# Beyond Barker: Infant Mortality at Birth
and Ischaemic Heart Disease in Older Age

Samuel Baker University of Bristol. E-mail<EMAIL_ADDRESS>Pietro
Biroli University of Bologna and University of Zürich. E-mail:
<EMAIL_ADDRESS>Hans van Kippersluis Erasmus University Rotterdam.
E-mail<EMAIL_ADDRESS>Stephanie von Hinke Corresponding author:
University of Bristol; Erasmus University Rotterdam. E-mail:
<EMAIL_ADDRESS>We would like to thank Alice Carter, seminar
participants at the VU University Amsterdam, the Centre for Health Economics,
and the Alpine Population Conference for valuable comments. We gratefully
acknowledge employees and participants of the 23andMe, Inc cohort for sharing
GWAS summary statistics for educational attainment. This work is based on data
provided through www.VisionofBritain.org.uk and uses historical material which
is copyright of the Great Britain Historical GIS Project and the University of
Portsmouth. We gratefully acknowledge financial support from NORFACE DIAL
(462-16-100) and the Medical Research Council (MR/N0137941/1). The project
DONNI has also received funding from the European Research Council (ERC) under
the European Union’s Horizon 2020 research and innovation programme (grant
agreement No. 851725).

(August 27, 2024)

###### Abstract

Adverse conditions in early life can have consequential impacts on
individuals’ health in older age. In one of the first papers on this topic,
Barker and Osmond (1986) show a strong positive relationship between infant
mortality rates in the 1920s and ischaemic heart disease in the 1970s. We go
‘beyond Barker’, first by showing that this relationship is robust to the
inclusion of local geographic area fixed effects, but not family fixed
effects. Second, we explore whether the average effects conceal underlying
heterogeneity: we examine if the infant mortality effect offsets or reinforces
one’s genetic predisposition for heart disease. We find considerable
heterogeneity that is robust to within-area as well as within-family analyses.
Our findings show that the effects of one’s early life environments mainly
affect individuals with the highest genetic risk for developing heart disease.
Put differently, in areas with the lowest infant mortality rates, the effect
of one’s genetic predisposition effectively vanishes. These findings suggests
that advantageous environments can cushion one’s genetic risk of developing
heart disease.

Keywords: Barker hypothesis; Developmental origins; Gene-environment interplay
JEL Classifications: I10, I14, I19

##  1 Introduction

Adverse conditions during the prenatal and early childhood period can have
significant and potentially irreversible impacts on individuals’ health and
well-being in older age (for a recent review, see e.g. Gluckman et al., 2016;
Gage et al., 2016; Conti et al., 2019).111The vast literature on the so-called
Developmental Origins of Health and Disease (DOHaD) hypothesis spans both the
medical and social sciences (see e.g., Gillman, 2005; Gluckman et al., 2008;
Almond and Currie, 2011a, b; Almond et al., 2018; Heindel et al., 2017; Arima
and Fukuoka, 2020). Correlational and causal linkages have been found between
several measures of early life adversities and health outcomes—such as
cardiovascular disease, metabolic syndrome, and obesity—as well as socio-
economic outcomes—such as educational attainment and wages. The best known
proponent of this hypothesis is the British physician and epidemiologist David
Barker. In one of the first of a set of papers, Barker and colleagues showed a
strong positive geographical relationship between the infant mortality rate in
the 1920s and ischaemic heart disease mortality in the 1970s in the UK (Barker
and Osmond, 1986). They conclude that “poor nutrition in early life increases
susceptibility to the effects of an affluent diet.”222Some of his other papers
on this topic include, e.g. Barker and Osmond (1987), focusing on maternal
mortality as a proxy for the early life environment, and Barker et al. (1989),
where early life neonatal and postneonatal mortality predict later life
mortality from stroke. In other work, they show that low birth weight
associates with mortality from ischaemic heart disease in older age (Barker et
al., 1989).

In addition to such ‘environmental’ circumstances affecting the development of
heart disease, genetic factors are known to play an important role. For
example, twin studies have shown that the disease is heritable, and more
recent Genome-Wide Association Studies (GWAS) have started to unravel the
specific genetic variants implied in the disease (see e.g. Samani et al.,
2007; Helgadottir et al., 2007; McPherson et al., 2007; Schunkert et al.,
2011; Deloukas et al., 2013; Nikpay et al., 2015; Howson et al., 2017; Nelson
et al., 2017; van der Harst and Verweij, 2018). These gene-discovery studies
have linked dozens of independent genetic loci to heart disease, facilitated a
better understanding of the causal risk factors, and informed the development
of new therapeutics (see e.g. Khera and Kathiresan, 2017).

Whereas the role of these environmental and genetic main effects are
established and widely-documented, the understanding of their interplay—so-
called Gene-Environment (GxE) interplay—is still in its infancy.333Some
studies discuss the potential role of gene-environment interactions for
cardiovascular disease, but none of these focus on the long-held DOHaD
hypothesis (see e.g., Visvikis-Siest and Siest, 2008; Hirvonen, 2009; Flowers
et al., 2012).

In this paper, we start by laying out different mechanisms through which
genetic variation can moderate the effect of environmental conditions. We
highlight the potential role of epigenetics, but argue that this is neither a
sufficient, nor a necessary condition for the existence of gene-environment
interaction effects. We then empirically confirm the independent importance of
genetic variation and adverse conditions during gestation and early childhood
(as proxied by infant mortality rates at birth as in Barker and Osmond, 1986),
in explaining later life heart disease. We then go ‘beyond Barker’ in two
ways. First, we explore the robustness of the Barker hypothesis to the
inclusion of local geographical area fixed effects, exploiting only variation
in infant mortality rates and heart disease within a local area over time. We
also investigate the sensitivity to the inclusion of family fixed effects,
comparing later life heart disease among full siblings who were exposed to
different rates of infant mortality at birth, whilst controlling for
individuals’ age at the monthly level. Second, we move ‘beyond Barker’ by
studying the importance of gene-environment interplay in ischaemic heart
disease. This allows us to answer questions such as “does genetic
susceptibility aggravate adverse early life circumstances?”, or – vice versa –
“can advantageous early life environments cushion genetic risk?”

We do this using individual-level data on over 370,000 individuals in the UK
Biobank (Sudlow et al., 2015), of whom we identify $\sim$33,000 full siblings
from their genetic data. Ischaemic heart disease is the most common cause of
death in the developed world, accounting for more than nine million deaths
worldwide in 2016 (e.g., WHO, 2018).444Ischaemic heart disease occurs when the
arteries cannot transfer enough oxygen-rich blood to the heart. The most
common type is coronary artery disease, where the blood-flow to the heart is
restricted by atherosclerosis, or plague. Advancing our knowledge on the
interplay between genetic and environmental factors that drive the world’s
major killer is therefore not just a fundamental scientific advance, but may
also inform governments on how environmental policies can reduce the –
arguably unfair – inequalities in heart disease arising from one’s genetic
variation.

Our first contribution is to digitize historical data on infant mortality
rates in the UK555We start with the already-digitized data from the Great
Britain Historical Database (GBHD; Ell et al., 2020), see also the Vision of
Britain website. The GBHD contains birth, death, and infant mortality counts,
as well as population estimates from 1930 to 1973 but lacked the years 1958 to
1962. We digitized these remaining years, and systematically quality-
controlled the entirety of the database., link these to the UK Biobank and use
them to replicate the analysis by Barker and Osmond (1986) on the relationship
between early life conditions and later life outcomes. Rather than specifying
area-level outcomes, as in the original study, we have individual-level
outcomes for those born between the late 1930s and early 1970s. As a measure
of the early life environment, we use the infant mortality rate that each
individual was exposed to in their year and local area of birth, as published
in the Registrar General reports for England and Wales (HMSO, 1934). The
spatial units we employ are Local Government Districts, including over 1400
small geographic regions covering the whole of England and Wales. We then
merge this information with the UK Biobank, a major resource that follows the
health and well-being of around 5 million individuals in the UK aged 40-69
between 2006-2010. Linking these two datasets provides previously unexplored
information on the early life conditions in the $\sim$35 years and $\sim$1400
districts of birth of the UK Biobank participants. This in turn allows us to
explore the determinants of heart disease, combining spatial variation in
infant mortality rates across small geographical regions (à la Barker and
Osmond, 1986) as well as temporal variation in infant mortality rates across
individuals’ year of birth (à la Kermack et al., 1934). Indeed Kermack et al.
(1934) were among the first to suggest that one’s childhood environment is
crucial in shaping individuals’ health in adulthood, but they largely ignore
spatial variation in childhood environments. In contrast, Barker and Osmond
(1986) focus on such geographical variation, but do not explore temporal
variation in adverse environments. Our data allow us to combine both sources
of variation and investigate the relationship between early life adversity and
later life health for a period characterised by a substantial improvement in
early life conditions.

