1. bookTom 76 (2022): Zeszyt 1 (January 2022)
Informacje o czasopiśmie
License
Format
Czasopismo
eISSN
1732-2693
Pierwsze wydanie
20 Dec 2021
Częstotliwość wydawania
1 raz w roku
Języki
Angielski
Otwarty dostęp

Assessment of dietary intake by self-report in adult patients with type 1 diabetes treated with a personal insulin pump

Data publikacji: 09 Aug 2022
Tom & Zeszyt: Tom 76 (2022) - Zeszyt 1 (January 2022)
Zakres stron: 315 - 323
Otrzymano: 02 Dec 2021
Przyjęty: 07 Apr 2022
Informacje o czasopiśmie
License
Format
Czasopismo
eISSN
1732-2693
Pierwsze wydanie
20 Dec 2021
Częstotliwość wydawania
1 raz w roku
Języki
Angielski
Abstract Introduction

Appropriate nutrition is an element affecting the metabolic control of patients with diabetes. There are only a few studies assessing the implementation of dietary recommendations in adult patients with type 1 diabetes; none of them assessed the implementation of nutritional standards. Our study aimed to assess the implementation of dietary recommendations and their relation to metabolic control in adults with T1DM treated with personal insulin pumps.

Materials and Methods

The study included 48 adult patients who were divided into two subgroups and compared, based on HbA1c above and below 6.5%. Each patient's nutrient, vitamin, and mineral intake was assessed on self-reported 3-day 24-hour surveys of food consumption. Records were introduced into the dietetic software DietaPro, (source: http://www.dietapro.eu/) which revealed nutrient content. We evaluated the percentage of patients with nutrient consumption below recommended values based on current recommendations.

Results

The studied population was characterized by insufficient consumption of most nutrients and vitamins: sodium, potassium, calcium, magnesium, iron, zinc, copper, iodine, manganese, vitamin A, vitamin D, vitamin E, thiamine, riboflavin, niacin, vitamin B6, folate, vitamin B12, vitamin C. Patients’ diet did supply correct amounts of phosphorus, and too much fatty acid and cholesterol. There were no statistically significant differences in most of the nutrient intakes across the two groups. Nevertheless, we observed a significant difference in the polyunsaturated fatty acids, sodium, niacin, and calcium intakes.

Conclusions

The studied patients consumed too much saturated fatty acid and dietary cholesterol. The consumption amounts of most nutrients and vitamins were associated with the risk of deficiency. The obtained results indicate the need for further dietary education for patients with T1DM.

Keywords

Introduction

One of the most important factors to affect general health is appropriate nutrition [1, 2]. Medical nutrition therapy is of paramount importance in the modern treatment of diabetes [3, 4]. Dietary management approaches that are tailored to meet the needs of people with type 1 diabetes must reflect recommendations aimed at reducing the risk of acute and chronic complications. Unfortunately, dietary intake in young populations with type 1 diabetes often does not follow dietary guidelines [5]. These patients should pay attention not only to the intake of carbohydrates but also to the nutritional quality of the meals. The exaggerated focus on planning meals only in relation to carbohydrate intake can pose a risk to an otherwise generally healthy diet [6]. Available research seems to indicate that diets focusing on higher carbohydrate [7, 8], lower fat [7, 8, 9], higher fiber [9], and higher fruit and vegetable [10, 11] intake have a positive influence on glycemic control. On the other hand, some studies show no such association [12, 13, 14], and several studies suggest the opposite – that lower carbohydrate consumption [15] produces better glycemic control. As far as rational nutrition is concerned, there is hardly any difference between the recommendations for individuals with type 1 diabetes, and the general population of healthy people [3, 4].

Diabetes-related malnutrition can be caused by poor dietary intake or malabsorption, which can lead to protein deficiency and other nutrients, affecting energy levels as well as several organs and systems, and being responsible for alterations in the digestive, immune, and muscular functions [16]. Contemporary advances in diabetes management, such as personal insulin pumps, allowed for lifestyle flexibility for people with T1DM to such an extent that the individuals can be tempted not to follow the prevailing guidelines for a healthy diet. This can prove especially important in terms of its effect on the development of long-term macrovascular complications of the disease [17, 18].

Our study aimed to assess the implementation of dietary recommendations and their relation to metabolic control in a group of young adults with T1DM treated with personal insulin pumps.

Materials and Methods
Study population

The study included 48 adult patients with type 1 diabetes treated with a personal insulin pump, under constant outpatient care at the Department of Metabolic Diseases, University Hospital in Cracow. Diagnosis of type 1 diabetes was made based on World Health Organization criteria: the presence of typical clinical symptoms and insulin therapy requirement from the onset of the disease. Patients were divided into two subgroups and compared based on glycated hemoglobin A1C. Measurement was performed in a reference laboratory using analytic methods certified by the National Glycohemoglobin Standardization Program (NGSP). The cut-off point was set at 6.5%, according to the 2021 Guidelines on the management of patients with diabetes type 1 [3]. The exclusion criteria included pregnancy and treatment with multiple insulin injections (i.e., pens). The study had been approved by the Bioethical Commission at the Jagiellonian University No. 1072.6120.113.2017., all the participants signed written consent.

Dietary Data

Each patient's nutrients, vitamin, and mineral intake were assessed on self-reported 3-day 24-hour dietary surveys of their food consumption. These records were introduced into dietetic software DietaPro (available at: http://www.dietapro.eu/), which revealed the actual nutrient content of the participant's diet. The program DietaPro was positively assessed by the Department of Epidemiology and Preventive Medicine, Jagiellonian University Medical College; it is widely used as a tool for the education of future doctors and dieticians. The program used the most recent Polish food database (2017).

We evaluated the calories’ sources, as in whether the calories came from protein, carbohydrates, lipids, including their subclasses, and consumption of nutrients. Table 2 shows the percentage of patients with nutrient consumption below recommended values. Both the estimated average requirement (EAR) and adequate intake (AI) were based on Guidelines from the Polish Institute of Food and Nutrition (2020) [19]. Tables 3 and 4 present the comparison of calorie sources and intake of nutrients in two subgroups respectively.

Statistical Analyses

Statistical analyses were performed using Statistica v. 13.0. The normality of the distribution was checked using the Shapiro-Wilk test; to assess the equality of variance the Levene's test was performed. The Student's t-test and its non-parametric counterpart, the Mann-Whitney test, were used when appropriate to analyze quantitative variables in two subgroups. For normally distributed data we provided mean and standard deviation and for non-normally distributed variables we provided median [lower quartile – upper quartile]. To analyze patients with nutrient consumption below EAR/AI, a chi-squared test was performed.

Results

Our results include data from 48 patients: 15 females (31.2%) and 33 males (68.8%) with type 1 diabetes with a median age of 25.6 years [22.2–28.3] and 15 years of diabetes duration (± 6.6). Most of the patients have their BMI within a normal range 32 (66.7%), 13 (27.1%) were overweight, 2 (4.2%) were obese and 1 (2.1%) was underweight. All the participants were Caucasian. Detailed patient characteristics are presented in Table 1.

Participants’ characteristics

Variable Whole group (N=48) HbA1<6.5% (N=20)Mean ± SD / Median [Q1–Q3]/n HbA1≥ 6.5% (N=28)Mean ± SD / Median [Q1–Q3]/n p-value
Sex, female/male; n 15/33, 48 7/13 8/20 -
Age [year] 25.6 [22.2–28.3] 23.0 [26.0–29.4] 21.1 [24.7–28.2] 0.254
BMI [kg/m2] 23.7 ± 3.0 22.7 ± 2.4 25.6 ± 6.2 0.037
Height [cm] 175.0 ± 9.5 174.5 ± 9.8 175.4 ± 9.6 0.738
Weight [kg] 73.1 ± 13.3 69.3 ± 11.4 75.8 ± 14.1 0.098
Duration of type 1 diabetes [year] 13.2 ± 6.4 15.0 ± 6.6 11.9 ± 6.0 0.098
Time on continuous subcutaneous insulin infusion (CSII) [year] 7.5 ± 4.1 4.5 [8.0–11.0] 4.0 [6.0–10.0] 0.391
General characteristics of nutrition

The average caloric value of a daily food ration for all participants (n=48) was 2011.8 ± 684.0 kcal. Analyzed menus delivered 51.1% (± 7.03) calories from carbohydrates, 33.2% (± 7.2) from fat, and 16.1% [14.2–19.1] from proteins. Median cholesterol intake was 287.6 mg/day [228.0–395.4], saturated fat was 13.0% ± 3.6. Diets were characterized by 4.7% ± 1.7 calories from polyunsaturated fatty acids (PUFA) intake and 11.7% [9–14.3] from monounsaturated fatty acids (MUFA) (Table 3).

