You are what your
mother eats: evidence for maternal pre-conception diet influencing fetal sex in
humans.
- http://journals.royalsociety.org/content/w260687441pp64w5/fulltext.pdf
Fiona
Mathews1*, Paul J. Johnson2 and Andrew Neil3
1 School of Biosciences, University of Exeter, Hatherly
Laboratories, Prince of Wales Road, Exeter, EX4 4PS, UK.
2 Wildlife Conservation Research Unit, Department of
Zoology, University of Oxford, South Parks Road, Oxford, OX1 3PS, UK.
3 Division of Public Health and Primary Health Care,
Institute of Health Sciences, University of Oxford, PO Box 777, Oxford, OX3
7LF, UK
*Author for correspondence (f.mathews@exeter.ac.uk)
Electronic
supplementary material (ESM)
Discussion of measurement
error in dietary studies
The
within-subject variability associated with dietary assessments will attenuate
the observed associations between the outcome and exposure measure, depressing
odds ratios towards zero. The sources
of this variability are measurement error (the difference between true intake
and that actually recorded), and the variation of individuals around their true
mean due temporal variation in diet (when measured repeatedly with a valid
instrument). The depression of the
strength of associations occurs because of the statistical phenomenon of
regression to the mean, where true values are less extreme than those
measured. The phenomenon arises as
follows. Any group of individuals with a measured exposure x will be a
mixture of individuals with true values that are higher and lower than x. However, if there is a bell-shaped
distribution of these true measurements - as is usually the case - then it
follows that individuals with true values closer to the population mean will
predominate, and there will be few people with extreme values. Thus the mean true exposure for a group with
a measured exposure x will be intermediate between x and the
population mean. The overall effect is
to flatten the slope of regression lines.
The importance of this kind of error has only been recognised relatively
recently. By comparison, the more
familiar type of error (random between-person) implies that an over-estimation
for some individuals is counterbalanced by underestimation for others, so that
the mean for a large group of subjects is the true mean of the group. This increases the width of confidence
intervals without affecting the slope of regression lines. It is important to note that either type of
error will not act to generate of spurious relationships in our data: this
would generally require differential misreporting of diet by women carrying
male rather than female fetuses (Willett 1990; Clayton & Gill 1991).
Evolutionary context
In humans, the
extended period of juvenile dependency means that the unit cost of producing
offspring is high, and males appear more expensive than females (Clutton-Brock
& Iason 1986; Frank 1987; Hrdy 1999).
Bearing male offspring may also be expensive in the long-term by
increasing mortality in women (Helle et al. 2002; Hurt et al.
2006) – though this is disputed (Cesarini et al. 2007) – and reducing
the lifetime reproductive output of subsequent offspring (Rickard et al.
2007). According to evolutionary
theory, these are circumstances in which differential investment in a
particular sex according to resource availability is to be expected (Koziel
& Ulijaszek 2001). Most research
has focused on the relationship between parental status (measured by financial
resources etc.) and offspring sex. There
are a variety of scenarios where high status might be expected to confer more benefits
on the reproductive success of a male than a female child. For example, in most societies, inheritance
of wealth and property passes to sons rather than daughters (Hrdy & Judge
1993) whereas women seek mates of high status more than men do and tend to
marry up the socio-economic scale (Buss 1989; Lazarus 2002). High status men can therefore gain more
mates and obtain them earlier than lower status ones, whereas this is less true
for women (Buss 1992). Yet only about
half of the known studies of parental status and offspring sex have found
associations in the expected direction (Lazarus 2002).
