Effects of diabetes and obesity on cognitive impairment and mortality in older mexicans

Milani, Sadaf Arefi; López, David S.; Downer, Brian; Samper‐Ternent, Rafael; Wong, Rebeca

doi:10.1016/j.archger.2021.104581

Cited by 17 publications

(17 citation statements)

References 36 publications

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“…This contrasts with our previous study of patients with obesity class II/III (mean (SD) age: 44.1 (12.1) years) which found obesity, not T2d, associated with poorer cognition, as measured by the NIHTB‐CB 16 . The observed associations in the Pima American Indians, are comparable to studies comprised of older adults (mean ages: 65.0 17 and 69.6 18 years), which found that obesity was less of a risk factor than T2d for cognitive impairment 17,18 . Therefore, one possible explanation for findings in the present study is that Pima American Indians had longer T2d duration at a given age than other race/ethnic groups, possibly amplifying the effect of T2d as a risk factor for cognition relative to obesity.…”

Section: Discussioncontrasting

confidence: 95%

“…19 Currently, the underlying mechanisms linking cognition with both T2d, and obesity are not well understood. However, results from studies that measured the relative effects of obesity and T2d on cognitive impairment, 17,18 along with the results from the present study indicate that although obesity and T2d are often comorbid, there are likely independent underlying pathophysiologic mechanisms linking these risk factors with cognition.…”

Section: Discussionmentioning

confidence: 46%

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Association between brain health outcomes and metabolic risk factors in persons with diabetes

Reynolds,

Votruba,

Jack

et al. 2023

Ann Clin Transl Neurol

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We performed a cross‐sectional study to determine associations between cognition and MRI‐derived brain outcomes, with obesity, diabetes duration, and metabolic risk factors in 51 Pima American Indians with longstanding type 2 diabetes (T2d) (mean [SD] age: 48.4 [11.3] years, T2d duration: 20.1 [9.1] years). Participants had similar cognition (NIH Toolbox Cognition Battery composite: 45.3 [9.8], p = 0.64, n = 51) compared to normative data. T2d duration, but not other metabolic risk factors, associated with decreased cortical thickness (Point Estimate (PE): −0.0061, 95%CI: −0.0113, −0.0009, n = 45), gray matter volume (PE: −830.39, 95%CI: −1503.14, −157.64, n = 45), and increased white matter hyperintensity volume (PE: 0.0389, 95%CI: 0.0049, 0.0729, n = 45).

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Section: Discussioncontrasting

confidence: 95%

Section: Discussionmentioning

confidence: 46%

Association between brain health outcomes and metabolic risk factors in persons with diabetes

Reynolds,

Votruba,

Jack

et al. 2023

Ann Clin Transl Neurol

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“…Relative to normal weight without diabetes, overweight without diabetes (RR, 0.86 [95% CI, 0.67–1.11]) and obesity without diabetes (RR, 0.88 [95% CI, 0.63–1.20]) were not associated with higher risk of dementia, but normal BMI with diabetes (RR, 2.01 [95% CI, 1.40–2.87]), overweight with diabetes (RR, 1.42 [95% CI, 1.02–1.97]), and obesity with diabetes (RR, 1.70 [95% CI, 1.16–2.48]) were associated with higher dementia risk. 113…”

Section: Brain Healthmentioning

confidence: 99%

2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

Martin,

Aday,

Almarzooq

et al. 2024

Circulation

434

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BACKGROUND: The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS: The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year’s worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year’s edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains. RESULTS: Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS: The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

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“…While there is evidence that these removed markers are associated with the development of cognitive impairment, several studies have reported findings similar to ours. Three previously published studies have indicated that BMI is associated with cognitive function ( 37 – 39 ); specifically, for every 1 kg/m2 increase in BMI, the prevalence of cognitive impairment increased by 3% ( 40 ), while another study reported the opposite result ( 41 ). A previous study based on large population data revealed a potential relationship between several obesity-related indicators, including WC, waist-to-hip ratio (WHR), BMI, LDL-C, and cognitive impairment ( 42 ).…”

Section: Discussionmentioning

confidence: 99%

A risk prediction model based on machine learning for early cognitive impairment in hypertension: Development and validation study

Zhong

Jiang

et al. 2023

Front. Public Health

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BackgroundClinical practice guidelines recommend early identification of cognitive impairment in individuals with hypertension with the help of risk prediction tools based on risk factors.ObjectiveThe aim of this study was to develop a superior machine learning model based on easily collected variables to predict the risk of early cognitive impairment in hypertensive individuals, which could be used to optimize early cognitive impairment risk assessment strategies.MethodsFor this cross-sectional study, 733 patients with hypertension (aged 30–85, 48.98% male) enrolled in multi-center hospitals in China were divided into a training group (70%) and a validation group (30%). After least absolute shrinkage and selection operator (LASSO) regression analysis with 5-fold cross-validation determined the modeling variables, three machine learning classifiers, logistic regression (LR), XGBoost (XGB), and gaussian naive bayes (GNB), were developed. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, and F1 score were used to evaluate the model performance. Shape Additive explanation (SHAP) analysis was performed to rank feature importance. Further decision curve analysis (DCA) assessed the clinical performance of the established model and visualized it by nomogram.ResultsHip circumference, age, education levels, and physical activity were considered significant predictors of early cognitive impairment in hypertension. The AUC (0.88), F1 score (0.59), accuracy (0.81), sensitivity (0.84), and specificity (0.80) of the XGB model were superior to LR and GNB classifiers.ConclusionThe XGB model based on hip circumference, age, educational level, and physical activity has superior predictive performance and it shows promise in predicting the risk of cognitive impairment in hypertensive clinical settings.

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Effects of diabetes and obesity on cognitive impairment and mortality in older mexicans

Cited by 17 publications

References 36 publications

Association between brain health outcomes and metabolic risk factors in persons with diabetes

Association between brain health outcomes and metabolic risk factors in persons with diabetes

2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

A risk prediction model based on machine learning for early cognitive impairment in hypertension: Development and validation study

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