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

[Construction of a Predictive Model for Diabetes Mellitus Type 2 in Middle-Aged and Elderly Populations Based on the Medical Checkup Data of National Basic Public Health Service]

Objective

To establish a universally applicable logistic risk prediction model for diabetes mellitus type 2 (T2DM) in the middle-aged and elderly populations based on the results of a Meta-analysis, and to validate and confirm the efficacy of the model using the follow-up data of medical check-ups of National Basic Public Health Service.

Methods

Cohort studies evaluating T2DM risks were identified in Chinese and English databases. The logistic model utilized Meta-combined effect values such as the odds ratio (OR) to derive β, the partial regression coefficient, of the logistic model. The Meta-combined incidence rate of T2DM was used to obtain the parameter α of the logistic model. Validation of the predictive performance of the model was conducted with the follow-up data of medical checkups of National Basic Public Health Service. The follow-up data came from a community health center in Chengdu and were collected between 2017 and 2022 from 7602 individuals who did not have T2DM at their baseline medical checkups done at the community health center. This community health center was located in an urban-rural fringe area with a large population of middle-aged and elderly people.

Results

A total of 40 cohort studies were included and 10 items covered in the medical checkups of National Basic Public Health Service were identified in the Meta-analysis as statistically significant risk factors for T2DM, including age, central obesity, smoking, physical inactivity, impaired fasting glucose, a reduced level of high-density lipoprotein cholesterol (HDL-C), hypertension, body mass index (BMI), triglyceride glucose (TYG) index, and a family history of diabetes, with the OR values and 95% confidence interval (CI) being 1.04 (1.03, 1.05), 1.55 (1.29, 1.88), 1.36 (1.11, 1.66), 1.26 (1.07, 1.49), 3.93 (2.94, 5.24), 1.14 (1.06, 1.23), 1.47 (1.34, 1.61), 1.11 (1.05, 1.18), 2.15 (1.75, 2.62), and 1.66 (1.55, 1.78), respectively, and the combined β values being 0.039, 0.438, 0.307, 0.231, 1.369, 0.131, 0.385, 0.104, 0.765, and 0.507, respectively. A total of 37 studies reported the incidence rate, with the combined incidence being 0.08 (0.07, 0.09) and the parameter α being -2.442 for the logistic model. The logistic risk prediction model constructed based on Meta-analysis was externally validated with the data of 7602 individuals who had medical checkups and were followed up for at least once. External validation results showed that the predictive model had an area under curve (AUC) of 0.794 (0.771, 0.816), accuracy of 74.5%, sensitivity of 71.0%, and specificity of 74.7% in the 7602 individuals.

Conclusion

The T2DM risk prediction model based on Meta-analysis has good predictive performance and can be used as a practical tool for T2DM risk prediction in middle-aged and elderly populations.

Associations between eating speed and food temperature and type 2 diabetes mellitus: a cross-sectional study

OBJECTIVE

This study aimed to evaluate the relationship between eating speed and food temperature and type 2 diabetes mellitus (T2DM) in the Chinese population.

METHODS

A cross-sectional survey was conducted between December 2020 to March 2022 from the department of Endocrinology at the Shandong Provincial Hospital. All recruited participants were asked to complete structured questionnaires on their eating behaviors at the time of recruitment. Clinical demographic data such as gender, age, height, weight, familial history of T2DM, prevalence of T2DM and various eating behaviors were collected. Univariate and multivariate logistic regression analyses were used to analyze the associations between eating behaviors and T2DM.

RESULTS

A total of 1,040 Chinese adults were included in the study, including 344 people with T2DM and 696 people without T2DM. Multivariate logistic regression analysis of the general population showed that gender (OR = 2.255, 95% CI: 1.559-3.260, p < 0.001), age (OR = 1.091, 95% CI: 1.075-1.107, p < 0.001), BMI (OR = 1.238, 95% CI: 1.034-1.483, p = 0.020), familial history of T2DM (OR = 5.709, 95% CI: 3.963-8.224, p < 0.001), consumption of hot food (OR = 4.132, 95% CI: 2.899-5.888, p < 0.001), consumption of snacks (OR = 1.745, 95% CI: 1.222-2.492, p = 0.002), and eating speed (OR = 1.292, 95% CI:1.048-1.591, p = 0.016) were risk factors for T2DM.

