Next Article in Journal
Hypovitaminosis D in Young Basketball Players: Association with Jumping and Hopping Performance Considering Gender
Next Article in Special Issue
Premature Aging in Chronic Kidney Disease: The Outcome of Persistent Inflammation beyond the Bounds
Previous Article in Journal
Mapping the Burden of Hypertension in South Africa: A Comparative Analysis of the National 2012 SANHANES and the 2016 Demographic and Health Survey
Previous Article in Special Issue
Sedentary Time, Physical Activity Levels and Physical Fitness in Adults with Intellectual Disabilities
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 18
Issue 10
10.3390/ijerph18105444
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

High Carbohydrate Diet Is Associated with Severe Clinical Indicators, but Not with Nutrition Knowledge Score in Patients with Multiple Myeloma

by
Ema Borsi
1,
Costela Lacrimioara Serban
2,*,
Cristina Potre
1,3,
Ovidiu Potre
1,3,
Salomeia Putnoky
4,*,
Miruna Samfireag
3,5,
Raluca Tudor
6,
Ioana Ionita
1,3 and
Hortensia Ionita
1,3
1
Discipline of Hematology, Department of Internal Medicine, Victor Babes University of Medicine and Pharmacy, 300041 Timisoara, Timis County, Romania
2
Department of Functional Science, Victor Babes University of Medicine and Pharmacy, 300041 Timisoara, Timis County, Romania
3
Hematology Clinic, Timisoara’s Emergency City Hospital, 300723 Timisoara, Timis County, Romania
4
Department of Microbiology, Victor Babes University of Medicine and Pharmacy, 300041 Timisoara, Timis County, Romania
5
Department of Internal Medicine, Discipline of Clinical Practical Skills, Victor Babes University of Medicine and Pharmacy, 300041 Timisoara, Timis County, Romania
6
Department of Neurology, Victor Babes University of Medicine and Pharmacy, 300041 Timisoara, Timis County, Romania
*
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2021, 18(10), 5444; https://doi.org/10.3390/ijerph18105444
Submission received: 18 April 2021 / Revised: 14 May 2021 / Accepted: 17 May 2021 / Published: 19 May 2021
(This article belongs to the Special Issue Research on Lifestyle, Nutrition, Consumer Behavior and Family Health)
Browse Figure
Review Reports Versions Notes

Abstract

:
Although the survival rate of patients diagnosed with multiple myeloma has doubled over the last few decades, due to the introduction of new therapeutic lines and improvement of care, other potential contributors to the therapeutic response/relapse of disease, such as nutrient intake, along with nutrition knowledge, have not been assessed during the course of the disease. The purpose of this research was to assess nutrition knowledge and diet quality in a group of patients with a diagnosis of multiple myeloma. Anthropometric, clinical and biological assessments and skeletal survey evaluations, along with the assessment of nutritional intake and general nutrition knowledge, were performed on 61 patients with a current diagnosis of multiple myeloma. A low carbohydrate diet score was computed, classified in tertiles, and used as a factor in the analysis. Patients in tertiles indicative of high carbohydrate or low carbohydrate intake showed significant alteration of clinical parameters, such as hemoglobin, uric acid, albumin, total proteins, beta-2 microglobulin, percentage of plasmacytes in the bone marrow and D-dimers, compared to patients in the medium carbohydrate intake tertile. Nutrition knowledge was not associated with clinical indicators of disease status, nor with patterns of nutrient intake. Better knowledge of food types and nutritional value of foods, along with personalized nutritional advice, could encourage patients with MM to make healthier decisions that might extend survival.

1. Introduction

In 2020, the global age-standardized incidence rates for multiple myeloma (MM) were 2.2/100,000 for males and 1.5/100,000 for females, with an age-standardized mortality rate of 1.1/100,000 [1]. Recognized and relatively constant risk factors are older age, male gender and African ethnicity [2]. Due to new therapies and constant improvements in care, survival has more than doubled over recent decades. Recently, Usmani et al. [3] recognized that in newly diagnosed transplant eligible MM patients, age (<65 years), non-IgA isotype, normal albumin levels, low beta-2 microglobulin ≤ 3.5 mg/dl, serum creatinine levels < 2 mg/dL, hemoglobin levels ≥10 g/dL and platelet count ≥ 150 k/μL had a positive effect on the 10-year survival, but elevated serum LDH levels and any cytogenetic abnormalities did not negatively predict 10-year survival. Still, the survival of patients with MM is impaired, with only 10–15% reaching the lifespan of the general population [3].
Recently, the relationship between diet and the risk of MM, as well as the relationship between diet and prognosis of patients with MM, has been tackled in several publications; however, most of the relationships that were studied pertained to pre-diagnosis diet. Protective factors for MM in adulthood included increased intake of fruits (over three servings per week) [4], increased intake of fish [5] and current alcohol intake in both genders compared to non-drinkers [6]. Risk factors for MM included the intake of more than one serving of artificially sweetened drinks [7]. Lee et al. [8] determined that an unhealthy overall diet pre-diagnosis is associated with poor survival rates in patients with MM.
Although the relationship between nutrition knowledge, food choices and food intake is complex, little is known about the level of nutrition knowledge in patients diagnosed with MM and the quality of their diets after diagnosis. Our aim was to assess the nutritional knowledge and quality of diets in a cohort of patients with MM, and prepare an intervention study to increase general and specific nutrition knowledge, including nutrition counseling.

2. Materials and Methods

2.1. Participants and Samples

From a cohort of 76 patients followed up in the Hematology Department of the Municipal Emergency Hospital, Timisoara, Romania, 61 participants were included in this research (Figure 1). Patients were recruited between September 2020 and January 2021, during the second wave of the COVID-19 pandemic. This research was designed as a cross-sectional investigation pilot study. Nevertheless, the sample size was sufficient, taking into account a minimum sample of 59 participants, calculated by Viechtbauer et al. [9] for pilot studies.
Diagnosis, staging and therapy were established using ESMO clinical practice guidelines [10]. Data were collected during follow-up visits, and included anthropometric, hematological, biochemical and immunological parameters, radiological data, whole body MRI and type of and response to treatment, as well as survival duration in months (from the time of diagnosis up to the end of the study). For this pilot study, the following inclusion criteria were used: age >18 years, diagnosis of MM in clinical stage 1–3 and a follow-up of at least 6 months, according to the protocol. For the purposes of analysis, the exclusion criteria were set as follows: allogeneic SCT, monoclonal gammopathy of undetermined significance and drug-naïve patients. The exclusion criteria also took into consideration the inability to provide informed consent, and the inability to provide accurate anamnestic data.
Patients were included only after signing the informed consent. The entire study was conducted according to the principles stated in the Declaration of Helsinki, and was approved by the Ethics Committee of the “Victor Babes” University of Medicine and Pharmacy, Timisoara, Romania, with no. 6/2019.

