For decades, scholars have wrestled with the notion that old age is characterized by social isolation. However, there has been no systematic, nationally representative evaluation of this possibility in terms of social network connectedness. In this paper, the authors develop a profile of older adults' social integration with respect to nine dimensions of connectedness to interpersonal networks and voluntary associations. The authors use new data from the National Social Life, Health, and Aging Project (NSHAP), a population-based study of non-institutionalized older Americans aged 57-85 conducted in [2005][2006]. Findings suggest that among older adults, age is negatively related to network size, closeness to network members, and number of non-primary-group ties. On the other hand, age is positively related to frequency of socializing with neighbors, religious participation, and volunteering. In addition, it has a U-shaped relationship with volume of contact with network members. These findings are inconsistent with the notion that old age has a universal negative influence on social connectedness. Instead, life course factors have divergent consequences for different forms of social connectedness. Some later life transitions, like retirement and bereavement, may prompt greater connectedness. The authors close by urging increased dialogue between social gerontological and social network research.

Media portrayals of a loneliness "epidemic" are premised on an increase in the proportion of people living alone and decreases in rates of civic engagement and religious affiliation over recent decades. However, loneliness is a subjective perception that does not correspond perfectly with objective social circumstances. In this study, we examined whether perceived loneliness is greater among the Baby Boomers-individuals born 1948 -1965-relative to those born 1920 -1947 and whether older adults have become lonelier over the past decade (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016). We used data from the National Social Life, Health and Aging Project and from the Health and Retirement Study collected during 2005-2016 to estimate differences in loneliness associated with age, birth year, and survey time point. Overall, loneliness decreased with age through the early 70s, after which it increased. We found no evidence that loneliness is substantially higher among the Baby Boomers or that it has increased over the past decade. Loneliness is, however, associated with poor health, living alone or without a spouse-partner, and having fewer close family and friends, which together accounted for the overall increase in loneliness after age 75. Although these data do not support the idea that older adults are becoming lonelier, the actual number of lonely individuals may increase as the Baby Boomers age into their 80s and beyond. Our results suggest that attention to social factors and improving health may help to mitigate this.

Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.

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