NiOSP/dP nanocomposite attenuates hyperglycemia, oxidative stress, and inflammation in gestational diabetic rats through modulation of the TLR4/MyD88/NF-κB signaling pathway

Junmei Liu; Zixuan Su; Jiayue Ding; Yan Fang

doi:10.25259/AJC_459_2025

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Original Article

2025

:18;

4592025

doi:

10.25259/AJC_459_2025

NiOSP/dP nanocomposite attenuates hyperglycemia, oxidative stress, and inflammation in gestational diabetic rats through modulation of the TLR4/MyD88/NF-κB signaling pathway

Junmei Liu, Zixuan Su, Jiayue Ding^,, Yan Fang^,*

Department of Obstetrics and Gynecology, Qilu Hospital, Shandong University, 107 Wenhua Xi Road, Jinan, Shandong, 250012, P.R China.

* Corresponding author: E-mail address: fangyan7801@outlook.com (Y. Fang)

Received: 2025-05-02, Accepted: 2025-05-27, Epub ahead of print: 2025-07-31, Published: 2025-08-04

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Abstract

Gestational diabetes mellitus (GDM) has emerged as a significant global health challenge, posing risks to maternal, fetal, and neonatal health and contributing to long-term metabolic complications. The increasing prevalence of GDM, driven by rising obesity rates, sedentary lifestyles, and metabolic disorders, underscores the urgent need for effective interventions. While conventional therapies effectively manage maternal glycaemia, they often fall short in addressing associated metabolic risks and may impact fetal development. In this study, we formulated a nanocomposite with phytochemical d-pinitol and hypothesized to study its efficacy in regulating glycemic levels and ameliorating hyperglycemia-induced complications in the GDM rat model. This study involves the development of a nanocomposite incorporating the phytochemical D-pinitol, with the hypothesis that it can efficiently regulate glycemic levels and alleviate complications induced by hyperglycemia in the GDM rat model. The successful synthesis of the NiOSP/dP nanocomposite was validated through multiple analytical techniques. UV-Vis spectroscopy confirmed the synthesis, showing a prominent absorbance peak at 271 nm. Dynamic Light Scattering (DLS) analysis indicated a relatively narrow size distribution, with particle sizes predominantly ranging between 100 and 160 nm. X-ray Diffraction (XRD) analysis revealed several sharp peaks within the 2θ range of 20° to 70°, indicating a well-defined crystalline structure. Fourier-Transform Infrared (FTIR) spectroscopy confirmed the biomolecular composition of the nanocomposite, while Field Emission Scanning Electron Microscopy coupled with Energy-Dispersive X-ray Analysis (FESEM-EDAX) verified its morphology and elemental composition. The NiOSP/dP nanocomposite protected both the fetus from GDM-induced complications and the mother rats, as confirmed by histopathological analysis. It effectively regulated hyperglycemia and hypercholesterolemia in the GDM-induced rats. Additionally, the nanocomposite enhanced antioxidant status and reduced the inflammatory response. Notably, NiOSP/dP treatment significantly downregulated the TLR4/MyD88/NF-κB signaling pathway proteins. Overall, this research demonstrates the therapeutic potential of the NiOSP/dP nanocomposite against GDM-induced rats. It shows potential as an innovative treatment option for addressing GDM in the future.

Keywords

d-pinitol

Gestational diabetes

Nanocomposites

Oxidative stress

TLR4/MyD88/NF-κB pathway

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1. Introduction

As obesity and its associated metabolic conditions continue to rise worldwide, gestational diabetes mellitus (GDM) has become a prominent concern in obstetric care. It poses serious health risks for both maternal and neonatal outcomes, making it a critical focus in contemporary medical research and prenatal care [1]. In 2021, the International Diabetes Federation (IDF) stated that GDM, often regarded as hyperglycemia diagnosed during pregnancy, impacted an estimated 21.1 million live births, accounting for 16.7% of global births [2]. GDM is recognized as a prevalent complication during pregnancy. In the United States, diabetes affected around 7% of pregnancies, with GDM accounting for 86% of these cases [3]. Meanwhile, in Europe, GDM was estimated to have a prevalence of 10.9% [4]. Globally, its prevalence ranges broadly between 1% and 28%, depending on regional and population-specific factors such as obesity, hypertension, and variations in lifestyle [5].

GDM develops through a multifaceted interaction of genetic, hormonal, environmental, and socio-economic determinants [6,7]. Prominent risk factors encompass older maternal age and a history of type 2 diabetes mellitus in the family, abnormal weight gain during pregnancy, inadequate access to healthcare services, and physical inactivity, all of which contribute to elevated insulin resistance [8,9]. GDM arises due to the incompetence of pancreatic β-cells to sufficiently offset the heightened insulin resistance induced by pregnancy-related hormonal changes, culminating in hyperglycemia [10]. These functional impairments of β-cells often predate pregnancy and may exacerbate over time, with a heightened risk of developing type 2 diabetes mellitus later in life [11,12].

Untreated GDM poses considerable health threats for both the mother and fetus, leading to diverse obstetric and neonatal complications [13]. In pregnant women, GDM is accompanied by an elevated risk of spontaneous abortion, congenital anomalies, preeclampsia, and cesarean delivery due to fetal macrosomia [14]. Newborns delivered by mothers with uncontrolled GDM are vulnerable to complications such as neonatal hypoglycemia, hyperbilirubinemia, perinatal asphyxia, and elevated perinatal mortality [15]. Over the long term, GDM predisposes mothers to chronic conditions including type 2 diabetes mellitus, while their progenies are at an elevated risk of obesity, metabolic dysfunctions, and developmental delays [16]. Suboptimal glycemic control during pregnancy amplifies these risks; however, effective management of blood glucose levels can significantly enhance neonatal outcomes and reduce adverse events [15]. International guidelines recommend a pharmacological strategy for managing GDM when lifestyle modifications fail to achieve optimal glycemic control [17]. When lifestyle interventions fail, oral agents such as metformin and glyburide are used as alternatives; metformin is preferred because of its ease of administration, lower hypoglycemia risk, and potential for better long-term outcomes [18]. Both medications are pregnancy category B and considered safe, though their long-term safety data are limited [19]. However, glyburide has been associated with higher NICU admissions for fetal hypoglycemia, intrauterine deaths, hypoglycemia, macrosomia [20], while metformin may lead to increased offspring weight, visceral fat, and higher blood glucose levels in children at age nine [21]. Insulin therapy is currently regarded as the primary treatment option for GDM due to its proven safety profile and limited placental transfer [22,23]. While insulin effectively restores maternal glycemic control, its metabolic benefits are not consistently observed in fetal and neonatal physiology. Furthermore, insulin therapy raises concerns about potential modifications in placental structure, umbilical vessel function, and the development of the fetus and neonate [24].

Traditional medicine employs various plant-based materials for the management of diabetes mellitus, including Trigonella foenum-graecum, Bougainvillea spectabilis, Retama raetam, and Sutherlandia frutescens, which are rich in D-pinitol [25-27]. While phytochemicals offer promising therapeutic potential for chronic diseases, their clinical effectiveness is often limited by limited systemic availability, primarily due to limited dissolution and stability [28]. Advances in drug delivery systems can address these limitations, with nanocomposites demonstrating significant improvements in targeted delivery through controlled release mechanisms. Such systems help to reduce adverse side effects while enhancing therapeutic outcomes [29]. In this study, a nanocomposite composed of nickel oxide, sodium alginate, PEG, and D-pinitol was developed and evaluated for its capability to modulate hyperglycemia in a rat model of GDM.

2. Materials and Methods

2.1. Synthesis of NiOSP/dP nanocomposite

NiO nanoparticles were synthesized by thermally decomposing Ni(OH)₂, following a method adapted from El-Kemary et al [30]. To create NiOSP/dP/NCs, D-pinitol was first dispersed in 20 mL of DMSO and then blended with a NiO solution under continuous stirring until a stable suspension formed. Ultrasonication was used to minimize the size of the nanoparticle droplets. During the coating process, the suspension containing D-pinitol and NiO was introduced into a polymer matrix made of sodium alginate and polyethylene glycol. Once the coating phase was complete, the nanocomposite powder was obtained through spray pyrolysis dehydration, carried out at temperatures between 80–100°C with controlled airflow, as described by Lim et al [31].

2.2. Characterization of NiOSP/dP nanocomposite

2.2.1. UV-Vis Spectroscopy analysis

The UV-visible spectroscopy was utilized to confirm the NiO/SP/dP nanocomposites synthesis in suspension. Using a Shimadzu-1700 UV-vis spectrophotometer from Japan, the samples were analyzed to identify the surface plasmon resonance peak characteristic of the nanocomposites. The spectroscopic measurements were conducted over a wavelength range from 1200 nm to 200 nm. This procedure was performed three times to ensure reproducibility of the results, and the data collected from each measurement were carefully recorded for further analysis.

2.2.2. Dynamic light scattering (DLS) analysis

The NiO/SP/dP nanocomposite was evaluated using DLS with a Nano-ZS instrument, Malvern, UK. To prepare the samples, the nanocomposites were first suspended in MilliQ water and subjected to sonication for 30 s to ensure proper dispersion. Following this step, 100 µL of the nanocomposite suspension was diluted in a 1:10 ratio with MilliQ water to reduce the concentration suitable for DLS analysis. The resulting diluted suspension was then transferred to the DLS instrument for zeta potential measurements, which were conducted to assess the stability and surface charge characteristics of the nanocomposite particles in suspension.

2.2.3. X-Ray diffraction (XRD) study

The NiO/SP/dP nanocomposite was analyzed using XRD with a Bruker-AXS D5005 instrument. The XRD measurements were conducted over 2θ range of 10° to 80° to capture the crystallographic structure of the samples. The instrument was operated at a voltage of 40 kW and a current of 45 mA to ensure optimal performance during analysis. A fixed step size of 0.02 s was employed to allow for accurate data collection, while the scanning time was programmed to be 0.5 s for each measurement. The scanning rate was maintained at 2° per minute, facilitating a detailed examination of any changes in the crystallization of the nanocomposite. This protocol was designed to provide a comprehensive assessment of the nanocomposite’s structural characteristics through XRD analysis.

2.2.4. Fourier-transform infrared (FTIR) spectroscopy

NiO/SP/dP nanocomposites were examined using FTIR spectroscopy with a Nicolet iS50 instrument. For analysis, the prepared nanocomposite was blended thoroughly with KBr to form pellets in a 1:100 weight ratio. The mixture was then pressed into a pellet to enable effective infrared transmission. FTIR spectra were collected across a range of 500 to 4000 cm⁻¹, using a spectral resolution of 4 cm⁻¹. To improve data reliability and signal clarity, 50 scans were averaged for each measurement. The resulting spectra were processed with WINFIRST software, allowing for the identification and examination of the different functional groups within the NiO/SP/dP nanocomposite. This approach provides insights into the chemical bonding and functional group composition, aiding in understanding the material’s overall structure and characteristics.

2.2.5. Field Emission Scanning Electron Microscopy-Energy Dispersive X-ray (FESEM-EDAX) analysis

The elemental composition and morphological characteristics of the synthesized NiO/SP/dP nanocomposite were analyzed using a Carl Zeiss Ultra 55. A small amount of the nanocomposite was placed on a substrate to ensure a homogeneous layer for imaging. The FESEM provided high-resolution images to assess the sample’s surface morphology and dispersion. Subsequently, EDAX was employed to determine the elemental components by analyzing the X-ray signals emitted from the nanocomposite when subjected to an electron beam. This combined approach allowed for a comprehensive comprehension of the structural and elemental characteristics of the NiO/SP/dP nanocomposite.

