Evaluation of the Japanese Metabolic Syndrome Risk Score (JAMRISC): a newly developed questionnaire used as a screening tool for diagnosing metabolic syndrome and insulin resistance in Japan

A meta-analysis of diabetes risk prediction models applied to prediabetes screening

AIM

To provide a systematic overview of diabetes risk prediction models used for prediabetes screening to promote primary prevention of diabetes.

METHODS

The Cochrane, PubMed, Embase, Web of Science and China National Knowledge Infrastructure (CNKI) databases were searched for a comprehensive search period of 30 August 30, 2023, and studies involving diabetes prediction models for screening prediabetes risk were included in the search. The Quality Assessment Checklist for Diagnostic Studies (QUADAS-2) tool was used for risk of bias assessment and Stata and R software were used to pool model effect sizes.

RESULTS

A total of 29 375 articles were screened, and finally 20 models from 24 studies were included in the systematic review. The most common predictors were age, body mass index, family history of diabetes, history of hypertension, and physical activity. Regarding the indicators of model prediction performance, discrimination and calibration were only reported in 79.2% and 4.2% of studies, respectively, resulting in significant heterogeneity in model prediction results, which may be related to differences between model predictor combinations and lack of important methodological information.

CONCLUSIONS

Numerous models are used to predict diabetes, and as there is an association between prediabetes and diabetes, researchers have also used such models for screening the prediabetic population. Although it is a new clinical practice to explore, differences in glycaemic metabolic profiles, potential complications, and methods of intervention between the two populations cannot be ignored, and such differences have led to poor validity and accuracy of the models. Therefore, there is no recommended optimal model, and it is not recommended to use existing models for risk identification in alternative populations; future studies should focus on improving the clinical relevance and predictive performance of existing models.

Association between the Finnish Diabetes Risk Score and cancer in middle-aged and older adults: Involvement of inflammation

BACKGROUND

Diabetes is associated with increased risk of common cancers. However, evidence of cancer risk in individuals with different diabetes risk is still scarce, and the underlying mechanism remains unknown. Therefore, we aimed to evaluate the relationship between the Finnish Diabetes Risk Score (FINDRISC) and risks of cancer incidence and mortality in a prospective study, and to explore whether low-grade inflammation partially mediated the association.

METHODS

A total of 330,384 participants aged 37 to 73 at baseline from the UK Biobank database was included in this study. The Cox proportional hazards model was used to examine the relationship of the FINDRISC and low-grade inflammation with risks of cancer incidence and mortality. Then, we estimated the contribution of higher FINDRISC to risks of overall and site-specific cancers. In addition, the role of low-grade inflammation in the association between FINDRISC and cancer risks was investigated through mediation analysis.

RESULTS

The increased FINDRISC was dose-dependently associated with higher incidence and mortality risks of overall cancer and an overwhelming majority of site-specific cancers. The higher FINDRISC was a strong contributor to incidence of eighteen site-specific cancers and mortality of fourteen site-specific cancers, with a population-attributable risk of 8.1 %-39.1 %, 14.2 %-39.7 %, respectively. Additionally, low-grade inflammation mainly mediated the association between the FINDRISC and risks of incidence and mortality of overall cancer, colorectal cancer, etc. CONCLUSIONS: Our findings highlighted the higher FINDRISC as critical risk factors of cancer incidence and mortality, partially mediated by low-grade inflammation. Individuals with increased risk of diabetes are also needed to be concerned about cancer prevention.

Non-Laboratory-Based Risk Prediction Tools for Undiagnosed Pre-Diabetes: A Systematic Review

Early detection of pre-diabetes (pre-DM) can prevent DM and related complications. This review examined studies on non-laboratory-based pre-DM risk prediction tools to identify important predictors and evaluate their performance. PubMed, Embase, MEDLINE, CINAHL were searched in February 2023. Studies that developed tools with: (1) pre-DM as a prediction outcome, (2) fasting/post-prandial blood glucose/HbA1c as outcome measures, and (3) non-laboratory predictors only were included. The studies’ quality was assessed using the CASP Clinical Prediction Rule Checklist. Data on pre-DM definitions, predictors, validation methods, performances of the tools were extracted for narrative synthesis. A total of 6398 titles were identified and screened. Twenty-four studies were included with satisfactory quality. Eight studies (33.3%) developed pre-DM risk tools and sixteen studies (66.7%) focused on pre-DM and DM risks. Age, family history of DM, diagnosed hypertension and obesity measured by BMI and/or WC were the most common non-laboratory predictors. Existing tools showed satisfactory internal discrimination (AUROC: 0.68–0.82), sensitivity (0.60–0.89), and specificity (0.50–0.74). Only twelve studies (50.0%) had validated their tools externally, with a variance in the external discrimination (AUROC: 0.31–0.79) and sensitivity (0.31–0.92). Most non-laboratory-based risk tools for pre-DM detection showed satisfactory performance in their study populations. The generalisability of these tools was unclear since most lacked external validation.

