The AORTA Gene score for detection and risk stratification of ascending aortic dilation.

BACKGROUND AND AIMS: This study assessed whether a model incorporating clinical features and a polygenic score for ascending aortic diameter would improve diameter estimation and prediction of adverse thoracic aortic events over clinical features alone. METHODS: Aortic diameter estimation models were built with a 1.1 million-variant polygenic score (AORTA Gene) and without it. Models were validated internally in 4394 UK Biobank participants and externally in 5469 individuals from Mass General Brigham (MGB) Biobank, 1298 from the Framingham Heart Study (FHS), and 610 from All of Us. Model fit for adverse thoracic aortic events was compared in 401 453 UK Biobank and 164 789 All of Us participants. RESULTS: AORTA Gene explained more of the variance in thoracic aortic diameter compared to clinical factors alone: 39.5% (95% confidence interval 37.3%-41.8%) vs. 29.3% (27.0%-31.5%) in UK Biobank, 36.5% (34.4%-38.5%) vs. 32.5% (30.4%-34.5%) in MGB, 41.8% (37.7%-45.9%) vs. 33.0% (28.9%-37.2%) in FHS, and 34.9% (28.8%-41.0%) vs. 28.9% (22.9%-35.0%) in All of Us. AORTA Gene had a greater area under the receiver operating characteristic curve for identifying diameter ≥ 4 cm: 0.836 vs. 0.776 (P < .0001) in UK Biobank, 0.808 vs. 0.767 in MGB (P < .0001), 0.856 vs. 0.818 in FHS (P < .0001), and 0.827 vs. 0.791 (P = .0078) in All of Us. AORTA Gene was more informative for adverse thoracic aortic events in UK Biobank (P = .0042) and All of Us (P = .049). CONCLUSIONS: A comprehensive model incorporating polygenic information and clinical risk factors explained 34.9%-41.8% of the variation in ascending aortic diameter, improving the identification of ascending aortic dilation and adverse thoracic aortic events compared to clinical risk factors.

Association of bone fracture with 30-year body mass index (BMI) trajectories: findings from the Framingham Heart Study : Bone fracture and 30-year BMI trajectories.

A knowledge gap exists in associating later life's osteoporotic fracture and middle adulthood's BMI trajectories. We observed an association showing those transitioning from overweight to normal weight face a higher fracture risk in late adulthood, emphasizing the potential benefits of maintaining a stable BMI to reduce late-life fractures. PURPOSE: Numerous studies on the relationship between obesity and fractures have relied on body mass index (BMI) at a single time point, yielding inconclusive results. This study investigated the association of BMI trajectories over middle adulthood with fracture risk in late adulthood. METHODS: This prospective cohort study analyzed 1772 qualified participants from the Framingham Original Cohort Study, with 292 (16.5%) incident fractures during an average of 17.1-year follow-up. We constructed BMI trajectories of age 35-64 years based on latent class mixed modeling and explored their association with the risk of fracture after 65 years using the Cox regression. RESULTS: The result showed that compared to the BMI trajectory Group 4 (normal to slightly overweight; see "Methods" for detailed description), Group 1 (overweight declined to normal weight) had a higher all-fracture risk after age 65 (hazard ratio [HR], 2.22, 95% CI, 1.13-4.39). The secondary analysis focusing on lower extremity fractures (pelvis, hip, leg, and foot) showed a similar association pattern. CONCLUSIONS: This study suggested that people whose BMI slightly increased from normal weight to low-level overweight during 30 years of middle adulthood confer a significantly lower risk of fracture in later life than those whose BMI declined from overweight to normal weight. This result implies the potentially beneficial effects of avoiding weight loss to normal weight during middle adulthood for overweight persons, with reduced fracture risk in late life.

Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models.