Percent of the study population below EAR/AI

Table 2 presents the percentage of the study population with nutrient consumption below recommended values. The population of patients was characterized by insufficient consumption below EAR/AI of most nutrients and vitamins: sodium, potassium, calcium, magnesium, iron, zinc, copper, iodine, manganese, vitamin A, vitamin D, vitamin E, thiamine, riboflavin, niacin, vitamin B6, folate, vitamin B12, vitamin C.

Percentage of patients with nutrient consumption below EAR/AI

Variable Whole group (N=48)% below EAR/AI HbA1<6,5% (N=20)% below EAR/AI HbA1≥6,5% (N=28)% below EAR/AI Test Chi2 PersonaP-value
Sodium intake in regard to AI [%] 20.8 30.0 14.3 .186
Potassium intake in regard to AI [%] 75.0 80.0 71.4 .499
Calcium intake in regard to EAR [%] 54.2 60.0 50.0 .493
Phosphorus intake in regard to EAR [%] 0.0 0.0 0.0 1.000
Magnesium intake in regard to EAR [%] 58.3 65.0 53.6 .428
Iron intake in regard to EAR [%] 14.6 15.0 14.3 .944
Zinc intake in regard to EAR [%] 29.2 35.0 25.0 .452
Copper intake in regard to EAR [%] 12.5 20.0 7.1 .184
Iodine intake in regard to EAR [%] 100.0 100.0 100.0 1.000
Manganese intake in regard to AI [%] 10.4 15.0 7.1 .380
Vitamin A intake in regard to EAR [%] 39.6 30.0 46.4 .251
Vitamin D intake in regard to AI [%] 100.0 100.0 100.0 1.000
Vitamin E intake in regard to AI [%] 58.3 70.0 50.0 .166
Thiamine intake in regard to EAR [%] 52.1 65.0 42.9 .130
Riboflavin intake in regard to EAR [%] 14.6 20.0 10.7 .369
Niacin intake in regard to EAR [%] 14.6 20.0 10.7 .369
Vitamin B6 intake in regard to EAR [%] 14.6 20.0 10.7 .369
Folate intake in regard to EAR [%] 62.5 70.0 57.1 .364
Vitamin B12 intake in regard to EAR [%] 22.9 30.0 17.9 .324
Vitamin C intake in regard to EAR [%] 45.8 40.0 50.0 .493

The analysis was also performed in subgroups based on glycated hemoglobin (HbA1C): patients with HbA1C equal and more than 6.5%, and patients with HbA1C below 6.5%. The comparison revealed the same deficiency as in the whole group and no statistical differences (p>0.05). In the whole group and the subgroups, patients did consume the right amount of phosphorus regarding EAR (0% below EAR, p=1.00).

Nutrient intake

There were no statistically significant differences in most nutrient intakes across the two groups. Nevertheless, we observed a significant differences in the polyunsaturated fatty acids (PUFA), sodium, niacin, and calcium intakes (Tables 3 & 4). The median PUFA, sodium, and niacin intakes were significantly higher in the group of patients with HbA1c≥6,5%. P-value was 0.029, 0.046, 0.023 respectively. The mean calcium intakes were significantly higher in the group of patients with HbA1c≥6.5%, p=0.043. These differences disappeared when we grouped participants into two groups (first group: patients with [HbA1c] less than 7%, second group: patients with [HbA1c] equal to or more than 7%).

Calorie sources in two groups of patients (with optimal and above-optimal metabolic control)

Variable Whole group (N=48)Mean ± SD / Median [Q1–Q3] HbA1<6,5% (N=20)Mean ± SD / Median [Q1–Q3] HbA1≥6,5% (N=28)Mean ± SD / Median [Q1–Q3] p-value
Total energy per day [kcal] 2011.8 ± 684.0 1819.0 ± 506.7 2149.6 ± 765.9 0.099
Protein [kcal] 338.4 ± 128.3 248.8 [289.8–335.1] 270.7 [359.0–442.0] 0.081
Carbohydrate [kcal] 1006.5 ± 310.7 937.7 ± 223.9 1055.70 ± 356.0 0.198
Fat [kcal] 635.7 [468.0–902.5] 604.1 ± 223.1 750.1 ± 382.6 0.132
Saturated fat (SFA) [kcal] 263.7 ± 115.7 251.8 ± 105.4 272.1 ± 123.7 0.555
Monounsaturated fatty acids (MUFA) [kcal] 213.2 [162.2–342.5] 189.7 [163.7–267.7] 234.5 [162.2–354.5] 0.397
Polyunsaturated fatty acids (PUFA) [kcal] 85.7 [58.0–114.0] 67.2 [51.3–94.9] 96.5 [70.0–147.3] 0.029
Percentage of calories from protein [%] 16.1 [14.2–19.1] 17.7 [13.2–19.1] 15.7 [14.3–19.4] 0.778
Percentage of calories from carbohydrates [%] 51.1 ± 7.0 52.3 ± 5.0 50.2 ± 8.2 0.322
Percentage of calories from fat [%] 33.2 ± 7.2 32.7 ± 5.6 33.5 ± 8.2 0.691
Percentage of calories from SFA [%] 13.0±3.6 13.6 ± 3.9 12.5 ± 3.4 0.290
Percentage of calories from MUFA [%] 11.7 [10.0–14.2] 11.2 [10.7–13.8] 12.1 [9.2–14.3] 0.875
Percentage of calories from PUFA [%] 4.7 ± 1.7 3.9 [3.4–4.7] 4.9 [4.0–6.2] 0.037
Total carbohydrates [g/day] 251.6 ± 77.7 234.4 ± 56.0 263.9 ± 89.0 0.198
Digestible carbohydrates [g/day] 223.9 ± 72.9 211.5 ± 51.2 232.8 ± 85.0 0.324
Starch [g/day] 128.4 ± 49.2 135.0 [101.4–154.7] 122.1 [85.4–158.3] 0.810
Fiber [g/day] 18.1 [14.1–24.9] 19.1 ± 7.3 19.90 ± 7,35 0.716
Total protein [g/day] 84.6 ± 32.1 72.5 [62.2–83.8] 89.8 [67.7–110.5] 0.081
Animal protein [g/day] 42.3 [28.2–55.2] 35.0 [28.2–54.2] 44.0 [32.1–55.2] 0.510
Plant protein [g/day] 24.8 ± 8.2 24.5 ± 7.3 24.9 ± 8.9 0.857
Fat [g/day] 70.6 [52.0–100.3] 67.1 ± 24.8 83.4 ± 42.5 0.132
SFA [mg/day] 29.3 ± 12.9 28.0 ± 11.7 30.2 ± 13.8 0.555
MUFA [mg/day] 23.7 [18.0–38.1] 21.1 [18.2–29.8] 26.1 [18.0–39.4] 0.397
PUFA [mg/day] 9.5 [6.4–12.7] 7.5 [5.7–10.5] 7.8 [10.7–16.4] 0.029
Cholesterol [mg/day] 287.6 [228.0–395.4] 263.9 [221.1–427.6] 307.9 [235.8–378.0] 0.975

Consumption of various nutrients in two groups of patients (with optimal and above-optimal metabolic control)

