Identification of Novel Genetic Risk Variants Associated with Hidradenitis Suppurativa in an Exome Sequencing Cohort of 92,455 Individuals.

INTRODUCTION: Hidradenitis suppurativa (HS) is a prevalent and persistent inflammatory skin disorder, lacking a known cure or effective biomarkers for early diagnosis at present. The genetic determinants of HS have not been fully documented, but it is believed to result from a combination of genetic and environmental factors. METHODS: To identify relevant HS gene variants in sporadic HS patients, this study utilized longitudinal electronic health records (EHRs) and whole-exome sequencing. DNA exome sequencing data from 92,455 participant samples in the MyCode biobank, linked to Geisinger's EHR, were analyzed. This cohort included 1,092 HS cases and 91,363 healthy controls. The MyCode EHR has a median longitudinal follow-up of 15 years per participant, with an average of 87 clinical encounters, 687 laboratory tests, and 7 procedures. RESULTS: There were 1,092 (901 females and 191 males) participants aged 14-89 years (median 47 years) with HS (L73.2), indicating a 1.18% prevalence and accounting for a 4.7:1 female-to-male ratio among the individuals presenting for clinical care. γ-secretase complex, syndromic, and autoinflammatory gene variants were assessed. Potential pathogenic variants were identified among 66 individuals in the HS genes studied. Molecularly, the estimated HS variant prevalence was 1:1,400 in the cohort, 12.3% of variant carriers had HS diagnosis in EHR. CONCLUSIONS: Using longitudinal EHR data, genomic screening identified HS-associated gene variants in a defined group of sporadic HS patients to augment the clinical diagnosis, particularly in cases of ambiguity. Based on this study, the field of skin disorders can benefit from a personalized approach to HS diagnosis using large-scale sequencing.

Impact of a multi-disciplinary team-based care model for patients living with diabetes on health outcomes: a mixed-methods study.

BACKGROUND: Individuals facing socioeconomic hardship experience higher than average rates of chronic disease, such as diabetes, with less access to evidence-based treatment. One solution to address these inequities is a team-based care (TBC) model, defined as one in which at least two providers work collaboratively with a patient and their caregiver(s) to make healthcare decisions. This paper seeks to describe the implementation of a TBC model within a safety-net healthcare setting and determine the extent to which it can be an effective, patient-centered approach to treating individuals with diabetes. METHODS: Semi-structured interviews were conducted with staff (n = 15) and patients (n = 18). Clinical data were extracted from the electronic medical record of patients (n = 1,599) seen at a safety-net health system in Chicago, Illinois, United States. The mixed methods study was guided by implementation science and participatory research principles. Staff interviews were 60 min and covered patient care activities, work flow, perceived patient experience, and facilitators/barriers to care coordination. Patient interviews were 60 min and covered satisfaction, attitudes about diabetes management, quality of life, and technology. Patient interviews were co-analyzed by research staff and members of a patient advisory committee. Clinical data were collected at an index visit, two years prior and at one-year follow up (n = 1,599). RESULTS: Four themes emerged from the interviews: (1) patients perceived the TBC model to be patient centered and of high quality; (2) technology can be an innovative tool, but barriers exist; (3) diabetes management is a complex process; and (4) staff communication enhances care coordination, but misinterpreting roles reduces care coordination. From pre-enrollment to the follow-up period, we found a statistically significant increase in missed visits, decrease in hemoglobin A1c (HbA1c), decrease in body mass index, and decrease in the percent of patients with high blood pressure. We found that each medical visit during the follow-up period was associated with an HbA1c decrease of 0.26 points. CONCLUSIONS: A TBC model is a patient-centered approach to providing care to patients with complex health needs, such as diabetes, patients were satisfied with the care they were receiving, and the model was associated with an improvement in clinical outcomes.

Deregulated Long Non-Coding RNAs (lncRNA) as Promising Biomarkers in Hidradenitis Suppurativa.

