Disease Progression in Multiple System Atrophy-Novel Modeling Framework and Predictive Factors.

BACKGROUND: Multiple system atrophy (MSA) is a rare and aggressive neurodegenerative disease that typically leads to death 6 to 10 years after symptom onset. The rapid evolution renders it crucial to understand the general disease progression and factors affecting the disease course. OBJECTIVES: The aims of this study were to develop a novel disease-progression model to estimate a population-level MSA progression trajectory and predict patient-specific continuous disease stages describing the degree of progress into the disease. METHODS: The disease-progression model estimated a population-level progression trajectory of subscales of the Unified MSA Rating Scale and the Unified Parkinson's Disease Rating Scale using patients in the European MSA natural history study. The predicted disease continuum was validated via multiple analyses based on reported anchor points, and the effect of MSA subtype on the rate of disease progression was evaluated. RESULTS: The predicted disease continuum spanned approximately 6 years, with an estimated average duration of 51 months for a patient with global disability score 0 to reach the highest level of 4. The predicted continuous disease stages were shown to be correlated with time of symptom onset and predictive of survival time. MSA motor subtype was found to significantly affect disease progression, with MSA-parkinsonian (MSA-P) type patients having an accelerated rate of progression. CONCLUSIONS: The proposed modeling framework introduces a new method of analyzing and interpreting the progression of MSA. It can provide new insights and opportunities for investigating covariate effects on the rate of progression and provide well-founded predictions of patient-level future progressions. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Progression models for repeated measures: Estimating novel treatment effects in progressive diseases.

Mixed models for repeated measures (MMRMs) are ubiquitous when analyzing outcomes of clinical trials. However, the linearity of the fixed-effect structure in these models largely restrict their use to estimating treatment effects that are defined as linear combinations of effects on the outcome scale. In some situations, alternative quantifications of treatment effects may be more appropriate. In progressive diseases, for example, one may want to estimate if a drug has cumulative effects resulting in increasing efficacy over time or whether it slows the time progression of disease. This article introduces a class of nonlinear mixed-effects models called progression models for repeated measures (PMRMs) that, based on a continuous-time extension of the categorical-time parametrization of MMRMs, enables estimation of novel types of treatment effects, including measures of slowing or delay of the time progression of disease. Compared to conventional estimates of treatment effects where the unit matches that of the outcome scale (eg, 2 points benefit on a cognitive scale), the time-based treatment effects can offer better interpretability and clinical meaningfulness (eg, 6 months delay in progression of cognitive decline). The PMRM class includes conventionally used MMRMs and related models for longitudinal data analysis, as well as variants of previously proposed disease progression models as special cases. The potential of the PMRM framework is illustrated using both simulated and historical data from clinical trials in Alzheimer's disease with different types of artificially simulated treatment effects. Compared to conventional models it is shown that PMRMs can offer substantially increased power to detect disease-modifying treatment effects where the benefit is increasing with treatment duration.

Personalized prediction of progression in pre-dementia patients based on individual biomarker profile: A development and validation study.

INTRODUCTION: The prognosis of patients at the pre-dementia stage is difficult to define. The aim of this study is to develop and validate a biomarker-based continuous model for predicting the individual cognitive level at any future moment. In addition to personalized prognosis, such a model could reduce trial sample size requirements by allowing inclusion of a homogenous patient population. METHODS: Disease-progression modeling of longitudinal cognitive scores of pre-dementia patients (baseline Clinical Dementia Rating ≤ 0.5) was used to derive a biomarker profile that was predictive of patient's cognitive progression along the dementia continuum. The biomarker profile model was developed and validated in the MEMENTO cohort and externally validated in the Alzheimer's Disease Neuroimaging Initiative. RESULTS: Of nine candidate biomarkers in the development analysis, three cerebrospinal fluid and two magnetic resonance imaging measures were selected to form the final biomarker profile. The model-based prognosis of individual future cognitive deficit was shown to significantly improve when incorporating biomarker information on top of cognition and demographic data. In trial power calculations, adjusting the primary analysis for the baseline biomarker profile reduced sample size requirements by ≈10%. Compared to conventional cognitive cut-offs, inclusion criteria based on biomarker-profile cut-offs resulted in up to 28% reduced sample size requirements due to increased homogeneity in progression patterns. DISCUSSION: The biomarker profile allows prediction of personalized trajectories of future cognitive progression. This enables accurate personalized prognosis in clinical care and better selection of patient populations for clinical trials. A web-based application for prediction of patients' future cognitive progression is available online.