Variable Whole group (N=48)Mean ± SD / Median [Q1–Q3] HbA1<6.5% (N=20)Mean ± SD / Median [Q1–Q3] HbA1≥6.5% (N=28)Mean ± SD / Median [Q1–Q3] p-value
Sodium [mg/day] 2081.5 [1554.1–2732.2] 1899.1 [1469.7–2316.1] 2331.2 [1743.3–3123.0] 0.046
Potassium [mg/day] 2833.8 ± 903.3 2636.5 ± 961.0 2974.7 ± 849.1 0.204
Calcium [mg/day] 780.5 ± 345.5 661.9 ± 289.3 865.1 ± 362.1 0.043
Phosphorus [mg/day] 1387.1 [984.7–1577.8] 1087.3 [902.9–1518.6] 1491.5 [1007.6–1754.1] 0.140
Magnesium [mg/day] 295.2 ± 97.7 278.1 ± 102.6 307.3 ± 94.0 0.312
Iron [mg/day] 10.5 [8.2–12.9] 9.5 [8.2–12.0] 11.5 [8.2–14.6] 0.221
Zinc [mg/day] 9.9 [7.6–11.7] 9.0 [7.2–11.3] 10.0 [7.6–13.1] 0.291
Copper [mg/day] 1.1 [0.8–1.6] 1.0 [0.7–1.4] 1.3 [0.9–1.6] 0.158
Iodine [μg/day] 31.9 ± 16.0 25.1 [18.8–40.1] 34.1 [21.1–46.1] 0.281
Manganese [mg/day] 4.0 ± 1.8 4.0 ± 1.6 4.1 ± 2.0 0.857
Vitamin A [μg/day] 752.8 [502.7–1156.2] 806.4 [566.2–912.3] 691.6 [426.9–1224.2] 0.843
Retinol [μg/day] 356.5 [239.7–528.0] 352.0 [276.7–539.9] 361.4 [214.4–510.5] 0.524
ß-Carotene [μg/day] 1840.5 [1012.8–3629.2] 1840.5 [925.2–3140.7] 1839.0 [1136.7–4186.6] 0.579
Vitamin D [μg/day] 1.7 [1.0–2.5] 1.9 [1.0–2.5] 1.4 [1.0–2.4] 0.369
Vitamin E [mg/day] 8.4 [5.5–12.6] 7.2 [4.8–9.6] 9.6 [6.0–13.8] 0.119
Thiamine [mg/day] 1.0 [0.8–1.4] 1.0 [0.8–1.2] 1.1 [0.9–1.5] 0.171
Riboflavin [mg/day] 1.6 [1.2–2.0] 1.3 [1.2–1.9] 1.7 [1.4–2.0] 0.124
Niacin [mg/day] 17.7 [14.2–25.0] 15.3 [12.6–19.1] 21.9 [15.6–30.4] 0.023
Vitamin B6 [mg/day] 1.6 [1.3–2.2] 1.5 [1.1–2.2] 1.7 [1.4–2.3] 0.268
Folate [μg/day] 268.8 [204.5–370.1] 283.5 ± 112.4 306.6 ± 128.3 0.521
Vitamin B12 [μg/day] 3.3 [2.2–4.7] 2.6 [1.9–3.8] 3.8 [2.8–5.1] 0.058
Vitamin C [mg/day] 78.0 [48.8–145.8] 78.5 [49.3–146.3] 75.3 [48.3–145.8] 0.762
Discussion

In the present study, we have indicated that our cohort of patients with type 1 diabetes is characterized by insufficient consumption below EAR/AI of most nutrients and vitamins: sodium, potassium, calcium, magnesium, iron, zinc, copper, iodine, manganese, vitamin A, vitamin D, vitamin E, thiamine, riboflavin, niacin, vitamin B6, folate, vitamin B12, vitamin C. There was no significant difference for consumption of most nutrients regarding metabolic control (except PUFA, niacin, sodium, calcium).

The analyzed subgroups divided based on glycated hemoglobin A1C, did not differ significantly in baseline characteristics, except for BMI (22.7 ± 2.4 [HbA1c<6.5%] vs. 25.6 ± 6.2 [HbA1c≥6.5%], p<0.04). All the patients were free from advanced late complications of diabetes.

Mean total energy per day was 2012 ± 684 calories, there was no significant difference in subgroups. The daily supply of calories depends on various factors including gender, age, weight, and physical activity level (PAL). Observed value is appropriate for a man with low PAL or a woman with medium PAL. Most of our patients obtained fewer calories than recommended for total energy expenditure [TEE]. In the literature there is a strong suspicion of non-reporting all meals or inaccurate weighing: both lead to underestimation of calories (observed level of underestimation is between 6% and 40%) [20, 21, 22]. A recent meta-analysis has confirmed a significant underestimation of total energy intake [TEI] in population samples of adults when energy intake was estimated by various retrospective and prospective dietary assessment methods (24h diet recalls, estimated food records, weighed food records, and diet histories) in comparison to an objective reference measure of TEI using doubly labeled water. Using 24h diet recall, men underestimate total caloric in-take by a mean of 715 kcal and women by 633 kcal per day. No significant differences in underestimation were identified based on sex (except estimated food records, for which males underestimated energy intake more than females) [23]. As early as 1990 it was shown that underreporting increased with increased BMI [24]. Furthermore, according to the literature, it is acceptable to use a low-calorie diet in type 1 diabetic patients with low physical activity and accompanying overweight or obesity (which was the case in one-third of our cohort) [25, 26].

Based on our results, we may conclude that observed calorie deficiency can be partly attributed to underestimation due to the self-reporting method used but according to the literature, observed underestimation falls within the error range.

The comparison of subgroups revealed no difference in percentage intake of carbohydrates, proteins, and fats (Table 3). According to the 2021 Guidelines on the management of patients with diabetes, there is no sufficient evidence to determine the single optimal carbohydrate content in the diet. Carbohydrates should provide about 45% of the total calorie intake; if they are supplied in the form of low glycemic index and high fiber content products, their share in the total calorie intake may even be as high as 60%. A high-calorie diet is intended for people who are very physically active, in contrast to the low carbohydrate diet (25–45% in total calorie intake) which may be temporarily recommended in patients with little physical activity. In our subgroups, carbohydrates constituted 52.3% ± 5.0% vs. 50.2% ± 8.2%, p=0.32 respectively. The intake of carbohydrates was about 250 grams per day in each subgroup, therefore it can be classified as a high carbohydrate diet [27]. This type of diet is meant for subjects who are very physically active, otherwise, it could be responsible for increases in glycemic variability, time spent in hypoglycemia, and gaining weight [28]. The intake of fiber was 19.1 g ± 7.3 g vs. 19.9 g ± 7.4 g, p=0.72, respectively; these values are below the recommended 25 g per day or 15 g/1000 kcal [3]. A low-fiber diet is typically observed when using highly processed food, which is popular nowadays in Europe. The consequences, however, are less glycemic control, blood lipid disorders, and the appearance of inflammation [29]. Therefore, the addition of fiber supplements, particularly containing soluble fibers, should be considered [30]. The percentage of intake of calories from fats in the studied group was between recommended 25% and 40% in both groups. However, values of saturated fatty acids are higher than recommended (less than 10% of the total calorie intake). When the intake of both monounsaturated and polyunsaturated fats is insufficient, a diet rich in saturated fats leads to increased serum LDL cholesterol levels and cardiovascular consequences [31]. In our report, the percentage intake of polyunsaturated fatty acids was 3.9% (3.4%–4.7%) vs. 4.9% (4.0%–6.2%), p<0.04. In contrast to other studies, a higher intake of PUFA was present in a subgroup apart from the therapeutic goal [32]. Diets poor in unsaturated fatty acids are characteristic of populations in Central and Eastern Europe. One of the representatives of MUFA is oleic acid, which is olive oil, an important component of the Mediterranean diet. PUFA belong to linoleic and arachidonic acid, they are found in nuts, plant oils, and fatty fish [33]. Both unsaturated fatty acids have multidirectional action. One of the most important is the cardioprotective effect of PUFA, connected with a decrease in cardiovascular risk, which results in lower stroke and myocardial infarction rate [34]. The cardioprotective impact is due to antiarrhythmic, antithrombogenic, and anti-inflammatory effects [35]. Interestingly, PUFA could inhibit lipogenesis and increase the sense of satiety. Finally, it has a positive influence on intestinal microbiota [36]. The Guidelines indicate that protein intake should constitute about 15–20% of total calories, without details concerning sources. In our analysis, these values were within the normal limits; the reduction of intake of protein is recommended in the case of chronic kidney disease [37].

The sodium intake was above the recommended range, 126.6% (98.0%–154.4%) vs. 155.4% (116.2%–208.2%), p=0.05 (0.0458) (Table 4). High dietary sodium in T1D is common due to adding salt to highly processed food. High sodium relates to vascular dysfunction, independently of other dietary intakes, blood pressure, and glycemic control [38]. Sodium is also independently associated with all-cause mortality and end-stage renal disease [39]. Although the causality of these findings is poor, these support the opinion of exercising caution before applying salt restriction universally [39]. Calcium intake in regard to EAR was 82.7% ± 36.2% vs. 106.8% ± 44.8% p=0.05, phosphorus intake was 187.5% (155.7%–261.8%) vs. 253.5% (171.5%–302.4%) p=0.21; in both subgroups, the diet was rich in phosphates. Dietary calcium and phosphorus have an impact on bone, kidneys, and parathyroid glands; a typical modern diet, which is high in phosphorus and low in calcium, may cause secondary hyperparathyroidism and bone loss [40]. Another consequence of phosphate overload is crystal nephropathy, therefore increase of calcium in the diet or as a supplementation alleviates the detrimental effects of excess dietary phytate through excretion of undigested complexes as feces [41]. The intake of zinc in both subgroups is sufficient, without significant difference, p=0.18. The impact of zinc on glycemic control is not clear [42]; however, in the era of COVID-19, it could be important to receive the recommended dose of these micronutrients [43]. In our report, the intake of iodine is insufficient in both subgroups, 26.4% (19.8%–42.2%) vs. 35.9% (22.2%–48.5%) p=0.18. Nowadays, salt is iodized, which prevents iodine deficiency; this is especially important for pregnant women and children.

