Marie Fanelli Kuczmarski

A new DNA methylation biomarker, Dunedin Pace of Aging Calculated from the Epigenome (DunedinPACE), is associated with healthy lifespan in several European ancestry cohorts. Few studies have examined the relation between dietary quality and DunedinPACE in African American and White adults with longitudinal assessments. To assess the relationship between diet quality and DunedinPACE, we used longitudinal data from African American and White 30–64 year old adults living above and below poverty. Participants’ DunedinPACE scores and dietary assessments were calculated at two time points, approximately 5 years apart. Numbers of participants ( n = 421; mean age 49 years) were balanced by race, sex, and poverty status. Diet quality was assessed using two different dietary indexes: Dietary Inflammatory Index (DII) and Healthy Eating Index-2010 (HEI). Linear mixed model regression examined the longitudinal association of DunedinPACE with DII and HEI adjusted by age, race, poverty status, BMI, and smoking status. Initial mean values of DII were 3.34 (SD = 2.16) and HEI was 40.67 (SD = 11.69), indicating a pro-inflammatory dietary pattern and low diet quality in this cohort. The initial mean DunedinPACE score was 1.07. We found that a higher DII score was associated with higher DunedinPACE score (β = 0.009; p < 0.001), higher HEI score was associated with lower DunedinPACE score (β = − 0.001; p = 0.032), and that these relationships were consistent over time. Overall, lower dietary quality was associated with a faster pace of aging captured by DunedinPACE score. Our findings demonstrate the independent contribution of diet quality to healthy aging-related epigenetic mechanisms. Graphical Abstract

Objective: The aim of this study is to determine if protein intake, diet quality, or engagement in physical activity mediate the relationship between sleep quality or duration and handgrip strength. Methods: The sample consisted of 2171 middle-aged persons examined in the 2013–2017 Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) prospective cohort study. Those with sleep apnea (n = 222) and missing data were excluded, resulting in an analytical sample of 1308. Handgrip strength, an objectively measured variable, was determined using a Jamar Hydraulic Hand Dynamometer and expressed relative to body mass index (based on measured height and weight). Sleep quality and duration were measured using the Pittsburgh Sleep Quality Index questionnaire. Protein intake was calculated from two 24 h recalls collected using the USDA Automated Multiple-Pass Method and expressed as g per kg of body weight. Diet quality was assessed using the Healthy Eating Index (HEI) and the energy-adjusted Dietary Inflammatory Index (e-DII). Physical activity was self-reported and expressed as meeting the Life Simple 7 criterion (≥150 min/week, 0–149 min/week, 0 min/week). Mediation analysis was conducted using the Hayes PROCESS macro, model #4, for SPSS Version 4.2. Adjustment for the self-reported covariates of age (years); sex at birth (male, female); race (African American, White); poverty status (125% US HHS Poverty Guidelines); current cigarette smoker (yes, no); marijuana, opiate, and/or cocaine user (yes, no); medical conditions including diabetes, hypertension, and/or metabolic syndrome (yes, no); and mean energy (kcal, only protein model) was performed. Results: Protein intake, expressed as g per kg of body weight, mediated the relationship between sleep quality and sleep duration and handgrip strength (indirect effect = −0.0017 ± 0.0006, CI 95% (−0.0030, −0.0006, p < 0.05); indirect effect = 0.0057 ± 0.0019, CI 95% (0.0023, 0.0098, p < 0.05, respectively)). Diet quality, as measured using the HEI, mediated the relationship between sleep duration and handgrip strength (indirect effect = 0.0013 ± 0.0007, CI 95% (0.0001, 0.0030, p < 0.05). Conclusions: Protein intake and a healthy diet mediate the relationship between sleep and handgrip strength, suggesting that these factors may play a role in preserving muscle strength.

Health disparities have been associated with diet quality inequalities. Study objectives were to determine if race and/or income were associated with the diet quality of White and African American urban women and to compare core food and beverage categories consumed at eating occasions by diet quality within race and income groups. Two 24 h dietary recalls were collected on 1219 women interviewed in the Healthy Aging in Neighborhoods of Diversity across the Life Span study, 2013–2017. Healthy Eating Index (HEI)-2010 scores were calculated. Linear regression Model 1 included race, marital status, and age. Model 2 included Model 1 variables plus income, education, literacy, employment, enough money for food, and food security. Core food categories and most frequently consumed items were identified at five eating occasions within the first and third tertile HEI-2010. Diet quality was associated with age, education, literacy, and employment. More fruit and vegetable categories at meals and snacks, and more water as a top beverage, were observed for women whose diets were in the third HEI-2010 tertile. The majority of foods reported by women in the first HEI-2010 tertile would be considered ultra-processed. The health benefits of consuming more minimally processed foods and unsweetened beverages may reduce health disparity gaps.

Healthy dietary patterns rich in flavonoids may benefit cognitive performance over time. Among socioeconomically disadvantaged groups, the association between flavonoid intake and measures of cognition is unclear. This study sought to identify associations between flavonoid intake and cognitive performance among Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) study participants (n = 1947) across three study visits. Flavonoid intakes were assessed via two 24-h dietary recalls. Cognitive performance was assessed via the Trail Making Test (TMT)-A and TMT-B, which provide measures of attention and executive function, respectively. Mixed effects linear regression was used to model TMT scores over three study visits against visit 1 (v1) flavonoid intake, time (years from v1), and the interaction between v1 flavonoid intake and time, capturing both the cross-sectional association between flavonoid intake and time at v1 as well as the longitudinal association between v1 flavonoid intake and the change in TMT scores over time. Prior to adjustment, inverse cross-sectional associations at v1 were observed between (1) anthocyanidin intake and TMT-A scores for the overall sample and (2) total flavonoid, anthocyanidin, flavan-3-ol, flavone, and flavonol intake and TMT-B scores for the overall sample and among White adults. Only the association between anthocyanidin intake and TMT-B at v1 among White adults persisted after adjustment (for demographic characteristics such as age). One possible explanation for the few significant associations is universally low flavonoid intakes resulting from the consumption of an unhealthy dietary pattern.

