Andrew E Clark
email: andrew.clark@ens.fr.ad

Tom Lee
email: tom.lee@ifs.org.uk

Early-life correlates of later-life well-being: Evidence from the Wisconsin Longitudinal Study *

Keywords: Life-course, well-being, eudaimonia, health, happiness

We here use data from the Wisconsin Longitudinal Study (WLS) to provide one of the first analyses of the distal (early-life) and proximal (later-life) correlates of older-life subjective well-being. Unusually, we have two distinct measures of the latter: happiness and eudaimonia. Even after controlling for proximal covariates, outcomes at age 18 (IQ score, parental income and parental education) remain good predictors of well-being over 50 years later. In terms of the proximal covariates, mental health and social participation are the strongest predictors of both measures of well-being in older age. However, there are notable differences in the other correlates of happiness and eudaimonia. As such, well-being policy will depend to an extent on which measure is preferred.

Introduction

The improvement of individuals' lives should arguably be the fundamental task of policy-makers. There are actually two distinct parts to this task -how to measure the quality of life, and how policy can best affect it. In recent years, there has been a great deal of work that has appealed to subjective well-being to reflect overall life quality. 1 Although this work has led to fascinating insights about what is important for well-being, there is still no consensus about which well-being measure is actually the most appropriate.

There are loosely-speaking three types of subjective well-being measures -eudaimonic, 2 hedonic and cognitive. Although these are undoubtedly different in their focus, if those who fare well according to one measure also do so across all the others, then it may not greatly matter which measure is retained. By comparing the results from different measures for the same individuals, we can ascertain just how inter-related these different measures are. [START_REF] Clark | Is happiness different from flourishing? Crosscountry evidence from the ESS[END_REF] compare a number of different measures in the well-being module of Wave 3 of the European Social Survey (ESS), and find a broadly similar covariate pattern for happiness, life satisfaction and eudaimonia (in that the explanatory variables seem to be correlated in the same way with all the well-being measures). A similar approach is taken in Clark (2016a) for a number of different well-being measures found in the British Household Panel Survey (BHPS), ESS and the Office of National Statistics' National Well-Being Programme. Again, fairly high correlations are found across a variety of measures, especially within country.

Other work has come to different conclusions. In particular, the estimated coefficients on the Cantril ladder (asking individuals about where they stand between their worst possible and best possible life) and Eudaimonia in the Gallup World Poll in [START_REF] Graham | Bentham or Aristotle in the Development Process? An Empirical Investigation of Capabilities and Subjective Well-Being[END_REF] are not particularly strongly correlated. It seems fair to conclude that the question of the relationship between well-being variables remains open. OECD (2013) concludes that "eudaimonic measures of well-being cap-ture important aspects of people's subjective perceptions about their own well-being that are not covered by either life evaluations or affect"3 (p.32).

Most work in subjective well-being has used cross-section or panel data on adults (such as the BHPS or the German Socio-Economic Panel, SOEP). This has allowed the calculation of correlations between adult well-being and current outcomes and/or events that occurred to the individual in the relatively recent past. We can broadly call these proximal variables. 4 A more recent area of study has relied on long-run cohort studies to examine the effect of covariates in the distant past (called distal variables) on current outcomes. We do of course here require data in which the distal outcomes are measured at the time when they occurred, not as the adult recalls them many years later. This has led to the use of birth-cohort datasets, where respondents are followed from birth or young age through to adulthood. It is important to distinguish the proximal and distal correlates of well-being for policy reasons. If many of the proximal variables (like income and education, say) are mainly determined by distal variables (perhaps school quality or parental income), then policy should focus on the latter rather than the former.

A number of contributions here have emphasised the importance of early childhood for adult outcomes such as income and education. 5 The analysis of well-being in this context is much scarcer. Without providing an exhaustive list, some examples include [START_REF] Layard | What predicts a successful life? A life-course model of well-being[END_REF], who combine distal and proximal factors in a life-course model of adult outcomes, including well-being (see also [START_REF] Clark | The Origins of Happiness: The Science of Well-Being over the Life-Course[END_REF]. [START_REF] Frijters | Does childhood predict adult life satisfaction? Evidence from British cohort surveys[END_REF] also look at the predictors of adult life satisfaction using data from two British birth-cohort surveys. They find that childhood variables predict 7% of the variation in adult life satisfaction, whilst adding contemporaneous variables increases this predictive power to 15.6%. [START_REF] Fletcher | Origins of adulthood personality: The role of adverse childhood experiences[END_REF] find that maltreatment in childhood is a strong predictor of neuroticism and conscientiousness in adulthood. Mental and physical health appear to be the key channels through which adverse childhood experiences affect later-life outcomes. Last, [START_REF] Wehner | Personality and mental health: the role and substitution effect of emotional stability and conscientiousness[END_REF] show that low adolescent emotional stability is associated with poor adult mental health.

A key driver of this channel is conscientiousness -individuals who score badly on this metric are particularly affected by a lack of emotional stability.

We here contribute to this literature by providing one of the first analyses of well-being in older age (72). Our use of a very long panel allows us to establish the distal and proximal correlates of well-being over 50 years apart. We can thus address the question of whether the distal determinants of adult well-being fade away over time. 6 Last, we are able to compare our results over two different well-being measures (happiness and eudaimonia), finding a number of differences.

The remainder of the paper is organised as follows. Section 2 presents the WLS data and the construction of our key variables. Section 3 then presents the main results regarding the distal and proximal correlates of older-age well-being, and discusses the mediating role of adult outcomes in the effect of the childhood variables. Last, Section 4 concludes.

Data

Our data here come from the WLS, a cohort study that has been run from 1957 to the present day in Wisconsin, USA (see [START_REF] Herd | Cohort Profile: Wisconsin Longitudinal Study (WLS)[END_REF]. The initial data collection took place before high-school graduation in 1957 (so that the majority of the cohort were born in 1938/1939). This cohort has been periodically re-surveyed over time. Of particular interest are the last three waves (1993, 2004 and 2011), which contain multiple well-being measures for the respondent at ages of roughly 54, 65 and 72. Both the long age span of the study and the multiple well-being measures make the WLS an attractive dataset for the purpose of our research. The majority of cohort datasets used to analyse well-being are more recent. Some exceptions are the Douglas Cohort Study, which started in 1946, the The Lothian Birth Cohorts of 1921 and1936 (http://www.lothianbirthcohort.ed.ac.uk/) and the Aberdeen Birth Cohorts of the same two years (https://www.abdn.ac.uk/birth-cohorts/). Representative panel studies such as the BHPS (started in 1991) and the SOEP (started in 1984) provide invaluable data on contemporaneous outcomes but suffer from the relatively late starting years.7 Of course individuals born in 1938 may be very different to those born post-World War Two -this issue can be examined as other cohorts reach comparable ages in the future.

One drawback with the WLS is that it is not a birth cohort, as data collection started only on high-school graduation at age 18. As such, although the age-18 covariates may be good proxies for childhood, they are not as complete as those that are available in the UK British Cohort Study (BCS) or National Child Development Study (NCDS). There nonetheless remains more than sufficient adult data for analysis. The fact that the cohort is restricted to high-school graduates again implies that we should be careful about making generalisations to the whole population.

A second issue is that only the three most-recent waves (1993, 2004 and 2011) contain well-being information. It is therefore not possible to see how well-being evolves over all of adult life (the earliest well-being observation is at age 54). In addition, the same well-being/covariate questions are not asked across the three years. Happiness (and health covariates) only appear in the 2004 and 2011 waves, whereas measures of eudaimonia are only available in 1993 and 2011. As a result, we will here mostly present results from 2011 well-being (when the respondents were age 72) and will use the 2004 happiness information to check for continuity over time.

We now turn to the key variables used in the analysis, starting with well-being before moving on to the distal and proximal control variables.

Well-being

We focus on two well-being measures: happiness and eudaimonia.8

Happiness

The happiness variable comes from the emotion component of the Health Utilities Index (HUI). Individuals were first asked if they felt happy/unhappy with their life over the past 4 weeks. If the individual replied "happy" they were asked if they were somewhat happy or happy and interested in life. If unhappy, they were asked if they were somewhat unhappy, very unhappy or so unhappy that life is not worthwhile. The resulting happiness variable is then measured on a 1 to 5 scale ranging from "So unhappy that life is not worthwhile" to "Happy and interested in life". Figure 1 shows the distribution of happiness. It is evident that the majority of respondents chose the top happiness category, reducing the variation in this outcome. Even so, there is enough variation to allow us to examine the covariates that are associated with reporting being less than "happy and interested in life"

Eudaimonia

The eudaimonia scale comes from a psychological well-being model devised by Carol [START_REF] Ryff | Happiness is everything, or is it? Explorations on the meaning of psychological well-being[END_REF]. Questions are asked about six different aspects of life -autonomy, environmental mastery, personal growth, positive relations with others, purpose in life and self-acceptance -with the answers being combined to form a composite eudaimonic measure. Although the theoretical justification for the particular aspects chosen is debated in the literature, eudaimonia will prove a useful complement to the hedonic/cognitive happiness measure.9

Figure 2 shows the distribution of the Ryff psychological well-being scale. It is clear that there is much more variation here than the happiness measure above, with there being is no clear bunching at any one score.

