Operationalized research criteria for mild cognitive impairment with Lewy bodies (MCI-LB) were published in 2020. The aim of this systematic review and meta-analysis was to review the evidence for the diagnostic clinical features and biomarkers in MCI-LB set out in the criteria.MEDLINE, PubMed, and Embase were searched on 9/28/22 for relevant articles. Articles were included if they presented original data reporting the rates of diagnostic features in MCI-LB.Fifty-seven articles were included. The meta-analysis supported the inclusion of the current clinical features in the diagnostic criteria. Evidence for striatal dopaminergic imaging and meta-iodobenzylguanidine cardiac scintigraphy, though limited, supports their inclusion. Quantitative electroencephalogram (EEG) and fluorodeoxyglucose positron emission tomography (PET) show promise as diagnostic biomarkers.The available evidence largely supports the current diagnostic criteria for MCI-LB. Further evidence will help refine the diagnostic criteria and understand how best to apply them in clinical practice and research. The criteria identified core clinical features  and proposed biomarkers . These core clinical features and proposed biomarkers are included in the diagnostic algorithm (see The diagnosis of mild cognitive impairment (MCI) has become a routine part of clinical care in aging and dementia. In memory clinics, one in five attendees receives a diagnosis of MCI.The criteria were developed based on expert consensus and the authors advised that they should be validated in prospective cohorts before entering routine clinical use. Since the criteria were agreed, there has been a significant increase in publications relating to MCI-LB. The Alzheimer’s Association International Society to Advance Alzheimer’s Research and Treatment (ISTAART) Lewy Body Dementias Professional Interest Area (PIA) Prodromal DLB Working Group agreed that a review of all evidence for the MCI-LB criteria would be of great benefit to the field.The objective of this article was to systematically review the evidence for the prevalence rates of core clinical features, supportive clinical features, proposed biomarkers, and potential biomarkers in MCI-LB, including meta-analytical estimations of prevalence rates. Where possible, we compared these with the prevalence of these features in other forms of MCI, such as MCI-AD and stable MCI.2 |2.1 |4We followed the PRISMA guidelines for systematic reviews and meta-analyses.post mortem studies); (2) data from the MCI phase in participants diagnosed with clinical DLB at later follow-up assessments; (3) data from cohorts of clinically diagnosed MCI-LB, defined using recognized criteria . For prevalence rates of core clinical features, only data from articles demonstrating MCI-LB by methods 1 or 2 were included, to avoid circularity. For reports of diagnostic accuracy of proposed biomarkers, only cases diagnosed in the absence of the biomarker of interest were included, to avoid circularity. For inclusion, it was necessary that data were available relating exclusively to MCI-LB participants .Inclusion criteria: original data investigating the presence of one or more of the diagnostic features in MCI-LB. The presence of diagnostic features in MCI-LB could be demonstrated by (1) data from the MCI phase in cases of autopsy-confirmed Lewy body (LB) disease  2.2 |PubMed, MEDLINE, and Embase were searched for all English Language publications on 28 September 2022. The search strategies were as follows: OR  OR  OR  OR (mild cognitive impairment[MeSH Terms]) OR ) AND  OR  OR  OR  OR (diffuse lewy body disease[MeSH Terms]) OR  OR (synuclein[MeSH Terms]) OR (synucleins [MeSH Terms])) AND  AND 2.3 |A team of four reviewers evaluated articles to ensure rapid assessment . At the title and abstract screening stages, each article was independently assessed by two reviewers. In cases of disagreement, a third reviewer reviewed the article for inclusion or exclusion. At the full-text screening stage, all articles were independently reviewed by two reviewers. In cases of disagreement, the two reviewers met to discuss the article. Where a decision on inclusion/exclusion could not be reached, a third reviewer made the final decision.2.4 |5 by a single researcher (AH), who consulted with a second researcher (DF) in case of doubt.Data were extracted by a single reviewer for each article. Data were extracted for the percentage prevalence of diagnostic features in MCI-LB and any comparison MCI groups. Data were also extracted for the predictive validity of diagnostic features . The methodological quality of studies was assessed with the CASP checklist for case control studies,2.5 |Data for the meta-analysis was extracted by a single researcher (A.H.). Only features which were reported in three or more independent studies using comparable assessment methods were included in the meta-analysis. Publications from the same research group were closely examined for overlapping data sets. In case of overlap, only data from the largest cohort reporting the frequency of a feature was included in the meta-analysis. Clarification was sought from the authors where required. Articles presumed or known to contain overlapping data are reported together in the Tables.pP) were calculated for each MCI group, and post hoc paired comparisons were performed between the MCI-LB group and other MCI groups with a p value <0.05 deemed significant. Heterogeneity in these analyses was assessed through visual inspection of forest plots , and complementary meta-analytical parameters are provided in 6Variables included in the meta-analysis were size of MCI groups and frequency of features in each MCI group. Using random-effects models, weighted pooled proportions , with fluctuations and visual hallucinations showing the strongest relationship.22 However, another longitudinal cohort found that, within an MCI-LB cohort, the presence of core clinical features did not predict conversion to dementia  23  23 .3.2 |29 When comparing MCI-LB with MCI-AD, our meta-analyses showed that the pooled prevalence of anxiety (31% vs. 18%), depression (37% vs. 22%), apathy (47% vs. 23%) and delusions (11% vs. 4%) was higher in MCI-LB  were reported in up to seven studies.n MCI-LB . We founn MCI-LB .31  and MCI-AD .33 Two studies have measured olfactory function using standardized tests.34 Both studies found poorer olfactory function in MCI-LB compared with MCI-AD, with an AUROC of 0.67 34 and 0.85 20  and MCI-AD (19%). 0.85 20 .33Hypersomnia: Daytime sleepiness was present in the majority of MCI-LB participants in two studies , whereas also being relatively frequent in MCI-AD .33 We did not find any reports of rates of prolonged/recurrent delirium or sensitivity to antipsychotic agents in MCI-LB.Other supportive features: One study has reported that frequent falls were more common in MCI-LB patients (43%) than MCI-AD (11%), but subjectively reported balance problems, dizziness/fainting and transient loss of consciousness were not.3.3 |37 In one publication that combined data from two cohorts, 40/61 probable MCI-LB had a positive scan , compared with 7/26 possible MCI-LB and 5/57 MCI-AD .37 83% of patients with MCI-LB and a positive 123I-FP-CIT scan had parkinsonism, compared to 55% in those with a normal 123I-FP-CIT scan. In this study, the images were visually rated, with semi-quantitative specific binding ratios (SBRs) used to support visual rating.Striatal dopaminergic imaging: We identified data reporting the diagnostic accuracy of dopaminergic imaging in a total of 121 possible or probable MCI-LB patients.38 22/34 (65%) of the MCI-LB cohort were below this threshold.35Another study investigated semi-quantitative specific binding ratio in an MCI-LB cohort using an SBR z-score threshold of −0.82 for abnormality, determined from an autopsy cohort.39 The sensitivity to detect probable MCI-LB was 59% (95% CI 42–75%) and the specificity was 88% (75–96%).Cardiac MIBG: Only one study with 37 probable MCI-LB and 43 MCI-AD has reported the accuracy of cardiac denervation as a diagnostic tool in MCI-LB.39Combining data from FP-CIT and MIBG imaging in the same cohort, requiring both scans to be abnormal resulted in a sensitivity to detect MCI-LB of 50%, with a specificity of 100%.40 The median duration of the MCI phase was 2 years. A similar longitudinal cohort of people with polysomnography confirmed RBD found that MCI was a risk factor to develop dementia first rather than parkinsonism first in those who developed a neurodegenerative disease.41Polysomnography: No studies were found that directly reported the rates of REM sleep without atonia in MCI-LB. A longitudinal study of people with polysomnography confirmed RBD identified an MCI stage in 25/29 people who later developed DLB.3.4 |46 Two of these cohorts also found no difference between MCI-AD and MCI-LB 46 One cohort found greater atrophy in MCI-AD,45 although the degree of difference was modest .Preservation of medial temporal lobe structures: visually rated medial temporal lobe atrophy scores in MCI-LB did not differ with scores in controls or stable MCI in three cohorts 47 When performing voxel-based analyses across the whole brain, the same study reported volume loss in the right hippocampus in MCI-LB and MCI-AD compared with controls, but no difference between MCI-AD and MCI-LB.47 In another cohort, there were no differences between MCI-LB and controls or MCI-AD in gray matter volume of medial temporal lobe structures,49 whereas an analysis of cortical thinning in the same cohort identified a small area of greater thinning in MCI-AD compared to MCI-LB in the left parahippocampal region.46Hippocampal volume loss was reported in MCI-AD and MCI-LB compared with controls using an automated measure, but no difference between MCI-AD and MCI-LB was found.16 Similarly, in a longitudinal study comparing brain atrophy subtypes in converters from MCI to dementia, converters to DLB were over-represented in two subtypes with no hippocampal atrophy , with 60% of DLB cases categorized to these subtypes, compared with 29% of converters to AD.50In a longitudinal study, 85% of MCI cases that progressed to DLB had normal hippocampal volumes, whereas 61% of MCI cases that progressed to AD dementia had abnormal volumes.48 Cortical thinning was found in an area of the right insula in MCI-LB compared with MCI-AD.46 In the same cohort, gray matter volume loss was also identified in the insula bilaterally in MCI-LB compared with controls, but not compared with MCI-AD.49 Another cohort found insula volume was decreased in both MCI-LB and MCI-AD compared with controls, but found no significant difference between MCI-AD and MCI-LB in a region of interest analysis and no difference between the groups in the insula using a voxel-wise analysis.47Insular thinning and gray matter volume