As for the vitamins, in both analyzed subgroups, the intake of vitamin D is much below the adequate intake, 12.9% (6.9%–16.7%) vs. 9.6% (6.8%–16.1%) p=0.37, respectively. Vitamin D deficiency is an important public health problem worldwide and also in Poland. It is a significant risk for both skeletal and non-skeletal disorders and several lifelong negative health outcomes [44]. Patients with type 1 diabetes are at risk of vitamin D deficiency, supplementation should be implemented and followed up under the control of 25(OH)D concentrations, in order to maintain the optimal concentration of >30–50 ng/ml [45]. In both subgroups, the intake of niacin is above estimated average requirement, 132.9% (106.9%–167.0%) vs. 184.2% (137.0%–253.7%), p=0.01. There is a lack of control trials concerning the impact of niacin on lipids and glucose control in patients with type 1 diabetes, although the effect of these vitamins in patients with type 2 diabetes is better known [46]. Analysis of the results showed that niacin alone or in combination significantly improved lipid abnormalities but requires monitoring of glucose in long-term treatment. Another inconvenient side effect is flushing [47]. The intake of folate was a little below recommendation, 88.6% ± 35.1% vs. 95.7% ± 40.0%, p=0.52; this microelement is crucial for the neural tube development of the fetus, therefore supplementation before and during the first trimester of pregnancy is highly recommended [48]. All above observed deficiencies and excesses do not cause significant clinical manifestations in our cohort. This could be indirect proof that self-reported 24h diet recalls are not full (they may be underestimated), but on the other hand, these deficiencies are observed with no clinical manifestation in similar degrees in the literature of a healthy person. For example, the Polish population tends to take higher energy from saturated fatty acids than dietary recommendations [49].

The study has several limitations. First, the sample size is rather small, and the time of observation was only 3 days. Perhaps the introduction of a mobile application, which makes it easier to count mass and calories, would encourage patients to participate in this type of valuable project [50]. Secondly, due to questionnaire research and patients’ records, there is a risk of under- or overestimation of intake. Some of the observed deficiencies can be attributed to the underestimation of total caloric intake, but this underestimation falls within the range observed in the literature. Nevertheless, 24h diet reports spanning a few days remain the most popular research tool for collecting nutritional data. Another shortcoming is that the diet database does not include information about the possible supplementation used.

The novelty of our study is the fact that there are only a few dietary studies that concern young adults with T1DM [51]. An appropriate diet could improve their quality of life and prevent irreversible complications. Finally, these data may give feedback to patients on what to increase or reduce in their everyday diet [52]. The originality of the present study is a population of young adults with type 1 diabetes, who were being treated with personal insulin pumps. This group of patients is still not widely examined [53]. Also, the strength of our study is the complexity and extensive analysis of diet.

Conclusion

In the population of our patients, there is an evident percentage of patients with nutrient consumption below the recommended EAR/AI. The deficiency of vitamin D and iodine may be an indication for supplementation. Our study has shown that vitamin and mineral intakes among participants with type 1 diabetes, patients with HbA1c≥6.5%, and patients with HbA1c<6.5%, were similar. Interestingly, differences between PUFA, sodium, niacin, and calcium intakes were observed only when participants were grouped with respect to glycated hemoglobin at the level of 6.5% (nonsignificant differences for HbA1c 7%). A longer observation or follow-up study is necessary to investigate the impact of diet on metabolic control and control for underestimation results.

Consumption of various nutrients in two groups of patients (with optimal and above-optimal metabolic control)

Variable Whole group (N=48)Mean ± SD / Median [Q1–Q3] HbA1<6.5% (N=20)Mean ± SD / Median [Q1–Q3] HbA1≥6.5% (N=28)Mean ± SD / Median [Q1–Q3] p-value
Sodium [mg/day] 2081.5 [1554.1–2732.2] 1899.1 [1469.7–2316.1] 2331.2 [1743.3–3123.0] 0.046
Potassium [mg/day] 2833.8 ± 903.3 2636.5 ± 961.0 2974.7 ± 849.1 0.204
Calcium [mg/day] 780.5 ± 345.5 661.9 ± 289.3 865.1 ± 362.1 0.043
Phosphorus [mg/day] 1387.1 [984.7–1577.8] 1087.3 [902.9–1518.6] 1491.5 [1007.6–1754.1] 0.140
Magnesium [mg/day] 295.2 ± 97.7 278.1 ± 102.6 307.3 ± 94.0 0.312
Iron [mg/day] 10.5 [8.2–12.9] 9.5 [8.2–12.0] 11.5 [8.2–14.6] 0.221
Zinc [mg/day] 9.9 [7.6–11.7] 9.0 [7.2–11.3] 10.0 [7.6–13.1] 0.291
Copper [mg/day] 1.1 [0.8–1.6] 1.0 [0.7–1.4] 1.3 [0.9–1.6] 0.158
Iodine [μg/day] 31.9 ± 16.0 25.1 [18.8–40.1] 34.1 [21.1–46.1] 0.281
Manganese [mg/day] 4.0 ± 1.8 4.0 ± 1.6 4.1 ± 2.0 0.857
Vitamin A [μg/day] 752.8 [502.7–1156.2] 806.4 [566.2–912.3] 691.6 [426.9–1224.2] 0.843
Retinol [μg/day] 356.5 [239.7–528.0] 352.0 [276.7–539.9] 361.4 [214.4–510.5] 0.524
ß-Carotene [μg/day] 1840.5 [1012.8–3629.2] 1840.5 [925.2–3140.7] 1839.0 [1136.7–4186.6] 0.579
Vitamin D [μg/day] 1.7 [1.0–2.5] 1.9 [1.0–2.5] 1.4 [1.0–2.4] 0.369
Vitamin E [mg/day] 8.4 [5.5–12.6] 7.2 [4.8–9.6] 9.6 [6.0–13.8] 0.119
Thiamine [mg/day] 1.0 [0.8–1.4] 1.0 [0.8–1.2] 1.1 [0.9–1.5] 0.171
Riboflavin [mg/day] 1.6 [1.2–2.0] 1.3 [1.2–1.9] 1.7 [1.4–2.0] 0.124
Niacin [mg/day] 17.7 [14.2–25.0] 15.3 [12.6–19.1] 21.9 [15.6–30.4] 0.023
Vitamin B6 [mg/day] 1.6 [1.3–2.2] 1.5 [1.1–2.2] 1.7 [1.4–2.3] 0.268
Folate [μg/day] 268.8 [204.5–370.1] 283.5 ± 112.4 306.6 ± 128.3 0.521
Vitamin B12 [μg/day] 3.3 [2.2–4.7] 2.6 [1.9–3.8] 3.8 [2.8–5.1] 0.058
Vitamin C [mg/day] 78.0 [48.8–145.8] 78.5 [49.3–146.3] 75.3 [48.3–145.8] 0.762

Percentage of patients with nutrient consumption below EAR/AI

Variable Whole group (N=48)% below EAR/AI HbA1<6,5% (N=20)% below EAR/AI HbA1≥6,5% (N=28)% below EAR/AI Test Chi2 PersonaP-value
Sodium intake in regard to AI [%] 20.8 30.0 14.3 .186
Potassium intake in regard to AI [%] 75.0 80.0 71.4 .499
Calcium intake in regard to EAR [%] 54.2 60.0 50.0 .493
Phosphorus intake in regard to EAR [%] 0.0 0.0 0.0 1.000
Magnesium intake in regard to EAR [%] 58.3 65.0 53.6 .428
Iron intake in regard to EAR [%] 14.6 15.0 14.3 .944
Zinc intake in regard to EAR [%] 29.2 35.0 25.0 .452
Copper intake in regard to EAR [%] 12.5 20.0 7.1 .184
Iodine intake in regard to EAR [%] 100.0 100.0 100.0 1.000
Manganese intake in regard to AI [%] 10.4 15.0 7.1 .380
Vitamin A intake in regard to EAR [%] 39.6 30.0 46.4 .251
Vitamin D intake in regard to AI [%] 100.0 100.0 100.0 1.000
Vitamin E intake in regard to AI [%] 58.3 70.0 50.0 .166
Thiamine intake in regard to EAR [%] 52.1 65.0 42.9 .130
Riboflavin intake in regard to EAR [%] 14.6 20.0 10.7 .369
Niacin intake in regard to EAR [%] 14.6 20.0 10.7 .369
Vitamin B6 intake in regard to EAR [%] 14.6 20.0 10.7 .369
Folate intake in regard to EAR [%] 62.5 70.0 57.1 .364
Vitamin B12 intake in regard to EAR [%] 22.9 30.0 17.9 .324
Vitamin C intake in regard to EAR [%] 45.8 40.0 50.0 .493

