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Ramani, A. L. "Prediction of First Lactation Milk Yield on The Basis of Test Day Yield Using Multiple Linear Regression in Gir Cows." Indian Journal of Pure & Applied Biosciences 12, no. 3 (June 30, 2024): 33–36. http://dx.doi.org/10.18782/2582-2845.9086.
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The test-day model is a method of choice for the study of milk yield traits, and this method is very important in countries like India, where herd size is generally smaller and lacks a well-established milk recording system. The present investigation was carried out on 365 records of Gir cows maintained at a cattle breeding farm from 1986 to 2014 with the objective of predicting the first lactation milk yield using monthly test day milk records. MLR was used with the backward elimination method. The optimum equation had total five variables (test days) viz. TD1, TD2, TD3, TD4 and TD5. This equation gave an accuracy of prediction of 77.71%. Therefore, it is concluded that first lactation 305 day milk yield could be predicted as early as 5th month of lactation with higher degree of accuracy.
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Ríos, Rafael, Carmen Belén Lupiañez, Daniele Campa, Alessandro Martino, Joaquin Martínez-López, Manuel Martínez-Bueno, Judit Varkonyi, et al. "Type 2 diabetes-related variants influence the risk of developing multiple myeloma: results from the IMMEnSE consortium." Endocrine-Related Cancer 22, no. 4 (August 2015): 545–59. http://dx.doi.org/10.1530/erc-15-0029.
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Type 2 diabetes (T2D) has been suggested to be a risk factor for multiple myeloma (MM), but the relationship between the two traits is still not well understood. The aims of this study were to evaluate whether 58 genome-wide-association-studies (GWAS)-identified common variants for T2D influence the risk of developing MM and to determine whether predictive models built with these variants might help to predict the disease risk. We conducted a case–control study including 1420 MM patients and 1858 controls ascertained through the International Multiple Myeloma (IMMEnSE) consortium. Subjects carrying the KCNQ1rs2237892T allele or the CDKN2A-2Brs2383208G/G, IGF1rs35767T/T and MADDrs7944584T/T genotypes had a significantly increased risk of MM (odds ratio (OR)=1.32–2.13) whereas those carrying the KCNJ11rs5215C, KCNJ11rs5219T and THADArs7578597C alleles or the FTOrs8050136A/A and LTArs1041981C/C genotypes showed a significantly decreased risk of developing the disease (OR=0.76–0.85). Interestingly, a prediction model including those T2D-related variants associated with the risk of MM showed a significantly improved discriminatory ability to predict the disease when compared to a model without genetic information (area under the curve (AUC)=0.645 vs AUC=0.629; P=4.05×10−06). A gender-stratified analysis also revealed a significant gender effect modification for ADAM30rs2641348 and NOTCH2rs10923931 variants (Pinteraction=0.001 and 0.0004, respectively). Men carrying the ADAM30rs2641348C and NOTCH2rs10923931T alleles had a significantly decreased risk of MM whereas an opposite but not significant effect was observed in women (ORM=0.71 and ORM=0.66 vs ORW=1.22 and ORW=1.15, respectively). These results suggest that TD2-related variants may influence the risk of developing MM and their genotyping might help to improve MM risk prediction models.
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Klein, Matthias S., and Jane Shearer. "Metabolomics and Type 2 Diabetes: Translating Basic Research into Clinical Application." Journal of Diabetes Research 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/3898502.
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Type 2 diabetes (T2D) and its comorbidities have reached epidemic proportions, with more than half a billion cases expected by 2030. Metabolomics is a fairly new approach for studying metabolic changes connected to disease development and progression and for finding predictive biomarkers to enable early interventions, which are most effective against T2D and its comorbidities. In metabolomics, the abundance of a comprehensive set of small biomolecules (metabolites) is measured, thus giving insight into disease-related metabolic alterations. This review shall give an overview of basic metabolomics methods and will highlight current metabolomics research successes in the prediction and diagnosis of T2D. We summarized key metabolites changing in response to T2D. Despite large variations in predictive biomarkers, many studies have replicated elevated plasma levels of branched-chain amino acids and their derivatives, aromatic amino acids andα-hydroxybutyrate ahead of T2D manifestation. In contrast, glycine levels and lysophosphatidylcholine C18:2 are depressed in both predictive studies and with overt disease. The use of metabolomics for predicting T2D comorbidities is gaining momentum, as are our approaches for translating basic metabolomics research into clinical applications. As a result, metabolomics has the potential to enable informed decision-making in the realm of personalized medicine.
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Luo, Yufang, Zi Guo, Honghui He, Youbo Yang, Shaoli Zhao, and Zhaohui Mo. "Predictive Model of Type 2 Diabetes Remission after Metabolic Surgery in Chinese Patients." International Journal of Endocrinology 2020 (October 8, 2020): 1–13. http://dx.doi.org/10.1155/2020/2965175.
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Introduction. Metabolic surgery is an effective treatment for type 2 diabetes (T2D). At present, there is no authoritative standard for predicting postoperative T2D remission in clinical use. In general, East Asian patients with T2D have a lower body mass index and worse islet function than westerners. We aimed to look for clinical predictors of T2D remission after metabolic surgery in Chinese patients, which may provide insights for patient selection. Methods. Patients with T2D who underwent metabolic surgery at the Third Xiangya Hospital between October 2008 and March 2017 were enrolled. T2D remission was defined as an HbA1c level below 6.5% and an FPG concentration below 7.1 mmol/L for at least one year in the absence of antidiabetic medications. Results. (1) Independent predictors of short-term T2D remission (1-2 years) were age and C-peptide area under the curve (C-peptide AUC); independent predictors of long-term T2D remission (4–6 years) were C-peptide AUC and fasting plasma glucose (FPG). (2) The optimal cutoff value for C-peptide AUC in predicting T2D remission was 30.93 ng/ml, with a specificity of 67.3% and sensitivity of 75.8% in the short term and with a specificity of 61.9% and sensitivity of 81.5% in the long term, respectively. The areas under the ROC curves are 0.674 and 0.623 in the short term and long term, respectively. (3) We used three variables (age, C-peptide AUC, and FPG) to construct a remission prediction score (ACF), a multidimensional 9-point scale, along which greater scores indicate a better chance of T2D remission. We compared our scoring system with other reported models (ABCD, DiaRem, and IMS). The ACF scoring system had the best distribution of patients and prognostic significance according to the ROC curves. Conclusion. Presurgery age, C-peptide AUC, and FPG are independent predictors of T2D remission after metabolic surgery. Among these, C-peptide AUC plays a decisive role in both short- and long-term remission prediction, and the optimal cutoff value for C-peptide AUC in predicting T2D remission was 30.93 ng/ml, with moderate predictive values. The ACF score is a simple reliable system that can predict T2D remission among Chinese patients.
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Ortiz Zuñiga, Angel Michael, Rafael Simó, Octavio Rodriguez-Gómez, Cristina Hernández, Adrian Rodrigo, Laura Jamilis, Laura Campo, Montserrat Alegret, Merce Boada, and Andreea Ciudin. "Clinical Applicability of the Specific Risk Score of Dementia in Type 2 Diabetes in the Identification of Patients with Early Cognitive Impairment: Results of the MOPEAD Study in Spain." Journal of Clinical Medicine 9, no. 9 (August 24, 2020): 2726. http://dx.doi.org/10.3390/jcm9092726.
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Introduction: Although the Diabetes Specific Dementia Risk Score (DSDRS) was proposed for predicting risk of dementia at 10 years, its usefulness as a screening tool is unknown. For this purpose, the European consortium MOPEAD included the DSDRS within the specific strategy for screening of cognitive impairment in type 2 diabetes (T2D) patients attended in a third-level hospital. Material and Methods: T2D patients > 65 years, without known cognitive impairment, attended in a third-level hospital, were evaluated. As per MOPEAD protocol, patients with MMSE ≤ 27 or DSDRS ≥ 7 were referred to the memory clinic for complete neuropsychological assessment. Results: 112 T2D patients were recruited. A total of 82 fulfilled the criteria for referral to the memory unit (43 of them declined referral: 48.8% for associated comorbidities, 37.2% lack of interest, 13.95% lack of social support). At the Fundació ACE’s Memory Clinic, 34 cases (87.2%) of mild cognitive impairment (MCI) and 3 cases (7.7%) of dementia were diagnosed. The predictive value of DSDRS ≥ 7 as a screening tool of cognitive impairment was AUROC = 0.739, p 0.024, CI 95% (0.609–0.825). Conclusions: We found a high prevalence of unknown cognitive impairment in TD2 patients who attended a third-level hospital. The DSDRS was found to be a useful screening tool. The presence of associated comorbidities was the main factor of declining referral.
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Vettoretti, Martina, Enrico Longato, Alessandro Zandonà, Yan Li, José Antonio Pagán, David Siscovick, Mercedes R. Carnethon, Alain G. Bertoni, Andrea Facchinetti, and Barbara Di Camillo. "Addressing practical issues of predictive models translation into everyday practice and public health management: a combined model to predict the risk of type 2 diabetes improves incidence prediction and reduces the prevalence of missing risk predictions." BMJ Open Diabetes Research & Care 8, no. 1 (July 2020): e001223. http://dx.doi.org/10.1136/bmjdrc-2020-001223.
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IntroductionMany predictive models for incident type 2 diabetes (T2D) exist, but these models are not used frequently for public health management. Barriers to their application include (1) the problem of model choice (some models are applicable only to certain ethnic groups), (2) missing input variables, and (3) the lack of calibration. While (1) and (2) drives to missing predictions, (3) causes inaccurate incidence predictions. In this paper, a combined T2D risk model for public health management that addresses these three issues is developed.Research design and methodsThe combined T2D risk model combines eight existing predictive models by weighted average to overcome the problem of missing incidence predictions. Moreover, the combined model implements a simple recalibration strategy in which the risk scores are rescaled based on the T2D incidence in the target population. The performance of the combined model was compared with that of the eight existing models using data from two test datasets extracted from the Multi-Ethnic Study of Atherosclerosis (MESA; n=1031) and the English Longitudinal Study of Ageing (ELSA; n=4820). Metrics of discrimination, calibration, and missing incidence predictions were used for the assessment.ResultsThe combined T2D model performed well in terms of both discrimination (concordance index: 0.83 on MESA; 0.77 on ELSA) and calibration (expected to observed event ratio: 1.00 on MESA; 1.17 on ELSA), similarly to the best-performing existing models. However, while the existing models yielded a large percentage of missing predictions (17%–45% on MESA; 63%–64% on ELSA), this was negligible with the combined model (0% on MESA, 4% on ELSA).ConclusionsLeveraging on existing literature T2D predictive models, a simple approach based on risk score rescaling and averaging was shown to provide accurate and robust incidence predictions, overcoming the problem of recalibration and missing predictions in practical application of predictive models.
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Wen, Min, Song Yang, Augustin Vintzileos, Wayne Higgins, and Renhe Zhang. "Impacts of Model Resolutions and Initial Conditions on Predictions of the Asian Summer Monsoon by the NCEP Climate Forecast System." Weather and Forecasting 27, no. 3 (June 1, 2012): 629–46. http://dx.doi.org/10.1175/waf-d-11-00128.1.
