With the increase of technological and societal demands, more and more decision makers are involved in the process of group decision making, which is called large-scale group decision making (LGDM). ...For an LGDM problem with linguistic information, it is common that different decision makers tend to provide linguistic assessments defined on multigranular linguistic term sets due to the difference in knowledge and culture background, and that hesitant fuzzy linguistic term sets (HFLTSs) are used by decision makers to model the hesitancy of their assessments. This article proposes first an algorithm to represent a linguistic distribution assessment (LDA) using a hesitant linguistic distribution (HLD). Two other algorithms are then proposed to transform an unbalanced HFLTS into a balanced LDA and to transform a balanced LDA into an unbalanced LDA, respectively. An approach is then proposed to deal with multiattribute LGDM problems with multigranular unbalanced hesitant fuzzy linguistic information based on these algorithms. In the proposed approach, all unbalanced hesitant fuzzy linguistic information is transformed into LDAs defined on a balanced linguistic term set, and then an LDA-based clustering algorithm is devised to cluster decision makers. Based on the clustering result, decision makers' linguistic distribution decision matrices are further fused to obtain collective assessments of alternatives. In order to provide easy-to-understand linguistic results for decision makers, all LDAs of alternatives are represented by HLDs defined on each decision maker's initial linguistic term set. Finally, an example for the selection of subway lines is used to demonstrate the proposed approach.
•Associations of common genetic risk variants with GDM risk in the north Indian population were investigated.•Relative risk, population penetrance and attributable risk for risk allele variants was ...higher in GDM mother.•Four variants FTO, PPARG2, SLC30A8, and TCF7L2 were significantly associated with BMI, HbA1c and insulin.•Four variants FTO, PPARG2, SLC30A8, and TCF7L2 were significantly associated with GDM in North Indian population.
The current study sought to investigate the associations of common genetic risk variants with gestational diabetes mellitus (GDM) risk in the north Indian population and to evaluate their utility in identifying GDM cases. A case-control study, including 300 pregnant women, was included, and clinical and pathological information was collected. The amplification-refractory mutation system (ARMS) was used for genotyping four single nucleotide polymorphisms (SNPs), namely FTO (rs9939609), PPARG2 (rs1801282), SLC30A8 (rs13266634), and TCF7L2 (rs12255372). The odds ratio and confidence interval were determined for each SNP in different genetic models. Further, attributable risk, population penetrance, and relative risk were also calculated. The risk allele A of FTO (rs9939609) poses a two times higher risk of GDM (p = 0.02, OR = 2.5). The CG and GG genotypes of PPARG2 (rs1801282) have half a lower risk of GDM. In SLC30A8 (rs13266634), the recessive model analysis showed a two times higher risk of having GDM, while the recessive model (TT vs. GG + GT) analysis in TCF7L2 (rs12255372) indicates a lower risk of GDM. Finally, the relative risk, population penetrance, and attributable risk for risk allele in all four variants was higher in GDM mothers. All four polymorphisms were found to be significantly associated with BMI, HbA1c, and insulin. Our study first time confirmed a significant association with GDM for four variants, FTO, PPARG2, SLC30A8, and TCF7L2, in the North Indian population.
We investigated the proinflammatory functions of endoplasmic reticulum stress and peroxisome proliferator-activated receptor α (PPARα) in the development of gestational diabetes mellitus (GDM) and ...their relationship in regulating inflammation in GDM.
This study was performed on placentas of normal pregnant women, women with GDM, and HTR8 cells. Transmission electron microscopy, immunohistochemistry, Western blot analysis, and RT-PCR were performed to analyze ERS and PPARα expression on both normal and GDM pregnancy placentas. ELISA was performed to analyze inflammatory biomarkers. To generate models of the GDM-like state, placentas of normal pregnancy were treated with LPS and polyinosinic-polycytidylic acid (poly I:C). TG, CHOP plasmid, and CHOP siRNA were assessed as to their regulation of HTR8 cells to discern the relationship between ERS and PPARα in regulating the inflammation associated with GDM.