Health utility in preclinical and prodromal Alzheimer's disease for establishing the value of new disease-modifying treatments-EQ-5D data from the Swedish BioFINDER study.

Quality of life and health utility are important outcomes for patients with Alzheimer's disease (AD) and central for demonstrating the value of new treatments. Estimates in biomarker-confirmed AD populations are missing, potentially delaying payer approval of treatment. We examined whether health utility, assessed with the EuroQoL-5 3-level version (EQ-5D-3L), differed between individuals with a positive or negative amyloid beta (Aß) biomarker in patients with mild cognitive impairment (MCI) and cognitively unimpaired (CU) participants from the Swedish BioFINDER study (n = 578). Participants with prodromal AD (Aß-positive MCI) reported better health utility (n = 79, mean = 0.81, 95% confidence interval [CI] 0.77-0.85) than Aß-negative MCI (mean = 0.71, 95% CI 0.64-0.78), but worse than controls (Aß-negative CU, mean = 0.87, 95% CI 0.86-0.89). Health utility in preclinical AD (Aß-positive CU; mean = 0.86, 95% CI 0.83-0.89) was similar to controls. This relatively good health utility in prodromal AD suggests a larger value of delaying progression to dementia than previously anticipated and a great value of delaying clinical progression in preclinical AD.

Separating Timing, Movement Conditions and Individual Differences in the Analysis of Human Movement.

A central task in the analysis of human movement behavior is to determine systematic patterns and differences across experimental conditions, participants and repetitions. This is possible because human movement is highly regular, being constrained by invariance principles. Movement timing and movement path, in particular, are linked through scaling laws. Separating variations of movement timing from the spatial variations of movements is a well-known challenge that is addressed in current approaches only through forms of preprocessing that bias analysis. Here we propose a novel nonlinear mixed-effects model for analyzing temporally continuous signals that contain systematic effects in both timing and path. Identifiability issues of path relative to timing are overcome by using maximum likelihood estimation in which the most likely separation of space and time is chosen given the variation found in data. The model is applied to analyze experimental data of human arm movements in which participants move a hand-held object to a target location while avoiding an obstacle. The model is used to classify movement data according to participant. Comparison to alternative approaches establishes nonlinear mixed-effects models as viable alternatives to conventional analysis frameworks. The model is then combined with a novel factor-analysis model that estimates the low-dimensional subspace within which movements vary when the task demands vary. Our framework enables us to visualize different dimensions of movement variation and to test hypotheses about the effect of obstacle placement and height on the movement path. We demonstrate that the approach can be used to uncover new properties of human movement.

Effect of inspiration on airway dimensions measured in maximal inspiration CT images of subjects without airflow limitation.

OBJECTIVES: To study the effect of inspiration on airway dimensions measured in voluntary inspiration breath-hold examinations. METHODS: 961 subjects with normal spirometry were selected from the Danish Lung Cancer Screening Trial. Subjects were examined annually for five years with low-dose CT. Automated software was utilized to segment lungs and airways, identify segmental bronchi, and match airway branches in all images of the same subject. Inspiration level was defined as segmented total lung volume (TLV) divided by predicted total lung capacity (pTLC). Mixed-effects models were used to predict relative change in lumen diameter (ALD) and wall thickness (AWT) in airways of generation 0 (trachea) to 7 and segmental bronchi (R1-R10 and L1-L10) from relative changes in inspiration level. RESULTS: Relative changes in ALD were related to relative changes in TLV/pTLC, and this distensibility increased with generation (p < 0.001). Relative changes in AWT were inversely related to relative changes in TLV/pTLC in generation 3--7 (p < 0.001). Segmental bronchi were widely dispersed in terms of ALD (5.7 ± 0.7 mm), AWT (0.86 ± 0.07 mm), and distensibility (23.5 ± 7.7%). CONCLUSIONS: Subjects who inspire more deeply prior to imaging have larger ALD and smaller AWT. This effect is more pronounced in higher-generation airways. Therefore, adjustment of inspiration level is necessary to accurately assess airway dimensions. KEY POINTS: Airway lumen diameter increases and wall thickness decreases with inspiration. The effect of inspiration is greater in higher-generation (more peripheral) airways. Airways of generation 5 and beyond are as distensible as lung parenchyma. Airway dimensions measured from CT should be adjusted for inspiration level.

