Nutritional Status of Patients Diagnosed with Cervical Cancer in Tanzania

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Mwita, Ghada A. Soliman, Dativa F. Mushi, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7780798/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Cervical cancer is the most common cancer affecting Tanzanian women. The nutritional status of patients with cervical cancer may influence cervical cancer outcomes. However, no information is available regarding the nutritional status of the Tanzanian cervical cancer patients. Therefore, we evaluated the nutritional status of patients with cervical cancer treated at the Ocean Road Cancer Institute (ORCI) in Tanzania. Methods We conducted a cross-sectional study of 184 newly diagnosed patients with cervical cancer seen at the oncology clinic of ORCI from April to September 2023. Assessment of the nutritional status was based on the Patient-Generated Subjective Global Assessment (PG-SGA) scores. The overall nutritional status was evaluated using nutritional blood biomarkers, food intake frequencies, a categorical rating indicating the level of malnourishment, and a triage recommendation. Multivariate regression analysis was performed to predict nutritional status based on demographic and clinical variables. Results Among the total patient population, 39.1% reported consuming less food after diagnosis, especially the intake of cereals, dark leafy green vegetables, orange vegetables, other vegetables, as well as oils, and fats. The majority (84.8%) of patients required nutritional intervention based on their nutritional status. Almost half (48%) of patients with early-stage cervical cancer (stages I, II) and 78% of patients with late-stage cervical cancer (Stage III, IV), showed moderate or severe malnutrition. The risk of malnutrition was significantly higher in patients with late-stage vs early-stage cancer (8.0 ± 1.4 vs. 5.9 ± 0.8; p = 0.0173). Similarly, patients with late-stages had higher mean levels of white blood cells (late vs. early: 9.0 ± 1.1 vs. 7.6 ± 0.6; p = 0.0216), platelets (396.3 ± 39.4 vs. 347.5 ± 23.9; p = 0.0399), the ratio of platelet to neutrophil count (101.7 ± 13.8 vs. 82.3 ± 1.6; p = 0.0294), and the liver function test, aspartate aminotransferase (AST) enzyme (31.9 ± 5.9 vs. 24.5 ± 2.0; p = 0.0201). Late-stage patients were predicted to have a worse nutritional status (late vs. early: mean = 7.8 vs. 6.0, p = 0.04), after adjusting for sociodemographic covariates. Further adjustments by smoking, alcohol use and physical activity did not affect the model. Conclusion Nutritional status of patients with late-stage cervical cancer in Tanzania should be prioritized during the management of patients with cervical cancer, especially for late-stage patients. This study may have translation to other low-income countries facing challenges in cancer management, poverty, and challenges in food security. ORCI Tanzania Cervical Cancer HPV Nutrition PG-SGA Introduction Cervical cancer is a significant and most common cancer affecting women in Sub-Saharan Africa, including Tanzania [ 1 – 3 ]. Cancer mortality among patients with cervical cancer in Tanzania is high (age-standardized incidence rate = 42.2/100,000), and late-stage presentation, as well as short survival among Tanzanian patients with cervical cancer, call for interventions for improving patient management [ 4 ]. Tanzania has faced a high cervical cancer incidence (age-standardized mortality rate = 64.8/100,000) and malnourishment [ 2 , 4 – 6 ]. Cervical cancer is caused by the human papillomavirus (HPV) [ 7 ], and past research showed that the nutritional status of patients may modify cervical cancer outcomes and response to treatment [ 8 – 10 ]. Patients with cancer and poor nutritional status may suffer from neutropenia quickly and, therefore, may not be able to tolerate chemotherapy and radiotherapy treatment. Neutropenia is common among patients with cancer in Tanzania [ 11 , 12 ]. However, information regarding the nutritional status of patients with cervical cancer in Tanzania, which may be associated with lower white blood count and neutropenia, is lacking. Therefore, we conducted this study to characterize the nutritional status of patients with cervical cancer treated at the Ocean Road Cancer Institute (ORCI) in Dar es Salaam, Tanzania. Methods Study Location This cross-sectional study was conducted at the ORCI, Tanzania's largest and oldest cancer center. ORCI sees patients from all over Tanzania with diverse socioeconomic, geographic places of residence and demographic backgrounds. Patients seeking care at the ORCI also represent a wide range of disease stages, as ORCI offers chemotherapy and is one of the very few radiotherapy programs in the country. Study Population and Data Collection The study included all newly diagnosed patients with cervical cancer seen at the oncology clinic of ORCI during the period from April to September 2023. Patients with cervical cancer seen at ORCI represent the general patient population of Tanzania. Based on Tanzania’s latest census of 2022 [ 13 ] and our previous publications on cervical cancer at ORCI ([ 14 , 15 ] and Fuss et al., 2024), the most populous regions of Tanzania (Dar es Salaam, Mwanza, Tabora, Morogoro, and Dadoma) as well as the less populated regions of Tanzania are represented in the patient population of ORCI. All 184 patients who were approached for the study agreed to participate. All interviews were conducted at the beginning of the patients’ appointments, before any discussion by the medical team about diagnosis or treatment. Every patient included in the study provided verbal consent to participate, as documented in the ORCI IRB approval. The patients' mean age was 53.3 ± 11.1. Data collection was conducted through all the following three approaches: A) conducting dietary interviews of all newly diagnosed patients with cervical cancer who were seen at the oncology clinic of ORCI during the period from April to September 2023. Patients were asked about their dietary intake on a typical day before diagnosis and within 24 hours after diagnosis. The interviews used a Tanzania Food Frequency Questionnaire (FFQ). The questionnaires were administered by an experienced local nutritionist and a co-author (DFM). B) Clinical and demographic data of the patients interviewed were retrieved from their medical records, and C) existing laboratory biomarker data were abstracted from the medical records of all interviewed patients. Blood biomarker analysis is routinely performed on-site, after the initial clinical examination of all patients, and before any treatment is prescribed at ORCI. The study was approved by the ORCI Research Ethics Committee. The IRB approval of ORCI was done according to the Declaration of Helsinki. The City University of New York (CUNY) determined that this study is exempt from the Institutional Review Board (IRB) review because the work done at CUNY included only statistical analysis of de-identified datasets. Assessment of the Nutritional Status The nutrition assessment score (1–24) was derived from the validated Patient-Generated Subjective Global Assessment (PG-SGA) with modifications [ 13 ]. The PG-SGA questionnaire was in English, and the native speaking interviewer from ORCI translated the questions to the patients and translated their responses back into the questionnaires. The scores were calculated based on the nutritional and health indicators, including changes in food intake, weight change, symptoms, comorbidities, disease stage and its relation to nutritional requirements, activities, metabolic demands, and physical examination (loss of muscle mass or body fat, edema or ascites) that were captured in the clinical nutritional assessment form. The respective indicator included a change in weight change (0–1), food intake (0–4), symptoms (0–3), disease state (1–2), activities (0–3), metabolic demands (0–5) including Fever [0–3], cortisone [0–2], and degree of physical deficit (0–6) including loss of body fat or muscle mass [0–3], and edema or ascites [0–3], with higher scores indicating worse nutritional and health status (Table 2 ). Based on this scoring, the nutrition status of patients with cervical cancer is presented in Table 3 . The nutritional risk is based on the total PG-SGA point score: none: 0–1, mild: 2–3, moderate: 4–8, and severe: ≥ 9. Category A: well-nourished score: 1–3, Category B: moderate risk of malnutrition: 4–8, and Category C: severe malnutrition with scores: 9+. Food intake frequencies before and after diagnosis were recorded on a typical day before diagnosis and within 24 hours of the interview after the confirmed diagnosis, including the intake across the main food categories in Tanzania. Blood nutritional biomarkers included inflammation indicators, such as white blood cells (WBC), indicators of neutropenia (neutrophil counts), red blood cells (RBC), and total number of platelets in the blood (PLT), indicators of inflammation and autoimmune diseases, such as the ratio of platelet and lymphocyte count (PLCR) and the ratio of platelet and neutrophil count (PLCC), heart and liver function indicators, such as hemoglobin (HGB), aspartate aminotransferase enzyme levels (AST), and alanine transaminase enzyme level (ALT, liver function indicator), bone and liver function indicator such as alkaline phosphatase enzyme levels (ALP), and kidney function health indicators, such as urea concentration in blood (UREA), and creatinine concentration in the blood (CRE-E). These blood markers were collected on-site from the patient's medical records during management. The questionnaires for nutritional assessment are included in the Supplementary materials. Covariate Assessment Self-reported covariates included sociodemographic and lifestyle variables: age (years), marital status (married or not married), employment status (Yes/No), educational attainment (none, primary, secondary or above), number of people in the household, number of children, lack of housing security in the past six months (Yes/No), sold or thinking about selling property for treatment (Yes/No), lack of resources to buy medication (Yes/No), lack of food security (Yes/No), smoking status (former/never), alcohol use (current/former/never), physical activity (no/run/walk), transportation access (Yes/No), sexual activity (Yes/No), and HPV vaccine awareness (Yes/No). Clinical information included stage of cervical cancer (categorized as early-stage: I and II, and late-stage: III and IV), number of symptoms and comorbidities, edema (no, mild, moderate), weight change (no change/increase/decrease), change in food intake (no/normal food but < normal amount or eat less/ nutritional supplement or only liquids/ eat more), family history of cancer (Yes/No), physical function (mild/moderate/normal/severe), and body mass index (BMI, kg/m 2 , measured by the research assistant on-site). Statistical Analysis All the analyses were performed using SAS (version 9.4), and results were reported at a 2-tailed α-level of 0.05. Descriptive analyses of nutritional status were performed using mean and standard deviation (SD) for nutritional assessment scores, mean blood biomarker levels, and dietary intake on a typical day before diagnosis and within 24 hours of interview after diagnosis. A comparison of the mean