Classification-based data mining for identification of risk patterns associated with hypertension in Middle Eastern population: A 12-year longitudinal study

Assessment of Hypertensive Patients' Complex Metabolic Status Using Data Mining Methods

Cardiovascular diseases are among the leading causes of mortality worldwide. Hypertension is a preventable risk factor leading to major cardiovascular events. We have not found a comprehensive study investigating Central and Eastern European hypertensive patients' complex metabolic status. Therefore, our goal was to calculate the prevalence of hypertension and associated metabolic abnormalities using data-mining methods in our region. We assessed the data of adults who visited the University of Debrecen Clinical Center's hospital (n = 937,249). The study encompassed data from a period of 20 years (2001-2021). We detected 292,561 hypertensive patients. The calculated prevalence of hypertension was altogether 32.2%. Markedly higher body mass index values were found in hypertensive patients as compared to non-hypertensives. Significantly higher triglyceride and lower HDL-C levels were found in adults from 18 to 80 years old. Furthermore, significantly higher serum glucose and uric acid levels were measured in hypertensive subjects. Our study confirms that the calculated prevalence of hypertension is akin to international findings and highlights the extensive association of metabolic alterations. These findings emphasize the role of early recognition and immediate treatment of cardiometabolic abnormalities to improve the quality of life and life expectancy of hypertensive patients.

Research on early warning of renal damage in hypertensive patients based on the stacking strategy

Background

Among the problems caused by hypertension, early renal damage is often ignored. It can not be diagnosed until the condition is severe and irreversible damage occurs. So we decided to screen and explore related risk factors for hypertensive patients with early renal damage and establish the early-warning model of renal damage based on the data-mining method to achieve an early diagnosis for hypertensive patients with renal damage.

Methods

With the aid of an electronic information management system for hypertensive out-patients, we collected 513 cases of original, untreated hypertensive patients. We recorded their demographic data, ambulatory blood pressure parameters, blood routine index, and blood biochemical index to establish the clinical database. Then we screen risk factors for early renal damage through feature engineering and use Random Forest, Extra-Trees, and XGBoost to build an early-warning model, respectively. Finally, we build a new model by model fusion based on the Stacking strategy. We use cross-validation to evaluate the stability and reliability of each model to determine the best risk assessment model.

Results

According to the degree of importance, the descending order of features selected by feature engineering is the drop rate of systolic blood pressure at night, the red blood cell distribution width, blood pressure circadian rhythm, the average diastolic blood pressure at daytime, body surface area, smoking, age, and HDL. The average precision of the two-dimensional fusion model with full features based on the Stacking strategy is 0.89685, and selected features are 0.93824, which is greatly improved.

Conclusions

Through feature engineering and risk factor analysis, we select the drop rate of systolic blood pressure at night, the red blood cell distribution width, blood pressure circadian rhythm, and the average diastolic blood pressure at daytime as early-warning factors of early renal damage in patients with hypertension. On this basis, the two-dimensional fusion model based on the Stacking strategy has a better effect than the single model, which can be used for risk assessment of early renal damage in hypertensive patients.

Cashless Japan: Unlocking Influential Risk on Mobile Payment Service

In Japan, cashless is not yet popular but government and companies are devoted to the development of mobile payment methods. This research collected 241 Japanese users and applied decision trees algorithm. Six types of perceived risks (financial, privacy, performance, psychological, security, and time) were used and the categorized class is intention to use mobile payment (low, medium, and high). We also compared different competitive models to examine the performance, including decision trees, kNN, Naïve Bayes, SVM, and logistic regression and decision trees outperformed among all models. The findings indicated that privacy and performance risks are import to Japanese users. Safe, secured, reliable, and fast mobile payment environment are more important to low intention users (less concerns about financial risk). Financial loss, safe, secured, reliable, and fast mobile payment environment are more important to medium intention users (less concerns about time and security risk). Monetary loss, safe, reliable, and fast mobile payment environment are more important to high intention users (less concerns about security risk and psychological risk). The results can help Japanese companies unlock the perceived risk on mobile payment and furnish appropriate strategies to improve usage.

Evaluation of Risk Factors Associated With Antihypertensive Treatment Success Employing Data Mining Techniques

Objective:

This study aimed to evaluate the effects of potential risk factors on antihypertensive treatment success.

