Correlates of Treatment Patterns Among Youth With Type 2 Diabetes

OBJECTIVE

To describe treatment regimens in youth with type 2 diabetes and examine associations between regimens, demographic and clinical characteristics, and glycemic control.

RESEARCH DESIGN AND METHODS

This report includes 474 youth with a clinical diagnosis of type 2 diabetes who completed a SEARCH for Diabetes in Youth study visit. Diabetes treatment regimen was categorized as lifestyle alone, metformin monotherapy, any oral hypoglycemic agent (OHA) other than metformin or two or more OHAs, insulin monotherapy, and insulin plus any OHA(s). Association of treatment with demographic and clinical characteristics (fasting C-peptide [FCP], diabetes duration, and self-monitoring of blood glucose [SMBG]), and A1C was assessed by χ² and ANOVA. Multiple linear regression models were used to evaluate independent associations of treatment regimens and A1C, adjusting for demographics, diabetes duration, FCP, and SMBG.

RESULTS

Over 50% of participants reported treatment with metformin alone or lifestyle. Of the autoantibody-negative youth, 40% were on metformin alone, while 33% were on insulin-containing regimens. Participants on metformin alone had a lower A1C (7.0 ± 2.0%, 53 ± 22 mmol/mol) than those on insulin alone (9.2 ± 2.7%, 77 ± 30 mmol/mol) or insulin plus OHA (8.6 ± 2.6%, 70 ± 28 mmol/mol) (P < 0.001). These differences remained significant after adjustment (7.5 ± 0.3%, 58 ± 3 mmol/mol; 9.1 ± 0.4%, 76 ± 4 mmol/mol; and 8.6 ± 0.4%, 70 ± 4 mmol/mol) (P < 0.001) and were more striking in those with diabetes for ≥2 years (7.9 ± 2.8, 9.9 ± 2.8, and 9.8 ± 2.6%). Over one-half of those on insulin-containing therapies still experience treatment failure (A1C ≥8%, 64 mmol/mol).

CONCLUSIONS

Approximately half of youth with type 2 diabetes were managed with lifestyle or metformin alone and had better glycemic control than individuals using other therapies. Those with longer diabetes duration in particular commonly experienced treatment failures, and more effective management strategies are needed.     

Cobalamin Intake and Related Biomarkers: Examining Associations With Mortality Risk Among Adults With Type 2 Diabetes in NHANES

OBJECTIVEDespite that periodical monitoring of cobalamin (vitamin B12) in metformin-treated patients with diabetes is recommended, cobalamin-associated mortality benefits or risks remain unclear. We investigated the association between cobalamin intake and related biomarkers and mortality risk in adults with diabetes using metformin or not.RESEARCH DESIGN AND METHODSThis study included 3,277 adults with type 2 diabetes from the National Health and Nutrition Examination Survey (NHANES) and followed up until 31 December 2015. Weighted Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% CIs for mortality risk.RESULTSAmong 3,277 participants, 865 all-cause deaths occurred during a median follow-up of 7.02 years. There was no robust relationship between all-cause mortality and serum cobalamin or intake of foods or cobalamin supplements, regardless of metformin treatment (each P ≥ 0.120). The doubling of methylmalonic acid (MMA), a cobalamin-deficiency marker, was significantly associated with higher all-cause (HR 1.31 [95% CI 1.18–1.45], P < 0.001) and cardiac (HR 1.38 [95% CI 1.14–1.67], P = 0.001) mortality. Cobalamin sensitivity was assessed by the combination of binary B12_low/high and MMA_low/high (cutoff values: cobalamin 400 pg/mL, MMA 250 nmol/L). Patients with decreased cobalamin sensitivity (MMA_highB12_high) had the highest mortality risk. The multivariable-adjusted HRs (95% CIs) of all-cause mortality in MMA_lowB12_low, MMA_lowB12_high, MMA_highB12_low, and MMA_highB12_high groups were 1.00 (reference), 0.98 (0.75–1.28), 1.49 (1.16–1.92), and 1.96 (1.38–2.78), respectively. That association was especially significant in metformin nonusers.CONCLUSIONSSerum and dietary cobalamin were not associated with reduced mortality. Decreased cobalamin sensitivity was significantly associated with all-cause and cardiac mortality, particularly among metformin nonusers.     

Obstructive Sleep Apnea Among Obese Patients With Type 2 Diabetes

OBJECTIVE

To assess the risk factors for the presence and severity of obstructive sleep apnea (OSA) among obese patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

Unattended polysomnography was performed in 306 participants.

RESULTS

Over 86% of participants had OSA with an apnea-hypopnea index (AHI) ≥5 events/h. The mean AHI was 20.5 ± 16.8 events/h. A total of 30.5% of the participants had moderate OSA (15 ≤ AHI <30), and 22.6% had severe OSA (AHI ≥30). Waist circumference (odds ratio 1.1; 95% CI 1.0–1.1; P = 0.03) was significantly related to the presence of OSA. Severe OSA was most likely in individuals with a higher BMI (odds ratio 1.1; 95% CI 1.0–1.2; P = 0.03).

