Managing Ramadan queries in COVID-19

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The cardiometabolic phenotype of UK South Asian Men

Migrant South Asian populations in Europe, North America the Westernised countries have a greater cardiovascular disease (CVD) risk than their respective indigenous populations. Both overall and premature CVD morbidity and mortality is significantly higher in migrant South Asians than in white populations in the UK and globally. Despite this, the role of ultrasound as a screening tool for CVD risk assessment in South Asians has not been studied extensively. Data also suggest that increased susceptibility to the adverse effects of insulin resistance and type 2 diabetes mellitus (T2DM) may contribute to the increased CVD risk. South Asians living in the United Kingdom also have a 3-5 fold increased prevalence of T2DM, developing the disease around a decade earlier and at a lower body mass index (BMI) compared to white Europeans. Furthermore, non-diabetic South Asians have higher fasting glycaemia and are more insulin resistant than Europeans. Liver fat is also associated with insulin resistance and T2DM risk and is considered to play a causal role in diabetes. Limited data suggest that South Asians have higher liver fat content than age- and BMI-matched Europeans, but it is not currently clear whether this contributes to the observed ethnic difference in insulin resistance. The first aim was to determine the extent to which increased insulin resistance and fasting glycaemia in South Asian, compared to white European men, living in the UK, was due to lower cardiorespiratory fitness (maximal oxygen uptake [VO2max]) and physical activity. The second aim was to determine whether South Asians have increased liver fat compared to Europeans and the extent to which any such differences can explain the increased insulin resistance observed between these groups. The final aim was to determine whether South Asians had a difference in carotid intima-media thickness (cIMT) or carotid plaque presence compared to Europeans; and if so, whether any measured risk factors (if any) could account for any such observed differences in cIMT and/or carotid plaque disease. 100 SA and 100 age and BMI-matched European men without diagnosed diabetes, aged 40-70 years, had fasting blood taken for glucose concentration, insulin, plus other risk factors, and underwent 2-dimensional carotid ultrasound for measurement of intima-media thickness and carotid plaque analysis, assessment of physical activity (using accelerometry), VO2max, body size and composition, and demographic and other lifestyle factors. For addressing the first aim of this thesis, 13 South Asian and 1 European man had HbA1c levels >6.5% indicating potential undiagnosed diabetes and were excluded from the analyses relating. Linear regression models were used to determine the extent to which body size and composition, fitness and physical activity variables explained differences in insulin resistance (assessed by Homeostasis Assessment Model of Insulin Resistance, HOMAIR) and fasting glucose between SA and Europeans. For the second aim, 28 South Asian and 24 European participants were chosen at random (but matched for age) within 4 months of their original main study visit to undergo magnetic resonance spectroscopy for quantification of liver fat. HOMAIR and fasting glucose were 67% (p<0.001) and 3% (p<0.018) higher, respectively, in South Asians than Europeans. Lower VO2max, lower physical activity and greater total adiposity in SA individually explained 68% (95% confidence interval [CI], 45-91%), 29% (95% CI, 11-46%) and 52% (95% CI, 30-80%), respectively, and together explained 83% (95% CI, 50-119%) (all p<0.001), of the ethnic difference in HOMAIR. Lower VO2max and greater total adiposity respectively explained 61% (95% CI, 9-111%) and 39% (95% CI; 9-76%) (combined effect 63% (95% CI 8-115%); all p<0.05)) of the ethnic difference in fasting glucose. Unadjusted mean liver fat content did not differ significantly between South Asians compared to Europeans (5.28 (standard deviation [SD], 2.11)% vs 5.41 (SD,2.35)%, p=0.913), but following adjustment for alcohol consumption was significantly lower in South Asians than Europeans (5.30 (SD, 2.10)% vs 9.03 (SD, 2.22)% p=0.017). Adjustment for alcohol-adjusted liver fat did not attenuate the difference in HOMAIR between ethnic groups. There were no significant differences in unadjusted or age-adjusted in mean cIMT between South Asians and Europeans. There was an increased odds ratio for the presence of plaque disease in South Asians compared to Europeans, however this was not significant (OR 1.57, 95% CI 0.89-2.77, p=0.13). Lower cardiorespiratory fitness is a key factor associated with the excess insulin resistance and fasting glycaemia in middle-aged South Asian compared to European men living in the UK. Also, whilst clear associations between liver fat and insulin resistance were observed in South Asians and Europeans, these results challenge the notion that excess liver fat per se explains the greater insulin resistance observed in South Asians. Finally, cIMT is similar between South Asian and European men and there is also currently no clear evidence for more carotid plaques in South Asian compared to European men living in the UK. This important negative finding highlights the need for further studies on carotid plaque or research in alternative screening methods for CVD which are more sensitive in identifying subclinical CVD