ERS was elevated in GDM placentas, induced the secretion of IL-6 and TNF-α, and attenuated the expression of GLUT-4. PPARα was diminished in GDM placentas and inhibited the inflammatory responses via the NF-κB nuclear-transport process. 4-PBA reduced CHOP and augmented PPARα, and it decreased IL-6 and TNF-α in our GDM-like explant. However, with both 4-PBA and MK886 treatment, we noted no significant difference in CHOP expression. The level of PPARα was reduced, and that of NF-κB p65 in the nucleus was elevated with TG treatment in the HTR8/Svneo. Knockdown of CHOP increased PPARα and reduced NF-κB p65, while expression of PPARα declined, and that of NF-κB p65 rose with the application of CHOP when HTR8 cells were treated with TG.
ERS contributes to the pathophysiology of GDM in pregnancy via the CHOP-PPARα–NF–κB-signalling pathway by inducing aberrant activation of inflammation and insulin resistance.
•ERS is increased including CHOP and induced pro-inflammatory function in GDM placentas.•PPARα is downregulated and promotes the NF-κB nuclear-transport process in placental of GDM.•Regulation of ERS affects the expression of pro-inflammatory factors through CHOP-PPARα–NF–κB pathway in GDM.
Background: GDM affects approximately 7% of all pregnant women worldwide. India has one of the highest prevalence of Diabetes Mellitus in the world. In a field study in Tamil Nadu, the prevalence of ...GDM was 17.8% in the urban, 13.8% in the semi urban and 9.9% in the rural areas. It is estimated that at any given time, approximately 4 million women in India are affected by GDM. Though there is availability of national guidelines, screening and management of GDM is challenging and controversial in India due to conflicting guidelines and treatment protocol. There are huge challenges in GDM management both at the individual and provider level. Objective: To understand the challenges and possible solutions for successfully implementing the GDM program Methodology: Data were collected from both public and private health facilities in two districts of Maharashtra. Self-administered questionnaire, IDIs and FGDs were conducted with medical officers and staff of PHCs, RH, SDH and District Hospital. Thematic analysis was done. Results: There were lack of training to Staff and Doctor of the government and private health system regarding GDM guidelines. Infrastructure was lacking for conducting the OGTT. Staff were not willing to conduct OGT and MNT(Medical nutrition therapy) on regular basis. Conclusion: Improvement in infrastructure, manpower and training of the staff is needed. Training in OGTT and MNT is must for the filed staff. More involvement of private players is needed for better implementation of GoI guidleines.
Linguistic large-scale group decision making (LGDM) problems are more and more common nowadays. In such problems a large group of decision makers are involved in the decision process and elicit ...linguistic information that are usually assessed in different linguistic scales with diverse granularity because of decision makers' distinct knowledge and background. To keep maximum information in initial stages of the linguistic LGDM problems, the use of multigranular linguistic distribution assessments seems a suitable choice, however, to manage such multigranular linguistic distribution assessments, it is necessary the development of a new linguistic computational approach. In this paper, it is proposed a novel computational model based on the use of extended linguistic hierarchies, which not only can be used to operate with multigranular linguistic distribution assessments but also can provide interpretable linguistic results to decision makers. Based on this new linguistic computational model, an approach to linguistic large-scale multiattribute group decision making is proposed and applied to a talent selection process in universities.
Gestational diabetes mellitus affects up to 10% of pregnancies and is classified into subtypes gestational diabetes subtype A1 (GDMA1) (managed by lifestyle modifications) and gestational diabetes ...subtype A2 (GDMA2) (requiring medication). However, whether these subtypes are distinct clinical entities or more reflective of an extended spectrum of normal pregnancy endocrine physiology remains unclear.
Integrated bulk RNA-sequencing (RNA-seq), single-cell RNA-sequencing (scRNA-seq), and spatial transcriptomics harbors the potential to reveal disease gene signatures in subsets of cells and tissue microenvironments. We aimed to combine these high-resolution technologies with rigorous classification of diabetes subtypes in pregnancy. We hypothesized that differences between preexisting type 2 and gestational diabetes subtypes would be associated with altered gene expression profiles in specific placental cell populations.
In a large case-cohort design, we compared validated cases of GDMA1, GDMA2, and type 2 diabetes mellitus (T2DM) to healthy controls by bulk RNA-seq (n=54). Quantitative analyses with reverse transcription and quantitative PCR of presumptive genes of significant interest were undertaken in an independent and nonoverlapping validation cohort of similarly well-characterized cases and controls (n=122). Additional integrated analyses of term placental single-cell, single-nuclei, and spatial transcriptomics data enabled us to determine the cellular subpopulations and niches that aligned with the GDMA1, GDMA2, and T2DM gene expression signatures at higher resolution and with greater confidence.
