Abstract
Background and objective
Malnutrition and micronutrient deficiencies represent significant concerns in geriatric care, leading to adverse health outcomes in older adults. The study aimed to investigate the prevalence and determinants of micronutrient deficiencies in malnourished older hospitalized patients.
Design and setting
This prospective, observational study was conducted in a geriatric acute care unit.
Participants
The study included 156 malnourished older adults.
Measurements
Malnutrition was identified using the Mini Nutritional Assessment-Short Form. Micronutrient status was assessed through serum analysis of vitamins (A, B1, B6, B12, C, D, E, H, K, folic acid) and minerals (iron, zinc, copper, selenium) within 24 h post-admission.
Results
The average patient age was 82.3 ± 7.5 years, with 69% female. The results revealed a high prevalence of micronutrient deficiencies, with 90% of patients exhibiting deficiencies in three or more micronutrients. Notably, every patient presented at least one micronutrient deficiency. Common deficiencies were found in vitamins C (75%), D (65%), H (61%), and K (45%), as well as folic acid (37%), iron (31%), zinc (36%) and selenium (35%). In binary regression analysis, the amount of previous weight loss was significantly associated with a higher prevalence of multiple (>2) micronutrient deficiencies (P = 0.045). Other variables such age (P = 0.449), gender (P = 0.252), BMI (P = 0.265) and MNA-SF score (P = 0.200) did not show any significant association with the prevalence multiple micronutrient deficiencies.
Conclusion
The high prevalence of micronutrient deficiencies in malnourished older hospitalized patients underscore the urgent need for targeted interventions to address micronutrient deficiencies in this population, promoting their health status.
Keywords: Micronutrient deficiencies, Malnutrition, Weight loss, Older hospitalized patients
1. Introduction
Malnutrition represents a critical concern in geriatric care, driven by a multifactorial etiology and demonstrating high prevalence rates [1]. Significantly linked with increased risks of adverse health events such as infections, falls, diminished quality of life, extended hospital stay, and increased morbidity and mortality rates, malnutrition persistently challenges healthcare outcomes in older adults [2]. Prior research suggests that up to 50% of older patients may experience malnutrition [3,4]. Among the various manifestations of malnutrition in older adults, micronutrient deficiency stands out as a particularly prevalent issue [5,6]. Age-associated alterations and diseases often influence nutritional intake, potentially leading to deficiencies of essential nutrients including calcium, vitamin D, vitamin B12, iron, magnesium, and zinc [6]. As lean body mass and physical activity levels decrease with age, energy requirements and intake are also reduced, often resulting in unintentional weight loss, a major indicator of malnutrition. This underscores the potential risk for malnourished older adults developing specific micronutrient deficiencies in the absence of a diet enriched with micronutrient-dense foods [6].
The risk factors and causes of inadequate micronutrient supply in older adults are various and complex, ranging from socio-economic challenges such as poverty, to physical and physiological issues such as compromised appetite, metabolic changes, changes in taste and smell, poorly fitting dentures, reduced mobility, and the impact of multi-medication [7,8]. Collectively, these elements present significant challenges in maintaining an optimal micronutrient balance for older individuals. While malnutrition in older adults can often be detected through unintentional weight loss or inadequate food intake, the more subtle micronutrient deficiencies are frequently overlooked. A systematic review of observational cohort and longitudinal studies conducted on community-dwelling older adults (≥65 years) highlighted frequent deficiencies in vitamins D, thiamine, riboflavin, calcium, magnesium and selenium [7].