Participants’ characteristics

Variable Whole group (N=48) HbA1<6.5% (N=20)Mean ± SD / Median [Q1–Q3]/n HbA1≥ 6.5% (N=28)Mean ± SD / Median [Q1–Q3]/n p-value
Sex, female/male; n 15/33, 48 7/13 8/20 -
Age [year] 25.6 [22.2–28.3] 23.0 [26.0–29.4] 21.1 [24.7–28.2] 0.254
BMI [kg/m2] 23.7 ± 3.0 22.7 ± 2.4 25.6 ± 6.2 0.037
Height [cm] 175.0 ± 9.5 174.5 ± 9.8 175.4 ± 9.6 0.738
Weight [kg] 73.1 ± 13.3 69.3 ± 11.4 75.8 ± 14.1 0.098
Duration of type 1 diabetes [year] 13.2 ± 6.4 15.0 ± 6.6 11.9 ± 6.0 0.098
Time on continuous subcutaneous insulin infusion (CSII) [year] 7.5 ± 4.1 4.5 [8.0–11.0] 4.0 [6.0–10.0] 0.391

Calorie sources in two groups of patients (with optimal and above-optimal metabolic control)

Variable Whole group (N=48)Mean ± SD / Median [Q1–Q3] HbA1<6,5% (N=20)Mean ± SD / Median [Q1–Q3] HbA1≥6,5% (N=28)Mean ± SD / Median [Q1–Q3] p-value
Total energy per day [kcal] 2011.8 ± 684.0 1819.0 ± 506.7 2149.6 ± 765.9 0.099
Protein [kcal] 338.4 ± 128.3 248.8 [289.8–335.1] 270.7 [359.0–442.0] 0.081
Carbohydrate [kcal] 1006.5 ± 310.7 937.7 ± 223.9 1055.70 ± 356.0 0.198
Fat [kcal] 635.7 [468.0–902.5] 604.1 ± 223.1 750.1 ± 382.6 0.132
Saturated fat (SFA) [kcal] 263.7 ± 115.7 251.8 ± 105.4 272.1 ± 123.7 0.555
Monounsaturated fatty acids (MUFA) [kcal] 213.2 [162.2–342.5] 189.7 [163.7–267.7] 234.5 [162.2–354.5] 0.397
Polyunsaturated fatty acids (PUFA) [kcal] 85.7 [58.0–114.0] 67.2 [51.3–94.9] 96.5 [70.0–147.3] 0.029
Percentage of calories from protein [%] 16.1 [14.2–19.1] 17.7 [13.2–19.1] 15.7 [14.3–19.4] 0.778
Percentage of calories from carbohydrates [%] 51.1 ± 7.0 52.3 ± 5.0 50.2 ± 8.2 0.322
Percentage of calories from fat [%] 33.2 ± 7.2 32.7 ± 5.6 33.5 ± 8.2 0.691
Percentage of calories from SFA [%] 13.0±3.6 13.6 ± 3.9 12.5 ± 3.4 0.290
Percentage of calories from MUFA [%] 11.7 [10.0–14.2] 11.2 [10.7–13.8] 12.1 [9.2–14.3] 0.875
Percentage of calories from PUFA [%] 4.7 ± 1.7 3.9 [3.4–4.7] 4.9 [4.0–6.2] 0.037
Total carbohydrates [g/day] 251.6 ± 77.7 234.4 ± 56.0 263.9 ± 89.0 0.198
Digestible carbohydrates [g/day] 223.9 ± 72.9 211.5 ± 51.2 232.8 ± 85.0 0.324
Starch [g/day] 128.4 ± 49.2 135.0 [101.4–154.7] 122.1 [85.4–158.3] 0.810
Fiber [g/day] 18.1 [14.1–24.9] 19.1 ± 7.3 19.90 ± 7,35 0.716
Total protein [g/day] 84.6 ± 32.1 72.5 [62.2–83.8] 89.8 [67.7–110.5] 0.081
Animal protein [g/day] 42.3 [28.2–55.2] 35.0 [28.2–54.2] 44.0 [32.1–55.2] 0.510
Plant protein [g/day] 24.8 ± 8.2 24.5 ± 7.3 24.9 ± 8.9 0.857
Fat [g/day] 70.6 [52.0–100.3] 67.1 ± 24.8 83.4 ± 42.5 0.132
SFA [mg/day] 29.3 ± 12.9 28.0 ± 11.7 30.2 ± 13.8 0.555
MUFA [mg/day] 23.7 [18.0–38.1] 21.1 [18.2–29.8] 26.1 [18.0–39.4] 0.397
PUFA [mg/day] 9.5 [6.4–12.7] 7.5 [5.7–10.5] 7.8 [10.7–16.4] 0.029
Cholesterol [mg/day] 287.6 [228.0–395.4] 263.9 [221.1–427.6] 307.9 [235.8–378.0] 0.975

Nansel TR, Lipsky LM, Liu A. Greater diet quality is associated with more optimal glycemic control in a longitudinal study of youth with type 1 diabetes. Am J Clin Nutr. 2016; 104: 81–87. NanselTR LipskyLM LiuA Greater diet quality is associated with more optimal glycemic control in a longitudinal study of youth with type 1 diabetes Am J Clin Nutr 2016 104 81 87 10.3945/ajcn.115.126136491952627194309 Search in Google Scholar

Zhou L, Deng M, Zhai X, Yu R, Liu J, Yu M, Li Y, Xiao X. The effects of dietary nutrition intake on glycemic variability in type 1 diabetes mellitus adults. Diabetes Ther. 2021; 12: 1055–1071. ZhouL DengM ZhaiX YuR LiuJ YuM LiY XiaoX The effects of dietary nutrition intake on glycemic variability in type 1 diabetes mellitus adults Diabetes Ther 2021 12 1055 1071 10.1007/s13300-021-01028-8799448633641082 Search in Google Scholar

2021 Guidelines on the management of diabetic patients. A position of Diabetes Poland. Clin Diabetol. 2021; 8: 1–95. 2021 Guidelines on the management of diabetic patients A position of Diabetes Poland Clin Diabetol 2021 8 1 95 Search in Google Scholar

American Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2020. Diabetes Care. 2020; 43 (Suppl 1): S14–S31. American Diabetes Association 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2020 Diabetes Care 2020 43 Suppl 1 S14 S31 10.2337/dc20-S00231862745 Search in Google Scholar

Rovner AJ, Nansel TR. Are children with type 1 diabetes consuming a healthful diet?: A review of the current evidence and strategies for dietary change. Diabetes Educ. 2009; 35: 97–107. RovnerAJ NanselTR Are children with type 1 diabetes consuming a healthful diet?: A review of the current evidence and strategies for dietary change Diabetes Educ 2009 35 97 107 10.1177/0145721708326699277211119244565 Search in Google Scholar

Mehta SN, Haynie DL, Higgins LA, Bucey NN, Rovner AJ, Volkening LK, Nansel TR, Laffel LM. Emphasis on carbohydrates may negatively influence dietary patterns in youth with type 1 diabetes. Diabetes Care. 2009; 32: 2174–2176. MehtaSN HaynieDL HigginsLA BuceyNN RovnerAJ VolkeningLK NanselTR LaffelLM Emphasis on carbohydrates may negatively influence dietary patterns in youth with type 1 diabetes Diabetes Care 2009 32 2174 2176 10.2337/dc09-1302278297119741186 Search in Google Scholar

Delahanty LM, Nathan DM, Lachin JM, Hu FB, Cleary PA, Ziegler GK, Wylie-Rosett J, Wexler DJ. Association of diet with glycated hemoglobin during intensive treatment of type 1 diabetes in the diabetes control and complications trial. Am J Clin Nutr. 2009; 89: 518–524. DelahantyLM NathanDM LachinJM HuFB ClearyPA ZieglerGK Wylie-RosettJ WexlerDJ Association of diet with glycated hemoglobin during intensive treatment of type 1 diabetes in the diabetes control and complications trial Am J Clin Nutr 2009 89 518 524 10.3945/ajcn.2008.26498264751819106241 Search in Google Scholar