loss: Cortical thickness and gray mater volumes have been compared using voxel-wise analysis in MCI-LB.54 One recent study found that visual rating of FDG PET demonstrated an accuracy of 77% to differentiate MCI-LB from MCI-AD,52 whereas another found that 50% of MCI-LB had primary visual cortex hypometabolism, compared with 24% of MCI-AD.17 Another study found that using the cingulate:cuneus/precuneus ratio demonstrated sensitivity of 59% and specificity of 90% to differentiate MCI-LB from MCI-AD.15Low occipital uptake on metabolism/perfusion imaging: MCI-LB patients have consistently demonstrated reduced fluorodeoxyglucose (FDG) PET uptake in posterior brain areas 57 The posterior cingulate:precuneus ratio measured by MRI arterial spin labeling (similar to the cingulate:cuneus/precuneus ratio above) was significantly greater in MCI-LB compared with controls, but not when compared with MCI-AD.55A similar pattern of regional brain changes has been reported in brain perfusion studies.60 increased theta power,58 increased delta power,60 and decreased alpha and beta power.59 Similar changes were reported in MCI with polysomnography confirmed RBD (probable MCI-LB) compared with controls and MCI without RBD.61 Greater dominant frequency variability has also been observed in MCI-LB compared with controls/MCI-AD in one study,10 with another study finding no statistically significant difference.59Quantitative electroencephalography (EEG) slowing and dominant frequency variability: several studies have demonstrated evidence of quantitative resting-state EEG slowing in MCI-LB relative to controls and/or MCI-AD, including lower dominant/peak frequency,58 EEG changes also predicted conversion to dementia in this study. Another study found AUROC to differentiate MCI-LB and MCI-AD ranging from 0.60 to 0.71 for the same measures,59 though theta:alpha power ratio was associated with an increased risk of transition to dementia in the same cohort.62 A third study found two features that discriminated between MCI-LB and MCI-AD with an AUROC greater than 0.7: parietal delta power (AUROC 0.72) and temporal delta power (AUROC 0.71).60 A longitudinal study categorized EEGs based on abnormalities associated with DLB . These abnormalities were present in 100% of MCI-LB participants at baseline, whereas these changes were present in only 7% of MCI-AD.10 These findings remained stable over the course of the study.When discriminating MCI-LB from MCI-AD, a range of EEG measures were effective in one study, including theta power , beta power , and theta:alpha ratio .4 |3 There has been a substantial increase in the evidence for the clinical and biomarker presentation on MCI-LB since the diagnostic criteria were submitted for publication in October 2019. Half of the articles cited in this review were published since this time. Therefore, this is an appropriate time to review the extent to which the new evidence supports the published criteria.This article presents a systematic review and meta-analysis of the evidence for the 2020 diagnostic criteria for MCI-LB.4.1 |post mortem confirmation of LB pathological changes or longitudinal follow-up with a confirmed clinical diagnosis of DLB, while we excluded articles with a cross-sectional clinical diagnosis of MCI-LB, to avoid circularity. There were only two studies with post mortem confirmation (n = 17 MCI-LB) and only one of these studies reported rates of RBD and hallucinations. It is difficult to draw any conclusions from this limited data. However, from previous post mortem studies in DLB, we know that all four core clinical features are predictive of the presence of LB pathology.63Core clinical features are used to diagnose MCI-LB and DLB. When considering the prevalence of core clinical features in MCI-LB, we included articles with Longitudinal clinical cohorts found highly variable rates of core features, but all four core features were more common in MCI-LB than MCI-AD and stable MCI . The sym63 As a result, the sensitivity of the clinical core features in the MCI-LB diagnostic criteria is likely to be lower than in the DLB criteria.Parkinsonism and RBD were the most common symptoms in MCI-LB, with visual hallucinations and fluctuations being less common. The rates of these symptoms  were lower than the rates observed in DLB, where rates ≥ 75% are observed over the course of the disease.64 Lower rates of cognitive fluctuations may reflect later onset of this symptom, difficulty in assessing fluctuations in early disease stages, or a misattribution of symptoms to a non-neurodegenerative cause .The lower rates of visual hallucinations may reflect that this symptom tends to develop later than the other three core features of DLB.2 in these analyses was relatively low but we still observed variability across studies in reported rates of core features. This variability was expected and may be due to differences in participant recruitment  and differences in the assessment of core features . Studies relying on a clinical diagnosis of DLB at the dementia stage, rather than post mortem confirmation, will inevitably exclude people with DLB who do not present with typical clinical symptoms. As such, they may overestimate the rates of clinical features in the MCI stage. Larger cohorts of post mortem cases with clinical characterization during MCI will be needed to confirm the validity of the core clinical features for MCI-LB.The heterogeneity index I4.2 |The supportive clinical features are too numerous to deal with individually, but some general conclusions can be made. First, in this systematic review and meta-analysis we observe that none of the supportive clinical features has demonstrated the sensitivity or specificity across different studies to suggest that it should be promoted to the level of a core clinical feature for the diagnosis of MCI-LB. Second, despite their limited diagnostic accuracy, supportive clinical features are very common in MCI-LB, and should be enquired about in clinical encounters to support diagnosis and identify potentially treatable symptoms to improve the quality of life of the person with MCI-LB.31From our meta-analysis, there is clear evidence for the inclusion of anxiety, apathy, depression, delusions, constipation, and difficulty emptying the bladder as supportive symptoms of MCI-LB. There is also consistent evidence of increased overall autonomic symptoms in MCI-LB compared with MCI-AD.65 Furthermore, the presence of psychiatric diagnoses such as major depressive disorder and primary psychotic disorders are often exclusion criteria in MCI cohorts, therefore there is a risk that the prevalence of these symptoms is systematically and artificially reduced in these cohorts.Non-visual hallucinations and delusions are not commonly reported in MCI-LB. The low rates of symptoms of psychosis in MCI-LB may be because these features tend to present later in disease, or because MCI-LB cohorts are currently not identifying cases with a concurrent psychiatric presentation.Future research should focus on whether combinations of symptoms or the use of clinical scales to measure severity of symptoms, such as constipation and hyposmia, can improve the identification of MCI-LB. These could form important screening questions during clinical assessment or for participant recruitment for MCI-LB studies and clinical trials.4.3 |39 Unfortunately, the sensitivity in possible MCI-LB appears to be lower than probable MCI-LB.39 Further longitudinal studies are needed to understand the predictive validity of these biomarkers, particularly in possible MCI-LB. The potential for relatively high rates of false negative scans in MCI-LB may limit the utility of these biomarkers in research and clinical practice.Our systematic review revealed limited evidence for the diagnostic effectiveness of dopaminergic imaging and cardiac MIBG in MCI-LB. The studies available suggest that specificity is high in probable MCI-LB compared with MC-AD (88%), but sensitivity is lower (59%–66%).41 Furthermore, the presence of RBD has been associated with faster cognitive decline in LB diseases.66From longitudinal studies, it is clear that people with clinical RBD and polysomnography confirmed REM sleep without atonia, along with MCI (by definition probable MCI-LB under the current criteria) are at very high risk of conversion to DLB.4.4 |50 However, preserved hippocampal volume was also common in MCI-AD (39%) and stable MCI (76%).16 This would need to be considered before using preserved hippocampal volume as a method to enrich MCI samples with MCI-LB cases. The new signature of preserved hippocampal volume in combination with presence of cortical atrophy 67 may be more specific but it has only been investigated in one MCI-LB study so far.50Relative preservation of medial temporal lobe structures on structural imaging: Cross-sectional comparisons of medial temporal lobe atrophy in MCI-LB and MCI-AD have reported no significant differences between the groups in most studies. This suggests that visual rating and volumetric measures of medial temporal lobe atrophy might not effectively discriminate between MCI-LB and MCI-AD. Despite this, the absence of hippocampal atrophy in MCI has been reported to be associated with progression to DLB.Insular thinning and gray matter volume loss on MRI: There have been inconsistent reports of gray matter changes in the insula in MCI-LB compared with MCI-AD and controls. The diagnostic accuracy of this biomarker remains to be determined.23 This may be useful to enrich cohorts with dementia converters, though this finding requires replication.Beyond the findings for medial temporal lobe and insular cortex, visual assessment of global cortical atrophy and posterior atrophy were associated with a shorter time of progression to DLB in MCI-LB.57 Visual rating and quantitative measures aiming to measure the cingulate island sign using FDG PET have reported accuracy values similar to that found for the proposed biomarkers.52 However, a standardized method of reporting or analyzing these scans has not yet been established. This should be an area of focus for future research. Other quantitative measurements have also been reported, and deserve further investigation, including medial temporal:substantia nigra and occipital:medial temporal ratios.55At the group level, MCI-LB demonstrates posterior cerebral hypometabolism and hypoperfusion when compared with MCI-AD and controls 58 but not others.60 EEG abnormalities may increase the risk of conversion to dementia.62 An EEG marker that predicts conversion to dementia could be useful in enriching clinical trial cohorts, particularly in studies where conversion to dementia is an outcome measure. At present, the evidence available supports the inclusion of EEG slowing as a potential biomarker. In contrast, there is less consistent evidence