Low fertility may be a significant determinant of ovarian cancer worldwide: an ecological analysis of cross- sectional data from 182 countries

Ovarian Cancer (OC56, abbreviated as per the International Classification of Diseases published by the WHO) [1] ranks among the top ten most commonly diagnosed cancers and top five deadliest cancers in most countries [2, 3]. In 2015, OC56 was present in 1.2 million women and resulted in 161,100 deaths worldwide [4] . In the twenty-first century, a woman’s overall lifetime risk of developing OC56 is around 1.6% [2, 5, 6], and her chance of dying of the disease is 1 in 100 [2, 6].

Although OC56 has been known to medical scientists for over 150 years [7], the aetiology of this lethal disease is not well understood. Most research on the aetiology of OC56 has focused on genetic and environmental carcinogenic factors, such as talc, pesticides, red meat and alcohol in diet, smoking, and herbicides. However, to date, none of these factors has been consistently shown to be a major risk factor for the development of OC56 [8]. Alternative hypotheses for the aetiology of the disease have also been suggested. Several studies have suggested that, obese women (those with a body mass index of at least 30 kg/m²) may have a greater risk of developing OC56 because of their elevated levels of circulating estrogen [9‐11]. An accumulation of somatic mutations has been suggested as the mechanism for the higher incidence of the disease in women over the age of 45 [9]. Urbanization may have improved public hygiene, sanitation and access to health care for women [12], but it has been associated with public health issues, including OC56 [13] due to the changes in occupational, dietary and exercise patterns [6, 12, 14, 15].

Natural selection, as one of the key mechanisms of evolution, differentiates phenotypes’ survival and/or fertility that reflect genetic differences. The Biological State Index (I_bs) has been constructed to measure the opportunity for natural selection through differential mortality at the population level. The I_bs calculation combines life table function d_x (number of deaths at age x) with the age-specific completed relative fertility rate s_x (fraction of total fertility rate to a woman up to age x): I_bs = 1 – Σd_xs_x [16‐21]. I_bs can be used as a way of measuring the opportunity for an individual born into a given population to pass on its genes to the next generation [17, 19, 22‐24]. I_bs has been postulated to reflect changes in the mutation-selection balance as a result of the effect of improved healthcare on relaxing natural selection and thus measure the magnitude of accumulation of the deleterious genes [16], including those responsible for cancers such as OC56 [17], type 1 diabetes [18] and obesity [18, 19] in human populations.

The association between low fertility and OC56 risk has been well described and it has been postulated that this risk increases in women who have ovulated less over their lifetime either through infertility or administering the combined birth controls, such as contraceptive pills [6, 25‐32].

To the best of our knowledge, despite that low fertility is a well-established risk factor for OC56, no research has compared the contributing effects of fertility to OC56 with other OC56 risk factors, such as ageing, I_bs (index of magnitude of OC56 genes accumulation in human populations), obesity and socioeconomic factors (GDP and urbanization).

There is significant variation in the incidence of OC56 between different geographic regions globally [2, 3, 33‐35]. This phenomenon has also been observed in different populations [6, 13] within the same countries [36, 37]. A number of publications suggest that the disparity between regions and populations is related to socioeconomic level.

In this study, empirical macro-level data have been used to test the hypothesis that fertility (measured by the crude birth rate) is the principal determinant of developing OC56, and that it is fertility, instead of GDP, that is most important factor in shaping the regional variation of OC56 incidence rate.

The relationship identified in the scatterplots between fertility and OC56 was noted to be polynomial with a strong, but inverse (negative) correlation (R² = 0.485, p < 0.001, n = 179, Fig. 1).

The strong relationship between fertility and OC56 identified in the scatterplots was confirmed by the subsequent nonparametric and Pearson r analyses based on the log-transformed data.

Globally, fertility was significantly and negatively correlated to OC56 incidence (r = − 0.632 and rho = − 0.655, p < 0.001 respectively in Pearson and non-parametric analyses) (Table 1).

It is also found that ageing, GDP, I_bs, obesity and urbanization had strong and significant correlations to OC56 incidence in both Pearson and non-parametric analyses respectively (Table 1).

The relationship between OC56 and each independent variable (ageing, fertility, GDP, I_bs, obesity and urbanization) was tested by keeping the other five variables statistically constant in partial correlation analysis. Fertility was the only predictor showing a substantial significant correlation (r = − 0.448, p < 0.001) with OC56 independent of the other five variables (Table 2). Ageing showed significant, but weak correlation to OC56 (r = − 0.178, p < 0.05). The Fisher r-to-z transformation revealed that OC56 was in significant stronger correlation with fertility than with ageing (z = 2.68, p < 0.01). GDP, I_bs, obesity and urbanization showed significant correlation to OC56 in the bivariate correlation analyses respectively. However, none of these variables showed a significant correlation with OC56 independent of the other five predictors. This indicates that fertility is the only significant predictor of OC56 independent of the secondary association between OC56 incidence and I_bs (magnitude of OC56 accumulation) and environmental factors (ageing, fertility, GDP, obesity and urbanization).

