Predictors of attrition in a longitudinal population-based study of aging

ABSTRACT Background: Longitudinal studies predictably experience non-random attrition over time. Among older adults, risk factors for attrition may be similar to risk factors for outcomes such as cognitive decline and dementia, potentially biasing study results. Objective: To characterize participants lost to follow-up which can be useful in the study design and interpretation of results. Methods: In a longitudinal aging population study with 10 years of annual follow-up, we characterized the attrited participants (77%) compared to those who remained in the study. We used multivariable logistic regression models to identify attrition predictors. We then implemented four machine learning approaches to predict attrition status from one wave to the next and compared the results of all five approaches. Results: Multivariable logistic regression identified those more likely to drop out as older, male, not living with another study participant, having lower cognitive test scores and higher clinical dementia ratings, lower functional ability, fewer subjective memory complaints, no physical activity, reported hobbies, or engagement in social activities, worse self-rated health, and leaving the house less often. The four machine learning approaches using areas under the receiver operating characteristic curves produced similar discrimination results to the multivariable logistic regression model. Conclusions: Attrition was most likely to occur in participants who were older, male, inactive, socially isolated, and cognitively impaired. Ignoring attrition would bias study results especially when the missing data might be related to the outcome (e.g. cognitive impairment or dementia). We discuss possible solutions including oversampling and other statistical modeling approaches.

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994. Risk for Viral Rebound in the Era of U=U; A CNICS Analysis

Open Forum Infectious Diseases ◽

10.1093/ofid/ofaa439.1180 ◽

2020 ◽

Vol 7 (Supplement_1) ◽

pp. S525-S526

Author(s):

Blake Hansen ◽

Tao Liu ◽

Lauri Bazerman ◽

Mari-Lynn Drainoni ◽

Fizza S Gillani ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Regression Model ◽

Logistic Regression Model ◽

Viral Suppression ◽

Viral Rebound ◽

Research Support ◽

Multivariable Logistic Regression Model ◽

Multivariable Logistic Regression

Abstract Background The “Undetectable equals Untransmittable (U=U)” HIV prevention campaign is a cornerstone of HIV prevention. However, there are few recommendations to guide patients and providers in U=U implementation and limited data on risk factors for viral rebound among persons eligible for U=U. Methods We conducted a retrospective multi-center study using data from the CNICS HIV research network to identify risk factors for viral rebound among persons with established viral suppression [two viral loads (VL) and all VLs of < 200 copies/ul within a one-year period (U=U eligible)]. Demographics, patient-reported outcomes, and longitudinal clinical data from 21,359 persons with HIV were analyzed. To include missing data in the analysis, they were treated as a separate category. The primary outcome of viral rebound was defined as any VL > 200 copies/ul within two years after U=U eligibility. A univariable logistic regression model was conducted to identify predictors of viral rebound. Significant variables (p< 0.05) were included in a multivariable logistic regression model. Predictive values of individual variables were captured by adjusted odds ratios (aORs). Results From 2011-2019, 12,150 patients met criteria for U=U eligibility and had two years of follow up data. The median age was 46 (IQR: 38-53); 68% male; 51% were white, 39% black. 1544 (13%) experienced viral rebound during follow-up. Forest plot summaries of univariable and multivariable logistic regression models are in Figures 1&2. In multivariable analysis, Black race (aOR=1.56, p< 0.001); MSM-IDU risk (aOR=1.38, p=0.006); lower QoL score (aOR=1.49, p=0.005); poorer ART adherence (aOR=1.84, p< 0.001); duration of lifetime ART [aOR=1.47 (10+yrs), = 1.37 (5-10 yrs); and = 1.28 (2-5 yrs), p< 0.001]; use of InSTIs after eligibility (aOR=1.60, p< 0.001); current smoker (aOR=1.49, p< 0.001), current amphetamine (aOR=1.83, p< 0.001) or cocaine use (aOR=1.46, p=0.012), were associated with viral rebound. In both analyses, older age was protective against viral rebound. Figure 1. Summary of Univariate Logistic Regression Model Figure 2. Summary of Multivariable Logistic Regression Model Conclusion We identified multiple risk factors for viral rebound among PWH with viral suppression. Further research is needed to identify synergistic risk factors that increase probability of viral rebound to inform optimal implementation of U=U. Disclosures Edward Cachay, MD, MAS, Gilead (Consultant, Grant/Research Support)Merck Sciences (Grant/Research Support) Heidi Crane, MD, MPH, ViiV (Grant/Research Support) Benigno Rodriguez, MD, Gilead (Speaker’s Bureau)ViiV (Speaker’s Bureau)

