Social and Behavior Factors of Alzheimer's Disease and Related Dementias: A National Study in the U.S.

INTRODUCTION: Considerable research has linked many risk factors to Alzheimer's Disease and Related Dementias (ADRD). Without a clear etiology of ADRD, it is advantageous to rank the known risk factors by their importance and determine if disparities exist. Statistical-based ranking can provide insight into which risk factors should be further evaluated. METHODS: This observational, population-based study assessed 50 county-level measures and estimates related to ADRD in 3,155 counties in the U.S. using data from 2010 to 2021. Statistical analysis was performed in 2022-2023. The machine learning method, eXtreme Gradient Boosting, was utilized to rank the importance of these variables by their relative contribution to the model performance. Stratified ranking was also performed based on a county's level of disadvantage. Shapley Additive exPlanations (SHAP) provided marginal contributions for each variable. RESULTS: The top three ranked predictors at the county level were insufficient sleep, consuming less than one serving of fruits/vegetables per day among adults, and having less than a high school diploma. In both disadvantaged and non-disadvantaged counties, demographic variables such as sex and race were important in predicting ADRD. Lifestyle factors ranked highly in non-disadvantaged counties compared to more environmental factors in disadvantaged counties. CONCLUSIONS: This ranked list of factors can provide a guided approach to ADRD primary prevention strategies in the U.S., as the effects of sleep, diet, and education on ADRD can be further developed. While sleep, diet, and education are important nationally, differing prevention strategies could be employed based on a county's level of disadvantage.

Narcolepsy genetic marker HLA DQB1*06:02 and excessive daytime sleepiness in Parkinson disease patients treated with dopaminergic agents.

OBJECTIVE: To determine whether narcolepsy Human Leukocyte Antigen (HLA) risk allele DQB1*0602 is associated with excessive daytime sleepiness (EDS) and inappropriate sleep in patients with Parkinson disease (PD). BACKGROUND: EDS is a common and disabling non-motor manifestation of PD, affecting quality of life and driving performance. DQB1*0602 is an HLA risk allele for narcolepsy. It is present in 12-30% of the general population. We hypothesize that DQB1*0602 is associated with an increased risk of EDS and inappropriate sleep in PD patients. METHODS: This was a cross-sectional observational study of 150 PD individuals on dopaminergic agents. Main outcome measures were DQB1*0602 status and the modified Epworth Sleepiness Scale. Individuals with dementia, loss of independence, narcolepsy and untreated sleep apnea were excluded. Confounding variables for EDS were assessed using Parkinson Disease Sleep Scale, Mayo Sleep Questionnaire, Unified PD Rating Scale, Hoehn and Yahr scale. RESULTS: DQB1*06:02 positive PD patients were approximately three times more likely to experience EDS and fall asleep inappropriately during activities that required sustained alertness (e.g. driving, eating, attending work etc.). Exploratory post hoc analysis showed a dopaminergic drug dose- and type- dependent effect on daytime sleepiness in DQB1*06:02 positive individuals. No significant differences were found in confounding variables. CONCLUSION: PD individuals are more likely to experience EDS and fall asleep inappropriately during activities if DQB1*0602 positive. Genetic vulnerability may explain EDS risk in PD.

Association between gestational phthalate exposure and newborn head circumference; impacts by race and sex.

Observational and experimental studies report associations between gestational phthalate exposure and fetal development, yet few data exist to characterize phthalate effects on head circumference (HC) or to estimate the impact of race or sex. To address this data gap, we enrolled 152 African American and 158 white mothers with uncomplicated singleton pregnancies from the Charleston, South Carolina (USA) metropolitan area in a prospective birth cohort. Study participants provided up to two urine specimens during mid and late gestation, completed a study questionnaire, and allowed access to hospital birth records. We measured eight phthalate monoester metabolites using liquid chromatography with tandem mass spectrometry, and calculated molar sums of phthalate parent diesters. After specific gravity correction, we tested for associations between phthalates and neonatal HC (cm) and cephalization index (cm/g) using multiple informant linear regression with inverse probability weighting to account for selection bias between repeated urine sampling, adjusted for maternal race, age, body mass index, education, and smoking. We explored interactions by maternal race and infant sex. A doubling of urinary monoethyl phthalate (MEP) concentration was associated with a -0.49% (95%CI: -0.95%, -0.02%) smaller head circumference, although seven other phthalate metabolites were null. There were no statistically significant associations with cephalization index. HC was larger for whites than African American newborns (p < 0.0001) but similar for males and females (p = 0.16). We detected interactions for maternal race with urinary monobutyl phthalate (MBP; p = 0.03), monobenzyl phthalate (MBzP; p = 0.01), monoethylhexyl phthalate (MEHP; p = 0.05), monomethyl phthalate (MMP; p = 0.02), and the sum of dibutyl phthalate metabolites (∑DBP; p = 0.05), in which reduced HC circumference associations were stronger among whites than African Americans, and interactions for sex with MBP (p = 0.08) and MiBP (p = 0.03), in which associations were stronger for females than males. Our results suggest that gestational phthalate exposure is associated with smaller neonatal HC and that white mothers and female newborns have greater susceptibility.

Multivariate analysis of spatial-temporal scales in melanoma prevalence.

Melanoma is a particularly deadly form of skin cancer arising from diverse biological and physical origins, making the characterization and quantification of relationships with recognized risk factors very complex. Melanoma has known associations with ultraviolet light exposure. Natural variations in solar electromagnetic irradiation, length of exposure, and intensity operate on different and therefore uncorrelated time scale frequencies. It is necessary to separate and investigate the principal components, such as the annual and solar cycle components, free from confounding influences. Kolmogorov-Zurbenko spatial filters applied to melanoma prevalence and environmental factors affecting solar irradiation exposure are able to identify and separate the independent space and time scale components of melanoma. Multidimensional analysis in space and time produces significantly improved model fit of what is in effect a linear regression of maps, or motion picture, in different time scales between melanoma rates and prominent factors. The resulting multivariate model coefficients of influence for each unique spatial-temporal melanoma component help quantify the relationships and are valuable to future research and prevention.

Skin cancer, irradiation, and sunspots: the solar cycle effect.

Skin cancer is diagnosed in more than 2 million individuals annually in the United States. It is strongly associated with ultraviolet exposure, with melanoma risk doubling after five or more sunburns. Solar activity, characterized by features such as irradiance and sunspots, undergoes an 11-year solar cycle. This fingerprint frequency accounts for relatively small variation on Earth when compared to other uncorrelated time scales such as daily and seasonal cycles. Kolmogorov-Zurbenko filters, applied to the solar cycle and skin cancer data, separate the components of different time scales to detect weaker long term signals and investigate the relationships between long term trends. Analyses of crosscorrelations reveal epidemiologically consistent latencies between variables which can then be used for regression analysis to calculate a coefficient of influence. This method reveals that strong numerical associations, with correlations >0.5, exist between these small but distinct long term trends in the solar cycle and skin cancer. This improves modeling skin cancer trends on long time scales despite the stronger variation in other time scales and the destructive presence of noise.