Dimensional reduction of the bulk RNA-seq data revealed that the most common source of placental gene expression variation was the diabetic disease subtype. Relative to controls, we found 2052 unique and significantly differentially expressed genes (−2<Log2fold-change>2 thresholds; q<0.05 Wald Test) among GDMA1 placental specimens, 267 among GDMA2, and 1520 among T2DM. Several candidate marker genes (chorionic somatomammotropin hormone 1 CSH1, period circadian regulator 1 PER1, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit beta PIK3CB, forkhead box O1 FOXO1, epidermal growth factor receptor EGFR, interleukin 2 receptor subunit beta IL2RB, superoxide dismutase 3 SOD3, dedicator of cytokinesis 5 DOCK5, suppressor of glucose, and autophagy associated 1 SOGA1) were validated in an independent and nonoverlapping validation cohort (q<0.05 Tukey). Functional enrichment revealed the pathways and genes most impacted for each diabetes subtype, and the degree of proximal similarity to other subclassifications. Surprisingly, GDMA1 and T2DM placental signatures were more alike by virtue of increased expression of chromatin remodeling and epigenetic regulation genes, while albumin was the top marker for GDMA2 with increased expression of placental genes in the wound healing pathway. Assessment of these gene signatures in single-cell, single-nuclei, and spatial transcriptomics data revealed high specificity and variability by placental cell and microarchitecture types. For example, at the cellular and spatial (eg, microarchitectural) levels, distinguishing features were observed in extravillous trophoblasts (GDMA1) and macrophages (GDMA2). Lastly, we utilized these data to train and evaluate 4 machine learning models to estimate our confidence in predicting the control or diabetes status of placental transcriptome specimens with no available clinical metadata.
Consistent with the distinct association of perinatal outcome risk, placentae from GDMA1, GDMA2, and T2DM-affected pregnancies harbor unique gene signatures that can be further distinguished by altered placental cellular subtypes and microarchitectural niches.
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Gestational diabetes mellitus (GDM) is defined as "glucose intolerance that is first diagnosed during pregnancy". Mothers with GDM and their infants may experience both short and long term ...complications. Dietary intervention is the first therapeutic strategy. If good glycaemic control is not achieved, insulin therapy is recommended. There is no consensus on which nutritional approach should be used in GDM. In the last few years, there has been growing evidence of the benefits of a low glycaemic index (LGI) diet on diabetes and cardiovascular disease. The effect of a LGI diet on GDM incidence has been investigated as well. Several studies observed a lower incidence of GDM in LGI diet arms, without adverse maternal and fetal outcomes. The main positive effect of the LGI diet was the reduction of 2-h post-prandial glucose (PPG). Several studies have also evaluated the effect of the LGI diet in GDM treatment. Overall, the LGI diet might have beneficial effects on certain outcomes, such as 2-h PPG, fasting plasma glucose and lipid profile in patients with GDM. Indeed, most studies observed a significant reduction in insulin requirement. Overall, according to current evidence, the LGI nutritional approach is safe and it might therefore be considered in clinical care for GDM.
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•Comparative study of different classical consensus reaching processes applied to large-scale group decision making (LGDM).•Analyze the performance of the models studied by using ...AFRYCA, that is a framework used to simulate different experts behavior patterns during the consensus reaching processes.•New challenges that consensus reaching processes should face to deal with LGDM.
Consensus reaching processes (CRPs) in group decision making (GDM) attempt to reach a mutual agreement among a group of decision makers before making a common decision. Different consensus models have been proposed by different authors in the literature to facilitate CRPs. Classical CRP models focus on achieving an agreement on GDM problems in which few decision makers participate. However, nowadays, societal and technological trends that demand the management of larger scale of decision makers add new requirements to the solution of consensus-based GDM problems. This paper presents a comparative study of different classical CRPs applied to large-scale GDM in order to analyze their performance and find out which are the main challenges that these processes face in large-scale GDM. Such analyses will be developed in a java-based framework (AFRYCA 2.0) simulating different scenarios in large scale GDM.
