Pregnancy Glossary: A to Z Guide to Pregnancy Terminology
Racial/Ethnic Differences In Autoimmune Disease Prevalence In US Claims/EHR Data
This analysis uses claims and electronic health records from 2021 to examine racial and ethnic variations in immune-mediated inflammatory diseases in the United States.
ABSTRACT
Objectives: Few studies have evaluated racial and ethnic differences in several immune-mediated inflammatory diseases (IMIDs) or overlap syndrome (the co-occurrence of ≥ 2 IMIDs). We assessed associations between race and ethnicity and prevalence of IMIDs and overlap syndrome using US claims and electronic health records from 2021.
Study Design: Retrospective cohort study of 10.8 million adults.
Methods: We identified the 10 most prevalent IMIDs among frequently discussed IMIDs. We estimated associations between the 5 most prevalent IMIDs and overlap syndrome in Hispanic and non-Hispanic Asian and Black adults using non-Hispanic White adults as the referent and stratifying by sex and age (20-39, 40-59, and ≥ 60 years).
Results: Inflammatory bowel disease (IBD), systemic lupus erythematosus (SLE), multiple sclerosis (MS), and rheumatoid arthritis (RA) were the most prevalent IMIDs in all races and ethnicities. We observed positive associations (P < .0001) between Hispanic and non-Hispanic Black adults and SLE, Asian women of all ages and Asian men younger than 60 years and SLE, Black women younger than 60 years and MS, and Hispanic and non-White women 60 years or older and RA. Hispanic and non-White adults of all age groups had inverse associations (P < .0001) with IBD. Non-Hispanic Black adults of all ages and Hispanic and non-Hispanic Asian women 40 years or older had inverse associations (P < .0001) with psoriasis/psoriatic arthritis. Overlap syndrome was rare among all groups, with some variation in which IMIDs co-occurred.
Conclusions: We found racial and ethnic differences in the prevalence and co-occurrence of IMIDs in this sample of US adults. Because misdiagnoses are relatively frequent for patients with IMIDs, awareness of racial and ethnic variations in IMIDs could aid early diagnosis and improve disease management.
Am J Manag Care. 2024;30(1):e4-e10. Https://doi.Org/10.37765/ajmc.2024.89488
_____
Takeaway Points
We use claims/electronic health record (EHR) data to evaluate associations between race and ethnicity and immune-mediated inflammatory diseases (IMIDs) in the United States in 2021.
_____
The public health burden of immune-mediated inflammatory diseases (IMIDs), those in which a person's immune system attacks its own organs and tissues, is high. Currently, more than 24 million US adults are estimated to have an IMID, according to the National Institutes of Health.1 If left undiagnosed or poorly managed, IMIDs can significantly affect quality of life, leading to disability2 and death.3,4
Although research has provided valuable information about IMID management and treatment, the focus has been mainly on select IMIDs or select risk factors. Among the areas that can benefit from further study are associations between race and ethnicity and IMIDs and overlap syndrome (ie, the concurrence of 2 or more IMIDs in an individual). Such associations, if significant, can highlight health disparities and potential genetic, socioeconomic, behavioral, and/or environmental factors that predispose an individual to IMIDs or complications from them. For example, findings from studies about systemic lupus erythematosus (SLE) have suggested that Hispanic patients have a distinct disease profile from non-Hispanic White, non-Hispanic Black, and non-Hispanic Asian patients.5-8
Although findings from population- and claims-based studies5-12 conducted in the United States have highlighted racial and ethnic differences in the prevalence of IMIDs among non-Hispanic White, non-Hispanic Black, Hispanic, and non-Hispanic Asian adults, those studies have focused on a few specific IMIDs such as SLE,5-8 psoriasis/psoriatic arthritis (Pso/PsA),9-12 and ankylosing spondylitis.12 Furthermore, IMID studies with a racial/ethnic component do not always capture race and ethnicity categories in their entirety. For example, a US health care–based study of several IMIDs evaluated the effects of race but not ethnicity.13 One study conducted among patients with giant cell arteritis evaluated differences in prevalence by Hispanic ethnicity, but the sample was small (N < 300) and limited to only White patients.14 Moreover, many of these studies are based on data collected more than 7 to 10 years ago and seldom cover overlap syndrome.
