Associations Between Modifiable Risk Factors and Changes in Glycemic Status Among Individuals With Prediabetes

Associations between modifiable risk factors and frailty progression among individuals with pre-frailty

BACKGROUND

In the context of the present global aging phenomenon, the senior population and pace of aging in China have emerged as prominent issues on the worldwide stage. Frailty, a complicated condition that is closely linked to the clinical syndrome of advancing age, poses a considerable health risk to older individuals. Frailty status was assessed by the frailty index (FI) ranging from 0 to 1, pre-frailty was defined as >0.10 to <0.25, and frailty was defined as ≥0.25. To look at the connection between modifiable risk factors and frailty progression among individuals in the pre-frailty population.

METHODS

Using pre-frailty patients as characterized by the 32-frailty index, the study focused on middle-aged and elderly persons from China and ultimately recruited 5,411 participants for analysis. The relationship between modifiable factors and changes in pre-frailty status throughout follow-up was investigated. Modifiable factors were body mass index (BMI), abdominal obesity, smoking status, alcohol use, and sleep status. We employed logistic regression to examine the relationships between modifiable risk factors and changes in pre-frailty status, as well as the associations between modifiable factors scores and the corresponding pre-frailty progression. Additionally, we generated the modifiable factors scores and examined how these related to modifications in the pre-frailty stage.

RESULTS

In this study, after a mean follow-up of 6 years, (OR = 0.59, 95%CI: 0.48-0.71) for BMI ≥ 25 kg/m2 and (OR = 0.74, 95%CI: 0.63-0.89) for concomitant abdominal obesity were significantly associated with lower reversal to a healthy state; (OR = 1.24, 95%CI:1.07-1.44) and (OR = 1.25, 95%CI: 1.10-1.42) for the group that negatively progressed further to frailty were significantly associated with increased frailty progression profile. Subsequently, investigation of modifiable factor scores and changes of pre-frailty status found that as scores increased further, frailty developed (OR = 1.12, 95%CI:1.05-1.18), with scores of 3 and 4 of (OR = 1.38, 95%CI: 1.08-1.77) and (OR = 1.52, 95%CI:1.09-2.14). Finally, we also performed a series of stratified analyses and found that rural unmarried men aged 45 to 60 years with less than a high school degree were more likely to develop a frailty state once they developed abdominal obesity.

CONCLUSION

In pre-frailty individuals, maintaining more favorable controllable variables considerably enhances the chance of return to normal and, conversely, increase the risk of progressing to the frailty.

Lifestyle Intervention With Smartphone App and isCGM for People at High Risk of Type 2 Diabetes: Randomized Trial

AIMS

Although conventional interventions for people at high risk of developing type 2 diabetes are usually conducted face-to-face, such interventions are burdensome for health care providers. We developed a lifestyle intervention program combining lifestyle coaching via a smartphone application augmented by intermittently scanned continuous glucose monitoring without burdening health care providers. Its effectiveness for glycemic control and body weight reduction in people at risk of type 2 diabetes was investigated.

MATERIALS AND METHODS

For this 12-week randomized unblinded trial with offline recruitment, participants with a hemoglobin A1c level of 5.6% to 6.4% or a fasting blood glucose of 110 to 125 mg/dL and body mass index (BMI) >23 kg/m2 but <40 kg/m2 were randomly assigned to the intervention group (App) and control group (C). The primary endpoint was the difference in time in range of blood glucose between 70 and 140 mg/dL (3.9-7.8 mmol/L) before and after the study period between the 2 groups.

RESULTS

Among 168 patients (mean age, 48.1 years; mean BMI, 26.6 kg/m2; and male, 80.4%), 82 and 86 were assigned to the App group and C group, respectively. After 12 weeks, time in range of blood glucose at 70 to 140 mg/dL significantly improved in the App group compared with the C group (-2.6 minutes/day vs +31.5 minutes/day, P = .03). Changes in time above range did not differ, whereas time below range (blood glucose <70 mg/dL; +23.5 minutes/day vs -8.9 minutes/day, P = .02) improved in the App group. BMI (-0.26 vs -0.59, P = .017) was reduced in the App group compared with the C group.

CONCLUSION

Intervention with a smartphone app and intermittently scanned continuous glucose monitoring increased glycemic control accompanied by decreased carbohydrate intake and weight loss. Further trials are needed to confirm whether these interventions can reduce incident type 2 diabetes.

