Associations of plasma proteomics and age-related outcomes with brain age in a diverse cohort.

Machine learning models are increasingly being used to estimate "brain age" from neuroimaging data. The gap between chronological age and the estimated brain age gap (BAG) is potentially a measure of accelerated and resilient brain aging. Brain age calculated in this fashion has been shown to be associated with mortality, measures of physical function, health, and disease. Here, we estimate the BAG using a voxel-based elastic net regression approach, and then, we investigate its associations with mortality, cognitive status, and measures of health and disease in participants from Atherosclerosis Risk in Communities (ARIC) study who had a brain MRI at visit 5 of the study. Finally, we used the SOMAscan assay containing 4877 proteins to examine the proteomic associations with the MRI-defined BAG. Among N = 1849 participants (age, 76.4 (SD 5.6)), we found that increased values of BAG were strongly associated with increased mortality and increased severity of the cognitive status. Strong associations with mortality persisted when the analyses were performed in cognitively normal participants. In addition, it was strongly associated with BMI, diabetes, measures of physical function, hypertension, prevalent heart disease, and stroke. Finally, we found 33 proteins associated with BAG after a correction for multiple comparisons. The top proteins with positive associations to brain age were growth/differentiation factor 15 (GDF-15), Sushi, von Willebrand factor type A, EGF, and pentraxin domain-containing protein 1 (SEVP 1), matrilysin (MMP7), ADAMTS-like protein 2 (ADAMTS), and heat shock 70 kDa protein 1B (HSPA1B) while EGF-receptor (EGFR), mast/stem-cell-growth-factor-receptor (KIT), coagulation-factor-VII, and cGMP-dependent-protein-kinase-1 (PRKG1) were negatively associated to brain age. Several of these proteins were previously associated with dementia in ARIC. These results suggest that circulating proteins implicated in biological aging, cellular senescence, angiogenesis, and coagulation are associated with a neuroimaging measure of brain aging.

Association of Lifestyle Intervention With Risk for Cardiovascular Events Differs by Level of Glycated Hemoglobin.

PURPOSE: We reevaluated the Action for Health in Diabetes (Look AHEAD) intensive lifestyle intervention (ILI) to assess whether the effect of ILI on cardiovascular disease (CVD) prevention differed by baseline glycated hemoglobin (HbA1c). METHODS: Look AHEAD randomized 5145 adults, aged 45 to 76 years with type 2 diabetes and overweight/obesity to ILI or a diabetes support and education (DSE) control group for a median of 9.6 years. ILI focused on achieving weight loss through decreased caloric intake and increased physical activity. We assessed the parent trial's primary composite CVD outcome. We evaluated additive and multiplicative heterogeneity of the intervention on CVD risk by baseline HbA1c. RESULTS: Mean baseline HbA1c was 7.3% (SD 1.2) and ranged from 4.4% (quintile 1) to 14.5% (quintile 5). We observed additive and multiplicative heterogeneity of the association between ILI and CVD (all P < .001) by baseline HbA1c. Randomization to ILI was associated with lower CVD risk for HbA1c quintiles 1 [hazard ratio (HR): 0.68, 95% confidence interval (CI): 0.53, 0.88] and 2 (HR: 0.80, 95% CI: 0.66, 0.96) and associated with higher CVD risk for HbA1c quintile 5 (HR: 1.27, 95% CI: 1.02, 1.58), compared to DSE. CONCLUSION: Among adults with type 2 diabetes and overweight/obesity, randomization to a lifestyle intervention was differentially associated with CVD risk by baseline HbA1c such that it was associated with lower risk at lower HbA1c levels and higher risk at higher HbA1c levels. There is a critical need to develop and tailor lifestyle interventions to be successful for individuals with type 2 diabetes and high HbA1c.

Rationale, design, and cohort characteristics of the Action for Health in Diabetes Aging study.

