Pediatric population health analysis of southern and central Illinois region: A cross sectional retrospective study using association rule mining and multiple logistic regression

Cholesterol alone or in combination is associated with frailty among community-dwelling older adults: A cross-sectional study

BACKGROUND

Biological markers contribute to the precise intervention across the continuum of frailty severity. Few studies have explored the advantages of biological markers collected as part of primary care data among community-dwelling older adult population and controversy remains regarding the classic biological markers for frailty.

METHODS

We recruited a total of 8791 adults with a mean age of 71.95 years who met the inclusion and exclusion criteria in Guancheng District and Dalang Town, Dongguan, China. Frailty was assessed by a Chinese frailty evaluation scale. Frailty status was classified with 33-item modified frailty index and latent class analysis was applied to explore the latent classes (subtypes) of frailty. We measured biological markers on blood samples collected. We identify association between specific biological markers or patterns and frailty by logistic regression and association rule mining (ARM) based on the Apriori algorithm.

RESULTS

Multivariable analysis of our data showed that an elevated white blood cell (WBC) count and high cholesterol (CHOL) level were associated with pre-frailty (adjusted odds ratio [aOR] = 1.231, 95 % confidence interval [CI] = 1.009-1.501; aOR = 0.703, 95 % CI = 0.623-0.793) and frailty (aOR = 1.500, 95 % CI = 1.130-1.993; aOR = 0.561, 95 % CI = 0.461-0.684) compared with the normal groups. Importantly, significantly high level of CHOL was associated with a lower risk of four frailty subtypes compared with relatively healthy participants with the most power of association in the multi-frail group (aOR = 0.182, 95 % CI = 0.086-0.386). Based on ARM technique to develop correlation analysis to identify important high-risk clusters among older adult transitions from non-frail to frailty, patterns for normal level of CHOL co-occurred with an elevated creatinine (CREA) level have a significant association with the risk of frailty (aOR = 7.787, 95 % CI = 1.978-30.648) after adjusting for targeted confounders.

CONCLUSIONS

Our study highlights the correlation between classic biological markers, especially CHOL and frailty status and subtypes among community-dwelling older adult, in the primary care setting. Further large-scale prospective studies are still needed to confirm the role of classic biological markers in frailty.

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.