Covariates

Table 1 presents the summary statistics of the distal covariates for three different attrition sub-groups.10 Sample attrition is a concern, as the number of everyquestion respondents (3086) is considerably lower than the initial number of individuals surveyed (10317) and every-question respondents have different characteristics to dropouts. Across all covariates, the Every-question Respondent sub-group is different to both of the others, meaning that an uncorrected statistical analysis that only focuses on 2011 respondents will lead to biased estimates. Probability weighting [START_REF] Wooldridge | Inverse probability weighted M-estimators for sample selection, attrition, and stratification[END_REF] and multiple imputation [START_REF] Rubin | Multiple imputation for nonresponse in surveys[END_REF]) will be used to address this problem.11

Regarding the last panel of Table 1, there is a substantial literature looking at the predictors of premature mortality in the WLS. Of particular interest, is the importance of adolescent IQ in predicting early death. In a well-known contribution, Note: Every-question Respondent refers to the sub-group of respondents who provided information on all of the distal (1957) and proximal (2011) covariates. [START_REF] Gottfredson | Intelligence: is it the epidemiologists' elusive "fundamental cause" of social class inequalities in health?[END_REF] argued that early-life intelligence in itself was an important determinant of later-life health outcomes. More recent work using the WLS, by Hauser & Palloni (2012) and [START_REF] Maenner | Association between mild intellectual disability and early mortality in men and women: evidence from a population-based cohort study[END_REF], has instead suggested that educational attainment is in fact a stronger predictor of longevity than raw intelligence. Although longevity is not the focus of this paper, we also find that the best early-life predictor of premature mortality is education. A number of contributions have looked at other determinants of mortality using WLS data: Pudrovska & Anikputa (2013) show that although breast-cancer incidence is higher for more-educated women, the mortality rate is lower; [START_REF] Yonker | Hypothalamic pituitary gonadal axis homeostasis predicts longevity[END_REF] find that later onset of the menopause is associated with lower mortality; and in [START_REF] Reither | Predicting Adult Health and Mortality from Adolescent Facial Characteristics in Yearbook Photographs[END_REF] overweight adolescents are twice as likely to suffer premature death as their non-overweight counterparts. Note: These statistics are calculated using the Every-question Respondent sub-group.

Table 2 presents the proximal covariate summary statistics from the Everyquestion respondent sub-group. Most of the variables are fairly self-explanatory, although mental and physical health probably require greater illumination. The summary statistics provide a useful snapshot of the life circumstances of WLS respondents at age 72. A sizeable number of individuals are widowed (12%), the majority have retired (64%) and most are in fairly good health.

Our mental-health variable comes from the CES-D depression scale. 12 This scale 12 Reverse coded so that a high Mental Health score indicates a low CES-D score.

is calculated from 20 questions about how many days over the past week the individual has felt certain emotions (16 negative, 4 positive) e.g. "On how many days during the past week did you feel lonely?". The test was designed in 1977 by Lenore Radloff, not as a test for clinical depression per se, but rather as an indicator of psychological distress. Mental health is different from happiness. In [START_REF] Flèche | Do More of Those in Misery Suffer from Poverty, Unemployment or Mental Illness?[END_REF], mental health and life satisfaction are not particularly strongly correlated (with correlation coefficients between 0.1 and 0.4); the analogous figure in our WLS data is also 0.4. They also do not have the same correlates (as we will see below in our data in Figures 3 and5). We would ideally like to have diagnostic measures of mental health, as often used in [START_REF] Clark | The Origins of Happiness: The Science of Well-Being over the Life-Course[END_REF]. This kind of measure is not available in the WLS. However, the analysis of life satisfaction in [START_REF] Clark | The Origins of Happiness: The Science of Well-Being over the Life-Course[END_REF] using diagnostic mental-health measures yields results that are in line with those using survey mental-health measures (such as the GHQ-12 or the SF-36).

Physical Health is a composite score calculated from six of the eight Health Utilities Index Mark 3 (HUI) measures -vision, hearing, speech, ambulation, dexterity and pain. 13

Results

We now ask how much of the variation across individuals in our two later-life well-being variables can be explained by the distal and proximal correlations. To do so we consider three different specifications: (1) distal-only, (2) proximal-only and (3) both together. All variables (except for gender) are standardised so that their estimated coefficients reveal their relative importance in explaining well-being at age 72. 14 Figure 3 depicts the relationships between the different covariates and happiness, evaluated at age 72 in 2011. 15 Looking at the distal-only specification, we can see that the biggest single happiness effect comes from being female. This gender difference in well-being is well-known in the literature: women often report higher values 13 Emotion (where the happiness well-being measure comes from) and cognition (not directly related to physical health) are the two excluded measures.

14 The squared value of a beta coefficient represents the variable's independent contribution (ignoring its covariance with the other explanatory variables) to the R 2 of the regression. 15 The analogous happiness results at age 65 in 2004 can be found in Appendix E. There are no great differences between the two sets of results.

on cognitive-evaluate measures of well-being than do men, but also higher stress scores (Nolen-Hoeksema and Rusting, 1999: see also [START_REF] Kahneman | High income improves evaluation of life but not emotional well-being[END_REF] and Chapter 6 of the 2015 World Happiness Report). 16 There is also a moderate positive effect from parental education. The fact that variables from 50+ years ago have predictive power over later-life well-being over 50 years later is in itself of interest. The fraction of well-being explained by the childhood variables is only small here. This is not necessarily a reason to neglect the findings as unimportant. If our results here at age 72 are representative of adulthood, then a change in childhood will be felt through every single year of adulthood, making for a large cumulative effect. It may of course even be that childhood factors have a larger effect under the age of 72, in which case we will underestimate the cumulative effect.

The introduction of the proximal variables produces some changes in the distal coefficients (as would be expected if these latter predict the proximal outcomes). The coefficient on Female becomes small and insignificant, indicating that the proximal variables almost entirely mediate the effect of gender. Moreover, the IQ score flips sign from positive to negative. Although intelligence in the reduced form has a small positive coefficient, once we control for the later-life outcomes that are correlated with intelligence, the effect of the IQ score becomes negative. This "residual" effect of IQ on well-being perhaps reflects the greater expectations of those with more education, or that education is associated with reference groups with higher income (to the extent that, despite own higher absolute income, relative income falls). Education was found to be negatively correlated with the GHQ measure of well-being in the first wave of the BHPS in [START_REF] Clark | Unhappiness and Unemployment[END_REF]. In [START_REF] Clark | Rising Aspirations Dampen Satisfaction[END_REF], education was explicitly shown to be positively correlated with how happy the individuals thought that they should be. [START_REF] Oreopoulos | Priceless: the Nonpecuniary Benefits of Schooling[END_REF] find that a rise in the school-leaving age increased the well-being of those affected by the change, although [START_REF] Clark | Does Compulsory Education Really Increase Life Satisfaction? PSE[END_REF] cannot replicate the UK results in BHPS data, with a life-satisfaction effect that is either zero or negative.

Regarding the proximal variables, by far the strongest predictor of age-72 happiness is mental health. This is in line with the results in the UK BCS and NCDS in Flèche et al. (2017) at ages up to the early 50s. A one standard-deviation rise in mental health is associated with 41% of a standard deviation higher happiness. On the contrary, physical health is nowhere near as important. Although there is a positive relationship with happiness, the coefficient is only one-fifth of the size of that on mental health. Other notable proximal factors at the bottom of Figure 1 are unemployment and widowhood, with negative estimated coefficients, and social participation, for which the estimated coefficient is positive.

Figure 4 shows the corresponding coefficients for eudamonia. In the distal-only specification, female, childhood IQ and parental income and education are all associated with higher eudaimonia scores at age 72; with the inclusion of the proximal variables all of these bar parental education continue to have independent effects on later-life eudaimonia. As for happiness above, the coefficient on IQ score flips with the inclusion of proximal variables -providing further evidence consistent with expectation effect. It is unclear quite why parental income should be a good predictor of eudaimonia 50+ years later: it may be the case that higher parental income at a young age is associated with more choice over life-decisions such as education and career path. [START_REF] Graham | Bentham or Aristotle in the Development Process? An Empirical Investigation of Capabilities and Subjective Well-Being[END_REF] find that own income is positively correlated with eudaimonia in Gallup World Poll data. We here find no role for proximal income but only for parental income, perhaps suggesting that income matters more early on in life. The fact that women have higher eudaimonia scores goes against the ESS findings of [START_REF] Clark | Is happiness different from flourishing? Crosscountry evidence from the ESS[END_REF], although it should be underlined that the WLS and ESS eudaimonia measures are not the same, and of course the age ranges are very different.

Looking at the proximal covariates, we again see that mental health is a strong predictor of well-being, as is social participation. This latter may however reflect one of the six components of the eudaimonia score being Positive Relationships. It is worth noting here that social participation is also positively correlated with happiness, where there is of course no mechanical relationship. There is no significant correlation between physical health and eudaimonia, indicating that a sense of flourishing is much more closely linked to mental/social processes.

Unemployment is associated with lower happiness scores, but not with significantly lower eudaimonia. The latter may reveal that labour-force status is less relevant at age 72 than at younger ages. [START_REF] Clark | Is happiness different from flourishing? Crosscountry evidence from the ESS[END_REF] consistently find that the unemployed report lower levels of eudaimonia. Note: The lines around the bars are the 90% confidence intervals. The full regression table appears in Appendix C. The R 2 figures for the distal-only and the distal plus proximal specifications are 0.015 and 0.241 respectively. The proximal-only specification is not shown as the results there are very similar to those in the joint specification.

Two of the most-commonly examined "economic" variables -income and years of schooling -do not have strong effects on well-being. Years of schooling does turn out to be a good predictor of eudaimonia (as in [START_REF] Clark | Is happiness different from flourishing? Crosscountry evidence from the ESS[END_REF] but not of happiness. This may show that education is linked to the ability to make good life decisions. Household income has no independent effect on either happiness or eudaimonia. This is contrary to the ESS results in [START_REF] Clark | Is happiness different from flourishing? Crosscountry evidence from the ESS[END_REF], where income is positively correlated with life satisfaction, happiness and four measures of eudaimonia. It is important to note here that this may reflect income not being a good measure of financial resources in later-life (wealth may matter more at this age). Alternatively, financial concerns in later life may have become of only secondary importance compared to factors such as health and social participation. If this is the case, a speculative conclusion is that some government spending on income support may be usefully diverted to provide a more comprehensive health and social-care system.

In general, the R 2 figures from the distal-only specification are small. However, as noted above, even a small effect over 50 years can end up producing a quite large cumulative contribution. A second point is that our set of distal variables is restricted (as we only start measurement at high-school graduation) and we do not have information on childhood emotional health, which was found to be so important in [START_REF] Clark | The Origins of Happiness: The Science of Well-Being over the Life-Course[END_REF], for example.

Overall, there are a number of differences in the correlates of happiness and eudaimonia in our sample of older Americans. Of the 17 variables in the joint distal and proximal specification (columns 3 and 6 in Appendix Table C1), only two are significant and of the same sign in both the happiness and eudaimonia regressions, with another seven being insignificant in both. The correlation coefficient between the joint determinants of happiness and eudaimonia here is 0.92, which is primarily driven by the importance of mental health for both well-being measures. The high correlation coefficient is similar to the analogous figures in the ESS, BHPS and ONS in Clark (2016a).

