Development of a new risk score for incident type 2 diabetes using updated diagnostic criteria in middle-aged and older chinese

Risk prediction models for incident type 2 diabetes in Chinese people with intermediate hyperglycemia: a systematic literature review and external validation study

BACKGROUND

People with intermediate hyperglycemia (IH), including impaired fasting glucose and/or impaired glucose tolerance, are at higher risk of developing type 2 diabetes (T2D) than those with normoglycemia. We aimed to evaluate the performance of published T2D risk prediction models in Chinese people with IH to inform them about the choice of primary diabetes prevention measures.

METHODS

A systematic literature search was conducted to identify Asian-derived T2D risk prediction models, which were eligible if they were built on a prospective cohort of Asian adults without diabetes at baseline and utilized routinely-available variables to predict future risk of T2D. These Asian-derived and five prespecified non-Asian derived T2D risk prediction models were divided into BASIC (clinical variables only) and EXTENDED (plus laboratory variables) versions, with validation performed on them in three prospective Chinese IH cohorts: ACE (n = 3241), Luzhou (n = 1333), and TCLSIH (n = 1702). Model performance was assessed in terms of discrimination (C-statistic) and calibration (Hosmer-Lemeshow test).

RESULTS

Forty-four Asian and five non-Asian studies comprising 21 BASIC and 46 EXTENDED T2D risk prediction models for validation were identified. The majority were at high (n = 43, 87.8%) or unclear (n = 3, 6.1%) risk of bias, while only three studies (6.1%) were scored at low risk of bias. BASIC models showed poor-to-moderate discrimination with C-statistics 0.52-0.60, 0.50-0.59, and 0.50-0.64 in the ACE, Luzhou, and TCLSIH cohorts respectively. EXTENDED models showed poor-to-acceptable discrimination with C-statistics 0.54-0.73, 0.52-0.67, and 0.59-0.78 respectively. Fifteen BASIC and 40 EXTENDED models showed poor calibration (P < 0.05), overpredicting or underestimating the observed diabetes risk. Most recalibrated models showed improved calibration but modestly-to-severely overestimated diabetes risk in the three cohorts. The NAVIGATOR model showed the best discrimination in the three cohorts but had poor calibration (P < 0.05).

CONCLUSIONS

In Chinese people with IH, previously published BASIC models to predict T2D did not exhibit good discrimination or calibration. Several EXTENDED models performed better, but a robust Chinese T2D risk prediction tool in people with IH remains a major unmet need.

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.

Machine Learning For Tuning, Selection, And Ensemble Of Multiple Risk Scores For Predicting Type 2 Diabetes

Background

This study proposes the use of machine learning algorithms to improve the accuracy of type 2 diabetes predictions using non-invasive risk score systems.

Methods

We evaluated and compared the prediction accuracies of existing non-invasive risk score systems using the data from the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals: A Longitudinal Study). Two simple risk scores were established on the bases of logistic regression. Machine learning techniques (ensemble methods) were used to improve prediction accuracies by combining the individual score systems.

Results

Existing score systems from Western populations performed worse than the scores from Eastern populations in general. The two newly established score systems performed better than most existing scores systems but a little worse than the Chinese score system. Using ensemble methods with model selection algorithms yielded better prediction accuracy than all the simple score systems.

Conclusion

Our proposed machine learning methods can be used to improve the accuracy of screening the undiagnosed type 2 diabetes and identifying the high-risk patients.

Meta-analytic evaluation for the spatio-temporal patterns of the associations between common risk factors and type 2 diabetes in mainland China