2.2.6. Experimental animals

This research was performed using Wistar albino rats of both sexes, with weights ranging from 210 ± 30 g. The animals were kept in a controlled laboratory environment, with temperature maintained between 22°C and 26°C and humidity levels ranging from 40% to 70%. They were subjected to a 12-h light and 12-h dark cycle. Throughout the experiment, the rats had unrestricted access to clean water and standard feed. All experimental protocols were conducted in accordance with the guidelines approved by the institutional animal ethics review board (2024-11/7).

2.2.7. Induction of gestational diabetes

Experiment animals were placed on a high-fat diet regimen for a duration of eight weeks, during which their body weight gain was systematically monitored. Following this period, the rats underwent an overnight fasting phase, after which daily vaginal swabs were performed to determine their estrous stage. Female rats in the estrous stage were paired with healthy and active males at a ratio of two females to one male. Following a 24-h cohabitation period, pregnancy was confirmed by the presence of sperm, designating day 0 of gestation. Pregnant rats were separated from the group, while non-pregnant rats were excluded from subsequent experiments. GDM was induced in the pregnant rats through an intraperitoneal injection of streptozotocin (STZ) at a dosage of 40 mg/kg. In contrast, control rats received an equivalent volume of saline solution instead of STZ.

2.2.8. Treatment regiment

Cluster I consisted of six healthy rats, serving as controls. The rats with GDM were assorted into three clusters: Cluster II (GDM rats without treatment), Cluster III (GDM rats treated with NiO/SP/dP nanocomposites at a dosage of 5 µg/kg), and Cluster IV (GDM rats treated with NiO/SP/dP nanocomposites at a dosage of 10 µg/kg). The nanocomposite treatment was administered orally over a 14-day period. Upon completion of the treatment regimen, the animals were euthanized, and biological samples were collected. Serum samples were prepared for further analytical studies. The experimental groups were assessed for fetal weight, placental weight, and placental index. Additionally, liver, kidney, and pancreatic tissues were carefully excised and subjected to subsequent analyses.

2.2.9. Histopathological analysis

After the rats were sacrificed, the kidneys, pancreas, and liver were carefully harvested. The isolated organs from each cluster were then fixed in 10% formalin to preserve their structure for subsequent analysis. Following fixation, the samples underwent a dehydration process using 10% ethylenediamine tetraacetic acid (EDTA). Once dehydrated, the tissues were embedded in paraffin to create a solid matrix that supports the tissue structure during sectioning. Thin sections of approximately 5 µm were cut from the paraffin-embedded samples using a microtome. Tissue sections were then stained with Hematoxylin and Eosin (H&E) to highlight cellular structures and features. The tissue morphology was then examined under a light microscope.

2.2.10. Quantification of glycemic profile

The assessment of the glycemic profile was conducted using serum samples collected from gestational diabetes-induced untreated rats and those treated with NiO/SP/dP nanocomposites. Levels of glycated hemoglobin (HbA1c), hepatic glycogen, fasting insulin, C-peptide, and free fatty acids (FFA) were measured using commercially available kits. The Rat Hemoglobin A1c (HbA1c) Assay Kit, Crystal Chem, and Rat Insulin and C-Peptide ELISA Kit were sourced from Elabscience, USA. Additionally, Free Fatty Acid Assay Kit and Glycogen Assay Kit were obtained from MyBiosource, USA and Abcam, USA for the respective analyses. The respective assay kit was used to conduct the assays, adhering to the instructions provided by the manufacturer.

2.2.11. Quantification of lipid profile

The lipid profile, including cholesterol (Ch), triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and very low-density lipoprotein (VLDL), was analyzed in the blood samples of experimental animals. Specific assay kits were utilized for the assessments, adhering to the protocols provided by the manufacturer (BioAssay Systems, USA).

2.2.12. Quantification of antioxidants

The levels of oxidative stress markers, including glutathione peroxidase (GPx), glutathione (GSH), catalase (CAT), and superoxide dismutase (SOD), were quantified with respective Cayman Chemicals assay kits, following the protocols provided by the manufacturer. Malondialdehyde (MDA) was measured using colorimetric kits obtained from Elabscience (USA).

2.2.13. Assessment of inflammatory cytokines

Inflammatory cytokine levels in the gestational diabetes induced untreated and NiO/SP/dP nanocomposites treated rats were quantified using the ELISA kits. TNF-α ELISA kit was purchased from Abcam, USA, and the IL-6, IL-1β, and IL-10 ELISA kits were obtained from Cusabio, USA. The assays were performed with the instructions provided in the kit’s manual.

2.2.14. Analysis of TLR4/MyD88/NF-κB Signaling Pathway

Impact of NiO/SP/dP nanocomposites on the TLR4/MyD88/NF-κB Signaling Pathway was assessed in the gestational diabetic rats. The levels of Toll-Like Receptor 4 (TLR-4), Myeloid Differentiation Primary Response 88 (MyD88), Nuclear factor kappa B (NFkB), Nucleotide-Binding Domain, Leucine-Rich-Containing Family, Pyrin Domain-Containing-3 (NLRP3) were quantified using the ELISA kits obtained from Cusabio, USA. The assays were done in triplicates as per the guideline prescribed by the manufacturer.

2.2.15. Statistics

Data were analyzed using GraphPad Prism version 7.0. The outcomes were expressed as mean ± standard deviation (SD) from three separate experiments. Statistical differences were assessed using one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test for multiple comparisons. Values with a p-value below 0.05 were deemed statistically significant.

3. Results and Discussion

3.1. Characterization of NiOSP/dP nanocomposites

3.1.1. XRD study

The XRD examination of the NiOSP/dP nanocomposite has been illustrated in Figure 1, confirming its purity and crystalline nature. As shown in Figure 2(a), the pattern exhibits several sharp peaks within the 2θ range of 20° to 70°, indicative of its well-defined crystalline structure.

Characterization of NiOSP/dP nanocomposite. (a) XRD (b) FTIR spectroscopic analysis of formulated Nickel oxide-sodium alginate-PEG-D-Pinitol nanocomposite..

Characterization of NiOSP/dP nanocomposite. (a) Field emission scanning electron microscopic analysis (b) EDAX spectroscopic analysis of formulated nickel oxide-sodium alginate-PEG-D-pinitol nanocomposite.

3.1.2. FTIR spectroscopy

Figure 1 depicts the FTIR spectrum of the NiOSP/dP nanocomposite, highlighting characteristic absorption bands at various frequencies. Notable peaks are observed at 3402 cm⁻¹, 2900 cm⁻¹, 2421 cm⁻¹, 1505 cm⁻¹, 1351 cm⁻¹, 1241 cm⁻¹, 1097 cm⁻¹, 963 cm⁻¹, 833 cm⁻¹, 765 cm⁻¹, and 522 cm⁻¹. The prominent bands at 3402 cm⁻¹ and 2900 cm⁻¹ are attributed to O-H stretching and aliphatic C-H stretching, respectively, originating from sodium alginate. Peaks at 1351 cm⁻¹ and 1241 cm⁻¹ correspond to bending vibrations of C-H and C-O bonds, as well as the symmetric vibrations of the carboxylate (–COO) groups. The peaks at 1097 cm⁻¹ and 963 cm⁻¹ are indicative of C-O stretching vibrations, while those at 833 cm⁻¹ and 765 cm⁻¹ are linked to H-O bond vibrations. Additionally, the peak at 522 cm⁻¹ is associated with NiO stretching within the alginate matrix.

3.1.3. Field Emission Scanning Electron Microscopy- Energy Dispersive X-ray (FESEM-EDAX) analysis

To analyze the surface morphology and elemental composition of the synthesized NiOSP/dP nanocomposite, FESEM and EDAX spectroscopy were conducted, with the results presented in Figure 2. The FESEM images reveal that the nanocomposite particles predominantly exhibit a cubic-like morphology, characterized by uniform dispersion and smooth surface textures (Figure 2a). This morphological uniformity suggests effective synthesis conditions that promote controlled particle growth and distribution. The EDAX spectrum (Figure 2b) confirms the elemental constituents of the nanocomposite, indicating the presence of nickel, nitrogen, carbon, and oxygen. The detected peaks correspond precisely to these elements, affirming the successful incorporation of these elements within the nanostructure, which is consistent with the expected composition of the NiOSP/dP material. This combined morphological and elemental analysis underscores the uniformity and compositional integrity of the synthesized nanocomposite.

3.1.4. UV-Vis Spectroscopy analysis

The UV-visible spectrum of the NiOSP/dP nanocomposite has been presented in Figure 3(a). The absorbance was recorded across a wavelength range of 200 to 1,000 nm. The successful NiOSP/dP nanocomposite formation was confirmed by the prominent absorbance peak observed at 271 nm.

Characterization of NiOSP/dP nanocomposite. (a) UV-Vis spectroscopy analysis (b) DLS analysis of formulated Nickel oxide-sodium alginate-PEG-D-Pinitol nanocomposite.

3.1.5. DLS analysis

The DLS examination of the synthesized NiOSP/dP nanocomposite has been depicted in Figure 3(b). The results reveal distinct peaks corresponding to particle sizes predominantly between 100 and 160 nm, indicating a relatively narrow size distribution.

3.1.6. NiOSP/dP nanocomposite prevented GDM induced fetal health impairments

Figure 4 illustrates the impact of NiOSP/dP nanocomposite on the fetal health of GDM-induced rats. Gestational diabetes induction in the experimental group decreased the weight of the fetus significantly. The fetal weight of the NiOSP/dP nanocomposite-treated GDM-induced rats was significantly increased compared to the fetal weight of the untreated GDM-induced rats. The placental weight and the placental index were increased in the GDM untreated rats compared to the other experimental groups. Treatment with NiOSP/dP nanocomposite in the GDM induced rats decreased both the placental weight and the placental index.

NiOSP/dP nanocomposite prevented GDM-induced fetal health impairments. (a) Fetal weight, (b) Placental weight, (c) Placental index of the control, GDM-induced, GDM-induced + 5µg/kg NiOSP/dP nanocomposite, GDM-induced + 10 µg/kg NiOSP/dP nanocomposite-treated rats. Results are expressed as mean ± SD for six rats per group. Data were analyzed using one-way ANOVA followed by Tukey’s post hoc test. **indicates a significant difference between GDM untreated rats and normal pregnant rats; #denotes differences between GDM-untreated rats and those treated with NiOSP/dP nanocomposite at doses of 5 µg/kg and 10 µg/kg.

3.1.7. Protective effects of NiOSP/dP nanocomposite on liver, kidney, and pancreatic tissues in GDM-induced rats

Histopathological analysis was conducted on the liver, kidney, and pancreatic tissues of GDM-induced rats, both untreated and treated with NiOSP/dP nanocomposite, with the results illustrated in Figure 5. In normal pregnant rats, the hepatic and pancreatic tissues displayed organized cellular architecture without any signs of inflammation or steatosis. Conversely, in the GDM-induced untreated rats, mild to moderate inflammatory responses were evident, characterized by infiltration of inflammatory cells and the presence of steatosis in the liver (Figure 5a). Additionally, these rats showed contraction of pancreatic islets and increased adipose tissue size. Notably, treatment with NiOSP/dP nanocomposite in GDM animals significantly mitigated pancreatic lesions and reduced adipose tissue expansion compared to untreated GDM rats (Figure 5b).