Methods:

Patients with hypertension who were treated with antihypertensive medications were included in this study. Data from the last visit were analyzed retrospectively for each patient. To evaluate the predictive models for antihypertensive treatment success, data mining algorithms (logistic regression, decision tree, random forest, and artificial neural network) using 5-fold cross-validation were applied. Additionally, study parameters between patients with controlled and uncontrolled hypertension were statistically compared and multiple regression analyses were conducted for secondary endpoints.

Results:

The data of 592 patients were included in the analysis. The overall blood pressure control rate was 44%. The performance of random forest algorithm (accuracy = 97.46%, precision = 97.08%, F1 score = 97.04%) was slightly higher than other data mining algorithms including logistic regression (accuracy = 87.31%, precision = 86.21%, F1 score = 85.74%), decision tree (accuracy = 76.94%, precision = 70.64%, F1 score = 76.54%), and artificial neural network (accuracy = 86.47%, precision = 83.85%, F1 score = 84.86%). The top 5 important categorical variables (predictive correlation value) contributed the most to the prediction of antihypertensive treatment success were use of calcium channel blocker (−0.18), number of antihypertensive medications (0.18), female gender (0.10), alcohol use (−0.09) and attendance at regular follow up visits (0.09), respectively. The top 5 numerical variables contributed the most to the prediction of antihypertensive treatment success were blood urea nitrogen (−0.12), glucose (−0.12), hemoglobin A1c (−0.12), uric acid (−0.09) and creatinine (−0.07), respectively. According to the decision tree model; age, gender, regular attendance at follow-up visits, and diabetes status were identified as the most critical patterns for stratifying the patients.

Conclusion:

Data mining algorithms have the potential to produce predictive models for screening the antihypertensive treatment success. Further research on larger populations and longitudinal datasets are required to improve the models.

Hypertension-Related Drug Activity Identification Based on Novel Ensemble Method

Hypertension is a chronic disease and major risk factor for cardiovascular and cerebrovascular diseases that often leads to damage to target organs. The prevention and treatment of hypertension is crucially important for human health. In this paper, a novel ensemble method based on a flexible neural tree (FNT) is proposed to identify hypertension-related active compounds. In the ensemble method, the base classifiers are Multi-Grained Cascade Forest (gcForest), support vector machines (SVM), random forest (RF), AdaBoost, decision tree (DT), Gradient Boosting Decision Tree (GBDT), KNN, logical regression, and naïve Bayes (NB). The classification results of nine classifiers are utilized as the input vector of FNT, which is utilized as a nonlinear ensemble method to identify hypertension-related drug compounds. The experiment data are extracted from hypertension-unrelated and hypertension-related compounds collected from the up-to-date literature. The results reveal that our proposed ensemble method performs better than other single classifiers in terms of ROC curve, AUC, TPR, FRP, Precision, Specificity, and F1. Our proposed method is also compared with the averaged and voting ensemble methods. The results reveal that our method could identify hypertension-related compounds more accurately than two classical ensemble methods.

Long-term Trends in Hypertension Prevalence, Awareness, Treatment, and Control Rate in the Middle East and North Africa: a Systematic Review and Meta-analysis of 178 Population-Based Studies

PURPOSE OF REVIEW

This study investigated and pooled the long-term trends in prevalence, awareness, treatment, and control of hypertension (HTN) in the Middle East and North Africa (MENA) region. In this systematic review and meta-analysis, we searched MEDLINE/PubMed, Web of Science, Google Scholar, EMBASE, and Scopus between database inception and November 2020. All cross-sectional studies that investigated the prevalence of pre-HTN, HTN, awareness, treatment, and control in the MENA counties were included. The selection study, data extraction, and quality assessment were conducted by two investigators independently. Heterogeneity between studies was assessed using Cochran's Q test and I-squared, and due to sever heterogeneity between studies, the random effect model was used to pool the estimates. Sensitivity analysis was performed to estimate the long-term trends in prevalence, awareness, treatment, and control rates of HTN according to definition of HTN as systolic blood pressure of 140 mm Hg or more, or diastolic blood pressure of 90 mm Hg or more, or being on pharmacological treatment for HTN.

