Impact of Diabetes Mellitus, Its Duration, and Associated Complications on Nutritional Intake in Patients at Risk of Malnutrition: A Focused Nutritional Assessment

Abstract

Background/Objectives: Diabetes mellitus (DM) is a disorder which affects carbohydrate metabolism and has been associated with other conditions such as disease-related malnutrition (DRM), leading to specific challenges in its management. This study aims to evaluate the implications of DM, its complications, and its duration on energy and protein intake in patients at high risk of malnutrition. Methods: Descriptive cross-sectional study in 179 patients with high risk of malnutrition. Patients were compared based on the presence or absence of DM, diabetes duration and the presence of complications. Age, gender, body mass index, bioimpedanciometry, ultrasonography and dynamometry parameters and the diagnosis of sarcopenia were recorded. Energy and protein requirements were calculated, and the composition of the patients’ three-day diet was analyzed. Results: DM was associated with lower weight-adjusted energy intake (Overall: 29.15 (9.99) vs. DM: 24.87 (8.28) vs. NoDM: 30.65 (10.13) kcal/kg/day; p < 0.01) and lower weight-adjusted protein intake (Overall: 1.29 (0.47) vs. DM: 1.18 (0.39) vs. NoDM: 1.33 (0.49) g of protein/kg/day; p < 0.05). DM was a risk factor for poorer compliance with energy requirements (OR: 2.38, 95% CI: 1.13–5.01, p < 0.05). Similarly, the occurrence of complications was identified as a risk factor associated with reduced adherence to energy intake requirements (OR: 1.78, 95% CI 1.06–2.98; p < 0.05), and a duration of diabetes mellitus exceeding 10 years was linked to lower adherence to protein intake requirements (OR 1.79, 95% CI 1.08–2.99; p < 0.05). Conclusions: Among patients at high risk of malnutrition, diabetes mellitus was associated with suboptimal adherence to both caloric and protein intake requirements. The presence of diabetes and its related complications emerged as risk factors for inadequate fulfillment of energy needs, while a longer disease duration was specifically linked to reduced compliance with protein requirements.

1. Introduction

Diabetes mellitus (DM) is a carbohydrate metabolism disorder which causes sustained high blood glucose levels, leading to alterations in various organs and systems in affected individuals. Based on its etiology, it can be divided into: type 1 diabetes mellitus (T1D), caused by antibodies that destroy pancreatic beta cells, generating insulinopenia; type 2 diabetes mellitus (T2D), due to insulin resistance mechanisms at the level of peripheral organs and tissues, and which is usually classified within the metabolic syndrome; and type 3C diabetes mellitus, caused by local or systemic pathology affecting the pancreas. The latter includes, among others, genetic diabetes or MODY (Maturity Onset Diabetes of the Young), in genetically predisposed subjects [1,2].

Paradoxically, although T2D is the most common subtype of diabetes and this, in turn, is associated with obesity and metabolic syndrome, having DM is a risk factor for disease-related malnutrition (DRM) and sarcopenia [3,4], mediated by different mechanisms. Insulin resistance, arteriosclerosis, chronic inflammation, oxidative stress and mitochondrial dysfunction are undoubtedly some of these [5,6]. More specifically, hyperglycaemia, hypoglycaemia, antidiabetic drugs, the presence of complications, the duration of DM, physical inactivity and a restrictive dietary intake can influence the development of DRM and sarcopenia, especially in older individuals [5,6,7]. The European Society for Clinical Nutrition and Metabolism (ESPEN) [8] guidelines on clinical nutrition and hydration in geriatrics recommend an energy intake of approximately 30 kcal/kg body weight/day for the elderly, as well as a minimum protein intake of 1.0 g/kg body weight/day; reaching 1.2–1.5 g/kg body weight/day for elderly subjects with acute or chronic diseases, although additional considerations are indicated to individually adjust these values based on nutritional status, level of physical activity, disease status and tolerance.