Protective effects of NiOSP/dP Nanocomposite on liver, kidney, and pancreatic tissues in GDM-induced rats. Representative images of hematoxylin & eosin stained (a) Liver, (b) Pancreas, (c) Kidney tissues of control, GDM induced, GDM induced + 5µg/kg NiOSP/dP nanocomposite, GDM induced + 10µg/kg NiOSP/dP nanocomposite treated rats. Black arrows: Infiltration of inflammatory cells; Blue arrows: Presence of steatosis; Red arrows: Contraction of pancreatic islets; Green arrows: Increased adipose tissue size.

In the kidneys of GDM rats, histological examination revealed marked thickening of the basement membranes of glomeruli and renal tubules, along with severe mesangial proliferation and extracellular matrix hyperplasia. Glomerular expansion was uneven, with areas of balloon adhesion and markedly dilated capillary lumens. Tubular atrophy, dilation, necrosis, detachment of tubular epithelial cells, and substantial infiltration of interstitial inflammatory cells were also observed. In contrast, control rats exhibited normal renal architecture, with intact glomeruli and tubules, and absence of inflammatory infiltration or vascular congestion. Treatment with NiOSP/dP nanocomposite in GDM rats was associated with minimal kidney tissue alterations, including sporadic desquamation of degenerated renal cells and slight dilation of peri-tubular capillaries (Figure 5c).

3.1.8. NiOSP/dP nanocomposite modulated glycemic level in GDM induced rats

Figure 6 depicts the glycemic profile of the GDM-induced untreated and NiOSP/dP nanocomposite-treated rats. Significant elevations in fasting blood glucose and HbA1c levels were observed in the serum of GDM rats compared to normal pregnant controls. Additionally, GDM animals showed a marked increase in hepatic glycogen content and C-peptide levels relative to the control group. Conversely, free fatty acid (FFA) concentrations were notably decreased in untreated GDM rats. Notably, administration of NiOSP/dP nanocomposite at doses of 5 µg/kg and 10 µg/kg resulted in a considerable reduction in HbA1c, FBG, hepatic glycogen, and C-peptide levels compared to untreated GDM rats. Furthermore, treatment with the nanocomposite led to a significant increase in FFA levels in GDM rats, indicating an amelioration of metabolic disturbances associated with GDM.

NiOSP/dP nanocomposite modulated glycemic levels in GDM-induced rats. (a) HbA1c (b) Hepatic glycogen (c) Fasting blood glucose (d) c-Peptide (e) Free Fatty acid levels of the control, GDM induced, GDM induced + 5 µg/kg NiOSP/dP nanocomposite, GDM induced + 10 µg/kg NiOSP/dP nanocomposite treated rats. Results are expressed as mean ± SD for six rats per group. Data were analyzed using one-way ANOVA followed by Tukey’s post hoc test. **indicates a significant difference between GDM-untreated rats and normal pregnant rats; #denotes differences between GDM-untreated rats and those treated with NiOSP/dP nanocomposite at doses of 5 µg/kg and 10 µg/kg.

3.1.9. NiOSP/dP nanocomposite demonstrated hypocholesterolemic effects in GDM induced rats

The lipid profiles of GDM-induced untreated rats and those treated with NiOSP/dP nanocomposite were assessed, with the results depicted in Figure 7(a-e). GDM induction in pregnant rats led to hypercholesterolemia with increased levels of LDL. Conversely, HDL Ch levels were markedly reduced in untreated GDM rats compared to the other experimental groups. Administration of NiOSP/dP nanocomposite exhibited a hypocholesterolemic effect, with reduction of total Ch, TGs, LDL, and VLDL, alongside an increase in HDL Ch in the GDM rats treated with the nanocomposite.

NiOSP/dP nanocomposite demonstrated hypocholesterolemic effects in GDM-induced rats. (a) Total Ch (b) TGs (c) Low density Lipoprotein Ch (d) Very Low density Lipoprotein Ch (e) High density Lipoprotein Ch levels of the control, GDM induced, GDM induced + 5µg/kg NiOSP/dP nanocomposite, GDM induced + 10µg/kg NiOSP/dP nanocomposite treated rats. Results are expressed as mean ± SD for six rats per group. Data were analyzed using one-way ANOVA followed by Tukey’s post hoc test. **indicates a significant difference between GDM-untreated rats and normal pregnant rats; #denotes differences between GDM-untreated rats and those treated with NiOSP/dP nanocomposite at doses of 5 µg/kg and 10 µg/kg.

3.1.10. NiOSP/dP nanocomposite enhanced the antioxidant status in GDM-induced rats

The antioxidant capacity of NiOSP/dP nanocomposite was evaluated in GDM-induced rats, with findings summarized in Figure 8(a-d). Rats treated with the nanocomposite demonstrated a significant enhancement in their antioxidant defenses compared to untreated GDM rats. Specifically, levels of key enzymatic antioxidants SOD, CAT and GPX were markedly elevated following treatment. Similarly, the non-enzymatic antioxidant GSH showed a dose-dependent increase, indicating a robust reinforcement of the antioxidant system.

NiOSP/dP nanocomposite enhanced the antioxidant status in GDM-induced rats. (a) SOD (b) CAT (c) GPx (d) GSH levels of the control, GDM induced, GDM-induced + 5 µg/kg NiOSP/dP nanocomposite, GDM induced + 10 µg/kg NiOSP/dP nanocomposite treated rats. Results are expressed as mean ± SD for six rats per group. Data were analyzed using one-way ANOVA followed by Tukey’s post hoc test. **indicates a significant difference between GDM-untreated rats and normal pregnant rats; #denotes differences between GDM-untreated rats and those treated with NiOSP/dP nanocomposite at doses of 5 µg/kg and 10 µg/kg.

3.1.11. NiOSP/dP nanocomposite attenuated the inflammatory response in GDM-induced rats

Figure 9(a-d) represents the inflammatory cytokines levels in GDM-induced rats treated with two doses of NiOSP/dP nanocomposite (5 µg/kg and 10 µg/kg). GDM in pregnant rats resulted in a notable rise of inflammation-stimulating cytokines such as TNF-β, IL-6, and IL-1β, while concurrently causing a decline in the anti-inflammatory cytokine IL-10. Administration of NiOSP/dP nanocomposite caused a notable decrease in TNF-β, IL-6, and IL-1β levels, indicating an inflammatory attenuating effect. Treatment enhanced IL-10 levels, suggesting a shift towards an anti-inflammatory state.

NiOSP/dP nanocomposite attenuated the inflammatory response in GDM-induced rats. (a) Tumor Necrosis Factor alpha (b) Interleukin 6 (c) Interleukin 1β (d) Interleukin 6 levels of the control, GDM-induced, GDM-induced + 5µg/kg NiOSP/dP nanocomposite, GDM-induced + 10 µg/kg NiOSP/dP nanocomposite treated rats. Results are expressed as mean ± SD for six rats per group. Data were analyzed using one-way ANOVA followed by Tukey’s post hoc test. **indicates a significant difference between GDM-untreated rats and normal pregnant rats; #denotes differences between GDM-untreated rats and those treated with NiOSP/dP nanocomposite at doses of 5 µg/kg and 10 µg/kg.

3.1.12. NiOSP/dP nanocomposite regulated TLR4/MyD88/NF-κB Signaling in GDM induced rats

Figure 10(a-d) displays the levels of signaling proteins involved in the TLR4/MyD88/NF-κB pathway in the experimental groups. GDM in pregnant rats caused a substantial elevation of TLR-4, MyD88, NF-κB, and NLRP3 proteins compared to normal control rats. Treatment with NiOSP/dP nanocomposite markedly reduced the expression of these proteins in GDM-induced rats. The extent of protein level reduction was dose-dependent, indicating that higher doses of the nanocomposite more effectively suppressed the activation of this inflammatory pathway.

NiOSP/dP nanocomposite regulated TLR4/MyD88/NF-κB Signaling in GDM-induced rats. (a) Toll-like receptor 4 (b) Myeloid differentiation primary response 88 (c) Nuclear factor kappa B (d) Nucleotide-Binding Domain, Leucine-Rich–Containing Family, Pyrin Domain–Containing-3 levels of the control, GDM-induced, GDM-induced + 5µg/kg NiOSP/dP nanocomposite, GDM-induced + 10µg/kg NiOSP/dP nanocomposite treated rats. Results are expressed as mean ± SD for six rats per group. Data were analyzed using one-way ANOVA followed by Tukey’s post hoc test. **indicates a significant difference between GDM-untreated rats and normal pregnant rats; #denotes differences between GDM-untreated rats and those treated with NiOSP/dP nanocomposite at doses of 5 µg/kg and 10 µg/kg.

D-Pinitol, also known as 3-o-ortho-methyl-D-chiro-inositol, is a cyclitol predominantly found in the Leguminosae and Pinaceae plant families [32]. In the past few years, increasing research emphasis has been placed on D-pinitol due to its presence in various edible and medicinal plants such as soybean, fenugreek seeds, ice plant, and certain Retama species [33-35]. Additionally, D-pinitol has been associated with multiple biological activities, including antioxidant properties [36], anti-inflammatory action [37], antidiabetic effects [38], and potential in cancer prevention [39].

Phytomolecules such as d-pinitiol obtained from therapeutic plants are in high demand within the therapeutic industry due to their therapeutic potential. However, these compounds often face limitations such as low absorption, high toxicity, adverse effects, poor bioavailability, and reduced efficacy [40]. The integration of nanotechnology provides a promising solution to overcome these challenges. Recent advancements have centered on developing nanostructured composite materials, where bioactive compounds are incorporated into an inorganic nanostructured matrix to enhance their performance and stability [41].

Nanoscale formulations have revolutionized drug delivery systems by surpassing the limitations of conventional approaches. These advanced formulations enhance the therapeutic effectiveness of drugs by enhancing their biopharmaceutical properties, pharmacokinetic profiles, and target specificity [42]. The integration of nanocarriers with ligands further refines their targeting ability, offering the additional benefit of safeguarding encapsulated drugs from degradation [43]. Nanoformulations have demonstrated significant potential in addressing metabolic disorders, such as diabetes mellitus, by facilitating the delivery of insulin and oral hypoglycemic agents. Nanoparticles engineered as carriers for hypoglycemic agents enhance the functionality of these drugs by improving their targeted delivery, extending the duration of their hypoglycemic effects, and minimizing potential side effects [44].

In this research, we formulated Nickel oxide-sodium alginate-polyethylene glycol (PEG)-D-Pinitol nanocomposite. In which NiO provides core nanostructure stability, sodium alginate which serves as a biocompatible, biodegradable polymer matrix that encapsulates and stabilizes the nanocomposite, aiding in controlled release and improving biocompatibility. PEG is used to stabilize and prevent nanoparticle aggregation, and to enhance the bioavailability of the therapeutic agent d-pinitol. Our UV-Vis spectroscopic and FTIR analyses collectively validate the successful synthesis of the NiOSP/dP nanocomposite. Additionally, characterization techniques such as XRD, DLS, FESEM, and energy dispersive X-ray spectroscopy (EDAX) have been employed to further examine the nanocomposite. These analyses confirmed that the NiOSP/dP nanostructure possesses an appropriate shape and size, within the optimal range for biomedical applications, ensuring its suitability for targeted therapeutic uses.