INTRODUCTION: Identification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for early intervention and selection of clinical trials. METHODS: We applied natural language processing techniques along with machine learning methods to develop a method for automated prediction of progression to AD within 6 years using speech. The study design was evaluated on the neuropsychological test interviews of n = 166 participants from the Framingham Heart Study, comprising 90 progressive MCI and 76 stable MCI cases. RESULTS: Our best models, which used features generated from speech data, as well as age, sex, and education level, achieved an accuracy of 78.5% and a sensitivity of 81.1% to predict MCI-to-AD progression within 6 years. DISCUSSION: The proposed method offers a fully automated procedure, providing an opportunity to develop an inexpensive, broadly accessible, and easy-to-administer screening tool for MCI-to-AD progression prediction, facilitating development of remote assessment. HIGHLIGHTS: Voice recordings from neuropsychological exams coupled with basic demographics can lead to strong predictive models of progression to dementia from mild cognitive impairment. The study leveraged AI methods for speech recognition and processed the resulting text using language models. The developed AI-powered pipeline can lead to fully automated assessment that could enable remote and cost-effective screening and prognosis for Alzehimer's disease.

Leptin bioavailability and markers of brain atrophy and vascular injury in the middle age.

INTRODUCTION: We investigated the associations of leptin markers with cognitive function and magnetic resonance imaging (MRI) measures of brain atrophy and vascular injury in healthy middle-aged adults. METHODS: We included 2262 cognitively healthy participants from the Framingham Heart Study with neuropsychological evaluation; of these, 2028 also had available brain MRI. Concentrations of leptin, soluble leptin receptor (sOB-R), and their ratio (free leptin index [FLI]), indicating leptin bioavailability, were measured using enzyme-linked immunosorbent assays. Cognitive and MRI measures were derived using standardized protocols. RESULTS: Higher sOB-R was associated with lower fractional anisotropy (FA, ß = -0.114 ± 0.02, p < 0.001), and higher free water (FW, ß = 0.091 ± 0.022, p < 0.001) and peak-width skeletonized mean diffusivity (PSMD, ß = 0.078 ± 0.021, p < 0.001). Correspondingly, higher FLI was associated with higher FA (ß = 0.115 ± 0.027, p < 0.001) and lower FW (ß = -0.096 ± 0.029, p = 0.001) and PSMD (ß = -0.085 ± 0.028, p = 0.002). DISCUSSION: Higher leptin bioavailability was associated with better white matter (WM) integrity in healthy middle-aged adults, supporting the putative neuroprotective role of leptin in late-life dementia risk. HIGHLIGHTS: Higher leptin bioavailability was related to better preservation of white matter microstructure. Higher leptin bioavailability during midlife might confer protection against dementia. Potential benefits might be even stronger for individuals with visceral obesity. DTI measures might be sensitive surrogate markers of subclinical neuropathology.

Plasma Protein Biomarkers of Healthy Dietary Patterns: Results from the Atherosclerosis Risk in Communities Study and the Framingham Heart Study.

BACKGROUND: Molecular mechanisms underlying the benefits of healthy dietary patterns are poorly understood. Identifying protein biomarkers of dietary patterns can contribute to characterizing biological pathways influenced by food intake. OBJECTIVES: This study aimed to identify protein biomarkers associated with four indexes of healthy dietary patterns: Healthy Eating Index-2015 (HEI-2015); Alternative Healthy Eating Index-2010 (AHEI-2010); DASH diet; and alternate Mediterranean Diet (aMED). METHODS: Analyses were conducted on 10,490 Black and White men and women aged 49-73 y from the ARIC study at visit 3 (1993-1995). Dietary intake data were collected using a food frequency questionnaire, and plasma proteins were quantified using an aptamer-based proteomics assay. Multivariable linear regression models were used to examine the association between 4955 proteins and dietary patterns. We performed pathway overrepresentation analysis for diet-related proteins. An independent study population from the Framingham Heart Study was used for replication analyses. RESULTS: In the multivariable-adjusted models, 282 out of 4955 proteins (5.7%) were significantly associated with at least one dietary pattern (HEI-2015: 137; AHEI-2010: 72; DASH: 254; aMED: 35; P value < 0.05/4955 = 1.01 × 10-5). There were 148 proteins that were associated with only one dietary pattern (HEI-2015: 22; AHEI-2010: 5; DASH: 121; aMED: 0), and 20 proteins were associated with all four dietary patterns. Five unique biological pathways were significantly enriched by diet-related proteins. Seven out of 20 proteins associated with all dietary patterns in the ARIC study were available for replication analyses, and 6 out of these 7 proteins were consistent in direction and significantly associated with at least 1 dietary pattern in the Framingham Heart Study (HEI-2015: 2; AHEI-2010: 4; DASH: 6; aMED: 4; P value < 0.05/7 = 7.14 × 10-3). CONCLUSIONS: A large-scale proteomic analysis identified plasma protein biomarkers that are representative of healthy dietary patterns among middle-aged and older US adult population. These protein biomarkers may be useful objective indicators of healthy dietary patterns.