RECENT FINDINGS

Overall, 178 studies met the inclusion criteria. Studies comprised 2,262,797 participants with a mean age of 45.72 ± 8.84 years. According to random effect model, the pooled prevalence of pre-HTN and HTN was 33% (95% CI 28, 39) and 26% (25, 27), respectively. Over the past three decades, prevalence of hypertension increased significantly in the region. The pooled awareness, treatment, and control rates were 50% (48, 53), 41% (38, 44), and 19% (17, 21), receptively. The pooled awareness, treatment, and control rates of HTN were lower significantly in men than women. According to definition of HTN as blood pressures above 140/90 mm Hg, over the past three decades, although the awareness and treatment rates did not change significantly, the control rates improved significantly in the region. The findings showed that HTN is a significant public health problem in the MENA region. Although there are low levels of pooled awareness, treatment, and control rates, the control rates improved over the past three decades in the region.

Potential value and impact of data mining and machine learning in clinical diagnostics

Data mining involves the use of mathematical sciences, statistics, artificial intelligence, and machine learning to determine the relationships between variables from a large sample of data. It has previously been shown that data mining can improve the prediction and diagnostic precision of type 2 diabetes mellitus. A few studies have applied machine learning to assess hypertension and metabolic syndrome-related biomarkers, as well as refine the assessment of cardiovascular disease risk. Machine learning methods have also been applied to assess new biomarkers and survival outcomes in patients with renal diseases to predict the development of chronic kidney disease, disease progression, and renal graft survival. In the latter, random forest methods were found to be the best for the prediction of chronic kidney disease. Some studies have investigated the prognosis of nonalcoholic fatty liver disease and acute liver failure, as well as therapy response prediction in patients with viral disorders, using decision tree models. Machine learning techniques, such as Sparse High-Order Interaction Model with Rejection Option, have been used for diagnosing Alzheimer's disease. Data mining techniques have also been applied to identify the risk factors for serious mental illness, such as depression and dementia, and help to diagnose and predict the quality of life of such patients. In relation to child health, some studies have determined the best algorithms for predicting obesity and malnutrition. Machine learning has determined the important risk factors for preterm birth and low birth weight. Published studies of patients with cancer and bacterial diseases are limited and should perhaps be addressed more comprehensively in future studies. Herein, we provide an in-depth review of studies in which biochemical biomarker data were analyzed using machine learning methods to assess the risk of several common diseases, in order to summarize the potential applications of data mining methods in clinical diagnosis. Data mining techniques have now been increasingly applied to clinical diagnostics, and they have the potential to support this field.

Predicting hypertension using machine learning: Findings from Qatar Biobank Study

Background and objective

Hypertension, a global burden, is associated with several risk factors and can be treated by lifestyle modifications and medications. Prediction and early diagnosis is important to prevent related health complications. The objective is to construct and compare predictive models to identify individuals at high risk of developing hypertension without the need of invasive clinical procedures.

Methods

This is a cross-sectional study using 987 records of Qataris and long-term residents aged 18₊ years from Qatar Biobank. Percentages were used to summarize data and chi-square tests to assess associations. Predictive models of hypertension were constructed and compared using three supervised machine learning algorithms: decision tree, random forest, and logistics regression using 5-fold cross-validation. The performance of algorithms was assessed using accuracy, positive predictive value (PPV), sensitivity, F-measure, and area under the receiver operating characteristic curve (AUC). Stata and Weka were used for analysis.

Results

Age, gender, education level, employment, tobacco use, physical activity, adequate consumption of fruits and vegetables, abdominal obesity, history of diabetes, history of high cholesterol, and mother’s history high blood pressure were important predictors of hypertension. All algorithms showed more or less similar performances: Random forest (accuracy = 82.1%, PPV = 81.4%, sensitivity = 82.1%), logistic regression (accuracy = 81.1%, PPV = 80.1%, sensitivity = 81.1%) and decision tree (accuracy = 82.1%, PPV = 81.2%, sensitivity = 82.1%. In terms of AUC, compared to logistic regression, while random forest performed similarly, decision tree had a significantly lower discrimination ability (p-value<0.05) with AUC’s equal to 85.0, 86.9, and 79.9, respectively.

Conclusions

Machine learning provides the chance of having a rapid predictive model using non-invasive predictors to screen for hypertension. Future research should consider improving the predictive accuracy of models in larger general populations, including more important predictors and using a variety of algorithms.

Association of wrist circumference with cardio-metabolic risk factors: a systematic review and meta-analysis

BACKGROUND AND AIMS

The association of Wrist Circumference (WrC) with cardio-metabolic risk factors is still contradictory. We aimed to systematically review the association of WrC with cardio-metabolic diseases among the general populations.