The initial manifestation of micronutrient deficiencies is often mild, unspecific and subclinical, thereby being easily dismissed. Deficiencies in B vitamins could lead to a mild cognitive decline [9], thiamine insufficiency might predispose to the development of type 2 diabetes [10], deficiencies in vitamin B12 and folate may affect the hematological and nervous system and elevate homocysteine levels—a risk factor for cardiovascular diseases [11], and deficiency in iron, vitamin D, and vitamin B6 could compromise immune function [12]. In addition, inadequate intake of antioxidant micronutrients like vitamin E, carotenoids, and vitamin C has been associated with impaired muscle strength and physical performance in older adults [[13], [14], [15]]. Therefore, preventive and therapeutic strategies against micronutrient deficiencies could play a significant role in managing malnutrition in older adults, potentially resulting in considerable healthcare savings compared to treating diseases arising from malnutrition.
In light of the critical role of micronutrients in chronic disease prevention and general health promotion, it's critical to assess the extent of micronutrient deficiency and identify at-risk subgroups within the older population. Thus, the present study seeks to explore the prevalence of micronutrient deficiency among malnourished older hospitalized adults, a group probably at highest risk of developing micronutrient deficiencies. In addition, the study aims to determine whether malnutrition or weight loss serves as an independent risk factor for micronutrient deficiency in this population.
2. Subjects and methods
This research was designed as a prospective, observational study and performed at the geriatric acute care unit of Marien Hospital Herne, university hospital of Ruhr-University Bochum, Germany. This investigation serves as a component of a broader interventional study which primarily aims to improve treatment protocols for malnutrition in older hospitalized patients. Briefly, the central objective of this overarching study was to evaluate the effectiveness of an optimized protocol for the treatment of malnutrition by measuring the differences in clinical outcome between those receiving the optimized intervention and those subjected to nutritional standard care. The detailed methodology, interventions, and results have been published in a separate manuscript [16].
Within the framework of this larger project, comprehensive and individualized management of micronutrient deficiencies was incorporated to optimize patient outcomes. The population under investigation comprised 156 malnourished, older hospitalized patients who were admitted between May 2019 and October 2020. Criteria for participant eligibility included: a Mini Nutritional Assessment Short Form (MNA-SF) score less than 8 or a body weight loss exceeding 10% within the preceding six months, being 65 years or older, need for nutritional support, expected stay in the hospital's geriatric department for at least 14 days, and ability and willingness to give informed consent. Conversely, the exclusion criteria entailed severe dementia, dysphagia and severe depression, an expected need for tube feeding and a palliative care situation.
The data generated throughout the study were managed via the REDCap electronic data capture tool [17], hosted at Marien Hospital Herne. This secure, web-based software ensured an efficient and dependable collection and storage of participant data, thus maintaining the accuracy and consistency of the data throughout the investigation.
2.1. Geriatric assessment
During the initial days following hospital admission, a comprehensive geriatric assessment was administered to all study participants. The MNA-SF, a validated tool for assessing nutritional status, was utilized, taking into consideration factors such as food intake, weight loss over the past three months, mobility, psychological stress or acute diseases, neuropsychological issues, and body mass index (BMI) [18]. This scoring system categorizes patients into three distinct nutritional groups: normal nutritional status (12–14 points), at-risk of malnutrition (8–11 points), and malnourished (0–7 points). The German version of the Barthel Index (BI) [19] was employed to assess functional capability in daily living activities. The index operates on a 0–100 point scale, with a score of 100 denoting complete independence. Frailty was evaluated using the FRAIL scale, designating scores of 1–-2 as pre-frail and 3–5 as frail [20]. The risk of sarcopenia was assessed through the SARC-F questionnaire, using a 0–10 scale, with scores exceeding 4 pointing towards possible sarcopenia [21,22]. The Depression in Old Age Scale (DIA-S) was implemented to screen for depressive symptoms, stratifying patients into three categories: no depression (0–2 points), suspected depression (3 points), and probable depression (4–10 points) [23]. Cognitive status was assessed utilizing the Montreal Cognitive Assessment (MoCA) [24], where a score below 26 suggested cognitive impairment. The Parker Mobility Score was used to evaluate the mobility status of patients. This metric encompasses three mobility-oriented questions, each scored from 0 to 3, with the total score ranging from 0 to 9, based on the patient's ability to perform tasks related to home navigation, stepping outside, and managing shopping tasks. A maximum score of 9 implies optimal mobility [25].
