Global Longitudinal Health Monitoring and Blood Sample Collection Study to Promote Early-stage Disease Detection and Personalized/Precision Care Using Innovative Research Platforms
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 15,000
- 试验地点
- 1
- 主要终点
- Establish Personalized Molecular Baselines
研究概览
简要总结
The investigators propose a prospective, longitudinal, observational study to improve health assessment by analyzing blood plasma molecular patterns in each individual over time using artificial intelligence (AI) to identify key measurements for early detection of NCDs. It will develop personalized reference ranges and screening methods, laying the foundation for population-based early detection. This study focus on collecting health data and biospecimen samples to understand early molecular changes linked to disease.
详细描述
Background:
According to the WHO, non-communicable diseases (NCDs) cause about 70% of global deaths-over 43 million in 2021- with up to 90% in high-income countries. These diseases are linked to risk factors such as unhealthy diets, inactivity, tobacco, and alcohol, leading to long-term health issues and economic burdens. Current screening methods mainly detect clinical signs but lack early sensitivity. Emerging approaches focus on biomarkers, advanced technologies, and machine learning to improve early detection and personalized prevention, aiming to reduce NCD impact and improve health outcomes.
An individual's blood parameters are usually stable and reflect their unique physiology. Comparing current results to personal baseline ranges is more sensitive for detecting health issues than using general population standards. This personalized approach aims to identify early molecular changes indicating potential NCDs. This study builds upon the ongoing Health for Hungary (H4H) (https://www.h4h.hu/en/) project conducted by the Center for Molecular Fingerprinting, a non-profit research institution in Hungary led by 2023 Nobel Laureate in Physics, Prof. Ferenc Krausz (https://www.physics.hku.hk/people/academic\_staff/teaching\_staff/f\_krausz/).
Aim:
The primary scientific goal of the project is to quantitatively parametrize health in terms of time series of integrated molecular parameters and molecular pattern recognition from human blood plasma, and leverage AI to discover the minimum set of molecular data of blood that reliably assess and predict any changes in human health. The overarching aim of the study is to establish the technological and economic basis for a population-based health screening for major NCDs.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 40 Years 至 70 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Signed informed consent form (ICF) of the study.
- •Male or female participants.
- •Age at Visit 1: 40 years - 70 years.
- •For Low-risk Arm: Assessed as healthy (free of acute or chronic disease) with no cardiovascular risk conditions listed in Inclusion criteria
- •Participants may have mild disorders that do not require regular therapeutic (pharmacological) intervention; For High-risk Arm: Assessed as healthy (free of acute or chronic diseases) with cardiovascular risk conditions listed in Inclusion criteria
- •Participant may have mild disorders that do not require regular therapeutic (pharmacological) intervention.
- •For Low-risk Arm: Has low risk to contract an NCD in the upcoming years, according to the following criteria: a) Non-hypertensive person according to criteria of the relevant national guideline, who never received antihypertensive medication; b) Total cholesterol: < 5.2 mmol/L (<200 mg/dL) with no history of lipid-lowering (e.g., statin) treatment; c) Non smoker or with no significant smoking history (<5 pack-years); For High-risk Arm: Has high risk to contract an NCD in the upcoming years, confirmed by the presence of at least 2 out of the following 3 criteria (a, b, c): a) Medically controlled hypertension: participants with diagnosed hypertension receiving antihypertensive medication ; b) Medically controlled dyslipidemia or hypercholesterolemia: participants with diagnosed dyslipidemia or hypercholesterolemia receiving lipid-lowering medication; c) Significant smoking history (tobacco exposure of >20 pack-years) and/or 1st degree family member with history of lung cancer.
- •BMI: 18.5 - 35.0 kg/m^
- •Willingness to fill in the study questionnaire.
- •Willingness to participate in future visits and medical investigations as defined per protocol.
- •Willingness to be followed-up on disease outcome through data linkage to the participant's health-related records.
排除标准
- •Pregnancy at Visit 1 (self-reported, no test required).
- •For low-risk arm: Past medical history (PMH) of target NCDs, any other significant health conditions, clinical symptoms, abnormalities of blood parameters or medical tests suggesting the presence of abnormal health conditions at Visit
- •Any condition that is inadequately controlled. The sponsor should be contacted for advice in case of any uncertainties; For high-risk arm: Except for the conditions mentioned under inclusion criteria (point 5a and b), participants with PMH of target NCDs, any other health conditions, clinical symptoms, abnormalities of blood parameters or medical tests suggesting the presence of abnormal health conditions at Visit 1 are excluded from the study. Any condition that is inadequately controlled. Sponsor should be contacted for advice in case of any uncertainties.
- •For low-risk arm: Any chronic, systemic drug therapy at Visit 1 (prescription); For high-risk arm: Any chronic, systemic drug therapy at Visit 1 except for conditions mentioned under Inclusion criteria (point 5a and b).
- •History of HIV, HBV, HCV or HEV infection at Visit
- •Vulnerable participants.
- •Foreseeable lack of compliance.
研究组 & 干预措施
Low-risk arm
a cohort of 7,500 participants with the absence of modifiable cardiovascular risk factors, who are at low risk of contracting selected NCDs
High-risk arm
a cohort of 7,500 participants with the presence of cardiovascular risk factors, who are at high risk of contracting selected NCDs
结局指标
主要结局
Establish Personalized Molecular Baselines
时间窗: 10 years
Longitudinal blood sampling from participants in a healthy state (i.e., free from NCDs) will allow comparison between the intra- and inter-individual stability of thousands of molecular variables. Stable molecular signatures will be defined within their personalized reference range, which characterizes an individual's current health state.
Establishment of Health Screening Algorithm
时间窗: 10 years
Create an AI-driven health screening tool for predicting diseases based on personalized molecular profiles and health parameters. This algorithm will be made to find early signs of disease before clinical symptoms show up by looking for any deviations from an individual's personalized molecular baseline.
Identification of molecular signatures
时间窗: 10 years
Identify subtle molecular signatures that precede the clinical manifestation of diseases
次要结局
未报告次要终点
研究者
Prof Dennis Kai-Ming Ip
Clinical Associate Professor
The University of Hong Kong