Background/Objectives: In recent times, epigenetics alterations in Hidradenitis suppurativa (HS) have been explored and exploited translationally to guide investigation of new therapeutic approaches. On the other hand, long noncoding RNAs (LncRNAs), main regulators of the epigenetic status of the human genome, have been scarcely investigated, notwithstanding their potential relevance in broad pathogenesis comprehension. Here, we aim to explore the methylation pattern of lncRNAs in HS. Methods: In this case-control study, 24 HS patients and age-, sex- and BMI-matched controls were analyzed to characterize the methylome of lncRNA genes in peripheral blood cells. Gene ontology analysis (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, protein-protein interaction (PPI) network, and MCODE analysis were performed. Results: A set of fifteen lncRNA genes exhibited significantly differential methylation patterns, with ten of them showing hypomethylation and five displaying hypermethylation at specific CpG sites. The hypomethylated lncRNA genes were DLEU2, MESTIT1, CASC2, TUG1, KCNQ1DN, PSORS1C3, PCA3, DSCR8, RFPL1S, and PVT1, while the hypermethylated ones were HAR1A, FAM66B, SNHG9, HCG9, and HCP5. These lncRNA genes have been linked to various important biological processes, including cell proliferation, apoptosis, inflammation, chronic inflammatory skin diseases, and wound healing. Their altered methylation status suggests potential roles in regulating these processes, and may contribute to HS pathogenesis and healing mechanisms. Conclusions: This study revealed an interesting dysregulation pattern of definite lncRNAs in the methylome which is linked to both the development of HS and its comorbidities. Epigenetically altered lncRNAs genes could represent useful biomarkers, and could help in guiding innovative treatment strategies.

Hidradenitis suppurativa associated telomere-methylome dysregulations in blood.

BACKGROUND: Hidradenitis suppurativa (HS) is a chronic debilitating disease with a significant burden of both organic and psychological comorbidities. It has been shown that certain telomere-related genes (TRGs) affect a wide range of diseases, including HS and its associated comorbidities, but their exact role in HS pathogenesis is still unknown. OBJECTIVES: To determine whether TRG methylomes can be used as biomarkers in HS. METHODS: Using the Illumina HumanMethylation450 BeadChip array, we examined methylation variations associated with TRGs in HS cases and age-, sex- and ethnicity-matched healthy controls. The study utilized integrated bioinformatics statistical methods, such as a false discovery rate (FDR), the area under the receiver operating characteristic curve (AUC) and principal component analysis. RESULTS: There were a total of 585 different differentially methylated CpG sites identified in 585 TRGs associated with HS (474 hypomethylated and 111 hypermethylated) (FDR p-value < 0.05). A number of these CpGs have been identified as being involved in increased pain sensitivity including EPAS1, AHR, CSNK1D, DNMT1, IKBKAP, NOS3, PLCB1 and PRDM16 genes; GABRB3 as a potential alcohol addiction marker; DDB1, NSMCE2 and HNRNPA2B1 associated with cancers. Pathway analysis identified 67 statistically significant pathways, including DNA repair, telomere maintenance, mismatch repair and cell cycle control (p < 0.001). CONCLUSION: The disruption of TRGs leads to the shortening of telomeres, which is associated with HS progression, ageing, cellular senescence and an increased risk of various diseases, including cancer and associated comorbidities, such as metabolic syndrome, cardiovascular disease and inflammatory disorders. Further research is necessary to better understand the underlying mechanisms and establish causal links between TRGs and HS. The present study is the first effort to comprehend potential pathomechanisms of sporadic HS cases concentrating on PBMC methylome since ours.

Cause-Specific Mortality and Racial Differentials in Life Expectancy, Chicago 2018-2019.