for dominant frequency variability as a biomarker for MCI-LB.59Quantitative EEG showing slowing and dominant frequency variability: resting state EEG studies have demonstrated consistent evidence of slowing  in MCI-LB compared with controls and MCI-AD. The discriminant ability of EEG is less clear, with some studies demonstrating excellent discriminant ability 59 This raises the possibility that EEG may be a more effective biomarker in MCI-LB with more significant cognitive impairment and early visual hallucinations (“top down” disease spread), whereas FP-CIT and cardiac MIBG may be most effective in those with early RBD and parkinsonism (“bottom up” disease spread). This should be tested in future research.Interestingly, EEG slowing was associated with visual hallucinations and poorer cognitive performance.4.5 |This is the first systematic review and meta-analysis of the evidence for the research diagnostic criteria for MCI-LB. Our report includes a significant amount of new information that was unpublished at the time the criteria were written. We hope that the findings will be useful to researchers who are developing MCI-LB cohorts.68 With longer-term clinical follow-up of these cohorts and, crucially, post mortem confirmation of diagnosis, greater differences between MCI-LB and MCI-AD may become evident.The limitations of this systematic review include the variability between studies in case ascertainment and the method of determining the presence of diagnostic features. LB disease biomarkers were often used in diagnosis, but most cohorts did not use biomarkers to confirm Alzheimer’s disease pathology in the MCI-AD groups. The clinical diagnosis on MCI-AD has low diagnostic specificity.We found little evidence for one of the most critical aspects of the diagnostic criteria – proposed biomarkers. This was in part due to our selection criteria, which excluded articles that included the symptom or biomarker of interest as part of the diagnostic process. For example, if abnormal dopaminergic imaging was used in the diagnosis MCI-LB, we chose not to use this cohort to identify the prevalence of abnormal dopaminergic imaging in MCI-LB. This was crucial, to avoid circularity in our evidence, and increases the reliability of the findings presented.While all included studies were of an overall good quality, the CASP criteria indicated that matching of demographic variables between groups and/or correction for these confounders should be considered in future studies. In addition, investigation of the potential impact of medications on clinical features and disease biomarkers should also be considered.4.6 |69 Assays have been developed to detect abnormal alpha-synuclein in skin, cerebrospinal fluid (CSF), and blood,72 although the development of alpha-synuclein imaging ligands is less advanced.73 A widely available, sensitive and specific test for synucleinopathy would revolutionize the in-vivo diagnosis of LB disease at all clinical stages and would represent a significant advance for the field. Assays to detect abnormal alpha-synuclein in the skin and CSF are already commercially available in some regions.74 There is evidence for the accuracy of CSF seeding assays in differentiating MCI-LB from controls and MCI-AD.70 We are unaware of evidence for the detection of alpha-synuclein in skin in MCI-LB, but there is evidence that skin alpha-synuclein can differentiate idiopathic RBD from controls.75The diagnosis of prodromal disease is challenging, as by its nature, it reflects a stage of disease where symptoms and biomarker results will be less clear. As a result of recent advances in imaging and biofluid analysis, diagnosis in Alzheimer’s disease is moving toward a biological definition.76 The aim of these tests would be to achieve high sensitivity, with acceptable specificity. To date, most clinical features achieve acceptable specificity with suboptimal sensitivity, therefore a combination of factors  may improve this. Artificial intelligence tools may help to increase the accuracy of single biomarkers and facilitate combining multiple clinical and biomarker features, to improve diagnostic accuracy.77 Novel digital biomarkers  could be developed to identify diagnostic features at an earlier stage, such as cognitive fluctuation, motor changes, and REM sleep behavior disorder.The identification of MCI-LB may benefit from a two-stage process, where screening measures of clinical symptoms and signs are used to detect those at risk, followed by more specialized biomarkers for diagnostic confirmation. There are many potential avenues to investigate for initial screening measures, including simple screening questionnaires that may include core and suggestive clinical features reviewed here. Diagnostic tools have been validated for the dementia stage of DLB, but there are none, at present, for MCI-LB.78 and quantitative susceptibility mapping,79 do not appear to currently be specific markers for MCI-LB, but interest in their potential to assess neurodegeneration in DLB has recently been rekindled.80 Other modalities remain to be investigated, such as susceptibility-weighted and neuromelanin imaging of the substantia nigra 81 and voxel-wise whole-brain dopaminergic imaging.82 Novel EEG measures are also currently under investigation. In spatiotemporal analysis, MCI-LB show more frequent alterations in microstates than controls.83 However, alpha reactivity on eye opening 84 and on measures of inter- and intra-hemispheric alpha connectivity 85 do not appear to differ between MCI-LB and MCI-AD at present.More advanced MRI modalities, such as fMRI connectivity 4.7 |87 Recruitment strategies and inclusion/exclusion criteria for longitudinal cohorts should seek to include the broad spectrum of presentations of prodromal DLB.The DLB research community and funders should support ongoing MCI-LB cohorts and the development of new MCI-LB cohorts in order to increase the evidence available on which to base our diagnostic criteria for this disease stage and validate the criteria for routine clinical use. This review has focused on MCI-LB, but research is also needed into presentation prior to the onset of MCI, and other prodromal presentations, such as psychiatric-onset and delirium-onset DLB, which may occur with or without concurrent MCI.MCI-LB cohorts should, wherever possible, collect dopaminergic imaging and cardiac MIBG in order to determine the accuracy of these biomarkers and their predictive validity at this disease stage. Established cohorts which have not yet published articles that allow extraction of the sensitivity and specificity of these biomarkers when they are excluded from the diagnostic algorithm  should consider doing so. Similarly, cohorts with data on core and suggestive clinical features should present these data in future publications.Promising results have been reported for quantitative EEG and FDG PET. Further studies are needed to determine their diagnostic utility.Wherever possible, MCI cohorts should include the option of brain donation for participants. This will allow the comparison of diagnostic features and biomarkers with pathological diagnosis.88Cohorts should seek to co-ordinate assessment schedules and consider establishing questionnaires to ascertain the presence or absence of clinical features in order to improve comparability between centers. Where possible, this should include the collection of data on olfactory function and autonomic dysfunction. Ideally, large cohorts investigating subjective cognitive impairment and MCI should collect data on LB disease features, given that a significant proportion of their cases are likely to have LB disease.post mortem studies, and there is evidence that the presence of co-pathology can influence clinical presentation.89 The influence of AD and other co-pathologies on clinical presentation and disease progression in MCI-LB should be investigated.Co-pathology is common in the dementia The accuracy of the MCI-LB diagnostic criteria to differentiate between MCI-LB and atypical AD presentations and neurodegenerative diseases other than AD that may present with MCI  should be investigated.90Many of the cohorts included in this review were predominantly male and of European descent. The difference in clinical presentation and disease biomarkers in males and females and in diverse populations with MCI-LB should be investigated.5 |The available evidence largely supports the current diagnostic criteria for MCI-LB, although there is a limited amount of evidence for proposed biomarkers, at present. The research criteria for MCI-LB present a rational framework that will allow sites across the world to recruit longitudinal cohorts of MCI-LB. Over the coming years, further evidence should emerge to help refine the diagnostic criteria to improve their sensitivity to identify MCI-LB and understand how best to apply them in clinical practice and research.Appendix 1Supplementary FiguresSupplementary TablesSupplementary Information


Migrants are over-represented in SARS-CoV-2 infections globally; however, evidence is limited for migrants in England and Wales. Household overcrowding is a risk factor for SARS-CoV-2 infection, with migrants more likely to live in overcrowded households than UK-born individuals. We aimed to estimate the total effect of migration status on SARS-CoV-2 infection and to what extent household overcrowding mediated this effect.We included a subcohort of individuals from the Virus Watch prospective cohort study during the second SARS-CoV-2 wave (1 September 2020–30 April 2021) who were aged ≥18 years, self-reported the number of rooms in their household and had no evidence of SARS-CoV-2 infection pre-September 2020. We estimated total, indirect and direct effects using Buis’ logistic decomposition regression controlling for age, sex, ethnicity, clinical vulnerability, occupation, income and whether they lived with children.In total, 23 478 individuals were included. 