levels of these continuous factors by cancer stage was done using a t-test. A comparison between mean food intake frequencies before and after diagnosis was conducted using a paired t-test. Counts and percentages were calculated for categorical level patients’ characteristics, clinical information, lifestyle factors, and nutritional status outcomes such as global categorical rating and nutrition triage recommendation. A comparison of categorical level factors according to cancer stage was performed using the Chi-square test. Multiple imputation was used to impute the missing data in stage (n = 6), food intake (prior to and after diagnosis, n = 2), and blood nutritional markers (missing rate < 5%, n = 9). Multivariable-adjusted generalized linear regression models were adopted to estimate the least square means and 95% confidence intervals (CIs) for nutrition assessment comparison between early and late cancer stages. Multivariable-adjusted logistic regression models were used to estimate the association between cancer stage and global category ratings with odds ratios and their 95% CIs. A backward stepwise selection procedure was adopted in the model to include sociodemographic and lifestyle covariates such as age, education, BMI (included in model 1), smoking, alcohol use, and physical activity (further included in model 2). Results Study population characteristics Of the 184 patients, the majority were diagnosed with stage II and III cervical cancer (87.5%) and did not have a family history of cancer (81%) (Table 1 ). A higher proportion of patients were married, unemployed, had lower physical activity levels and educational attainment, and lacked reliable transportation and awareness of human papillomavirus (HPV) vaccination. Most of the patients did not actively smoke or drink alcohol. More than half of the patients had worsened physical function, and they did not engage in recent sexual activity. Patients experienced an average of 1.4 symptoms and comorbidities. Approximately 50% of patients consumed less food or relied on nutritional supplements or liquids (Table 3 ), and the majority of patients (58.7%) experienced weight loss (Table 1 ). Table 1 Characteristics of patients with cervical cancer in Tanzania (N = 184). Descriptive Information of Study Population Patients’ Characteristics Age, Mean ± S.D. 53.3 ± 11.1 Married (yes), n (%) 106 (57.6) Unemployed (yes), n (%) 177 (96.2) Education, % None 50 (27.2) Primary 122 (66.3) Secondary or above 12 (6.5) Patients’ Lifestyles # People living in the household 4.9 ± 2.4 # Children 4.5 ± 2.2 Lack of housing security in the past 6 months (yes), n (%) 129 (70.1) Sold or thinking about selling property to finance treatment (yes), n (%) 75 (40.8) Lack of food security within the past 6 months (yes), n (%) 28 (15.2) Smoking, n (%) Former smoker 3 (1.6) Never smoker 181 (98.4) Alcohol status, n (%) Current 4 (2.2) Former 93 (50.5) Never 87 (47.3) Physical Activity, n (%) No 169 (91.9) Run 5 (2.7) Walk 10 (5.4) Having transportation (yes), n (%) 80 (43.5) Sexually active (yes), n (%) 54 (29.4) Decided not to purchase medications for high expense within the past 6 months (yes), n (%) 53 (28.8) Having HPV knowledge (yes), n (%) 62 (33.7) Patients’ Clinical Information BMI, Mean ± S.D. 24.3 ± 5.4 Stage of cervical cancer, n (%) I 14 (7.6) II 123 (66.9) III 38 (20.6) IV 9 (4.9) Symptoms, Mean ± S.D. 1.4 ± 1.8 Comorbidities, Mean ± S.D. 1.4 ± 0.5 Edema, n (%) No 178 (96.8) Mild 3 (1.6) Moderate 3 (1.6) Table 1 . Characteristics of patients with cervical cancer in Tanzania (N = 184) (Cont.) Descriptive Information of Study Population (Continued) Weight change, n (%) No change 62 (33.7) Increase 14 (7.6) Decrease 108 (58.7) Family history of cancer (yes), n (%) 35 (19.0) Change in physical function, n (%) No change 85 (46.2) Mild 67 (36.4) Moderate 25 (13.6) Severe 7 (3.8) S.D: standard deviation Tables 3 and 4 describe the patients' overall nutritional status and dietary changes (before and after diagnosis). The mean nutritional risk score (1–24) was 6.5 ± 5.2 (median = 5) (Table 3 ). Based on the PG-GSA nutritional assessment score, 84.8% of patients are recommended for nutrition intervention to alleviate the risk of mild (27.7%), moderate (24.5%), or severe malnutrition (32.6%). 39.1% of the patients reported consuming everyday foods, but in smaller amounts than before the onset of cancer. A decrease in the mean daily intake before and after cancer diagnosis was observed in the intakes of cereals ( p < 0.001), vegetables (dark leafy green vegetables ( p = 0.04), orange vegetables ( p < 0.001), and other vegetables ( p < 0.001), and oils and fats ( p < 0.001) (Table 4 ). Table 2 Nutrition Assessment Score Categories Item # Category Max-Score Range 1 Weight Decreased or not 1 0–1 2 Food Intake Normal or decrease 4 0–4 3 Symptoms No symptoms, NVCD* 3 0–3 4 Disease state Cancer = 1, co-morbidity = 2 2 1–2 5 Activities Active to bedridden (0–3) 3 0–3 6 Metabolic Demands Fever (0–3), cortisone (0–2) 5 0–5 7 Physical Examination loss of fat or muscle mass (0–3) 3 0–3 edema or ascites (0–3) 3 0–3 Max. Score 24 1–24 *NVCD = Nausea, vomiting, constipation, or diarrhea Metabolic demands include fever and cortisone medications Table 3 Overall nutritional status of patients with cervical cancer included in the study Overall Nutritional Status Nutrition assessment score (derived from the PG-SGA) Mean ± S.D.; Median (Range) 6.5 ± 5.2; 5.0 (1–24) Category rating , n (%) A: well-nourished 81 (44.0) B: moderate/suspected risk of malnutrition 65 (35.3) C: Severe risk of malnutrition 38 (20.7) Recommendation (nutritional intervention per total score), n (%) None (0–1) 28 (15.2) Mild (2–3) 51 (27.7) Moderate (4–8) 45 (24.5) Severe (≥ 9) 60 (32.6) Change in food intake , n (%) No change 93 (50.5) Normal food but < normal amounts/eat less 72 (39.1) Nutrition supplement/only liquids 15 (8.2) Eat more 4 (2.2) Table 4 Daily diet before and after diagnosis (N = 184) Occurrence of dietary intake/day Usual diet before diagnosis, Mean ± S.D. Diet after diagnosis, Mean ± S.D. Mean Difference P value Cereals 2.09 ± 0.40 0.98 ± 0.10 1.1 ± 0.4 < 0.001 Orange vegetables 1.32 ± 0.50 0.98 ± 0.13 0.3 ± 0.5 < 0.001 White tubers 0.44 ± 0.51 0.44 ± 0.50 0.01 ± 0.07 0.3186 Dark leafy green vegetables 0.96 ± 0.29 0.93 ± 0.25 0.02 ± 0.15 0.0452 Other vegetables 1.14 ± 0.42 0.97 ± 0.16 0.16 ± 0.4 < 0.0001 Vitamin A rich fruits 0.37 ± 0.49 0.37 ± 0.49 0 ± 0.1 1.0 Other fruits 0.01 ± 0.10 0.01 ± 0.10 0 ± 0 - Organ meats 0.05 ± 0.22 0.04 ± 0.19 0.01 ± 0.1 0.1579 Flesh meats 0.57 ± 0.51 0.56 ± 0.50 0.01 ± 0.2 0.3186 Eggs 0.20 ± 0.40 0.20 ± 0.40 0 ± 0 - Fish 0.60 ± 0.50 0.60 ± 0.49 -0.01 ± 0.2 0.1841 Legumes, nuts, and seeds 0.57 ± 0.56 0.54 ± 0.50 0.02 ± 0.2 0.1579 Dairy products 0.44 ± 0.53 0.42 ± 0.50 0.02 ± 0.2 0.258 Oils and fats 1.13 ± 0.46 0.97 ± 0.23 0.16 ± 0.38 < 0.0001 Sweets 0.90 ± 0.30 0.90 ± 0.31 0.01 ± 0.07 0.3186 Spices, condiments, and beverages 0.91 ± 0.31 0.89 ± 0.31 0.02 ± 0.13 0.0833 Table 5 shows the distribution of nutritional status by the early and late stages of cancer. A higher proportion of patients with moderate and severe risk of malnutrition was observed among those with late-stage disease (moderate malnutrition risk: late vs. early stage, 46.8% vs. 31.4%; risk of severe malnutrition: late vs. early, 31.9% vs. 16.8%; p = 0.001). Regarding the blood biomarkers, the higher mean levels of WBC (late vs. early: 9.0 ± 1.1 vs. 7.6 ± 0.6; p = 0.0216), PLT (late vs. early: 396.3 ± 39.4 vs. 347.5 ± 23.9; p = 0.0399), and PLCC (late vs. early: 101.7 ± 13.8 vs. 82.3 ± 1.6; p = 0.0294) can be observed in the patients with late-stage, as compared to the patients with early-stage, indicating a more severe inflammation level among patients with late-stage. The mean level of AST, a liver function test, was also higher in patients with late-stage (mean = 31.9 ± 5.9 for late-stage vs. mean = 24.5 ± 2.0 for early-stage; p = 0.0201). While similar patterns were also observed in other biomarkers such as ALT ( p = 0.3839), ALP ( p = 0.2910), Urea ( p = 0.1832) and CRE-E ( p = 0.123), these findings were not statistically significant. Table 5 Nutritional status by cervical cancer stage (N = 184) Early stage (Stage I, II) Late stage (Stage III, IV) P value Category rating, n (%) 0.0011 A: well-nourished 71 (51.8) 10 (21.3) B: moderate/suspected risk of malnutrition 43 (31.4) 22 (46.8) C: Severe risk of malnutrition 23 (16.8) 15 (31.9) Total 137 (100) 47 (100) Recommendation (nutritional intervention per total score), n (%) 0.0061 None (0–1) 45 (32.9) 6 (12.8) Mild (2–3) 28 (20.4) 17 (36.1) Moderate (4–8) 24 (17.5) 4 (8.5) Severe (≥ 9) 40 (29.2) 20 (42.6) Total 137 (100) 47 (100) Total Score (total count of nutritional indicators: 1–24), Mean ± S.D. 5.9 ± 0.8 8.0 ± 1.4 0.0173 Blood Nutritional Biomarkers Mean ± S.D. White blood cells (WBC), \(\:{10}^{9}\) /L 7.6 ± 0.6 9.0 ± 1.1 0.0216 Red blood cells (RBC), \(\:{10}^{12}\) /L 4.2 ± 0.1 4.1 ± 0.2 0.4841 Platelets (PLT), \(\:{10}^{9}\) /L 347.5 ± 23.9 396.3 ± 39.4 0.0399 Ratio of the platelet and lymphocyte count (PLCR), % 26.6 ± 1.5 25.7 ± 2.0 0.5631 Ratio of the platelet and neutrophil count (PLCC), % 82.3 ± 1.6 101.7 ± 13.8 0.0294 Hemoglobin (HGB), g/dL 10.4 ± 0.4 9.8 ± 0.8 0.1111 Alanine transaminase enzyme levels (ALT), U/L 18.7 ± 2.7 21.3 ± 4.5 0.3839 Alkaline phosphatase enzyme levels (ALP), U/L 82.3 ± 1.6 101.7 ± 36.6 0.2910 Aspartate Aminotransferase enzyme levels (AST), U/L 24.5 ± 2.0 31.9 ± 5.9 0.0201 Urea concentration (UREA), mmol/L 4.2 ± 1.2 6.2 ± 2.8 0.1832 Creatinine (CRE-E), mmol/L 80.9 ± 6.3 93.3 ± 14.7 0.1230 For total score and blood biomarkers, t-test was used to examine the mean difference. A chi-square test was performed to examine the proportion difference for category rating and triage recommendation. Table 6 reveals the association between cervical cancer stage and nutritional status after adjusting for sociodemographic and lifestyle covariates. Patients with early-stage cervical cancer score 6.0 (95% CI 5.2, 6.9), while patients with late-stage cervical cancer have a score of 7.8 (95% CI 6.3, 9.2, p = 0.04). Further adjustment for other covariates, such as smoking, alcohol use or physical activity, did not have an additional effect. Table 6 Prediction of the nutritional status by stage of cervical cancer diagnosis by consideration of the possible confounders. (N = 184) Associations Total score, least square mean (95% CI) Model 1 Model 2 Early stage 6.0 (5.2, 6.9) 8.9 (6.3, 11.4) Late stage 7.8 (6.3, 9.2) 9.2 (6.5, 11.9) p-value 0.04 0.56 For the total score, a multivariable generalized linear regression model was used. For category rating, multivariable logistic regression was used. Compared to early-stage, it was less likely for a patient with late-stage cervical cancer to get a good category rating such as A or B. Model 1: Adjusted for age, education, and BMI. Model 2: Further adjusted for smoking, alcohol use, and physical activity. Discussion To our knowledge, this is the first study to report on the nutritional status of patients with cervical cancer in Tanzania. The study revealed that the majority of patients with cervical cancer in this study had a low intake of food, including cereals, vegetables (such as orange vegetables, dark leafy green vegetables, and other vegetables), and oils and fats. The study also showed