GDM is allied with a range of obstetric and perinatal difficulties that stem from disruptions in placental development, as well as anatomical and functional changes [45]. The placenta serves as a critical controller of maternal functions and fetal hemodynamics, and its dysregulation in GDM has significant pathological implications. Hormonal alterations within the placenta, combined with enhanced output of pro-inflammatory cytokines, perpetuate the chronic low-grade inflammatory state characteristic of GDM. This inflammatory milieu drives morphological and functional changes within the placental tissue, including variations in placental size, vascular lesions, and microscopic necrosis. Such alterations compromise placental function, thereby affecting its regulatory capacity and contributing to adverse fetal outcomes [46]. The administration of the NiOSP/dP nanocomposite to GDM-induced rats resulted in a reduction in placental index and placental weight, alongside an increase in fetal weight when compared to untreated GDM rats. Furthermore, NiOSP/dP nanocomposite treatment mitigated GDM-induced pathological damage in maternal liver, pancreas, and kidney tissues, as demonstrated by histopathological analysis.

During normal pregnancy, oxidative stress increases due to higher reactive oxygen species (ROS) production by the placenta, but this is usually balanced by increased antioxidant production [47]. In GDM, oxidative stress is amplified, with decreased antioxidant capacity and increased inflammation, raising cardiovascular risks and postpartum insulin resistance [48]. Elevated lipid peroxidation markers are seen in GDM placentas, and when oxidative stress exceeds antioxidant defenses, damage can affect other tissues. [49,50] In our study, we created a gestational diabetic model through the administration of STZ to gestational rats. STZ induces cytotoxic effects on pancreatic β-cells through DNA alkylation and the formation of toxic by-products, such as superoxide anions and nitric oxide [51]. This results in DNA damage and structural degradation of β-cells, ultimately leading to apoptosis [52].

Histological analysis of our research revealed that STZ administration induced diabetes led to considerable damage to the pancreatic β-cells of the islets of Langerhans, impairing their insulin synthesis at physiologically relevant levels. These effects were noted in diabetic pregnant rats, with β-cells showing a decrease in size. It is further confirmed with increased levels of HbA1C, fasting blood glucose (FBG), hepatic glycogen levels and decreased FFA levels. However, treatment with NiO/SP/dP nanocomposites demonstrated significant improvements in β-cell structure and insulin-producing functionality, thereby regulating the hyperglycemic levels in GDM induced rats. This therapeutic effect is attributed to the nanocomposite’s antioxidant properties, which reduce oxidative stress by enhancing the total antioxidant capacity. This effect was evidenced by increased expression of key antioxidant enzymes, which collectively mitigated oxidative damage and restored cellular homeostasis in treated animals.

Maternal Ch holds a vital role in supporting the hormonal and physical adaptations that occur during early pregnancy [53]. Circulating LDL Ch serves as a primary substrate for the production of placental progesterone [54,55]. The normal rise in Ch levels later in pregnancy appears to serve an adaptive purpose, aiding in maintaining pregnancy and promoting fetal development [56]. However, elevated maternal Ch levels have additionally been linked to potential harm, including increased Ch accumulation in the fetal aorta [57], which may cause cardiovascular issues later [58,59]. Several studies have recommended that increased maternal Ch levels might be associated with increase chances of preterm birth and growth restrictions in the fetus [60,61]. Administration of NiO/SP/dP nanocomposites to GDM-induced rats resulted in a reduction in total Ch levels and an increase in HDL levels. These findings highlight the nanocomposite’s therapeutic potential in mitigating dyslipidemia associated with GDM, thereby demonstrating its ameliorative effects during gestational diabetes.

Systemic and localized inflammation is a key factor in the progression of insulin resistance (IR) in GDM, with inflammatory mediators such as c-reactive protein (CRP) [62], tumor necrosis factor alpha (TNF-α) [63], and interleukin 6 (IL-6) being critical contributors [64,65]. Among the inflammatory pathways, the Toll-like receptor 4 pathway is a key candidate, as it mediates pro-inflammatory responses by recognizing pathogen-associated molecular patterns (PAMPs) and damage-associated signals [66,67]. Activation of TLR4 by lipopolysaccharides (LPS) and metabolic by-products triggers the MyD88-dependent pathway, leading to natriuretic factor (NF)-κB, activator protein (AP-1), and interferon regulatory factors (IRFs) activation, which interfere with insulin signaling [68,69]. Elevated TLR4 expression has been observed in diabetes models and patients, as well as in GDM patients [70-72]. Studies suggest that inhibiting TLR4 signaling can ameliorate insulin resistance, with evidence showing that hepatocyte-specific TLR4 deficiency improves insulin sensitivity [73]. In our study, we investigated the efficacy of NiO/SP/dP nanocomposite in inhibiting TLR4 signaling in GDM-induced rats. Administration of NiO/SP/dP nanocomposites significantly attenuated TLR4 signaling, leading to decreased levels of MyD88 and later suppressing the upregulation of NF-κB and NLRP3 proteins. The inhibition of TLR4 signaling by NiO/SP/dP nanocomposites in GDM-induced rats was further validated by reduced levels of inflammatory-stimulating cytokines, including TNF-α, IL-1β, and IL-6, alongside an increased level of the inflammatory attenuating cytokine IL-10. These results underscore the possibility of NiO/SP/dP nanocomposites in modulating inflammatory pathways associated with GDM.

4. Conclusions

In conclusion, the developed NiOSP/dP nanocomposite exhibits promising therapeutic efficacy in managing GDM by effectively regulating blood glucose and lipid levels, reducing inflammation, and protecting fetal development. Its ability to modulate key inflammatory signaling pathways highlights its efficacy as a novel, targeted therapeutic selection for GDM. Further research and medical validation could pave the way for its application as a safe and efficient therapeutic agent in GDM management, ultimately improving maternal and neonatal outcomes. Future studies will focus on investigating the long-term efficacy and safety of NiOSP/dP nanocomposite in gestational diabetes management. Additionally, research will be conducted to explore the potential of this nanocomposite in preventing diabetes-related complications and improving fetal outcomes. Further mechanistic studies will also be undertaken to elucidate the precise molecular interactions underlying the therapeutic effects of NiOSP/dP nanocomposite.

CRediT authorship contribution statement

Junmei Liu: Conceptualization, Methodology, Software, Zixuan Su: Data curation, Writing-Original draft preparation, Jiayue Ding: Visualization, Investigation, Yan Fang: Supervision.

Declaration of competing interest

The authors declare no competing interest.

Declaration of Generative AI and AI-assisted technologies in the writing process

The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.