9.07% (187/2062) of migrants had evidence of infection during the study period vs 6.27% (1342/21 416) of UK-born individuals. Migrants had 22% higher odds of infection during the second wave . Household overcrowding accounted for approximately 36% (95% CI −4% to 77%) of these increased odds .Migrants had higher odds of SARS-CoV-2 infection during the second wave compared with UK-born individuals and household overcrowding explained 36% of these increased odds. Policy interventions to reduce household overcrowding for migrants are needed as part of efforts to tackle health inequalities during the pandemic and beyond. Migrants in England and Wales may be at greater risk of exposure to SARS-CoV-2 due to unique risk factors, including over-representation in front-line jobs, an increased likelihood of living in multigenerational households and difficulties in accessing primary care. Research shows that migrants in high-income countries have been disproportionally infected with SARS-CoV-2. It is likely that, due to their pre-existing vulnerabilities, this is similarly the case for migrants in England and Wales; however, quantitative evidence addressing this is lacking.We investigated the effect of being a migrant on SARS-CoV-2 infection during the second wave of the pandemic in a cohort in England and Wales. We also determined the proportion of the effect mediated by household overcrowding after controlling for age, sex, ethnicity, clinical vulnerability, occupation, income and the presence of children in the household. Migrants had 22% higher odds of being infected with SARS-CoV-2 than their UK-born counterparts, and household overcrowding accounted for approximately 36% of these increased odds.Our findings highlight the role of household overcrowding in the disproportionate impact of SARS-CoV-2 infections on migrants. They also demonstrate the urgent need for policy interventions that improve housing conditions as part of efforts to reduce health inequalities. Further research investigating other causes of migrants’ over-representation in infection cases is also needed to inform further targeted policy interventions.Globally, the UK has the fifth largest migrant (non UK-born) population comprising approximately 9.57 million people in 2020.The built environment is a wider determinant of health.Household overcrowding is particularly relevant to migrants. In London, 13%–16% of migrant households were overcrowded compared with just 4% of UK-born households between 2016 and 2018.We used data from Virus Watch, a prospective community cohort study of COVID-19 in England and Wales from 1 June 2020.Virus Watch cohort data were linked to the second-generation surveillance system (SGSS) containing laboratory SARS-CoV-2 test results from swabs taken during hospitalisation (pillar 1) and community testing (pillar 2).Participants were aged ≥18 years and reported the number of rooms in their household in the February 2021 survey. Participants with evidence of SARS-CoV-2 infection before the start of the second wave (September 2020) were excluded as first infection, rather than reinfection, was our focus.Country of birth was the exposure, defined as migrant  or UK-born (a UK country of birth). The outcome was evidence of first SARS-CoV-2 infection during the analysis period defined as either a PCR or lateral flow test self-reported during a weekly survey, a positive test for SARS-CoV-2 anti-nucleocapsid antibodies, a positive test for anti-spike antibodies or a positive PCR test identified via the SGSS.In the February 2021 survey, participating households were asked how many rooms were available for their exclusive use . PPR was calculated by dividing the total number of people in the household (including children) by the number of available rooms, excluding bathrooms or kitchens. For households reporting ≥2 rooms, one room was subtracted from the total, assuming that one of the rooms was a kitchen. For households reporting one room, it was assumed that this room is likely to be a bedroom/studio. Households with PPRs less than one were defined as ‘underoccupied’, equal to one as ‘balanced’ and greater than one as ‘overcrowded’.Potential confounders were identified using a directed acyclic graph DAG; , baselin10.1136/jech-2022-220251.supp1Supplementary dataHouseholds were assigned a geographical region  and a local area-level Index of Multiple Deprivation quintile (where 1 represents the most deprived and 5 the least) by linking their postcode to the May 2020 ONS Postcode Lookup.We found no evidence of colinearity between ethnicity and migrant status . We usedAs eligibility was not dependent on households responding to all weekly surveys throughout the analysis period, we conducted a sensitivity analysis including only participants who had either linked data or, for those without linked data, self-reported follow-up for every full week of the analysis period . Positive SARS-CoV-2 anti-nucleocapsid antibody or anti-spike antibody tests during the study period may indicate evidence of older SARS-CoV-2 infection prior to the second wave or postvaccination seroconversions. Therefore, we carried out a sensitivity analysis using only swab-confirmed infections. We also conducted a sensitivity analysis where household overcrowding was represented by the continuous PPR variable.Individuals with missing country-of-birth responses were classified as UK-born; however, in a sensitivity analysis, we excluded these individuals. There were no missing age data and 363 participants (1.5%) had missing data for sex, 478 (2.0%) for ethnicity, 1405 (6.0%) for clinical vulnerability, 4120 (17.5%) for household income and 3400 (14.5%) for occupation. All missing values were included in the model under a ‘missing’ category. We conducted sensitivity analyses to assess the effect of missing data using a complete-case analysis and multivariate imputation by chained equations using the mice package with 5 datasets and 50 iterations per dataset  individuals met our inclusion criteria .Migrants were generally younger  with a higher percentage identifying as female  of individuals had evidence of infection compared with 6.27% (1342/21 416) of UK-born individuals . InfectiMigrants had 22% higher odds of SARS-CoV-2 infection during the second wave versus UK-born individuals , determined using logistic decomposition regression adjusted for age, sex, ethnicity, clinical vulnerability, baseline total household income, occupation and the presence of children in the household  were classified as UK-born. While this could introduce misclassification bias whereby true migrants are classified as being UK-born, the impact is likely small and would underestimate the effect of migration status on infection. Additionally, results from the sensitivity analysis excluding individuals with a missing country of birth were generally consistent with the main analysis.We did not adjust for vaccination status at baseline as no individuals had been vaccinated at the start of the analysis period (roll-out began in England on 8 December 2021). However, by 25 April 2021, 91.5% (22 644 679) of individuals aged ≥45 years had received at least one dose.Other limitations are the exclusion of children (<18 years), a group which requires further research. Additionally, Virus Watch enrolled more individuals aged over 60 years or of white British ethnicity versus England’s and Wales’s general population, and included more higher income households. These biases likely contribute to an underestimation of the effect of migration status on infection. Finally, a limitation of the methodology that we used to estimate the indirect effect through household overcrowding is that we have potentially adjusted for intermediate confounders . This has the potential to remove some of the true causal effect of the exposure (migration status) on the outcome (SARS-CoV-2 infection).The disproportionate effect of household overcrowding on individuals in the migrant group compared with UK-born individuals builds on previous Virus Watch results showing household overcrowding as a risk factor for SARS-CoV-2 infection.Efforts to address overcrowding are complex and require engagement with multiple stakeholders, including both in government and the private sector. Short-term efforts to prevent spread include providing hotel accommodation for infected individuals and ensuring that existing advice on preventing spread within the household To conclude, we show that migrants were over-represented in SARS-CoV-2 infections early in the pandemic, with household overcrowding playing a significant role in driving over-representation. Our findings highlight the implications of inadequate housing on health and underscore the importance of policy interventions for tackling household overcrowding. As we continue to live with COVID-19, it will be important to address these inequalities in health outcomes and housing to ensure than we build back fairer


The effect of smoking on the resolution of magnetic resonance imaging (MRI) lesions in patients with neuromyelitis optica spectrum disorders with aquaporin-4 positive antibody (NMOSD-AQP4) and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) has not been studied before.We aimed to determine the effect of smoking on lesion resolution in MRI and assess its correlation with clinical recovery after a relapse.We conducted a cohort study including NMOSD-AQP4 and MOGAD patients with acute and follow-up MRI scans. We collected demographic, clinical, imaging and smoking data. Logistic regression models were fitted to predict the effect of smoking on lesion resolution and to assess whether clinical recovery was associated with MRI lesion resolution.p < 0.001). Additionally, the presence of lesion resolution was associated with better clinical recovery .A total of 105 patients were included (57 with NMOSD-AQP4 and 48 with MOGAD). Current and past smoking was associated with a higher risk of persistent lesions in NMOSD-AQP4 and MOGAD (risk ratio (RR) = 3.4, 95% confidence interval (CI) = 2.5–4.7, Smoking is associated with worse MRI lesion resolution in patients with NMOSD-AQP4 and MOGAD, and lesion resolution correlates with clinical recovery. Our findings suggest a detrimental effect of smoking in inflammatory central nervous system (CNS) diseases. However, disease course, clinical relapse recovery and lesion evolution differ between these disorders, which may be linked to differences in the resolution of relapse-associated inflammation profile.68 In NMOSD, previous studies reported that smoking affects the severity of the disease and may be associated with poor relapse recovery in NMOSD-AQP4. However, the impact of smoking on MRI lesion resolution, and the role of MRI as a surrogate for clinical improvement, has not been studied before.Vascular risk factors have a known detrimental effect in inflammatory diseases of the central nervous system (CNS) such as MS, with smoking in particular being associated with greater disability and a higher burden of MRI lesions.We aimed to determine the effect of smoking on MRI lesion resolution and the association of lesion resolution with clinical recovery in people with NMOSD-AQP4 and MOGAD. or the MOGAD diagnostic criteria, who gave consent according to local ethic regulations, were included. Additional inclusion criteria comprised: (1) available details of smoking history and (2) available acute MRI scan within 6 weeks of a first relapse in a certain CNS region and follow-up MRI (⩾4 months after baseline) without a relapse in between. The main outcome was lesion resolution in MRI across smoking status. As a secondary endpoint, we evaluated the relationship between lesion resolution and clinical recovery.We conducted a retrospective