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Machine Learning to Identify Interaction of Single-Nucleotide Polymorphisms as a Risk Factor for Chronic Drug-Induced Liver Injury

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph182010603 ◽

2021 ◽

Vol 18 (20) ◽

pp. 10603

Author(s):

Roland Moore ◽

Kristin Ashby ◽

Tsung-Jen Liao ◽

Minjun Chen

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Logistic Regression ◽

Liver Injury ◽

Genetic Variants ◽

Learning Approaches ◽

Nucleotide Polymorphisms ◽

Drug Induced ◽

Single Nucleotide ◽

Drug Induced Liver Injury

Drug-induced liver injury (DILI) is a major cause of drug development failure and drug withdrawal from the market after approval. The identification of human risk factors associated with susceptibility to DILI is of paramount importance. Increasing evidence suggests that genetic variants may lead to inter-individual differences in drug response; however, individual single-nucleotide polymorphisms (SNPs) usually have limited power to predict human phenotypes such as DILI. In this study, we aim to identify appropriate statistical methods to investigate gene–gene and/or gene–environment interactions that impact DILI susceptibility. Three machine learning approaches, including Multivariate Adaptive Regression Splines (MARS), Multifactor Dimensionality Reduction (MDR), and logistic regression, were used. The simulation study suggested that all three methods were robust and could identify the known SNP–SNP interaction when up to 4% of genotypes were randomly permutated. When applied to a real-life DILI chronicity dataset, both MARS and MDR, but not logistic regression, identified combined genetic variants having better associations with DILI chronicity in comparison to the use of individual SNPs. Furthermore, a simple decision tree model using the SNPs identified by MARS and MDR was developed to predict DILI chronicity, with fair performance. Our study suggests that machine learning approaches may help identify gene–gene interactions as potential risk factors for better assessing complicated diseases such as DILI chronicity.

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Machine Learning Approaches for Predicting Bisphosphonate-Related Osteonecrosis in Women with Osteoporosis Using VEGFA Gene Polymorphisms

Journal of Personalized Medicine ◽

10.3390/jpm11060541 ◽

2021 ◽

Vol 11 (6) ◽

pp. 541

Author(s):

Jin-Woo Kim ◽

Jeong Yee ◽

Sang-Hyeon Oh ◽

Sun-Hyun Kim ◽

Sun-Jong Kim ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Logistic Regression ◽