Non-exercise equations to estimate fitness in white European and South Asian men

Cardiorespiratory fitness is a strong, independent predictor of health, whether it is measured in an exercise test or estimated in an equation. The purpose of this study was to develop and validate equations to estimate fitness in middle-aged white European and South Asian men.Multiple linear regression models (n=168, including 83 white European and 85 South Asian men) were created using variables that are thought to be important in predicting fitness (VO2 max, mL⋅kg⋅min): age (years); BMI (kg·m); resting heart rate (beats⋅min); smoking status (0=never smoked, 1=ex or current smoker); physical activity expressed as quintiles (0=quintile 1, 1=quintile 2, 2=quintile 3, 3=quintile 4, 4=quintile 5), categories of moderate- to vigorous-intensity physical activity (0=&lt;75 min⋅wk, 1=75-150 min⋅wk, 2=&gt;150-225 min⋅wk, 3=&gt;225-300 min⋅wk, 4=&gt;300 min⋅wk), or minutes of moderate- to vigorous-intensity physical activity (min⋅wk); and, ethnicity (0=South Asian, 1=white). The leave-one-out-cross-validation procedure was used to assess the generalizability and the bootstrap and jackknife resampling techniques were used to estimate the variance and bias of the models.Around 70% of the variance in fitness was explained in models with an ethnicity variable, such as: VO2 max = 77.409 - (age*0.374) - (BMI*0.906) - (ex or current smoker*1.976) + (physical activity quintile coefficient) - (resting heart rate*0.066) + (white ethnicity*8.032), where physical activity quintile 1 is 1, 2 is 1.127, 3 is 1.869, 4 is 3.793, and 5 is 3.029. Only around 50% of the variance was explained in models without an ethnicity variable. All models with an ethnicity variable were generalizable and had low variance and bias.These data demonstrate the importance of incorporating ethnicity in non-exercise equations to estimate cardiorespiratory fitness in multi-ethnic populations

Diabetes, driving and fasting during Ramadan:the interplay between secular and religious law

A large proportion of the Muslim population fasts during Ramadan. The risk of hypoglycemia is increased with fasting during Ramadan in people with diabetes who are on insulin and insulin secretagogues. Therefore, the combination of fasting with diabetes and driving presents a challenging situation, with legal implications for such individuals and their healthcare professionals. This novel, narrative, non-systematic review discusses the importance of addressing hypoglycemia in fasting with reference to secular legal guidance on driving with diabetes. We discuss religious aspects relating to fasting and driving in Islam. While there is no clear guidance or legal position on diabetes and driving for individuals who are fasting, Islamic law provides a logical framework to address this. Healthcare professionals need to raise and facilitate discussions on this often-overlooked topic with people with diabetes who are planning on fasting to minimize the potential for public harm. For some individuals fasting perhaps should be avoided when driving and that this religiously compatible position would best be adopted when one is dependent on driving for livelihood. Ultimately further research on glycemic control and management when fasting and driving, as well as a formal legal guidance on this topic, is required to safeguard healthcare professionals and the public from the potential dangers of driving with diabetes and fasting

Fasting with adrenal insufficiency: practical guidance for healthcare professionals managing patients on steroids during Ramadan

There are limited recommendations for fasting in many chronic diseases such as adrenal insufficiency (AI). Research in such situations highlights potential for complications and need for education for patients with AI undertaking fasting during Ramadan. This article aims to provide up‐to‐date guidance for healthcare professionals to educate, discuss and manage patients with AI who are considering fasting in Ramadan and is religiously compatible. Latest guidance on this topic and the evidence base for steroid dosing are reviewed and discussed. Risk stratification for patients with AI and optimal strategies for management, including steroid dosing, are detailed. Our review highlights that patients with AI wishing to fast should undergo a thorough risk assessment ideally several months before Ramadan. ‘High risk’ and ‘Very high risk’ patients should be encouraged to explore alternative options to fasting discussed below. Prior to the commencement of Ramadan, all patients must receive up‐to‐date education on sick day rules, instructions on when to terminate their fast or abstain from fasting, carry steroid warning information and must have a valid intramuscular (IM) hydrocortisone pack and know how to administer this. Switching patients with AI desiring to fast from multiple daily hydrocortisone replacement to prednisolone 5 mg once daily at dawn (during Suhoor or Sehri) is recommended and discussed. Patients on fludrocortisone for AI should be advised to take their total dose at dawn. We provide practically relevant case‐based scenarios to help with the application of this guidance. Future efforts need to focus on healthcare professional awareness and further research in this setting