Background: Pathways explaining racial/ethnic disparities in dementia risk are under‐evaluated.Method: We examine those disparities and their related pathways among UK biobank study respondents (50‐74y, N = 323,483; 3.6% non‐White minorities) using a series of Cox proportional hazards and generalized structural equations models (GSEM).Result: After ≤15 years, 5,491 all‐cause dementia cases were diagnosed. Minority status increased dementia risk by 24% (HR = 1.24, 95% CI: 1.07‐1.45, P = 0.005), an association attenuated by socio‐economic status (SES), (HR = 1.12, 95% CI: 0.96‐1.31). Total race‐dementia effect was mediated through both SES and Life’s essential 8 lifestyle sub‐score (LE8LIFESTYLE), combining diet, smoking, physical activity, and sleep factors. SES was inversely related to dementia risk (HR = 0.69, 95% CI: 0.67, 0.72, P<0.001). Pathways explaining excess dementia risk among minorities included ‘RACE_ETHN(‐)→SES(‐)→◊ DEMENTIA’, ‘RACE_ETHN(‐)→ SES(‐)→ COGN(+)→ DEMENTIA’ and ‘RACE_ETHN(‐)→SES(+)→ LE8LIFESTYLE(‐)→`DEMENTIA’, with SES reflecting higher socio‐economic status, COGN reflecting poorer cognitive performance and LE8LIFESTYLE reflecting better cardiovascular health, specifically lifestyle factors, with higher scores. RACE_ETHN is the Non‐White minority vs. White contrast.Conclusion: Pending future interventions, lifestyle factors including diet, smoking, physical activity and sleep are crucial for reducing racial and socio‐economic disparities in dementia.

Loneliness is considered a predictor of poor health through numerous pathways. Mediators of this association has not been extensively explored. The study objective was to determine if diet quality and physical activity are parallel mediators with body mass index (BMI) as the third mediator in the association of loneliness with diabetes. The sample, middle-aged and older African American and White adults, 36–77 years, participated in the second follow-up wave of the prospective Healthy Aging in Neighborhoods of Diversity across the Life Span study, 2013–2017. Loneliness was measured by the UCLA 3-item loneliness scale. Participants were categorized as not diabetic, pre-diabetic, or diabetic based on fasting blood glucose, self-reports, or taking medication for diabetes. The Mean Healthy Eating Index-2010 score was calculated from two 24 h dietary recalls collected using the USDA automated multiple pass method. Physical activity was derived from the Baecke questionnaire. The Hayes PROCESS macro, model #80, was used to perform the mediational analysis. Covariates were age, sex at birth, race, income, alcohol intake, and education. Loneliness was inversely and significantly associated with diet quality and physical activity. The only significant indirect path was loneliness > physical activity > BMI > diabetes. Better understanding of modifiable lifestyle behaviors when developing interventions may improve mental health, thereby improving health.

Diet quality is a modifiable risk factor for frailty, but research on the association of frailty with dietary inflammatory potential is limited. The objective was to determine associations between diet quality assessed by the dietary inflammatory index (DII) with frailty status over time. Participants with both dietary and frailty data from the longitudinal Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) study were used (n = 2901, 43.5% male, 43.8% African American, 48.5 y mean baseline age, with a mean 8.7 y of follow-up). Group-based trajectory modeling identified two frailty (remaining non-frail or being pre-frail/frail over time) and three diet quality trajectory groups (high or medium pro-inflammatory and anti-inflammatory potentials). Multiple logistic regression found both medium pro-inflammatory and anti-inflammatory DII trajectory groups, compared to the high pro-inflammatory group, were positively associated with being non-frail over time for the overall sample, both sexes and races. Kaplan–Meier curves and log-rank test revealed anti-inflammatory DII scores were associated with lower risk for being pre-frail or frail. No longitudinal relationship existed between frailty status at baseline and annualized DII change, a check on reverse causality. This study contributes to our current knowledge providing longitudinal evidence of the link between anti-inflammatory DII score with lower frailty risk.

Limited investigation has been done on diet quality trajectories over adulthood. The main study objectives were to determine the diet quality group trajectories (GTs) over time and to detect changes in a socio-economically and racially diverse middle-aged cohort. Data from three waves of the Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) study were used to determine diet quality with group-based trajectory modeling (GBTM). Three quality indices—the Healthy Eating Index (HEI), the Dietary Inflammatory Index (DII), and the Mean Adequacy Ratio (MAR)—were explored. The rate of change in quality over time was determined by mixed-effects regression analysis. Three diet quality GTs, low, middle, and high quality, were identified for each index and confirmed with spaghetti plots. Within each GT, only small changes in diet quality scores were observed, with improvements for the HEI and DII indices and a slight decline in MAR scores. Weighted kappa values revealed that the DII had better agreement with the HEI-2010 and MAR indices compared with the agreement between the HEI-2010 and MAR. Bayesian estimates revealed that the annualized rate of change in diet quality per person across the GTs was similar. There was minimal change in diet quality over time, regardless of the diet quality index used.