A Healthy Diet is Associated with a Lower Risk of Hepatic Fibrosis.

BACKGROUND: Higher diet quality is associated with a lower risk of NAFLD. OBJECTIVES: We examined the relationship between diet quality and hepatic fibrosis. METHODS: We analyzed cross-sectional associations between 3 a priori diet quality scores-the Dietary Approaches to Stop Hypertension (DASH) score, the Alternative Healthy Eating Index (AHEI), and a modified Mediterranean-style Diet Score (MDS)-and hepatic fat [controlled attenuation parameter (CAP)] and fibrosis [liver stiffness measurement (LSM)] measured by vibration-controlled transient elastography (VCTE) in 2532 Framingham Heart Study (FHS) participants and 3295 participants of the National Health and Nutrition Examination Survey (NHANES). RESULTS: Higher diet quality scores were associated with lower LSM in both FHS and NHANES after adjustment for demographic and lifestyle factors. Additional adjustment for CAP or BMI attenuated the observed associations. Association strength was similar across all 3 diet quality scores. Fixed-effect meta-analysis demonstrated that, under CAP-adjusted models, the LSM decreases associated with 1-SD increase of the DASH, AHEI, and MDS scores were 2% (95% CI: 0.7%, 3.3%; P = 0.002), 2% (95% CI: 0.7%, 3.3%; P = 0.003), and 1.7% (95% CI: 0.7%, 2.6%; P = 0.001), respectively, whereas in the meta-analysis of BMI-adjusted models, LSM reductions associated with 1-SD increase of the DASH, AHEI, and MDS scores were 2.2% (95% CI: -0.1%, 2.2%; P = 0.07), 1.5% (95% CI: 0.3%, 2.7%; P = 0.02), and 0.9 (95% CI: -0.1%, 1.9%; P = 0.07), respectively. CONCLUSIONS: We demonstrated associations of higher diet quality with favorable hepatic fat and fibrosis measures. Our data suggest that a healthy diet may reduce the likelihood of obesity and hepatic steatosis as well as the progression of steatosis to fibrosis.

High consumption of whole grain foods decreases the risk of dementia and Alzheimer's disease: Framingham Offspring Cohort.

AIMS: To examine the longitudinal associations between total and individual whole grain (WG) food intake and the risk of all-cause dementia and Alzheimer's disease (AD) dementia. METHODS: This study included 2958 subjects (mean age at baseline was 61 ± 9 years) from the Framingham Offspring Cohort. Standardized interviews, physician examinations, and laboratory tests were collected approximately every 4 years, and the Food Frequency Questionnaire (FFQ) was conducted in cycle 5. Proportional hazards models and cubic spline regression examined associations between WG foods and all-cause dementia and AD dementia. RESULTS: Over an average of 12.6 years of follow-up, there were 322 dementia cases, of which 247 were AD dementia. After multivariate and dietary adjustments, individuals with the highest category for total WG food consumption had a lower risk of all-cause dementia [HR 0.66, 95% confidence interval (CI) 0.51-0.81] and AD dementia (HR 0.60, 95% CI 0.46-0.78) than individuals with the lowest category. The results remained comparable in different subgroups stratifying for age, sex, education, body mass index, and smoking status without significant interaction. Moreover, these inverse associations were seen for most individual WG foods except popcorn. A nonlinear dose-response association was shown between total WG intake and all-cause dementia and AD dementia, where the rate reduction slightly plateaued at more than one and two servings/day, respectively. CONCLUSIONS: Higher consumption of total and several common individual WG foods was strongly associated with a lower risk of all-cause dementia and AD dementia.

A genomic approach identifies sRAGE as a putatively causal protein for asthma.