Regarding oral intake recommendations for patients with DM, they are mostly based on reducing rapidly absorbed carbohydrates in favor of those with medium or slow absorption, teaching patients to control their intake and learn how to count carbohydrate grams to adjust insulin therapy, reduce saturated fat intake, increasing other more adequate fat alternatives (monounsaturated fatty acids and polyunsaturated fatty acids) and ensure protein intake [9,10]. However, in clinical practice, patients with diabetes mostly follow diets focused on calorie restriction at the expense of drastically reducing carbohydrate, fat or protein intake, perpetuating the vicious cycle between DM, DRM and sarcopenia [5,7].

Therefore, especially in older patients with DM, it may be necessary to shift dietary therapy strategies from treating obesity/metabolic syndrome to preventing DRM [5,8]. However, the assessment of oral intake, and especially protein intake, remains a challenge today, due, in part, to the absence of standardized tests for its assessment and the need to spend a large number of resources and time to categorize and quantify it.

For all these reasons, this study was conducted. Its aims were: first, to evaluate differences in dietary intake in patients at risk of malnutrition based on the presence or absence of DM; and second, to evaluate the relationship between the presence of complications and duration of diabetes with energy–protein intake in patients at risk of malnutrition

2. Materials and Methods

2.1. Study Design

A cross-sectional study was conducted in 179 outpatients at high risk of malnutrition who were referred to the Clinical Nutrition Unit of the University Clinical Hospital of Valladolid (Spain) between January 2021 and December 2024. The study was conducted in accordance with the Declaration of Helsinki. All procedures performed on the patients were approved by the Ethics and Drug Investigation Committees (CEIm) of the Eastern Area of Valladolid. The study was registered with PI code 22-2907 and was approved on 13 October 2022.

The selected patients met the following inclusion criteria: outpatients referred to the Nutrition Unit of the University Clinical Hospital of Valladolid for specialized support, aged over 18 years, and with a body mass index (BMI) less than 25 kg/m². The patients were ambulatory and were referred by primary care physicians or from hospital specialists for evaluation and treatment of malnutrition. Patients with stage IV chronic kidney disease or higher or decompensated chronic liver disease were excluded.

After signing informed consent, patients were assessed using a nutritional history, anthropometric measurements, bioimpedance analysis, ultrasound of the quadriceps rectus femoris muscle, muscle function with handgrip strength, and biochemical tests.

Subjects were stratified based on the presence or absence of diabetes, related complications (microvascular and macrovascular) and a time course of DM greater than or equal to 10 years. Both the diagnosis and type of DM were established according to the American Diabetes Association (ADA) diagnostic criteria: HbA1c equal to or greater than 6.5%; fasting blood glucose equal to or greater than 126 mg/dL; blood glucose 2 h after an oral glucose tolerance test equal to or greater than 200 mg/dL; random blood glucose equal to or greater than 200 mg/dL in an individual with cardinal symptoms of DM or hyperglycemic decompensation [1].

2.2. Parameters Studied

Clinical variables: Age (in years), sex (male/female), presence of diabetes, type and duration of diabetes, and the presence of associated complications were taken into account. Pathologies causing DRM were recorded and DRM was defined using the GLIM criteria, requiring the presence of at least one phenotypic criterion (weight loss > 5% in the last 6 months or >10% in the last year; BMI < 20 kg/m² for individuals younger than 70 years or <22 kg/m² for those older than 70 years; or decreased muscle mass) and one etiological criterion (reduced food intake or assimilation or an inflammatory burden due to an acute or chronic disease) [11]. The European Working Group on Sarcopenia on Older People (EWGSOP2) criteria were used to determine the diagnosis of sarcopenia: low muscle mass measured by BIA (appendicular skeletal muscle adjusted for height less than 7.0 kg/m² in men, less than 5.5 kg/m² in women) and low muscle strength measured by handgrip strength (less than 27 kg in men, less than 16 kg in women) or chair stand test (more than 15 s for five rises) [12].