Multivariable Analysis ◽

Significant Risk ◽

Machine Learning Algorithms ◽

Receiver Operating Curve ◽

Support Vector ◽

Learning Approaches ◽

Wild Type

Objective: This nested case–control study aimed to investigate the effects of VEGFA polymorphisms on the development of bisphosphonate-related osteonecrosis of the jaw (BRONJ) in women with osteoporosis. Methods: Eleven single nucleotide polymorphisms (SNPs) of the VEGFA were assessed in a total of 125 patients. Logistic regression was performed for multivariable analysis. Machine learning algorithms, namely, fivefold cross-validated multivariate logistic regression, elastic net, random forest, and support vector machine, were developed to predict risk factors for BRONJ occurrence. Area under the receiver-operating curve (AUROC) analysis was conducted to assess clinical performance. Results: The VEGFA rs881858 was significantly associated with BRONJ development. The odds of BRONJ development were 6.45 times (95% CI, 1.69–24.65) higher among carriers of the wild-type rs881858 allele compared with variant homozygote carriers after adjusting for covariates. Additionally, variant homozygote (GG) carriers of rs10434 had higher odds than those with wild-type allele (OR, 3.16). Age ≥ 65 years (OR, 16.05) and bisphosphonate exposure ≥ 36 months (OR, 3.67) were also significant risk factors for BRONJ occurrence. AUROC values were higher than 0.78 for all machine learning methods employed in this study. Conclusion: Our study showed that the BRONJ occurrence was associated with VEGFA polymorphisms in osteoporotic women.

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Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

10.31234/osf.io/eqcdx ◽

2019 ◽

Author(s):

Oskar Flygare ◽

Jesper Enander ◽

Erik Andersson ◽

Brjánn Ljótsson ◽

Volen Z Ivanov ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Random Forests ◽

Clinical Utility ◽

Body Dysmorphic Disorder ◽

Prediction Models ◽

Behavioral Therapy ◽

Learning Approach ◽

Learning Approaches ◽

Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

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POS1002 BASELINE CALPROTECTIN AND VISFATIN LEVELS PREDICT RADIOGRAPHIC SPINAL PROGRESSION AFTER 2 YEARS IN ANKYLOSING SPONDYLITIS PATIENTS ON TNF INHIBITOR THERAPY

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.3059 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 769.2-770

Author(s):