A second contribution is to deepen our understanding of the relationship
between early life environments and later life outcomes by incorporating
recent advances in the understanding and collection of individual-level
molecular genetic data. More specifically, we investigate whether the early
life environment exacerbates or mitigates any genetic effects on heart
disease. We begin by showing the strong predictive power of the early life
environment as well as the relevant polygenic score666Polygenic scores measure
one’s genetic ‘predisposition’ towards a trait (here: heart disease). We
discuss this in more detail below., confirming the importance of both
components. Next, we investigate the interaction between the polygenic score
and the infant mortality rate at the time and location of birth. In other
words, we shed light not only on whether improving the early life environment
reduces the average disease prevalence in older age, as suggested by Barker,
but also whether such improvements reduce the inequality in disease prevalence
for individuals with different genetic predispositions for the disease. In the
other direction, our interaction results enable testing whether individuals’
genetic predisposition exacerbates or protects individuals against adverse
early life conditions in developing ischaemic heart disease.

We have three main findings. First, we replicate Barker’s result that the
infant mortality rate in one’s local district and year of birth is associated
with ischaemic heart disease later in life using well-powered individual-level
data from the UK Biobank. However, our interpretation of these findings
differs from Barker on two counts. First, we show that infant mortality rates
are systematically associated to socioeconomic disadvantage, and hence are
likely to additionally capture other characteristics of the environment than
just “nutritional deficiencies” (as in Barker and Osmond, 1986). Indeed, areas
with high infant mortality rates are poorer and of lower socioeconomic
position, and this in itself may negatively affect individuals’ health in
later life. Second, we explore the robustness of our findings to the inclusion
of Local Government District fixed effects, as well as to family fixed
effects. In the former, we compare people born in the same district over time
and exploit variation in infant mortality rates and outcomes within districts.
In the latter, we compare siblings born in different years and exploit
variation in infant mortality rates and outcomes within families. We find that
the main effect of the infant mortality rate is robust (albeit attenuated) to
accounting for time-invariant variation within districts, but not within
families. This suggests that the infant mortality rate does not mainly capture
within-family variation in nutritional circumstances, but rather that it
proxies for other unobserved characteristics that vary between families, such
as poverty or socio-economic status. Accounting for these using a family fixed
effects approach renders the average effect of the infant mortality rate
insignificant.

A second main finding is that the genetic signal in ischaemic heart disease
largely derives from direct (or causal) genetic effects. As pointed out by
Davey Smith and Ebrahim (2003); Howe et al. (2021); Biroli et al. (2022) among
others, family designs enable us to estimate the direct genetic effect on the
outcome, accounting for demography (e.g. population stratification,
assortative mating) and indirect genetic effects (e.g., genetic nurture, where
parental genotypes influence offspring outcomes through environmental
channels). We find that the genetic effects are robust to the inclusion of
district and family fixed effects, suggesting that demography and indirect
genetic effects are not driving the association between polygenic scores and
heart disease. To clarify: by direct genetic effects we make a probabilistic
statement about a counterfactual, i.e., those with higher polygenic scores are
more likely to develop ischaemic heart disease later in life. We do not mean
that these effects are purely biological, immutable or deterministic. Indeed,
even though these effects are downstream consequences of genetic variation
across individuals, they could be entirely mediated by malleable environmental
conditions.

The third main finding is that the null effect of infant mortality conceals
underlying genetic heterogeneity: we find evidence of non-negligible
interactions between genes and the environment. Our findings imply that among
those with high genetic risk for ischaemic heart disease, the infant mortality
rate significantly aggravates the risk. In contrast, in districts with the
lowest infant mortality rates, the effect of genetic predisposition
effectively vanishes. We find that these results are robust (albeit
attenuated) to the inclusion of district as well as family fixed effects. This
not only reveals previously masked heterogeneity in the DOHaD literature, but
additionally provides evidence that environmental interventions may moderate
and even mitigate genetic susceptibility to heart disease.777Although we know
from the epigenetics literature that early-life circumstances may trigger
differential epigenetic expression (see e.g., Heijmans et al., 2008), much
remains to be learned. First, epigenetic studies generally do not investigate
how epigenetic changes affect later life outcomes. Instead, they tend to
explore how different circumstances, or environments, affect DNA methylation.
Second, not all epigenetic effects are expected to translate into
developmental differences, and they could arise in segments of the DNA where
there is no variation across individuals. In fact, as we argue below,
epigenetics is one potential channel through which gene-environment interplay
exists, but it is neither necessary, nor sufficient. This finding strongly
debunks genetic determinism of later life diseases such as heart disease, and
establishes that the disease is the product of complex interactions between
genes and the environment. As such, it suggests that improving the early life
environment can significantly reduce the variation driven by ‘genetic risk’ in
the population.

The rest of this paper is structured as follows: Section 2 provides a
background to the paper, discussing the importance of ‘nature’ (genetics),
‘nurture’ (proxied here by the infant mortality rate) and why or how the two
may interact to shape individuals’ outcomes later in life. The data is
described in Section 3, followed by the empirical specification in Section 4.
The results are discussed in Section 5, with the robustness analysis in
Section 6. We conclude in Section 7.

##  2 Background

###  2.1 Nature: Genetics

The human genome consists of over 3 billion base pairs in each cell nucleus,
with four possible bases: adenine (A), thymine (T), guanine (G) and cytosine
(C).888A base pair is set of two bases, with A always pairing with T, and C
always pairing with G. Comparing any two unrelated human beings, over 99% of
their genome is identical. The remaining $<$1% differs between individuals,
with a Single Nucleotide Polymorphism (or SNP, pronounced snip) being the most
common form of genetic variation. A SNP is a one base-pair substitution at a
particular location (locus) on the human genome.

To identify genetic variants that are associated with a particular trait of
interest, such as coronary heart disease, so-called Genome-Wide Association
Studies (GWAS) relate each SNP to the trait in a hypothesis free-approach.
Stringent p-values are then used to identify SNPs that are robustly associated
with the trait of interest, and replication is performed in other, independent
samples. Most human complex traits are polygenic, meaning that they are
affected by many SNPs, each with a very small effect size. To increase the
predictive power of SNPs, they can be aggregated into a so-called polygenic
score (PGS, also referred to as a polygenic index or PGI), defined as a
weighted sum of the individual SNPs:

$PGS_{i}=\sum_{j=1}^{J}\beta_{j}x_{ij}$

where $x_{ij}$ is a count of the number of ‘effect’ alleles at SNP $j$ of
individual $i$, and $\beta_{j}$ is the associated effect size, obtained from
an independent GWAS. Polygenic scores have been shown to be powerful tools to
identify patients with increased risk for coronary artery disease, atrial
fibrillation, type 2 diabetes, inflammatory bowel disease, and breast cancer.
Indeed, Khera and Kathiresan (2017) propose the use of polygenic prediction in
clinical care.

###  2.2 Nurture: The infant mortality environment

The infant mortality rate in the year and location of birth can be seen as a
broad measure of the environment that an individual was exposed to around
birth. Different mechanisms have been suggested that explain how such early
life environments can affect outcomes in older age. First, adverse conditions
in early life may lead to permanent changes in body structure, physiology and
metabolism (see e.g. Gluckman et al., 2008; Belsky, 2019; Colich et al., 2020;
McDermott et al., 2021). For instance, they may lead to compensatory patterns
of growth, with nutritional resources being diverted from child development
towards survival, potentially affecting the development of the body more
generally. Similarly, adverse circumstances may cause the body to
automatically protect the growth of one organ (e.g., the brain) at the expense
of other organs (e.g., the heart), which in turn can affect the development of
future disease (see e.g. Campbell et al., 1967; Rudolph, 1984; Hales and
Barker, 2001).

