Academic literature on the topic 'Universal glucose predictor'

ObjectiveThe International Association of Diabetes and Pregnancy Study Groups (IADPSG) recently proposed new criteria for diagnosing gestational diabetes mellitus (GDM). We compared prevalence rates, risk factors, and the effect of ethnicity using the World Health Organization (WHO) and modified IADPSG criteria.MethodsThis was a population-based cohort study of 823 (74% of eligible) healthy pregnant women, of whom 59% were from ethnic minorities. Universal screening was performed at 28±2 weeks of gestation with the 75 g oral glucose tolerance test (OGTT). Venous plasma glucose (PG) was measured on site. GDM was diagnosed as per the definition of WHO criteria as fasting PG (FPG) ≥7.0 or 2-h PG ≥7.8 mmol/l; and as per the modified IADPSG criteria as FPG ≥5.1 or 2-h PG ≥8.5 mmol/l.ResultsOGTT was performed in 759 women. Crude GDM prevalence was 13.0% with WHO (Western Europeans 11%, ethnic minorities 15%,P=0.14) and 31.5% with modified IADPSG criteria (Western Europeans 24%, ethnic minorities 37%,P< 0.001). Using the WHO criteria, ethnic minority origin was an independent predictor (South Asians, odds ratio (OR) 2.24 (95% confidence interval (CI) 1.26–3.97); Middle Easterners, OR 2.13 (1.12–4.08)) after adjustments for age, parity, and prepregnant body mass index (BMI). This increased OR was unapparent after further adjustments for body height (proxy for early life socioeconomic status), education and family history of diabetes. Using the modified IADPSG criteria, prepregnant BMI (1.09 (1.05–1.13)) and ethnic minority origin (South Asians, 2.54 (1.56–4.13)) were independent predictors, while education, body height and family history had little impact.ConclusionGDM prevalence was overall 2.4-times higher with the modified IADPSG criteria compared with the WHO criteria. The new criteria identified many subjects with a relatively mild increase in FPG, strongly associated with South Asian origin and prepregnant overweight.

Bacillus species form an heterogeneous group of Gram-positive bacteria that include members that are disease-causing, biotechnologically-relevant, and can serve as biological research tools. A common feature of Bacillus species is their ability to survive in harsh environmental conditions by formation of resistant endospores. Genes encoding the universal stress protein (USP) domain confer cellular and organismal survival during unfavorable conditions such as nutrient depletion. As of February 2012, the genome sequences and a variety of functional annotations for at least 123 Bacillus isolates including 45 Bacillus cereus isolates were available in public domain bioinformatics resources. Additionally, the genome sequencing status of 10 of the B. cereus isolates were annotated as finished with each genome encoded 3 USP genes. The conservation of gene neighborhood of the 140 aa universal stress protein in the B. cereus genomes led to the identification of a predicted plasmid-encoded transcriptional unit that includes a USP gene and a sulfate uptake gene in the soil-inhabiting Bacillus megaterium. Gene neighborhood analysis combined with visual analytics of chemical ligand binding sites data provided knowledge-building biological insights on possible cellular functions of B. megaterium universal stress proteins. These functions include sulfate and potassium uptake, acid extrusion, cellular energy-level sensing, survival in high oxygen conditions and acetate utilization. Of particular interest was a two-gene transcriptional unit that consisted of genes for a universal stress protein and a sirtuin Sir2 (deacetylase enzyme for NAD+-dependent acetate utilization). The predicted transcriptional units for stress responsive inorganic sulfate uptake and acetate utilization could explain biological mechanisms for survival of soil-inhabiting Bacillus species in sulfate and acetate limiting conditions. Considering the key role of sirtuins in mammalian physiology additional research on the USP-Sir2 transcriptional unit of B. megaterium could help explain mammalian acetate metabolism in glucose-limiting conditions such as caloric restriction. Finally, the deep-rooted position of B. megaterium in the phylogeny of Bacillus species makes the investigation of the functional coupling acetate utilization and stress response compelling.