BACKGROUND: Asthma is a complex respiratory condition caused by environmental and genetic factors. Although lower concentrations of the anti-inflammatory protein soluble receptor for advanced glycation end products (sRAGE) have been associated with asthma in humans and mouse models, it is uncertain whether sRAGE plays a causal role in asthma. OBJECTIVE: We designed a 2-stage study of sRAGE in relation to asthma with association analysis in FHS participants as well as causal inference testing using Mendelian randomization (MR). METHODS: We measured plasma levels of sRAGE and performed cross-sectional analysis to examine the association between plasma sRAGE concentration and asthma status in 6546 FHS participants. We then used sRAGE protein advanced glycation end products (pQTLs) derived from a genome-wide association study of plasma sRAGE levels in ∼7000 FHS participants with UK Biobank asthma genome-wide association study in MR to consider sRAGE as a putatively causal protein for asthma. We also performed replication MR using an externally derived sRAGE pQTL from the INTERVAL study. Last, we conducted colocalization using cis-pQTL variants at the advanced glycosylation end-product specific receptor (AGER) locus with variants from the UK Biobank asthma genome-wide association study. RESULTS: Association analysis revealed that each 1 SD increment in sRAGE concentration was associated with a 14% lower odds of asthma in FHS participants (95% CI 0.76-0.96). MR identified sRAGE as putatively causal for and protective against asthma on the basis of self-reported (odds ratio [per 1 SE increment in inverse-rank-normalized sRAGE] = 0.97, 95% CI 0.95-0.99; P = .005) and doctor-diagnosed asthma (odds ratio = 0.97, 95% CI 0.95-0.99; P = .011). CONCLUSION: Through this genomic approach, we identified sRAGE as a putatively causal, biologically important, and protective protein in relation to asthma. Functional studies in cell/animal models are needed to confirm our findings.

Examination of potentially modifiable dementia risk factors across the adult life course: The Framingham Heart Study.

INTRODUCTION: We examined for associations between potentially modifiable risk factors across the adult life course and incident dementia. METHODS: Participants from the Framingham Heart Study were included (n = 4015). Potential modifiable risk factors included education, alcohol intake, smoking, body mass index (BMI), physical activity, social network, diabetes, and hypertension. Cox models were used to examine associations between each factor and incident dementia, stratified by early adult life (33-44 years), midlife (45-65 years), and late life (66-80 years). RESULTS: Increased dementia risk was associated with diabetes (hazard ratio [HR] = 1.62; 95% confidence interval [CI] = 1.07-2.46) and physical inactivity (HR = 1.57; 95% CI = 1.12-2.20) in midlife, and with obesity (HR = 1.76; 95% CI = 1.08-2.87) in late life. Having multiple potential modifiable risk factors in midlife and late life was associated with greater risk. DISCUSSION: Potentially modifiable risk factors individually have limited impact on dementia risk in this population across the adult life course, although in combination they may have a synergistic effect. HIGHLIGHTS: Diabetes and physical inactivity in midlife is associated with increased dementia risk. Obesity in late life is associated with increased dementia risk. Having more potentially modifiable risk factors in midlife and late life is associated with greater dementia risk.

Higher Dietary Inflammatory Index scores are associated with brain MRI markers of brain aging: Results from the Framingham Heart Study Offspring cohort.

INTRODUCTION: We investigated cross-sectional associations between the Dietary Inflammatory Index (DII) and measures of brain volume and cerebral small vessel disease among participants of the Framingham Heart Study Offspring cohort. METHODS: A total of 1897 participants (mean ± standard deviation, age 62±9) completed Food Frequency Questionnaires and brain magnetic resonance imaging (MRI). RESULTS: Higher (pro-inflammatory) DII scores, averaged across a maximum of three time points, were associated with smaller total brain volume (beta ± standard error: -0.16 ± 0.03; P < .0001) after adjustment for demographic, clinical, and lifestyle covariates. In addition, higher DII scores were associated with smaller total gray matter volume (-0.08 ± 0.03; P = .003) and larger lateral ventricular volume (0.04 ± 0.02; P = .03). No associations were observed with other brain MRI measures. DISCUSSION: Our findings showed associations between higher DII scores and global brain MRI measures. As we are one of the first groups to report on the associations between higher DII scores and brain volume, replication is needed to confirm our findings.

Assisted clustering of gene expression data using regulatory data from partially overlapping sets of individuals.

