Validation of a nomogram for predicting regression from impaired fasting glucose to normoglycaemia to facilitate clinical decision making

Development of a nomogram to predict medication nonadherence risk in patients with rheumatoid arthritis

OBJECTIVES

Poor adherence among patients with chronic diseases including inflammatory rheumatic diseases (IRDs) is a complex and serious global health care problem. This study aimed to develop an intelligent nomogram using retrospectively collected patient clinical data for predicting nonadherence to biologic treatment in rheumatoid arthritis (RA) patients.

METHODS

The clinical characteristics of 102 RA patients were collected from outpatients and inpatients at the Orthopedic Departments of Ningxia General Hospital of Ningxia Medical University and Ningxia Hui Autonomous Region People's Hospital from October 2020 to September 2021. Adherence was evaluated using the proportion of treatment days covered within 6 months as the outcome event. A least absolute shrinkage and selection operator (LASSO) regression analysis was used to identify risk predictors, and then multivariate logistic regression analysis was applied to construct the risk prediction model. Furthermore, the nomogram was plotted by multivariable logistic regression.

RESULTS

Among the 102 patients analyzed, 43 patients did not adhere to biologic therapy for various reasons. LASSO regression analysis identified age, sex, education level, disease activity, monthly income, medical insurance, and adverse drug reactions as the significant risk predictors. By incorporating these factors, the nomogram was plotted which showed good discrimination, calibration, and clinical value. The C-index was 0.759 (95% CI: 0.665-0.853), and the area under the receiver operating characteristic (ROC) curve was 0.7416 with a good calibration ability. Decision curve analysis showed that the prediction effect of this model could benefit about 75% of the patients without compromising the interests of other patients.

CONCLUSIONS

This nomogram could help medical staff identify patients with higher risk of nonadherence early, so that intervention measures can be taken in time.

Development and validation of a diabetes mellitus and prediabetes risk prediction function for case finding in primary care in Hong Kong: a cross-sectional study and a prospective study protocol paper

INTRODUCTION

Diabetes mellitus (DM) is a major non-communicable disease with an increasing prevalence. Undiagnosed DM is not uncommon and can lead to severe complications and mortality. Identifying high-risk individuals at an earlier disease stage, that is, pre-diabetes (pre-DM), is crucial in delaying progression. Existing risk models mainly rely on non-modifiable factors to predict only the DM risk, and few apply to Chinese people. This study aims to develop and validate a risk prediction function that incorporates modifiable lifestyle factors to detect DM and pre-DM in Chinese adults in primary care.

METHODS AND ANALYSIS

A cross-sectional study to develop DM/Pre-DM risk prediction functions using data from the Hong Kong's Population Health Survey (PHS) 2014/2015 and a 12-month prospective study to validate the functions in case finding of individuals with DM/pre-DM. Data of 1857 Chinese adults without self-reported DM/Pre-DM will be extracted from the PHS 2014/2015 to develop DM/Pre-DM risk models using logistic regression and machine learning methods. 1014 Chinese adults without a known history of DM/Pre-DM will be recruited from public and private primary care clinics in Hong Kong. They will complete a questionnaire on relevant risk factors and blood tests on Oral Glucose Tolerance Test (OGTT) and haemoglobin A1C (HbA1c) on recruitment and, if the first blood test is negative, at 12 months. A positive case is DM/pre-DM defined by OGTT or HbA1c in any blood test. Area under receiver operating characteristic curve, sensitivity, specificity, positive predictive value and negative predictive value of the models in detecting DM/pre-DM will be calculated.

ETHICS AND DISSEMINATION

Ethics approval has been received from The University of Hong Kong/Hong Kong Hospital Authority Hong Kong West Cluster (UW19-831) and Hong Kong Hospital Authority Kowloon Central/Kowloon East Cluster (REC(KC/KE)-21-0042/ER-3). The study results will be submitted for publication in a peer-reviewed journal.

TRIAL REGISTRATION NUMBER

US ClinicalTrial.gov: NCT04881383; HKU clinical trials registry: HKUCTR-2808; Pre-results.

Daytime Napping and Nighttime Sleep Duration with Incident Diabetes Mellitus: A Cohort Study in Chinese Older Adults

Background: We aimed to examine the longitudinal associations between daytime napping and nighttime sleep duration with the risk of diabetes mellitus (DM) among Chinese elderly using data from the China Health and Retirement Longitudinal Study (CHARLS). Methods: A cohort study was conducted among 2620 participants aged 60 years or above. Information on daytime napping and nighttime sleep duration was self-reported during the 2011 baseline survey. DM status during the 2015 follow-up survey was confirmed according to the American Diabetes Association criteria. Results: Individuals with long daytime napping (>1 h/day) had increased risk of developing DM than non-nappers (adjusted RR = 1.52, 95%CI: 1.10, 2.10). In addition, we observed a U-shaped association between nighttime sleep duration and incident DM risk. We further found that nappers with <4 h of nighttime sleep, and those with >1 h of daytime napping and >6 h nighttime sleep had approximately two-fold elevated risk of DM, compared to non-nappers with 6–8 h of nighttime sleep. Conclusion: Long daytime napping and extreme nighttime sleep duration were associated with increased DM risk among Chinese elderly. There was a joint effect of long daytime napping and nighttime sleep duration on the risk of DM.