that a more advanced cancer stage was associated with a worse nutritional status. Finally, the study revealed that levels of inflammatory markers, such as WBC, PLCC, and heart and liver function markers, such as AST, were higher in patients with late-stage cervical cancer. The decrease in food intake was a common observation in patients with cervical cancer in this study. Patients cannot maintain a good nutrition status when food intake is reduced to less than normal [ 16 ]. As such, malnutrition may likely occur in patients with cervical cancer. The reported significant reduction in the intake of certain foods rich in micronutrients, such as vegetables and cereals, can aggravate patient malnutrition, weakening patients’ overall health and diminishing their survival probability [ 17 , 18 ]. Specifically, the reduced food groups, such as vegetables of all kinds, can be detrimental to cervical cancer patients’ health and survival outcomes. This is because consumption of vegetables and their included nutrients (such as vitamin C, vitamin E, carotene, carotenoid, zinc, iron, potassium, and antioxidants) may have protective effects against cervical cancer development, progression, and response to treatment [ 19 – 21 ]. Significant associations between late-stage cervical cancer and worsened nutritional status were found. By contrast, previous studies focused on malnutrition and survival outcomes. The scored PG-SGA is a well-documented tool to evaluate the nutritional status in patients with cervical or gynecological cancer [ 18 , 22 – 25 ]. Studies have shown that PG-SGA could identify patients at nutritional risk ([ 25 ], and toxicity from chemotherapy is associated with malnutrition as identified by PG-GSA in patients with cervical cancer [ 22 ]. Several existing cohort studies reported malnutrition to be a predictor of a higher risk of premature death and lower cancer survival rates [ 17 , 18 ]. The findings of the cohort studies are consistent with a retrospective cohort study, in which the poor nutritional status (reflected by the lower nutritional index assessed using blood nutritional biomarker and weight) predicted worsened survival rates and shortened survival of patients with advanced-stage cervical cancer [ 26 ]. Thus, there is a lack of research determining nutritional status based on the cervical cancer stage. The significant association between late-stage disease and the risk of severe malnutrition revealed in our study could help fill gaps in the existing literature, facilitating a deeper understanding of the determinants of malnourishment and their impact on patients' survival and quality of life in developing countries. Also, the study findings showed the importance of providing nutritional support for patients with late-stage cervical cancer in developing countries. Furthermore, higher mean levels of inflammatory markers (WBC, PLT) and liver and heart health markers (AST) were observed in patients with the late-stage disease. This finding was consistent with the literature that showed high levels of inflammatory markers in patients with late-stage cancer and can predict worsened survival outcomes [ 26 ]. Notably, the higher mean level of AST indicates that liver dysfunction may be unrelated to cervical cancer itself but to comorbidities common in late-stage patients. The findings of the study highlight the importance of the provision of nutritional interventions, especially for late-stage cervical cancer, and also for other late-stage cancers. While cancer diagnosis and treatment have expanded in Tanzania [ 20 , 21 ], efforts are needed to enhance patient care and provide nutritional and dietary support to augment treatment effectiveness. This is the first study on nutrition and diet among cancer patients in Tanzania. This study utilized a dietary questionnaire of local food items and combined both dietary and laboratory markers for nutritional assessment. The study has limitations. Dietary information collected from self-reported FFQs through interviews may have included measurement errors [ 27 ], which might lead to null-toward biased results and underestimated postulated associations. Blood nutritional biomarkers were assessed at a single point in time, rather than through repeated measures during treatment. However, the nutritional status of patients is expected to worsen during the cancer treatment. The high proportion of patients who lacked financial support for transportation to the cancer center shows the likelihood of patients’ sacrifice of their food intake for saving money for afford the transportation to the cancer treatment facility. The severity of the risk of malnutrition reported in this study was statistically associated with late-stage cervical cancer in Tanzania, suggesting that referral to nutritionists and nutrition intervention and support at hospital and community levels should be prioritized for the participants diagnosed with late-stage cervical cancer for better survival and quality of life in developing countries, like Tanzania. Declarations a. Ethics approval and consent to participate. As listed in the manuscript, the study was approved by the Ocean Road Cancer Institute (ORCI). The City University of New York (CUNY) determined that the study is not subject to IRB review as the work performed at CUNY only included aggregate analysis of de-identified data. Every single patient included in the study verbally consented to participate in the study, as included in the ORCI IRB approval. b. Consent for publication All authors have approved the manuscript for submission and publication. c. Availability of data and materials The datasets analyzed for this study can be found and obtained pending e-mail requests to the corresponding author. d. Competing interests The authors declare that the research was conducted without commercial or financial relationships that could create a conflict of interest. The authors declare no potential competing interests. e. Funding This study was supported in part through funding from the Cancer Epidemiology Education in Special Populations (CEESP) Program from the National Cancer Institute, Grant # R25 CA112383. f. Authors' contributions LZ: wrote the first draft; LZ, CJM, GS, DFM, GL, AS, and EMN: methodology, investigation, review, writing, editing, and final approval; GS, CJM, DFM, and AS: conceptualization, data collection, and resources; Data analysis and curation: LZ; Project supervision: AS. All authors contributed to the article and approved the submitted version. g. Acknowledgments We thank ORCI and CUNY Graduate School of Public Health for their support of our research. h Helsinki declaration statement The IRB approval of ORCI was done according to the Declaration of Helsinki. Consent for publication Not applicable was added under the consent.” i. Consent for publication Not Applicable References International W, Cervical cancer statistics World Cancer Research Fund International. (n.d.). https://www.wcrf.org/cancer-trends/cervical-cancer-statistics/ . In.; 2023. Runge AS, Bernstein ME, Lucas AN, Tewari KS. Cervical cancer in Tanzania: A systematic review of current challenges in six domains. Gynecol Oncol Rep. 2019;29:40–7. Centre IIHI. Tanzania human papillomavirus and related cancers, Fact Sheet 2023. In.; 2023. Ferlay J, Ervik M, Lam F, Laversanne M, Colombet M, Mery L, Piñeros M, Znaor A, Soerjomataram I, Bray F. Global Cancer Observatory: Cancer Today. Lyon, France: International Agency for Research on Cancer. In.; 2024. 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Meneses-Urrea LA, Vaquero-Abellán M, Villegas Arenas D, Benachi Sandoval N, Hernández-Carrillo M, Molina-Recio G. Association between Cervical Cancer and Dietary Patterns in Colombia. Nutrients 2023, 15(23). Carter LW. Influences of nutrition and stress on people at risk for neutropenia: nursing implications. Oncol Nurs Forum. 1993;20(8):1241–50. Safari LC, Mloka D, Minzi O, Dharsee NJ, Reuben R. Prevalence of blood stream infections and associated factors among febrile neutropenic cancer patients on chemotherapy at Ocean Road Cancer Institute, Tanzania. Infect Agent Cancer. 2023;18(1):52. Ministry of Finance and Planning, NBoSaPsOFaP, October, Office of the Chief Government Statistician., Zanzibar. The 2022 Population and Housing Census: Initial Results. Dodoma, Tanzania. 2022: 2022. Fuss CG, Msami K, Kahesa C, Mwaiselage J, Gordon A, Sohler N, Mattick LJ, Soliman AS. The impact of in-house pathology services on downstaging cervical cancer in Tanzania over an 18-year period. Cancer Causes Control. 2024;35(1):93–101. Mattick LJ, Ochs-Balcom HM, Mwaiselage J, Kahesa C, Gard AC, Shalan F, Soliman AS. Downstaging of cervical cancer in Tanzania over a 16-year period. Cancer Causes Control. 2021;32(4):401–7. Saunders J, Smith T. Malnutrition: causes and consequences. Clin Med (Lond). 2010;10(6):624–7. Laan J, van Lonkhuijzen L, Hinnen K, Pieters B, Dekker I, Stalpers L, Westerveld H. Malnutrition is associated with poor survival in women receiving radiotherapy for cervical cancer. Int J Gynecol Cancer 2024. Argefa TG, Roets L. Malnutrition and the Survival of Cervical Cancer Patients: A Prospective Cohort Study Using the PG-SGA Tool. Nutr Cancer. 2022;74(2):605–12. García-Closas R, Castellsagué X, Bosch X, González CA. The role of diet and nutrition in cervical carcinogenesis: a review of recent evidence. Int J Cancer. 2005;117(4):629–37. Zhang YY, Lu L, Abliz G, Mijit F. Serum carotenoid, retinol and tocopherol concentrations and risk of cervical cancer among Chinese women. Asian Pac J Cancer Prev. 2015;16(7):2981–6. Nazari E, Hasanzadeh M, Rezvani R, Rejali M, Badpeyma M, Delaram Z, Mousavi-Seresht L, Akbari M, Khazaei M, Ferns GA, et al. Association of dietary intake and cervical cancer: a prevention strategy. Infect Agent Cancer. 2023;18(1):42. Mota AP, Aredes MA, De Oliveira Miguel J, Chaves GV. Nutritional status assessed by Patient-Generated Subjective Global Assessment is associated with toxicity to chemoradiotherapy in women with cervical cancer: a prospective study. Eur J Clin Nutr. 2022;76(12):1740–7. Chantragawee C, Achariyapota V. Utilization of a Scored Patient-Generated Subjective Global Assessment in Detecting a Malnourished Status in Gynecologic Cancer Patients. Asian Pac J Cancer Prev. 2016;17(9):4401–4. Laura FC, Lucely CP, Tatiana GC, Roberto JL, Dulce GI, Arturo PS, Maricarmen GG, Lilia CM. Handgrip Strength, Overhydration and Nutritional Status as a Predictors of Gastrointestinal Toxicity in Cervical Cancer Patients. A Prospective Study. Nutr Cancer. 2022;74(7):2444–50. Evrimler S, Iscan SC, Iscan G, Raoufi J, Erdemoglu E. The comparison of the prognostic value of scored patient generated subjective global assessment and Computed Tomography measured sarcopenia in patients with gynecological cancer. Clin Nutr ESPEN. 2022;48:253–8. Wang HB, Xu XT, Tian MX, Ding CC, Tang J, Qian Y, Jin X. Prognostic values of the prognostic nutritional index, geriatric nutritional risk index, and systemic inflammatory indexes in patients with stage IIB-III cervical cancer receiving radiotherapy. Front Nutr. 2023;10:1000326. Subar AF, Freedman LS, Tooze JA, Kirkpatrick SI, Boushey C, Neuhouser ML, Thompson FE, Potischman N, Guenther PM, Tarasuk V, et al. Addressing Current Criticism Regarding the Value of Self-Report Dietary Data. J Nutr. 2015;145(12):2639–45. Additional Declarations No competing interests reported. Supplementary Files DietarydiversitytoolquestionnaireTFNC.doc NutritionalAssessment.docx nutritionscreening.