CONCLUSION

In addition to traditional risk factors such as gender, age, BMI, familial history of T2DM, eating behaviors associated with Chinese culture, including consumption of hot food, consumption of snacks, and fast eating have shown to be probable risk factors for T2DM.

Predicting the Risk of Type 2 Diabetes Mellitus with the New Chinese Diabetes Risk Score in a Cohort Study

Objectives: The New Chinese Diabetes Risk Score (NCDRS) is a noninvasive tool to assess the risk of type 2 diabetes mellitus (T2DM) in the Chinese population. Our study aimed to evaluate the performance of the NCDRS in predicting T2DM risk with a large cohort. Methods: The NCDRS was calculated, and participants were categorized into groups by optimal cutoff or quartiles. Hazard ratios (HRs) and 95% confidential intervals (CIs) in Cox proportional hazards models were used to estimate the association between the baseline NCDRS and the risk of T2DM. The performance of the NCDRS was assessed by the area under the curve (AUC). Results: The T2DM risk was significantly increased in participants with NCDRS ≥25 (HR = 2.12, 95% CI 1.88-2.39) compared with NCDRS <25 after adjusting for potential confounders. T2DM risk also showed a significant increasing trend from the lowest to the highest quartile of NCDRS. The AUC was 0.777 (95% CI 0.640-0.786) with a cutoff of 25.50. Conclusion: The NCDRS had a significant positive association with T2DM risk, and the NCDRS is valid for T2DM screening in China.

Establishment of dynamic nomogram and risk score models for T2DM: a retrospective cohort study in Beijing

BACKGROUND

Health interventions can delay or prevent the occurrence and development of diabetes. Dynamic nomogram and risk score (RS) models were developed to predict the probability of developing type 2 diabetes mellitus (T2DM) and identify high-risk groups.

METHODS

Participants (n = 44,852) from the Beijing Physical Examination Center were followed up for 11 years (2006-2017); the mean follow-up time was 4.06 ± 2.09 years. Multivariable Cox regression was conducted in the training cohort to identify risk factors associated with T2DM and develop dynamic nomogram and RS models using weighted estimators corresponding to each covariate derived from the fitted Cox regression coefficients and variance estimates, and then undergone internal validation and sensitivity analysis. The concordance index (C-index) was used to assess the accuracy and reliability of the model.

RESULTS

Of the 44,852 individuals at baseline, 2,912 were diagnosed with T2DM during the follow-up period, and the incidence density rate per 1,000 person-years was 16.00. Multivariate analysis indicated that male sex (P < 0.001), older age (P < 0.001), high body mass index (BMI, P < 0.05), high fasting plasma glucose (FPG, P < 0.001), hypertension (P = 0.015), dyslipidaemia (P < 0.001), and low serum creatinine (sCr, P < 0.05) at presentation were risk factors for T2DM. The dynamic nomogram achieved a high C-index of 0.909 in the training set and 0.905 in the validation set. A tenfold cross-validation estimated the area under the curve of the nomogram at 0.909 (95% confidence interval 0.897-0.920). Moreover, the dynamic nomogram and RS model exhibited acceptable discrimination and clinical usefulness in subgroup and sensitivity analyses.

CONCLUSIONS

The T2DM dynamic nomogram and RS models offer clinicians and others who conduct physical examinations, respectively, simple-to-use tools to assess the risk of developing T2DM in the urban Chinese current or retired employees.

Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms

Objective

The prevention of hypertension in primary care requires an effective and suitable hypertension risk assessment model. The aim of this study was to develop and compare the performances of three machine learning algorithms in predicting the risk of hypertension for residents in primary care in Shanghai, China.