INTRODUCTION: Diabetes and overweight/obesity are described as accelerating aging processes, yet many individuals with these conditions maintain high levels of cognitive and physical function and independence late into life. The Look AHEAD Aging study is designed to identify 20-year trajectories of behaviors, risk factors, and medical history associated with resilience against geriatric syndromes and aging-related cognitive and physical functional deficits among individuals with these conditions. METHODS: Look AHEAD Aging extends follow-up of the cohort of the former 10-year Look AHEAD trial. The original cohort (N = 5145) was enrolled in 2001 to 2004 when participants were aged 45 to 76 years and randomly assigned to a multidomain intensive lifestyle intervention (ILI) or a diabetes support and education (DSE) condition. The trial interventions ceased in 2012. Clinic-based follow-up continued through 2020. In 2021, the cohort was invited to enroll in Look AHEAD Aging, an additional 4-year telephone-based follow-up (every 6 months) enhanced with Medicare linkage. Standardized protocols assess multimorbidity, physical and cognitive function, health care utilization, and health-related quality of life. RESULTS: Of the original N = 5145 Look AHEAD participants, N = 1552 active survivors agreed to participate in Look AHEAD Aging. At consent, the cohort's mean age was 76 (range 63 to 94) years and participants had been followed for a mean of 20 years. Of the original Look AHEAD enrollees, those who were younger, female, or with no history of cardiovascular disease were more likely to be represented in the Look AHEAD Aging cohort. Intervention groups were comparable with respect to age, diabetes duration, body mass index, insulin use, hypertension, cardiovascular disease, and cognitive function. ILI participants had significantly lower deficit accumulation index scores. DISCUSSION: By continuing the long-term follow-up of an extensively characterized cohort of older individuals with type 2 diabetes, Look AHEAD Aging is well positioned to identify factors associated with resilience against aging-related conditions.

Defining Demographic-specific Coronary Artery Calcium Percentiles in the Population Aged ≥75: The ARIC Study and MESA.

BACKGROUND: Current clinical guidelines recommend a coronary artery calcium (CAC) score of 100 Agatston Units or demographic-specific 75th percentile as high-risk thresholds for guiding atherosclerotic cardiovascular disease preventive therapy. Meanwhile, low CAC can help derisk individuals who may safely defer statin therapy. However, limited data from the early 2000s, including just 208 older Black individuals, inform CAC percentiles for adults aged 75 to 85 years, and none have been established in adults aged ≥85 years. This study aims to characterize the distribution of CAC and establish demographic-specific CAC percentiles in the population aged ≥75 years. METHODS: We assessed 2886 participants aged ≥75 years without clinical coronary heart disease from the ARIC study (Atherosclerosis Risk in Communities) visit 7 (2018-2019; n=2217) and the MESA (Multi-Ethnic Study of Atherosclerosis) visit 5 (2010-2011; n=669). Prevalence of any CAC >0 and sex- and race-specific CAC percentiles across age were estimated nonparametrically with locally weighted regression models and pooled residual ranking. RESULTS: The median age was 80 (interquartile interval, 77-83) years, and 60% were female. The prevalence of zero CAC was lowest in White males (4%), followed by Black males (13%), White females (14%), and highest in Black females (18%). Regardless of sex and race, most participants had CAC>100 (62.5%). CAC scores increased with age, with CAC identified in ≈95% of participants aged ≥90 years across sex-race subgroups. The 75th percentile corresponded to higher CAC scores for Black older adults (n=741), especially females, than currently used thresholds. CONCLUSIONS: In community-dwelling adults aged ≥75 years free of clinical coronary heart disease, the prevalence of zero CAC was 11%, and CAC >100 as a threshold for high ASCVD risk would categorize most of this older population as high risk. Demographic-specific CAC percentiles from this study are a valuable tool for interpreting CAC in the population aged ≥75 years.

Genome-wide association meta-analysis identifies 17 loci associated with nonalcoholic fatty liver disease.