Nationally representative prospective population-based studies are often well suited to evaluate effects of race and ethnicity on IMID prevalence because of their consideration of racial and ethnic representativeness. However, these surveys might not capture recent data for most IMIDs, as in the case with the National Health and Nutrition Examination Survey (NHANES).15 Electronic health records (EHRs) and claims are increasingly being used by researchers to better characterize and understand patient populations.12,13 Such health care data include demographics and comprehensive medical information, from diagnoses and laboratory results to pharmacy data, treatments, and hospitalizations, captured through several health care contacts spanning several years; this volume of information from a single patient allows for data triangulation and hypothesis generation that could otherwise not be possible with data collected for research purposes. Furthermore, because these health care databases are large, with data from millions of patients in any single year, analytical studies of rare diseases, such as many IMIDs, are feasible.
In this study, we used US health care data from 2021 with the goal of understanding the following:
Our study results provide greater understanding of the varied associations between race and ethnicity and IMIDs in the United States; these insights may enable accurate diagnosis in the earlier stages of these diseases, leading to better disease management and improved treatment options. Furthermore, because research on overlap syndrome can provide insights about shared pathology and risk factors,16,17 our findings may shed light on groups at greatest risk for poor quality of life, disability, and mortality as well as areas requiring greater patient education, treatment monitoring, and medical research. Lastly, through our assessment of potential effect measure modification by age and gender, we aim to provide a more accurate picture of the association between race and ethnicity and IMIDs.
METHODS
Study Participant Selection
We conducted this study using Clarivate's Real-World Data (RWD) Product (ie, claims and EHR databases).18 These databases consist of deidentified data (in compliance with the Health Insurance Portability and Accountability Act privacy law) of insured individuals with health care (hospitals, physicians, pharmacies, clinics) contacts since 2012. Data are obtained from an open network of claims (sourced from clearinghouses) and EHR data (from EHR software vendors) from all US states and are normalized and cleaned using proprietary methods; the product provides data on more than 300 million patients, 100 million EHRs, and 2 million health care providers, representing approximately 70% to 80% of the insured US population. More details about Clarivate's RWD Product can be found elsewhere.18
Inclusion/Exclusion Criteria
We included individuals who (1) were present in both the claims and EHR databases; (2) were 20 years or older in 2021; (3) had no missing data on age, sex, race, or ethnicity; and (4) did not have "other," "declined," or "unknown" listed for both race and ethnicity (~30% of adults with race/ethnicity recorded). Of note, only the EHR database includes information on race and ethnicity.
We restricted the analyses to those enrolled from January 1 to December 31, 2021, to allow for annual prevalence estimates and to include individuals likely to be actively enrolled (and not disenrolled) in the database. We assumed active individuals to be those with at least the mean number of claims per age group.
Four racial and ethnic groups were created for the purposes of this study: (1) Hispanic, (2) non-Hispanic Asian, (3) non-Hispanic Black, and (4) non-Hispanic White. In the EHR database, the terms race and ethnicity were often used interchangeably. Thus, we classified individuals as follows: Adults who self-identified as Hispanic or Hispanic/Latina with respect to either race or ethnicity were classified as Hispanic regardless of their racial identification. Among non-Hispanic adults, those who self-identified as Black or African American with respect to race or ethnicity were classified as non-Hispanic Black, those who self-identified as Asian were classified as non-Hispanic Asian, and those who self-identified as White or Caucasian were classified as non-Hispanic White. To preserve sample size and because less than 0.01% of the non-Hispanic adults were of mixed race, we classified mixed-race White or Asian and Black/African American individuals as non-Hispanic Black adults and mixed-race White and Asian individuals as non-Hispanic Asian adults. Adults who had both race and ethnicity recorded as "other," "declined," or "unknown" were recoded as missing. The "other" category included American Indian, Pacific Islander, and Alaska Native individuals and people of North African or Arabic descent, among others.
We identified the 10 most prevalent IMIDs in the database from 22 IMIDs frequently discussed13,19,20:SLE, multiple sclerosis (MS), rheumatoid arthritis (RA), inflammatory bowel disease (IBD), Pso/PsA, Graves disease, autoimmune thyroiditis, pernicious anemia, vitiligo, Sjögren syndrome, cutaneous lupus erythematosus, type 1 diabetes, celiac disease, dermatomyositis/polymyositis, systemic sclerosis, mixed connective tissue disease, myasthenia gravis, granulomatosis with polyangiitis, microscopic polyangiitis, giant cell arteritis, other vasculitis, and Marfan syndrome (eAppendix Table [available at ajmc.Com]). Because misdiagnoses and misclassification can occur with IMIDs,21 we reestimated the prevalence of each IMID to minimize misclassification by ascertaining patients who had at least 2 or more International Statistical Classification of Diseases, Tenth Revision (ICD-10) codes separated by a period of 3 months or more. Moreover, we estimated the prevalence of overlap syndrome, defined as the presence of 2 or more concurrent IMIDs (of the 10 more frequent IMIDs), in the overall sample and in each racial and ethnic category.