Methods

A dataset of 40,261 subjects over the age of 35 years was extracted from Electronic Healthcare Records of 47 community health centers from 2017 to 2019 in the Pudong district of Shanghai. Embedded methods were applied for feature selection. Machine learning algorithms, XGBoost, random forest, and logistic regression analyses were adopted in the process of model construction. The performance of models was evaluated by calculating the area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, accuracy and F1-score.

Results

The XGBoost model outperformed the other two models and achieved an AUC of 0.765 in the testing set. Twenty features were selected to construct the model, including age, diabetes status, urinary protein level, BMI, elderly health self-assessment, creatinine level, systolic blood pressure measured on the upper right arm, waist circumference, smoking status, low-density lipoprotein cholesterol level, high-density lipoprotein cholesterol level, frequency of drinking, glucose level, urea nitrogen level, total cholesterol level, diastolic blood pressure measured on the upper right arm, exercise frequency, time spent engaged in exercise, high salt consumption, and triglyceride level.

Conclusions

XGBoost outperformed random forest and logistic regression in predicting the risk of hypertension in primary care. The integration of this risk assessment model into primary care facilities may improve the prevention and management of hypertension in residents.

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.

Association of weight status and the risks of diabetes in adults: a systematic review and meta-analysis of prospective cohort studies

Obesity is a known risk factor for type 2 diabetes mellitus (T2DM); however, the associations between underweight and T2DM and between weight status and prediabetes have not been systematically reviewed. We aimed to estimate the relative risks (RRs) of prediabetes/T2DM in underweight/overweight/obesity relative to normal weight. PubMed, Embase, Web of Science, and Cochrane Library were searched from inception to December 8, 2021. Prospective cohort studies with a minimum 12-month follow-up period reporting the association between baseline body mass index (BMI) categories and risk of prediabetes/T2DM in adults were included. Study quality was assessed using the Newcastle-Ottawa Scale. The main analyses of T2DM risk were performed using the ethnic-specific (Asian/non-Asian) BMI classification and additional analyses of prediabetes/T2DM risk by including all eligible studies. Random-effects models with inverse variance weighting were used. Subgroup analyses and meta-regression were conducted to explore the potential effects of pre-specified modifiers. The study protocol was registered with PROSPERO (CRD42020215957). Eighty-four articles involving over 2.69 million participants from 20 countries were included. The pooled RR of prediabetes risk was 1.24 (95% CI: 1.19-1.28, I² = 9.7%, n = 5 studies) for overweight/obesity vs. normal weight. The pooled RRs of T2DM based on the ethnic-specific BMI categories were 0.93 (95% CI: 0.75-1.15, I² = 55.5%, n = 12) for underweight, 2.24 (95% CI: 1.95-2.56, I² = 92.0%, n = 47) for overweight, 4.56 (95% CI: 3.69-5.64, I² = 96%, n = 43) for obesity, and 22.97 (95% CI: 13.58-38.86, I² = 92.1%, n = 6) for severe obesity vs. normal weight. Subgroup analyses indicated that underweight is a protective factor against T2DM in non-Asians (RR = 0.68, 95% CI: 0.40-0.99, I² = 56.1%, n = 6). The magnitude of the RR of T2DM in overweight/obesity decreased with age and varied by region and the assessment methods for weight and T2DM. Overweight/obesity was associated with an increased prediabetes/T2DM risk. Further studies are required to confirm the association between underweight and prediabetes/T2DM, particularly in Asian populations.

The prospective effects of long-term exposure to ambient PM2.5 and constituents on mortality in rural East China

