This study showed that the prevalence of gestational diabetes mellitus in Blantyre is low. It also showed a wide discrepancy in the prevalence when IADPSG criteria are used compared to the WHO criteria with a 12-fold increase in the prevalence when the IADPSG criteria are used. To our knowledge this is the first description of the prevalence of gestational diabetes in the Malawian population.
The HAPO study, with an average BMI of 27 among its participants, showed a direct correlation between obesity and poor outcomes.  Our study population however, being largely young with few obese women (1% based on MUAC), was different from that described in other studies of risk factors for GDM. Furthermore, half of the women had normal BMI during pregnancy.
In the nationwide WHO Steps survey  , the prevalence of overweight and obesity among Malawian women was 16% and 2% respectively. The age of the women screened was 25-64 years, but the majority of the women screened were young as 46% of the women were between ages 25-34. Our GDM study similarly screened a young population of women and the prevalence of overweight and obesity were 9% and 1% respectively. From both studies, obesity appears to be rare amongst Malawian women.
In another 2007 study of 620 patients attending the adult diabetic clinic at QECH, the average BMI in Type 2 DM patients was 28.7.  These observations suggest that obesity may not be the main driver for the diabetes epidemic in Malawi and that other factors such as genetics, low birth weight and stunting may play a larger role.
Risk Factors for Gestational Diabetes
Advanced maternal age, high parity and attending government ANCs were associated with GDM, the older women being more likely to have high parity than the young consistent with traditional risk factors for GDM. Other known risk factors for GDM such as a family history of diabetes, a history of macrosomia or previous miscarriages or stillbirths or MUAC were not associated with GDM. As observed in the STEPS survey, the majority of diabetes in the population is undiagnosed; as such a negative family history of diabetes may in part be a reflection of this. The overall picture however highlights the fact that risk factors for developing GDM may be population specific and there may be genetic variability inherent to the population to explain such differences. This raises a cause for exploring population specific risk factors other than those stated in the WHO guidelines or those from high income countries.
Women attending private hospitals are generally perceived as having a higher socioeconomic status and more likely to adopt a diet rich in refined foods and a sedentary lifestyle than their counterparts. By including private ANCs, we anticipated to show that this group would tend to be more obese and have a higher risk of developing GDM. Our findings though were contrary to this expectation as there was no difference in terms of nutritional status between women from government facilities and those from private hospitals. Furthermore, women at private ANCs were less likely to have GDM than those in government hospitals. Diet differences between the two groups were not explored in particular but it appears that the risk that may be conferred by sedentary habits or a westernized diet may be balanced by better health seeking behavior and ready access to screening and diagnostic services in the private hospitals.
Random blood glucose measurements were largely normal as only 3 women had RBG >11.1mmol/L and 75% of the study population had an RBG below 5.5 mmol/L. Other than the RBG being an insensitive screening tool, it was also observed on random questioning that many of the women at the health centres had not eaten for some time before the measurement particularly those that had to leave their homes early in the morning to attend clinic on time. Their results may reflect a fasting rather than random blood glucose and may explain the large proportion of women with normal RBG. There was no correlation between the random blood glucose and gestational diabetes diagnosed by OGTT or risk factors for diabetes. Random blood glucose may therefore not be a sensitive screening tool or used as a proxy for OGTTs in this population.
The prevalence of GDM using WHO criteria of 1.6% was lower than that described in other African studies using the same diagnostic criteria (3.8% - South Africa and 3.8% in Ethiopia) [11,12] but comparable with what was expected by local obstetricians who estimated prevalence between 2-3% amongst women attending antenatal clinics (B. Makanani personal communication). GDM was rare even amongst those with traditional risk factors for GDM suggesting there may be a unique environmental or genetic influence on risk factors for GDM in this population.
Using IADPSG criteria, the prevalence of GDM was 12 times higher compared to the WHO criteria and interestingly showed a higher prevalence in government ANCs compared to private ANCs.
We anticipated to find a higher prevalence of GDM using IADPSG criteria as compared to WHO criteria as has been described in other studies. There are no other published studies from African populations for comparison. Many studies have compared prevalence using the two criteria with some finding the two to be comparable.  The decision to change the criteria depends on performing careful cost analysis and weighing the risk benefit ratio particularly in a population that is different from the HAPO population – performance in a non HAPO population thought to be lower.  In a low income setting, particularly where maternal obesity which was shown to be an independent risk factor for poor perinatal outcomes (Catalano) is rare, priority should probably be placed on treating those diagnosed with GDM based on WHO criteria.
There was a large loss to follow up amongst the women diagnosed with GDM which precludes definitive conclusions on outcome. The causes of the four miscarriages among the women diagnosed with GDM were not explored further.