Managing epilepsy in Ramadan: guidance for healthcare providers and patients

Ramadan is a regularly recurring period of fasting that takes place in the ninth month of the Islamic calendar. For this period, adult Muslims refrain from eating and drinking between dawn and sunset. The variation in summer daylight hours means that at temperate latitudes, fasting can last up to 20 h. It is already recognized that epilepsy control can deteriorate during Ramadan, and this may be explained by fasting-related changes to adherence to antiseizure drug regimes. This article provides specific advice to help Muslim patients prepare for Ramadan and reduce chances of exacerbation in epilepsy. In addition to advice around sleep hygiene, it explores the use of drugs or preparations of drugs that will demonstrate reduced variation during periods of fasting

Should physical activity recommendations be ethnicity-specific? Evidence from a cross-sectional study of south Asian and European men

Background Expert bodies and health organisations recommend that adults undertake at least 150 min.week−1 of moderate-intensity physical activity (MPA). However, the underpinning data largely emanate from studies of populations of European descent. It is unclear whether this level of activity is appropriate for other ethnic groups, particularly South Asians, who have increased cardio-metabolic disease risk compared to Europeans. The aim of this study was to explore the level of MPA required in South Asians to confer a similar cardio-metabolic risk profile to that observed in Europeans undertaking the currently recommended MPA level of 150 min.week−1.&lt;p&gt;&lt;/p&gt; Methods Seventy-five South Asian and 83 European men, aged 40–70, without cardiovascular disease or diabetes had fasted blood taken, blood pressure measured, physical activity assessed objectively (using accelerometry), and anthropometric measures made. Factor analysis was used to summarise measured risk biomarkers into underlying latent ‘factors’ for glycaemia, insulin resistance, lipid metabolism, blood pressure, and overall cardio-metabolic risk. Age-adjusted regression models were used to determine the equivalent level of MPA (in bouts of ≥10 minutes) in South Asians needed to elicit the same value in each factor as Europeans undertaking 150 min.week−1 MPA.&lt;p&gt;&lt;/p&gt; Findings For all factors, except blood pressure, equivalent MPA values in South Asians were significantly higher than 150 min.week−1; the equivalent MPA value for the overall cardio-metabolic risk factor was 266 (95% CI 185-347) min.week−1.&lt;p&gt;&lt;/p&gt; Conclusions South Asian men may need to undertake greater levels of MPA than Europeans to exhibit a similar cardio-metabolic risk profile, suggesting that a conceptual case can be made for ethnicity-specific physical activity guidance. Further study is needed to extend these findings to women and to replicate them prospectively in a larger cohort.&lt;p&gt;&lt;/p&gt

Non-exercise equations to estimate fitness in white European and South Asian men

© 2015 American College of Sports Medicine PURPOSE: Cardiorespiratory fitness is a strong, independent predictor of health, whether it is measured in an exercise test or estimated in an equation. The purpose of this study was to develop and validate equations to estimate fitness in middle-aged white European and South Asian men. METHODS: Multiple linear regression models (n=168, including 83 white European and 85 South Asian men) were created using variables that are thought to be important in predicting fitness (VO2 max, mL⋅kg⋅min): age (years); BMI (kg·m); resting heart rate (beats⋅min); smoking status (0=never smoked, 1=ex or current smoker); physical activity expressed as quintiles (0=quintile 1, 1=quintile 2, 2=quintile 3, 3=quintile 4, 4=quintile 5), categories of moderate- to vigorous-intensity physical activity (0=150-225 min⋅wk, 3=>225-300 min⋅wk, 4=>300 min⋅wk), or minutes of moderate- to vigorous-intensity physical activity (min⋅wk); and, ethnicity (0=South Asian, 1=white). The leave-one-out-cross-validation procedure was used to assess the generalizability and the bootstrap and jackknife resampling techniques were used to estimate the variance and bias of the models. RESULTS: Around 70% of the variance in fitness was explained in models with an ethnicity variable, such as: VO2 max = 77.409 - (age*0.374) – (BMI*0.906) – (ex or current smoker*1.976) + (physical activity quintile coefficient) – (resting heart rate*0.066) + (white ethnicity*8.032), where physical activity quintile 1 is 1, 2 is 1.127, 3 is 1.869, 4 is 3.793, and 5 is 3.029. Only around 50% of the variance was explained in models without an ethnicity variable. All models with an ethnicity variable were generalizable and had low variance and bias. CONCLUSION: These data demonstrate the importance of incorporating ethnicity in non-exercise equations to estimate cardiorespiratory fitness in multi-ethnic populations