BACKGROUND: In Chicago in 2018, the average life expectancy (ALE) for NH Blacks was 71.5 years, 9.1 fewer years than for NH Whites (80.6 years). Inasmuch as some causes of death are increasingly recognized products of structural racism, in urban areas, such causes may have potential for reducing racial inequities through public health intervention. Our purpose is to allocate racial inequities in ALE in Chicago to differentials in cause-specific mortality. METHODS: Using multiple decrement processes and decomposition analysis, we examine cause-specific mortality in Chicago to determine the causes of death that contribute to the gap in life expectancy between NH Blacks and NH Whites. RESULTS: Among females, the racial difference in ALE was 8.21 years; for males, it was 10.53 years. We find that cancer and heart disease mortality account for 3.03 years or 36% of the racial gap in average life expectancy among females. Differences in homicide and heart disease mortality rates comprised over 45% of the disparity among males. CONCLUSIONS: Strategies for improving inequities in life expectancy should account for differences between males and females in cause-specific mortality rates. In urban areas with high levels of segregation, reducing inequities in ALE may be possible by dramatically reducing mortality rates from some causes. CONTRIBUTION: This paper illustrates the state of inequities in ALE between NH Blacks and NH Whites in Chicago for the period just prior to the onset of the COVID-19 pandemic, using a well-established method of decomposing mortality differentials for sub-populations.

Pre-pandemic trends and Black:White inequities in life expectancy across the 30 most populous U.S. cities: a population-based study.

BACKGROUND: Racial inequities in life expectancy, driven by structural racism, have been documented at the state and county levels; however, less information is available at the city level where local policy change generally happens. Furthermore, an assessment of life expectancy during the decade preceding COVID-19 provides a point of comparison for life expectancy estimates and trends post COVID-19 as cities recover. METHODS: Using National Vital Statistics System mortality data and American Community Survey population estimates, we calculated the average annual city-level life expectancies for the non-Hispanic Black (Black), non-Hispanic White (White), and total populations. We then calculated the absolute difference between the Black and White life expectancies for each of the 30 cities and the U.S. We analyzed trends over four time periods (2008-2010, 2011-2013, 2014-2016, and 2017-2019). RESULTS: In 2017-2019, life expectancies ranged from 72.75 years in Detroit to 83.15 years in San Francisco (compared to 78.29 years for the U.S.). Black life expectancy ranged from 69.94 years in Houston to 79.04 years in New York, while White life expectancy ranged from 75.18 years in Jacksonville to 86.42 years in Washington, DC. Between 2008-2010 and 2017-2019, 17 of the biggest cities experienced a statistically significant improvement in life expectancy, while 9 cities experienced a significant decrease. Black life expectancy increased significantly in 14 cities and the U.S. but decreased significantly in 4 cities. White life expectancy increased significantly in 17 cities and the U.S. but decreased in 8 cities. In 2017-2019, the U.S. and all but one of the big cities had a significantly longer life expectancy for the White population compared to the Black population. There was more than a 13-year difference between Black and White life expectancies in Washington, DC (compared to 4.18 years at the national level). From 2008-2010 to 2017-2019, the racial gap decreased significantly for the U.S. and eight cities, while it increased in seven cities. CONCLUSION: Urban stakeholders and equity advocates need data on mortality inequities that are aligned with city jurisdictions to help guide the allocation of resources and implementation of interventions.

Integrative Analysis Unveils the Correlation of Aminoacyl-tRNA Biosynthesis Metabolites with the Methylation of the SEPSECS Gene in Huntington's Disease Brain Tissue.

The impact of environmental factors on epigenetic changes is well established, and cellular function is determined not only by the genome but also by interacting partners such as metabolites. Given the significant impact of metabolism on disease progression, exploring the interaction between the metabolome and epigenome may offer new insights into Huntington's disease (HD) diagnosis and treatment. Using fourteen post-mortem HD cases and fourteen control subjects, we performed metabolomic profiling of human postmortem brain tissue (striatum and frontal lobe), and we performed DNA methylome profiling using the same frontal lobe tissue. Along with finding several perturbed metabolites and differentially methylated loci, Aminoacyl-tRNA biosynthesis (adj p-value = 0.0098) was the most significantly perturbed metabolic pathway with which two CpGs of the SEPSECS gene were correlated. This study improves our understanding of molecular biomarker connections and, importantly, increases our knowledge of metabolic alterations driving HD progression.