Anthropometric variables: Body height (m) was measured using a calibrated height scale (Omron, Los Angeles, CA, USA). Body weight (kg) was measured while subjects were minimally clothed and unshod using digital scales (Omron, Los Angeles, CA, USA). BMI was calculated using the formula: weight in kilograms divided by height in meters squared (kg/m²).

Body composition:

• Bioimpedance measurement (BIA) was performed using the NUTRILAB device (EFG, Akern, Milan, Italy) between 8:00 and 9:15, after an overnight fast and 15 min in the supine position. Resistance (R) and reactance (Xc) parameters were measured. The phase angle (PA) was calculated as: PA = ((Xc/R) × 180°/π) [13].
• Muscle ultrasound of the rectus femoris (RF) of the dominant lower extremity was performed with a 10 to 12 MHz probe and a multifrequency linear array (Mindray Z60, Madrid, Spain). The measurement was made with the patient in the supine position, without compression, at the level of the lower third from the superior pole of the patella and the anterior superior iliac spine [14]. The parameters measured to evaluate muscle mass were anteroposterior muscle thickness (Y axis), RF muscle area (RFMA), and subcutaneous fat thickness (cm).

Muscle function: Muscle function was measured using handgrip strength (JAMAR^® dynamometer, Preston, Jackson, MO, USA). Measurements were made with the patient seated with the dominant arm at a right angle. Three measurements were taken, and the highest value was chosen. Low muscle strength (or dynapenia) was considered as a hand grip strength of less than 16 kg in women and less than 27 kg in men.

Biochemical markers: Glycated haemoglobin A1c (HbA1c) was measured using a turbidimetric inhibition immunoassay standardized according to the National Glycohemoglobin Standardization Program (NGSP) (Roche Diagnostics, Geneva, Switzerland).

Energy–Protein Requirements: Energy requirements were assessed using the Harris–Benedict equation [15]: Resting metabolic rate (RMR) for men: (9.65 × weight in kg) + (573 × height in m) − (5.08 × age in years) + 260; RMR for women: (7.38 × weight in kg) + (607 × height in m) − (2.31 × age in years) + 43. These were multiplied by an activity or stress factor [16]. Protein requirements were calculated as 1.5 g/kg/day following the recommendations of the ESPEN guidelines for elderly multi-pathological patients (8). The upper limit of the recommended protein intake (1.5 g/kg/day) was applied due to the patients’ high nutritional risk. Considering the elevated risk of sarcopenia in individuals with diabetes mellitus [3,4], and the absence of restrictions based on our exclusion criteria (such as chronic kidney disease), we deemed it essential to estimate the maximum protein requirement. Compliance with the calculated calorie and protein requirements was assessed as a percentage of consumed versus estimated requirements: (Actual caloric intake/Estimated caloric consumption) × 100; (Actual protein consumption/Estimated protein consumption) × 100. A detailed analysis of macronutrients was performed.

Dietary Intake: All patients documented their dietary intake to facilitate an accurate estimate of their average daily calorie and macronutrient consumption. The dietary records covered a total of three days—two weekdays and one weekend day—to provide a representative snapshot of typical dietary patterns. These collected records were subsequently analyzed using specialized nutritional assessment software (Dietsource^®, Nestlé, Geneva, Switzerland), ensuring an accurate and standardized assessment of nutrient intake. Caloric (kcal/day) and protein (g/day) intakes were calculated. The intake of monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), saturated fatty acids (SFA), cholesterol and fiber was also calculated, as well as their consumption as a percentage of total caloric value.

