Glucose Estimation Research Articles

A vision-based approach to estimate enzymatic glucose concentration and to investigate the effects of temperature and time on colorimetric responses

Abstract The estimation of glucose is important for managing health conditions, like diabetes and supporting advancements in non-invasive diagnostic tools. The propose work introduces a simple, fast, reliable, and cost-effective vision-based colorimetric method for detection of enzymatic glucose by using a Raspberry Pi. Unlike traditional methods, which often require invasive procedures, complex equipment, or expert intervention, the proposed approach leverages image processing and open-source computing to provide a non-invasive, portable, and efficient solution. The concentration of sample solutions varying from 0.02 M to 1.20 M is prepared for detection. The principle of the proposed technique is based on the change of color by using ammonium metavanadate and sulphuric acid upon interaction with glucose solution. A camera module interfaced with a Raspberry Pi single-board computer captures the colorimetric changes. The captured images are studied by using HSV (Hue, Saturation, and Value) color model. This model is chosen for its ability to separate color information (hue) from intensity (value), enabling more accurate quantification of colorimetric responses. We present here the results of the effect of temperature and time on the colorimetric responses of the prepared samples. The novelty of the technique is its simple design, portability, rapid analysis, cost-effectiveness, suitable for environmental conditions, non-invasive in nature, and simple measurement procedure.

Molecularly imprinted polymer-based biosensor for detection of salivary glucose in diabetes.

Diabetes is a disorder attributed to impaired production or utilization of insulin and requires rapid precise monitoring of glucose levels. The fabrication of nanotechnology-based non-invasive biosensors for glucose detection holds significant promise for improved diabetes care and point-of-care diagnostics. The study demonstrates a novel molecularly imprinted polymers (ADMIPs) based sensitive biosensor for glucose estimation in saliva using three distinct sensing platforms -cotton swab, paper strip and polymeric film by colorimetric assay. The device employed a TCS3200 RGB sensor for effectively sensing color change based on the presence of D-GSE. Various techniques such as semi-covalent imprinting method, colorimetry, and microcontroller were utilized in device fabrication. The fabricated non-imprinted polymers (ADNIPs) and ADMIPs displayed particle size <1000 nm with quantified glucose readings from saliva via glucose re-binding. Furthermore, ADMIPs were coated onto cotton swabs, paper strips, and polymer films to develop sensing devices with low LODs (0.9 mg/dL - 3.9 mg/dL) and strong correlational values (0.99-0.98) with blood glucose levels in diabetic patients, offering a potential non-invasive alternative for glucose monitoring. These findings highlight the potential utility of MIP-based sensing platforms for non-invasive glucose monitoring in biofluids such as saliva with implications for diabetes management and thorough diagnostic care.

Vasant Kusumakar Rasa Ameliorates Diabetic Encephalopathy by Reducing Oxidative Stress and Neuroinflammation and Improving Neurotransmitter Levels in Experimental Animals.

Diabetic encephalopathy (DE) is one of the complications of diabetes that affects the brain. In the Ayurveda system of medicine, Vasant Kusumakar Rasa (VKR) is cited as a classical herbo-mineral formulation for diabetes. However, the role of VKR in DE is still unclear. High-fat diet and streptozotocin (35 mg/kg, i.p.) were used to induce type 2 DE in Sprague Dawley rats. VKR at doses 28 mg/kg and 56 mg/kg was given via intragastric route to diabetic rats for 16 weeks. Estimation of plasma glucose, serum insulin, glycohemoglobin, and C-reactive protein (C-RP) was analyzed. Furthermore, the Morris water maze test was performed to assess cognitive behavior. Pro-inflammatory, such as TNF-α, IL-1β, and IL-6, were measured in brain tissue homogenate. Antioxidant enzyme assays were performed to estimate the levels of malondialdehyde, reduced glutathione, superoxide dismutase, and catalase in brain tissue. Histopathology of brain sections was performed using hematoxylin and eosin (H & E) staining. Neurotransmitters (viz., serotonin (5-HT), dopamine (DA), and norepinephrine (NE)) were estimated in the brain by high-performance liquid chromatography (HPLC). The data were analyzed by using ANOVA, followed by Dunnett's multiple comparison test. VKR treatment, at a dose of 28 and 56 mg/kg, reduced the plasma glucose level significantly (236.7±17.08 and 221.8±17.50, respectively; p<0.001) when compared with diabetic control (461.7±13.03). The treatment also reduced serum insulin and glycated hemoglobin levels and improved the escape latency in VKR-treated animals as compared to diabetic animals. Brain tissue pro-inflammatory marker levels were reduced, and oxidative stress enzymes showed positive marks in diabetic rats treated with VKR. Histopathology of the brain demonstrated a reduction in neuronal damage in the VKR-treated diabetic animals. VKR treatment at doses of 28 and 56 mg/kg also improved the levels of 5-HT (1.78±0.11and 1.72±0.18, respectively)when compared with diabetic control (0.91±0.08) significantly (p<0.01). DA levels were significantly (p<0.01) increased in VKR-treated animals when compared with diabetic animals.The treatment of VKR for 16 weeks also improved the NE levels significantly when compared with diabetic control animals. The result of the study indicates that the treatment with VKR for 16 weeks has significant therapeutic potential in the management of type 2 DE.

