Risk score prediction model for dementia in patients with type 2 diabetes

A Nomogram Including Total Cerebral Small Vessel Disease Burden Score for Predicting Mild Vascular Cognitive Impairment in Patients with Type 2 Diabetes Mellitus

Background

Total cerebral small vessel disease (CSVD) burden score is an important predictor of vascular cognitive impairment (VCI). However, few predictive models of VCI in type 2 diabetes mellitus (T2DM) patients have included the total CSVD burden score, especially in the early stage of VCI.

Objective

To develop and validate a nomogram that includes the total CSVD burden score to predict mild VCI in patients with T2DM.

Methods

A total of 322 eligible participants with T2DM who were divided into mild and normal cognitive groups were enrolled in this retrospective study. Demographic data, laboratory data and imaging markers of CSVD were collected. The total CSVD burden score was calculated by combining the different CSVD markers. Step-backward multivariable logistic regression analysis with the Akaike information criterion was applied to select significant predictors and develop a best-fit predictive nomogram. The performance of the nomogram was assessed in terms of discriminative ability, calibrated ability, and clinical usefulness.

Results

The nomogram model consisted of five variables: age, education, hemoglobin A1c level, serum homocysteine level, and total CSVD burden score. A nomogram with these variables showed good discriminative ability (area under the receiver operating characteristic curve was 0.801 in internal verification). In addition, the Hosmer-Lemeshow test (χ2 =9.226, P=0.417) and bootstrap-corrected calibration plot indicated that the nomogram had good calibration. The Brier score of the predictive model was 0.178. Decision curve analysis demonstrated that when the threshold probability ranged between 16% and 98%, the use of the nomogram to predict mild VCI in patients with T2DM provide a greater net benefit.

Conclusions

The nomogram, composed of age, education, stroke, HbA1c level, Hcy level, and total CSVD burden score, had good predictive accuracy and may provide clinicians with a practical tool for predicting the risk of mild VCI in T2DM patients.

Development and Validation of a Risk Score for Mild Cognitive Impairment in Individuals with Type 2 Diabetes in China: A Practical Cognitive Prescreening Tool

Aim

Numerous evidence suggests that diabetes increases the risk of cognitive impairment. This study aimed to develop and validate a multivariable risk score model to identify mild cognitive impairment (MCI) in patients with type 2 diabetes mellitus (T2DM).

Methods

This cross-sectional study included 1256 inpatients (age: 57.5 ± 11.2 years) with T2DM in a tertiary care hospital in China. MCI was diagnosed according to the criteria recommended by the National Institute on Aging-Alzheimer's Association Workgroup, and a MoCA score of 19-25 indicated MCI. Participants were randomly allocated into the derivation and validation sets at 7:3 ratio. Logistic regression models were used to identify predictors for MCI in the derivation set. A scoring system based on the predictors' beta coefficient was developed. Predictive ability of the risk score was tested by discrimination and calibration methods.

Results

Totally 880 (285 with MCI, 32.4%) and 376 (167 with MCI, 33.8%) patients were allocated in the derivation and validation set, respectively. Age, education, HbA1c, self-reported history of severe hypoglycemia, and microvascular disease were identified as predictors for MCI and constituted the risk score. The AUCs (95% CI) of the risk score were 0.751 (0.717, 0.784) in derivation set and 0.776 (0.727, 0.824) in validation set. The risk score showed good apparent calibration of observed and predicted MCI probabilities and was capable of stratifying individuals into 3 risk categories by two cut-off points (low risk: ≤ 3, medium risk: 4-13, and high risk ≥ 14).

Conclusion

The risk score based on age, education, HbA1c, self-reported history of severe hypoglycemia, and microvascular disease can effectively assess MCI risk in adults with T2DM at different age. It can serve as a practical prescreening tool for early detection of MCI in daily diabetes care.