Predictive value of individual serum neurofilament light chain levels in short-term disease activity in relapsing multiple sclerosis.

Background: The assessment of serum neurofilament light chain (sNFL) has emerged as a diagnostic and prognostic tool in monitoring multiple sclerosis (MS). However, the application of periodic measurement in daily practice remains unclear. Objective: To evaluate the predictive value of individual sNFL levels in determining disease activity in patients with relapsing MS (RMS). Methods: In this two-year prospective study, 129 RMS patients underwent quarterly sNFL assessments and annual MRI scans. The study analyzed the correlation between individual NFL levels and past, current, and future disease activity. Group-level Z-scores were employed as a comparative measure. Results: Among the 37 participants, a total of 61 episodes of disease activity were observed. sNFL levels proved valuable in distinct ways; they were confirmatory of previous and current clinical and/or radiological activity and demonstrated a high negative predictive value for future 90 days activity. Interestingly, Z-scores marginally outperformed sNFL levels in terms of predictive accuracy, indicating the potential for alternative approaches in disease activity assessment. In our cohort, sNFL cut-offs of 10.8 pg./mL (sensitivity 27%, specificity 90%) and 14.3 pg./mL (sensitivity 15%, specificity 95%) correctly identified 7 and 4 out of 26 cases of radiological activity within 90 days, respectively, with 14 and 15% false negatives. When using lower cut-off values, individuals with sNFL levels below 5 pg/mL (with a sensitivity of 92%, specificity of 25%, and negative predictive value of 94%) were less likely to experience radiological activity within the next 3 months. Conclusion: Individual sNFL levels may potentially confirm prior or current disease activity and predict short-term future radiological activity in RMS. These findings underscore its periodic measurement as a valuable tool in RMS management and decision-making, enhancing the precision of clinical evaluation in routine practice.

Progression analysis versus traditional methods to quantify slowing of disease progression in Alzheimer's disease.

BACKGROUND: The clinical meaningfulness of the effects of recently approved disease-modifying treatments (DMT) in Alzheimer's disease is under debate. Available evidence is limited to short-term effects on clinical rating scales which may be difficult to interpret and have limited intrinsic meaning to patients. The main value of DMTs accrues over the long term as they are expected to cause a delay or slowing of disease progression. While awaiting such evidence, the translation of short-term effects to time delays or slowing of progression could offer a powerful and readily interpretable representation of clinical outcomes. METHODS: We simulated disease progression trajectories representing two arms, active and placebo, of a hypothetical clinical trial of a DMT. The placebo arm was simulated based on estimated mean trajectories of clinical dementia rating scale-sum of boxes (CDR-SB) recordings from amyloid-positive subjects with mild cognitive impairment (MCI) from Alzheimer's Disease Neuroimaging Initiative (ADNI). The active arm was simulated to show an average slowing of disease progression versus placebo of 20% at each visit. The treatment effects in the simulated trials were estimated with a progression model for repeated measures (PMRM) and a mixed model for repeated measures (MMRM) for comparison. For PMRM, the treatment effect is expressed in units of time (e.g., days) and for MMRM in units of the outcome (e.g., CDR-SB points). PMRM results were implemented in a health economics Markov model extrapolating disease progression and death over 15 years. RESULTS: The PMRM model estimated a 19% delay in disease progression at 18 months and 20% (~ 7 months delay) at 36 months, while the MMRM model estimated a 25% reduction in CDR-SB (~ 0.5 points) at 36 months. The PMRM model had slightly greater power compared to MMRM. The health economic model based on the estimated time delay suggested an increase in life expectancy (10 months) without extending time in severe stages of disease. CONCLUSION: PMRM methods can be used to estimate treatment effects in terms of slowing of progression which translates to time metrics that can be readily interpreted and appreciated as meaningful outcomes for patients, care partners, and health care practitioners.

Association between clinical dementia rating and clinical outcomes in Alzheimer's disease.

