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Record W4296163277 · doi:10.1016/s2214-109x(22)00356-4

One hundred years of solitude—underrepresentation of Indigenous and minority groups in diabetes trials

2022· article· en· W4296163277 on OpenAlexaffabout
Shohinee Sarma, Lisa Richardson, John Neary

Bibliographic record

VenueThe Lancet Global Health · 2022
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsSt. Joseph’s Healthcare HamiltonUniversity Health NetworkMount Sinai HospitalMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsMedicineDiabetes mellitusPopulationGestational diabetesEthnic groupType 2 diabetesIndigenousPregnancyGerontologyDemographyEnvironmental healthEndocrinologyGestation

Abstract

fetched live from OpenAlex

100 years after the discovery of insulin in Toronto, Indigenous people in Canada face the highest risk of developing diabetes, but are not well represented in diabetes research. Indigenous people have 3·5 times the age-standardised prevalence rate of diabetes and suffer complications such as end-stage renal disease twice as frequently compared to the general Canadian population.1Jiang Y Osgood N Lim HJ et al.Differential mortality and the excess burden of end-stage renal disease among First Nations people with diabetes mellitus: a competing-risks analysis.CMAJ. 2014; 186: 103-109Google Scholar Indigenous women face higher gestational diabetes rates, highlighting the intergenerational impact of diabetes.2Oster RT King M Morrish DW et al.Diabetes in pregnancy among First Nations women in Alberta, Canada: a retrospective analysis.BMC Pregnancy Childbirth. 2014; 14: 136Google Scholar Similarly, other racialised groups are twice as likely to develop type 2 diabetes (and its macrovascular complications) than are the general population.3Cheng YJ Kanaya AM Araneta MRG et al.Prevalence of diabetes by race and ethnicity in the United States, 2011–2016.JAMA. 2019; 322: 2389-2398Google Scholar However, the proportion of people from many ethnic groups enrolled in diabetes clinical trials remains negligible. Landmark clinical trials in diabetes demonstrated 0% enrolment of Indigenous individuals and less than 10% representation of other non-white ethnicities. In the Diabetes Control and Complications Trial done in Canada and the USA from 1993, 96% of enrolled participants were white.4Diabetes Control and Complications Trial Research GroupThe effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus.N Engl J Med. 1993; 329: 977-986Google Scholar The UK Prospective Diabetes Study similarly underrepresented minority groups with the highest risk of macrovascular disease: 5% of patients were Indian Asian, 8% were Afro-Caribbean, and 1% were classed as other.5UK Prospective Diabetes Study (UKPDS) GroupEffect of intensive blood-glucose control with metformin on complications in overweight patients with type 2 diabetes (UKPDS 34).Lancet. 1998; 352: 854-865Google Scholar These studies are applied to all people globally despite population level data demonstrating variable cardiovascular risk and mortality outcomes by race, independent of therapy.6Ke CH Morgan S Smolina K Gasevic D Qian H Khan NA Is cardiovascular risk reduction therapy effective in South Asian, Chinese and other patients with diabetes? A population-based cohort study from Canada.BMJ Open. 2017; 7e013808Google Scholar Recent trials of SGLT-2 inhibitors and GLP-1 receptor agonists showed similar rates of enrolment of diverse racial groups.7Ahmed R de Souza RJ Anand SS Assessing non-white ethnic participation in type 2 diabetes mellitus randomized clinical trials: a meta-analysis.medRxiv. 2022; (published online June 29.) (preprint).https://doi.org/10.1101/2022.06.28.22275821Google Scholar Barriers to inclusive research persist. The CHANCE trial,8Wang Y Wang Y Zhao X et al.Clopidogrel with aspirin in acute minor stroke or transient ischemic attack.N Engl J Med. 2013; 369: 11-19Google Scholar conducted in China, demonstrated that dual antiplatelet therapy prevented recurrent stroke compared to aspirin alone; its generalisability was questioned based on the ethnicity of its patient population. The POINT trial,9Johnston SC Easton JD Farrant M et al.Clopidogrel and aspirin in acute ischemic stroke and high-risk TIA.N Engl J Med. 2018; 379: 215-225Google Scholar conducted in centres across North America, Europe, Australia, and New Zealand, showed similar results to CHANCE. POINT was deemed relevant to a wider patient population, although is not evident why its population (75% White, 20% Black, less than 4% Asian, and <1% other) was meaningfully more representative than that of CHANCE. These differences in interpretation suggest the need for continued work in dismantling structural bias in scientific research. In the era of global research consortiums, we also need a standard framework for what generalisable should mean in terms of proportionate enrolment of diverse groups in clinical trials. The impacts of major clinical trials are felt globally. There are 89·5 million individuals with type 2 diabetes in China, 67·8 million in India, 21·0 million in Indonesia, and 13·1 million in Mexico, illustrating the need for improved representation.10Beran D Laing RO Kaplan W et al.A perspective on global access to insulin: a descriptive study of the market, trade flows and prices.Diabet Med. 2019; 36: 726-733Google Scholar The prevalence of type 1 diabetes is also rising worldwide, highlighting the need for diabetes research that is inclusive and representative of the world. However, researchers in low-income and middle-income countries have difficulty in accessing funding for RCTs, meet significant barriers to publishing in high-quality journals, and face publication costs that are often insurmountable. Although increasing the inclusivity of clinical research is a laudable goal, the ethics of conducting clinical trials in vulnerable populations are complex. Biomedicine has a long history of unethical and exploitative research in such populations, such as the Pima Indian Diabetes Dataset,11Sterling RL Genetic research among the Havasupai: a cautionary tale.Virtual Mentor. 