Spectrum of cutaneous manifestations in patients with type 2 diabetes mellitus

Background: Skin disorders, usually neglected and frequently under diagnosed among diabetic patients. These are common complications and encounter a broad spectrum of disorders in diabetes. Aims and Objectives: Present study, aimed to evaluate various cutaneous manifestations among type 2 diabetes mellitus (DM) patients. Materials and Methods: The present study was conducted at Dermatology, Diabetic Clinic of tertiary care centre hospital. After approval from the Institutional Ethics Committee and consent from study subjects, 240 subjects were included. All subjects underwent clinical examination-special emphasis on cutaneous manifestations. Blood samples were collected and used for the estimation of blood glucose, liver function tests and renal function tests, complete blood count, bacterial infections-Gram stain and culture, fungal infections-KOH (potassium hydroxide) mount, Gram stain (for Candida), and culture. Results: Out of 200, 140 (58.4%) were males and 100 (41.6%) were females. The majority of the patients were 5th, 6thand 7th decades. Acrochordons 22 (9.1%) and candidal balanoposthitis 21 (8.7%) were predominantly observed. Fungal, bacterial, and non-infective were 90 (37.5%), 54 (22.5%), and 112 (46.6%), respectively. On bacterial culture, Pyogenic ulcer was observed in 8, furunculosis in 6, etc. KOH mount positive in 22 patients and culture positive was seen in 16 patients. In non-infective dermatoses, Acrochordons were observed in 24 patients. The majority of the patients were on oral hypoglycemic agents. Conclusion: This study’s results showed demographic, social factors, and the prevalence of various dermatological manifestations in type 2 DM patients.

The Impact of Clinical Parameters on LSTM-based Blood Glucose Estimate in Type 1 Diabetes

Accurate forecasting of blood sugar levels is essential for managing diabetes, especially Type-1 reducing incidences, and diminishing care, costs in patients. In this study, a Long Short-Term Memory Recurrent Neural Network (LSTM) model has been employed to predict blood glucose levels using clinical data. The research focuses on identifying and analyzing several key parameters that play a significant role in determining future blood glucose levels, ensuring a robust and reliable prediction framework. We have considered patient-specific features: Insulin-Sensitivity-Factor (ISF), total daily dose (TDD) of insulin, HbA1C levels, height and weight of a patient, and age and gender while analyzing the prediction performance for Blood Glucose. We thought training LSTM models on a large dataset and studying the most important predictors with their predictive power would be beneficial. The results indicate that including these clinical parameters improves the accuracy of blood glucose prediction and provides valuable information for individuals to control diabetes. This analysis highlights the efficiency of LSTM networks in making use of patient data to improve prediction models, eventually aiding more effective and individualized treatment strategies for Type 1 diabetic patients (T1D). This work also examines the extent to which each parameter influences the prediction of future blood glucose levels, providing deeper insights into their relative impact and significance in the predictive model.

Joint quaternion valued features and quaternion valued long short term memory based recurrent neural network for non-invasive blood glucose estimation

For the four channel photoplethysmograms (PPGs), this paper proposes a quaternion valued based method for performing the non-invasive blood glucose estimation. First, these four channel PPGs form a quaternion valued signal. Then, the quaternion valued means and the quaternion valued medians of this quaternion valued PPGs are employed as features. Next, the quaternion valued long short term memory (QLSTM) based recurrent neural network (RNN) is employed for estimating the non-invasive blood glucose values. Unlike the traditional methods based on extracting the features in each channel of the PPGs independently and fusing all the features together in the conventional neural networks, the quaternion valued operations performed on the quaternion valued PPGs only fuse the same features among these four channels together while do not fuse different features among these four channels together. Hence, the physical interpretations of the features preserve. To demonstrate the effectiveness and the robustness of our proposed method, two datasets are evaluated. The computer numerical simulation results show that our proposed method can yield 81.43% and 83.09% of the test data falling in the zone A of the Clark error grid for the test data in the first dataset and in the second dataset, respectively. Also, it can yield the mean absolute relative difference (MARD) at 14.41% and 14.31% for the test data in the first dataset and the second dataset, respectively. This demonstrates the superior performance of our proposed method over the existing methods for the test data in the both datasets.