Multi-domain prognostic models used in middle-aged adults without known cognitive impairment for predicting subsequent dementia

BACKGROUND

Dementia, a global health priority, has no current cure. Around 50 million people worldwide currently live with dementia, and this number is expected to treble by 2050. Some health conditions and lifestyle behaviours can increase or decrease the risk of dementia and are known as 'predictors'. Prognostic models combine such predictors to measure the risk of future dementia. Models that can accurately predict future dementia would help clinicians select high-risk adults in middle age and implement targeted risk reduction.

OBJECTIVES

Our primary objective was to identify multi-domain prognostic models used in middle-aged adults (aged 45 to 65 years) for predicting dementia or cognitive impairment. Eligible multi-domain prognostic models involved two or more of the modifiable dementia predictors identified in a 2020 Lancet Commission report and a 2019 World Health Organization (WHO) report (less education, hearing loss, traumatic brain injury, hypertension, excessive alcohol intake, obesity, smoking, depression, social isolation, physical inactivity, diabetes mellitus, air pollution, poor diet, and cognitive inactivity). Our secondary objectives were to summarise the prognostic models, to appraise their predictive accuracy (discrimination and calibration) as reported in the development and validation studies, and to identify the implications of using dementia prognostic models for the management of people at a higher risk for future dementia.

SEARCH METHODS

We searched MEDLINE, Embase, PsycINFO, CINAHL, and ISI Web of Science Core Collection from inception until 6 June 2022. We performed forwards and backwards citation tracking of included studies using the Web of Science platform.  SELECTION CRITERIA: We included development and validation studies of multi-domain prognostic models. The minimum eligible follow-up was five years. Our primary outcome was an incident clinical diagnosis of dementia based on validated diagnostic criteria, and our secondary outcome was dementia or cognitive impairment determined by any other method.

DATA COLLECTION AND ANALYSIS

Two review authors independently screened the references, extracted data using a template based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS), and assessed risk of bias and applicability of included studies using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). We synthesised the C-statistics of models that had been externally validated in at least three comparable studies.  MAIN RESULTS: We identified 20 eligible studies; eight were development studies and 12 were validation studies. There were 14 unique prognostic models: seven models with validation studies and seven models with development-only studies. The models included a median of nine predictors (range 6 to 34); the median number of modifiable predictors was five (range 2 to 11). The most common modifiable predictors in externally validated models were diabetes, hypertension, smoking, physical activity, and obesity. In development-only models, the most common modifiable predictors were obesity, diabetes, hypertension, and smoking. No models included hearing loss or air pollution as predictors. Nineteen studies had a high risk of bias according to the PROBAST assessment, mainly because of inappropriate analysis methods, particularly lack of reported calibration measures. Applicability concerns were low for 12 studies, as their population, predictors, and outcomes were consistent with those of interest for this review. Applicability concerns were high for nine studies, as they lacked baseline cognitive screening or excluded an age group within the range of 45 to 65 years. Only one model, Cardiovascular Risk Factors, Ageing, and Dementia (CAIDE), had been externally validated in multiple studies, allowing for meta-analysis. The CAIDE model included eight predictors (four modifiable predictors): age, education, sex, systolic blood pressure, body mass index (BMI), total cholesterol, physical activity and APOEƐ4 status. Overall, our confidence in the prediction accuracy of CAIDE was very low; our main reasons for downgrading the certainty of the evidence were high risk of bias across all the studies, high concern of applicability, non-overlapping confidence intervals (CIs), and a high degree of heterogeneity. The summary C-statistic was 0.71 (95% CI 0.66 to 0.76; 3 studies; very low-certainty evidence) for the incident clinical diagnosis of dementia, and 0.67 (95% CI 0.61 to 0.73; 3 studies; very low-certainty evidence) for dementia or cognitive impairment based on cognitive scores. Meta-analysis of calibration measures was not possible, as few studies provided these data.