Another mechanism is ‘foetal programming’. This refers to the intrauterine
environment giving the foetus a forecast of the circumstances into which it
will be born (Gluckman and Hanson, 2006a; Gluckman et al., 2008). Individuals’
metabolism may then adapt to ensure optimal survival under similar conditions.
Hence, foetal programming is believed to reflect foetal adaptation. Whilst
foetal adaptation may be beneficial to short-term survival, it could be
detrimental to health in adulthood if later life circumstances are very
different from those prenatally (Barker, 1995). For example, if the foetus was
exposed to a famine prenatally, the body may develop in a way to ensure
survival under similar conditions post-birth. If it is then exposed to an
environment with sufficient nutrition (or over-nutrition), this may impact on
the development of disease later in life.999For studies looking at the effects
of intrauterine exposure to famines on later life outcomes, see e.g. those on
the Dutch Hunger Winter (Smith, 1947; Stein et al., 1975; Schultz, 2010; Conti
et al., 2021), famines in China (St Clair et al., 2005), Finland (Kannisto et
al., 1997), the Netherlands (Lindeboom, 2010), and Leningrad (Stanner et al.,
1997).

Finally, rather than the infant mortality rate having a causal effect on later
life health, it is likely to also capture socio-economic differences. Indeed,
the literature on infant mortality rates in the 20th century shows a strong
social gradient: infant mortality is substantially higher among the unskilled
social classes compared to the professional classes, and in northern compared
to more southern regions within the UK. It is also higher among illegitimate
children (Adelstein et al., 1980).

###  2.3 The potential interaction between ‘nature’ and ‘nurture’

There are multiple reasons why ‘nature’ might interact with ‘nurture’ to shape
individuals’ later life outcomes. First, there may be a biological channel:
genes may predispose individuals to certain health conditions or behaviours,
but the extent to which these genes are expressed – their phenotypic effect –
can depend on environmental circumstances (Gluckman and Hanson, 2006a; Rutter,
2006). For example, if adverse circumstances early in life cause permanent
changes in the structure of certain organs (e.g., the heart), genetic variants
may change their expression in that organ. This can in turn affect the
development of the organ and with that, future disease. This channel is also
known as environmentally-induced epigenetic regulation (see e.g., Gluckman and
Hanson, 2006b); switching genes on and off depending on environmental
circumstances. The epigenetic literature tends to focus on the effects of
certain exposures on the epigenome, with some studies establishing epigenetic
expressions driven by early life circumstances (e.g., Heijmans et al., 2008).

However, even if early life circumstances cause epigenetic changes, this does
not necessarily imply that they affect later life outcomes. Indeed, there
could be compensatory investments by the individual or her environment that
offset the development of any epigenetic effects on later life phenotypes. For
example, parental investments in children may differ in response to early life
circumstances, potentially mitigating or reinforcing their effects (see e.g.
Yi et al., 2015; Adhvaryu and Nyshadham, 2016; Grätz and Torche, 2016; Molina,
2021; Majid, 2018; Muslimova et al., 2020). This may be a conscious reaction
to environmental circumstances, but it could also be an unconscious response.
Similarly, if prenatal malnutrition leads to epigenetic expression, but
parents fully compensate for this harmful exposure, we would not necessarily
detect any differences in later-life outcomes. In other words, epigenetic
expression in response to environmental circumstances is not a sufficient
condition for the existence of gene-environment interaction effects on later
life outcomes.

Furthermore, an interaction between nature and nurture need not be epigenetic.
For example, one’s genetic predisposition for certain health conditions or
behaviours may simply reflect the ‘type’ of individual, with different ‘types’
responding differently to different environments. For example, Fletcher (2012)
finds that variation in the nicotinic acetylcholine receptor moderates the
influence of tobacco taxation on smoking, showing that only those with the
protective polymorphism respond to tobacco taxation and reduce their tobacco
use (see also, e.g., Slob and Rietveld, 2021). A differential response to
tobacco taxation is unlikely to occur due to epigenetic changes or
compensatory effects, and is more likely to indicate that those with the
protective polymorphism can be seen as different ‘types’ who respond
differently to different environments. In other words, epigenetics is also not
a necessary condition for the existence of gene-environment interaction
effects.

In this study, we cannot identify the specific channel that may be driving our
gene-by-environment interaction effects, and hence we are unable to
distinguish between the different potential mechanisms. Instead, we quantify
the extent to which genes and environments interact in shaping individuals’
later life health, which is an essential precursor to analyses seeking to
identify epigenetic changes or other channels through which the early-life
environments and genetic predisposition interact in the development of heart
disease.

##  3 Data

The data we use is the UK Biobank; a major resource that follows the health
and well-being of approximately 500,000 individuals in the UK aged 40-69
between 2006-2010. Participants have provided information on their health and
well-being, and given blood, urine and saliva samples. They have also been
genotyped.

An advantage of the UK Biobank is that it is a very large sample of
individuals for whom we observe an extensive amount of relevant health (as
well as social and economic) outcomes later in life. Our main outcome of
interest is motivated by the existing literature: we define a binary indicator
measuring whether the individual has been diagnosed with ischaemic heart
disease (IHD). We identify individuals using the ICD-10 codes (I20-I25)
obtained from mortality records as well as primary and secondary diagnoses in
individuals’ hospital inpatient records that are linked to the UK Biobank.
Around 11% of our sample has been diagnosed with IHD.

Another advantage of these data is that it includes a relatively large sample
of siblings. This means that the analysis can hold constant any time-invariant
observed and unobserved family characteristics that may affect both the
exposure and outcome of interest, using family fixed effects. We do this in
the analysis below.

One of the main disadvantages of the UK Biobank, however, in particular for
our research, is that there is almost no information on the early life
environment, other than the location of birth.101010The data also include
(self-reported) birth weight, an indicator for whether the participants were
breastfed, and whether the mother smoked during pregnancy. To allow us to
explore the effects of early life circumstances on later life health, we
exploit the the eastings and northings of the location of birth to identify
the Local Government District in which individuals were born. We then merge in
data on individuals’ environmental conditions in their year of birth at the
Local Government District level (henceforth: district-level). In the absence
of information on individual- or family-level circumstances around birth, this
allows us to characterise the birth environment for each individual in the UK
Biobank, providing a rich resource to add to these existing data.111111Note
that the districts we observe suffer from the modifiable areal unit problem
(MAUP, Netrdová et al., 2020), in that they change over time in terms of their
shape, name and type (e.g., rural or urban district). A frequently-used
solution is aggregation (Gregory and Ell, 2005) to larger time-invariant
geographic units, but this leads to a loss of data and specificity. An
alternative is to create weights for smaller spatial sub-units (representing,
e.g., its population) and use these to construct time-invariant districts and
their associated (weighted) statistics. We use WeightGIS (Baker, 2020) to do
the latter, creating time-invariant districts based on their parish-level
population; we standardise districts to the 1951 census shapefile provided by
Vision of Britain (University of Portsmouth, 2011).

To this end, we take the Great Britain Historical Database (GBHD; Ell et al.,
2020) as a starting point, which contains district-level birth, death, and
infant mortality counts, as well as population estimates for the years
1930–1957 and 1963–1973. We collect and digitize this information for the
remaining years 1958–1962, and systematically quality-control the entirety of
the database. We then link the infant mortality rate in the year and district
of birth, defined as the total number of deaths within the first year of life
per 1000 live births, for all UK Biobank participants born in England or
Wales. This local mortality rate is a proxy for the quality of the early life
environment that each individual was exposed to. The year and district-
specific infant mortality data are extracted from the Registrar General
Statistical Reviews of England and Wales from 1934-1971 (HMSO, 1934).

We use the genetic data in the UK Biobank to create a polygenic score for
heart disease, using the GWAS summary statistics from Nikpay et al. (2015).
This GWAS excludes the UK Biobank, ensuring the discovery sample is
independent from the analysis sample, meaning we do not suffer from over-
prediction.121212In the robustness analyses, where we focus on the sibling
sample, we explore the sensitivity of our findings to the use of an
alternative polygenic score, obtained from a GWAS on the UK Biobank sample
that excludes siblings and related individuals. We then use these GWAS
estimates to construct a polygenic score on the analysis sample of siblings.
Our results are not sensitive to this alternative construction of the
polygenic score. We correct for co-inheritance between SNPs using LDpred
(Vilhjálmsson et al., 2015), assuming the fraction of causal SNPs is 1. We
standardise all polygenic scores to have mean 0, standard deviation 1.

