Nature Reviews Nephrology

Hypertension affects two-thirds of people aged >60 years and significantly increases the risk of both vascular cognitive impairment and Alzheimer’s disease. Hypertension compromises the structural and functional integrity of the cerebral microcirculation, promoting microvascular rarefaction, cerebromicrovascular endothelial dysfunction and neurovascular uncoupling, which impair cerebral blood supply. In addition, hypertension disrupts the blood–brain barrier, promoting neuroinflammation and exacerbation of amyloid pathologies. Ageing is characterized by multifaceted homeostatic dysfunction and impaired cellular stress resilience, which exacerbate the deleterious cerebromicrovascular effects of hypertension. Neuroradiological markers of hypertension-induced cerebral small vessel disease include white matter hyperintensities, lacunar infarcts and microhaemorrhages, all of which are associated with cognitive decline. Use of pharmaceutical and lifestyle interventions that reduce blood pressure, in combination with treatments that promote microvascular health, have the potential to prevent or delay the pathogenesis of vascular cognitive impairment and Alzheimer’s disease in patients with hypertension.

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The lymphatics in kidney health and disease

Michael D. Donnan, Yael Kenig-Kozlovsky & Susan E. Quaggin

doi : 10.1038/s41581-021-00438-y

Nature Reviews Nephrology volume 17, pages655–675 (2021)

The mammalian vascular system consists of two networks: the blood vascular system and the lymphatic vascular system. Throughout the body, the lymphatic system contributes to homeostatic mechanisms by draining extravasated interstitial fluid and facilitating the trafficking and activation of immune cells. In the kidney, lymphatic vessels exist mainly in the kidney cortex. In the medulla, the ascending vasa recta represent a hybrid lymphatic-like vessel that performs lymphatic-like roles in interstitial fluid reabsorption. Although the lymphatic network is mainly derived from the venous system, evidence supports the existence of lymphatic beds that are of non-venous origin. Following their development and maturation, lymphatic vessel density remains relatively stable; however, these vessels undergo dynamic functional changes to meet tissue demands. Additionally, new lymphatic growth, or lymphangiogenesis, can be induced by pathological conditions such as tissue injury, interstitial fluid overload, hyperglycaemia and inflammation. Lymphangiogenesis is also associated with conditions such as polycystic kidney disease, hypertension, ultrafiltration failure and transplant rejection. Although lymphangiogenesis has protective functions in clearing accumulated fluid and immune cells, the kidney lymphatics may also propagate an inflammatory feedback loop, exacerbating inflammation and fibrosis. Greater understanding of lymphatic biology, including the developmental origin and function of the lymphatics and their response to pathogenic stimuli, may aid the development of new therapeutic agents that target the lymphatic system.

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Big Data in Nephrology

Navchetan Kaur, Sanchita Bhattacharya & Atul J. Butte

doi : 10.1038/s41581-021-00439-x

Nature Reviews Nephrology volume 17, pages676–687 (2021)

A huge array of data in nephrology is collected through patient registries, large epidemiological studies, electronic health records, administrative claims, clinical trial repositories, mobile health devices and molecular databases. Application of these big data, particularly using machine-learning algorithms, provides a unique opportunity to obtain novel insights into kidney diseases, facilitate personalized medicine and improve patient care. Efforts to make large volumes of data freely accessible to the scientific community, increased awareness of the importance of data sharing and the availability of advanced computing algorithms will facilitate the use of big data in nephrology. However, challenges exist in accessing, harmonizing and integrating datasets in different formats from disparate sources, improving data quality and ensuring that data are secure and the rights and privacy of patients and research participants are protected. In addition, the optimism for data-driven breakthroughs in medicine is tempered by scepticism about the accuracy of calibration and prediction from in silico techniques. Machine-learning algorithms designed to study kidney health and diseases must be able to handle the nuances of this specialty, must adapt as medical practice continually evolves, and must have global and prospective applicability for external and future datasets.

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Non-invasive molecular imaging of kidney diseases

Barbara M. Klinkhammer, Twan Lammers, Felix M. Mottaghy, Fabian Kiessling, Jürgen Floege & Peter Boor

doi : 10.1038/s41581-021-00440-4

Nature Reviews Nephrology volume 17, pages688–703 (2021)

In nephrology, differential diagnosis or assessment of disease activity largely relies on the analysis of glomerular filtration rate, urinary sediment, proteinuria and tissue obtained through invasive kidney biopsies. However, currently available non-invasive functional parameters, and most serum and urine biomarkers, cannot capture intrarenal molecular disease processes specifically. Moreover, although histopathological analyses of kidney biopsy samples enable the visualization of pathological morphological and molecular alterations, they only provide information about a small part of the kidney and do not allow longitudinal monitoring. These limitations not only hinder understanding of the dynamics of specific disease processes in the kidney, but also limit the targeting of treatments to active phases of disease and the development of novel targeted therapies. Molecular imaging enables non-invasive and quantitative assessment of physiological or pathological processes by combining imaging technologies with specific molecular probes. Here, we discuss current preclinical and clinical molecular imaging approaches in nephrology. Non-invasive visualization of the kidneys through molecular imaging can be used to detect and longitudinally monitor disease activity and can therefore provide companion diagnostics to guide clinical trials, as well as the safe and effective use of drugs.

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Publisher Correction: Non-invasive molecular imaging of kidney diseases

Barbara M. Klinkhammer, Twan Lammers, Felix M. Mottaghy, Fabian Kiessling, Jürgen Floege & Peter Boor

doi : 10.1038/s41581-021-00464-w

Nature Reviews Nephrology volume 17, page704 (2021)

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Publisher Correction: Sustainable Development Goals relevant to kidney health: an update on progress

Valerie A. Luyckx, Ziyad Al-Aly, Aminu K. Bello, Ezequiel Bellorin-Font, Raul G. Carlini, June Fabian, Guillermo Garcia-Garcia, Arpana Iyengar, Mohammed Sekkarie, Wim van Biesen, Ifeoma Ulasi, Karen Yeates & John Stanifer

doi : 10.1038/s41581-021-00473-9

Nature Reviews Nephrology volume 17, page704 (2021)

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