The Life Style Patterns and the Development Trend of Chronic Diseases in Healthy and Sub-healthy Groups Were Analyzed by Using Data-mining Techniques

Used multi-year health examination member profile by multi-algorithms technology, to find comprehensive key hazard factors or important high-risk group components for metabolic syndrome and chronic kidney disease or more common chronic diseases.

Study Overview

• Status: Active, not recruiting
• Conditions: Metabolic Syndrome; Chronic Disease; Chronic Kidney Disease Stage 5; Chronic Kidney Disease Stage 3; Chronic Kidney Disease Stage 4; Chronic Kidney Disease Stage 2; Chronic Kidney Disease Stage 1
• Intervention / Treatment: Other

Detailed Description

The proportion of the population over the age of 65 in Taiwan reached 7.10% in 1993. After Taiwan became an 「aging country」, the originally slow growth of the elderly population (9.9% in 2006) started to increase, and it reached 14.05% in 2018, which was almost 2 times that in 1993. In addition, Taiwan formally became an 「aged country」as defined globally. According to the statistical data from the Ministry of the Interior and the data from the National Development Council, it is estimated that the population over the age of 65 is rapidly growing. It is expected that 6 years later (by 2026), the elderly population in Taiwan will exceed 20%. Taiwan will formally become the「super-aged country」as defined globally, with a population structure similar to that in Japan, South Korea, Singapore, and some European countries (Department of Statistics, 2018; National Development Council, 2019). In order to effectively prevent and treat chronic diseases of sub-health populations and develop health management prediction systems that have unlimited market opportunities and potentials, the author intends to extend the achievements of individual projects sponsored by the Ministry of Science and Technology in recent years. By multi-year complete health examination member profile, this project used multiple algorithms, such as Logistic regression (LR); Classification And Regression Trees (CART); Hierarchical Linear Modeling (HLM); Random forests (RF); Support-Vector Machines (SVM); eXtreme Gradient Boosting (xGBoost); Light Gradient Boosting Machine (LightGBM) and multiple analysis tools to explore the common potential health hazard variables of the sub-health population to establish a comprehensive assessment health management system that can detect chronic diseases early, the research results will be provided for reference in related fields.

• Study Type: Observational
• Enrollment (Anticipated): 81108

Contacts and Locations

Study Locations

• Taiwan
  • Pan-Chiao Dist.
    • New Taipei City, Pan-Chiao Dist., Taiwan, 22061
      • Oriental Institute of Technology / Far Eastern Memorial Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: ADULT; OLDER_ADULT; CHILD
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

In 2006-2017 of the MJ Health Research Foundation's member, of the de-linked and de-identified annual health check database, about 71,108 people; 2015-2020, the Health2Sync provides [Intelligent Anti-Glucose System], the biometric and behavioral item de-linked and de-identified data, about 10,000 people.

Description

Inclusion Criteria:

• Continuously health screening twice or more in MJ health reports.
• Chronic kidney disease
• Metabolic syndrome
• Or more, common chronic diseases

Exclusion Criteria:

• Participants who have received clinical treatment
• Subjects of other related research diseases

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Number of participants with metabolic syndrome in kidney disease-related adverse events as assessed by estimated glomerular filtration rate	Physiological information of participants with chronic kidney disease related adverse events as assessed in metabolic syndrome, by natural longitudinal change from baseline in estimated glomerular filtration rate at 2 years recent in health screening in participants.	2 year

Collaborators and Investigators

• Sponsor: Far Eastern Memorial Hospital

Study record dates

Study Major Dates

• Study Start (ACTUAL): March 3, 2021
• Primary Completion (ANTICIPATED): July 3, 2024
• Study Completion (ANTICIPATED): July 31, 2024

Study Registration Dates

• First Submitted: October 21, 2021
• First Submitted That Met QC Criteria: January 26, 2022
• First Posted (ACTUAL): February 4, 2022

Study Record Updates

• Last Update Posted (ACTUAL): May 10, 2022
• Last Update Submitted That Met QC Criteria: May 9, 2022
• Last Verified: March 1, 2022

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Pathologic Processes; Glucose Metabolism Disorders; Metabolic Diseases; Urologic Diseases; Disease Attributes; Renal Insufficiency; Insulin Resistance; Hyperinsulinism; Kidney Diseases; Renal Insufficiency, Chronic; Metabolic Syndrome; Chronic Disease
• Other Study ID Numbers: 110027-E

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No