Valerie Lohner

In a cohort of persons with chronic coronary syndrome, we investigated associations of blood-based biomarkers of neurodegenerative diseases with cognitive scores and mild cognitive impairment (MCI) after ten years. Biomarker levels were measured in baseline samples of serum (glial fibrillary acid protein, neurofilament light chain (NfL), phosphorylated tau181; n = 363) or plasma (amyloid-β _42/40 -ratio, total-tau; n = 232). Cognitive scores and MCI were measured at ten-year follow-up using the Cognitive Telephone Screening Instrument. At follow-up, 14.7% had MCI. The amyloid-β _42/40 -ratio was predictive of cognitive scores, while NfL was associated with increased risk of MCI, suggesting that these biomarkers might be utilized for risk stratification.

Background: Cerebral small vessel disease (CSVD) is a common neurological condition that contributes to strokes, dementia, disability, and mortality worldwide. We conducted a systematic review and meta-analysis to investigate the use of neuroimaging CSVD markers in machine learning (ML) based diagnosis and prognosis of cognitive impairment and dementia, and identify both methodological changes over time and barriers to clinical translation. Methods: Following the PRISMA guidelines, we systematically searched for original studies that used both neuroimaging CSVD markers and ML methods for diagnosing and prognosing neurodegenerative diseases (preregistration in PROSPERO: CRD42022366767). Each paper was independently reviewed by a pair of reviewers at all stages, with a third consulted to resolve conflicts. We meta-analysed the effectiveness of ML models to distinguish healthy controls from Alzheimer’s dementia and cognitive impairment, using area under the curve (AUC) as the performance metric. Results: We identified 75 studies: 43 on diagnosis, 27 on prognosis, and 5 on both. Nearly 60% of studies were published in the past two years, reflecting a growing interest in using CSVD markers in ML-based diagnosis and prognosis of neurodegenerative diseases, especially Alzheimer’s dementia. This rising interest may be linked to the strong performance of such models: according to our meta-analysis, ML approaches using CSVD markers perform well in differentiating healthy controls from Alzheimer’s dementia (AUC 0.88 [95%-CI 0.85–0.92]) and cognitive impairment (AUC 0.84 [95%-CI 0.74–0.95]). However, the growing interest has not been matched by methodological rigour: only 16 studies met the criteria for inclusion in the meta-analysis due to inconsistent reporting, only five assessed the generalisability of their models on external datasets, and six lacked clear diagnostic criteria. Conclusions: Interest in incorporating CSVD markers into ML models for neurodegenerative disease classification is on the rise, and their performance suggests that this is worth further exploration. Serious methodological issues, including inconsistent reporting, limited generalisability testing, and other potential biases, are unfortunately common and hinder further adoption. Our targeted recommendations provide a roadmap to accelerate the integration of ML into clinical practice.

Plasma neurofilament light chain (NfL) level is a sensitive yet aspecific marker of neurodegeneration. Its neuroanatomical and functional correlates in the general population are not fully elucidated. We thus assessed how brain's macrostructures and microstructures and cognitive function are related to plasma NfL levels in cognitively unimpaired adults over a wide age range. Our analyses were based on cross-sectional data from the Rhineland Study, a community-based prospective cohort study. This study includes people from the age of 30 onwards who live in 2 geographically defined areas in Bonn, Germany, and have sufficient command of the German language. Plasma NfL levels were measured using the Simoa platform and then log-transformed and adjusted for plate position, batch number, and Analyzer (HD-1 or HD-X). Brain imaging data were collected on a 3 Tesla scanner and included volumetric measures, metrics of the diffusion tensor and the neurite orientation dispersion and density imaging model, and white matter hyperintensity load. Memory performance, processing speed, and executive function were assessed using traditional cognitive tasks and an eye movement battery. We used multivariable regression models to assess the relations between brain structure and plasma NfL levels and between plasma NfL levels and cognitive performance. The study sample consisted of 5,589 participants aged 30-95 years (mean age 55 ± 13.7 years, 56.1% women) without neurodegenerative diseases. Higher plasma NfL levels were associated with lower isotropic volume fraction (-0.030; 95% CI -0.051 to -0.010; p_FDR = 0.011), lower neurite density index (ß = -0.031; 95% CI -0.053 to -0.008; p_FDR = 0.014), and higher axial diffusivity (ß = 0.037; 95% CI 0.013-0.062; p = 0.005; p_FDR = 0.011). The strongest association was with the orientation dispersion index (ß = -0.063; 95% CI -0.085 to -0.041; p_FDR < 0.001). Furthermore, higher plasma NfL levels tended to be associated with a lower processing speed domain score (ß = -0.046; 95% CI -0.084 to -0.009; p = 0.014; p_FDR = 0.056) and longer prosaccade latency (ß = 0.039; 95% CI 0.000-0.078; p = 0.049; p_FDR = 0.480). Higher plasma NfL levels mainly reflect worse white matter microstructural integrity, especially lower axonal density, in a relatively healthy, community-based sample. This suggests that plasma NfL levels allow for early detection of subtle differences in brain microstructure.

