This dataset consists of a single data table in .docx Word document format, holding aggregated data on cardiovascular risk factors for men in Paramaribo, Suriname.In related studies from 2013-2015, the population of Suriname was found to have a high cardiovascular risk factor burden. Around 40% of the general population was hypertensive, 15% had diabetes, and the large majority had one or more risk factors for cardiovascular disease. However, it was not possible to assess time trends in these risk factors as historical data were lacking.

This dataset holds rediscovered and hitherto unpublished aggregated data of what was apparently the first population study on measured blood pressure, diabetes, and cardiovascular health in men in Suriname, assessed in 1973. These are presented alongside 2013 data for the same variables. These data may help understand the cardiovascular risk factor escalation of the local population in time as well as aid in projections of future cardiovascular disease in this middle income country. The variables reported in the data table are: sample size (%), sampling method, African ancestry (%), Regular leisure exercise (%), Ever smoked tobacco (%), Hypertension (%) and Diabetes (%).

The Type 1 Diabetes Genetics Consortium (T1DGC) was an international, multicenter program organized to promote research to identify genes and alleles that determine an individual's risk for type 1 diabetes. The program had two primary goals: (1) to identify genomic regions and candidate genes whose variants modify an individual’s risk of type 1 diabetes and help explain the clustering of the disease in families and (2) to make research data available to and establish resources that can be used by the research community. The T1DGC assembled a resource of affected sib-pair families, parent-child trios, and case-control collections with banks of DNA, serum, plasma, and EBV-transformed cell lines. In addition to T1DGC-recruited ASP families, the T1DGC recruited trio families from ethnic groups with lower prevalence of type 1 diabetes. The T1DGC also welcomed the inclusion of earlier ascertained case-control collections (from the UK, Denmark, etc.). Research with T1DGC data has included genome-wide linkage scans, evaluation of the human major histocompatibility complexes, examination of published candidate genes for type 1 diabetes, and examination of autoimmune disease genes and those affecting β-cell function in type 2 diabetes.

In 2007, the T1DGC incorporated over 7,000 cases from the UK (the JDRF/WT case series, aka GRID). GRID samples are available here, and data from dbGaP, the European Genome-phenome Archive (EGA) and data and documentation at the JDRF/WT DIL.

Abstract

Objective The objective of the study is to assess the burden and the spectrum of environmental and genetic determinants of T2D and selected associated microvascular complications in SSA. Methods A multi-national study integrating epidemiological and genomic techniques designed as case-series and population based cross-sectional surveys at 10 sites in 7 countries spanning west, east and southern Africa. Up to 6000 cases of T2D from health facilities and 6000 population based controls will be recruited. This design makes it possible to efficiently draw cases of T2D from health facilities and align them to controls from an appropriate base population while providing an opportunity to estimate prevalence from the survey component of the study. The integrated approach provides a framework for assessing burden, spectrum, and environmental and genetic risk factors for T2D and associated clinical complications.

Geographic coverage

Cameroon, Guinea, Malawi, Nigeria, South Africa, Tanzania, Uganda

Analysis unit

The unit of analysis is the human individual. Each record corresponds to an individual.

Universe

The population in both the survey and clinic arms of the study was of self-identified black Africans, 18 years or older and resident in their respective localities. The inclusion and exclusion criteria are in the table below.

Inclusion and Exclusion criteria

Clinic Arm Inclusion
Age=>25 years. Clinically diagnosed T2D based on data extracted from patient medical records according to current ADA and WHO definitions. Fasting plasma glucose (FPG) =>7.0mmol/ (=>126mg/dl) OR o Two-hours post-load glucose (2h-PG) =>11.1 mmol/l (=>200mg/dl) OR o Symptoms of diabetes and random plasma glucose => 11.1 mmol/l (=200mg/dl) OR o On oral or insulin treatment for diabetes. Individual of African origin (Black) Signed informed consent.

Exclusion · Pregnant women - can participate six months after childbirth · Diabetes classified other than T2D or doubt as to classification · Living outside the geographical sampling frame for the relevant site · Self-defined ethnic group regarded as other than African (Black) · Unable to give informed consent

Survey Arm Inclusion
Resident in the relevant geographical sampling frame Age=>18 years · Individual of African origin (Black) Signed informed consent

Exclusion · Pregnant women - can participate six months after childbirth · Resident outside the relevant geographical sampling frame · Self-defined ethnic group regarded as other than African (Black) · Unable to give informed consent

Kind of data

Includes data on:Socio-demographic, biophysical and anthropometric, biochemical factors as well as fundus image grading.

Sampling procedure

T2D cases were recruited purposively selected from health facilities within the geographical location of study centres using patient registers as sampling frames. Surveys were conducted in the catchment areas of the selected health facilities using a two-stage cluster sampling design involving predefined geographical areas such as administrative units and households. Listings of administrative units and households were obtained from each country's National Statistics Office or equivalent agency, or generated with the help of a local leader of the area to provide a sampling frame.

