Prediction of incident type 2 diabetes mellitus based on a twenty-year follow-up of the Ventimiglia heart study

Prediction Models for Type 2 Diabetes Risk in the General Population: A Systematic Review of Observational Studies

OBJECTIVES

This study aimed to provide an overview of prediction models of undiagnosed type 2 diabetes mellitus (U-T2DM) or the incident T2DM (I-T2DM) using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) checklist and the prediction model risk of the bias assessment tool (PROBAST).

DATA SOURCES

Both PUBMED and EMBASE databases were searched to guarantee adequate and efficient coverage.

STUDY SELECTION

Articles published between December 2011 and October 2019 were considered.

DATA EXTRACTION

For each article, information on model development requirements, discrimination measures, calibration, overall performance, clinical usefulness, overfitting, and risk of bias (ROB) was reported.

RESULTS

The median (interquartile range; IQR) number of the 46 study populations for model development was 5711 (1971 - 27426) and 2457 (2060 - 6995) individuals for I-T2DM and U-T2DM, respectively. The most common reported predictors were age and body mass index, and only the Qrisk-2017 study included social factors (e.g., Townsend score). Univariable analysis was reported in 46% of the studies, and the variable selection procedure was not clear in 17.4% of them. Moreover, internal and external validation was reported in 43% the studies, while over 63% of them reported calibration. The median (IQR) of AUC for I-T2DM models was 0.78 (0.74 - 0.82); the corresponding value for studies derived before October 2011 was 0.80 (0.77 - 0.83). The highest discrimination index was reported for Qrisk-2017 with C-statistics of 0.89 for women and 0.87 for men. Low ROB for I-T2DM and U-T2DM was assessed at 18% and 41%, respectively.

CONCLUSIONS

Among prediction models, an intermediate to poor quality was reassessed in several aspects of model development and validation. Generally, despite its new risk factors or new methodological aspects, the newly developed model did not increase our capability in screening/predicting T2DM, mainly in the analysis part. It was due to the lack of external validation of the prediction models.

Diabetes mellitus risk among Jordanians in a dental setting: a cross-sectional study

BACKGROUND

Dental offices can be useful to screen and identify patients at risk of developing diabetes mellitus (DM) using risk prediction tools. The Finnish Diabetes Risk Score (FINDRISC) is a validated, questionnaire-based tool used to predict the 10-year risk of developing type II DM.

OBJECTIVES

To determine the 10-year DM risk among Jordanians using the FINDRISC questionnaire in a dental setting.

MATERIALS AND METHODS

Participants attending two university dental teaching centres between March 2017 and February 2018 were interviewed using an Arabic translated version of the FINDRISC questionnaire. Anthropometrics including weight, height, waist circumference (WC) and body mass index (BMI) were recorded. Random capillary blood glucose level was measured for each participant. Statistical analysis was done using Chi-square and independent t-tests.

RESULTS

A total of 1,247 (436 males and 811 females) participants were included. As defined by BMI, 1,012 (81.2%) participants were either overweight or obese. Abdominal adiposity as determined by WC was seen in 738 (59.2%) participants. The mean (± SD) FINDRISC score for females (11.3 ± 4.3) was significantly higher (P = 0.001) than males (10.4 ± 4.9). After age adjustment, more females were in the high-risk categories (FINDRISC ≥ 15) compared with males. This trend was seen among all age groups, but was statistically significant in the older age groups; 55-64 years (P = 0.037) and ≥ 65 years (P = 0.004).

CONCLUSION

In a developing Middle Eastern country such as Jordan, almost half of Jordanians attending university dental clinics are at a moderate to high risk of developing type II DM in 10 years. The risk of DM should be considered in dental patients, particularly older females.

Detecting persons at risk for diabetes mellitus type 2 using FINDRISC: results from a community pharmacy-based study

Background

This cross-sectional study has been developed within the framework of the Italian project 'We love your heart' ('Ci sta a cuore il tuo cuore') and reports the results of the initial type 2 diabetes mellitus (T2DM) risk assessment carried out in a big network of community pharmacies in Italy and Spain.

