Neonatal hypoglycaemia remains one of the most common metabolic disturbances encountered in the early neonatal period and poses a significant risk to short- and long-term neurodevelopmental outcomes if not identified and corrected promptly [1,2]. The clinical presentation is often subtle or even absent — many neonates remain asymptomatic — underscoring the importance of systematic monitoring rather than reliance on clinical signs alone [3,4].

Multiple perinatal and maternal factors have been implicated in increasing the risk of neonatal hypoglycaemia. According to a recent meta-analysis of 12 studies, small gestational age, maternal gestational diabetes, gestational hypertension, and neonatal respiratory distress syndrome were significantly associated with hypoglycaemia [1]. Preterm birth and low birth weight have also been repeatedly highlighted as important determinants, potentially due to inadequate glycogen and fat stores, as well as immature gluconeogenesis mechanisms in the preterm or small-for-gestational-age infant [2,5].

Further, maternal metabolic conditions — especially diabetes during pregnancy — are strongly associated with neonatal hypoglycaemia. Infants born to diabetic mothers often experience hyperinsulinemia in utero; following birth, abrupt cessation of maternal glucose supply may precipitate hypoglycaemia in the context of persistently elevated insulin levels [6,7].

Despite recognition of these risk factors, the relative contribution of each — especially in resource-limited settings and tertiary-care units in developing countries — remains underexplored. Additionally, symptomatic hypoglycaemia represents only a subset of affected neonates, implying that many cases could remain undetected without proactive screening.

Given this background, the present study was undertaken in a tertiary care NICU in Bhuj, Gujarat, with the aim of systematically documenting demographic, perinatal, and clinical risk factors associated with neonatal hypoglycaemia. Our objective was to generate local data that may support targeted screening and early intervention protocols, thereby reducing potential neonatal morbidity.

Study Design: This investigation employed a cross-sectional, observational design conducted within a tertiary-level hospital environment.

Study Setting and Study Population: The study enrolled neonates who either presented with hypoglycaemia at admission or developed low blood glucose levels during their stay in the Neonatal Intensive Care Unit (NICU) of Gujarat Adani Institute of Medical Sciences (GAIMS) and G.K. General Hospital (GKGH), Bhuj, Gujarat.

Study Duration: Data collection and related study activities were undertaken over an 18-month period, extending from July 2024 to December 2024.

Sample Size and Sampling Technique: The sample size estimation was based on the formula used for determining a single population proportion with relative precision:
n=(Z²₁–α/₂×P×Q)/ε², where Z₁–α/₂ corresponds to 1.96 for a 95% confidence interval, P represents the anticipated prevalence of neonatal hypoglycaemia (51% as per Thinesh Kumar J. et al.), Q = 100 – P, and ε denotes the permissible relative precision (20% of P). The calculated minimum sample size was 92; however, to ensure adequate representation, a rounded sample size of 100 neonates was selected. Participants were recruited using a convenience sampling method.

Inclusion Criteria: Neonates with documented hypoglycaemia, defined as a random blood sugar (RBS) value below 45 mg/dL.

Exclusion Criteria: Neonates whose parents or legal guardians declined to provide written informed consent.

Ethical Considerations: Prior to initiation, ethical approval was obtained from the Institutional Ethics Committee of Gujarat Adani Institute of Medical Sciences, Bhuj. Written informed consent was taken from parents or guardians of all eligible neonates. Participant confidentiality and anonymity were maintained at all stages of the study.

Data Collection Procedures: Prospective data collection was conducted from September 2024 to February 2025. Parents or guardians received an information sheet describing study objectives and procedures. For illiterate respondents, the information sheet was read aloud, and consent was obtained through signature or thumb impression.

Clinical data were recorded using a structured proforma. Blood glucose assessment was performed via heel-prick sampling using a glucometer. These devices function on amperometric principles, where the electrochemical current generated at a fixed potential is directly proportional to glucose concentration. Modern test strips contain a disposable enzymatic biosensor system with working, reference, and counter electrodes. Enzymes such as glucose oxidase or glucose dehydrogenase catalyse glucose conversion to an electroactive product, generating a measurable current corresponding to the blood glucose level.

Neonates with RBS values below 45 mg/dL underwent confirmatory plasma glucose estimation, which remains the reference standard. Laboratory analyses were conducted using glucose oxidase-based colorimetric assays or electrode-based methods used in blood gas analysers. To avoid post-collection glycolysis, samples were processed promptly and collected in tubes containing glycolytic inhibitors, such as sodium fluoride.

Demographic and clinical characteristics—including gestational age, birth weight, maternal history, and perinatal conditions—were systematically documented. Risk factors such as maternal diabetes, large-for-gestational-age status, pregnancy-induced hypertension, intrauterine growth restriction, sepsis, respiratory distress syndrome, and meningitis were also recorded. Outcomes including discharge, death, and discharge against medical advice (DAMA) were noted. All data were transcribed into Microsoft Excel (2021) for digital storage and subsequent analysis.

Data Analysis: Statistical analysis was performed using SPSS version 29.0.2.0. The distribution of sociodemographic and clinical variables was evaluated using normality plots. Descriptive statistics were computed as mean and standard deviation for continuous variables and as frequencies and percentages for categorical variables. Associations between clinical features and neonatal outcomes were examined using suitable statistical tests, with a significance threshold of p < 0.05. Findings were summarised using tables and graphical illustrations.