References

Kim K.S., Hong S., Han K., Park C.Y.. The clinical characteristics of gestational diabetes mellitus in Korea: A national health information database study. Endocrinology and Metabolism (Seoul, Korea). 2021;36:628-636. https://doi.org/10.3803/EnM.2020.948
[Google Scholar]
ACOG practice bulletin No. 190: gestational diabetes mellitus. Obstetrics and Gynecology. ;131:e49-e64. https://doi.org/10.1097/AOG.0000000000002501
[Google Scholar]
Quintanilla Rodriguez B.S., Vadakekut E.S., Mahdy H. Gestational diabetes. In: StatPearls. Treasure Island (FL): StatPearls Publishing; 2025 Jan. [last accessed 2024 Jul 14]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK545196/
[Google Scholar]
Paulo M.S., Abdo N.M., Bettencourt-Silva R., Al-Rifai R.H.. Gestational diabetes mellitus in Europe: A systematic review and meta-analysis of prevalence studies. Frontiers in Endocrinology. 2021;12:691033. https://doi.org/10.3389/fendo.2021.691033
[Google Scholar]
Tsokkou S., Tzintros S.T., Konstantinidis I., Keramas A., Georgaki M.N., Stamoula E., Matsas A.. Assessment of environmental risk factors for gestational diabetes mellitus: A ten-year systematic review and meta-analysis. Journal of Clinical Medicine. 2025;14:1646. https://doi.org/10.3390/jcm14051646
[Google Scholar]
Goueslard K., Cottenet J., Mariet A.S., Giroud M., Cottin Y., Petit J.M., Quantin C.. Early cardiovascular events in women with a history of gestational diabetes mellitus. Cardiovascular Diabetology. 2016;15:15. https://doi.org/10.1186/s12933-016-0338-0
[Google Scholar]
Fuchs O., Sheiner E., Meirovitz M., Davidson E., Sergienko R., Kessous R.. The association between a history of gestational diabetes mellitus and future risk for female malignancies. Archives of Gynecology and Obstetrics. 2017;295:731-736. https://doi.org/10.1007/s00404-016-4275-7
[Google Scholar]
Perrin M.C., Terry M.B., Kleinhaus K., Deutsch L., Yanetz R., Tiram E., Calderon R., Friedlander Y., Paltiel O., Harlap S.. Gestational diabetes as a risk factor for pancreatic cancer: A prospective cohort study. BMC Medicine. 2007;5:25. https://doi.org/10.1186/1741-7015-5-25
[Google Scholar]
Keskesiadou G.N., Tsokkou S., Konstantinidis I., Georgaki M.N., Sioga A., Papamitsou T., Karachrysafi S.. Endocrine-disrupting chemicals and the development of diabetes mellitus type 1: A 5-year systematic review. International Journal of Molecular Sciences. 2024;25:10111. https://doi.org/10.3390/ijms251810111
[Google Scholar]
Tsokkou S., Konstantinidis I., Georgaki M.N., Kavvadas D., Papadopoulou K., Keramas A., Sioga A., Papamitsou T., Karachrysafi S.. Gestational diabetes mellitus and its correlation in the development of pancreatic cancer: A 10-year systematic review. Cancers. 2024;16:1840. https://doi.org/10.3390/cancers16101840
[Google Scholar]
Bellamy L., Casas J.P., Hingorani A.D., Williams D.. Type 2 diabetes mellitus after gestational diabetes: A systematic review and meta-analysis. Lancet (London, England). 2009;373:1773-1779. https://doi.org/10.1016/S0140-6736(09)60731-5
[Google Scholar]
Vounzoulaki E., Khunti K., Abner S.C., Tan B.K., Davies M.J., Gillies C.L.. Progression to type 2 diabetes in women with a known history of gestational diabetes: Systematic review and meta-analysis. BMJ (Clinical Research ed.). 2020;369:m1361. https://doi.org/10.1136/bmj.m1361
[Google Scholar]
Beharier O., Sergienko R., Kessous R., Szaingurten-Solodkin I., Walfisch A., Shusterman E., Tsumi E., Sheiner E.. Gestational diabetes mellitus is a significant risk factor for long-term ophthalmic morbidity. Archives of Gynecology and Obstetrics. 2017;295:1477-1482. https://doi.org/10.1007/s00404-017-4362-4
[Google Scholar]
Farahvar S., Walfisch A., Sheiner E.. Gestational diabetes risk factors and long-term consequences for both mother and offspring: A literature review. Expert Review of Endocrinology & Metabolism. 2019;14:63-74. https://doi.org/10.1080/17446651.2018.1476135
[Google Scholar]
Beharier O., Shoham-Vardi I., Pariente G., Sergienko R., Kessous R., Baumfeld Y., Szaingurten-Solodkin I., Sheiner E.. Gestational diabetes mellitus is a significant risk factor for long-term maternal renal disease. The Journal of Clinical Endocrinology and Metabolism. 2015;100:1412-1416. https://doi.org/10.1210/jc.2014-4474
[Google Scholar]
Wang Y.X., Mitsunami M., Manson J.E., Gaskins A.J., Rich-Edwards J.W., Wang L., Zhang C., Chavarro J.E.. Association of gestational diabetes with subsequent long-term risk of mortality. JAMA Internal Medicine. 2023;183:1204-1213. https://doi.org/10.1001/jamainternmed.2023.4401
[Google Scholar]
American Diabetes Association. Management of diabetes in pregnancy. Diabetes Care. ;38:S77-S79. https://doi.org/10.2337/dc15-s015
[Google Scholar]
Rowan J.A., Hague W.M., Gao W., Battin M.R., Moore M.P.. Metformin versus insulin for the treatment of gestational diabetes. The New England Journal of Medicine. 2008;358:2003-2015. https://doi.org/10.1056/NEJMoa0707193
[Google Scholar]
Grant RW, Kirkman MS.. Standards of medical care in diabetes. Diabetes Care. 2005;28:s4-s36. https://doi.org/10.2337/diacare.28.suppl_1.s4
[Google Scholar]
Zeng Y.C., Li M.J., Chen Y., Jiang L., Wang S.M., Mo X.L., Li B.Y.. The use of glyburide in the management of gestational diabetes mellitus: A meta-analysis. Advances in Medical Sciences. 2014;59:95-101. https://doi.org/10.1016/j.advms.2014.03.001
[Google Scholar]
Rowan J.A., Rush E.C., Plank L.D., Lu J., Obolonkin V., Coat S., Hague W.M.. Metformin in gestational diabetes: The offspring follow-up (MiG TOFU): Body composition and metabolic outcomes at 7-9 years of age. BMJ Open Diabetes Research & Care. 2018;6:e000456. https://doi.org/10.1136/bmjdrc-2017-000456
[Google Scholar]
Mukherjee S.M., Dawson A.. Diabetes: How to manage gestational diabetes mellitus. Drugs in Context. 2022;11:2021-2029. https://doi.org/10.7573/dic.2021-9-12
[Google Scholar]
Tanu G., Amreen S., Renu S., Shabihe R.B.S., Monisha B.. Pharmacological therapy for gestational diabetes mellitus: A comprehensive overview. Obesity Medicine. 2025;54:100587. https://doi.org/10.1016/j.obmed.2025.100587
[Google Scholar]
Subiabre M., Silva L., Toledo F., Paublo M., López M.A., Boric M.P., Sobrevia L.. Insulin therapy and its consequences for the mother, foetus, and newborn in gestational diabetes mellitus. Biochimica et Biophysica Acta. Molecular Basis of Disease. 2018;1864:2949-2956. https://doi.org/10.1016/j.bbadis.2018.06.005
[Google Scholar]
Bates S.H., Jones R.B., Bailey C.J.. Insulin-like effect of pinitol. British Journal of Pharmacology. 2000;130:1944-1948. https://doi.org/10.1038/sj.bjp.0703523
[Google Scholar]
Lahuta L.ławB., Szablińska J., Ciak M., Górecki R.J., ózef. The occurrence and accumulation of d-pinitol in fenugreek (Trigonella foenum graecum L.) Acta Physiologiae Plantarum. 2018;40:155-166. https://doi.org/10.1007/s11738-018-2734-4
[Google Scholar]
León-González A.J., Navarro I., Acero N., Muñoz Mingarro D., Martín-Cordero C.. Genus Retama: A review on traditional uses, phytochemistry, and pharmacological activities. Phytochemistry Reviews. 2018;17:701-731. https://doi.org/10.1007/s11101-018-9555-3
[Google Scholar]
Aqil F., Munagala R., Jeyabalan J., Vadhanam M.V.. Bioavailability of phytochemicals and its enhancement by drug delivery systems. Cancer Letters. 2013;334:133-141. https://doi.org/10.1016/j.canlet.2013.02.032
[Google Scholar]
El-Say K.M., El-Sawy H.S.. Polymeric nanoparticles: Promising platform for drug delivery. International Journal of Pharmaceutics. 2017;528:675-691. https://doi.org/10.1016/j.ijpharm.2017.06.052
[Google Scholar]
El-Kemary M., Nagy N., El-Mehasseb I.. Nickel oxide nanoparticles: Synthesis and spectral studies of interactions with glucose. Materials Science in Semiconductor Processing. 2013;16:1747-1752. https://doi.org/10.1016/j.mssp.2013.05.018
[Google Scholar]
Lim H.-R., Jung S.-J., Hwang T.-Y., Lee J., Kim K.H., Cho H.-baek, Choa Y.-H.. Electromagnetic wave absorption properties of Fe/MgO composites synthesized by a simple ultrasonic spray pyrolysis method. Applied Surface Science. 2019;473:1009-1013. https://doi.org/10.1016/j.apsusc.2018.12.222
[Google Scholar]
Sánchez-Hidalgo M., León-González A.J., Gálvez-Peralta M.. D-Pinitol: A cyclitol with versatile biological and pharmacological activities. Phytochem Rev. 2021;20:211-224. https://link.springer.com/article/10.1007/s11101-020-09677-6
[Google Scholar]
Lee B.H., Lee C.C., Wu S.C.. Ice plant (Mesembryanthemum crystallinum) improves hyperglycaemia and memory impairments in a Wistar rat model of streptozotocin-induced diabetes. Journal of the Science of Food and Agriculture. 2014;94:2266-2273. https://doi.org/10.1002/jsfa.6552
[Google Scholar]
González-Mauraza N.H., León-González A.J., Espartero J.L., Gallego-Fernández J.B., Sánchez-Hidalgo M., Martin-Cordero C.. Isolation and quantification of pinitol, a bioactive cyclitol, in retama spp. Natural Product Communications. 2016;11:405-406. https://doi.org/10.1007/s10886-016-0775-y
[Google Scholar]
Zhang C., Wu W., Xin X., Li X., Liu D.. Extract of ice plant (Mesembryanthemum crystallinum) ameliorates hyperglycemia and modulates the gut microbiota composition in type 2 diabetic Goto-Kakizaki rats. Food & Function. 2019;10:3252-3261. https://doi.org/10.1039/c9fo00119k
[Google Scholar]
Lee E., Lim Y., Kwon S.W., Kwon O.. Pinitol consumption improves liver health status by reducing oxidative stress and fatty acid accumulation in subjects with non-alcoholic fatty liver disease: A randomized, double-blind, placebo-controlled trial. The Journal of Nutritional Biochemistry. 2019;68:33-41. https://doi.org/10.1016/j.jnutbio.2019.03.006
[Google Scholar]
Zheng K., Zhao Z., Lin N., Wu Y., Xu Y., Zhang W.. Protective effect of pinitol against inflammatory mediators of rheumatoid arthritis via inhibition of protein tyrosine phosphatase non-receptor type 22 (PTPN22) Medical Science Monitor: International Medical Journal of Experimental and Clinical Research. 2017;23:1923-1932. https://doi.org/10.12659/msm.903357
[Google Scholar]
Gao Y., Zhang M., Wu T., Xu M., Cai H., Zhang Z.. Effects of d-pinitol on insulin resistance through the PI₃K/AKT signaling pathway in type 2 diabetes mellitus rats. Journal of Agricultural and Food Chemistry. 2015;63:6019-6026. https://doi.org/10.1021/acs.jafc.5b01238
[Google Scholar]
Rengarajan T., Nandakumar N., Balasubramanian M.P.. d-Pinitol a low-molecular cyclitol prevents 7,12-Dimethylbenz [a] anthracene induced experimental breast cancer through regulating anti-apoptotic protein Bcl-2, mitochondrial and carbohydrate key metabolizing enzymes. Biomedicine & Preventive Nutrition. 2012;2:25-30. https://doi.org/10.1016/j.bionut.2011.11.001
[Google Scholar]
Kaplan A.. The nanocomposites designs of phytomolecules from medicinal and aromatic plants: Promising anticancer-antiviral applications. Beni-Suef University Journal of Basic and Applied Sciences. 2022;11:17. https://doi.org/10.1186/s43088-022-00198-z
[Google Scholar]
Böttcher H.. Bioactive sol-gel coatings. Journal for Practical Chemistry. 2000;342:427-436. https://doi.org/10.1002/1521-3897(200006)342:5<427::aid-prac427>3.0.co;2-b
[Google Scholar]
Kesharwani P., Gorain B., Low S.Y., Tan S.A., Ling E.C.S., Lim Y.K., Chin C.M., Lee P.Y., Lee C.M., Ooi C.H., Choudhury H., Pandey M.. Nanotechnology based approaches for anti-diabetic drugs delivery. Diabetes Research and Clinical Practice. 2018;136:52-77. https://doi.org/10.1016/j.diabres.2017.11.018
[Google Scholar]
Patra J.K., Das G., Fraceto L.F., Campos E.V.R., Rodriguez-Torres M.D.P., Acosta-Torres L.S., Diaz-Torres L.A., Grillo R., Swamy M.K., Sharma S., Habtemariam S., Shin H.S.. Nano based drug delivery systems: Recent developments and future prospects. Journal of Nanobiotechnology. 2018;16:71. https://doi.org/10.1186/s12951-018-0392-8
[Google Scholar]
Dewanjee S., Chakraborty P., Mukherjee B., De Feo V.. Plant-based antidiabetic nanoformulations: The emerging paradigm for effective therapy. International Journal of Molecular Sciences. 2020;21:2217. https://doi.org/10.3390/ijms21062217
[Google Scholar]
Gauster M., Desoye G., Tötsch M., Hiden U.. The placenta and gestational diabetes mellitus. Current Diabetes Reports. 2012;12:16-23. https://doi.org/10.1007/s11892-011-0244-5
[Google Scholar]
Calvo M.J., Parra H., Santeliz R., Bautista J., Luzardo E., Villasmil N., Martínez M.S., Chacín M., Cano C., Checa-Ros A.D.’, Marco L., Bermúdez V., De Sanctis J.B.. The placental role in gestational diabetes mellitus: A molecular perspective. TouchREVIEWS in Endocrinology. 2024;20:10-18. https://doi.org/10.17925/EE.2024.20.1.5
[Google Scholar]
Cindrova-Davies T., van Patot M.T., Gardner L., Jauniaux E., Burton G.J., Charnock-Jones D.S.. Energy status and HIF signalling in chorionic villi show no evidence of hypoxic stress during human early placental development. Molecular Human Reproduction. 2015;21:296-308. https://doi.org/10.1093/molehr/gau105
[Google Scholar]
Lappas M., Hiden U., Desoye G., Froehlich J., Hauguel-de Mouzon S., Jawerbaum A.. The role of oxidative stress in the pathophysiology of gestational diabetes mellitus. Antioxidants & Redox Signaling. 2011;15:3061-3100. https://doi.org/10.1089/ars.2010.3765
[Google Scholar]
Abell S.K., De Courten B., Boyle J.A., Teede H.J.. Inflammatory and other biomarkers: Role in pathophysiology and prediction of gestational diabetes mellitus. International Journal of Molecular Sciences. 2015;16:13442-13473. https://doi.org/10.3390/ijms160613442
[Google Scholar]
Saucedo R., Ortega-Camarillo C., Ferreira-Hermosillo A., Díaz-Velázquez M.F., Meixueiro-Calderón C., Valencia-Ortega J.. Role of oxidative stress and inflammation in gestational diabetes mellitus. Antioxidants (Basel, Switzerland). 2023;12:1812. https://doi.org/10.3390/antiox12101812
[Google Scholar]
Lenzen S.. The mechanisms of alloxan- and streptozotocin-induced diabetes. Diabetologia. 2008;51:216-226. https://doi.org/10.1007/s00125-007-0886-7
[Google Scholar]
Mythili M.D., Vyas R., Akila G., Gunasekaran S.. Effect of streptozotocin on the ultrastructure of rat pancreatic islets. Microscopy Research and Technique. 2004;63:274-281. https://doi.org/10.1002/jemt.20039
[Google Scholar]
Woollett L.A.. The origins and roles of cholesterol and fatty acids in the fetus. Current Opinion in Lipidology. 2001;12:305-312. https://doi.org/10.1097/00041433-200106000-00010
[Google Scholar]
Tuckey R.C.. Progesterone synthesis by the human placenta. Placenta. 2005;26:273-281. https://doi.org/10.1016/j.placenta.2004.06.012
[Google Scholar]
Henson M.C., Shi W., Greene S.J., Reggio B.C.. Effects of pregnant human, nonpregnant human, and fetal bovine sera on human chorionic gonadotropin, estradiol, and progesterone release by cultured human trophoblast cells. Endocrinology. 1996;137:2067-2074. https://doi.org/10.1210/endo.137.5.8612549
[Google Scholar]
Tranquilli A.L., Cester N., Giannubilo S.R., Corradetti A., Nanetti L., Mazzanti L.. Plasma lipids and physicochemical properties of the erythrocyte plasma membrane throughout pregnancy. Acta Obstetricia et Gynecologica Scandinavica. 2004;83:443-448. https://doi.org/10.1111/j.0001-6349.2004.00341.x
[Google Scholar]
Napoli C.D.’, Armiento F.P., Mancini F.P., Postiglione A., Witztum J.L., Palumbo G., Palinski W.. Fatty streak formation occurs in human fetal aortas and is greatly enhanced by maternal hypercholesterolemia. Intimal accumulation of low density lipoprotein and its oxidation precede monocyte recruitment into early atherosclerotic lesions. The Journal of Clinical Investigation. 1997;100:2680-2690. https://doi.org/10.1172/JCI119813
[Google Scholar]
Napoli C., Glass C.K., Witztum J.L., Deutsch R.D.’, Armiento F.P., Palinski W.. Influence of maternal hypercholesterolaemia during pregnancy on progression of early atherosclerotic lesions in childhood: Fate of early lesions in children (FELIC) study. Lancet (London, England). 1999;354:1234-1241. https://doi.org/10.1016/S0140-6736(99)02131-5
[Google Scholar]
Skilton M.R., Evans N., Griffiths K.A., Harmer J.A., Celermajer D.S.. Aortic wall thickness in newborns with intrauterine growth restriction. Lancet (London, England). 2005;365:1484-1486. https://doi.org/10.1016/S0140-6736(05)66419-7
[Google Scholar]
Catov J.M., Newman A.B., Roberts J.M., Kelsey S.F., Sutton-Tyrrell K., Harris T.B., Colbert L., Rubin S.M., Satterfield S., Ness R.B.. Preterm delivery and later maternal cardiovascular disease risk. Epidemiology (Cambridge, Mass.). 2007;18:733-739. https://doi.org/10.1097/EDE.0b013e3181567f96
[Google Scholar]
Misra V.K., Trudeau S., Perni U.. Maternal serum lipids during pregnancy and infant birth weight: The influence of prepregnancy BMI. Obesity (Silver Spring, Md.). 2011;19:1476-1481. https://doi.org/10.1038/oby.2011.43
[Google Scholar]
Bo S., Signorile A., Menato G., Gambino R., Bardelli C., Gallo M.L., Cassader M., Massobrio M., Pagano G.F.. C-reactive protein and tumor necrosis factor-alpha in gestational hyperglycemia. Journal of Endocrinological Investigation. 2005;28:779-786. https://doi.org/10.1007/BF03347566
[Google Scholar]
Kinalski M., Telejko B., Kuźmicki M., Kretowski A., Kinalska I.. Tumor necrosis factor alpha system and plasma adiponectin concentration in women with gestational diabetes. Hormone and Metabolic Research = Hormon- und Stoffwechselforschung = Hormones et Metabolisme. 2005;37:450-454. https://doi.org/10.1055/s-2005-870238
[Google Scholar]
Glass C.K., Olefsky J.M.. Inflammation and lipid signaling in the etiology of insulin resistance. Cell Metabolism. 2012;15:635-645. https://doi.org/10.1016/j.cmet.2012.04.001
[Google Scholar]
Eleftheriades M., Papastefanou I., Lambrinoudaki I., Kappou D., Lavranos D., Akalestos A.. Elevated placental growth factor concentrations at 11-14 weeks of gestation to predict gestational diabetes mellitus. Metabolism: Clinical and Experimental.. 2014;63:1419-1425. https://doi.org/10.1016/j.metabol.2014.07.016
[Google Scholar]
Kumar H., Kawai T., Akira S.. Toll-like receptors and innate immunity. Biochemical and Biophysical Research Communications. 2009;388:621-625. https://doi.org/10.1016/j.bbrc.2009.08.062
[Google Scholar]
Feng H., Su R., Song Y., Wang C., Lin L., Ma J., Yang H.. Positive correlation between enhanced expression of TLR4/MyD88/NF-κB with insulin resistance in placentae of gestational diabetes mellitus. PloS One. 2016;11:e0157185. https://doi.org/10.1371/journal.pone.0157185
[Google Scholar]
O’Neill L.A., Golenbock D., Bowie A.G.. The history of toll-like receptors—redefining innate immunity. Nature Reviews Immunology.. 2013;13:453-60. https://doi.org/10.1038/nri3446
[Google Scholar]
Glass C.K., Olefsky J.M.. Inflammation and lipid signaling in the etiology of insulin resistance. Cell Metabolism. 2012;15:635-645. https://doi.org/10.1016/j.cmet.2012.04.001
[Google Scholar]
Li H., Sun B.. Toll-like receptor 4 in atherosclerosis. Journal of Cellular and Molecular Medicine. 2007;11:88-95. https://doi.org/10.1111/j.1582-4934.2007.00011.x
[Google Scholar]
Liu X., Ukai T., Yumoto H., Davey M., Goswami S., Gibson F.C., Genco C.A.. Toll-like receptor 2 plays a critical role in the progression of atherosclerosis that is independent of dietary lipids. Atherosclerosis. 2008;196:146-154. https://doi.org/10.1016/j.atherosclerosis.2007.03.025
[Google Scholar]
Michelsen K.S., Wong M.H., Shah P.K., Zhang W., Yano J., Doherty T.M., Akira S., Rajavashisth T.B., Arditi M.. Lack of toll-like receptor 4 or myeloid differentiation factor 88 reduces atherosclerosis and alters plaque phenotype in mice deficient in apolipoprotein E. Proceedings of the National Academy of Sciences of the United States of America. 2004;101:10679-10684. https://doi.org/10.1073/pnas.0403249101
[Google Scholar]
Jia L., Vianna C.R., Fukuda M., Berglund E.D., Liu C., Tao C., Sun K., Liu T., Harper M.J., Lee C.E., Lee S., Scherer P.E., Elmquist J.K.. Hepatocyte Toll-like receptor 4 regulates obesity-induced inflammation and insulin resistance. Nature Communications. 2014;5:3878. https://doi.org/10.1038/ncomms4878
[Google Scholar]