Objective: The aim of this study is to identify the first trimester markers that may be associated with gestational diabetes mellitus (GDM) and to evaluate whether those markers might be used for prediction of gestational diabetes or not. Methods: Pregnant women between 11 and 14 weeks of gestation applying to the university hospital between August 2018 and March 2019 were included in the study. Body mass index calculation and blood tests including complete blood count, TSH, T3, T4, HbA1c, uric acid, CRP, procalcitonin, PAPP-A and b-hCG levels were done during assessment followed by 50 grams of glucose challenge test between the 24 and 28 weeks of gestation for each woman. Patients with positive results were further evaluated with a 3-hour, 100-g OGTT. According to the diagnostic test results, the relationship between biochemical markers during the first trimester, BMI and GDM was statistically analyzed. Results: A hundred and eighty-two pregnant women participated in the study. Fifty-four women had positive glucose challenge test (GCT) results while 128 women had negative results. Pregnant women with positive GCT results underwent 3-hour, 100-g OGTT and, 24 pregnant women were diagnosed with GDM, while 158 pregnant women were considered healthy according to the results. There was no statistically significant difference between GDM and non-GDM groups in terms of age, height, TSH, T3, T4, b-hCG-mom, PAPP-A, PAPP-A-mom, uric acid and procalcitonin (p>0.05). The mean body weight, body mass index and HbA1c levels were higher and b-hCG levels were lower in the GDM group compared to the non-GDM group, and these findings were statistically significant (p<0.001). Conclusion: The use of first trimester markers in GDM prediction seems to have no significance. There is a need for extensive, randomized studies with universal criteria.

Whether the insulin resistance commonly observed in patients with inflammatory arthritis is a disease-specific feature and/or is limited to a disease phase (i.e., it occurs only during phases of high disease activity) is unknown. Fifty-three rheumatoid arthritis (RA) and 44 psoriatic arthritis (PsA) patients were recruited consecutively along with 194 controls matched for age, sex and body mass index for a case-control study. All underwent an oral glucose tolerance test, the results of which were analysed to derive the following indexes: homeostatic model of insulin resistance (HOMA-IR), insulin sensitivity index (ISI) and early insulin sensitivity index (EISI). These data were related to anthropometric, clinical and laboratory findings. Metabolic parameters of patients and controls were similar. Neither inflammatory markers nor disease activity scores were related to glucose metabolism for the generality of RA and PsA patients; however, by restricting the analysis to the subset of RA patients with residual disease activity, an association emerged between erythrocyte sedimentation rate, on the one hand, and fasting insulin (β=0.46, p=0.047) and HOMA-IR (β=0.44, p=0.02), on the other. Moreover, C-reactive protein (CRP) levels were associated with plasma glucose and insulin levels measured 120 min after the glucose load (β=0.91, p=0.0003 and β=0.77, p=0.0006, respectively); ISI and EISI were predicted by CRP (β=-0.79, p=0.0006; β=-0.80, p=0.0001, respectively). The same did not hold true for PsA patients. The association between systemic inflammation and insulin resistance indexes is a feature of RA with residual disease activity, not a universal feature of inflammatory arthritides.

Gestational diabetes mellitus (GDM) is one of the most common pregnancy complications and one of the main causes of adverse pregnancy outcomes. An early diagnosis of GDM is of fundamental importance in clinical practice. However, the major professional organizations recommend universal screening for GDM, using a 75 g oral glucose tolerance test at 24–28 weeks of gestation. A selective screening at an early stage of pregnancy is recommended only if there are maternal risk factors for diabetes. As a result, the GDM diagnosis is often delayed and established after the appearance of complications. The manifestation of GDM is directly related to insulin resistance, which is closely associated with endothelial dysfunction. The placenta, the placental peptides and hormones play a pivotal role in the manifestation and progression of insulin resistance during pregnancy. Recently, the placental growth factor (PlGF) and plasma-associated protein-A (PAPP-A), have been shown to significantly affect both insulin sensitivity and endothelial function. The principal function of PAPP-A appears to be the cleavage of circulating insulin-like growth factor binding protein-4 while PlGF has been shown to play a central role in the development and maturation of the placental vascular system and circulation. On one hand, these factors are widely used as early predictors (11–13 weeks of gestation) of complications during pregnancy, such as preeclampsia and fetal aneuploidies, in most countries. On the other hand, there is increasing evidence for their predictive role in the development of carbohydrate disorders, but some studies are rather controversial. Therefore, this review aims to summarize the available literature about the potential of serum levels of PlGF and PAPP-A as early predictors in the diagnosis of GDM.

