiens)
showed a decrease. Of the seven species assessed for oxidative damagelevels, the four species Apodemus agrarius, A. uralensis, B. taurus andH. sapiens showed an increase in oxidative markers, while Rattus
norvegicus, M. arvalis and M. rutilus did not differ signiﬁcantly fromzero (Fig. 4). However, the analysis of absolute values demonstrated
that mean effect size for R. norvegicus consisted of signiﬁcant positiveand signiﬁcant negative effects of oxidative damage. This analysis also
showed that A. sylvaticus, A. uralensis and M. glareolus had none tovery weak effects (Fig. 4). Such heterogeneity may indicate that somespecies are negatively impacted by ionizing radiation while others are
more resistant.
Variation in effect among biological matrices within species can obscure the analysis of relationships, as seen in R. norvegicus and H. rustica(Fig. 4). While showing a non-signiﬁcant level of signed oxidative effect,
the average absolute effect size for these species was signiﬁcantly large.
Thus we suggest care when considering the particular marker analyzed
and the predicted direction of effects when combining studies that are
not homogeneous in the protocol used for analyses.
The literature sources that we used in the present study reported
various measurements of LDIR on human or animal populations. For example, Souchkevitch et al. (1997) and Ovsyannikova et al. (2010) reported individual effective dose of exposure in milliSieverts (mSv),
whereas Paranich et al. (1998), Drobinska and Moroz (1998) andLyashenko (2000) used centiGray (cGy) as a unit of absorbed dose,and Verhoglyad et al. (1991) reported an older unit, millirem (mrem).
Yet these units are cross-convertible with some minor assumptions(Thaul and O'Maonaigh, 1999), while others provide a vague estimate
of individual radiation impact, reporting spatial contamination or rather
the equivalent dose rate. Among such studies were those by Shishkina
et al. (2005), with a commonly reported exposure rate in μSv/h. Likewise, Belov et al. (1997) estimated radionuclide intake in Bq/day,while other studies like Mirzoev et al. (1999) or Neyfakh et al. (1998)
reported terrestrial contamination in kBq/m2 or Ci/km2, respectively.
Thus, because of methodological inconsistencies in reporting radiation
dosimetry among studies, we were unable to infer dose–response relationships to explain variation. Therefore, we call for the use of estimatesof acquired dose of LDIR by individuals rather than reporting measurements of environmental contamination.
Young age classes are considered to be more susceptible to oxidative
damage because juveniles produce large amounts of free radicals as aconsequence of their development and growth process, whilst they
have an enzymatic antioxidant machinery that takes time to become
fully mature (Costantini, 2014; Surai, 2002). In addition, low levels of dietary antioxidants deposited by mothers into eggs or passed to the offspring through milk may result in embryos exposed to LDIR starting
their development with low levels of antioxidants (Møller et al.,2005). Thus we expected that studies of early developmental stages
would demonstrate greater sensitivity to increased oxidative damage.
Levels of natural LDIR vary considerably with studies in high natural
background radiation areas ﬁnding weak, but signiﬁcant health effects
associated with natural LDIR (Møller and Mousseau, 2013). This conclusion suggests that there is selection acting on the ability to sustain LDIR,
and that there may be scope for adaptation to radiation. Indeed, studiesof birds at Chernobyl and Fukushima have shown that species withcarotenoid- and pheomelanin-based pigments in their plumage show
stronger negative impact of LDIR on population density (Galván et al.,2011; Møller and Mousseau, 2007b, 2009; Møller et al., 2012). At thesame time, Galván et al. (2014) showed that glutathione (an important
intracellular antioxidant) levels and body condition increased, and oxidative damage and DNA damage decreased, with increasing background radiation in some species of birds. This effect was independent
Table 3
Ordinary least squares model of effect size of oxidative damage weighted by sample size in
relation to species, biological matrix and age. The model had the statistics F = 2.413, d.f. =16, 252, r2 = 0.08, P = 0.0022.