BACKGROUND: As omics measurements profiled on different molecular layers are interconnected, integrative approaches that incorporate the regulatory effect from multi-level omics data are needed. When the multi-level omics data are from the same individuals, gene expression (GE) clusters can be identified using information from regulators like genetic variants and DNA methylation. When the multi-level omics data are from different individuals, the choice of integration approaches is limited. METHODS: We developed an approach to improve GE clustering from microarray data by integrating regulatory data from different but partially overlapping sets of individuals. We achieve this through (1) decomposing gene expression into the regulated component and the other component that is not regulated by measured factors, (2) optimizing the clustering goodness-of-fit objective function. We do not require the availability of different omics measurements on all individuals. A certain amount of individual overlap between GE data and the regulatory data is adequate for modeling the regulation, thus improving GE clustering. RESULTS: A simulation study shows that the performance of the proposed approach depends on the strength of the GE-regulator relationship, degree of missingness, data dimensionality, sample size, and the number of clusters. Across the various simulation settings, the proposed method shows competitive performance in terms of accuracy compared to the alternative K-means clustering method, especially when the clustering structure is due mostly to the regulated component, rather than the unregulated component. We further validate the approach with an application to 8,902 Framingham Heart Study participants with data on up to 17,873 genes and regulation information of DNA methylation and genotype from different but partially overlapping sets of participants. We identify clustering structures of genes associated with pulmonary function while incorporating the predicted regulation effect from the measured regulators. We further investigate the over-representation of these GE clusters in pathways of other diseases that may be related to lung function and respiratory health. CONCLUSION: We propose a novel approach for clustering GE with the assistance of regulatory data that allowed for different but partially overlapping sets of individuals to be included in different omics data.

Associations Between the Digital Clock Drawing Test and Brain Volume: Large Community-Based Prospective Cohort (Framingham Heart Study).

BACKGROUND: The digital Clock Drawing Test (dCDT) has been recently used as a more objective tool to assess cognition. However, the association between digitally obtained clock drawing features and structural neuroimaging measures has not been assessed in large population-based studies. OBJECTIVE: We aimed to investigate the association between dCDT features and brain volume. METHODS: This study included participants from the Framingham Heart Study who had both a dCDT and magnetic resonance imaging (MRI) scan, and were free of dementia or stroke. Linear regression models were used to assess the association between 18 dCDT composite scores (derived from 105 dCDT raw features) and brain MRI measures, including total cerebral brain volume (TCBV), cerebral white matter volume, cerebral gray matter volume, hippocampal volume, and white matter hyperintensity (WMH) volume. Classification models were also built from clinical risk factors, dCDT composite scores, and MRI measures to distinguish people with mild cognitive impairment (MCI) from those whose cognition was intact. RESULTS: A total of 1656 participants were included in this study (mean age 61 years, SD 13 years; 50.9% women), with 23 participants diagnosed with MCI. All dCDT composite scores were associated with TCBV after adjusting for multiple testing (P value <.05/18). Eleven dCDT composite scores were associated with cerebral white matter volume, but only 1 dCDT composite score was associated with cerebral gray matter volume. None of the dCDT composite scores was associated with hippocampal volume or WMH volume. The classification model for differentiating MCI and normal cognition participants, which incorporated age, sex, education, MRI measures, and dCDT composite scores, showed an area under the curve of 0.897. CONCLUSIONS: dCDT composite scores were significantly associated with multiple brain MRI measures in a large community-based cohort. The dCDT has the potential to be used as a cognitive assessment tool in the clinical diagnosis of MCI.

Automated detection of mild cognitive impairment and dementia from voice recordings: A natural language processing approach.

INTRODUCTION: Automated computational assessment of neuropsychological tests would enable widespread, cost-effective screening for dementia. METHODS: A novel natural language processing approach is developed and validated to identify different stages of dementia based on automated transcription of digital voice recordings of subjects' neuropsychological tests conducted by the Framingham Heart Study (n = 1084). Transcribed sentences from the test were encoded into quantitative data and several models were trained and tested using these data and the participants' demographic characteristics. RESULTS: Average area under the curve (AUC) on the held-out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal cognition from Dementia, Normal or Mild Cognitive Impairment (MCI) from Dementia, and Normal from MCI, respectively. DISCUSSION: The proposed approach offers a fully automated identification of MCI and dementia based on a recorded neuropsychological test, providing an opportunity to develop a remote screening tool that could be adapted easily to any language.

Genetic association tests in family samples for multi-category phenotypes.