2.3. Statistical Analysis

Data were analysed using the SPSS 23.0 statistical package (SPSS Inc., Chicago, IL, USA), which was officially licensed for use by the University of Valladolid. For continuous variables, normality was assessed using the Kolmogorov–Smirnov test. These variables were expressed as mean ± standard deviation. Differences in parametric variables were analysed using paired and unpaired Student’s t tests, while nonparametric variables were analysed using the Friedman test, Wilcoxon test, Kruskal–Wallis test, and Mann–Whitney U-test. When comparisons between more than two groups were required, one-way ANOVA with Bonferroni post hoc tests were applied. Variables were analysed across different time points using multivariate analysis of variance (ANOVA). Qualitative variables were expressed as percentages (%), and their differences were assessed using the Chi-square test, applying Fisher and Yates corrections when necessary. The relationship between the presence of diabetes mellitus, its duration greater than 10 years, or complications was assessed, adjusting for energy and protein requirements, using binary logistic regression. Risk was calculated by estimating an odds ratio (OR) with 95% confidence intervals (CI). A p-value less than 0.05 was considered statistically significant.

3. Results

The sample consisted of 179 patients. 117 patients were women (65.4% of the sample). The mean age was 64.65 (15.43) years. 46 of the patients (25.7% of the sample) had DM (Figure 1): 1 patient had T1D (2.17% of all the patients with DM); 40 patients had T2D (86.96% of all the patients with DM); 4 patients had type 3 DM (8.70% of all the patients with DM); and 1 patient had MODY DM (2.17% of all the patients with DM). The mean duration of diabetes was 10.57 ± 7.03 years and the mean HbA1c 6.12 ± 0.95%. 19 patients had diabetes of more than 10 years’ duration (10.6% of the total, 40.43% of patients with DM). 19 patients had some type of diabetes-related complication (10.6% of the total; 40.43% of the total number of patients with DM).

145 patients (81% of the sample) presented DRM. 48 of the patients (26.8% of the total) had sarcopenia. The average caloric intake was 1551 ± 441 kcal/day and the average protein intake was 69.0 ± 21.7 g protein/day. The median percentage of adjustment to caloric requirements was 100.6 (78.8–121.4)%; and 82.4 (62.5–104.7)% was the median percentage of adjustment to protein requirements.

Regarding the remaining chronic pathologies besides DM, almost half of the sample had oncological pathologies (49.2%). The next most common conditions were pulmonary disease (16.8% of patients), non-oncological digestive diseases (10.6% of patients), neurological disorders (7.3% of patients), autoimmune diseases (6.7%), and psychiatric conditions (4.5%). The distribution of these pathologies was not different in people with DM compared to those without DM (p = 0.55).

The differences in body composition and function, as well as the diagnosis of oncological pathologies, malnutrition and sarcopenia according to the presence or absence of DM are shown in

Table 1. Differences in anthropometric, bioimpedance, nutritional ultrasound and dynamometry parameters, as well as in the diagnosis of oncological pathologies, malnutrition and sarcopenia, based on the presence of diabetes.

	Total	DM	NoDM	p-Value
Age (years)	64.7 ± 15.4	68.3 ± 11.8	63.4 ± 16.3	0.06
Gender (%M/%W)	34.6/65.4	50/50	29.3/70.7	0.01 *
HbA1c (%)	5.9 ± 1.0	6.1 ± 1.0	5.8 ± 1.0	<0.01
ANTHROPOMETRY
BMI (kg/m2)	21.4 ± 3.7	22.7 ± 3.3	21.0 ± 3.7	<0.01 *
BIOIMPEDANCIOMETRY
Resistance/height (ohm/m)	373 ± 73	351.7 ± 70	380 ± 72	0.02 *
Reactance/height (ohm/m)	31.1 ± 7.3	28.9 ± 7.1	31.9 ± 7.3	0.02 *
Phase angle (°)	4.8 ± 0.8	4.7 ± 1.0	4.8 ± 0.8	0.61
NUTRITIONAL ULTRASOUND
Area (cm2)	2.9 ± 1.0	2.8 ± 1.0	2.9 ± 1.0	0.41
Y-axis (cm)	0.9 ± 0.3	0.9 ± 0.3	0.9 ± 0.3	0.40
Thickness of subcutaneous fat (cm)	0.7 ± 0.4	0.6 ± 0.3	0.8 ± 0.4	<0.05 *
HANDGRIP STRENGTH
Dynamometry (kg)	22.0 ± 8.4	21.0 ± 9.1	22.4 ± 8.1	0.34
OTHER DIAGNOSIS
Oncological pathology (%)	49.2%	54.3%	47.4%	0.41
Malnutrition (%)	81	80.4	81.2	0.79
Sarcopenia (%)	26.8	39.1	22.6	0.03 *