Nonalcoholic fatty liver disease (NAFLD) is common and partially heritable and has no effective treatments. We carried out a genome-wide association study (GWAS) meta-analysis of imaging (n = 66,814) and diagnostic code (3,584 cases versus 621,081 controls) measured NAFLD across diverse ancestries. We identified NAFLD-associated variants at torsin family 1 member B (TOR1B), fat mass and obesity associated (FTO), cordon-bleu WH2 repeat protein like 1 (COBLL1)/growth factor receptor-bound protein 14 (GRB14), insulin receptor (INSR), sterol regulatory element-binding transcription factor 1 (SREBF1) and patatin-like phospholipase domain-containing protein 2 (PNPLA2), as well as validated NAFLD-associated variants at patatin-like phospholipase domain-containing protein 3 (PNPLA3), transmembrane 6 superfamily 2 (TM6SF2), apolipoprotein E (APOE), glucokinase regulator (GCKR), tribbles homolog 1 (TRIB1), glycerol-3-phosphate acyltransferase (GPAM), mitochondrial amidoxime-reducing component 1 (MARC1), microsomal triglyceride transfer protein large subunit (MTTP), alcohol dehydrogenase 1B (ADH1B), transmembrane channel like 4 (TMC4)/membrane-bound O-acyltransferase domain containing 7 (MBOAT7) and receptor-type tyrosine-protein phosphatase δ (PTPRD). Implicated genes highlight mitochondrial, cholesterol and de novo lipogenesis as causally contributing to NAFLD predisposition. Phenome-wide association study (PheWAS) analyses suggest at least seven subtypes of NAFLD. Individuals in the top 10% and 1% of genetic risk have a 2.5-fold to 6-fold increased risk of NAFLD, cirrhosis and hepatocellular carcinoma. These genetic variants identify subtypes of NAFLD, improve estimates of disease risk and can guide the development of targeted therapeutics.

Methods for estimating insulin resistance from untargeted metabolomics data.

CONTEXT: Insulin resistance is associated with multiple complex diseases; however, precise measures of insulin resistance are invasive, expensive, and time-consuming. OBJECTIVE: Develop estimation models for measures of insulin resistance, including insulin sensitivity index (SI) and homeostatic model assessment of insulin resistance (HOMA-IR) from metabolomics data. DESIGN: Insulin Resistance Atherosclerosis Family Study (IRASFS). SETTING: Community based. PARTICIPANTS: Mexican Americans (MA) and African Americans (AA). MAIN OUTCOME: Estimation models for measures of insulin resistance, i.e. SI and HOMA-IR. RESULTS: Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net regression were used to build insulin resistance estimation models from 1274 metabolites combined with clinical data, e.g. age, sex, body mass index (BMI). Metabolite data were transformed using three approaches, i.e. inverse normal transformation, standardization, and Box Cox transformation. The analysis was performed in one MA recruitment site (San Luis Valley, Colorado (SLV); N = 450) and tested in another MA recruitment site (San Antonio, Texas (SA); N = 473). In addition, the two MA recruitment sites were combined and estimation models tested in the AA recruitment sample (Los Angeles, California; N = 495). Estimated and empiric SI were correlated in the SA (r2 = 0.77) and AA (r2 = 0.74) testing datasets. Further, estimated and empiric SI were consistently associated with BMI, low-density lipoprotein cholesterol (LDL), and triglycerides. We applied similar approaches to estimate HOMA-IR with similar results. CONCLUSIONS: We have developed a method for estimating insulin resistance with metabolomics data that has the potential for application to a wide range of biomedical studies and conditions.

Prediabetes, intervening diabetes and subsequent risk of dementia: the Atherosclerosis Risk in Communities (ARIC) study.

AIMS/HYPOTHESIS: The aim of this work was to evaluate whether the association of prediabetes with dementia is explained by the intervening onset of diabetes. METHODS: Among participants of the Atherosclerosis Risk in Communities (ARIC) study we defined baseline prediabetes as HbA1c 39-46 mmol/mol (5.7-6.4%) and subsequent incident diabetes as a self-reported physician diagnosis or use of diabetes medication. Incident dementia was ascertained via active surveillance and adjudicated. We quantified the association of prediabetes with dementia risk before and after accounting for the subsequent development of diabetes among ARIC participants without diabetes at baseline (1990-1992; participants aged 46-70 years). We also evaluated whether age at diabetes diagnosis modified the risk of dementia. RESULTS: Among 11,656 participants without diabetes at baseline, 2330 (20.0%) had prediabetes. Before accounting for incident diabetes, prediabetes was significantly associated with the risk of dementia (HR 1.12 [95% CI 1.01, 1.24]). After accounting for incident diabetes, the association was attenuated and non-significant (HR 1.05 [95% CI 0.94, 1.16]). Earlier age of onset of diabetes had the strongest association with dementia: HR 2.92 (95% CI 2.06, 4.14) for onset before 60 years; HR 1.73 (95% CI 1.47, 2.04) for onset at 60-69 years; and HR 1.23 (95% CI 1.08, 1.40) for onset at 70-79 years. CONCLUSIONS/INTERPRETATION: Prediabetes is associated with dementia risk but this risk is explained by the subsequent development of diabetes. Earlier age of onset of diabetes substantially increases dementia risk. Preventing or delaying progression of prediabetes to diabetes will reduce dementia burden.