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Feb, 2026 Reviews received at journal 16 Feb, 2026 Reviewers agreed at journal 15 Feb, 2026 Reviewers agreed at journal 10 Feb, 2026 Reviews received at journal 29 Dec, 2025 Reviewers agreed at journal 18 Dec, 2025 Reviewers agreed at journal 16 Dec, 2025 Reviewers invited by journal 16 Dec, 2025 Editor assigned by journal 15 Dec, 2025 Editor invited by journal 10 Dec, 2025 Submission checks completed at journal 06 Dec, 2025 First submitted to journal 06 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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affecting women in Sub-Saharan Africa, including Tanzania [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Cancer mortality among patients with cervical cancer in Tanzania is high (age-standardized incidence rate\u0026thinsp;=\u0026thinsp;42.2/100,000), and late-stage presentation, as well as short survival among Tanzanian patients with cervical cancer, call for interventions for improving patient management [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTanzania has faced a high cervical cancer incidence (age-standardized mortality rate\u0026thinsp;=\u0026thinsp;64.8/100,000) and malnourishment [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Cervical cancer is caused by the human papillomavirus (HPV) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and past research showed that the nutritional status of patients may modify cervical cancer outcomes and response to treatment [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePatients with cancer and poor nutritional status may suffer from neutropenia quickly and, therefore, may not be able to tolerate chemotherapy and radiotherapy treatment. Neutropenia is common among patients with cancer in Tanzania [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, information regarding the nutritional status of patients with cervical cancer in Tanzania, which may be associated with lower white blood count and neutropenia, is lacking. Therefore, we conducted this study to characterize the nutritional status of patients with cervical cancer treated at the Ocean Road Cancer Institute (ORCI) in Dar es Salaam, Tanzania.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy Location\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study was conducted at the ORCI, Tanzania's largest and oldest cancer center. ORCI sees patients from all over Tanzania with diverse socioeconomic, geographic places of residence and demographic backgrounds. Patients seeking care at the ORCI also represent a wide range of disease stages, as ORCI offers chemotherapy and is one of the very few radiotherapy programs in the country.\u003c/p\u003e\n\u003cp\u003eStudy Population and Data Collection\u003c/p\u003e\n\u003cp\u003eThe study included all newly diagnosed patients with cervical cancer seen at the oncology clinic of ORCI during the period from April to September 2023. Patients with cervical cancer seen at ORCI represent the general patient population of Tanzania. Based on Tanzania\u0026rsquo;s latest census of 2022 [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e] and our previous publications on cervical cancer at ORCI ([\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e] and Fuss et al., 2024), the most populous regions of Tanzania (Dar es Salaam, Mwanza, Tabora, Morogoro, and Dadoma) as well as the less populated regions of Tanzania are represented in the patient population of ORCI.\u003c/p\u003e\n\u003cp\u003eAll 184 patients who were approached for the study agreed to participate. All interviews were conducted at the beginning of the patients\u0026rsquo; appointments, before any discussion by the medical team about diagnosis or treatment. Every patient included in the study provided verbal consent to participate, as documented in the ORCI IRB approval. The patients' mean age was 53.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1. Data collection was conducted through all the following three approaches: A) conducting dietary interviews of all newly diagnosed patients with cervical cancer who were seen at the oncology clinic of ORCI during the period from April to September 2023. Patients were asked about their dietary intake on a typical day before diagnosis and within 24 hours after diagnosis. The interviews used a Tanzania Food Frequency Questionnaire (FFQ). The questionnaires were administered by an experienced local nutritionist and a co-author (DFM). B) Clinical and demographic data of the patients interviewed were retrieved from their medical records, and C) existing laboratory biomarker data were abstracted from the medical records of all interviewed patients. Blood biomarker analysis is routinely performed on-site, after the initial clinical examination of all patients, and before any treatment is prescribed at ORCI. The study was approved by the ORCI Research Ethics Committee. The IRB approval of ORCI was done according to the Declaration of Helsinki. The City University of New York (CUNY) determined that this study is exempt from the Institutional Review Board (IRB) review because the work done at CUNY included only statistical analysis of de-identified datasets.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eAssessment of the Nutritional Status\u003c/h2\u003e\n\u003cp\u003eThe nutrition assessment score (1\u0026ndash;24) was derived from the validated Patient-Generated Subjective Global Assessment (PG-SGA) with modifications [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. The PG-SGA questionnaire was in English, and the native speaking interviewer from ORCI translated the questions to the patients and translated their responses back into the questionnaires. The scores were calculated based on the nutritional and health indicators, including changes in food intake, weight change, symptoms, comorbidities, disease stage and its relation to nutritional requirements, activities, metabolic demands, and physical examination (loss of muscle mass or body fat, edema or ascites) that were captured in the clinical nutritional assessment form. The respective indicator included a change in weight change (0\u0026ndash;1), food intake (0\u0026ndash;4), symptoms (0\u0026ndash;3), disease state (1\u0026ndash;2), activities (0\u0026ndash;3), metabolic demands (0\u0026ndash;5) including Fever [0\u0026ndash;3], cortisone [0\u0026ndash;2], and degree of physical deficit (0\u0026ndash;6) including loss of body fat or muscle mass [0\u0026ndash;3], and edema or ascites [0\u0026ndash;3], with higher scores indicating worse nutritional and health status (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Based on this scoring, the nutrition status of patients with cervical cancer is presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The nutritional risk is based on the total PG-SGA point score: none: 0\u0026ndash;1, mild: 2\u0026ndash;3, moderate: 4\u0026ndash;8, and severe: \u0026ge; 9. Category A: well-nourished score: 1\u0026ndash;3, Category B: moderate risk of malnutrition: 4\u0026ndash;8, and Category C: severe malnutrition with scores: 9+.\u003c/p\u003e\n\u003cp\u003eFood intake frequencies before and after diagnosis were recorded on a typical day before diagnosis and within 24 hours of the interview after the confirmed diagnosis, including the intake across the main food categories in Tanzania.\u003c/p\u003e\n\u003cp\u003eBlood nutritional biomarkers included inflammation indicators, such as white blood cells (WBC), indicators of neutropenia (neutrophil counts), red blood cells (RBC), and total number of platelets in the blood (PLT), indicators of inflammation and autoimmune diseases, such as the ratio of platelet and lymphocyte count (PLCR) and the ratio of platelet and neutrophil count (PLCC), heart and liver function indicators, such as hemoglobin (HGB), aspartate aminotransferase enzyme levels (AST), and alanine transaminase enzyme level (ALT, liver function indicator), bone and liver function indicator such as alkaline phosphatase enzyme levels (ALP), and kidney function health indicators, such as urea concentration in blood (UREA), and creatinine concentration in the blood (CRE-E). These blood markers were collected on-site from the patient's medical records during management.\u003c/p\u003e\n\u003cp\u003eThe questionnaires for nutritional assessment are included in the Supplementary materials.\u003c/p\u003e\n\u003cp\u003eCovariate Assessment\u003c/p\u003e\n\u003cp\u003eSelf-reported covariates included sociodemographic and lifestyle variables: age (years), marital status (married or not married), employment status (Yes/No), educational attainment (none, primary, secondary or above), number of people in the household, number of children, lack of housing security in the past six months (Yes/No), sold or thinking about selling property for treatment (Yes/No), lack of resources to buy medication (Yes/No), lack of food security (Yes/No), smoking status (former/never), alcohol use (current/former/never), physical activity (no/run/walk), transportation access (Yes/No), sexual activity (Yes/No), and HPV vaccine awareness (Yes/No). Clinical information included stage of cervical cancer (categorized as early-stage: I and II, and late-stage: III and IV), number of symptoms and comorbidities, edema (no, mild, moderate), weight change (no change/increase/decrease), change in food intake (no/normal food but \u0026lt;\u0026thinsp;normal amount or eat less/ nutritional supplement or only liquids/ eat more), family history of cancer (Yes/No), physical function (mild/moderate/normal/severe), and body mass index (BMI, \u003cem\u003ekg/m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e, measured by the research assistant on-site).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n\u003cp\u003eAll the analyses were performed using SAS (version 9.4), and results were reported at a 2-tailed \u0026alpha;-level of 0.05. Descriptive analyses of nutritional status were performed using mean and standard deviation (SD) for nutritional assessment scores, mean blood biomarker levels, and dietary intake on a typical day before diagnosis and within 24 hours of interview after diagnosis. A comparison of the mean levels of these continuous factors by cancer stage was done using a t-test. A comparison between mean food intake frequencies before and after diagnosis was conducted using a paired t-test. Counts and percentages were calculated for categorical level patients\u0026rsquo; characteristics, clinical information, lifestyle factors, and nutritional status outcomes such as global categorical rating and nutrition triage recommendation. A comparison of categorical level factors according to cancer stage was performed using the Chi-square test. Multiple imputation was used to impute the missing data in stage (n\u0026thinsp;=\u0026thinsp;6), food intake (prior to and after diagnosis, n\u0026thinsp;=\u0026thinsp;2), and blood nutritional markers (missing rate\u0026thinsp;\u0026lt;\u0026thinsp;5%, n\u0026thinsp;=\u0026thinsp;9).