Katz ML, Mehta S, Nansel T, Quinn H, Lipsky LM, Laffel LM. Associations of nutrient intake with glycemic control in youth with type 1 diabetes: Differences by insulin regimen. Diabetes Technol Ther. 2014; 16: 512–518. KatzML MehtaS NanselT QuinnH LipskyLM LaffelLM Associations of nutrient intake with glycemic control in youth with type 1 diabetes: Differences by insulin regimen Diabetes Technol Ther 2014 16 512 518 10.1089/dia.2013.0389411580224766666 Search in Google Scholar

Maffeis C, Morandi A, Ventura E, Sabbion A, Contreas G, Tomasselli F, Tommasi M, Fasan I, Costantini S, Pinelli L. Diet, physical, and biochemical characteristics of children and adolescents with type 1 diabetes: Relationship between dietary fat and glucose control. Pediatr Diabetes. 2011; 13: 137–146. MaffeisC MorandiA VenturaE SabbionA ContreasG TomasselliF TommasiM FasanI CostantiniS PinelliL Diet, physical, and biochemical characteristics of children and adolescents with type 1 diabetes: Relationship between dietary fat and glucose control Pediatr Diabetes 2011 13 137 146 10.1111/j.1399-5448.2011.00781.x21672107 Search in Google Scholar

Overby NC, Margeirsdottir HD, Brunborg C, Andersen LF, Dahl-Jørgensen K. The influence of dietary intake and meal pattern on blood glucose control in children and adolescents using intensive insulin treatment. Diabetologia. 2007; 50: 2044–2051. OverbyNC MargeirsdottirHD BrunborgC AndersenLF Dahl-JørgensenK The influence of dietary intake and meal pattern on blood glucose control in children and adolescents using intensive insulin treatment Diabetologia 2007 50 2044 2051 10.1007/s00125-007-0775-017687538 Search in Google Scholar

Lodefalk M, Aman J. Food habits, energy and nutrient intake in adolescents with type 1 diabetes mellitus. Diabet Med. 2006; 23: 1225–1232. LodefalkM AmanJ Food habits, energy and nutrient intake in adolescents with type 1 diabetes mellitus Diabet Med 2006 23 1225 1232 10.1111/j.1464-5491.2006.01971.x17054600 Search in Google Scholar

Michaliszyn SF, Shaibi GQ, Quinn L, Fritschi C, Faulkner MS. Physical fitness, dietary intake, and metabolic control in adolescents with type 1 diabetes. Pediatr Diabetes. 2009; 10: 389–394. MichaliszynSF ShaibiGQ QuinnL FritschiC FaulknerMS Physical fitness, dietary intake, and metabolic control in adolescents with type 1 diabetes Pediatr Diabetes 2009 10 389 394 10.1111/j.1399-5448.2009.00500.x278320319364393 Search in Google Scholar

Nansel TR, Haynie DL, Lipsky LM, Laffel LM, Mehta SN. Multiple indicators of poor diet quality in children and adolescents with type 1 diabetes are associated with higher body mass index percentile but not glycemic control. J Acad Nutr Diet. 2012; 112: 1728–1735. NanselTR HaynieDL LipskyLM LaffelLM MehtaSN Multiple indicators of poor diet quality in children and adolescents with type 1 diabetes are associated with higher body mass index percentile but not glycemic control J Acad Nutr Diet 2012 112 1728 1735 10.1016/j.jand.2012.08.029398555323102173 Search in Google Scholar

Wolever TM, Hamad S, Chiasson JL Josse RG, Leiter LA, Rodger NW, Ross SA, Ryan EA. Day-to-day consistency in amount and source of carbohydrate intake associated with improved blood glucose control in type 1 diabetes. J Am Coll Nutr. 1999; 18: 242–247. WoleverTM HamadS ChiassonJL JosseRG LeiterLA RodgerNW RossSA RyanEA Day-to-day consistency in amount and source of carbohydrate intake associated with improved blood glucose control in type 1 diabetes J Am Coll Nutr 1999 18 242 247 10.1080/07315724.1999.1071885810376780 Search in Google Scholar

Meissner T, Wolf J, Kersting M, Fröhlich-Reiterer E, Flechtner-Mors M, Salgin B, Stahl-Pehe A, Holl RW. Carbohydrate intake in relation to BMI, HbA1c and lipid profile in children and adolescents with type 1 diabetes. Clin Nutr. 2014; 33: 75–78. MeissnerT WolfJ KerstingM Fröhlich-ReitererE Flechtner-MorsM SalginB Stahl-PeheA HollRW Carbohydrate intake in relation to BMI, HbA1c and lipid profile in children and adolescents with type 1 diabetes Clin Nutr 2014 33 75 78 10.1016/j.clnu.2013.03.01723642393 Search in Google Scholar

Saintrain MVL, Sandrin RLESP, Bezerra CB, Lima AOP, Nobre MA, Braga DRA. Nutritional assessment of older adults with diabetes mellitus. Diabetes Res Clin Pract. 2019; 155: 107819. SaintrainMVL SandrinRLESP BezerraCB LimaAOP NobreMA BragaDRA Nutritional assessment of older adults with diabetes mellitus Diabetes Res Clin Pract 2019 155 107819 10.1016/j.diabres.2019.10781931425770 Search in Google Scholar

Homma TK, Endo CM, Saruhashi T, Mori AP, Noronha RM, Monte O, Calliari LE. Dyslipidemia in young patients with type 1 diabetes mellitus. Arch Endocrinol Metab. 2015; 59: 215–219. HommaTK EndoCM SaruhashiT MoriAP NoronhaRM MonteO CalliariLE Dyslipidemia in young patients with type 1 diabetes mellitus Arch Endocrinol Metab 2015 59 215 219 10.1590/2359-399700000004026154088 Search in Google Scholar

Ng DS. Diabetic dyslipidemia: From evolving pathophysiological insight to emerging therapeutic targets. Can J Diabetes. 2013; 37: 319–326. NgDS Diabetic dyslipidemia: From evolving pathophysiological insight to emerging therapeutic targets Can J Diabetes 2013 37 319 326 10.1016/j.jcjd.2013.07.06224500559 Search in Google Scholar

Jarosz M, Rychlik E, Stoś K, Charzewska J. Normy żywienia dla populacji Polski i ich zastosowanie. NIZP-PZH, 2020. JaroszM RychlikE StośK CharzewskaJ Normy żywienia dla populacji Polski i ich zastosowanie NIZP-PZH 2020 Search in Google Scholar

Jarosz M, Rychlik E, Cichocka A, Białkowska M. Energia. In: Normy żywienia dla populacji Polski. M. Jarosz (ed.), Instytut Żywności i Żywienia, Warszawa, 2017: 21–39. JaroszM RychlikE CichockaA BiałkowskaM Energia In: Normy żywienia dla populacji Polski JaroszM. (ed.), Instytut Żywności i Żywienia Warszawa 2017 21 39 Search in Google Scholar

Poslusna K, Ruprich J, de Vries JH, Jakubikova M, van’t Veer P. Misreporting of energy and micronutrient intake estimated by food records and 24 hour recalls, control and adjustment methods in practice. Br J Nutr. 2009; 101, Suppl 2: S73–S85. PoslusnaK RuprichJ de VriesJH JakubikovaM van’t VeerP Misreporting of energy and micronutrient intake estimated by food records and 24 hour recalls, control and adjustment methods in practice Br J Nutr 2009 101 Suppl 2 S73 S85 10.1017/S000711450999060219594967 Search in Google Scholar

Ravelli MN, Schoeller DA. Traditional self-reported dietary instruments are prone to inaccuracies and new approaches are needed. Front Nutr. 2020; 7: 90. RavelliMN SchoellerDA Traditional self-reported dietary instruments are prone to inaccuracies and new approaches are needed Front Nutr 2020 7 90 10.3389/fnut.2020.00090735052632719809 Search in Google Scholar

McKenzie BL, Coyle DH, Santos JA, Burrows T, Rosewarne E, Peters SAE, Carcel C, Jaacks LM, Norton R, Collins CE, et al. Investigating sex differences in the accuracy of dietary assessment methods to measure energy intake in adults: a systematic review and meta-analysis. Am J Clin Nutr. 2021; 113: 1241–1255. McKenzieBL CoyleDH SantosJA BurrowsT RosewarneE PetersSAE CarcelC JaacksLM NortonR CollinsCE Investigating sex differences in the accuracy of dietary assessment methods to measure energy intake in adults: a systematic review and meta-analysis Am J Clin Nutr 2021 113 1241 1255 10.1093/ajcn/nqaa370810676233564834 Search in Google Scholar