J. Rademacher ◽

M. Siderius ◽

L. Gellert ◽

F. Wink ◽

M. Verba ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Ankylosing Spondylitis ◽

Bone Formation ◽

Bone Turnover ◽

Radiographic Progression ◽

Serum Levels ◽

Tnf Inhibitor ◽

Research Support

Background:Radiographic spinal progression determinates functional status and mobility in ankylosing spondylitis (AS)1.Objectives:To analyse whether biomarker of inflammation, bone turnover and adipokines at baseline or their change after 3 months or 2 years can predict spinal radiographic progression after 2 years in AS patients treated with TNF-α inhibitors (TNFi).Methods:Consecutive AS patients from the Groningen Leeuwarden Axial Spondyloarthritis (GLAS) cohort2 starting TNFi between 2004 and 2012 were included. The following serum biomarkers were measured at baseline, 3 months and 2 years of follow-up with ELISA: - Markers of inflammation: calprotectin, matrix metalloproteinase-3 (MMP-3), vascular endothelial growth factor (VEGF) - Markers of bone turnover: bone-specific alkaline phosphatase (BALP), serum C-terminal telopeptide (sCTX), osteocalcin (OC), osteoprotegerin (OPG), procollagen typ I and II N-terminal propeptide (PINP; PIINP), sclerostin. - Adipokines: high molecular weight (HMW) adiponectin, leptin, visfatinTwo independent readers assessed spinal radiographs at baseline and 2 years of follow-up according to the modified Stoke Ankylosing Spondylitis Spine Score (mSASSS). Radiographic spinal progression was defined as mSASSS change ≥2 units or the formation of ≥1 new syndesmophyte over 2 years. Logistic regression was performed to examine the association between biomarker values at baseline, their change after 3 months and 2 years and radiographic spinal progression. Multivariable models for each biomarker were adjusted for mSASSS or syndesmophytes at baseline, elevated CRP (≥5mg/l), smoking status, male gender, symptom duration, BMI, and baseline biomarker level (the latter only in models with biomarker change).Results:Of the 137 included AS patients, 72% were male, 79% HLAB27+; mean age at baseline was 42 years (SD 10.8), ASDAScrp 3.8 (0.8) and mSASSS 10.6 (16.1). After 2 years of follow-up, 33% showed mSASSS change ≥2 units and 24% had developed ≥1 new syndesmophyte. Serum levels of biomarkers of inflammation and bone formation showed significant changes under TNFi therapy, whereas adipokine levels were not altered from baseline (Figure 1).Univariable logistic regression revealed a significant association of baseline visfatin (odds ratio OR [95% confidence interval] 1.106 [1.007-1.215]) and sclerostin serum levels (OR 1.006 [1.001-1.011]) with mSASSS progression after 2 years. Baseline sclerostin levels were also associated with syndesmophyte progression (OR 1.007 [1.001-1.013]). In multivariable logistic analysis, only baseline visfatin level remained significantly associated (OR 1.465 [1.137-1.889]) with mSASSS progression. Furthermore, baseline calprotectin showed a positive association with both, mSASSS (OR 1.195 [1.055-1.355]) and syndesmophyte progression (OR 1.107 [1.001-1.225]) when adjusting for known risk factors for radiographic progression.Univariable logistic regression showed that change of sclerostin after 3 months was associated with syndesmophytes progression (OR 1.007 [1.000-1.015), change of PINP level after 2 years was associated with mSASSS progression (OR 1.027 [1.003-1.052]) and change of visfatin after 2 years was associated with both measures of radiographic progression – mSASSS (OR 1.108 [1.004-1.224]) and syndesmophyte formation (OR 1.115; [1.002-1.24]). However, those associations were lost in multivariable analysis.Conclusion:Independent of known risk factors, baseline calprotectin and visfatin levels were associated with radiographic spinal progression after 2 years of TNFi. Although biomarkers of inflammation and bone formation showed significant changes under TNFi therapy, these changes were not significantly related to radiographic spinal progression in our cohort of AS patients.References:[1]Poddubnyy et al 2018[2]Maas et al 2019Acknowledgements:Dr. Judith Rademacher is participant in the BIH-Charité Clinician Scientist Program funded by the Charité –Universitätsmedizin Berlin and the Berlin Institute of Health.Disclosure of Interests:Judith Rademacher: None declared, Mark Siderius: None declared, Laura Gellert: None declared, Freke Wink Consultant of: AbbVie, Maryna Verba: None declared, Fiona Maas: None declared, Lorraine M Tietz: None declared, Denis Poddubnyy: None declared, Anneke Spoorenberg Consultant of: Abbvie, Pfizer, MSD, UCB, Lilly and Novartis, Grant/research support from: Abbvie, Pfizer, UCB, Novartis, Suzanne Arends Grant/research support from: Pfizer.

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Logistic regression analysis on risk factors of augmented vertebra recompression after percutaneous vertebral augmentation

Journal of Orthopaedic Surgery and Research ◽

10.1186/s13018-021-02480-9 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Zhongcheng An ◽