\u003c/p\u003e\n\u003cp\u003eMultivariable-adjusted generalized linear regression models were adopted to estimate the least square means and 95% confidence intervals (CIs) for nutrition assessment comparison between early and late cancer stages. Multivariable-adjusted logistic regression models were used to estimate the association between cancer stage and global category ratings with odds ratios and their 95% CIs. A backward stepwise selection procedure was adopted in the model to include sociodemographic and lifestyle covariates such as age, education, BMI (included in model 1), smoking, alcohol use, and physical activity (further included in model 2).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eStudy population characteristics\u003c/p\u003e\n\u003cp\u003eOf the 184 patients, the majority were diagnosed with stage II and III cervical cancer (87.5%) and did not have a family history of cancer (81%) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). A higher proportion of patients were married, unemployed, had lower physical activity levels and educational attainment, and lacked reliable transportation and awareness of human papillomavirus (HPV) vaccination. Most of the patients did not actively smoke or drink alcohol. More than half of the patients had worsened physical function, and they did not engage in recent sexual activity. Patients experienced an average of 1.4 symptoms and comorbidities. Approximately 50% of patients consumed less food or relied on nutritional supplements or liquids (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), and the majority of patients (58.7%) experienced weight loss (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCharacteristics of patients with cervical cancer in Tanzania (N\u0026thinsp;=\u0026thinsp;184).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eDescriptive Information of Study Population\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePatients\u0026rsquo; Characteristics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;S.D.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMarried (yes), n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e106 (57.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnemployed (yes), n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e177 (96.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEducation, %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50 (27.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrimary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e122 (66.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSecondary or above\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (6.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePatients\u0026rsquo; Lifestyles\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e# People living in the household\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e# Children\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLack of housing security in the past 6 months (yes), n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e129 (70.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSold or thinking about selling property to finance treatment (yes), n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75 (40.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLack of food security within the past 6 months (yes), n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28 (15.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSmoking, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFormer smoker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (1.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNever smoker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e181 (98.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlcohol status, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCurrent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (2.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFormer\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93 (50.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNever\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87 (47.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhysical Activity, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e169 (91.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRun\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (2.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWalk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (5.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHaving transportation (yes), n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80 (43.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSexually active (yes), n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54 (29.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecided not to purchase medications for high expense within the past 6 months (yes), n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53 (28.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHaving HPV knowledge (yes), n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62 (33.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePatients\u0026rsquo; Clinical Information\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;S.D.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage of cervical cancer, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (7.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e123 (66.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38 (20.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (4.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSymptoms, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;S.D.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eComorbidities, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;S.D.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEdema, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e178 (96.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMild\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (1.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModerate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (1.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Characteristics of patients with cervical cancer in Tanzania (N\u0026thinsp;=\u0026thinsp;184) (Cont.)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDescriptive Information of Study Population (Continued)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeight change, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo change\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62 (33.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (7.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e108 (58.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFamily history of cancer (yes), n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35 (19.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChange in physical function, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo change\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e85 (46.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMild\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67 (36.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModerate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25 (13.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSevere\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (3.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\"\u003eS.D: standard deviation\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e describe the patients' overall nutritional status and dietary changes (before and after diagnosis). The mean nutritional risk score (1\u0026ndash;24) was 6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2 (median\u0026thinsp;=\u0026thinsp;5) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Based on the PG-GSA nutritional assessment score, 84.8% of patients are recommended for nutrition intervention to alleviate the risk of mild (27.7%), moderate (24.5%), or severe malnutrition (32.6%). 39.1% of the patients reported consuming everyday foods, but in smaller amounts than before the onset of cancer. A decrease in the mean daily intake before and after cancer diagnosis was observed in the intakes of cereals (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), vegetables (dark leafy green vegetables (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04), orange vegetables (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and other vegetables (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and oils and fats (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eNutrition Assessment Score Categories\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eItem #\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCategory\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMax-Score\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRange\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeight\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eDecreased or not\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026ndash;1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFood Intake\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNormal or decrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026ndash;4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSymptoms\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNo symptoms, NVCD*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026ndash;3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease state\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCancer\u0026thinsp;=\u0026thinsp;1, co-morbidity\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u0026ndash;2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eActivities\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eActive to bedridden (0\u0026ndash;3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026ndash;3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMetabolic Demands\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eFever (0\u0026ndash;3), cortisone (0\u0026ndash;2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026ndash;5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhysical Examination\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eloss of fat or muscle mass (0\u0026ndash;3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026ndash;3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eedema or ascites (0\u0026ndash;3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026ndash;3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMax. Score\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e24\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1\u0026ndash;24\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003e*NVCD\u0026thinsp;=\u0026thinsp;Nausea, vomiting, constipation, or diarrhea\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eMetabolic demands include fever and cortisone medications\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eOverall nutritional status of patients with cervical cancer included in the study\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOverall Nutritional Status\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNutrition assessment score\u003c/strong\u003e (derived from the PG-SGA)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;S.D.; Median (Range)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2; 5.0 (1\u0026ndash;24)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCategory rating\u003c/strong\u003e, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA: well-nourished\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81 (44.