There is a dearth of accurate information about the associations between risk factors and type 2 diabetes in mainland China. We conducted a systematic review and meta-analysis to explore the spatio-temporal patterns of the associations between common risk factors and type 2 diabetes in adults at least 18 years old in mainland China.We searched English and Chinese databases from January 1st, 1997 to December 31st, 2017 for relevant observational studies. Overall and stratification analyses including secular trends and temporal distributions were conducted, odds ratio (OR) and 95% confidence interval (CI) were calculated by applying random-effects model.Thirty-five studies were included. Type 2 diabetes was positively associated with a family history of type 2 diabetes (OR 2.89, 95%CI 2.38-3.49), hypertension (OR 2.73, 95%CI 2.25-3.36), central obesity (OR 2.28, 95%CI 1.94-2.68), dyslipidemia (OR 2.23, 95%CI 1.70-2.91), hypertriglyceridemia (OR 2.18, 95%CI 1.64-2.92), general obesity (OR 1.90, 95%CI 1.66-2.18), hypercholesterolemia (OR 1.65, 95%CI 1.32-2.06), smoking (OR 1.26, 95%CI 1.13-1.40), and drinking (OR 1.20, 95%CI 1.05-1.36), whereas a negative association with female gender (OR 0.87, 95%CI 0.78-0.97) existed. Except for female gender and drinking, the pooled effects of temporal and spatial stratification for the other five risk factors were consistent with the above results. For temporal stratification, the ORs of general obesity increased gradually during the periods of 1992 to 2005, 2006 to 2010, and 2011 to 2017, while the ORs of a family history declined. For regional stratification, the magnitudes of ORs for hypertension, dyslipidemia, and hypercholesterolemia in northern areas were larger than that in southern areas, while opposite situation occurred for a family history. Except for the factor a family history, provincial results for the other nine risk factors differed from the overall results and among provinces.Effect differences existed for modifiable and non-modifiable risk factors in secular trends and regional distribution, which is of potential public health importance for type 2 diabetes prevention.

Differing Contributions of Classical Risk Factors to Type 2 Diabetes in Multi-Ethnic Malaysian Populations

The prevalence of type 2 diabetes is escalating rapidly in Asian countries, with the rapid increase likely attributable to a combination of genetic and lifestyle factors. Recent research suggests that common genetic risk variants contribute minimally to the rapidly rising prevalence. Rather, recent changes in dietary patterns and physical activity may be more important. This nested case-control study assessed the association and predictive utility of type 2 diabetes lifestyle risk factors in participants from Malaysia, an understudied Asian population with comparatively high disease prevalence. The study sample comprised 4077 participants from The Malaysian Cohort project and included sub-samples from the three major ancestral groups: Malay (n = 1323), Chinese (n = 1344) and Indian (n = 1410). Association of lifestyle factors with type 2 diabetes was assessed within and across ancestral groups using logistic regression. Predictive utility was quantified and compared between groups using the Area Under the Receiver-Operating Characteristic Curve (AUC). In predictive models including age, gender, waist-to-hip ratio, physical activity, location, family history of diabetes and average sleep duration, the AUC ranged from 0.76 to 0.85 across groups and was significantly higher in Chinese than Malays or Indians, likely reflecting anthropometric differences. This study suggests that obesity, advancing age, a family history of diabetes and living in a rural area are important drivers of the escalating prevalence of type 2 diabetes in Malaysia.

Development of type 2 diabetes mellitus in people with intermediate hyperglycaemia

BACKGROUND

Intermediate hyperglycaemia (IH) is characterised by one or more measurements of elevated blood glucose concentrations, such as impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and elevated glycosylated haemoglobin A1c (HbA1c). These levels are higher than normal but below the diagnostic threshold for type 2 diabetes mellitus (T2DM). The reduced threshold of 5.6 mmol/L (100 mg/dL) fasting plasma glucose (FPG) for defining IFG, introduced by the American Diabetes Association (ADA) in 2003, substantially increased the prevalence of IFG. Likewise, the lowering of the HbA1c threshold from 6.0% to 5.7% by the ADA in 2010 could potentially have significant medical, public health and socioeconomic impacts.

OBJECTIVES

To assess the overall prognosis of people with IH for developing T2DM, regression from IH to normoglycaemia and the difference in T2DM incidence in people with IH versus people with normoglycaemia.

SEARCH METHODS

We searched MEDLINE, Embase, ClincialTrials.gov and the International Clinical Trials Registry Platform (ICTRP) Search Portal up to December 2016 and updated the MEDLINE search in February 2018. We used several complementary search methods in addition to a Boolean search based on analytical text mining.

SELECTION CRITERIA

We included prospective cohort studies investigating the development of T2DM in people with IH. We used standard definitions of IH as described by the ADA or World Health Organization (WHO). We excluded intervention trials and studies on cohorts with additional comorbidities at baseline, studies with missing data on the transition from IH to T2DM, and studies where T2DM incidence was evaluated by documents or self-report only.

