Multifactorial Diseases of the Heart, Kidneys, Lungs, and Liver and Incident Cancer: Epidemiology and Shared Mechanisms

Mendelian randomization analyses reveal causal relationship between liver volume and stroke

BACKGROUND

Observational studies have suggested a potential association between abdominal viscera volume and increased risk of stroke. However, the causal relationship remains unclear. This study aims to utilize Mendelian randomization (MR) to explore the genetic causal relationship between them.

METHODS

We conducted MR analysis to study the causal effects of five abdominal viscera volumes on stroke. The genetic variations of abdominal viscera volume were obtained from the UK Biobank, and the summary data for stroke and ischemic stroke were acquired from the MEGASTROKE consortium. This study employed inverse variance weighting (IVW), MR Egger, and weighted median methods. IVW served as the primary MR analysis method, supplemented by other sensitivity analyses to validate the robustness of the results.

RESULTS

We found that liver volume can causally increase the risk of stroke [odds ratio (OR): 1.13, 95 % confidence interval (CI): 1.03-1.25, P = 0.013] and ischemic stroke (OR: 1.14, 95 % CI: 1.03-1.26, P = 0.012). No causal relationships between other abdominal viscera volumes and stroke and ischemic stroke appeared to be present (P > 0.05). Sensitivity analyses confirmed the robustness of the results.

CONCLUSION

Our research findings indicate a causal relationship between liver volume and stroke, highlighting the potential role of liver volume in the onset of stroke. However, further basic and clinical research is needed to delve into the specific mechanisms underlying the relationship between liver volume and stroke, and to implement interventions aimed at reducing the impact of liver volume on stroke risk.

Metabolic dysfunction-associated profiles and subsequent site-specific risk of obesity-related cancers among Chinese patients with diabetes: a retrospective cohort study

OBJECTIVES

To compare metabolic dysfunction-associated profiles between patients with diabetes who developed different obesity-related site-specific cancers and those who remained free of cancer during follow-up.

DESIGN

Retrospective cohort study.

SETTING

Public general outpatient clinics in Hong Kong.

PARTICIPANTS

Patients with diabetes without a history of malignancy (n=391 921).

PRIMARY OUTCOME MEASURES

The outcomes of interest were diagnosis of site-specific cancers (colon and rectum, liver, pancreas, bladder, kidney and stomach) during follow-up. Cox proportional hazards regression was applied to assess the associations between metabolic dysfunction and other clinical factors with each site-specific cancer.

RESULTS

Each 0.1 increase in waist-to-hip ratio was associated with an 11%-35% elevated risk of colorectal, bladder and liver cancers. Each 1% increase in glycated haemoglobin was linked to a 4%-9% higher risk of liver and pancreatic cancers. While low-density lipoprotein cholesterol and triglycerides were inversely associated with the risk of liver and pancreatic cancers, high-density lipoprotein cholesterol was negatively associated with pancreatic, gastric and kidney cancers, but positively associated with liver cancer. Furthermore, liver cirrhosis was linked to a 56% increased risk of pancreatic cancer. No significant association between hypertension and cancer risk was found.

CONCLUSIONS

Metabolic dysfunction-associated profiles contribute to different obesity-related cancer outcomes differentially among patients with diabetes. This study may provide evidence to help identify cancer prevention targets during routine diabetes care.

Redefining cancer research for therapeutic breakthroughs

Cancer research has played a pivotal role in improving patient outcomes. However, despite the significant investment in fundamental cancer research over the past few decades, the translation of funding into substantial advancements in cancer treatment has been limited. This perspective article employs a detailed analysis to outline strategies for promoting innovation and facilitating discoveries within the field of cancer research.

In utero exposure to ionizing radiation and metabolic regulation: perspectives for future multi- and trans-generation effects studies

PURPOSE

The radiation protection community has been particularly attentive to the risks of delayed effects on offspring from low dose or low dose-rate exposures to ionizing radiation. Despite this, the current epidemiologic studies and scientific data are still insufficient to provide the necessary evidence for improving risk assessment guidelines. This literature review aims to inform future studies on multigenerational and transgenerational effects. It primarily focuses on animal studies involving in utero exposure and discusses crucial elements for interpreting the results. These elements include in utero exposure scenarios relative to the developmental stages of the embryo/fetus, and the primary biological mechanisms responsible for transmitting heritable or hereditary effects to future generations. The review addresses several issues within the contexts of both multigenerational and transgenerational effects, with a focus on hereditary perspectives.

CONCLUSIONS

Knowledge consolidation in the field of Developmental Origins of Health and Disease (DOHaD) has led us to propose a new study strategy. This strategy aims to address the transgenerational effects of in utero exposure to low dose and low dose-rate radiation. Within this concept, there is a possibility that disruption of epigenetic programming in embryonic and fetal cells may occur. This disruption could lead to metabolic dysfunction, which in turn may cause abnormal responses to future environmental challenges, consequently increasing disease risk. Lastly, we discuss methodological limitations in our studies. These limitations are related to cohort size, follow-up time, model radiosensitivity, and analytical techniques. We propose scientific and analytical strategies for future research in this field.