INTRODUCTION: We examined associations between the Clinical Dementia Rating Scale (CDR) and function (Functional Assessment Scale [FAS]), neuropsychiatric symptoms (Neuropsychiatric Inventory Questionnaire [NPI-Q]), and cognitive impairment in Alzheimer's disease (AD). METHODS: We used data from the National Alzheimer's Coordinating Center Uniform Data Set and defined cognitively unimpaired and AD stages using CDR-global. RESULTS: Functional and neuropsychiatric symptoms occur as early as the mild cognitive impairment (MCI) phase. The adjusted lest square mean FAS (95% confidence interval [CI]) was lowest in cognitively unimpaired (3.88 [3.66, 4.11] to 5.01 [4.76, 5.26]) and higher with more advanced AD (MCI: 8.17 [6.92, 9.43] to 20.87 [19.53, 22.20]; mild: 18.54 [17.57, 19.50] to 28.13 [27.14, 29.12]; moderate: 26.01 [25.31, 26.70] to 29.42 [28.73, 30.10]). FAS and NPI-Q scores increased steeply with MCI (NPI-Q: 5.55 [4.89, 6.20] to 7.11 [6.43, 7.78]) and mild AD dementia (NPI-Q: 6.66 [5.72, 7.60] to 8.32 [7.32, 9.33]). DISCUSSION: CDR-global staged AD by capturing differences in relevant outcomes along AD progression. Highlights: There were strong associations among CDR and the various outcomes relevant to healthcare providers, patients, and their care givers, such as activities of daily living.Overall, activities of daily living, neuropsychiatric symptoms, and cognitive function outcomes deteriorated over time and can be observed in early stages of AD (MCI or mild dementia).Our findings directly inform the current understanding of AD progression and can aid in care planning and benefit assessments of early AD interventions to delay the progression of AD to more advanced stages.

Relationships of change in Clinical Dementia Rating (CDR) on patient outcomes and probability of progression: observational analysis.

BACKGROUND: Understanding the relationship among changes in Clinical Dementia Rating (CDR), patient outcomes, and probability of progression is crucial for evaluating the long-term benefits of disease-modifying treatments. We examined associations among changes in Alzheimer's disease (AD) stages and outcomes that are important to patients and their care partners including activities of daily living (ADLs), geriatric depression, neuropsychiatric features, cognitive impairment, and the probabilities of being transitioned to a long-term care facility (i.e., institutionalization). We also estimated the total time spent at each stage and annual transition probabilities in AD. METHODS: The study included participants with unimpaired cognition, mild cognitive impairment (MCI) due to AD, and mild, moderate, and severe AD dementia in the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (UDS) database. The associations among change in AD stages and change in relevant outcomes were estimated using linear mixed models with random intercepts. The probability of transitioning to long-term care facilities was modeled using generalized estimating equations. The total length of time spent at AD stages and annual transition probabilities were estimated with multistate Markov models. RESULTS: The estimated average time spent in each stage was 3.2 years in MCI due to AD and 2.2, 2.0, and 2.8 years for mild, moderate, and severe AD dementia, respectively. The annual probabilities of progressing from MCI to mild, moderate, and severe AD dementia were 20, 4, and 0.7%, respectively. The incremental change to the next stage of participants with unimpaired cognition, MCI, and mild, moderate, and severe AD dementia (to death) was 3.2, 20, 26.6, 31, and 25.3%, respectively. Changes in ADLs, neuropsychiatric features, and cognitive measures were greatest among participants who transitioned from MCI and mild AD dementia to more advanced stages. Participants with MCI and mild and moderate AD dementia had increasing odds of being transitioned to long-term care facilities over time during the follow-up period. CONCLUSIONS: The findings demonstrated that participants with early stages AD (MCI or mild dementia) were associated with the largest changes in clinical scale scores. Early detection, diagnosis, and intervention by disease-modifying therapies are required for delaying AD progression. Additionally, estimates of transition probabilities can inform future studies and health economic modeling.

Longitudinal changes in hippocampal texture from healthy aging to Alzheimer's disease.