Gestational diabetes mellitus is a common metabolic complication of pregnancy. Universal guidelines on gestational diabetes have been impeded by the long-term controversies on its definition and screening strategies. The prevalence of gestational diabetes is rising all over the world, is significantly influenced by ethnicity and its rise is mainly attributed to increasing maternal obesity and age. Gestational diabetes mellitus has important long-term implications, including gestational diabetes recurrence, increased risk for developing type 2 diabetes, metabolic syndrome and cardiovascular disease for the mother. Gestational diabetes mellitus may be viewed as a chronic metabolic disorder that is identified in women during gestation and may provide a unique opportunity for the early identification and primary prevention of type 2 diabetes mellitus and cardiovascular disease in these women. In this mini-review, the evolution of screening tests for gestational diabetes and guidelines are briefly described and metabolic and cardiovascular long-term consequences of women with a history of gestational diabetes are summarized. A summary of our own St. Mary’s Hospital-UK Research series on long-term metabolic consequences of 368 women with a history of gestational diabetes of 3 different ethnic groups and 482 control women is also included. We found that approximately 2 years following delivery, 37% of women with a history of gestational diabetes had abnormal glucose concentrations, but, most importantly, even those who were normoglycaemic, postpartum displayed metabolic abnormalities on detailed testing. Future research needs to focus on the prevention of gestational diabetes long-term complications, but also in identification of pre-pregnancy predictors and risk reduction before conception.

Background: Many observational studies have shown a protective effect of physical activity on cognitive ageing, but interventional studies have been less convincing. This may be due to short time scales of interventions, suboptimal interventional regimes or lack of lasting effect. Confounding through common genetic and developmental causes is also possible. Objectives: We aimed to test whether muscle fitness (measured by leg power) could predict cognitive change in a healthy older population over a 10-year time interval, how this performed alongside other predictors of cognitive ageing, and whether this effect was confounded by factors shared by twins. In addition, we investigated whether differences in leg power were predictive of differences in brain structure and function after 12 years of follow-up in identical twin pairs. Methods: A total of 324 healthy female twins (average age at baseline 55, range 43-73) performed the Cambridge Neuropsychological Test Automated Battery (CANTAB) at two time points 10 years apart. Linear regression modelling was used to assess the relationships between baseline leg power, physical activity and subsequent cognitive change, adjusting comprehensively for baseline covariates (including heart disease, diabetes, blood pressure, fasting blood glucose, lipids, diet, body habitus, smoking and alcohol habits, reading IQ, socioeconomic status and birthweight). A discordant twin approach was used to adjust for factors shared by twins. A subset of monozygotic pairs then underwent magnetic resonance imaging. The relationship between muscle fitness and brain structure and function was assessed using linear regression modelling and paired t tests. Results: A striking protective relationship was found between muscle fitness (leg power) and both 10-year cognitive change [fully adjusted model standardised β-coefficient (Stdβ) = 0.174, p = 0.002] and subsequent total grey matter (Stdβ = 0.362, p = 0.005). These effects were robust in discordant twin analyses, where within-pair difference in physical fitness was also predictive of within-pair difference in lateral ventricle size. There was a weak independent effect of self-reported physical activity. Conclusion: Leg power predicts both cognitive ageing and global brain structure, despite controlling for common genetics and early life environment shared by twins. Interventions targeted to improve leg power in the long term may help reach a universal goal of healthy cognitive ageing.