CONCLUSIONS

Physicians should be particularly cognizant of the likelihood of OSA in obese patients with type 2 diabetes, especially among individuals with higher waist circumference and BMI.We report the prevalence of obstructive sleep apnea (OSA) and the factors that increase the risk and severity of OSA among 306 obese patients with type 2 diabetes enrolled in Sleep AHEAD, a four-site ancillary study of the Look AHEAD Trial (Action for Health in Diabetes).     

Factors Associated With Psychological Insulin Resistance in Individuals With Type 2 Diabetes

OBJECTIVE

To describe the predictive relationships of selected sociodemographic, biomedical, and psychosocial variables to reluctance to use insulin among patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

A total of 178 patients with type 2 diabetes participated in this cross-sectional, observational study. Data were obtained by patient interview using validated measures of diabetes attitude, knowledge, self-efficacy, care communication, and perceived barriers to treatment, as well as sociodemographic and biomedical data.

RESULTS

Women and ethnic minorities with type 2 diabetes have more psychological barriers to insulin treatment (P < 0.05). The final regression model showed that individuals who believed in the value of tight glucose control, had strong self-efficacy, and had better interpersonal processes with their healthcare providers were less reluctant to use insulin treatment (R² = 0.403; P < 0.0001).

CONCLUSIONS

Diabetes self-efficacy and better interaction with clinicians were important in decreasing patients'' reluctance to use insulin, known as psychological insulin resistance.Despite the known benefits of insulin, many patients are reluctant to use insulin therapy (1–3). A patient''s reluctance to initiate insulin may be called “psychological insulin resistance” (PIR).Little is known about what factors influence PIR. We examined the relationships among PIR, sociodemographic, biomedical, and psychosocial factors identified in previous studies (4–7) and tested a predictive model of PIR.     

Diabetes Genetic Predisposition Score and Cardiovascular Complications Among Patients With Type 2 Diabetes

OBJECTIVE

To examine the association between genetic predisposition to type 2 diabetes (T2D) and risk of cardiovascular disease (CVD) among patients with T2D.

RESEARCH DESIGN AND METHODS

The current study included 1,012 men and 1,310 women with T2D from the Health Professionals Follow-up Study and Nurses’ Health Study, including 677 patients with CVD and 1,645 non-CVD control subjects. A genetic predisposition score (GPS) was calculated on the basis of 36 established independent diabetes-predisposing variants.

RESULTS

Each additional diabetes-risk allele in the GPS was associated with a 3% increased risk of CVD (odds ratio [OR] 1.03 [95% CI 1.00–1.06]). The OR was 1.47 (1.11–1.95) for CVD risk by comparing extreme quartiles of the GPS (P for trend = 0.01). We also found that the GPS was positively associated with hemoglobin A_1c levels (P = 0.009).

CONCLUSIONS

Genetic predisposition to T2D is associated with an increased risk of cardiovascular complications in patients with T2D.It has been postulated that type 2 diabetes (T2D) and cardiovascular disease (CVD) might spring from a “common soil” where both conditions share common genetic and environmental antecedents (1). Identification of the shared genetic risk factors may improve our understanding of the etiological link of these two diseases. A recent study reported a significant association between a genetic score based on multiple diabetes-predisposing variants and increased risk of coronary heart disease (CHD) in a general population (2). In this study, we constructed a genetic predisposition score (GPS) on the basis of 36 established T2D-predisposing variants, and examined its association with cardiovascular complications among people with T2D.     

HDL Cholesterol and Cancer Risk Among Patients With Type 2 Diabetes

OBJECTIVETo investigate the relationship between HDL cholesterol (HDL-C) and cancer risk among type 2 diabetic patients.RESULTSDuring a mean follow-up period of 6.4 years, 3,711 type 2 diabetic patients had a cancer diagnosis. A significant inverse association between HDL-C and the risk of cancer was found among men and women. The multivariable-adjusted hazard ratios (HRs) of cancer at various levels of HDL-C at baseline (<30, 30–39.9, 40–49.9, 50–59.9, 60–69.9, 70–79.9, and ≥80 mg/dL) were 1.00, 0.87, 0.95, 1.01, 0.61, 0.45, and 0.37, respectively, in men (P_trend = 0.027) and 1.00, 0.98, 0.88, 0.85, 0.84, 0.86, and 0.84, respectively, in women (P_trend = 0.025). When stratified by race, BMI, smoking status, or medication use, the inverse association was still present. With an updated mean of HDL-C used in the analysis, the inverse association of HDL-C with cancer risk did not change. The inverse association substantially attenuated after excluding patients who died of or were diagnosed with cancer during the first 2 years of follow-up.CONCLUSIONSThe study suggests an inverse association of HDL-C with cancer risk among men and women with type 2 diabetes, whereas the effect of HDL-C was partially mediated by reverse causation.     

