Prediction of 5-year risk of diabetes mellitus in relatively low risk middle-aged and elderly adults

The Interaction Between Age and Risk Factors for Diabetes and Prediabetes: A Community-Based Cross-Sectional Study

OBJECTIVE

This study aimed to investigate the interaction between age groups and risk factors for diabetes and prediabetes in Shanghai communities and to identify the effect of age on other risk factors for diabetes and prediabetes.

METHODS

This study recruited 3540 participants with undiagnosed diabetes or prediabetes in 14 communities in Shanghai from February to August 2019. All participants underwent a comprehensive examination, including filling out a detailed questionnaire, physical examination, 75 g oral glucose tolerance test, and blood sample collection. In addition, logistic regression was used to analyze the interaction between age and risk factors for prediabetes and diabetes.

RESULTS

The statistical analysis included 2776 people. In this study, the prevalence of diabetes and prediabetes were 15.1% and 52.3%, respectively. The prevalence of diabetes and prediabetes is higher in the elderly than in the middle-aged group. Among the risk factors for diabetes, overweight was associated with higher age (P-interaction 0.028). In addition, among the risk factors for prediabetes, a high level of education was associated with higher age (P-interaction 0.039) and elevated serum cholesterol level was associated with lower age (P-interaction 0.019).

CONCLUSION

This study confirmed an interaction between age and other influencing factors, which may be important in explaining differences in risk factors for diabetes and prediabetes in the middle-aged and elderly populations. Community health facilities can provide health guidance to people of different age groups to prevent and control prediabetes and diabetes.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-022-01622-5.

Association of body mass index with risk of prediabetes in Chinese adults: a population-based cohort study

Aims/Introduction

Overweight and obesity in adults are strongly associated with an increased risk of prediabetes, and this study set out to gain a better understanding of the optimal body mass index (BMI) range for assessing the risk of prediabetes in the Chinese population.

Materials and Methods

The cohort study included 100,309 Chinese adults who underwent health screening. Participants were divided into six groups based on the cut‐off point for BMI recommended by the World Health Organization (underweight: <18.5 kg/m², normal‐weight: 18.5–24.9 kg/m², pre‐obese: 25.0–29.9 kg/m², obese class I: 30.0–34.9 kg/m², obese class II: 35.0–39.9 kg/m², and obese class III ≥40 kg/m²). The association of BMI with prediabetes and the shape of the correlation were modeled using multivariate Cox regression and restricted cubic spline regression, respectively.

Results

In the multivariate Cox regression model, with normal weight as the control group, underweight people had a lower risk of developing prediabetes, whereas obese and pre‐obese people had a higher risk of prediabetes. Additionally, in the restricted cubic spline model, we found that the association of BMI with prediabetes follows a positive dose–response relationship, but does not conform to the pattern of obesity paradox. Among the general population in China, a BMI of 23.03 kg/m² might be a potential intervention threshold for prediabetes.

Conclusions

The national cohort study found that the association of BMI with prediabetes follows a positive dose–response relationship, rather than a pattern of obesity paradox. For Chinese people with normal weight, more attention should be paid to glucose metabolism when BMI exceeds 23.03 kg/m².

New risk score model for identifying individuals at risk for diabetes in southwest China

The prevalence of diabetes is increasing rapidly and becoming a major public health issue worldwide. We aimed to develop a novel nomogram model for long-term diabetic risk prediction in a Chinese population. A prospective cohort study was performed on 687 nondiabetic individuals who underwent routine physical examination in 1992 and 2007. Using the least absolute shrinkage and selection operator model to optimize feature selection. Multiple Cox regression analysis was performed, and a simple nomogram was constructed. The area under receiver operating characteristic curve (AUC) and calibration plot were conducted to assess the predictive accuracy of the model. The model was subjected to bootstrap internal validation. Of the 687 participants without diabetes at baseline, 74 developed diabetes during the follow-up time. This simple nomogram model was constructed by family history of diabetes, height, waist circumference, triglycerides, fasting plasma glucose and white blood cell count. The AUCs were 0.812 (95% CI: 0.729-0.895) and 0.794 (95% CI: 0.734-0.854) for 10-year and 15-year diabetic risk. The bootstrap corrected c-index was 0.771 (95% CI: 0.721-0.821). The calibration plot also achieved good agreement between observational and actual diabetic incidence. The stratification into different risk groups by optimal cut-off value of 12.8 allowed significant distinction between cumulative diabetic incidence curves in the whole cohort and several subgroups. We established and internally validated a novel nomogram which can provide individual diabetic risk prediction for Chinese population and this practical screening model may help clinicians to identify individuals at high risk of diabetes.

The causal association between body mass index and type 2 diabetes mellitus-evidence based on regression discontinuity design

OBJECTIVE

This study aimed to investigate and determine the precise causal association between body mass index (BMI) and type 2 diabetes mellitus (T2DM) using a regression discontinuity design (RDD).