Chronic Kidney Disease, Heart Failure, and Adverse Cardiac Remodeling in Older Adults: The ARIC Study.

BACKGROUND: The associations of kidney dysfunction and damage with heart failure with reduced ejection fraction (HFrEF) and heart failure with preserved ejection fraction (HFpEF), as well as adverse cardiac remodeling, in late-life remain incompletely understood. OBJECTIVES: The authors sought to define the associations between kidney dysfunction and damage and incident HFrEF and HFpEF and cardiac structure and function in late-life. METHODS: This study included 5,170 adults initially free of a heart failure (HF) diagnosis who had estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (UACR) measured at visit 5 (2011-2013) of the ARIC (Atherosclerosis Risk In Communities) study. Multivariable Cox proportional hazards models were used to estimate the associations of eGFR and UACR with incident HF, HFrEF, and HFpEF through 2019. Multivariable linear regression models were used to investigate the associations of eGFR and UACR at visit 5 with changes in cardiac structure and function between visits 5 and 7 in 2,313 participants with available echocardiograms. RESULTS: The mean age of participants was 76 ± 5 years, and 2,225 (43%) were men. The mean eGFR and median UACR were 66 ± 18 mL/min/1.73 m2 and 11 mg/g (25th, 75th percentile: 6, 22 mg/g), respectively. In fully adjusted models, both lower eGFR and higher UACR were associated with greater risk of any HF, HFrEF, and HFpEF. Lower eGFR was associated with larger increases in left ventricular end-diastolic volume index and worsening of diastolic measures. UACR did not associate with changes in cardiac structure or function. CONCLUSIONS: Mild to moderate kidney dysfunction and damage associate with incident HF and adverse cardiac remodeling in late-life.

Metabolomic profiling of glucose homeostasis in African Americans: the Insulin Resistance Atherosclerosis Family Study (IRAS-FS).

INTRODUCTION: African Americans are at increased risk for type 2 diabetes. OBJECTIVES: This work aimed to examine metabolomic signature of glucose homeostasis in African Americans. METHODS: We used an untargeted liquid chromatography-mass spectrometry metabolomic approach to comprehensively profile 727 plasma metabolites among 571 African Americans from the Insulin Resistance Atherosclerosis Family Study (IRAS-FS) and investigate the associations between these metabolites and both the dynamic (SI, insulin sensitivity; AIR, acute insulin response; DI, disposition index; and SG, glucose effectiveness) and basal (HOMA-IR and HOMA-B) measures of glucose homeostasis using univariate and regularized regression models. We also compared the results with our previous findings in the IRAS-FS Mexican Americans. RESULTS: We confirmed increased plasma metabolite levels of branched-chain amino acids and their metabolic derivatives, 2-aminoadipate, 2-hydroxybutyrate, glutamate, arginine and its metabolic derivatives, carbohydrate metabolites, and medium- and long-chain fatty acids were associated with insulin resistance, while increased plasma metabolite levels in the glycine, serine and threonine metabolic pathway were associated with insulin sensitivity. We also observed a differential ancestral effect of glutamate on glucose homeostasis with significantly stronger effects observed in African Americans than those previously observed in Mexican Americans. CONCLUSION: We extended the observations that metabolites are useful biomarkers in the identification of prediabetes in individuals at risk of type 2 diabetes in African Americans. We revealed, for the first time, differential ancestral effect of certain metabolites (i.e., glutamate) on glucose homeostasis traits. Our study highlights the need for additional comprehensive metabolomic studies in well-characterized multiethnic cohorts.

The Academic Learning Health System: A Framework for Integrating the Multiple Missions of Academic Medical Centers.