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Abstract A series of 60-day hindcasts by the Climate Forecast System (CFS) of the National Centers for Environmental Prediction is analyzed to understand the impacts of atmospheric model resolutions and initial conditions on predictions of the Asian summer monsoon. The experiments, for the time period 2002–06 and with 14 ensemble members, are conducted at resolutions of T62, T126, and T254. They are initialized every 5 days from May to August, using the operational global atmospheric data assimilation system and operational global ocean data assimilation. It is found that, in predicting the magnitude and the timing of monsoon rainfall over lands, high model resolutions overall perform better than lower model resolutions. The increase in prediction skills with model resolution is more apparent over South Asia than over Southeast Asia. The largest improvement is seen over the Tibetan Plateau, at least for precipitation. However, the increase in model resolution does not enhance the skill of the predictions over oceans. Overall, model resolution has larger impacts than do the initial conditions on predicting the development of the Asian summer monsoon in the early season. However, higher model resolutions such as T382 may be needed for the CFS to simulate and predict many features of the monsoon more realistically.
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Kumar, Mukkesh, Li Ting Ang, Cindy Ho, Shu E. Soh, Kok Hian Tan, Jerry Kok Yen Chan, Keith M. Godfrey, et al. "Machine Learning–Derived Prenatal Predictive Risk Model to Guide Intervention and Prevent the Progression of Gestational Diabetes Mellitus to Type 2 Diabetes: Prediction Model Development Study." JMIR Diabetes 7, no. 3 (July 5, 2022): e32366. http://dx.doi.org/10.2196/32366.
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Background The increasing prevalence of gestational diabetes mellitus (GDM) is concerning as women with GDM are at high risk of type 2 diabetes (T2D) later in life. The magnitude of this risk highlights the importance of early intervention to prevent the progression of GDM to T2D. Rates of postpartum screening are suboptimal, often as low as 13% in Asian countries. The lack of preventive care through structured postpartum screening in several health care systems and low public awareness are key barriers to postpartum diabetes screening. Objective In this study, we developed a machine learning model for early prediction of postpartum T2D following routine antenatal GDM screening. The early prediction of postpartum T2D during prenatal care would enable the implementation of effective strategies for diabetes prevention interventions. To our best knowledge, this is the first study that uses machine learning for postpartum T2D risk assessment in antenatal populations of Asian origin. Methods Prospective multiethnic data (Chinese, Malay, and Indian ethnicities) from 561 pregnancies in Singapore’s most deeply phenotyped mother-offspring cohort study—Growing Up in Singapore Towards healthy Outcomes—were used for predictive modeling. The feature variables included were demographics, medical or obstetric history, physical measures, lifestyle information, and GDM diagnosis. Shapley values were combined with CatBoost tree ensembles to perform feature selection. Our game theoretical approach for predictive analytics enables population subtyping and pattern discovery for data-driven precision care. The predictive models were trained using 4 machine learning algorithms: logistic regression, support vector machine, CatBoost gradient boosting, and artificial neural network. We used 5-fold stratified cross-validation to preserve the same proportion of T2D cases in each fold. Grid search pipelines were built to evaluate the best performing hyperparameters. Results A high performance prediction model for postpartum T2D comprising of 2 midgestation features—midpregnancy BMI after gestational weight gain and diagnosis of GDM—was developed (BMI_GDM CatBoost model: AUC=0.86, 95% CI 0.72-0.99). Prepregnancy BMI alone was inadequate in predicting postpartum T2D risk (ppBMI CatBoost model: AUC=0.62, 95% CI 0.39-0.86). A 2-hour postprandial glucose test (BMI_2hour CatBoost model: AUC=0.86, 95% CI 0.76-0.96) showed a stronger postpartum T2D risk prediction effect compared to fasting glucose test (BMI_Fasting CatBoost model: AUC=0.76, 95% CI 0.61-0.91). The BMI_GDM model was also robust when using a modified 2-point International Association of the Diabetes and Pregnancy Study Groups (IADPSG) 2018 criteria for GDM diagnosis (BMI_GDM2 CatBoost model: AUC=0.84, 95% CI 0.72-0.97). Total gestational weight gain was inversely associated with postpartum T2D outcome, independent of prepregnancy BMI and diagnosis of GDM (P=.02; OR 0.88, 95% CI 0.79-0.98). Conclusions Midgestation weight gain effects, combined with the metabolic derangements underlying GDM during pregnancy, signal future T2D risk in Singaporean women. Further studies will be required to examine the influence of metabolic adaptations in pregnancy on postpartum maternal metabolic health outcomes. The state-of-the-art machine learning model can be leveraged as a rapid risk stratification tool during prenatal care. Trial Registration ClinicalTrials.gov NCT01174875; https://clinicaltrials.gov/ct2/show/NCT01174875
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Di Camillo, Barbara, Liisa Hakaste, Francesco Sambo, Rafael Gabriel, Jasmina Kravic, Bo Isomaa, Jaakko Tuomilehto, et al. "HAPT2D: high accuracy of prediction of T2D with a model combining basic and advanced data depending on availability." European Journal of Endocrinology 178, no. 4 (April 2018): 331–41. http://dx.doi.org/10.1530/eje-17-0921.
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Objective Type 2 diabetes arises from the interaction of physiological and lifestyle risk factors. Our objective was to develop a model for predicting the risk of T2D, which could use various amounts of background information. Research design and methods We trained a survival analysis model on 8483 people from three large Finnish and Spanish data sets, to predict the time until incident T2D. All studies included anthropometric data, fasting laboratory values, an oral glucose tolerance test (OGTT) and information on co-morbidities and lifestyle habits. The variables were grouped into three sets reflecting different degrees of information availability. Scenario 1 included background and anthropometric information; Scenario 2 added routine laboratory tests; Scenario 3 also added results from an OGTT. Predictive performance of these models was compared with FINDRISC and Framingham risk scores. Results The three models predicted T2D risk with an average integrated area under the ROC curve equal to 0.83, 0.87 and 0.90, respectively, compared with 0.80 and 0.75 obtained using the FINDRISC and Framingham risk scores. The results were validated on two independent cohorts. Glucose values and particularly 2-h glucose during OGTT (2h-PG) had highest predictive value. Smoking, marital and professional status, waist circumference, blood pressure, age and gender were also predictive. Conclusions Our models provide an estimation of patient’s risk over time and outweigh FINDRISC and Framingham traditional scores for prediction of T2D risk. Of note, the models developed in Scenarios 1 and 2, only exploited variables easily available at general patient visits.
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Zhu, Jianlong, Dehui Guo, Liying Liu, and Jing Zhong. "Serum Galectin-3 Predicts Mortality in Venoarterial Extracorporeal Membrane Oxygenation Patients." Cardiology Research and Practice 2023 (September 30, 2023): 1–8. http://dx.doi.org/10.1155/2023/3917156.
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Objective. We investigated the potential use of galectin-3 (Gal-3) as a prognostic indicator for patients with cardiogenic shock and developed a predictive mortality model for venoarterial extracorporeal membrane oxygenation (VA-ECMO). Methods. We prospectively studied patients (survivors and nonsurvivors) who received VA-ECMO for cardiogenic shock from 2019 to 2021. We recorded baseline data, Gal-3, and B-type natriuretic peptide (BNP) before ECMO and 24–72 h after ECMO. We used multivariable logistic regression to analyze significant risk factors and construct a VA-ECMO death prediction model. Receiver operating characteristic (ROC) curves were plotted to assess the predictive efficacy of the model. Results. We enrolled 73 patients with cardiogenic shock who received VA-ECMO support; 38 (52.05%) died in hospital. The median age was 57 years (interquartile range (IQR): 48–67 years); the median duration of ECMO therapy was 5.8 days (IQR: 4.62–7.57 days); and the median intensive care unit stay was 19.04 days (IQR: 13.92–26.15 days). Compared with the nonsurvivors, survivors had lower acute physiology and chronic health evaluation (APACHE) II scores ( p < 0.001), increased left ventricular ejection fraction ( p < 0.05), lower Gal-3 levels at 24 and 72 h (both p = 0.001), lower BNP levels at 24 and 72 h (both p = 0.001), and higher platelet counts ( p = 0.009). Further multivariable analysis showed that APACHE II score, BNP-T72, and Gal-3-T72 were independent risk factors for death in VA-ECMO patients. Gal-3 and BNP were positively correlated ( p < 0.05) and decreased significantly during ECMO treatment. The areas under the ROC curve (AUC) for APACHE II score, Gal-3-T72, and BNP-T72 were 0.687, 0.799, and 0.723, respectively. We constructed a combined prediction model with an AUC of 0.884 ( p < 0.01). Conclusion. Gal-3 may serve as a prognostic indicator for patients receiving VA-ECMO for cardiogenic shock. The combined early warning score is a simple and effective tool for predicting mortality in VA-ECMO patients.
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Chikowore, Tinashe, Kenneth Ekoru, Marijana Vujkovi, Dipender Gill, Fraser Pirie, Elizabeth Young, Manjinder S. Sandhu, et al. "Polygenic Prediction of Type 2 Diabetes in Africa." Diabetes Care 45, no. 3 (January 11, 2022): 717–23. http://dx.doi.org/10.2337/dc21-0365.
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OBJECTIVE Polygenic prediction of type 2 diabetes (T2D) in continental Africans is adversely affected by the limited number of genome-wide association studies (GWAS) of T2D from Africa and the poor transferability of European-derived polygenic risk scores (PRSs) in diverse ethnicities. We set out to evaluate if African American, European, or multiethnic-derived PRSs would improve polygenic prediction in continental Africans. RESEARCH DESIGN AND METHODS Using the PRSice software, ethnic-specific PRSs were computed with weights from the T2D GWAS multiancestry meta-analysis of 228,499 case and 1,178,783 control subjects. The South African Zulu study (n = 1,602 case and 981 control subjects) was used as the target data set. Validation and assessment of the best predictive PRS association with age at diagnosis were conducted in the Africa America Diabetes Mellitus (AADM) study (n = 2,148 case and 2,161 control subjects). RESULTS The discriminatory ability of the African American and multiethnic PRSs was similar. However, the African American–derived PRS was more transferable in all the countries represented in the AADM cohort and predictive of T2D in the country combined analysis compared with the European and multiethnic-derived scores. Notably, participants in the 10th decile of this PRS had a 3.63-fold greater risk (odds ratio 3.63; 95% CI 2.19–4.03; P = 2.79 × 10−17) per risk allele of developing diabetes and were diagnosed 2.6 years earlier than those in the first decile. CONCLUSIONS African American–derived PRS enhances polygenic prediction of T2D in continental Africans. Improved representation of non-European populations (including Africans) in GWAS promises to provide better tools for precision medicine interventions in T2D.
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Pan, Dikang, Hui Wang, Sensen Wu, Jingyu Wang, Yachan Ning, Jianming Guo, Cong Wang, and Yongquan Gu. "Unveiling the Hidden Burden: Estimating All-Cause Mortality Risk in Older Individuals with Type 2 Diabetes." Journal of Diabetes Research 2024 (January 20, 2024): 1–10. http://dx.doi.org/10.1155/2024/1741878.