1. Introduction

2. Materials and Methods

2.1. Synthesis of NiOSP/dP nanocomposite
2.2. Characterization of NiOSP/dP nanocomposite

3. Results and Discussion

3.1. Characterization of NiOSP/dP nanocomposites

4. Conclusions

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[1] Kim K.S., Hong S., Han K., Park C.Y.. The clinical characteristics of gestational diabetes mellitus in Korea: A national health information database study. Endocrinology and Metabolism (Seoul, Korea). 2021;36:628-636. https://doi.org/10.3803/EnM.2020.948
[Google Scholar]

[2] ACOG practice bulletin No. 190: gestational diabetes mellitus. Obstetrics and Gynecology. ;131:e49-e64. https://doi.org/10.1097/AOG.0000000000002501
[Google Scholar]

[3] Quintanilla Rodriguez B.S., Vadakekut E.S., Mahdy H. Gestational diabetes. In: StatPearls. Treasure Island (FL): StatPearls Publishing; 2025 Jan. [last accessed 2024 Jul 14]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK545196/
[Google Scholar]

[4] Paulo M.S., Abdo N.M., Bettencourt-Silva R., Al-Rifai R.H.. Gestational diabetes mellitus in Europe: A systematic review and meta-analysis of prevalence studies. Frontiers in Endocrinology. 2021;12:691033. https://doi.org/10.3389/fendo.2021.691033
[Google Scholar]

[5] Tsokkou S., Tzintros S.T., Konstantinidis I., Keramas A., Georgaki M.N., Stamoula E., Matsas A.. Assessment of environmental risk factors for gestational diabetes mellitus: A ten-year systematic review and meta-analysis. Journal of Clinical Medicine. 2025;14:1646. https://doi.org/10.3390/jcm14051646
[Google Scholar]

[6] Goueslard K., Cottenet J., Mariet A.S., Giroud M., Cottin Y., Petit J.M., Quantin C.. Early cardiovascular events in women with a history of gestational diabetes mellitus. Cardiovascular Diabetology. 2016;15:15. https://doi.org/10.1186/s12933-016-0338-0
[Google Scholar]

[7] Fuchs O., Sheiner E., Meirovitz M., Davidson E., Sergienko R., Kessous R.. The association between a history of gestational diabetes mellitus and future risk for female malignancies. Archives of Gynecology and Obstetrics. 2017;295:731-736. https://doi.org/10.1007/s00404-016-4275-7
[Google Scholar]

[8] Perrin M.C., Terry M.B., Kleinhaus K., Deutsch L., Yanetz R., Tiram E., Calderon R., Friedlander Y., Paltiel O., Harlap S.. Gestational diabetes as a risk factor for pancreatic cancer: A prospective cohort study. BMC Medicine. 2007;5:25. https://doi.org/10.1186/1741-7015-5-25
[Google Scholar]

[9] Keskesiadou G.N., Tsokkou S., Konstantinidis I., Georgaki M.N., Sioga A., Papamitsou T., Karachrysafi S.. Endocrine-disrupting chemicals and the development of diabetes mellitus type 1: A 5-year systematic review. International Journal of Molecular Sciences. 2024;25:10111. https://doi.org/10.3390/ijms251810111
[Google Scholar]

[10] Tsokkou S., Konstantinidis I., Georgaki M.N., Kavvadas D., Papadopoulou K., Keramas A., Sioga A., Papamitsou T., Karachrysafi S.. Gestational diabetes mellitus and its correlation in the development of pancreatic cancer: A 10-year systematic review. Cancers. 2024;16:1840. https://doi.org/10.3390/cancers16101840
[Google Scholar]

[11] Bellamy L., Casas J.P., Hingorani A.D., Williams D.. Type 2 diabetes mellitus after gestational diabetes: A systematic review and meta-analysis. Lancet (London, England). 2009;373:1773-1779. https://doi.org/10.1016/S0140-6736(09)60731-5
[Google Scholar]

[12] Vounzoulaki E., Khunti K., Abner S.C., Tan B.K., Davies M.J., Gillies C.L.. Progression to type 2 diabetes in women with a known history of gestational diabetes: Systematic review and meta-analysis. BMJ (Clinical Research ed.). 2020;369:m1361. https://doi.org/10.1136/bmj.m1361
[Google Scholar]