We make the following sample selection: First, we drop those with missing
birth co-ordinates since these cannot be geo-located to a Local Government
District. We also drop those born outside England and Wales, as the infant
mortality data does not contain reports for other parts of the United Kingdom.
This leaves us with 405,180 individuals. We next drop those of non-European
ancestry, those with missing data on ischaemic heart disease, or on infant
mortality rates. Our final sample size includes 378,838 individuals. In
further analysis, we restrict our estimation to the sibling sample of the UK
Biobank. As there is no self-reported information on family members, we
identify siblings using the (genetic) kinship matrix provided by the UK
Biobank. Our analysis sample includes 33,060 full siblings.

###  3.1 The Infant Mortality Rate

Figure 1 shows the distribution of infant mortality rates across England and
Wales for the 1951 Census year. The different geographical regions shown are
the 1472 Local Government Districts for which we observe annual infant
mortality rates. These are relatively small geographic areas, showing
substantial variation in infant mortality rates across England and Wales.

Figure 1: Geographic distribution of the infant mortality rate (infant deaths
per 1000 live births) in 1951 for England and Wales. This work is based on
data provided through www.VisionofBritain.org.uk and uses historical material
which is copyright of the Great Britain Historical GIS Project and the
University of Portsmouth (2011).

To better understand the temporal and spatial variation in infant mortality
rates, Figure 2 presents a box plot of the district-level variation in infant
mortality rates over time. The white line in the centre of the box is the
median, with the box representing the inter-quartile range. This shows two
main points. First, there has been a large reduction in the infant mortality
rate over time, from approximately 50–60 infant deaths per 1000 live births in
the late 1930s to just under 20 infant deaths per 1000 live births in the
1960s. Second, although the inter-quartile range reduces over time, it remains
relatively large, even in the late 1960s, suggesting there is sufficient
variation in infant mortality rates across districts to investigate its effect
on later life outcomes.

Figure 2: Trend and variation in infant mortality rates for England and Wales;
1935–1970

To explore the extent to which the infant mortality rate is correlated with
other socio-economic indicators, we merge district-level data on social class
from the UK Censuses in 1951, 1961 and 1971 to the district-level data on
infant mortality rates in these years from the Registrar General reports.
Social class is measured as the proportion of households in each district that
is of social class I (professional), II (managerial/technical), III (skilled
non-manual/manual), IV (semi-skilled manual) and V (unskilled manual). We also
link data on illegitimacy; recently proposed as a proxy for social class
(Lührmann and Wilson, 2018). We then regress the infant mortality rate on the
illegitimacy rate (i.e., the number of illegitimate children per 1000 live
births) and the social class indicators. subsection 3.1 presents the
estimates, showing a strong social gradient in infant mortality, with
districts that have larger proportions of individuals in lower social classes
experiencing significantly higher infant mortality rates. This is true for the
pooled sample (column 1), as well as for the individual Census years (columns
2–4), though the magnitude reduces over time and it is no longer significant
in 1971. The strong correlation between the infant mortality rate and social
class is important, as this suggests that the former is likely to partially
capture different socio-economic indicators of the district. We find some
suggestive evidence that the illegitimacy rate is positively correlated with
infant mortality, though only in 1971.

Table 1: Correlations between district level infant mortality rates, social
class and illegitimacy

| All years | 1951 | 1961 | 1971
---|---|---|---|---
Illegitimacy rate | 0.0105 | 0.0050 | -0.0048 |  0.0303***
| (0.0091) | (0.0238) | (0.0145) | (0.0106)
Proportion social class I |  -0.3373*** |  -0.7362*** |  -0.4171** | -0.2087
| (0.1240) | (0.2654) | (0.2100) | (0.1519)
Proportion social class II | -0.0298 | 0.1144 | -0.0626 | -0.0826
| (0.0375) | (0.0875) | (0.0499) | (0.0629)
Proportion social class IV |  0.0996** |  0.1783*** | 0.0584 |  -0.1452**
| (0.0421) | (0.0650) | (0.0693) | (0.0725)
Proportion social class V |  0.2447*** |  0.3718*** | 0.2311 | 0.1053
| (0.0750) | (0.1142) | (0.1478) | (0.1369)
R2 | 0.17 | 0.03 | 0.03 | 0.03
No. of observations | 4297 | 1468 | 1409 | 1420

Notes: Column (1) additionally includes census year dummies. Social class III
is the reference category. Robust standard errors, clustered by district, in
parentheses. * $p<0.1$, ** $p<0.05$, *** $p<0.01$.

###  3.2 The Polygenic Score

We next provide descriptive statistics on the polygenic score and how it
correlates with the outcome of interest. The top left hand graph of subsection
3.1 presents the density of the (standardized) polygenic score for heart
disease. We divide the PGS into 200 bins, and plot the average outcome (i.e.,
the probability of being diagnosed with ischaemic heart disease) for each bin.
These are the black dots. The line through the dots is obtained from a kernel-
weighted local polynomial regression of the outcome on its polygenic score.
This shows a strong correlation between the polygenic score and ischaemic
heart disease. We report the statistical strength of this relationship in
regressions below. The figures also show some suggestion of convexity in the
relationship between the polygenic score and the outcome. We model this
directly below and explore the sensitivity of our findings to linear
specifications of the polygenic score in the robustness analysis.

The middle graph in the top row of subsection 3.1 shows a similar plot, but
with the Infant Mortality Rate (IMR) on the horizontal axis. This shows a
strong correlation between the infant mortality rate at birth and the
likelihood of being diagnosed with ischaemic heart disease in later life.
However, the graph suggests that the relationship is somewhat non-linear, with
little difference in ischaemic heart disease for those born in areas
characterised by low (i.e. more than 1 standard deviation below the mean)
rates of infant mortality, but with increasing risk of ischaemic heart disease
among those with (standardised) infant mortality rates above -1.

The top right hand graph in subsection 3.1 shows the extent to which the PGS
and the infant mortality rate are correlated with one another. Positive
(negative) gene-environment correlation – $rGE$ – indicates that individuals
with a higher genetic predisposition are more (less) likely to be observed in
environments with higher infant mortality rates. The figure shows some
suggesting evidence of positive $rGE$, with districts exposed to higher than
average infant mortality rates also having higher than average polygenic
scores, although – as we show below – this correlation does not survive in our
sibling sample.

The middle set of three graphs in subsection 3.1 show the same relationships
as above, but on the sibling sample only. This still shows a positive
correlation between the polygenic score and ischaemic heart disease (figure on
the left). As we show in the regressions below, the strength of this
relationship is similar in this reduced sample. The correlation between the
infant mortality rate and ischaemic heart disease (graph in the middle) is
again positive, but does not suggest strong non-linearities. Furthermore, we
find no evidence of $rGE$ in the reduced sibling sample (graph on the right),
with the slope of the line being close to zero.

Figure 3: Correlation between the polygenic score, infant mortality rates, and
ischaemic heart disease in the full estimation sample (row 1), the sibling
sample (between families; row 2) and sibling sample (within families, row 3).

Finally, the bottom three graphs only exploit within-family variation. In
other words, we use the UK Biobank sibling sample and correlate the residuals
of the variables shown on the axes, after taking out the family fixed effects.
This still shows a positive correlation between the polygenic score and
ischaemic heart disease (bottom left figure), though – as we show below – the
magnitude of the effect is slightly smaller, since the within-family analysis
accounts for shared environments and parental genotypes (see e.g. Selzam et
al., 2019; Koellinger and Harden, 2018; Kong et al., 2018; Lee et al., 2018).
Accounting for time-invariant characteristics within families, we still find a
positive correlation between the infant mortality rate and ischaemic heart
disease, as shown in the bottom middle graph, though this is effects is again
attenuated. Finally, we find no evidence of gene-environment correlation
($rGE$; bottom right graph). This is reassuring, as it suggests we are
identifying true gene-environment interactions, rather than spurious gene-gene
or environment-environment interactions (for more detail on the interpretation
of the $G\times E$ coefficient in the presence of $rGE$, see Biroli et al.
(2022)).