2.2. Laboratory parameters
Blood samples were collected and analyzed according to standard clinical procedures, centrifugated and frozen within 1 h after taking the blood sample. Serum concentrations of vitamins A, B1, B6, B12, C, D, E, H, K, folic acid, along with iron, ferritin, transferrin, transferrin saturation, zinc, copper, and selenium were determined within 24 h post-admission. The reference values for these parameters, within the context of a healthy population, are presented in Table 2. Due to a change in the laboratory method during the course of the study, two different methods were utilized to measure vitamin B1 (thiamine) which was individually considered. Following these procedures, patients were classified into two groups based on micronutrient deficiencies: those having zero, one, or two deficiencies, and those with three or more deficiencies.
Table 2.
Mean serum concentration of micronutrients including classification on admission.
| Total population (n = 156) | Reference values | |
|---|---|---|
| Iron (μg/dl) | 54.6 ± 30.8 | 37−145 |
| Low (n; %) | 48 (31) | |
| Normal (n; %) | 104 (67) | |
| High (n; %) | 3 (2) | |
| Ferritin (ng/mL) | 289.3 ± 301.6 | 13−150 |
| Low (n; %) | 2 (1) | |
| Normal (n; %) | 54 (35) | |
| High (n; %) | 98 (64) | |
| Transferrin (mg/dl) | 191.2 ± 48.6 | 200−360 |
| Low (n; %) | 96 (62) | |
| Normal (n; %) | 58 (38) | |
| High (n; %) | 0 (0) | |
| Transferrin saturation (%) | 20.9 ± 12.4 | 16−45 |
| Low (n; %) | 64 (42) | |
| Normal (n; %) | 84 (54) | |
| High (n; %) | 6 (4) | |
| Folic acid (ng/mL) | 8.0 ± 5.9 | 4.6−18.7 |
| Low (n; %) | 56 (37) | |
| Normal (n; %) | 78 (51) | |
| High (n; %) | 18 (12) | |
| Vitamin B12 (pg/mL) | 538.6 ± 369.1 | 197−866 |
| Low (n; %) | 10 (7) | |
| Normal (n; %) | 125 (81) | |
| High (n; %) | 19 (12) | |
| Zinc (μg/mL) | 0.7 ± 0.2 | 0.7−1.3 |
| Low (n; %) | 54 (36) | |
| Normal (n; %) | 95 (64) | |
| High (n; %) | 0 (0) | |
| Copper (μg/dl) | 141.6 ± 30.3 | 70−150 |
| Low (n; %) | 1 (1) | |
| Normal (n; %) | 93 (65) | |
| High (n; %) | 48 (34) | |
| Selenium (ng/mL) | 67.7 ± 22.2 | 60−110 |
| Low (n; %) | 52 (35) | |
| Normal (n; %) | 93 (62) | |
| High (n; %) | 5 (3) | |
| Vitamin A (ng/mL) | 469.6 ± 189.7 | 300−800 |
| Low (n; %) | 28 (18) | |
| Normal (n; %) | 113 (75) | |
| High (n; %) | 10 (7) | |
| Vitamin C (mg/l) | 2.8 ± 2.6 | 4−15 |
| Low (n; %) | 113 (75) | |
| Normal (n; %) | 38 (25) | |
| High (n; %) | 0 (0) | |
| Vitamin D (ng/mL) | 19.2 ± 17.6 | >20 |
| Low (n; %) | 98 (65) | |
| Normal (n; %) | 52 (35) | |
| High (n; %) | 0 (0) | |
| Vitamin E (μg/mL) | 13.3 ± 3.5 | 5−18 |
| Low (n; %) | 1 (1) | |
| Normal (n; %) | 140 (93) | |
| High (n; %) | 10 (6) | |
| Vitamin H (pg/mL) | 265.6 ± 186.3 | >250 |
| Low (n; %) | 90 (61) | |
| Normal (n; %) | 58 (39) | |
| Vitamin K (ng/mL) | 0.4 ± 2.0 | 0.1−2.2 |
| Low (n; %) | 67 (45) | |
| Normal (n; %) | 82 (54) | |
| High (n; %) | 1 (1) | |
| aVitamin B1 (ng/mL) | 72.6 ± 34.7 | 20−100 |
| Low (n; %) | 0 (0) | |
| Normal (n; %) | 49 (86) | |
| High (n; %) | 8 (14) | |
| aVitamin B1 (nmol/l) | 111.9 ± 41.1 | 70−180 |
| Low (n; %) | 7 (7) | |
| Normal (n; %) | 80 (86) | |
| High (n; %) | 6 (7) | |
| Vitamin B6 (nmol/l) | 69.6 ± 80.5 | 12.5−138 |
| Low (n; %) | 26 (17) | |
| Normal (n; %) | 111 (75) | |
| High (n; %) | 11 (8) |
Values are given as mean ± SD or number (%).