Coronary heart disease (CHD) is a well‐known risk factor for cognitive impairment and dementia, and blood biomarkers of neurodegenerative diseases may be utilised to identify people at higher risk of cognitive decline. Here, we aimed to investigate prospective associations between these biomarkers and mild neurocognitive disorder (MiND) after a follow‐up of ten years in patients with stable CHD, and potential effect modification by hypercholesterolemia and ApoE genotype. Biomarkers of neurodegenerative diseases (glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), and phosphorylated tau181 (p‐tau181)) were measured in baseline blood serum samples using the Single‐Molecule Array (Simoa) Technology (Quanterix, USA) in a subset (n = 363) of a cohort of patients with stable CHD. MiND was defined as scores ≤ 21.8 on the Cognitive Telephone Screening Instrument (COGTEL). Hypercholesterolemia was categorised into none (normal total cholesterol (TC) levels without statin use), normal TC levels with statin use, or increased TC levels independent of statin use. We evaluated prospective associations of biomarkers of neurodegenerative disease with MiND using multivariable logistic regression models, adjusted for age, sex, study centre, hearing impairment, and comorbidities. We additionally checked for biomarker×ApoE genotype and biomarker×hypercholesterolemia interactions. At follow‐up, 55 (15.2%) patients had developed MiND. Higher levels of NfL were associated with increased risks of developing MiND (OR (95%‐CI) per SD increase: 1.44 (1.01‐2.04)). Associations of p‐tau181 with MiND were depending on hypercholesterolemia, but not on ApoE genotype. Higher levels of p‐tau181 were associated with lower odds of developing MiND in patients without hypercholesterolemia (OR (95%‐CI) per SD increase: 0.09 (0.01‐0.39)) and higher odds of developing MiND in patients with increased TC levels (OR (95%‐CI) per SD increase: 7.83 (1.72‐103.20)), but not in patients with normal TC levels using statins. Levels of GFAP were not associated with MiND. Preliminary analyses suggest that NfL and p‐tau181 predict MiND after ten years in patients with stable CHD, and that the association of p‐tau181 with MiND was modified by hypercholesterolemia. This might imply that a deterioration in cognitive performance in this population might be halted through early management of hypercholesterolemia, however, more research is warranted.

We quantified the fraction of cardiovascular deaths attributable to smoking in Germany over time, accounting for population ageing. We calculated population-attributable fractions to quantify cardiovascular deaths attributable to smoking for 1992 to 2021, and compared actual with age-standardized figures. We found a significant decline in the number of cardiovascular deaths attributable to smoking: from about 71 900 cases in 1992 to around 42 000 cases in 2021, with a steeper decline in men. Age-standardized figures suggest that population ageing partially offset this trend. Forward projections indicate increasing trends over the next decade. The results highlight the need for targeted smoking cessation programmes.

Introduction Machine learning (ML) algorithms using neuroimaging markers of cerebral small vessel disease (CSVD) are a promising approach for classifying cognitive impairment and dementia. Methods We systematically reviewed and meta-analysed studies that leveraged CSVD features for ML-based diagnosis and/or prognosis of cognitive impairment and dementia. Results We identified 75 relevant studies: 43 on diagnosis, 27 on prognosis, and 5 on both. CSVD markers are becoming important in ML-based classifications of neurodegenerative diseases, mainly Alzheimer’s dementia, with nearly 60% of studies published in the last two years. Regression and support vector machine techniques were more common than other approaches such as ensemble and deep-learning algorithms. ML-based classification performed well for both Alzheimer’s dementia (AUC 0.88 [95%-CI 0.85–0.92]) and cognitive impairment (AUC 0.84 [95%-CI 0.74–0.95]). Of 75 studies, only 16 were suitable for meta-analysis, only 11 used multiple datasets for training and validation, and six lacked clear definitions of diagnostic criteria. Discussion ML-based models using CSVD neuroimaging markers perform well in classifying cognitive impairment and dementia. However, challenges in inconsistent reporting, limited generalisability, and potential biases hinder adoption. Our targeted recommendations provide a roadmap to accelerate the integration of ML into clinical practice.