[13] Beharier O., Sergienko R., Kessous R., Szaingurten-Solodkin I., Walfisch A., Shusterman E., Tsumi E., Sheiner E.. Gestational diabetes mellitus is a significant risk factor for long-term ophthalmic morbidity. Archives of Gynecology and Obstetrics. 2017;295:1477-1482. https://doi.org/10.1007/s00404-017-4362-4
[Google Scholar]

[14] Farahvar S., Walfisch A., Sheiner E.. Gestational diabetes risk factors and long-term consequences for both mother and offspring: A literature review. Expert Review of Endocrinology & Metabolism. 2019;14:63-74. https://doi.org/10.1080/17446651.2018.1476135
[Google Scholar]

[15] Beharier O., Shoham-Vardi I., Pariente G., Sergienko R., Kessous R., Baumfeld Y., Szaingurten-Solodkin I., Sheiner E.. Gestational diabetes mellitus is a significant risk factor for long-term maternal renal disease. The Journal of Clinical Endocrinology and Metabolism. 2015;100:1412-1416. https://doi.org/10.1210/jc.2014-4474
[Google Scholar]

[16] Wang Y.X., Mitsunami M., Manson J.E., Gaskins A.J., Rich-Edwards J.W., Wang L., Zhang C., Chavarro J.E.. Association of gestational diabetes with subsequent long-term risk of mortality. JAMA Internal Medicine. 2023;183:1204-1213. https://doi.org/10.1001/jamainternmed.2023.4401
[Google Scholar]

[17] American Diabetes Association. Management of diabetes in pregnancy. Diabetes Care. ;38:S77-S79. https://doi.org/10.2337/dc15-s015
[Google Scholar]

[18] Rowan J.A., Hague W.M., Gao W., Battin M.R., Moore M.P.. Metformin versus insulin for the treatment of gestational diabetes. The New England Journal of Medicine. 2008;358:2003-2015. https://doi.org/10.1056/NEJMoa0707193
[Google Scholar]

[19] Grant RW, Kirkman MS.. Standards of medical care in diabetes. Diabetes Care. 2005;28:s4-s36. https://doi.org/10.2337/diacare.28.suppl_1.s4
[Google Scholar]

[20] Zeng Y.C., Li M.J., Chen Y., Jiang L., Wang S.M., Mo X.L., Li B.Y.. The use of glyburide in the management of gestational diabetes mellitus: A meta-analysis. Advances in Medical Sciences. 2014;59:95-101. https://doi.org/10.1016/j.advms.2014.03.001
[Google Scholar]

[21] Rowan J.A., Rush E.C., Plank L.D., Lu J., Obolonkin V., Coat S., Hague W.M.. Metformin in gestational diabetes: The offspring follow-up (MiG TOFU): Body composition and metabolic outcomes at 7-9 years of age. BMJ Open Diabetes Research & Care. 2018;6:e000456. https://doi.org/10.1136/bmjdrc-2017-000456
[Google Scholar]

[22] Mukherjee S.M., Dawson A.. Diabetes: How to manage gestational diabetes mellitus. Drugs in Context. 2022;11:2021-2029. https://doi.org/10.7573/dic.2021-9-12
[Google Scholar]

[23] Tanu G., Amreen S., Renu S., Shabihe R.B.S., Monisha B.. Pharmacological therapy for gestational diabetes mellitus: A comprehensive overview. Obesity Medicine. 2025;54:100587. https://doi.org/10.1016/j.obmed.2025.100587
[Google Scholar]

[24] Subiabre M., Silva L., Toledo F., Paublo M., López M.A., Boric M.P., Sobrevia L.. Insulin therapy and its consequences for the mother, foetus, and newborn in gestational diabetes mellitus. Biochimica et Biophysica Acta. Molecular Basis of Disease. 2018;1864:2949-2956. https://doi.org/10.1016/j.bbadis.2018.06.005
[Google Scholar]

[25] Bates S.H., Jones R.B., Bailey C.J.. Insulin-like effect of pinitol. British Journal of Pharmacology. 2000;130:1944-1948. https://doi.org/10.1038/sj.bjp.0703523
[Google Scholar]

[26] Lahuta L.ławB., Szablińska J., Ciak M., Górecki R.J., ózef. The occurrence and accumulation of d-pinitol in fenugreek (Trigonella foenum graecum L.) Acta Physiologiae Plantarum. 2018;40:155-166. https://doi.org/10.1007/s11738-018-2734-4
[Google Scholar]

[27] León-González A.J., Navarro I., Acero N., Muñoz Mingarro D., Martín-Cordero C.. Genus Retama: A review on traditional uses, phytochemistry, and pharmacological activities. Phytochemistry Reviews. 2018;17:701-731. https://doi.org/10.1007/s11101-018-9555-3
[Google Scholar]

[28] Aqil F., Munagala R., Jeyabalan J., Vadhanam M.V.. Bioavailability of phytochemicals and its enhancement by drug delivery systems. Cancer Letters. 2013;334:133-141. https://doi.org/10.1016/j.canlet.2013.02.032
[Google Scholar]

[29] El-Say K.M., El-Sawy H.S.. Polymeric nanoparticles: Promising platform for drug delivery. International Journal of Pharmaceutics. 2017;528:675-691. https://doi.org/10.1016/j.ijpharm.2017.06.052
[Google Scholar]

[30] El-Kemary M., Nagy N., El-Mehasseb I.. Nickel oxide nanoparticles: Synthesis and spectral studies of interactions with glucose. Materials Science in Semiconductor Processing. 2013;16:1747-1752. https://doi.org/10.1016/j.mssp.2013.05.018
[Google Scholar]

[31] Lim H.-R., Jung S.-J., Hwang T.-Y., Lee J., Kim K.H., Cho H.-baek, Choa Y.-H.. Electromagnetic wave absorption properties of Fe/MgO composites synthesized by a simple ultrasonic spray pyrolysis method. Applied Surface Science. 2019;473:1009-1013. https://doi.org/10.1016/j.apsusc.2018.12.222
[Google Scholar]

[32] Sánchez-Hidalgo M., León-González A.J., Gálvez-Peralta M.. D-Pinitol: A cyclitol with versatile biological and pharmacological activities. Phytochem Rev. 2021;20:211-224. https://link.springer.com/article/10.1007/s11101-020-09677-6
[Google Scholar]

[33] Lee B.H., Lee C.C., Wu S.C.. Ice plant (Mesembryanthemum crystallinum) improves hyperglycaemia and memory impairments in a Wistar rat model of streptozotocin-induced diabetes. Journal of the Science of Food and Agriculture. 2014;94:2266-2273. https://doi.org/10.1002/jsfa.6552
[Google Scholar]

[34] González-Mauraza N.H., León-González A.J., Espartero J.L., Gallego-Fernández J.B., Sánchez-Hidalgo M., Martin-Cordero C.. Isolation and quantification of pinitol, a bioactive cyclitol, in retama spp. Natural Product Communications. 2016;11:405-406. https://doi.org/10.1007/s10886-016-0775-y
[Google Scholar]

[35] Zhang C., Wu W., Xin X., Li X., Liu D.. Extract of ice plant (Mesembryanthemum crystallinum) ameliorates hyperglycemia and modulates the gut microbiota composition in type 2 diabetic Goto-Kakizaki rats. Food & Function. 2019;10:3252-3261. https://doi.org/10.1039/c9fo00119k
[Google Scholar]

[36] Lee E., Lim Y., Kwon S.W., Kwon O.. Pinitol consumption improves liver health status by reducing oxidative stress and fatty acid accumulation in subjects with non-alcoholic fatty liver disease: A randomized, double-blind, placebo-controlled trial. The Journal of Nutritional Biochemistry. 2019;68:33-41. https://doi.org/10.1016/j.jnutbio.2019.03.006
[Google Scholar]

[37] Zheng K., Zhao Z., Lin N., Wu Y., Xu Y., Zhang W.. Protective effect of pinitol against inflammatory mediators of rheumatoid arthritis via inhibition of protein tyrosine phosphatase non-receptor type 22 (PTPN22) Medical Science Monitor: International Medical Journal of Experimental and Clinical Research. 2017;23:1923-1932. https://doi.org/10.12659/msm.903357
[Google Scholar]

[38] Gao Y., Zhang M., Wu T., Xu M., Cai H., Zhang Z.. Effects of d-pinitol on insulin resistance through the PI₃K/AKT signaling pathway in type 2 diabetes mellitus rats. Journal of Agricultural and Food Chemistry. 2015;63:6019-6026. https://doi.org/10.1021/acs.jafc.5b01238
[Google Scholar]

[39] Rengarajan T., Nandakumar N., Balasubramanian M.P.. d-Pinitol a low-molecular cyclitol prevents 7,12-Dimethylbenz [a] anthracene induced experimental breast cancer through regulating anti-apoptotic protein Bcl-2, mitochondrial and carbohydrate key metabolizing enzymes. Biomedicine & Preventive Nutrition. 2012;2:25-30. https://doi.org/10.1016/j.bionut.2011.11.001
[Google Scholar]

[40] Kaplan A.. The nanocomposites designs of phytomolecules from medicinal and aromatic plants: Promising anticancer-antiviral applications. Beni-Suef University Journal of Basic and Applied Sciences. 2022;11:17. https://doi.org/10.1186/s43088-022-00198-z
[Google Scholar]

[41] Böttcher H.. Bioactive sol-gel coatings. Journal for Practical Chemistry. 2000;342:427-436. https://doi.org/10.1002/1521-3897(200006)342:5<427::aid-prac427>3.0.co;2-b
[Google Scholar]

[42] Kesharwani P., Gorain B., Low S.Y., Tan S.A., Ling E.C.S., Lim Y.K., Chin C.M., Lee P.Y., Lee C.M., Ooi C.H., Choudhury H., Pandey M.. Nanotechnology based approaches for anti-diabetic drugs delivery. Diabetes Research and Clinical Practice. 2018;136:52-77. https://doi.org/10.1016/j.diabres.2017.11.018
[Google Scholar]

[43] Patra J.K., Das G., Fraceto L.F., Campos E.V.R., Rodriguez-Torres M.D.P., Acosta-Torres L.S., Diaz-Torres L.A., Grillo R., Swamy M.K., Sharma S., Habtemariam S., Shin H.S.. Nano based drug delivery systems: Recent developments and future prospects. Journal of Nanobiotechnology. 2018;16:71. https://doi.org/10.1186/s12951-018-0392-8
[Google Scholar]

[44] Dewanjee S., Chakraborty P., Mukherjee B., De Feo V.. Plant-based antidiabetic nanoformulations: The emerging paradigm for effective therapy. International Journal of Molecular Sciences. 2020;21:2217. https://doi.org/10.3390/ijms21062217
[Google Scholar]

[45] Gauster M., Desoye G., Tötsch M., Hiden U.. The placenta and gestational diabetes mellitus. Current Diabetes Reports. 2012;12:16-23. https://doi.org/10.1007/s11892-011-0244-5
[Google Scholar]

[46] Calvo M.J., Parra H., Santeliz R., Bautista J., Luzardo E., Villasmil N., Martínez M.S., Chacín M., Cano C., Checa-Ros A.D.’, Marco L., Bermúdez V., De Sanctis J.B.. The placental role in gestational diabetes mellitus: A molecular perspective. TouchREVIEWS in Endocrinology. 2024;20:10-18. https://doi.org/10.17925/EE.2024.20.1.5
[Google Scholar]