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Background. The mortality rate among older persons with diabetes has been steadily increasing, resulting in significant health and economic burdens on both society and individuals. The objective of this study is to develop and validate a predictive nomogram for estimating the 5-year all-cause mortality risk in older persons with T2D (T2D). Methods. We obtained data from the National Health and Nutrition Survey (NHANES). A random 7 : 3 split was made between the training and validation sets. By linking the national mortality index up until December 31, 2019, we ensured a minimum of 5 years of follow-up to assess all-cause mortality. A nomogram was developed in the training cohort using a logistic regression model as well as a least absolute shrinkage and selection operator (LASSO) regression model for predicting the 5-year risk of all-cause mortality. Finally, the prediction performance of the nomogram is evaluated using several validation methods. Results. We constructed a comprehensive prediction model based on the results of multivariate analysis and LASSO binomial regression. These models were then validated using data from the validation cohort. The final model includes four independent predictors: age, gender, estimated glomerular filtration rate, and white blood cell count. The C-index values for the training and validation cohorts were 0.748 and 0.762, respectively. The calibration curve demonstrates satisfactory consistency between the two cohorts. Conclusions. The newly developed nomogram proves to be a valuable tool in accurately predicting the 5-year all-cause mortality risk among older persons with diabetes, providing crucial information for tailored interventions.
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Ha, Jane, Mi Jang, Yeongkeun Kwon, Young Suk Park, Do Joong Park, Joo-Ho Lee, Hyuk-Joon Lee, et al. "Metabolomic Profiles Predict Diabetes Remission after Bariatric Surgery." Journal of Clinical Medicine 9, no. 12 (December 1, 2020): 3897. http://dx.doi.org/10.3390/jcm9123897.
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Background: Amino acid metabolites (AAMs) have been linked to glucose homeostasis and type 2 diabetes (T2D). We investigated whether (1) baseline AAMs predict T2D remission 12 months after bariatric surgery and (2) whether AAMs are superior for predicting T2D remission postoperatively compared with existing prediction models. Methods: Among 24 participants undergoing bariatric surgery, 16 diabetes-related AAMs were quantified at baseline and postoperative 3 and 12 months. Existing prediction models included the ABCD, DiaRem, and IMS models. Results: Baseline L-dihydroxyphenylalanine (L-DOPA) (areas under receiver operating characteristic curves (AUROC), 0.92; 95% confidence interval (CI), 0.75 to 1.00) and 3-hydroxyanthranilic acid (3-HAA) (AUROC, 0.85; 95% CI, 0.67 to 1.00) better predicted T2D remission 12 months postoperatively than the ABCD model (AUROC, 0.81; 95% CI, 0.54 to 1.00), which presented the highest AUROC value among the three models. The superior prognostic performance of L-DOPA (AUROC at 3 months, 0.97; 95% CI, 0.91 to 1.00) and 3-HAA (AUROC at 3 months, 0.86; 95% CI, 0.63 to 1.00) continued until 3 months postoperatively. Conclusions: The AAM profile predicts T2D remission after bariatric surgery more effectively than the existing prediction models.
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Kurasawa, Hisashi, Kayo Waki, Tomohisa Seki, Akihiro Chiba, Akinori Fujino, Katsuyoshi Hayashi, Eri Nakahara, Tsuneyuki Haga, Takashi Noguchi, and Kazuhiko Ohe. "Enhancing Type 2 Diabetes Treatment Decisions With Interpretable Machine Learning Models for Predicting Hemoglobin A1c Changes: Machine Learning Model Development." JMIR AI 3 (July 18, 2024): e56700. http://dx.doi.org/10.2196/56700.
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Background Type 2 diabetes (T2D) is a significant global health challenge. Physicians need to assess whether future glycemic control will be poor on the current trajectory of usual care and usual-care treatment intensifications so that they can consider taking extra treatment measures to prevent poor outcomes. Predicting poor glycemic control from trends in hemoglobin A1c (HbA1c) levels is difficult due to the influence of seasonal fluctuations and other factors. Objective We sought to develop a model that accurately predicts poor glycemic control among patients with T2D receiving usual care. Methods Our machine learning model predicts poor glycemic control (HbA1c≥8%) using the transformer architecture, incorporating an attention mechanism to process irregularly spaced HbA1c time series and quantify temporal relationships of past HbA1c levels at each time point. We assessed the model using HbA1c levels from 7787 patients with T2D seeing specialist physicians at the University of Tokyo Hospital. The training data include instances of poor glycemic control occurring during usual care with usual-care treatment intensifications. We compared prediction accuracy, assessed with the area under the receiver operating characteristic curve, the area under the precision-recall curve, and the accuracy rate, to that of LightGBM. Results The area under the receiver operating characteristic curve, the area under the precision-recall curve, and the accuracy rate (95% confidence limits) of the proposed model were 0.925 (95% CI 0.923-0.928), 0.864 (95% CI 0.852-0.875), and 0.864 (95% CI 0.86-0.869), respectively. The proposed model achieved high prediction accuracy comparable to or surpassing LightGBM’s performance. The model prioritized the most recent HbA1c levels for predictions. Older HbA1c levels in patients with poor glycemic control were slightly more influential in predictions compared to patients with good glycemic control. Conclusions The proposed model accurately predicts poor glycemic control for patients with T2D receiving usual care, including patients receiving usual-care treatment intensifications, allowing physicians to identify cases warranting extraordinary treatment intensifications. If used by a nonspecialist, the model’s indication of likely future poor glycemic control may warrant a referral to a specialist. Future efforts could incorporate diverse and large-scale clinical data for improved accuracy.
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Guasch-Ferré, Marta, Miguel Ruiz-Canela, Jun Li, Yan Zheng, Mònica Bulló, Dong D. Wang, Estefanía Toledo, et al. "Plasma Acylcarnitines and Risk of Type 2 Diabetes in a Mediterranean Population at High Cardiovascular Risk." Journal of Clinical Endocrinology & Metabolism 104, no. 5 (November 13, 2018): 1508–19. http://dx.doi.org/10.1210/jc.2018-01000.
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Abstract Context The potential associations between acylcarnitine profiles and incidence of type 2 diabetes (T2D) and whether acylcarnitines can be used to improve diabetes prediction remain unclear. Objective To evaluate the associations between baseline and 1-year changes in acylcarnitines and their diabetes predictive ability beyond traditional risk factors. Design, Setting, and Participants We designed a case-cohort study within the PREDIMED Study including all incident cases of T2D (n = 251) and 694 randomly selected participants at baseline (follow-up, 3.8 years). Plasma acylcarnitines were measured using a targeted approach by liquid chromatography–tandem mass spectrometry. We tested the associations between baseline and 1-year changes in individual acylcarnitines and T2D risk using weighted Cox regression models. We used elastic net regressions to select acylcarnitines for T2D prediction and compute a weighted score using a cross-validation approach. Results An acylcarnitine profile, especially including short- and long-chain acylcarnitines, was significantly associated with a higher risk of T2D independent of traditional risk factors. The relative risks of T2D per SD increment of the predictive model scores were 4.03 (95% CI, 3.00 to 5.42; P < 0.001) for the conventional model and 4.85 (95% CI, 3.65 to 6.45; P < 0.001) for the model including acylcarnitines, with a hazard ratio of 1.33 (95% CI, 1.08 to 1.63; P < 0.001) attributed to the acylcarnitines. Including the acylcarnitines into the model did not significantly improve the area under the receiver operator characteristic curve (0.86 to 0.88, P = 0.61). A 1-year increase in C4OH-carnitine was associated with higher risk of T2D [per SD increment, 1.44 (1.03 to 2.01)]. Conclusions An acylcarnitine profile, mainly including short- and long-chain acylcarnitines, was significantly associated with higher T2D risk in participants at high cardiovascular risk. The inclusion of acylcarnitines into the model did not significantly improve the T2D prediction C-statistics beyond traditional risk factors, including fasting glucose.
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YANG, JIN MIN. "PROBING NEW PHYSICS FROM TOP QUARK FCNC PROCESS AT LHC: A MINI REVIEW." International Journal of Modern Physics A 23, no. 21 (August 20, 2008): 3343–47. http://dx.doi.org/10.1142/s0217751x08042092.
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Since the top quark FCNC processes are extremely supressed in the Standard Model (SM) but could be greatly enhanced in some new physics models, they could serve as a smoking gun for new physics hunting at the LHC. In this brief review we summarize the new physics predictions for various top quark FCNC processes at the LHC by focusing on two typical models: the minimal supersymmetric model (MSSM) and the topcolor-assisted technicolor (TC2) model. The conclusion is: (1) Both new physics models can greatly enhance the SM predictions by several orders; (2) The TC2 model allows for largest enhancement, and for each channel the maximal prediction is much larger than in the MSSM; (3) Compared with the 3σ sensitivity at the LHC, only a couple of channels are accessible for the MSSM while most channles are accesible for the TC2 model.
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Papandreou, Christopher, Mònica Bulló, Miguel Ruiz-Canela, Courtney Dennis, Amy Deik, Daniel Wang, Marta Guasch-Ferré, et al. "Plasma metabolites predict both insulin resistance and incident type 2 diabetes: a metabolomics approach within the Prevención con Dieta Mediterránea (PREDIMED) study." American Journal of Clinical Nutrition 109, no. 3 (February 23, 2019): 626–34. http://dx.doi.org/10.1093/ajcn/nqy262.
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ABSTRACT Background Insulin resistance is a complex metabolic disorder and is often associated with type 2 diabetes (T2D). Objectives The aim of this study was to test whether baseline metabolites can additionally improve the prediction of insulin resistance beyond classical risk factors. Furthermore, we examined whether a multimetabolite model predicting insulin resistance in nondiabetics can also predict incident T2D. Methods We used a case-cohort study nested within the Prevención con Dieta Mediterránea (PREDIMED) trial in subsets of 700, 500, and 256 participants without T2D at baseline and 1 and 3 y. Fasting plasma metabolites were semiquantitatively profiled with liquid chromatography–tandem mass spectrometry. We assessed associations between metabolite concentrations and the homeostasis model of insulin resistance (HOMA-IR) through the use of elastic net regression analysis. We subsequently examined associations between the baseline HOMA-IR–related multimetabolite model and T2D incidence through the use of weighted Cox proportional hazard models. Results We identified a set of baseline metabolites associated with HOMA-IR. One-year changes in metabolites were also significantly associated with HOMA-IR. The area under the curve was significantly greater for the model containing the classical risk factors and metabolites together compared with classical risk factors alone at baseline [0.81 (95% CI: 0.79, 0.84) compared with 0.69 (95% CI: 0.66, 0.73)] and during a 1-y period [0.69 (95% CI: 0.66, 0.72) compared with 0.57 (95% CI: 0.53, 0.62)]. The variance in HOMA-IR explained by the combination of metabolites and classical risk factors was also higher in all time periods. The estimated HRs for incident T2D in the multimetabolite score (model 3) predicting high HOMA-IR (median value or higher) or HOMA-IR (continuous) at baseline were 2.00 (95% CI: 1.58, 2.55) and 2.24 (95% CI: 1.72, 2.90), respectively, after adjustment for T2D risk factors. Conclusions The multimetabolite model identified in our study notably improved the predictive ability for HOMA-IR beyond classical risk factors and significantly predicted the risk of T2D.