2.2. Clinical and Biological Evaluation

Height and weight were measured as per the international guidelines, and were used for the calculation of body mass index (BMI; BMI = weight (kg)/height (m)2). The nutritional status of the participants was determined using the following BMI thresholds: underweight (BMI below 18.5 kg/m2), normal weight (18.5–24.9 kg/m2), overweight (25–29.9 kg/m2) and obese (over 30 kg/m2). All patients underwent whole-body low-dose computed tomography. All blood samples were collected in the morning. Complete blood count implied the collection of venous blood in a sterile EDTA vacutainer, using fluorescence flow cytometry. Coagulation tests consisted of the following: dosing of fibrinogen through a coagulometric method, and performing quantitative tests to dose D-dimer; analysis of venous blood collected in a vacutainer with sodium citrate, with plasma separation by centrifugation, and processing using a latex method through automatic agglutination with photometric detection. The samples for serum blood urea nitrogen, creatinine, calcium and LDH testing were collected in a sterile vacutainer without anticoagulant, with/without gel separator, by a spectrophotometric technique. Beta-2 microglobulin was assessed using the nephelometric method. The samples underwent analysis via serum protein electrophoresis with immunofixation, serum-free light chain quantification, heavy/light chain quantification, immunoglobulin and total proteins. All patients underwent bone marrow aspiration.
The diagnosis of chronic kidney disease was established by measuring serum creatinine levels, in order to calculate the glomerular filtration rate, and by measuring the urinary albumin/creatinine ratio, in order to detect proteinuria [11]. Osteoporosis was diagnosed based on bone mineral density determination obtained from dual energy X-ray absorptiometry assessment [12]. Major depression was diagnosed by a psychiatrist, using the DSM-5 criteria [13], and clinical assessments were conducted in order to diagnose any potential peripheral neuropathy. Information about diarrhea/constipation was obtained during the anamnestic process, and information about any infections was extracted from clinical records.

2.3. Nutritional Assessment

Dietary intake assessment was performed using a validated FFQ, consisting of 53 food items; it investigated food intake during the last 30 days [14,15]. For each item, the frequency and the usual amount of consumed food items were investigated. To give an estimate of the quantity of fat or added sugar, additional questions were asked in relation to some of the items. The intakes were converted to grams, in accordance with the guidelines of household scales [16]. Using a computer program specially created for this purpose, the energy and macronutrient intakes were calculated for each individual. Macronutrients were transformed into a percentage of contribution to total energy, and then the adequacy of intake was calculated as per the European Food and Safety Authority (EFSA) recommendations [17]. “Inadequate intake” had different meanings for different macronutrients: for proteins, “inadequate” meant lower than the recommended values; for fat, “inadequate” represented only the values above the upper recommended threshold; as for carbohydrates, the values were both lower and upper thresholds. The energy-adjusted values of intake were also computed for saturated fat, fibers and alcohol.
A low carbohydrate diet (LCD) score was computed using deciles of the percentage of macronutrients. For carbohydrates, the lowest decile received a score of 10, and the highest decile received a score of one. For fat and protein, the lowest decile received a score of one, and the highest received a score of 10. All individual macronutrient scores were added to obtain the LCD score, which ranged from a possible lowest score of 3 to a highest score of 30, with higher scores indicating higher adherence to a low carbohydrate diet [18,19]. The LCD score was transformed into tertiles and further used as a three-layer factor in statistical analysis.

2.4. Nutrition Knowledge Assessment

A previously validated questionnaire [20] was used to assess nutrition knowledge. The nutritional knowledge assessment included 88 items. Each item allowed for only one answer, and the obtained points were added for each correct answer provided by the responders, in order to determine the sub-score per section and the total knowledge score.

2.5. Statistical Analysis

Statistical analysis was performed using the Statistical Package for Social Sciences (SPSS Corp, version 18, Chicago, IL, USA). Frequency and percentage were used for the description of categorical data, and mean and standard deviation were used for continuous data. The normality of data was evaluated by the Shapiro–Wilk test. All continuous variables were normally distributed, therefore parametric tests (t-test and ANOVA) were employed for the comparisons of means between the categories. Chi-square or Mann–Whitney or Kruskal–Wallis tests were used to compare the characteristics of participants for categorical data.

3. Results

Within the group, 13.1% (8) of participants were in disease stage 1, 21.3% (13) were in disease stage 2 and 65.6% (40) were in disease stage 3. Females represented 57.4%. Mean age was 65.2 +/− 9.5 years, within the age range of 41–84 years. The mean BMI was 26.2 +/− 4.2 kg/m2, with 57.4% (35) being overweight and obese, and the remaining 42.6% (26) having a weight within normal limits. In Table 1, the clinical features of the cohort of patients diagnosed with MM are presented using disease stage as a factor. The proportions of genders, kappa or lambda light chains in blood and urine, the presence of infections, increased viscosity, depression and therapeutic response did not statistically differ between stages 1 and 2 versus stage 3. Additionally, mean age, BMI and number of therapeutic lines were not statistically different between the two categories. All the rest of the clinical variables showed statistical differences between stages 1 and 2 versus stage 3.
Unadjusted energy (kcal), macronutrient intake (g) and total sugar (g), fiber (g) and alcohol (g) by tertiles of energy are presented in Table 2. Table 2 also contains the energy-adjusted macronutrients (as a percentage to energy contribution), and adjusted sugar, fiber and alcohol per 1000 kcal of intake. While unadjusted intakes are statistically different, the adjusted intakes are not statistically different between tertiles of energy. A lower carbohydrate diet score is observed in the first tertile of energy, as compared to other tertiles. A linear trend of the adequacy of intake was observed for proteins, but not for fat or carbohydrates.
In Table 3, the demographic and clinical features are presented by tertiles of low carbohydrate diet scores, with the first tertile indicating higher carbohydrate intake, the second tertile indicating medium carbohydrate intake and the third tertile indicating low carbohydrate intake. Females are more likely than men to report a high carbohydrate diet. The low carbohydrate tertile has significantly higher proportions of higher than adequate fat intake, as compared to medium carbohydrate tertile. Energy-adjusted saturated fat, total fiber and alcohol show significant differences between tertiles of the LCD score. Other clinical characteristics that show differences between tertiles of the LCD score include hemoglobin, uric acid, albumin, total proteins, beta-2 microglobulin, percentage of plasmacytes in the bone marrow and D-dimers. Clinical indicators, namely types of response to therapy and the number of therapeutic lines, are not associated with tertiles of the LCD score.
Table 4 shows the percentages of nutrition knowledge score per each section and the total score. Compared to the previously published knowledge scores, calculated for the Romanian population [20], Section 1, Section 3 and Section 4 scores were lower, but the score for Section 2 and the total score were similar. The sub-scores per section and the total score did not differ when using demographic and clinical factors (Table 4).

