Comorbidity study on type 2 diabetes mellitus using data mining

Diabetes management, dietary supplements use and the effect of coronavirus pandemic on diabetes patients in Serbia: a cross-sectional study

OBJECTIVES

Diabetes mellitus (DM) is a significant public health challenge in Serbia, mirroring the situation in other European middle-income countries. The aims of this study were to examine the disease-related characteristics and management of diabetes, as well as the prevalence of use of dietary supplements (DS) among diabetes patients in Serbia, and to analyze the effects of the coronavirus pandemic on DM patients in Serbia.

METHODS

The study was carried out as an online, observational, cross-sectional study involving 422 adult diabetes type 1 (DM1) and type 2 (DM2) patients residing in Serbia.

RESULTS

DM1 patients were more likely than DM2 patients to self-control glucose levels (p < 0.001). Almost one-third of DM2 patients (31.4%) did not know their HbA1c value. Polypharmacy has been reported by 9.7% of DM1 patients and 23.5% of DM2 patients. During the coronavirus pandemic increased anxiety levels for one-third of respondents was noticed. The prevalence of DS use among DM patients was very high (95.3%), with vitamin C, zinc, vitamin D and magnesium being the most commonly used. Women were more likely to use vitamin D (p = 0.001) and magnesium DS (p = 0.005) than men. Most patients (76.9%) faced limited access to healthcare services during the coronavirus pandemic with, sometimes, detrimental consequences. A significant portion of respondents (41.2%) consulted a pharmacist more often in 2021 than in previous years.

CONCLUSIONS

Special caution is needed regarding the potential interactions of DS with chronic therapy. To enhance diabetes care, Serbia needs more accessible mental health support, improved diabetes education, expanded CGM availability, and carefully planned emergency healthcare measures for chronic patients.

Prescription pattern analysis of Type 2 Diabetes Mellitus: a cross-sectional study in Isfahan, Iran

BACKGROUND

Patients with Type 2 Diabetes Mellitus (T2DM) are at a higher risk of polypharmacy and more susceptible to irrational prescriptions; therefore, pharmacological therapy patterns are important to be monitored. The primary objective of this study was to highlight current prescription patterns in T2DM patients and compare them with existing Standards of Medical Care in Diabetes. The second objective was to analyze whether age and gender affect prescription patterns.

METHOD

This cross-sectional study was conducted using the Iran Health Insurance Organization (IHIO) prescription database. It was mined by an Association Rule Mining (ARM) technique, FP-Growth, in order to find co-prescribed drugs with anti-diabetic medications. The algorithm was implemented at different levels of the Anatomical Therapeutic Chemical (ATC) classification system, which assigns different codes to drugs based on their anatomy, pharmacological, therapeutic, and chemical properties to provide an in-depth analysis of co-prescription patterns.

RESULTS

Altogether, the prescriptions of 914,652 patients were analyzed, of whom 91,505 were found to have diabetes. According to our results, prescribing Lipid Modifying Agents (C10) (56.3%), Agents Acting on The Renin-Angiotensin System (C09) (48.9%), Antithrombotic Agents (B01) (35.7%), and Beta Blocking Agents (C07) (30.1%) were meaningfully associated with the prescription of Drugs Used in Diabetes. Our study also revealed that female diabetic patients have a higher lift for taking Thyroid Preparations, and the older the patients were, the more they were prone to take neuropathy-related medications. Additionally, the results suggest that there are gender differences in the association between aspirin and diabetes drugs, with the differences becoming less pronounced in old age.

CONCLUSIONS

Almost all of the association rules found in this research were clinically meaningful, proving the potential of ARM for co-prescription pattern discovery. Moreover, implementing level-based ARM was effective in detecting difficult-to-spot rules. Additionally, the majority of drugs prescribed by physicians were consistent with the Standards of Medical Care in Diabetes.

