Factors correlated with targeted prevention for prediabetes classified by impaired fasting glucose, impaired glucose tolerance, and elevated HbA1c: A population-based longitudinal study.

Background: There is still controversy surrounding the precise characterization of prediabetic population. We aim to identify and examine factors of demographic, behavioral, clinical, and biochemical characteristics, and obesity indicators (anthropometric characteristics and anthropometric prediction equation) for prediabetes according to different definition criteria of the American Diabetes Association (ADA) in the Chinese population. Methods: A longitudinal study consisted of baseline survey and two follow-ups was conducted, and a pooled data were analyzed. Prediabetes was defined as either impaired fasting glucose (IFG), impaired glucose tolerance (IGT), or elevated glycosylated hemoglobin (HbA1c) according to the ADA criteria. Robust generalized estimating equation models were used. Results: A total of 5,713 (58.42%) observations were prediabetes (IGT, 38.07%; IGT, 26.51%; elevated HbA1c, 23.45%); 9.66% prediabetes fulfilled all the three ADA criteria. Among demographic characteristics, higher age was more evident in elevated HbA1c [adjusted OR (aOR)=2.85]. Female individuals were less likely to have IFG (aOR=0.70) and more likely to suffer from IGT than male individuals (aOR=1.41). Several inconsistency correlations of biochemical characteristics and obesity indicators were detected by prediabetes criteria. Body adiposity estimator exhibited strong association with prediabetes (D10: aOR=4.05). For IFG and elevated HbA1c, the odds of predicted lean body mass exceed other indicators (D10: aOR=3.34; aOR=3.64). For IGT, predicted percent fat presented the highest odds (D10: aOR=6.58). Conclusion: Some correlated factors of prediabetes under different criteria differed, and obesity indicators were easily measured for target identification. Our findings could be used for targeted intervention to optimize preventions to mitigate the obviously increased prevalence of diabetes.

Short-term effects of lifestyle intervention in the reversion to normoglycemia in people with prediabetes.

AIMS: To evaluate the short-term effect of lifestyle intervention in people with prediabetes. METHODS: A stratified multistage sampling method was used in the recruitment of residents of the Jiangsu Province, China in 2017, who had no previous diagnosis of diabetes. Physical examination and laboratory tests were performed, and questionnaires were completed. Those with a prediabetes diagnosis at baseline were included in the cohort and participants were randomized to the intervention group or the control group. The intervention group received a lifestyle intervention strategy, which included exercise, diet and peer educations. The control group received general health education. Participants were followed up in 2018. RESULTS: A total of 2005 individuals were included in the analysis. At follow-up, there were 516 (36.7%) individuals in the intervention group and 207 (34.5%) individuals in the control group with normal blood glucose levels. The decline in waist circumference and fasting plasma glucose levels was significantly higher in the intervention group than in the control group. This was still observed after adjusting for variables (odds ratio 1.32, P = 0.02). Females or younger individuals who had lower body mass index and plasma glucose levels at baseline were more likely to reverse to normoglycemia at follow-up. CONCLUSIONS: Compared with a strategy of general health education, a lifestyle intervention strategy could reverse glucose levels to normoglycemia in individuals with prediabetes.

Effect of Metabolic Health and Obesity Phenotype on Risk of Diabetes Mellitus: A Population-Based Longitudinal Study.

BACKGROUND: Epidemiologic evidence on body mass index (BMI)-metabolic status phenotypes and diabetes risk remains controversial, especially for metabolically healthy obesity (MHO). We aimed to examine the effect of metabolic health and obesity phenotype on diabetes risk in the Chinese population. METHODS: A population-based cohort study was carried out. The baseline survey was conducted in 2017, with two follow-up visits in 2018 and 2020. Diabetes was defined based on the criteria of the World Health Organization. Robust generalized estimating equation models with a binary distribution using a log link and exchange structure were applied for the pooled analysis sample. RESULTS: A total sample of 9623 observations was pooled for the longitudinal data analysis. The average follow-up time was 1.64 years per person and the overall incidence density of diabetes was 6.94% person-years. Decreased diabetes risk was found in metabolically healthy overweight phenotype (RR = 0.65; 95% CI = 0.47-0.90) and no significant associations were detected for the MHO individuals (RR = 0.99; 95% CI = 0.63-1.53) compared with those of metabolically healthy normal weight, in contrast to metabolically unhealthy normal weight (MU-NW) (RR = 1.81; 95% CI = 1.28-2.55), metabolically unhealthy overweight (MU-OW) (RR = 2.02; 95% CI = 1.57-2.61) and metabolically unhealthy obesity (MUO) (RR = 2.48; 95% CI = 1.89-3.26) phenotypes. Significant associations between BMI-metabolic status phenotypes and diabetes were found in both males and females. CONCLUSION: The MUO phenotype needs to be accorded much more importance. MU-NW and MU-OW are also important component for targeted prevention. Our findings can be targeted for optimizing preventive strategies to mitigate the obviously increased prevalence of diabetes.