Parametric variables are represented as mean ± standard deviation. M: male; W: women; DM: diabetes mellitus; HbA1c: glycosylated haemoglobin; BMI: body mass index. * Statistical Significance: p < 0.05.

.

As shown in

Table 2. Differences in caloric and protein requirements and dietary parameters depending on the presence or absence of diabetes.

	Total	DM	NoDM	p-Value
BMR (kcal/day)	1206 ± 178	1235 ± 182	1198 ± 177	0.24
TMR (kcal/day)	1569 ± 232	1605 ± 237	1556 ± 230	0.24
Energy intake (kcal/day)	1551 ± 441	1419 ± 382	1597 ± 452	0.02
Protein intake (g of prot/day)	69 ± 21	67 ± 18	69 ± 22	0.61
Adjusted energy intake (kcal/kg/day)	29.2 ± 10.0	24.9 ± 8.3	30.7 ± 10.1	<0.01
Adjusted protein intake (g of prot/kg/day)	1.3 ± 0.5	1.2 ± 0.4	1.3 ± 0.5	<0.05
Adjustment to energy requirements (%)	100 ± 29	90 ± 25	104 ± 29	<0.01
Adjustment to protein requirements (%)	86 ± 31	79 ± 25	88 ± 33	<0.05
%TCV Carbohydrates	41.1 ± 8.7	42.4 ± 10.0	40.7 ± 8.2	0.24
Fiber (g)	13.6 ± 6.8	14.0 ± 6.7	13.5 ± 6.8	0.65
%TCV fats	39.6 ± 7.4	37.7 ± 7.0	40.2 ± 7.4	<0.05
SFA (g)	20.3 ± 9.8	17.3 ± 8.1	21.4 ± 10.1	0.01
%TCV SFA	11.7 ± 4.1	10.8 ± 3.8	12.0 ± 4.2	0.08
MUFA (g)	27.7 ± 11.7	24.8 ± 9.0	28.7 ± 12.4	0.05
%TCV MUFA	16.1 ± 5.2	15.8 ± 3.9	16.2 ± 5.5	0.59
PUFA (g)	8.1 ± 5.4	6.4 ± 4.0	8.8 ± 5.6	<0.01
%TCV PUFA	4.6 ± 2.3	3.9 ± 1.9	4.8 ± 2.5	0.03
Cholesterol (mg)	333 ± 154	283 ± 116	350 ± 162	0.01
%TCV proteins	18.1 ± 4.1	19.7 ± 4.9	17.6 ± 3.7	<0.01

DM: diabetes mellitus; BMR: basal metabolic rate; TMR: total metabolic rate. TCV: total caloric volume; SFA: Saturated Fatty Acids; MUFA: Mono-Unsaturated Fatty Acids, PUFA: Polyunsaturated Fatty acids.

, a lower overall caloric intake and a tendency to a lower overall protein intake was observed in patients with DM compared to non-diabetics. When adjusting for requirements, significantly lower caloric and protein intake values were observed in the group of subjects with DM.

Figure 2 and Figure 3 show the adjustment to energy and protein requirements of patients with DM according to their duration and the presence or absence of complications. As it can be seen in Figure 2, there was a lower adjustment to energy requirements in patients with a duration of DM of more than 10 years and in those with complications derived from DM. Those patients with longer duration and complications of DM presented a lower protein intake adjusted to requirements, although not significant (Figure 3).