Early detection of Alzheimer's disease is essential to develop preventive treatment strategies. Detectible change in brain volume emerges relatively late in the pathogenic progression of disease, but microstructural changes caused by early neuropathology may cause subtle changes in the MR signal, quantifiable using texture analysis. Texture analysis quantifies spatial patterns in an image, such as smoothness, randomness and heterogeneity. We investigated whether the MRI texture of the hippocampus, an early site of Alzheimer's disease pathology, is sensitive to changes in brain microstructure before the onset of cognitive impairment. We also explored the longitudinal trajectories of hippocampal texture across the Alzheimer's continuum in relation to hippocampal volume and other biomarkers. Finally, we assessed the ability of texture to predict future cognitive decline, over and above hippocampal volume. Data were acquired from the Alzheimer's Disease Neuroimaging Initiative. Texture was calculated for bilateral hippocampi on 3T T1-weighted MRI scans. Two hundred and ninety-three texture features were reduced to five principal components that described 88% of total variance within cognitively unimpaired participants. We assessed cross-sectional differences in these texture components and hippocampal volume between four diagnostic groups: cognitively unimpaired amyloid-ß- (n = 406); cognitively unimpaired amyloid-ß+ (n = 213); mild cognitive impairment amyloid-ß+ (n = 347); and Alzheimer's disease dementia amyloid-ß+ (n = 202). To assess longitudinal texture change across the Alzheimer's continuum, we used a multivariate mixed-effects spline model to calculate a 'disease time' for all timepoints based on amyloid PET and cognitive scores. This was used as a scale on which to compare the trajectories of biomarkers, including volume and texture of the hippocampus. The trajectories were modelled in a subset of the data: cognitively unimpaired amyloid-ß- (n = 345); cognitively unimpaired amyloid-ß+ (n = 173); mild cognitive impairment amyloid-ß+ (n = 301); and Alzheimer's disease dementia amyloid-ß+ (n = 161). We identified a difference in texture component 4 at the earliest stage of Alzheimer's disease, between cognitively unimpaired amyloid-ß- and cognitively unimpaired amyloid-ß+ older adults (Cohen's d = 0.23, Padj = 0.014). Differences in additional texture components and hippocampal volume emerged later in the disease continuum alongside the onset of cognitive impairment (d = 0.30-1.22, Padj < 0.002). Longitudinal modelling of the texture trajectories revealed that, while most elements of texture developed over the course of the disease, noise reduced sensitivity for tracking individual textural change over time. Critically, however, texture provided additional information than was provided by volume alone to more accurately predict future cognitive change (d = 0.32-0.63, Padj < 0.0001). Our results support the use of texture as a measure of brain health, sensitive to Alzheimer's disease pathology, at a time when therapeutic intervention may be most effective.

Personalising Alzheimer's Disease progression using brain atrophy markers.

INTRODUCTION: Neuroanatomical normative modelling can capture individual variability in Alzheimer's Disease (AD). We used neuroanatomical normative modelling to track individuals' disease progression in people with mild cognitive impairment (MCI) and patients with AD. METHODS: Cortical thickness and subcortical volume neuroanatomical normative models were generated using healthy controls (n~58k). These models were used to calculate regional Z-scores in 4361 T1-weighted MRI time-series scans. Regions with Z-scores <-1.96 were classified as outliers and mapped on the brain, and also summarised by total outlier count (tOC). RESULTS: Rate of change in tOC increased in AD and in people with MCI who converted to AD and correlated with multiple non-imaging markers. Moreover, a higher annual rate of change in tOC increased the risk of MCI progression to AD. Brain Z-score maps showed that the hippocampus had the highest rate of atrophy change. CONCLUSIONS: Individual-level atrophy rates can be tracked by using regional outlier maps and tOC.

Decision making in clinical trials: Interim analyses, innovative design, and biomarkers.

The efficient and accurate execution of clinical trials testing novel treatments for Alzheimer's disease (AD) is a critical component of the field's collective efforts to develop effective disease-modifying treatments for AD. The lengthy and heterogeneous nature of clinical progression in AD contributes to the challenges inherent in demonstrating a clinically meaningful benefit of any potential new AD therapy. The failure of many large and expensive clinical trials to date has prompted a focus on optimizing all aspects of decision making, to not only expedite the development of new treatments, but also maximize the value of the information that each clinical trial yields, so that all future clinical trials (including those that are negative) will contribute toward advancing the field. To address this important topic the Alzheimer's Association Research Roundtable convened December 1-2, 2020. The goals focused around identifying new directions and actionable steps to enhance clinical trial decision making in planned future studies.