4. Discussion

To the best of our knowledge, neither the LCD score nor the nutrition knowledge has been assessed for patients diagnosed with MM. LCD score has been used for several years in relation to the risk of chronic diseases [18,19] and mortality [21]. Nutrition knowledge was previously associated with healthier food choices [22,23,24] and is a necessary, but not a sufficient, component of behavioral change.
By tertiles of energy, the energy-adjusted intake of nutrients did not reach statistical significance, meaning that the increase in energy is not due to a particular macronutrient source of energy. A higher LCD score, associated with a diet lower in carbohydrates, was observed in the lowest tertile of energy (Table 3). Proportions of the adequacy of protein intake showed a linear trend, with the lowest tertile being associated with the lowest proportion of adequacy of intake and reaching up to 100% in the third LCD tertile.
Similar to other populational studies [25], by tertiles of LCD score, women are more likely than men to have higher intakes of carbohydrates. Low carbohydrate diets have become popular due to short-term weight loss, with better results when associated with high protein diets [26,27], but recent research is inconsistent regarding the long-term effects on diabetes [28,29]. High mortality rates and increased risk of chronic diseases were reported among people consuming low carbohydrate diets, especially when low carbohydrate diets were accompanied by high fat intake, especially saturated fat, and a low fiber intake [18,19,21,30,31,32]. In our sample (Table 3), low carbohydrate diets were associated with high saturated fat and alcohol intake and low fiber intake, situating our patients in the high-risk group. Yet, some clinical indicators, such as a significantly lower hemoglobin level and higher prevalence of reported peripheral neuropathy, were reported in our low carbohydrate–high saturated fat tertile.
Then again, high carbohydrate diets are also associated with high morbidity and mortality rates [33,34,35]. As demonstrated by our small sample, compared to the medium tertile of LCD score, which has the highest proportions of macronutrient adequacy (94.4% for carbohydrate, 77.8% for fat and 66.7% for protein), the high carbohydrate tertile, with 52.4% for carbohydrate, 95.2% for fat and 52.4% for protein, has the worst clinical indicators (Table 3). Anemia has a broad implication, i.e., the low hemoglobin and hematocrit, and induces or aggravates hypoxia, impacts the cardiovascular system, is associated with poor quality of life and performance and impairs daily activity. Considering that most MM patients are elderly, the clinical aspects previously presented may be even more important [36]. Anemia is often associated with and aggravated by chronic kidney disease [37].
Several hemostatic and thrombotic anomalies have been reported in patients with MM, with D-dimers being the most commonly reported prothrombotic marker [38]. D-dimers are associated with poor prognosis in patients with cancer [39]. Recent research has shown that hypercalcemia and bone disease are significantly associated with a worse prognosis [40]. The International Staging System, which is based on serum beta-2 microglobulin and albumin levels, is the most widely adopted in multiple myeloma and is also correlated with the prognosis of the disease [40]. Assessment of bone marrow involvement by malignant plasma cells is an important element in the diagnosis and follow-up of patients with multiple myeloma and other plasma cell dyscrasias [40,41].
Patients with MM suffer diagnostic delays due to the complex nature of the disease and non-specificity of symptoms [42,43,44]. Often, due to these delays, patients are diagnosed in the late stage of disease, associated with poor prognosis [40]. In our experience, patients are often diagnosed in stage 3 disease, and this is reflected in our sample by shorter time to follow-up, compared to patients in stages 1 and 2 (Table 1). Patients in disease stage 3 are more likely to report either high carbohydrate intake or low carbohydrate intake, the latter not reaching statistical significance after Bonferroni correction (Table 3). In our sample, patients from the high carbohydrate diet tertile have lower hemoglobin and albumin levels and higher D-dimers, calcium, uric acid, percentage of plasmacytes in the bone marrow and beta-2 microglobulin levels, when compared to patients from the medium carbohydrate tertile. Since our study is cross-sectional, we cannot assume causality, only association. Further research will be able to clarify some of these mechanisms observed between clinical outcomes and patterns of nutrient intake, and the relationships between clinical indicators of disease status.
There are several proposed mechanisms to explain the association of anemia with MM, and the most important seems to be inadequate erythropoietin (EPO) production related to inflammatory cytokines [45] and the high levels of hepcidin, the iron regulatory hormone that works by restricting the iron supply for erythropoiesis [46]. Several dietary factors can aggravate anemia, and phytates, fiber and starches were considered to have inhibitory effects on iron absorption [47]. In a 10-year retrospective study in patients with MM [48], low folate levels were associated with lower levels of hemoglobin. High BMI has a protective effect for anemia, but even in this population, increasing the dietary fat/carbohydrate ratio increases the risk of anemia [49].
Purine-rich foods, high protein intake, alcohol and fructose are known risk factors for increasing the levels of uric acid [50]. Fructose directly regulates uric acid production by increasing ATP degradation to AMP, a uric acid precursor [50]. Intervention studies have shown that the relationship between carbohydrate intake and uric acid production is mediated by the glycemic index of food, with the intake of food with low glycemic index lowering the levels of uric acid [51].
Total nutrition knowledge scores (Table 4) were low, but were similar to those of recent research [20] that used the same instrument. In the sections Expert recommendations, Healthy food choices and Diet, disease and weight associations, our sample scored lower than the general population, so this could represent a starting point in targeted diet interventions. The nutrition knowledge score did not differ by demographics, intake or clinical indicators, though some trends were observed, but did not reach statistical significance.
Patients with MM may gain benefits if they receive nutritional counseling and healthy eating guidance, in order to learn how to sustain a balanced diet, because nutritional needs change during the course of the disease and survivorship [52]. A study by Lee et al. [8] suggests that increased scores in different patterns of intake, such as in the Alternate Healthy Eating Index (AHEI)-2010, Alternate Mediterranean Diet or Dietary Approaches to Stop Hypertension were associated with better prognosis and survival in patients with MM. Intermittent fasting has been proposed as an alternative option in cancer therapy due to autophagy, but has been controversial [53,54,55].
Epigenetic mechanisms via DNA methylation, histone modifications and non-coding RNAs were found to be associated with better evolution and therapeutic response in patients with MM [56]. Although adequate intake of methyl donors such as folate, methionine, choline and vitamins B2, B6, B12 are likely to impact epigenetic mechanisms [57], better understanding of the different polymorphisms from the genes involved in one-carbon metabolism could shed new light on nutrition counseling for patients with MM [58,59].
A major limitation of our study is the small number of participants since recruitment occurred during the second wave of the COVID-19 pandemic. Secondly, participants were all recruited in a single university clinic, thus the results cannot be generalized. A larger multicentric sample would be necessary to be able to generalize these findings. The participants were volunteers, which might have biased selection toward individuals more interested in food, diet and health and with a genuine interest in these matters, as compared to those who declined participation. Since the current study did not receive external funding, not all participants received metabolic panel analyses. Subsequent studies should plan to overcome this limitation.

5. Conclusions

Stage 3 disease in patients with MM is associated with either a high carbohydrate or low carbohydrate–high saturated fat diet. Other clinical indicators, besides beta-2 microglobulin, which have been related to poor prognosis, are associated in our sample with a high carbohydrate diet. During disease treatment and survivorship, the nutritional needs of MM patients change, thus a better knowledge of foods and nutritional value could encourage them to make healthier decisions that might extend survival.

Author Contributions

Conceptualization, E.B. and H.I.; methodology, C.L.S., E.B., H.I., I.I.; software, C.L.S. and S.P.; validation, E.B., S.P., C.P., O.P., M.S. and R.T.; formal analysis, C.L.S.; investigation, E.B., C.P., O.P., M.S. and R.T.; resources, I.I. and H.I.; data curation, E.B. and C.L.S.; writing—original draft preparation, E.B.; writing—review and editing, C.L.S. and H.I.; supervision, H.I. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was approved by the Ethics Committee of the “Victor Babes” University of Medicine and Pharmacy (6/2019), Timisoara, Romania, and conducted in accordance with the Declaration of Helsinki.