The learning health system (LHS) has emerged over the past 15 years as a concept for improving health care delivery. Core aspects of the LHS concept include: promoting improved patient care through organizational learning, innovation, and continuous quality improvement; identifying, critically assessing, and translating knowledge and evidence into improved practices; building new knowledge and evidence around how to improve health care and health outcomes; analyzing clinical data to support learning, knowledge generation, and improved patient care; and engaging clinicians, patients, and other stakeholders in processes of learning, knowledge generation, and translation. However, the literature has paid less attention to how these LHS aspects may integrate with the multiple missions of academic medical centers (AMCs). The authors define an academic learning health system (aLHS) as an LHS built around a robust academic community and central academic mission, and they propose 6 features that emphasize how an aLHS differs from an LHS. An aLHS capitalizes on embedded academic expertise in health system sciences; engages the full spectrum of translational investigation from mechanistic basic sciences to population health; builds pipelines of experts in LHS sciences and clinicians with fluency in practicing in an LHS; applies core LHS principles to the development of curricula and clinical rotations for medical students, housestaff, and other learners; disseminates knowledge more broadly to advance the evidence for clinical practice and health systems science methods; and addresses social determinants of health, creating community partnerships to mitigate disparities and improve health equity. As AMCs evolve, the authors expect that additional differentiating features and ways to operationalize the aLHS will be identified and hope this article stimulates further discussion around the intersection of the LHS concept and AMCs.

Trends in incidence of youth-onset type 1 and type 2 diabetes in the USA, 2002-18: results from the population-based SEARCH for Diabetes in Youth study.

BACKGROUND: The incidence of diabetes is increasing in children and young people. We aimed to describe the incidence of type 1 and type 2 diabetes in children and young people aged younger than 20 years over a 17-year period. METHODS: The SEARCH for Diabetes in Youth study identified children and young people aged 0-19 years with a physician diagnosis of type 1 or type 2 diabetes at five centres in the USA between 2002 and 2018. Eligible participants included non-military and non-institutionalised individuals who resided in one of the study areas at the time of diagnosis. The number of children and young people at risk of diabetes was obtained from the census or health plan member counts. Generalised autoregressive moving average models were used to examine trends, and data are presented as incidence of type 1 diabetes per 100 000 children and young people younger than 20 years and incidence of type 2 diabetes per 100 000 children and young people aged between 10 years and younger than 20 years across categories of age, sex, race or ethnicity, geographical region, and month or season of diagnosis. FINDINGS: We identified 18 169 children and young people aged 0-19 years with type 1 diabetes in 85 million person-years and 5293 children and young people aged 10-19 years with type 2 diabetes in 44 million person-years. In 2017-18, the annual incidence of type 1 diabetes was 22·2 per 100 000 and that of type 2 diabetes was 17·9 per 100 000. The model for trend captured both a linear effect and a moving-average effect, with a significant increasing (annual) linear effect for both type 1 diabetes (2·02% [95% CI 1·54-2·49]) and type 2 diabetes (5·31% [4·46-6·17]). Children and young people from racial and ethnic minority groups such as non-Hispanic Black and Hispanic children and young people had greater increases in incidence for both types of diabetes. Peak age at diagnosis was 10 years (95% CI 8-11) for type 1 diabetes and 16 years (16-17) for type 2 diabetes. Season was significant for type 1 diabetes (p=0·0062) and type 2 diabetes (p=0·0006), with a January peak in diagnoses of type 1 diabetes and an August peak in diagnoses of type 2 diabetes. INTERPRETATION: The increasing incidence of type 1 and type 2 diabetes in children and young people in the USA will result in an expanding population of young adults at risk of developing early complications of diabetes whose health-care needs will exceed those of their peers. Findings regarding age and season of diagnosis will inform focused prevention efforts. FUNDING: US Centers for Disease Control and Prevention and US National Institutes of Health.

Exploring Racial and Ethnic Differences in Arterial Stiffness Among Youth and Young Adults With Type 1 Diabetes.

