Gestational diabetes mellitus (GDM) is a condition of glucose intolerance during pregnancy (1). It usually develops in the second or third trimester and may resolve after delivery. GDM results from insufficient insulin production to regulate blood glucose levels during pregnancy. The prevalence of GDM worldwide ranges from <1 to 28%, with an 8% prevalence in Pakistan (2). According to recent estimates, approximately 18.6 million women have experienced various hyperglycemic conditions, with 18.6% of them suffering from GDM (3). The prevalence of GDM is higher in the South Asian region, with a recorded prevalence of 24.2% (4). The WHO and International Association of the Diabetes and Pregnancy Study Groups criteria (IADPSGC) note that neonatal and maternal complications such as macrosomia, respiratory distress, perinatal mortality, shoulder dystocia, childhood obesity, and neonatal hypoglycemia may arise in women with GDM (5, 6).

Abnormal glycolysis may have a negative impact on lipid metabolism. Placental hormones such as estrogen, prolactin, and human placental lactogen are significant contributors to insulin resistance (IR) and maternal adiposity, resulting in atherogenic dyslipidemia (AD) (7). Hyper-dyslipidemia during the latter half of pregnancy is considered a necessary mechanism for providing nutrients and metabolic fuel to the fetus (8). A primary risk factor for developing gestational diabetes (GDM) and change in lipid metabolism is Maternal obesity (9, 10). A prior study demonstrated that overweight women with increased triglycerides are at a greater risk of developing GDM than lean women with higher HDL (9). Additional risk factors for dyslipidemia include activity level, smoking status, age, blood glucose level, gender, and overall patient health (11). Pregnancy and GDM have a cumulative effect on the lipid profile. In diabetes, HbA1c (glycated hemoglobin) is a commonly used indicator for measuring blood sugar levels, reporting the average plasma glucose over approximately 8–10 weeks. Elevated HbA1c disrupts the lipid profile, with a 1% increase in HbA1c resulting in an 18% increase in the lipid profile and a higher risk of several cardiovascular diseases (12). During pregnancy, dyslipidemia characterized by elevated triglycerides (TG) or total cholesterol (TC) may increase the risk of preterm delivery, the second most common cause of death in children under 5 years of age (13). Moreover, it raises the risk of metabolic derangement later in life.

Hemoglobin is a protein that carries oxygen and circulates in the bloodstream, supplying oxygen to bodily tissues. During pregnancy, there is an increased demand for red blood cells, which in turn increases the demand for iron and vitamins. This increased demand for iron and vitamins affects the level of hemoglobin. Iron deficiency may result in reduced hemoglobin production, leading to iron deficiency anemia, which is a common cause of anemia during pregnancy. According to the World Health Organization (WHO), a woman is considered anemic if her hemoglobin level is less than 11 g/dL (14). Consuming iron-rich foods or taking iron supplements can help compensate for the iron deficiency and meet the body’s needs. However, increasing iron levels can have adverse effects: a previous study has shown that the prevalence of GDM is strongly linked with high levels of iron in the blood (15).

This study aims to investigate the significant correlation between Hb levels, dyslipidemia, and HbA1c with GDM. To the best of our knowledge, this is the first report on the correlation of GDM with various biochemical parameters (including Hb, HbA1c, and lipid profile) in a single population in Pakistan.

This study aims to investigate the correlation between hemoglobin (Hb) levels and lipid profiles with gestational diabetes mellitus (GDM) in a case-control manner. Previous studies have typically examined the correlation between Hb or lipid profile and GDM in discrete cohorts, rather than in a single population. Our findings demonstrate that age is a contributing factor for GDM, as it differed significantly between the two groups (p < 0.05). These results align with other studies that have found advanced maternal age, in addition to other maternal characteristics, to be a predisposing factor for GDM (16, 17). A meta-analysis of approximately 120 million participants also reported maternal age as a risk factor for GDM (11). The meta-analysis included 24 studies, which reported a linear relationship between maternal age and GDM (P trend <0.001). Furthermore, it revealed that for Asian women, every one-year increase in maternal age results in a 12.74% increase in the risk of developing GDM.

There was a statistically significant difference in Hb levels between the two study populations (p < 0.05). Women with GDM had lower levels of iron in their blood compared to the non-GDM group. During gestation, many women experience lower Hb levels in their blood and may take iron supplements to compensate for iron deficiency. Variations in iron levels affect maternal glucose homeostasis and may lead to the development of GDM (18). Our findings showed a mild negative correlation (r = −0.294) between Hb and HbA1c. This contravenes a study from Korea, which reported an association between higher Hb levels and GDM (19). Most previous studies have not examined the correlation of Hb and lipid profile with GDM in a single population, which makes our findings particularly noteworthy. Additionally, our results indicate that advanced maternal age is a significant contributing factor for GDM, which is consistent with other studies (11, 16, 17).

HbA1c is an indicator of the average blood glucose levels for 3–4 months and is considered a reliable measure of the sugar level of diabetic patients. In our study, we found statistically significant differences in HbA1c levels between the two groups (p < 0.05). An independent sample t-test was used to compare the mean and standard deviation of HbA1c between the GDM and non-GDM groups. Our findings are consistent with a previous study that reported higher HbA1c levels in the GDM group compared to the non-GDM group and a positive linear correlation between HbA1c and blood glucose levels (p < 0.001) (20).

Our analysis revealed significant differences in TC, HDL, and LDL between the GDM and non-GDM groups (p < 0.05). These findings are consistent with the results of a previous hospital-based study that showed significant differences in lipid parameters between the two groups (p < 0.05) (21). However, our results contradict those of a previous study from Nigeria, which found no significant association between lipid profiles and GDM status (22).