Due to the laboratory change during the study, vitamin B1 measurement was conducted using different method for certain patients.
In addition, information about patients' prior consumption of single or multi-vitamin supplements, along with data on unintentional weight loss and its corresponding timeline, were collected through patient interviews or from their medical history, and in cases where the patient was unable to communicate effectively, details were acquired through interactions with family members.
3. Statistical analysis
The data analysis was conducted using the SPSS statistical software package (SPSS Statistics for Windows, IBM Corp, Version 29.0, Armonk, NY, USA). A power calculation was conducted based on the data of the larger interventional study. Anticipating a substantial average increase of 24 points in the Barthel index for the intervention group (and 19 in the control group), and considering a realistically high standard deviation of ±17 points, we determined that a sample size of N = 143 in a 1:1 design would provide a statistical power of 0.8 and a type I error rate of 0.05 (http://PowerAndSampleSize.com). Micronutrient deficiencies were only measured in the intervention group, which is described in the current study. Descriptive statistics were used to present the continuous data with a normal distribution as mean values and standard deviations (SDs). For non-normally distributed data, median values along with interquartile ranges (IQR) were reported. Categorical variables were presented as number and proportions (n, %). To assess the impact of risk factors (i.e., age, BMI, MNA-SF scores, gender and the amount of previous weight loss) as independent variables on the occurrence of micronutrient deficiencies (those with up to 2 deficiencies and those with 3 or more deficiencies), binary logistic regression analysis was performed. In addition, linear regression analysis was conducted to investigate the impact of all micronutrient levels (independent variables) on the prevalence of frailty and sarcopenia (dependent variables) in our study population. Furthermore, we utilized k-means clustering to identify and analyze patterns of micronutrient deficiencies among our cohort of malnourished patients. A significance level of less than 0.05 was considered statistically significant.
4. Results
4.1. Baseline characteristics
The baseline characteristics of study participants are described in detail in Table 1. The study involved 156 participants, aged between 64 and 100 years, with a higher representation of females (69%). The entire study population was malnourished, as indicated by a median MNA-SF score of six. Notably, eight patients initially at risk of malnutrition were re-categorized as malnourished due to substantial weight loss exceeding 10% within the preceding six months. A significant majority of patients (97%) reported previous weight loss, with an average weight loss of 11.5 kg over a mean duration of eight months. The patient group showed a significant prevalence of frailty and cognitive decline, with frailty affecting 90% (n = 140) and cognitive impairment seen in 93% (n = 145). The SARC-F identified probable sarcopenia in 89% (n = 138) of the patients. Based on the DIA-S, 42% (n = 65) of patients exhibited no depressive symptoms, while 16% (n = 25) and 41% (n = 63) demonstrated suspected and probable depression, respectively. In addition, previous intake of single and multi-vitamin supplementation was seen in 28% (n = 42) and 4% (n = 6) of patients, respectively, with vitamin D supplementation being the most prevalent.