Schoeller DA, Bandini LG, Dietz WH. Inaccuracies in self-reported in-take identified by comparison with the doubly labelled water method. Can J Physiol Pharmacol. 1990; 68: 941–949. SchoellerDA BandiniLG DietzWH Inaccuracies in self-reported in-take identified by comparison with the doubly labelled water method Can J Physiol Pharmacol 1990 68 941 949 10.1139/y90-1432200586 Search in Google Scholar

Musil F, Smahelová A, Bláha V, Hyšpler R, Tichá A, Lesná J, Zadák Z, Sobotka L. Effect of low calorie diet and controlled fasting on insulin sensitivity and glucose metabolism in obese patients with type 1 diabetes mellitus. Physiol Res. 2013; 62: 267–276. MusilF SmahelováA BláhaV HyšplerR TicháA LesnáJ ZadákZ SobotkaL Effect of low calorie diet and controlled fasting on insulin sensitivity and glucose metabolism in obese patients with type 1 diabetes mellitus Physiol Res 2013 62 267 276 10.33549/physiolres.93238123489182 Search in Google Scholar

Sellahewa L, Khan C, Lakkunarajah S, Idris I. A systematic review of evidence on the use of very low calorie diets in people with diabetes. Curr Diabetes Rev. 2017; 13: 35–46. SellahewaL KhanC LakkunarajahS IdrisI A systematic review of evidence on the use of very low calorie diets in people with diabetes Curr Diabetes Rev 2017 13 35 46 10.2174/157339981266615100512343126435354 Search in Google Scholar

Matejko B, Tota Ł, Mrozińska S, Morawska M, Pałka T, Kieć-Wilk B, Klupa T, Malecki MT. Predictors of the maximal oxygen consumption in adult patients with type 1 diabetes treated with personal insulin pumps. J Diabetes Investig. 2021; 12: 1377–1385. MatejkoB TotaŁ MrozińskaS MorawskaM PałkaT Kieć-WilkB KlupaT MaleckiMT Predictors of the maximal oxygen consumption in adult patients with type 1 diabetes treated with personal insulin pumps J Diabetes Investig 2021 12 1377 1385 10.1111/jdi.13490835450233378577 Search in Google Scholar

Schmidt S, Christensen MB, Serifovski N, Damm-Frydenberg C, Jensen JB, Fløyel T, Størling J, Ranjan A, Nørgaard K. Low versus high carbohydrate diet in type 1 diabetes: A 12-week randomized open-label crossover study. Diabetes Obes Metab. 2019; 21: 1680–1688. SchmidtS ChristensenMB SerifovskiN Damm-FrydenbergC JensenJB FløyelT StørlingJ RanjanA NørgaardK Low versus high carbohydrate diet in type 1 diabetes: A 12-week randomized open-label crossover study Diabetes Obes Metab 2019 21 1680 1688 10.1111/dom.1372530924570 Search in Google Scholar

Feinman RD, Pogozelski WK, Astrup A, Bernstein RK, Fine EJ, Westman EC, Accurso A, Frassetto L, Gower BA, McFarlane SI, et al. Dietary carbohydrate restriction as the first approach in diabetes management: Critical review and evidence base. Nutrition. 2015; 31: 1–13. FeinmanRD PogozelskiWK AstrupA BernsteinRK FineEJ WestmanEC AccursoA FrassettoL GowerBA McFarlaneSI Dietary carbohydrate restriction as the first approach in diabetes management: Critical review and evidence base Nutrition 2015 31 1 13 10.1016/j.nut.2014.06.01125287761 Search in Google Scholar

Reynolds AN, Akerman AP, Mann J. Dietary fibre and whole grains in diabetes management: Systematic review and meta-analyses. PLoS Med. 2020; 17: e1003053. ReynoldsAN AkermanAP MannJ Dietary fibre and whole grains in diabetes management: Systematic review and meta-analyses PLoS Med 2020 17 e1003053 10.1371/journal.pmed.1003053705990732142510 Search in Google Scholar

Clifton PM, Keogh JB. A systematic review of the effect of dietary saturated and polyunsaturated fat on heart disease. Nutr Metab Cardiovasc Dis. 2017; 27: 1060–1080. CliftonPM KeoghJB A systematic review of the effect of dietary saturated and polyunsaturated fat on heart disease Nutr Metab Cardiovasc Dis 2017 27 1060 1080 10.1016/j.numecd.2017.10.01029174025 Search in Google Scholar

Imamura F, Micha R, Wu JH, de Oliveira Otto MC, Otite FO, Abioye AI, Mozaffarian D. Effects of saturated fat, polyunsaturated fat, monounsaturated fat, and carbohydrate on glucose-insulin homeostasis: A systematic review and meta-analysis of randomised controlled feeding trials. PLoS Med. 2016; 13: e1002087. ImamuraF MichaR WuJH de Oliveira OttoMC OtiteFO AbioyeAI MozaffarianD Effects of saturated fat, polyunsaturated fat, monounsaturated fat, and carbohydrate on glucose-insulin homeostasis: A systematic review and meta-analysis of randomised controlled feeding trials PLoS Med 2016 13 e1002087 10.1371/journal.pmed.1002087495114127434027 Search in Google Scholar

Whelan J. Linoleic Acid. Adv Nutr. 2013; 4: 311–312. WhelanJ Linoleic Acid Adv Nutr 2013 4 311 312 10.3945/an.113.003772365050023674797 Search in Google Scholar

25.2019 ESC/EAS, Guidelines for the management of dyslipidaemias: Lipid modification to reduce cardiovascular risk. Eur Heart J. 2020; 41:111–188. 25.2019 ESC/EAS Guidelines for the management of dyslipidaemias: Lipid modification to reduce cardiovascular risk Eur Heart J 2020 41 111 188 10.1093/eurheartj/ehz455 Search in Google Scholar

Innes JK, Calder PC. Marine omega-3 (N-3) fatty acids for cardiovascular health: An update for 2020. Int J Mol Sci. 2020; 21: 1362. InnesJK CalderPC Marine omega-3 (N-3) fatty acids for cardiovascular health: An update for 2020 Int J Mol Sci 2020 21 1362 10.3390/ijms21041362 Search in Google Scholar

Bellenger J, Bellenger S, Escoula Q, Bidu C, Narce M. N-3 polyunsatu-rated fatty acids: An innovative strategy against obesity and related metabolic disorders, intestinal alteration and gut microbiota dysbiosis. Biochimie. 2019; 159: 66–71. BellengerJ BellengerS EscoulaQ BiduC NarceM N-3 polyunsatu-rated fatty acids: An innovative strategy against obesity and related metabolic disorders, intestinal alteration and gut microbiota dysbiosis Biochimie 2019 159 66 71 10.1016/j.biochi.2019.01.017 Search in Google Scholar

Ahola AJ, Forsblom C, Harjutsalo V, Groop PH. Dietary intake in type 1 diabetes at different stages of diabetic kidney disease. Diabetes Res Clin Pract. 2019; 155: 107775. AholaAJ ForsblomC HarjutsaloV GroopPH Dietary intake in type 1 diabetes at different stages of diabetic kidney disease Diabetes Res Clin Pract 2019 155 107775 10.1016/j.diabres.2019.06.016 Search in Google Scholar

Anderson J, Couper JJ, Toome S, Mpundu-Kaambwa C, Giles LC, Gent R, Coppin B, Peña AS. Dietary sodium intake relates to vascular health in children with type 1 diabetes. Pediatr Diabetes. 2018; 19: 138–142. AndersonJ CouperJJ ToomeS Mpundu-KaambwaC GilesLC GentR CoppinB PeñaAS Dietary sodium intake relates to vascular health in children with type 1 diabetes Pediatr Diabetes 2018 19 138 142 10.1111/pedi.12537 Search in Google Scholar

Thomas MC, Moran J, Forsblom C, Harjutsalo V, Thorn L, Ahola A, Wadén J, Tolonen N, Saraheimo M, Gordin D, et al. The association between dietary sodium intake, ESRD, and all-cause mortality in patients with type 1 diabetes. Diabetes Care. 2011; 34: 861–866. ThomasMC MoranJ ForsblomC HarjutsaloV ThornL AholaA WadénJ TolonenN SaraheimoM GordinD The association between dietary sodium intake, ESRD, and all-cause mortality in patients with type 1 diabetes Diabetes Care 2011 34 861 866 10.2337/dc10-1722 Search in Google Scholar

Calvo MS. Dietary phosphorus, calcium metabolism and bone. J Nutr. 1993; 123: 1627–1633. CalvoMS Dietary phosphorus, calcium metabolism and bone J Nutr 1993 123 1627 1633 10.1093/jn/123.9.1627 Search in Google Scholar

