Sex-specific associations between skeletal muscle mass and incident diabetes: A population-based cohort study

AIMS

To investigate the sex-specific associations between predicted skeletal muscle mass index (pSMI) and incident type 2 diabetes in a retrospective longitudinal cohort of Chinese men and women.

MATERIALS AND METHODS

We enrolled Chinese adults without diabetes at baseline from WATCH (West chinA adulT health CoHort), a large health check-up-based database. We calculated pSMI to estimate skeletal muscular mass, and measured blood glucose variables and assessed self-reported history to identify new-onset diabetes. The nonlinear association between pSMI and incident type 2 diabetes was modelled using the penalized spline method. The piecewise association was estimated using segmented linear splines in weighted Cox proportional hazards regression models.

RESULTS

Of 47 885 adults (53.2% women) with a median age of 40 years, 1836 developed type 2 diabetes after a 5-year median follow-up. In women, higher pSMI was associated with a lower risk of incident type 2 diabetes (Pnonlinearity  = 0.09, hazard ratio [HR] per standard deviation increment in pSMI: 0.79 [95% confidence interval {CI} 0.68, 0.91]). A nonlinear association of pSMI with incident type 2 diabetes was detected in men (Pnonlinearity  < 0.001). In men with pSMI lower than 8.1, higher pSMI was associated with a lower risk of incident type 2 diabetes (HR 0.58 [95% CI 0.40, 0.84]), whereas pSMI was not significantly associated with incident diabetes in men with pSMI equal to or greater than 8.1 (HR 1.08 [95% CI 0.93, 1.25]).

CONCLUSIONS

In females, a larger muscular mass is associated with a lower risk of type 2 diabetes. For males, this association is significant only among those with diminished muscle mass.

Two Distinct Groups Are Shown to Be at Risk of Diabetes by Means of a Cluster Analysis of Four Variables

Recent attempts to classify adult-onset diabetes using only six diabetes-related variables (GAD antibody, age at diagnosis, BMI, HbA1c, and homeostatic model assessment 2 estimates of b-cell function and insulin resistance (HOMA2-B and HOMA2-IR)) showed that diabetes can be classified into five clusters, of which four correspond to type 2 diabetes (T2DM). Here, we classified nondiabetic individuals to identify risk clusters for incident T2DM to facilitate the refinement of prevention strategies. Of the 1167 participants in the population-based Iwaki Health Promotion Project in 2014 (baseline), 868 nondiabetic individuals who attended at least once during 2015-2019 were included in a prospective study. A hierarchical cluster analysis was performed using four variables (BMI, HbA1c, and HOMA2 indices). Of the four clusters identified, cluster 1 (n = 103), labeled as "obese insulin resistant with sufficient compensatory insulin secretion", and cluster 2 (n = 136), labeled as "low insulin secretion", were found to be at risk of diabetes during the 5-year follow-up period: the multiple factor-adjusted HRs for clusters 1 and 2 were 14.7 and 53.1, respectively. Further, individuals in clusters 1and 2 could be accurately identified: the area under the ROC curves for clusters 1and 2 were 0.997 and 0.983, respectively. The risk of diabetes could be better assessed on the basis of the cluster that an individual belongs to.

Lipid Accumulation Product Index Predicts New-Onset Type 2 Diabetes Among Non-Obese Koreans: A 12-Year Longitudinal Study

PURPOSE

The lipid accumulation product (LAP) has been a potential indicator of central lipid accumulation status. This study aimed to assess the longitudinal association between LAP index and incident type 2 diabetes among non-obese Korean adults using a large, community-based Korean cohort observed over 12 years.

PATIENTS AND METHODS

This study included 4281 non-diabetic adults without generalized obesity and abdominal obesity and aged 40-69 years from the Korean Genome and Epidemiology Study. The participants were divided into four groups according to LAP index quartiles, calculated as (waist circumference [cm] - 65) x (triglycerides [mmol/L]) in men and (waist circumference [cm] - 58) x (triglycerides [mmol/L]) in women. We prospectively assessed hazard ratios (HRs) with 95% confidential intervals (CIs) for incident type 2 diabetes using multivariate Cox proportional hazard regression models.

RESULTS

Overall, 608 (14.2%) participants developed type 2 diabetes during the follow-up period. HRs for incident type 2 diabetes in the second, third, and fourth LAP quartile were 1.32 (95% CI: 0.97-1.79), 1.51 (95% CI: 1.11-2.06), and 2.14 (95% CI: 1.56-2.94), respectively, after adjusting for age, sex, body mass index, smoking status, alcohol intake, physical activity, mean arterial blood pressure, family history of diabetes, and impaired glucose tolerance.

CONCLUSION

A high LAP index can be an additional indicator for new-onset T2DM among middle-aged and elderly non-obese Koreans.