Table 1.
Characteristic of study population on admission.
| All (n = 156) | |
|---|---|
| Gender (n, %) | |
| Female | 108 (69) |
| Male | 48 (31) |
| Age (y) | 82.3 ± 7.5 |
| Height (m) | 1.65 ± 0.08 |
| Body weight (kg) | 62.8 ± 13.9 |
| BMI (kg/m2) | 23.1 ± 4.5 |
| Geriatric assessment | |
| MNA-SF, Median (IQR) | 6 (5−7) |
| Risk of malnutrition (n; %) | 8 (5) |
| Malnourished (n; %) | 148 (95) |
| Barthel-Index, Median (IQR) | 45 (40−59) |
| Parker mobility score, Median (IQR) | 3 (2−5) |
| Frail Simple score, Median (IQR) | 4 (4−5) |
| SARC‐F scores, Median (IQR) | 7 (5−8) |
| Depression score (DIA-S), Median (IQR) | 3 (1−5) |
| Cognitive function (MoCA), Median (IQR) | 17 (13−21) |
| Handgrip strength, Median (IQR) | 14.5 (8−20) |
| Previous weight loss | |
| Yes (n; %) | 151 (97) |
| Previous weight loss (kg) | 11.5 ± 7.1 |
| Duration of previous weight loss (months) | 8.1 ± 7.1 |
| No (n; %) | 4 (3) |
| Multivitamin supplementation before admission (n, %) | |
| Yes | 6 (4) |
| No | 143 (96) |
| Single vitamin supplementation before admission (n, %) | |
| Yes | 42 (28) |
| No | 106 (72) |
| Living situation at admission (n, %) | |
| Home | 150 (96) |
| Long term care | 6 (4) |
| Discharge to (n, %) | |
| Home | 122 (80) |
| Short term-care | 23 (15) |
| Long term-care | 3 (2) |
| Rehabilitation clinic | 1 (1) |
| Other hospital | 5 (3) |
MNA-SF, Mini Nutritional Assessment Short Form; DIA-S scores, Depression in Old Age Scale; MoCA, Montreal Cognitive Assessment; Values are given as number (%), mean ± SD or median (IQR, interquartile range).
4.2. Outcome measures
Table 2 demonstrates the average serum concentration of micronutrients, including their respective classifications and the corresponding reference values. A significant 90% (n = 138) of patients exhibited deficiencies in three or more micronutrients, whereas every patient presented at least a single micronutrient deficiency. Notably, only 10% (n = 17) of the subjects exhibited deficiencies in two or less micronutrients.
Vitamins C, D, H, and K deficiencies were particularly prevalent, identified in 75% (n = 113), 65% (n = 98), 61% (n = 90), and 45% (n = 67) of patients, respectively. In addition, low folic acid levels were identified in 37% (n = 56) of patients, while iron deficiency was seen in 31% (n = 48). Zinc and selenium deficiencies were similarly common, with 36% of patients (n = 54) displaying low zinc levels, and 35% (n = 52) showing insufficient selenium concentrations. Furthermore, deficiencies in vitamin B12, vitamin B1, vitamin E, ferritin, and copper had a comparatively low prevalence, observed in 7%, 7%, 1%, 1%, and 1% of patients, respectively.
4.3. Results of logistic and linear regression analysis
In an effort to investigate the potential predictors of multiple (>2) micronutrient deficiencies, a binary logistic regression analysis was performed (Table 3). The dependent variable was micronutrient deficiencies, which was categorized into two groups: those with two deficiencies and those with 3 or more deficiencies. Independent variables included age, BMI, MNA-SF scores, gender, and the amount of previous weight loss. Among the predictor variables, the amount of previous weight loss (Exp(B) = 1.122, P = 0.045) was the only one to demonstrate a statistically significant association with the prevalence of multiple micronutrient deficiencies (P = 0.045). Other variables such as age (P = 0.449), gender (P = 0.252), BMI (P = 0.265) and MNA-SF score (P = 0.200) did not show a significant association with the prevalence of multiple micronutrient deficiencies in this analysis.