cohort study of patients followed at the national commissioned service for NMOSD/MOGAD in Oxford. All patients fulfilling the NMOSD-AQP4 criteriaWe retrieved demographic and clinical data, including age at time of relapse, sex, disease duration until relapse, follow-up time between MRI scans, smoking status , vascular risk factors , diagnosis, relapse type/region , lesion location  and relapse recovery. Standard clinical MRI scans with varying scanner fields and protocols according to clinical practice, including T1 and T2 weighted sequences from the CNS region of relapse , were used to assess lesion resolution.Lesion resolution was scored as complete (no lesion remaining in follow-up imaging), partial  or absent  by an investigator (D.B.) with previous neuroradiological experience. Inter-rater variability was tested on 12.4% of randomly selected subset of patients by another rater (M.L.R.). Both raters were blinded for clinical data, including smoking status, at the time of MRI scoring.Clinical recovery from relapse was scored by the patient’s clinician as complete (full recovery with improvement to baseline), partial  or absent . The outcome for the fitted model was the risk ratio (RR) of lesion persistence, obtained by converting lesion resolution into a binary outcome . We included diagnosis, age at relapse, sex, disease duration until relapse, region of relapse, CNS lesion location and follow-up time between imaging studies as possible confounders. We performed a post hoc analysis of the effect of lesion location on lesion resolution. Statistical significance was set at p < 0.05. All analyses were performed using Stata version 16.1 .Inter-rater agreement was measured by weighted Cohen’s kappa coefficient. We performed a multivariate log regression model to determine the effect of smoking on MRI lesion resolution. A modified Poisson approach with robust error variances was used to account for convergence and outcome prevalence.Supplemental Figure 1). Demographic, clinical and imagiological characteristics of the cohort according to smoking status are summarized in Of the 638 eligible patients in the cohort, 105 patients met the inclusion criteria, comprising 57 NMOSD-AQP4 patients and 48 MOGAD patients . There was an overall almost perfect inter-rater agreement (weighted κ = 0.81) in the subset of patients selected for imaging review (p < 0.001).Lesion persistence was significantly more frequent in current-smokers (93%) than in past-smokers (62%), and in both smoker groups than in never-smokers (3%). Additionally, proportions of partial and complete resolution were higher in never-smokers and lower in current-smokers . None ofp < 0.001) and past-smokers  compared with never-smokers (p = 0.01), independently of smoking status, with patients with NMOSD-AQP4 having overall worse lesion resolution. Adjusting for other vascular risk factors did not impact the effect of smoking on lesion resolution, but due to small number of patients with available information on hypertension (n = 8), dyslipidaemia (n = 2) and diabetes (n = 8), these were not included in the multivariate model to avoid overfitting and decreasing sample size.In a regression model to predict the association of lesion persistence with smoking and adjusting for predefined covariates, including diagnosis of NMOSD-AQP4 or MOGAD, the risk of lesion persistence in follow-up MRI was increased in current-smokers . Nearly all patients with partial or complete lesion resolution had some degree of relapse recovery . Additionally, most patients with complete resolution on MRI experienced complete clinical relapse recovery .The presence of MRI lesion resolution was significantly associated with better clinical recovery in both NMOSD-AQP4 and MOGAD groups  or complete MRI lesion resolution . The detrimental effect of smoking was observed in both NMOSD-AQP4 and MOGAD patients, with persistent lesions being more frequent in current- and past-smokers compared with never-smokers . Additionally, the proportion of complete lesion resolution was higher in never-smokers in both NMOSD-AQP4 and MOGAD .Compared with MOGAD patients, a lower proportion of patients in the NMOSD-AQP4 subgroup reached complete clinical recovery (p = 0.03) and spinal cord lesions . However, after correction for covariates, there was no significant relationship between CNS lesion location and persistence of lesions in MRI. Additionally, the effect of smoking on lesion persistence was independent of lesion location in the multivariate regression model explained above  than in infratentorial lesions (11%) or spinal cord lesions (25%). Supratentorial lesions were associated with a higher risk of lesion persistence compared with infratentorial lesions , smoking appears to be associated with increased disability,16 a higher number of juxta-cortical lesions and of Dawson’s fingers in MRI. In general, there seems to be a consistent detrimental effect of smoking in neuroinflammatory disorders, possibly mediated by an increased inflammatory activity,15 and/or, in NMOSD-AQP4, by poor tissue damage recovery after relapses. Smoking increases the release of pro-inflammatory cytokines and activation of the complement pathway,1719 which may enhance tissue damage and hinder recovery after the initial inflammatory response is triggered.Previous studies suggested the association of smoking with higher number of relapses and increased severity of attacks in NMOSD. We found a previously unreported association between MRI lesion resolution and clinical recovery after a relapse in NMOSD-AQP4 and MOGAD, which could highlight the relevance of imaging as a biomarker of tissue damage and recovery.MRI lesion resolution in inflammatory CNS diseases has not been extensively studied, and its impact in clinical outcomes in NMOSD-AQP4 and MOGAD is unclear. A recent study reported better lesion resolution in MOGAD compared with NMOSD-AQP4 or MS, which was not associated with comorbidities, or clinical recovery, but smoking status was not included.2022 and spinal cord in MOGAD. The higher frequency of lesion resolution in these lesions could be related to the greater predominance of non-destructive inflammatory lesions in these CNS locations. However, we did not find an effect of lesion location in the multivariate analysis, suggesting that the smoking effect on lesion resolution is independent of CNS lesion location.There was worse resolution of lesions in the supratentorial compartment compared with the spinal cord and infratentorial lesions, which is consistent with previous reports of higher reversibility of lesions in the infratentorial compartment in NMOSD-AQP4Our study has several limitations. First, there was limited access to detailed smoking information due to the retrospective design of our study. This may have reduced our power to detect differences between past- and current-smoker groups. Additionally, smoking was self-reported by patients which could influence effect in past-smokers, as patients may not report ongoing smoking habits. Despite these limitations, we were able to detect a clear correlation of smoking with poor lesion resolution, which supports an underlying strong biological effect. There was also limited information about other risk factors that may be associated with smoking; however, the addition of hypertension, diabetes and dyslipidaemia in the multivariate model, despite the small numbers, did not affect the smoking risk. Finally, because we used clinical scans with heterogeneous scanner fields and protocols, more crude qualitative assessment of MRI images was necessary. To mitigate this, we used a uniform scoring system and tested rater variability, which found an almost perfect inter-rater agreement. The results of this study report a previous undisclosed effect of smoking on the imaging of NMOSD-AQP4 and MOGAD and should be confirmed by a prospective study designed to collect detailed smoking history and perform standardized research MRIs for quantitative assessment of lesion evolution.Smoking is associated with worse MRI resolution of inflammatory lesions in patients with NMOSD-AQP4 and MOGAD, which is in turn associated with clinical recovery from acute relapses. Overall, our findings support a detrimental effect of smoking in inflammatory CNS diseases. The role of smoking in exacerbating inflammatory disease, as well as vascular disease, may strengthen educational programmes. Additionally, smoking status may be taken into consideration in the clinical management of these conditions, and understanding the underlying pathogenic processes may help develop novel treatments.Click here for additional data file.Supplemental material, sj-docx-1-msj-10.1177_13524585231188485 for The effect of smoking on MRI lesion resolution in NMOSD-AQP4 and MOGAD by David Berhanu, Miguel Leal Rato, Silvia Messina, Maria Isabel Leite, Ruth Geraldes and Jacqueline Palace in Multiple Sclerosis Journal


White blood cell (WBC) count profiles in anti-aquaporin-4 antibody-positive neuromyelitis optica spectrum disorder (AQP4-NMOSD) and anti-myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) are still unknown. This study evaluated the total WBC count, differential WBC counts, monocyte-to-lymphocyte ratio (MLR), and neutrophil-to-lymphocyte ratio (NLR) in patients with these diseases within three months from an attack before acute treatment or relapse prevention and compared the profiles with those in matched volunteers or in multiple sclerosis (MS) patients. AQP4-NMOSD patients (n = 13) had a higher neutrophil count (p = 0.0247), monocyte count (p = 0.0359), MLR (p = 0.0004), and NLR (p = 0.0037) and lower eosinophil (p = 0.0111) and basophil (p = 0.0283) counts than those of AQP4-NMOSD-matched volunteers (n = 65). Moreover, patients with MOGAD (n = 26) had a higher overall WBC count (p = 0.0001), neutrophil count (p < 0.0001), monocyte count (p = 0.0191), MLR (p = 0.0320), and NLR (p = 0.0002) than those of MOGAD-matched volunteers (n = 130). The three demyelinating diseases showed similar levels of the total and differential WBC counts; however, MOGAD and MS showed different structures in the hierarchical clustering and distributions on a two-dimensional canonical plot using differential WBC counts from the other three groups. WBC count profiles were similar in patients with MOGAD and MS but differed from profiles in matched volunteers or patients with AQP4-NMOSD. Lesions associated with these diseases may involve any part of the brain, optic nerve, or spinal cord5. Efficient relapse prevention, including immunosuppressants and monoclonal antibodies administration, is essential as the neurological disability in patients with these diseases often progresses due to relapses9. Many of the currently available effective relapse prevention strategies affect circulating lymphocytes, complement activation