The heterogeneity of reversion to normoglycemia according to prediabetes type is not explained by lifestyle factors

Healthy lifestyle interventions and drug therapies are proven to have a positive preventative influence on normal glucose regulation in prediabetes. However, little is known on the specific role that these factors play on reversion to normal glycemia according to type of prediabetes. We used data from the Observational prospective cohort study, The Cohort study in Primary Health Care on the Evolution of Patients with Prediabetes from 2012 to 2015. A total of 1184 individuals aged 30-74 years old were included and classified based on the ADA in three mutually exclusive groups using either fasting plasma glucose (FPG) levels (from 100 to 125 mg/dl, FPG group), HbA_1c (5.7-6.4%, HbA1c group) or both impaired parameters. Information on lifestyle factors and biochemical parameters were collected at baseline. Reversion to normal glucose regulation was calculated at third year of follow-up. Relationship of lifestyle factor and type of prediabetes with reversion were estimated using odds ratios (ORs) with 95% confidence intervals (95% CIs) adjusting by different groups of confounders. Proportion of reversion rates were 31% for FPG group, 31% for HbA1c group and 7.9% for both altered parameters group, respectively. Optimal life style factors such as BMI < 25 kg/m²[OR (95% CI): 1.90 (1.20-3.01)], high adherence to Mediterranean diet 1.78 (1.21-2.63) and absence of abdominal obesity 1.70 (1.19-2.43) were the strongest predictors for reversion to normal glucose. However, those did not modify the ORs of reversion to normal glucose. Taking as reference those with both impaired parameters, subjects with FPG impairment (FPG group) had an OR of 4.87 (3.10-7.65) and 3.72 (2.39-5.78) for HbA1c group. These estimates remained almost the same after further adjustment for biochemical parameters and lifestyle factors (4.55(2.84-7.28) and 3.09 (1.92-4.97), respectively). Optimal lifestyle factors showed to be a positive predictor for reversion to normal glucose regulation however, the differences of reversion risk according type of prediabetes are not explained by lifestyle factors.

Clinical and metabolomic predictors of regression to normoglycemia in a population at intermediate cardiometabolic risk

Background

Impaired fasting glucose (IFG) is a prevalent and potentially reversible intermediate stage leading to type 2 diabetes that increases risk for cardiometabolic complications. The identification of clinical and molecular factors associated with the reversal, or regression, from IFG to a normoglycemia state would enable more efficient cardiovascular risk reduction strategies. The aim of this study was to identify clinical and biological predictors of regression to normoglycemia in a non-European population characterized by high rates of type 2 diabetes.

Methods

We conducted a prospective, population-based study among 9637 Mexican individuals using clinical features and plasma metabolites. Among them, 491 subjects were classified as IFG, defined as fasting glucose between 100 and 125 mg/dL at baseline. Regression to normoglycemia was defined by fasting glucose less than 100 mg/dL in the follow-up visit. Plasma metabolites were profiled by Nuclear Magnetic Resonance. Multivariable cox regression models were used to examine the associations of clinical and metabolomic factors with regression to normoglycemia. We assessed the predictive capability of models that included clinical factors alone and models that included clinical factors and prioritized metabolites.

Results

During a median follow-up period of 2.5 years, 22.6% of participants (n = 111) regressed to normoglycemia, and 29.5% progressed to type 2 diabetes (n = 145). The multivariate adjusted relative risk of regression to normoglycemia was 1.10 (95% confidence interval [CI] 1.25 to 1.32) per 10 years of age increase, 0.94 (95% CI 0.91–0.98) per 1 SD increase in BMI, and 0.91 (95% CI 0.88–0.95) per 1 SD increase in fasting glucose. A model including information from age, fasting glucose, and BMI showed a good prediction of regression to normoglycemia (AUC = 0.73 (95% CI 0.66–0.78). The improvement after adding information from prioritized metabolites (TG in large HDL, albumin, and citrate) was non-significant (AUC = 0.74 (95% CI 0.68–0.80), p value = 0.485).

Conclusion

In individuals with IFG, information from three clinical variables easily obtained in the clinical setting showed a good prediction of regression to normoglycemia beyond metabolomic features. Our findings can serve to inform and design future cardiovascular prevention strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-021-01246-1.