Table 3.
Binary regression analysis of risk factors associated with multiple (≥3) micronutrient deficiencies (n = 156).
| Micronutrient deficiencies |
||||
|---|---|---|---|---|
| B | Std. Error | Beta | P value | |
| Age (year) | −0.031 | 0.041 | 0.969 | 0.449 |
| Gender | 0.800 | 0.699 | 2.227 | 0.252 |
| BMI | −0.087 | 0.078 | 0.917 | 0.265 |
| MNA-SF score | 0.318 | 0.248 | 1.375 | 0.200 |
| Previous weight loss (kg) | 0.115 | 0.057 | 1.122 | 0.045 |
MNA-SF, Mini Nutritional Assessment Short Form; Micronutrient deficiencies was categorized into two groups: those with 2 deficiencies and those with 3 or more deficiencies.
In order to investigate the association of micronutrient levels with frailty and sarcopenia, we conducted linear regression analyses. These models were designed to assess the relationship between all measured micronutrients such as vitamins A, B1, B6, B12, C, D, E, H, K, folic acid, along with iron, zinc, copper, and selenium (independent variables) and the prevalence of frailty and sarcopenia (dependent variables) in our study population. The analysis regarding frailty revealed no significant association with any of the micronutrient levels. However, when examining sarcopenia, a significant association between vitamin K levels and the presence of sarcopenia (P = 0.020) was found. Additionally, while not statistically significant, there were suggestive trends indicating potential associations between sarcopenia and the levels of vitamins B1 (P = 0.052) and C (P = 0.060).
4.4. Cluster analysis of micronutrient deficiencies
K-means clustering resulted in four distinct clusters within the study population. Cluster 1, comprising 11 patients, was characterized by deficiencies in vitamin C and D. Cluster 2, with 29 patients, displayed deficiencies in vitamins C and H. The largest group, Cluster 3, included 67 patients and demonstrated deficiencies in vitamins C, D, and H. Cluster 4 had 23 patients with adequate levels of vitamin D but a deficiency in vitamin C. The analysis highlighted vitamin C deficiency across all clusters and varying levels of vitamin D and H deficiencies.
5. Discussion
Our study aimed to investigate the prevalence and determinants of micronutrient deficiencies in malnourished older hospitalized patients, illuminating the complex nutritional challenges faced by this vulnerable population. While protein-energy-malnutrition is defined by low energy- and protein-intake, it is frequently accompanied by low micronutrient intake. The results revealed a remarkably high prevalence of micronutrient deficiencies, with a significant 90% of patients exhibiting deficiencies in three or more micronutrients. Additionally, every patient in the study presented at least one micronutrient deficiency, indicating the widespread nature of nutritional inadequacies in this group. Consistent with recent research, a cohort study on older hospitalized patients with COVID-19 (median age 67.0 years) showed that 79% of patients had at least one deficient micronutrient level, and 33% had ≥3 deficiencies, with selenium, vitamin D, vitamin A, and zinc being the most prevalent deficiencies [26].
Specifically, our study identified common deficiencies in vitamins C, D, H, and K, as well as folic acid, iron, zinc, and selenium among older hospitalized patients. These results align with the second German National Nutrition Survey (NVS II), which reported insufficient dietary intake of vitamin D, folic acid, and calcium in older adults aged 65 years and over [27]. Additionally, that study identified older women experiencing deficiencies in critical micronutrients, including vitamins B1, B2, B12 and iron [27]. Similar observations were noted in the Population-Based KORA-Age Study, which found subclinical micronutrient deficiencies in community-dwelling older individuals, including vitamin D, vitamin B12, iron, and folate insufficiencies [6]. Furthermore, a National Diet and Nutrition Survey (NDNS) in the UK highlighted the frequent inadequacy of dietary intakes of vitamin D, vitamin K, magnesium, and selenium in community-dwelling elderly individuals [28]. These studies identified common deficiencies in the general older population. We tried to identify which micronutrients are critical in the malnourished older population.