Chen Chen ◽

Junjie Wang ◽

Yuchen Zhu ◽

Liqiang Dong ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Bone Cement ◽

Vertebral Compression Fracture ◽

Univariate Analysis ◽

Compression Fracture ◽

Osteoporotic Vertebral Compression Fracture ◽

Vertebral Augmentation ◽

Surgical Methods

Abstract Objective To explore the high-risk factors of augmented vertebra recompression after percutaneous vertebral augmentation (PVA) in the treatment of osteoporotic vertebral compression fracture (OVCF) and analyze the correlation between these factors and augmented vertebra recompression after PVA. Methods A retrospective analysis was conducted on 353 patients who received PVA for a single-segment osteoporotic vertebral compression fracture from January 2017 to December 2018 in our department according to the inclusion criteria. All cases meeting the inclusion and exclusion criteria were divided into two groups: 82 patients in the recompression group and 175 patients in the non-compression group. The following covariates were reviewed: age, gender, body mass index (BMI), injured vertebral segment, bone mineral density (BMD) during follow-up, intravertebral cleft (IVC) before operation, selection of surgical methods, unilateral or bilateral puncture, volume of bone cement injected, postoperative leakage of bone cement, distribution of bone cement, contact between the bone cement and the upper or lower endplates, and anterior height of injured vertebrae before operation, after surgery, and at the last follow-up. Univariate analysis was performed on these factors, and the statistically significant factors were substituted into the logistic regression model to analyze their correlation with the augmented vertebra recompression after PVA. Results A total of 257 patients from 353 patients were included in this study. The follow-up time was 12–24 months, with an average of 13.5 ± 0.9 months. All the operations were successfully completed, and the pain of patients was relieved obviously after PVA. Univariate analysis showed that in the early stage after PVA, the augmented vertebra recompression was correlated with BMD, surgical methods, volume of bone cement injected, preoperative IVC, contact between bone cement and the upper or lower endplates, and recovery of anterior column height. The difference was statistically significant (P < 0.05). Among them, multiple factors logistic regression elucidated that more injected cement (P < 0.001, OR = 0.558) and high BMD (P = 0.028, OR = 0.583) were negatively correlated with the augmented vertebra recompression after PVA, which meant protective factors (B < 0). Preoperative IVC (P < 0.001, OR = 3.252) and bone cement not in contact with upper or lower endplates (P = 0.006, OR = 2.504) were risk factors for the augmented vertebra recompression after PVA. The augmented vertebra recompression after PVP was significantly less than that of PKP (P = 0.007, OR = 0.337). Conclusions The augmented vertebra recompression after PVA is due to the interaction of various factors, such as surgical methods, volume of bone cement injected, osteoporosis, preoperative IVC, and whether the bone cement is in contact with the upper or lower endplates.

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Efficacy and safety of 5 mg olanzapine for nausea and vomiting management in cancer patients receiving carboplatin: integrated study of three prospective multicenter phase II trials

BMC Cancer ◽

10.1186/s12885-021-08572-3 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Senri Yamamoto ◽

Hirotoshi Iihara ◽

Ryuji Uozumi ◽

Hitoshi Kawazoe ◽

Kazuki Tanaka ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Cancer Patients ◽

Phase Ii ◽

Low Dose ◽

Nausea And Vomiting ◽

Efficacy And Safety ◽

Multivariable Logistic Regression ◽

Related Risk ◽

Total Control

Abstract Background The efficacy of olanzapine as an antiemetic agent in cancer chemotherapy has been demonstrated. However, few high-quality reports are available on the evaluation of olanzapine’s efficacy and safety at a low dose of 5 mg among patients treated with carboplatin regimens. Therefore, in this study, we investigated the efficacy and safety of 5 mg olanzapine for managing nausea and vomiting in cancer patients receiving carboplatin regimens and identified patient-related risk factors for carboplatin regimen-induced nausea and vomiting treated with 5 mg olanzapine. Methods Data were pooled for 140 patients from three multicenter, prospective, single-arm, open-label phase II studies evaluating the efficacy and safety of olanzapine for managing nausea and vomiting induced by carboplatin-based chemotherapy. Multivariable logistic regression analyses were performed to determine the patient-related risk factors. Results Regarding the endpoints of carboplatin regimen-induced nausea and vomiting control, the complete response, complete control, and total control rates during the overall study period were 87.9, 86.4, and 72.9%, respectively. No treatment-related adverse events of grade 3 or higher were observed. The multivariable logistic regression models revealed that only younger age was significantly associated with an increased risk of non-total control. Surprisingly, there was no significant difference in CINV control between the patients treated with or without neurokinin-1 receptor antagonist. Conclusions The findings suggest that antiemetic regimens containing low-dose (5 mg) olanzapine could be effective and safe for patients receiving carboplatin-based chemotherapy.

