Associations between the characteristics of general practitioners and their patients with type 2 diabetes O besity is an important cardiovascular risk factor in type 2 diabetes (1). Physician characteristics such as age and sex are related to counseling for overweight (2). Other physician characteristics may also be related. Patients indicated greater confidence in nonobese versus obese physicians. Whether this translates into increased success in obesity management is unknown (3). We aimed to study associations between the weight of general practitioners and their type 2 diabetic patients.A postal survey was performed among 36 general practitioners participating in a shared-care diabetes project in 2000. It contained questions about the general practitioners' age, sex, weight, height, smoking behavior, work experience, practice population size, and opinion regarding how much influence they have on a patient's weight and smoking cessation. The project's target population consisted of type 2 diabetic patients who were exclusively treated in primary care. Patients who were cotreated in secondary care or who were terminally ill or had dementia were excluded. Participating patients (n ϭ 1,441) represented 87% of the target population. Data on patient age, sex, diabetes duration, BMI, and smoking were collected by nurses. We performed a regression analysis with the mean BMI of patients as the dependent variable and the above variables as predictors.The survey response rate was 100%. Most general practitioners were nonsmoking (94%) men (83%) with a mean (ϮSD) age of 51.1 Ϯ 7.0 years and work experience of 18.1 Ϯ 8.9 years. The general practitioners' mean BMI was 24.4 Ϯ 3.5 kg/m 2 (BMI Ͻ25 in 72%). For patients (per general practitioner), mean age was 68.2 Ϯ 2.9 years, diabetes duration 7.2 Ϯ 1.3 years, and BMI 29.3 Ϯ 0.85 kg/m 2 ; 44 Ϯ 9% were men. The mean BMI of patients showed the strongest correlation with the BMI of general practitioners: Ϫ0.40 (partial correlation) and unstandardized coefficient B of Ϫ1.05 (95% CI Ϫ0.197 to Ϫ0.013). The optimal model (P ϭ 0.07) had a multiple correlation (R) of 0.56, and explained variance (R 2 ) was 31% (adjusted 17%). We found a negative correlation between the BMI of type 2 diabetic patients and their general practitioners. Obese doctors had lean patients. Our study is limited by the cross-sectional design; associations were found, not causal relations.Hash et al. (3) showed that patients indicated greater confidence in nonobese physicians. However, we found no translation into increased success in obesity management. On the contrary, patients of nonobese general practitioners had a higher BMI compared with patients of obese general practitioners. A discernable negative impact of patient weight on physician behavior was shown earlier (4). Could it be that nonobese general practitioners lack motivation to treat overweight patients? Is it time for general practitioners to search our own hearts? • LIELITH J. UBINK-VELTMAAT, MD Sudden sensorineural hearing loss (SSNHL) is define...

Background The burden of atherosclerosis has led to treatment prioritization on high-risk individuals without established cardiovascular disease based on risk estimates. We investigated the effects of biological variation in risk factors on risk estimate accuracy and whether current primary prevention screening (risk assessment) models correctly categorize patients.Methods A population of 10 000 'perfect' individuals with 100 simulants affected by biological and analytical variation for systolic blood pressure, total cholesterol, high-density lipoprotein-cholesterol was mathematically modelled. Coronary heart disease (CHD) risks were calculated using the Framingham study algorithm and the mathematical properties of the screening system were evaluated.Results At internationally recommended 10-year CHD risk treatment threshold levels of 15, 20 and 30%, the 95% confidence intervals were 7 5.1, 7 6.0 and 7 6.9% for single-point (singlicate), 7 3.6, 7 4.2 and 7 4.9% for duplicate and 7 2.8, 7 3.3 and 7 3.9% for triplicate estimates respectively (i.e. for singlicate 15% risk, 95% confidence interval is 9.9-20.1%). Consequently, using the 30% risk threshold from the National Service Framework (NSF) for CHD with singlicate estimation, 30% of patients who should receive treatment would be denied it and 20% would receive treatment unnecessarily. Multiple measurements improve precision but cannot absolutely define risk. Blood pressure should be measured to the greatest accuracy possible and not rounded prior to averaging.Conclusions This study suggests biological variation in cardiovascular risk factors has profound consequences on calculated risk for therapeutic decision-making. Current guidelines recommending multiple measurements are usually ignored. Triplicate measurement is required to allow risk to be identified and clinical judgement has to be exercised in interpretation of the results.