High-Sensitivity C-Reactive Protein Is Associated With Incident Type 2 Diabetes Among African Americans: The Jackson Heart Study

OBJECTIVE

Previous studies on the association between hs-CRP and incident type 2 diabetes among African Americans have been inconclusive. We examined the association between hs-CRP and incident diabetes in a large African American cohort (Jackson Heart Study).

RESEARCH DESIGN AND METHODS

hs-CRP was measured in 3,340 participants. Incident diabetes was defined by fasting glucose ≥126 mg/dL, physician diagnosis, use of diabetes drugs, or A1C ≥6.5% (48 mmol/mol) at follow-up. Cox regression was used to estimate hazard ratios (HRs) for incident diabetes, adjusting for age, sex, education, diabetes family history, alcohol, HDL, triglycerides, hypertension status, hypertension medications, physical activity, BMI, HOMA-insulin resistance (HOMA_IR), and waist circumference.

RESULTS

Participants (63% women) were aged 53.3 ± 12.5 years. During a median follow-up of 7.5 years, 17.4% developed diabetes (23.1/1,000 person-years, 95% CI 21.3–25.1). After adjustment, the HR (hs-CRP third vs. first tertile) was 1.64 (95% CI 1.26–2.13). In separate models, further adjustment for BMI and waist circumference attenuated this association (HR 1.28 [95% CI 0.97–1.69] and 1.35 [95% CI 1.03–1.78, P < 0.05 for trend], respectively). Upon adding HOMA_IR in the models, the association was no longer significant. In adjusted HOMA_IR-stratified analysis, the hs-CRP–diabetes association appeared stronger in participants with HOMA_IR <3.0 compared with HOMA_IR ≥3.0 (P < 0.0001 for interaction). The association was also stronger among nonobese participants, although not significant when adjusted for HOMA_IR.

CONCLUSIONS

Low-grade inflammation, as measured by hs-CRP level, may have an important role in the development of diabetes among African Americans with a lesser degree of insulin resistance.     

Chronotype Is Independently Associated With Glycemic Control in Type 2 Diabetes

OBJECTIVE

To examine whether chronotype and daily caloric distribution are associated with glycemic control in patients with type 2 diabetes independently of sleep disturbances.

RESEARCH DESIGN AND METHODS

Patients with type 2 diabetes had a structured interview and completed questionnaires to collect information on diabetes history and habitual sleep duration, quality, and timing. Shift workers were excluded. A recently validated construct derived from mid-sleep time on weekends was used as an indicator of chronotype. One-day food recall was used to compute the temporal distribution of caloric intake. Hierarchical linear regression analyses controlling for demographic and sleep variables were computed to determine whether chronotype was associated with HbA_1c values and whether this association was mediated by a higher proportion of caloric intake at dinner.

RESULTS

We analyzed 194 completed questionnaires. Multiple regression analyses adjusting for age, sex, race, BMI, insulin use, depressed mood, diabetes complications, and perceived sleep debt found that chronotype was significantly associated with glycemic control (P = 0.001). This association was partially mediated by a greater percentage of total daily calories consumed at dinner.

CONCLUSIONS

Later chronotype and larger dinner were associated with poorer glycemic control in patients with type 2 diabetes independently of sleep disturbances. These results suggest that chronotype may be predictive of disease outcomes and lend further support to the role of the circadian system in metabolic regulation.The circadian system, controlled by the master circadian clock located in the suprachiasmatic nuclei of the hypothalamus, plays a major role in regulating daily rhythms of sleep/wake and various metabolic outputs, such as feeding behavior, peripheral tissue metabolism, and hormone secretions (1–3). Despite having this genetically regulated master circadian clock, humans living in modern industrialized societies with 24-h access to light often engage in behaviors that are inappropriately timed relative to their endogenous circadian rhythms. This mismatch in timing is termed “circadian misalignment” and has been associated with a number of negative health outcomes. Night shift work is an example of severe circadian misalignment, as workers are awake, active, and eating during their circadian night and trying to sleep and fast during their circadian day. Epidemiologic studies reveal that shift work is associated with health problems including peptic ulcer disease, coronary heart disease, and metabolic syndrome, as well as certain types of cancers (4). In controlled laboratory studies, experimentally induced circadian misalignment in healthy human volunteers resulted in impaired glucose tolerance (5,6). In animal experiments, mice fed a high-fat diet during their inactive period gained significantly more weight than mice fed during their active phase, despite consuming the equivalent amount of calories (7). Taken together, these data suggest that severe circadian misalignment involving eating and sleeping at an abnormal circadian time leads to impaired energy metabolism.Many individuals in modern society experience a form of mild circadian misalignment, especially during the work or school week as they follow social rhythms imposed by professional obligation, school schedules, family, and other commitments (8). The degree of misalignment is dependent on the individual’s “chronotype” (8). Chronotype is a construct that captures an individual’s preference for being a “morning” or “evening” person. Late chronotype is typically associated with a greater degree of misalignment between social rhythms and the circadian clock (8). This misalignment phenomenon has been termed “social jetlag,” as it resembles the condition experienced after traveling across time zones (8) and can be observed by comparing the difference in sleep timing between work/school days and free days. In a large population study, larger amounts of social jetlag were recently reported to be associated with higher BMI in overweight individuals (9). In addition, a recent study found that patients with type 2 diabetes had significantly later bedtimes and wake times than participants without diabetes, suggesting that chronotype may play a role in glucose metabolism (10).In addition to chronic circadian misalignment, late chronotypes or “evening types” tend to minimize or skip breakfast (11,12). Therefore, the daily distribution of food intake may be mismatched with circadian-controlled metabolic rhythms. It is well recognized that glucose tolerance is worse in the evening (13), suggesting that eating late may result in adverse metabolic consequences. Indeed, a study of healthy volunteers reported that the amount of calories consumed after 8:00 p.m. predicted a higher BMI after controlling for sleep timing and duration (14), suggesting that the timing of food intake across the waking day is of metabolic relevance.To date, little is known about chronotypic variations in patients with type 2 diabetes and the potential associations with glycemic control. There is abundant evidence that sleep disturbances such as short sleep duration and poor sleep quality are linked to the risk of diabetes and obesity, as well as glycemic control in subjects with type 2 diabetes (15,16), but little is known about the association between chronotype and metabolism independently of these sleep characteristics. The aim of this study was to examine whether chronotype was independently associated with glycemic control in patients with type 2 diabetes. We hypothesized that late chronotype would be associated with worse glycemic control independently of sleep disturbances. Because the distribution of food intake across the day is associated with chronotype, we also examined whether daily caloric distribution contributed to glycemic control. We hypothesized that a greater percentage of daily calories consumed at dinner would be associated with worse glycemic control.     

