We included 5,791 older adults in the Atherosclerosis Risk in Communities Study who attended visit 5 (2011–2013; ages 66–90 years). We compared prediabetes (HbA_1c 5.7% to <6.5%), newly diagnosed diabetes (HbA_1c ≥6.5%, prior diagnosis <1 year, or taking antihyperglycemic medications <1 year), short-duration diabetes (duration ≥1 year but <10 years [median]), and long-standing diabetes (duration ≥10 years). Outcomes were all-cause and cardiovascular mortality (median follow-up of 5.6 years).

Participants were 58% female, and 24% had prevalent cardiovascular disease. All-cause mortality rates, per 1,000 person-years, were 21.2 (95% CI 18.7, 24.1) among those without diabetes, 23.7 (95% CI 20.8, 27.1) for those with prediabetes, 33.8 (95% CI 25.2, 45.5) among those with recently diagnosed diabetes, 29.6 (95% CI 25.0, 35.1) for those with diabetes of short duration, and 48.6 (95% CI 42.4, 55.7) for those with long-standing diabetes. Cardiovascular mortality rates, per 1,000 person-years, were 5.8 (95% CI 4.6, 7.4) among those without diabetes, 6.6 (95% CI 5.2, 8.5) for those with prediabetes, 11.5 (95% CI 7.0, 19.1) among those with recently diagnosed diabetes, 8.2 (95% CI 5.9, 11.3) for those with diabetes of short duration, and 17.3 (95% CI 13.8, 21.7) for those with long-standing diabetes. After adjustment for other cardiovascular risk factors, prediabetes and newly diagnosed diabetes were not significantly associated with a higher risk of all-cause mortality (hazard ratio [HR] 1.03 [95% CI 0.85, 1.23] and HR 1.31 [95% CI 0.94, 1.82], respectively) or cardiovascular mortality (HR 1.00 [95% CI 0.70, 1.43] and HR 1.35 [95% CI 0.74, 2.49], respectively). Excess mortality risk was primarily concentrated among those with long-standing diabetes (all-cause: HR 1.71 [95% CI 1.40, 2.10]; cardiovascular: HR 1.72 [95% CI 1.18, 2.51]).

In older adults, long-standing diabetes has a substantial and independent effect on short-term mortality. Older individuals with prediabetes remained at low mortality risk over a median 5.6 years of follow-up.

Medical resource use data and responses to the EuroQol 5-Dimension (EQ-5D) instrument were collected at baseline and throughout the trial. Medical resources and medications were assigned values by using U.S. Medicare payments and wholesale acquisition costs, respectively. Secondary analyses used English costs.

Patients were followed for an average of 3.3 years, during which time those randomized to EQW experienced 0.41 fewer inpatient days (7.05 vs. 7.46 days; relative rate ratio 0.91; P = 0.05). Rates of outpatient medical visits were similar, as were total inpatient and outpatient costs. Mean costs for nonstudy diabetes medications over the study period were ~$1,600 lower with EQW than with placebo (P = 0.01). Total within-study costs, excluding study medication, were lower in the EQW arm than in the placebo arm ($28,907 vs. $30,914; P ≤ 0.01). When including the estimated cost of EQW, total mean costs were significantly higher in the EQW group than in the placebo group ($42,697 vs. $30,914; P < 0.01). With English costs applied, mean total costs, including exenatide costs, were £1,670 higher in the EQW group than the placebo group (£10,874 vs. £9,204; P < 0.01). There were no significant differences in EQ-5D health utilities between arms over time.

Medical costs were lower in the EQW arm than the placebo arm, but total costs were significantly higher once the cost of branded exenatide was incorporated.

At baseline, 178 lipids were measured by mass spectrometry in 3,668 participants without diabetes from the Malmö Diet and Cancer Study. The population was randomly split into discovery (n = 1,868, including 257 incident cases) and replication (n = 1,800, including 249 incident cases) sets. We used orthogonal projections to latent structures discriminant analyses, extracted a predictive component for T2DM incidence (lipid-PC_DM), and assessed its association with T2DM incidence using Cox regression and lifestyle factors using general linear models.