Channels

The fact that the distal coefficients changed with the introduction of proximal variables to the regression reveals mechanically that the distal and contemporaneous variables are correlated. Figure 3 considers the size of the different distal-proximal channels. Note: These coefficients come from separate regressions of each proximal variable on the set of distal variables. The five distal and proximal variables that appear in each panel of the figure above are those with the greatest channel effect (the highest partial R 2 ). The lines around the bars are the 90% confidence intervals. The full regression results appear in Appendix C.

Of particular interest is the large change in the estimated coefficient on the childhood IQ score with the addition of the proximal variables: from zero to negative for happiness and from positive to negative for eudaimonia. Figure 5 shows that the early-life IQ score is positively correlated with a number of age-72 outcomes, including mental health (as in Flèche et al., 2017). Later-life mental health is one of the main correlates of adult life satisfaction, as can be seen in Figures 3 and4, which explains why the estimated coefficient on IQ Score falls with the introduction of the proximal variables. A similar story can be told for years of schooling and eudaimonia: a higher childhood IQ score predicts adult years of schooling, which in turn is associated with higher levels of later-life eudaimonia.

The effect of any policy interventions that take place in early life should be predicted using the distal-only (reduced-form) specification. A policy aimed at childhood cognitive performance or parental education will thus be predicted to improve later-life well-being (as well, of course, as higher well-being throughout life) through a variety of channels. This is not of course to say that childhood interventions are the only ones that matter. As the bottom panels of Figures 1 and2 show, conditional on childhood, policies aimed at education success and later-life mental health and social participation are also predicted to increase well-being at older ages.

Conclusion

This analysis has shown a clear reduced-form impact of IQ scores, parental income and parental education on well-being 50+ years later. Family and early-life outcomes are thus associated with higher levels of well-being well into old age, and more so for eudaimonia than for happiness. This arguably underlines the large returns (as they last for so long) of interventions in these domains at young ages. Adding proximal variables to the analysis allows us to estimate the indirect and direct effect of the distal factors and the corresponding channels of influence. We find strong channel effects (in particular through mental health and years of schooling), with some early-life outcomes remaining significant predictors of well-being at age 72 even when the proximal variables are controlled for. This is of course not to say that we should neglect proximal variables in determining policy for well-being. In particular, interventions that target later-life social participation and mental health could have a substantial impact on later-life well-being.

One important caveat to any these results is that we do not control for fixed effects. It may be that individuals with better distal outcomes also had some genetic predisposition to being content with life. In that case, early-life interventions may not affect well-being. The same argument applies to social participation: extroverted/optimistic individuals may enjoy more social participation and report greater subjective well-being, and it may be that making unsociable people socialise more will actually reduce their well-being. Controlling for fixed effects using panel data would defeat the purpose of our analysis here: we only have one childhood, which remains constant through our adult lives. We can however imagine identification strategies for some adult outcomes, and perhaps greater analysis of the role of both personality variables (although these might be thought to be partly endogenous: see [START_REF] Boyce | Is personality fixed? Personality changes as much as "variable" economic factors and more strongly predicts changes to life satisfaction[END_REF] and genetic factors [START_REF] Pluess | Genetics of Psychological Well-Being: The Role of Heritability and Genetics in Positive Psychology[END_REF] in determining adult well-being.

B1: Mental Health

The mental health covariates come from the CES-D scale -based on 20 question (16 negative + 4 positive) about how many days over the past week respondents felt a certain way in this past week. The CES-D scale is the total number of days across the 20 questions (negative questions are reverse-coded). The questions are as follows:

Negative Questions (reverse coded):

1. On how many days during the past week did you feel you could not shake off the blues even with help from your family and friends?

2. On how many days during the past week did you feel bothered by things that usually don't bother you?

3. On how many days during the past week did you think your life had been a failure?

4. On how many days during the past week did you feel that people were unfriendly?

5. On how many days during the past week did you feel lonely?

6. On how many days during the past week did you have crying spells?

7. On how many days during the past week did you feel that people disliked you?

8. On how many days during the past week did you feel sad?

9. On how many days during the past week did you feel depressed?

On how many days during the past week did you have trouble keeping your mind on what you were doing?

11. On how many days during the past week did you not feel like eating, your appetite was poor?

12. On how many days during the past week did you feel you were just as good as other people?

13. On how many days during the past week did you feel fearful?

14. On how many days during the past week did you sleep restlessly?

15. On how many days during the past week did you talk less than usual?

16. On how many days during the past week did you feel you could not get going?

Positive questions:

1. On how many days during the past week did you feel happy?

2. On how many days during the past week did you enjoy life?

3. On how many days during the past week did you feel hopeful about the future?

4. On how many days during the past week did you feel you were just as good as other people?

B2: Physical Health

Physical health is calculated from six of the eight components of the Health Utilities Index -vision, hearing, speech, ambulation, dexterity and pain. Emotion (where happiness well-being measure comes from) and cognition (not directly related to physical health) are the two excluded measures. Health over each of these components is ascertained by asking how the respondent has fared over the past four weeks. The six different domains and their associated questions are listed below -possible responses are Yes or No, unless otherwise stated17

Vision

• During the past 4 weeks, have you been able to see well enough to read ordinary newsprint without glasses or contact lenses?

• During the past 4 weeks, have you been able to see well enough to read ordinary newsprint with glasses or contact lenses?

• During the past 4 weeks, have you been able to see at all?

• During the past 4 weeks, have you been able to see well enough to recognize a friend on the other side of the street without glasses or contact lenses?

• During the past 4 weeks, have you been able to see well enough to recognize a friend on the other side of the street with glasses or contact lenses?

Level Description 1

The respondent has been able to see well enough to read ordinary newsprint and recognize a friend on the other side of the street, without glasses or contact lenses 2

The respondent has been able to see well enough to read ordinary newsprint and recognize a friend on the other side of the street, but with glasses. 3

The respondent has been able to read ordinary newsprint with or without glasses but unable to recognize a friend on the other side of the street, even with glasses. 4

The respondent has been able to recognize a friend on the other side of the street with or without glasses but unable to read ordinary newsprint, even with glasses. 5

The respondent has been unable to read ordinary newsprint and unable to recognize a friend on the other side of the street, even with glasses. 6

The respondent has been unable to see at all.

Hearing

• During the past 4 weeks, have you been able to hear what is said while in a group conversation with at least three other people without a hearing aid?

• During the past 4 weeks, have you been able to hear what is said in a group conversation with at least three other people with a hearing aid? The respondent has been able to hear what is said in a conversation with one other person in a quiet room with a hearing aid, and able to hear what is said in a group conversation with at least three other people, with a hearing aid. 4

The respondent has been able to hear what is said in a conversation with one other person in a quiet room without a hearing aid, but unable to hear what is said in a group conversation with at least three other people even with a hearing aid. 5

The respondent has been able to hear what is said in a conversation with one other person in a quiet room without a hearing aid, but unable to hear what is said in a group conversation with at least three other people even with a hearing aid. 6

The respondent has been unable to hear at all.

Speech

• During the past 4 weeks, have people who do not know you understood you completely when you speak?

• During the past 4 weeks, have people who do not know you understood you partially when you speak?

• During the past 4 weeks, have people who know you well understood you completely when you speak?

• During the past 4 weeks, have people who know you well understood you partially when you speak?

• During the past 4 weeks, have you been able to speak at all?

Level Description 1

The respondent has been able to be understood completely when speaking with strangers or friends. 2

The respondent has been able to be understood partially when speaking with strangers but able to be understood completely when speaking with people who know me well. 3

The respondent has been able to be understood partially when speaking with strangers or people who know me well. . 4

The respondent has been unable to be understood when speaking with strangers but able to be understood partially by people who know me well. 5

The respondent has been unable to be understood when speaking to other people (or unable to speak at all).

Ambulation

• During the past 4 weeks, have you been able to bend, lift, jump and run without difficulty and without help or equipment of any kind?

• During the past 4 weeks, have you been able to walk around the neighborhood without difficulty and without help or equipment of any kind?

• During the past 4 weeks, have you been able to walk around the neighborhood with difficulty but without help or equipment of any kind?

• During the past 4 weeks, have you been able to walk at all?

• During the past 4 weeks, have you needed mechanical support, such as braces or a cane or crutches, to be able to walk around the neighborhood?

• During the past 4 weeks, have you needed the help of another person to walk?

• During the past 4 weeks, have you needed a wheelchair to get around the neighborhood?

• During the past 4 weeks, have you needed the help of another person to get around in the wheelchair?

Level Description 1

The respondent has been able to walk around the neighborhood without difficulty, and without walking equipment. 2

The respondent has been able to walk around the neighborhood with difficulty; but does not require walking equipment or the help of another person. 3

The respondent has been able to walk around the neighborhood with walking equipment, but without the help of another person. 4

The respondent has been able to walk only short distances with walking equipment, and requires a wheelchair to get around the neighborhood. 5

The respondent has been unable to walk alone, even with walking equipment. 6

The respondent could not walk at all.

Dexterity

• During the past 4 weeks, have you had full use of both hands and 10 fingers?

• During the past 4 weeks, have you needed the help of another person because of limitations in the use of your hands or fingers?

• During the past 4 weeks, due to limitations in the use of your hands or fingers, have you needed the help of another person with some tasks, most tasks, or all tasks?

• During the past 4 weeks, have you needed special equipment, for example, special tools to help with dressing or eating, because of limitations in the use of your hands or fingers?

• During the past 4 weeks, have you been able to eat, bathe, dress and use the toilet without difficulty?

• During the past 4 weeks, have you needed the help of another person to eat, bathe, dress or use the toilet?

• During the past 4 weeks, have you needed special equipment or tools to eat, bathe, dress or use the toilet?