(Invited) Wearable Electrochemical Biosensors for Continuous Biomarker Monitoring in Daily-Life

Wearable biosensors represent a promising opportunity to monitor human physiology through dynamic measurements of (bio)chemical markers in bio-fluids such as sweat, tears, saliva, and interstitial fluid in continuous and non-invasive way. Such new platforms can thus offer real-time (bio)chemical information toward a more comprehensive view of a wearer’s health, performance, or stress at the molecular level in daily life. Continuous biomonitoring addresses the limitations of traditional invasive blood testing and provides the opportunity for early diagnostic and therapeutic interventions. My talk will focus on developing wearable electrochemical biosensors towards non-invasive health monitoring opportunities and evaluating the potential impact of such wearable point-of-care devices on our daily life and clinical settings. It will cover various types of salivary, sweat and tear fluid based wearable biosensors utilizing mouthguard, tattoo patch, and contact lens form-factors. Significant effort have been made on developing enzymatic electrochemical sensors for continuous metabolite monitoring towards healthcare in daily-life or managing diabetes like chronic disease. Recently, we demonstrated personalization strategy for accurate estimation of blood glucose utilizing non-invasive biofluids. Lastly, the talk will also cover the latest efforts on developing stress hormone (cortisol) monitoring wearable sensors, focusing on nanoscale molecularly imprinted polymer via quantum electrochemical detection.

Validation of Electrochemical Biosensor based Hand-held Device for the Measurement of Blood Glucose in Lactating Buffaloes (Bubalus bubalis)

Background: Bovine ketosis is a major metabolic disorder emerging as a result of negative energy balance in the post calving period and subsequent lactation of lactating dairy animals. The measurement of the concentration of blood glucose was reported to be helpful for the early diagnosis of subclinical ketosis in buffaloes. Also, blood glucose will provide the overall energy status of the buffalo. Methods: The experiment was conducted on 56 healthy lactating Murrah buffaloes. Blood samples were collected in two slots for two consecutive days in the morning hour in an anticoagulant-added sodium fluoride bulb and in clot activator plain tubes by jugular venipuncture. Also, glucose concentration was measured by a handheld device (“Gluco One”, Model BG-03) by puncturing the small branches of the ear vein and the results were recorded. The estimation of blood glucose plasma and serum was done by standard laboratory enzymatic method i.e. Glucose Oxidase- Peroxidase (GOD-POD) on an automated chemistry analyzer. Result: There was no significant difference between the mean values of plasma glucose measured by the GOD-POD method and by the biosensor. But the regression analysis showed the blood glucose in plasma do not have a significant correlation with that measured by biosensor. The glucose measured in serum is significantly less than in plasma and measured by the biosensor. It is advisable to use fluoride-added preservative anticoagulant tubes and not to use a serum for the estimation of blood glucose.

Convolutional neural network for colorimetric glucose detection using a smartphone and novel multilayer polyvinyl film microfluidic device

Detecting glucose levels is crucial for diabetes patients as it enables timely and effective management, preventing complications and promoting overall health. In this endeavor, we have designed a novel, affordable point-of-care diagnostic device utilizing microfluidic principles, a smartphone camera, and established laboratory colorimetric methods for accurate glucose estimation. Our proposed microfluidic device comprises layers of adhesive poly-vinyl films stacked on a poly methyl methacrylate (PMMA) base sheet, with micro-channel contours precision-cut using a cutting printer. Employing the gold standard glucose-oxidase/peroxidase reaction on this microfluidic platform, we achieve enzymatic glucose determination. The resulting colored complex, formed by phenol and 4-aminoantipyrine in the presence of hydrogen peroxide generated during glucose oxidation, is captured at various glucose concentrations using a smartphone camera. Raw images are processed and utilized as input data for a 2-D convolutional neural network (CNN) deep learning classifier, demonstrating an impressive 95% overall accuracy against new images. The glucose predictions done by CNN are compared with ISO 15197:2013/2015 gold standard norms. Furthermore, the classifier exhibits outstanding precision, recall, and F1 score of 94%, 93%, and 93%, respectively, as validated through our study, showcasing its exceptional predictive capability. Next, a user-friendly smartphone application named “GLUCOLENS AI” was developed to capture images, perform image processing, and communicate with cloud server containing the CNN classifier. The developed CNN model can be successfully used as a pre-trained model for future glucose concentration predictions.