AUTHORS' CONCLUSIONS

We identified 14 unique multi-domain prognostic models used in middle-aged adults for predicting subsequent dementia. Diabetes, hypertension, obesity, and smoking were the most common modifiable risk factors used as predictors in the models. We performed meta-analyses of C-statistics for one model (CAIDE), but the summary values were unreliable. Owing to lack of data, we were unable to meta-analyse the calibration measures of CAIDE. This review highlights the need for further robust external validations of multi-domain prognostic models for predicting future risk of dementia in middle-aged adults.

A prediction model of cognitive impairment risk in elderly illiterate Chinese women

Objective

To establish and validate a targeted model for the prediction of cognitive impairment in elderly illiterate Chinese women.

Methods

1864 participants in the 2011-2014 cohort and 1,060 participants in the 2014-2018 cohort from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) were included in this study. The Chinese version of the Mini-Mental State Examination (MMSE) was used to measure cognitive function. Demographics and lifestyle information were collected to construct a risk prediction model by a restricted cubic spline Cox regression. The discrimination and accuracy of the model were assessed by the area under the curve (AUC) and the concordance index, respectively.

Results

A total of seven critical variables were included in the final prediction model for cognitive impairment risk, including age, MMSE score, waist-to-height ratio (WHtR), psychological score, activities of daily living (ADL), instrumental abilities of daily living (IADL), and frequency of tooth brushing. The internal and external validation AUCs were 0.8 and 0.74, respectively; and the receiver operating characteristic (ROC) curves indicated good performance ability of the constructed model.

Conclusion

A feasible model to explore the factors influencing cognitive impairment in elderly illiterate women in China and to identify the elders at high risk was successfully constructed.

External validation of existing dementia prediction models on observational health data

BACKGROUND

Many dementia prediction models have been developed, but only few have been externally validated, which hinders clinical uptake and may pose a risk if models are applied to actual patients regardless. Externally validating an existing prediction model is a difficult task, where we mostly rely on the completeness of model reporting in a published article. In this study, we aim to externally validate existing dementia prediction models. To that end, we define model reporting criteria, review published studies, and externally validate three well reported models using routinely collected health data from administrative claims and electronic health records.

METHODS

We identified dementia prediction models that were developed between 2011 and 2020 and assessed if they could be externally validated given a set of model criteria. In addition, we externally validated three of these models (Walters' Dementia Risk Score, Mehta's RxDx-Dementia Risk Index, and Nori's ADRD dementia prediction model) on a network of six observational health databases from the United States, United Kingdom, Germany and the Netherlands, including the original development databases of the models.

RESULTS

We reviewed 59 dementia prediction models. All models reported the prediction method, development database, and target and outcome definitions. Less frequently reported by these 59 prediction models were predictor definitions (52 models) including the time window in which a predictor is assessed (21 models), predictor coefficients (20 models), and the time-at-risk (42 models). The validation of the model by Walters (development c-statistic: 0.84) showed moderate transportability (0.67-0.76 c-statistic). The Mehta model (development c-statistic: 0.81) transported well to some of the external databases (0.69-0.79 c-statistic). The Nori model (development AUROC: 0.69) transported well (0.62-0.68 AUROC) but performed modestly overall. Recalibration showed improvements for the Walters and Nori models, while recalibration could not be assessed for the Mehta model due to unreported baseline hazard.

CONCLUSION

We observed that reporting is mostly insufficient to fully externally validate published dementia prediction models, and therefore, it is uncertain how well these models would work in other clinical settings. We emphasize the importance of following established guidelines for reporting clinical prediction models. We recommend that reporting should be more explicit and have external validation in mind if the model is meant to be applied in different settings.