Kim OH, Booth CJ, Choi HS, Lee J, Kang J, Hur J, Jung WJ, Jung YS, Choi HJ, Kim H, et al. High-phytate/low-calcium diet is a risk factor for crystal nephropathies, renal phosphate wasting, and bone loss. Elife. 2020; 9: e52709. KimOH BoothCJ ChoiHS LeeJ KangJ HurJ JungWJ JungYS ChoiHJ KimH High-phytate/low-calcium diet is a risk factor for crystal nephropathies, renal phosphate wasting, and bone loss Elife 2020 9 e52709 10.7554/eLife.52709.sa2 Search in Google Scholar

de Sena KC, Arrais RF, das Graças Almeida M, de Araújo DM, dos Santos MM, de Lima VT, de Fãtima Campos Pedrosa L. Effects of zinc supplementation in patients with type 1 diabetes. Biol Trace Elem Res. 2005; 105: 1–9. de SenaKC ArraisRF das Graças AlmeidaM de AraújoDM dos SantosMM de LimaVT de Fãtima Campos PedrosaL Effects of zinc supplementation in patients with type 1 diabetes Biol Trace Elem Res 2005 105 1 9 10.1385/BTER:105:1-3:001 Search in Google Scholar

Iddir M, Brito A, Dingeo G, Fernandez Del Campo SS, Samouda H, La Frano MR, Bohn T. Strengthening the immune system and reducing inflammation and oxidative stress through diet and nutrition: Considerations during the COVID-19 crisis. Nutrients. 2020; 12: 1562. IddirM BritoA DingeoG Fernandez Del CampoSS SamoudaH La FranoMR BohnT Strengthening the immune system and reducing inflammation and oxidative stress through diet and nutrition: Considerations during the COVID-19 crisis Nutrients 2020 12 1562 10.3390/nu12061562735229132471251 Search in Google Scholar

Rusińska A, Płudowski P, Walczak M, Borszewska-Kornacka MK, Bossowski A, Chlebna-Sokół D, Czech-Kowalska J, Dobrzańska A, Franek E, Helwich E, et al. Vitamin D supplementation guidelines for general population and groups at risk of vitamin D deficiency in Poland - Recommendations of the Polish Society of Pediatric Endocrinology and Diabetes and the expert panel with participation of national specialist consultants and representatives of scientific societies-2018 update. Front Endocrinol. 2018; 9: 246. RusińskaA PłudowskiP WalczakM Borszewska-KornackaMK BossowskiA Chlebna-SokółD Czech-KowalskaJ DobrzańskaA FranekE HelwichE Vitamin D supplementation guidelines for general population and groups at risk of vitamin D deficiency in Poland - Recommendations of the Polish Society of Pediatric Endocrinology and Diabetes and the expert panel with participation of national specialist consultants and representatives of scientific societies-2018 update Front Endocrinol 2018 9 246 10.3389/fendo.2018.00246599087129904370 Search in Google Scholar

Kamińska S, Pikala M, Dziankowska-Zaborszczyk E, Bielecki W, Rębowska E, Kozakiewicz K, Nadrowski P, Drygas W, Kwaśniewska M. Vitamin D - dietary intake, supplementation and metabolic status of Polish adults. Int J Occup Med Environ Health. 2020; 33: 107–118. KamińskaS PikalaM Dziankowska-ZaborszczykE BieleckiW RębowskaE KozakiewiczK NadrowskiP DrygasW KwaśniewskaM Vitamin D - dietary intake, supplementation and metabolic status of Polish adults Int J Occup Med Environ Health 2020 33 107 118 10.13075/ijomeh.1896.0140031942873 Search in Google Scholar

Ding Y, Li Y, Wen A. Effect of niacin on lipids and glucose in patients with type 2 diabetes: A meta-analysis of randomized, controlled clinical trials. Clin Nutr. 2015; 34: 838–844. DingY LiY WenA Effect of niacin on lipids and glucose in patients with type 2 diabetes: A meta-analysis of randomized, controlled clinical trials Clin Nutr 2015 34 838 844 10.1016/j.clnu.2014.09.01925306426 Search in Google Scholar

Grundy SM, Vega GL, McGovern ME, Tulloch BR, Kendall DM, Fitz-Patrick D, Ganda OP, Rosenson RS, Buse JB, Robertson DD, et al. Diabetes Multicenter Research Group. Efficacy, safety, and tolerability of once-daily niacin for the treatment of dyslipidemia associated with type 2 diabetes: results of the assessment of diabetes control and evaluation of the efficacy of niaspan trial. Arch Intern Med. 2002; 162: 1568–1576. GrundySM VegaGL McGovernME TullochBR KendallDM Fitz-PatrickD GandaOP RosensonRS BuseJB RobertsonDD Diabetes Multicenter Research Group Efficacy, safety, and tolerability of once-daily niacin for the treatment of dyslipidemia associated with type 2 diabetes: results of the assessment of diabetes control and evaluation of the efficacy of niaspan trial Arch Intern Med 2002 162 1568 1576 10.1001/archinte.162.14.156812123399 Search in Google Scholar

Kozlowska A, Jagielska AM, Okreglicka KM, Dabrowski F, Kanecki K, Nitsch-Osuch A, Wielgos M, Bomba-Opon D. Dietary vitamin and mineral intakes in a sample of pregnant women with either gestational diabetes or type 1 diabetes mellitus, assessed in comparison with Polish nutritional guidelines. Ginekol Pol. 2018; 89: 581–586. KozlowskaA JagielskaAM OkreglickaKM DabrowskiF KaneckiK Nitsch-OsuchA WielgosM Bomba-OponD Dietary vitamin and mineral intakes in a sample of pregnant women with either gestational diabetes or type 1 diabetes mellitus, assessed in comparison with Polish nutritional guidelines Ginekol Pol 2018 89 581 586 10.5603/GP.a2018.010030508208 Search in Google Scholar

Ilow R, Regulska-Ilow B, Różańska D, Zatońska K, Dehghan M, Zhang X, Szuba A, Vatten L, Janik-Koncewicz K, Mańczuk M et al. Assessment of dietary intake in a sample of Polish population-baseline assessment from the prospective cohort ‘PONS’ study. Ann Agricult Environ Med. 2011; 18: 2. IlowR Regulska-IlowB RóżańskaD ZatońskaK DehghanM ZhangX SzubaA VattenL Janik-KoncewiczK MańczukM Assessment of dietary intake in a sample of Polish population-baseline assessment from the prospective cohort ‘PONS’ study Ann Agricult Environ Med 2011 18 2 Search in Google Scholar

Boushey CJ, Spoden M, Zhu FM, Delp EJ, Kerr DA. New mobile methods for dietary assessment: review of image-assisted and image-based dietary assessment methods. Proc Nutr Soc. 2017; 76: 283–294. BousheyCJ SpodenM ZhuFM DelpEJ KerrDA New mobile methods for dietary assessment: review of image-assisted and image-based dietary assessment methods Proc Nutr Soc 2017 76 283 294 10.1017/S002966511600291327938425 Search in Google Scholar

Seo HE, Kim M, Doo EY, Choi J. Process of diabetes management in young adults with type 1 diabetes. West J Nurs Res. 2020; 42: 278–285. SeoHE KimM DooEY ChoiJ Process of diabetes management in young adults with type 1 diabetes West J Nurs Res 2020 42 278 285 10.1177/019394591986086531347471 Search in Google Scholar

Krzyżowska S, Matejko B, Kieć-Wilk B, Wilk M, Małecki M, Klupa T. Assessment of selected food intake frequency in patients with type 1 diabetes treated with personal insulin pumps. Rocz Panstw Zakl Hig. 2019; 70: 259–265. KrzyżowskaS MatejkoB Kieć-WilkB WilkM MałeckiM KlupaT Assessment of selected food intake frequency in patients with type 1 diabetes treated with personal insulin pumps Rocz Panstw Zakl Hig 2019 70 259 265 10.32394/rpzh.2019.007631515985 Search in Google Scholar

Wojtasik A, Kunachowicz H, Pietraś E. Błonnik pokarmowy (włókno pokarmowe). In: Normy żywienia dla populacji Polski. M. Jarosz (ed.), Instytut Żywności i Żywienia, Warszawa, 2017: 115–129. WojtasikA KunachowiczH PietraśE Błonnik pokarmowy (włókno pokarmowe) In: Normy żywienia dla populacji Polski JaroszM. (ed.), Instytut Żywności i Żywienia Warszawa 2017 115 129 Search in Google Scholar

Polecane artykuły z Trend MD

Zaplanuj zdalną konferencję ze Sciendo