Pearson’s correlation was used to estimate the correlation between lipid profile and HbA1C. Statistically significant results were found for all four lipid parameters: TC, HDL, LDL, and TG, with glycated hemoglobin. All lipid parameters, except HDL (which showed a negative correlation), were positively correlated with HbA1C. The negative correlation between HDL and HbA1C indicates that an increase in HbA1C levels may lead to a decrease in good cholesterol, resulting in various complications. In gestational diabetes mellitus, an increase in maternal lipid profile may cause the accumulation of fetal fat as it increases the activity of lipoprotein lipase, which results in a higher rate of fatty acid transfer through the placenta (8). Our findings are consistent with previous studies conducted in China, which identified the lipid profile as an independent risk factor for GDM (23, 24). While changes in lipid profile during pregnancy are considered normal, the risk of blood lipid disorders is higher in gestational diabetes mellitus, and monitoring of the lipid profile during early pregnancy is recommended (25).

GDM represents a prevalent pregnancy complication, often manifesting between the 24th and 28th gestational week (26). This condition engenders diverse maternal and fetal complications. Maternally, it may contribute to recurrent vaginal infections, heightening the risk of cesarean section, and inducing polyhydramnios (27). Conversely, the fetus can experience macrosomia and various metabolic perturbations. Our study offers indispensable insights into the multifactorial determinants of GDM and underscores the significance of vigilant maternal health surveillance during pregnancy. These revelations carry profound implications for the clinical management and risk stratification of expectant mothers grappling with GDM. Primarily, the cornerstone of GDM management entails dietary modifications and structured exercise regimens (28). Remarkably, our research corroborates that a substantial proportion of pregnant women with GDM, ranging between 70 and 90%, can achieve glycemic control through non-pharmacological interventions exclusively (29). Furthermore, the National Institute for Health and Care Excellence (NICE) formally advocates for exercise, dietary adjustments, and lifestyle modifications as crucial non-pharmacological interventions for GDM (30). Supplementary to these measures, the incorporation of Myo-inositol within a tailored therapeutic regimen, which may encompass insulin therapy, oral hypoglycemic agents, and metformin, can confer additional benefits to the patient (31). Furthermore, owing to the inherent maternal and fetal risks associated with GDM, healthcare professionals have a professional obligation to engage in comprehensive discussions with patients. These dialogs should encompass an elucidation of the procedural risks and benefits, as well as potential alternatives (32). The medical community is increasingly embracing the concept of personalized medicine (33), necessitating a keen focus on individualized care and patient priorities (34). In a broader context, emerging studies have illuminated the potential of artificial intelligence, centered around human expertise, to augment and enhance the capabilities of healthcare professionals concerning reproductive medical issues (35). The correlations unearthed by our study advocate for the vigilant monitoring and management of hemoglobin, dyslipidemia, and HbA1c levels during pregnancy. This proactive approach may serve as a pivotal tool in the early identification and prospective prevention or management of GDM.

The limitations of this study include the impact of selection bias, confounding factors, and a heterogeneous population. Future studies should address these limitations in their study design. Nonetheless, this study provides valuable guidance for healthcare professionals in identifying factors associated with GDM, which can lead to personalized and prioritized care for patients.

Certainly, based on our study’s findings, we can provide the following possible recommendations for healthcare professionals who are involved in the care and management of pregnant women with GDM: Conduct a thorough risk assessment for each pregnant woman, considering factors such as hemoglobin levels, dyslipidemia, and HbA1c during pregnancy. Utilize these risk assessments to stratify patients into low, moderate, or high-risk categories. Tailor care plans based on the individual risk profiles of pregnant women with GDM. Customize dietary recommendations and exercise programs to match each patient’s unique needs and risk factors. Emphasize the importance of non-pharmacological interventions, including diet and exercise, as first-line treatments for GDM. Educate the patients about the benefits of lifestyle modifications and their potential to achieve glycemic control without medication. Engage in open and patient-centered discussions with pregnant women diagnosed with GDM. Explain the risks and benefits of different treatment options, allowing the patient to make informed decisions about their care. Provide alternatives and address any concerns or questions the patient may have regarding their treatment plan. Implement regular monitoring of glycemic control, hemoglobin levels, and lipid profiles throughout the pregnancy to track progress and make necessary adjustments to the care plan. Adjust the care plan as needed to ensure that glycemic targets are met and that the patient remains within their individualized risk category. Stay current with the latest research and clinical guidelines related to GDM management. Be prepared to adapt care plans and recommendations based on evolving evidence and best practices. Collaborate with other healthcare professionals, such as dietitians, obstetricians, and endocrinologists, to provide comprehensive care for pregnant women with GDM. Ensure that the patient receives holistic care addressing both diabetes management and overall maternal and fetal health.

These recommendations are intended to guide the healthcare professionals in optimizing the care and management of pregnant women with GDM. By incorporating these findings into clinical practice, healthcare providers can contribute to improved outcomes and the well-being of both mothers and their babies.

This study provides essential insights into the factors associated with GDM and the importance of monitoring maternal health during pregnancy. These findings may have crucial implications for clinical management and risk stratification for pregnant women with GDM. However, the study is limited by selection bias, confounders, and heterogeneous population, which should be considered while interpreting the results. Future studies should take into account these limitations and continue to investigate factors associated with GDM to improve the care of pregnant women and their infants.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

This research was approved by the Ethical Review Committee of GCUF (Ref. No. GCUF/ERC/37). This research involved human participants and all the protocols about human participants were followed. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

MA contributed to project administration, conceptualization, study design, and manuscript writing. SN contributed to formal analysis, sample detection, data processing, and literature search. KR contributed to conceptualization, study design, data curation, and revising it critically for intellectual content. AN contributed to funding acquisition and recourses management. MK contributed to manuscript review. All authors contributed to the article and approved the submitted version.