Level Description 1

The respondent has had full use of two hands and ten fingers. 2

The respondent has had limitations in the use of hands or fingers, but does not require special tools or help of another person. 3

The respondent has had limitations in the use of hands or fingers; s/he is independent with use of special tools and does not require the help of another person. 4

The respondent has had limitations in the use of hands or fingers; s/he requires the help of another person for some tasks and is not independent even with use of special tools. 5

The respondent has had limitations in the use of hands or fingers; s/he requires the help of another person for most tasks and is not independent even with use of special tools. 6

The respondent has had limitations in use of hands or fingers; s/he requires the help of another person for all tasks and is not independent even with use of special tools.

Pain

• During the past 4 weeks, have you had any trouble with pain or discomfort?

• During the past 4 weeks, how many of your activities were limited by pain or discomfort? [Possible responses: None, A few, Some, Most, All]

Level Description 1

The respondent has been free of pain and discomfort 2

The respondent has been in mild to moderate pain that prevents no activities. 3

The respondent has been in moderate pain that prevents a few activities. 4

The respondent has been in moderate to severe pain that prevents some activities. 5

The respondent has been in severe pain that prevents most activities.

Calculation

In order to obtain a single overall measure of health the different domains must be combined. Each level is assigned a domain-specific score between 0 and 1, as shown in Table B.1, and the average score is taken.

Figure 1 :

 1 Figure 1: The Distribution of Happiness at age 72 in the WLS

Figure 2 :

 2 Figure 2: The Distribution of Eudaimonia at age 72 in the WLS

Figure 3 :

 3 Figure 3: The Distal and Proximal Correlates of Happiness at age 72

Figure 4 :

 4 Figure 4: The Distal and Proximal Correlates of Eudaimonia at age 72

Figure 5

 5 Figure 5: Distal-Proximal Channel Coefficients

Table 1 :

 1 Distal Covariate Summary Statistics by Attrition Sub-group

	Variable	Mean Std. Dev. Min. Max.	N
	Every-question Respondent
	IQ Score	104.1	14.2	61	145
	Log Parental Income	10.6	0.7	6.6	13.6
	Single-Parent Household	0.06	0.24	0	1
	Number of Siblings	3.1	2.4	0	26
	Parental Education	10.2	2.7	0	21
	Birth Order	2.3	1.7	1	14
	Age of Mother at Birth	27.6	5.9	9	54
	Female	0.52	0.5	0	1
	Alive -Missed 1+ 2011 Question
	IQ Score	98.9	14.9	61	145
	Log Parental Income	10.5	0.7	6.7	13.6
	Single-Parent Household	0.12	0.33	0	1
	Number of Siblings	3.3	2.6	0	18
	Parental Education	10.1	2.8	1.5	21
	Birth Order	2.6	2.1	1	16
	Age of Mother at Birth	27.9	6.3	9	69
	Female	0.56	0.5	0	1
	Dead pre-2011
	IQ Score	99.0	15.1	61	145
	Log Parental Income	10.5	0.7	6.7	13.6
	Single-Parent Household	0.12	0.32	0	1
	Number of Siblings	3.3	2.6	0	22
	Parental Education	10.1	2.6	2	21.5
	Birth Order	2.5	2.0	1	21
	Age of Mother at Birth	27.2	6.1	9	51
	Female	0.43	0.5	0	1

Table 2 :

 2 Proximal Covariate Summary Statistics

	Variable	Mean Std. Dev. Min. Max.	N
	Years of Schooling	13.8	2.4	12	20	3086
	Log Household Income	10.0	1.0	1.1	13.3	3086
	Separated	0.10	0.30	0	1	3086
	Widowed	0.12	0.33	0	1	3086
	Never Married	0.03	0.18	0	1	3086
	Unemployed	0.06	0.24	0	1	3086
	Retired	0.64	0.48	0	1	3086
	Social Participation (0-64)	22.8	7.0	0	58	3086
	Mental Health (0-140 )	125.4	14.7	8	140	3086
	Physical Health (0.6-1)	0.99	0.02	0.78	1	3086
	Female	0.52	0.5	0	1	3086

•

  During the past 4 weeks, have you been able to hear what is said in a conversation with one other person in a quiet room without a hearing aid? • During the past 4 weeks, have you been able to hear what is said in a conversation with one other person in a quiet room with a hearing aid? The respondent has been able to hear what is said in a group conversation with at least three other people, without a hearing aid. 2 The respondent has been able to hear what is said in a conversation with one other person in a quiet room without a hearing aid, but requires a hearing aid to hear what is said in a group conversation with at least three other people. 3

	Level Description
	1

Table B

 B Standard errors in parentheses, * p < 0.1, * * p < 0.05, * * * p < 0.01 Standard errors in parentheses, * p < 0.1, * * p < 0.05, * * * p < 0.01

							Table C2: 2011 Channels
				(1)		(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
				Years of Schooling Log Household Income Widowed Mental Health Social Participation Separated Never Married Unemployed Retired Physical Health
		IQ Score		0.321 * * *		0.138 * * *	-0.0402 * *	0.110 * * *	0.0831 * * *	-0.000321	0.0182	-0.0270	-0.0130	0.00270
				(0.0147)	(0.0183)	(0.0199)	(0.0210)	(0.0195)	(0.0192)	(0.0197)	(0.0208)	(0.0197)	(0.0204)
		Number of Siblings		-0.0362 * *	-0.0370	0.0155	0.0111	0.0363	-0.0000215	-0.0230	-0.0343	-0.0476 *	0.0186
				(0.0179)	(0.0237)	(0.0299)	(0.0306)	(0.0251)	(0.0261)	(0.0185)	(0.0268)	(0.0254)	(0.0258)
		Log Parental Income		0.0772 * * *	0.0663 * * *	0.0157	0.0208	-0.00332	-0.00144	0.00385	0.0308	0.00442	0.00775
				(0.0162)	(0.0179)	(0.0193)	(0.0205)	(0.0202)	(0.0217)	(0.0168)	(0.0198)	(0.0203)	(0.0208)
		Single Parent Household	-0.00358	0.0344 * * *	0.0301	0.0277	-0.0123	0.00548	0.00669	0.0234	0.00994	-0.0480 *
				(0.0137)	(0.0130)	(0.0206)	(0.0194)	(0.0185)	(0.0190)	(0.0190)	(0.0215)	(0.0183)	(0.0255)
		Parental Education		0.214 * * *		0.0406 * *	-0.0522 * *	0.0327	0.0557 * * *	0.0308	-0.00295	-0.0282	0.0232	0.0598 * * *
				(0.0168)	(0.0193)	(0.0210)	(0.0207)	(0.0215)	(0.0213)	(0.0208)	(0.0219)	(0.0204)	(0.0206)
		IQ Score Age of Mother at Birth IQ Score	0.00797 0.103 * * * (0.0189) 0.0146	-0.0480 * * 0.0319 (0.0207) -0.0535 * * *	0.0576 * * * -0.0240 (0.0236) 0.0624 * * *	0.0396 (0.0273)	-0.0427 * * 0.0671 * * * (0.0228) -.0446 * * *	-0.0357 (0.0245)	0.0594 * * (0.0260)	-0.0249 (0.0292)	-0.0480 * * (0.0238)	0.0147 (0.0242)
		Birth Order		(0.0197) -0.0401 * (0.0154)		(0.0192) 0.0490 * (0.0147)	(0.0196) -0.0370 (0.0149)	-0.0162	(0.0183) -0.0646 * * (0.0139)	0.0191	-0.0199	-0.00594	0.0923 * * *	-0.0191
		Number of Siblings Log Parental Income Female Log Parental Income Observations Parental Education	-0.0147 (0.0299) 0.0224 (0.0227) -0.383 * * * 0.0110 (0.0292) (0.0159) 3089 0.0583 * * *	-0.0250 (0.0248) 0.0145 (0.0267) -0.363 * * * 0.0083 (0.0348) (0.0142) 3089 0.0348 * *	-0.0201 (0.0246) 0.0523 * * * (0.0312) 0.385 * * * 0.0500 * * * (0.0360) (0.0192) 3089 0.0494 * *	(0.0321) -0.147 * * * (0.0378) 3089	-0.0259 (0.0210) 0.0330 * (0.0302) 0.0128 0.0364 * * * (0.0362) (0.0142) 3089 0.0034	(0.0303) 0.206 * * * (0.0367) 3089	(0.0277) -0.00425 (0.0358) 3089	(0.0340) 0.0593 (0.0375) 3089	(0.0300) 0.177 * * * (0.0368) 3089	(0.0316) -0.0848 * * (0.0373) 3089
		Adjusted R 2		(0.0192) 0.294 (0.0154)		(0.0173) 0.068 (0.0143)	(0.0196) 0.040 (0.0200)	0.023	(0.0174) 0.015 (0.0142)	0.009	0.004	0.003	0.009	0.006
		Single Parent Household Number of Siblings		0.0160 -0.0093		0.0104 -0.0174	-0.00436 0.0010		-0.0135 0.0001
				(0.0166) (0.0156)		(0.0160) (0.0139)	(0.0170) (0.0175)		(0.0151) (0.0133)
		Parental Education Single Parent Household	0.0583 * * * 0.0254 *		0.0338 * 0.0141	0.0375 * 0.0360 * * *		-0.0122 0.0100
				(0.0208) (0.0138)		(0.0190) (0.0127)	(0.0201) (0.0135)		(0.0182) (0.0127)
		Age of Mother at Birth Birth Order		0.0324 -0.0121		0.00872 -0.0026	0.0355 -0.0153		-0.00275 0.0014
				(0.0287) (0.0156)		(0.0234) (0.0139)	(0.0238) (0.0175)		(0.0209) (0.0115)
		Birth Order Age of Mother at Birth		-0.0137 0.0325		-0.00254 0.0104	0.00128 0.0572 * * *		0.0202 0.0202
				(0.0359) (0.0199)		(0.0318) (0.0239)	(0.0290) (0.0181)		(0.0253) (0.0164)
		PV Proximal Covariates
		Years of Schooling Years of Schooling			0.00607 0.0142	0.00672 0.0113		0.106 * * * 0.945 * * *	0.120 * * * 0.101 * * *
					(0.0161) (0.0127)	(0.0182) (0.0145)		(0.0167) (0.0135)	(0.0190) (0.0148)
		Log Household Income Log Household Income			0.00105 -0.0153 .1: Domain Level Score 0.00226 -0.0021 (0.0185) (0.0185) (0.0138) (0.0137)	0.0331 * 0.0320 * * (0.0177) (0.0162)	0.0342 * 0.0413 * * * (0.0179) (0.0150)
		Unemployed Social Participation		-0.0513 * * -0.0540 * * * 0.0311 * * * 0.0333 * *		-0.00479 0.119 * * *	-0.00665 0.120 * * *
	(0.0208) (0.0137) Level Vision Hearing Speech Ambulation Dexterity Pain (0.0208) (0.0185) (0.0185) (0.0131) (0.0216) (0.0144) 1 1.00 1.00 1.00 1.00 1.00 Retired -0.0156 -0.0174 -0.00394 -0.00506 Separated -0.0238 * -0.0252 * 0.0147 0.0147 1.00 (0.0170) (0.0171) (0.0172) (0.0172) (0.0138) (0.0138) (0.0152) (0.0123)
	2	0.98 Never Married Widowed	0.95	0.94 -0.0213 -0.0370 * * -0.0400 * * * -0.0220 (0.0184) (0.0185) (0.0152) (0.0151)	0.93	0.95 -0.0356 * * -0.0361 * * -0.0252 * -0.0100 (0.0155) (0.0154) (0.0153) (0.0131)	0.96
	3 4	0.89 Widowed Never Married 0.84 Separated Unemployed	0.89 0.80	0.89 -0.0278 -0.0104 0.81 (0.0185) (0.0133) -0.00333 -0.0377 * *	-0.0284 -0.0058 (0.0185) (0.0134) -0.00413 -0.0393 * *	0.86 0.73	0.88 -0.00341 -0.00413 -0.0208 -0.0207 * 0.76 (0.0170) (0.0170) (0.0134) (0.0119) -0.00122 -0.000714 -0.0036 0.0022	0.90 0.77
	5	0.75 Social Participation Retired	0.74	0.68 (0.0180) (0.0160) 0.0419 * * -0.0051	(0.0179) (0.0162) 0.0427 * * 0.0044	0.65	0.65 (0.0168) (0.0168) (0.0149) (0.0143) 0.137 * * * 0.139 * * * -0.0070 -0.0068	0.55
	6	0.61 Mental Health Mental Health	0.61	(0.0179) (0.0138) 0.428 * * * 0.432 * * *	(0.0178) (0.0137) 0.429 * * * 0.436 * * *	0.58	(0.0192) (0.0161) 0.56 (0.0191) (0.0159) 0.411 * * * 0.414 * * * 0.418 * * * 0.421 * * *
					(0.0242) (0.0196)	(0.0242) (0.0196)		(0.0177) (0.0144)	(0.0178) (0.0145)
		Physical Health Physical Health		0.0734 * * * 0.0591 * * *	0.0715 * * * 0.0567 * * *		0.0173 0.0139	0.0156 0.0173
					(0.0195) (0.0155)	(0.0196) (0.0155)		(0.0162) (0.0144)	(0.0163) (0.0152)
		Female Female	-0.0914 * * -0.0687 * *	-0.00806 0.0153	-0.00125 0.0176	0.0701 * 0.110 * *	0.184 * * * 0.217 * * *	0.192 * * * 0.220 * * *
				(0.0366) (0.0284)	(0.0337) (0.260)	(0.0343) (0.0266)	(0.0367) (0.0281)	(0.0345) (0.0375)	(0.0347) (0.0261)
		Observations Observations		3086 5070	3086 5070	3086 5070	3086 5070	3086 5070	3086 5070
		Adjusted R 2		0.007	0.219	0.221	0.015	0.240	0.241
		Note: An indicator variable is included for extreme values of age of mother at birth (<15 or >50)
		Standard errors in parentheses, * p < 0.1, * * p < 0.05, * * * p < 0.01