Methods

In total 4002 pharmacists from 854pharmacies were specifically trained to collect data and perform the evaluation of the probability of developing T2DM among pharmacy customers. The risk of developing T2DM within 10 years was evaluated using the FINDRISC.

Results

Overall, 7234 (22.1%) subjects were at low risk to develop the disease, whereas 43.3% were at slightly elevated risk (scores 7-11), 19.3% were at moderate (scores 12-14), 13.9% were at high (scores 15-20), and 1.4% were at very high risk (scores > 20). Spanish participants showed higher levels of risk than Italian (16.7 vs. 14.7%) taking the cut-off FINDRISC ≥ 15.

Conclusion

This study shows that considerable percentage of persons is likely to develop diabetes in the next 10 years. Analyses of the risk factors indicate that men were more susceptible to develop this disease, as well as the Spanish participants respect to Italian.

Association between familial hypobetalipoproteinemia and the risk of diabetes. Is this the other side of the cholesterol-diabetes connection? A systematic review of literature

Statin therapy is beneficial in reducing LDL cholesterol (LDL-C) levels and cardiovascular events, but it is associated with the risk of incident diabetes mellitus (DM). Familial hypercholesterolemia (FH) is characterized by genetically determined high levels of plasma LDL-C and a low prevalence of DM. LDL-C levels seem then inversely correlated with prevalence of DM. Familial hypobetalipoproteinemia (FHBL) represents the genetic mirror of FH in terms of LDL-C levels, very low in subjects carrying mutations of APOB, PCSK9 (FHBL1) or ANGPTL3 (FHBL2). This review explores the hypothesis that FHBL might represent also the genetic mirror of FH in terms of prevalence of DM and that it is expected to be increased in FHBL in comparison with the general population. A systematic review of published literature on FHBL was made by searching PubMed (1980-2016) for articles presenting clinical data on FHBL probands and relatives. The standardized prevalence rates of DM in FHBL1 were similar to those of the reference population, with a prevalence rate of 8.2 and 9.2%, respectively, while FHBL2 showed a 4.9% prevalence of DM. In conclusion, low LDL-C levels of FHBL do not seem connected to DM as it happens in subjects undergoing statin therapy and the diabetogenic effect of statins has to be explained by mechanisms that do not rely exclusively on the reduced levels of LDL-C. The review also summarizes the published data on the effects of FHBL on insulin sensitivity and the relationships between FH, statin therapy, FHBL1 and intracellular cholesterol metabolism, evaluating possible diabetogenic pathways.

Sex specific incidence rates of type 2 diabetes and its risk factors over 9 years of follow-up: Tehran Lipid and Glucose Study

Objective

To investigate the population-based incidence of type 2 diabetes and its potential risk factors in a sex-split cohort of Iranian population.

Materials and Methods

A total of 8400 non-diabetic participants, aged ≥20 years (3620 men and 4780 women) entered the study. Crude and age standardized incidence rates per 1000 person-years were calculated for whole population and each sex separately. Cox proportional hazard models were used to evaluate hazard ratios (HR) and 95% confidence intervals for all potential risk factors in both uni-variable and multivariable models.

Results

During a median follow-up of 9.5 years, 736 new cases of diabetes were identified, including 433 women and 303 men. The annual crude and age-standardized incidence rates (95% CI) of diabetes in the total population were 10.6 (9.92–11.4) and 9.94 (7.39–13.6) per 1000 person-years of follow-up and the corresponding sex specific rates were 10.2 (9.13–11.4) and 9.36 (5.84–14.92) in men and 11.0 (9.99–12.0) and 10.1 (7.24–13.9) in women, respectively. In the multivariable model, the risk for incident diabetes was significantly associated with fasting and 2 hour post challenge plasma glucose as well as family history of diabetes in both men and women. However, among women, only the contribution of wrist circumference to incident diabetes achieved statistical significance [HR: 1.16 (1.03–1.31)] with waist/height ratio being marginally significant [HR: 1.02 (0.99–1.04)]; while among men, only body mass index was a significant predictor [HR: 1.12 (1.02–1.22)]. Additionally, low education level conferred a higher risk for incident diabetes only among men [HR: 1.80 (1.23–2.36); P for interaction with sex = 0.003].