##  4 Empirical strategy

#### Baseline specification:

To explore the relationship between ischaemic heart disease, the infant
mortality rate at birth, the polygenic score and their interaction, we write
the baseline empirical specification as:

$\displaystyle Y_{id}=\alpha$
$\displaystyle+\beta_{1}IMR_{d,t=0}+\beta_{2}IMR^{2}_{d,t=0}+\beta_{3}PGS_{i}+\beta_{4}PGS^{2}_{i}$
$\displaystyle+\beta_{5}PGS_{i}\times IMR_{d,t=0}+\beta_{6}PGS^{2}_{i}\times
IMR^{2}_{d,t=0}$
$\displaystyle+\gamma\mathbf{X_{i}}+f_{1}(IMR,\mathbf{X})+f_{2}(PGS,\mathbf{X})+u_{id}$
(1)

where $Y_{id}$ is equal to one if individual $i$, born in district $d$, has
been diagnosed with ischaemic heart disease, and zero otherwise. The district-
level infant mortality rate in the year of birth (i.e., $t=0$) is given by
$IMR_{d,t=0}$, which is standardised to have mean 0 and standard deviation 1.
The relevant polygenic score is given by $PGS_{i}$, also standardised to have
mean 0 and standard deviation 1. The gene-environment interaction is denoted
by $PGS_{i}\times IMR_{d,t=0}$. We follow Biroli et al. (2022) and include
non-linear terms of $PGS_{i}$, $IMR_{d,t=0}$, as well as $PGS_{i}\times
IMR_{d,t=0}$, though we explore the robustness of our results to linear
specifiations in Section 6 below; we show that this does not change our main
conclusions.

Within $\mathbf{X_{i}}$, we control for the individual’s gender and we include
dummies for each year-month of birth (i.e., [(12 months $\times$ 38 years)-1]
separate dummy variables). The latter will account for the fact that older
individuals are more likely to be exposed to higher infant mortality rates,
and are also more likely to have worse health, on average. Hence, including
dummies for each year-month of birth is the most flexible specification to
account for individuals’ age at the monthly-level. We also include the first
10 principal components of the genetic relatedness matrix to control for any
remaining genetic differences across ancestry groups, as is custom in the
literature (see e.g. Price et al., 2006).131313Although it is not necessary to
control for the principal components in the within-family analysis, we include
them to allow the comparison to the between-family analysis. Excluding them
does not affect our conclusions. Finally, the function $f_{1}(IMR,\mathbf{X})$
and $f_{2}(PGS,\mathbf{X})$ denotes interactions between $IMR_{d,t=0}$ and
$\mathbf{X_{i}}$ and between $PGS_{i}$ and $\mathbf{X_{i}}$ respectively (as
in Keller, 2014). The error is denoted by $u_{id}$; we report
heteroskedasticity-robust standard errors, clustered by either by district (in
full sample) or by family and district (in the family fixed effects analysis
below).

We start by estimating the relationship between the local infant mortality
rate in the year of birth and later life ischaemic heart disease (i.e.
Equation (4) without $PGS_{i}$, $PGS^{2}_{i}$ and the interaction terms). This
analysis is meant as a replication of the original Barker hypothesis, showing
the strength of the relationship between infant mortality rates in the year
and region of birth and ischaemic heart disease, and allowing for a non-linear
(quadratic) effect. However, note that, in contrast to Barker and Osmond
(1986), our outcome of interest is measured at the individual level, as
opposed to the regional level. We next explore the statistical relationship
between the outcome of interest and the relevant polygenic score (i.e.,
Equation (4) without $IMR_{d,t=0}$, $IMR^{2}_{d,t=0}$ and the interaction
terms), aiming to corroborate the results from existing GWAS that genetic
scores are predictive of heart disease, as well as to quantify this
relationship.

Finally, by including the main effects as well as their interactions, as in
Equation (4), we explore whether being born in a district characterised by low
infant mortality rates can reduce not only the mean disease prevalence in
older age, but also its variation predicted by the polygenic score. Or vice
versa, whether an individual’s polygenic score exacerbates or protects against
the effects of being born in high infant mortality rate districts.

Since we are exploring the effects of early life circumstances (and in
particular: mortality) on later life health, it is important to take into
account the potential for selection. Indeed, a high infant mortality rate
implies that only a selected sample remains (i.e., those who survived).
Assuming that the survivors are healthier than those who did not survive, we
are likely to underestimate the effects of interest.

#### District Fixed Effects:

Equation (4) compares individuals born in districts with high infant mortality
rates to those born in districts with lower infant mortality rates,
conditional on the other covariates including the year-month of birth.
However, districts with a high infant mortality rate may be systematically
different from districts with a low infant mortality rate. Indeed, subsection
3.1 shows that the population in high infant mortality rate districts is
likely to be of lower socio-economic class. To take this into account, we next
add district fixed effects, only exploiting variation in infant mortality
rates within districts over time. Such district fixed effects specifications
ensure that the parameters are identified only off of within-district
variation, comparing individuals born in the same district, yet exposed to
different infant mortality rates depending on their year of birth.

#### Family Fixed Effects:

Whereas district fixed effects go a long way in controlling for socio-economic
differences across districts, the socio-economic composition of districts may
change over time. As such, an increase in the infant mortality rate may still
reflect an increase in regional poverty, which may be driving the results from
the regressions above. We therefore also exploit the fact that the UK Biobank
includes a sample of full siblings, meaning we can look at variation within
full-sibling pairs from the same family. The family fixed effects
specification is, given by:

$\displaystyle Y_{ijd}=\alpha$
$\displaystyle+\beta_{1}IMR_{ijd,t=0}+\beta_{2}IMR^{2}_{ijd,t=0}+\beta_{3}PGS_{ij}+\beta_{4}PGS^{2}_{ij}$
$\displaystyle+\beta_{5}PGS_{ij}\times
IMR_{ijd,t=0}+\beta_{6}PGS^{2}_{ij}\times IMR^{2}_{ijd,t=0}$
$\displaystyle+\gamma\mathbf{X_{ij}}+f(IMR,PGS,\mathbf{X})+\eta_{j}+u_{ijd}$
(2)

where $\eta_{j}$ are the family fixed effects; the other variables are defined
above. As such, Equation (4) exploits the fact that some individuals are born
in years with low infant mortality rates, but their siblings, who largely
share the same family environment, may be born in years with higher or lower
infant mortality rates. Hence, this specification exploits variation in infant
mortality, holding any other time-invariant (observed or unobserved) family
characteristics fixed and as such accounts for any confounders that differ
between households that may bias the estimates from Equation (4).

An additional advantage of the sibling sample is that estimation of the
genetic effects is purged from endogeneity concerns. That is, even though
one’s genotype is fixed at conception and therefore by definition not affected
by early-life circumstances, parental genotypes can act as potential
confounders. More specifically, the parental genotype can shape the
environment in which children are raised, also known as ‘genetic nurture’, and
therefore act as a potential confounder in the relationship between one’s own
genotype and the outcome (see, e.g., Kong et al., 2018; Wertz et al., 2019).
Whilst this may be more of a concern for polygenic scores related to socio-
economic outcomes such as educational attainment, we cannot rule out
endogeneity of the polygenic score for heart disease. Controlling for family
fixed effects exploits only variation in genetic variants within sibling
pairs, the allocation of which is randomly assigned by Mendel’s law.

In sum, given the random inheritance of genetic variants within families, the
coefficients $\beta_{3}$ and $\beta_{4}$ in Equation (4) capture the direct
genetic effect. Moreover, since our environmental measure – the infant
mortality rate in the district and year of birth – is uncorrelated with our
polygenic score in the within-family analysis (see Section 3.2), we are able
to estimate a genuine ‘$G\times E$’ interaction, as opposed to spurious
‘$G\times G$’ or ‘$E\times E$’ due to e.g., gene-environment correlations or
genetic nurture.141414We show in subsection 3.1 in Appendix A that the infant
mortality rate is uncorrelated to a wide range of other polygenic scores, and
similarly, that the polygenic score for heart disease is uncorrelated with a
range of alternative early life environments (subsection 3.1). Indeed, finding
evidence of systematic $rGE$ between the infant mortality rate and alternative
polygenic scores would suggest that our $G\times E$ estimate may in fact be
picking up a gene-gene interaction effect. Vice versa, strong correlations
between the polygenic score for heart disease and alternative early life
environments would suggest that our $G\times E$ estimate may instead be
capturing an environment-environment interaction effect. Our analysis shows no
strong evidence of $rGE$ in either case, with a wide range of polygenic scores
and early life environments, reinforcing the argument that we are identifying
genuine $G\times E$ interactions.