DATA COLLECTION AND ANALYSIS

One review author extracted study characteristics, and a second author checked the extracted data. We used a tailored version of the Quality In Prognosis Studies (QUIPS) tool for assessing risk of bias. We pooled incidence and incidence rate ratios (IRR) using a random-effects model to account for between-study heterogeneity. To meta-analyse incidence data, we used a method for pooling proportions. For hazard ratios (HR) and odds ratios (OR) of IH versus normoglycaemia, reported with 95% confidence intervals (CI), we obtained standard errors from these CIs and performed random-effects meta-analyses using the generic inverse-variance method. We used multivariable HRs and the model with the greatest number of covariates. We evaluated the certainty of the evidence with an adapted version of the GRADE framework.

MAIN RESULTS

We included 103 prospective cohort studies. The studies mainly defined IH by IFG5.6 (FPG mmol/L 5.6 to 6.9 mmol/L or 100 mg/dL to 125 mg/dL), IFG6.1 (FPG 6.1 mmol/L to 6.9 mmol/L or 110 mg/dL to 125 mg/dL), IGT (plasma glucose 7.8 mmol/L to 11.1 mmol/L or 140 mg/dL to 199 mg/dL two hours after a 75 g glucose load on the oral glucose tolerance test, combined IFG and IGT (IFG/IGT), and elevated HbA1c (HbA1c5.7: HbA1c 5.7% to 6.4% or 39 mmol/mol to 46 mmol/mol; HbA1c6.0: HbA1c 6.0% to 6.4% or 42 mmol/mol to 46 mmol/mol). The follow-up period ranged from 1 to 24 years. Ninety-three studies evaluated the overall prognosis of people with IH measured by cumulative T2DM incidence, and 52 studies evaluated glycaemic status as a prognostic factor for T2DM by comparing a cohort with IH to a cohort with normoglycaemia. Participants were of Australian, European or North American origin in 41 studies; Latin American in 7; Asian or Middle Eastern in 50; and Islanders or American Indians in 5. Six studies included children and/or adolescents.Cumulative incidence of T2DM associated with IFG5.6, IFG6.1, IGT and the combination of IFG/IGT increased with length of follow-up. Cumulative incidence was highest with IFG/IGT, followed by IGT, IFG6.1 and IFG5.6. Limited data showed a higher T2DM incidence associated with HbA1c6.0 compared to HbA1c5.7. We rated the evidence for overall prognosis as of moderate certainty because of imprecision (wide CIs in most studies). In the 47 studies reporting restitution of normoglycaemia, regression ranged from 33% to 59% within one to five years follow-up, and from 17% to 42% for 6 to 11 years of follow-up (moderate-certainty evidence).Studies evaluating the prognostic effect of IH versus normoglycaemia reported different effect measures (HRs, IRRs and ORs). Overall, the effect measures all indicated an elevated risk of T2DM at 1 to 24 years of follow-up. Taking into account the long-term follow-up of cohort studies, estimation of HRs for time-dependent events like T2DM incidence appeared most reliable. The pooled HR and the number of studies and participants for different IH definitions as compared to normoglycaemia were: IFG5.6: HR 4.32 (95% CI 2.61 to 7.12), 8 studies, 9017 participants; IFG6.1: HR 5.47 (95% CI 3.50 to 8.54), 9 studies, 2818 participants; IGT: HR 3.61 (95% CI 2.31 to 5.64), 5 studies, 4010 participants; IFG and IGT: HR 6.90 (95% CI 4.15 to 11.45), 5 studies, 1038 participants; HbA1c5.7: HR 5.55 (95% CI 2.77 to 11.12), 4 studies, 5223 participants; HbA1c6.0: HR 10.10 (95% CI 3.59 to 28.43), 6 studies, 4532 participants. In subgroup analyses, there was no clear pattern of differences between geographic regions. We downgraded the evidence for the prognostic effect of IH versus normoglycaemia to low-certainty evidence due to study limitations because many studies did not adequately adjust for confounders. Imprecision and inconsistency required further downgrading due to wide 95% CIs and wide 95% prediction intervals (sometimes ranging from negative to positive prognostic factor to outcome associations), respectively.This evidence is up to date as of 26 February 2018.

