Routinely collected general practice data aids identification of people with hyperglycaemia and metabolic syndrome

Back on track-Smoking cessation and weight changes over 9 years in a community-based cohort study

OBJECTIVE

To examine the impact of smoking cessation on body weight compared with normal long-term weight development.

METHODS

Of 1970 adults (20-69 years) in a rural town in Denmark invited to take part in the study in 1998-2000, 1374 (70%) participated. After 9 years, 1121 participated in the follow-up study. Weight changes were compared using multivariable regression models.

RESULTS

The mean baseline weight of never-smokers was 76.4 kg (SD 16.0). The adjusted weight of smokers and ex-smokers differed by -4.2 kg (95% CI: -5.9, -2.6), and -0.7 kg (95% CI: -2.5, 1.1), respectively. The adjusted weight gain rate (kg/year) of never-smokers, smokers, and ex-smokers was 0.213, 0.127, and 0.105, respectively. The absolute post cessation weight gain (PCWG) was 5.0 kg (SD 7.0), and the adjusted PCWG was 2.8 kg (95% CI: 1.7, 3.9) compared with never-smokers, and 3.5 kg (95% CI: 2.3, 4.8) compared with smokers. The follow-up weight did not differ between quitters and never-smokers (0.1 kg; 95% CI: -2.4, 2.6).

CONCLUSION

Smokers weigh less than never-smokers. By quitting, they gain weight and end up weighing the same as comparable never-smokers. Weight gain rates differ by smoking status. Consequently, PCWG depends on the length of follow-up. Our graphical model indicates that smoking cessation results in a return to normal weight development.

Systematic Review and Meta-Analysis of Response Rates and Diagnostic Yield of Screening for Type 2 Diabetes and Those at High Risk of Diabetes

Background

Screening for type 2 diabetes (T2DM) and individuals at risk of diabetes has been advocated, yet information on the response rate and diagnostic yield of different screening strategies are lacking.

Methods

Studies (from 1998 to March/2015) were identified through Medline, Embase and the Cochrane library and included if they used oral glucose tolerance test (OGTT) and WHO-1998 diagnostic criteria for screening in a community setting. Studies were one-step strategy if participants were invited directly for OGTT and two, three/four step if participants were screened at one or more levels prior to invitation to OGTT. The response rate and diagnostic yield were pooled using Bayesian random-effect meta-analyses.

Findings

47 studies (422754 participants); 29 one-step, 11 two-step and seven three/four-step were identified. Pooled response rate (95% Credible Interval) for invitation to OGTT was 65.5% (53.7, 75.6), 63.1% (44.0, 76.8), and 85.4% (76.4, 93.3) in one, two and three/four-step studies respectively. T2DM yield was 6.6% (5.3, 7.8), 13.1% (4.3, 30.9) and 27.9% (8.6, 66.3) for one, two and three/four-step strategies respectively. The number needed to invite to the OGTT to detect one case of T2DM was 15, 7.6 and 3.6 in one, two, and three/four-step strategies. In two step strategies, there was no difference between the response or yield rates whether the first step was blood test or risk-score. There was evidence of substantial heterogeneity in rates across study populations but this was not explained by the method of invitation, study location (rural versus urban) and developmental index of the country in which the study was performed.

Conclusions

Irrespective of the invitation method, developmental status of the countries and or rural/urban location, using a multi-step strategy increases the initial response rate to the invitation to screening for diabetes and reduces the number needed to have the final diagnostic test (OGTT in this study) for a definite diagnosis.

Predictive modeling for incident and prevalent diabetes risk evaluation

With half of individuals with diabetes undiagnosed worldwide and a projected 55% increase of the population with diabetes by 2035, the identification of undiagnosed and high-risk individuals is imperative. Multivariable diabetes risk prediction models have gained popularity during the past two decades. These have been shown to predict incident or prevalent diabetes through a simple and affordable risk scoring system accurately. Their development requires cohort or cross-sectional type studies with a variable combination, number and definition of included risk factors, with their performance chiefly measured by discrimination and calibration. Models can be used in clinical and public health settings. However, the impact of their use on outcomes in real-world settings needs to be evaluated before widespread implementation.

Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic review

Missing values are common in health research and omitting participants with missing data often leads to loss of statistical power, biased estimates and, consequently, inaccurate inferences. We critically reviewed the challenges posed by missing data in medical research and approaches to address them. To achieve this more efficiently, these issues were analyzed and illustrated through a systematic review on the reporting of missing data and imputation methods (prediction of missing values through relationships within and between variables) undertaken in risk prediction studies of undiagnosed diabetes. Prevalent diabetes risk models were selected based on a recent comprehensive systematic review, supplemented by an updated search of English-language studies published between 1997 and 2014. Reporting of missing data has been limited in studies of prevalent diabetes prediction. Of the 48 articles identified, 62.5% (n = 30) did not report any information on missing data or handling techniques. In 21 (43.8%) studies, researchers opted out of imputation, completing case-wise deletion of participants missing any predictor values. Although imputation methods are encouraged to handle missing data and ensure the accuracy of inferences, this has seldom been the case in studies of diabetes risk prediction. Hence, we elaborated on the various types and patterns of missing data, the limitations of case-wise deletion and state-of the-art methods of imputations and their challenges. This review highlights the inexperience or disregard of investigators of the effect of missing data in risk prediction research. Formal guidelines may enhance the reporting and appropriate handling of missing data in scientific journals.

A routine biomarker-based risk prediction model for metabolic syndrome in urban Han Chinese population

Background

Many MetS related biomarkers had been discovered, which provided the possibility for building the MetS prediction model. In this paper we aimed to develop a novel routine biomarker-based risk prediction model for MetS in urban Han Chinese population.

Methods

Exploring Factor analysis (EFA) was firstly conducted in MetS positive 13,345 males and 3,212 females respectively for extracting synthetic latent predictors (SLPs) from 11 routine biomarkers. Then, depending on the cohort with 5 years follow-up in 1,565 subjects (male 1,020 and female 545), a Cox model for predicting 5 years MetS was built by using SLPs as predictor; Area under the ROC curves (AUC) with 10 fold cross validation was used to evaluate its power. Absolute risk (AR) and relative absolute risk (RAR) were calculated to develop a risk matrix for visualization of risk assessment.

Results

Six SLPs were extracted by EFA from 11 routine health check-up biomarkers. Each of them reflected the specific pathogenesis of MetS, with inflammatory factor (IF) contributed by WBC & LC & NGC, erythrocyte parameter factor (EPF) by Hb & HCT, blood pressure factor (BPF) by SBP & DBP, lipid metabolism factor (LMF) by TG & HDL-C, obesity condition factor (OCF) by BMI, and glucose metabolism factor (GMF) by FBG with the total contribution of 81.55% and 79.65% for males and females respectively. The proposed metabolic syndrome synthetic predictor (MSP) based predict model demonstrated good performance for predicting 5 years MetS with the AUC of 0.802 (95% CI 0.776-0.826) in males and 0.902 (95% CI 0.874-0.925) in females respectively, even after 10 fold cross validation, AUC was still enough high with 0.796 (95% CI 0.770-0.821) in males and 0.897 (95% CI 0.868-0.921) in females. More importantly, the MSP based risk matrix with a series of risk warning index provided a feasible and practical tool for visualization of risk assessment in the prediction of MetS.

Conclusions

MetS could be explained by six SLPs in Chinese urban Han population. The proposed MSP based predict model demonstrated good performance for predicting 5 years MetS, and the MetS-based matrix provided a feasible and practical tool.

Electronic supplementary material

The online version of this article (doi:10.1186/s12889-015-1424-z) contains supplementary material, which is available to authorized users.

Independent external validation and comparison of prevalent diabetes risk prediction models in a mixed-ancestry population of South Africa

BACKGROUND

Guidelines increasingly encourage the use of multivariable risk models to predict the presence of prevalent undiagnosed type 2 diabetes mellitus worldwide. However, no single model can perform well in all settings and available models must be tested before implementation in new populations. We assessed and compared the performance of five prevalent diabetes risk models in mixed-ancestry South Africans.

METHODS

Data from the Cape Town Bellville-South cohort were used for this study. Models were identified via recent systematic reviews. Discrimination was assessed and compared using C-statistic and non-parametric methods. Calibration was assessed via calibration plots, before and after recalibration through intercept adjustment.