It is worth noting that the cluster analysis reveals significant insights into frequent patterns of micronutrient deficiencies in malnourished older hospitalized adults, with distinct combinations of vitamin C, D, and H deficiencies across different clusters. The widespread deficiency of vitamin C in all clusters raises concerns, likely reflecting dietary gaps, as this vitamin is typically sourced from fruits and vegetables – foods that older adults might not consume sufficiently. Similarly, the common deficiency of vitamin D across multiple clusters correlates with the known challenges of limited sun exposure and dietary sources in older patients. The occurrence of vitamin H deficiency, especially in clusters 2 and 3, also warrants additional investigation into dietary intake and nutrient absorption. These findings underscore the complexity of micronutrient deficiencies in this group and emphasize the need for individualized nutritional strategies to enhance patient outcomes.
Vitamins in the human body can be categorized into two main groups: water-soluble and fat-soluble, each with unique storage mechanisms influencing their retention and susceptibility to deficiencies [29]. Water-soluble vitamins, such as vitamin C and B-complex vitamins including Biotin (vitamin H) are stored in limited quantities and are rapidly excreted via urine, making them more prone to becoming deficient first, especially without sufficient dietary intake. Our study substantiates this phenomenon, identifying vitamin C and H deficiencies in 75% and 61% of patients, respectively. Conversely, fat-soluble vitamins, including vitamins A, D, E, and K, are stored in larger amounts within the liver and adipose tissue, providing a more sustained supply lasting for several weeks to months [29]. This difference in storage capacity underscores the importance of maintaining a regular and balanced dietary intake, particularly for water-soluble vitamins, to prevent potential deficiencies. However, our study findings further revealed a high prevalence of vitamin D and K deficiencies, identified in 65% and 45% of patients, respectively. Vitamin K deficiency typically arises from sustained insufficient intake, resulting in the gradual depletion of reserves. While Vitamin D can be partially obtained through diet, it is primarily synthesized through skin exposure to radiation, a process that diminishes with age and poor sunlight-exposure [30].
In light of these findings, it becomes evident that the widespread issue of micronutrient deficiencies in older hospitalized patients are frequently not addressed. This lack of supplementation can stem from several barriers, including the absence of universally accepted guidelines, diagnostic challenges, and concerns over potential interactions with medications commonly prescribed to the older adults [31]. The routine analysis of multiple micronutrients is expensive but not analyzing them entails the risk of over-supplementation, especially with fat-soluble vitamins, which could result in toxicity, thereby compounding clinicians' hesitancy to implement such interventions without stronger evidence [32]. However, the results of a meta-analysis demonstrated that the addition of micronutrient supplementation to oral nutritional supplements does not lead to improved outcomes [33]. Future research should aim to establish clear protocols for supplementation, assess the long-term effects on health outcomes, and explore the economic implications of such interventions in the healthcare system.
Another significant finding from our study is the notable association between unintentional weight loss and multiple micronutrient deficiencies in older hospitalized patients. Unintentional weight loss in this population is often influenced by a complex interplay of factors, such as impaired nutrient absorption, increased nutritional needs and most important reduced food intake, mediated by various medical, social and psychological influencing factors [8]. These factors may concomitantly contribute to deficiencies in essential vitamins and minerals, which are vital for numerous physiological functions. Our regression analysis supports that the amount of previous weight loss plays a crucial role in determining the prevalence and extent of micronutrient deficiencies. For each kg decrease of body weight, the odds of having three or more micronutrient deficiencies, compared to less than 3 deficiencies, increased by 12.2%. This finding seems intuitive as unintentional weight reflects the degree of global nutritional insufficiency in older adults. In other words, the greater the weight loss, the higher the vulnerability to multiple micronutrient deficiencies. However, our regression analysis did not reveal a significant association between malnutrition as determined by MNA-SF and micronutrient deficiencies. Several reasons may account for this lack of correlation. Firstly, the MNA-SF is designed as a general screening tool for malnutrition and takes into account various risk factors beyond weight loss such as psychological stress, acute diseases, and neuropsychological problems. As a result, it detects the accumulation of risk factors for malnutrition without being a direct measure of weight loss and severity of malnutrition. Moreover, all the patients in our study were classified as malnourished according to MNA-SF, leading to low variability in the scores, which might have reduced the ability to detect other determinants micronutrient deficiencies. Conversely, the amount of weight loss may represent the severity of malnutrition, and thus, show a clearer association with micronutrient deficiencies.
