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Objectives: The aim of the current study was to investigate the health-related quality of life (HRQol) of the family caregiver in MCI, explore possible determinants and study possible differences with mild dementia. Methods: This secondary data analysis included 145 persons with MCI and 154 persons with dementia and their family caregivers from two Dutch cohort studies. HRQoL was measured with the VAS of the EuroQol-5D-3L version. Regressions analyses were conducted to examine potential demographic and clinical determinants of the caregiver’s HRQoL. Results: The mean EQ5D-VAS in family caregivers of persons with MCI was 81.1 (SD 15.7), and did not significantly differ from family caregivers in mild dementia (81.9 (SD 13.0)). In MCI, patient measurements were not significantly associated with caregiver mean EQ5D-VAS. Concerning caregiver characteristics, being a spouse and a lower educational level were associated with a lower mean EQ5D-VAS (in a multiple linear regression model: unstandardized B −8.075, p = 0.013 and unstandardized B −6.162, p = 0.037 resp.). In mild dementia, the NPI item irritability showed an association with caregiver EQ5D-VAS in bivariate linear regression analyses. Conclusion: Results indicate that especially family caregiver characteristics seem to influence family caregiver HRQoL in MCI. Future research should include other potential determinants such as burden, coping strategies and relationship quality.

Importance: One characteristic histopathological event in Alzheimer disease (AD) is cerebral amyloid aggregation, which can be detected by biomarkers in cerebrospinal fluid (CSF) and on positron emission tomography (PET) scans. Prevalence estimates of amyloid pathology are important for health care planning and clinical trial design.Objective: To estimate the prevalence of amyloid abnormality in persons with normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia and to examine the potential implications of cutoff methods, biomarker modality (CSF or PET), age, sex, APOE genotype, educational level, geographical region, and dementia severity for these estimates.Design, Setting, and Participants: This cross-sectional, individual-participant pooled study included participants from 85 Amyloid Biomarker Study cohorts. Data collection was performed from January 1, 2013, to December 31, 2020. Participants had normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia. Normal cognition and subjective cognitive decline were defined by normal scores on cognitive tests, with the presence of cognitive complaints defining subjective cognitive decline. Mild cognitive impairment and clinical AD dementia were diagnosed according to published criteria.Exposures: Alzheimer disease biomarkers detected on PET or in CSF.Main Outcomes and Measures: Amyloid measurements were dichotomized as normal or abnormal using cohort-provided cutoffs for CSF or PET or by visual reading for PET. Adjusted data-driven cutoffs for abnormal amyloid were calculated using gaussian mixture modeling. Prevalence of amyloid abnormality was estimated according to age, sex, cognitive status, biomarker modality, APOE carrier status, educational level, geographical location, and dementia severity using generalized estimating equations.Results: Among the 19 097 participants (mean [SD] age, 69.1 [9.8] years; 10 148 women [53.1%]) included, 10 139 (53.1%) underwent an amyloid PET scan and 8958 (46.9%) had an amyloid CSF measurement. Using cohort-provided cutoffs, amyloid abnormality prevalences were similar to 2015 estimates for individuals without dementia and were similar across PET- and CSF-based estimates (24%; 95% CI, 21%-28%) in participants with normal cognition, 27% (95% CI, 21%-33%) in participants with subjective cognitive decline, and 51% (95% CI, 46%-56%) in participants with mild cognitive impairment, whereas for clinical AD dementia the estimates were higher for PET than CSF (87% vs 79%; mean difference, 8%; 95% CI, 0%-16%; P = .04). Gaussian mixture modeling-based cutoffs for amyloid measures on PET scans were similar to cohort-provided cutoffs and were not adjusted. Adjusted CSF cutoffs resulted in a 10% higher amyloid abnormality prevalence than PET-based estimates in persons with normal cognition (mean difference, 9%; 95% CI, 3%-15%; P = .004), subjective cognitive decline (9%; 95% CI, 3%-15%; P = .005), and mild cognitive impairment (10%; 95% CI, 3%-17%; P = .004), whereas the estimates were comparable in persons with clinical AD dementia (mean difference, 4%; 95% CI, -2% to 9%; P = .18).Conclusions and Relevance: This study found that CSF-based estimates using adjusted data-driven cutoffs were up to 10% higher than PET-based estimates in people without dementia, whereas the results were similar among people with dementia. This finding suggests that preclinical and prodromal AD may be more prevalent than previously estimated, which has important implications for clinical trial recruitment strategies and health care planning policies.

Clinical reasoning and research in modern geriatrics often prioritises the disease concept. This is understandable as it has brought impressive advances in medicine (e.g. antibiotics, vaccines, successful cancer treatment and many effective surgeries). However, so far the disease framework has not succeeded in getting us to root causes of many age-related chronic diseases (e.g. Alzheimer’s disease, diabetes, osteoarthritis). Moreover, in aging and disease constructs alone fail to explain the variability in illness presentations. Therefore, we propose to apply the underused illness concept in a new way by reconsidering the importance of common symptoms in the form of a dynamic network of symptoms as a complementary framework. We show that concepts and methods of complex system thinking now enable to fruitfully monitor and analyse the multiple interactions between symptoms in such in networks, offering new routes for prognosis and treatment. Moreover, close attention to the symptoms that bother older persons may also improve weighing the therapeutic objectives of well-being and survival and aligning treatment targets with the patients’ priorities.