METHODS

We systematically searched electronic databases such as PubMed/Medline, Web of Sciences, and Scopus without language restriction until March 2017. Observational studies that examined the association of WrC with any cardio-metabolic risk factors were included. Pooled association of WrC with metabolic syndrome (MetS) was estimated using a random-effect model, and heterogeneity among studies was assessed by I² index and Q test.

RESULTS

A total of 14 papers including cohort study (n = 9), cross-sectional study (n = 4), and case-control study (n = 1) met the criteria and included. The eligible papers have been examined the association of WrC with any cardiovascular disorders (n = 8), metabolic syndrome (n = 4), insulin resistance (IR) (n = 5), diabetes mellitus (n = 2), impaired glucose tolerance (n = 1), cardio-metabolic risk factors (n = 2) and obesity/overweight (n = 1). In the whole population (both adults and pediatric population), high WrC increased the risk of MetS by 33% (Pooled OR = 1.33; 95% CI 1.20, 1.48; I² = 60.2%, p = 0.04), while the pooled OR in adult populations was 1.27 (95% CI 1.15-1.41; I²: 32.8%, p = 0.21). Qualitative synthesis showed that associations of WrC with other cardio-metabolic risk factors are conflicting.

CONCLUSION

High WrC increased the risk of MetS and other cardio-metabolic risk factors. However, due to limited studies, particularly in children, results should be declared with great caution. Further cohort studies are needed to clarify whether WrC is a suitable anthropometric index to predict cardio-metabolic disorders in adult and children populations in different societies.

LEVEL OF EVIDENCE

Level 1, systematic review and meta-analysis.

Spousal metabolic risk factors and incident hypertension: A longitudinal cohort study in Iran

We investigated the association between metabolic risk factors in one spouse with incident hypertension in the other. Study sample included 1528 men and 1649 women aged ≥20 years from the Tehran lipid and glucose study with information on body mass index (BMI), waist circumference (WC), hypertension, type 2 diabetes mellitus (DM), and dyslipidemia. The hazard ratio (HR) and 95% confidence interval (95% CI) were estimated for the association of spousal metabolic factors and incident hypertension among men and women separately. A total of 604 and 566 cases of incident hypertension were observed in men and women, respectively. Among men, spousal DM was associated with a 40% (CI: 1.07-1.83) excess risk of hypertension after adjusting for the men's own and their spouse's risk factors including BMI, DM, smoking, and physical activity level. Among women, spousal DM was associated with more than two times (2.11, 1.69-2.63) higher risk of hypertension. After further adjustment for the women's own and their spouse's risk factors, the association was attenuated and remained marginally significant (1.25, 0.99-1.58; P value = .053). Having a spouse with DM increases an individual's risk of hypertension, which raises the possibility of using preexisting information of one partner to guide the screening of the other partner.

Hypertension and Pre-Hypertension Among Iranian Adults Population: a Meta-Analysis of Prevalence, Awareness, Treatment, and Control

PURPOSE OF REVIEW

This meta-analysis and systematic review was conducted to evaluate hypertension and prehypertension prevalence, awareness, treatment, and control in Iranian adults population.

RECENT FINDINGS

In this study, six international and national databases were searched from inception until August 30, 2018. Forty-eight studies performed on 417,392 participants were included in the meta-analysis. Based on the results of random effect method (95% CI), the overall prevalence of pre-hypertension, hypertension, awareness, treatment, and control were 31.6% (95% CI 24.9, 38.3; I2 = 99.7%), 20.4% (95% CI 16.5, 24.4; I2 = 99.9%), 49.3% (95% CI 44.8, 53.8; I2 = 98.5%), 44.8% (95% CI 28.3, 61.2; I2 = 99.9%), 37.4% (95% CI 29.0, 45.8; I2 = 99.3%), respectively. Considering the increasing prevalence of pre-hypertension, hypertension, as well as more than half of the participants were unaware of their disease and were not treated, the results of the present study can help policy-makers to increase hypertension awareness, control, and treatment, especially in high-risk individuals.