Evaluation of Processing Speed of Different Cognitive Functions Across the Life Span Using Cognitive Mobile Games

Objective: Processing speed (PS) is an important indicator of cognitive functioning and normal aging. However, the tools used to evaluate these are often rather simplistic and only assess one cognitive component. The aim of this study was to use cognitive mobile games (CMG) to evaluate the evolution of reaction times over the life span during different cognitive tasks. Methodology: We carried out a retrospective observational study in which we obtained anonymized results of 15,000 subjects. Scores of five CMG that train arithmetic, vocabulary, response control, visual attention and recognition, and working memory were analyzed. Results: Overall, we observed a highly statistically significant decrease (P < 0.001) in PS and a decrease of accuracy (P < 0.001) with increasing participant age, indicating that for each cognitive function tested, older participants performed cognitive tasks more slowly than younger participants. We also observed an interaction between the age of the participants and the number of errors. These results are consistent with physiological data with respect to aging and cognition. Conclusion: Owing to their wide availability and ease of use, CMG could be used as a simple tool to monitor cognitive function such as PS. Further studies are needed to study the influence of pathologies on those variables.

Dementia Risk Scores and Their Role in the Implementation of Risk Reduction Guidelines

Dementia prevention is a global health priority. In 2019, the World Health Organisation published its first evidence-based guidelines on dementia risk reduction. We are now at the stage where we need effective tools and resources to assess dementia risk and implement these guidelines into policy and practice. In this paper we review dementia risk scores as a means to facilitate this process. Specifically, we (a) discuss the rationale for dementia risk assessment, (b) outline some conceptual and methodological issues to consider when reviewing risk scores, (c) evaluate some dementia risk scores that are currently in use, and (d) provide some comments about future directions. A dementia risk score is a weighted composite of risk factors that reflects the likelihood of an individual developing dementia. In general, dementia risks scores have a wide range of implementations and benefits including providing early identification of individuals at high risk, improving risk perception for patients and physicians, and helping health professionals recommend targeted interventions to improve lifestyle habits to decrease dementia risk. A number of risk scores for dementia have been published, and some are widely used in research and clinical trials e.g., CAIDE, ANU-ADRI, and LIBRA. However, there are some methodological concerns and limitations associated with the use of these risk scores and more research is needed to increase their effectiveness and applicability. Overall, we conclude that, while further refinement of risk scores is underway, there is adequate evidence to use these assessments to implement guidelines on dementia risk reduction.

Vascular complications of diabetes: natural history and corresponding risks of dementia in a national cohort of adults with diabetes

AIMS

This study aimed to determine the trajectory of diabetic vascular diseases and to investigate the association between vascular diseases and dementia.

METHODS

We included adults aged ≥ 50 years with newly diagnosed type 2 diabetes (n = 173,118) from 2001 to 2005 who were followed-up until December 31, 2013 in the Taiwan's National Health Insurance Research Database. Multivariable Cox regression models were constructed to estimate hazard ratios (HRs) and confidence limits (CLs) for all-cause dementia in relation to the number, types, and occurrence patterns of vascular disease.

RESULTS

Within 1 year of diabetes diagnosis, 26.3% of adults developed their first vascular disease. During the 1,864,279 person-years of follow-up, 17,426 adults had all-cause dementia, corresponding to an incidence of 97.9 cases/10,000 person-years in 127,718 adults with at least one vascular disease and 67.5 cases/10,000 person-years in 45,400 adults without vascular diseases. Across all age groups, adults who subsequently developed a vascular disease in two one-year windows since diabetes diagnosis had the highest incidence of all-cause dementia. In comparison with adults without vascular diseases, HR for all-cause dementia was 1.99 (CL: 1.92-2.07) for those with one vascular disease only; 2.04 (CL: 1.98-2.13) for two or more vascular diseases; 3.56 (CL: 3.44-3.70) for stroke only; and 2.06 (CL: 1.99-2.14) for neuropathy alone. Similar associations were also observed with a smaller magnitude for adults with nephropathy, retinopathy, cardiovascular disease, or peripheral arterial disease.

CONCLUSIONS

Patients with diabetes-related complications, particularly stroke and neuropathy, and those with rapidly developed vascular diseases appeared to have a high risk of dementia.