Statistical Analyses
We ranked the 5 most common IMIDs in the overall population and within each racial and ethnic group (Figure). We evaluated effect measure modification by age and sex by including interaction terms between race and ethnicity and these variables in a multivariable logistic model. In the event of significant effect measure modification, we conducted logistic regression analyses stratified by age groups (20-39 vs 40-59 vs ≥ 60 years) based on ages of disease onset for different IMIDs20 and sex.
All analyses were conducted using SAS 9.4 (SAS Institute). Our analyses were stratified by 3 age groups and sex with 3 dummy variables included for race/ethnicity. Because we were testing 90 associations using 30 logistic regression models, statistical significance was set at a P value less than .0001.
RESULTS
Table 1 represents the demographic characteristics of study participants and the prevalence of IMIDs and chronic conditions in the overall population and by race and ethnicity. We included 10.8 million individuals in our analyses (7.4 million non-Hispanic White, 1.5 million non-Hispanic Black, 1.4 million Hispanic, and 0.4 million non-Hispanic Asian adults). We observed overlap syndrome in 1707 adults in the overall sample and among 0.02% of non-Hispanic White, 0.01% of non-Hispanic Black, 0.02% of Hispanic, and 0.02% of non-Hispanic Asian adults. Most individuals with overlap syndrome had only 2 IMIDs, with few having 3 or more. The most frequently co-occurring autoimmune diseases were IBD and Pso/PsA overall, IBD and Pso/PsA among non-Hispanic White adults, and SLE and RA among non-Hispanic Black, Hispanic, and Asian adults.
In the overall sample and among non-Hispanic White adults, the 5 most prevalent IMIDs ranked in descending order were IBD, MS, Pso/PsA, SLE, and RA. Among other races and ethnicities, the most prevalent IMIDwas SLE followed by MS and IBD in non-Hispanic Black adults and IBD and Pso/PsA in Hispanic and Asian adults. Among non-Hispanic Asian and non-Hispanic Black adults, Graves disease was among the 5 most prevalent IMIDs, ranking fourth and fifth, respectively. Pso/PsA was also among the 5 most common IMIDs in all racial and ethnic groups except in non-Hispanic Black individuals.
We detected significant effect measure modification by age and sex for the associations between Hispanic and non-Hispanic Asian and Black races and ethnicities and the 5 most common autoimmune conditions in the study population (P < .0001 for 1 or more interaction terms for all outcomes).
Table 2 provides ORs for Hispanic and non-Hispanic Asian and Black adults vs non-Hispanic White adults for the 5 most prevalent IMIDs in the database stratified by age and sex. In Hispanic or non-White adults of all ages, except Asian men 60 years or older, we observed positive associations (P < .0001) with SLE. Further, we observed positive associations (P < .0001) between non-Hispanic Black women younger than 60 years and MS but inverse associations (P < .0001) between Hispanic adults (women ≥ 40 years and men ≥ 60 years), Asian women of all ages, and Asian men 40 years or older and MS. IBD was inversely associated (P < .0001) with Hispanic or non-White adults of all ages, and Pso/PsA was inversely associated (P < .0001) with non-Hispanic Black adults of all ages and Hispanic and Asian women 40 years or older (P < .0001). We observed positive associations (P < .0001) between RA and Hispanic women 40 years or older and non-Hispanic Asian and Black women 60 years or older. Interestingly, an inverse association (P < .0001) was observed between Black men 60 years or older and RA. We did not observe positive or inverse associations that reached statistical significance (P < .0001) between Hispanic or non-White adults of any age and sex and overlap syndrome.