Urinary Cytokines as Potential Biomarkers of Mild Cognitive Impairment and Alzheimer's Disease: A Pilot Study.

Background: Alzheimer's disease (AD) is the most common form of dementia, accounting for 80% of all cases. Mild cognitive impairment (MCI) is a transitional state between normal aging and AD. Early detection is crucial, as irreversible brain damage occurs before symptoms manifest. Objective: This study aimed to identify potential biomarkers for early detection of AD by analyzing urinary cytokine concentrations. We investigated 37 cytokines in AD, MCI, and cognitively normal individuals (NC), assessing their associations with AD development. Methods: Urinary cytokine concentrations were measured in AD (n = 25), MCI (n = 25), and NC (n = 26) patients. IL6ST and MMP-2 levels were compared between AD and NC, while TNFRSF8, IL6ST, and IL-19 were assessed in AD versus MCI. Diagnostic models distinguished AD from NC, and in-silico analysis explored molecular mechanisms related to AD. Results: Significant perturbations in IL6ST and MMP-2 concentrations were observed in AD urine compared to NC, suggesting their potential as biomarkers. TNFRSF8, IL6ST, and IL-19 differed significantly between AD and MCI, implicating them in disease progression. Diagnostic models exhibited promising performance (AUC: 0.59-0.79, sensitivity: 0.72-0.80, specificity: 0.56-0.78) in distinguishing AD from NC. In-silico analysis revealed molecular insights, including relevant non-coding RNAs, microRNAs, and transcription factors. Conclusion: This study establishes significant associations between urinary cytokine concentrations and AD and MCI. IL6ST, MMP-2, TNFRSF8, IL6ST, and IL-19 emerge as potential biomarkers for early detection of AD. In-silico analysis enhances understanding of molecular mechanisms in AD. Further validation and exploration of these biomarkers in larger cohorts are warranted to assess their clinical utility.

Hidradenitis suppurativa presents a methylome dysregulation capable to explain the pro-inflammatory microenvironment: Are these DNA methylations potential therapeutic targets?

BACKGROUND: Hidradenitis suppurativa (HS) is a chronic, systemic, inflammatory skin condition with elusive pathogenesis that affects therapeutic intervention directly. OBJECTIVE: To characterize epigenetic variations in cytokines genes contributing to HS. METHODS: Epigenome-wide DNA methylation profiling with the Illumina Epic array was performed on blood DNA samples from 24 HS patients and 24 age- and sex-matched controls to explore DNA methylation changes in cytokine genes. RESULTS: We identified 170 cytokine genes including 27 hypermethylated CpG sites and 143 genes with hypomethylated sites respectively. Hypermethylated genes, including LIF, HLA-DRB1, HLA-G, MTOR, FADD, TGFB3, MALAT1 and CCL28; hypomethylated genes, including NCSTN, SMAD3, IGF1R, IL1F9, NOD2, NOD1, YY1, DLL1 and BCL2 may contribute to the pathogenesis of HS. These genes were enriched in the 117 different pathways (FDR p-values ≤ 0.05), including IL-4/IL-13 pathways and Wnt/ß-catenin signalling. CONCLUSIONS: The lack of wound healing, microbiome dysbiosis and increased tumour susceptibility are all sustained by these dysfunctional methylomes, hopefully, capable to be targeted in the next future. Since methylome describes and summarizes genetic and environmental contributions, these data may represent a further step towards a feasible precision medicine also for HS patients.

An Updated Mutation Spectrum of the γ-Secretase Complex: Novel NCSTN Gene Mutation in an Indian Family with Hidradenitis Suppurativa and Acne Conglobata.