RESULTS

Seven hundred thirty-seven participants (27 % male), mean age, 52.2 years, were included, among whom 130 (17.6 %) had prevalent undiagnosed diabetes. The highest c-statistic for the five prediction models was recorded with the Kuwaiti model [C-statistic 0.68: 95 % confidence: 0.63-0.73] and the lowest with the Rotterdam model [0. 64 (0.59-0.69)]; with no significant statistical differences when the models were compared with each other (Cambridge, Omani and the simplified Finnish models). Calibration ranged from acceptable to good, however over- and underestimation was prevalent. The Rotterdam and the Finnish models showed significant improvement following intercept adjustment.

CONCLUSIONS

The wide range of performances of different models in our sample highlights the challenges of selecting an appropriate model for prevalent diabetes risk prediction in different settings.

Risk scores based on self-reported or available clinical data to detect undiagnosed type 2 diabetes: a systematic review

OBJECTIVE

To systematically review published primary research on the development or validation of risk scores that require only self-reported or available clinical data to identify undiagnosed Type 2 Diabetes Mellitus (T2DM).

METHODS

A systematic literature search of Medline and EMBASE was conducted until January 2011. Studies focusing on the development or validation of risk scores to identify undiagnosed T2DM were included. Risk scores to predict future risk of T2DM were excluded.

RESULTS

Thirty-one studies were included; 17 developed a new risk score, 14 validated existing scores. Twenty-six studies were conducted in high-income countries. Age and measures of body mass/fat distribution were the most commonly used predictor variables. Studies developing new scores performed better than validation studies, with 11 reporting an AUC of >0.80 compared to one validation study. Fourteen validation studies reported sensitivities of <80%. The performance of scores did not differ by the number of variables included or the country setting.

CONCLUSIONS

There is a proliferation of newly developed risk scores using similar variables, which sometimes perform poorly upon external validation. Future research should explore the recalibration, validation and applicability of existing scores to other settings, particularly in low/middle income countries, and on the utility of scores to improve diabetes-related outcomes.

Similar cardiovascular risk factor profile in screen-detected and known type 2 diabetic subjects

OBJECTIVE. To compare the cardiovascular disease (CVD) risk factor profile in subjects with screen-detected type 2 diabetes (SDM) and subjects with known type 2 diabetes (KDM). DESIGN. Population-based, cross-sectional survey. SETTING AND SUBJECTS. In a single, semi-rural general practice 2082 subjects were between 20 and 69 years. Of those, 1970 subjects were invited, and a total of 1374 (69.7%) subjects were examined by blood tests, anthropometric measures, and self-administered questionnaires. RESULTS. Before the survey 19 persons were known to have type 2 diabetes. The screening revealed another 31 individuals with type 2 diabetes, diagnosed according to the 1999 World Health Organization criteria. Age, levels of blood pressure, BMI, and dyslipidaemia, and markers of haemostasis and inflammation were comparable in the two groups. Median age in the KDM group was 58 vs. 57 years in the SDM group, p = 0.82, 79% were male vs. 61%, p = 0.23. In both groups 74% had blood pressure ≥ 130/85 mmHg, p = 1.00. In both groups 90% had BMI ≥ 25, p = 1.00, and about half in both groups had BMI ≥ 30, p = 0.56. In the KDM group 63% had dyslipidaemia (low HDL cholesterol or elevated triglycerides) vs. 80% in the SDM group, p = 0.32. Median levels of plasminogen-activator-inhibitor (PAI-1), tissue plasminogen activator (t-PA), as well as fibrinogen and C-reactive protein (CRP) were without statistically significant differences in the two groups, p > 0.1. In contrast, in markers of glycaemic regulation statistically significant differences were found between groups. Median HbA1 was 8.0 vs. 6.5, p < 0.001. Median fasting whole blood glucose level was 8.8 mmol/L vs. 6.3 mmol/L, p < 0.001, and glucose at two hours during OGTT was 16.9 mmol/L vs. 11.2 mmol/L, p < 0.001. Median fasting serum insulin level was 52 pmol/L vs. 80 pmol/L, p = 0.039 and at two hours 127 pmol/L vs. 479 pmol/L, p < 0.001. CONCLUSIONS. The CVD risk-factor profile of SDM patients was similar to the expected adverse profile of patients with KDM. This indicates an already increased risk of cardiovascular disease in diabetic patients before the diabetes becomes clinically manifest, supporting the need for early diagnosis.