AUTHORS' CONCLUSIONS

Overall prognosis of people with IH worsened over time. T2DM cumulative incidence generally increased over the course of follow-up but varied with IH definition. Regression from IH to normoglycaemia decreased over time but was observed even after 11 years of follow-up. The risk of developing T2DM when comparing IH with normoglycaemia at baseline varied by IH definition. Taking into consideration the uncertainty of the available evidence, as well as the fluctuating stages of normoglycaemia, IH and T2DM, which may transition from one stage to another in both directions even after years of follow-up, practitioners should be careful about the potential implications of any active intervention for people 'diagnosed' with IH.

Development of a new scoring system to predict 5-year incident diabetes risk in middle-aged and older Chinese

AIMS

The aim of this study was to develop a new risk score system to predict 5-year incident diabetes risk among middle-aged and older Chinese population.

METHODS

This prospective study included 17,690 individuals derived from the Dongfeng-Tongji cohort. Participants were recruited in 2008 and were followed until October 2013. Incident diabetes was defined as self-reported clinician diagnosed diabetes, fasting glucose ≥7.0 mmol/l, or the use of insulin or oral hypoglycemic agent. A total of 1390 incident diabetic cases were diagnosed during the follow-up period. β-Coefficients were derived from Cox proportional hazard regression model and were used to calculate the risk score.

RESULTS

The diabetes risk score includes BMI, fasting glucose, hypertension, hyperlipidemia, current smoking status, and family history of diabetes. The β-coefficients of these variables ranged from 0.139 to 1.914, and the optimal cutoff value was 1.5. The diabetes risk score was calculated by multiplying the β-coefficients of the significant variables by 10 and rounding to the nearest integer. The score ranges from 0 to 36. The area under the receiver operating curve of the score was 0.751. At the optimal cutoff value of 15, the sensitivity and specificity were 65.6 and 72.9%, respectively. Based upon these risk factors, this model had the highest discrimination compared with several commonly used diabetes prediction models.

CONCLUSIONS

The newly established diabetes risk score with six parameters appears to be a reliable screening tool to predict 5-year risk of incident diabetes in a middle-aged and older Chinese population.

Risk score model of type 2 diabetes prediction for rural Chinese adults: the Rural Deqing Cohort Study

OBJECTIVE

Risk score (RS) model is a cost-effective tool to identify adults who are at high risk for diabetes. This study was to develop an RS model of type 2 diabetes (T2DM) prediction specifically for rural Chinese adults.

METHODS

A prospective whole cohort study (n = 28,251) and a sub-cohort study (n = 3043) were conducted from 2006-2014 and 2006-2008 to 2015 in rural Deqing, China. All participants were free of T2DM at baseline. Incident T2DM cases were identified through electronic health records, self-reported and fasting plasma glucose testing for the sub-cohort, respectively. RS models were constructed with coefficients (β) of Cox regression. Receiver-operating characteristic curves were plotted and the area under the curve (AUC) reflected the discriminating accuracy of an RS model.

RESULTS

By 2015, the incidence of T2DM was 3.3 and 7.7 per 1000 person-years in the whole cohort and the sub-cohort, respectively. Based on data from the whole cohort, the non-invasive RS model included age (4 points), overweight (2 points), obesity (4 points), family history of T2DM (3 points), meat diet (3 points), and hypertension (2 points). The plus-fasting plasma glucose (FPG) model added impaired fasting glucose (4 points). The AUC was 0.705 with a positive predictive value of 2.5% for the non-invasive model, and for the plus-FPG model the AUC was 0.754 with a positive predictive value of 2.5%. These models performed better as compared with 12 existing RS models for the study population.

CONCLUSIONS

Our non-invasive RS model can be used to identify individuals who are at high risk of T2DM as a simple, fast, and cost-effective tool for rural Chinese adults.

Risk Score for Detecting Dysglycemia: A Cross-Sectional Study of a Working-Age Population in an Oil Field in China

Background

Dysglycemia (pre-diabetes or diabetes) in young adults has increased rapidly. However, the risk scores for detecting dysglycemia in oil field staff and workers in China are limited. This study developed a risk score for the early identification of dysglycemia based on epidemiological and health examination data in an oil field working-age population with increased risk of diabetes.

Material/Methods

Multivariable logistic regression was used to develop the risk score model in a population-based, cross-sectional study. All subjects completed the questionnaires and underwent physical examination and oral glucose tolerance tests. The performance of the risk score models was evaluated using the area under the receiver operating characteristic curve (AUC).