Global Impacts of Western Diet and Its Effects on Metabolism and Health: A Narrative Review

The Western diet is a modern dietary pattern characterized by high intakes of pre-packaged foods, refined grains, red meat, processed meat, high-sugar drinks, candy, sweets, fried foods, conventionally raised animal products, high-fat dairy products, and high-fructose products. The present review aims to describe the effect of the Western pattern diet on the metabolism, inflammation, and antioxidant status; the impact on gut microbiota and mitochondrial fitness; the effect of on cardiovascular health, mental health, and cancer; and the sanitary cost of the Western diet. To achieve this goal, a consensus critical review was conducted using primary sources, such as scientific articles, and secondary sources, including bibliographic indexes, databases, and web pages. Scopus, Embase, Science Direct, Sports Discuss, ResearchGate, and the Web of Science were used to complete the assignment. MeSH-compliant keywords such "Western diet", "inflammation", "metabolic health", "metabolic fitness", "heart disease", "cancer", "oxidative stress", "mental health", and "metabolism" were used. The following exclusion criteria were applied: (i) studies with inappropriate or irrelevant topics, not germane to the review's primary focus; (ii) Ph.D. dissertations, proceedings of conferences, and unpublished studies. This information will allow for a better comprehension of this nutritional behavior and its effect on an individual's metabolism and health, as well as the impact on national sanitary systems. Finally, practical applications derived from this information are made.

Variations in comorbidity burden in people with type 2 diabetes over disease duration: A population-based analysis of real world evidence

Background

The prevalence of type 2 diabetes (T2DM) is increasing, but increasing longevity among persons with diagnosed diabetes may be is associated with more extensive and diverse types of morbidity. The extent and breadth of morbidity and how this varies across sub-groups is unclear and could have important clinical and public health implications. We aimed to estimate comorbidity profiles in people with T2DM and variations across sub-groups and over time.

Methods

We identified approximately 224,000 people with T2DM in the Discover-NOW dataset, a real-world primary care database from 2000 to 2020 covering 2.5 million people across North-West London, England, linked to hospital records. We generated a mixed prevalence and incidence study population through repeated annual cross sections, and included a broad set of 35 comorbidities covering traditional T2DM conditions, emerging T2DM conditions and other common conditions.We estimated annual age-standardised prevalence of comorbidities, over the course of the disease in people with T2DM and several sub-groups.

Findings

Multimorbidity (two or more chronic conditions) is common in people with T2DM and increasing, but the comorbidity profiles of people with T2DM vary substantially. Nearly 30% of T2DM patients had three or more comorbidities at diagnosis, increasing to 60% of patients ten years later. Two of the five commonest comorbidities at diagnosis were traditional T2DM conditions (hypertension (37%) and ischaemic heart disease (10%)) the other three were not (depression (15%), back pain (25%) and osteoarthritis (11%)). The prevalence of each increased during the course of the disease, with more than one in three patients having back pain and one in four having depression ten years post diagnosis.People with five or more comorbidities at diagnosis had higher prevalence of each of the 35 comorbidities. Hypertension (73%) was the commonest comorbidity at diagnosis in this group; followed by back pain (69%), depression (67%), asthma (45%) and osteoarthritis (36%). People with obesity at diagnosis had substantially different comorbidity profiles to those without, and the five commonest comorbidities were 50% more common in this group.

Interpretation

Preventative and clinical interventions alongside care pathways for people with T2DM should transition to reflect the diverse set of causes driving persistent morbidity. This would benefit both patients and healthcare systems alike.

Funding

The study was funded by the National Institute for Health and Care Excellence (NICE).

Comorbidity Patterns of Older Lung Cancer Patients in Northeast China: An Association Rules Analysis Based on Electronic Medical Records

Purposes: This study aims to identify the comorbidity patterns of older men with lung cancer in China. Methods: We analyzed the electronic medical records (EMRs) of lung cancer patients over age 65 in the Jilin Province of China. The data studied were obtained from 20 hospitals of Jilin Province in 2018. In total, 1510 patients were identified. We conducted a rank–frequency analysis and social network analysis to identify the predominant comorbidities and comorbidity networks. We applied the association rules to mine the comorbidity combination with the values of confidence and lift. A heatmap was utilized to visualize the rules. Results: Our analyses discovered that (1) there were 31 additional medical conditions in older patients with lung cancer. The most frequent comorbidities were pneumonia, cerebral infarction, and hypertension. (2) The network-based analysis revealed seven subnetworks. (3) The association rules analysis provided 41 interesting rules. The results revealed that hypertension, ischemic cardiomyopathy, and pneumonia are the most frequent comorbid combinations. Heart failure may not have a strong implicating role in these comorbidity patterns. Cerebral infarction was rarely combined with other diseases. In addition, glycoprotein metabolism disorder comorbid with hyponatremia or hypokalemia increased the risk of anemia by more than eight times in older lung cancer patients. Conclusions: This study provides evidence on the comorbidity patterns of older men with lung cancer in China. Understanding the comorbidity patterns of older patients with lung cancer can assist clinicians in their diagnoses and contribute to developing healthcare policies, as well as allocating resources.