Having said that, we acknowledge that we cannot claim to estimate the causal
effect of the infant mortality rate in one’s district and year of birth. Even
though the district– and family fixed effects specifications allow us to rule
out that the infant mortality rate is merely picking up district- or family-
specific time-invariant (socioeconomic) conditions, and so we are confident
that the infant mortality rate reflects early-life environmental conditions,
we cannot open the black box of which early-life environmental factors are the
driving causal mechanism. In other words, it is likely that the infant
mortality rate proxies for other early life ‘environments’.

Finally, whereas the sibling sample is helpful to reduce endogeneity concerns,
a downside is that it is significantly smaller compared to the main sample.
There are approximately 33,000 siblings in our analysis sample; substantially
less than the full sample. This has two (related) implications, both leading
to a loss of power. First, using the smaller sample will directly inflate the
standard errors. Second, including family fixed effects means we are only
exploiting variation within families. As most siblings are born relatively
close together and infant mortality rates do not change dramatically over the
course of a few years within a given district, there is relatively little
variation in infant mortality rates over time within the same family. Hence,
for both these reasons, the within-family analysis has much less power than
the between-family analysis, and this will be reflected in the standard
errors. We therefore focus more on the magnitude of the estimates, as opposed
to its precision.

##  5 Results

#### Baseline specification:

Column 1 in section 5 presents the estimates from a simple regression of the
binary indicator of ischaemic heart disease on the infant mortality rate in
the district and year of birth as well as its square, controlling for gender
and year $\times$ month of birth dummies. This confirms the results in Barker
and Osmond (1986), showing a significant relationship between adverse early
life conditions and later life cardiovascular health. In fact, the estimates
suggest an inverse U-shaped association. Column 2 shows the predictive power
of the polygenic score, corroborating the findings from existing GWAS and
showing a significant positive relationship between the polygenic score for
heart disease and the individual diagnosis, which is stronger for those with
higher polygenic scores. Including the infant mortality rate and the polygenic
score simultaneously, as in column 3 and column 4, shows effects of similar
magnitude. Column 4 further adds the interaction term between genetic
variation and the early life environment, showing a positive and significant
effect, with no evidence of non-linearities in the squared interaction. In
other words, an increase in the infant mortality rate in the district and year
of birth increases the probability of being diagnosed with ischaemic heart
disease by more for those with a high polygenic score for heart disease.

Table 2: Gene-environment interplay for ischaemic heart disease

Notes: Covariates include gender and all year$\times$month of birth dummies.
‘Mean’ is the mean of the dependent variable. Robust standard errors clustered
by district in parentheses. * $p<0.1$, ** $p<0.05$, *** $p<0.01$.

Figure 4 graphically presents the regression results from column 4 in section
5, showing a fanning out of the regression lines. In other words, a less
advantageous early life environment is associated with an increased
probability of being diagnosed with ischaemic heart disease. However, the
magnitude of this effect increases substantially with individuals’ polygenic
score: the association with the infant mortality rate is much stronger for
those with higher genetic risk for developing ischaemic heart disease. The
graph also suggests that genetic risk plays a minor role in the probability of
being diagnosed with ischaemic heart disease among those exposed to low infant
mortality rates at birth, with little variation in the outcome of interest
across polygenic scores for those born into low infant mortality rate
districts. This suggests that one’s genetic predisposition matters less for
the development of ischaemic heart disease among those born in advantageous,
healthier environments.

Figure 4: $G\times E$ interplay for Ischaemic Heart Disease; full sample

To explore more flexible non-linearities in the gene-environment interaction
effect, we next plot the relationship between the residualised outcome and the
infant mortality rate at birth using local polynomial plots, where the
residual is obtained from a regression of ischaemic heart disease on the
covariates listed in Equation (4). The left hand panel of Figure 5 shows this
for the quintiles of the polygenic score, whereas the panel on the right
presents it for the polygenic score deciles. This shows similar trends to
those in Figure 4, illustrating a diverging pattern as the infant mortality
rate increases. Again, the figure suggests that one’s genetic predisposition
makes little difference in the probability of developing ischaemic heart
disease among those born in more advantageous environments, yet plays a major
role when exposed to an adverse early life environment.

Figure 5: $G\times E$ interplay for Ischaemic Heart Disease, local polynomial
plots; full sample

#### District Fixed Effects:

To explore the extent to which these coefficients are capturing unobserved
differences across districts in infant mortality rates and later life
ischaemic heart disease, column 5 in section 5 adds district of birth fixed
effects, taking into account that, e.g., districts with high infant mortality
rates are systematically poorer than districts with lower infant mortality
rates. Hence, we now only exploit variation in infant mortality rates within
districts over time, comparing individuals born in the same district but
exposed to different rates of infant mortality due to their year of birth. The
findings suggest that over half of the main infant mortality rate effect is
due to time-invariant differences between districts. A one standard deviation
increase in infant mortality rates in the district and year of birth is
associated with a 0.5 percentage point (4%) increase in the probability of
being diagnosed with ischaemic heart disease (with its square being
insignificantly different from zero). The coefficients on the polygenic score
and the interaction do not change with the addition of district fixed effects,
suggesting there is no systematic variation in these variables across
districts, once we account for the main effect of the infant mortality rate.

#### Family Fixed Effects:

subsection 3.1 reproduces the regression results for the full sample without
district fixed effects (columns 1–3); the results for the reduced sibling
sample without family fixed effects (columns 4–6), and the results for sibling
sample including family fixed effects (columns 7–9). Reducing the sample size
to include only siblings causes a substantial increase in all standard errors,
yet does not lead to big changes in the estimated coefficients of the infant
mortality rate and the polygenic score. However, the estimate on the
interaction halves, suggesting that the sibling sample is different from the
full UK Biobank. Indeed, the sibling sample is more likely to be born in urban
areas, with more limited representation of the rural districts in England and
Wales. Nevertheless, it remains statistically significant, even on the reduced
sample.

Moving to the estimates from the family fixed effects specifications (columns
7–9), we find a very small reduction in the parameter estimate on the
polygenic score, suggesting that demography and indirect genetic effects do
not play a large role for ischaemic heart disease. However, the coefficient on
the infant mortality rate approaches zero and is no longer significant. This
therefore suggests that the infant mortality rate partially proxies for other
unobserved characteristics that vary between families. Once these are
accounted for via the family fixed effects, the coefficient on the infant
mortality rate is no longer significant. The interaction effects, however,
remain very similar to the point estimate in the between-family analysis
(column 6). In other words, the results suggest that the infant mortality rate
does not increase the probability of being diagnosed with ischaemic heart
disease for those with an average polygenic score, yet that it does increase
this probability for those with a high polygenic score. In the other
direction, a one standard deviation increase in the polygenic score increases
the probability of being diagnosed with ischaemic heart disease, and this
effect is larger for those exposed to higher infant mortality rates at birth.

Table 3: Restricting the sample size and including family fixed effects

Notes: Columns (1)-(3) show robust standard errors clustered by district on
the full sample. Columns (4)–(6) show robust standard errors clustered by
district on the sibling sample. Columns (7)–(9) use two-way clustering by
family and district on the sibling sample. * $p<0.1$, ** $p<0.05$, ***
$p<0.01$.

Figure 6 shows the non-linear local polynomial plots from the residualised
outcome, as in Figure 5 above, but now also residualised for family fixed
effects. This again shows rising divergence in ischaemic heart disease with
increasing infant mortality rates. The probability of developing ischaemic
heart disease is very similar for those exposed to low infant mortality rates
at birth, with one’s polygenic score being more important for those exposed to
high infant mortality rates.

Figure 6: $G\times E$ interplay for Ischaemic Heart Disease, local polynomial
plots; sibling sample, accounting for family fixed effects.