A T2DM-predictive lipid-PC_DM derived from the discovery set was independently associated with T2DM incidence in the replication set, with hazard ratio (HR) among subjects in the fifth versus first quintile of lipid-PC_DM of 3.7 (95% CI 2.2–6.5). In comparison, the HR of T2DM among obese versus normal weight subjects was 1.8 (95% CI 1.2–2.6). Clinical lipids did not improve T2DM risk prediction, but adding the lipid-PC_DM to all conventional T2DM risk factors increased the area under the receiver operating characteristics curve by 3%. The lipid-PC_DM was also associated with a dietary risk score for T2DM incidence and lower level of physical activity.

A lifestyle-related lipidomic profile strongly predicts T2DM development beyond current risk factors. Further studies are warranted to test if lifestyle interventions modifying this lipidomic profile can prevent T2DM.

We included 3,257 patients aged 60–80 years (80% male) with a median time since MI of 3.5 years from the Alpha Omega Cohort and who were initially free of type 2 diabetes. At baseline (2002–2006), plasma LA was measured in cholesteryl esters, and dietary LA was estimated with a 203-item food-frequency questionnaire. Incident type 2 diabetes was ascertained through self-reported physician diagnosis and medication use. Hazard ratios (with 95% CIs) were calculated by Cox regressions, in which dietary LA isocalorically replaced the sum of saturated (SFA) and trans fatty acids (TFA).

Mean ± SD circulating and dietary LA was 50.1 ± 4.9% and 5.9 ± 2.1% energy, respectively. Plasma and dietary LA were weakly correlated (Spearman r = 0.13, P < 0.001). During a median follow-up of 41 months, 171 patients developed type 2 diabetes. Plasma LA was inversely associated with type 2 diabetes risk (quintile [Q]5 vs. Q1: 0.44 [0.26, 0.75]; per 5%: 0.73 [0.62, 0.86]). Substitution of dietary LA for SFA+TFA showed no association with type 2 diabetes risk (Q5 vs. Q1: 0.78 [0.36, 1.72]; per 5% energy: 1.18 [0.59, 2.35]). Adjustment for markers of de novo lipogenesis attenuated plasma LA associations.

In our cohort of post-MI patients, plasma LA was inversely related to type 2 diabetes risk, whereas dietary LA was not related. Further research is needed to assess whether plasma LA indicates metabolic state rather than dietary LA in these patients.

We analyzed admissions data listing DKA or HHS at two academic hospitals. We determined 1) the frequency distributions of HHS, DKA, and combined DKA-HHS (DKA criteria plus elevated effective osmolality); 2) the relationship of markers of severity of illness and clinical comorbidities with 30-day all-cause mortality; and 3) the relationship of hospital complications associated with insulin therapy (hypoglycemia and hypokalemia) with mortality.

There were 1,211 patients who had a first admission with confirmed hyperglycemic crises criteria, 465 (38%) who had isolated DKA, 421 (35%) who had isolated HHS, and 325 (27%) who had combined features of DKA-HHS. After adjustment for age, sex, BMI, race, and Charlson Comorbidity Index score, subjects with combined DKA-HHS had higher in-hospital mortality compared with subjects with isolated hyperglycemic crises (adjusted odds ratio [aOR] 2.7; 95% CI 1.4, 4.9; P = 0.0019). In all groups, hypoglycemia (<40 mg/dL) during treatment was associated with a 4.8-fold increase in mortality (aOR 4.8; 95% CI 1.4, 16.8). Hypokalemia ≤3.5 mEq/L was frequent (55%). Severe hypokalemia (≤2.5 mEq/L) was associated with increased inpatient mortality (aOR 4.9; 95% CI 1.3, 18.8; P = 0.02).

Combined DKA-HHS is associated with higher mortality compared with isolated DKA or HHS. Severe hypokalemia and severe hypoglycemia are associated with higher hospital mortality in patients with hyperglycemic crises.

We studied 706 single autoantibody–positive pediatric TrialNet participants (ages 1.6–18.6 years at baseline). Cumulative excess BMI (ceBMI) was calculated for each participant based on longitudinally accumulated BMI ≥85th age- and sex-adjusted percentile. Recursive partitioning analysis and multivariable modeling defined the age cut point differentiating the risk for progression to multiple positive autoantibodies.