Perspectivas do uso de mineração de dados e aprendizado de máquina em saúde e segurança no trabalho

Resumo Introdução: a variedade, volume e velocidade de geração de dados (big data) possibilitam novas e mais complexas análises. Objetivo: discutir e apresentar técnicas de mineração de dados (data mining) e de aprendizado de máquina (machine learning) para auxiliar pesquisadores de Saúde e Segurança no Trabalho (SST) na escolha da técnica adequada para lidar com big data. Métodos: revisão bibliográfica com foco em data mining e no uso de análises preditivas com machine learning e suas aplicações para auxiliar diagnósticos e predição de riscos em SST. Resultados: a literatura indica que aplicações de data mining com algoritmos de machine learning para análises preditivas em saúde pública e em SST apresentam melhor desempenho em comparação com análises tradicionais. São sugeridas técnicas de acordo com o tipo de pesquisa almejada. Discussão: data mining tem se tornado uma alternativa cada vez mais comum para lidar com bancos de dados de saúde pública, possibilitando analisar grandes volumes de dados de morbidade e mortalidade. Tais técnicas não visam substituir o fator humano, mas auxiliar em processos de tomada de decisão, servir de ferramenta para a análise estatística e gerar conhecimento para subsidiar ações que possam melhorar a qualidade de vida do trabalhador.

Optimum cutoff values of anthropometric indices of obesity for predicting hypertension: more than one decades of follow-up in an Iranian population

We determined cutoff points of body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR), for predicting hypertension in an Iranian population. Study sample included 6359 (3678 female) participants aged ≥20 and <60 years of a prospective cohort. The sex stratified multivariate hazard ratios (HRs) for all indices were estimated using Cox regression in two age groups (20-39 and 40-59 years). Receiver operating characteristic (ROC) was used to evaluate the predictive ability and determine the optimal cut-off values of the indices. In both genders and two age groups, the confounders adjusted HRs were significant for general and central obesity measures indices. AUCs of the indices were similar in men; however, among women 40-59 years, WC and WHtR had significantly higher AUC compared to BMI. Generally, the optimal cut-off values were higher in the 40-59 year age group. Optimal BMI, WC and WHR and WHtR cut-off values were 24.15 kg/m², 90.5 cm, 0.90 and 0.49 among men, aged 20-39 years; the corresponding values were 28.41 kg/m², 86.5 cm, 0.96 and 0.50 in men aged 40-59 years, respectively. In women, the aforementioned values were 26.38 kg/m², 83.5 cm, 0.79 and 0.51 in the age group of 20-39 years, and 29.57 kg/m², 90.5 cm, 0.88 and 0.59 in the 40-59 year age group, respectively. Our results suggest that gender and age differences in the association between anthropometric indices and hypertension should be considered.

Wrist circumference is associated with increased systolic blood pressure in children with overweight/obesity

Wrist circumference is a clinical marker for insulin-resistance in overweight/obese children and adolescents. Insulin resistance is considered a major determinant of increased vascular resistance and hypertension. The aim of the study was to investigate the association between wrist circumference and systolic (S) and diastolic (D) blood pressure (BP) in a population of overweight/obese children and adolescents. A population of 1133 overweight/obese children and adolescents (n = 1133) were consecutively enrolled. Multivariate regression analyses were used to investigate the influence of independent variables on the variance of BP. The prevalence of hypertension was 21.74% in males and 28.95% in females (p = 0.048). The results showed that SBP was significantly associated with wrist circumference in both genders (p < 0.0001 for both comparisons). We found no association between DBP and wrist circumference in either gender. Wrist circumference accounted for 17% of the total variance of SBP in males and 14% in females. Wrist circumference, a marker of insulin resistance, is associated with increased SBP in overweight/obese children and adolescents, suggesting a role of insulin resistance in the pathogenesis and development of hypertension.

Application of survival tree analysis for exploration of potential interactions between predictors of incident chronic kidney disease: a 15-year follow-up study

Background

Chronic kidney disease (CKD) is a growing public health challenges worldwide. Various studies have investigated risk factors of incident CKD; however, a very few studies examined interaction between these risk factors. In an attempt to clarify the potential interactions between risk factors of CKD, we performed survival tree analysis.

Methods

A total of 8238 participants (46.1% men) aged > 20 years without CKD at baseline [(1999–2001) and (2002–2005)], were followed until 2014. The first occurrence of CKD, defined as the estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m², was set as the main outcome. Multivariable Cox proportional hazard (Cox PH) regression was used to identify significant independent predictors of CKD; moreover, survival tree analysis was performed to gain further insight into the potential interactions between predictors.