pathways, cytokines, and chemokines. This implies the impact of peripherally circulating white blood cells on the development of these diseases13. It has been recently reported that white blood cell (WBC) count profiles in patients with MS are different from those in matched healthy volunteers and are characterized with elevated neutrophil, monocyte, and basophil counts14. However, the exact profiles of the total and differential white blood cell (WBC) counts in AQP4-IgG-positive NMOSD and MOGAD, for which disease concept has been recently established, is not determined yet. Elucidating detailed WBC count profiles in these diseases will help us to understand their mechanisms and find appropriate therapeutic strategies. Therefore, in this study, we evaluated the total and differential WBC counts in patients with these diseases and compared them with counts in matched volunteers or patients with MS to understand the abnormalities and characteristics of WBC count profiles in AQP4-IgG-positive NMOSD and MOGAD.Anti-aquaporin-4 antibody (AQP4-IgG)-positive neuromyelitis optica spectrum disorder (NMOSD), anti-myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), and multiple sclerosis (MS) are distinct autoimmune-related relapsing neurological diseases of the central nervous system (CNS)This study aimed to clarify the WBC count profiles of patients with AQP4-IgG-positive NMOSD and MOGAD during the acute to subacute phases before treatment. WBC count profiles were collected from patients with AQP4-IgG-positive NMOSD or MOGAD within three months from the last clinical attack before starting acute treatment  or relapse prevention. The control group included age- and sex-matched volunteers for each disease. Matching was performed using propensity scores calculated according to age and sex. Moreover, data from patients with MS before starting acute treatment or disease-modifying drugs were also collected to further clarify the characteristics of WBC count profiles in patients with AQP4-IgG-positive NMOSD and MOGAD. All blood test data were collected during the patients’ first visit to Tohoku University Hospital, Japan, between April 2013 and March 2022. Patients whose first blood tests were performed before April 2013 were not enrolled as the current automated hematology analyzer was introduced at the university hospital in April.15, and all enrolled patients with AQP4-IgG-positive NMOSD and MOGAD met the international consensus on the diagnosis of these diseases1. From the initially recruited patients, a woman in her 30 s with MS  was excluded because of a recent history of trauma with active inflammation at the time of blood test. Age- and sex-matched volunteers with 1:5 matching  were prepared for each of the two neurological diseases using propensity score matching. Age- and sex-matched volunteers were enrolled from a database of volunteers registered in the Tohoku Medical Megabank Organization (ToMMo) Community-Based Cohort Study since 2013. The detailed profiles of the volunteers have been previously reported17.Eligible patients with each of the afore-mentioned diseases were enrolled before the initiation of any acute or chronic treatment for neurological diseases. Patients who had already initiated immune-modulatory agents or disease-modifying drugs at the time of blood tests were excluded. All enrolled patients with MS met the latest version of the McDonald diagnostic criteria19. Titers were calculated semi-quantitatively by performing serial two-fold dilutions.Demographic data including age and sex were obtained. Furthermore, the total WBC count [/μL], and the differential counts of neutrophils, eosinophils, basophils, lymphocytes, and monocytes were obtained from the blood test data. According to these differential counts, the platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and neutrophil-to-lymphocyte ratio (NLR) were further calculated. All patients with the three demyelinating diseases were assessed for the presence of serum AQP4-IgG and MOG-IgG. Microscopic live cell-based assays using AQP4-expressing or MOG-expressing HEK293 cells and Alexa Fluor 488-conjugated secondary antibodies were conducted to check for these antibodies in the serum20. Statistical and graph-building analyses were performed using JMP Pro version 14 .WBC count profiles were compared between patients with AQP4-IgG-positive NMOSD or MOGAD and the matched control group using the Mann–Whitney U (MWU) test. In addition, comparisons between the three disease groups  were performed using the analysis of covariance (ANCOVA) with age and sex as covariates. Statistically significant differences between each pair of disease groups were evaluated using Tukey’s post-hoc tests. Furthermore, hierarchical clustering analyses were performed by using the five differential WBC counts for each group to evaluate their relationship. Violin and box-whisker plots were plotted for each group to visually evaluate and compare the distribution of WBC counts. Moreover, canonical plots based on the canonical structures of the five differential WBC counts were constructed to visually understand the different profiles of differential WBC counts between the diseases. The statistical significance was consistently set at p < 0.05 and was not adjusted for the number of multiple tests because of the exploratory nature of the present study21. A written informed consent was obtained from all participants.This study was approved by the Institutional Review Board of the Tohoku University Graduate School of Medicine . It was performed in accordance with the current version of the Declaration of Helsinki, as revised in 2013, and the STROBE reporting guidelines for observational studiesThe number of enrolled participants for each disease group was as follows: 13  patients with AQP4-IgG-positive NMOSD, 26  patients with MOGAD, and 70  patients with MS. We initially enrolled 23,433 individuals with the full dataset of WBC count data from the ToMMo community-based cohort, in which the age- and sex-matched control groups were prepared. The number of the one-to-five-matched control group for each of AQP4-IgG-positive NMOSD and MOGAD was 65  volunteers in AQP4-matched control group and 130  volunteers in MOGAD-matched control group, respectively. The median and interquartile range  of the age at the time of the blood tests were 47 (39–57) years in the AQP4-IgG-positive NMOSD group, 43 (34.5–56) years in the MOGAD group, 33 (27–43) years in the MS group, 47 (39–57) years in the AQP4-matched control group, and 43 (34–57) years in the MOGAD-matched control group. All 13 patients with AQP4-IgG-positive NMOSD experienced their first clinical attack  at the blood test, whereas 3 out of the 26 patients with MOGAD experienced an undiagnosed neurological episode (one with myelitis and two with optic neuritis) more than 10 years ago without subsequent relapse preventions.The distributions of the total WBC counts, five differential WBC counts, PLR, MLR, and NLR in the evaluated disease groups are shown in Fig. Furthermore, the total WBC count, differential WBC counts, and derivatives were compared among the three disease groups (excluding the two matched control groups) after adjusting for age and sex using ANCOVA, followed by the Tukey’s post hoc test. As a result, there were no statistically significant differences in the total WBC count , neutrophil count (p = 0.4236), eosinophil count (p = 0.2179), basophil count (p = 0.0995), lymphocyte count (p = 0.1225), monocyte count (p = 0.4597), PLR (p = 0.2685), MLR (p = 0.1713), and NLR (p = 0.0534). For reference, the Tukey’s post hoc test following ANCOVA between MS and AQP4-IgG-positive NMOSD groups in NLR showed statistical significance, with estimated least square means (95% CI) of 2.363 (2.084–2.642) for MS and 3.236 (2.593–3.879) for AQP4-IgG-positive NMOSD (p = 0.0415).Moreover, hierarchical clustering analysis using the five differential WBC counts were performed for each group to visually evaluate their relationship. The cluster structures are shown in Fig. Canonical plots based on the five differential WBC counts were plotted for all five groups to further investigate the different WBC count profiles among them. The obtained two-dimensional canonical plot is shown in Fig. Finally, correlations between WBC count profiles and clinical profiles in AQP4-IgG-positive NMOSD and MOGAD groups were comprehensively evaluated. These profiles included the serum titer levels of the disease-specific antibodies (AQP4-IgG and MOG-IgG), clinical phenotype of the neurological episode at the time of the blood test , neurological disability level 1 year after the neurological episode in the blood test, and the presence of clinical relapses in the first 2 years after the neurological episode. None of the evaluated WBC count profiles, including MLR and NLR, were significantly correlated with the evaluated clinical profiles. Whether the correlations were evaluated using the Pearson’s R or the Spearman’s rho correlation coefficient, these results did not change. Furthermore, based on the marginally non-significant statistical results in the platelet count between the MOGAD and the matched control groups, the associations between the mean platelet volume (MPV) or platelet distribution width (PDW) and the evaluated clinical outcomes were additionally evaluated within each of the AQP4-IgG-positive NMOSD and MOGAD groups. As a result, neither MPV nor PDW showed significant correlations with any of the evaluated clinical outcomes in both of the two disease groups.24. In rodent models of MS, neutrophils have contributed to acute inflammation of the CNS through cytotoxicity and compromised blood–brain barrier, which result in the development of CNS lesions in animal models25. Circulating monocyte counts have predicted subsequent neurological disability levels in patients with MS26. Similar to neutrophils, monocytes secrete various cytokines and microbial factors27, thus attracting other immune cells to the focus of inflammation and contributing to chronic inflammation. Previous studies have implied that NLR and MLR may be better predictors for estimating disease activity or outcomes than simple counts of neutrophils and monocytes. NLR is associated with many autoimmune diseases, neurological diseases including MS, and malignancies31. This ratio has been reported to be linked to outcomes in some of these conditions, such as shorter survival in some malignancies and long-term neurological disability or relapse activity in MS34. MLR is also linked to the development of many autoimmune-related neurological diseases, including MS and Guillain-Barré syndrome35. Furthermore, MLR is associated with the outcomes of stroke and MS37. These findings collectively imply that both NLR and MLR are easily available markers for estimating inflammation levels in various