Who attends a UK diabetes screening programme? Findings from the ADDITION-Cambridge study

AIMS

One of the factors influencing the cost-effectiveness of population screening for Type 2 diabetes may be uptake. We examined attendance and practice- and individual-level factors influencing uptake at each stage of a diabetes screening programme in general practice.

METHODS

A stepwise screening programme was undertaken among 135, 825 people aged 40-69 years without known diabetes in 49 general practices in East England. The programme included a score based on routinely available data (age, sex, body mass index and prescribed medication) to identify those at high risk, who were offered random capillary blood glucose (RBG) and glycosylated haemoglobin tests. Those screening positive were offered fasting capillary blood glucose (FBG) and confirmatory oral glucose tolerance tests (OGTT).

RESULTS

There were 33 539 high-risk individuals invited for a RBG screening test; 24 654 (74%) attended. Ninety-four per cent attended the follow-up FBG test and 82% the diagnostic OGTT. Seventy per cent of individuals completed the screening programme. Practices with higher general practitioner staff complements and those located in more deprived areas had lower uptake for RBG and FBG tests. Male sex and a higher body mass index were associated with lower attendance for RBG testing. Older age, prescription of antihypertensive medication and a higher risk score were associated with higher attendance for FBG and RBG tests.

CONCLUSIONS

High attendance rates can be achieved by targeted stepwise screening of individuals assessed as high risk by data routinely available in general practice. Different strategies may be required to increase initial attendance, ensure completion of the screening programme, and reduce the risk that screening increases health inequalities.

Chinese metabolic syndrome risk score

BACKGROUND

The metabolic syndrome (MetS) was first proposed to predict the occurrence of cardiovascular disease and type 2 diabetes. However, it is difficult to identify subjects with MetS early. No previous studies designed to develop a predictive model for MetS in the Chinese population exists; this study was designed to fill that gap.

METHODS

A middle-aged Chinese cohort of 198 men and 154 women were followed for two years. The binary logistic regression and receiver operation characteristic (ROC) curve were used to develop a predictive model for the future development of MetS.

RESULTS

Over two years of follow up, 30 of the 352 subjects (8.52%) without MetS at baseline subsequently developed MetS. Triglycerides (TG) had the highest area under the curve (AUC), while diastolic blood pressure had the lowest. In order to increase the prediction power, MetS components were arranged in the ROC model according to their AUC. After adding waist circumference (WC) to TG (model 1), the AUC was significantly higher than for TG alone. Adding other components into the model did not increase the AUC significantly. A risk score cutoff (0.078) was selected for the best predictive power of model 1 (sensitivity of 76.7%, specificity of 63.4%, with AUC of 76.8%).

CONCLUSIONS

These results imply that WC and TG are related to the pathophysiologies of MetS, and model 1 could also be used clinically for screening subjects at high risks for MetS.

Impact of an informed choice invitation on uptake of screening for diabetes in primary care (DICISION): trial protocol

Background

Screening invitations have traditionally been brief, providing information only about population benefits. Presenting information about the limited individual benefits and potential harms of screening to inform choice may reduce attendance, particularly in the more socially deprived. At the same time, amongst those who attend, it might increase motivation to change behavior to reduce risks. This trial assesses the impact on attendance and motivation to change behavior of an invitation that facilitates informed choices about participating in diabetes screening in general practice. Three hypotheses are tested:

1. Attendance at screening for diabetes is lower following an informed choice compared with a standard invitation.

2. There is an interaction between the type of invitation and social deprivation: attendance following an informed choice compared with a standard invitation is lower in those who are more rather than less socially deprived.

3. Amongst those who attend for screening, intentions to change behavior to reduce risks of complications in those subsequently diagnosed with diabetes are stronger following an informed choice invitation compared with a standard invitation.