Identifying Glucose Metabolism Status in Nondiabetic Japanese Adults Using Machine Learning Model with Simple Questionnaire

We aimed to identify the glucose metabolism statuses of nondiabetic Japanese adults using a machine learning model with a questionnaire. In this cross-sectional study, Japanese adults (aged 20–64 years) from Tokyo and surrounding areas were recruited. Participants underwent an oral glucose tolerance test (OGTT) and completed a questionnaire regarding lifestyle and physical characteristics. They were classified into four glycometabolic categories based on the OGTT results: category 1: best glucose metabolism, category 2: low insulin sensitivity, category 3: low insulin secretion, and category 4: combined characteristics of categories 2 and 3. A total of 977 individuals were included; the ratios of participants in categories 1, 2, 3, and 4 were 46%, 21%, 14%, and 19%, respectively. Machine learning models (decision tree, support vector machine, random forest, and XGBoost) were developed for identifying the glycometabolic category using questionnaire responses. Then, the top 10 most important variables in the random forest model were selected, and another random forest model was developed using these variables. Its areas under the receiver operating characteristic curve (AUCs) to classify category 1 and the others, category 2 and the others, category 3 and the others, and category 4 and the others were 0.68 (95% confidence intervals: 0.62–0.75), 0.66 (0.58–0.73), 0.61 (0.51–0.70), and 0.70 (0.62–0.77). For external validation of the model, the same dataset of 452 Japanese adults in Hokkaido was obtained. The AUCs to classify categories 1, 2, 3, and 4 and the others were 0.66 (0.61–0.71), 0.57 (0.51–0.62), 0.60 (0.50–0.69), and 0.64 (0.57–0.71). In conclusion, our model could identify the glucose metabolism status using only 10 factors of lifestyle and physical characteristics. This model may help the larger general population without diabetes to understand their glucose metabolism status and encourage lifestyle improvement to prevent diabetes.

Development and Validation of a Simple Risk Model for Predicting Metabolic Syndrome (MetS) in Midlife: A Cohort Study

Purpose

To develop and validate a simple risk model for predicting metabolic syndrome in midlife using a prospective cohort data.

Design

Prospective cohort study.

Participants

A total of 7626 members of the 1958 British birth cohort (individuals born in the first week of March 1958) participated in the biomedical survey at age 45 and have completed information on metabolic syndrome.

Methods

Variables utilised were obtained prospectively at birth, 7, 16, 23 and 45 years. Multivariable logistic regression was used to develop a total of ten (10) MetS risk prediction models taking the life course approach. Measures of discrimination and calibration were used to evaluate the performance of the models. A pragmatic criteria developed was used to select one model with the most potential to be useful. The internal validity (overfitting) of the selected model was assessed using bootstrap technique of Stata.

Main Outcome Measure

Metabolic syndrome was defined based on the NCEP-ATP III clinical criteria.

Results

There is high prevalence of MetS among the cohort members (19.6%), with males having higher risk as compared to females (22.8% vs 16.4%, P < 0.001). Individuals with MetS are more likely to have higher levels of HbA1c and low HDL-cholesterol. Similarly, regarding the individual components of MetS, male cohort members are more likely to have higher levels of glycaemia (HbA1c), BP and serum triglycerides. In contrast, female cohort members have lower levels of HDL-cholesterol and higher levels of waist circumference. Furthermore, a total of ten (10) MetS risk prediction models were developed taking the life course approach. Of these, one model with the most potential to be applied in practical setting was selected. The model has good accuracy (AUROC 0.91 (0.90, 0.92)), is well calibrated (Hosmer-Lemeshow 6.47 (0.595)) and has good internal validity.

Conclusion

Early life factors could be included in a risk model to predict MetS in midlife. The developed model has been shown to be accurate and has good internal validity. Therefore, interventions targeting socioeconomic inequality could help in the wider prevention of MetS. However, the validity of the developed model needs to be further established in an external population.

Characteristic of FINDRISC Score and Association with Diabetes Development in 6-Year Follow-Up in PURE Poland Cohort Study

Purpose

The aim of the study was to assess if FINDRISC score was associated with diabetes development after 6 years of observation.

Methods

Polish cohort is a part of global PURE study. Hereby analysis presents data from baseline (2007–2010) and 6-year follow-up (2013–2016) and was conducted on 1090 participants (702 women) from urban and rural areas in Lower Silesia region (Poland) without diabetes at the baseline and with complete data throughout course of the study.

