Chronic Diseases and Translational Medicine最新文献

In the article titled, “Effects of long-term blood pressure variability on renal function in community population” published in pages 149–152, vol. 10 of Chronic Diseases and Translational Medicine,¹ the order of the first author's name is incorrect. The first author's name should be Feng Zhao.

Chronic diseases such as heart disease, cancer, and diabetes are leading drivers of mortality worldwide, underscoring the need for improved efforts around early detection and prediction. The pathophysiology and management of chronic diseases have benefitted from emerging fields in molecular biology like genomics, transcriptomics, proteomics, glycomics, and lipidomics. The complex biomarker and mechanistic data from these “omics” studies present analytical and interpretive challenges, especially for traditional statistical methods. Machine learning (ML) techniques offer considerable promise in unlocking new pathways for data-driven chronic disease risk assessment and prognosis. This review provides a comprehensive overview of state-of-the-art applications of ML algorithms for chronic disease detection and prediction across datasets, including medical imaging, genomics, wearables, and electronic health records. Specifically, we review and synthesize key studies leveraging major ML approaches ranging from traditional techniques such as logistic regression and random forests to modern deep learning neural network architectures. We consolidate existing literature to date around ML for chronic disease prediction to synthesize major trends and trajectories that may inform both future research and clinical translation efforts in this growing field. While highlighting the critical innovations and successes emerging in this space, we identify the key challenges and limitations that remain to be addressed. Finally, we discuss pathways forward toward scalable, equitable, and clinically implementable ML solutions for transforming chronic disease screening and prevention.

High blood pressure is a major public health issue and a leading risk factor for death worldwide, with the number of attributable deaths increased from 6.8 million in 1990 to 10.8 million in 2019.^{1, 2} According to the World Health Organization, the age-standardized prevalence of hypertension was estimated to be 33%, affecting approximately 1.3 billion adults aged 30–79 years in 2019 worldwide, doubling from 1990.³ In addition to the heavy disease burden, the economic burden associated with hypertension is also substantial, accounting for approximately 10% of the global healthcare expenditure.⁴ Furthermore, despite improvements in diagnostic and treatment capacities, the status of hypertension care remains suboptimal, especially in low- and middle-income countries. For instance, in China, in 2019, the awareness, treatment, and control rates of hypertension were 38.3%, 34.6%, and 12.0%, respectively.⁵ Accumulating evidence calls for more comprehensive and effective prevention and management of hypertension.

Multiple risk factors for hypertension have been well established, including genetic background, high sodium intake, smoking, physical inactivity, obesity, and alcohol intake.^{6, 7} Other potential risk factors (e.g., air pollution, psychological disorders, sleep habits, and noise exposure) have received increasing attention in recent years.⁴ To enhance the efficacy of prevention, identifying the potential risk factors for hypertension within specific populations and developing advanced tools to improve adherence to interventions remain imperative. In addition to lifestyle modifications, pharmacological treatment is essential for managing hypertension. Moreover, hypertension may cause other diseases such as cardiovascular disease, chronic kidney disease, and dementia.^{8, 9} Additionally, hypertension during pregnancy is associated with adverse consequences for offspring, including preterm birth, vascular dysfunction, and cognitive impairment.^{10, 11} Therefore, it is important to systematically understand the current research status of hypertension and related diseases.

In this special issue, authors from the United States, United Kingdom, China, India, and Pakistan report the latest research findings on hypertension and related diseases. The issue comprises eight publications, including four original articles, two brief reports, one study protocol, and one correspondence. In summary, the aforementioned studies employed different epidemiological designs to assess the risk factors for hypertension, identify molecular biomarkers for pre-eclampsia (PE), depict the adverse impact of blood pressure on various outcomes, and provide potential evidence for hypertension management and treatment.

Zhang et al.¹² offered valuable