Furthermore, in a multivariate analysis (Figure 4), DM was identified as a risk factor for lower adjustment to energy requirements (OR: 2.38, 95% CI: 1.13–5.01, p < 0.05) adjusted for gender and age. This lower adjustment to energy intake was defined as that which did not reach the median percentage of adjustment to energy requirements (100.60%). For protein intake requirements there was no significant relationship with DM (OR 1.95, 95% CI 0.94–4.04; p > 0.05). Lower adjustment to protein intake was defined as not reaching the median percentage of adjustment to protein requirements (82.41%).

The relationship between the duration of diabetes greater than 10 years and the presence of complications with the adjustment to requirements was also analyzed and is shown in Table S1 with an OR of 1.78 (95% CI 1.06–2.98; p < 0.05) between the presence of complications and lower consumption of caloric requirements and OR 1.79 (95% CI 1.08–2.99; p < 0.05) between duration of DM longer than 10 years and lower protein intake, both adjusted for gender and age (Table S2).

4. Discussion

This study demonstrates that, in patients at risk of disease-related malnutrition (DRM), those with diabetes mellitus exhibited poorer adherence to recommended protein and energy intake compared to non-diabetic individuals. Diabetic complications and extended disease duration were linked to reduced caloric consumption and a trend toward inadequate compliance with protein intake recommendations. These results underscore the significance of diabetes and its complications as key risk factors for insufficient energy intake, while long-standing disease appears to particularly impair appropriate protein consumption.

This study aligns with previous works that relate the presence of DM with DRM [3,17,18], going a step further by investigating the caloric and protein intake of each group of subjects. The results show that patients with DM had lower energy and protein intakes after adjusting both for body weight. This could be due to the higher proportion of males in the DM group, which would result in a higher total metabolic rate (TMR) when calculated using the Harris–Benedict equation [16] and a difficulty to reach this rate; however, it is observed that in both groups the TMR is similar. Other possible explanation is that patients with DM had a higher BMI (they had mostly T2D), something previously observed in studies such as the VIDA study or an unicenter study [3,18]. This fact may lead to an inadequate perception of nutritional status and a delay in the adoption of nutritional measures [18]; however, there are patients with normal body weight, similar TMR and with pathologies at risk for DRM. In fact, when breaking down the proportion of macronutrients in the diet, it can be seen that in the DM group the proportion of fat (which is a major contributor to calorie intake) is lower, while the percentage of carbohydrates consumed is similar in both groups.

Dietary fat reduction is a rational approach in the management of patients with type 2 diabetes (T2D), given its frequent association with metabolic syndrome. However, in our case we are dealing with a cohort with normal BMI, and with a significant percentage of oncological pathology, so we are probably not so interested in this reduction and should prioritize the risk of malnutrition over cardiovascular risk. In our study, we observed that the group of patients with DM had a lower consumption of fats at the expense of both saturated fats and PUFA, of which omega-3 and omega-6 fatty acids are part. As we know, PUFA cannot be synthesized by the body, so they must be obtained through the diet, and they play important roles in cardiovascular health, neurological development and inflammation control, so they should not be restricted in these patients [19]. On the other hand, it has been described that excess energy in the diet can lead to worse glycaemic control in patients with DM [20], but, on the other hand, restrictive diets in patients with DM can contribute to loss of muscle mass and the appearance of fragility or sarcopenia, so the diet must provide the necessary amount of energy [21,22,23].

Regarding protein intake, as previously mentioned, a minimum intake of 1.2–1.5 g of protein/kg of body weight/day is recommended in patients with chronic/acute pathologies, such as those in our study. In our cohort, it was observed that patients with DM hardly reached these recommendations, while in the case of non-diabetics they did (adjusted protein intake of 1.18 vs. 1.33 g protein/kg/day). It should be noted, however, that an exception would be patients with advanced kidney disease, requiring a lower protein intake (approximately 0.8 g of protein/kg of body weight/day) [5,20,24]. Given that the most common cause of chronic kidney disease in the world is diabetes [25,26], it is possible that there was a higher percentage of patients with this complication in the group of patients with DM, although patients with stage IV chronic kidney disease and above were excluded from the study to avoid this interference, and the overall protein intake was similar in both groups. As we already know, inadequate protein intake as well as low physical activity are associated with sarcopenia due to decreased muscle protein synthesis [21,27].