Abstract Uncontrolled blood glucose (BG) is associated with increased risk of infection, complications, and mortality in hospitalized patients. American Diabetes Association guidelines currently recommend basal insulin for all hospitalized, non-critically ill patients requiring insulin and state that “use of only a sliding scale insulin regimen in the inpatient hospital setting is strongly discouraged”. In practice, however, sliding scale only is used not infrequently. Here, we challenge the recommendation for universal basal insulin use and leverage machine learning to predict which inpatients would indeed benefit from basal insulin at time of admission. Querying inpatient electronic health record data for hospitalizations between 2008–2020, we identified a cohort of 16,868 unique patients who achieved a day of “good control”, defined as ≥ 3 BGs that were within 100–180 mg/dL without any values outside that range. Inclusion criteria were adult inpatients receiving subcutaneous insulin with BG of 100-180mg/dL on one calendar day. If patients had more than one “good day”, the first day of their most recent hospitalization was chosen. We excluded patients ordered for insulin pumps, insulin infusion, any insulin type that is rarely used (ordered &lt; 25 times), TPN or PPN, or tube feeds. We also excluded patients with missing weights. We aimed to predict which patients would require &gt; 6 units of insulin. We chose this threshold clinically, as patients with a total daily dose (TDD) of insulin &lt; 6 units could reasonably be managed on sliding scale insulin alone. Using the threshold of 6 units, we used an ensemble machine learning method, called SuperLearner, to model a binary classification for high vs. low insulin users. Features included in the algorithm were collected prior to prediction time, including weight, height, age, sex, race, insurance status, A1c categories (normal, high, panic high, and missing), creatinine, diet, steroid use in prior 48 hours, admission BG, summary statistics of BG, numerous counts of relevant lab values in quantiles, history of basal insulin use, and counts of major diagnosis code groups. Prior insulin doses were not considered to better simulate admission insulin dosing. Compared to using only weight in the model, with an area under the receiver operating curve (AUROC) of 0.59, our machine learning algorithm showed excellent predictive ability, with an AUROC of 0.85 (95% CI: 0.84 - 0.87) and area under the precision recall curve (AUPRC) of .65 (95% CI: 0.64 - 0.68) vs 0.29 with the weight-only model. Although it will need to be validated prospectively, our algorithm could be used to emphasize basal-bolus insulin on admission in patients predicted to require more insulin, whereas those predicted to require less could be started on sliding scale insulin or considered for oral anti-hyperglycemics.