Potential of Near-Infrared Optical Techniques for Non-invasive Blood Glucose Measurement: A Pilot Study

BackgroundDiabetes mellitus manifested by escalated blood glucose, demands periodic or continuous glucose monitoring in the pursuit of improved control, optimal management, and appropriate medication. The existing state-of-the-art for blood glucose estimation are invasive methodologies necessitating the acquisition of blood sample through either a finger-stick or venipuncture. MethodsWe propose a dual Near-infrared wavelength integrated in a 3D printed enclosure as the optical prototype for non-invasive glucose estimation. We acquired data from both diabetic and healthy subjects with the developed system and subsequently validated the system. ResultsThe system demonstrated commendable clinical accuracy, as evidenced by the alignment of data pairs representing actual blood glucose levels and blood glucose levels predicted by our optical system within the A+B zones of the Parkes error grid and zones of no-risk and lower risk as defined by the surveillance error grid. We achieved compliant pairs percentage of 95.6%, which satisfies the accuracy requirements of the blood glucose monitoring surveillance study. The mean absolute percentage error attained with the proposed device (5.99%) was significant in predicting the blood glucose. ConclusionWe successfully deployed the NIR wavelengths functionality as the promising approach for glucose monitoring, offering new possibilities for improved medical interventions.

Efficacy and Clinical Utility of Sodium Fluoride Tubes Versus Serum Tubes for Blood Glucose Estimation.

Precise and accurate measurement of blood glucose is the key factor in both the diagnosis and effective management of diabetes mellitus. The blood glucose levels are influenced by the time interval between sample collection and measurement as glycolysis continues to take place in the blood cells. Laboratories make efforts to minimize this change. This study aimed to analyze the differences in blood glucose levels between routine serum tubes and sodium fluoride (NaF) tubes over time intervals. The study included 50 participants from whom blood was collected in both serum and NaF tubes. The blood glucose values between the two were compared using the Wilcoxon signed-rank sum test at time intervals of one, two, and six hours and the level of agreement in measuring the blood glucose levels by the Bland-Altman plot. There were statistically significant variations in median glucose values between serum and NaF tubes across all the time intervals. The study found that 62%, 70%, and 100% of patients showed decreased glucose values in serum tubes at the end of one, two, and six hours, respectively, compared to NaF tubes. The Bland-Altman plot analysis indicated high agreement at all three time points. However, the difference in blood glucose levels varied from 5.6 mg/dL to 18.74 mg/dL from one to six hours, which was clinically very significant.All the values are clinically very significant as it would lead to underdiagnosing of diabetes mellitus and gestational diabetes mellitus. The study demonstrated that NaF tubes may offer better preservation of glucose levels, especially over extended periods implying that NaF tubes will help prevent under-diagnosis of diabetes mellitus.

Accuracy enhancement of metabolic index-based blood glucose estimation with a screening process for low-quality data.

Many researchers have proposed various non-invasive glucose monitoring (NIGM) approaches using wearable or portable devices. However, due to the limited capacity of detectors for such compact devices and the movement of the body during measurement, the precision of the acquired data frequently diminishes, which can cause problems during actual use in daily life. In addition, intensive smoothing is often used in post-processing to mitigate the effects of erroneous values. However, this requires a considerable amount of data and results in a delay in the response to the actual blood glucose level (BGL). Instead of just applying data smoothing in the post-process of the data acquisition, we propose an active low-quality data screening method in the pre-process. In the proposal phase of the screening process, we employ an analytical approach to examine and formulate factors that might affect the BGL estimation accuracy. A signal quality index inspired by the standard deviation concept is introduced to detect visually apparent noise on signals. Furthermore, the total estimation error in the metabolic index (MI) is calculated based on potential perturbations defined by the signal-to-noise ratio (SNR) and the uncertainty due to discrete sampling. Thereafter, the acquired data were screened by these quality indices. By applying the proposed data screening process to the data obtained from a commercially available smartwatch device in the pre-process, the estimation accuracy of the MI-based BGL was improved significantly. Adopting the proposed screen process improves BGL estimation accuracy in the smartwatch-based prototype. Applying the proposed screen process will facilitate the integration of wearable and continuous BGL monitoring into size- and SNR-limited devices such as smartwatches and smart rings.