Method/Design

1500 people aged 40–69 years without known diabetes but at high risk are identified from four general practice registers in the east of England. 1200 participants are randomized by households to receive one of two invitations to attend for diabetes screening at their general practices. The intervention invitation is designed to facilitate informed choices, and comprises detailed information and a decision aid. A comparison invitation is based on those currently in use. Screening involves a finger-prick blood glucose test. The primary outcome is attendance for diabetes screening. The secondary outcome is intention to change health related behaviors in those attenders diagnosed with diabetes. A sample size of 1200 ensures 90% power to detect a 10% difference in attendance between arms, and in an estimated 780 attenders, 80% power to detect a 0.2 sd difference in intention between arms.

Discussion

The DICISION trial is a rigorous pragmatic denominator based clinical trial of an informed choice invitation to diabetes screening, which addresses some key limitations of previous trials.

Trial registration

Current Controlled Trials ISRCTN73125647

A simple risk score identifies individuals at high risk of developing Type 2 diabetes: a prospective cohort study

BACKGROUND

Randomized trials have demonstrated that Type 2 diabetes is preventable among high-risk individuals. To date, such individuals have been identified through population screening using the oral glucose tolerance test.

OBJECTIVE

To assess whether a risk score comprising only routinely collected non-biochemical parameters was effective in identifying those at risk of developing Type 2 diabetes.

METHODS

Population-based prospective cohort (European Prospective Investigation of Cancer-Norfolk). Participants aged 40-79 recruited from UK general practices attended a health check between 1993 and 1998 (n = 25 639) and were followed for a mean of 5 years for diabetes incidence. The Cambridge Diabetes Risk Score was computed for 24 495 individuals with baseline data on age, sex, prescription of steroids and anti-hypertensive medication, family history of diabetes, body mass index and smoking status. We examined the incidence of diabetes across quintiles of the risk score and plotted a receiver operating characteristic (ROC) curve to assess discrimination.

RESULTS

There were 323 new cases of diabetes, a cumulative incidence of 2.76/1000 person-years. Those in the top quintile of risk were 22 times more likely to develop diabetes than those in the bottom quintile (odds ratio 22.3; 95% CI: 11.0-45.4). In all, 54% of all clinically incident cases occurred in individuals in the top quintile of risk (risk score > 0.37). The area under the ROC was 74.5%.

CONCLUSION

The risk score is a simple, effective tool for the identification of those at risk of developing Type 2 diabetes. Such methods may be more feasible than mass population screening with biochemical tests in defining target populations for prevention programmes.

Additional contribution of emerging risk factors to the prediction of the risk of type 2 diabetes: evidence from the Western New York Study

OBJECTIVE

To examine whether several biomarkers of endothelial function and inflammation improve prediction of type 2 diabetes over 5.9 years of follow-up, independent of traditional risk factors.

METHODS AND PROCEDURES

A total of 1,455 participants from the Western New York Study, free of type 2 diabetes at baseline, were selected. Incident type 2 diabetes was defined as fasting glucose exceeding 125 mg/dl or on antidiabetic medication at the follow-up visit. Sixty-one people who met the case definition (8/1,000 person years) were identified and individually matched with up to three controls on gender, race, year of study enrollment, and baseline fasting glucose (<110 or 110-125 mg/dl). Biomarkers were measured from frozen baseline samples.

RESULTS

In conditional logistic regression analyses accounting for traditional risk factors (age, family history of diabetes, smoking, drinking status, and BMI), E-selectin was positively related (3rd vs. 1st tertile: odds ratio 2.77, 95% confidence interval (CI) 1.13-6.79, P for linear trend = 0.023) and serum albumin was inversely related (3rd vs. 1st tertile: odds ratio 0.36, 95% CI 0.14-0.93, P for linear trend = 0.032) to type 2 diabetes incidence. The addition of E-selectin, serum albumin, and leukocyte count to a basic risk factor model including only traditional risk factors significantly increased the area under the receiver operating characteristic curve (AUC) (from 0.646 to 0.726, P value = 0.04).

DISCUSSION

These results support the role of endothelial dysfunction and subclinical inflammation as important mechanisms in the etiopathogenesis of type 2 diabetes; moreover, they indicate that novel biomarkers may improve the prediction of type 2 diabetes beyond the use of traditional risk factors alone.