Background: Hidradenitis suppurativa (HS) is a complex, chronic inflammatory skin disorder whose pathophysiology is poorly understood. Genetic studies have shown that HS is predisposed by mutations in the γ-secretase gene, but only a proportion of familial and partial sporadic cases have been shown to possess such mutations. HS has high genetic heterogeneity and is thought to be triggered by a combination of genetics and environmental factors. Aims: The study aimed to investigate the genetic causes of HS in a large cohort of patients and to update the mutation spectrum of γ-secretase complex genes. Methods: We conducted mutational screening of 95 sporadic HS cases and one large family with both HS and acne conglobata (AC) to identify mutations in the coding and splice junction region of γ-secretase complex genes (nicastrin (NCSTN), presenilin 1 (PSEN1), presenilin enhancer 2 (PSENEN), and aph-1 homolog B, gamma-secretase subunit (APH1B)). Results: Our study identified a nucleotide substitution of 1876C>T in the NCSTN gene, which caused a stop codon (p.Arg626X) in the affected members of a large family with HS and AC. No pathogenic variants were detected in 95 sporadic cases of HS, indicating there is possible genetic heterogeneity. Conclusion: We report a new family with a nonsense mutation in the NCSTN gene that supports the role of the γ-secretase complex genes in HS with AC. The updated γ-secretase mutation spectrum for HS now includes 78 mutations.

Association of Metabolomic Biomarkers with Sleeve Gastrectomy Weight Loss Outcomes.

This prospective observational study aimed to evaluate the association of metabolomic alterations with weight loss outcomes following sleeve gastrectomy (SG). We evaluated the metabolomic profile of serum and feces prior to SG and three months post-SG, along with weight loss outcomes in 45 adults with obesity. The percent total weight loss for the highest versus the lowest weight loss tertiles (T3 vs. T1) was 17.0 ± 1.3% and 11.1 ± 0.8%, p < 0.001. Serum metabolite alterations specific to T3 at three months included a decrease in methionine sulfoxide concentration as well as alterations to tryptophan and methionine metabolism (p < 0.03). Fecal metabolite changes specific to T3 included a decrease in taurine concentration and perturbations to arachidonic acid metabolism, and taurine and hypotaurine metabolism (p < 0.002). Preoperative metabolites were found to be highly predictive of weight loss outcomes in machine learning algorithms, with an average area under the curve of 94.6% for serum and 93.4% for feces. This comprehensive metabolomics analysis of weight loss outcome differences post-SG highlights specific metabolic alterations as well as machine learning algorithms predictive of weight loss. These findings could contribute to the development of novel therapeutic targets to enhance weight loss outcomes after SG.

Life Expectancy Gaps Among Black and White Persons and Contributing Causes of Death in 3 Large US Cities, 2018-2019.

Importance: US cities have substantial, but varying, levels of racial mortality inequities, a consequence of structural racism. As committed partners increasingly pledge to eliminate health inequities, local data are required to focus and unify efforts. Objective: To analyze the contributions of 26 cause-of-death categories to Black to White life expectancy gaps within 3 large US cities. Design, Setting, and Participants: In this cross-sectional study, data were extracted from the 2018 and 2019 National Vital Statistics System Multiple Cause of Death Restricted Use data files for deaths by race, ethnicity, sex, age, place of residence, and underlying and contributing causes of death in Baltimore, Maryland; Houston, Texas; and Los Angeles, California. Life expectancy at birth was calculated for non-Hispanic Black and non-Hispanic White populations overall and by sex using abridged life tables with 5-year age intervals. Data analysis was performed from February to May 2022. Main Outcomes and Measures: Using the Arriaga method, the proportion of the Black to White life expectancy gap was calculated overall and by sex for each city that was attributable to 26 cause-of-death categories defined using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes for underlying and contributing causes of death. Results: A total of 66 321 death records from 2018 to 2019 were analyzed, with 29 057 individuals (44%) were identified as Black, 34 745 (52%) as male, and 46 128 (70%) as aged 65 years and older. Black to White life expectancy gaps were 7.60 years for Baltimore, 8.06 years for Houston, and 9.57 years for Los Angeles. Circulatory diseases, cancer, injuries, and diabetes and endocrine disorders were top contributors to the gaps, although the order and magnitude varied by city. The contribution of circulatory diseases was 11.3 percentage points higher in Los Angeles than in Baltimore (3.76 years [39.3%] vs 2.12 years [28.0%]). The contribution of injuries to Baltimore's racial gap (2.22 years [29.3%]) was twice as large as in Houston (1.11 years [13.8%]) and Los Angeles (1.36 years [14.2%]). Conclusions and Relevance: By assessing the composition of Black to White life expectancy gaps for 3 large US cities and categorizing deaths at a more granular level than past studies, this study provides insight into the differing underpinnings of urban inequities. This type of local data can support local resource allocation that more effectively addresses racial inequities.