Results

The crude incidence rates of CKD were 20.2 and 35.2 per 1000 person-years in men and women, respectively. The Cox PH identified the main effect of significant predictors of CKD incidence in men and women. In addition, using a limited number of predictors, survival trees identified 12 and 10 subgroups among men and women, respectively, with different survival probability. Accordingly, a group of men with eGFR > 74 ml/min/1.73 m², age ≤ 46 years, low level of physical activity, waist circumference ≤ 100 cm and FPG ≤ 4.7 mmol/l had the lowest risk of CKD incidence; while men with eGFR ≤ 63.4 ml/min/1.73 m², age > 50 years had the highest risk for CKD compared to men in the lowest risk group [hazard ratio (HR), 70.68 (34.57–144.52)]. Also, a group of women aged ≤ 45 years and eGFR > 83.5 ml/min/1.73 m² had the lowest risk; while women with age > 48 years and eGFR ≤ 69 ml/min/1.73 m² had the highest risk compared to low risk group [HR 27.25 (19.88–37.34)].

Conclusion

In this post hoc analysis, we found the independent predictors of CKD using Cox PH; furthermore, by applying survival tree analysis we identified several numbers of homogeneous subgroups with different risk for incidence of CKD. Our study suggests that two methods can be used simultaneously to provide new insights for intervention programs and improve clinical decision making.

A new look at risk patterns related to coronary heart disease incidence using survival tree analysis: 12 Years Longitudinal Study

We identified risk patterns associated with incident coronary heart disease (CHD) using survival tree, and compared performance of survival tree versus Cox proportional hazards (Cox PH) in a cohort of Iranian adults. Data on 8,279 participants (3,741 men) aged ≥30 yr were used to analysis. Survival trees identified seven subgroups with different risk patterns using four [(age, non-HDL-C, fasting plasma glucose (FPG) and family history of diabetes] and five [(age, systolic blood pressure (SBP), non-HDL-C, FPG and family history of CVD] predictors in women and men, respectively. Additional risk factors were identified by Cox models which included: family history of CVD and waist circumference (in both genders); hip circumference, former smoking and using aspirin among men; diastolic blood pressure and lipid lowering drug among women. Survival trees and multivariate Cox models yielded comparable performance, as measured by integrated Brier score (IBS) and Harrell’s C-index on validation datasets; however, survival trees produced more parsimonious models with a minimum number of well recognized risk factors of CHD incidence, and identified important interactions between these factors which have important implications for intervention programs and improve clinical decision making.

Identifying Future Drinkers: Behavioral Analysis of Monkeys Initiating Drinking to Intoxication is Predictive of Future Drinking Classification

BACKGROUND

The Monkey Alcohol Tissue Research Resource (MATRR) is a repository and analytics platform for detailed data derived from well-documented nonhuman primate (NHP) alcohol self-administration studies. This macaque model has demonstrated categorical drinking norms reflective of human drinking populations, resulting in consumption pattern classifications of very heavy drinking (VHD), heavy drinking (HD), binge drinking (BD), and low drinking (LD) individuals. Here, we expand on previous findings that suggest ethanol drinking patterns during initial drinking to intoxication can reliably predict future drinking category assignment.

METHODS

The classification strategy uses a machine-learning approach to examine an extensive set of daily drinking attributes during 90 sessions of induction across 7 cohorts of 5 to 8 monkeys for a total of 50 animals. A Random Forest classifier is employed to accurately predict categorical drinking after 12 months of self-administration.

RESULTS

Predictive outcome accuracy is approximately 78% when classes are aggregated into 2 groups, "LD and BD" and "HD and VHD." A subsequent 2-step classification model distinguishes individual LD and BD categories with 90% accuracy and between HD and VHD categories with 95% accuracy. Average 4-category classification accuracy is 74%, and provides putative distinguishing behavioral characteristics between groupings.

CONCLUSIONS

We demonstrate that data derived from the induction phase of this ethanol self-administration protocol have significant predictive power for future ethanol consumption patterns. Importantly, numerous predictive factors are longitudinal, measuring the change of drinking patterns through 3 stages of induction. Factors during induction that predict future heavy drinkers include being younger at the time of first intoxication and developing a shorter latency to first ethanol drink. Overall, this analysis identifies predictive characteristics in future very heavy drinkers that optimize intoxication, such as having increasingly fewer bouts with more drinks. This analysis also identifies characteristic avoidance of intoxicating topographies in future low drinkers, such as increasing number of bouts and waiting longer before the first ethanol drink.