BackgroundHyperprogressive disease (HPD) is a distinct pattern of rapid tumor progression observed in patients with cancer who are undergoing immune checkpoint inhibitor therapy. Despite the growing evidence, a universal definition of HPD remains to be established, and incidence rates vary based on the defining criteria. Therefore, a refinement of currently existing criteria is warranted to better characterize this phenomenon and evaluate its incidence.MethodsTwo independent investigators performed a systematic literature search in EMBASE and MEDLINE using keywords selected in Park et al.1: checkpoint, immunotherapy, pd1, pdl1, ctla4, ipilimumab, nivolumab, pembrolizumab, atezolizumab, avelumab, durvalumab and hyperprogress. Studies published from March 3, 2020 to April 20, 2020 that included the incidence and definition of HPD in patients receiving immunotherapy were included for analysis. Selected studies were then combined with those included in the meta-analysis by Park et al.1 Duplicates were removed, and the study with a larger cohort was selected in instances of overlap between two cohorts. In total, 50 studies were included for meta-analysis.2–51 Pooled incidence rates of HPD and prespecified subgroup analyses based on four categories defining HPD (tumor growth rate ratio, tumor growth kinetics ratio, early tumor burden increase, and combination) were obtained with 95% confidence intervals (CI) using a random effects model performed on R.ResultsA total of 6009 patients from 50 studies were included in the meta-analysis. Incidences varied from 0.0% to 43.1% (figure 1), and the overall pooled incidence of HPD was 12.9% (95%CI, 11.1%–14.7%). Significant heterogeneity was observed (I2= 77%; p<0.01). Studies were also grouped into one of 4 categories (table 1) based on the definition of HPD used to calculate the tumor growth acceleration: tumor growth rate ratio (pooled incidence of HPD 10.5%; 95% CI, 7.9%–13.0%), tumor growth kinetics ratio (pooled incidence, 14.8%; 95% CI, 12.0%–17.5%), early tumor burden increase (pooled incidence, 17.2%; 95% CI, 9.7%–24.7%), and combinations of the above (pooled incidence, 12.2%; 95% CI, 9.2%–15.2%).Abstract 238 Table 1Subgroup analyses based on definitions of HPDAbbreviationTGR, tumor growth rate; TGK, tumor growth kinetics.Abstact 238 Figure 1Overall pooled incidence of HPD. The overall pooled incidence of HPD was 12.9% (95% CI, 11.1%–14.7%). Significant heterogeneity was observed (I2 = 77%; p<0.01).ConclusionsThe overall incidence of HPD from 50 studies was 12.9% (95%CI, 11.1%–14.7%). HPD incidence varied from 0% to 43.1% depending on the definition each investigator chose. There is a growing need for a more uniform definition of HPD that does not underestimate or overestimate its incidence.ReferencesPark HJ, Kim KW, Won SE, et al. Definition, incidence, and challenges for assessment of hyperprogressive disease during cancer treatment with immune checkpoint inhibitors: a systematic review and meta-analysis. JAMA Netw Open 2021;4(3):1–16. doi:10.1001/jamanetworkopen.2021.1136Champiat S, Dercle L, Ammari S, et al. Hyperprogressive disease is a new pattern of progression in cancer patients treated by anti-PD-1/PD-L1. Clin Cancer Res 2017;23(8):1920–1928. doi:10.1158/1078-0432.CCR-16-1741Kato S, Goodman A, Walavalkar V, Barkauskas DA, Sharabi A, Kurzrock R. Hyperprogressors after immunotherapy: analysis of genomic alterations associated with accelerated growth rate. Clin Cancer Res 2017;23(15):4242–4250. doi:10.1158/1078-0432.CCR-16-3133Saâda-Bouzid E, Defaucheux C, Karabajakian A, et al. 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MDM2, MDM4 and EGFR amplifications and hyperprogression in metastatic acral and mucosal melanoma. Cancers (Basel) 2020;12(3). doi:10.3390/cancers12030540Petrioli R, Mazzei MA, Giorgi S, et al. Hyperprogressive disease in advanced cancer patients treated with nivolumab: a case series study. Anticancer Drugs. Published online 2020:190–195. doi:10.1097/CAD.0000000000000864Refae S, Gal J, Brest P, et al. Author correction: hyperprogression under immune checkpoint inhibitor: a potential role for germinal immunogenetics (Scientific Reports, (2020), 10, 1, (3565), 10.1038/s41598-020-60437-0). Sci Rep 2020;10(1):1–8. doi:10.1038/s41598-020-66841-wRuiz-Patiño A, Arrieta O, Cardona AF, et al. Immunotherapy at any line of treatment improves survival in patients with advanced metastatic non-small cell lung cancer (NSCLC) compared with chemotherapy (Quijote-CLICaP). Thorac Cancer 2020;11(2):353–361. doi:10.1111/1759-7714.13272Kim CG, Kim C, Yoon SE, et al. 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Pathol Res Pract 2021;219(February):153364. doi:10.1016/j.prp.2021.153364

We proposed a diffuse imaging approach for universal noninvasive blood glucose measurements based on visible light, which can predict the blood glucose concentration without personal calibration. The proposed approach used a CCD to obtain diffuse images from human index finger pulp. The denoising autoencoder algorithm adopted effectively extracted the scattering information highly related to blood glucose concentration from the diffuse images, and the gradient boosting regression algorithm enabled an accurate calculation of blood glucose concentration without prior personalized calibration. In vivo experimental results showed that the proposed approach had a mean absolute error of 19.44 mg/dl, with all the predicted results observed within the clinically acceptable region (Region A: 78.9%) in the Clarke error grid analysis. Compared to other blood glucose concentration measurement methods of scattering coefficient, this new method does not require individual calibration, therefore it is easier to implement and popularize, which is critical for the noninvasive monitoring of blood glucose concentration.