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Fenton, G. A., and D. V. Griffiths. "Bearing-capacity prediction of spatially random c ϕ soils." Canadian Geotechnical Journal 40, no. 1 (February 1, 2003): 54–65. http://dx.doi.org/10.1139/t02-086.
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Soils with spatially varying shear strengths are modeled using random field theory and elasto-plastic finite element analysis to evaluate the extent to which spatial variability and cross-correlation in soil properties (c and ϕ) affect bearing capacity. The analysis is two dimensional, corresponding to a strip footing with infinite correlation length in the out-of-plane direction, and the soil is assumed to be weightless with footing placed on the soil surface. Theoretical predictions of the mean and standard deviation of bearing capacity, for the case where c and ϕ are independent, are derived using a geometric averaging model and then verified via Monte Carlo simulation. The standard deviation prediction is found to be quite accurate, while the mean prediction is found to require some additional semi-empirical adjustment to give accurate results for "worst case" correlation lengths. Combined, the theory can be used to estimate the probability of bearing-capacity failure, but also sheds light on the stochastic behaviour of foundation bearing failure.Key words: bearing capacity, probability, random fields, geometric averaging, cϕ soil, Monte Carlo simulation.
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Deberneh, Henock M., and Intaek Kim. "Prediction of Type 2 Diabetes Based on Machine Learning Algorithm." International Journal of Environmental Research and Public Health 18, no. 6 (March 23, 2021): 3317. http://dx.doi.org/10.3390/ijerph18063317.
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Prediction of type 2 diabetes (T2D) occurrence allows a person at risk to take actions that can prevent onset or delay the progression of the disease. In this study, we developed a machine learning (ML) model to predict T2D occurrence in the following year (Y + 1) using variables in the current year (Y). The dataset for this study was collected at a private medical institute as electronic health records from 2013 to 2018. To construct the prediction model, key features were first selected using ANOVA tests, chi-squared tests, and recursive feature elimination methods. The resultant features were fasting plasma glucose (FPG), HbA1c, triglycerides, BMI, gamma-GTP, age, uric acid, sex, smoking, drinking, physical activity, and family history. We then employed logistic regression, random forest, support vector machine, XGBoost, and ensemble machine learning algorithms based on these variables to predict the outcome as normal (non-diabetic), prediabetes, or diabetes. Based on the experimental results, the performance of the prediction model proved to be reasonably good at forecasting the occurrence of T2D in the Korean population. The model can provide clinicians and patients with valuable predictive information on the likelihood of developing T2D. The cross-validation (CV) results showed that the ensemble models had a superior performance to that of the single models. The CV performance of the prediction models was improved by incorporating more medical history from the dataset.
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Nixon, J. F. (Derick). "Discrete ice lens theory for frost heave beneath pipelines." Canadian Geotechnical Journal 29, no. 3 (June 1, 1992): 487–97. http://dx.doi.org/10.1139/t92-053.
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The discrete ice lens theory of frost heave in one-dimensional soil columns was developed to provide a better physical basis for engineering predictions of frost heave in soils. The theory has now been extended to the two-dimensional heat- and mass-flow situation beneath a buried chilled pipeline. Although the frozen and unfrozen soil regions beneath a buried cold pipeline are two dimensional, and the temperature and water-flow fields are potentially complex, considerable simplifications can be made by invoking the so-called quasi-static approach for estimating temperature fields around the buried pipeline. It is proposed that the curved, quasi-static temperature profiles available from published relationships are appropriate for frost-heave predictions in the two-dimensional region beneath a pipeline. Using these curved temperature profiles in the same program and solution procedure developed previously for one-dimensional soil columns allows frost-heave predictions for a buried pipeline to be carried out with a minimum of computational effort. Therefore, the lengthy and tedious numerical procedures that have been a feature of previous attempts to model heat and mass flow and the resulting frost heave in two dimensions can be avoided. The procedure has been used to predict the frost depth and heave beneath two well-documented pipeline test sections at Calgary, Alta., and Caen, France, with very good agreement between prediction and observation. Some predictions for a practical field situation indicate the initial ground temperature plays an important role in frost heave, frost penetration, and the time at which the final ice lens forms in the freezing soil. Key words : frost heave, discrete ice lens, pipeline, segregation potential, hydraulic conductivity of frozen soil.
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Zhang, Meilin, Li Zheng, Ping Li, Yufeng Zhu, Hong Chang, Xuan Wang, Weiqiao Liu, Yuwen Zhang, and Guowei Huang. "4-Year Trajectory of Visceral Adiposity Index in the Development of Type 2 Diabetes: A Prospective Cohort Study." Annals of Nutrition and Metabolism 69, no. 2 (2016): 142–49. http://dx.doi.org/10.1159/000450657.
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Background/Aims: Our aim was to evaluate whether visceral adiposity index (VAI) could predict the risk of type 2 diabetes (T2D) in different genders and to compare the predictive ability between VAI and other fatness indices. Methods: Four thousand seventy-eight participants including 1,817 men and 2,261 women, aged 18 and older and free of T2D at baseline were enrolled in 2010 and followed up for 4 years. New cases of T2D were identified via the annual medical examination. Cox regression analysis was used to assess the association between VAI and incidence of T2D. Receiver operating characteristic curve and area under the curves (AUC) were applied to compare the prediction ability of T2D between VAI and other fatness indices. Results: During the 4-year follow-up, 153 (8.42%) of 1,817 men and 88 (3.89%) of 2,261 women developed T2D. The multivariable-adjusted hazards ratios for developing T2D in the highest tertile of VAI scores were 2.854 (95% CI 1.815-4.487) in men and 3.551 (95% CI 1.586-7.955) in women. The AUC of VAI was not higher than that of other fatness indices. Conclusions: VAI could predict the risk of T2D among Chinese adults, especially in women. However, the prediction ability of T2D risk for VAI was not higher than that of the other fatness indices.
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Fu, Yuanyuan, Ling Hu, Hong-Wei Ren, Yi Zuo, Shaoqiu Chen, Qiu-Shi Zhang, Chen Shao, et al. "Prognostic Factors for COVID-19 Hospitalized Patients with Preexisting Type 2 Diabetes." International Journal of Endocrinology 2022 (January 17, 2022): 1–13. http://dx.doi.org/10.1155/2022/9322332.
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Background. Type 2 diabetes (T2D) as a worldwide chronic disease combined with the COVID-19 pandemic prompts the need for improving the management of hospitalized COVID-19 patients with preexisting T2D to reduce complications and the risk of death. This study aimed to identify clinical factors associated with COVID-19 outcomes specifically targeted at T2D patients and build an individualized risk prediction nomogram for risk stratification and early clinical intervention to reduce mortality. Methods. In this retrospective study, the clinical characteristics of 382 confirmed COVID-19 patients, consisting of 108 with and 274 without preexisting T2D, from January 8 to March 7, 2020, in Tianyou Hospital in Wuhan, China, were collected and analyzed. Univariate and multivariate Cox regression models were performed to identify specific clinical factors associated with mortality of COVID-19 patients with T2D. An individualized risk prediction nomogram was developed and evaluated by discrimination and calibration. Results. Nearly 15% (16/108) of hospitalized COVID-19 patients with T2D died. Twelve risk factors predictive of mortality were identified. Older age (HR = 1.076, 95% CI = 1.014–1.143, p = 0.016 ), elevated glucose level (HR = 1.153, 95% CI = 1.038–1.28, p = 0.0079 ), increased serum amyloid A (SAA) (HR = 1.007, 95% CI = 1.001–1.014, p = 0.022 ), diabetes treatment with only oral diabetes medication (HR = 0.152, 95%CI = 0.032–0.73, p = 0.0036 ), and oral medication plus insulin (HR = 0.095, 95%CI = 0.019–0.462, p = 0.019 ) were independent prognostic factors. A nomogram based on these prognostic factors was built for early prediction of 7-day, 14-day, and 21-day survival of diabetes patients. High concordance index (C-index) was achieved, and the calibration curves showed the model had good prediction ability within three weeks of COVID-19 onset. Conclusions. By incorporating specific prognostic factors, this study provided a user-friendly graphical risk prediction tool for clinicians to quickly identify high-risk T2D patients hospitalized for COVID-19.
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Cao, Yang, Ingmar Näslund, Erik Näslund, Johan Ottosson, Scott Montgomery, and Erik Stenberg. "Using a Convolutional Neural Network to Predict Remission of Diabetes After Gastric Bypass Surgery: Machine Learning Study From the Scandinavian Obesity Surgery Register." JMIR Medical Informatics 9, no. 8 (August 19, 2021): e25612. http://dx.doi.org/10.2196/25612.
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Background Prediction of diabetes remission is an important topic in the evaluation of patients with type 2 diabetes (T2D) before bariatric surgery. Several high-quality predictive indices are available, but artificial intelligence algorithms offer the potential for higher predictive capability. Objective This study aimed to construct and validate an artificial intelligence prediction model for diabetes remission after Roux-en-Y gastric bypass surgery. Methods Patients who underwent surgery from 2007 to 2017 were included in the study, with collection of individual data from the Scandinavian Obesity Surgery Registry (SOReg), the Swedish National Patients Register, the Swedish Prescribed Drugs Register, and Statistics Sweden. A 7-layer convolution neural network (CNN) model was developed using 80% (6446/8057) of patients randomly selected from SOReg and 20% (1611/8057) of patients for external testing. The predictive capability of the CNN model and currently used scores (DiaRem, Ad-DiaRem, DiaBetter, and individualized metabolic surgery) were compared. Results In total, 8057 patients with T2D were included in the study. At 2 years after surgery, 77.09% achieved pharmacological remission (n=6211), while 63.07% (4004/6348) achieved complete remission. The CNN model showed high accuracy for cessation of antidiabetic drugs and complete remission of T2D after gastric bypass surgery. The area under the receiver operating characteristic curve (AUC) for the CNN model for pharmacological remission was 0.85 (95% CI 0.83-0.86) during validation and 0.83 for the final test, which was 9%-12% better than the traditional predictive indices. The AUC for complete remission was 0.83 (95% CI 0.81-0.85) during validation and 0.82 for the final test, which was 9%-11% better than the traditional predictive indices. Conclusions The CNN method had better predictive capability compared to traditional indices for diabetes remission. However, further validation is needed in other countries to evaluate its external generalizability.
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Sayyid, Hiba O., Salma A. Mahmood, and Saad S. Hamadi. "A Comparative Analysis of Machine Learning Models for Predicting Thyroid Disorders in Type 1 and Type 2 Diabetic Patients." Basrah Researches Sciences 50, no. 2 (December 31, 2024): 193–203. https://doi.org/10.56714/bjrs.50.2.16.