#### Potential mechanisms:

To examine potential mechanisms for the significant interaction between the
polygenic score for heart disease and the infant mortality rate in early life,
we investigate whether and how the main and interaction effects are associated
with specific behaviours and other outcomes that also associate with heart
disease, including BMI, blood pressure, height, drinking and smoking. Indeed,
if the main and interaction effects show no association with these alternative
outcomes, they cannot be a mediator in the relationship between infant
mortality, polygenic scores and heart disease. We define smoking and drinking
as dummy variables that equal to one if the individual currently smokes or
drinks, or has done so in the past, and zero otherwise.

subsection 3.1 presents the estimates, where the first column for each outcome
only controls for the covariates specified in Equation (4) and the second
additionally accounts for family fixed effects, as in Equation (4). All
analyses are based on the sibling sample and specify the polygenic score for
heart disease as the variable capturing the genetic component. Examining first
the estimates that do not control for family fixed effects, we find that the
infant mortality in one’s year and district of birth has an inverse U-shaped
association with BMI and systolic blood pressure, as well as a U-shaped
relationship with height in adulthood. There are no strong differences in
drinking and smoking among those born in areas characterised by different
infant mortality rates. Once we control for family fixed effects, however,
these associations reduce in magnitude and are generally no longer
significantly different from zero, though there is some evidence of marginal
non-linearities in BMI and systolic blood pressure.

Furthermore, we find that the polygenic score for heart disease predicts
variation in BMI, blood pressure, height, alcohol and smoking status, with the
estimates suggesting these associations are generally linear. When controlling
for family fixed effects, the estimates reduce, but they all remain
significantly different from zero. Finally, the coefficients on the $G\times
E$ terms in the analyses that do not include family fixed effects are
significant only for BMI and diastolic blood pressure, with no evidence of any
non-linearities, where a higher polygenic score for heart disease is
protective against the adverse effects of increased infant mortality rates.
However, including family fixed effects renders them insignificantly different
from zero. In other words, we find no strong evidence that BMI, blood
pressure, height, drinking, and smoking mediate the relationship between
polygenic scores and infant mortality rates in the development of ischaemic
heart disease.

Notes: BMI is measured as weight (in kilograms) divided by height (in metres
squared). DBP add SBP denote diastolic and systolic blood pressure,
respectively, and are measured in mmHg (millimeters of mercury). Height is
measured in cm. Alcohol and smoking are dummy variables indicating whether the
individual currently smokes or drinks. ‘Mean’ is the mean of the dependent
variable. Robust standard errors clustered by district (column 1) and family
and district (column 2) in parentheses. * $p<0.1$, ** $p<0.05$, *** $p<0.01$.

##  6 Robustness checks

This section explores the sensitivity of our main findings to a set of
robustness checks. First, we explore whether there are gender differences in
the relationship between the early life infant mortality rate and later life
heart disease. Second, we test the sensitivity of our findings to controlling
for additional covariates at the district level. Third, we explore the use of
an alternative polygenic score for heart disease, constructed from the summary
statistics of our tailor-made GWAS on the non-siblings (and non-relatives) of
the UK Biobank. Fourth, we examine the robustness of our results to specifying
the main and interaction effects as linear predictors (i.e., not allowing for
non-linearities in $PGS$ and $IMR$). Fifth, we compare our analysis to those
that use the infant mortality rate in the years around birth (as opposed to
the year of birth) as the main variable of interest. Other than our first
robustness check, all are based on the family fixed effects specification.

###  6.1 Gender differences

We start by exploring whether the relationship between ischaemic heart disease
and infant mortality rates at birth is similar for men and women. subsection
6.1 shows slightly larger estimates for men compared to women, possibly
reflecting a higher incidence of heart disease among men. In addition, we find
that the polygenic score is more predictive among men, with the magnitude of
the interaction term also being slightly larger. Nevertheless, for both groups
do we find evidence of main as well as interaction effects for ischaemic heart
disease. Furthermore, we find evidence of non-linearities of the interaction
term for women, though not for men.

Table 5: $G\times E$ interplay for ischaemic heart disease, by gender

Notes: These analysis are based on the full UK Biobank analysis sample,
controlling for district fixed effects in columns (5) and (10). ‘Mean’ is the
mean of the dependent variable. Robust standard errors clustered by district
in parentheses. * $p<0.1$, ** $p<0.05$, *** $p<0.01$.

###  6.2 Controlling for additional district-level covariates

As shown in subsection 3.1, the infant mortality rate is likely to capture
variation in socio-economic characteristics. Although the district fixed
effects capture some of this variation, it may not be able to account for
time-varying characteristics that correlate with the infant mortality rate. We
therefore next explore whether our analysis is robust to the inclusion of a
set of variables that vary over time within districts. For this, we include
district-level illegitimacy, birth and death rates in the regressions, as
measured during individuals’ year of birth, aiming to capture some socio-
economic differences between districts. Column (1) of subsection 6.2
replicates the main estimates from column (9) of subsection 3.1 for
comparison. Column (2) reports the estimates that control for the additional
covariates, showing very similar coefficients on the infant mortality rate,
the polygenic score, and the interactions, with no significant effects of the
additional covariates.

Table 6: Robustness analyses of gene-by-environment interplay for ischaemic
heart disease

Notes: All estimates are from family-fixed effects specifications.
‘Covariates’ refers to gender and year-month dummies; ‘PCs’ are the first 10
principal components of the genetic relatedness matrix. ‘Additional controls’
refer to the district-level covariates illegitimacy rate, birth rate and death
rate, added in Column (2). Robust standard errors in parentheses, clustered by
family and district throughout. Column (4) uses a polygenic score constructed
from our own GWAS on non-siblings of the UK Biobank. All analyses control for
the interactions between $IMR$ and $X$ and between $PGS$ and $X$. * $p<0.1$,
** $p<0.05$, *** $p<0.01$.

###  6.3 Alternative polygenic score

The polygenic score used in our main analysis is constructed from the summary
statistics in Nikpay et al. (2015). It is well-known, however, that the GWAS
estimates are specific to the environmental context and demographic
characteristics of the discovery sample (see e.g., Domingue et al., 2020). We
therefore examine the sensitivity of our findings to the use of an alternative
polygenic score, constructed using the summary statistics from our own tailor-
made GWAS on the non-siblings of the UK Biobank sample (also excluding any
other relatives of the sibling sample). The latter ensures that the discovery
sample is independent of the analysis sample. It also ensures that the two
samples are from the same environmental context, likely increasing the
predictive power of the polygenic score.

Column (3) of subsection 6.2 presents the results. Although the main effects
of the infant mortality rate are not substantially affected, we find a
somewhat larger main effect of the polygenic score. In addition, the estimate
on the interaction term between the polygenic score and the infant mortality
rate is larger. These findings confirm the existence of important interplay
between the early life environment and individuals’ genetic predisposition in
the development of heart disease in older age.

###  6.4 Modelling $G$ and $E$ as linear effects

Our main analysis allows for non-linear (quadratic) effects in the infant
mortality rate at birth, the polygenic score, as well as their interaction. To
explore the robustness of our findings, we next re-estimate Equation (4) but
restrict the main effects as well as the interaction terms to enter the
function linearly. The estimates, presented in Column (4) of subsection 6.2,
are very similar to the specification that allows for non-linearities, and
show evidence of an interaction effect between the infant mortality rate in
one’s year and district of birth and one’s polygenic score in shaping
individuals’ probability of being diagnosed with heart disease in later life.

###  6.5 Exploring timing of the infant mortality rate

Barker and Osmond (1986) show similar correlations between ischaemic heart
disease in adulthood and regional infant mortality rates, irrespective of
whether neonatal or postneonatal infant mortality rates are used. We here
explore whether our estimates are robust to different timings of the
measurement of the infant mortality rate, ranging from four years before birth
to four years after birth. Figure 7 and Figure 8 show the estimates,
respectively, of the main infant mortality rate and the $G\times E$
interaction effects on the vertical axis, with the horizontal axis denoting
the timing relative to the year of birth.151515For ease of interpretation and
plotting of the estimates, we here model the probability of being diagnosed
with ischaemic heart disease as a function of $G$, $E$, and $G\times E$, but
do not allow for the squared terms. The estimates in Figure 7 are based on a
model that includes district fixed effects, whilst the estimates in Figure 8
are based on a model that includes family fixed effects). The dashed
horizontal line is the estimate from the analysis above, using the infant
mortality rate measured in the year of birth; the solid line is at zero.

Figure 7: Exploring the timing of the infant mortality rate for Ischaemic
Heart Disease; full sample, controlling for district fixed effects. Figure 8:
Exploring the timing of the infant mortality rate for Ischaemic Heart Disease;
sibling sample, controlling for family fixed effects.