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eB: moderate/suspected risk of malnutrition\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65 (35.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC: Severe risk of malnutrition\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38 (20.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRecommendation\u003c/strong\u003e (nutritional intervention per total score), n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNone (0\u0026ndash;1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28 (15.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMild (2\u0026ndash;3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51 (27.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModerate (4\u0026ndash;8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45 (24.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSevere (\u0026ge;\u0026thinsp;9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60 (32.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eChange in food intake\u003c/strong\u003e, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo change\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93 (50.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNormal food but \u0026lt;\u0026thinsp;normal amounts/eat less\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72 (39.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNutrition supplement/only liquids\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (8.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEat more\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (2.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDaily diet before and after diagnosis (N\u0026thinsp;=\u0026thinsp;184)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOccurrence of dietary intake/day\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUsual diet before diagnosis,\u003c/p\u003e\n\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;S.D.\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDiet after diagnosis,\u003c/p\u003e\n\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;S.D.\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean Difference\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCereals\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOrange vegetables\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWhite tubers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3186\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDark leafy green vegetables\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.0452\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther vegetables\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVitamin A rich fruits\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther fruits\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOrgan meats\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1579\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFlesh meats\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3186\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEggs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFish\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1841\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLegumes, nuts, and seeds\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1579\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDairy products\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.258\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOils and fats\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSweets\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3186\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSpices, condiments,\u003c/p\u003e\n\u003cp\u003eand beverages\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0833\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the distribution of nutritional status by the early and late stages of cancer. A higher proportion of patients with moderate and severe risk of malnutrition was observed among those with late-stage disease (moderate malnutrition risk: late vs. early stage, 46.8% vs. 31.4%; risk of severe malnutrition: late vs. early, 31.9% vs. 16.8%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Regarding the blood biomarkers, the higher mean levels of WBC (late vs. early: 9.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 vs. 7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0216), PLT (late vs. early: 396.3\u0026thinsp;\u0026plusmn;\u0026thinsp;39.4 vs. 347.5\u0026thinsp;\u0026plusmn;\u0026thinsp;23.9; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0399), and PLCC (late vs. early: 101.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8 vs. 82.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0294) can be observed in the patients with late-stage, as compared to the patients with early-stage, indicating a more severe inflammation level among patients with late-stage. The mean level of AST, a liver function test, was also higher in patients with late-stage (mean\u0026thinsp;=\u0026thinsp;31.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9 for late-stage vs. mean\u0026thinsp;=\u0026thinsp;24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 for early-stage; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0201). While similar patterns were also observed in other biomarkers such as ALT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3839), ALP (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.2910), Urea (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1832) and CRE-E (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.123), these findings were not statistically significant.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eNutritional status by cervical cancer stage (N\u0026thinsp;=\u0026thinsp;184)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEarly stage (Stage I, II)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLate stage\u003c/p\u003e\n\u003cp\u003e(Stage III, IV)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCategory rating, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.0011\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA: well-nourished\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71 (51.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (21.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eB: moderate/suspected risk of\u003c/p\u003e\n\u003cp\u003emalnutrition\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43 (31.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 (46.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC: Severe risk of malnutrition\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 (16.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (31.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e137 (100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47 (100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRecommendation (nutritional intervention per total score), n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.0061\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNone (0\u0026ndash;1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45 (32.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (12.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMild (2\u0026ndash;3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28 (20.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (36.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModerate (4\u0026ndash;8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24 (17.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (8.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSevere (\u0026ge;\u0026thinsp;9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40 (29.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20 (42.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e137 (100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47 (100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal Score (total count of nutritional indicators: 1\u0026ndash;24), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;S.D.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.0173\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlood Nutritional Biomarkers\u003c/p\u003e\n\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;S.D.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWhite blood cells (WBC), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{10}^{9}\\)\u003c/span\u003e\u003c/span\u003e/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.0216\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRed blood cells (RBC), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{10}^{12}\\)\u003c/span\u003e\u003c/span\u003e/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4841\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePlatelets (PLT), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{10}^{9}\\)\u003c/span\u003e\u003c/span\u003e/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e347.5\u0026thinsp;\u0026plusmn;\u0026thinsp;23.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e396.3\u0026thinsp;\u0026plusmn;\u0026thinsp;39.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.0399\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRatio of the platelet and\u003c/p\u003e\n\u003cp\u003elymphocyte count (PLCR), %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5631\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRatio of the platelet and neutrophil\u003c/p\u003e\n\u003cp\u003ecount (PLCC), %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e101.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.0294\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHemoglobin (HGB), g/dL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1111\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlanine transaminase enzyme\u003c/p\u003e\n\u003cp\u003elevels (ALT), U/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3839\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlkaline phosphatase enzyme\u003c/p\u003e\n\u003cp\u003elevels (ALP), U/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e101.7\u0026thinsp;\u0026plusmn;\u0026thinsp;36.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2910\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAspartate Aminotransferase\u003c/p\u003e\n\u003cp\u003eenzyme levels (AST), U/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.0201\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUrea concentration (UREA),\u003c/p\u003e\n\u003cp\u003emmol/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1832\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCreatinine (CRE-E), mmol/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1230\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003eFor total score and blood biomarkers, t-test was used to examine the mean difference.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003eA chi-square test was performed to examine the proportion difference for category rating and triage recommendation.