Results

The study population consisted of 1995 participants, 20–64 years old (49.4% males), with undiagnosed diabetes or pre-diabetes who underwent periodic health examinations from March 2014 to June 2015 in Dagang oil field, Tianjin, China. Age, sex, body mass index, history of high blood glucose, smoking, triglyceride, and fasting plasma glucose (FPG) constituted the Dagang dysglycemia risk score (Dagang DRS) model. The performance of Dagang DRS was superior to m-FINDRISC (AUC: 0.791; 95% confidence interval (CI), 0.773–0.809 vs. 0.633; 95% CI, 0.611–0.654). At the cut-off value of 5.6 mmol/L, the Dagang DRS (AUC: 0.616; 95% CI, 0.592–0.641) was better than the FPG value alone (AUC: 0.571; 95% CI, 0.546–0.596) in participants with FPG <6.1 mmol/L (n=1545, P=0.028).

Conclusions

Dagang DRS is a valuable tool for detecting dysglycemia, especially when FPG <6.1 mmol/L, in oil field workers in China.

High-sensitive C-reactive protein and risk of incident type 2 diabetes: a case-control study nested within the Singapore Chinese Health Study

BACKGROUND

The liver-derived C-reactive protein (CRP) is a sensitive and systemic biomarker of inflammation, and has been associated with increased risk of developing type 2 diabetes in populations other than Chinese. Therefore, we prospectively examined the relation between plasma levels of CRP and risk of type 2 diabetes (T2D) among a Chinese population.

METHODS

Plasma high-sensitive CRP (hs-CRP) concentrations were assayed among 571 T2D cases and 571 controls nested in the prospective cohort of the Singapore Chinese Health Study. Both cases and controls were free of physician-diagnosed diabetes, cardiovascular disease and cancer at blood collections (1999-2004). Incident physician-diagnosed T2D cases were self-reported during the follow-up visits (2006-2010), and controls were matched for age (±3 years) and date (±6 months) of blood collection and gender. Multivariable logistic regression models were used to compute the odds ratio (OR) and the corresponding 95% confidence intervals (CIs).

RESULTS

The mean (SD) concentrations of hs-CRP were 2.79 (2.65) and 1.86 (2.03) mg/L, respectively, in cases and controls (P < 0.001). After multivariate adjustment for T2D risk factors such as lifestyle, body mass index, plasma triglycerides and HDL cholesterol, the OR comparing the extreme quartiles of hs-CRP was 1.74 [95% CI 1.12-2.70; P for trend = 0.016]. When the analysis was limited to 279 cases who had HbA1c ≥6.5% at the time of blood collection and their controls, the OR comparing the extreme quartiles of hs-CRP was 2.43 (95% CI 1.25-4.71; P for trend = 0.003). When confined to the other 292 subjects with HbA1c <6.5% and their controls, the corresponding OR was 1.24 (95% CI 0.64-2.39; P for trend = 0.93).

CONCLUSIONS

We found that CRP was not associated with increased risk of incident diabetes in this cohort of Chinese in Singapore. Previous positive findings from prospective studies might be partly due to undiagnosed T2D among the cases during blood collection.

Prediction of the 20-year incidence of diabetes in older Chinese: Application of the competing risk method in a longitudinal study

The competing risk method has become more acceptable for time-to-event data analysis because of its advantage over the standard Cox model in accounting for competing events in the risk set. This study aimed to construct a prediction model for diabetes using a subdistribution hazards model.We prospectively followed 1857 community residents who were aged ≥ 55 years, free of diabetes at baseline examination from August 1992 to December 2012. Diabetes was defined as a self-reported history of diabetes diagnosis, taking antidiabetic medicine, or having fasting plasma glucose (FPG) ≥ 7.0 mmol/L. A questionnaire was used to measure diabetes risk factors, including dietary habits, lifestyle, psychological factors, cognitive function, and physical condition. Gray test and a subdistribution hazards model were used to construct a prediction algorithm for 20-year risk of diabetes. Receiver operating characteristic (ROC) curves, bootstrap cross-validated Wolber concordance index (C-index) statistics, and calibration plots were used to assess model performance.During the 20-year follow-up period, 144 cases were documented for diabetes incidence with a median follow-up of 10.9 years (interquartile range: 8.0-15.3 years). The cumulative incidence function of 20-year diabetes incidence was 11.60% after adjusting for the competing risk of nondiabetes death. Gray test showed that body mass index, FPG, self-rated heath status, and physical activity were associated with the cumulative incidence function of diabetes after adjusting for age. Finally, 5 standard risk factors (poor self-rated health status [subdistribution hazard ratio (SHR) = 1.73, P = 0.005], less physical activity [SHR = 1.39, P = 0.047], 55-65 years old [SHR = 4.37, P < 0.001], overweight [SHR = 2.15, P < 0.001] or obesity [SHR = 1.96, P = 0.003], and impaired fasting glucose [IFG] [SHR = 1.99, P < 0.001]) were significantly associated with incident diabetes. Model performance was moderate to excellent, as indicated by its bootstrap cross-validated discrimination C-index (0.74, 95% CI: 0.70-0.79) and calibration plot.Poor self-rated health, physical inactivity, being 55 to 65 years of age, overweight/obesity, and IFG were significant predictors of incident diabetes. Early prevention with a goal of achieving optimal levels of all risk factors should become a key element of diabetes prevention.