Background We examined arterial stiffness in individuals with type 1 diabetes, and explored whether differences between Hispanic, non-Hispanic Black (NHB), and non-Hispanic White (NHW) individuals were attributable to modifiable clinical and social factors. Methods and Results Participants (n=1162; 22% Hispanic, 18% NHB, and 60% NHW) completed 2 to 3 research visits from ≈10 months to ≈11 years post type 1 diabetes diagnosis (mean ages of ≈9 to ≈20 years, respectively) providing data on socioeconomic factors, type 1 diabetes characteristics, cardiovascular risk factors, health behaviors, quality of clinical care, and perception of clinical care. Arterial stiffness (carotid-femoral pulse wave velocity [PWV], m/s) was measured at ≈20 years of age. We analyzed differences in PWV by race and ethnicity, then explored the individual and combined impact of the clinical and social factors on these differences. PWV did not differ between Hispanic (adjusted mean 6.18 [SE 0.12]) and NHW (6.04 [0.11]) participants after adjustment for cardiovascular risks (P=0.06) and socioeconomic factors (P=0.12), or between Hispanic and NHB participants (6.36 [0.12]) after adjustment for all factors (P=0.08). PWV was higher in NHB versus NHW participants in all models (all P<0.001). Adjustment for modifiable factors reduced the difference in PWV by 15% for Hispanic versus NHW participants; by 25% for Hispanic versus NHB; and by 21% for NHB versus NHW. Conclusions Cardiovascular and socioeconomic factors explain one-quarter of the racial and ethnic differences in PWV of young people with type 1 diabetes, but NHB individuals still experienced greater PWV. Exploration of pervasive inequities potentially driving these persistent differences is needed.

Proteomic Predictors of Incident Diabetes: Results From the Atherosclerosis Risk in Communities (ARIC) Study.

OBJECTIVE: The plasma proteome preceding diabetes can improve our understanding of diabetes pathogenesis. RESEARCH DESIGN AND METHODS: In 8,923 Atherosclerosis Risk in Communities (ARIC) Study participants (aged 47-70 years, 57% women, 19% Black), we conducted discovery and internal validation for associations of 4,955 plasma proteins with incident diabetes. We externally validated results in the Singapore Multi-Ethnic Cohort (MEC) nested case-control (624 case subjects, 1,214 control subjects). We used Cox regression to discover and validate protein associations and risk-prediction models (elastic net regression with cardiometabolic risk factors and proteins) for incident diabetes. We conducted a pathway analysis and examined causality using genetic instruments. RESULTS: There were 2,147 new diabetes cases over a median of 19 years. In the discovery sample (n = 6,010), 140 proteins were associated with incident diabetes after adjustment for 11 risk factors (P < 10-5). Internal validation (n = 2,913) showed 64 of the 140 proteins remained significant (P < 0.05/140). Of the 63 available proteins, 47 (75%) were validated in MEC. Novel associations with diabetes were found for 22 the 47 proteins. Prediction models (27 proteins selected by elastic net) developed in discovery had a C statistic of 0.731 in internal validation, with ΔC statistic of 0.011 (P = 0.04) beyond 13 risk factors, including fasting glucose and HbA1c. Inflammation and lipid metabolism pathways were overrepresented among the diabetes-associated proteins. Genetic instrument analyses suggested plasma SHBG, ATP1B2, and GSTA1 play causal roles in diabetes risk. CONCLUSIONS: We identified 47 plasma proteins predictive of incident diabetes, established causal effects for 3 proteins, and identified diabetes-associated inflammation and lipid pathways with potential implications for diagnosis and therapy.

Weight changes during the COVID-19 shutdown in older individuals with type 2 diabetes: the Look AHEAD Study.

OBJECTIVE: The aims of this study were as follows: 1) examine weight changes in older adults (mean age = 76 years) with type 2 diabetes and overweight or obesity during the COVID-19 shutdown; and 2) compare the behavioral and psychosocial effects of the shutdown in those who had large weight losses (>5%), those who had small weight losses (2%-5%), those who remained weight stable (±2%), or those who gained weight (>2%). METHODS: Look AHEAD (Action for Health in Diabetes) participants (N = 2544) were surveyed during the COVID-19 shutdown (2020), and they self-reported their current weight, reasons for weight change, weight-related behaviors, psychosocial measures, and negative and positive effects of the pandemic on their lives. RESULTS: Comparing self-reported weight during the COVID-19 shutdown with earlier measured weight, Look AHEAD participants lost, on average, 2.2 kg during the COVID-19 shutdown: 47% lost >2%, and only 18% gained >2% (p < 0.0001). Decreases in physical activity and increases in screen time were reported frequently in all weight-change categories. Similarly, there were few differences among the categories on standardized psychosocial measures or self-reported effects of the shutdown on participants' lives. However, when differences were seen, the most negative impact was in those who gained weight. CONCLUSIONS: Although weight loss appeared more common than weight gain during the shutdown, the weight-change groups did not differ on most psychosocial and behavioral variables.