BACKGROUND: Advancements in statistical methods and sequencing technology have led to numerous novel discoveries in human genetics in the past two decades. Among phenotypes of interest, most attention has been given to studying genetic associations with continuous or binary traits. Efficient statistical methods have been proposed and are available for both types of traits under different study designs. However, for multinomial categorical traits in related samples, there is a lack of efficient statistical methods and software. RESULTS: We propose an efficient score test to analyze a multinomial trait in family samples, in the context of genome-wide association/sequencing studies. An alternative Wald statistic is also proposed. We also extend the methodology to be applicable to ordinal traits. We performed extensive simulation studies to evaluate the type-I error of the score test, Wald test compared to the multinomial logistic regression for unrelated samples, under different allele frequency and study designs. We also evaluate the power of these methods. Results show that both the score and Wald tests have a well-controlled type-I error rate, but the multinomial logistic regression has an inflated type-I error rate when applied to family samples. We illustrated the application of the score test with an application to the Framingham Heart Study to uncover genetic variants associated with diabesity, a multi-category phenotype. CONCLUSION: Both proposed tests have correct type-I error rate and similar power. However, because the Wald statistics rely on computer-intensive estimation, it is less efficient than the score test in terms of applications to large-scale genetic association studies. We provide computer implementation for both multinomial and ordinal traits.

Whole- and Refined-Grain Consumption and Longitudinal Changes in Cardiometabolic Risk Factors in the Framingham Offspring Cohort.

BACKGROUND: Greater whole grain (WG) consumption is associated with reduced risk of cardiovascular disease (CVD); however, few prospective studies have examined WG or refined grain (RG) intake and intermediate cardiometabolic risk factors. OBJECTIVES: We examined the longitudinal association between WG and RG intake on changes in waist circumference (WC); fasting HDL cholesterol, triglyceride, and glucose concentrations; and blood pressure. METHODS: Subjects were participants in the Framingham Offspring cohort study [n = 3121; mean ± SD baseline age: 54.9 ± 0.2 y; BMI (kg/m2) 27.2 ± 0.1]. FFQ, health, and lifestyle data were collected approximately every 4 y over a median 18-y follow-up. Repeated measure mixed models were used to estimate adjusted mean changes per 4-y interval in risk factors across increasing categories of WG or RG intake. RESULTS: Greater WG intake was associated with smaller increases in WC (1.4 ± 0.2 compared with 3.0 ± 0.1 cm in the highest compared with the lowest category, respectively; P-trend < 0.001), fasting glucose concentration (0.7 ± 0.4 compared with 2.6 ± 0.2 mg/dL; P-trend < 0.001), and systolic blood pressure (SBP; 0.2 ± 0.5 compared with 1.4 ± 0.3 mm Hg; P-trend < 0.001) per 4-y interval. When stratified by sex, a stronger association with WC was observed among females than males. Higher intake of WG was associated with greater increases in HDL cholesterol and declines in triglyceride concentrations; however, these differences did not remain significant after adjustment for change in WC. Conversely, greater RG intake was associated with greater increases in WC (2.7 ± 0.2 compared with 1.8 ± 0.1 cm, P-trend < 0.001) and less decline in triglyceride concentration (-0.3 ± 1.3 compared with -7.0 ± 0.7 mg/dL, P-trend < 0.001). CONCLUSIONS: Among middle- to older-age adults, replacing RG with WG may be an effective dietary modification to attenuate abdominal adiposity, dyslipidemia, and hyperglycemia over time, thereby reducing the risk of cardiometabolic diseases.

Trends in food consumption by degree of processing and diet quality over 17 years: results from the Framingham Offspring Study.