Analysis of Inflammatory Mediators in Prediabetes and Newly Diagnosed Type 2 Diabetes Patients

This study evaluated the inflammatory markers in prediabetes and newly diagnosed type 2 diabetes mellitus (T2DM). Inflammatory markers levels were analyzed using one-way analysis of covariance and the association with prediabetes or T2DM risks was examined by logistic regression models. Our data showed increased levels of hypersensitivity C-reactive protein (hs-CRP), interleukin (IL-4), IL-10, and tryptase in prediabetes subjects and hs-CRP, immunoglobulin E (IgE), IL-4, and IL-10 in T2DM subjects. We concluded that Hs-CRP, IgE, IL-4, IL-10, and tryptase were positively associated with prediabetes or T2DM. Further large prospective studies are warranted to assess a temporal relation between inflammatory biomarkers and incidence of prediabetes or T2DM and its associated chronic diseases.

Causes of type 2 diabetes in China

The prevalence of diabetes in China has increased substantially over recent decades, with more than 100 million people estimated to be affected by the disease presently. During this period there has been an increase in the rates of obesity and a reduction in physical activity. Many of the changes in lifestyle and diet are a result of increased economic development and urbanisation. In addition to an increasingly westernised diet, the traditional Chinese diet also plays a part, with the quantity and quality of rice intake linked to the risk of type 2 diabetes. Familial factors including inherited genetic variants are important, although differences in the genetic architecture suggest a different combination of genetic variants could be most relevant in Chinese when compared with Europeans. Recent advances have also emphasised the role of early life factors in the epidemic of diabetes and non-communicable diseases: maternal undernutrition, maternal obesity, and gestational diabetes are all linked to increased risk of diabetes in offspring. A mismatch between developmentally programmed biology and the modern environment is relevant for countries like China where there has been rapid economic transformation. Multisectoral efforts to address the risks will be needed at different stages throughout the lifecourse to reduce the burden of diabetes.

Evidence Gap on the Prevalence of Non-conventional Risk Factors for Type 2 Diabetes in Iran

OBJECTIVES

Robust scientific evidence exists about the role of non-conventional risk factors in type 2 diabetes worldwide. The current epidemiological pattern of the disease in Iran suggests a precipitating role for these non-conventional risk factors. This review was performed to examine the research evidence suggesting a higher prevalence of non-conventional type 2 diabetes risk factors in Iran.

METHODS

MeSH keywords were applied to search several databases, including PUBMED, MEDLINE, AMED, EMBASE, Iran DOC, and the Scientific Information Database without a time limit from inception to September 2011. The quality of the non-interventional and population-based studies on Iranians included in these databases was assessed by the authors and any disagreement was resolved with consensus.

RESULTS

The literature search yielded 1847 publications, of which 62 were included in this study after eliminating non-relevant and overlapping papers. No study was found that verified a higher prevalence of the non-conventional type 2 diabetes risk factors in the Iranian population.

CONCLUSION

The identified evidence gap about the role of prominent non-conventional risk factors of type 2 diabetes in the Iranian population could be a major caveat in the application of an evidence-based approach to endorse or reject existing hypothesis about these risk factors. Studies on the prevalence of non-conventional biomarkers of type 2 diabetes among Iranians could be a promising area of research.