Rocha C, V. Maurer-Morelli C, Perez Orrico SR, Cirelli JA, Von Zuben FJ, Mantuaneli Scarel-Caminaga R. Using association rule mining to jointly detect clinical features and differentially expressed genes related to chronic inflammatory diseases

OBJECTIVE

It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus (T2DM), dyslipidemia (DLP) and periodontitis (PD), which are chronic inflammatory diseases. More studies able to capture unknown relationships among these diseases will contribute to raise biological and clinical evidence. The aim of this study was to apply association rule mining (ARM) to discover whether there are consistent patterns of clinical features (CFs) and differentially expressed genes (DEGs) relevant to these diseases. We intend to reinforce the evidence of the T2DM-DLP-PD-interplay and demonstrate the ARM ability to provide new insights into multivariate pattern discovery.

METHODS

We utilized 29 clinical glycemic, lipid and periodontal parameters from 143 patients divided into five groups based upon diabetic, dyslipidemic and periodontal conditions (including a healthy-control group). At least 5 patients from each group were selected to assess the transcriptome by microarray. ARM was utilized to assess relevant association rules considering: (i) only CFs; and (ii) CFs+DEGs, such that the identified DEGs, specific to each group of patients, were submitted to gene expression validation by quantitative polymerase chain reaction (qPCR).

RESULTS

We obtained 78 CF-rules and 161 CF+DEG-rules. Based on their clinical significance, Periodontists and Geneticist experts selected 11 CF-rules, and 5 CF+DEG-rules. From the five DEGs prospected by the rules, four of them were validated by qPCR as significantly different from the control group; and two of them validated the previous microarray findings.

CONCLUSIONS

ARM was a powerful data analysis technique to identify multivariate patterns involving clinical and molecular profiles of patients affected by specific pathological panels. ARM proved to be an effective mining approach to analyze gene expression with the advantage of including patient's CFs. A combination of CFs and DEGs might be employed in modeling the patient's chance to develop complex diseases, such as those studied here.

Inferring Relationship of Blood Metabolic Changes and Average Daily Gain With Feed Conversion Efficiency in Murrah Heifers: Machine Learning Approach

Machine learning algorithms were employed for predicting the feed conversion efficiency (FCE), using the blood parameters and average daily gain (ADG) as predictor variables in buffalo heifers. It was observed that isotonic regression outperformed other machine learning algorithms used in study. Further, we also achieved the best performance evaluation metrics model with additive regression as the meta learner and isotonic regression as the base learner on 10-fold cross-validation and leaving-one-out cross-validation tests. Further, we created three separate partial least square regression (PLSR) models using all 14 parameters of blood and ADG as independent (explanatory) variables and FCE as the dependent variable, to understand the interactions of blood parameters, ADG with FCE each by inclusion of all FCE values (i), only higher FCE values (negative RFI) (ii), and inclusion of only lower FCE (positive RFI) values (iii). The PLSR model including only the higher FCE values was concluded the best, based on performance evaluation metrics as compared to PLSR models developed by inclusion of the lower FCE values and all types of FCE values. IGF1 and its interactions with the other blood parameters were found highly influential for higher FCE measures. The strength of the estimated interaction effects of the blood parameter in relation to FCE may facilitate understanding of intricate dynamics of blood parameters for growth.