Impact of age at onset on symptom profiles, treatment characteristics and health-related quality of life in Parkinson's disease.

Parkinson's disease (PD) is typically considered an age-related disease, but the age at disease onset can vary by decades between patients. Aging and aging-associated diseases can affect the movement system independently of PD, and advanced age has previously been proposed to be associated with a more severe PD phenotype with accelerated progression. In this work, we investigated how interactions between PD progression and aging affect a wide range of outcomes related to PD motor and nonmotor symptoms as well as Health Related Quality of Life (HRQoL) and treatment characteristics. This population-based cohort study is based on 1436 PD patients from southern Sweden followed longitudinally for up to approximately 7.5 years from enrollment (3470 visits covering 2285 patient years, average follow-up time 1.7 years). Higher age at onset was generally associated with faster progression of motor symptoms, with a notable exception of dyskinesia and other levodopa-associated motor fluctuations that had less severe trajectories for patients with higher age at onset. Mixed results were observed for emergence of non-motor symptoms, while higher age at onset was generally associated with worse HRQoL trajectories. Accounting for these identified age-associated differences in disease progression could positively impact patient management and drug development efforts.

Timing of Puberty, Pubertal Growth, and Adult Height in Short Children Born Small for Gestational Age Treated With Growth Hormone.

CONTEXT: Growth hormone (GH) is used to treat short children born small for gestational age (SGA); however, the effects of treatment on pubertal timing and adult height are rarely studied. OBJECTIVE: To evaluate adult height and peak height velocity in short GH-treated SGA children. METHODS: Prospective longitudinal multicenter study. Participants were short children born SGA treated with GH therapy (n = 102). Adult height was reported in 47 children. A reference cohort of Danish children was used. Main outcome measures were adult height, peak height velocity, age at peak height, and pubertal onset. Pubertal onset was converted to SD score (SDS) using Danish reference data. RESULTS: Gain in height SDS from start of treatment until adult height was significant in both girls (0.94 [0.75; 1.53] SDS, P = .02) and boys (1.57 [1.13; 2.15] SDS, P < .001). No difference in adult height between GH dosage groups was observed. Peak height velocity was lower than a reference cohort for girls (6.5 [5.9; 7.6] cm/year vs 7.9 [7.4; 8.5] cm/year, P < .001) and boys (9.5 [8.4; 10.7] cm/year vs 10.1 [9.7; 10.7] cm/year, P = .002), but no difference in age at peak height velocity was seen. Puberty onset was earlier in SGA boys than a reference cohort (1.06 [-0.03; 1.96] SDS vs 0 SDS, P = .002) but not in girls (0.38 [-0.19; 1.05] SDS vs 0 SDS, P = .18). CONCLUSION: GH treatment improved adult height. Peak height velocity was reduced, but age at peak height velocity did not differ compared with the reference cohort. SGA boys had an earlier pubertal onset compared with the reference cohort.

Dynamic Changes of Reproductive Hormones in Male Minipuberty: Temporal Dissociation of Leydig and Sertoli Cell Activity.

CONTEXT: The male hypothalamic-pituitary-gonadal (HPG) axis is transiently active during the first months of life with surging serum concentrations of reproductive hormones. This period, termed minipuberty, appears to be essential for priming testicular function. Despite the central role for male reproductive function, longitudinal data on HPG axis activation in infancy is sparse. OBJECTIVE: To explore the dynamics of HPG hormone activity in healthy male infants, to assess the association of HPG axis activity and testicular volume, and to establish reference curves for serum levels of reproductive hormones. DESIGN: Prospective, longitudinal birth cohort (the COPENHAGEN Minipuberty Study, 2016-2018, 1-year follow-up). SETTING: Population-based. PATIENTS OR OTHER PARTICIPANTS: Healthy, male, term, singleton newborns were followed from birth on with repeated clinical examinations including blood sampling during a 1-year follow-up. A total of 128 boys contributed to this study, while 119 participated in the postnatal follow-up. MAIN OUTCOME MEASURES: Serum reproductive hormone concentrations and testicular volume. RESULTS: Reproductive hormone concentrations showed marked dynamics during the first 6 months of age. Gonadotropins, total testosterone, and insulin-like factor 3 peaked at around 1 month of age. Inhibin B, anti-Müllerian hormone, and testicular volume peaked at around 4 to 5 months. Correlations largely recapitulated typical HPG axis pathways but also differed significantly from adult men. CONCLUSIONS: We demonstrate a temporal dissociation of Leydig and Sertoli cell activity during male minipuberty and provide reference curves for reproductive hormones.