Association of Type 2 Diabetes Subgroups With Cognitive Status Without Modification From Lifestyle Intervention.

CONTEXT: Type 2 diabetes is a risk factor for incident dementia but whether risk and treatment/prevention strategies differ by diabetes subgroup is unknown. OBJECTIVE: We assessed (1) whether specific type 2 diabetes (T2D) subgroups are associated with mild cognitive impairment (MCI) or probable dementia (PD), and (2) whether T2D subgroups modified the association of the Action for Health in Diabetes (Look AHEAD) multidomain intensive lifestyle intervention (ILI) with MCI/PD. METHODS: We included 3760 Look AHEAD participants with T2D and overweight or obesity randomly assigned to 10 years of ILI or diabetes support and education. We used k-means clustering techniques with data on age of diabetes diagnosis, body mass index, waist circumference, and glycated hemoglobin (HbA1c) to characterize diabetes subgroups at randomization. Prevalent MCI/PD were centrally adjudicated based on standardized cognitive tests and other health information 10 to 13 years after randomization. We estimated marginal probabilities for prevalent MCI/PD among T2D subgroups with adjustment for potential confounders and attrition and examined whether ILI modified any associations. RESULTS: Four distinct T2D subgroups were identified, characterized by older age at diabetes onset (43% of sample), high HbA1c (13%), severe obesity (23%), and younger age at onset (22%). Unadjusted prevalence of MCI/PD (314 cases, 8.4%) differed across T2D subgroup (older onset = 10.5%, severe obesity = 9.0%, high HbA1c = 7.9%, and younger onset = 4.0%). Adjusted probability for MCI/PD within T2D subgroup was highest for the severe obesity subgroup and lowest for the younger onset subgroup but did not differ by ILI arm (interaction P value = 0.84). CONCLUSIONS: Among individuals with T2D and overweight or obesity, probability of MCI/PD differed by T2D subgroup. Probability of MCI/PD was highest for a subgroup characterized by severe obesity. CLINICALTRIALS.GOV IDENTIFIER: NCT00017953.

Is an MRI-derived anatomical measure of dementia risk also a measure of brain aging?

Machine learning methods have been applied to estimate measures of brain aging from neuroimages. However, only rarely have these measures been examined in the context of biologic age. Here, we investigated associations of an MRI-based measure of dementia risk, the Alzheimer's disease pattern similarity (AD-PS) scores, with measures used to calculate biological age. Participants were those from visit 5 of the Atherosclerosis Risk in Communities Study with cognitive status adjudication, proteomic data, and AD-PS scores available. The AD-PS score estimation is based on previously reported machine learning methods. We evaluated associations of the AD-PS score with all-cause mortality. Sensitivity analyses using only cognitively normal (CN) individuals were performed treating CNS-related causes of death as competing risk. AD-PS score was examined in association with 32 proteins measured, using a Somalogic platform, previously reported to be associated with age. Finally, associations with a deficit accumulation index (DAI) based on a count of 38 health conditions were investigated. All analyses were adjusted for age, race, sex, education, smoking, hypertension, and diabetes. The AD-PS score was significantly associated with all-cause mortality and with levels of 9 of the 32 proteins. Growth/differentiation factor 15 (GDF-15) and pleiotrophin remained significant after accounting for multiple-testing and when restricting the analysis to CN participants. A linear regression model showed a significant association between DAI and AD-PS scores overall. While the AD-PS scores were created as a measure of dementia risk, our analyses suggest that they could also be capturing brain aging.

Associations of Vascular Risk and Amyloid Burden with Subsequent Dementia.