Affect refers to mood or emotional state.

See Frey & Stutzer (2010) and[START_REF] Clark | Four Decades of the Economics of Happiness: Where Next?[END_REF] for overviews of the range of insights provided by the economics of well-being literature.

See for example[START_REF] Sacerdote | How large are the effects from changes in family environment? A study of Korean American adoptees[END_REF],[START_REF] Cunha | Formulating, identifying and estimating the technology of cognitive and noncognitive skill formation[END_REF] and[START_REF] Heckman | The economics of human development and social mobility[END_REF].

In Flèche et al. (2017), there was little attenuation of the effect of childhood on adult life satisfaction between early adulthood and middle age.

There is no data on individuals born in 1938 before the ages of 52 and 45 in the BHPS and SOEP respectively.

The full description of these well-being measures appears in Appendix A.

See Springer & Hauser (2006) for an assessment of the scale, using in part data from the WLS.

See Appendix B for the detailed description of all of the variables. The correlation matrix between all variables appears in Appendix F.

The results in the main text use inverse-probability weighting; the multiple-imputation results (with inverse-probability weighting) can be found in Appendix D. There are only small differences between these two sets of results.

This gender happiness gap disappears in countries where women's rights are reduced: see[START_REF] Graham | Gender and well-being around the world[END_REF].

It is important to note that not all questions are asked to every respondent, rather the responses given affect the number of questions asked. For instance replying Yes to During the past 4 weeks, have you been able to see well enough to read ordinary newsprint without glasses or contact lenses? stops all further Vision questions and the highest vision level is assigned.

Etilé, Stephen Jenkins, Richard Layard, and participants at the Well-Being over the Life Course Support from CEPREMAP, the US National Institute on Aging (Grant R01AG040640), the John Templeton Foundation and the What Works Centre for Wellbeing is gratefully acknowledged. This research uses data from the Wisconsin Longitudinal Study (WLS) of the University of Wisconsin-Madison. Since 1991, the WLS has been supported principally by the National Institute on Aging (AG-9775, AG-21079, AG-033285, and AG-041868), with additional support from the Vilas Estate Trust, the National Science Foundation, the Spencer Foundation, and the Graduate School of the University of Wisconsin-Madison. Since 1992, data have been collected by the University of Wisconsin Survey Center. A public use file of data from the Wisconsin Longitudinal Study is available from the Wisconsin Longitudinal Study, University of Wisconsin-Madison, 1180 Observatory Drive, and at http://www.ssc.wisc.edu/wlsresearch/data/.

Appendix A: Well-being Measures

A.1 Happiness

The happiness measure comes directly from the emotion component of the Health Utilities Index, the score taken being the level achieved (reverse-coded).

Level Description 1

The respondent has been happy and interested in life. 2

The respondent has been somewhat happy. 3

The respondent has been somewhat unhappy. 4

The respondent has been very unhappy. [START_REF] Boyce | Is personality fixed? Personality changes as much as "variable" economic factors and more strongly predicts changes to life satisfaction[END_REF] The respondent has been unhappy that life is not worthwhile

A.2 Eudaimonia

Eudaimonia is ascertained by asking individuals thirty-two questions over six eudaimonic aspects of life. These are self-acceptance, purpose in life, positive relationships, environmental mastery, personal growth and autonomy. All questions are answered on a six-point scale:

1. Agree Strongly 2. Agree Moderately 3. Agree Slightly 4. Disagree Slightly 5. Disagree Moderately

Disagree Strongly

The score across the questions is then summed (reverse coded for positive questions) to calculate the psychological well-being score.

Appendix B: Covariates

Distal Variables

IQ Score (1957) IQ score mapped from raw Henmon-Nelson test score Log Parental Income (1957-60) Average Annual Parental Income (1957-1960) -from tax data (Corrected for inflation using the CPI index from the Bureau of Labor Statistics) Parental Education (1957) Average number of years of schooling of parents Birth Order (1957) Birth order of respondent Single Parent Household (1975)


Selim Fares
email: selimfares8@gmail.com

Sophie Hajjar

Marc Romana

Philippe Connes

Malik Acomat

Coralie Zorobabel

Kevin Zuber

Elise Boulanger-Scemama

Maryse Etienne-Julan

Thierry David

Maryse Etienne- Julan

Laurence Beral

Sickle Cell Maculopathy: Microstructural Analysis Using OCTA and Identification of Genetic, Systemic, and Biological Risk Factors

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction:

Sickle cell disease (SCD) is the most common genetic disease affecting around 300,000 to 400,000 newborns every year. [START_REF] Kato | Sickle cell disease[END_REF][START_REF] Piel | Sickle cell disease[END_REF] SCD is caused by a mutation in the βglobin gene resulting in the production of an abnormal hemoglobin called hemoglobin S (HbS). Hemoglobin S polymerizes under hypoxic conditions, which causes a mechanical distortion of red blood cells into a sickle-like shape. These sickled red blood cells are fragile and rigid, resulting in chronic hemolytic anemia and frequent vaso-occlusive like complications in multiple organs, including the eyes. [START_REF] Modell | Global epidemiology of haemoglobin disorders and derived service indicators[END_REF] SCD is a generic term encompassing different syndromes according to genotype: homozygotes HbSS or compound heterozygotes (HbSvariant). Sickle cell retinopathy (SCR) has been well described, with proliferative sickle cell retinopathy (PSR) being a major sight-threatening complication. [START_REF] Elagouz | Sickle cell disease and the eye: old and new concepts[END_REF] Since the development of Spectral Domain Optical Coherence Tomography (SD-OCT), macular vascular abnormalities in SCD are now commonly detected. [START_REF] Rees | Sickle-cell disease[END_REF] Sickle cell maculopathy (SCM) is defined as patchy areas of severe retinal thinning in the temporal macula. The exact etiology of macular thinning remains unclear but it may be related to the temporal macula ending along the horizontal raphe making it a watershed zone between the vascular arcades of the retinal circulation and thus more susceptible to vascular occlusions. [START_REF] Abdalla Elsayed Mea | Sickle cell retinopathy. A focused review[END_REF] By allowing depth-resolved visualization of macular vascular network with high resolution, Optical Coherence Tomography Angiography (OCTA) recently detected much more macular vascular alterations than previously and provided additional features, including enlargement and irregularities of the foveal avascular zone (FAZ), hairpin venular loops and areas of flow loss responsible for irreversible macular thinning. [START_REF] Mathew | Spectral domain optical coherence tomography in patients with sickle cell disease[END_REF][START_REF] Stevens | Sickling hemoglobinopathies: Macular and perimacular vascular abnormalities[END_REF] Previous studies showed that HbSC genotype, male gender, low fetal hemoglobin (HbF), high hemoglobin levels and high blood viscosity (particularly in HbSS genotype) were associated with severe PSR. [START_REF] Minvielle | Macular microangiopathy in sickle cell disease using optical coherence tomography angiography[END_REF][START_REF] Han | Evaluation of macular vascular abnormalities identified by optical coherence tomography angiography in sickle cell disease[END_REF][START_REF] Fox | Risk factors for proliferative sickle retinopathy[END_REF] However, no study has identified genetic or biological risk factors of SCM. Identifying risk factors could lead to reliable prediction models for SCM management and minimize the onset of irreversible macular scotoma, decreased retinal sensitivity or vision loss described in eyes with severe macular thinning. [START_REF] Duan | Clinical and ophthalmic factors associated with the severity of sickle cell retinopathy[END_REF][START_REF] Lemaire | Severe proliferative retinopathy is associated with blood hyperviscosity in sickle cell hemoglobin-C disease but not in sickle cell anemia[END_REF] The primary purpose of this study was to identify the systemic, biological and genetic risk factors of SCM on OCT. The secondary purpose was to analyze macular microvascular network using OCTA and test if FAZ area and macular ischemia could be useful to identify more specific risk factors of SCM.