A total of 100 neonates diagnosed with hypoglycaemia were included in the study. Of these, 62% were male and 38% were female (Table 1). With respect to gestational maturity, late preterm infants constituted the largest subgroup (34%), followed by term neonates (31%). Moderate preterm and early-term groups accounted for 13% and 16% of the cohort, respectively, while very preterm and extreme preterm infants represented smaller proportions (5% and 1%).

Birth weight assessment demonstrated that nearly half of the neonates (48%) had a weight above 2.5 kg, corresponding to the normal weight category (Table 2). Low birth weight (LBW) infants constituted 44% of the study population, with a mean weight of 2.25 ± 0.23 kg. Very low birth weight (VLBW) neonates accounted for 7%, and extremely low birth weight (ELBW) neonates comprised 1% of the cohort. The overall mean birth weight of the study sample was 2.74 ± 0.80 kg.

Evaluation of potential etiological factors revealed that more than half of the neonates (56%) had sepsis, making it the most frequently identified risk condition (Table 3). Pregnancy-induced hypertension (PIH) in the mother was noted in 42% of cases. Infants of diabetic mothers (IDM) constituted 27% of the cohort. Other significant contributors included intrauterine growth restriction (IUGR) in 33% of neonates and polycythaemia in 21%. Additional comorbidities were observed in 32% of the neonates.

Most neonates were asymptomatic at presentation (Table 4). Among those with symptoms, jitteriness was observed in 6% of cases, while lethargy and seizures were each reported in 4%. Refusal to feed was noted in 2% of neonates. No additional symptoms were documented in the study population.

Table 1: Baseline profile of study participants

Table 2: Distribution of Neonates by Birth Weight

Table 3: Distribution of Risk factors

Table 4: Distribution of Clinical Symptoms among Neonates with Hypoglycemia

In our cohort of 100 neonates with hypoglycaemia, several risk factors — notably sepsis, maternal pregnancy‑induced hypertension (PIH), maternal diabetes, and extremes of birth weight and gestational age — were frequently observed. These findings broadly align with recent evidence. A 2024 meta‑analysis summarizing 12 studies reported that small gestational age (SGA), maternal gestational diabetes, gestational hypertension, and respiratory distress syndrome were significantly associated with neonatal hypoglycaemia [1].

The high proportion of neonates with sepsis (56%) in our sample underscores the role of infection/inflammation as a contributor to hypoglycaemia in NICU settings. In resource‑limited and intensive‑care settings, neonatal sepsis has been repeatedly identified as a significant risk factor for low glucose levels, likely through increased metabolic demand and impaired glucose homeostasis [8,9].

Similarly, maternal metabolic status and antenatal complications contribute meaningfully to neonatal glucose disturbances. A large retrospective study involving infants born to mothers with gestational diabetes mellitus (GDM) reported that poor maternal glycemic control and excessive gestational weight gain were strongly associated with neonatal hypoglycaemia [6,10]. In our study, 27% of neonates were infants of diabetic mothers (IDM), which aligns with the recognized increased risk in this group.

Our birth-weight distribution — with 44% low-birth-weight (LBW) and only 48% normal-weight neonates — suggests that low or suboptimal birth weight remains a critical determinant of neonatal hypoglycaemia, likely due to reduced glycogen and fat stores, limited hepatic glycogenolysis, and immature gluconeogenesis. This notion echoes prior reports that preterm and LBW infants are particularly vulnerable to early postnatal hypoglycaemia [8,11-13].

An important observation in our cohort was the low frequency of overt clinical symptoms: only 6% had jitteriness, 4% lethargy, 4% had seizures, and 2% feeding refusal. This highlights a well-known challenge: many hypoglycaemic neonates remain asymptomatic or present with non-specific signs, reducing the likelihood of timely detection based on clinical observation alone. Similar observations have been reported in other settings, reinforcing the need for routine glucose screening in high-risk neonates [2,9].

Taken together, our findings support a multifactorial model of neonatal hypoglycaemia in tertiary-care settings, where maternal factors (e.g., gestational diabetes, PIH), perinatal events (e.g., sepsis, IUGR), and neonatal parameters (birth weight, prematurity) interact to influence risk. This complexity underscores the need for vigilant, risk‑based surveillance protocols rather than reliance solely on clinical signs.

Several limitations must be acknowledged. First, the use of convenience sampling may impose selection bias and limit generalizability. Second, confirmatory plasma glucose measurements were performed only when glucometer readings were low — some borderline or fluctuating hypoglycaemia may have been missed. Third, our study did not include long-term follow-up to evaluate neurodevelopmental outcomes associated with neonatal hypoglycaemia; such data would provide deeper insight into the clinical significance of the detected hypoglycaemia.

Given that many neonates remain asymptomatic despite hypoglycaemia, our data support institutional protocols for routine blood‑glucose monitoring in neonates with risk factors (e.g., maternal diabetes, PIH, LBW, sepsis) — particularly within the first 24–72 hours of life. Future research in similar settings should aim for larger sample sizes, prospective designs with serial glucose monitoring, and long-term neurodevelopmental follow-up to better define the burden and consequences of neonatal hypoglycaemia.

Neonatal hypoglycaemia in the studied tertiary care setting was influenced by multiple perinatal and maternal risk factors, with sepsis, pregnancy-induced hypertension, and maternal diabetes emerging as the most prevalent contributors. While a substantial proportion of affected neonates were asymptomatic, early identification through routine blood glucose monitoring remains critical to prevent potential morbidity. Gestational age, birth weight, and inborn versus outborn status were important determinants of risk, underscoring the need for targeted surveillance in high-risk neonates. Prompt recognition and management of these risk factors can improve clinical outcomes and reduce the incidence of complications associated with neonatal hypoglycaemia.

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