Black-White Inequities in Kidney Disease Mortality Across the 30 Most Populous US Cities.

OBJECTIVES: To examine city-level kidney disease mortality rates and Black:White racial inequities for the USA and its largest cities, and to determine if these measures changed over the past decade. METHODS: We used National Vital Statistics System mortality data and American Community Survey population estimates to calculate age-standardized kidney disease mortality rates for the non-Hispanic Black (Black), non-Hispanic White (White), and total populations for the USA and the 30 most populous US cities. We examined two time points, 2008-2013 (T1) and 2014-2018 (T2), and assessed changes in rates and inequities over time. Racial inequities were measured with Black:White mortality rate ratios and rate differences. RESULTS: Kidney disease mortality rates varied from 2.5 (per 100,000) in San Diego to 24.6 in Houston at T2. The Black kidney disease mortality rate was higher than the White rate in the USA and all cities studied at both time points. In T2, the Black mortality rate ranged from 7.9 in New York to 45.4 in Charlotte, while the White mortality rate ranged from 2.0 in San Diego to 18.6 in Indianapolis. At T2, the Black:White rate ratio ranged from 1.79 (95% CI 1.62-1.99) in Philadelphia to 5.25 (95% CI 3.40-8.10) in Washington, DC, compared to the US rate ratio of 2.28 (95% CI 2.25-2.30). Between T1 and T2, only one city (Nashville) saw a significant decrease in the Black:White mortality gap. CONCLUSIONS: The largest US cities experience widely varying kidney disease mortality rates and widespread racial inequities. These local data on racial inequities in kidney disease mortality can be used by city leaders and health stakeholders to increase awareness, guide the allocation of limited resources, monitor trends over time, and support targeted population health strategies.

Suicide Rates and Differences in Rates Between Non-Hispanic Black and Non-Hispanic White Populations in the 30 Largest US Cities, 2008-2017.

OBJECTIVES: Suicide is a leading cause of death in the United States, and rates vary by race and ethnicity. An analysis of suicide across large US cities is absent from the literature. The objective of this study was to examine suicide rates among the total population, non-Hispanic Black population, and non-Hispanic White population in the United States and in the 30 largest US cities. METHODS: We used data from the National Vital Statistics System to calculate non-Hispanic White, non-Hispanic Black, and total age-adjusted suicide rates for the 30 largest US cities and for the entire nation during 2 periods: 2008-2012 and 2013-2017. We also examined absolute and relative differences in suicide rates among non-Hispanic White populations and non-Hispanic Black populations in each city. RESULTS: The overall age-adjusted suicide rate per 100 000 population in the United States increased significantly from 12.3 in 2008-2012 to 13.5 in 2013-2017. Total suicide rates were stable in most cities; rates increased significantly in only 1 city (Louisville), and rates decreased significantly in 2 cities (Boston and Memphis). The non-Hispanic White suicide rate was significantly higher-1.3 to 4.3 times higher-than the non-Hispanic Black suicide rate in 24 of 26 study cities during 2013-2017. From 2008-2012 to 2013-2017, non-Hispanic White suicide rates decreased significantly in 3 cities and increased significantly in 3 cities; non-Hispanic Black suicide rates increased significantly in 5 cities and decreased in none. Absolute differences in suicide rates among non-Hispanic White populations and non-Hispanic Black populations increased significantly in 1 city (Louisville) and decreased significantly in 2 cities (Memphis and Boston). CONCLUSIONS: Our study may inform the use of evidence-based programs and practices to address population-level risk factors for suicide.

HIV mortality across the 30 largest U.S. cities: assessing overall trends and racial inequities.