Ethnic differences in Glycaemic control in people with type 2 diabetes mellitus living in Scotland

Background and Aims: Previous studies have investigated the association between ethnicity and processes of care and intermediate outcomes of diabetes, but there are limited population-based studies available. The aim of this study was to use population-based data to investigate the relationships between ethnicity and glycaemic control in men and women with diabetes mellitus living in Scotland.&lt;p&gt;&lt;/p&gt; Methods: We used a 2008 extract from the population-based national electronic diabetes database of Scotland. The association between ethnicity with mean glycaemic control in type 2 diabetes mellitus was examined in a retrospective cohort study, including adjustment for a number of variables including age, sex, socioeconomic status, body mass index (BMI), prescribed treatment and duration of diabetes.&lt;p&gt;&lt;/p&gt; Results: Complete data for analyses were available for 56,333 White Scottish adults, 2,535 Pakistanis, 857 Indians, 427 Chinese and 223 African-Caribbeans. All other ethnic groups had significantly (p&#60;0.05) greater proportions of people with suboptimal glycaemic control (HbA1c &#62;58 mmol/mol, 7.5%) compared to the White Scottish group, despite generally younger mean age and lower BMI. Fully adjusted odds ratios for suboptimal glycaemic control were significantly higher among Pakistanis and Indians (1.85, 95% CI: 1.68–2.04, and 1.62,95% CI: 1.38–1.89) respectively.&lt;p&gt;&lt;/p&gt; Conclusions: Pakistanis and Indians with type 2 diabetes mellitus were more likely to have suboptimal glycaemic control than the white Scottish population. Further research on health services and self-management are needed to understand the association between ethnicity and glycaemic control to address ethnic disparities in glycaemic control.&lt;p&gt;&lt;/p&gt

Glycated hemoglobin, prediabetes and the links to cardiovascular disease: data from UK Biobank

OBJECTIVE: HbA1c levels are increasingly measured in screening for diabetes; we investigated whether HbA1c may simultaneously improve cardiovascular disease (CVD) risk assessment, using QRISK3, American College of Cardiology/American Heart Association (ACC/AHA), and Systematic COronary Risk Evaluation (SCORE) scoring systems. RESEARCH DESIGN AND METHODS: UK Biobank participants without baseline CVD or known diabetes (n = 357,833) were included. Associations of HbA1c with CVD was assessed using Cox models adjusting for classical risk factors. Predictive utility was determined by the C-index and net reclassification index (NRI). A separate analysis was conducted in 16,596 participants with known baseline diabetes. RESULTS: Incident fatal or nonfatal CVD, as defined in the QRISK3 prediction model, occurred in 12,877 participants over 8.9 years. Of participants, 3.3% (n = 11,665) had prediabetes (42.0–47.9 mmol/mol [6.0–6.4%]) and 0.7% (n = 2,573) had undiagnosed diabetes (≥48.0 mmol/mol [≥6.5%]). In unadjusted models, compared with the reference group (&lt;42.0 mmol/mol [&lt;6.0%]), those with prediabetes and undiagnosed diabetes were at higher CVD risk: hazard ratio (HR) 1.83 (95% CI 1.69–1.97) and 2.26 (95% CI 1.96–2.60), respectively. After adjustment for classical risk factors, these attenuated to HR 1.11 (95% CI 1.03–1.20) and 1.20 (1.04–1.38), respectively. Adding HbA1c to the QRISK3 CVD risk prediction model (C-index 0.7392) yielded a small improvement in discrimination (C-index increase of 0.0004 [95% CI 0.0001–0.0007]). The NRI showed no improvement. Results were similar for models based on the ACC/AHA and SCORE risk models. CONCLUSIONS: The near twofold higher unadjusted risk for CVD in people with prediabetes is driven mainly by abnormal levels of conventional CVD risk factors. While HbA1c adds minimally to cardiovascular risk prediction, those with prediabetes should have their conventional cardiovascular risk factors appropriately measured and managed