Materials and Methods:

Patients: This study received the ethical approval of the Regional Ethics Committee (CPP Sud-Ouest Outre-Mer III, Bordeaux, France) and adhered to the principles set by the Declaration of Helsinki. Written informed consent was obtained from each participant. This cross-sectional monocentric study concerned 117 patients with confirmed SCD diagnosis using standard methods and undergoing regular ophthalmological evaluation at the Ophthalmology Department of the University Hospital of Guadeloupe (French West Indies) between January 1 st , 2019 and January 1 st , 2020. Exclusion criteria were age under 18, history of other confounding retinal vascular disease such as hypertension, diabetes or retinal vein occlusion, prior retinal surgery, spherical equivalent (SE) > ± 1 diopter, retinal involvement such as p. 3 vitreomacular traction syndrome, epiretinal membrane or druses, poor quality images or ocular media opacities preventing detailed imaging. Sixty-nine eyes of 37 patients met these criteria and were excluded, so that 151 eyes of 78 patients were analyzed. An ethnic-matched control group of 20 healthy African Caribbean subjects were also recruited and underwent the same ocular procedures.

Multimodal imaging: Ophthalmic examination included best corrected visual acuity (BCVA) measured with Snellen chart, slit-lamp evaluation, dilated fundus examination. SCR was graded according to the Goldberg classification including peripheral arteriolar occlusions (stage I), peripheral arteriovenous anastomoses (stage II), preretinal neovascularization (stage III), vitreous hemorrhage (stage IV) and retinal detachment (stage V). [START_REF] Martin | Visual function in asymptomatic patients with homozygous sickle cell disease and temporal macular atrophy[END_REF] Eyes with no retinopathy were graded as Goldberg stage 0. Patients with previously performed focal laser photocoagulation for peripheral neovascularization were classified as stage III. Analyses were performed using three categories of retinopathy: no retinopathy (stage 0), non-proliferative retinopathy (stage I and II) and proliferative retinopathy (stage III, IV and V). All patients and healthy controls underwent a macular SD-OCT and OCTA using the Spectralis HRA+OCT (Heidelberg Engineering, Heidelberg, Germany). For each eye, the standard scanning protocol included an automated « posterior pole » 30x25 cuboid volume scan of 61 high resolution OCT scans and 10° and 20° OCTA scans centered on the fovea. Multiple scans were acquired as needed to secure adequate quality criteria and the best quality scans were selected by the lead grader for analysis. Images were anonymized and analyzed by two independent retina fellowship trained graders in a blinded fashion. In case of disagreement, scans were reviewed and arbitrary graded together. Sickle cell maculopathy was identified as patchy retinal thinning areas on OCT (i.e., blue areas of markedly decreased thickness on retinal thickness color-coded map). The first step involved manual delineation of the full thickness retina FAZ border taken from 10° OCTA scans using the « area » function of the software (Figure 1). Scans were then qualitatively graded by the investigators for macular ischemia from no (0) to severe (3) macular ischemia (including extension of flow loss areas, vessel density, enlargement of the FAZ, presence of perifoveal venular loops and focal disruption of the perifoveal anastomotic capillary arcade). Figure 2 illustrates our macular ischemia graduation.

Clinical and biological data collection: Clinical data were retrospectively collected from patient's charts at the last routine clinical visit to the Sickle cell disease center of Guadeloupe and included : SCD type (genotype, α-thalassemia status), medical and surgery history (other systemic condition, spleen status, cholecystectomy), current treatment (hydroxyurea, chelation therapy, chronic transfusion, anticoagulants) and history of SCD systemic complications (acute chest syndrome, cerebrovascular accident, severe vaso-occlusive crisis frequency, osteonecrosis). Frequency of severe vaso-occlusive crises was defined using Duan et al's method, i.e. as rare if once a year or less, occasional if twice a year and frequent if three times a year or more, averaged over the two years prior to the ocular examination. [START_REF] Han | Evaluation of macular vascular abnormalities identified by optical coherence tomography angiography in sickle cell disease[END_REF] Severe vasoocclusive crises were defined as requiring acute Emergency Department visit or inpatient admission. We also collected laboratory results including fetal hemoglobin (HbF) level, baseline hemoglobin level, hematocrit, C-reactive protein (CRP), prothrombin ratio (PR), reticulocytes, leucocytes, neutrophils and platelets count, lactate dehydrogenase p. 4

(LDH), total bilirubin, serum ferritin and fibrinogen levels and G6PD deficiency. Biological parameters were obtained at steady-state.

Statistical analysis:

Mann-Whitney tests were used to compare the different demographic, clinical and ocular characteristics between the two groups. A chi 2 was used for qualitative data. For the three groups comparisons, a Kruskal-Wallis test was used followed by a Dunn post-hoc test when appropriate. P-value < 0.05 was considered as significant. Three ocular criteria were included in the analysis: the occurrence of maculopathy, the FAZ enlargement and the severity of the macular ischemia. Because simple comparison of the distribution of systemic and biological factors between patients can be misleading as certain factors are interdependent or age related, we used multiple logistic regression analysis, adjusted by genders, genotypes and factors showing a p-value < 0.1 in univariate analysis to identify risk factors independently associated with sickle cell maculopathy, FAZ enlargement and macular ischemia in an ordinal Generalized Estimating Equations (GEE) model. Complete data were not available for all patients, so we used a simple imputation with a predictive mean matching (PMM) method for the missing data. Matched controls were used to compare FAZ measures with SCD patients using a Mann-Whitney test. Statistical analyses were performed using R statistical software. Data are expressed in mean ± SD or percentages for quantitative and qualitative parameters, respectively.

Results:

Demographic results: Table 1 summarizes patients' demographical and clinical characteristics. All participants were of African Caribbean origin. 151 eyes of 78 patients (25 men and 53 women) with a mean age of 37 years (range 18 -61 years old) were enrolled in this study. Five eyes were excluded due to unilateral high myopia (1 eye), phthisis bulbi (1 eye), poor acquisition due to no fixation (2 eyes) and a large foveal vascular pigmentary epithelium drusen (1 eye). Several sickle cell syndromes were analyzed including homozygotes HbSS (43 patients, 55%) and compound heterozygotes HbSvariant (35 patients, 45%) including HbSC (30 patients), HbS/β+ (4 patients) and HbSLepore (1 patient). Visual acuity ranged from 20/25 to 20/20 for all patients. The most frequent systemic events in our population were painful vaso-occlusive crisis (49%), acute chest syndrome (42%) and osteonecrosis (31%). There was no statistical difference in age, gender, functional αglobin gene number, vaso-occlusive crisis, osteonecrosis, anticoagulant medication and chronic transfusion between HbSS and HbSvariant groups. However, HbSS patients exhibited more ulcers (p = 0.019), more acute chest syndrome (p < 0.01) and more cerebrovascular accidents (p < 0.01). Lower hemoglobin and hematocrit (p < 0.001), higher LDH and total bilirubin level (p < 0.001) were found in the HbSS group. Finally, HbSS patients seemed to exhibit a pro-inflammatory state: higher leucocytes and neutrophils levels (p < 0.001 and <0.02, respectively) and a trend through higher level of CRP (p=0.087).

Sickle cell maculopathy: SCM was present in 66 eyes (43.7%), especially in HbSS patients (71.2%, p=0.004). Univariate analysis (Table 2) showed that patients with SCM had lower hemoglobin (p = 0.001), lower hematocrit (p = 0.01), lower PR (p = 0.038) and higher LDH levels (p = 0.007) than those without. In addition, we p. 5 observed higher occurrence of SCM in eyes with proliferative sickle cell retinopathy (p = 0.04). Chronic transfusion tended to be associated with the occurrence of SCM (p = 0.073). As expected, maculopathy was associated with FAZ enlargement (p = 0.021) and more severe macular ischemia (p < 0.001). A cut-off level of HbF > 15% based on the prior studies about sickle cell retinopathy did not show any association with maculopathy. [START_REF] Chow | Structural and functional correlation in sickle cell retinopathy using spectral-domain optical coherence tomography and scanning laser ophthalmoscope microperimetry[END_REF] Multivariate regression analysis revealed that HbSS genotype and lower prothrombin ratio (PR) were the most predictive risk factors of SCM (p = 0.01).