Informed Consent Statement

Written informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Raw data for variables of the cohort are available on request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Cancer Today. Available online: http://gco.iarc.fr/today/home (accessed on 9 February 2021).
  2. Padala, S.; Barsouk, A.; Barsouk, A.; Rawla, P.; Vakiti, A.; Kolhe, R.; Kota, V.; Ajebo, G. Epidemiology, Staging, and Management of Multiple Myeloma. Med. Sci. 2021, 9, 3. [Google Scholar] [CrossRef]
  3. Usmani, S.Z.; Hoering, A.; Cavo, M.; Miguel, J.S.; Goldschimdt, H.; Hajek, R.; Turesson, I.; Lahuerta, J.J.; Attal, M.; Barlogie, B.; et al. Clinical predictors of long-term survival in newly diagnosed transplant eligible multiple myeloma—An IMWG Research Project. Blood Cancer J. 2018, 8, 1–7. [Google Scholar] [CrossRef] [Green Version]
  4. Thordardottir, M.; Lindqvist, E.K.; Lund, S.H.; Costello, R.; Burton, D.; Steingrimsdottir, L.; Korde, N.; Mailankody, S.; Eiriksdottir, G.; Launer, L.J.; et al. Dietary intake is associated with risk of multiple myeloma and its precursor disease. PLoS ONE 2018, 13, e0206047. [Google Scholar] [CrossRef] [PubMed]
  5. Fritschi, L.; Ambrosini, G.L.; Kliewer, E.V.; Johnson, K.C. Canadian Cancer Registries Epidemiologic Research Group. Dietary fish intake and risk of leukaemia, multiple myeloma, and non-Hodgkin lymphoma. Cancer Epidemiol. Prev. Biomark. 2004, 13, 532–537. [Google Scholar]
  6. Andreotti, G.; Birmann, B.; De Roos, A.J.; Spinelli, J.; Cozen, W.; Camp, N.J.; Moysich, K.; Chiu, B.; Steplowski, E.; Krzystan, J.; et al. A Pooled Analysis of Alcohol Consumption and Risk of Multiple Myeloma in the International Multiple Myeloma Consortium. Cancer Epidemiol. Biomark. Prev. 2013, 22, 1620–1627. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Schernhammer, E.S.; Bertrand, K.; Birmann, B.M.; Sampson, L.; Willett, W.C.; Feskanich, D. Consumption of artificial sweetener- and sugar-containing soda and risk of lymphoma and leukemia in men and women. Am. J. Clin. Nutr. 2012, 96, 1419–1428. [Google Scholar] [CrossRef] [PubMed]
  8. Lee, D.H.; Fung, T.T.; Tabung, F.K.; Marinac, C.R.; Devore, E.E.; Rosner, B.A.; Ghobrial, I.M.; Colditz, G.A.; Giovannucci, E.L.; Birmann, B.M. Prediagnosis dietary pattern and survival in patients with multiple myeloma. Int. J. Cancer 2020, 147, 1823–1830. [Google Scholar] [CrossRef]
  9. Viechtbauer, W.; Smits, L.; Kotz, D.; Budé, L.; Spigt, M.; Serroyen, J.; Crutzen, R. A simple formula for the calculation of sample size in pilot studies. J. Clin. Epidemiol. 2015, 68, 1375–1379. [Google Scholar] [CrossRef] [Green Version]
  10. Dimopoulos, M.A.; Moreau, P.; Terpos, E.; Mateos, M.V.; Zweegman, S.; Cook, G. Multiple myeloma: EHA-ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 2021, 32, 309–322. [Google Scholar] [CrossRef]
  11. Baumgarten, M.; Gehr, T. Chronic kidney disease: Detection and evaluation. Am. Fam. Physician 2011, 84, 1138–1148. [Google Scholar]
  12. Jeremiah, M.P.; Unwin, B.K.; Greenawald, M.H.; Casiano, V.E. Diagnosis and Management of Osteoporosis. Am. Fam. Physician. 2015, 92, 261–268. [Google Scholar] [PubMed]
  13. Ng, C.W.M.; How, C.H.; Ng, Y.P. Major depression in primary care: Making the diagnosis. Singap. Med. J. 2016, 57, 591–597. [Google Scholar] [CrossRef] [Green Version]
  14. Haftenberger, M.; Heuer, T.; Heidemann, C.; Kube, F.; Krems, C.; Mensink, G.B. Relative validation of a food frequency questionnaire for national health and nutrition monitoring. Nutr. J. 2010, 9, 36. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Serban, C.; Banu, A.; Putnoky, S.; Butica, S.I.; Niculescu, M.D.; Putnoky, S. Relative validation of a four weeks retrospective food frequency questionnaire versus 7-day paper-based food records in estimating intake of energy and nutrients in adults. Nutr. Diet. Suppl. Dove Press 2021, in press. [Google Scholar]
  16. U.S. Food and Drug Administration (FDA). Guidance for Industry: Guidelines for Determining Metric Equivalents of Household Measures. 2020. Available online: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/guidance-industry-guidelines-determining-metric-equivalents-household-measures (accessed on 19 February 2021).
  17. Dietary Reference Values for nutrients Summary report. EFSA Support Publ. 2017, 14, e15121E.
  18. Halton, T.L.; Willett, W.C.; Liu, S.; Manson, J.E.; Albert, C.; Rexrode, K.; Hu, F.B. Low-Carbohydrate-Diet Score and the Risk of Coronary Heart Disease in Women. N. Engl. J. Med. 2006, 355, 1991–2002. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. De Koning, L.; Fung, T.T.; Liao, X.; Chiuve, S.E.; Rimm, E.B.; Willett, W.C. Low-carbohydrate diet scores and risk of type 2 diabetes in men. Am. J. Clin. Nutr. 2011, 93, 844–850. [Google Scholar] [CrossRef]
  20. Putnoky, S.; Banu, A.M.; Moleriu, L.C.; Putnoky, S.; Șerban, D.M.; Niculescu, M.D.; Șerban, C.L. Reliability and validity of a General Nutrition Knowledge Questionnaire for adults in a Romanian population. Eur. J. Clin. Nutr. 2020, 74, 1576–1584. [Google Scholar] [CrossRef]
  21. Fung, T.T.; van Dam, R.M.; Hankinson, S.E.; Stampfer, M.; Willett, W.C.; Hu, F.B. Low-carbohydrate diets and all-cause and cause-specific mortality: Two cohort studies. Ann. Intern. Med. 2010, 153, 289–298. [Google Scholar] [CrossRef]
  22. Wardle, J.; Parmenter, K.; Waller, J. Nutrition knowledge and food intake. Appetite 2000, 34, 269–275. [Google Scholar] [CrossRef]
  23. Hill, C.R.; Blekkenhorst, L.C.; Radavelli-Bagatini, S.; Sim, M.; Woodman, R.J.; Devine, A.; Shaw, J.E.; Hodgson, J.M.; Daly, R.M.; Lewis, J.R. Fruit and Vegetable Knowledge and Intake within an Australian Population: The AusDiab Study. Nutrients 2020, 12, 3628. [Google Scholar] [CrossRef] [PubMed]
  24. McKinnon, L.; Giskes, K.; Turrell, G. The contribution of three components of nutrition knowledge to socio-economic differences in food purchasing choices. Public Health Nutr. 2013, 17, 1814–1824. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Leblanc, V.; Bégin, C.; Corneau, L.; Dodin, S.; Lemieux, S. Gender differences in dietary intakes: What is the contribution of motivational variables? J. Hum. Nutr. Diet. 2014, 28, 37–46. [Google Scholar] [CrossRef] [PubMed]
  26. Clifton, P.; Condo, D.; Keogh, J. Long term weight maintenance after advice to consume low carbohydrate, higher protein diets—A systematic review and meta analysis. Nutr. Metab. Cardiovasc. Dis. 2014, 24, 224–235. [Google Scholar] [CrossRef]