DISCUSSION
We evaluated associations between race and ethnicity and IMIDs using EHR and claims data of insured adults from all US states. The most prevalent IMIDs in this sample were IBD, MS, Pso/PsA, SLE, RA, and Graves disease; the prevalence of any given IMID was greatest among non-Hispanic White adults, followed by non-Hispanic Black, Hispanic, and non-Hispanic Asian adults. Overall, we did observe significant variations in the prevalence of these conditions by race and ethnicity (χ2P < .0001). Further, we observed significant effect measure modification of these associations by age and sex (P of interaction terms < .0001). Lastly, we found a small proportion of the sample with overlap syndrome, with the prevalence of having at least 2 IMIDs relatively similar across all racial/ethnic groups, between 0.01% and 0.02%.
When reviewing the 5 most prevalent IMIDs by race and ethnicity, the greatest increase in odds was observed between non-Hispanic Black women and Hispanic men aged 20 to 49 years and SLE, who had 5 to 6 times the odds compared with non-Hispanic White women and men aged 20 to 49 years, respectively.
We discovered some findings consistent with prior literature as well as some contradictory and new findings when focusing on the most common IMIDs. Our results confirm recent findings from studies that have reported an increasing prevalence of MS in Black adults compared with non-Hispanic White adults.22-24 MS is a condition that was traditionally believed to have a greater disease burden in White individuals than in those of other races. For example, results from a study based on a 2010-2016 US health care database found MS prevalence to be lower among African American than Caucasian individuals.13 However, results from a study conducted in 2009-2010 found a comparable prevalence,23 and results from a more recent study found a greater prevalence.24 This increasing trend is replicated in our data for Black women younger than 60 years, who had 14% to 77% increased odds of MS compared with White women younger than 60 years. Interestingly, we observed an inverse association in Black women 60 years or older.
Our findings for SLE are consistent with prior research, in which greater prevalence or risk of SLE has been suggested among Hispanic or non-Hispanic Black or Asian individuals in population- or claims-based studies conducted in different regions of the United States.5-8,13 Further, our results confirm certain findings from a study conducted on the NHANES that evaluated the prevalence of RA by race and Hispanic ethnicity.25 This study reported a higher RA prevalence among Hispanic and non-Hispanic Black women compared with non-Hispanic White women,25 a result that we observed in Hispanic women 40 years or older and non-Hispanic Asian and Black women 60 years or older. Contrary to our study results, however, the NHANES study findings also reported a positive association with non-Hispanic Black men. These differences may have resulted from differences in the study cohorts or the methodology used to report RA diagnoses (self-report in NHANES vs ICD-10 codes in ours). Such differences call for additional studies that evaluate prevalence of RA by race and ethnicity.
Few recent studies have examined associations between race/ethnicity and Pso/PsA in the United States.9-12 Earlier studies conducted more than 9 years ago9-11 found a higher prevalence of Pso/PsA in non-Hispanic White adults compared with other racial and ethnic groups. Our study results confirm these findings for non-Hispanic Black adults and Hispanic and Asian women older than 40 years. Furthermore, we found racial and ethnic differences in IBD by age and gender, with a lower prevalence in other races and ethnicities compared with non-Hispanic White adults. Although no recent study representative of the general population has estimated the prevalence of IBD by race and ethnicity, results from a study conducted among Medicare fee-for-service recipients older than 67 years found a similar increased prevalence in non-Hispanic White adults compared with other groups.26
The prevalence of overlap syndrome was lower than reported by other studies,16,17 but this could be due to differences in how overlap syndrome was defined, including what IMIDs were assessed. We could not compare our findings regarding overlap syndrome with those of other studies due to the lack of other studies that reported the prevalence of overlap syndrome. However, the co-occurrence of SLE and RA in general has been previously documented.27 Further research is needed to understand possible reasons for the higher co-occurrence of SLE and RA among non-White or Hispanic racial and ethnic groups.