Few cohort studies explored the associations of long-term exposure to ambient fine particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5) and its chemical constituents with mortality risk in rural China. We conducted a 12-year prospective study of 28,793 adults in rural Deqing, China from 2006 to 2018. Annual mean PM2.5 and its constituents, including black carbon (BC), organic carbon (OC), ammonium (NH4+), nitrate (NO3-), sulfate (SO42-), and soil dust were measured at participants' addresses at enrollment from a satellite-based exposure predicting model. Cox proportional hazard model was used to estimate hazard ratios (HRs) and 95% confidence intervals (95%CIs) of long-term exposure to PM2.5 for mortality. A total of 1960 deaths were identified during the follow-up. We found PM2.5, BC, OC, NH4+, NO3-, and SO42- were significantly associated with an increased risk of non-accidental mortality. The HR for non-accidental mortality was 1.17 (95%CI: 1.07, 1.28) for each 10 μg/m3 increase in PM2.5. As for constituents, the strongest association was found for BC (HR = 1.21, 95%CI: 1.11, 1.33), followed by NO3-, NH4+, SO42-, and OC (HR = 1.14-1.17 per interquartile range). A non-linear relationship was found between PM2.5 and non-accidental mortality. Similar associations were found for cardio-cerebrovascular and cancer mortality. Associations were stronger among men and ever smokers. Conclusively, we found long-term exposure to ambient PM2.5 and its chemical constituents (especially BC and NO3-) increased mortality risk. Our results suggested the importance of adopting effective targeted emission control to improve air quality for health protection in rural East China.

Associations between serum amino acids and incident type 2 diabetes in Chinese rural adults

BACKGROUND AND AIMS

Some amino acids (AAs) may be associated with type 2 diabetes (T2DM). This study aimed to determine the associations of individual AAs with the development of T2DM in rural Chinese adults.

METHODS AND RESULTS

A cohort study of 1199 individuals aged 18 years or older was conducted from 2006 to 2008 in a rural community of Deqing, China, a repeated survey was done in 2015 and data linkage with the electronic health records system was performed each year for identifying new T2DM cases. A high-performance liquid chromatography approach was used to measure the baseline serum concentrations of 15 AAs. Cox proportional hazards models were used to examine the associations between AAs and the risk of incident T2DM. A total of 98 new T2DM cases were identified during the follow-up of 12 years on average. Among 15 AAs, proline was associated with an increased risk of incident T2DM after adjusted for age, sex, body mass index, fasting plasma glucose, family history of T2DM, smoking status, alcohol use, and history of hypertension, the adjusted hazard ratio for 1-standard deviation increment was 1.20 (95% confidence interval: 1.00, 1.43). The association tended to be more marked in subjects younger than 60 years and overweight/obese subjects. Among participants without hypertension, proline and phenylalanine were associated with an increased risk of incident T2DM, while aspartic acid was associated with a decreased risk.

CONCLUSION

Serum proline was associated with the risk of incident T2DM in rural Chinese adults and might be a potential predictor.

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.

Underweight rather than adiposity is an important predictor of death in rural Chinese adults: a cohort study

BACKGROUND

To assess the associations of body mass index (BMI) with all-cause and cause-specific mortalities among rural Chinese.

METHODS

A prospective study of 28 895 individuals was conducted from 2006 to 2014 in rural Deqing, China. Height and weight were measured. The association of BMI with mortality was assessed by using Cox proportional hazards model and restricted cubic spline regression.

RESULTS

There were a total of 2062 deaths during an average follow-up of 7 years. As compared with those with BMI of 22.0-24.9 kg/m², an increased risk of all-cause mortality was found for both underweight men (BMI <18.5 kg/m²) (adjusted HR (aHR): 1.45, 95% CI: 1.18 to 1.79) and low normal weight men (BMI of 18.5-21.9 kg/m²) (aHR: 1.20, 95% CI: 1.03 to 1.38). A J-shaped association was observed between BMI and all-cause mortality in men. Underweight also had an increased risk of cardiovascular disease and cancer mortalities in men. The association of underweight with all-cause mortality was more pronounced in ever smokers and older men (60+ years). The results remained after excluding participants who were followed up less than 1 year.

CONCLUSION

The present study suggests that underweight is an important predictor of mortality, especially for elderly men in the rural community of China.

Cardiometabolic risk prediction algorithms for young people with psychosis: a systematic review and exploratory analysis

OBJECTIVE

Cardiometabolic risk prediction algorithms are common in clinical practice. Young people with psychosis are at high risk for developing cardiometabolic disorders. We aimed to examine whether existing cardiometabolic risk prediction algorithms are suitable for young people with psychosis.