  27. Maula, A.; Kai, J.; Woolley, A.K.; Weng, S.; Dhalwani, N.; Griffiths, F.E.; Khunti, K.; Kendrick, D. Educational weight loss interventions in obese and overweight adults with type 2 diabetes: A systematic review and meta-analysis of randomized controlled trials. Diabet. Med. 2019, 37, 623–635. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Larsen, R.N.; Mann, N.J.; MacLean, E.; Shaw, J.E. The effect of high-protein, low-carbohydrate diets in the treatment of type 2 diabetes: A 12 month randomised controlled trial. Diabetologia 2011, 54, 731–740. [Google Scholar] [CrossRef] [PubMed]
  29. Huntriss, R.; Campbell, M.; Bedwell, C. The interpretation and effect of a low-carbohydrate diet in the management of type 2 diabetes: A systematic review and meta-analysis of randomised controlled trials. Eur. J. Clin. Nutr. 2017, 72, 311–325. [Google Scholar] [CrossRef] [Green Version]
  30. Noto, H.; Goto, A.; Tsujimoto, T.; Noda, M. Low-Carbohydrate Diets and All-Cause Mortality: A Systematic Review and Meta-Analysis of Observational Studies. PLoS ONE 2013, 8, e55030. [Google Scholar] [CrossRef] [Green Version]
  31. Lagiou, P.; Sandin, S.; Lof, M.; Trichopoulos, D.; Adami, H.-O.; Weiderpass, E. Low carbohydrate-high protein diet and incidence of cardiovascular diseases in Swedish women: Prospective cohort study. BMJ 2012, 344, e4026. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Fechner, E.; Smeets, E.T.; Schrauwen, P.; Mensink, R.P. The Effects of Different Degrees of Carbohydrate Restriction and Carbohydrate Replacement on Cardiometabolic Risk Markers in Humans—A Systematic Review and Meta-Analysis. Nutrients 2020, 12, 991. [Google Scholar] [CrossRef] [Green Version]
  33. Dehghan, M.; Mente, A.; Zhang, X.; Swaminathan, S.; Li, W.; Mohan, V.; Iqbal, R.; Kumar, R.; Wentzel-Viljoen, E.; Rosengren, A.; et al. Associations of fats and carbohydrate intake with cardiovascular disease and mortality in 18 countries from five continents (PURE): A prospective cohort study. Lancet 2017, 390, 2050–2062. [Google Scholar] [CrossRef] [Green Version]
  34. Seidelmann, S.B.; Claggett, B.; Cheng, S.; Henglin, M.; Shah, A.; Steffen, L.M.; Folsom, A.R.; Rimm, E.B.; Willett, W.C.; Solomon, S.D. Dietary carbohydrate intake and mortality: A prospective cohort study and meta-analysis. Lancet Public Health 2018, 3, e419–e428. [Google Scholar] [CrossRef] [Green Version]
  35. Mente, A.; Yusuf, S. Evolving evidence about diet and health. Lancet Public Health 2018, 3, e408–e409. [Google Scholar] [CrossRef] [Green Version]
  36. Wouters, H.J.; Van Der Klauw, M.M.; De Witte, T.; Stauder, R.; Swinkels, D.W.; Wolffenbuttel, B.H.; Huls, G. Association of anemia with health-related quality of life and survival: A large population-based cohort study. Haematologica 2018, 104, 468–476. [Google Scholar] [CrossRef] [PubMed]
  37. Banaszkiewicz, M.; Małyszko, J.; Vesole, D.H.; Woziwodzka, K.; Jurczyszyn, A.; Żórawski, M.; Krzanowski, M.; Małyszko, J.; Batko, K.; Kuźniewski, M.; et al. New Biomarkers of Ferric Management in Multiple Myeloma and Kidney Disease-Associated Anemia. J. Clin. Med. 2019, 8, 1828. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Gogia, A.; Sikka, M.; Sharma, S.; Rusia, U. Hemostatic Abnormalities in Multiple Myeloma Patients. Asian Pac. J. Cancer Prev. 2012, 19, 127–130. [Google Scholar]
  39. Ay, C.; Dunkler, D.; Pirker, R.; Thaler, J.; Quehenberger, P.; Wagner, O.; Zielinski, C.; Pabinger, I. High D-dimer levels are associated with poor prognosis in cancer patients. Haematologica 2012, 97, 1158–1164. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Nakaya, A.; Fujita, S.; Satake, A.; Nakanishi, T.; Azuma, Y.; Tsubokura, Y.; Hotta, M.; Yoshimura, H.; Ishii, K.; Ito, T.; et al. Impact of CRAB Symptoms in Survival of Patients with Symptomatic Myeloma in Novel Agent Era. Hematol. Rep. 2017, 9, 6887. [Google Scholar] [CrossRef] [Green Version]
  41. Cazaubiel, T.; Mulas, O.; Montes, L.; Schavgoulidze, A.; Avet-Loiseau, H.; Corre, J.; Perrot, A. Risk and Response-Adapted Treatment in Multiple Myeloma. Cancers 2020, 12, 3497. [Google Scholar] [CrossRef]
  42. Lyratzopoulos, G.; Neal, R.D.; Barbiere, J.M.; Rubin, G.P.; Abel, G. Variation in number of general practitioner consultations before hospital referral for cancer: Findings from the 2010 National Cancer Patient Experience Survey in England. Lancet Oncol. 2012, 13, 353–365. [Google Scholar] [CrossRef] [Green Version]
  43. Lyratzopoulos, G.; Wardle, J.; Rubin, G. Rethinking diagnostic delay in cancer: How difficult is the diagnosis? BMJ 2014, 349, g7400. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Koshiaris, C. Methods for reducing delays in the diagnosis of multiple myeloma. Int. J. Hematol. Oncol. 2019, 8, IJH13. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Mittelman, M. The Implications of Anemia in Multiple Myeloma. Clin. Lymphoma 2003, 4, S23–S29. [Google Scholar] [CrossRef] [PubMed]
  46. Maes, K.; Nemeth, E.; Roodman, G.D.; Huston, A.; Esteve, F.; Freytes, C.; Callander, N.; Katodritou, E.; Tussing-Humphreys, L.; Rivera, S.; et al. In anemia of multiple myeloma, hepcidin is induced by increased bone morphogenetic protein 2. Blood 2010, 116, 3635–3644. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. De Rozo, M.P.; Van Campen, D.; Miller, D.D. Effects of some carbohydrates on iron absorption. Arch. Latinoam. Nutr. 1986, 36, 688–700. [Google Scholar] [PubMed]
  48. Wongrakpanich, S.; George, G.; Chaiwatcharayut, W.; Candelario, N.; Mittal, V.; Pomerantz, S.; Varadi, G. Frequency of folate deficiency in multiple myeloma patients: A 10-year retrospective study. Int. J. Lab. Hematol. 2015, 38, e19–e22. [Google Scholar] [CrossRef]
  49. Chang, J.-S.; Chen, Y.-C.; Owaga, E.; Palupi, K.C.; Pan, W.-H.; Bai, C.-H. Interactive Effects of Dietary Fat/Carbohydrate Ratio and Body Mass Index on Iron Deficiency Anemia among Taiwanese Women. Nutrients 2014, 6, 3929–3941. [Google Scholar] [CrossRef] [Green Version]
  50. Rho, Y.H.; Zhu, Y.; Choi, H.K. The Epidemiology of Uric Acid and Fructose. Semin. Nephrol. 2011, 31, 410–419. [Google Scholar] [CrossRef] [Green Version]
  51. Juraschek, S.P.; McAdams-DeMarco, M.; Gelber, A.C.; Sacks, F.M.; Appel, L.J.; White, K.J.; Miller, E.R. Effects of Lowering Glycemic Index of Dietary Carbohydrate on Plasma Uric Acid Levels: The OmniCarb Randomized Clinical Trial. Arthritis Rheumatol. 2015, 68, 1281–1289. [Google Scholar] [CrossRef] [Green Version]
  52. Rock, C.L.; Doyle, C.; Demark-Wahnefried, W.; Meyerhardt, J.; Courneya, K.S.; Schwartz, A.L. Nutrition and physical activity guidelines for cancer survivors. CA Cancer J. Clin. 2012, 62, 242–274. [Google Scholar] [CrossRef] [Green Version]