The new clinical classification of metastatic spinal malignancies serves as a vital reference for surgical management: a retrospective case-control study

BACKGROUND

It is commonly accepted that surgical treatment is an essential component of the comprehensive management of metastatic spinal malignancies. However, up until now, the clinical classification of metastatic spinal malignancies has not been well-structured.

METHODS

After IRB approval, 86 patients with metastatic spinal malignancies were adopted. According to the vascular distribution, stability of vertebrae, and degree of nerve compression, metastatic spinal malignancies can be classified into five types. Tumors classified as type I typically appear in the vertebral body. Type II tumors are those that develop in the transverse processes, superior and inferior articular processes, and spinal pedicles. Type III denotes malignancies that are present in the spinous process and vertebral plate. Types IVa and IVb are included in type IV. Type IVa combines type I and type II, whereas type IVb combines type II and type III. Type V tumors are those of types I, II, and III that co-occur and spread in different directions into the spinal canal. 20 of included 86 patients who did not receive segmental arterial embolization were set as the non-embolization group. The embolization group included 24 patients who received segmental arterial embolization on both sides of the diseased vertebrae. 42 patients were included in the offending embolization group after receiving responsible arterial embolization. A surgical intervention was performed within 24 h following an embolization. Surgical intervention with the purpose of removing as much of the tumor as possible and providing an effective reconstruction of the spinal column.

RESULTS

In comparison with the non-embolization group and embolization group, the offending embolization group presented unique advantages in terms of bleeding volume (p<0.001), operation time (p<0.001), and local recurrence rate within 12 months (p=0.006).

CONCLUSION

By significantly reducing surgical trauma and local recurrence rate (12 months), responsible arterial vascular embolization procedures together with associated surgical protocols developed on the basis of the clinical classification of metastatic spinal malignancies, are worthy of clinical dissemination.

Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective

Artificial intelligence (AI) in healthcare plays a pivotal role in combating many fatal diseases, such as skin, breast, and lung cancer. AI is an advanced form of technology that uses mathematical-based algorithmic principles similar to those of the human mind for cognizing complex challenges of the healthcare unit. Cancer is a lethal disease with many etiologies, including numerous genetic and epigenetic mutations. Cancer being a multifactorial disease is difficult to be diagnosed at an early stage. Therefore, genetic variations and other leading factors could be identified in due time through AI and machine learning (ML). AI is the synergetic approach for mining the drug targets, their mechanism of action, and drug-organism interaction from massive raw data. This synergetic approach is also facing several challenges in data mining but computational algorithms from different scientific communities for multi-target drug discovery are highly helpful to overcome the bottlenecks in AI for drug-target discovery. AI and ML could be the epicenter in the medical world for the diagnosis, treatment, and evaluation of almost any disease in the near future. In this comprehensive review, we explore the immense potential of AI and ML when integrated with the biological sciences, specifically in the context of cancer research. Our goal is to illuminate the many ways in which AI and ML are being applied to the study of cancer, from diagnosis to individualized treatment. We highlight the prospective role of AI in supporting oncologists and other medical professionals in making informed decisions and improving patient outcomes by examining the intersection of AI and cancer control. Although AI-based medical therapies show great potential, many challenges must be overcome before they can be implemented in clinical practice. We critically assess the current hurdles and provide insights into the future directions of AI-driven approaches, aiming to pave the way for enhanced cancer interventions and improved patient care.

Back to the Basics: Usefulness of Naturally Aged Mouse Models and Immunohistochemical and Quantitative Morphologic Methods in Studying Mechanisms of Lung Aging and Associated Diseases

Aging-related molecular and cellular alterations in the lung contribute to an increased susceptibility of the elderly to devastating diseases. Although the study of the aging process in the lung may benefit from the use of genetically modified mouse models and omics techniques, these approaches are still not available to most researchers and produce complex results. In this article, we review works that used naturally aged mouse models, together with immunohistochemistry (IHC) and quantitative morphologic (QM) methods in the study of the mechanisms of the aging process in the lung and its most commonly associated disorders: cancer, chronic obstructive pulmonary disease (COPD), and infectious diseases. The advantage of using naturally aged mice is that they present characteristics similar to those observed in human aging. The advantage of using IHC and QM methods lies in their simplicity, economic accessibility, and easy interpretation, in addition to the fact that they provide extremely important information. The study of the aging process in the lung and its associated diseases could allow the design of appropriate therapeutic strategies, which is extremely important considering that life expectancy and the number of elderly people continue to increase considerably worldwide.