Study Limitations and Strengths
A limitation of our study is that approximately 30% of individuals had race and ethnicity coded as "other," "unknown," or "declined." Further, data from racial and ethnic groups such as Native Americans and Pacific Islander individuals, who are at increased risk for IMIDs such as SLE,13 were not analyzed because they were coded as "other." Moreover, the data were limited to those with any insurance coverage. To assess potential bias due to insurance status and missing race and ethnicity data, we compared our analytical sample (ie, RWD) with the racial and ethnic distribution of the 2020 US Census28 and found relatively similar distributions. However, our study population slightly overrepresented women (57% in RWD vs 52% in the US Census), individuals older than 60 years (33% in RWD vs 23% in the US Census), and non-Hispanic White adults (67% in RWD vs 59% in the US Census) and underrepresented Hispanic (13% in RWD vs 18% in the US Census) and non-Hispanic Asian (4% in RWD vs 5% in the US Census) adults compared with their distribution in the United States in 2020.28
Further, because we restricted the data to those with greater than the mean number of claims per given age group, it is possible that individuals included in our analytical sample were less healthy or had more severe disease. However, we found mean length of hospitalizations to be similar in samples with and without the restriction. Furthermore, we compared the prevalence of chronic conditions in our study sample with national data (eg, reported by the CDC) and found a relatively similar prevalence29-32 (diagnosed diabetes: 14% in RWD vs 11% in national data29; coronary artery disease30,31 and chronic obstructive pulmonary disease32: 5% and 6%, respectively, in either RWD or national data).
Lastly, due to limited sample size, we could only assess the co-occurrence of the most common IMIDs, potentially leading to underestimation of overlap syndrome.
Despite the limitations, this study has several key strengths, most notably the use of a large US health care database with comprehensive data on IMIDs and demographics. This allowed analyses to assess effect measure modification by age and sex in the associations between race and ethnicity and several IMIDs, including rarer diseases. We were also able to minimize misclassification of IMIDs by reviewing multiple health care visits with diagnosis codes. These data also allowed for validation analyses of prior studies. To our knowledge, our study is the only study to report the prevalence of several IMIDs and overlap syndrome by race and Hispanic ethnicity using a large US health care database.
CONCLUSIONS
Our findings indicate a relatively similar prevalence of any given IMID across racial and ethnic groups, with some IMIDs, such as IBD, being more prevalent among non-Hispanic White adults and others, such as SLE, being more prevalent in Hispanic and non-Hispanic Asian and Black adults. Furthermore, although the prevalence of 2 or more co-occurring IMIDs was similarly low among all groups, differences by race and ethnicity were noted in which 2 co-occurred most frequently. Awareness of racial and ethnic differences with respect to IMIDs could shorten time to accurate diagnosis, result in significantly improved disease management and better treatment opportunities, and curtail disease progression, morbidity, and mortality.
Author Affiliations: Department of Epidemiology, Clarivate (SDG, SD, ST, EPD), Ann Arbor, MI.
Source of Funding: None.
Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (SDG, EPD); acquisition of data (SDG, ST); analysis and interpretation of data (SDG, SD, ST, EPD); drafting of the manuscript (SDG, SD, EPD); critical revision of the manuscript for important intellectual content (SDG, SD, ST, EPD); statistical analysis (SDG); administrative, technical, or logistic support (SD, EPD); and supervision (EPD).
Address Correspondence to: Sunali D. Goonesekera, MS, Department of Epidemiology, Clarivate (Ann Arbor), 789 E Eisenhower Parkway, Ann Arbor, MI 48008. Email: sunali2@yahoo.Com.
REFERENCES