ObjectivesSince the onset of the COVID-19 pandemic in 2020, there have been plausible suggestions about the need to augment vitamin D intake by supplementation in order to prevent SARS-CoV2 infection and reduce mortality. Some groups have advocated supplementation for all adults, but governmental agencies have advocated targeted supplementation. We sought to explore the effect of the COVID-19 pandemic on both vitamin D status and on the dose of new-to-market vitamin D supplements.SettingUniversity hospital, Dublin, Ireland.ParticipantsLaboratory-based samples of circulating 25-hydroxyvitamin D (25OHD) (n=100 505).Primary and secondary outcome measuresPrimary outcomes: comparing yearly average 25OHD prior to the pandemic (April 2019 to March 2020) with during the pandemic (April 2020 to March 2021) and comparing the dose of new-to-market vitamin D supplements between 2017 and 2021 (n=2689). Secondary outcome: comparing prevalence of vitamin D deficiency and vitamin D excess during the two time periods.ResultsThe average yearly serum 25OHD measurement increased by 2.8 nmol/L (61.4, 95% CI 61.5 to 61.7 vs 58.6, 95% CI 58.4 to 58.9, p<0.001), which was almost threefold higher than two similar trend analyses that we conducted between 1993 and 2016. There was a lower prevalence of low 25OHD and a higher prevalence of high 25OHD. The dose of new-to-market vitamin D supplements was higher in the years 2020–2021 compared with the years 2017–2019 (p<0.001).ConclusionsWe showed significant increases in serum 25OHD and in the dose of new-to-market vitamin D supplements. The frequency of low vitamin D status reduced indicating benefit, but the frequency of vitamin D excess increased indicating risk of harm. Rather than a blanket recommendation about vitamin D supplementation for all adults, we recommend a targeted approach of supplementation within current governmental guidelines to at-risk groups and cautioning consumers about adverse effects of high dose supplements on the market.

Associations between the characteristics of general practitioners and their patients with type 2 diabetes O besity is an important cardiovascular risk factor in type 2 diabetes (1). Physician characteristics such as age and sex are related to counseling for overweight (2). Other physician characteristics may also be related. Patients indicated greater confidence in nonobese versus obese physicians. Whether this translates into increased success in obesity management is unknown (3). We aimed to study associations between the weight of general practitioners and their type 2 diabetic patients.A postal survey was performed among 36 general practitioners participating in a shared-care diabetes project in 2000. It contained questions about the general practitioners' age, sex, weight, height, smoking behavior, work experience, practice population size, and opinion regarding how much influence they have on a patient's weight and smoking cessation. The project's target population consisted of type 2 diabetic patients who were exclusively treated in primary care. Patients who were cotreated in secondary care or who were terminally ill or had dementia were excluded. Participating patients (n ϭ 1,441) represented 87% of the target population. Data on patient age, sex, diabetes duration, BMI, and smoking were collected by nurses. We performed a regression analysis with the mean BMI of patients as the dependent variable and the above variables as predictors.The survey response rate was 100%. Most general practitioners were nonsmoking (94%) men (83%) with a mean (ϮSD) age of 51.1 Ϯ 7.0 years and work experience of 18.1 Ϯ 8.9 years. The general practitioners' mean BMI was 24.4 Ϯ 3.5 kg/m 2 (BMI Ͻ25 in 72%). For patients (per general practitioner), mean age was 68.2 Ϯ 2.9 years, diabetes duration 7.2 Ϯ 1.3 years, and BMI 29.3 Ϯ 0.85 kg/m 2 ; 44 Ϯ 9% were men. The mean BMI of patients showed the strongest correlation with the BMI of general practitioners: Ϫ0.40 (partial correlation) and unstandardized coefficient B of Ϫ1.05 (95% CI Ϫ0.197 to Ϫ0.013). The optimal model (P ϭ 0.07) had a multiple correlation (R) of 0.56, and explained variance (R 2 ) was 31% (adjusted 17%).We found a negative correlation between the BMI of type 2 diabetic patients and their general practitioners. Obese doctors had lean patients. Our study is limited by the cross-sectional design; associations were found, not causal relations.Hash et al. (3) showed that patients indicated greater confidence in nonobese physicians. However, we found no translation into increased success in obesity management. On the contrary, patients of nonobese general practitioners had a higher BMI compared with patients of obese general practitioners. A discernable negative impact of patient weight on physician behavior was shown earlier (4). Could it be that nonobese general practitioners lack motivation to treat overweight patients? Is it time for general practitioners to search our own hearts? • S udden sensorineural hearing loss (SSNHL) is defined as the sudden onset of unilat...