Several competing hypotheses, developed with other
species, have been proposed to explain associations between resource
availability and the differential investment in offspring of one sex rather
than the other. These include the
Trivers-Willard effect (when resources are plentiful, parents should invest in
males since these achieve a greater increase in reproductive success from a
given level of investment)
(Trivers &
Willard 1973) and the cost of reproduction hypothesis (females in poor
condition should not invest in the more costly sex to minimise the risk of
failure and/or to increase the prospects that they will breed again
successfully in the future) (Myers 1978; Gangestad & Simpson 1990). Local conditions, such as competition or
co-operation between non-dispersive offspring and their parents or kin, may
also determine parental investment patterns, since they will influence the
parent’s inclusive fitness (Clark 1978; Borgerhoff Mulder 1998). Finally, according to Fisher’s theory,
selection should favour shifts in sex ratio against any current population bias
(Werren & Charnov 1978). This
could explain the increase in birth sex ratio during war time (James 1971;
Graffelman & Hoekstra 2000) since
local adult sex ratios are low at such times.
Such Fisherian effects may counteract the influence of maternal resource
availability, and make wartime datasets difficult to interpret (Stein et al.
2004). Unfortunately it is hard to
provide definitive tests of such theories using observational data from humans,
due to the difficulty of obtaining long-term datasets. Distinguishing between the relevant theories
would require not only the measurement of the reproductive output of the parent
and the cost of each sex, but also the reproductive success of all
offspring.
Table 1. Usual daily dietary intakes*of women
between 16 and 28 week’s gestation by fetal sex.
|
 
|
median (lower, upper quartile)
male
fetus female
fetus
(n=327) (n=334)
|
Chi-square
|
p-value
|
|
energy (kcal)
|
2218
(1828, 2692)
|
2165
(1808, 2636)
|
53122.0
|
0.287
|
|
fat (g)
|
81.2
(65.2, 105.6)
|
83.4
(66.7, 100.1)
|
55355.0
|
0.860
|
|
% energy from fat
|
33.7
(30.5, 36.8)
|
34.4
(31.3, 36.9)
|
52087.5
|
0.139
|
|
protein (g)
|
87.3
(72.0, 105.4)
|
85.1
(67.9, 103.7)
|
52980.5
|
0.262
|
|
% energy from protein
|
15.7
(13.9, 17.2)
|
15.6
(13.9, 17.2)
|
55226.0
|
0.825
|
|
carbohydrate (g)
|
321
(264, 385)
|
306
(253, 373)
|
51632.0
|
0.097
|
|
% energy from carbohydrate
|
57.5
(53.8, 61.1)
|
56.4
(52.8, 60.2)
|
51025.0
|
0.057
|
|
vitamin C (mg)
|
108
(74, 148)
|
108
(69, 148)
|
54942.0
|
0.738
|
|
vitamin E (mg)
|
6.9
(5.5, 8.6)
|
6.9
(5.7, 8.4)
|
55007.5
|
0.758
|
|
ß-carotene (µg)
|
1477
(998, 2525)
|
1591
(942, 2478)
|
54442.0
|
0.593
|
|
retinol (µg)
|
424
(311, 579)
|
425
(305, 558)
|
54246.0
|
0.540
|
|
vitamin B12 (µg)
|
6.4
(4.6, 9.9)
|
6.4
(4.0, 9.3)
|
53220.0
|
0.305
|
|
folate (µg)
|
366
(299, 452)
|
347
(286, 432)
|
50856.0
|
0.049
|
|
iron (mg)
|
12.5
(10.4, 15.3)
|
12.3
(10.1, 14.9)
|
52192.0
|
0.151
|
|
zinc (mg)
|
10.6
(9.0, 13.4)
|
10.8
(8.4, 13.6)
|
54503.5
|
0.610
|
|
sodium (mg)†
|
3870
(3151, 4690)
|
3720
(2985, 4631)
|
51213.0
|
0.067
|
|
calcium (mg)
|
1323
(1013, 1613)
|
1269
(936, 1665)
|
53261.0
|
0.313
|
|
 potassium (mg)
|
4314
(3706, 5057)
|
4217
(3391, 5105)
|
52798.0
|
0.232
|
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Last edited: 25-Apr-08 01:16 PM