1. Autoimmune diseases. National Institute of Environmental Health Sciences. Updated May 31, 2022. Accessed January 15, 2023. Https://www.Niehs.Nih.Gov/health/topics/conditions/autoimmune/index.Cfm
2. Allaire S, Wolfe F, Niu J, Lavalley MP. Contemporary prevalence and incidence of work disability associated with rheumatoid arthritis in the US. Arthritis Rheum. 2008;59(4):474-480. Doi:10.1002/art.23538
3. Mitratza M, Klijs B, Hak AE, Kardaun JWPF, Kunst AE. Systemic autoimmune disease as a cause of death: mortality burden and comorbidities. Rheumatology (Oxford). 2021;60(3):1321-1330. Doi:10.1093/rheumatology/keaa537
4. Thomas SL, Griffiths C, Smeeth L, Rooney C, Hall AJ. Burden of mortality associated with autoimmune diseases among females in the United Kingdom. Am J Public Health. 2010;100(11):2279-2287. Doi:10.2105/AJPH.2009.180273
5. Dall'Era M, Cisternas MG, Snipes K, Herrinton LJ, Gordon C, Helmick CG. The incidence and prevalence of systemic lupus erythematosus in San Francisco County, California: the California Lupus Surveillance Project. Arthritis Rheumatol. 2017;69(10):1996-2005. Doi:10.1002/art.40191
6. Izmirly PM, Wan I, Sahl S, et al. The incidence and prevalence of systemic lupus erythematosus in New York County (Manhattan), New York: the Manhattan Lupus Surveillance Program. Arthritis Rheumatol. 2017;69(10):2006-2017. Doi:10.1002/art.40192
7. Lim SS, Bayakly AR, Helmick CG, Gordon C, Easley KA, Drenkard C. The incidence and prevalence of systemic lupus erythematosus, 2002-2004: the Georgia Lupus Registry. Arthritis Rheumatol. 2014;66(2):357-368. Doi:10.1002/art.38239
8. Somers EC, Marder W, Cagnoli P, et al. Population-based incidence and prevalence of systemic lupus erythematosus: the Michigan Lupus Epidemiology and Surveillance program. Arthritis Rheumatol. 2014;66(2):369-378. Doi:10.1002/art.38238
9. Armstrong AW, Mehta MD, Schupp CW, Gondo GC, Bell SJ, Griffiths CEM. Psoriasis prevalence in adults in the United States. JAMA Dermatol. 2021;157(8):940-946. Doi:10.1001/jamadermatol.2021.2007
10. Rachakonda TD, Schupp CW, Armstrong AW. Psoriasis prevalence among adults in the United States. J Am Acad Dermatol. 2014;70(3):512-516. Doi:10.1016/j.Jaad.2013.11.013
11. Helmick CG, Lee-Han H, Hirsch SC, Baird TL, Bartlett CL. Prevalence of psoriasis among adults in the U.S.: 2003-2006 and 2009-2010 National Health and Nutrition Examination Surveys. Am J Prev Med. 2014;47(1):37-45. Doi:10.1016/j.Amepre.2014.02.012
12. Ogdie A, Matthias W, Thielen RJ, Chin D, Saffore CD. Racial differences in prevalence and treatment for psoriatic arthritis and ankylosing spondylitis by insurance coverage in the USA. Rheumatol Ther. 2021;8(4):1725-1739. Doi:10.1007/s40744-021-00370-4
13. Roberts MH, Erdei E. Comparative United States autoimmune disease rates for 2010-2016 by sex, geographic region, and race. Autoimmun Rev. 2020;19(1):102423. Doi:10.1016/j.Autrev.2019.102423
14. Lam BL, Wirthlin RS, Gonzalez A, Dubovy SR, Feuer WJ. Giant cell arteritis among Hispanic Americans. Am J Ophthalmol. 2007;143(1):161-163. Doi:10.1016/j.Ajo.2006.07.048
15. National Health and Nutrition Examination Survey. CDC. Accessed January 16, 2023. Https://www.Cdc.Gov/nchs/nhanes/index.Htm
16. Cooper GS, Bynum ML, Somers EC. Recent insights in the epidemiology of autoimmune diseases: improved prevalence estimates and understanding of clustering of diseases. J Autoimmun. 2009;33(3-4):197-207. Doi:10.1016/j.Jaut.2009.09.008
17. Somers EC, Thomas SL, Smeeth L, Hall AJ. Are individuals with an autoimmune disease at higher risk of a second autoimmune disorder? Am J Epidemiol. 2009;169(6):749-755. Doi:10.1093/aje/kwn408
18. Real-World Data. Clarivate. Accessed July 24, 2023. Https://clarivate.Com/products/real-world-data/
19. Conrad N, Verbeke G, Molenberghs G, et al. Autoimmune diseases and cardiovascular risk: a population-based study on 19 autoimmune diseases and 12 cardiovascular diseases in 22 million individuals in the UK. Lancet. 2022;400(10354):733-743. Doi:10.1016/S0140-6736(22)01349-6
20. Wang L, Wang FS, Gershwin ME. Human autoimmune diseases: a comprehensive update. J Intern Med. 2015;278(4):369-395. Doi:10.1111/joim.12395