BACKGROUND: Despite decreases in overall HIV mortality in the U.S., large racial inequities persist. Most previous analyses of HIV mortality and mortality inequities have utilized national- or state-level data. METHODS: Using vital statistics mortality data and American Community Survey population estimates, we calculated HIV mortality rates and Black:White HIV mortality rate ratios (RR) for the 30 most populous U.S. cities at two time points, 2010-2014 (T1) and 2015-2019 (T2). RESULTS: Almost all cities (28) had HIV mortality rates higher than the national rate at both time points. At T2, HIV mortality rates ranged from 0.8 per 100,000 (San Jose, CA) to 15.2 per 100,000 (Baltimore, MD). Across cities, Black people were approximately 2-8 times more likely to die from HIV compared to White people at both time points. Over the decade, these racial disparities decreased at the national level (T1: RR = 11.0, T2: RR = 9.8), and in one city (Charlotte, NC). DISCUSSION: We identified large geographic and racial inequities in HIV mortality in U.S. urban areas. These city-specific data may motivate change in cities and can help guide city leaders and other health advocates as they implement, test, and support policies and programming to decrease HIV mortality.

Influenza and Pneumonia Mortality Across the 30 Biggest U.S. Cities: Assessment of Overall Trends and Racial Inequities.

Influenza and pneumonia account for substantial morbidity in the United States and show a demonstrated racial inequity. Detailed race-specific analysis at the city level can be used to guide targeted prevention efforts within the most at-risk communities. The purpose of this study is to analyze city-level data of influenza/pneumonia mortality rates and racial disparities across the 30 biggest U.S. cities over time. We assess racial inequities in influenza/pneumonia mortality in the 30 biggest cities and compare city-level trends overtime through age-adjusted overall and race-specific mortality rates calculated from public death records for the years 2008-2017. The national influenza/pneumonia mortality rate significantly decreased as did 45% of the cities included in the study. Nationally, the Black mortality rate was 16% higher than White mortality rate, and a significant disparity was seen within about one-third of the biggest cities. Over half (56%) of the cities showed reductions in both Black and White mortality; however, there was no overall trend in racial equity with some cities reducing the inequities between the Blacks and Whites and others increasing the inequities. Elevated mortality rates in communities of color can be traced to structural racism, social factors, and access to treatment and prevention services. We recommend an approach utilizing community outreach administered through localized public health organizations and supported by data at the city level.

Precision cardiovascular medicine: artificial intelligence and epigenetics for the pathogenesis and prediction of coarctation in neonates.

BACKGROUND: Advances in omics and computational Artificial Intelligence (AI) have been said to be key to meeting the objectives of precision cardiovascular medicine. The focus of precision medicine includes a better assessment of disease risk and understanding of disease mechanisms. Our objective was to determine whether significant epigenetic changes occur in isolated, non-syndromic CoA. Further, we evaluated the AI analysis of DNA methylation for the prediction of CoA. METHODS: Genome-wide DNA methylation analysis of newborn blood DNA was performed in 24 isolated, non-syndromic CoA cases and 16 controls using the Illumina HumanMethylation450 BeadChip arrays. Cytosine nucleotide (CpG) methylation changes in CoA in each of 450,000 CpG loci were determined. Ingenuity pathway analysis (IPA) was performed to identify molecular and disease pathways that were epigenetically dysregulated. Using methylation data, six artificial intelligence (AI) platforms including deep learning (DL) was used for CoA detection. RESULTS: We identified significant (FDR p-value ≤ .05) methylation changes in 65 different CpG sites located in 75 genes in CoA subjects. DL achieved an AUC (95% CI) = 0.97 (0.80-1) with 95% sensitivity and 98% specificity. Gene ontology (GO) analysis yielded epigenetic alterations in important cardiovascular developmental genes and biological processes: abnormal morphology of cardiovascular system, left ventricular dysfunction, heart conduction disorder, thrombus formation, and coronary artery disease. CONCLUSION: In an exploratory study we report the use of AI and epigenomics to achieve important objectives of precision cardiovascular medicine. Accurate prediction of CoA was achieved using a newborn blood spot. Further, we provided evidence of a significant epigenetic etiology in isolated CoA development.