Cost-Effectiveness of Aspirin Use Among Persons With Newly Diagnosed Type 2 Diabetes

OBJECTIVE

To assess the long-term cost-effectiveness of aspirin use among adults aged ≥40 years with newly diagnosed type 2 diabetes.

RESEARCH DESIGN AND METHODS

We used a validated cost-effectiveness model of type 2 diabetes to assess the lifetime health and cost consequences of use or nonuse of aspirin. The model simulates the progression of diabetes and accompanying complications for a cohort of subjects with type 2 diabetes. The model predicts the outcomes of type 2 diabetes along five disease paths (nephropathy, neuropathy, retinopathy, coronary heart disease, and stroke) from the time of diagnosis until age 94 years or until death.

RESULTS

Over a lifetime, aspirin users gained 0.31 life-years (LY) or 0.19 quality-adjusted LYs (QALYs) over nonaspirin users, at an incremental cost of $1,700; the incremental cost-effectiveness ratio (ICER) of aspirin use was $5,428 per LY gained or $8,801 per QALY gained. In probabilistic sensitivity analyses, the ICER was <$30,000 per QALY in all of 2,000 realizations in two scenarios.

CONCLUSIONS

Regular use of aspirin among people with newly diagnosed diabetes is cost-effective.Diabetes is a major risk factor for cardiovascular disease (CVD) among people with diabetes. The risk of developing coronary heart disease (CHD) is two to four times higher for people with diabetes than those without diabetes (1). Aspirin decreases CHD incidence in adults at risk for CVD (2,3).The American Diabetes Association recommends aspirin use for primary prevention of CVD in diabetic patients aged >40 years or in all people aged >30 years if they have risk factors for CVD and no aspirin contraindications (4). However, the cost-effectiveness of aspirin use for primary prevention in a diabetic population has not been evaluated. Previous studies have evaluated the cost-effectiveness of aspirin therapy for primary prevention of CVD in the general population (5,6). These studies concluded that aspirin use was cost saving or cost-effective. It is not known if the same conclusion holds for people with diabetes. The cost-effectiveness of aspirin therapy could differ between people with diabetes and the general population because of the additional cost and health consequences related to diabetes and its complications. Our study evaluates the lifetime cost-effectiveness of aspirin use in adults aged ≥40 years who have newly diagnosed type 2 diabetes.     

Genetic Polymorphisms in Genes Encoding Antioxidant Enzymes Are Associated With Diabetic Retinopathy in Type 1 Diabetes

OBJECTIVE

Oxidative stress plays an important role in the development of microangiopathic complications in type 1 diabetes. We investigated polymorphic markers in genes encoding enzymes regulating production of reactive oxygen species in association with diabetic retinopathy or diabetic nephropathy.

RESEARCH DESIGN AND METHODS

A total of 124 patients with type 1 diabetes were investigated in this case-control study. All subjects were matched for sex, age, and duration of diabetes. Genotyping was conducted using real-time PCR for p.Val16Ala polymorphism in the MnSOD gene and c.C−262T in the promoter region of the CAT gene. Multiplex PCR method was used for determination of GSTM1 and GSTT1 polymorphic deletions. Fluorescence-labeled PCR amplicons and fragment analysis was used for assessing the number of pentanucleotide (CCTTT)n repeats in inducible nitric oxide synthase.