Modifiable Risk Factors and Trends in Changes in Glucose Regulation during the First Three Years Postdelivery: The St Carlos Gestational Diabetes Mellitus Prevention Cohort

OBJECTIVE

Evaluation of the influence of potential risk factors (RFs) on glycemic changes at 3 years postpartum.

METHODS

The glycemic status of 1400 women, in absence of a new pregnancy, was evaluated at 3 months (3 m) and 3 years (3 y) postpartum, after participation in the St. Carlos Gestational Study (2228 normoglycemic pregnant women followed from before gestational week 12 to delivery, from 2015-2017). Abnormal glucose regulation (AGR) was defined as fasting serum glucose ≥ 100 mg/dL and/or HbA1c ≥ 5.7% and/or 2 h 75 g OGTT glucose ≥ 140 mg/dL. In total, 12 modifiable and 3 unmodifiable RFs were analyzed.

RESULTS

3 m postpartum, 110/1400 (7.9%) women had AGR; 3 y postpartum, 137 (9.8%) women exhibited AGR (110 with 3 m normal glucose tolerance [NGT]); 1263 (90.2%) had NGT (83 with 3 m AGR). More women with gestational diabetes mellitus (GDM) progressed to AGR at 3 y (OR: 1.60 [1.33-1.92]) than women without GDM. Yet, most women with 3 m and/or 3 y AGR had no GDM history. Having ≥2 unmodifiable RFs was associated with increased risk for progression to AGR (OR: 1.90 [1.28-2.83]) at 3 y postpartum. Having >5/12 modifiable RFs was associated with increased progression from NGT to AGR (OR: 1.40 [1.00-2.09]) and AGR persistence (OR: 2.57 [1.05-6.31]). Pregestational BMI ≥ 25 kg/m2 (OR: 0.59 [0.41-0.85]), postdelivery weight gain (OR: 0.53 [0.29-0.94]), and waist circumference > 89.5 cm (OR: 0.54 [0.36-0.79]) reduced the likelihood of NGT persisting at 3 y.

CONCLUSIONS

3-month and/or 3-year postpartum AGR can be detected if sought in women with no prior GDM. Modifiable and unmodifiable RF predictors of AGR at 3 y postpartum were identified. Universal screening for glycemic alterations should be considered in all women following delivery, regardless of prior GDM. These findings could be useful to design personalized strategies in women with risk factors for 3 y AGR.

Predictive performance of triglyceride glucose index (TyG index) to identify glucose status conversion: a 5-year longitudinal cohort study in Chinese pre-diabetes people

OBJECTIVE

Triglyceride glucose index (TyG index) has been recommended as an alternative indicator of insulin resistance. However, the association between TyG and regression from prediabetes to normoglycemia remains to be elucidated.

METHODS

This retrospective cohort study involved 25,248 subjects with prediabetes at baseline conducted from 2010 to 2016. A Cox proportional hazard regression model was designed to evaluate the role of TyG in identifying people at converting from prediabetes to normoglycemia. Cox proportional hazards regression with cubic spline functions and smooth curve fitting was used to dig out the nonlinear relationship between them. Detailed evaluations for TyG were also performed using sensitivity and subgroup analyse.

RESULTS

Among the included prediabetes subjects (n = 25,248), the mean age was 49.27 ± 13.84 years old, and 16,701 (66.15%) were male. The mean TyG was 8.83 ± 0.60. The median follow-up time was 2.96 ± 0.90 years. 11,499 (45.54%) individuals had a final diagnosis of normoglycemia. After adjusting for covariates, TyG was negatively affecting the results of glucose status conversion in prediabetes people (HR 0.895, 95% CI 0.863, 0.928). There was a nonlinear connection between TyG and normoglycemia in prediabetes people, and the inflection point was 8.88. The effect sizes (HR) on the left and right sides of the inflection point were 0.99 (0.93, 1.05) and 0.79 (0.74, 0.85), respectively. Sensitivity analysis confirmed the robustness of these results. Subgroup analysis showed that TyG was more strongly associated with incident glucose status conversion in male, BMI ≥ 25. In contrast, there was a weaker relationship in those with female, BMI < 25.

CONCLUSION

Based on sample of subjects evaluated between 2010 and 2016, TyG index appears to be a promising marker for predicting normoglycemic conversion among prediabetes people in China. This study demonstrates a negative and non-linear association between TyG and glucose status conversion from prediabetes to normoglycemia. TyG is strongly related to glucose status conversion when TyG is above 8.88. From a therapeutic point of view, it is meaningful to maintain TyG levels within the inflection point to 8.88.