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Machine learning (ML) is increasingly indispensable in modern medicine, particularly for disease prediction and improving patient outcomes. This study applies ML techniques to predict thyroid disorders in diabetic patients, a critical task given the frequent co-occurrence and complex interplay between these conditions. six ML classifiers namely Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), and Naive Bayes (NB) were evaluated across three experiments on a local dataset: (1) a balanced dataset using Random Under-Sampling (RUS), (2) a subset of Type 2 diabetes (T2D) patients, and (3) a subset of Type 1 diabetes (T1D) patients. Random Forest classifier consistently outperformed other classifiers, achieving the highest accuracy (0.85) and F1-score (0.83) in the T2D-focused dataset and showing robust performance on the balanced dataset using RUS. These results highlight the suitability of Random Forest for deployment in clinical settings and underscore the importance of balancing techniques like RUS in improving predictive accuracy. However, challenges remain in predicting thyroid disorders among T1D patients due to the low prevalence of thyroid disorders in this group. The findings reinforce the potential of ML in advancing diagnostics and personalized care in diabetic populations.
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Wu, Chung-Ze, Li-Ying Huang, Fang-Yu Chen, Chun-Heng Kuo, and Dong-Feng Yeih. "Using Machine Learning to Predict Abnormal Carotid Intima-Media Thickness in Type 2 Diabetes." Diagnostics 13, no. 11 (May 23, 2023): 1834. http://dx.doi.org/10.3390/diagnostics13111834.
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Carotid intima-media thickness (c-IMT) is a reliable risk factor for cardiovascular disease risk in type 2 diabetes (T2D) patients. The present study aimed to compare the effectiveness of different machine learning methods and traditional multiple logistic regression in predicting c-IMT using baseline features and to establish the most significant risk factors in a T2D cohort. We followed up with 924 patients with T2D for four years, with 75% of the participants used for model development. Machine learning methods, including classification and regression tree, random forest, eXtreme gradient boosting, and Naïve Bayes classifier, were used to predict c-IMT. The results showed that all machine learning methods, except for classification and regression tree, were not inferior to multiple logistic regression in predicting c-IMT in terms of higher area under receiver operation curve. The most significant risk factors for c-IMT were age, sex, creatinine, body mass index, diastolic blood pressure, and duration of diabetes, sequentially. Conclusively, machine learning methods could improve the prediction of c-IMT in T2D patients compared to conventional logistic regression models. This could have crucial implications for the early identification and management of cardiovascular disease in T2D patients.
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Hu, W. P., Q. A. Shen, M. Zhang, Q. C. Meng, and X. Zhang. "Corrosion–Fatigue Life Prediction for 2024-T62 Aluminum Alloy Using Damage Mechanics-Based Approach." International Journal of Damage Mechanics 21, no. 8 (December 21, 2011): 1245–66. http://dx.doi.org/10.1177/1056789511432791.
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An approach based on the continuum damage mechanics was applied to predict corrosion–fatigue crack initiation life of 2024-T62 aluminum alloy. A fatigue test in air, a pre-corrosion–fatigue test, and a corrosion–fatigue test on smooth standard specimens were performed. The fatigue lives are strongly reduced by the corrosive environment of 5 wt.% NaCl continuous salt spray compared with non-corroded specimens. Damage evolution models for fatigue in air and pre-corrosion–fatigue of smooth specimens were established, which forms the basis for solving the corrosion–fatigue problem. Finally, the method of corrosion–fatigue life prediction was presented. The predictions comply with the experimental data.
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G, Revathi, and Gnanambal Ilango. "Topological Approaches to Diabetes Prediction Using TDA." Journal of Research in Applied Mathematics 10, no. 9 (September 2024): 09–14. http://dx.doi.org/10.35629/0743-10090914.
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The concept of shape and significance in data is fundamental to Topological Data Analysis (TDA). This method has increased process efficiency across multiple sectors, including computational biology and healthcare. Utilizing the Kaggle Pima Indian Diabetes Dataset, persistent homology and persistent landscape were used in the present study to predict diabetes, demonstrating the significance of TDA in the medical field. To guide the investigation, we determined the persistent homology of a point cloud represented by a Vietoris-Rips complex. Additionally, we used the persistent landscape to identify six important features in the dataset. Consequently, we found that our prediction accuracy for diabetes is 75%.
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Elhefnawy, Marwa Elsaeed, Siti Maisharah Sheikh Ghadzi, and Sabariah Noor Harun. "Predictors Associated with Type 2 Diabetes Mellitus Complications over Time: A Literature Review." Journal of Vascular Diseases 1, no. 1 (August 4, 2022): 13–23. http://dx.doi.org/10.3390/jvd1010003.
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Early detection of type 2 diabetes mellitus (T2DM) complications is essential to prevent disability and death. Risk prediction models are tools to estimate the probability that an individual with specific risk factors will develop a future condition within a certain time period. A predictive model that incorporates time to quantify the risk of T2DM complications such as cardiovascular diseases (CVD) event is still lacking. Well-established and validated predictive models of T2D complications are vital to stratify patients based on their risks; thus, individualization therapy could be optimized. New approaches (e.g., the parametric approach) are needed in developing predictive models of T2DM complications by incorporating new and time-varying predictors that may improve the existing models’ predictive ability. This review aimed (1) to summarize the reported predictors for the five main complications of T2DM, which include cardiovascular diseases, ischemic stroke, diabetic nephropathy, diabetic neuropathy, and diabetic retinopathy, and (2) to highlight the persistent need for future risk score models as screening tools for the early prevention of T2D complications.
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Ayensa-Vazquez, Jose Angel, Alfonso Leiva, Pedro Tauler, Angel Arturo López-González, Antoni Aguiló, Matías Tomás-Salvá, and Miquel Bennasar-Veny. "Agreement between Type 2 Diabetes Risk Scales in a Caucasian Population: A Systematic Review and Report." Journal of Clinical Medicine 9, no. 5 (May 20, 2020): 1546. http://dx.doi.org/10.3390/jcm9051546.
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Early detection of people with undiagnosed type 2 diabetes (T2D) is an important public health concern. Several predictive equations for T2D have been proposed but most of them have not been externally validated and their performance could be compromised when clinical data is used. Clinical practice guidelines increasingly incorporate T2D risk prediction models as they support clinical decision making. The aims of this study were to systematically review prediction scores for T2D and to analyze the agreement between these risk scores in a large cross-sectional study of white western European workers. A systematic review of the PubMed, CINAHL, and EMBASE databases and a cross-sectional study in 59,042 Spanish workers was performed. Agreement between scores classifying participants as high risk was evaluated using the kappa statistic. The systematic review of 26 predictive models highlights a great heterogeneity in the risk predictors; there is a poor level of reporting, and most of them have not been externally validated. Regarding the agreement between risk scores, the DETECT-2 risk score scale classified 14.1% of subjects as high-risk, FINDRISC score 20.8%, Cambridge score 19.8%, the AUSDRISK score 26.4%, the EGAD study 30.3%, the Hisayama study 30.9%, the ARIC score 6.3%, and the ITD score 3.1%. The lowest agreement was observed between the ITD and the NUDS study derived score (κ = 0.067). Differences in diabetes incidence, prevalence, and weight of risk factors seem to account for the agreement differences between scores. A better agreement between the multi-ethnic derivate score (DETECT-2) and European derivate scores was observed. Risk models should be designed using more easily identifiable and reproducible health data in clinical practice.
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Jiang, Shimin, Jinying Fang, Tianyu Yu, Lin Liu, Guming Zou, Hongmei Gao, Li Zhuo, and Wenge Li. "Novel Model Predicts Diabetic Nephropathy in Type 2 Diabetes." American Journal of Nephrology 51, no. 2 (December 19, 2019): 130–38. http://dx.doi.org/10.1159/000505145.
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Background: Clinical indicators for accurately distinguishing diabetic nephropathy (DN) from non-diabetic renal disease in type 2 diabetes (T2D) are lacking. This study aimed to develop and validate a nomogram for predicting DN in T2D patients with kidney disease. Methods: A total of 302 consecutive patients with T2D who underwent renal biopsy at China-Japan Friendship Hospital between January 2014 and June 2019 were included in the study. The data were randomly split into a training set containing 70% of the patients (n = 214) and a validation set containing the remaining 30% of patients (n = 88). Multivariable logistic regression analyses were applied to develop a prediction nomogram incorporating the candidates selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the prediction model were assessed using a concordance index (C-index), calibration plot, and decision curve analysis. Both internal and external validations were assessed. Results: A multivariable model that included gender, diabetes duration, diabetic retinopathy, hematuria, glycated hemoglobin A1c, anemia, blood pressure, urinary protein excretion, and estimated glomerular filtration rate was represented as the nomogram. The model demonstrated very good discrimination with a C-index of 0.934 (95% CI 0.904–0.964). The calibration plot diagram of predicted probabilities against observed DN rates indicated excellent concordance. The C-index value was 0.91 for internal validation and 0.875 for external validation. Decision curve analysis demonstrated that the novel nomogram was clinically useful. Conclusion: The novel model was very useful for predicting DN in patients with T2D and kidney disease, and thereby could be used by clinicians either in triage or as a replacement for biopsy.
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Hong, Eun Pyo, Seong Gu Heo, and Ji Wan Park. "The Liability Threshold Model for Predicting the Risk of Cardiovascular Disease in Patients with Type 2 Diabetes: A Multi-Cohort Study of Korean Adults." Metabolites 11, no. 1 (December 24, 2020): 6. http://dx.doi.org/10.3390/metabo11010006.
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Personalized risk prediction for diabetic cardiovascular disease (DCVD) is at the core of precision medicine in type 2 diabetes (T2D). We first identified three marker sets consisting of 15, 47, and 231 tagging single nucleotide polymorphisms (tSNPs) associated with DCVD using a linear mixed model in 2378 T2D patients obtained from four population-based Korean cohorts. Using the genetic variants with even modest effects on phenotypic variance, we observed improved risk stratification accuracy beyond traditional risk factors (AUC, 0.63 to 0.97). With a cutoff point of 0.21, the discrete genetic liability threshold model consisting of 231 SNPs (GLT231) correctly classified 87.7% of 2378 T2D patients as high or low risk of DCVD. For the same set of SNP markers, the GLT and polygenic risk score (PRS) models showed similar predictive performance, and we observed consistency between the GLT and PRS models in that the model based on a larger number of SNP markers showed much-improved predictability. In silico gene expression analysis, additional information was provided on the functional role of the genes identified in this study. In particular, HDAC4, CDKN2B, CELSR2, and MRAS appear to be major hubs in the functional gene network for DCVD. The proposed risk prediction approach based on the liability threshold model may help identify T2D patients at high CVD risk in East Asian populations with further external validations.
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Mormile, Ilaria, Francescopaolo Granata, Aikaterini Detoraki, Daniela Pacella, Francesca Della Casa, Felicia De Rosa, Antonio Romano, Amato de Paulis, and Francesca Wanda Rossi. "Predictive Response to Immunotherapy Score: A Useful Tool for Identifying Eligible Patients for Allergen Immunotherapy." Biomedicines 10, no. 5 (April 22, 2022): 971. http://dx.doi.org/10.3390/biomedicines10050971.