21. Seitz M. Autoimmune diseases: frequent misdiagnosis. Article in German. Schweiz Med Wochenschr. 1997;127(9):349-354.
22. Amezcua L, McCauley JL. Race and ethnicity on MS presentation and disease course. Mult Scler. 2020;26(5):561-567. Doi:10.1177/1352458519887328
23. Langer-Gould AM, Gonzales EG, Smith JB, Li BH, Nelson LM. Racial and ethnic disparities in multiple sclerosis prevalence. Neurology. 2022;98(18):e1818-e1827. Doi:10.1212/WNL.0000000000200151
24. Romanelli RJ, Huang Q, Lacy J, Hashemi L, Wong A, Smith A. Multiple sclerosis in a multi-ethnic population from Northern California: a retrospective analysis, 2010-2016. BMC Neurol. 2020;20(1):163. Doi:10.1186/s12883-020-01749-6
25. Xu Y, Wu Q. Prevalence trend and disparities in rheumatoid arthritis among US adults, 2005-2018. J Clin Med. 2021;10(15):3289. Doi:10.3390/jcm10153289
26. Xu F, Carlson SA, Liu Y, Greenlund KJ. Prevalence of inflammatory bowel disease among Medicare fee-for-service beneficiaries - United States, 2001-2018. MMWR Morb Mortal Wkly Rep. 2021;70(19):698-701. Doi:10.15585/mmwr.Mm7019a2
27. Ahmad R, Ahsan H. Dual autoimmune diseases: rheumatoid arthritis with systemic lupus erythematosus and type 1 diabetes mellitus with multiple sclerosis. Rheumatol Autoimmun. 2022;2(3):120-128. Doi:10.1002/rai2.12037
28. American Community Survey: S0101 age and sex. US Census Bureau. 2021. Accessed January 15, 2023. Https://data.Census.Gov/table?Tid=ACSST1Y2021.S0101
29. National Diabetes Statistics Report. CDC. Updated November 29, 2023. Accessed July 23, 2023. Https://www.Cdc.Gov/diabetes/data/statistics-report/diagnosed-diabetes.Html
30. Heart disease facts. CDC. Updated May 15, 2023. Accessed July 23, 2023. Https://www.Cdc.Gov/heartdisease/facts.Htm
31. Tsao CW, Aday AW, Almarzooq ZI, et al; American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics-2023 update: a report from the American Heart Association. Circulation. 2023;147(8):e93-e621. Doi:10.1161/CIR.0000000000001123
32. National trends in COPD. CDC. Updated July 20, 2022. Accessed July 23, 2023. Https://www.Cdc.Gov/copd/data-and-statistics/national-trends.Html
A History Of Race And The Emerging Role Of Genetics In Primary Care
In 2003, the Human Genome Project became the culmination of the history of genetics research, allowing us to understand the role of genetics in human health and disease. As part of The Clinical Advisor's 20th Anniversary, this article explores the influence of race and genetics in clinical practice.Medical professions, including the field of genetics, have long struggled with defining race. "Race" remains an especially fraught and imprecise term.1 In the first few decades of the 20th century, race was defined by the notion that one member of a race was thought to share the same social and physical traits as other members of that race.2 Historically, race has been mapped into continental populations and described in the context of evolutionary biology. In recent years, however, the concept of race has become a way to understand the frequency of individual genes in diverse human populations.2 Today, new genetic data have enabled researchers to re-examine the relationship between human genetic variation and race.3
Race remains prominent in health research and clinical guidelines and is routinely invoked in clinical practice.4 Recent studies suggest that race is important to consider in clinical care because it provides common assumptions about biologic genetics and cultural characteristics as these factors relate to health.4 The need for a genetically competent workforce that can assure advanced care practitioners' ongoing participation in translation of genomic discoveries into everyday health care will be vital for inclusion of race in health research, evidence-based practice (EBP), and clinical guidelines. This article will address genetic disorders in relation to race, analyze trends in contemporary issues, and discuss potential innovations for healthcare practice, as well as evaluate the evidence for appropriateness when applying an intervention or a treatment modality into clinical practice.
Literature and studies
The term "race" in scientific literature refers to biologic differences between groups who are assumed to have different biogeographic ancestries or genetic makeup.5 Race is a construct of human variability based on perceived differences in biology, physical appearance, and behavior.5 In the United States, the majority of racial differences in health are explained by socioeconomic status and factors surrounding access to medical care.6 To fully understand the role of race in clinical practice, it is important to consider the contribution of genetic factors. Researchers describe the variation in gene alteration or gene expression by race or ancestry.6
There are various barriers to genomic medicine for racial minorities such as socioeconomic status, rural settings, and lack of participation. Moreover, clinical trial research, including biomarkers and drug development, continues to lack inclusion of and participation by minorities.6 This lack of representation presents a disadvantage for minority groups such as people from African ancestries. Most large-scale, genome-wide association studies have been conducted in populations of European ancestry.6 Although there has been an improvement in research on genetics and race, further studies need to be conducted as precise genetic information on race could affect clinical practice.