RESULTS

A positive association of MnSOD genotype Val/Val (odds ratio [OR] 2.49, 95% CI 1.00–6.16, P = 0.045) and GSTM1–1 genotype (2.63, 1.07–6.47, P = 0.031) with diabetic retinopathy but not with diabetic nephropathy was demonstrated. Additionally, the combination of the two genotypes conveyed an even higher risk (4.24, 1.37–13.40, P = 0.009). No other investigated genetic polymorphisms were associated with either diabetic retinopathy or diabetic nephropathy.

CONCLUSIONS

Selected polymorphisms in genes encoding MnSOD and GSTM1 could be added to a panel of genetic markers for identification of individuals with type 1 diabetes at an increased risk for developing diabetic retinopathy.Several studies have suggested that reactive oxygen species (ROS) are implicated in the etiology of type 1 diabetes (1) as well as in the development of severe microangiopathic complications such as diabetic retinopathy and diabetic nephropathy (2). Chronic extracellular hyperglycemia in diabetes stimulates ROS production and increases oxidative stress (3). The oxidation of high levels of glucose inside diabetic cells produces more electron donors (NADH and FADH₂) and increases the electron transfer, thereby generating superoxide (4).Excess generation of ROS such as superoxide (O₂^•−), hydrogen peroxide (H₂O₂), and hydroxyl radical (^•OH) and reactive nitrogen species such as nitric oxide oxidize target celular proteins, nucleic acids, or membrane lipids and damage their cellular structure and function (4). Hyperglycemia stimulates the expression of inducible nitric oxide synthase (iNOS) and increases production of nitric oxide, an intracellular second messenger (2). Increased nitric oxide generation accompanied by the superoxide overproduction favors the formation of peroxynitrite, a highly reactive oxidant (5). Evidence suggests that ROS also regulate the expression of genes encoding for proteins involved in inflammation, immune response, and cell death (6).Antioxidant enzymes such as manganese superoxide dismutase (MnSOD) and catalase (CAT) directly eliminate ROS, while glutathione-S-transferases (GSTs) detoxify cytotoxic secondary metabolites of ROS. Together they represent a protective mechanism against the damage caused by the oxidative stress. Most of the enzymes involved in the defense against oxidative stress are polymorphic.Associations between MnSOD Val16Ala single nucleotide polymorphism (SNP) and diabetic nephropathy (7) or diabetic retinopathy in type 2 diabetes (8) and polyneuropathy in type 1 diabetes (9) emphasize the importance of polymorphism in these genes. Individuals carrying the homozygous TT or heterozygous CT genotype in the promoter region of the CAT gene have significantly higher enzyme activity, which offers a degree of protection against development of polyneuropathy (10). Human GSTM1 and GSTT1 polymorphic deletion are associated with the age of onset of type 1 diabetes (11). Variable expression of the human iNOS gene polymorphic pentanucleotide (CCTTT)n repeat is associated with different autoimmune diseases including type 1 diabetes (12). All described polymorphisms could add to the interindividual variability in patients with type 1 diabetes for the development of microangiopathic complications.The aim of our study was to evaluate the association of polymorphic markers in genes encoding antioxidant enzymes, which share a common detoxification pathway (MnSOD, CAT, GSTM1, and GSTT1) or regulate ROS production (iNOS) with diabetic retinopathy or diabetic nephropathy in a cohort of patients with type 1 diabetes.     

2型糖尿病患者饮食控制行为及其相关信念的研究

目的　研究 2型糖尿病患者饮食控制行为与相关信念的关系。方法　采用个人基本资料问卷、饮食控制行为量表、结果预期量表和自我效能量表对 130例 2型糖尿病门诊患者进行调查。结果　 2型糖尿病患者对饮食控制的结果预期和自我效能均与饮食控制行为呈正相关 (P <0 .0 0 1) ;多元逐步回归分析 ,自我效能可解释患者饮食控制行为 6 2 8%的变异量。结论　结果预期和自我效能是 2型糖尿病患者控制饮食的重要影响因素 ,且自我效能具有决定性作用。     

Glycation Gap Is Associated With Macroproteinuria but Not With Other Complications in Patients With Type 2 Diabetes

OBJECTIVE

We investigated whether glycation gap (G-Gap), an index of intracellular glycation of proteins, was associated with diabetes complications.

RESEARCH DESIGN AND METHODS

We measured concomitantly HbA_1c and fructosamine in 925 patients with type 2 diabetes to calculate the G-Gap, defined as the difference between measured HbA_1c, and fructosamine-based predicted HbA_1c. Patients were explored for retinopathy, nephropathy, peripheral neuropathy, cardiac autonomic neuropathy (n = 512), and silent myocardial ischemia (n = 506).