chronic diseases, including many neurological diseases of the CNS. This study showed significantly different distributions in MLR and NLR between each of the disease groups and matched control group, but not in PLR. The platelet count, MPV, and PDW failed to show significant correlations with the evaluated clinical outcomes both in the AQP4-IgG-positive NMOSD and MOGAD groups. Although the circulating platelet count and the distribution of the size of platelets were not associated with the evaluated three neurological diseases in this study, a previous study reported that platelets are significantly activated in MS38. Additional assessment of platelet activities, such as plasma platelet microparticles and P-selectin expression, may benefit future researches evaluating potential roles of platelets in AQP4-IgG-positive NMOSD and MOGAD.In this study, the total and differential WBC counts and their derivatives were compared between each of the groups with evaluated relapsing neurological diseases of the CNS  and matched control groups. WBC count profiles were further investigated using hierarchical clustering analyses and two-dimensional canonical plots. The results suggested that the total WBC, neutrophil, and monocyte counts in patients with these diseases were higher than those in their matched controls. The structures of obtained tree with hierarchical clustering in each group implied that the lymphocyte count was more linked with the counts of eosinophils and basophils in the MOGAD and MS groups, whereas it was more linked with the counts of neutrophils and monocytes in the AQP4-IgG-positive NMOSD and two control groups. Moreover, the NLR and MLR values were higher in patients with AQP4-IgG-positive NMOSD and MOGAD than in their matched controls. Neutrophils and monocytes are innate immune cells with phagocytic and antigen-presenting roles. They both play pivotal roles in acute and chronic inflammatory responses against external pathogens and are associated with the mechanisms of some chronic diseases based on systemic inflammation, including MS40. The different backgrounds of patients with different comorbid conditions and preceding clinical episodes heralding attacks in AQP4-IgG-positive NMOSD may be a possible explanation for this discrepancy41. Patients with AQP4-IgG-positive NMOSD are considered to have several predisposing medical histories, including malignancies or autoimmune-related systemic diseases such as Sjögren's syndrome45. Such complications are rare in patients with MS or MOGAD, and this suggests different profiles of immune cells in these diseases. Further studies are required to elucidate the exact roles of neutrophils, monocytes, and lymphocytes in these three neurological diseases.Although the total and differential WBC counts did not differ significantly among the three demyelinating disease groups, hierarchical clusters and canonical plots suggested different WBC count profiles between AQP4-IgG-positive NMOSD group and the other two disease groups. This may be incompatible with the current understanding of these three diseases. Generally, the clinical and radiological findings in MOGAD are thought to be more similar to the findings in AQP4-IgG-positive NMOSD than in MS. Furthermore, the cerebrospinal fluid cytokine profile in patients with MOGAD is more similar to the profile in those with AQP4-IgG-positive NMOSD than to the profile in those with MS, with a predominant representation of Th17-related cytokines such as IL-6, IL-8, and IL-17+ T cells, and CD8+ T cells. Further studies evaluating these detailed cellular surface markers to discriminate the cellular types of lymphocytes will be helpful in obtaining deeper insights into the exact role of circulating lymphocytes in the mechanisms of these diseases. Finally, the disease groups and matched control groups in this study did not exclude the individuals with smoking history or comorbidities that may affect the WBC count profiles, such as systemic autoimmune diseases or hematologic diseases. This was because this study aimed to elucidate the overview of the WBC count profiles in the overall patients with AQP4-IgG-positive NMOSD or MOGAD. In future researches, it may be better to exclude the individuals who are with conditions that may affect the WBC count profiles.This study had several limitations. First, there were a few eligible patients with AQP4-IgG-positive NMOSD. The absence of statistically significant differences between patients and matched HCs in total WBC, neutrophil, and monocyte counts could be mainly due to the small number of patients. Further studies with more enrolled patients are required to determine the statistical significance of these cell types. Second, almost all enrolled patients and volunteers in this study were of East Asian ancestry. Therefore, it is uncertain whether the findings of this study can be generalized to other patients from other countries and races. Furthermore, this study did not evaluate the cell types among lymphocytes in detail, such as B cells, CD4In conclusion, the total WBC count, neutrophil count, monocyte count, MLR, and NLR in patients with AQP4-IgG-positive NMOSD and MOGAD were higher than those in their matched controls. Although the total and differential WBC counts did not significantly differ among AQP4-IgG-positive NMOSD, MOGAD, and MS, the relationship between the five differential WBC counts in AQP4-IgG-positive NMOSD may differ from those in the other two diseases. Further studies on the relationship between each differential WBC type and relapsing neurological diseases in the CNS are warranted.Supplementary Information.


The Omicron variant of SARS-CoV-2 led to a steep rise in transmissions, and emerging variants continue to influence case rates across the US. As public tolerance for isolation abated, CDC guidance on duration of at-home isolation of COVID-19 cases was shortened to five days if no symptoms, with no laboratory test requirement, despite more cautious approaches advocated by other federal experts.We conducted a decision tree analysis of alternative protocols for ending COVID-19 isolation, estimating net costs (direct and productivity), secondary infections, and incremental cost-effectiveness ratios. Sensitivity analyses assessed the impact of input uncertainty.Per 100 individuals, five-day isolation had 23 predicted secondary infections and a net cost of $33,000. Symptom check on day five (CDC guidance) yielded a 23% decrease in secondary infections (to 17.8), with a net cost of $45,000. Antigen testing on day six yielded 2.9 secondary infections and $63,000 in net costs. This protocol, compared to the next best protocol of antigen testing on day five of a maximum eight-day isolation, cost an additional $1,300 per secondary infection averted. Antigen or polymerase chain reaction testing on day five were dominated (more expensive and less effective) versus antigen testing on day six. Results were qualitatively robust to uncertainty in key inputs.A six-day isolation with antigen testing to confirm the absence of contagious virus appears the most effective and cost-effective de-isolation protocol to shorten at-home isolation of individuals with COVID-19.The online version contains supplementary material available at 10.1186/s12889-023-15762-0. The SARS-CoV-2 B.1.1.529 (Omicron) variant was designated a variant of concern by the World Health Organization in November 2021, as it had several mutations that are suspected to impact its transmissibility and disease severity  public hWhile quantitative studies on the viral kinetics and pathophysiology of the Omicron variant are underway, decision makers must offer timely guidance that balances public health and economic considerations in their COVID-19 isolation recommendations. As new variants appear and influence case rates, antigen testing could offer benefits over using symptom status as a marker of infectivity given the high rate of asymptomatic COVID-19 infections . FurtherWe used a cost-effectiveness study design to model six different protocols for ending COVID-19 isolation. Using a decision tree adapted from our previous work , we compInterventions were selected to demonstrate current policy options as well as alternatives that might reduce transmissions while also shortening isolation duration. While not exhaustive, these protocols represent a variety of options that might be acceptable to policymakers and the general public alike and warrant further evaluation. In all strategies, we assumed that individuals leaving isolation would follow best practices for infection prevention, which at the time of the analysis included mask wearing. Individuals could leave their home the day after their isolation ended .Five-day isolation. Person with confirmed COVID-19 stays at home for five days, then can leave without any further consideration.Ten-day isolation with symptom check on day five . Person with confirmed COVID-19 stays at home for five days. On day five, they review their symptoms. Those who were asymptomatic or fever free for 24 h can end isolation, while those with persisting symptoms continue to isolate until day ten.Ten-day isolation with antigen test on day five. Person with confirmed COVID-19 stays at home for five days. On day five, they perform a rapid antigen test. Those who test negative can end isolation while those who test positive continue to isolate until day ten.Ten-day isolation with PCR test on day five. Person with confirmed COVID-19 stays at home for five days. On day five, they conduct a PCR test. Those who test negative can end isolation while those who test positive continue to isolate until day ten. We assumed results are obtained within 24 h.Ten-day isolation with antigen test on day six. Person with confirmed COVID-19 stays at home for six days. On day six, they perform a rapid antigen test. Those who test negative can end isolation while those who test positive continue to isolate until day ten.Eight-day isolation with antigen test on day five. Person with confirmed COVID-19 stays at home for five days. On day five, they perform a rapid antigen test. Those who test negative can end isolation while those who test positive continue to isolate until day eight instead of day ten (no re-test is done).We assumed that no one in the cohort was SARS-CoV-2-naïve . As such, individuals were either still carrying contagious virus or had cleared all viable virus. We defined a frontloaded distribution for infectivity over ten days following symptom onset , based on empirical data on culture-positivity of patient samples , 18, 19.We used data specific to the Omicron variant when available to parameterize the model. Otherwise, we used data generated during the wildtype (Alpha) and B.1.617.2 (Delta) variant waves. Key model inputs are presented in Table Health inputs. The probability of carrying viable SARS-CoV-2 was 90% on day five and 70% on day six, quickly dropping to zero by day ten from the start of isolation, based on studies of previous variants [eff), which implicitly accounts for infection prevention measures that were in place such as mask wearing, to calculate the number of secondary infections that occur per index case. As of January 16th, 2022, Reff was estimated as 1.2 in the US [eff for de-isolation protocols requiring longer isolation periods.variants , 18, 19.Test performance. 