Faz area: On OCTA, FAZ was measured in 142 eyes (94%) of SCD patients and in all healthy control eyes. In 9 eyes, it was not detectable by the software used related to extensive macular ischemia (Figure 3) due to central arterial occlusion (4 eyes) and extensive perifoveal capillary remodeling with a poorly defined FAZ (5 eyes). For these 9 eyes, the quantitative value of their surely enlarged FAZ could be misleading in the analysis. Accordingly, we used a censored statistical model that assigned 1.0 to larger FAZ area than 1.0. Univariate analysis showed that FAZ enlargement was associated with HbSS genotype (p = 0.04), hydroxyurea treatment (p = 0.04), anticoagulant medication (p = 0.05), positive history of acute chest syndrome (p = 0.02), occasional vaso-occlusive crises (p = 0.01), lower hematocrit and hemoglobin levels (p = 0.01) and higher LDH level (p = 0.02). Unexpectedly, we also found that male SCD patients had a smaller FAZ size (p = 0.01). In multivariate analysis only high LDH level was independently associated with FAZ enlargement (a rise of 0.03 µm [START_REF] Piel | Sickle cell disease[END_REF] for every 100 units of LDH, p = 0.02). Interestingly, positive history for acute chest syndrome was marginally associated with FAZ enlargement (p = 0.07). Comparison between SCD patients with or without SCM and age, ethnic and sexmatched controls revealed that the FAZ area was larger in our patients than controls (respectively 0.52 ± 0.19 vs 0.38 ± 0.17, p < 0.01).

Macular ischemia: Macular ischemia was subjectively identified using SD-OCT and OCTA with quantitative and qualitative criteria: irregularity of the FAZ, the extension of no flow areas and the general vascular density. We graded ischemia from no (0) to severe (3) for all 151 eyes of the study. The size of the grade 3 (severe) group of macular ischemia was not large enough to perform statistical analysis and was regrouped with the grade 2 (moderate) group for the analysis. Thus, we had 49 eyes (32.5%) with no ischemia, 52 with mild ischemia (34.4%) and 50 with moderate or severe ischemia (33.1%). Areas of no flow were more frequent in the temporal superior (36%) subfield then the temporal inferior (27%), nasal superior (9%) and nasal inferior (5%) subfields. Seventy eyes had irregularities of the FAZ borders (46%). Thirty-eight eyes had perifoveal venular loops (25%). Abnormal macular flow loss was visible even in SCD eyes with no macular thinning on OCT (36 eyes, 42%).

To have sufficient number of patients in groups for the identification of potential risk factors of macular ischemia, we regrouped moderate (2) and severe (3) eyes. Univariate analysis revealed that 80% of the eyes with moderate and severe macular ischemia are detected in HbSS patients (p < 0.001). We also showed a significant association between the severity of macular ischemia and hemoglobin level (p < 0.001), hematocrit and PR (p = 0.01 and < 0.001, respectively), platelet level (p = 0.01) and LDH (p = 0.003). In multivariate analysis, low hemoglobin level (p = 0.004) and low PR (p = 0.01) remained independently associated with macular ischemia severity.

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Discussion:

The current study is the largest study to date evaluating SCD patients with OCTA. The analysis revealed that HbSS genotype, lower PR and proliferative sickle cell retinopathy are independently associated with sickle cell maculopathy. Correlation between proliferative sickle cell retinopathy and maculopathy is an expected outcome. However, the association between genotype and sickle cell maculopathy has been poorly described. Even though it is well accepted that sickle cell retinopathy is more common and more severe in HbSC patients than in HbSS, [START_REF] Martin | Visual function in asymptomatic patients with homozygous sickle cell disease and temporal macular atrophy[END_REF][START_REF] Goldberg | Classification and pathogenesis of proliferative sickle retinopathy[END_REF] Mathew et al. reported a higher rate of retinal thinning in HbSS patients than in HbSC patients (48% vs 35%), [START_REF] Rees | Sickle-cell disease[END_REF] whereas other studies found no statistical difference. [START_REF] Stevens | Sickling hemoglobinopathies: Macular and perimacular vascular abnormalities[END_REF][START_REF] Estepp | Protection from sickle cell retinopathy is associated with elevated HbF levels and hydroxycarbamide use in children[END_REF] Our study demonstrated that retinal thinning occurs more frequently in HbSS eyes compared to the other syndromes of SCD, especially in HbSC eyes. This result is in agreement with prior studies suggesting that higher rate of vaso-occlusive crisis in HbSS patients could lead to repeated ischemic episodes in the eyes and thus the development of extensive retinal atrophy. [START_REF] Minvielle | Macular microangiopathy in sickle cell disease using optical coherence tomography angiography[END_REF][START_REF] Downes | Incidence and natural history of proliferative sickle cell retinopathy[END_REF][START_REF] Dell'arti | Sickle cell maculopathy: Identification of systemic risk factors, and microstructural analysis of individual retinal layers of the macula[END_REF] On the other hand, milder degree of retinal ischemia in HbSC genotype would be not enough to infarct the retina but could result in the production of angiogenic factors and a higher risk of peripheral neovascularization. [START_REF] Ballas | Beyond the definitions of the phenotypic complications of sickle cell disease: An update on management[END_REF][START_REF] Lopes | Key endothelial cell angiogenic mechanisms are stimulated by the circulating milieu in sickle cell disease and attenuated by hydroxyurea[END_REF] Natural history of sickle cell retinopathy is unique because peripheral neovessels can infarct over time. [START_REF] Downes | Incidence and natural history of proliferative sickle cell retinopathy[END_REF] In this study, we graded as PSR the few eyes with focal photocoagulation for a prior neovascularization, so PSR eyes may have been overrepresented in our analysis. However, our results suggest a strong relationship between maculopathy and retinopathy. This relationship may be explained by the fact that the temporal macular area and the retinal periphery are both supplied by small caliber terminal arterioles, therefore vascular occlusion can easily damage these areas. [START_REF] Mathew | Spectral domain optical coherence tomography in patients with sickle cell disease[END_REF] Interestingly, PR was detected at lower level in patients with maculopathy. Low PR means a prolonged prothrombin time which is common in SCD even if the reason is not well established. [START_REF] Raffini | Prolongation of the prothrombin time and activated partial thromboplastin time in children with sickle cell disease[END_REF][START_REF] Nilesh | Haemostatic alterations in patients of sickle cell trait and homozygous sickle cell disease -A hospital based case control study[END_REF] It is likely that prolonged prothrombin time is a marker of chronic vascular inflammation among others like platelets activation and high plasma levels of adhesion molecules. Prolonged PR could be related to several conditions such as liver dysfunction that decreases the synthesis of clotting factors or dysfunctional coagulation factors on vitamin K deficiency due to cholestasis for example.

OCTA enabled us to determine valuable findings of maculopathy and to identify or confirm specific risk factors for macular damages in SCD. FAZ area and macular ischemia were correlated with several biological factors. Indeed, high serum LDH level was significantly associated with FAZ area enlargement in multivariate analysis with a rise of 0.03 µm 2 for every 100 units of LDH. Also, lower hemoglobin level was significantly associated with more severe macular ischemia in multivariate analysis with a rise of 0.36 µm 2 for every unit of hemoglobin. Two mechanisms have been proposed to be involved in the pathophysiology of SCD: chronic hemolysis that could promote the development of several complications (such as leg ulcers, stroke, pulmonary hypertension, priapism) and blood hyper-viscosity that would increase the risks for vaso-occlusive like complications. [START_REF] Kato | Vasculopathy in sickle cell disease: Biology, pathophysiology, genetics, translational medicine, and new research directions[END_REF] Few studies found that severe sickle cell retinopathy was more frequent in patients with high hemoglobin and hematocrit levels, suggesting blood hyperviscosity could be a potential risk factor. [START_REF] Fox | Risk factors for proliferative sickle retinopathy[END_REF][START_REF] Dembélé | Prevalence and risk factors for sickle retinopathy in a sub-Saharan comprehensive Sickle Cell Center[END_REF] These studies served as a basis to use phlebotomy to treat blood hyperviscosity and prevent retinopathy. In our study, we failed to found any statistical association p. 7

between SCM and complications belonging to the "blood hyperviscosity" phenotype. Moreover, the association found between hemolysis and FAZ area enlargement in our study suggest that hemolysis, and not blood hyperviscosity, could play a leading role in the development of sickle cell maculopathy. [START_REF] Kato | Vasculopathy in sickle cell disease: Biology, pathophysiology, genetics, translational medicine, and new research directions[END_REF] Concerning hydroxyurea use, this compound induces an increase of HbF preventing HbS polymerization and red blood cells sickling. [START_REF] Kato | Vasculopathy in sickle cell disease: Biology, pathophysiology, genetics, translational medicine, and new research directions[END_REF] Prior studies suggest a protective effect of HbF on retinopathy, in particular when HbF is higher than 15%. [START_REF] Estepp | Protection from sickle cell retinopathy is associated with elevated HbF levels and hydroxycarbamide use in children[END_REF][START_REF] Dembélé | Prevalence and risk factors for sickle retinopathy in a sub-Saharan comprehensive Sickle Cell Center[END_REF] Even if Dell'Arti et al. showed a correlation between lower HbF and sickle cell maculopathy, the current study failed to show any statistically significant association with hydroxyurea use. [START_REF] Dell'arti | Sickle cell maculopathy: Identification of systemic risk factors, and microstructural analysis of individual retinal layers of the macula[END_REF] On OCT, the occurrence of maculopathy in patients with SCD was 43.7%, which is in agreement with prior studies showing that maculopathy frequency varied between 44% to 60%. [START_REF] Mathew | Spectral domain optical coherence tomography in patients with sickle cell disease[END_REF][START_REF] Hoang | Central macular splaying and outer retinal thinning in asymptomatic sickle cell patients by spectral-domain optical coherence tomography[END_REF][START_REF] Guerra | Sickle cell retinopathy: What we now understand using optical coherence tomography angiography. A systematic review[END_REF] A great advantage of OCTA is to detect microvascular abnormalities before retinal thinning occurs. For instance, Minvielle et al already detected perifoveal abnormalities in fluorescence angiography in only 50% of eyes whereas OCTA detected in 100% of cases. [START_REF] Minvielle | Macular microangiopathy in sickle cell disease using optical coherence tomography angiography[END_REF] Our results are in agreement with prior studies. [START_REF] Mathew | Spectral domain optical coherence tomography in patients with sickle cell disease[END_REF][START_REF] Hood | Temporal macular atrophy as a predictor of neovascularization in sickle cell retinopathy[END_REF] Areas of no flow on OCTA were present in 36/85 eyes without maculopathy on OCT. This observation suggests that OCTA may detect early signs of macular changes in patients and can likely be considered as a clinically useful marker in the screening and the evaluation of SCD progression or even for preventive treatment.