Optical Sensor Based Continuous Blood Glucose Estimation Using Lightweight Distributed Architecture

Optical Sensor Based Continuous Blood Glucose Estimation Using Lightweight Distributed Architecture

Noninvasive Monitoring of Glycemia Level in Diabetic Patients by Wearable Advanced Biosensors.

We report on the possibility of noninvasive diabetes monitoring through continuous analysis of sweat. The prediction of the blood glucose level in diabetic patients is possible on the basis of their sweat glucose content due to the positive correlation discovered. The ratio between the blood glucose and sweat glucose concentrations for a certain diabetic subject is stable within weeks, excluding requirements for frequent blood probing. The glucose variations in sweat display allometric (non-linear) dependence on those in blood, allowing more precise blood glucose estimation. Selective (avoiding false-positive responses) and sensitive (sweat glucose is on average 30-50 times lower) detection is possible with biosensors based on the glucose oxidase enzyme coupled with a Prussian Blue transducer. Reliable glucose detection in just secreted sweat would allow noninvasive monitoring of the glycemia level in diabetic patients.

Benchmarking Point-of-Care Glucometers: A Comparative Study Using the Hexokinase Test and International Organization for Standardization (ISO) Standards.

Introduction The International Diabetes Federation states that India accounts for one in seven of all adults with diabetes. Adherence to self-monitoring of blood glucose is essential for effective management of diabetes. Despite the wide variety of glucometers and their clinical applicability, there is a lot of ambiguity regarding their accuracy. Therefore, this study aimed to evaluate if three point-of-care glucometers using three different methods for the estimation of blood glucose fulfil the minimum accuracy needed by ISO15197:2013 when compared with the gold standard hexokinase test. Methods A cross-sectional study was conducted at two primary health care centers in Ernakulam district, which included 73 participants with diabetes and 73 without diabetes. We evaluated three different enzymes in the test glucometers: glucose dehydrogenase with pyrroloquinoline quinone (GDH-PQQ), glucose dehydrogenase with flavin adenine dinucleotide (GDH-FAD), and glucose oxidase (GOD). The glucometer readings were compared with the gold standard hexokinase test and ISO15197:2013 standards. To compare the accuracy of each glucometer with the reference method, the Bland-Altman plot was used and to evaluate the clinical significance of the differences between the glucometer and reference value the Clarke error grid analysis was performed. Results When compared with the gold standard hexokinase result, only 78.08% (GOD), 92.4% (GDH-PQQ), and 95.8 (GDH-FAD) of results were within the limits stipulated by ISO15197:2013. All three glucometers showed blood glucose results within zones A and B of the consensus error grid, implying that it is clinically acceptable, and the Bland-Altman plot showed that the GDH-FAD method had the narrowest range between the upper and lower limit of agreement and a minimum bias of -0.89. Conclusion Only one test glucometer with the GDH-FAD method met the ISO15197:2013 criteria. Glucometers must meet the accuracy standards for the safety of the patients.

Effect of Single Low-Dose Dexamethasone on Peri-operative Blood Glucose Levels

Background: Post-operative nausea and vomiting (PONV) is the second most common complaint reported after surgery with an incidence of 30%-80%. PONV is associated with numerous morbidities, hence prevention is beneficial. Dexamethasone is used as a prophylactic anti-emetic agent, however, being a corticosteroid, it could cause hyperglycaemia which is associated with a number of other adverse effects. Objectives: To study the effect of a single low-dose intravenous dexamethasone given as a prophylactic anti-emetic agent on perioperative blood glucose levels. Methods: Eighty-eight women who had elective myomectomy or hysterectomy under combined spinal-epidural (CSE) anaesthesia were randomised into two groups of 44 each. Following baseline blood glucose estimation, one group had 4 mg (1 ml) of intravenous dexamethasone 1-2 seconds after blood glucose check while the control group had 1 ml of normal saline. Blood glucose was measured every hour intra-operatively and two-hourly post-operatively till 12 hours after drug administration. Nausea, vomiting, dyspepsia, pain, motor block duration and urinary output were assessed hourly. Results: The mean maximum change in blood glucose from baseline in the dexamethasone group was statistically significantly higher than in the control group, 51.00±26.57 mg/dl vs. 35.80±25.71 mg/dl (p = 0.007) so also was the mean maximum blood glucose, 153.93±31.63 mg/dl vs. 139.52±28.95 mg/dl (p = 0.028) respectively. Conclusion: Intravenous dexamethasone at a single, low-dose (4 mg) used as a prophylaxis for nausea and vomiting is associated with significant increase in blood glucose concentration.