2011; 13: 113-117Google Scholar in which genetic data from members of the Havasupai Tribe of Arizona was provided for diabetes research but continued to be unethically used for a wider range of studies without informed consent from the participants. In Canada, deplorable nutrition experiments done in residential schools exposed young children to chronic malnutrition, stunting, anaemia, and the risk of developing metabolic disorders such as obesity and type 2 diabetes.12Mosby I Galloway T “Hunger was never absent”: how residential school diets shaped current patterns of diabetes among Indigenous peoples in Canada.CMAJ. 2017; 189: E1043-E1045Google Scholar The challenges of historic and present inequity and ethics also require transparent oversight by institutional boards. In Canada, the First Nations Information Governance Centre provides collaborations, training, community engagement, knowledge translation, and research oversight for equitable research practices focusing on Indigenous values and successes. These frameworks are specific to research in Indigenous health, but provide the necessary foundations for governance, oversight, and partnership for equitable health research in all populations. Echoing these frameworks, we outline themes that researchers should consider when planning clinical trials to increase engagement of minority groups (panel).PanelFramework for culturally inclusive themes in inclusive clinical trialsLanguage•Availability of language resources in trial handouts•Use of trained health interpreters and translators during informed consent•Availability of trained translators throughout the study recruitment, enrollment, and follow-up stagesParticipatory community engagement•Partnership with members and leaders from underrepresented communities during the planning and protocol writing stages•Consideration of research techniques such as the Delphi Method to understand barriers and consider meaningful and value-based research practices, adverse effects, and health outcomes important to research participantsParticipatory recruitment and enrollment•Partnerships with community members and leaders to incorporate community-based recruitment strategies•Development of a collaborative protocol with community members to identify representative percentage recruitment in trials•Understanding of cost and cultural barriers for improvement of participant retentionParticipatory leadership•Diverse advisory and data monitoring boards with leaders and members from participating communitiesEthical consent•Continued focus on meaningful informed consent and ethical recruitment practices with involvement of diverse ethics teamsEthical review, oversight, and inclusivity•Inclusion of leaders and members with diverse experiences on institutional research ethics boards (REB)•Ability of REBs to acknowledge and address concerns raised by community and public on ongoing studies•Development of community-based REBs with inclusion of community-appointed membersAuthorship•Inclusion of community members, leaders, and scientists as protocol and manuscript authors based on collaboration by use of community participatory research techniques (ie, Delphi consensus, focus groups)Knowledge translation•Development of programmes, initiatives, information sources for better understanding of research results by communitiesData ownership•Consideration of community data ownership practices (eg, the Canadian Ownership, Control, Access, and Possession statement by the First Nations Information Governance Centre) Language •Availability of language resources in trial handouts•Use of trained health interpreters and translators during informed consent•Availability of trained translators throughout the study recruitment, enrollment, and follow-up stages Participatory community engagement •Partnership with members and leaders from underrepresented communities during the planning and protocol writing stages•Consideration of research techniques such as the Delphi Method to understand barriers and consider meaningful and value-based research practices, adverse effects, and health outcomes important to research participants Participatory recruitment and enrollment •Partnerships with community members and leaders to incorporate community-based recruitment strategies•Development of a collaborative protocol with community members to identify representative percentage recruitment in trials•Understanding of cost and cultural barriers for improvement of participant retention Participatory leadership •Diverse advisory and data monitoring boards with leaders and members from participating communities Ethical consent •Continued focus on meaningful informed consent and ethical recruitment practices with involvement of diverse ethics teams Ethical review, oversight, and inclusivity •Inclusion of leaders and members with diverse experiences on institutional research ethics boards (REB)•Ability of REBs to acknowledge and address concerns raised by community and public on ongoing studies•Development of community-based REBs with inclusion of community-appointed members Authorship •Inclusion of community members, leaders, and scientists as protocol and manuscript authors based on collaboration by use of community participatory research techniques (ie, Delphi consensus, focus groups) Knowledge translation •Development of programmes, initiatives, information sources for better understanding of research results by communities Data ownership •Consideration of community data ownership practices (eg, the Canadian Ownership, Control, Access, and Possession statement by the First Nations Information Governance Centre) Although diabetes treatment has come a long way in 100 years, we are overdue to develop and practice a framework for research that is both meaningful and representative of all patients. Ultimately, inclusivity is a matter of choice and prioritisation. Barriers and costs are not insurmountable in research; the first step requires the philosophy that all human beings deserve to benefit from fair and equitable research that represents them. We hope that the next 100 years will allow this philosophy to be enacted into practice. SS identifies as a physician of visible minority. LR identifies as mixed Anishinaabe descent. We declare no competing interests.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.108
GPT teacher head0.420
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations36
Published2022
Admission routes2
Has abstractyes

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