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Learning chemistry: exploring the suitability of machine learning for the task of structure-based chemical ontology classification

Journal of Cheminformatics ◽

10.1186/s13321-021-00500-8 ◽

2021 ◽

Vol 13 (1) ◽

Cited By ~ 1

Author(s):

Janna Hastings ◽

Martin Glauer ◽

Adel Memariani ◽

Fabian Neuhaus ◽

Till Mossakowski

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Short Term Memory ◽

Chemical Space ◽

Chemical Data ◽

Learning Approaches ◽

Class Prediction ◽

Chemical Structures ◽

Chemical Ontology ◽

Chemical Ontologies

AbstractChemical data is increasingly openly available in databases such as PubChem, which contains approximately 110 million compound entries as of February 2021. With the availability of data at such scale, the burden has shifted to organisation, analysis and interpretation. Chemical ontologies provide structured classifications of chemical entities that can be used for navigation and filtering of the large chemical space. ChEBI is a prominent example of a chemical ontology, widely used in life science contexts. However, ChEBI is manually maintained and as such cannot easily scale to the full scope of public chemical data. There is a need for tools that are able to automatically classify chemical data into chemical ontologies, which can be framed as a hierarchical multi-class classification problem. In this paper we evaluate machine learning approaches for this task, comparing different learning frameworks including logistic regression, decision trees and long short-term memory artificial neural networks, and different encoding approaches for the chemical structures, including cheminformatics fingerprints and character-based encoding from chemical line notation representations. We find that classical learning approaches such as logistic regression perform well with sets of relatively specific, disjoint chemical classes, while the neural network is able to handle larger sets of overlapping classes but needs more examples per class to learn from, and is not able to make a class prediction for every molecule. Future work will explore hybrid and ensemble approaches, as well as alternative network architectures including neuro-symbolic approaches.

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Replication study reveals miR-483-5p as an important target in prevention of cardiometabolic disease

BMC Cardiovascular Disorders ◽

10.1186/s12872-021-01964-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Widet Gallo ◽

Filip Ottosson ◽

Cecilia Kennbäck ◽

Amra Jujic ◽

Jonathan Lou S. Esguerra ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Cardiometabolic Risk ◽

Carotid Atherosclerosis ◽

Tissue Injury ◽

Control Sample ◽

Cardiometabolic Risk Factors ◽

Cardiometabolic Disease ◽

Micro Rnas

Abstract Background Alterations in levels of circulating micro-RNAs might reflect within organ signaling or subclinical tissue injury that is linked to risk of diabetes and cardiovascular risk. We previously found that serum levels of miR-483-5p is correlated with cardiometabolic risk factors and incidence of cardiometabolic disease in a case–control sample from the populations-based Malmö Diet and Cancer Study Cardiovascular Cohort (MDC-CC). We here aimed at replicating these findings and to test for association with carotid atherosclerosis. Methods We measured miR-483-5p in fasting serum of 1223 healthy subjects from the baseline examination of the population-based, prospective cohort study Malmö Offspring Study (MOS) and correlated miR-483-5p to cardiometabolic risk factors and to incidence of diabetes mellitus and coronary artery disease (CAD) during 3.7 (± 1.3) years of follow-up using logistic regression. In both MOS and MDC-CC we related mir-483-5p to carotid atherosclerosis measured with ultrasound. Results In cross-sectional analysis miR-483-5p was correlated with BMI, waist circumference, HDL, and sex. After adjustment for age and sex, the association remained significant for all risk factors except for HDL. Logistic regression analysis showed significant associations between miR-483-5p and new-onset diabetes (OR = 1.94, 95% CI 1.06–3.56, p = 0.032) and cardiovascular disease (OR = 1.99, 95% CI 1.06–3.75, p = 0.033) during 3.7 (± 1.3) years of follow-up. Furthermore, miR-483-5p was significantly related with maximum intima-media thickness of the carotid bulb in MDC-CC (p = 0.001), but not in MOS, whereas it was associated with increasing number of plaques in MOS (p = 0.007). Conclusion miR-483-5p is related to an unfavorable cardiometabolic risk factor profile and predicts diabetes and CAD, possibly through an effect on atherosclerosis. Our results encourage further studies of possible underlying mechanisms and means of modifying miR-483-5p as a possible interventional target in prevention of cardiometabolic disease.

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