At baseline, 175 children (25%) had a BMI ≥85th percentile. ceBMI range was –9.2 to 15.6 kg/m² (median –1.91), with ceBMI ≥0 kg/m² corresponding to persistently elevated BMI ≥85th percentile. Younger age increased the progression to multiple autoantibodies, with age cutoff of 9 years defined by recursive partitioning analysis. Although ceBMI was not significantly associated with progression from single to multiple autoantibodies overall, there was an interaction with ceBMI ≥0 kg/m², age, and HLA (P = 0.009). Among children ≥9 years old without HLA DR3-DQ2 and DR4-DQ8, ceBMI ≥0 kg/m² increased the rate of progression from single to multiple positive autoantibodies (hazard ratio 7.32, P = 0.004) and conferred a risk similar to that in those with T1D-associated HLA haplotypes. In participants <9 years old, the effect of ceBMI on progression to multiple autoantibodies was not significant regardless of HLA type.

These data support that elevated BMI may exacerbate islet autoimmunity prior to clinical T1D, particularly in children with lower risk based on age and HLA. Interventions to maintain normal BMI may prevent or delay the progression of islet autoimmunity.

Using Oregon Medicaid administrative data from 1 January 2014 to 30 September 2016, a retrospective cohort study was conducted with propensity score–matched Medicaid eligibility groups (newly and previously eligible). Outcome measures included total per-member per-month (PMPM) Medicaid expenditures and PMPM expenditures in the following 12 categories: inpatient visits, emergency department visits, primary care physician visits, specialist visits, prescription drugs, transportation services, tests, imaging and echography, procedures, durable medical equipment, evaluation and management, and other or unknown services.

Total PMPM Medicaid expenditures for newly eligible enrollees with diabetes were initially considerably lower compared with PMPM expenditures for matched previously eligible enrollees during the first postexpansion quarter (mean values $561 vs. $793 PMPM, P = 0.018). Within the first three postexpansion quarters, PMPM expenditures of the newly eligible increased to a similar but slightly lower level. Afterward, PMPM expenditures of both groups continued to increase steadily. Most of the overall PMPM expenditure increase among the newly eligible was due to rapidly increasing prescription drug expenditures.

Newly eligible Medicaid enrollees with diabetes had slightly lower PMPM expenditures than previously eligible Medicaid enrollees. The increase in PMPM prescription drug expenditures suggests greater access to treatment over time.

A cost-effectiveness analysis was conducted to estimate lifetime costs and health gains using a stochastic microsimulation model of oral health conditions, T2D, T2D-related microvascular diseases, and CVD of the U.S. population. Model parameters were obtained from the nationally representative National Health and Nutrition Examination Survey (NHANES) (2009–2014) and randomized trials of periodontal treatment among patients with T2D.

Expanding periodontal treatment coverage among patients with T2D and periodontitis would be expected to avert tooth loss by 34.1% (95% CI –39.9, –26.5) and microvascular diseases by 20.5% (95% CI –31.2, –9.1), 17.7% (95% CI –32.7, –4.7), and 18.4% (95% CI –34.5, –3.5) for nephropathy, neuropathy, and retinopathy, respectively. Providing periodontal treatment to the target population would be cost saving from a health care perspective at a total net savings of $5,904 (95% CI –6,039, –5,769) with an estimated gain of 0.6 quality-adjusted life years per capita (95% CI 0.5, 0.6).

Providing nonsurgical periodontal treatment to patients with T2D and periodontitis would be expected to significantly reduce tooth loss and T2D-related microvascular diseases via improved glycemic control. Encouraging patients with T2D and poor oral health conditions to receive periodontal treatment would improve health outcomes and still be cost saving or cost-effective.

The Environmental Determinants of Diabetes in the Young (TEDDY) study followed 7,522 genetically high-risk children in Sweden, Finland, Germany, and the U.S. from birth for a median of 9.0 years (interquartile range 5.7–10.6) with available growth data. Of these, 761 (10.1%) children developed IA and 290 (3.9%) children were diagnosed with T1D. Bayesian two-phase piecewise linear mixed models with a random change point were used to estimate children’s individual growth trajectories. Cox proportional hazards models were used to assess the effects of associated growth parameters on the risks of IA and progression to T1D.