Brain training using cognitive apps can improve cognitive performance and processing speed in older adults

Managing age-related decrease of cognitive function is an important public health challenge, especially in the context of the global aging of the population. Over the last years several Cognitive Mobile Games (CMG) have been developed to train and challenge the brain. However, currently the level of evidence supporting the benefits of using CMG in real-life use is limited in older adults, especially at a late age. In this study we analyzed game scores and the processing speed obtained over the course of 100 sessions in 12,000 subjects aged 60 to over 80 years. Users who trained with the games improved regardless of age in terms of scores and processing speed throughout the 100 sessions, suggesting that old and very old adults can improve their cognitive performance using CMG in real-life use.

A risk score model with five long non-coding RNAs for predicting prognosis in gastric cancer: an integrated analysis combining TCGA and GEO datasets

Background

Gastric cancer (GC) is one of the most common carcinomas of the digestive tract, and the prognosis for these patients may be poor. There is evidence that some long non-coding RNAs(lncRNAs) can predict the prognosis of patients with GC. However, few lncRNA signatures have been used to predict prognosis. Herein, we aimed to construct a risk score model based on the expression of five lncRNAs to predict the prognosis of patients with GC and provide new potential therapeutic targets.

Methods

We performed differentially expressed and survival analyses to identify differentially expressed survival-ralated lncRNAs by using GC patient expression profile data from The Cancer Genome Atlas (TCGA) database. We then established a formula including five lncRNAs to predict the prognosis of patients with GC. In addition, to verify the prognostic value of this risk score model, two independent Gene Expression Omnibus (GEO) datasets, GSE62254 (N = 300) and GSE15459 (N = 200), were employed as validation groups.

Results

Based on the characteristics of five lncRNAs, patients with GC were divided into high or low risk subgroups. The prognostic value of the risk score model with five lncRNAs was confirmed in both TCGA and the two independent GEO datasets. Furthermore, stratification analysis results showed that this model had an independent prognostic value in patients with stage II-IV GC. We constructed a nomogram model combining clinical factors and the five lncRNAs to increase the accuracy of prognostic prediction. Enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) suggested that the five lncRNAs are associated with multiple cancer occurrence and progression-related pathways.

Conclusion

The risk score model including five lncRNAs can predict the prognosis of patients with GC, especially those with stage II-IV, and may provide potential therapeutic targets in future.

Risk of idiopathic peripheral neuropathy in end-stage renal disease: A population-based cohort study

BACKGROUND

Whether patients with end-stage renal disease (ESRD) have a higher risk of idiopathic polyneuropathy (IPN) than those without ESRD remains unclear. We hypothesised that carpal tunnel syndrome (CTS) is a prodrome of IPN in patients with ESRD.

METHODS

Data were collected from the Taiwan National Health Insurance research database (NHIRD) for the 2000-2011 period. Two matching strategies, age- and sex-matching and propensity matching, were used, which yielded 2596 age- and sex-matched patients with ESRD and 2210 propensity-matched patients with ESRD. The comparison cohort was chosen in a 1:4 ratio for the age- and sex-matched method and in a 1:1 ratio for the propensity-matching method. The primary outcome was the incidence of IPN. Cox proportional hazards modelling was used.

RESULTS

In the age- and sex-matched cohort, the IPN incidence was 7.64 and 2.88 per 1000 person-years for the ESRD and controls cohorts, respectively. After we adjusted for age, sex, comorbidities and medications relative to controls, having ESRD was significantly associated with increased risk of IPN (hazard ratio [HR] = 2.45, 95% confidence interval [CI] = 1.76-3.41). Competing risk of death as sensitivity analysis revealed that having ESRD with CTS was still associated with higher risk of IPN than having CTS without ESRD (HR = 2.85, 95% CI = 1.87-4.34).

CONCLUSION

Patients with ESRD with CTS had higher incidences of idiopathic peripheral neuropathy than those without ESRD with CTS.