From the June 01, 2018 Issue of Clinical Advisor
Unraveling The Genetic Puzzle Of Alzheimer's Disease
As Australia's population ages, we're hearing a growing number of stories about complex diseases with cognitive and behavioral effects.
Cognitive changes can impact memory, attention, and problem-solving. Meanwhile, behavioral effects involve alterations in how individuals act or respond to their environment. These kinds of symptoms can be the result of neurodegenerative diseases, such as dementia. The rate of neurodegenerative diseases is expected to double over the next 40 years. So, chances are someone you know will be affected by dementia's most common form, Alzheimer's disease.
Complex diseases are, well, complex. Understanding how they develop, their signs, and how they can be managed is tricky. One aspect of complex diseases that can be especially difficult to research is heritability, or the way genes are passed along in a family.
Let's go back to the age-old question: Do our physical traits depend on our genetics or our environment? The answer is both.
Say, you have a close relative who has been diagnosed with Alzheimer's disease. You wonder whether you'll also develop it because it's "in your genes." As expected, the answer to this is not simple.
Everyone has observable traits or characteristics they are born with. These are called phenotypes. A good example is brown eyes. Having brown eyes is an observable expression of a certain genetic makeup.
Disease states have phenotypes as well. Disease phenotypes are the characteristics we can see when a person has a disease. For example, someone with Alzheimer's disease may present with memory loss.
Many characteristics depend on a mixture of our genetics and our environment. Figuring out how much a trait is influenced by either factor is necessary to manage and treat diseases.
Getting to the bottom of Alzheimer's heritabilityThe heritability of Alzheimer's has been estimated as high as 80%. You might look at this number and wonder, "Does this mean that I have an 80% chance of getting Alzheimer's if one of my parents has it?" Not exactly.
Heritability is a measure of how much the variation of a trait within a population is caused by a variation in genes. It is usually shown as a percentage value. This means that we can only apply a heritability value to a group of people—not an individual.
The exception is a form of Alzheimer's disease called familial Alzheimer's, where symptoms usually develop early on in a person's life. The chances of passing it on to their offspring is 50%.
The missing pieces in the genetic disease puzzleThis is where '"missing heritability" comes in. It's a big issue encountered in Alzheimer's disease research.
Missing heritability refers to when scientists know that a disease is heritable but are unable to find the underlying genetic cause.
For simple diseases, scientists can understand their heritability by finding genetic variants. Variants are different versions of the same gene which cause different phenotypes. However, with a complex disease like Alzheimer's, individual variants are not the sole cause of the disease. This makes solving the puzzle much more complicated.
Scientists now know that Alzheimer's is caused not only by gene variants, but also by the relationships between different genes.
We call these relationships gene interactions. Gene interactions can occur between the roughly 20,000 genes, meaning there are endless possibilities to how they can influence disease phenotypes.
Finding these interactions gives scientists the opportunity to highlight variants that can act as "modulators" in Alzheimer's disease. Modulators can either reduce or increase the chance of someone developing a disease, so identifying them is crucial to understanding Alzheimer's.
Traditional approaches to studying genetics are not powerful enough to find all these gene interactions. If scientists don't know what to look for, there is no way to find it. And doctors can't catch it until it's too late.
This is why curing and treating Alzheimer's is a challenge.
AI and machine learning for early genetic disease detectionUntil recently, scientists have lacked tools powerful enough to capture the impacts of gene interactions. With the development of artificial intelligence and machine learning approaches such as our VariantSpark tool, we are beginning to see the full extent of how relationships between genes can help us understand disease.
The key to Alzheimer's research is adding the pieces of this missing heritability to the puzzle. This allows scientists to understand the causes and warning signs of the disease. It could allow at risk people to be identified sooner and enable early intervention strategies—both of which are vital to improving outcomes for patients.
Each piece of missing heritability we uncover brings us closer to more effective treatments and early intervention strategies. This progress is not just about solving scientific puzzles—it's about changing lives, offering hope, and building a future where diseases like Alzheimer's can be effectively managed.
Citation: Unraveling the genetic puzzle of Alzheimer's disease (2024, January 18) retrieved 28 January 2024 from https://medicalxpress.Com/news/2024-01-unraveling-genetic-puzzle-alzheimer-disease.Html
This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.
Comments
Post a Comment