Polychlorinated Biphenyls (PCBs) and Polybrominated Diphenyl Ethers (PBDEs) are extensively present in humans and may disturb glucose metabolism during pregnancy. However, previous reports on the ...associations between PCBs/PBDEs levels and gestational diabetes mellitus (GDM) have been inconsistent. We performed a nested case-control study to measure the serum levels of 6 PCB and 7 PBDE congeners in early pregnancy, and to assess their associations with GDM risk and blood glucose levels. Totally, 208 serum samples (104 GDM cases and 104 controls) were included based on a prospective cohort which was carried out in Jiangsu province, China, from 2020 to 2022. The results showed that PCB-153 was the major PCB congener, whereas PBDE-47 was the predominant PBDE congener. The continuous concentrations of PCB-153, PBDE-28, and total PCB were significantly related to an increased risk of GDM, with adjusted ORs (95%CI) of 1.25 (1.04–1.50), 1.19 (1.02–1.39), and 1.37 (1.05–1.79), respectively. Potential dose-response relationships were also observed between serum levels of PCB-153 (P = 0.011), PBDE-28 (P = 0.028), total PCB (P = 0.048), and total PCB/PBDE (P = 0.010) and GDM risk. Moreover, PCB-153, PBDE-28 and total PCB levels were positively related to 1-h OGTT blood glucose (adjusted βPCB-153: 0.14, 95%CI: 0.00–0.28; adjusted βPBDE-28: 0.20, 95%CI: 0.08–0.32; adjusted βtotal PCB: 0.30, 95%CI: 0.09–0.50), whereas none of the PCBs/PBDEs were statistically related to fasting blood glucose and 2-h OGTT blood glucose (all P > 0.05). Further meta-analysis also supported the association of PCBs exposure with GDM risk. Our study provides further evidence that PCBs/PBDEs exposure may increase GDM risk during pregnancy.
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•Serum levels of PCBs/PBDEs in early pregnancy in Jiangsu, China were investigated.•PCB-153 was the major PCBs, whereas PBDE-47 was the predominant PBDEs.•Levels of PCB-153, PBDE-28 and total PCB were positively related to GDM risk.•PCB-153, PBDE-28 and total PCB were associated with 1-h OGTT blood glucose.•Meta-Analysis also supported the association of PCBs exposure with GDM risk.
This study explores the impact of gestational diabetes mellitus (GDM) subtypes classified by oral glucose tolerance test (OGTT) values on maternal and perinatal outcomes.
This multicenter prospective ...cohort study (May 2019-December 2022) included participants from the Mexican multicenter cohort study
(CME). Women were classified into four groups per 75-g 2-h OGTT: 1) normal glucose tolerance (normal OGTT), 2) GDM-Sensitivity (isolated abnormal fasting or abnormal fasting in combination with 1-h or 2-h abnormal results), 3) GDM-Secretion (isolated abnormal values at 1-h or 2-h or their combination), and 4) GDM-Mixed (three abnormal values). Cesarean delivery, neonates large for gestational age (LGA), and pre-term birth rates were among the outcomes compared. Between-group comparisons were analyzed using either the
-test, chi-square test, or Fisher's exact test.
Of 2,056 Mexican pregnant women in the CME cohort, 294 (14.3%) had GDM; 53.7%, 34.4%, and 11.9% were classified as GDM-Sensitivity, GDM-Secretion, and GDM-Mixed subtypes, respectively. Women with GDM were older (p = 0.0001) and more often multiparous (p = 0.119) vs without GDM. Cesarean delivery (63.3%; p = 0.02) and neonate LGA (10.7%; p = 0.078) were higher in the GDM-Mixed group than the overall GDM group (55.6% and 8.4%, respectively). Pre-term birth was more common in the GDM-Sensitivity group than in the overall GDM group (10.2% vs 8.5%, respectively; p=0.022). At 6 months postpartum, prediabetes was more frequent in the GDM-Sensitivity group than in the overall GDM group (31.6% vs 25.5%). Type 2 diabetes was more common in the GDM-Mixed group than in the overall GDM group (10.0% vs 3.3%).
GDM subtypes effectively stratified maternal and perinatal risks. GDM-Mixed subtype increased the risk of cesarean delivery, LGA, and type 2 diabetes postpartum. GDM subtypes may help personalize clinical interventions and optimize maternal and perinatal outcomes.
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