Diabetes-specific dementia risk score (DSDRS) predicts cognitive performance in patients with type 2 diabetes at high cardio-renal risk

AIM

To investigate the relationship between the diabetes-specific dementia risk score (DSDRS) and concurrent and future cognitive impairment (CI) in type 2 diabetes (T2D).

METHODS

DSDRS were calculated for participants with T2D aged ≥60 years from the CARMELINA-cognition substudy (ClinicalTrials.gov Identifier: NCT01897532). Cognitive assessment included Mini-Mental State Examination (MMSE) and a composite attention and executive functioning score (A&E). The relation between baseline DSDRS and probability of CI (MMSE < 24) and variation in cognitive performance was assessed at baseline (n = 2241) and after 2.5 years follow-up in patients without baseline CI (n = 1312).

RESULTS

Higher DSDRS was associated with a higher probability of CI at baseline (OR = 1.17 per point, 95% CI 1.12-1.22) and follow-up (OR = 1.24 per point, 95% CI 1.14-1.35). Moreover, in patients without baseline CI, higher DSDRS was also associated with lower baseline cognitive performance (MMSE: F(1, 1930) = 47.07, p < .0001, R² = 0.02); A&E z-score: (F(1, 1871) = 33.44 p < .0001, R² = 0.02) and faster cognitive decline at follow-up (MMSE: F(3, 1279) = 38.41, p < .0001; A&E z-score: F(3, 1206) = 148.48, p < .0001).

CONCLUSIONS

The DSDRS identifies patients with T2D at risk of concurrent as well as future CI. The DSDRS may thus be a supportive tool in screening strategies for cognitive dysfunction in patients with T2D.

Development and Validation of a Nomogram for Predicting the 6-Year Risk of Cognitive Impairment Among Chinese Older Adults

OBJECTIVE

Although some people with mild cognitive impairment may not suffer from dementia lifelong, about 5% of them will progress to dementia within 1 year in community settings. However, a general tool for predicting the risk of cognitive impairment was not adequately studied among older adults.

DESIGN

Prospective cohort study.

SETTING

Community-living, older adults from 22 provinces in China.

PARTICIPANTS

We included 10,066 older adults aged 65 years and above (mean age, 83.2 ± 11.1 years), with normal cognition at baseline in the 2002-2008 cohort and 9354 older adults (mean age, 83.5 ± 10.8 years) in the 2008-2014 cohort of the Chinese Longitudinal Healthy Longevity Survey.

METHODS

We measured cognitive function using the Chinese version of the Mini-Mental State Examination. Demographic, medical, and lifestyle information was used to develop the nomogram via a Lasso selection procedure using a Cox proportional hazards regression model. We validated the nomogram internally with 2000 bootstrap resamples and externally in a later cohort. The predictive accuracy and discriminative ability of the nomogram were measured by area-under-the-curves and calibration curves, respectively.

RESULTS

Eight factors were identified with which to construct the nomogram: age, baseline of the Mini-Mental State Examination, activities of daily living and instrumental activities of daily living score, chewing ability, visual function, history of stroke, watching TV or listening to the radio, and growing flowers or raising pets. The area-under-the-curves for internal and external validation were 0.891 and 0.867, respectively, for predicting incident cognitive impairment. The calibration curves showed good consistency between nomogram-based predictions and observations.

CONCLUSIONS AND IMPLICATIONS

The nomogram-based prediction yielded consistent results in 2 separate large cohorts. This feasible prognostic nomogram constructed using readily ascertained information may assist public health practitioners or physicians to provide preventive interventions of cognitive impairment.

Association between the Use of Dipeptidyl Peptidase 4 Inhibitors and the Risk of Dementia among Patients with Type 2 Diabetes in Taiwan

STUDY OBJECTIVES

Diabetes mellitus per se and its related therapy have been frequently associated with an increased risk of developing dementia. However, studies that explored the risk of dementia from the use of the novel oral antidiabetic medication dipeptidyl peptidase 4 inhibitor (DPP-4i) have been limited, especially in Asian populations. The present study aimed to determine the effect of DPP-4i on the subsequent risk of dementia among patients with type 2 diabetes (T2D) in Taiwan.