MorbiNet: multimorbidity networks in adult general population. Analysis of type 2 diabetes mellitus comorbidity

Multimorbidity has great impact on health care. We constructed multimorbidity networks in the general population, extracted subnets focused on common chronic conditions and analysed type 2 diabetes mellitus (T2DM) comorbidity network. We used electronic records from 3,135,948 adult people in Catalonia, Spain (539,909 with T2DM), with at least 2 coexistent chronic conditions within the study period (2006-2017). We constructed networks from odds-ratio estimates adjusted by age and sex and considered connections with OR > 1.2 and p-value < 1e-5. Directed networks and trajectories were derived from temporal associations. Interactive networks are freely available in a website with the option to customize characteristics and subnets. The more connected conditions in T2DM undirected network were: complicated hypertension and atherosclerosis/peripheral vascular disease (degree: 32), cholecystitis/cholelithiasis, retinopathy and peripheral neuritis/neuropathy (degree: 31). T2DM has moderate number of connections and centrality but is associated with conditions with high scores in the multimorbidity network (neuropathy, anaemia and digestive diseases), and severe conditions with poor prognosis. The strongest associations from T2DM directed networks were to retinopathy (OR: 23.8), glomerulonephritis/nephrosis (OR: 3.4), peripheral neuritis/neuropathy (OR: 2.7) and pancreas cancer (OR: 2.4). Temporal associations showed the relevance of retinopathy in the progression to complicated hypertension, cerebrovascular disease, ischemic heart disease and organ failure.

Mental disorders and medical comorbidities: Association rule mining approach

PURPOSE

This study explored the medical comorbidities of mental disorders using association rule mining.

DESIGN AND METHODS

Patients diagnosed with mental disorders between 2002 and 2010 were identified. An equal number of nonmental disorder subjects were randomly selected and matched with case group by age and gender.

FINDINGS

Sleep disorders and digestive diseases were frequent comorbidities among mental disorders. The specific medical comorbidities were diabetes mellitus, chronic liver disease, extrapyramidal diseases, disorders of stomach function, general symptoms, sleep disturbance, and family circumstances.

PRACTICE IMPLICATIONS

The results suggest that education of professional knowledge of comorbid conditions should be provided to nurses for caring patients with mental illnesses.

Examining Development Processes for Text Messaging Interventions to Prevent Cardiovascular Disease: Systematic Literature Review

Background

Interventions delivered by mobile phones have the potential to prevent cardiovascular disease (CVD) by supporting behavior change toward healthier lifestyles and treatment adherence. To allow replication and adaptation of these interventions across settings, it is important to fully understand how they have been developed. However, the development processes of these interventions have not previously been systematically examined.

Objective

This study aimed to systematically describe and compare the development process of text messaging interventions identified in the Text2PreventCVD systematic review.

Methods

We extracted data about the development process of the 9 interventions identified in the Text2PreventCVD systematic review. Data extraction, which was guided by frameworks for the development of complex interventions, considered the following development stages: intervention planning, design, development, and pretesting. Following data extraction, we invited the developers of the interventions to contribute to our study by reviewing the accuracy of the extracted data and providing additional data not reported in the available publications.

Results

A comprehensive description of the development process was available for 5 interventions. Multiple methodologies were used for the development of each intervention. Intervention planning involved gathering information from stakeholder consultations, literature reviews, examination of relevant theory, and preliminary qualitative research. Intervention design involved the use of behavior change theories and behavior change techniques. Intervention development involved (1) generating message content based on clinical guidelines and expert opinions; (2) conducting literature reviews and primary qualitative research to inform decisions about message frequency, timing, and level of tailoring; and (3) gathering end-user feedback concerning message readability, intervention acceptability, and perceived utility. Intervention pretesting involved pilot studies with samples of 10 to 30 participants receiving messages for a period ranging from 1 to 4 weeks.

Conclusions

The development process of the text messaging interventions examined was complex and comprehensive, involving multiple studies to guide decisions about the scope, content, and structure of the interventions. Additional research is needed to establish whether effective messaging systems can be adapted from work already done or whether this level of development is needed for application in other conditions and settings.

Anesthesia in children with comorbid pathology - clinical assessment of the most common pathological conditions in the practice of anesthesiologist

Currently, there is an increase in the number of children who have multiple connected or “independent” pathological conditions, hospitalized to General hospitals with severe pain. It significantly worsens the disease course and complicates differential diagnosis in these patients, requires a deeper treatment approach. Thus, it requires attention and discussion and is urgent to cover the concept of comorbidity, combined with pain syndrome in Pediatrics. In this article is carried out the analysis of comorbid pathology in surgery from the point of view of the children’s anaesthetist. There were allocated comorbidities that are commonly encountered in daily practice and are of danger in surgical practice. We have analyzed analgesia for obesity, blood diseases, congenital heart defects, concomitant respiratory diseases – during surgery and in the postoperative period. To improve the efficiency of medical care for these patients it is important to develop an algorithm of physician's actions in the most common comorbid conditions. It is concluded that there is a need for additional studies for further clarifying the optimal management of pain and the effect of pain in case of comorbidities.