Standard multiple linear regression (enter) analysis was applied to predict OC56 incidence when ageing, fertility, GDP, obesity and urbanization were included as the independent predicting variables.

When fertility was excluded as one of the independent variables, GDP PPP (β = 0.471, p < 0.001) and I_bs (β = 0.250, p < 0.05) were the two significant variables related to OC56 incidence. However, when fertility was included as an independent predictor, only the correlation between fertility and OC56 incidence was strong and significant. None of the other five predictors showed strong and significant correlation to OC56 (Table 3). Similarly, in a stepwise linear regression model, when fertility was not included as one of the independent predictors, GDP and I_bs were selected as the variables having the greatest influence on the development of OC56. However, when fertility was included together with the other five independent variables, only fertility was selected as the most influential predictor of OC56 with the R² increase from 0.278 to 0.434. This suggested that GDP and I_bs did not appear to account for the major part of the impact on OC56 incidence. This finding supports our previous suggestion that fertility is the significant predictor of OC56 incidence in partial correlation analysis.

Table 4 showed that the mean OC56 incidence rate was lowest in Africa (4.19) and highest in Europe (8.70). The means of OC56 in the other four regions were 5.89 (Americas), 5.19 (Eastern Mediterranean), 5.90 (South East Asia) and 6.63 (Western Pacific). A post hoc Bonferroni analysis conducted on the multiple comparisons of means revealed that there were a number of significant differences in mean OC56 incidence rates between different WHO regions (Table 4). Mean of OC56 incidence in Europe was significantly greater than in Africa, Americas, East Mediterranean, South East Asia and West Pacific. Mean of OC56 in Americas was significantly greater than in Africa. The regions with greater means of fertility had lower means of OC56 incidence rates (r = 0.985, p < 0.001, n = 6).

A subsequent ANOVA with post hoc Bonferroni procedure performed on the means of “Residual of OC56 standardised on fertility” in different WHO regions showed there was no significant difference among and between regions (Table 4). Whilst the same procedure was performed on the means of “Residual of OC56 standardised on GDP PPP”, the developed region, Europe still had the significantly higher “Residual of OC56 standardised on GDP PPP” than Africa, Americas and East Mediterranean (Table 4). The results from the post hoc Bonferroni tests conducted on comparisons between the WHO regions suggested that regional variations of OC56 incidence may only reach statistically significant levels if the contributing effect of their respective fertility was included. In other words, except for fertility, the total contribution of the other OC56 risk factors to OC56 incidence may not be sufficient for the difference in mean rates to reach significance level. This result was supported by the findings identified in our previous partial correlation (Table 2) and multiple linear regression analyses (Table 3) that fertility is the critical risk factor for OC56.

The worldwide secular trend of increased OC56 incidence may have multiple etiologies, which may act through multiple mechanisms at different magnitudes. By examining the correlations of OC56 with low fertility, ageing, GDP, I_bs, obesity and urbanization respectively, this study has shown that only fertility and aging were correlated with the OC56 incidence significantly, although the latter was not as strongly. Statistically, this may suggest that low fertility was the most significant risk factor for OC56 when compared to ageing, GDP, I_bs, obesity and urbanization. This finding is in agreement with three studies conducted by Hankinson et al. [32], Vachon et al. [31] and Cramer et al. [52] respectively which concluded that fertility is a significantly greater predictor of OC56 risk than other commonly used epidemiological variables.

The relationship between female reproductive performance and gynecological cancers has been known for over 300 years [30, 53]. Previous studies in multiple different populations have shown that nulliparous women have a 30–60% greater risk than parous women [52, 54]. Studies also reported that each additional full-term pregnancy lowers OC56 risk by approximately 15% [54, 55]. The mechanism of the influence of childbearing on reducing OC56 risk may be that full-term pregnancy, post-partum period and sometimes the subsequent lactation involve anovulation, suppress secretion of pituitary gonadotropins, lower levels of oestrogen [56‐59], lessen exposure of the ovaries to chronic inflammation and mutation [60], and reduce proliferation of malignant transformations in the inclusion cysts and clefts which are invaginated and formed in the ovarian epithelium during ovulation [61].

Recent studies suggested that women with greater fertility may receive the protection against developing OC56 because:

This study revealed that low fertility determines the variation of OC56 incidence rate among the WHO regions. This finding contradicts the WHO and IARC’s statement that socioeconomic level is the determinant of regional variation of OC56 incidence rate [2, 6, 34]. This may suggest that the correlation between fertility and socioeconomic status (SES) is spurious – caused by the correlation of both variables (SES and OC56 incidence) to the same one (fertility) [80‐82].

The strength of this study is that it uses an ecological study approach, different from hitherto used approaches, to demonstrate that low fertility is a significant determinant of OC56 risk.

We need to note several limitations of this study:

Low fertility appears to be a significant and strong determinant of OC56 risk independent of ageing, GDP, I_bs, obesity and urbanization. These findings may be helpful for governments, policy-makers, funders, clinicians and researchers when determining future screening and primary presentation strategies for the disease [32, 83, 84].