OBJECTIVE: Midlife vascular risk factors (MVRFs) are associated with incident dementia, as are amyloid ß (Aß) deposition and neurodegeneration. Whether vascular and Alzheimer disease-associated factors contribute to dementia independently or interact synergistically to reduce cognition is poorly understood. METHODS: Participants in the Atherosclerosis Risk in Communities-Positron Emission Tomography study were followed from 1987-1989 (45-64 years old) through 2016-2017 (74-94 years old), with repeat cognitive assessment and dementia adjudication. In 2011-2013, dementia-free participants underwent brain magnetic resonance imaging (with white matter hyperintensity [WMH] and brain volume measurement) and florbetapir (Aß) positron emission tomography. The relative contributions of vascular risk and injury (MVRFs, WMH volume), elevated Aß standardized uptake value ratio (SUVR), and neurodegeneration (smaller temporoparietal brain regions) to incident dementia were evaluated with adjusted Cox models. RESULTS: In 298 individuals, 36 developed dementia (median follow-up = 4.9 years). Midlife hypertension and Aß each independently predicted dementia risk (hypertension: hazard ratio [HR] = 2.57, 95% confidence interval [CI] = 1.16-5.67; Aß SUVR [per standard deviation (SD)]: HR = 2.57, 95% CI = 1.72-3.84), but did not interact significantly, whereas late life diabetes (HR = 2.50, 95% CI = 1.18-5.28) and Aß independently predicted dementia risk. WMHs (per SD: HR = 1.51, 95% CI = 1.03-2.20) and Aß SUVR (HR = 2.52, 95% CI = 1.83-3.47) independently contributed to incident dementia, but WMHs lost significance when MVRFs were included. Smaller temporoparietal brain regions were associated with incident dementia, independent of Aß and MVRFs (HR = 2.18, 95% CI = 1.18-4.01). INTERPRETATION: Midlife hypertension and late life Aß are independently associated with dementia risk, without evidence for synergy on a multiplicative scale. Given the independent contributions of vascular and amyloid mechanisms, multiple pathways should be considered when evaluating interventions to reduce the burden of dementia. ANN NEUROL 2022;92:607-619.

Impact of COVID-19 on life experiences reported by a diverse cohort of older adults with diabetes and obesity.

OBJECTIVE: This study aimed to measure the impact of the COVID-19 pandemic on self-reported life experiences in older adults with diabetes and obesity. METHODS: Participants were surveyed in 2020 regarding negative and positive impacts of the pandemic across domains of personal, social, and physical experiences. A cumulative negative risk index (a count of all reported negative impacts of 46 items) and a positive risk index (5 items) were characterized in relation to age, sex, race/ethnicity, BMI, and multimorbidity. RESULTS: Response rate was high (2950/3193, 92%), average age was 76 years, 63% were women, and 39% were from underrepresented populations. Women reported more negative impacts than men (6.8 vs. 5.6; p < 0.001 [of 46 items]) as did persons with a greater multimorbidity index (p < 0.001). Participants reporting African American/Black race reported fewer negative impacts than White participants. Women also reported more positive impacts than men (1.9 vs. 1.6; p < 0.001 [of 5 items]). CONCLUSIONS: Older adults with diabetes and obesity reported more positive impacts of the pandemic than negative impacts, relative to the number of positive (or negative) items presented. Some subgroups experienced greater negative impacts (e.g., for women, a greater multimorbidity index). Efforts to reestablish personal, social, and physical health after the pandemic could target certain groups.

Comparing data-driven and hypothesis-driven MRI-based predictors of cognitive impairment in individuals from the Atherosclerosis Risk in Communities (ARIC) study.

INTRODUCTION: A data-driven index of dementia risk based on magnetic resonance imaging (MRI), the Alzheimer's Disease Pattern Similarity (AD-PS) score, was estimated for participants in the Atherosclerosis Risk in Communities (ARIC) study. METHODS: AD-PS scores were generated for 839 cognitively non-impaired individuals with a mean follow-up of 4.86 years. The scores and a hypothesis-driven volumetric measure based on several brain regions susceptible to AD were compared as predictors of incident cognitive impairment in different settings. RESULTS: Logistic regression analyses suggest the data-driven AD-PS scores to be more predictive of incident cognitive impairment than its counterpart. Both biomarkers were more predictive of incident cognitive impairment in participants who were White, female, and apolipoprotein E gene (APOE) ε4 carriers. Random forest analyses including predictors from different domains ranked the AD-PS scores as the most relevant MRI predictor of cognitive impairment. CONCLUSIONS: Overall, the AD-PS scores were the stronger MRI-derived predictors of incident cognitive impairment in cognitively non-impaired individuals.