Effects of dipeptidyl peptidase-4 inhibitors on blood pressure in patients with type 2 diabetes: A systematic review and meta-analysis

This review was undertaken to assess the effects of dipeptidyl peptidase-4 (DPP-4) inhibitors on blood pressure (BP) in patients with type 2 diabetes (T2DM). We searched three main databases (PubMed, Web of Science, and Scopus) for relevant articles. Randomized controlled trials which reported BP changes from baseline to study endpoint in patients with T2DM receiving treatment of DPP-4 inhibitors were included for analysis. Random effects models were used to measure the mean differences with 95% confidence intervals (CIs). Fifteen trials involving 5636 participants were identified. When compared with placebo or nontreatment, DPP-4 inhibitors achieved greater reductions for both SBP (mean difference, -3.04  mmHg; 95% CI, -4.37 to -1.72; P < 0.00001) and DBP (mean difference, -1.47  mmHg; 95% CI, -1.79 to -1.15; P < 0.00001). But the BP-lowering effects of sodium-glucose cotransporter 2 inhibitors were more significant than those of DPP-4 inhibitors for both SBP (mean difference, 4.44  mmHg; 95% CI, 2.67-6.22; P < 0.00001) and DBP (mean difference, 2.15  mmHg; 95% CI, 1.08-3.21; P < 0.00001). No significant differences in BP changes were shown between DPP-4 inhibitors with other antidiabetic agents including glucagon-like peptide 1 receptor agonists, pioglitazone, sulphonylureas, metformin, and α-glucosidase inhibitors. DPP-4 inhibitors may exert modest BP-lowering effects compared with placebo or nontreatment for patients with T2DM, but no significant BP improvement was seen with this drug class when compared with other antidiabetic medications.

Behavior Correlates of Post-Stroke Disability Using Data Mining and Infographics

PURPOSE

Disability is a potential risk for stroke survivors. This study aims to identify disability risk factors associated with stroke and their relative importance and relationships from a national behavioral risk factor dataset.

METHODS

Data of post-stroke individuals in the U.S (n=19,603) including 397 variables were extracted from a publically available national dataset and analyzed. Data mining algorithms including C4.5 and linear regression with M5s methods were applied to build association models for post-stroke disability using Weka software. The relative importance and relationship of 70 variables associated with disability were presented in infographics for clinicians to understand easily.

RESULTS

Fifty-five percent of post-stroke patients experience disability. Exercise, employment and satisfaction of life were relatively important factors associated with disability among stroke patients. Modifiable behavior factors strongly associated with disability include exercise (OR: 0.46, P<0.01) and good rest (OR 0.37, P<0.01).

CONCLUSIONS

Data mining is promising to discover factors associated with post-stroke disability from a large population dataset. The findings can be potentially valuable for establishing the priorities for clinicians and researchers and for stroke patient education. The methods may generalize to other health conditions.

Predicting Metabolic Syndrome Using the Random Forest Method

Aims. This study proposes a computational method for determining the prevalence of metabolic syndrome (MS) and to predict its occurrence using the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria. The Random Forest (RF) method is also applied to identify significant health parameters. Materials and Methods. We used data from 5,646 adults aged between 18–78 years residing in Bangkok who had received an annual health check-up in 2008. MS was identified using the NCEP ATP III criteria. The RF method was applied to predict the occurrence of MS and to identify important health parameters surrounding this disorder. Results. The overall prevalence of MS was 23.70% (34.32% for males and 17.74% for females). RF accuracy for predicting MS in an adult Thai population was 98.11%. Further, based on RF, triglyceride levels were the most important health parameter associated with MS. Conclusion. RF was shown to predict MS in an adult Thai population with an accuracy >98% and triglyceride levels were identified as the most informative variable associated with MS. Therefore, using RF to predict MS may be potentially beneficial in identifying MS status for preventing the development of diabetes mellitus and cardiovascular diseases.