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e reveals the association between cervical cancer stage and nutritional status after adjusting for sociodemographic and lifestyle covariates. Patients with early-stage cervical cancer score 6.0 (95% CI 5.2, 6.9), while patients with late-stage cervical cancer have a score of 7.8 (95% CI 6.3, 9.2, p\u0026thinsp;=\u0026thinsp;0.04). Further adjustment for other covariates, such as smoking, alcohol use or physical activity, did not have an additional effect.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePrediction of the nutritional status by stage of cervical cancer diagnosis by consideration of the possible confounders. (N\u0026thinsp;=\u0026thinsp;184)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAssociations\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTotal score, least square mean (95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eModel 1 Model 2\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEarly stage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.0 (5.2, 6.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.9 (6.3, 11.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLate stage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.8 (6.3, 9.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.2 (6.5, 11.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.56\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003eFor the total score, a multivariable generalized linear regression model was used.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003eFor category rating, multivariable logistic regression was used.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003eCompared to early-stage, it was less likely for a patient with late-stage cervical cancer to get a good category rating such as A or B.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003eModel 1: Adjusted for age, education, and BMI.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003eModel 2: Further adjusted for smoking, alcohol use, and physical activity.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this is the first study to report on the nutritional status of patients with cervical cancer in Tanzania. The study revealed that the majority of patients with cervical cancer in this study had a low intake of food, including cereals, vegetables (such as orange vegetables, dark leafy green vegetables, and other vegetables), and oils and fats. The study also showed that a more advanced cancer stage was associated with a worse nutritional status. Finally, the study revealed that levels of inflammatory markers, such as WBC, PLCC, and heart and liver function markers, such as AST, were higher in patients with late-stage cervical cancer.\u003c/p\u003e \u003cp\u003eThe decrease in food intake was a common observation in patients with cervical cancer in this study. Patients cannot maintain a good nutrition status when food intake is reduced to less than normal [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. As such, malnutrition may likely occur in patients with cervical cancer. The reported significant reduction in the intake of certain foods rich in micronutrients, such as vegetables and cereals, can aggravate patient malnutrition, weakening patients\u0026rsquo; overall health and diminishing their survival probability [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Specifically, the reduced food groups, such as vegetables of all kinds, can be detrimental to cervical cancer patients\u0026rsquo; health and survival outcomes. This is because consumption of vegetables and their included nutrients (such as vitamin C, vitamin E, carotene, carotenoid, zinc, iron, potassium, and antioxidants) may have protective effects against cervical cancer development, progression, and response to treatment [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSignificant associations between late-stage cervical cancer and worsened nutritional status were found. By contrast, previous studies focused on malnutrition and survival outcomes. The scored PG-SGA is a well-documented tool to evaluate the nutritional status in patients with cervical or gynecological cancer [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Studies have shown that PG-SGA could identify patients at nutritional risk ([\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and toxicity from chemotherapy is associated with malnutrition as identified by PG-GSA in patients with cervical cancer [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Several existing cohort studies reported malnutrition to be a predictor of a higher risk of premature death and lower cancer survival rates [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The findings of the cohort studies are consistent with a retrospective cohort study, in which the poor nutritional status (reflected by the lower nutritional index assessed using blood nutritional biomarker and weight) predicted worsened survival rates and shortened survival of patients with advanced-stage cervical cancer [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Thus, there is a lack of research determining nutritional status based on the cervical cancer stage. The significant association between late-stage disease and the risk of severe malnutrition revealed in our study could help fill gaps in the existing literature, facilitating a deeper understanding of the determinants of malnourishment and their impact on patients' survival and quality of life in developing countries. Also, the study findings showed the importance of providing nutritional support for patients with late-stage cervical cancer in developing countries.\u003c/p\u003e \u003cp\u003eFurthermore, higher mean levels of inflammatory markers (WBC, PLT) and liver and heart health markers (AST) were observed in patients with the late-stage disease. This finding was consistent with the literature that showed high levels of inflammatory markers in patients with late-stage cancer and can predict worsened survival outcomes [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Notably, the higher mean level of AST indicates that liver dysfunction may be unrelated to cervical cancer itself but to comorbidities common in late-stage patients.\u003c/p\u003e \u003cp\u003eThe findings of the study highlight the importance of the provision of nutritional interventions, especially for late-stage cervical cancer, and also for other late-stage cancers. While cancer diagnosis and treatment have expanded in Tanzania [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], efforts are needed to enhance patient care and provide nutritional and dietary support to augment treatment effectiveness.\u003c/p\u003e \u003cp\u003eThis is the first study on nutrition and diet among cancer patients in Tanzania. This study utilized a dietary questionnaire of local food items and combined both dietary and laboratory markers for nutritional assessment. The study has limitations. Dietary information collected from self-reported FFQs through interviews may have included measurement errors [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], which might lead to null-toward biased results and underestimated postulated associations. Blood nutritional biomarkers were assessed at a single point in time, rather than through repeated measures during treatment. However, the nutritional status of patients is expected to worsen during the cancer treatment.\u003c/p\u003e \u003cp\u003eThe high proportion of patients who lacked financial support for transportation to the cancer center shows the likelihood of patients\u0026rsquo; sacrifice of their food intake for saving money for afford the transportation to the cancer treatment facility. The severity of the risk of malnutrition reported in this study was statistically associated with late-stage cervical cancer in Tanzania, suggesting that referral to nutritionists and nutrition intervention and support at hospital and community levels should be prioritized for the participants diagnosed with late-stage cervical cancer for better survival and quality of life in developing countries, like Tanzania.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003ea. Ethics approval and consent to participate.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs listed in the manuscript, the study was approved by the Ocean Road Cancer Institute (ORCI). \u0026nbsp;The City University of New York (CUNY) determined that the study is not subject to IRB review as the work performed at CUNY only included aggregate analysis of de-identified data. Every single patient included in the study verbally consented to participate in the study, as included in the ORCI IRB approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb. Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have approved the manuscript for submission and publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec. Availability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed for this study can be found and obtained pending e-mail requests to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed. Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted without commercial or financial relationships that could create a conflict of interest. \u0026nbsp;The authors declare no potential competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee. Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported in part through funding from the Cancer Epidemiology Education in Special Populations (CEESP) Program from the National Cancer Institute, Grant # R25 CA112383.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ef. Authors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLZ: wrote the first draft; LZ, CJM, GS, DFM, GL, AS, and EMN: methodology, investigation, review, writing, editing, and final approval; GS, CJM, DFM, and AS: conceptualization, data collection, and resources; Data analysis and curation: LZ; Project supervision: AS. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003ch1\u003eg. Acknowledgments\u003c/h1\u003e\n\u003cp\u003eWe thank ORCI and CUNY Graduate School of Public Health for their support of our research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eh Helsinki declaration statement\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The IRB approval of ORCI was done according to the Declaration of Helsinki. Consent for publication Not applicable was added under the consent.”\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u003cstrong\u003ei. Consent for publication\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not Applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eInternational W, Cervical cancer statistics World Cancer Research Fund International. (n.d.). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wcrf.org/cancer-trends/cervical-cancer-statistics/\u003c/span\u003e\u003cspan address=\"https://www.wcrf.org/cancer-trends/cervical-cancer-statistics/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. In.; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRunge AS, Bernstein ME, Lucas AN, Tewari KS. Cervical cancer in Tanzania: A systematic review of current challenges in six domains. Gynecol Oncol Rep. 2019;29:40\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCentre IIHI. Tanzania human papillomavirus and related cancers, Fact Sheet 2023. In.; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerlay J, Ervik M, Lam F, Laversanne M, Colombet M, Mery L, Pi\u0026ntilde;eros M, Znaor A, Soerjomataram I, Bray F. Global Cancer Observatory: Cancer Today. Lyon, France: International Agency for Research on Cancer. In.; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of Health CD, Gender E, Children, Ministry of Health, National Bureau of Statistics, Office of the Chief Government Statistician., and ICF: Tanzania demographic and health survey and malaria indicator survey (TDHS-MIS) 2015-16. \u003cem\u003eDar es Salaam, Tanzania, and Rockville, Maryland, USA: MoHCDGEC, MoH, NBS, OCGS, and ICF\u003c/em\u003e 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSunguya BF, Zhu S, Mpembeni R, Huang J. Trends in prevalence and determinants of stunting in Tanzania: an analysis of Tanzania demographic health surveys (1991\u0026ndash;2016). Nutr J. 2019;18(1):85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkunade KS. Human papillomavirus and cervical cancer. J Obstet Gynaecol. 2020;40(5):602\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePiyathilake CJ, Badiga S, Thao N, Jolly PE. Micronutrients and prevention of cervical pre-cancer in HPV vaccinated women: a cross-sectional study. Korean J Community Nutr. 2023;28(1):61\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePotischman N, Brinton LA. Nutrition and cervical neoplasia. Cancer Causes Control. 1996;7(1):113\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeneses-Urrea LA, Vaquero-Abell\u0026aacute;n M, Villegas Arenas D, Benachi Sandoval N, Hern\u0026aacute;ndez-Carrillo M, Molina-Recio G. Association between Cervical Cancer and Dietary Patterns in Colombia. Nutrients 2023, 15(23).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarter LW. Influences of nutrition and stress on people at risk for neutropenia: nursing implications. Oncol Nurs Forum. 1993;20(8):1241\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSafari LC, Mloka D, Minzi O, Dharsee NJ, Reuben R. Prevalence of blood stream infections and associated factors among febrile neutropenic cancer patients on chemotherapy at Ocean Road Cancer Institute, Tanzania. Infect Agent Cancer. 2023;18(1):52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of Finance and Planning, NBoSaPsOFaP, October, Office of the Chief Government Statistician., Zanzibar. The 2022 Population and Housing Census: Initial Results. Dodoma, Tanzania. 2022: 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFuss CG, Msami K, Kahesa C, Mwaiselage J, Gordon A, Sohler N, Mattick LJ, Soliman AS. The impact of in-house pathology services on downstaging cervical cancer in Tanzania over an 18-year period. Cancer Causes Control. 2024;35(1):93\u0026ndash;101.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMattick LJ, Ochs-Balcom HM, Mwaiselage J, Kahesa C, Gard AC, Shalan F, Soliman AS. Downstaging of cervical cancer in Tanzania over a 16-year period. Cancer Causes Control. 2021;32(4):401\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaunders J, Smith T. Malnutrition: causes and consequences. Clin Med (Lond). 2010;10(6):624\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaan J, van Lonkhuijzen L, Hinnen K, Pieters B, Dekker I, Stalpers L, Westerveld H. Malnutrition is associated with poor survival in women receiving radiotherapy for cervical cancer. Int J Gynecol Cancer 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArgefa TG, Roets L. Malnutrition and the Survival of Cervical Cancer Patients: A Prospective Cohort Study Using the PG-SGA Tool. Nutr Cancer. 2022;74(2):605\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Closas R, Castellsagu\u0026eacute; X, Bosch X, Gonz\u0026aacute;lez CA. The role of diet and nutrition in cervical carcinogenesis: a review of recent evidence. Int J Cancer. 2005;117(4):629\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang YY, Lu L, Abliz G, Mijit F. Serum carotenoid, retinol and tocopherol concentrations and risk of cervical cancer among Chinese women. Asian Pac J Cancer Prev. 2015;16(7):2981\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNazari E, Hasanzadeh M, Rezvani R, Rejali M, Badpeyma M, Delaram Z, Mousavi-Seresht L, Akbari M, Khazaei M, Ferns GA, et al. Association of dietary intake and cervical cancer: a prevention strategy. Infect Agent Cancer. 2023;18(1):42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMota AP, Aredes MA, De Oliveira Miguel J, Chaves GV. Nutritional status assessed by Patient-Generated Subjective Global Assessment is associated with toxicity to chemoradiotherapy in women with cervical cancer: a prospective study. Eur J Clin Nutr. 2022;76(12):1740\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChantragawee C, Achariyapota V. Utilization of a Scored Patient-Generated Subjective Global Assessment in Detecting a Malnourished Status in Gynecologic Cancer Patients. Asian Pac J Cancer Prev. 2016;17(9):4401\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaura FC, Lucely CP, Tatiana GC, Roberto JL, Dulce GI, Arturo PS, Maricarmen GG, Lilia CM. Handgrip Strength, Overhydration and Nutritional Status as a Predictors of Gastrointestinal Toxicity in Cervical Cancer Patients. A Prospective Study. Nutr Cancer. 2022;74(7):2444\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvrimler S, Iscan SC, Iscan G, Raoufi J, Erdemoglu E. The comparison of the prognostic value of scored patient generated subjective global assessment and Computed Tomography measured sarcopenia in patients with gynecological cancer. Clin Nutr ESPEN. 2022;48:253\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang HB, Xu XT, Tian MX, Ding CC, Tang J, Qian Y, Jin X. Prognostic values of the prognostic nutritional index, geriatric nutritional risk index, and systemic inflammatory indexes in patients with stage IIB-III cervical cancer receiving radiotherapy. Front Nutr. 2023;10:1000326.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubar AF, Freedman LS, Tooze JA, Kirkpatrick SI, Boushey C, Neuhouser ML, Thompson FE, Potischman N, Guenther PM, Tarasuk V, et al. Addressing Current Criticism Regarding the Value of Self-Report Dietary Data. J Nutr. 2015;145(12):2639\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ORCI, Tanzania, Cervical Cancer, HPV, Nutrition, PG-SGA","lastPublishedDoi":"10.21203/rs.3.rs-7780798/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7780798/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCervical cancer is the most common cancer affecting Tanzanian women. The nutritional status of patients with cervical cancer may influence cervical cancer outcomes. However, no information is available regarding the nutritional status of the Tanzanian cervical cancer patients. Therefore, we evaluated the nutritional status of patients with cervical cancer treated at the Ocean Road Cancer Institute (ORCI) in Tanzania.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a cross-sectional study of 184 newly diagnosed patients with cervical cancer seen at the oncology clinic of ORCI from April to September 2023. Assessment of the nutritional status was based on the Patient-Generated Subjective Global Assessment (PG-SGA) scores. The overall nutritional status was evaluated using nutritional blood biomarkers, food intake frequencies, a categorical rating indicating the level of malnourishment, and a triage recommendation. Multivariate regression analysis was performed to predict nutritional status based on demographic and clinical variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the total patient population, 39.1% reported consuming less food after diagnosis, especially the intake of cereals, dark leafy green vegetables, orange vegetables, other vegetables, as well as oils, and fats. The majority (84.8%) of patients required nutritional intervention based on their nutritional status. Almost half (48%) of patients with early-stage cervical cancer (stages I, II) and 78% of patients with late-stage cervical cancer (Stage III, IV), showed moderate or severe malnutrition. The risk of malnutrition was significantly higher in patients with late-stage vs early-stage cancer (8.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 vs. 5.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0173). Similarly, patients with late-stages had higher mean levels of white blood cells (late vs. early: 9.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 vs. 7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0216), platelets (396.3\u0026thinsp;\u0026plusmn;\u0026thinsp;39.4 vs. 347.5\u0026thinsp;\u0026plusmn;\u0026thinsp;23.9; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0399), the ratio of platelet to neutrophil count (101.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8 vs. 82.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0294), and the liver function test, aspartate aminotransferase (AST) enzyme (31.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9 vs. 24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0201). Late-stage patients were predicted to have a worse nutritional status (late vs. early: mean\u0026thinsp;=\u0026thinsp;7.8 vs. 6.0, p\u0026thinsp;=\u0026thinsp;0.04), after adjusting for sociodemographic covariates. Further adjustments by smoking, alcohol use and physical activity did not affect the model.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eNutritional status of patients with late-stage cervical cancer in Tanzania should be prioritized during the management of patients with cervical cancer, especially for late-stage patients. This study may have translation to other low-income countries facing challenges in cancer management, poverty, and challenges in food security.\u003c/p\u003e","manuscriptTitle":"Nutritional Status of Patients Diagnosed with Cervical Cancer in Tanzania","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-18 17:11:15","doi":"10.21203/rs.3.rs-7780798/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-16T19:42:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T14:47:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150888995516508249722735050639551668659","date":"2026-02-15T09:40:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221776311399234524702925586767908096584","date":"2026-02-10T09:39:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-29T07:14:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234928765049253656077577585892859441841","date":"2025-12-18T09:55:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214316325585795457735609364852488957292","date":"2025-12-16T10:09:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-16T09:40:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-15T07:39:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-10T11:52:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-07T01:36:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nutrition","date":"2025-12-07T01:32:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4b86df5d-eee7-4251-8845-917e5902f1f1","owner":[],"postedDate":"December 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T17:09:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-18 17:11:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7780798","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7780798","identity":"rs-7780798","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}