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To prevent the spread of COVID-19, lockdown was imposed in many countries with rigid restrictions on all outdoor activities, also limiting attendance at diabetes clinics. In patients with diabetes, lockdown implies lifestyle changes related to physical activity, stress, and nutrition that are likely to adversely affect glycemic control (1). Conversely, during lockdown, individuals with type 1 diabetes (T1D) are to be expected to have a more regular lifestyle, more closely respecting time schedules and insulin administration timing. We evaluated the impact of lockdown on glucose control in 207 Italian adults with T1D attending the Diabetes Outpatient Clinic of the Federico II University Hospital, Naples: 96 females/111 males, mean ± SD age 38.4 ± 12.7 years, 104 on multiple daily insulin injections (MDI), and 103 on insulin pump (continuous subcutaneous insulin infusion [CSII]). Inclusion criteria were continuous glucose monitoring (CGM) for at least 6 months, including a 2-week period with CGM use >70% before (January–February) and during (March–April 2020) lockdown, while maintaining the same device: FreeStyle ( n = 130), Guardian 3 ( n = 47), Dexcom G6 ( n = 18), and Eversense ( n = 12). Each participant gave informed consent for the use of her or his data. No participant reported …

OBJECTIVE To evaluate whether fat quality, in the context of meals with high– (HGI) or low–glycemic index (LGI), influences postprandial blood glucose (PPG) response in patients with type 1 diabetes. RESEARCH DESIGN AND METHODS According to a randomized crossover design, 13 patients with type 1 diabetes on insulin pump consumed two series (HGI or LGI) of meals with the same carbohydrate quantity while differing for amount and quality of fat: 1) low in fat (“low fat”), 2) high in saturated fat (butter), or 3) high in monounsaturated fat (extra-virgin olive oil) (EVOO). Premeal insulin doses were based on insulin–to–glycemic load ratios. Continuous glucose monitoring was performed and 6-h PPG evaluated. RESULTS PPG was significantly different between HGI and LGI meals (P = 0.005 for time × glycemic index interaction by repeated-measures analysis [RMA]), being significantly higher during the first 3 h after the HGI meals with a later tendency to an opposite pattern. In the context of HGI meals, PPG was significantly lower after EVOO than after low fat or butter (P < 0.0001 for time × meal interaction by RMA), with a marked difference in the 0- to 3-h glucose incremental area under the curve between EVOO (mean ± SD 198 ± 274 mmol/L × 180 min) and either low fat (416 ± 329) or butter (398 ± 355) (P < 0.05). No significant differences were observed in PPG between the three LGI meals. CONCLUSIONS Carbohydrate quality of a mixed meal influences shape and extent of PPG. Besides, using EVOO in a HGI meal attenuates the early postprandial glucose response observed when this meal is consumed with either low fat or butter. Therefore, an optimal prandial insulin administration would require considering, in addition to the quantity of carbohydrates, the quality of both carbohydrate and fat.

OBJECTIVE—We explore the relationship among BMI, habitual diet, and the Pro12Ala polymorphism in the peroxisome proliferator–activated receptor (PPAR)γ2. RESEARCH DESIGN AND METHODS—The Pro12Ala variant was characterized in 343 unrelated type 2 diabetic patients who were consecutively seen at the outpatient clinic of a health district of the province of Naples. Anthropometric and laboratory parameters were measured; habitual diet was assessed by a validated semiquantitative food frequency questionnaire. RESULTS—The overall frequency of Ala12 was 12% (n = 42). BMI was significantly higher in Ala carriers than non-Ala carriers, whereas total daily energy intake or macronutrient composition of the diet were similar in the two groups. For further analysis, participants were stratified according to genotype and sex-specific quartiles of energy intake. BMI increased in both genotype groups with increasing energy intake (P < 0.03). BMI was similar in Ala carriers and non-Ala carriers (30.0 vs. 30.1 kg/m2, P > 0.10) in the lower quartile of energy intake but significantly higher in Ala carriers in the upper quartile (36.0 vs. 32.1 kg/m2, P < 0.001). Average daily energy intake and diet composition were comparable within each quartile for carriers or noncarriers of the Ala allele. Relative to the noncarriers, Ala carriers had a significantly lower energy intake per kilogram body weight, thus suggesting that the Ala allele is associated with a higher food efficiency. The confounding role of medications, glucose control, and physical exercise was ruled out. CONCLUSIONS—This study provides evidence of a differential susceptibility to fat accumulation, and, hence, weight gain, in response to habitual high energy intake for Ala carriers compared with Pro/Pro homozygotes.

Peroxisome proliferator-activated receptor (PPAR)-gamma is a major regulator of adipogenesis and insulin sensitivity. The PPAR-gamma gene generates two isoforms through alternative splicing, PPAR-gamma1 and -gamma2, the latter having an additional stretch of 28 amino acids at its NH2-terminus in the ligand-independent activation domain. This extension renders PPAR-gamma2 more sensitive to insulin action. Since there is a Pro12Ala substitution in this domain, we tested whether it is related to type 2 diabetes or insulin resistance. Therefore, 131 type 2 diabetic patients and 312 normoglycemic control subjects were screened for the presence of the mutation and for major clinical and metabolic features. The frequency of the mutation did not differ significantly between diabetic patients and control subjects. BMI, insulin, and other metabolic and anthropometric variables were also not associated with the mutation. Although the study was carried out on a sufficiently large sample, the conclusions do not support a major role for the Pro12Ala substitution of the PPAR-gamma gene in the etiology of type 2 diabetes.