As expected, FAZ area was found larger in SCD patients than controls. However, this finding should be read with caution because the loss of capillaries may have been the result of blood flow velocity below the detection threshold of the OCTA rather than no perfusion. In addition, it is important to acknowledge that FAZ area can vary with ethnicities. [START_REF] Wagner-Schuman | Race-and sex-related differences in retinal thickness and foveal pit morphology[END_REF] An enlarged FAZ area is not specific of patients with SCD but also a physiological variation for healthy subjects from African origin. To avoid any bias related to ethnicity effect, the control group was selected in the same population studied. We also noticed a substantial variability in FAZ area and irregularity between patients and controls. Some studies performed on healthy eyes suggest that this parameter can be ambiguous and thus may be insufficient for discriminating the occurrence and severity of maculopathy in SCD. [START_REF] Linderman | Variability of foveal avascular zone metrics derived from optical coherence tomography angiography images[END_REF][START_REF] Magrath | Variability in foveal avascular zone and capillary density using optical coherence tomography angiography machines in healthy ayes[END_REF] Because of the extended disruption in the FAZ of 9 concerned eyes beyond the borders of the OCTA scans, causing misleading quantitative values with clinical implication, we did not exclude them from analysis but used a censored statistical model instead. As such, eyes with more severe macular vascular pathology have been included in this study in order to establish association with potential risk factors. We did not detect statistical difference between male and female in the occurrence of maculopathy and the macular ischemia, but men exhibited a lower FAZ area than women. Interestingly, some studies have questioned a protective female effect in SCR. Indeed, Fox et al. found that HbSS men had a 2.5-fold higher risk of developing severe SCR than women. [START_REF] Fox | Risk factors for proliferative sickle retinopathy[END_REF] They attributed the female protective effect to estrogen levels through a vascular protective effect by increasing endothelial nitric oxide synthase activity. Further studies are warranted to address this issue.

Finally, our analysis also revealed that the occurrence of the maculopathy or the severity of macular ischemia can vary significantly between two eyes of the same p. 8 patient. Future prospective studies may elucidate the ocular risk factors that may predispose the injury of one eye compared to the other.

Our study had several limitations. First, it was a cross-sectional study, so we could not estimate the incidence and the evolution of the sickle cell maculopathy. Further studies are warranted to evaluate macular microvascular changes with OCTA in the same patients over time in a longitudinal manner. Secondly, we used strict inclusion and exclusion criteria resulting in a modest cohort of patients, which limited the multivariate analysis for potential risk factors. Yet, we did not exclude patients with prior photocoagulation because we believe that it reflects a real-life situation and their exclusion can underestimate a number of severe patients in the present analysis. Another limitation concerned image acquisition and the software used. OCTA has the advantage to be a non-invasive dye-free technique and to provide structural microvascular information. However, it requires a precise and stable ocular fixation from the patients for several seconds. Furthermore, the software we used could not provide any vascular density, so we could not have any quantitative criteria for ischemia. For this reason, we subjectively graded macular ischemia because we suspect that OCTA is a better tool to reveal maculopathy than OCT. However, the study may underestimate the frequency of macular abnormalities in patients with more severe systemic events.

In conclusion, the identification of systemic, genetic and biological factors associated with the occurrence and the severity of maculopathy confirms the complexity of this disease. Association between sickle cell maculopathy and other organ damages in SCD is a significant question. OCTA is a highly sensitive method to detect microvascular macular abnormalities before the onset of macular thinning that commonly define the maculopathy status. Avoiding the occurrence of these abnormalities is critical to minimize and to manage the functional implication of retinal thinning in these young patients. Prospective studies are needed to determine the natural history of SCM and its relevance regarding preventive strategies for SCD patients. Furthermore, the emergence of novel technology such as OCTA could provide valuable information to contribute in a public health screening and follow up of patients with SCD.

Figure captions : Figure 1 .

 :1 Figure captions:

Figure 2 .

 2 Figure 2. Subjective graduation of macular ischemia based on SD-OCT and OCTA using the following criteria: extension of blue area in retinal thickness map, the areas of flow loss in OCTA 10° and 20°, the irregularity and enlargement of the FAZ. A. No ischemia, B. mild ischemia, C. moderate ischemia, D. severe ischemia.

Figure 3 .

 3 Figure 3. Example of difficulty to manually delineate FAZ area due to extensive macular ischemia.

Figure 1 .

 1 Figure 1. Flow chart of the study.

Table 1 .

 1 Baseline demographic characteristics of the sickle cell disease population.

	EXCLUDED :
	69 eyes
	HBP* (12)
	Diabetes (8)
	Retinal surgery (2)
	RVO* (1)
	VMT* (2)
	Drusen (2)
	SE* > ± 1 D* (22)
	Poor image quality (37)
	ANALYZED :

Table 2 .

 2 Univariate analysis for occurrence of sickle cell maculopathy

	Maculopathy	No maculopathy P value

Table 3 .

 3 Univariate analysis of FAZ area

			FAZ area
		Estimator Std error t	P
					value
	SYSTEMIC CHARACTERISTICS
	Male	-0.09	0.03	7.30	0.01
	HbSS Genotype	0.07	0.03	2.07	0.04
	Hydroxyurea use	0.07	0.03	2.05	0.04
	Anticoagulation	0.09	0.04	1.97	0.05
	Chronic transfusion	0.12	0.06	1.91	0.06
	Regular phlebotomy	-0.12	0.03	-3.53 <0.01
	Osteonecrosis	0.04	0.04	1.28	0.20
	CVA*	0.00	0.06	0.00	0.98
	ACS* history	0.08	0.03	5.83	0.02
	VOC* frequency/2 years
	Rare	-0.01	0.03	0.10	0.75
	Occasional	0.09	0.03	6.75	0.01
	Frequent	0.06	0.04	3.14	0.08
	OCULAR CHARACTERISTICS
	Macular ischemia index
	1	0.001	0.018	0.00	0.94
	2	0.028	0.027	1.10	0.29
	3	0.036	0.037	0.94	0.33
	Proliferative retinopathy	-0.03	0.02	1.24	0.27
	BIOLOGICAL CRITERIA
	Hemoglobin	-0.03	0.01	-2.73	0.01
	HbF* < 15%	-0.03	0.03	0.99	0.32
	Hematocrit	-0.01	0.00	6.84	0.01
	Platelets	0.00	0.00	2.03	0.15
	PR*	-0.0004	0.00	0.24	0.62
	LDH*	0.0003	0.00	5.85	0.02
	GGT*	0.00	0.00	2.11	0.15
	α globin gene number	-0.01	0.03	-0.21	0.83
	* ACS = acute chest syndrome; CVA = cerebrovascular accident; VOC = vaso-
	occlusive crisis; PR = Prothrombin ratio; LDH = lactate dehydrogenase; GGT =
	gamma-glutamyl transferase

Table 4 .

 4 Univariate analysis of risk factors in macular ischemia ACS = acute chest syndrome; HbF = hemoglobin F; PR = Prothrombin ratio; CRP = C-reactive protein; LDH = lactate dehydrogenase; GGT = gamma-glutamyl transferase

		No	Mild	Moderate	P value
		ischemia	ischemia	or severe
				ischemia
	Number of eyes (%)	49 (32.5)	52 (34.4)	50 (33.1)	-
	Female, eyes (%)	33 (67.3)	41 (78.8)	28 (56.0)	0.40
	HbSS genotype, eyes (%)	17 (34.7)	28 (53.9)	40 (80.0)	<0.001
	Age	34.2 ± 11.2 41.0 ± 10.2	34.0 ± 9.0	0.912
	SYSTEMIC CHARACTERISTICS
	Hydroxyurea use, eyes (%)	11 (22.4)	21 (40.4)	22 (44.0)	0.055
	Regular phlebotomy, eyes	17 (34.7)	18 (34.6)	10 (20.0)	0.16
	(%)
	Chronic transfusion, eyes	2 (4.1)	1 (1.9)	8 (16.0)	0.09
	(%)
	Osteonecrosis, eyes (%)	11 (22.4)	22 (42.3)	13 (26.0)	0.66
	ACS* history, eyes (%)	17 (34.7)	21 (40.4)	26 (52.0)	0.16
	OCULAR CHARACTERISTICS
	Occurrence of maculopathy,
	eyes
	No maculopathy (%)	49 (100)	34 (65)	2 (4)	< 0.001
	Maculopathy (%)	0 (0)	18 (35)	48 (96)	-
	Proliferative retinopathy,	24 (49.0)	28 (53.9)	37 (74.0)	0.53
	eyes (%)
	Photocoagulation, eyes (%)	12 (24.5)	19 (36.5)	21 (42.0)	0.123
	BIOLOGICAL CRITERIA
	Hemoglobin (g/dL)	10.2 ± 1.7	9.0 ± 1.4	8.6 ± 1.6	<0.001
	HbF* > 15% (%)	5 (10.2)	10 (19.2)	8 (16.0)	0.42
	Hematocrit, mean ± SD	30.2 ± 5.1	27.3 ± 4.2	25.6 ± 5.6	0.01
	Platelets (G/L)	298.8 ±	311.4 ±	395.3 ±	0.01
		140.1	141.4	163.4
	CRP* (mg/L)	5.9 ± 6.1	6.0 ±7.0	5.6 ±5.2	0.842
	PR* (%)	86.2 ± 8.9 82.7 ± 17.1 77.6 ± 13.5	<0.001
	LDH* (IU/L)	323.4 ±	399.8 ±	465.5 ±	0.003
		140.5	183.5	176.9
	Total bilirubin (µmol/L)	30.7 ± 27.1 33.4 ± 25.0 47.1 ± 33.7	0.156
	GGT* (IU/L)	50.8 ± 46.5 68.7 ± 60.8 55.4 ± 46.2	0.597

*

Acknowledgments: We would like to thank Unity of Sickle Cell Disease -Pointe à Pitre, Guadeloupe, for providing the clinical, genetic and biologic data and the patients who participated to the study.

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Declaration of interest statement: None of the following authors have any proprietary interests or conflicts of interest related to this submission.

Financial disclosures: No financial disclosures. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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