Polysomnographic Phenotypes of Obstructive Sleep Apnea and Incident Type 2 Diabetes: Results from the DREAM Study

Rationale: Obstructive sleep apnea (OSA) is associated with cardiovascular disease and incident type 2 diabetes (T2DM). Seven OSA phenotypes, labeled on the basis of their most distinguishing polysomnographic features, have been shown to be differentially associated with incident cardiovascular disease. However, little is known about the relevance of polysomnographic phenotypes for the risk of T2DM. Objectives: To assess whether polysomnographic phenotypes are associated with incident T2DM and to compare the predictive value of baseline polysomnographic phenotypes with the Apnea-Hypopnea Index (AHI) for T2DM. Methods: The study included 840 individuals without baseline diabetes from a multisite observational U.S. veteran cohort who underwent OSA evaluation between 2000 and 2004, with follow-up through 2012. The primary outcome was incident T2DM, defined as no diagnosis at baseline and a new physician diagnosis confirmed by fasting blood glucose >126 mg/dL during follow-up. Relationships between the seven polysomnographic phenotypes (1. mild, 2. periodic limb movements of sleep [PLMS], 3. non-rapid eye movement and poor sleep, 4. rapid eye movement and hypoxia, 5. hypopnea and hypoxia, 6. arousal and poor sleep, and 7. combined severe) and incident T2DM were investigated using Cox proportional hazards regression and competing risk regression models with and without adjustment for baseline covariates. Likelihood ratio tests were conducted to compare the predictive value of the phenotypes with the AHI. Results: During a median follow-up period of 61 months, 122 (14.5%) patients developed incident T2DM. After adjustment for baseline sociodemographics, fasting blood glucose, body mass index, comorbidities, and behavioral risk factors, hazard ratios among persons with "hypopnea and hypoxia" and "PLMS" phenotypes as compared with persons with "mild" phenotype were 3.18 (95% confidence interval [CI], 1.53-6.61] and 2.26 (95% CI, 1.06-4.83) for incident T2DM, respectively. Mild OSA (5 ⩽ AHI < 15) (vs. no OSA) was directly associated with incident T2DM in both unadjusted and multivariable-adjusted regression models. The addition of polysomnographic phenotypes, but not AHI, to known T2DM risk factors greatly improved the predictive value of the computed prediction model. Conclusions: Polysomnographic phenotypes "hypopnea and hypoxia" and "PLMS" independently predict risk of T2DM among a predominantly male veteran population. Polysomnographic phenotypes improved T2DM risk prediction comared with the use of AHI.

Gene-lifestyle interaction on risk of type 2 diabetes: A systematic review

The pathophysiological influence of gene-lifestyle interactions on the risk to develop type 2 diabetes (T2D) is currently under intensive research. This systematic review summarizes the evidence for gene-lifestyle interactions regarding T2D incidence. MEDLINE, EMBASE, and Web of Science were systematically searched until 31 January 2019 to identify publication with (a) prospective study design; (b) T2D incidence; (c) gene-diet, gene-physical activity, and gene-weight loss intervention interaction; and (d) population who are healthy or prediabetic. Of 66 eligible publications, 28 reported significant interactions. A variety of different genetic variants and dietary factors were studied. Variants at TCF7L2 were most frequently investigated and showed interactions with fiber and whole grain on T2D incidence. Further gene-diet interactions were reported for, eg, a western dietary pattern with a T2D-GRS, fat and carbohydrate with IRS1 rs2943641, and heme iron with variants of HFE. Physical activity showed interaction with HNF1B, IRS1, PPARγ, ADRA2B, SLC2A2, and ABCC8 variants and weight loss interventions with ENPP1, PPARγ, ADIPOR2, ADRA2B, TNFα, and LIPC variants. However, most findings represent single study findings obtained in European ethnicities. Although some interactions have been reported, their conclusiveness is still low, as most findings were not yet replicated across multiple study populations.

Characterization of diabetes risk factors in patients prescribed chronic statin therapy

OBJECTIVE

Determine the incidence of major diabetes risk factors over time in patients prescribed chronic statin therapy.

METHODS

Retrospective observational chart review of adult patients without diabetes in primary care who initiated statin therapy between 2005 and 2010. Presence of diabetes risk factors were determined 1 year prior to statin initiation and continued up to a maximum of 7 years. Diabetes risk factors included impaired fasting glucose, body mass index (BMI) ≥30 kg/m(2), hemoglobin A1c >6% and metabolic syndrome. Descriptive statistics were used to describe the incidence of diabetes risk factors over time.

RESULTS

A total of 98 patients met study criteria; mean age was 57 ± 13 years, 43% were men and 71% self-identified as Caucasian/white. Mean baseline values were A1c of 5.97%, fasting glucose of 104 mg/dl and BMI of 28 kg/m(2). There were zero diabetes risk factors over time in 54% ± 7% of patients. The incidence over time of 1 risk factor was 25 ± 9%, 2 risk factors was 17 ± 5% and 3 risk factors was 3 ± 2%. A total of 12 patients were diagnosed with type 2 diabetes during the course of the study period.

CONCLUSION

The incidence of diabetes risk factors did not change over time in an ambulatory adult population prescribed chronic statin therapy. Larger population studies assessing the incidence of and change in diabetes risk factors in patients on chronic statin therapy may help assess the association between statin therapy and presence of such risk factors.