Hypertriglyceridaemic-waist phenotype and risk of diabetes in people with impaired fasting glucose in primary care: a cohort study

AIM

We aimed to determine the prospective association between baseline triglyceridaemic-waist phenotypes and diabetic mellitus incidence in individuals with impaired fasting glucose seen in primary care.

METHODS

A cohort of 1101 participants (84.4% of the recruited individuals) with impaired fasting glucose were recruited from three primary care clinics during regular follow-ups to monitor their chronic conditions. Baseline triglyceridaemic-waist phenotypes were divided into four groups: (1) normal waistline and triglyceride level (n = 252); (2) isolated central obesity (n = 518); (3) isolated high triglyceride level (n = 80); and (4) central obesity with high triglyceride level (i.e. hypertriglyceridaemic-waist phenotype) (n = 251). The presence of diabetes at follow-up was determined by fasting plasma glucose (≥ 7.0 mmol/l) and/or 2-h 75-g oral glucose tolerance test (≥ 11.1 mmol/l) and/or HbA_1c (47.5 mmol/mol; ≥ 6.5%) according to American Diabetes Association diagnostic criteria. Multivariable Cox proportional hazards regressions were established to assess the impact of different triglyceridaemic-waist phenotypes on time to diabetes onset.

RESULTS

After a mean follow-up period of 6.5 months (sd 4.7 months), the number of diabetes cases was significantly higher in the group with hypertriglyceridaemic-waist phenotype (52.2%) compared with the other three phenotype groups (group 1: 28.2%; group 2: 34.6%; group 3: 30.0%). Only the hypertriglyceridaemic-waist phenotype showed an increased risk of developing diabetes (hazard ratio 1.581, 95% CI 1.172-2.134; P = 0.003) compared with the group with normal waistline and triglyceride level after controlling for confounders.

CONCLUSIONS

The combination of central obesity and hypertriglyceridaemia is associated with > 50% risk of progression to diabetes within 6 months among individuals with impaired fasting glucose seen in primary care.

Fetuin-A levels and risk of type 2 diabetes mellitus: a systematic review and meta-analysis

AIMS

Fetuin-A has been linked to insulin resistance and obesity. Its role in the pathogenesis of type 2 diabetes (T2DM) has also been discussed. We aimed to investigate the prospective association of fetuin-A and the risk of T2DM in a systematic review and meta-analysis.

METHODS

A systematic search of studies from the MEDLINE, EMBASE, Pubmed and Web of Science using fetuin-A, diabetes and various synonyms was conducted up to June 5, 2017. Relevant studies were extracted by two reviewers independently. The quality of studies was assessed using Newcastle-Ottawa scales. Overall estimates were pooled using fixed effect with inverse variance meta-analysis. Subgroup analyses by gender, study population, techniques of assessing fetuin-A, diabetes ascertainment methods, follow-up duration and measures of association were conducted.

RESULTS

Seven studies comprising a total of 11,497 individuals and 2176 cases of T2DM were included in the systematic review and meta-analysis. Overall, one SD increment of fetuin-A level was associated with a 23% greater risk of incident T2DM (RR: 1.23, 95% CI 1.16-1.31). No significant heterogeneity or publication bias was found. The association was relatively stable across different subgroups. However, the association seemed only evident in women, but not in men.

CONCLUSIONS

Higher circulating fetuin-A levels were associated with increased risk of T2DM. However, the causality deserved further analysis.

The association between daytime napping and risk of diabetes: a systematic review and meta-analysis of observational studies

OBJECTIVE

To investigate the association between daytime napping and prevalent/incident diabetes mellitus (DM) based on systematic review and meta-analytic data.

METHODS

The electronic databases of Embase, Medline, Pubmed and Web of Science were searched. Relevant studies were extracted by two reviewers independently. The associations between daytime napping (irrespective of duration), long nap (≥1 h/day) and short nap (<1 h/day), and risk of DM were assessed according to study types. Overall estimates were pooled using either fixed- or random-effect with inverse variance meta-analysis. Heterogeneity of included studies was assessed using the I² test and possible cause of the heterogeneity was examined by meta-regression analyses.

RESULTS

Ten studies (four cross-sectional and six longitudinal cohort) comprising a total of 304,885 individuals and 20,857 cases of DM were included in the systematic review, with an average napping prevalence of 47%. Nappers were found to have increased risk of DM in both cross-sectional and cohort studies. However, significant heterogeneity was present. Long nap (≥1 h/day) was associated with both prevalent and incident DM; in particular, those with a daily nap over 1 h had a 31% increased risk of developing DM during follow-up (95% confidence interval: 2-67%). Conversely, no such association was found in individuals with short naps (<1 h/day) in cohort studies.

CONCLUSIONS

Long daytime napping over 1 h per day was associated with increased risk of both prevalent and incident DM. Further studies are needed to confirm the findings.