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A specific predictive tool of allergen immunotherapy (AIT) outcome has not been identified yet. This study aims to evaluate the efficacy of a disease score referred to as Predictive Response to Immunotherapy Score (PRIS) to predict the response to AIT and identify eligible patients. A total of 110 patients diagnosed with allergic rhinitis with or without concomitant asthma were enrolled in this study. Before beginning sublingual immunotherapy (SLIT), patients were evaluated by analyzing clinical and laboratory parameters. A specific rating was assigned to each parameter to be combined in a total score named PRIS. At baseline (T0) and follow-up [after 12 (T12) and 24 months (T24) of SLIT], a Visual Analogue Scale (VAS) was used to calculate a mean symptom score (MSS). Finally, the percentage variation between the MSS at T0 and at T12 [ΔMSS-12(%)] and T24 [ΔMSS-24 (%)] was measured. We observed a significant improvement of symptoms at T12 and T24 compared to T0 in all groups undergoing SLIT. PRIS was effective in predicting ΔMSS-24 (%) in patients treated with single-allergen SLIT. In addition, PRIS was effective in predicting ΔMSS-24 (%) in both patients with only rhinitis and with concomitant asthma. PRIS assessment can represent a useful tool to individuate potential responders before SLIT prescription.
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Kleeman, Richard. "Limits, Variability, and General Behavior of Statistical Predictability of the Midlatitude Atmosphere." Journal of the Atmospheric Sciences 65, no. 1 (January 1, 2008): 263–75. http://dx.doi.org/10.1175/2007jas2234.1.
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Abstract The nature of statistical predictability is analyzed in a T42 global atmospheric model that is able to adequately capture the main features of the midlatitude atmosphere. Key novel features of the present study include very large prediction ensembles and information theoretic techniques. It is found globally that predictability declines in a quasi-linear fashion with time for short-term predictions (3–25 days), while for long ranges (30–45 days) there is an exponential tail. In general, beyond 45 days the prediction and climatological ensembles have essentially converged, which means that beyond that point, atmospheric initial conditions are irrelevant to atmospheric statistical prediction. Regional predictions show considerable variation in behavior. Both of the (northern) winter storm-track regions show a close-to-quasi-linear decline in predictability toward a cutoff at around 40 days. The (southern) summer storm track shows a much more exponential and considerably slower decline with a small amount of predictability still in evidence even at 90 days. Because the winter storm tracks dominate global variance the behavior of their predictability tends to dominate the global measure, except at longer lags. Variability in predictability with respect to initial conditions is also examined, and it is found that this is related more strongly to ensemble signal rather than ensemble spread. This result may serve to explain why the relation between weather forecast skill and ensemble spread is often observed to be significantly less than perfect. Results herein suggest that the ensemble signal as well as spread variations may be a major contributor to skill variations. Finally, it is found that the sensitivity of the calculated global predictability to changes in model horizontal resolution is not large; results from a T85 resolution model are not qualitatively all that different from the T42 case.
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Yu, Daohua, Xin Zhou, Yu Pan, Zhendong Niu, Xu Yuan, and Huafei Sun. "University Academic Performance Development Prediction Based on TDA." Entropy 25, no. 1 (December 23, 2022): 24. http://dx.doi.org/10.3390/e25010024.
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With the rapid development of higher education, the evaluation of the academic growth potential of universities has received extensive attention from scholars and educational administrators. Although the number of papers on university academic evaluation is increasing, few scholars have conducted research on the changing trend of university academic performance. Because traditional statistical methods and deep learning techniques have proven to be incapable of handling short time series data well, this paper proposes to adopt topological data analysis (TDA) to extract specified features from short time series data and then construct the model for the prediction of trend of university academic performance. The performance of the proposed method is evaluated by experiments on a real-world university academic performance dataset. By comparing the prediction results given by the Markov chain as well as SVM on the original data and TDA statistics, respectively, we demonstrate that the data generated by TDA methods can help construct very discriminative models and have a great advantage over the traditional models. In addition, this paper gives the prediction results as a reference, which provides a new perspective for the development evaluation of the academic performance of colleges and universities.
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Kiseleva, A. V., A. G. Soplenkova, V. A. Kutsenko, E. A. Sotnikova, Yu V. Vyatkin, А. A. Zharikova, A. I. Ershova, et al. "Validation of genetic risk scores for type 2 diabetes on a Russian population sample from the biobank of the National Medical Research Center for Therapy and Preventive Medicine." Cardiovascular Therapy and Prevention 22, no. 11 (December 10, 2023): 3746. http://dx.doi.org/10.15829/1728-8800-20233746.
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Aim. To validate and evaluate the accuracy of 14 genetic risk scores (GRSs) for type 2 diabetes (T2D), created earlier in other countries, using a Russian population sample from the biobank of the National Medical Research Center for Therapy and Preventive Medicine.Material and methods. For genetic analysis, next generation sequencing data was used on a sample from the Russian population (n=1165) based on the biobank collection. The study included 14 GRSs associated with T2D.Results. The study demonstrated that the predictive power of 12 out of 14 GRSs for T2D was replicated in the Russian population. As quality metrics, we used the area under the ROC curve, which for models including only GRS varied from 54,49 to 59,46%, and for models including GRS, sex and age — from 77,56 to 78,75%.Conclusion. For the first time in Russia, a study of 14 T2D GRSs developed on other populations was conducted. Twelve GRSs have been validated and can be used in the future to improve risk prediction and prevention of T2D in Russia.
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Cavati, Guido, Filippo Pirrotta, Daniela Merlotti, Elena Ceccarelli, Marco Calabrese, Luigi Gennari, and Christian Mingiano. "Role of Advanced Glycation End-Products and Oxidative Stress in Type-2-Diabetes-Induced Bone Fragility and Implications on Fracture Risk Stratification." Antioxidants 12, no. 4 (April 14, 2023): 928. http://dx.doi.org/10.3390/antiox12040928.
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Type 2 diabetes (T2D) and osteoporosis (OP) are major causes of morbidity and mortality that have arelevant health and economic burden. Recent epidemiological evidence suggests that both of these disorders are often associated with each other and that T2D patients have an increased risk of fracture, making bone an additional target of diabetes. As occurs for other diabetic complications, the increased accumulation of advanced glycation end-products (AGEs) and oxidative stress represent the major mechanisms explaining bone fragility in T2D. Both of these conditions directly and indirectly (through the promotion of microvascular complications) impair the structural ductility of bone and negatively affect bone turnover, leading to impaired bone quality, rather than decreased bone density. This makes diabetes-induced bone fragility remarkably different from other forms of OP and represents a major challenge for fracture risk stratification, since either the measurement of BMD or the use of common diagnostic algorithms for OP have a poor predictive value. We review and discuss the role of AGEs and oxidative stress on the pathophysiology of bone fragility in T2D, providing some indications on how to improve fracture risk prediction in T2D patients.
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Sun, Yue, Hao-Yu Gao, Zhi-Yuan Fan, Yan He, and Yu-Xiang Yan. "Metabolomics Signatures in Type 2 Diabetes: A Systematic Review and Integrative Analysis." Journal of Clinical Endocrinology & Metabolism 105, no. 4 (November 29, 2019): 1000–1008. http://dx.doi.org/10.1210/clinem/dgz240.
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Abstract Objective Metabolic signatures have emerged as valuable signaling molecules in the biochemical process of type 2 diabetes (T2D). To summarize and identify metabolic biomarkers in T2D, we performed a systematic review and meta-analysis of the associations between metabolites and T2D using high-throughput metabolomics techniques. Methods We searched relevant studies from MEDLINE (PubMed), Embase, Web of Science, and Cochrane Library as well as Chinese databases (Wanfang, Vip, and CNKI) inception through 31 December 2018. Meta-analysis was conducted using STATA 14.0 under random effect. Besides, bioinformatic analysis was performed to explore molecule mechanism by MetaboAnalyst and R 3.5.2. Results Finally, 46 articles were included in this review on metabolites involved amino acids, acylcarnitines, lipids, carbohydrates, organic acids, and others. Results of meta-analysis in prospective studies indicated that isoleucine, leucine, valine, tyrosine, phenylalanine, glutamate, alanine, valerylcarnitine (C5), palmitoylcarnitine (C16), palmitic acid, and linoleic acid were associated with higher T2D risk. Conversely, serine, glutamine, and lysophosphatidylcholine C18:2 decreased risk of T2D. Arginine and glycine increased risk of T2D in the Western countries subgroup, and betaine was negatively correlated with T2D in nested case-control subgroup. In addition, slight improvements in T2D prediction beyond traditional risk factors were observed when adding these metabolites in predictive analysis. Pathway analysis identified 17 metabolic pathways may alter in the process of T2D and metabolite-related genes were also enriched in functions and pathways associated with T2D. Conclusions Several metabolites and metabolic pathways associated with T2D have been identified, which provide valuable biomarkers and novel targets for prevention and drug therapy.
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Kocbek, Simon, Primoz Kocbek, Andraz Stozer, Tina Zupanic, Tudor Groza, and Gregor Stiglic. "Building interpretable models for polypharmacy prediction in older chronic patients based on drug prescription records." PeerJ 6 (October 12, 2018): e5765. http://dx.doi.org/10.7717/peerj.5765.
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Background Multimorbidity presents an increasingly common problem in older population, and is tightly related to polypharmacy, i.e., concurrent use of multiple medications by one individual. Detecting polypharmacy from drug prescription records is not only related to multimorbidity, but can also point at incorrect use of medicines. In this work, we build models for predicting polypharmacy from drug prescription records for newly diagnosed chronic patients. We evaluate the models’ performance with a strong focus on interpretability of the results. Methods A centrally collected nationwide dataset of prescription records was used to perform electronic phenotyping of patients for the following two chronic conditions: type 2 diabetes mellitus (T2D) and cardiovascular disease (CVD). In addition, a hospital discharge dataset was linked to the prescription records. A regularized regression model was built for 11 different experimental scenarios on two datasets, and complexity of the model was controlled with a maximum number of dimensions (MND) parameter. Performance and interpretability of the model were evaluated with AUC, AUPRC, calibration plots, and interpretation by a medical doctor. Results For the CVD model, AUC and AUPRC values of 0.900 (95% [0.898–0.901]) and 0.640 (0.635–0.645) were reached, respectively, while for the T2D model the values were 0.808 (0.803–0.812) and 0.732 (0.725–0.739). Reducing complexity of the model by 65% and 48% for CVD and T2D, resulted in 3% and 4% lower AUC, and 4% and 5% lower AUPRC values, respectively. Calibration plots for our models showed that we can achieve moderate calibration with reducing the models’ complexity without significant loss of predictive performance. Discussion In this study, we found that it is possible to use drug prescription data to build a model for polypharmacy prediction in older population. In addition, the study showed that it is possible to find a balance between good performance and interpretability of the model, and achieve acceptable calibration at the same time.
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Komine, Hideo, and Nobuhide Ogata. "New equations for swelling characteristics of bentonite-based buffer materials." Canadian Geotechnical Journal 40, no. 2 (April 1, 2003): 460–75. http://dx.doi.org/10.1139/t02-115.