Neuroimaging has an essential role in studies of brain health and of cerebrovascular and neurodegenerative diseases, requiring the availability of versatile magnetic resonance imaging (MRI) acquisition and processing protocols. We designed and developed a multipurpose high-resolution MRI protocol for large-scale and long-term population neuroimaging studies that includes structural, diffusion-weighted and functional MRI modalities. This modular protocol takes almost 1 h of scan time and is, apart from a concluding abdominal scan, entirely dedicated to the brain. The protocol links the acquisition of an extensive set of MRI contrasts directly to the corresponding fully automated data processing pipelines and to the required quality assurance of the MRI data and of the image-derived phenotypes. Since its successful implementation in the population-based Rhineland Study (ongoing, currently more than 11,000 participants, target participant number of 20,000), the proposed MRI protocol has proved suitable for epidemiological and clinical cross-sectional and longitudinal studies, including multisite studies. The approach requires expertise in magnetic resonance image acquisition, in computer science for the data management and the execution of processing pipelines, and in brain anatomy for the quality assessment of the MRI data. The protocol takes ~1 h of MRI acquisition and ~20 h of data processing to complete for a single dataset, but parallelization over multiple datasets using high-performance computing resources reduces the processing time. By making the protocol, MRI sequences and pipelines available, we aim to contribute to better comparability, interoperability and reusability of large-scale neuroimaging data.

Introduction: Electronic cigarettes (e-cigarettes) are considered a potential aid for smoking cessation. This study investigates the neurobiological mechanisms underlying the motivational properties of e-cigarettes compared to tobacco cigarettes and provides insights into their addictive potential and implications for smoking cessation. Aims and Methods: The study included 47 daily e-cigarette users, the majority of whom were former smokers (n = 43), and 28 nicotine-naïve participants as controls. Among the e-cigarette users, 32 individuals currently used e-cigarettes exclusively, while 15 also used tobacco cigarettes (dual users). Functional magnetic resonance imaging was used to examine anticipatory brain activation to reward-predictive stimuli for money, e-liquid, or tobacco cigarettes, as well as the instrumental response to obtain each reward. Results: Exclusive e-cigarette users demonstrated increased activation in limbic regions, for example, the nucleus accumbens, during the anticipation of e-liquid and money over tobacco cigarettes. Prefrontal regions showed greater activation during the anticipation of money compared to e-liquid or tobacco cigarettes. Dual users showed greater activation for money compared to tobacco cigarettes in the hippocampal limbic system. No significant differences were observed in the anticipation of e-liquid and tobacco cigarettes among dual users. Conclusions: E-cigarettes are perceived as equally rewarding as money, highlighting their addictive potential. Among exclusive e-cigarette users, money and e-liquid have stronger motivational properties than tobacco cigarettes (even among former smokers), suggesting that e-cigarettes may be an effective tool for tobacco cessation. The comparable appeal of e-liquid and tobacco cigarettes among dual users may suggest that a complete switch to e-cigarettes may be more effective for smoking cessation. Implications: It is important to understand the motivational properties of e-cigarettes, particularly compared to traditional tobacco products. E-cigarettes with little or no addictive potential might not be effective as a substitute for tobacco cigarettes; however, a high addictive potential would, conversely, pose a risk to nonsmokers. Our data suggest that e-cigarettes have a sufficiently high addictive potential to make them an attractive alternative for smokers, especially if they switch completely from tobacco to e-cigarettes.

Background: Numerous studies have shown that e-cigarettes are addictive. For example, we previously showed that users of e-cigarette online forums discuss experiences of addiction in a netnographic analysis. However, it is unclear what makes e-cigarettes addictive apart from nicotine. In a focus group analysis, we recently identified 3 unique features of e-cigarettes that users linked to experiences of addiction: the pleasant taste, unobtrusiveness, and unlimited usability of e-cigarettes. Objective: This study aimed to validate the previously identified features of e-cigarette addictive potential by triangulating data from the netnographic analysis and focus group discussions. Methods: Drawing on a netnographic analysis of 3 popular, German-language e-cigarette forums, we studied whether experiences of addiction were linked to specific e-cigarette features. We included 451 threads in the analysis that had been coded for addictive experiences in a previous study by our team. First, we conducted a deductive analysis with preregistered codes to determine whether the features of pleasant taste, unobtrusiveness, and unlimited usability were mentioned in relation to the addictive potential of e-cigarettes in the online forums. Second, an inductive approach was chosen to identify further possible addictive features of e-cigarettes. Results: Our deductive analysis confirmed that the features highlighted in our previous focus group study (pleasant taste, unobtrusiveness, and unlimited usability) were also frequently discussed in online forums in connection to addictive symptoms. In addition, our inductive analysis identified nicotine dosage as a significant feature linked to addiction. Users reported varying their nicotine doses for different reasons, leading to the identification of four distinct user types based on dosing patterns: (1) high doses for intermittent, (2) high doses for constant use, (3) low doses for constant use, and (4) switching between high and low doses depending on the situation. Conclusions: Our comprehensive analysis of online forum threads revealed that users’ experiences of addiction are linked to 4 specific features unique to e-cigarettes: pleasant taste, unobtrusiveness, unlimited usability, and nicotine dosage. Recognizing these addictive features of e-cigarettes is crucial for designing cessation programs and informing public health policies to reduce the addictiveness of e-cigarettes.