METHODS

This study utilized data from the Longitudinal Health Insurance Database between 2008 and 2015. We enrolled 2903 patients aged ≥50 years, who were on DPP-4i for a diagnosis of T2D and had no dementia. A total of 11,612 subjects were included and compared with a propensity score-matched control group who did not use DPP-4i (non-DPP-4i group). Survival analysis was performed to estimate and compare the risk of dementia-including Alzheimer's disease, vascular dementia, and other dementia types-between the two groups. Results: Both groups had a mean age of 68 years, had a preponderance of women (61.8%), and were followed up for a mean duration of 7 years. The risk of all-cause dementia was significantly lower in the DPP-4i group than in the non-DPP-4i group (hazard ratio (HR) 0.798; 95% confidence interval (CI) 0.681-0.883; p < 0.001), with a class effect. This trend was particularly observed for vascular dementia (HR 0.575; 95% CI 0.404-0.681; p < 0.001), but not in Alzheimer's disease (HR 0.891; 95% CI 0.712-1.265; p = 0.297). The Kaplan-Meier analysis showed that the preventive effect on dementia was positively correlated with the cumulative dose of DPP-4i. Conclusions: DPP-4i decreased the risk of dementia with a class effect, especially vascular dementia, but not in Alzheimer's disease. Our results provide important information on the drug choice when managing patients with T2D in clinical practice.

Cognitive Dysfunction in Persons with Type 2 Diabetes Mellitus: A Concept Analysis

Although cognitive dysfunction is related to type 2 diabetes mellitus (T2DM), the concept has not yet been well defined. The purpose of this study was to define the concept of cognitive dysfunction in persons with T2DM and examine its defining attributes, antecedents, and consequences. Literature was retrieved from 2008 to 2018 by systematically searching the PubMed, CINAHL, and PsycINFO databases. Based on 37 included studies, three defining attributes were identified: cognitive dysfunction is a recognized or unrecognized symptom, is characterized by a subtle decline in one or more cognitive domains, and is accompanied by pronounced structural changes observed in brain imaging. One major antecedent was diabetes-related or diabetes-specific pathological changes. Consequences included interference with diabetes self-management, nonadherence to recommended self-management behaviors, and a higher risk of having hypoglycemic events. The concept analysis provides a theoretical foundation that can be used to guide evaluations and interventions related to cognitive dysfunction in individuals with T2DM.

Models for predicting risk of dementia: a systematic review

BACKGROUND

Information from well-established dementia risk models can guide targeted intervention to prevent dementia, in addition to the main purpose of quantifying the probability of developing dementia in the future.

METHODS

We conducted a systematic review of published studies on existing dementia risk models. The models were assessed by sensitivity, specificity and area under the curve (AUC) from receiver operating characteristic analysis.

RESULTS

Of 8462 studies reviewed, 61 articles describing dementia risk models were identified, with the majority of the articles modelling late life risk (n=39), followed by those modelling prediction of mild cognitive impairment to Alzheimer's disease (n=15), mid-life risk (n=4) and patients with diabetes (n=3). Age, sex, education, Mini Mental State Examination, the Consortium to Establish a Registry for Alzheimer's Disease neuropsychological assessment battery, Alzheimer's Disease Assessment Scale-cognitive subscale, body mass index, alcohol intake and genetic variables are the most common predictors included in the models. Most risk models had moderate-to-high predictive ability (AUC>0.70). The highest AUC value (0.932) was produced from a risk model developed for patients with mild cognitive impairment.

CONCLUSION

The predictive ability of existing dementia risk models is acceptable. Population-specific dementia risk models are necessary for populations and subpopulations with different characteristics.