In relation to the study’s second objective, patients with diabetes mellitus lasting more than 10 years and those with diabetes-related complications exhibited poorer alignment with energy intake requirements. This is in line with what was described in the VIDA study, where an association between malnutrition and micro or macrovascular complications such as diabetic nephropathy and diabetic foot ulcers was observed [3]. Moreover, the presence of complicated DM, with poorer glycaemic control, has been associated with the presence of sarcopenia [28,29]. Undoubtedly, this type of more advanced patients (due to time or complications) could benefit from nutritional strategies by adapting the diet or adding specific enteral formulas for diabetic patients that help us to increase this caloric and protein load, thus preventing DRM and sarcopenia [5,8,20].

The results of the multivariate analysis suggest that DM is a significant risk factor for lower adjustment to energy requirements, even after controlling for age and sex (OR: 2.38; 95% CI: 1.13–5.01; p < 0.05). This means that people with DM are more than twice as likely to fail to achieve at least the median percentage of energy adjustment (100.6%) compared to those without this condition. On the other hand, no statistically significant association was observed between DM and adjustment to protein requirements, although the odds ratio close to 2 suggests a possible trend that could reach significance with greater statistical power or a larger sample, or it may be influenced by other factors not adjusted for in the model. Relating these facts to clinical practice, these data reinforce the importance of individualized nutritional assessment in outpatients with DM, particularly with regard to energy intake, to prevent DRM or clinical deterioration related to inadequate intake.

A primary limitation of this study is the exclusion of individuals with a BMI > 25 kg/m², which precluded the assessment of patients with coexisting malnutrition and obesity, including those with sarcopenic obesity. Furthermore, although diabetes mellitus was a key focus, participants also presented with a variety of chronic comorbidities—ranging from oncological to psychiatric disorders—that may have influenced their nutritional status. The outpatient nature of the cohort also limits the comparability of these findings with previous studies, thereby constraining their generalizability.

Nonetheless, the study offers several strengths. It is among the few investigations that examine dietary intake differences in relation to diabetes status, specifically distinguishing between energy and protein requirements. While prior studies have addressed malnutrition in patients with long-standing diabetes or diabetes-related complications, this work contributes novel insight by characterizing the specific malnutrition profile in such individuals. Additionally, the incorporation of advanced body composition assessments—such as bioelectrical impedance analysis (BIA) and muscle ultrasound—beyond traditional anthropometric measures enhances the precision of subject characterization. To our knowledge, this is one of the few studies to explore these aspects within an outpatient population.

5. Conclusions

Our findings revealed that individuals at risk of disease-related malnutrition (DRM) who also had diabetes mellitus demonstrated poorer adherence to recommended protein and energy intake compared to their non-diabetic counterparts. Both the presence of diabetes-related complications and a longer disease duration were associated with reduced caloric intake and a trend toward insufficient compliance with protein intake recommendations. Diabetes and its complications emerged as significant risk factors for inadequate adaptation to energy requirements, while prolonged disease duration was specifically linked to suboptimal protein intake.

In patients with diabetes mellitus who are at risk of DRM, an accurate estimation of energy and protein requirements is essential to guide appropriate nutritional intervention. Tailored nutritional education that considers both the metabolic alterations of diabetes and the specific nutritional deficiencies associated with malnutrition may contribute to optimizing dietary intake. Such individualized strategies could improve the adequacy of macro- and micronutrient consumption, thereby supporting glycaemic control, preserving lean body mass, and enhancing overall clinical outcomes.