Ultraprocessed foods provide the majority of energy content in the American diet, yet little is known regarding consumption trends over time. We determined trends in diet processing level and diet quality from 1991 to 2008 within the prospective Framingham Offspring Cohort. Dietary intakes were collected by FFQ quadrennially 1991-2008 (total of four examinations). The analytical sample included 2893 adults with valid dietary data for ≥3 examinations (baseline mean age = 54 years). Based on the NOVA framework, we classified foods as: unprocessed/minimally processed foods; processed culinary ingredients (salt/sugar/fats/oils); and processed foods and ultraprocessed foods. We evaluated diet quality using the Dietary Guidelines for Americans Adherence Index (DGAI) 2010. Trends in consumption of foods within each processing level (servings/d) and diet quality over the four examinations were evaluated using mixed effects models with subject-specific random intercepts. Analyses were stratified by sex, BMI (<25 kg/m2, 25-29·9 kg/m2, ≥30 kg/m2) and smoking status. Over 17 years of follow-up, ultraprocessed food consumption decreased from 7·5 to 6·0 servings/d and minimally processed food consumption decreased from 11·9 to 11·3 servings/d (Ptrend < 0·001). Changes in intakes of processed foods, culinary ingredients and culinary preparations were minimal. Trends were similar by sex, BMI and smoking status. DGAI-2010 score increased from 60·1 to 61·5, P < 0·001. The current study uniquely describes trends in diet processing level in an ageing US population, highlighting the longstanding presence of ultraprocessed foods in the American diet. Given the poor nutritional quality of ultraprocessed foods, public health efforts should be designed to limit their consumption.

Adherence of Mobile App-Based Surveys and Comparison With Traditional Surveys: eCohort Study.

BACKGROUND: eCohort studies offer an efficient approach for data collection. However, eCohort studies are challenged by volunteer bias and low adherence. We designed an eCohort embedded in the Framingham Heart Study (eFHS) to address these challenges and to compare the digital data to traditional data collection. OBJECTIVE: The aim of this study was to evaluate adherence of the eFHS app-based surveys deployed at baseline (time of enrollment in the eCohort) and every 3 months up to 1 year, and to compare baseline digital surveys with surveys collected at the research center. METHODS: We defined adherence rates as the proportion of participants who completed at least one survey at a given 3-month period and computed adherence rates for each 3-month period. To evaluate agreement, we compared several baseline measures obtained in the eFHS app survey to those obtained at the in-person research center exam using the concordance correlation coefficient (CCC). RESULTS: Among the 1948 eFHS participants (mean age 53, SD 9 years; 57% women), we found high adherence to baseline surveys (89%) and a decrease in adherence over time (58% at 3 months, 52% at 6 months, 41% at 9 months, and 40% at 12 months). eFHS participants who returned surveys were more likely to be women (adjusted odds ratio [aOR] 1.58, 95% CI 1.18-2.11) and less likely to be smokers (aOR 0.53, 95% CI 0.32-0.90). Compared to in-person exam data, we observed moderate agreement for baseline app-based surveys of the Physical Activity Index (mean difference 2.27, CCC=0.56), and high agreement for average drinks per week (mean difference 0.54, CCC=0.82) and depressive symptoms scores (mean difference 0.03, CCC=0.77). CONCLUSIONS: We observed that eFHS participants had a high survey return at baseline and each 3-month survey period over the 12 months of follow up. We observed moderate to high agreement between digital and research center measures for several types of surveys, including physical activity, depressive symptoms, and alcohol use. Thus, this digital data collection mechanism is a promising tool to collect data related to cardiovascular disease and its risk factors.

Association Between the Digital Clock Drawing Test and Neuropsychological Test Performance: Large Community-Based Prospective Cohort (Framingham Heart Study).

BACKGROUND: The Clock Drawing Test (CDT) has been widely used in clinic for cognitive assessment. Recently, a digital Clock Drawing Text (dCDT) that is able to capture the entire sequence of clock drawing behaviors was introduced. While a variety of domain-specific features can be derived from the dCDT, it has not yet been evaluated in a large community-based population whether the features derived from the dCDT correlate with cognitive function. OBJECTIVE: We aimed to investigate the association between dCDT features and cognitive performance across multiple domains. METHODS: Participants from the Framingham Heart Study, a large community-based cohort with longitudinal cognitive surveillance, who did not have dementia were included. Participants were administered both the dCDT and a standard protocol of neuropsychological tests that measured a wide range of cognitive functions. A total of 105 features were derived from the dCDT, and their associations with 18 neuropsychological tests were assessed with linear regression models adjusted for age and sex. Associations between a composite score from dCDT features were also assessed for associations with each neuropsychological test and cognitive status (clinically diagnosed mild cognitive impairment compared to normal cognition). RESULTS: The study included 2062 participants (age: mean 62, SD 13 years, 51.6% women), among whom 36 were diagnosed with mild cognitive impairment. Each neuropsychological test was associated with an average of 50 dCDT features. The composite scores derived from dCDT features were significantly associated with both neuropsychological tests and mild cognitive impairment. CONCLUSIONS: The dCDT can potentially be used as a tool for cognitive assessment in large community-based populations.