An application of association rule mining to extract risk pattern for type 2 diabetes using tehran lipid and glucose study database

BACKGROUND

Type 2 diabetes, common and serious global health concern, had an estimated worldwide prevalence of 366 million in 2011, which is expected to rise to 552 million people, by 2030, unless urgent action is taken.

OBJECTIVES

The aim of this study was to identify risk patterns for type 2 diabetes incidence using association rule mining (ARM).

PATIENTS AND METHODS

A population of 6647 individuals without diabetes, aged ≥ 20 years at inclusion, was followed for 10-12 years, to analyze risk patterns for diabetes occurrence. Study variables included demographic and anthropometric characteristics, smoking status, medical and drug history and laboratory measures.

RESULTS

In the case of women, the results showed that impaired fasting glucose (IFG) and impaired glucose tolerance (IGT), in combination with body mass index (BMI) ≥ 30 kg/m(2), family history of diabetes, wrist circumference > 16.5 cm and waist to height ≥ 0.5 can increase the risk for developing diabetes. For men, a combination of IGT, IFG, length of stay in the city (> 40 years), central obesity, total cholesterol to high density lipoprotein ratio ≥ 5.3, low physical activity, chronic kidney disease and wrist circumference > 18.5 cm were identified as risk patterns for diabetes occurrence.

CONCLUSIONS

Our study showed that ARM is a useful approach in determining which combinations of variables or predictors occur together frequently, in people who will develop diabetes. The ARM focuses on joint exposure to different combinations of risk factors, and not the predictors alone.

Divisive Hierarchical Clustering towards Identifying Clinically Significant Pre-Diabetes Subpopulations

Type 2 Diabetes Mellitus is a progressive disease with increased risk of developing serious complications. Identifying subpopulations and their relevant risk factors can contribute to the prevention and effective management of diabetes. We use a novel divisive hierarchical clustering technique to identify clinically interesting subpopulations in a large cohort of Olmsted County, MN residents. Our results show that our clustering algorithm successfully identified clinically interesting clusters consisting of patients with higher or lower risk of diabetes than the general population. The proposed algorithm offers fine control over the granularity of the clustering, has the ability to seamlessly discover and incorporate interactions among the risk factors, and can handle non-proportional hazards, as well. It has the potential to significantly impact clinical practice by recognizing patients with specific risk factors who may benefit from an alternative management approach potentially leading to the prevention of diabetes and its complications.

Metabolic Syndrome and its Profound Effect on Prevalence of Ischemic Stroke

Ischemic stroke represents a leading cause of death worldwide and the leading cause of disability in the United States. Greater than 8% of all deaths are attributed to ischemic stroke. This rate is consistent with the heightened burden of cardiovascular disease deaths. Treatments for acute ischemic stroke remain limited to tissue plasminogen activator and mechanical thrombolysis, both of which require significant medical expertise and can only be applied to a select number of patients based on time of presentation, imaging, and absence of contraindications. Over 1,000 compounds that were successful in treating ischemic stroke in animal models have failed to correlate to success in clinical trials. The search for alternative treatments is ongoing, drawing greater attention to the importance of preclinical models that more accurately represent the clinical population through incorporation of common risk factors. This work reviews the contribution of these commonly observed risk factors in the clinical population highlighting both the pathophysiology as well as current clinical diagnosis and treatment standards. We also highlight future potential therapeutic targets, areas requiring further investigation, and recent changes in best-practice clinical care.

Survival association rule mining towards type 2 diabetes risk assessment

Type-2 Diabetes Mellitus is a growing epidemic that often leads to severe complications. Effective preventive measures exist and identifying patients at high risk of diabetes is a major health-care need. The use of association rule mining (ARM) is advantageous, as it was specifically developed to identify associations between risk factors in an interpretable form. Unfortunately, traditional ARM is not directly applicable to survival outcomes and it lacks the ability to compensate for confounders and to incorporate dosage effects. In this work, we propose Survival Association Rule (SAR) Mining, which addresses these shortcomings. We demonstrate on a real diabetes data set that SARs are naturally more interpretable than the traditional association rules, and predictive models built on top of these rules are very competitive relative to state of the art survival models and substantially outperform the most widely used diabetes index, the Framingham score.