A higher rate of weight gain in infancy was associated with increased IA risk (hazard ratio [HR] 1.09 [95% CI 1.02, 1.17] per 1 kg/year). A height growth pattern with a lower rate in infancy (HR 0.79 [95% CI 0.70, 0.90] per 1 cm/year), higher rate in early childhood (HR 1.48 [95% CI 1.22, 1.79] per 1 cm/year), and younger age at the phase transition (HR 0.76 [95% CI 0.58, 0.99] per 1 month) was associated with increased risk of progression from IA to T1D. A higher rate of weight gain in early childhood was associated with increased risk of progression from IA to T1D (HR 2.57 [95% CI 1.34, 4.91] per 1 kg/year) in children with first-appearing GAD autoantibody only.

Growth patterns in early life better clarify how specific growth phases are associated with the development of T1D.

A validated survey of 585 eligible family or general internal medicine practices seeing ≥30 adult patients with diabetes in or near Minnesota during 2017 evaluated the presence of 62 SysCMs. From 419 (72%) practices completing the survey, NQF#0729 was determined in 396 (95%) from electronic health records, including 215,842 patients with type 1 or type 2 diabetes.

Three SysCMs were associated with higher rates of meeting performance targets across all practices: 1) a systematic process for shared decision making with patients (P = 0.001), 2) checklists of tests or interventions needed for prevention or monitoring of diabetes (P = 0.002), and 3) physician reminders of guideline-based age-appropriate risk assessments due at the patient visit (P = 0.002). When all three were in place, an additional 10.8% of the population achieved recommended performance measures. In subgroup analysis, 15 additional SysCMs were associated with better care in particular types of practices.

Diabetes care outcomes are better in primary care settings that use a patient-centered approach to systematically engage patients in decision making, remind physicians of age-appropriate risk assessments, and provide checklists for recommended diabetes interventions. Practice size and location are important considerations when redesigning delivery systems to improve performance.

We examined the association between SH and cognitive function in 718 older adults with T1D from the Study of Longevity in Diabetes (SOLID). Subjects self-reported recent SH (previous 12 months) and lifetime history of SH resulting in inpatient/emergency department utilization. Global and domain-specific cognition (language, executive function, episodic memory, and simple attention) were assessed. The associations of SH with cognitive function and impaired cognition were evaluated via linear and logistic regression models, respectively.

Thirty-two percent of participants (mean age 67.2 years) reported recent SH and 50% reported lifetime SH. Compared with those with no SH, subjects with a recent SH history had significantly lower global cognition scores. Domain-specific analyses revealed significantly lower scores on language, executive function, and episodic memory with recent SH exposure and significantly lower executive function with lifetime SH exposure. Recent SH was associated with impaired global cognition (odds ratio [OR] 3.22, 95% CI 1.30, 7.94) and cognitive impairment on the language domain (OR 3.15, 95% CI 1.19, 8.29).

Among older adults with T1D, recent SH and lifetime SH were associated with worse cognition. Recent SH was associated with impaired global cognition. These findings suggest a deleterious role of SH on the brain health of older patients with T1D and highlight the importance of SH prevention.

We monitored 4,372 women with a history of GDM participating in the Nurses’ Health Study II for incident type 2 diabetes over 25 years up to 2017. Lactation history was obtained through follow-up questionnaires to calculate lactation duration. Follow-up blood samples were collected from a subset of these women at median age of 58 years through the Diabetes & Women’s Health Study.

We documented 873 incident cases of type 2 diabetes during 87,411 person-years of follow-up. Longer duration of lactation was associated with lower risk of type 2 diabetes for both total lactation (hazard ratio 1.05 [95% CI 0.83–1.34] for up to 6 months, 0.91 [0.72–1.16] for 6–12 months, 0.85 [0.67–1.06] for 12–24 months, and 0.73 [0.57–0.93] for >24 months, compared with 0 months; P-trend = 0.003) and exclusive breastfeeding (P-trend = 0.002) after adjustment for age, ethnicity, family history of diabetes, parity, age at first birth, smoking, diet quality, physical activity, and prepregnancy BMI. Longer duration of lactation was also associated with lower HbA_1c, fasting plasma insulin, and C-peptide concentrations among women without type 2 diabetes at follow-up (all adjusted P-trend ≤0.04).

Longer duration of lactation is associated with a lower risk of type 2 diabetes and a favorable glucose metabolic biomarker profile among women with a history of GDM. The underlying mechanisms and impact on diabetes complications, morbidity, and mortality remain to be determined.