METHODS

The cross-sectional data of 8550 participants were from the China Health and Nutrition Survey (CHNS) in 2015. Influencing factors with statistically significant were selected with logistic regression analysis, and a risk prediction model was established to obtain the risk of individuals suffering from T2DM. RDD was performed with BMI as the grouping variable and the risk of individuals suffering from T2DM as the outcome variable.

RESULTS

The predictive factors in the T2DM risk prediction model were age, gender, BMI, habitation, education, physical activity level, preference for sugary beverages, walking, self-evaluation health status and history of hypertension. The AUC (area under receiver operating characteristic curve) of the T2DM risk prediction model was 0.849 (95% CI: 0.833, 0.866). BMI was an independent risk factor for T2DM (OR = 1.109, p < 0.001); at BMI = 31 kg/m² , the risk of T2DM increased sharply by 5.03% (p = 0.006).

CONCLUSIONS

There was a positive causal association between BMI and T2DM; when BMI = 31 kg/m² , the risk of individuals suffering from T2DM was sharply increased.

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.

Comparative evaluation of inflammatory parameters and substitute insulin resistance indices in elderly women with and without type 2 diabetes mellitus

PURPOSE

To analyze the influence of inflammatory parameters and substitute insulin resistance indices on the risk of type 2 diabetes mellitus (DM) development in elderly women, as well as to compare anthropometric measures and metabolic parameters according to the presence of type 2 DM and HbA1c levels.

PATIENTS AND METHODS

One hundred and twenty elderly women (67.9 ± 6.0 years) were submitted to anthropometric analysis, determination of inflammatory and metabolic parameters. They also underwent indices of lipid accumulation product (LAP), high density triglyceride/lipoprotein ratio (TG/HDL), triglyceride glucose index (TyG), as well as TyG by body mass index (BMI) ratio (TyG-BMI) assessment.

RESULTS

Body mass index, tumor necrosis factor alpha, interleukin-2, blood glucose, TG, LAP, TG/HDL, TyG and TyG-BMI were significantly higher in elderly women with DM compared to non-diabetic women. LAP ≥ 55.4 (OR = 2.29; P = .027); TyG ≥ 8.8 (OR = 3.52; P < .001) and TyG-BMI ≥ 264.8 (OR = 3.54; P = .001) were identified as risk factors for DM.

CONCLUSION

High pro-inflammatory parameters, low levels of anti-inflammatory markers and higher levels of substitute insulin resistance indices are risk predictors for DM development in elderly women in primary health care.

The Relationship between Body Mass Index and Incident Diabetes Mellitus in Chinese Aged Population: A Cohort Study

OBJECTIVES

Previous studies reported that overweight older adults had a lower mortality after cardiovascular diseases attack, indicating being thinner might not always be better. However, there is an ongoing debate about what is the optimal range of body mass index (BMI) for the aged population. We aimed to evaluate the value of BMI for the prediction of incident diabetes mellitus (DM) in the Chinese elderly population.

METHODS

A total number of 6,911 Chinese elderly people (4,110 men and 2,801 women, aged 71 ± 6.0 years) were included in this cohort study. BMI was measured at baseline (Jan 1, 2014, to Dec 31, 2014). All the participants were further classified into six groups: <18.5 kg/m², 18.5 to <22.5 kg/m², 22.5 to <25.0 kg/m², 25.0 to <27.5 kg/m², 27.5 to <30.0 kg/m², and ≥30.0 kg/m². Fasting blood glucose (FBG) and glycated hemoglobin A1c (HbA1c) were annually measured during follow-up (Jan 1, 2015-May 31, 2019). DM was confirmed if either FBG ≥ 7.0 mmol/L or HbA1c ≥ 6.5%. We used the Cox proportional hazard regression model to evaluate the association between BMI and the prediction of incident DM.

RESULTS

Comparing individuals with a BMI range of 18.5 to <22.5 kg/m² (reference), the hazard ratio for incident DM was 2.13 (95% CI: 1.54~2.95), 2.14 (95% CI: 1.53~3.00), 3.17 (95% CI: 2.19~4.59), 3.15 (95% CI: 1.94~5.09), and 3.14 (95% CI: 1.94~5.09) for the group with a BMI range of 22.5 to <25.0 kg/m², 25.0 to <27.5 kg/m², 27.5 to <30.0 kg/m², and ≥30.0 kg/m² after adjusting for baseline age, sex, blood pressure, lipid profiles, and eGFR (P trend < 0.001), after adjusting for the abovementioned confounders. The association tended to be closer in men and young participants, compared with their counterparts.

CONCLUSIONS

High BMI was associated with a high risk of developing DM in the Chinese aged population. Thus, it is optimal for the aged population to maintain their body weight within a reasonable range to prevent chronic diseases.