IntroductionA high prevalence of diabetes and diabetes-related complications in people from minority ethnic communities in high income countries is of significant concern. Several studies have indicated low adherence rates to antidiabetic medication in ethnic minority groups. Poor adherence to antidiabetic medication leads to a higher risk of complications and potential mortality. This review aims to qualitatively explore the barriers to and facilitators of adherence to antidiabetic medication among ethnic minority groups in high-income countries.MethodsA comprehensive search of MEDLINE, Embase, CINAHL, PsycINFO, and Global Health databases for qualitative studies exploring the barriers to or facilitators of adherence to antidiabetic medication in minority ethnic groups was conducted from database inception to March 2023 (PROSPERO CRD42022320681). A quality assessment of the included studies was conducted using the Critical Appraisal Skills Programme (CASP) tool. Key concepts and themes from relevant studies were synthesised using a meta-ethnographic approach. The Grading of Recommendations Assessment, Development and Evaluation Confidence in the Evidence from Reviews of Qualitative research (GRADE-CERQual) approach was used to assess the Confidence in the review findings.ResultOf 13,994 citations screened, 21 studies that included primary qualitative studies were selected, each of which involved people from minority ethnic communities from eight high income countries. This qualitative evidence synthesis has identified three overarching themes around the barriers to and facilitators of adherence to antidiabetic medication among ethnic minority groups.: 1) cultural underpinnings, 2) communication and building relationships, and 3) managing diabetes during visiting home countries. Based on the GRADE-CERQual assessment, we had mainly moderate- and high-confidence findings.ConclusionMultiple barriers and facilitators of adherence to antidiabetic medication among people from minority ethnic communities in high-income countries have been identified. A medication adherence intervention focusing on identified barriers to adherence to antidiabetic medication in these communities may help in improving diabetes outcomes in these groups.

Objective: To study the global distribution of risk factors, causes and 3-month mortality of young ischemic stroke patients, by performing a patient data meta-analysis form different cohorts worldwide.

Methods: We did a pooled analysis of individual patient data from cohort studies which included consecutive ischemic stroke patients aged 18-50 years. We studied differences in prevalence of risk factors and causes between different ethnic groups, geographic regions and countries with different income levels. We investigated differences in 3-month mortality by mixed-effects multivariable logistic regression.

Results: We included 17,663 patients from 32 cohorts in 29 countries. Hypertension and diabetes were most prevalent in Blacks (hypertension, 52.1%; diabetes, 20.7%) and Asians (hypertension 46.1%, diabetes, 20.9%). Large vessel atherosclerosis and small vessel disease were more often cause of stroke in high-income countries (HICs; both p<0.001), whereas ‘’other determ...

Introduction: While the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria is widely adopted in many countries, clinicians have questioned the applicability of these diagnostic thresholds for different races/ethnicities. We first compared the prevalence of gestational diabetes mellitus (GDM) diagnosed with different criteria including IADPSG, World Health Organization (WHO) 1999 and Sri Lankan national guidelines, and subsequently related individual guidelines-specific GDM prevalence to offspring birthweight in Sri Lanka.Materials and Methods: We retrospectively collected data on singleton pregnancies (n = 795) from two tertiary hospitals in Sri Lanka. We applied three diagnostic guidelines to define GDM, namely IADPSG criteria, the Sri Lankan national and WHO 1999 guidelines. We calculated the age- and first booking BMI-adjusted prevalence rates of GDM and assessed the association of GDM (using each guideline) with birthweight.Results: The age- and first booking BMI-adjusted GDM prevalence rates were 31.2, 28.0, and 13.1% for IADPSG criteria, Sri Lankan national and WHO 1999 guidelines, respectively. The IADPSG criteria identified 90 distinctive GDM cases at a lower cut-off of fasting glucose (from 5.1 to 5.5 mmol/L) while Sri Lankan national guideline identified 15 distinctive GDM cases at a lower cut-off for 2-h glucose (from 7.8 to 8.4 mmol/L). After adjusting for age, GDM diagnosed by IADPSG criteria was associated with higher birthweight [90.8 g, 95% CI: 10.8, 170.9], while the associations for GDM diagnosed either by Sri Lankan national or WHO 1999 guidelines were not significant.Conclusion: Adopting the IADPSG criteria for diagnosing GDM may be important in Sri Lankan pregnant population.

