Today’s official numbers show that participation in late-stage dementia studies is disturbingly low compared to other major diseases. This is despite the fact there are currently no authorized medicines to reduce or stop the progression of the disorders that cause dementia. According to research by the Alzheimer’s Association, the scenario highlights the urgent need for leadership to prioritize funding in late-stage dementia research.

Regardless, scientists are still taking significant steps in finding possible novel approaches to diagnosing, treating, and possibly preventing Alzheimer’s and associated dementias. These advancements are possible due to the participation of thousands of individuals in clinical trials and other investigations.

Why Is Clinical Trial Diversity Important?

Researchers need individuals of diverse races and ethnicities, genders, geographical areas, and sexual orientations. When research is conducted on a similar set of individuals, the conclusions reached may not apply to or benefit everyone. When diverse volunteers are included in clinical studies, the research outcomes are far more likely to have a broader application.

Researchers may be better able to understand how dementia affects diverse groups, why distinct dementias disproportionately affect some populations, and whether therapies or preventative measures may be more effective in particular groups by including a wider range of people in studies.

Trial study challenges

Although there is a global need for dementia research, dementia studies are difficult to carry out. Agreements with pharmaceutical firms to use their therapies are made more difficult by the time restrictions on patents, which could expire before research is complete.

Drug studies may contain difficulties linked to using invalidated biomarkers, including the disclosure of biomarker data to participants. The appropriate therapy target is not yet known, and there are no assurances that any treatment will succeed. Furthermore, based on a record of severe setbacks, the likelihood of succeeding is bleak.

Pharmaceutical companies frequently withdraw from clinical studies due to the high cost and risk of failure. As dementia typically progresses slowly, there is a significant time gap between starting studies and receiving results. There are several professionals who can treat dementia, including neurologists, geriatricians, radiologists, etc. Researchers must work together with peers from diverse subspecialties and regulatory organizations in order to conduct studies effectively.

Alzheimer’s and other similar neurodegenerative illnesses are typically incurable and sometimes difficult to diagnose accurately. However, early and accurate neurodegenerative disease detection can lead to more successful therapy. As a result, research efforts are shifting toward early detection of high-risk people and disease progression prevention via biomarkers.

Dementia and biomarker studies were hampered by dropouts, problems comparing data sets, divergent biomarker sets, the availability of histopathological confirmation at death, and nonlinear fluctuations in cognitive domains as the disease advances in vivo in subjects. This chapter evaluates the challenges in making an early diagnosis of dementia and outlines the issues to be aware of when doing dementia and biomarker investigations.

Data collection and analysis challenges

Longitudinal studies are more effective than cross-sectional studies in establishing causal directions. However, recruiting Mild Cognitive Impairment (MCI) subjects is challenging, especially for long-term dementia research.

A lack of awareness, a lack of benefits for the participant, stringent eligibility criteria, the older age of study volunteers, comorbidity factors, disability, limited mobility, requiring the cooperation of a partner or carer, transportation, medication administration, too many tests, and intensive monitoring of the individual’s condition and progress may all affect eligibility for enrollment.

Due to slow enrollment and the nature of the disease’s progression, dementia studies often take at least 5–6 years to determine whether a treatment works. This slow rate of development has several ramifications beyond the slow progress of finding a cure — it increases the expenses connected with clinical trials, making them less likely to be funded, and influence the trustworthiness of trial outcomes due to changing factors that come naturally with time (such as staff, scanners, economic cycles, and more).

Studies may work to make recruiting criteria more strict about lessening the variability usually present in a memory clinic to increase internal validity. However, for studies to be more relevant to doctors, they must also be clinically grounded, which implies that recruitment criteria cannot be too difficult for participants to be included. Simplifying recruiting enrolling requirements and screening procedures is one strategy to boost the number of volunteers. By being less rigorous on acceptable subjects for recruitment, more people may be registered, encouraging doctor recommendations.

Problems with comparing data sets

As a result of retrofitting criteria and statistical models developed for one cohort to another with different demographic characteristics, the results will differ. The variability of results can be further compounded by various combinations of measurements, cutoffs, subjects, and follow-up periods between samples.

Merging datasets in dementia research is also not a simple task. It is challenging to compare data from various studies that employed different approaches. When data from separate scanners are combined, noise is introduced. PET and MRI scanners have different scanner and software combinations. Inter-scanner variability is eliminated if both cross-sectional and longitudinal scans are taken on the same scanner, but this is impractical.

Lack of standardization impedes result comparability and replication, raises analytical variability, and complicates technique assessment. The accuracy of biomarker analysis varies depending on the technology used. Dropouts and missing data are handled differently. The time lag between acquiring a clinical diagnosis of subjective cognitive impairment (SCI) or mild cognitive impairment (MCI) and enrolling varies in every study.

Suppose there is a considerable time gap between diagnosis and enrollment. In that case, one SCI or MCI cohort may include more stable participants, making them less likely to proceed to a dementia subtype. Neuropsychological exams are administered using various demographic norms and neuropsychological test batteries.

Given that various factors might impact mental stability in the near term, assessing what variables are accounted for when reading published findings is crucial. As previously stated, one disadvantage of robustly constructed studies that are typically useful because they control for numerous variables is that they may not accurately reflect regular clinical practice.

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