Conclusion

Overall, sex did not significantly modify the impact of risk factors associated with diabetes among Iranian adults; however, among modifiable risk factors, the independent role of lower education and general adiposity in men and central adiposity in women might require different preventive strategies.

Long-term predictors of impaired fasting glucose and type 2 diabetes in subjects with family history of type 2 diabetes: a 12-years follow-up of the Brisighella Heart Study historical cohort

AIM

To identify and quantify the role of different risk factors in the long-term development of IFG and T2DM in a rural Italian population sample with family history of T2DM.

METHODS

We selected a sample of 1271 adult subjects from among those 1851 consecutively visited during four consecutive Brisighella Heart Study surveys (1996-2008), then selecting those ones with a family history of T2DM. Thus, we obtained a final sample including 545 subjects and for which a full clinical and ematochemistry data set was available.

RESULTS

The Cox-regression model better predicting the incident IFG and T2DM included age, gender, FPG, TG and SUA. The model best predicting the incident IFG status alone (without T2DM) is very similar to that predicting both IFG and T2DM, including the same predictors. Finally, the model best predicting T2DM (excluding IFG) simply includes FPG, BMI and ALT/AST ratio. Repeating the Cox-regression analysis using BMI as a covariate, TG appears to be also a significant predictor of T2DM (HR 1.018 95% CI 1.009-1.041, p=0.013).

CONCLUSION

In a sample of subjects with a family history of diabetes the best long-term predictors of IFG are age, gender, FPG, TG and SUA, while those of T2DM are FPG and BMI.

Predicting the short-term risk of diabetes in HIV-positive patients: the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) study

Introduction

HIV-positive patients receiving combination antiretroviral therapy (cART) frequently experience metabolic complications such as dyslipidemia and insulin resistance, as well as lipodystrophy, increasing the risk of cardiovascular disease (CVD) and diabetes mellitus (DM). Rates of DM and other glucose-associated disorders among HIV-positive patients have been reported to range between 2 and 14%, and in an ageing HIV-positive population, the prevalence of DM is expected to continue to increase. This study aims to develop a model to predict the short-term (six-month) risk of DM in HIV-positive populations and to compare the existing models developed in the general population.

Methods

All patients recruited to the Data Collection on Adverse events of Anti-HIV Drugs (D:A:D) study with follow-up data, without prior DM, myocardial infarction or other CVD events and with a complete DM risk factor profile were included. Conventional risk factors identified in the general population as well as key HIV-related factors were assessed using Poisson-regression methods. Expected probabilities of DM events were also determined based on the Framingham Offspring Study DM equation. The D:A:D and Framingham equations were then assessed using an internal-external validation process; area under the receiver operating characteristic (AUROC) curve and predicted DM events were determined.

Results

Of 33,308 patients, 16,632 (50%) patients were included, with 376 cases of new onset DM during 89,469 person-years (PY). Factors predictive of DM included higher glucose, body mass index (BMI) and triglyceride levels, and older age. Among HIV-related factors, recent CD4 counts of<200 cells/µL and lipodystrophy were predictive of new onset DM. The mean performance of the D:A:D and Framingham equations yielded AUROC of 0.894 (95% CI: 0.849, 0.940) and 0.877 (95% CI: 0.823, 0.932), respectively. The Framingham equation over-predicted DM events compared to D:A:D for lower glucose and lower triglycerides, and for BMI levels below 25 kg/m².

Conclusions

The D:A:D equation performed well in predicting the short-term onset of DM in the validation dataset and for specific subgroups provided better estimates of DM risk than the Framingham.