A genetic analysis identifies haplotype at adiponectin locus: Association with the metabolic health and obesity phenotypes.

BACKGROUND: Obesity and metabolic syndrome frequently co-exist and define obese individuals into different obesity phenotypes, such as metabolically healthy obese (MHO), metabolically unhealthy obese (MUO) and metabolically unhealthy normal weight (MUNW). Growing evidence suggests that genetic predisposition and environmental factors can explain the heterogeneity among these phenotypes. METHODS: We conducted a case-control study including 130 MHO, 251 MUNW, 208 MUO and 336 health controls by genotyping 2 SNPs (rs2241766, rs1501299) in ADIPOQ to investigate possible associations between SNPs in the ADIPOQ gene with susceptibility to three obese phenotypes respectively in Chinese Han population. Unconditional logistic regressions were used to detect the association between ADIPOQ SNPs and MHO/MUNW/MUO risks. RESULTS: Variant G allele of rs2241766 was associated with a reduced odds of MUO (additive model: Adjusted OR = 0.55; 95% CI = 0.40-0.75; P < 0.001) and no evidence of any significant association between rs2241766 and MHO phenotype (additive model: Adjusted OR = 0.84; 95% CI = 0.61-1.16; P = 0.306) or MUNW phenotype (additive model: Adjusted OR = 0.95; 95% CI = 0.73-1.24; P = 0.720) was found. Minor allele T of rs1501299 were significantly associated with decreased risk of MHO (Adjusted OR = 0.53; 95% CI = 0.37-0.76; P < 0.001) and MUNW (Adjusted OR = 0.63; 95% CI = 0.48-0.83; P = 0.001) in additive genetic model after correction for multiple testing. CONCLUSIONS: The variant G allele of rs2241766 was negatively associated with risk of MUO and variant T allele of rs1501299 exhibited reduced odds for MHO and MUNW. Beyond that, future studies are warranted to validate and extend our findings.

Use of capillary glucose combined with other non-laboratory examinations to screen for diabetes and prediabetes.

AIM: To evaluate the value and feasibility of capillary glucose assessment, combined with other non-laboratory measures, in screening for diabetes and prediabetes in the community. METHODS: In this cross-sectional study, we assessed fasting capillary glucose, fasting plasma glucose, and both capillary glucose and plasma glucose values after 2-h oral glucose tolerance tests in a total of 3736 samples. We determined the optimal threshold of capillary glucose using receiver-operating characteristic curve analysis. The effect of screening methods using capillary glucose combined with other variables, such as age, BMI and waist circumference, was assessed according to area under the receiver-operating characteristic curve. RESULTS: There was a strong positive correlation between capillary glucose and venous plasma glucose. The area under the curve for the model using fasting capillary glucose to screen for impaired fasting glucose was 0.722, while that for the model using capillary glucose after a 2-h oral glucose tolerance test to screen for impaired glucose tolerance was 0.916. The area under the curve for the model using fasting capillary glucose to screen for diabetes was 0.835, while that for the model using 2-h oral glucose tolerance test capillary glucose was 0.912. The area under the curve for the model using fasting capillary glucose + 2-h oral glucose tolerance test capillary glucose to screen for diabetes was 0.945. The discriminatory capability of models using capillary glucose was somewhat improved by adding non-laboratory variables. CONCLUSIONS: Capillary glucose could be an alternative for screening for diabetes and prediabetes, especially in low-resource areas.