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Compacted bentonite and sandbentonite mixtures are attracting greater attention as buffer material for repositories of high-level nuclear waste. This buffer material is expected to fill up the space between the canisters containing the waste and the surrounding ground by swelling. To produce the specifications, such as dry density, sandbentonite mass ratio, and dimensions, of the buffer material, the swelling characteristics of compacted bentonite and sandbentonite mixtures must be evaluated quantitatively. New equations for evaluating the swelling behavior of compacted bentonite and sandbentonite mixtures are presented that can accommodate the influences of the sandbentonite mass ratio and the exchangeable-cation composition of bentonite. The new method for predicting swelling characteristics is presented by combining the new equations with the theoretical equations of the GouyChapman diffuse double layer theory and of the van der Waals force, which can evaluate the repulsive and attractive forces of montmorillonite mineral (i.e., the swelling clay mineral in bentonite). Furthermore, the applicability of the new prediction method has been confirmed by comparing the predicted results with laboratory test results on the swelling deformation and swelling pressure of compacted bentonites and sandbentonite mixtures.Key words: bentonite, diffuse double layer theory, van der Waals force, nuclear waste disposal, swelling deformation, swelling pressure.
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SUN, De-Chun, Zu-Jun LIU, and Ke-Chu YI. "Double-Scale Channel Prediction for Precoded TDD-MIMO Systems." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E96.A, no. 3 (2013): 745–46. http://dx.doi.org/10.1587/transfun.e96.a.745.
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Chauhan, Kinsuk, Girish N. Nadkarni, Fergus Fleming, James McCullough, Cijiang J. He, John Quackenbush, Barbara Murphy, Michael J. Donovan, Steven G. Coca, and Joseph V. Bonventre. "Initial Validation of a Machine Learning-Derived Prognostic Test (KidneyIntelX) Integrating Biomarkers and Electronic Health Record Data To Predict Longitudinal Kidney Outcomes." Kidney360 1, no. 8 (June 30, 2020): 731–39. http://dx.doi.org/10.34067/kid.0002252020.
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BackgroundIndividuals with type 2 diabetes (T2D) or the apolipoprotein L1 high-risk (APOL1-HR) genotypes are at increased risk of rapid kidney function decline (RKFD) and kidney failure. We hypothesized that a prognostic test using machine learning integrating blood biomarkers and longitudinal electronic health record (EHR) data would improve risk stratification.MethodsWe selected two cohorts from the Mount Sinai BioMe Biobank: T2D (n=871) and African ancestry with APOL1-HR (n=498). We measured plasma tumor necrosis factor receptors (TNFR) 1 and 2 and kidney injury molecule-1 (KIM-1) and used random forest algorithms to integrate biomarker and EHR data to generate a risk score for a composite outcome: RKFD (eGFR decline of ≥5 ml/min per year), or 40% sustained eGFR decline, or kidney failure. We compared performance to a validated clinical model and applied thresholds to assess the utility of the prognostic test (KidneyIntelX) to accurately stratify patients into risk categories.ResultsOverall, 23% of those with T2D and 18% of those with APOL1-HR experienced the composite kidney end point over a median follow-up of 4.6 and 5.9 years, respectively. The area under the receiver operator characteristic curve (AUC) of KidneyIntelX was 0.77 (95% CI, 0.75 to 0.79) in T2D, and 0.80 (95% CI, 0.77 to 0.83) in APOL1-HR, outperforming the clinical models (AUC, 0.66 [95% CI, 0.65 to 0.67] and 0.72 [95% CI, 0.71 to 0.73], respectively; P<0.001). The positive predictive values for KidneyIntelX were 62% and 62% versus 46% and 39% for the clinical models (P<0.01) in high-risk (top 15%) stratum for T2D and APOL1-HR, respectively. The negative predictive values for KidneyIntelX were 92% in T2D and 96% for APOL1-HR versus 85% and 93% for the clinical model, respectively (P=0.76 and 0.93, respectively), in low-risk stratum (bottom 50%).ConclusionsIn patients with T2D or APOL1-HR, a prognostic test (KidneyIntelX) integrating biomarker levels with longitudinal EHR data significantly improved prediction of a composite kidney end point of RKFD, 40% decline in eGFR, or kidney failure over validated clinical models.
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Palmer, Daniel, Larissa Henze, Hugo Murua Escobar, Uwe Walter, Axel Kowald, and Georg Fuellen. "Multicohort study testing the generalisability of the SASKit-ML stroke and PDAC prognostic model pipeline to other chronic diseases." BMJ Open 14, no. 9 (September 2024): e088181. http://dx.doi.org/10.1136/bmjopen-2024-088181.
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ObjectivesTo validate and test the generalisability of the SASKit-ML pipeline, a prepublished feature selection and machine learning pipeline for the prediction of health deterioration after a stroke or pancreatic adenocarcinoma event, by using it to identify biomarkers of health deterioration in chronic disease.DesignThis is a validation study using a predefined protocol applied to multiple publicly available datasets, including longitudinal data from cohorts with type 2 diabetes (T2D), inflammatory bowel disease (IBD), rheumatoid arthritis (RA) and various cancers. The datasets were chosen to mimic as closely as possible the SASKit cohort, a prospective, longitudinal cohort study.Data sourcesPublic data were used from the T2D (77 patients with potential pre-diabetes and 18 controls) and IBD (49 patients with IBD and 12 controls) branches of the Human Microbiome Project (HMP), RA Map (RA-MAP, 92 patients with RA, 22 controls) and The Cancer Genome Atlas (TCGA, 16 cancers).MethodsData integration steps were performed in accordance with the prepublished study protocol, generating features to predict disease outcomes using 10-fold cross-validated random survival forests.Outcome measuresHealth deterioration was assessed using disease-specific clinical markers and endpoints across different cohorts. In the HMP-T2D cohort, the worsening of glycated haemoglobin (HbA1c) levels (5.7% or more HbA1c in the blood), fasting plasma glucose (at least 100 mg/dL) and oral glucose tolerance test (at least 140) results were considered. For the HMP-IBD cohort, a worsening by at least 3 points of a disease-specific severity measure, the "Simple Clinical Colitis Activity Index" or "Harvey-Bradshaw Index" indicated an event. For the RA-MAP cohort, the outcome was defined as the worsening of the "Disease Activity Score 28" or "Simple Disease Activity Index" by at least five points, or the worsening of the "Health Assessment Questionnaire" score or an increase in the number of swollen/tender joints were evaluated. Finally, the outcome for all TCGA datasets was the progression-free interval.ResultsModels for the prediction of health deterioration in T2D, IBD, RA and 16 cancers were produced. The T2D (C-index of 0.633 and Integrated Brier Score (IBS) of 0.107) and the RA (C-index of 0.654 and IBS of 0.150) models were modestly predictive. The IBD model was uninformative. TCGA models tended towards modest predictive power.ConclusionsThe SASKit-ML pipeline produces informative and useful features with the power to predict health deterioration in a variety of diseases and cancers; however, this performance is disease-dependent.
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KUMAR, VIJAY, A. K. CHAKRAVARTY, ANKIT MAGOTRA, C. S. PATIL, and P. R. SHIVAHRE. "Comparative study of ANN and conventional methods in forecasting first lactation milk yield in Murrah buffalo." Indian Journal of Animal Sciences 89, no. 11 (December 4, 2019). http://dx.doi.org/10.56093/ijans.v89i11.95887.
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Present investigation was undertaken to predict first lactation 305-day milk yield (FL305DMY) using monthly test day milk records. Under this study, multiple linear regression (MLR) and artificial neural network (ANN) approach were used. Effectiveness of both methods was also compared for prediction of FL305DMY in Murrah buffalo. The data on 3336 monthly test day milk yields records of first lactation pertaining to 556 Murrah buffaloes maintained at National Dairy Research Institute, Karnal; Central Institute for research on buffalo; Guru Angad Dev Veterinary and Animal Sciences University (GADVASU), Ludhiana and Choudhary Charan Singh Haryana Agricultural University (CCSHAU), Hisar were used in this study. In MLR study, it was observed that model 14 having four independent variable, i.e. FSP, TD2, TD4 and TD6 fulfilled most criteria such as highest R2, lowest MSE, lowest RMSE, lowest CP, lowest MAE, lowest MAPE, and lowest U value. In the present investigation, the accuracy of prediction obtained from ANN was almost similar to MLR for prediction of FL305DMY using monthly test day milk records in Murrah buffalo. The best ANN algorithm achieved 76.8% accuracy of prediction for optimum model, whereas the MLR explained 76.9% of accuracy of prediction of FL305DMY in Murrah buffalo. MLR method is simple as compared to ANN, hence MLR method could be preferred.
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"Prediction of first lactation milk yield on the basis of test day yield using artificial neural network versus multiple linear regression in Gir cows." Indian Journal of Dairy Science 77, no. 1 (2024): 91–96. https://doi.org/10.33785/ijds.2024.v77i01.013.
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The test- day model is a method of choice for the study of milk yield traits and this method is very important in countries like India where herd size is generally smaller and lacking well-established milk recording system. The present study was aimed to predict first lactation milk yield on the basis of test day yield in Gir cows and comparison was made between the relative efficiency of Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models. First lactation records of 513Gir cows sired by 75 bulls spread over a period of 34 years (1981to 2014), maintained at Cattle Breeding Farm, Junagadh were used for the study. The data of monthly test-day milk yield (MTMY)was divided into seven sets. ANN was used with backpropagation Bayesian regularization (BR) algorithm and MLR was used with backward elimination method. The accuracy of prediction of first lactation milk yield in MLR was lower than the accuracy of ANN in all the test data sets. The Root Mean Square Errors (RMSE) of prediction were lower in ANN as compared to MLR. The optimum equation had total four variables (test days)viz. TD2 to TD5 for prediction of First Lactation 305-Days Milk Yield (FL305DMY). This equation gave an accuracy of prediction of 76.02% by MLR and 87.69% by ANN model till 125th days of lactation i.e. 5th monthly test day.
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Jaeger, Byron C., Ramon Casanova, Brian Wells, Yitbarek Demesie, Jeanette Stafford, and Michael Bancks. "Abstract P131: Individualized Risk Prediction for Type 2 Diabetes: A Secondary Analysis of the Diabetes Prevention Program." Circulation 149, Suppl_1 (March 19, 2024). http://dx.doi.org/10.1161/circ.149.suppl_1.p131.
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Introduction: Type 2 diabetes (T2D) risk prediction models that predict risk with individualized preventive intervention effects can help targeted prevention. Hypothesis: An individualized T2D risk prediction model developed in the Diabetes Prevention Program (DPP) randomized trial that incorporates differential treatment effects based on an individual’s fasting glucose (FG) level and body mass index (BMI) will provide more accurate predictions than a model without individualized treatment effects. Methods: We included 2640 DPP participants randomized to the placebo, metformin, or lifestyle arms. Using 50% of the DPP sample, we developed a non-individualized Cox model predicting T2D risk over 3 years with adjustment for sex, hemoglobin A1c, triglycerides, FG, BMI, and treatment, and an individualized model with adjustment for these variables plus interactions between treatment and age, FG, and BMI. In the remaining 50%, we evaluated the prediction accuracy of both models using the concordance (C)-statistic. We repeated this process 100 times with different random splits. We externally validated models among Multi-Ethnic Study of Atherosclerosis (MESA) adults with prediabetes (n=1067) where model predictions were computed for each intervention scenario and averaged for all participants. Results: Mean (standard deviation) age of participants was 51 (11) years in the DPP and 64 (10) in MESA, with 67% and 54% women, respectively. Mean C-statistics for our individualized and non-individualized prediction models (100 splits) in DPP were 0.71 and 0.70, respectively (table). The individualized model predicted higher benefit of lifestyle intervention at lower BMI levels and higher benefit of both metformin and lifestyle at higher FG. The C-statistic of the individualized model was 0.78 in the MESA validation. Conclusion: Creating individualized estimates of intervention effects can improve accuracy of T2D risk prediction models, allowing patients to make more informed decisions about prevention strategies.