Dairy Intake in 2 American Adult Cohorts Associates with Novel and Known Targeted and Nontargeted Circulating Metabolites.

BACKGROUND: The role of dairy in health can be elucidated by investigating circulating metabolites associated with intake. OBJECTIVES: We sought to identify metabolites associated with quantity and type of dairy intake in the Framingham Heart Study Offspring and Third Generation (Gen3) cohorts. METHODS: Dairy intake (total dairy, milk, cheese, yogurt, and cream/butter) was analyzed in relation to targeted (Offspring, n = 2205, 55.1 ± 9.8 y, 52% female, 217 signals; Gen3, n = 866, 40.5 ± 8.8 y, 54.9% female, 79 signals) and nontargeted metabolites (Gen3, ∼7031 signals) in a 2-step analysis including orthogonal projections to latent structures with discriminant analysis (OPLS-DA) in discovery subsets to identify metabolites distinguishing between high and low intake; and linear regression in confirmation subsets to assess putative associations, subsequently tested in the total samples. Previously reported associations were also investigated. RESULTS: OPLS-DA in the Offspring targeted discovery subset resulted in a variable importance in projection (VIP) >1 of 65, 60, 58, 66, and 60 metabolites for total dairy, milk, cream/butter, cheese, and yogurt, respectively, of which 5, 3, 1, 6, and 4 metabolites, respectively, remained after confirmation. In the Gen3 targeted discovery subset, OPLS-DA resulted in a VIP >1 of 17, 15, 13, 7, and 6 metabolites for total dairy, milk, cream/butter, cheese, and yogurt, respectively. In the Gen3 nontargeted discovery subset, OPLS-DA resulted in a VIP >2 of 203, 503, 78, 186, and 206 metabolites, respectively. Combining targeted and nontargeted results in Gen3, significant associations of 7 (6 unannotated), 2, 12 (11 unannotated), 0, and 61 (all unannotated) metabolites, respectively, remained. Candidate identities of unannotated signals included fatty acids and food flavorings. Results supported relations previously reported for C14:0 sphingomyelin, and marginal associations for deoxycholates. CONCLUSIONS: Dairy in 2 American adult cohorts associated with numerous circulating metabolites. Reports about diet-metabolite relations and confirmation of previous findings might be limited by specificity of dietary intake and breadth of measured metabolites.

Relations of liver fat with prevalent and incident chronic kidney disease in the Framingham Heart Study: A secondary analysis.

BACKGROUND & AIMS: Prior studies demonstrated an association between non-alcoholic fatty liver disease and chronic kidney disease (CKD), though data are conflicting. We examined the association between liver fat and prevalent and incident CKD in the Framingham Heart Study (FHS). METHODS: We included FHS participants who underwent computed tomography (CT) from 2002 to 2005 (n = 1315). After excluding heavy alcohol use (n = 211) and missing covariates (n = 117), the final sample included 987 participants. For the incident CKD analysis, we excluded 73 participants with prevalent CKD. Liver fat was measured by the average liver attenuation on CT. Estimated glomerular filtration rate (eGFR) was obtained using the CKD Epidemiology Collaboration Creatinine-Cystatin C equation, and CKD was defined as eGFR < 60 ml/min/1.73 m2 . Microalbuminuria was defined by sex-specific urinary albumin-creatinine ratio cut-offs. Multivariable-adjusted regression models were performed to determine the association between liver fat and CKD. RESULTS: The prevalence of hepatic steatosis and CKD were 19% and 14% respectively (55.9% women, mean age 60 ± 9 years). After adjusting for covariates, we observed no significant associations between liver fat and CKD, microalbuminuria or eGFR in cross-sectional analyses. We observed positive associations between liver fat, incident microalbuminuria and reduced eGFR in age- and sex-adjusted models; these relationships were not significant in multivariable-adjusted models. CONCLUSIONS: In this community-based cohort study, we did not observe significant associations between liver fat and prevalent or incident CKD with a median follow-up time of 12.5 years. The association between NAFLD and CKD may be accounted for by shared risk factors; confirmatory studies are needed.