The left panel of Figure 7 suggests that it matters when the infant mortality
rate is measured. Using the infant mortality rate in the year of birth or the
year prior to birth (we loosely refer to this as the year of pregnancy), the
estimates are almost double the size compared to using earlier or later years,
though we cannot statistically distinguish them from each other. The evidence
is suggestive however that the prenatal and the first year of life are the
most important in shaping later-life disease risk. The right panel of Figure 7
shows that the interaction term is largest in magnitude for the infant
mortality rate in the year of birth, although the other years show a
remarkably consistent interaction effect that differs significantly from zero
in all specifications.

Figure 8 shows the estimates from the family fixed effects specification.
These suggest no significant main effects of the infant mortality rate in the
year of birth, but a significant positive effect for the infant mortality rate
in the year of pregnancy. Taken at face value, this suggests that the prenatal
period is more important than the neonatal period in shaping later-life
disease risk. The interaction estimates, shown in the panel on the right, do
not suggest that the timing of measurement matters, with the estimates being
relatively constant across the different specifications. All show positive
interaction effects, confirming that the early life environment interacts with
individuals’ genetic risk for heart disease. However, the fact that the
estimates are similar across the different specifications suggests that the
effects are not driven by factors that are specific to the year of birth.
Instead, it looks like the infant mortality rate is a broader indicator of
adverse environments in early life.

##  7 Conclusion

The Barker hypothesis states that adverse circumstances in the prenatal and
early life period can lead to disease in older age. We confirm this hypothesis
in the UK Biobank, using a large sample of individuals in England and Wales
born between the 1930s and 1970s. We then explore whether this relationship
holds exploiting only variation within local areas or variation within
families, and we investigate any heterogeneity of the association with respect
to individuals’ genetic predisposition. Our analysis shows a strong inverse
U-shaped correlation between the local infant mortality rate at birth and
later life heart disease. When using only variation in exposure to infant
mortality rates and polygenic scores within siblings, we show that the
correlation is not significant for those with an average polygenic score, but
only exists among individuals with the highest genetic risk for developing
heart disease. Although we still interpret our estimates as correlational
rather than causal, our findings do show that gene-environment interplay in
heart disease is strong and robust to the inclusion of district and family
fixed effects.

Our findings suggest that improvements in the early life environment reduce
the role of genetic risk for heart disease. More specifically, being exposed
to low infant mortality rates in one’s year and region of birth reduces the
risk of being diagnosed with ischaemic heart disease, with very little
variation in this risk between those with high or low genetic predispositions
for heart disease. In contrast, being exposed to high infant mortality rates
increases the risk of ischaemic heart disease, and this risk is increased even
further among those with a high genetic predisposition for heart disease.

One important issue to take into account in the interpretation of the results
is selection and scarring. First, it is well known that the UK Biobank is not
representative of the UK population, with its participants being more likely
to live in urban areas and being of higher socio-economic status, on average
(Fry et al., 2017). This will affect the generalisability of our findings.
Indeed, we cannot simply extrapolate our results to the full UK population,
though our analysis shows that the positive correlation between infant
mortality at birth and disease in old age also holds for this slightly higher
SES (UK Biobank) population. If anything, we would expect this relationship to
be stronger among individuals of lower SES, suggesting that we may
underestimate the effects of interest. Second, individuals born in districts
with high infant mortality rates may not be observed in the data, as they may
have already passed away. If this is the case, our sample is a selected
(healthier) sample of individuals, meaning that our estimates are likely to be
a lower bound. Third, individuals born in districts with high infant mortality
rates may have been ‘scarred’ in early life because of their exposure to an
adverse environment. In fact, this is one of the potential mechanisms
suggested in the literature, that adverse conditions in early life may lead to
permanent changes in the body structure, physiology and metabolism. Indeed,
this is what we would likely be capturing in the analysis.

Our findings have at least two implications. First, our findings clearly show
that ‘genetic determinism’, or the idea that human behaviour is controlled by
an individual’s genes with no role for non-genetic/environmental influences,
is incorrect. Indeed, we find strong evidence that ‘nature’ and ‘nurture’
complement each other. Our findings therefore suggest a more nuanced
understanding of later life health, in which the effect of one’s genetic
predisposition depends on the environment in which individuals live and grow
up (see also, e.g., Caspi, 2010). This suggests that improving the early life
environment not only reduces the mean prevalence in heart disease, but also
the variation in heart disease for those with different genetic
predispositions. This is an interesting contrast with findings for education
and cognition outcomes, where genetic effects are typically weaker in deprived
environments (Heath et al., 1985; Tucker-Drob and Bates, 2016; Harden, 2021).

Second, despite the fact that our study looks at infant mortality rates
between the 1930s and 1970s, our findings do have policy relevance. Indeed,
they suggest that improving the early life environment for children born now,
will likely positively affect individuals’ future health. Although we cannot
pinpoint exactly what the infant mortality rate captures, and therefore what
the main cause is of the change in disease prevalence in older age, our
findings do suggest that a general improvement in such early life
circumstances have long-run effects on the health and well-being of the
population, in particular among those with high genetic risk for developing
heart disease.

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## Appendix A: Gene-environment correlation

To further explore whether gene-environment correlation ($rGE$) may be driving
our results, subsection 3.1 presents the same (between-family and within-
family) figures as subsection 3.1, focusing on the sibling sample, but instead
of using the polygenic score for heart disease, we use polygenic scores that
are relevant to other traits. In subsection 3.1, we show the correlation
between the polygenic score for heart disease and alternative early life
environments. Indeed, if the environment of interest (the infant mortality
rate), is somehow correlated to one’s other polygenic score, or if one’s
polygenic score for heart disease is somehow correlated to other early life
environments, it is unclear whether the environment-coefficient partially
captures one’s genetic predisposition, and vice versa, through $rGE$. In that
case, it is unclear whether the coefficient on the gene-environment
interaction ($G\times E$) is truly a $G\times E$, or whether it captures a
$G\times G$ or $E\times E$.

For example, if individuals with a high polygenic score for educational
attainment are more likely to select into healthier and wealthier environments
(see e.g. Abdellaoui et al., 2019), we may see a negative correlation between
the polygenic score for educational attainment and the infant mortality rate.
It is then unclear whether the $G\times E$ effect in the main analysis
captures a true interaction effect between the polygenic score for heart
disease and the early life infant mortality rate, or whether this is partially
capturing an interaction between the the polygenic score for heart disease and
the polygenic score for educational attainment ($G\times G$). Similarly, if
individuals with higher polygenic scores for heart disease are more likely to
live in areas characterised by higher illegitimacy rates, it is unclear
whether the interaction effect captures a true $G\times E$ effect, or whether
this picks up an interaction between the infant mortality rate and the
illegitimacy rate ($E\times E$).

As genetic variants are randomly allocated within families, we do not
encounter this interpretational issue with the coefficient on the polygenic
score in the within-family analysis. Indeed, the coefficient on the genetic
term in the within-family analysis captures a genetic effect161616Though note
that the genetic effect is potentially attenuated; see Trejo and Domingue
(2019). In the between-family analysis, it may capture either a genetic or
environmental effect; the latter via genetic nurture or $rGE$.

subsection 3.1 shows the gene-environment correlations between the infant
mortality rate at birth and the polygenic scores for BMI (using GWAS summary
statistics from Locke, 2015), educational attainment (23andMe), and height
(Locke, 2015). The left panel of each row of graphs shows the raw correlation
between the infant mortality rate and the relevant polygenic score for the
sibling sample. The right panel shows this same relationship exploiting only
within-family variation, i.e., taking the residuals of the variable shown on
the axes after taking out the family fixed effect.

Figure A.1: Correlation between infant mortality rates, and the polygenic
scores for BMI, educational attainment and height in the sibling sample
without (column 1) and with (column 2) family fixed effects.

subsection 3.1 shows the gene-environment correlations between the polygenic
score for heart disease and alternative district-level variables: the
district-level illegitimacy rate, birth rate and death rate. The left panel
again shows the raw correlation for the sibling sample, whilst the panel on
the right only exploits within-family variation.

For both subsection 3.1 and subsection 3.1, the slope of the lines are close
to zero. In other words, we find no evidence of any strong gene-environment
correlation for any of the polygenic scores and early life environments used
here. This again is reassuring, suggesting we are identifying true $G\times E$
interactions, rather than spurious $G\times G$ or $E\times E$ interactions.

Figure A.2: Correlation between the polygenic score for heart disease and
three alternative early life environments: the district-level illegitimacy
rate, birth rate and death rate using the sibling sample without (column 1)
and with (column 2) family fixed effects.