RESULTS

Macroproteinuria was the only complication that was associated with G-Gap (prevalence in the first, second, and third tertile of G-Gap: 2.9, 6.2, and 11.0%, respectively; P < 0.001). The G-Gap was higher in patients with macroproteinuria than in those without (1.06 ± 1.62 vs. 0.03 ± 1.30%; P < 0.0001). Because HbA_1c was associated with both G-Gap (HbA_1c 7.0 ± 1.4, 7.9 ± 1.4, and 10.1 ± 1.8% in the first, second, and third G-Gap tertile, respectively; P < 0.0001) and macroproteinuria (HbA_1c 8.8 ± 2.2% if macroproteinuria, 8.3 ± 2.0% if none; P < 0.05), and because it could have been a confounder, we matched 54 patients with macroproteinuria and 200 patients without for HbA_1c. Because macroproteinuria was associated with lower serum albumin and fructosamine levels, which might account for higher G-Gap, we calculated in this subpopulation albumin-indexed fructosamine and G-Gap; macroproteinuria was independently associated with male sex (odds ratio [OR] 3.2 [95% CI 1.5–6.7]; P < 0.01), hypertension (2.9 [1.1–7.5]; P < 0.05), and the third tertile of albumin-indexed G-Gap (2.3 [1.1–4.4]; P < 0.05) in multivariate analysis.

CONCLUSIONS

In type 2 diabetic patients, G-Gap was associated with macroproteinuria, independently of HbA_1c, albumin levels, and confounding factors, suggesting a specific role of intracellular glycation susceptibility on kidney glomerular changes.Protein glycation is involved in diabetes complications, and glycated hemoglobin (HbA_1c) level is associated with diabetes complications. Because glycation starts with glucose, it has been assumed that mean blood glucose is at the beginning of this association. However, even if there has been a close correlation between HbA_1c and mean blood glucose level over the previous 3 months, one-fifth (1) to one-third (2) of HbA_1c variance cannot be explained by mean blood glucose. Nonglycemic determinants of HbA_1c actually also could account for diabetes complications. One of the involved mechanisms might be an interindividual variation in the intracellular glycation of proteins, independently of glucose levels: the higher the protein glycation in target tissues (such as retina, kidney, neuronal tissues, and vessels), the more prevalent the tissue damages would be.Some indexes have been developed to estimate nonglycemic determinants of HbA_1c: hemoglobin glycation index is the difference between observed HbA_1c and the value calculated from its regression with mean plasma glucose (3), and glycation gap (G-Gap) (previously called glycosylation gap) is the difference between observed HbA_1c and the value calculated from its regression with fructosamine (4). There are several advantages to consider fructosamine rather than mean blood glucose to evaluate nonglycemic determinants of HbA_1c. First, fructosamine level is more stable than glucose itself. Second, fructosamine represents the 2-week blood glucose exposure, whereas frequent 7-point blood glucose profiles or continuous blood glucose monitoring is required to evaluate mean glucose. Moreover, fructosamine, unlike mean blood glucose, can be used to compare protein glycation in the extracellular space (fructosamine) and in the intracellular space (HbA_1c in red cells and, by assumption, in target tissues).G-Gap has been shown to be consistent over time in type 2 (5,6) and type 1 diabetic patients (4,6). In a study including 40 patients with type 1 diabetes for >15 years, a 1% increase in G-Gap was associated with a 2.9-fold greater frequency of progression in the nephropathy stage. The data demonstrated that nephropathy correlated better with G-Gap than with HbA_1c or fructosamine alone (4). Furthermore, it recently has been shown that G-Gap predicted the progression of nephropathy in type 2 diabetic patients independently of fructosamine, even after adjustment for HbA_1c (5). Nevertheless, in type 1 diabetes, controversial data have been published on G-Gap and retinopathy (7), whereas in type 2 diabetes there has been no report on the relations between G-Gap and complications outside of nephropathy.Thus, the aim of our study was to evaluate, in a large series of type 2 diabetic patients, the potential association between G-Gap and the presence of nephropathy, retinopathy, neuropathy, or silent myocardial ischemia independently of glycemic control.     

Increased CD36 Expression Signals Monocyte Activation Among Patients With Type 2 Diabetes

OBJECTIVE

To explore the hypothesis that CD36, a scavenger receptor and fatty acid translocase, is upregulated in peripheral blood mononuclear cells (PBMCs) among patients with type 2 diabetes and is a biomarker of PBMC activation and inflammation.

RESEARCH DESIGN AND METHODS

We used a cross-sectional observational design to study a multi-racial/ethnic population sample consisting of Caucasians, Hispanics, and Native Americans with type 2 diabetes (n = 33) and nondiabetic control subjects (n = 27). PBMC CD36 mRNA/protein and plasma high sensitivity (hs) C-reactive protein (hsCRP), hs–interleukin-6 (hsIL-6), and adiponectin were measured.

RESULTS

Unadjusted PBMC CD36 mRNA and protein were 1.56- and 1.63-fold higher, respectively, among type 2 diabetic subjects versus control subjects. PBMC CD36 protein was directly associated with CD36 mRNA, plasma hsCRP, and hsIL-6 and inversely associated with plasma adiponectin in both groups.