A randomized feasibility trial of time-restricted eating during pregnancy in people with increased risk of gestational diabetes

Time-restricted eating (TRE) is a nutritional intervention that confines the daily time-window for energy intake. TRE reduces fasting glucose concentrations in non-pregnant individuals, but whether this eating protocol is feasible and effective for glycemic control in pregnancy is unknown. The aim of this randomized controlled trial was to investigate the adherence to and effect of a 5-week TRE intervention (maximum 10 h daily eating window) among pregnant individuals at risk of gestational diabetes mellitus (GDM), compared with a usual-care control group. Participants underwent 2-h oral glucose tolerance tests and estimation of body composition, before and after the intervention. Interstitial glucose levels were continuously measured, and adherence rates and ratings of hunger were recorded daily. Thirty of 32 participants completed the trial. Participants allocated to TRE reduced their daily eating window from 12.3 (SD 1.3) to 9.9 (SD 1.0) h, but TRE did not affect glycemic measures, blood pressure, or body composition, compared with the control group. TRE increased hunger levels in the evening, but not in the morning, and induced only small changes in dietary intake. Adhering to a 5-week TRE intervention was feasible for pregnant individuals with increased risk of GDM but had no effect on cardiometabolic outcomes.

Co-crystals of thiobarbituric acid and N-heterocyclic compounds: Synthesis, structural, solubility, computational, and antilymphoma activity

Two new co-crystals (1–2) were prepared by the reaction of 2-thiobarbituric acid with N-heterocyclic compounds, and successfully characterized. X-ray analysis shows that 1 and 2 have zig-zag and ladder like supramolecular frameworks, respectively. HOMO-LUMO analysis of optimized geometries confirmed the lesser band gap of 1 and 2. Co-crystals 1–2 have solubility values of 19.737 and 25.652 mg/ml, respectively, and they may have good bioactivity. The cytotoxic activity of 1–2 indicated the tumor growth inhibitory ability of 1 and 2 against T cell lymphoma. Interestingly, apoptosis evaluation by Wright-Giemsa and DAPI staining suggested that 1 and 2 mediated tumor cell death through apoptosis induction. In addition, the estimation of glucose, pH, and NO also indicated the possible role of inhibited glucose uptake and augmented NO production in the cell death mediated by 1 and 2.

A comparative study of linear and nonlinear regression models for blood glucose estimation based on near-infrared facial images from 760 to 1650 nm wavelength

A comparative study of linear and nonlinear regression models for blood glucose estimation based on near-infrared facial images from 760 to 1650 nm wavelength

The Plasma Glucose Threshold Values Associated with Adverse Pregnancy Outcomes Among Asian Indian Pregnant Women: MAASTHI Birth Cohort Analysis.

To assess the association of adverse pregnancy and infant outcomes with different cut-off levels of glucose intolerance during pregnancy in the MAASTHI cohort. Pregnant women (n = 1470) underwent Oral glucose tolerance test between 24 and 36 weeks using a 75-g oral glucose load, with plasma glucose estimations measured at fasting and two hours later. Follow-up was done within 72 hours of delivery for recording type of delivery, infant weight, mid-upper arm circumference, and skinfold thickness. The odds of having higher skinfold thickness (>90th percentile) were 43% higher (AOR = 1.43; 95% CI: 1.18, 1.74) and the odds of being overweight at birth was 34% higher (AOR = 1.34; 95% CI: 1.09, 1.62) for every 1 standard deviation (9.9 mg/dL) increase in fasting plasma glucose (FPG) in male infants. The odds of delivering via caesarean section were 45% higher in women with female foetus (1.45,95% CI 1.15,1.82) for every one SD (23.4 mg/dl) increase in 2-h post-load Glucose. The impact of maternal glucose levels on infant and maternal outcomes differed notably between sex of the child. Compared to female infants, male infants exhibited a stronger association with elevated risks for adverse outcomes, including higher infant weight and increased skinfold thickness.