Percent of all visits which are opioid-related.

<p>Bar graph of the percent of all visits to the MGH ED which are opioid-related from each census tract in Charlestown, MA. The dashed line represents the median percent of visits which are opioid-related from all census tracts within Charlestown (1.4%).</p

Emergency department visits per capita.

<p>Bar graph of all visits (opioid-related and otherwise) to the MGH ED per capita from each census tract in Charlestown, MA based on the census tract level population from the 2010 US Census. The dashed line represents the median per capita visits across all census tracts in Charlestown (0.55).</p

Opioid-related ED visits and addresses compared to population levels.

<p>Bar graph of observed vs population-weighted expected opioid-related visits (Left), and addresses (Right) from each census tract in Charlestown, MA. Expected distributions were calculated by weighting the total number of opioid-related ED visits (Left) or addresses (Right) from Charlestown by the population in each census tract. Observed distributions are the actual number of opioid-related ED visits (Left) or addresses (Right) from each census tract. The horizontal dotted line represents a simple (i.e. not population-weighted) average obtained by distributing the total number of opioid-related ED visits (Left) or addresses (Right) equally across all census tracts.</p

Opioid-related ED visits from Charlestown, MA.

<p>Choropleth map of Charlestown, MA at 1:17,000 scale with census tracts colored by level of opioid-related ED visits to MGH. Grey areas show boundaries of census tracts outside of Charlestown. Compass arrow at upper left points north. Blue shaded areas represent water and show the confluence of the Massachusetts Bay and the heads of the Charles, Mystic, and Chelsea Rivers.</p

Change in IVC and Aorta diameter with Rehydration.

<p>Change in IVC and Aorta diameter with Rehydration.</p

ROC curves of Aorta-to-IVC ratio for predicting dehydration.

<p>ROC curves of Aorta-to-IVC ratio for predicting dehydration.</p

Patient enrollment.

<p>Patient enrollment.</p

Examples of genetic risk profiles in 4 study subjects (3 centenarians with ages at death 107, 108 and 119 years, and a control).

<p>281 nested SNP sets were used to compute the posterior probability of exceptional longevity in the 4 subjects (y-axis) and were plotted against the number of SNPs in each set (x-axis). In the 107 year old, the first 5 SNP sets Σ₁ = [rs2075650], Σ₂ = [Σ₁, rs1322048], …, Σ₅ = [Σ₄, rs6801173] determine a posterior probability of exceptional longevity ranging between 0.54 and 0.28. This subject carries genotypes AA, AG, AG, CC, AA for the 5 SNPs respectively and, with the exclusion of genotype AA of rs2075650 that is more common in centenarians, the other genotypes are more common in controls than centenarians and determine a posterior probability of exceptional longevity that is lower than the posterior probability of average longevity. The sixth SNP set, Σ₆ = [Σ₅, rs337656], predicts an almost 30% chance of exceptional longevity. The subject carries the AA genotype for the SNP rs337656 that is more frequent in centenarians (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029848#pone.0029848.s022" target="_blank">Table S1</a>), and carrying this genotype increases the posterior probability of exceptional longevity. The probability predicted by the next SNP sets increases steadily and all models with more than 20 SNPs predict more than a 50% chance of exceptional longevity. This genetic profile shows that the subject carries some combinations of SNP alleles that are associated with exceptional longevity, while other alleles are associated with “average longevity”. However, the overall genetic risk profile determined by all 281 SNP sets makes a strong case for exceptional longevity because the majority of models predict more than an 80% chance of exceptional longevity. The genetic risk profile of the centenarian who died at age 119 years is even more convincing: with the exception of the first SNP, all subsequent SNP sets determine more than a 70% chance of exceptional longevity, and 272 of the 281 models predict more than an 80% chance for exceptional longevity. This profile shows that this subject is highly enriched for SNPs alleles that are more common in centenarians (longevity associated variants) and that probably played a determinant role in the extreme survival. The profile of the third subject, age 108 years, shows that different SNP sets determine different chances for exceptional longevity, and only the overall trend of genetic risk provides evidence for exceptional longevity. The fourth plot displays the profile of a control, and shows that this subject carries some longevity associated variants; however, the overall trend of genetic risk points to average longevity rather than exceptional longevity.</p

Schematic showing the methodology used to discover genetic signatures of exceptional longevity (EL).

<p>The analysis included genetic matching to remove confounding by population stratification between cases and controls of the discovery and replication set 1, discovery and replication of single SNP associations, multivariate genetic risk modeling and generation of predictive genetic profiles, and cluster analysis of genetic risk profiles to discover genetic signatures of EL.</p

Distribution of NECS cases (row 2), NECS controls (row 3) and Illumina controls (row 4) in clusters of genetic ethnicity (columns).

<p>The table shows the 20 clusters of genetic ethnicity that were discovered using a clustering algorithm described in reference <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029848#pone.0029848-Solovieff1" target="_blank">[20]</a>. Note that no centenarians were allocated to cluster 1 or 15. These clusters are represented by full red dots in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029848#pone.0029848.s001" target="_blank">Figure S1</a></b> and denote Franks and Celtics- Alpine ethnicities.</p