METHODS

We conducted a systematic review and narrative synthesis of studies reporting the development and validation of cardiometabolic risk prediction algorithms for general or psychiatric populations. Furthermore, we used data from 505 participants with or at risk of psychosis at age 18 years in the ALSPAC birth cohort, to explore the performance of three algorithms (QDiabetes, QRISK3 and PRIMROSE) highlighted as potentially suitable. We repeated analyses after artificially increasing participant age to the mean age of the original algorithm studies to examine the impact of age on predictive performance.

RESULTS

We screened 7820 results, including 110 studies. All algorithms were developed in relatively older participants, and most were at high risk of bias. Three studies (QDiabetes, QRISK3 and PRIMROSE) featured psychiatric predictors. Age was more strongly weighted than other risk factors in each algorithm. In our exploratory analysis, calibration plots for all three algorithms implied a consistent systematic underprediction of cardiometabolic risk in the younger sample. After increasing participant age, calibration plots were markedly improved.

CONCLUSION

Existing cardiometabolic risk prediction algorithms cannot be recommended for young people with or at risk of psychosis. Existing algorithms may underpredict risk in young people, even in the face of other high-risk features. Recalibration of existing algorithms or a new tailored algorithm for the population is required.

Nomogram prediction for the 3-year risk of type 2 diabetes in healthy mainland China residents

Aims

To develop a precise personalized type 2 diabetes mellitus (T2DM) prediction model by cost-effective and readily available parameters in a Central China population.

Methods

A 3-year cohort study was performed on 5557 nondiabetic individuals who underwent annual physical examination as the training cohort, and a subsequent validation cohort of 1870 individuals was conducted using the same procedures. Multiple logistic regression analysis was performed, and a simple nomogram was constructed via the stepwise method. Receiver operating characteristic (ROC) curve and decision curve analyses were performed by 500 bootstrap resamplings to assess the determination and clinical value of the nomogram, respectively. We also estimated the optimal cutoff values of each risk factor for T2DM prediction.

Results

The 3-year cumulative incidence of T2DM was 10.71%. We developed simple nomograms that predict the risk of T2DM for females and males by using the parameters of age, BMI, fasting blood glucose (FBG), low-density lipoprotein cholesterol (LDLc), high-density lipoprotein cholesterol (HDLc), and triglycerides (TG). In the training cohort, the area under the ROC curve (AUC) showed statistical accuracy (AUC = 0.863 for female, AUC = 0.751 for male), and similar results were shown in the subsequent validation cohort (AUC = 0.847 for female, AUC = 0.755 for male). Decision curve analysis demonstrated the clinical value of this nomogram. To optimally predict the risk of T2DM, the cutoff values of age, BMI, FBG, systolic blood pressure, diastolic blood pressure, total cholesterol, LDLc, HDLc, and TG were 47.5 and 46.5 years, 22.9 and 23.7 kg/m², 5.1 and 5.4 mmol/L, 118 and 123 mmHg, 71 and 85 mmHg, 5.06 and 4.94 mmol/L, 2.63 and 2.54 mmol/L, 1.53 and 1.34 mmol/L, and 1.07 and 1.65 mmol/L for females and males, respectively.

Conclusion

Our nomogram can be used as a simple, plausible, affordable, and widely implementable tool to predict a personalized risk of T2DM for Central Chinese residents. The successful identification of at-risk individuals and intervention at an early stage can provide advanced strategies from a predictive, preventive, and personalized medicine perspective.

Electronic supplementary material

The online version of this article (10.1007/s13167-019-00181-2) contains supplementary material, which is available to authorized users.

Clinical inertia, reverse clinical inertia, and medication non-adherence in type 2 diabetes

PURPOSE

Clinical inertia and medication non-adherence are thought to contribute largely to the suboptimal glycemic control in many patients with type 2 diabetes. The present review explores the relations between A1C targets, clinical inertia and medication non-adherence in type 2 diabetes.

METHODS

We searched PubMed for English-language studies published from 2001 through June 1, 2018. We also manually searched the references of selected articles, reviews, meta-analyses, and practice guidelines. Selected articles were mutually agreed upon by the authors.