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Liu, Yang, Scott C. Ritchie, Shu Mei Teo, Matti O. Ruuskanen, Oleg Kambur, Qiyun Zhu, Jon Sanders, et al. "Integration of polygenic and gut metagenomic risk prediction for common diseases." Nature Aging, March 25, 2024. http://dx.doi.org/10.1038/s43587-024-00590-7.
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AbstractMultiomics has shown promise in noninvasive risk profiling and early detection of various common diseases. In the present study, in a prospective population-based cohort with ~18 years of e-health record follow-up, we investigated the incremental and combined value of genomic and gut metagenomic risk assessment compared with conventional risk factors for predicting incident coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer disease and prostate cancer. We found that polygenic risk scores (PRSs) improved prediction over conventional risk factors for all diseases. Gut microbiome scores improved predictive capacity over baseline age for CAD, T2D and prostate cancer. Integrated risk models of PRSs, gut microbiome scores and conventional risk factors achieved the highest predictive performance for all diseases studied compared with models based on conventional risk factors alone. The present study demonstrates that integrated PRSs and gut metagenomic risk models improve the predictive value over conventional risk factors for common chronic diseases.
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Fan, Yuting, Enwu Long, Lulu Cai, Qiyuan Cao, Xingwei Wu, and Rongsheng Tong. "Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes." Frontiers in Pharmacology 12 (June 22, 2021). http://dx.doi.org/10.3389/fphar.2021.665951.
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Purpose: The objective of this study was to evaluate the efficacy of machine learning algorithms in predicting risks of complications and poor glycemic control in nonadherent type 2 diabetes (T2D).Materials and Methods: This study was a real-world study of the complications and blood glucose prognosis of nonadherent T2D patients. Data of inpatients in Sichuan Provincial People’s Hospital from January 2010 to December 2015 were collected. The T2D patients who had neither been monitored for glycosylated hemoglobin A nor had changed their hyperglycemia treatment regimens within the last 12 months were the object of this study. Seven types of machine learning algorithms were used to develop 18 prediction models. The predictive performance was mainly assessed using the area under the curve of the testing set.Results: Of 800 T2D patients, 165 (20.6%) met the inclusion criteria, of which 129 (78.2%) had poor glycemic control (defined as glycosylated hemoglobin A ≥7%). The highest area under the curves of the testing set for diabetic nephropathy, diabetic peripheral neuropathy, diabetic angiopathy, diabetic eye disease, and glycosylated hemoglobin A were 0.902 ± 0.040, 0.859 ± 0.050, 0.889 ± 0.059, 0.832 ± 0.086, and 0.825 ± 0.092, respectively.Conclusion: Both univariate analysis and machine learning methods reached the same conclusion. The duration of T2D and the duration of unadjusted hypoglycemic treatment were the key risk factors of diabetic complications, and the number of hypoglycemic drugs was the key risk factor of glycemic control of nonadherent T2D. This was the first study to use machine learning algorithms to explore the potential adverse outcomes of nonadherent T2D. The performances of the final prediction models we developed were acceptable; our prediction performances outperformed most other previous studies in most evaluation measures. Those models have potential clinical applicability in improving T2D care.
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Marchiori, Marian, Alice Maguolo, Alexander Perfilyev, Marlena Maziarz, Mats Martinell, Maria F. Gomez, Emma Ahlqvist, Sonia García-Calzón, and Charlotte Ling. "Blood-based epigenetic biomarkers associated with incident chronic kidney disease in individuals with type 2 diabetes." Diabetes, December 23, 2024. https://doi.org/10.2337/db24-0483.
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There is an increasing need for new biomarkers improving prediction of chronic kidney disease (CKD) in individuals with type 2 diabetes (T2D). We aimed to identify blood-based epigenetic biomarkers associated with incident CKD and develop a methylation risk score (MRS) predicting CKD in newlydiagnosed individuals with T2D. DNA methylation was analysed epigenome-wide in blood from 487 newly-diagnosed individuals with T2D, of whom 88 developed CKD during 11.5-year follow-up. Weighted Cox regression was used to associate methylation with incident CKD. Weighted logistic models and cross-validation (k=5) were performed to test if the MRS could predict CKD. Methylation at 37 sites was associated with CKD development, based on FDR<5% and absolute methylation differences ≥5% between individuals with incident CKD and those free of CKD during follow-up. Notably, 15 genes annotated to these sites, e.g., TGFBI, SHISA3, and SLC43A2 (encoding LAT4), have been linked to CKD or related risk factors including blood pressure, BMI, or eGFR. Using a MRS including 37 sites and cross-validation for prediction of CKD, we generated ROC curves with AUC=0.82 for the MRS and AUC=0.87 for the combination of MRS and clinical factors. Importantly, ROC curves including the MRS had significantly better AUCs versus the one only including clinical factors (AUC=0.72). The combined epigenetic biomarker had high accuracy in identifying individuals free of future CKD (negative predictive value=94.6%). We discovered a high-performance epigenetic biomarker for predicting CKD, encouraging its potential role in precision medicine, risk stratification, and targeted prevention in T2D.
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Jiang, Mingyang, Fu Gan, Meishe Gan, Huachu Deng, Xuxu Chen, Xintao Yuan, Danyi Huang, et al. "Predicting the Risk of Diabetic Foot Ulcers From Diabetics With Dysmetabolism: A Retrospective Clinical Trial." Frontiers in Endocrinology 13 (July 12, 2022). http://dx.doi.org/10.3389/fendo.2022.929864.
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BackgroundDiabetic foot ulcer (DFU) in patients with type 2 diabetes mellitus (T2D) often leads to amputation. Early intervention to prevent DFU is urgently necessary. So far, there have been no studies on predictive models associated with DFU risk factors. Our study aimed to quantify the predictive risk value of DFU, promote health education, and further develop behavioral interventions to reduce the incidence of DFU.MethodsData from 973 consecutive patients with T2D was collected from two hospitals. Patients from the Guangxi Medical University First Affiliated Hospital formed the training cohort (n = 853), and those from the Wuming Hospital of Guangxi Medical University formed the validation cohort (n = 120). Independent variable grouping analysis and multivariate logistic regression analysis were used to determine the risk factors of DFUs. The prediction model was established according to the related risk factors. In addition, the accuracy of the model was evaluated by specificity, sensitivity, predictive value, and predictive likelihood ratio.ResultsIn total, 369 of the 853 patients (43.3%) and 60 of the 120 (50.0%) were diagnosed with DFUs in the two hospitals. The factors associated with DFU were old age, male gender, lower body mass index (BMI), longer duration of diabetes, history of foot disease, cardiac insufficiency, no use of oral hypoglycemic agent (OHA), high white blood cell count, high platelet count, low hemoglobin level, low lymphocyte absolute value, and high postprandial blood glucose. After incorporating these 12 factors, the nomogram drawn achieved good concordance indexes of 0.89 [95% confidence interval (CI): 0.87 to 0.91] in the training cohort and 0.84 (95% CI: 0.77 to 0.91) in the validation cohort in predicting DFUs and had well-fitted calibration curves. Patients who had a nomogram score of ≥180 were considered to have a low risk of DFU, whereas those having ≥180 were at high risk.ConclusionsA nomogram was constructed by combining 12 identified risk factors of DFU. These 12 risk factors are easily available in hospitalized patients, so the prediction of DFU in hospitalized patients with T2D has potential clinical significance. The model provides a reliable prediction of the risk of DFU in patients with T2D.
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Zhu, Yun, Ying Zhang, Jianhui Zhu, Jason G. Umans, Shelley Cole, Elisa T. Lee, Barbara V. Howard, et al. "Abstract 22: Novel Plasma Lipids Predict Risk of Diabetes: A Longitudinal Lipidomics Study in American Indians." Circulation 141, Suppl_1 (March 3, 2020). http://dx.doi.org/10.1161/circ.141.suppl_1.22.
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Background: American Indians (AIs) suffer disproportionately high rate of type 2 diabetes (T2D). Traditional biomarkers have limited value in predicting and tracking early onset and progression of T2D. There is an urgent need for early biomarkers in this high-risk but understudied minority population. Objective: To identify novel lipids predictive of T2D onset among American Indians, independent of standard risk factors. Methods: We studied 2,000 American Indians attending two clinical exams (2001-2003, 2006-2009, average 5-yr apart) in the Strong Heart Family Study (SHFS). Fasting plasma lipids were repeatedly measured by untargeted lipidomics using LC-MS/MS. All participants were free of overt CVD at baseline (2001-2003) and followed through 2017 (average 16-yr follow-up). Cox regression with frailty model was used to identify lipids predictive of T2D onset, adjusting for traditional risk factors including age, sex, smoking, BMI, triglyceride, HDL, insulin resistance, eGFR and dietary intake of protein. Longitudinal analysis was conducted by regressing changes in lipids on changes in T2D-related traits (e.g., fasting glucose, HbA1c, or insulin resistance) between baseline and 5-yr follow-up, adjusting for changes in BMI, triglyceride, HDL, and eGFR. Incremental prognostic value of lipids in diabetes risk prediction above traditional risk factors was estimated using area under the curve (AUC). Network analysis was conducted to examine the dynamic changes in lipid networks between baseline and 5-yr follow-up. Multiple testing was controlled by FDR. Results: Of 1,628 non-diabetic participants at baseline (mean age 39.8, 62% women), 189 and 359 individuals developed incident T2D after 5-yr and 16-yr follow-up, respectively. Our high-resolution lipidomics detected 1,826 lipids, of which 1,119 lipids (460 known, 659 unknown) passed stringent quality control. Seven lipids with known structures significantly predict over 30% increased (FA(18:1), FA(20:2), FA(22:2), SM(d34:0) ) or 19% decreased (PC(37:4), PC(37:5), PC(38:4)) risk of T2D onset at both follow-ups. Nine unknown lipids also predicted T2D onset. Longitudinal changes in CE(20:2), CE(22:6), PC(32:0) and two unknowns explain about 6.4% changes in fasting plasma glucose. These newly identified lipids significantly improve the performance of risk prediction over traditional risk factors (AUC increased from 0.787 to 0.803, P=0.002). Network topology analysis revealed that the network connectivity among lipids was much stronger in participants who developed new T2D compared to those who didn’t (degree of network connectivity, 4.2 vs 2.5, P<0.001). Conclusion: We identified several novel plasma lipids significantly predictive of incident T2D, independent of traditional risk factors. These findings highlight the importance of identifying novel lipid markers in early diabetes risk prediction.