Editorial: Small vessel disease: From diagnosis to organized management pathways
Bibliographic record
Abstract
Cerebral small vessel diseases (SVD) is a main underlying cause not only for ischemic and hemorrhagic stroke but also for cognitive impairment and dementia 1 . It includes several different diseases, both sporadic, e.g. arteriolosclerosis (type I SVD) and cerebral amyloid angiopathy (type II SVD) 2 , and inherited or genetic ones. Globally, SVDs have a standardized definition of neuroimaging markers with variable specificity for the different forms of diseases rather than mixed SVDs 3,4 . Its prevalence makes SVD a huge matter of public health for health care providers and politicians and its understanding and awareness in a broad medical community is of paramount importance for the prevention of stroke and cognitive impairment 5 , tailored treatment 6 , in particular in the comorbid patient and organization of pathways of care in the near future. Indeed, SVD has a great impact as a comorbidity in patients with stroke from other defined causes 7 and it increases the hemorrhagic risk and worsens the functional outcome in patients with stroke 8 in general and in patients treated with intravenous thrombolysis and/ or endovascular thrombectomy 9 . Neuroimaging markers of SVD are also predictive of intracranial bleeding risk in patients with cardioembolic stroke on anticoagulant treatment 10 . Therefore, SVD is gaining increasing attention in recent years at different levels, from preclinical and clinical research on disease mechanisms to its phenotyping and standardization of neuroradiological markers up to the treatment and management of patients in organized pathways. It is a condition with a high prevalence in its chronic manifestations, underpinning predominantly non-acute neuroimaging markers 3,4 , and with a high incidence in acute manifestations 5 , both ischemic and hemorrhagic ones. These facts highlight SVD as one of the most frequent vascular diseases in aging, a frequent comorbidity in patients presenting with acute cerebrovascular events from other causes, and an important cause of cognitive impairment and functional impairment, due to components of gait alterations. Some issues in SVD deserve particular attention and help to increase the awareness of the complex interplay of several vascular conditions in comorbid and aging people, i.e. the relationship with classical vascular risk factors and in particular arterial hypertension, the impact on gait and the correlation between neuroimaging markers and systemic inflammatory biomarkers.First, arterial hypertension is a strong contributor to SVD and to clinical overt and covert manifestations 6 . The relationship between office, ambulatory and 24 hours measurement of blood pressure (BP) and neuroimaging markers of SVD (cerebral microbleeds, lacunes, white matter hyperintensities, enlarged perivascular spaces, etc.) is an intriguing topic with relevant implications for pathophysiology of the cerebral microvascular damage and for prevention, also considering the role of sleep and sleeping hours on beta amyloid trafficking/removal in the brain 11 . Nighttime BP provides the most valuable prognostic information for adverse health outcomes 12 , sometimes being due to lack of antihypertensive therapy during the nighttime or to sympathetic modulation of the nighttime BP. One of the potential confounding factors is represented by the typical comorbidities of hypertensive patients, such as diabetes, dyslipidemia, or obesity. Diabetes is also related to SVD burden through autonomic disfunction, as suggested by the heart rate variability (HRV) measure. Indeed, HRV is included in risk stratification models in patients with cardiovascular diseases and in an inpatient's population of diabetic patients lower HRV was independently associated with total burden of SVD. In an individual approach to a complex and comorbid patient it is hard to identify and separate the different and interplaying pathophysiological changes in the cerebral small vessels with hypertension and other vascular risk factors or to distinguish the role of concurrent SVD (e.g. type I and type II SVD) 2 . It is also challenging to demonstrate which BP threshold should be addressed and reached for these patients with the aim to slow the rate of SVD burden accumulation within years and to prevent stroke and cognitive impairment 13 .Second, SVD neuroimaging markers are a common finding in aging and some of them, in particular age-related white matter lesions in the periventricular and deep frontal lobes, are associated with gait and balance impairment 14 . Moreover, white matter lesion load progression is associated with progressive gait impairment even in healthy elderly people. In other studies on older patients with SVD, cerebral microbleeds (CMB) in temporal, frontal, and basal ganglia regions were associated independently of other SVD markers with gait and balance impairment 15 . These SVD markers may be found also in patients with other subtypes of stroke 7 and in patients with atrial fibrillation [in the Swiss cohort small subcortical infarctions were found in 368 patients (21%), CMB in 372 (22%), and white matter lesions in 1715 (99%)]. 16 SVD burden may contribute to worsening the functional outcome, also affecting the balance and gait function and increasing the risk of falls. The association of white matter hyperintensities with arterial hypertension also in atrial fibrillation patients 17 is meaningful and requires an awareness in order to improve the prevention of cerebrovascular events and cognitive impairment.Third, the pathophysiology of cerebral damage in SVD is still a matter of debate, involving several mechanisms and triggers with a proposed role for an inflammatory mechanism 18 . In this regard, systemic inflammation has been proposed as a trigger of a proinflammatory environment in the central nervous system with further acceleration of the molecular cascade involved in SVD, including endothelial dysfunction and breakdown of the blood brain barrier 19 . Among the biomarkers of systemic inflammation, the neutrophil/lymphocyte ratio (NLR) has been linked to cerebrovascular diseases and their prognosis, both in ischemic and hemorrhagic stroke. Moreover, NLR is also a potential predictor of the risk of cognitive impairment. The mechanism underlying NLR with cognitive impairment in SVD is still unclear, but inflammation has been implicated as risk factor for SVD and immune activation increases the harmful effect of vascular risk factors 20 . Another potential issue in the acute evolution of lacunar infarctions is blood rheology as expressed by viscosity measures (e.g. hematocrit). This mechanism has implications in contributing to hypoperfusion in small vessels and early neurological deterioration from the clinical side.SVD is a multifaceted disease with several involved factors interplaying in an intricate and not always easily discriminable way. The relevance of SVD in a population setting makes it a huge issue for prevention, treatment and the organization of the health care pathways, both in high-income and in middle-low-income countries. Increased awareness about it together with a coordinated and multinational initiative for research and clinical management is needed in order to overcome the consequences of SVD starting from primary prevention models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.019 | 0.021 |
| Insufficient payload (model declined to judge) | 0.021 | 0.017 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".