Fig. 6. Forest plots for levels of mean and absolute values of antioxidant response andoxidative damage for the two age classes. Values are means and 95% bootstrap
conﬁdence intervals.

Term

d.f.

Sum of squares

Intercept
Species
Biological matrix
Age
Error

6
9
1
252

2173.441377.95
843.1438,947.77

F

P

0.212
2.344
0.991
5.46

0.646
0.032
0.448
0.020

Estimate

SE

0.286

0.624

−0.493

0.211

470

D. Einor et al. / Science of the Total Environment 548–549 (2016) 463–471

Table 4
Ordinary least squares model of effect size for antioxidant response weighted by sample
size in relation to species, biological matrix and age. The model had the statistics F =3.9151, d.f. = 18, 177, r2 = 0.17, P b 0.0001.
Term
Intercept
Species
Biological matrix
Age
Error

d.f.
10
7
1
177

Sum of squares
3578.30
3552.67
724.8625,787.78

F

P

Estimate

SE

7.182
2.449
3.484
4.975

0.008
0.009
0.00160.027−2.6600.992−0.2690.121

of a number of potentially confounding variables including effects ofsimilarity among taxa due to common phylogenetic descent. We only
found a signiﬁcant difference in the effect of LDIR on oxidative damage
among species and age classes in a meta-regression accounting for theeffects of other potentially confounding variables. In contrast, there
was a signiﬁcant effect of species and matrix on the level of antioxidant
response. While some species showed a positive response for oxidative
damage, other species did not. This dichotomy and the signiﬁcant heterogeneity among species are consistent with adaptation to LDIR although alternative explanations may also account for such
heterogeneity (Lademann et al., 2015).
In conclusion, we have found generally strong effects on oxidative
status in response to low-dose ionizing radiation. We showedsigniﬁcant heterogeneity among biological matrices, species and age
classes. These ﬁndings are consistent with some species apparently
being negatively impacted by ionizing radiation while others are not,or even showing evidence consistent with adaptation to radiation byhaving positive antioxidant responses at high levels of radiation.
The results of our meta-analysis have important implications forstudying the effects of LDIR in human populations and in the wild. For
example, over the last few decades exposure of the average American
to ionizing radiation has increased from 3.6 to 6.2 mSv/year (NCRP,
1987; NCRP, 2009). Generally, this low rate of exposure comes from natural sources in air, soil, rocks and cosmic rays (accounting for 2–
2.5 mSv/year), while the remainder is acquired from man-made sources
such as medical procedures (periodic X-ray, CT scans and others) andindustrial activities such as those associated with mining and processingof ores, minerals, nuclear fuels and oil. Such trends as those revealed in
the NCRP reports and elsewhere forecast an increase in exposure due tosuch artiﬁcial sources (Fazel et al., 2009; Pandey et al., 2010). In addition, the advent of the “Atomic age” has ushered in several nuclear accidents. Two of these, at Chernobyl NPP in 1986 and at Fukushima NPP in
2011, were classiﬁed at the highest level on the International Nuclear
and Radiological Event Scale (INES) scale of nuclear hazards leading to
global increases in radioactive contamination. Overall, man-madesources of LDIR appear to increase radioactive intake, which, as our analysis suggests, might be reﬂected in the metabolic sensitivity to LDIR.
Supplementary data to this article can be found online at http://dx.
doi.org/10.1016/j.scitotenv.2016.01.027.
Declaration of interest
The authors state that they have no conﬂicts of interest.
Acknowledgments
The authors would like to thank the anonymous reviewers for theirinsightful comments and suggestions. We also thank I. Kozeretska, theLibrary of the University of Kyiv and National Library of Ukraine forhelp in locating publications and ﬁnding scientiﬁc papers in the Ukrainian and Russian scientiﬁc literature. We thank M. Owens for editorialassistance. Funding was provided in part from the Samuel Freeman
Charitable Trust, the US Fulbright Program, the CNRS (France), theAmerican Council of Learned Societies, and the University of South Carolina College of Arts & Sciences.

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