Machine learning approaches for discerning intercorrelation of hematological parameters and glucose level for identification of diabetes mellitus

BACKGROUND

The aim of this study is to explore the relationship between hematological parameters and glycemic status in the establishment of quantitative population-health relationship (QPHR) model for identifying individuals with or without diabetes mellitus (DM).

METHODS

A cross-sectional investigation of 190 participants residing in Nakhon Pathom, Thailand in January-March, 2013 was used in this study. Individuals were classified into 3 groups based on their blood glucose levels (normal, Pre-DM and DM). Hematological (white blood cell (WBC), red blood cell (RBC), hemoglobin (Hb) and hematocrite (Hct)) and glucose parameters were used as input variables while the glycemic status was used as output variable. Support vector machine (SVM) and artificial neural network (ANN) are machine learning approaches that were employed for identifying the glycemic status while association analysis (AA) was utilized in discovery of health parameters that frequently occur together.

RESULTS

Relationship amongst hematological parameters and glucose level indicated that the glycemic status (normal, Pre-DM and DM) was well correlated with WBC, RBC, Hb and Hct. SVM and ANN achieved accuracy of more than 98 % in classifying the glycemic status. Furthermore, AA analysis provided association rules for defining individuals with or without DM. Interestingly, rules for the Pre-DM group are associated with high levels of WBC, RBC, Hb and Hct. Conclusion This study presents the utilization of machine learning approaches for identification of DM status as well as in the discovery of frequently occurring parameters. Such predictive models provided high classification accuracy as well as pertinent rules in defining DM.

Prevalence and comorbidities of known diabetes in northeastern Italy

AIMS/INTRODUCTION

We aimed at estimating the prevalence and at identifying the frequent comorbidities of diabetes mellitus in a region of northeastern Italy from administrative health data.

MATERIALS AND METHODS

The prevalence was estimated according to two disease definitions, based on administrative health data. Association rule mining was used to detect comorbid diagnoses that coexisted with a diagnosis of diabetes among patients admitted to the regional hospitals.

RESULTS

The prevalence of known diabetes in 2010 was 6.0-8.1%, with great variations by age class (from approximately 2% <60 years to more than 20% in some elderly age groups). Of 155,494 patients admitted to the hospital in 2011, 9,358 had a diagnosis of diabetes. A total of 12 rules satisfied our criteria for support (>0.5%) and confidence (>5%), and identified nine frequent isolated comorbidities and three pairs of comorbid diagnoses. The rule with the highest support (2.4%) and confidence (39.5%) identified the combination of diabetes and essential hypertension.

CONCLUSIONS

Association rule mining was useful, because it showed the complexity of diabetic patients. Clinical management of those patients cannot neglect comorbidities.

Quantitative population-health relationship (QPHR) for assessing metabolic syndrome

Metabolic syndrome (MS) is a condition that predisposes individuals to the development of cardiovascular diseases and type 2 diabetes mellitus. A cross-sectional investigation of 15,365 participants residing in metropolitan Bangkok who had received an annual health checkup in 2007 was used in this study. Individuals were classified as MS or non-MS according to the International Diabetes Federation criteria using BMI cutoff of ≥ 25 kg/m(2) plus two or more MS components. This study explores the utility of quantitative population-health relationship (QPHR) for predicting MS status as well as discovers variables that frequently occur together. The former was achieved by decision tree (DT) analysis, artificial neural network (ANN), support vector machine (SVM) and principal component analysis (PCA) while the latter was obtained by association analysis (AA). DT outperformed both ANN and SVM in MS classification as deduced from its accuracy value of 99 % as compared to accuracies of 98 % and 91 % for ANN and SVM, respectively. Furthermore, PCA was able to effectively classify individuals as MS and non-MS as observed from the scores plot. Moreover, AA was employed to analyze individuals with MS in order to elucidate pertinent rule from MS components that occur frequently together, which included TG+BP, BP+FPG and TG+FPG where TG, BP and FPG corresponds to triglyceride, blood pressure and fasting plasma glucose, respectively. QPHR was demonstrated to be useful in predicting the MS status of individuals from an urban Thai population. Rules obtained from AA analysis provided general guidelines (i.e. co-occurrences of TG, BP and FPG) that may be used in the prevention of MS in at risk individuals.