40% of relevant COVID-19 cases were asymptomatic, [tomatic,  and of ttomatic,  yieldingtomatic, . We redutomatic, .Cost inputs. Costs were calculated from a societal perspective and in 2022 US dollars. A rapid antigen test cost $10, while a PCR test cost $150 [ost $150 –27. We iost $150 , productost $150 , 30. We For each de-isolation protocol, we calculated the number of secondary infections, societal net costs, and, when appropriate, incremental cost per secondary infections averted. We first calculated path probabilities along each decision tree branch by multiplying the probability of carrying transmissible virus on test day with the probability of a positive or negative test conditional on true viral status. We then multiplied this by the residual R appropriate for the day of de-isolation to estimate the number of secondary infections. Net costs included direct medical costs due to these secondary infections, the cost of the test used in the de-isolation protocol, and productivity loss due isolating depending on duration. De-isolation protocols that led to fewer net costs and fewer secondary infections than their comparator were dominant; no cost per infection averted were calculated.We conducted deterministic and probabilistic sensitivity analyses to assess uncertainty in key inputs in Table eff (see Supplement 1). We defined a low-risk scenario representing the infected individual re-joining the household where everyone was fully vaccinated and continued to wear masks for the next five days . A medium-risk scenario reflected individuals starting to see few non-household members, all of whom were fully vaccinated, but mask-wearing was inconsistent . Finally, a high-risk scenario was defined in which both vaccination and mask-wearing was inconsistent, and a greater number of social contacts were occurring . We did not conduct multivariate sensitivity analyses on the scenarios.Additionally, we simulated three separate risk scenarios to evaluate how the environment individuals are re-entering upon ending isolation would affect outcomes. The change in the risk of infection due to varying vaccination rate, mask-wearing , 31–33, The model was built in Excel®  and sensitivity analyses were conducted using @RISK®  . The decWe present base-case results from a societal perspective  in Table Antigen testing on day five of an eight-day isolation period cost an additional $1,150 per secondary infection averted compared with symptom check. This drop in the additional cost per infection averted represents extended dominance  over theThe most cost-effective de-isolation protocol was performing an antigen test on day six of a ten-day isolation period. This protocol led to $63,200 in net costs and 2.9 secondary infections, costing an additional $1,293 per secondary infection averted versus an antigen test on day five of an eight-day isolation, again representing extended dominance. Both antigen and PCR testing on day five were dominated by antigen testing on day six; they led to greater net costs and more secondary infections.If productivity losses were omitted, leaving just direct costs, antigen testing on day six was strictly dominant  over all other de-isolation protocols Table .In one-way sensitivity analyses where key inputs were varied one at a time, antigen testing on day five prevented between 51 and 85% secondary infections over symptom check, depending primarily on antigen test sensitivity for transmissible viral loads. Secondary infections prevented with an antigen test on day six versus day five was mostly related to the relative reduction in viable viral load from day five to six and varied between 35 and 67%. Antigen test on day six, compared to the next most cost-effective option (antigen test on day five of eight-day isolation) prevented between 3.6 and 8.0 secondary infections. This value was most sensitive to the probability of having transmissible virus on day five  was necessary for antigen testing to prevent more secondary infections than the symptom check if done on day five. Similarly, PCR tests had greater benefit than symptom check when test availability was greater than approximately 27%. In a two-way sensitivity analysis, if symptom check adherence was below 70%, then < 20% antigen test or < 19% PCR test availability was sufficient for these tests to prevent a greater number of transmissions than the symptom check protocol on day five. Notably, antigen testing on day six prevented more secondary infections than symptom check on day five even when test availability was as low as 1%, due to the added day of isolation.We compared health and cost outcomes associated with different de-isolation protocols to end COVID-19 isolation for those with confirmed asymptomatic or mild COVID-19. All testing strategies had favorable cost-effectiveness ratios except for antigen or PCR test on day five of 10-day isolation. We found that while symptom check without testing on day five of isolation did reduce secondary transmissions after de-isolation by 23% compared to no testing, it still led to nearly 18 secondary infections per 100 individuals and had the least favorable cost-effectiveness ratio due to high medical costs for secondary infections. The most cost-effective protocol was to remain in isolation through day six and then perform an antigen test, which dominated both antigen testing and PCR testing on day five. Antigen testing on day six led to an overall 87% decrease in secondary infections compared to no testing and cost an additional $1,300 per secondary infection averted compared to the next best option.Notably, a threshold analysis on antigen test availability suggested that the benefit of antigen testing on day six might be primarily due to the extra day of isolation (during which the probability of still carrying infectious virus quickly begins to drop), rather than the ability of the test to identify those who still might have a transmissible viral load. This insight warrants further evaluation using emerging data on the viral dynamics of variants. Moreover, antigen testing on day six was associated with lower productivity loss than antigen testing on day five; even though everyone remained in isolation for one more day, more individuals were cleared for de-isolation on day six than would have been on day five. The four days gained by this portion of index cases offset the extra day lost by everyone. Workforce shortages have been an important adverse effect of COVID-19 isolation –41. AntiBy modeling different risk scenarios, we demonstrated that the de-isolation environment had a considerable impact on the cost-effectiveness of testing strategies. Regardless of the risk scenario, the optimal protocol remained antigen testing on day six, which became dominant over other protocols in high-risk situations and remained cost-effective, although cost-effectiveness was less favorable in low-risk situations. Nevertheless, these findings suggest that much like other public health policy decisions throughout the pandemic, de-isolation guidelines must evolve as the context of the pandemic shifts. For example, potential new surges may call for more stringent policies with longer minimum isolation and more sensitive tests, while declining transmissions may allow more lenient approaches. It is plausible that the CDC has reached this same conclusion and proposed a symptom check rather than an antigen test because of an expectation that transmissions would subside in the weeks following the announcement of the new guidance. While reasonable at first glance, this could be a risky approach; loosening infection prevention measures may have prevented the expected drop in transmissions, leading instead to a quick rise in cases that would have prohibited the loosened guidance being put in place to begin with. Indeed, our modeling of the CDC guidance resulted in a substantial number of secondary COVID-19 cases given the transmission rate at the time the guidance was issued, some of which were avoided with a different de-isolation approach.Evidence from earlier SARS-CoV-2 variants suggest that transmissibility of the virus peaks by approximately the fifth day from symptom onset, and swiftly drops afterward , 19, 42.eff over the 10 days following COVID-19 confirmation, but a portion of transmissions occur prior to the index case learning their COVID-19 status and entering isolation. As such, we have overestimated the number of secondary infections in our model and underestimated cost-effectiveness ratios. Decreasing the residual R would increase costs per secondary infection averted, but given favorable cost/infection averted ratios even in the low-risk scenario, we believe the implications of our findings would not be affected. More importantly, given the novelty of the Omicron variant at the time of analysis, we had to rely on studies of prior variants for these two important factors. While variance in neither of these inputs changed cost-effectiveness results qualitatively, they did have an impact on the number of further transmissions after isolation and thus the level of cost-effectiveness. Additional studies on the viral kinetics of recent variants are necessary to refine these estimates.This study had important limitations, especially regarding uncertainty in key inputs such as the viral kinetics of SARS-CoV-2 and sensitivity of antigen tests. First, we distributed RThe Omicron and following variants of SARS-CoV-2 continue to present a threat to public health due to high transmissibility and potential ability to evade vaccine-induced immunity. Cost-effectiveness analyses can help decision makers assess the trade-offs between the economic disadvantages and health risks of adopting different COVID-19 de-isolation guidance. Using a decision tree model and Omicron-specific data when available, we found that ending isolation in five days given a negative symptom check left substantial risk of transmission and was not the most cost-effective strategy even when high productivity losses of longer isolation were accounted for. Instead, our findings suggest a baseline isolation duration of six days, at which time an antigen test, if available, can be conducted to confirm that the individual no longer carries transmissible SARS-CoV-2.Below is the link to the electronic supplementary material.Supplementary Material 1