A hospital-based cross-sectional survey with retrospective data collection combined with a comparative component was carried out over a period of 24 consecutive months from January 2018 to December 2019.

The cross-sectional component was used in determining the prevalence of obesity among the infertile women.

The comparative component matched the infertile respondents (cases) with fertile women (controls) according to their ages in order to establish a statistical association between obesity and Sub-fertility in our setting.

The target population comprises all women who sought fertility treatment at the University of Cape Coast Hospital during the study period. The comparative population of the survey comprises clients who attended the hospital’s antenatal clinic for booking in the first trimester of pregnancy during the study period. The controls were matched to cases according to ages and were selected randomly from the hospital ANC registers.

2.2.1. Inclusion Criteria

The cases (Infertile women)

All women who sought treatment for subfertility at the University of Cape Coast Hospital within the period of January, 2018 to December, 2019.

The Control (Fertile women)

Clients who reported for antenatal booking during the first trimester in pregnancy at the University of Cape Coast Hospital within the period of January 2018 to December 2019 with documented pre-pregnancy weight or weight at booking. Those women are selected as control because being pregnant ruled out Sub-fertility and pregnancy weight during the first trimester can be a reflection of the pre pregnancy weight.

2.2.2. Exclusion Criteria

The cases (Infertile women)

Patients with incomplete data in their folders were excluded.

The Control (Fertile women)

All clients who reported for booking beyond the first trimester of pregnancy were excluded.

Census was carried out to include all cases of sub-fertility, who reported at the hospital during the study period and simple random sampling was used to select the controls.

A structured pre designed data extraction tool was used in data collection. The data extraction tool comprised items to collect socio-demographic, medical and obstetric history and the body mass index information.

Data was entered into Statistical Package for Social Sciences (SPSS) version 24 and analysed. Odds Ratio (OR) test, with a confidence interval at 95% was used to compare proportions.

Ethical clearance was obtained from the University of Cape Coast Institutional Review Board with IRB ID: UCCIRB/CHAS/2019/142, The data was anonymized.

This study has two main limitations that should be considered when interpreting the findings. First, it is a single centre retrospective study which limit the generalization of the findings to the city of Cape Coast and also the inability to confirm causal relationship.

Second, there is an absence of data on potential confounders like Polycystic Ovarian Syndrome (PCOs).

The mean age of the sub-fertile group was 35.51 years with a standard deviation of 5.53 years and the ages ranged from 23 and 48 years.

It was found that more than four out of ten (43.6%) and two out of ten (23.3%) were within the age groups of 23 to 34 and above 40 years respectively.

Table 1 represents the age distribution for the sub-fertile group.

Table 1. Age distribution of the infertile group. (n = 103)

The Results revealed that more than half of respondents (55.3%) were Traders/self employed, majority of them (83.5%) were married, and urban residence was the commonest (86.4%).

Table 2presents the results of some socio-demographic characteristics of the sub-fertile group of women.

Table 2. Socio-demographic characteristics of the infertile group. (n = 103)

During the study period (January 2018-December 2019), 672 patients visited the Gynecology department of the UCC Hospital for various reasons. Out of this number, 103 was diagnosed with subfertility which gives a prevalence of 15.3% (103/672). Primary sub-fertility (53.4%) was more prevalent than secondary subfertility (46.6%). Ovarian factors were the highest (41.7%) cause of sub-fertility followed by uterine (18.5% ) and male factors (8.5%). Tubal factors (11.6%). unexplained sub-fertility accounted for 9.7% of cases.

3.3.1. Prevalence of Obesity Among the Infertile Group

The prevalence of obesity among the Sub-fertile patient was 44.6% (46/103) and both overweight and obesity accounted for 74.6%.

Table 3presents the prevalence of the various BMI categorizations among the sub-fertile population.

Table 3. Prevalence of obesity among sub-fertile group. (n=103)

3.3.2. Prevalence of Obesity among Fertile Group

The total population of the fertile group was 120 respondents who were matched according to the age with the sub fertile group. It was found that more infertile women (44.6%) than sub-fertile ones (18.3%) were obese.

Table 4 compares the BMI categories between the sub-fertile and fertile groups.

Table 4. Comparison of BMI categories between sub-fertile and fertile group.

A test of Odds Ratio with a confidence level of 95% was performed for the comparative component of the survey. The results revealed that obesity alone versus other BMI categories (overweight, normal weight and underweight) was significantly associated with sub-fertility status, obese women are 3 times likely to be infertile (Odds Ratio = 3.59) with a confidence interval of (1.96; 6.57). Similarly when obesity and overweight are combined, there is significant association with sub-fertility status, overweight and obese women are almost 2 times likely to have sub-fertility (Odds Ratio = 1.90, with a confidence interval of [1.07; 3.39].

In this study, most women were in their reproductive age which ranged from 23 to 48years with a mean age of 35.5 years and standard deviation of 5.53 years. These findings were predictable, because sub-fertility affects women in their reproductive ages. These are similar to the study populations in other surveys related to sub-fertility conducted in Mangalore and in Pakistan where study participants were within the age ranges of 18 to 45years and 20 to 40 years respectively [11][12].

Our study has demonstrated that an increased Body Mass Index (overweight and obesity) yielded a higher percentage of sub-fertility. We found that more than four out of ten (44.6%) sub-fertile women were obese, three out of ten (30.0%) being overweight. Both overweight and obesity accounted for 74.6% among the sub-fertile subjects.

Similar observations were demonstrated by two studies conducted in India and Pakistan which also found that overweight and obesity collectively accounted for a higher percentage of women who were diagnosed with sub-fertility [11][12].

Other studies have also shown the negative effect of obesity on reproduction, such as the studies in Iran [13] and Greece [14].

It has been established in a number of studies that obesity and fertility statuses are closely related, with obesity having a negative impact on fertility. In our study, the test of association using Odds Ratio (OR) test, with a confidence interval at 95% conducted between obesity versus normal BMI and overweight and fertility statuses, there was a significant association, obese women are 3 times more likely to suffer from sub-fertility (OR = 3.59).

A similar result was obtained, when the same test was performed between obesity plus overweight versus normal BMI, and fertility statuses (OR = 1.9). This implied that there was an association between obesity status and the presence of sub-fertility. This is in line with the findings of a study in the Netherlands which revealed that obesity was associated with lower pregnancy rates in sub-fertile ovulatory women [15]. Similarly, a study in Australia also revealed that high body weight, which subsequently leads to obesity was strongly associated with various reproductive sequelae, most especially, sub-fertility [16]. In further support of previous findings which established an association between obesity and fertility status, a review found that increase in body mass index (BMI) was strongly associated with ovulatory subfertility and anovulatory sub-fertility and also had an impact on fertility treatment outcome [17].

There are scientific evidence linking obesity to sub-fertility, particularly female sub-fertility through a disruption of the hormonal control of oocyte maturation and endometrial priming for implantation via an alteration of the normal hypothalamic-pituitary-ovarian axis [18][19]. In addition, the adipose tissues produce signaling molecules which disrupt the normal menstrual cycle and its ovulation [19][20].