CONCLUSIONS

Increased CD36 expression is a biomarker of PBMC activation and inflammation and may become a useful tool in cardiovascular disease risk stratification.The prevalence of type 2 diabetes is increasing in epidemic proportions among minority populations. Cardiovascular disease (CVD) is a major cause of morbidity and mortality among diabetic patients. New methods for risk stratification are needed to reduce the burden of CVD among patients with diabetes.CD36, a 88-kDa transmembrane glycoprotein and a cell surface scavenger receptor for oxidized low-density lipoproteins (oxLDL), plays a critical role in the pathogenesis of atherosclerosis and CVD (1,2). Ligation of oxLDL by CD36 in macrophages induces activation of nuclear factor κ–light-chain enhancer of activated B-cells (NFκB) and production of proinflammatory cytokines, e.g., tumor necrosis factor α/β (TNF-α/β), interleukin (IL)-1β, IL-6, and interferon β/γ. Production of proinflammatory cytokines is reduced in CD36-deficient macrophages (2). The size of atherosclerotic lesions is reduced by inactivation of CD36 and increased by reintroduction of CD36 in apolipoprotein E/CD36-deficient mice. CD36 expression is increased in the presence of high glucose concentrations. Therefore, assessment of CD36 levels may become a valuable tool in CVD risk stratification among patients with type 2 diabetes.The majority of previous clinical studies of CD36 expression have been conducted in a single racial/ethnic group. In contrast, the present study explored the hypothesis that there is coordinated upregulation of peripheral blood mononuclear cell (PBMC) CD36 mRNA and protein, signaling PBMC activation and increased production of proinflammatory cytokines among non-Hispanic whites, Hispanics, and American Indians with type 2 diabetes.     

Insomnia With Objective Short Sleep Duration Is Associated With Type 2 Diabetes: A population-based study

OBJECTIVE

We examined the joint effects of insomnia and objective short sleep duration, the combination of which is associated with higher morbidity, on diabetes risk.

RESEARCH DESIGN AND METHODS

A total of 1,741 men and women randomly selected from Central Pennsylvania were studied in the sleep laboratory. Insomnia was defined by a complaint of insomnia with duration of ≥1 year, whereas poor sleep was defined as a complaint of difficulty falling asleep, staying asleep, or early final awakening. Polysomnographic sleep duration was classified into three categories: ≥6 h of sleep (top 50% of the sample); 5–6 h (approximately third quartile of the sample); and ≤5 h (approximately the bottom quartile of the sample). Diabetes was defined either based on a fasting blood glucose >126 mg/dl or use of medication. In the logistic regression model, we simultaneously adjusted for age, race, sex, BMI, smoking, alcohol use, depression, sleep-disordered breathing, and periodic limb movement.

RESULTS

Chronic insomnia but not poor sleep was associated with a higher risk for diabetes. Compared with the normal sleeping and ≥6 h sleep duration group, the highest risk of diabetes was in individuals with insomnia and ≤5 h sleep duration group (odds ratio [95% CI] 2.95 [1.2–7.0]) and in insomniacs who slept 5–6 h (2.07 [0.68–6.4]).

CONCLUSIONS

Insomnia with short sleep duration is associated with increased odds of diabetes. Objective sleep duration may predict cardiometabolic morbidity of chronic insomnia, the medical impact of which has been underestimated.Many studies have established that insomnia, the most common sleep disorder, is highly comorbid with psychiatric disorders and is a risk factor for the development of depression, anxiety, and suicide (1,2). In contrast with sleep-disordered breathing (SDB), the second most common sleep disorder, chronic insomnia has not been associated with significant medical morbidity, e.g., cardiovascular disorders (3,4).Recently, we demonstrated that insomnia with objective short sleep duration is associated with a high risk for hypertension (5). These data suggest that objective sleep measures in insomnia provide an index of the severity of the disorder and that the more severe form of insomnia is most likely associated with morbidity and possibly mortality. This hypothesis is further supported by physiological studies, which demonstrated that activation of the hypothalamic-pituitary-adrenal (HPA) axis and autonomic system, including increased heart rate, 24-h metabolic rate, and impaired heart rate variability, is present in insomniacs who meet both subjective and objective polysomnographic criteria (6–11). Given the association of the HPA axis and sympathetic system activation with the pathogenesis of metabolic disorders, including diabetes (12), we hypothesized that insomnia with objective short sleep duration will be associated with type 2 diabetes.Previous studies have shown that sleep disturbances or complaints are associated with increased incidence of type 2 diabetes (13–16). However, in these studies, the presence of sleep disturbances was based only on a subjective questionnaire and did not control for obstructive sleep apnea, a sleep disorder whose association with diabetes and insulin resistance is well established (12). Thus, it is not known whether insomnia per se is associated with an increased risk for diabetes.To test this hypothesis, we examined the joint effects of the complaints of chronic insomnia and poor sleep (a milder form of insomnia) and objective sleep duration on the prevalence of diabetes in a large cross-sectional population-based sample from Central Pennsylvania (Penn State Cohort).     