In older patients, the clinical implications of some vitamin deficiencies such as vitamin D, B12, and folate are well-established in previous studies and are linked to age-related health conditions. A lack of vitamin D increases the risk of osteoporosis [34] and falls [35], vitamin B1, B12 and folate deficiency may accelerate cognitive decline [36,37], and insufficient vitamin B12 and folate levels can exacerbate anemia [38]. However, the understanding of the clinical relevance of other vitamins' deficiencies in older adults, particularly in older hospitalized patients, is still low. The complexity of these deficiencies may be due to the factors like age-related changes in absorption, metabolism, or the interaction with chronic diseases and multi-medication commonly found in older hospitalized adults. Additionally, older patients may present unique nutritional needs and susceptibilities. Therefore, the ambiguous nature of some vitamin deficiencies in this age group calls for more research.
Some limitations of the current study should be discussed. Firstly, the cross-sectional design of the study limits our ability to establish causality between micronutrient deficiencies and weight loss. Longitudinal studies are required to better understand the relationship between these variables. Additionally, the study was conducted in a specific hospital setting, which may limit the generalizability of the findings to other populations or healthcare settings. Major reasons for hospitalization, including cardiovascular diseases, falls, fractures, post-stroke care, urinary tract infections, and primary neurodegenerative diseases, may have biased our results. For instance, patients with cardiovascular diseases often have altered nutrient metabolism, which could contribute to deficiencies in vitamins crucial for cardiovascular health [39,40]. Similarly, those hospitalized due to falls and fractures may frequently have underlying vitamin D deficiencies, leading to fall- and fracture-risk [41]. Further research involving different cohorts and settings is necessary to validate and extend our results.
6. Conclusion
A high prevalence of deficiencies in vitamins C, D, H, and K, as well as folic acid, iron, zinc, and selenium are particularly notable among our study cohort. The significant association between previous weight loss and multiple micronutrient deficiencies underscores the importance of recognizing the amount of unintentional weight loss as an indicator of nutritional inadequacies in malnourished older patients.
Author contributions
Conceptualization, M.P., K.Y. and R.W.; data curation and investigation D.D. and K.Y.; formal analysis, M.P. and K.Y.; methodology, M.P. and R.W, project administration, M.P.; supervision and validation, M.P. and R.W.; writing—original draft, M.P. and K.Y.; writing—review and editing, M.P., K.Y. and R.W. All authors have read and agreed to the published version of the manuscript.
Funding
This study was funded by the Faculty of Medicine at Ruhr-Universität Bochum through the FoRUM-Program (FoRUM No. F917R-2018).
Ethical standard
The authors declare that the study procedures comply with current ethical standards for research involving human participants in Germany. The study protocol had been approved by the ethical committee of Ruhr- University Bochum (Approval No. 17-6217, dated 24 January 2018). Written informed consent was obtained from all patients. This study was registered in the German Clinical Trials Register with the DRKS-ID: DRKS00017324 (https://www.drks.de/drks_web/setLocale_EN.do).
Conflicts of interest
The authors declare no conflict of interest.
Acknowledgments
We acknowledge the support we received from the Open Access Publication Fund of the Ruhr-Universität Bochum.
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