  53. Chen, N.; Karantza, V. Autophagy as a therapeutic target in cancer. Cancer Biol. Ther. 2011, 11, 157–168. [Google Scholar] [CrossRef] [PubMed]
  54. Galluzzi, L.; Pedro, J.M.B.-S.; Levine, B.; Green, D.R.; Kroemer, G. Pharmacological modulation of autophagy: Therapeutic potential and persisting obstacles. Nat. Rev. Drug Discov. 2017, 16, 487–511. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Antunes, F.; Erustes, A.G.; Costa, A.J.; Nascimento, A.C.; Bincoletto, C.; Ureshino, R.P.; Pereira, G.J.S.; Smaili, S.S. Autophagy and intermittent fasting: The connection for cancer therapy? Clinics 2018, 73. [Google Scholar] [CrossRef] [PubMed]
  56. Alzrigat, M.; Párraga, A.A.; Jernberg-Wiklund, H. Epigenetics in multiple myeloma: From mechanisms to therapy. Semin. Cancer Biol. 2018, 51, 101–115. [Google Scholar] [CrossRef] [PubMed]
  57. Kadayifci, F.Z.; Zheng, S.; Pan, Y.-X. Molecular Mechanisms Underlying the Link between Diet and DNA Methylation. Int. J. Mol. Sci. 2018, 19, 4055. [Google Scholar] [CrossRef] [Green Version]
  58. Zintzaras, E.; Giannouli, S.; Rodopoulou, P.; Voulgarelis, M. The role of MTHFR gene in multiple myeloma. J. Hum. Genet. 2008, 53, 499–507. [Google Scholar] [CrossRef]
  59. Weiner, A.S.; Boyarskikh, U.A.; Voronina, E.N.; Mishukova, O.V.; Filipenko, M.L. Methylenetetrahydrofolate reductase C677T and methionine synthase A2756G polymorphisms influence on leukocyte genomic DNA methylation level. Gene 2014, 533, 168–172. [Google Scholar] [CrossRef]
Ijerph 18 05444 g001 550
Figure 1. Study flow chart.
Figure 1. Study flow chart.
Ijerph 18 05444 g001
Table
Table 1. Clinical features of the cohort of patients with a diagnosis of MM by disease staging (N = 61).
Table 1. Clinical features of the cohort of patients with a diagnosis of MM by disease staging (N = 61).
Clinical FeaturesMeasureDisease Stages 1 and 2 (n1 = 21)Disease Stage 3 (n2 = 40)p-ValueTotal
Sex Fn (%) *12 (57.1%)23 (57.5%)0.97935 (57.4%)
Age (years)Mean +/− SD **65.1 +/− 11.765.2 +/− 8.20.96465.2 +/− 9.5
Body mass index (kg/m2)Mean +/− SD **26.3 +/− 5.126.2 +/− 3.70.91726.3 +/− 4.2
Follow-up since diagnosis (months)Mean +/− SD **32.9 +/− 19.722.1 +/− 14.30.01825.8 +/− 17.0
Blood smear alterationsn (%) *7 (33.3%)34 (85.0%)<0.00141 (67.2%)
Anemian (%) *15 (71.4%)40 (100.0%)0.00155 (90.2%)
Hemoglobin (g/dl)Mean +/− SD **10.9 +/− 1.77.3 +/− 0.9<0.0018.6 +/− 2.1
Hypercalcemian (%) *6 (28.6%)26 (65.0%)0.00732 (52.5%)
Serum calcium (mg/dL)Mean +/− SD **10.6 +/− 2.312.8 +/− 3.10.00412.0 +/− 3.0
Alkaline Phosphatase (IU/L)Mean +/− SD **71.9 +/− 18.5116.0 +/− 36.9<0.001100.8 +/− 38.0
Beta-2 microglobulin ≥ 3.5n (%) *13 (61.9%)40 (100.0%)<0.00153 (86.9%)
Beta-2 microglobulin (mg/dl)Mean +/− SD **3.7 +/− 0.911.2 +/− 3.1<0.0018.6 +/− 4.4
Percentage of plasmacytes > 60%n (%) *3 (14.3%)28 (70.0%)<0.00131 (50.8%)
Plasmacytes in bone marrow (%)Mean +/− SD **36.8 +/− 20.668.1 +/− 16.2<0.00157.3 +/− 23.2
Uric acid (mg/dL)Mean +/− SD **5.1 +/− 1.47.5 +/− 1.8<0.0016.7 +/− 2.0
Creatinine (mg/dL)Mean +/− SD **1.3 +/− 0.92.5 +/− 1.3<0.0012.1 +/− 1.3
Blood urea nitrogen (mg/dL)Mean +/− SD **42.4 +/− 17.367.3 +/− 32.3<0.00158.7 +/− 30.4
Albumin (g/L)Mean +/− SD **59.7 +/− 5.751.1 +/− 3.9<0.00154.0 +/− 6.1
Total protein (g/dL)Mean +/− SD **6.6 +/− 0.99.2 +/− 3.8<0.0018.3 +/− 3.3
Erythrocyte sedimentation rate (mm/hr)Mean +/− SD **87.8 +/− 40.9114.6 +/− 38.50.014105.4 +/− 41.1
C-reactive protein (mg/L)Mean +/− SD **8.5 +/− 9.821.7 +/− 14.4<0.00117.2 +/− 14.4
LDH (U/L)Mean +/− SD **226.0 +/− 63.0359.5 +/− 121.8<0.001313.6 +/− 122.7
Fibrinogen (mg/dL)Mean +/− SD **421.4 +/− 123.6507.5 +/− 152.60.030477.8 +/− 148.1
D-dimers (ng/mL)Mean +/− SD **220.7 +/− 59.7340.4 +/− 118.4<0.001299.1 +/− 116.5
Serum-free light chainKappan (%) *16 (76.2%)26 (65.0%)0.37042 (68.9%)
Lambda5 (23.8%)14 (35.0%)19 (31.1%)
Immunoglobulin typeIgGn (%) *15 (71.4%)23 (57.5%)0.42938 (62.3%)
IgA5 (23.8%)16 (40.0%)21 (34.4%)
IgM1 (4.8%)1 (2.5%)2 (3.3%)
Infectionsn (%) *2 (9.5%)7 (17.5%)0.4799 (14.8%)
Myelosuppressionn (%) *0 (0.0%)10 (25.0%)0.01110 (16.4%)
Chronic kidney diseasen (%) *3 (14.3%)21 (52.5%)0.00424 (39.3%)
Peripheral neuropathyn (%) *0 (0.0%)31 (77.5%)<0.00131 (50.8%)
Osteoporosisn (%) *1 (4.8%)17 (42.5%)0.00218 (29.5%)
Depressionn (%) *14 (66.7%)19 (47.5%)0.15333 (54.1%)
Constipation/diarrhean (%) *0 (0.0%)16 (40.0%)0.00116 (26.2%)
Autologous stem cell transplantationn (%) *0 (0.0%)9 (22.5%)0.0219 (14.8%)
ResponsePartial remissionn (%) *8 (38.1%)15 (37.5%)0.37023 (37.7%)
Total remission9 (42.9%)10 (25.0%)19 (31.1%)
Stable disease3 (14.3%)9 (22.5%)12 (19.7%)
Progressive disease1 (4.8%)6 (15.0%)7 (11.5%)
Therapeutic lines administeredMean +/− SD ***3.2 +/− 1.42.8 +/− 1.60.2472.9 +/− 1.5
Notes: Data are presented as mean +/− SD, or as n (%), as appropriate. * Chi-square test, ** independent samples t-test, *** Mann–Whitey test. p-values in bold are statistically significant. Abbreviations: SD = standard deviation, n = number of participants.
Table
Table 2. Nutrient intake per tertile of energy intake (N = 61).
Table 2. Nutrient intake per tertile of energy intake (N = 61).
Nutrient IntakeMeasureTertiles of Energy (kcal) Intakep-Value for Trend
First (n1 = 20)Second (n2 = 21)Third (n3 = 20)
Energy (kcal)Mean +/− SD *797.6 +/− 139.21259.1 +/− 164.02296.0 +/− 1030.9<0.001
Protein (g)Mean +/− SD *39.7 +/− 9.154.8 +/− 11.892.9 +/− 34.9<0.001
Fat total (g)Mean +/− SD *31.1 +/− 6.944.8 +/− 11.580.9 +/− 34.3<0.001
Carbohydrate (g)Mean +/− SD *89.4 +/− 20.2161.6 +/− 36.8301.5 +/− 188.4<0.001
Saturated fat (g)Mean +/− SD *10.1 +/− 2.215.7 +/− 4.827.0 +/− 9.9<0.001
Fiber total dietary (g)Mean +/− SD *6.9 +/− 2.213.8 +/− 6.525.0 +/− 25.70.002
Alcohol (g)Mean +/− SD *1.2 +/− 2.01.2 +/− 1.83.1 +/− 3.60.036
Percentage of energy from fatMean +/− SD *35.0 +/− 4.731.9 +/− 6.332.7 +/− 9.30.361
Percentage of energy from carbohydratesMean +/− SD *44.8 +/− 6.651.3 +/− 9.350.8 +/− 12.30.057
Saturated fat (g)/1000 kcalMean +/− SD *1.3 +/− 0.11.2 +/− 0.31.2 +/− 0.40.946
Total fiber (g)/1000 kcalMean +/− SD *8.6 +/− 2.510.9 +/− 4.79.9 +/− 5.30.237
Alcohol (g)/1000 kcalMean +/− SD *1.5 +/− 2.71.0 +/− 1.41.4 +/− 1.80.648
LCD scoreMean +/− SD **19.9 +/− 5.814.7 +/− 7.415.0 +/− 8.30.045
Protein adequate intake n (%)n (%) **7 (35.0%)14 (66.7%)20 (100.0%)<0.001
Fat adequate intake n (%)n (%) **10 (50.0%)16 (76.2%)11 (55.0%)0.193
Carbohydrate intakeBelow adequate n (%)n (%) **9 (45.0%)6 (28.6%)7 (35.0%)0.185
Adequate n (%)n (%) **11 (55.0%)11 (52.4%)7 (35.0%)