Results

At the baseline, women had significantly higher FINDRISC score than men (10.43 vs 8.91; p=0.000) and participants from rural areas had higher score than from urban areas (10.97 vs 9.33; p=0.000). At the baseline, 25.87% of the participants had low risk of diabetes according to FINDRISC score, 38.90% had slightly elevated risk, 16.79% moderate risk, 16.42% high risk and 2.02% very high risk. Participants, who were healthy at baseline, but developed diabetes after 6 years of observation had significantly higher FINDRISC, than those who did not (13.39 vs 9.36; p=0.000). In 6-year follow-up, diabetes was diagnosed in 2.8% of participants, who were ascertained to “low risk” according to FINDRISC score in baseline; in 9.9% of participants of “slightly elevated risk”, 17.5% of participants of “moderate risk”, 26.8% in participants of “high risk” and 50.0% of participants of “very high risk”.

Conclusions

Results of PURE Poland cohort study indicates that higher FINDRISC score at the baseline was associated with higher risk of diabetes development during 6 years of observation.

Metabolic Age, an Index Based on Basal Metabolic Rate, Can Predict Individuals That are High Risk of Developing Metabolic Syndrome

INTRODUCTION

Every 10 years, an adult's basal metabolic rate (BMR), independent of their BMI, decreases 1-2% due to skeletal muscle loss, thus decreasing an adult's energy requirement and promoting obesity. Increased obesity augments the risk of developing Metabolic Syndrome (MetS); however, an adult's healthy lifestyle, which increases BMR, can mitigate MetS development. To compare different BMRs for certain ages, Metabolic age (Met-age) was developed.

AIM

To assess the association between Met-age and MetS and to determine if Met-age is an indicator of high-risk individuals for MetS.

METHODS

Four hundred thirty-five attendees at 2 clinics agreed to participate and gave signed informed consent. MetS risk was assessed by the ESF-I questionnaire. Met-age was determined using a TANITA bio-analyzer. Strengthen of association was determined by calculating Spearman's rho and predictability was evaluated by the area-under-a-receiver-operating characteristic curve (AUC). Difference-in-age (DIA) = [chronological age - Met-age].

RESULTS

There was a difference between the low-risk (n = 155) and the high-risk (n = 280) groups' Met-age (37.8±16.7 v. 62.9±17.3) and DIA (1.3±17.4 v. - 10.5±20.8, p < 0.001). There was a positive correlation between the ESF-I questionnaire and Met-age (rho = - 0.624, p < 0.001) and a negative correlation for DIA (rho = - 0.358, p < 0.001). Met-age was strongly predictive (AUC = 0.84, 95% CI 0.80-0.88), suggesting a 45.5 years cutoff (sensitivity = 83.2%, specificity = 72.3%). DIA was a good predictor (AUC = 0.68, 95% CI 0.63-0.74) with a - 11.5 years cutoff (sensitivity = 52.5%, specificity = 82.8%).

CONCLUSION

Met-age highly associated with and is an indicator of high-risk individuals for MetS. This would suggest that increases in Met-age are associated with augmented MetS severity, independent of the individual's chronological age.

Validation of a non-laboratorial questionnaire to identify Metabolic Syndrome among a population in central Mexico

OBJECTIVE

To determine the reliability of a non-laboratorial questionnaire, the Encuesta de Identificación de Sujetos Metabólicamente Comprometidos en Fase-I (ESF-I) for identifying Metabolic Syndrome among a population in central Mexico.

METHODS

Clinical and biochemical parameters were collected for 232 participants from 1 June 2012 - 31 August 2013. Three definitions of Metabolic Syndrome (Harmonizing, National Cholesterol Education Program Expert Panel and Adult Treatment Panel III [ATPIII], and International Diabetes Federation [IDF]) were used to allocate subjects to either the normal or Metabolic Syndrome positive (MetS+) group. The predictability of the questionnaire was determined by the Area-Under-the-Receiver-Operating Characteristic curve (AUC). Youden's index was calculated and the highest score was considered the optimal cutoff value. Cohen´s kappa (κ) was calculated to determine the level of agreement between the ESF-I questionnaire (max score: 15 based on 15 items) and Metabolic Syndrome.

RESULTS

From 53.8% - 60.7% of the participants were determined to be MetS+. The average questionnaire score was significantly higher in the MetS+ group for each definition (4.0 vs. 8.0, P < 0.05). The ESF-I questionnaire was predictive for the Harmonizing definition (AUC = 0.841, 95%CI: 0.790 - 0.892), the ATPIII definition (AUC = 0.827, 95%CI: 0.774 - 0.880), and the IDF definition (AUC = 0.836, 95%CI: 0.785 - 0.887). A cutoff value of 7 was determined for each definition; therefore, the cohort was re-categorized based on questionnaire results. There was a strong agreement between the ESF-I questionnaire and MetS (Harmonizing: accuracy = 77.6%, κ = 0.554; ATPIII: accuracy = 74.1%, κ = 0.489; IDF: accuracy = 74.6%, κ = 0.495, P < 0.001).

CONCLUSION

The ESF-I questionnaire can identify MetS+ patients, and therefore, lead to earlier diagnoses, reduced number of consultations, and lower costs with easier application.