This was a retrospective new-user cohort study of female Medicare beneficiaries aged ≥65 years initiating glargine (203,159), detemir (67,012), or NPH (47,388) from September 2006 to September 2015, with follow-up through May 2017. Weighted Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% CIs for incidence of breast cancer according to ever use, cumulative duration of use, cumulative dose of insulin, length of follow-up time, and a combination of dose and length of follow-up time.

Ever use of glargine was not associated with an increased risk of breast cancer compared with NPH (HR 0.97; 95% CI 0.88–1.06) or detemir (HR 0.98; 95% CI 0.92–1.05). No increased risk was seen with glargine use compared with either NPH or detemir by duration of insulin use, length of follow-up, or cumulative dose of insulin. No increased risk of breast cancer was observed in medium- or high-dose glargine users compared with low-dose users.

Overall, glargine use was not associated with an increased risk of breast cancer compared with NPH or detemir in female Medicare beneficiaries.

We included 17,026 adults (8,346 men and 8,680 women) who were part of the China Health and Nutrition Survey (1991–2015) prospective cohort. Dietary intake was measured by three consecutive 24-h dietary recalls combined with a household food inventory. Diabetes cases were identified through a questionnaire. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% CIs.

A total of 547 men and 577 women developed diabetes during 202,138 person-years of follow-up. For men, the adjusted HRs (95% CIs) for quintiles of nonheme iron intake were 1.00, 0.77 (0.58–1.02), 0.72 (0.54–0.97), 0.63 (0.46–0.85), and 0.87 (0.64–1.19) (P-nonlinearity = 0.0015). The corresponding HRs (95% CIs) for women were 1.00, 0.63 (0.48–0.84), 0.57 (0.43–0.76), 0.58 (0.43–0.77), and 0.67 (0.49–0.91) (P-nonlinearity < 0.0001). The dose-response curves for the association between nonheme iron and total iron intake and diabetes followed a reverse J shape in men and an L shape in women. No significant associations were observed between heme iron intake and diabetes risk.

Total iron and nonheme iron intake was associated with diabetes risk, following a reverse J-shaped curve in men and an L-shaped curve in women. Sufficient intake of nonheme or total iron might be protective against diabetes, while excessive iron intake might increase the risk of diabetes among men.

We pooled individual-level data from 29 nationally representative surveys conducted between 2008 and 2016, totaling 588,574 participants aged ≥25 years. Diabetes prevalence and the subset with undiagnosed diabetes was calculated overall and by country, World Bank income group (WBIG), and geographic region. Multivariable Poisson regression models were used to estimate relative risk (RR).

Overall, prevalence of diabetes in 29 LMICs was 7.5% (95% CI 7.1–8.0) and of undiagnosed diabetes 4.9% (4.6–5.3). Diabetes prevalence increased with increasing WBIG: countries with low-income economies (LICs) 6.7% (5.5–8.1), lower-middle-income economies (LMIs) 7.1% (6.6–7.6), and upper-middle-income economies (UMIs) 8.2% (7.5–9.0). Compared with no formal education, greater educational attainment was associated with an increased risk of diabetes across WBIGs, after adjusting for BMI (LICs RR 1.47 [95% CI 1.22–1.78], LMIs 1.14 [1.06–1.23], and UMIs 1.28 [1.02–1.61]).

Among 29 LMICs, diabetes prevalence was substantial and increased with increasing WBIG. In contrast to the association seen in high-income countries, diabetes risk was highest among those with greater educational attainment, independent of BMI. LMICs included in this analysis may be at an advanced stage in the nutrition transition but with no reversal in the socioeconomic gradient of diabetes risk.

Data from 2,662 individuals with self-reported diabetes who participated in the Health and Retirement Study (HRS) were used. Participants were followed from 2006 through 2014. Financial hardship, psychosocial, and neighborhood-level social determinant factors were based on validated surveys from the biennial core interview and RAND data sets. All social determinant factors and measurements of HbA_1c from the time period were used and treated as time varying in analyses. SAS PROC GLIMMIX was used to fit a series of hierarchical linear mixed models. Models controlled for nonindependence among the repeated observations using a random intercept and treating each individual participant as a random factor. Survey methods were used to apply HRS weighting.

Before adjustment for demographics, difficulty paying bills (β = 0.18 [95% CI 0.02, 0.24]) and medication cost nonadherence (0.15 [0.01, 0.29]) were independently associated with increasing HbA_1c over time, and social cohesion (–0.05 [–0.10, –0.001]) was independently associated with decreasing HbA_1c over time. After adjusting for both demographics and comorbidity count, difficulty paying bills (0.13 [0.03, 0.24]) and religiosity (0.04 [0.001, 0.08]) were independently associated with increasing HbA_1c over time.