Over adequate n (%)n (%) **0 (0.0%)4 (19.0%)6 (30.0%)
Notes: Data are presented as mean +/− SD, or as n (%), as appropriate. * ANOVA, ** Kruskall–Wallis test; p-values in bold are statistically significant. Abbreviations: SD = standard deviation, n = number of participants, LCD = low carbohydrate diet.
Table
Table 3. Clinical and intake variables per tertile of LCD score.
Table 3. Clinical and intake variables per tertile of LCD score.
Clinical and Intake VariablesMeasureTertiles Of LCD Scorep−Values
First (High Carb Diet) n1 = 21Second (Medium Carb Diet) n2 = 18Third (Low Carb Diet) n3 = 22
SexMn (%) *6 (28.6%)5 (27.8%)15 (68.2%)0.011
F15 (71.4%)13 (72.2%)7 (31.8%)
AgeMean +/− SD **66.3 +/− 7.361.1 +/− 11.267.4 +/− 9.10.087
Follow-up since diagnosis (months)Mean +/− SD **25.0 +/− 17.729.6 +/− 16.323.5 +/− 17.10.524
Disease stage1 and 2n (%) *2 (9.5%)12 (66.7%)7 (31.8%)0.001
319 (90.5%) a6 (33.3%)15 (68.2%)
Adequate fat intakeIncreasedn (%) *1 (4.8%)4 (22.2%)19 (86.4%)<0.001
Yes20 (95.2%)14 (77.8%)3 (13.6%) a
Adequate carbohydrate intakeDecreasedn (%) *0 (0.0%)1 (5.6%)21 (95.5%)<0.001
Yes11 (52.4%)17 (94.4%)1 (4.5%)
Increased10 (47.6%)0 (0.0%)0 (0.0%)
Adequate protein intakeDecreasedn (%) *10 (47.6%)6 (33.3%)4 (18.2%)0.125
Yes11 (52.4%)12 (66.7%)18 (81.8%)
Saturated fat (g)/1000 kcalMean +/− SD *1.0 +/− 0.2 a1.2 +/− 0.21.5 +/− 0.2 a<0.001
Total fiber (g)/1000 kcalMean +/− SD *13.0 +/− 5.4 a9.8 +/− 2.56.8 +/− 1.6 a<0.001
Alcohol (g)/1000 kcalMean +/− SD *0.7 +/− 1.20.8 +/− 0.82.3 +/− 2.8 a0.013
Hemoglobin (g/dl)Mean +/− SD **8.1 +/− 1.7 a9.8 +/− 2.28.0 +/− 2.0 a0.010
Alkaline phosphatase (IU/L)Mean +/− SD **114.4 +/− 31.988.1 +/− 35.898.1 +/− 42.20.088
Erythrocyte sedimentation rate (mm/hr)Mean +/− SD **104.7 +/− 46.3102.8 +/− 40.9108.1 +/− 37.50.919
Serum calcium (mg/dL)Mean +/− SD **12.1 +/− 3.311.8 +/− 2.712.2 +/− 3.10.928
Uric acid (mg/dL)Mean +/− SD **7.7 +/− 1.8 a5.9 +/− 2.26.3 +/− 1.70.006
Creatinine (mg/dL)Mean +/− SD **2.2 +/− 0.91.6 +/− 1.22.4 +/− 1.50.131
Blood urea nitrogen (mg/dL)Mean +/− SD **58.7 +/− 21.246.9 +/− 21.068.4 +/− 40.40.083
Albumin (g/L)Mean +/− SD **51.3 +/− 4.1 a56.8 +/− 7.654.1 +/− 5.60.018
Total proteins (g/dL)Mean +/− SD **9.9 +/− 3.6 a7.2 +/− 2.47.6 +/− 3.20.018
Beta-2 microglobulin (mg/dl)Mean +/− SD **10.7 +/− 4.0 a5.9 +/− 3.48.8 +/− 4.50.002
C-reactive protein (mg/L)Mean +/− SD **20.3 +/− 17.712.0 +/− 9.718.3 +/− 13.50.179
Plasmacytes in bone marrow (%)Mean +/− SD **67.5 +/− 18.5 a44.4 +/− 23.558.2 +/− 22.70.006
LDH (U/L)Mean +/− SD **300.7 +/− 104.2276.8 +/− 107.3355.9 +/− 142.00.106
Fibrinogen (mg/dL)Mean +/− SD **503.1 +/− 139.9453.5 +/− 149.4473.5 +/− 157.40.580
D-dimers (ng/mL)Mean +/− SD **346.0 +/− 122.6 a252.4 +/− 114.3292.6 +/− 99.00.039
Response n (%)Partial remissionn (%) ***9 (42.9%)8 (44.4%)6 (27.3%)0.674
Total remission4 (19.0%)6 (33.3%)9 (40.9%)
Stabile disease5 (23.8%)3 (16.7%)4 (18.2%)
Progressive disease3 (14.3%)1 (5.6%)3 (13.6%)
Infectionsn (%) *6 (28.6%)1 (5.6%)2 (9.1%)0.087
Myelosuppressionn (%) *6 (28.6%)1 (5.6%)3 (13.6%)0.144
Chronic kidney diseasen (%) *8 (38.1%)7 (38.9%)9 (40.9%)0.982
Peripheral neuropathyn (%) *16 (76.2%) a3 (16.7%)12 (54.5%) a0.001
Osteoporosisn (%) *7 (33.3%)4 (22.2%)7 (31.8%)0.722
Depressionn (%) *12 (57.1%)10 (55.6%)11 (50.0%)0.888
Constipation/diarrhean (%) *7 (33.3%)1 (5.6%)8 (36.4%)0.061
Therapeutic linesMean +/− SD *2.76 +/− 1.73.22 +/− 1.42.86 +/− 1.50.628
Notes: Data are presented as mean +/- SD, or as n (%), as appropriate. * Kruskal–Wallis test; ** ANOVA; *** chi-square test. a Superscript letter denotes a significant difference, as compared to the second tertile using Mann–Whitney test with Bonferroni adjustment, or post hoc tests with Sidak adjustment, as appropriate. p-values in bold are statistically significant. Abbreviations: SD = standard deviation, n = number of participants, LCD = low carbohydrate diet.
Table
Table 4. Nutrition knowledge score percentages per sections and total score (N = 61 participants).
Table 4. Nutrition knowledge score percentages per sections and total score (N = 61 participants).
FactorsSection 1 Achievement (%)Expert RecommendationsSection 2 Achievement (%)Food GroupsSection 3 Achievement (%) Healthy Food ChoicesSection 4 Achievement Diet, Disease and Weight AssociationsTotal Score Achievement (%)
Median achievement64.0 +/− 9.4 *63.5 +/− 9.271.6 +/− 13.9 *65.0 +/− 11.2 *65.2 +/− 7.1
SexM63.7 +/− 10.363.1 +/− 8.471.6 +/− 16.267.2 +/− 9.365.5 +/− 7.7
F64.3 +/− 8.863.7 +/− 9.871.6 +/− 12.263.4 +/− 12.364.9 +/− 6.8
Age category≤65 years65.9 +/− 10.463.8 +/− 10.573.4 +/− 13.667.3 +/− 11.066.5 +/− 7.5
>65 years62.0 +/− 7.963.1 +/− 7.869.7 +/− 14.362.7 +/− 11.163.8 +/− 6.6
EducationHigh school or less61.8 +/− 10.263.9 +/− 9.073.6 +/− 14.665.9 +/− 10.165.4 +/− 7.4
At least college degree66.5 +/− 7.963.0 +/− 9.569.5 +/− 13.164.0 +/− 12.464.9 +/− 7.0
Living with underage individualsYes64.3 +/− 9.363.7 +/− 9.471.0 +/− 13.665.0 +/− 11.265.2 +/− 7.4
No61.1 +/− 10.561.6 +/− 6.476.9 +/− 16.965.1 +/− 12.364.6 +/− 4.9
Percentile group of LCD scoreFirst (high carb)62.4 +/− 9.162.8 +/− 8.073.3 +/− 12.661.9 +/− 12.164.1 +/− 5.2
Second (medium)67.0 +/− 8.863.1 +/− 11.768.8 +/− 13.665.3 +/− 11.465.3 +/− 8.6
Third (Low carb)63.1 +/− 9.964.4 +/− 8.272.4 +/− 15.767.7 +/− 9.766.1 +/− 7.6
AnemiaNo66.7 +/− 7.060.2 +/− 13.170.5 +/− 13.261.9 +/− 6.063.4 +/− 7.8
Yes63.7 +/− 9.663.8 +/− 8.771.7 +/− 14.165.4 +/− 11.665.4 +/− 7.1
HypercalcemiaNo65.5 +/− 6.462.9 +/− 9.473.2 +/− 13.663.7 +/− 9.065.2 +/− 6.8
Yes62.7 +/− 11.464.0 +/− 9.070.2 +/− 14.366.2 +/− 12.965.2 +/− 7.6
Chronic kidney diseaseNo65.3 +/− 8.363.7 +/− 9.473.0 +/− 13.366.8 +/− 10.266.2 +/− 6.8
Yes62.0 +/− 10.763.1 +/− 9.069.6 +/− 15.062.3 +/− 12.363.6 +/− 7.5
InfectionsNo65.2 +/− 8.964.4 +/− 9.172.0 +/− 13.665.3 +/− 11.565.9 +/− 7.2
Yes57.4 +/− 9.658.3 +/− 8.469.2 +/− 16.363.5 +/− 9.561.0 +/− 5.0
Notes: Data is presented as mean +/− SD. * Statistically significant lower achievement score, when compared to the population median, with the following values: Section 1—66.7%, Section 2—63.8%, Section 3—76.9%, Section 4—71.4%, total score—65.9% [20]; between factors comparisons with independent samples t-test or ANOVA, as appropriate.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Borsi, E.; Serban, C.L.; Potre, C.; Potre, O.; Putnoky, S.; Samfireag, M.; Tudor, R.; Ionita, I.; Ionita, H. High Carbohydrate Diet Is Associated with Severe Clinical Indicators, but Not with Nutrition Knowledge Score in Patients with Multiple Myeloma. Int. J. Environ. Res. Public Health 2021, 18, 5444. https://doi.org/10.3390/ijerph18105444