[47] Cindrova-Davies T., van Patot M.T., Gardner L., Jauniaux E., Burton G.J., Charnock-Jones D.S.. Energy status and HIF signalling in chorionic villi show no evidence of hypoxic stress during human early placental development. Molecular Human Reproduction. 2015;21:296-308. https://doi.org/10.1093/molehr/gau105
[Google Scholar]

[48] Lappas M., Hiden U., Desoye G., Froehlich J., Hauguel-de Mouzon S., Jawerbaum A.. The role of oxidative stress in the pathophysiology of gestational diabetes mellitus. Antioxidants & Redox Signaling. 2011;15:3061-3100. https://doi.org/10.1089/ars.2010.3765
[Google Scholar]

[49] Abell S.K., De Courten B., Boyle J.A., Teede H.J.. Inflammatory and other biomarkers: Role in pathophysiology and prediction of gestational diabetes mellitus. International Journal of Molecular Sciences. 2015;16:13442-13473. https://doi.org/10.3390/ijms160613442
[Google Scholar]

[50] Saucedo R., Ortega-Camarillo C., Ferreira-Hermosillo A., Díaz-Velázquez M.F., Meixueiro-Calderón C., Valencia-Ortega J.. Role of oxidative stress and inflammation in gestational diabetes mellitus. Antioxidants (Basel, Switzerland). 2023;12:1812. https://doi.org/10.3390/antiox12101812
[Google Scholar]

[51] Lenzen S.. The mechanisms of alloxan- and streptozotocin-induced diabetes. Diabetologia. 2008;51:216-226. https://doi.org/10.1007/s00125-007-0886-7
[Google Scholar]

[52] Mythili M.D., Vyas R., Akila G., Gunasekaran S.. Effect of streptozotocin on the ultrastructure of rat pancreatic islets. Microscopy Research and Technique. 2004;63:274-281. https://doi.org/10.1002/jemt.20039
[Google Scholar]

[53] Woollett L.A.. The origins and roles of cholesterol and fatty acids in the fetus. Current Opinion in Lipidology. 2001;12:305-312. https://doi.org/10.1097/00041433-200106000-00010
[Google Scholar]

[54] Tuckey R.C.. Progesterone synthesis by the human placenta. Placenta. 2005;26:273-281. https://doi.org/10.1016/j.placenta.2004.06.012
[Google Scholar]

[55] Henson M.C., Shi W., Greene S.J., Reggio B.C.. Effects of pregnant human, nonpregnant human, and fetal bovine sera on human chorionic gonadotropin, estradiol, and progesterone release by cultured human trophoblast cells. Endocrinology. 1996;137:2067-2074. https://doi.org/10.1210/endo.137.5.8612549
[Google Scholar]

[56] Tranquilli A.L., Cester N., Giannubilo S.R., Corradetti A., Nanetti L., Mazzanti L.. Plasma lipids and physicochemical properties of the erythrocyte plasma membrane throughout pregnancy. Acta Obstetricia et Gynecologica Scandinavica. 2004;83:443-448. https://doi.org/10.1111/j.0001-6349.2004.00341.x
[Google Scholar]

[57] Napoli C.D.’, Armiento F.P., Mancini F.P., Postiglione A., Witztum J.L., Palumbo G., Palinski W.. Fatty streak formation occurs in human fetal aortas and is greatly enhanced by maternal hypercholesterolemia. Intimal accumulation of low density lipoprotein and its oxidation precede monocyte recruitment into early atherosclerotic lesions. The Journal of Clinical Investigation. 1997;100:2680-2690. https://doi.org/10.1172/JCI119813
[Google Scholar]

[58] Napoli C., Glass C.K., Witztum J.L., Deutsch R.D.’, Armiento F.P., Palinski W.. Influence of maternal hypercholesterolaemia during pregnancy on progression of early atherosclerotic lesions in childhood: Fate of early lesions in children (FELIC) study. Lancet (London, England). 1999;354:1234-1241. https://doi.org/10.1016/S0140-6736(99)02131-5
[Google Scholar]

[59] Skilton M.R., Evans N., Griffiths K.A., Harmer J.A., Celermajer D.S.. Aortic wall thickness in newborns with intrauterine growth restriction. Lancet (London, England). 2005;365:1484-1486. https://doi.org/10.1016/S0140-6736(05)66419-7
[Google Scholar]

[60] Catov J.M., Newman A.B., Roberts J.M., Kelsey S.F., Sutton-Tyrrell K., Harris T.B., Colbert L., Rubin S.M., Satterfield S., Ness R.B.. Preterm delivery and later maternal cardiovascular disease risk. Epidemiology (Cambridge, Mass.). 2007;18:733-739. https://doi.org/10.1097/EDE.0b013e3181567f96
[Google Scholar]

[61] Misra V.K., Trudeau S., Perni U.. Maternal serum lipids during pregnancy and infant birth weight: The influence of prepregnancy BMI. Obesity (Silver Spring, Md.). 2011;19:1476-1481. https://doi.org/10.1038/oby.2011.43
[Google Scholar]

[62] Bo S., Signorile A., Menato G., Gambino R., Bardelli C., Gallo M.L., Cassader M., Massobrio M., Pagano G.F.. C-reactive protein and tumor necrosis factor-alpha in gestational hyperglycemia. Journal of Endocrinological Investigation. 2005;28:779-786. https://doi.org/10.1007/BF03347566
[Google Scholar]

[63] Kinalski M., Telejko B., Kuźmicki M., Kretowski A., Kinalska I.. Tumor necrosis factor alpha system and plasma adiponectin concentration in women with gestational diabetes. Hormone and Metabolic Research = Hormon- und Stoffwechselforschung = Hormones et Metabolisme. 2005;37:450-454. https://doi.org/10.1055/s-2005-870238
[Google Scholar]

[64] Glass C.K., Olefsky J.M.. Inflammation and lipid signaling in the etiology of insulin resistance. Cell Metabolism. 2012;15:635-645. https://doi.org/10.1016/j.cmet.2012.04.001
[Google Scholar]

[65] Eleftheriades M., Papastefanou I., Lambrinoudaki I., Kappou D., Lavranos D., Akalestos A.. Elevated placental growth factor concentrations at 11-14 weeks of gestation to predict gestational diabetes mellitus. Metabolism: Clinical and Experimental.. 2014;63:1419-1425. https://doi.org/10.1016/j.metabol.2014.07.016
[Google Scholar]

[66] Kumar H., Kawai T., Akira S.. Toll-like receptors and innate immunity. Biochemical and Biophysical Research Communications. 2009;388:621-625. https://doi.org/10.1016/j.bbrc.2009.08.062
[Google Scholar]

[67] Feng H., Su R., Song Y., Wang C., Lin L., Ma J., Yang H.. Positive correlation between enhanced expression of TLR4/MyD88/NF-κB with insulin resistance in placentae of gestational diabetes mellitus. PloS One. 2016;11:e0157185. https://doi.org/10.1371/journal.pone.0157185
[Google Scholar]

[68] O’Neill L.A., Golenbock D., Bowie A.G.. The history of toll-like receptors—redefining innate immunity. Nature Reviews Immunology.. 2013;13:453-60. https://doi.org/10.1038/nri3446
[Google Scholar]

[69] Glass C.K., Olefsky J.M.. Inflammation and lipid signaling in the etiology of insulin resistance. Cell Metabolism. 2012;15:635-645. https://doi.org/10.1016/j.cmet.2012.04.001
[Google Scholar]

[70] Li H., Sun B.. Toll-like receptor 4 in atherosclerosis. Journal of Cellular and Molecular Medicine. 2007;11:88-95. https://doi.org/10.1111/j.1582-4934.2007.00011.x
[Google Scholar]

[71] Liu X., Ukai T., Yumoto H., Davey M., Goswami S., Gibson F.C., Genco C.A.. Toll-like receptor 2 plays a critical role in the progression of atherosclerosis that is independent of dietary lipids. Atherosclerosis. 2008;196:146-154. https://doi.org/10.1016/j.atherosclerosis.2007.03.025
[Google Scholar]

[72] Michelsen K.S., Wong M.H., Shah P.K., Zhang W., Yano J., Doherty T.M., Akira S., Rajavashisth T.B., Arditi M.. Lack of toll-like receptor 4 or myeloid differentiation factor 88 reduces atherosclerosis and alters plaque phenotype in mice deficient in apolipoprotein E. Proceedings of the National Academy of Sciences of the United States of America. 2004;101:10679-10684. https://doi.org/10.1073/pnas.0403249101
[Google Scholar]

[73] Jia L., Vianna C.R., Fukuda M., Berglund E.D., Liu C., Tao C., Sun K., Liu T., Harper M.J., Lee C.E., Lee S., Scherer P.E., Elmquist J.K.. Hepatocyte Toll-like receptor 4 regulates obesity-induced inflammation and insulin resistance. Nature Communications. 2014;5:3878. https://doi.org/10.1038/ncomms4878
[Google Scholar]

NiOSP/dP nanocomposite attenuates hyperglycemia, oxidative stress, and inflammation in gestational diabetic rats through modulation of the TLR4/MyD88/NF-κB signaling pathway

Abstract

Keywords

d-pinitol

Gestational diabetes

Nanocomposites

Oxidative stress

TLR4/MyD88/NF-κB pathway

1. Introduction

2. Materials and Methods

2.1. Synthesis of NiOSP/dP nanocomposite

2.2. Characterization of NiOSP/dP nanocomposite

2.2.1. UV-Vis Spectroscopy analysis

2.2.2. Dynamic light scattering (DLS) analysis

2.2.3. X-Ray diffraction (XRD) study

2.2.4. Fourier-transform infrared (FTIR) spectroscopy

2.2.5. Field Emission Scanning Electron Microscopy-Energy Dispersive X-ray (FESEM-EDAX) analysis

2.2.6. Experimental animals

2.2.7. Induction of gestational diabetes

2.2.8. Treatment regiment

2.2.9. Histopathological analysis

2.2.10. Quantification of glycemic profile

2.2.11. Quantification of lipid profile

2.2.12. Quantification of antioxidants

2.2.13. Assessment of inflammatory cytokines

2.2.14. Analysis of TLR4/MyD88/NF-κB Signaling Pathway

2.2.15. Statistics

3. Results and Discussion

3.1. Characterization of NiOSP/dP nanocomposites

3.1.1. XRD study

3.1.2. FTIR spectroscopy

3.1.3. Field Emission Scanning Electron Microscopy- Energy Dispersive X-ray (FESEM-EDAX) analysis

3.1.4. UV-Vis Spectroscopy analysis

3.1.5. DLS analysis

3.1.6. NiOSP/dP nanocomposite prevented GDM induced fetal health impairments

3.1.7. Protective effects of NiOSP/dP nanocomposite on liver, kidney, and pancreatic tissues in GDM-induced rats

3.1.8. NiOSP/dP nanocomposite modulated glycemic level in GDM induced rats

3.1.9. NiOSP/dP nanocomposite demonstrated hypocholesterolemic effects in GDM induced rats

3.1.10. NiOSP/dP nanocomposite enhanced the antioxidant status in GDM-induced rats

3.1.11. NiOSP/dP nanocomposite attenuated the inflammatory response in GDM-induced rats

3.1.12. NiOSP/dP nanocomposite regulated TLR4/MyD88/NF-κB Signaling in GDM induced rats

4. Conclusions

CRediT authorship contribution statement

Declaration of competing interest

Declaration of Generative AI and AI-assisted technologies in the writing process

References

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