High risk of developing dementia in Parkinson's disease: a Swedish registry-based study.

Dementia have substantial negative impact on the affected individual, their care partners and society. Persons living with Parkinson's disease (PwP) are also to a large extent living with dementia. The aim of this study is to estimate time to dementia in PD using data from a large quality register with access to baseline clinical and patient reported data merged with Swedish national health registries. Persons with Parkinson's disease in the Swedish Neuro Registries/Parkinson's Disease Swedish PD Registry (PARKreg) in Sweden were included and linked to national health registries and matched by sex and age to controls without PD. Time to dementia was analysed with Cox regression models assuming proportional hazards, with time since diagnosis as the underlying time variable. In this large prospective cohort study, PwP had approximately four times higher risk of developing dementia as compared to age and sex-matched controls, a finding which remained after adjusting for potential confounders. The present results underline the high risk of dementia in PD and further emphasize the importance of developing symptomatic and ultimately disease modifying strategies to counteract this part of the non-motor symptomatology in PD.

Statistical Disease Progression Modeling in Alzheimer Disease.

Background: The characterizing symptom of Alzheimer disease (AD) is cognitive deterioration. While much recent work has focused on defining AD as a biological construct, most patients are still diagnosed, staged, and treated based on their cognitive symptoms. But the cognitive capability of a patient at any time throughout this deterioration reflects not only the disease state, but also the effect of the cognitive decline on the patient's pre-disease cognitive capability. Patients with high pre-disease cognitive capabilities tend to score better on cognitive tests that are sensitive early in disease relative to patients with low pre-disease cognitive capabilities at a similar disease stage. Thus, a single assessment with a cognitive test is often not adequate for determining the stage of an AD patient. Repeated evaluation of patients' cognition over time may improve the ability to stage AD patients, and such longitudinal assessments in combinations with biomarker assessments can help elucidate the time dynamics of biomarkers. In turn, this can potentially lead to identification of markers that are predictive of disease stage and future cognitive decline, possibly before any cognitive deficit is measurable. Methods and Findings: This article presents a class of statistical disease progression models and applies them to longitudinal cognitive scores. These non-linear mixed-effects disease progression models explicitly model disease stage, baseline cognition, and the patients' individual changes in cognitive ability as latent variables. Maximum-likelihood estimation in these models induces a data-driven criterion for separating disease progression and baseline cognition. Applied to data from the Alzheimer's Disease Neuroimaging Initiative, the model estimated a timeline of cognitive decline that spans ~15 years from the earliest subjective cognitive deficits to severe AD dementia. Subsequent analyses demonstrated how direct modeling of latent factors that modify the observed data patterns provides a scaffold for understanding disease progression, biomarkers, and treatment effects along the continuous time progression of disease. Conclusions: The presented framework enables direct interpretations of factors that modify cognitive decline. The results give new insights to the value of biomarkers for staging patients and suggest alternative explanations for previous findings related to accelerated cognitive decline among highly educated patients and patients on symptomatic treatments.

Utility of plasma neurofilament light and total tau for clinical trials in Alzheimer's disease.

INTRODUCTION: Several blood-based biomarkers are associated with neuronal injury, but their utility in interventional clinical trials is unclear. This study retrospectively evaluated the utility of plasma neurofilament light (NfL) and total tau (t-tau) in an 18-month trial in mild Alzheimer's disease (AD). METHODS: Correlation and conditional independence analyses and Gaussian graphical models were used to investigate cross-sectional and longitudinal relations between NfL, t-tau, and clinical scales. RESULTS: NfL had a stronger association than t-tau with clinical scales; t-tau did not hold additional information to that given by NfL (P > 0.05 at all time points). NfL held independent information about shorter-term (3- to 6-month) progression beyond patient age and clinical scores. However, no meaningful gain in power was found when adjusting a longitudinal analysis of cognitive scores for baseline NfL. DISCUSSION: Plasma NfL is superior to t-tau in mild AD. The ability of NfL to detect changes before clinical manifestations makes it a promising biomarker of drug response in trials of disease-modifying drugs.