Using a longitudinal cohort of older adults with diabetes, this study found that financial hardship factors, such as difficulty paying bills, were more consistently associated with worsening glycemic control over time than psychosocial and neighborhood factors.

Data on health care resource utilization from 100,391 people with type 2 diabetes were extracted from the electronic database used at the Catalan Health Institute. Multivariate regression models were carried out to test the impact of glycemic control (HbA_1c) on total health care, hospital admission, and medication costs; model 1 adjusted for a variety of covariates, and model 2 also included micro- and macrovascular complications. Glycemic control was classified as good for HbA_1c <7%, fair for ≥7% to <8%, poor for ≥8% to <10%, and very poor for ≥10%.

Mean per patient annual direct medical costs were 3,039 ± SD 6,581. Worse glycemic control was associated with higher total health care costs: compared with good glycemic control, health care costs increased by 18% (509.82) and 23% (661.35) in patients with very poor and poor glycemic control, respectively, when unadjusted and by 428.3 and 395.1, respectively, in model 2. Medication costs increased by 12% in patients with fair control and by 28% in those with very poor control (model 2). Patients with poor control had a higher probability of hospitalization than those with good control (5% in model 2) and a greater average cost when hospitalization occurred (811).

Poor glycemic control was directly related to higher total health care, hospitalization, and medication costs. Preventive strategies and good glycemic control in people with type 2 diabetes could reduce the economic impact associated with this disease.

We measured inpatient (n = 816) health care utilization for Bronx A1C participants using an administrative data set containing all hospital discharges for New York State from 2006 to 2014. Multilevel mixed modeling was used to assess changes in health care utilization and costs between the telephonic diabetes intervention (Tele/Pr) arm and print-only (PrO) control arm.

During follow-up, excess relative reductions in all-cause hospitalizations for the Tele/Pr arm compared with PrO arm were statistically significant for odds of hospital use (odds ratio [OR] 0.89; 95% CI 0.82, 0.97; P < 0.01), number of hospital stays (rate ratio [RR] 0.90; 95% CI 0.81, 0.99; P = 0.04), and hospital costs (RR 0.90; 95% CI 0.84, 0.98; P = 0.01). Reductions in hospital use and costs were even stronger for diabetes-related hospitalizations. These outcomes were not significantly related to changes observed in hemoglobin A_1c during individuals’ participation in the 1-year intervention.

These results indicate that the impact of the Bronx A1C intervention was not just on short-term improvements in glycemic control but also on long-term health care utilization. This finding is important because it suggests the benefits of the intervention were long-lasting with the potential to not only reduce hospitalizations but also to lower hospital-associated costs.

We analyzed data from a large representative sample of adults with T1D (N = 5,558). We assessed the presence of symptomatic neuropathy using the dichotomized (≥4) Michigan Neuropathy Screening Instrument Patient Questionnaire score. Logistic regression models were used to investigate associations between DPN and risk factors, as well as with other complications.

The burden of DPN is substantial with 13% prevalence overall. Adjusting for attained age, diabetes duration, and sex, the odds of DPN increased mainly with waist-to-hip ratio, lipids, poor glycemic control (odds ratio 1.51 [95% CI 1.21–1.89] for levels of 75 vs. 53 mmol/mol), ever versus never smoking (1.67 [1.37–2.03]), and worse renal function (1.96 [1.03–3.74] for estimated glomerular filtration rate levels <30 vs. ≥90 mL/min/1.73 m²). The odds significantly decreased with higher HDL cholesterol (0.77 [0.66–0.89] per mmol/L). Living in more deprived areas was associated with DPN (2.17 [1.78–2.65]) for more versus less deprived areas adjusted for other risk factors. Finally, individuals with prevalent DPN were much more likely than others to have other diabetes complications.

Diabetic neuropathy remains substantial, particularly affecting those in the most socioeconomically deprived groups. Those with clinically manifest neuropathy also have a higher burden of other complications and elevated levels of modifiable risk factors. These data suggest that there is considerable scope to reduce neuropathy rates and narrow the socioeconomic differential by better risk factor control.