Costs and Consequences Associated With Newer Medications for Glycemic Control in Type 2 Diabetes

OBJECTIVE

Newer medications offer more options for glycemic control in type 2 diabetes. However, they come at considerable costs. We undertook a health economic analysis to better understand the value of adding two newer medications (exenatide and sitagliptin) as second-line therapy to glycemic control strategies for patients with new-onset diabetes.

RESEARCH DESIGN AND METHODS

We performed a cost-effectiveness analysis for the U.S. population aged 25–64. A lifetime analytic horizon and health care system perspective were used. Costs and quality-adjusted life years (QALYs) were discounted at 3% annually, and costs are presented in 2008 U.S. dollars. We compared three glycemic control strategies: 1) glyburide as a second-line agent, 2) exenatide as a second-line agent, and 3) sitagliptin as a second-line agent. Outcome measures included QALYs gained, incremental costs, and the incremental cost-effectiveness ratio associated with each strategy.

RESULTS

Exenatide and sitagliptin conferred 0.09 and 0.12 additional QALYs, respectively, relative to glyburide as second-line therapy. In base case analysis, exenatide was dominated (cost more and provided fewer QALYs than the next most expensive option), and sitagliptin was associated with an incremental cost-effectiveness ratio of $169,572 per QALY saved. Results were sensitive to assumptions regarding medication costs, side effect duration, and side effect–associated disutilities.

CONCLUSIONS

Exenatide and sitagliptin may confer substantial costs to health care systems. Demonstrated gains in quality and/or quantity of life are necessary for these agents to provide economic value to patients and health care systems.Diabetes is increasingly endemic in the U.S. In 2007, 23.5 million Americans aged >20 years had diabetes compared with 18.0 million in 2002 (1). Diabetes was the seventh leading cause of death in 2006 (1). It remains the leading cause of blindness, end-stage renal disease, and nontraumatic amputations. A total of $116 billion in direct health care costs are attributable to diabetes annually (2).Large clinical trials from the U.S. and Europe have demonstrated that tighter glycemic control can prevent diabetes complications in individuals with recent-onset disease (3,4); in older individuals with longer disease duration, recent studies have found no cardiovascular benefit of tight control (5) and possible harm (6). In the past several years, the U.S. Food and Drug Administration (FDA) approved nine new products for glycemic control (7). Some are new forms or combinations of existing classes, whereas others belong to new therapeutic classes such as amylin analogs, glucagon-like peptide-1 receptor agonists, incretins, and dipeptidyl peptidase-IV inhibitors.Although these agents increase the management options available, they come at increased costs (8). Previous analyses of the health economics of glycemic control were published before the FDA approval of many new agents (9–11). Recent studies have examined the cost-effectiveness of exenatide or sitagliptin in European populations, reflecting costs and management appropriate for the modeled populations but not necessarily reflective of the U.S. (12–14).In this analysis, we estimate the costs associated with two of the most prescribed examples of these new medications: exenatide and sitagliptin. We project the gains in health outcomes necessary to have these newer medications pose good economic value for patients with new-onset diabetes, using the incremental cost-effectiveness ratio as our metric.     

Interpretable Machine Learning Framework Reveals Robust Gut Microbiome Features Associated With Type 2 Diabetes

OBJECTIVETo identify the core gut microbial features associated with type 2 diabetes risk and potential demographic, adiposity, and dietary factors associated with these features.RESEARCH DESIGN AND METHODSWe used an interpretable machine learning framework to identify the type 2 diabetes–related gut microbiome features in the cross-sectional analyses of three Chinese cohorts: one discovery cohort (n = 1,832, 270 cases of type 2 diabetes) and two validation cohorts (cohort 1: n = 203, 48 cases; cohort 2: n = 7,009, 608 cases). We constructed a microbiome risk score (MRS) with the identified features. We examined the prospective association of the MRS with glucose increment in 249 participants without type 2 diabetes and assessed the correlation between the MRS and host blood metabolites (n = 1,016). We transferred human fecal samples with different MRS levels to germ-free mice to confirm the MRS–type 2 diabetes relationship. We then examined the prospective association of demographic, adiposity, and dietary factors with the MRS (n = 1,832).RESULTSThe MRS (including 14 microbial features) consistently associated with type 2 diabetes, with risk ratio for per 1-unit change in MRS 1.28 (95% CI 1.23–1.33), 1.23 (1.13–1.34), and 1.12 (1.06–1.18) across three cohorts. The MRS was positively associated with future glucose increment (P < 0.05) and was correlated with a variety of gut microbiota–derived blood metabolites. Animal study further confirmed the MRS–type 2 diabetes relationship. Body fat distribution was found to be a key factor modulating the gut microbiome–type 2 diabetes relationship.CONCLUSIONSOur results reveal a core set of gut microbiome features associated with type 2 diabetes risk and future glucose increment.