Comparison of All-Cause Mortality Rates and Inequities Between Black and White Populations Across the 30 Most Populous US Cities.

Importance: To address elevated mortality rates and historically entrenched racial inequities in mortality rates, the United States needs targeted efforts at all levels of government. However, few or no all-cause mortality data are available at the local level to motivate and guide city-level actions for health equity within the country's biggest cities. Objectives: To provide city-level data on all-cause mortality rates and racial inequities within cities and to determine whether these measures changed during the past decade. Design, Setting, and Participants: This cross-sectional study used mortality data from the National Vital Statistics System and American Community Survey population estimates to calculate city-level mortality rates for the non-Hispanic Black (Black) population, non-Hispanic White (White) population, and total population from January 2016 to December 2018. Changes from January 2009 to December 2018 were examined with joinpoint regression. Data were analyzed for the United States and the 30 most populous US cities. Data analysis was conducted from February to November 2020. Exposure: City of residence. Main Outcomes and Measures: Total population and race-specific age-standardized mortality rates using 3-year averages, mortality rate ratios between Black and White populations, excess Black deaths, and annual average percentage change in mortality rates and rate ratios. Results: The study included 26 295 827 death records. In 2016 to 2018, all-cause mortality rates ranged from 537 per 100 000 population in San Francisco to 1342 per 100 000 in Las Vegas compared with the overall US rate of 759 per 100 000. The all-cause mortality rate among Black populations was 24% higher than among White populations nationally (rate ratio, 1.236; 95% CI, 1.233 to 1.238), resulting in 74 402 excess Black deaths annually. At the city level, this ranged from 6 excess Black deaths in El Paso to 3804 excess Black deaths every year in Chicago. The US rate remained constant during the study period (average annual percentage change, -0.10%; 95% CI, -0.34% to 0.14%; P = .42). The racial inequities in rates for the US decreased between 2008 and 2019 (annual average percentage change, -0.51%; 95% CI, -0.92% to -0.09%; P =0.02). Only 14 of 30 cities (46.7%) experienced improvements in overall mortality rates during the past decade. Racial inequities increased in more cities (6 [20.0%]) than in which it decreased (2 [6.7%]). Conclusions and Relevance: In this study, mortality rates and inequities between Black and White populations varied substantially among the largest US cities. City leaders and other health advocates can use these types of local data on the burden of death and health inequities in their jurisdictions to increase awareness and advocacy related to racial health inequities, to guide the allocation of local resources, to monitor trends over time, and to highlight effective population health strategies.

Diabetes mortality across the 30 biggest U.S. cities: Assessing overall trends and racial inequities.

AIMS: National data suggest that diabetes mortality disproportionately affects Blacks compared to whites. We aimed to (1) calculate diabetes mortality rates (where diabetes was an underlying cause of death) among the general population of the U.S. and the largest 30 cities; (2) calculate Black/white mortality rate ratios and rate differences; and (3) compare changes in mortality rates and inequities across two 5-year periods (2008-2012 (T1) and 2013-2017 (T2)). METHODS: We used vital statistics mortality data and American Community Survey population estimates. RESULTS: The U.S. diabetes mortality rate at T1 was 20.91 per 100,000, and significantly increased to 21.05 at T2. El Paso had the highest diabetes mortality rate at both time points (T1 = 33.06; T2 = 35.98), while San Francisco had the lowest rate (T1 = 11.41; T2 = 13.18). The U.S. Black mortality rate was 2.21 times higher than the white rate at T2 (95%CI [2.19-2.23]). Eleven cities had significantly higher rate ratios than the U.S. at T2. The Black:white rate ratio in Washington, D.C. was approximately three times higher than the national rate ratio. CONCLUSIONS: This city-level data is important to inform more targeted local policy interventions and programming to promote health equity, particularly within cities with the greatest inequities.