RESULTS

Clinical inertia is the failure of clinicians to initiate or intensify therapy when indicated, while medication non-adherence is the failure of patients to start or continue therapy that a clinician has recommended. Although clinical inertia may occur at all stages of diabetes treatment, the longest delays were reported for initiation or intensification of insulin. Medication non-adherence to antidiabetic drugs may range from 53 to 65% at 1 year and may be responsible for uncontrolled A1C in about 23% of cases. Reverse clinical inertia can be acknowledged as the failure to reduce or change therapy when no longer needed or indicated. Clinical inertia and medication non-adherence are difficult to address: clinician-and patient-targeted educational programs, more connected communications between clinicians and patients, the help of other health professional figures (nurse, pharmacist) have been explored with mixed results.

CONCLUSIONS

Both clinical inertia and medication non-adherence remain significant barriers to optimal glycemic targets in type 2 diabetes. Moreover, part of clinical inertia may be a way through which clinicians face current uncertainty in medicine, including some dissonance among therapeutic guidelines. Scientific associations should find an agreement about how to measure and report clinical inertia in clinical practice and should exhort clinicians to consider reverse clinical inertia as a cause of persisting inappropriate therapy in vulnerable patients.

Importance of family history of diabetes in computing a diabetes risk score in Korean prediabetic population

Prediabetic subjects represent a vulnerable population, requiring special care to reduce the risk of diabetes onset. We developed and validated a diabetes risk score for prediabetic subjects using the Korea National Diabetes Program (KNDP) cohort. Subjects included in the multicenter and prospective cohort (n = 1162) had high diabetes risk at baseline (2005) and were followed until 2012. Survival analysis was performed to analyze the prospective cohort over time, and the bootstrap method was used to validate our model. We confirmed our findings in an external cohort. A diabetes risk score was calculated and the cut-off defined using a receiver operating characteristic curve. Age, body mass index, total cholesterol, and family history of diabetes were associated with diabetes. The model performed well after correction for optimism (Cadj = 0.735). A risk score was defined with a cut-off of ≥5 that maximized sensitivity (72%) and specificity (62%), with an area under the curve of 0.73. Prediabetic subjects with a family history of diabetes had a higher probability of diabetes (risk score = 5) irrespective of other variables; this result was confirmed in the external cohort. Hence, prediabetic subjects with a family history of diabetes have a higher probability of developing diabetes, regardless of other clinical factors.

Using Neighborhood-Level Census Data to Predict Diabetes Progression in Patients with Laboratory-Defined Prediabetes

CONTEXT

Research on predictors of clinical outcomes usually focuses on the impact of individual patient factors, despite known relationships between neighborhood environment and health.

OBJECTIVE

To determine whether US census information on where a patient resides is associated with diabetes development among patients with prediabetes.

DESIGN

Retrospective cohort study of all 157,752 patients aged 18 years or older from Kaiser Permanente Northern California with laboratory-defined prediabetes (fasting plasma glucose, 100 mg/dL-125 mg/dL, and/or glycated hemoglobin, 5.7%-6.4%). We assessed whether census data on education, income, and percentage of households receiving benefits through the US Department of Agriculture's Supplemental Nutrition Assistance Program (SNAP) was associated with diabetes development using logistic regression controlling for age, sex, race/ethnicity, blood glucose levels, and body mass index.

MAIN OUTCOME MEASURE

Progression to diabetes within 36 months.

RESULTS

Patients were more likely to progress to diabetes if they lived in an area where less than 16% of adults had obtained a bachelor's degree or higher (odds ratio [OR] =1.22, 95% confidence interval [CI] = 1.09-1.36), where median annual income was below $79,999 (OR = 1.16 95% CI = 1.03-1.31), or where SNAP benefits were received by 10% or more of households (OR = 1.24, 95% CI = 1.1-1.4).

CONCLUSION

Area-level socioeconomic and food assistance data predict the development of diabetes, even after adjusting for traditional individual demographic and clinical factors. Clinical interventions should take these factors into account, and health care systems should consider addressing social needs and community resources as a path to improving health outcomes.