AMA Style

Borsi E, Serban CL, Potre C, Potre O, Putnoky S, Samfireag M, Tudor R, Ionita I, Ionita H. High Carbohydrate Diet Is Associated with Severe Clinical Indicators, but Not with Nutrition Knowledge Score in Patients with Multiple Myeloma. International Journal of Environmental Research and Public Health. 2021; 18(10):5444. https://doi.org/10.3390/ijerph18105444

Chicago/Turabian Style

Borsi, Ema, Costela Lacrimioara Serban, Cristina Potre, Ovidiu Potre, Salomeia Putnoky, Miruna Samfireag, Raluca Tudor, Ioana Ionita, and Hortensia Ionita. 2021. "High Carbohydrate Diet Is Associated with Severe Clinical Indicators, but Not with Nutrition Knowledge Score in Patients with Multiple Myeloma" International Journal of Environmental Research and Public Health 18, no. 10: 5444. https://doi.org/10.3390/ijerph18105444

APA Style

Borsi, E., Serban, C. L., Potre, C., Potre, O., Putnoky, S., Samfireag, M., Tudor, R., Ionita, I., & Ionita, H. (2021). High Carbohydrate Diet Is Associated with Severe Clinical Indicators, but Not with Nutrition Knowledge Score in Patients with Multiple Myeloma. International Journal of Environmental Research and Public Health, 18(10), 5444. https://doi.org/10.3390/ijerph18105444

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop