Developing Early Markers of Cognitive Decline and Dementia Derived From Survey Response Behaviors: Protocol for Analyses of Preexisting Large-scale Longitudinal Data

The efficacy of novel biomarkers for the early detection and management of acute kidney injury: A systematic review

Acute kidney injury (AKI) is a frequent clinical complication lacking early diagnostic tests and effective treatments. Novel biomarkers have shown promise for enabling earlier detection, risk stratification, and guiding management of AKI. We conducted a systematic review to synthesize evidence on the efficacy of novel biomarkers for AKI detection and management. Database searches yielded 17 relevant studies which were critically appraised. Key themes were biomarker efficacy in predicting AKI risk and severity before functional changes; potential to improve clinical management through earlier diagnosis, prognostic enrichment, and guiding interventions; emerging roles as therapeutic targets and prognostic tools; and ongoing challenges requiring further validation. Overall, novel biomarkers like neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), and cell cycle arrest markers ([TIMP-2] •[IGFBP7]) demonstrate capability for very early AKI prediction and accurate risk stratification. Their incorporation has potential to facilitate timely targeted interventions and personalized management. However, factors influencing biomarker performance, optimal cutoffs, cost-effectiveness, and impact on patient outcomes require robust validation across diverse settings before widespread implementation. Addressing these limitations through ongoing research can help translate novel biomarkers into improved detection, prognosis, and management of AKI in clinical practice.

Early Identification of Cognitive Impairment in Community Environments Through Modeling Subtle Inconsistencies in Questionnaire Responses: Machine Learning Model Development and Validation

BACKGROUND

The underdiagnosis of cognitive impairment hinders timely intervention of dementia. Health professionals working in the community play a critical role in the early detection of cognitive impairment, yet still face several challenges such as a lack of suitable tools, necessary training, and potential stigmatization.

OBJECTIVE

This study explored a novel application integrating psychometric methods with data science techniques to model subtle inconsistencies in questionnaire response data for early identification of cognitive impairment in community environments.

METHODS

This study analyzed questionnaire response data from participants aged 50 years and older in the Health and Retirement Study (waves 8-9, n=12,942). Predictors included low-quality response indices generated using the graded response model from four brief questionnaires (optimism, hopelessness, purpose in life, and life satisfaction) assessing aspects of overall well-being, a focus of health professionals in communities. The primary and supplemental predicted outcomes were current cognitive impairment derived from a validated criterion and dementia or mortality in the next ten years. Seven predictive models were trained, and the performance of these models was evaluated and compared.

RESULTS

The multilayer perceptron exhibited the best performance in predicting current cognitive impairment. In the selected four questionnaires, the area under curve values for identifying current cognitive impairment ranged from 0.63 to 0.66 and was improved to 0.71 to 0.74 when combining the low-quality response indices with age and gender for prediction. We set the threshold for assessing cognitive impairment risk in the tool based on the ratio of underdiagnosis costs to overdiagnosis costs, and a ratio of 4 was used as the default choice. Furthermore, the tool outperformed the efficiency of age or health-based screening strategies for identifying individuals at high risk for cognitive impairment, particularly in the 50- to 59-year and 60- to 69-year age groups. The tool is available on a portal website for the public to access freely.

CONCLUSIONS

We developed a novel prediction tool that integrates psychometric methods with data science to facilitate "passive or backend" cognitive impairment assessments in community settings, aiming to promote early cognitive impairment detection. This tool simplifies the cognitive impairment assessment process, making it more adaptable and reducing burdens. Our approach also presents a new perspective for using questionnaire data: leveraging, rather than dismissing, low-quality data.

The Understanding America Study (UAS)

PURPOSE

The Understanding America Study (UAS) is a probability-based Internet panel housed at the Center for Economic and Social Research at the University of Southern California (USC). The UAS serves as a social and health sciences infrastructure for collecting data on the daily lives of US families and individuals. The collected information includes survey data, DNA from saliva samples, information from wearables, contextual and administrative linkages, ecological momentary assessments, self-recorded narratives and electronic records of financial transactions. The information collected focuses on a defining challenge of our time-identifying factors explaining racial, ethnic, geographic and socioeconomic disparities over the life course, including racial discrimination, inequalities in access to education and healthcare, differences in physical, economic and social environments, and, more generally, the various opportunities and obstacles one encounters over the life course. The UAS infrastructure aims to optimise engagement with the wider research community both in data dissemination and in soliciting input on content and methods. To encourage input from the research community, we have reserved 100 000 min of survey time per year for outside researchers, who can propose to add survey questions four times a year.

PARTICIPANTS

The UAS currently comprises about 15 000 US residents (including a 3500-person California oversample) recruited by Address-Based Sampling and provided with Internet-enabled tablets if needed. Surveys are conducted in English and Spanish.

FINDINGS TO DATE

Since the founding of the UAS in 2014, we have conducted more than 600 surveys, including a sequence of surveys collecting biennial information on health and retirement (the complete Health and Retirement Study instrument), 11 cognitive assessments, personality, knowledge and use of information on Social Security programme rules, work disability and subjective well-being. Several hundreds of papers have been published based on the collected data in the UAS. Studies include documentations of the mental health effects of the COVID-19 pandemic and how this varied across socioeconomic groups; comparisons of physical activity measured with accelerometers and by self-reports showing the dramatic biases in the latter; extensive studies have shown the power of using paradata in gauging cognitive change over time; several messaging experiments have shown the effectiveness of information provision on the quality of decision-making affecting well-being at older ages.

FUTURE PLANS

The UAS national sample is planned to grow to 20 000 respondents by 2025, with subsamples of about 2500 African American, 2000 Asian and 3000 Hispanic participants and an oversample of rural areas. An increasing amount of non-interview data (contextual information, data from a suite of wearables and administrative linkages) is continually being added to the data files.

Attrition from longitudinal ageing studies and performance across domains of cognitive functioning: an individual participant data meta-analysis

OBJECTIVES

This paper examined the magnitude of differences in performance across domains of cognitive functioning between participants who attrited from studies and those who did not, using data from longitudinal ageing studies where multiple cognitive tests were administered.

DESIGN

Individual participant data meta-analysis.

PARTICIPANTS

Data are from 10 epidemiological longitudinal studies on ageing (total n=209 518) from several Western countries (UK, USA, Mexico, etc). Each study had multiple waves of data (range of 2-17 waves), with multiple cognitive tests administered at each wave (range of 4-17 tests). Only waves with cognitive tests and information on participant dropout at the immediate next wave for adults aged 50 years or older were used in the meta-analysis.

MEASURES

For each pair of consecutive study waves, we compared the difference in cognitive scores (Cohen's d) between participants who dropped out at the next study wave and those who remained. Note that our operationalisation of dropout was inclusive of all causes (eg, mortality). The proportion of participant dropout at each wave was also computed.

RESULTS

The average proportion of dropouts between consecutive study waves was 0.26 (0.18 to 0.34). People who attrited were found to have significantly lower levels of cognitive functioning in all domains (at the wave 2-3 years before attrition) compared with those who did not attrit, with small-to-medium effect sizes (overall d=0.37 (0.30 to 0.43)).

CONCLUSIONS

Older adults who attrited from longitudinal ageing studies had lower cognitive functioning (assessed at the timepoint before attrition) across all domains as compared with individuals who remained. Cognitive functioning differences may contribute to selection bias in longitudinal ageing studies, impeding accurate conclusions in developmental research. In addition, examining the functional capabilities of attriters may be valuable for determining whether attriters experience functional limitations requiring healthcare attention.