The quantification of diabetes-related quality of life (DR-QoL) is an essential step in making Type 2 Diabetes (T2DM) self-management arrangements. The European General Practitioners Research Network (EGPRN) initiated the EUROBSTACLE study to develop a broadly conceptualised DR-QoL instrument for diverse cultural and ethnic groups; high and low-income countries. In 2016 the Diabetes Obstacles Questionnaire-30 (DOQ-30) was introduced. The research aimed to study obstacles a patient with diabetes (PWD) may face in everyday life. First, we assessed how descriptive and clinical characteristics and the residential country were associated with the obstacles. Secondly, we calculated the proportion of respondents who expressed obstacles. Data were collected in 2009 in a cross-sectional survey in Belgium, France, Estonia, Serbia, Slovenia, and Turkey. Multiple linear regressions were computed to detect associations between descriptive and clinical characteristics, residential country, and obstacles. Percentages of respondents who perceived obstacles were calculated. We found that although descriptive and clinical characteristics varied to quite a great extent, they were weakly associated with the perception of obstacles. The residential country was most often associated with the existence of some obstacle. The highest percent (48%) of all respondents perceived ‘Uncertainty about Insulin Use’ as an obstacle. Descriptive and clinical characteristics were weakly associated with perceived obstacles. However, the residential country plays an essential role in the decline of the QoL of PWDs. Education of both PWDs and healthcare professionals (HCPs) plays an essential role in countering the fear of insulin.

BackgroundThere is a paucity of information on secular trends in the age-related process by which people develop overweight or obesity. Utilizing longitudinal data in the United Kingdom birth cohort studies, we investigated shifts over the past nearly 70 years in the distribution of body mass index (BMI) and development of overweight or obesity across childhood and adulthood.Methods and FindingsThe sample comprised 56,632 participants with 273,843 BMI observations in the 1946 Medical Research Council National Survey of Health and Development (NSHD; ages 2–64 years), 1958 National Child Development Study (NCDS; 7–50), 1970 British Cohort Study (BCS; 10–42), 1991 Avon Longitudinal Study of Parents and Children (ALSPAC; 7–18), or 2001 Millennium Cohort Study (MCS; 3–11). Growth references showed a secular trend toward positive skewing of the BMI distribution at younger ages. During childhood, the 50th centiles for all studies lay in the middle of the International Obesity Task Force normal weight range, but during adulthood, the age when a 50th centile first entered the overweight range (i.e., 25–29.9 kg/m2) decreased across NSHD, NCDS, and BCS from 41 to 33 to 30 years in males and 48 to 44 to 41 years in females. Trajectories of overweight or obesity showed that more recently born cohorts developed greater probabilities of overweight or obesity at younger ages. Overweight or obesity became more probable in NCDS than NSHD in early adulthood, but more probable in BCS than NCDS and NSHD in adolescence, for example. By age 10 years, the estimated probabilities of overweight or obesity in cohorts born after the 1980s were 2–3 times greater than those born before the 1980s (e.g., 0.229 [95% CI 0.219–0.240] in MCS males; 0.071 [0.065–0.078] in NSHD males). It was not possible to (1) model separate trajectories for overweight and obesity, because there were few obesity cases at young ages in the earliest-born cohorts, or (2) consider ethnic minority groups. The end date for analyses was August 2014.ConclusionsOur results demonstrate how younger generations are likely to accumulate greater exposure to overweight or obesity throughout their lives and, thus, increased risk for chronic health conditions such as coronary heart disease and type 2 diabetes mellitus. In the absence of effective intervention, overweight and obesity will have severe public health consequences in decades to come.

a. Age distribution of patients with newly coded CKD3a between 1/1/2015-31/12/2015, separated by the degree of albuminuria. CKD3aA1 = eGFR between 45-59mls/min/1.73m2 with uACR 30mg/mmol. b. Ethnicity of patients with newly coded CKD3a between 1/1/2015-31/12/2015, separated by the degree of albuminuria. CKD3aA1 = eGFR between 45-59mls/min/1.73m2 with uACR 30mg/mmol.

CKD3aA1 = eGFR between 45-59mls/min/1.73m2 with uACR 30mg/mmol. RAASi = Renin Angiotensin Aldosterone inhibitors. SGLT2i = Sodium-Glucose Cotransporter-2 Inhibitor.

BMI: Body Mass Index, IOTF: International Obesity Task Force, IQR: Inter-Quartile Range, UK: United Kingdom, NSHD: Medical Research Council National Survey of Health and Development, NCDS National Child Development Study, BCS: British Cohort Study, ALSPAC: Avon Longitudinal Study of Parents and Children, MCS: Millennium Cohort StudyaThinness, overweight, and obesity between 2–18 years of age were defined according to the IOTF cut-offs, which are centiles that link with the adulthood cut-offs at age 18 years (e.g., the 90.5th IOTF centile is used to define overweight in boys as this centile equals 25 kg/m2, the adulthood cut-off, at age 18 years).Description of BMI data in the five UK birth cohort studies.

NSHD: Medical Research Council National Survey of Health and Development, NCDS National Child Development Study, BCS: British Cohort Study, ALSPAC: Avon Longitudinal Study of Parents and Children, MCS: Millennium Cohort Study.Estimated probabilities of overweight or obesity (versus normal weight) from sex- and study-stratified multilevel logistic regression models.